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24th February 2022 International Class 2022 Energy and Environmental Management Towards Sustainable Energy: A Roadmap for Residential Buildings at Loop Head II Content List of abbreviations ................................................................................................................. VII List of tables .............................................................................................................................. IX List of figures ............................................................................................................................. XI List of equation ........................................................................................................................ XV Annex ..................................................................................................................................... XVI Executive summary ............................................................................................................... XVII 1. Introduction ......................................................................................................................... 1 1.1. Background .................................................................................................................. 1 1.2. Scope and objectives .................................................................................................. 3 1.2.1. Scope of work ................................................................................................... 3 1.2.2. Objectives ........................................................................................................... 4 2. Community engagement ..................................................................................................... 5 2.1. Stakeholders’ identification .......................................................................................... 6 2.2. Communication approach ........................................................................................... 7 2.3. Conducted workshops ............................................................................................... 10 2.3.1. Monitoring and evaluation ................................................................................ 12 2.4. Challenges and recommendation ............................................................................. 13 3. Case study approach to energy efficiency and technologies........................................... 14 3.1. Common definitions used throughout the energy assessments and case studies .. 14 3.2. Energy assessment of residential buildings .............................................................. 16 3.2.1. Cost analysis .................................................................................................... 23 3.3. Solar PV-Heat Pump System Sizing and Integration ............................................... 25 4. Energy solutions for residential buildings ......................................................................... 32 4.1. Types of wall and insulation ...................................................................................... 34 4.1.1. External wall insulation..................................................................................... 34 4.1.2. Internal wall insulation ...................................................................................... 35 4.1.3. Cavity wall insulation ........................................................................................ 35 4.2. Types of roofs and insulation .................................................................................... 35 III 4.2.1. Shed roof insulation ......................................................................................... 36 4.2.2. Pitched roof ...................................................................................................... 37 4.2.3. Mansard roof .................................................................................................... 37 4.3. Floor insulation .......................................................................................................... 37 4.4. Types of windows ...................................................................................................... 39 4.1. Heating technologies ................................................................................................. 40 4.1.1. Brine-to-Water Heat Pumps Technology ......................................................... 44 4.1.2. Air-to-Air Heat Pumps Technology .................................................................. 45 4.1.3. Air-to-Water Heat Pumps (AWHP) technology ................................................ 46 4.2. Heat emitters ............................................................................................................. 48 4.2.1. Steel radiators .................................................................................................. 48 4.2.2. Low-temperature fan assisted radiators .......................................................... 49 4.2.3. Underfloor heating ............................................................................................ 49 4.3. Solar PV ..................................................................................................................... 49 4.3.1. Introduction to solar technology ....................................................................... 49 4.3.2. Factors Affecting Solar Energy Generation ..................................................... 51 4.4. Solar PV System integration ..................................................................................... 56 4.4.1. Battery storage system .................................................................................... 57 4.4.2. Store energy as hot water ................................................................................ 57 4.4.3. Grid connection ................................................................................................ 58 4.5. Solar PV system component specifications .............................................................. 59 4.5.1. Solar PV panels................................................................................................ 60 4.5.2. Single-phase Inverter ....................................................................................... 61 4.5.3. DC Battery ........................................................................................................ 61 4.5.4. Mounting system .............................................................................................. 62 4.5.5. Balance of system components ....................................................................... 63 4.5.6. Hot water diverter ............................................................................................. 63 4.6. SEAI grants ................................................................................................................ 64 4.6.1. Home energy upgrade ..................................................................................... 64 IV 4.6.2. Heat pump grant............................................................................................... 67 4.6.3. Grants for Solar Electricity ............................................................................... 68 5. Case study I ...................................................................................................................... 72 5.1. Status quo .................................................................................................................. 72 5.1.1. Building Envelope............................................................................................. 72 5.1.2. Electricity usage pattern ................................................................................... 74 5.1.3. BER Rating ....................................................................................................... 76 5.2. Identified energy-saving measures ........................................................................... 76 5.2.1. Building envelope ............................................................................................. 76 5.2.2. Cost analysis of building retrofit ....................................................................... 78 5.3. Domestic Hot Water (DHW) and Space Heating Supply .......................................... 80 5.3.1. Space Heating .................................................................................................. 80 5.3.2. The Space Heating and DHW Excel Model..................................................... 85 5.3.3. Space Heating and DHW economic analysis .................................................. 88 5.3.4. Domestic Hot Water Heat Demand excel model update ................................. 89 5.4. Solar PV ..................................................................................................................... 89 5.4.1. Residential Demand ......................................................................................... 89 5.4.2. System Design ................................................................................................. 90 5.5. System integration ..................................................................................................... 93 5.5.1. Domestic hot water .......................................................................................... 93 5.5.2. Cost-effectiveness analysis ............................................................................. 98 5.6. Summarized key findings ........................................................................................ 101 6. Case study II ................................................................................................................... 101 6.1. Status quo ................................................................................................................ 101 6.1.1. Building Envelope........................................................................................... 101 6.1.2. Electricity usage pattern ................................................................................. 105 6.1.3. BER Rating ..................................................................................................... 107 6.2. Identified energy saving measures ......................................................................... 107 6.2.1. Building envelope ........................................................................................... 107 V 6.2.2. Cost analysis of Building retrofit..................................................................... 108 6.3. Existing Space Heating and DHW System ............................................................. 109 6.3.1. Existing Space Heating and DHW System Analysis ..................................... 110 6.4. Solar PV system design .......................................................................................... 111 6.4.1. Residential electricity demand ...................................................................... 111 6.4.2. Solar electricity generation ............................................................................. 112 6.4.3. Solar PV System Design ................................................................................ 113 6.4.4. Cost-effectiveness analysis ........................................................................... 117 6.5. Summarized key findings ....................................................................................... 119 7. Case study III .................................................................................................................. 120 7.1. Status quo ................................................................................................................ 120 7.1.1. Building Envelope........................................................................................... 120 7.1.2. Energy usage pattern ..................................................................................... 123 7.1.3. BER Rating ..................................................................................................... 124 7.2. Identified energy saving measures ......................................................................... 124 7.2.1. Building envelope ........................................................................................... 124 7.2.2. Cost analysis of Building retrofit..................................................................... 127 7.3. Solar PV ................................................................................................................... 129 7.3.1. Residential Electricity Demand ...................................................................... 129 7.3.2. Solar PV System Design Using Homer Pro Software: .................................. 130 7.3.3. Cost effectiveness analysis ............................................................................ 135 7.4. Summarized key findings ........................................................................................ 139 8. Local environmental and socio-economic impacts ........................................................ 139 8.1. Life cycle assessment ............................................................................................. 139 8.1.1. Goal and scope .............................................................................................. 139 8.1.2. Methodology ................................................................................................... 143 8.1.3. Life cycle inventory analysis .......................................................................... 145 8.1.4. Life cycle impact assessment ........................................................................ 151 8.1.5. Data interpretation .......................................................................................... 159 VI 8.1.6. Critical discussion........................................................................................... 169 8.2. Socio-economic impact assessment ....................................................................... 170 8.2.1. Introduction ..................................................................................................... 170 8.2.2. Methodology ................................................................................................... 171 8.2.3. Data collection ................................................................................................ 172 8.2.4. Result ............................................................................................................. 177 8.2.5. Critical discussion........................................................................................... 184 9. Next step to be taken for house owners ......................................................................... 185 9.1. Community energy capacity building ...................................................................... 186 9.2. Community ownership of renewable energy projects ............................................. 186 9.3. Solar community ...................................................................................................... 188 9.4. Community solar participation models .................................................................... 189 9.5. Benefit of solar community ...................................................................................... 190 10. Conclusion and recommendation ................................................................................... 191 11. References ...................................................................................................................... 195 12. Annex .............................................................................................................................. 207 VII List of abbreviations °C ....................................................................................................................... Degrees Celcius ∆T ............................................................ Temperature drop across the radiatoror surface area AWHP .................................................................................................... Air to Water Heat Pump BER ......................................................................................................... Building Energy Rating BOS ................................................................................................................. balance of system CE ......................................................................................................... Community Engagement CED ................................................................................................. Cumulative energy demand CEG ......................................................................................................Clean Energy Guarantee COCE ............................................................................................ Cost of Consumed Electricity COE .................................................................................................... Cost of generated Energy COP ................................................................................................... Coefficient of Performance CRU ................................................................................. Commission for Regulation of Utilities DCCAE .................................. Department of Communication Climate Action and Environment DEAP ........................................................................... Dwelling Energy Assessment Procedure DHW ............................................................................................................ Domestic Hot Water EEM ................................................................................................. Energy Efficiency Measures EPBT ....................................................................................................... Energy Pay Back Time Epv_gen .............................................................. Energy Generated from the Photovoltaic system EROI ............................................................................................. Energy Return On Investment GHE ....................................................................................................... Ground Heat Exchanger GWP .................................................................................................. Global Warming Potentials HLI ................................................................................................................. Heat Loss Indicator HP ............................................................................................................................... Heat Pump IC .................................................................................................................... International Class IEA .................................................................................................. International Energy Agency IO ............................................................................................................................... input-output ISEA............................................................................................. Irish Solar Energy Association ISO...................................................................... International Organization for Standardization kJ/kgK ...................................................................................... kiloJoule per Kelvin per kilogram kW................................................................................................................................... kilowatts kWh........................................................................................................................... kilowatt hour kWhthermal ................................................................................................................. Thermal kWh LCI ................................................................................................................ Life Cycle Inventory LCOE ............................................................................................... Levelized Cost of Electricity LEAP ............................................................................... Loop Head Energy Action Partnership LH ............................................................................................................................... Loop Head m ......................................................................................................................................... meter M&E .................................................................................................... Monitoring and Evaluation MG ............................................................................................................................ metallurgical MJ-eq ..........................................................................................................Megajoule equivalent ml .................................................................................................................................... millilitres mm .................................................................................................................................millimeter MPPT .......................................................................................... Maximum Power Point Tracker MSS ........................................................................................ Microgeneration Support Scheme NH3 ................................................................................................................................ Ammonia NO2 ..................................................................................................................... Nitrogen dioxide NPV ................................................................................................................ Net Present Value NREL ............................................................................ National Renewable Energy Laboratory NSMP ..................................................................................... National Smart Metering Program VIII Ƞ𝑔𝑟𝑖𝑑 ..................................................................................................... Grid conversion efficiency O&M................................................................................................. Operation and Maintenance PEinv ...................................................................................................... Primary Energy Invested PV ............................................................................................................................................. 49 RE ................................................................................................................. Renewable Fraction ROI ............................................................................................................ Return on Investment SEAI............................................................................... Sustainable Energy Authority of Ireland SG............................................................................................................................... solar grade SOX ........................................................................................................................... sulfur oxide STEM ....................................................... Science, Technology, Engineering, and Mathematics T ................................................................................................................................... Life Time W ........................................................................................................................................ Watts W/K .....................................................................................................................Watts per Kelvin W/m2 K ..................................................... U value is Watts per square meter per degree Kelvin IX List of tables Table 2.1: Questionnaire preparation key take-aways .............................................................. 8 Table 2.2: Indicative level of community engagement ............................................................ 12 Table 3.1: Summary of the main DEAP inputs ........................................................................ 22 Table 3.2: Domestic fuels prices .............................................................................................. 23 Table 3.3: Cost estimate classification matrix for the building and general construction industries” ....................................................................................................... 25 Table 3.4: Lifetime of components and equipment ................................................................. 32 Table 4.1: Summary of different types of solar panel .............................................................. 50 Table 4.2:Home Energy Grants options .................................................................................. 64 Table 4.3: SEAI Grant amounts of Building retrofits................................................................ 66 Table 4.4: Heat Pump Grants .................................................................................................. 68 Table 4.5: Solar PV Grants Available ...................................................................................... 68 Table 5.1: Status quo of Case study I...................................................................................... 72 Table 5.2: Energy cost savings and payback period of insulation .......................................... 77 Table 5.3: Upgrade recommendation with their U-Values of case study I ............................. 78 Table 5.4: Cost Analysis for different retrofitted steps of case study I .................................... 79 Table 5.5: Existing Radiators' Sizes and Heat Emittance ....................................................... 82 Table 5.6: Base Case Scenario Room Wise Heat Loss .......................................................... 82 Table 5.7: Refurbished house room wise heat loss ................................................................ 83 Table 5.8: New Radiators Sizes and Heat Emittance ............................................................. 84 Table 5.9: Least cost PV system configurations- Case study I .............................................. 91 Table 5.10: PV system performance of cost optimized system- Case study I ........................ 91 Table 5.11: Sensitivity analysis by varying tilt angle- case study I.......................................... 92 Table 5.12: PV system performance with battery storage and power diversion to hot water tank ................................................................................................................. 93 Table 5.13: Least cost PV system configuration for the system with heat pump electrical demand- Case study I..................................................................................... 94 Table 5.14: Performance of PV system including electrical demand from heat pump- Case I ....................................................................................................................... 95 Table 5.15: Cost analysis summary for Case I ........................................................................ 98 Table 5.16: Payback period and ROI for case study I ............................................................. 98 Table 5.17: Cost sensitivity analysis- Case I ........................................................................... 99 Table 6.1: Status quo of case study II ................................................................................... 102 Table 6.2: Upgrade Recommendations with their U-Values for case study 2 ...................... 107 Table 6.3: Cost Analysis for upgrade recommendation ........................................................ 108 X Table 6.4: Least cost PV system configurations ................................................................... 113 Table 6.5: Electricity generation from solar PV system ........................................................ 114 Table 6.6: Cost analysis summary of 5.92kWp solar PV system with 2.4kWh battery ........ 117 Table 6.7: Payback period and return on investment of 5.92kWp solar PV system with 2.4kWh battery .............................................................................................. 118 Table 6.8: Cost sensitivity analysis for the solar PV system with battery- Case II ............... 118 Table 7.1: Default U values .................................................................................................... 120 Table 7.2: Energy Cost Savings and payback period of insulation ....................................... 125 Table 7.3 Upgrade Recommendation with their U-Values of case study III ........................ 127 Table 7.4 Cost Analysis for different retrofitted steps of case study 3 .................................. 127 Table 7.5: Least cost PV system configuration ..................................................................... 130 Table 7.6: Electricity generation from selected solar PV system .......................................... 132 Table 7.7: Cost Analysis – Case Study III ............................................................................. 135 Table 7.8: Payback period and ROI for case study III ........................................................... 136 Table 7.9: Sensitivity Analysis for the 2.96 kWp system ....................................................... 136 Table 7.10: Sensitivity Analysis for the 4.4 kWp system ....................................................... 137 Table 8.1: ILCD 2011 MIDPOINT+ impact categories .......................................................... 152 Table 8.2: The impact Categories of Cumulative Energy Demand (CED) method .............. 160 Table 8.3: GVA multiplier II .................................................................................................... 175 Table 8.4: Technical cost breakdown structure of PV solar system corresponding the SIC sectors........................................................................................................... 175 Table 8.5: Summary of total investment and GVA of case study III...................................... 179 Table 8.6: Potential of job creation in Loop Head for 1,100 households .............................. 181 Table 8.7: Potential of job creation in Loop Head for 100 households ................................. 181 XI List of figures Figure 1.1: Scope of the IC 2022 work ...................................................................................... 3 Figure 2.1: Poster communication flow.................................................................................... 10 Figure 3.1: Building Energy Rating scales ............................................................................... 15 Figure 3.2: Energy assessment methodology ......................................................................... 16 Figure 3.3: Rooms distribution drawings ................................................................................. 21 Figure 3.4: Section of a pitchpitched roof ................................................................................ 21 Figure 3.5: Total floor area ....................................................................................................... 21 Figure 3.6: Rooms areas.......................................................................................................... 21 Figure 3.7: Section of a pitch roof ............................................................................................ 21 Figure 3.8: Rooms areas.......................................................................................................... 21 Figure 3.9: Wall elevations ....................................................................................................... 21 Figure 3.10: Walls elevations ................................................................................................... 21 Figure 3.11: Total floor area ..................................................................................................... 21 Figure 3.12: Case study approach for PV technology ............................................................. 26 Figure 3.13: Operating mechanism of Homer Pro Software ................................................... 28 Figure 3.14: Inflation rate in Ireland ......................................................................................... 30 Figure 4.1: Average house heat losses ................................................................................... 33 Figure 4.2: Types of insulation. ................................................................................................ 34 Figure 4.3: Types of roofs ........................................................................................................ 35 Figure 4.4: Roof insulation. ...................................................................................................... 36 Figure 4.5 Wooden floor insulation .......................................................................................... 38 Figure 4.6: Concrete floor insulation ........................................................................................ 39 Figure 4.7: Energy-efficient window. ........................................................................................ 39 Figure 4.8: Work Has to Be Done to Transfer Heat ................................................................ 41 Figure 4.9: Heat Pump Components ....................................................................................... 43 Figure 4.10: Geothermal heat pump schematic ...................................................................... 45 Figure 4.11: Air to Air Heat Pump Schematic .......................................................................... 46 Figure 4.12: Air to Water Heat Pump Schematic .................................................................... 48 Figure 4.13: Solar Irradiance ................................................................................................... 52 Figure 4.14: Monthly PV generation of 1kWp system ............................................................. 53 Figure 4.15: Average monthly solar irradiation ........................................................................ 54 Figure 4.16: Sun path chart of Loop Head, Peninsula ............................................................ 55 Figure 4.17: Domestic solar PV system integration priority .................................................... 57 Figure 4.18: Mobile app for solar PV system performance monitoring ................................... 59 Figure 4.19: Solar PV system configuration ............................................................................ 60 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619239 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619240 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619241 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619242 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619243 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619244 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619245 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619246 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619247 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619256 XII Figure 4.20: Ground mounting system solar PV panels .......................................................... 63 Figure 4.21: Hot water diverter configuration .......................................................................... 64 Figure 5.1 Infrared and digital image of the attic walls ............................................................ 73 Figure 5.2: Infrared and digital image of the external walls..................................................... 74 Figure 5.3: Annual estimated electricity demand profile ......................................................... 75 Figure 5.4 End-Use Breakdown of electrical appliances ......................................................... 75 Figure 5.5: Total investment cost and Discounted Payback Period of case study 1 .............. 80 Figure 5.6: Condensing oil boiler name plate .......................................................................... 81 Figure 5.7: Heat Pump Monthly Heat Generation Vs Electrical Consumption ....................... 86 Figure 5.8: Heat pump performance on the coldest day of the year ....................................... 87 Figure 5.9: Selected AWHP COP uration Curve ..................................................................... 88 Figure 5.10: Comparison of Synthetic and actual monthly load profile ................................... 90 Figure 5.11: Monthly PV performance for cost optimized system- Case I .............................. 92 Figure 5.12:Perfomence comparision for the lowest COE system ......................................... 95 Figure 5.13: Time-series plot of PV system during lowest irradiation- case study I ............... 96 Figure 5.14: Time-series plot of PV system during highest irradiation- case study I .............. 97 Figure 5.15: System performance throughout the year ........................................................... 97 Figure 5.16: Cost savings for case study I ............................................................................ 100 Figure 6.1: Infrared and digital image of the windows. .......................................................... 103 Figure 6.2: Infrared and digital image of the windows. .......................................................... 103 Figure 6.3: Infrared and digital image of the external walls................................................... 104 Figure 6.4: Infrared image of the porch. ................................................................................ 105 Figure 6.5: Annual estimatedl electricity consumption .......................................................... 106 Figure 6.6 End-Use Breakdown of electrical appliances ....................................................... 107 Figure 6.7: Total investment cost and Discounted Payback Period of case study 2 ............ 109 Figure 6.8: Existing geothermal heat pump heat generated against electricity consumed .. 111 Figure 6.8: Electricity consumption pattern ........................................................................... 112 Figure 6.9: Performance comparison for the lowest COE systems ..................................... 114 Figure 6.11: Monthly electricity generation by 5.92 kWp solar PV system with battery ....... 115 Figure 6.12: Time-series plot of PV system during low irradiation days ............................... 116 Figure 6.13: Time-series plot of PV system during high irradiation ...................................... 117 Figure 6.14: Cost savings for case study I ............................................................................ 119 Figure 7.1: Infrared image of single glazed window with aluminium frame. ......................... 121 Figure 7.2 Digital image of under-roof space ........................................................................ 122 Figure 7.3 Infrared image of the house roof from the outside ............................................... 122 Figure 7.4: Infrared image of an uninsulated external wall. .................................................. 122 Figure 7.5: Infrared image of the unused fireplace................................................................ 123 XIII Figure 7.6: Monthly electricity consumption of case study 3 ................................................. 123 Figure 7.7: End use energy breakdown of case study 3 ....................................................... 124 Figure 7.8: Total investment cost and Discounted Payback Period of case study 3 ............ 129 Figure 7.9: Comparison of the synthetic and monthly demand profile .................................. 130 Figure 7.10: Performance comparision for the lowest COE systems ................................... 131 Figure 7.11: Monthly electricity generation by 4.4 kWp solar PV system ............................. 133 Figure 7.12: Time series plot of electricity generation and demand during lowest irradiation day ................................................................................................................ 134 Figure 7.13: Time series plot of electricity generation and demand during highest irradiation day ............................................................................................... 134 Figure 7.14: Cost saving for 2.96 kWp PV system without battery case study III ................ 138 Figure 7.15: Cost saving for 4.4 kWp system with battery case study III ............................. 138 Figure 8.1: The scope and system boundaries of the solar PV life cycle analysis ............... 141 Figure 8.2: Transportation of solar system components from China to Ireland .................... 142 Figure 8.3: LCA methodology ................................................................................................ 143 Figure 8.4: Simplified process flow diagram of an LCA for a PV system Source: Author .... 147 Figure 8.5: Supply chain of sillicon-based PV electricity production ..................................... 147 Figure 8.6: Input/output data in OpenLCA for solar grade silicon production in PV module manufacturing. .............................................................................................. 148 Figure 8.7: Lifecycle CO2 emissions for the 2.96 kWp PV system components .................. 153 Figure 8.8: CO2 emissions for the 4.4 kWp PV system components .................................... 154 Figure 8.9: Total emissions comparison between the two PV systems and the Irish grid ... 155 Figure 8.10: Land use comparison, 2.9 kWp without battery and Irish electricity grid ......... 156 Figure 8.11 Land use comparison, 4.4 kWp with battery and Irish electricity grid................ 157 Figure 8.12 Acidification comparison, 2.9 kWp without battery and Irish electricity grid ...... 158 Figure 8.13 Acidification comparison, 4.4 kWp with battery and Irish electricity grid ........... 159 Figure 8.14: Non-renewable primary energy demand for the 2.96 kWp PV system ............ 161 Figure 8.15: Non-renewable primary energy demand for the 4.4 kWp PV system .............. 162 Figure 8.16: Electricity generation carbon intensity in Ireland, February 17 th, 2022 ............. 165 Figure 8.17: Irish system generation fuel mix, February 17th, 2022 ..................................... 166 Figure 8.18: The effect of the 2.96 kWp PV system on CO2 reduction, February 17th, 2022 ...................................................................................................................... 166 Figure 8.19: The effect of the 4.4 kWp PV system on CO2 reduction, February 17th, 2022 . 167 Figure 8.20: Electricity generation carbon intensity in Ireland, July 14th, 2021.................... 167 Figure 8.21: The effect of the 2.96 kWp PV system on CO2 reduction, July 14th, 2021 ..... 168 Figure 8.22:The effect of the 4.4 kWp PV system on CO2 reduction, July 14th, 2021 ........ 168 Figure 8.23: GVA comparison per industry, for case study III with and without battery ....... 178 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619321 XIV Figure 8.24: GVA comparison per type of expenditure, for case study III with and without battery ........................................................................................................... 179 Figure 8.25: Human resource requirements for workers in solar PV .................................... 183 Figure 9.1: Way forward LEAP project .................................................................................. 185 Figure 9.2: S Schematic of energy system based on the community-ownership business model ............................................................................................................ 187 Figure 9.3: Number of community energy initiatives from the nine European countries ...... 188 Figure 9.4: Energy community share per technology in Europe ........................................... 188 Figure 9.5: Community solar billing system ........................................................................... 189 Figure 9.6: Structure of P2P electricity trading model ........................................................... 191 List of equation XV List of equation Equation 3.1: Total investment cost ......................................................................................... 24 Equation 3.2: Total net saving ................................................................................................. 24 Equation 3.3: Simple payback period ...................................................................................... 24 Equation 3.4: Discounted payback period ............................................................................... 24 Equation 3.5: Electricity consumption of water heating ........................................................... 29 Equation 3.6: COCE ................................................................................................................. 29 Equation 3.7: Grid O&M cost ................................................................................................... 31 Equation 4.1: Change Of Entropy ............................................................................................ 40 Equation 4.2: Reversible System Entropy ............................................................................... 41 Equation 4.3: Irreversible System Entropy .............................................................................. 41 Equation 4.4: Work of A Heat Pump ........................................................................................ 41 Equation 4.5: Heat Pump COP ................................................................................................ 41 Equation 4.6: Heat Pump COP ................................................................................................ 42 Equation 4.7: Deemed export quantity calculation .................................................................. 71 Equation 8.1: Energy Pay Back Time .................................................................................... 163 Equation 8.2: Energy Return On Investment ......................................................................... 164 Equation 8.3: Emission factor ................................................................................................ 165 Equation 8.4: Industry output ................................................................................................. 173 Equation 8.5: GVA output ...................................................................................................... 173 Equation 8.6:The total output of each industry ...................................................................... 174 Equation 8.7: Input-output matrix ........................................................................................... 174 Equation 8.8: Linear equation in Leontief model ................................................................... 174 Equation 8.9 Leontief Inverse ................................................................................................ 174 Equation 8.10: Calculation of energy supply jobs - construction .......................................... 180 Equation 8.11: Calculation of energy supply jobs – O&M ..................................................... 180 Annex XVI Annex Annex 1: Stakeholder identification ....................................................................................... 207 Annex 2: Stakeholders mapping ............................................................................................ 209 Annex 3: Community engagement activities roadmap .......................................................... 210 Annex 4: Pre-survey questionnaire ........................................................................................ 211 Annex 5: Music gala ............................................................................................................... 217 Annex 6: Solar deep dive ...................................................................................................... 218 Annex 7: Solar deep dive arising questions .......................................................................... 219 Annex 8: Workshop 2 ............................................................................................................ 220 Annex 9: DEAP for New – Final and existing Home Survey Form ....................................... 221 Annex 10: Excel Data collection sheet .................................................................................. 224 Annex 11: FLIR B40 Technical Specifications....................................................................... 225 Annex 12: Link to thermographer inspection report – case study I, II, and III ...................... 225 Annex 13: Cost estimation for building retrofiting in Ireland .................................................. 225 Annex 14: AWHP ................................................................................................................... 226 Annex 15: AWHP Auxilary Equipment ................................................................................... 227 Annex 16: Space Heating and DHW Excel Model Interface ................................................. 229 Annex 17: Radiators ............................................................................................................... 229 Annex 18: Existing Geothermal Heat Pump Commissioning Sheet ..................................... 230 Annex 19: Solar Panel ........................................................................................................... 230 Annex 20: Solis Mini Inverter ................................................................................................. 233 Annex 21: Solis Single Phase Dual MPPT ............................................................................ 234 Annex 22: US2000B PYLONTECH ....................................................................................... 235 Annex 23: US3000 Pylontech ................................................................................................ 236 Annex 24: Triple Power .......................................................................................................... 237 Annex 25: Hot water power diverters..................................................................................... 238 Annex 26: Solar PV system with 2.2 kWp capacity (bill of quantity) .................................... 238 Annex 27: Solar PV system with 1.48 kWp capacity ............................................................. 239 Annex 28: Solar PV system with 2.96 kWp capacity ............................................................. 240 Annex 29: Solar PV system with 4.4 kWp capacity ............................................................... 241 Annex 30: Solar PV system with 5.18 kWp capacity without battery storage ....................... 243 Annex 31: Solar PV system with 5.92 kWp capacity with 3.6kWh battery storage .............. 244 file://///Users/fwu/Documents/IC2022%20-%20Group%20report/FINAL%20FOLDER/IC2022_FINAL_24Feb2022.docx%23_Toc96619179 Executive summary XVII Executive summary Residential buildings consume a considerable amount of energy to cater for the needs in lighting, space heating and cooling and other households’ electrical appliances. Inefficient use of this energy could imply unnecessary consumption which would translate to high energy expenses incurred by the building owners. Hence, this study builds up on prior work done by a group of engineering students from the University of Flensburg, Germany in 2020 and 2021 on sustainable energy in Loop Head, Ireland. This paper documents events, methodologies, findings and recommendations from the 5-weeks community energy project focussed on the Loop Head residential sector that was undertaken by the 2022 batch of students. A community engagement approach and activities designed to enable researchers to gain a better understanding of the residential demographics and household energy usage have been documented. Further, a set of criteria for the selection of 3 representative households for the case studies have been highlighted. For these houses, energy assessments have been conducted to advise on ways of eliminating electrical and heat losses from the buildings. DEAP 4.2.0 and Excel models have been used to collect buildings’ envelope data information and for the calculation of the building energy efficiency rating while the Sketchup software has been utilized for the building dimensions calculations. For case I, the building was estimated to have a heat loss indicator (HLI) of 4.89 W/K/m2 corresponding to a BER Rating of “D2” in the Sustainable Energy Authority of Ireland BER scale. Case II’s HLI of 1.94 W/k/m2 corresponded to a BER Rating of “C1” while Case III was estimated to have an HLI of 8 W/k/m2 which corresponds to BER Rating of “G”. Recommendations on improving the BER Rating, and hence the buildings’ energy efficiency, have been outlined for each of these 3 cases. Further, the electrical and heat demand for these houses have been analysed to size an efficient space heating system as well as a solar PV system. For space heating, different types of heat pumps are discussed including Brine-to-Water, Air-to-Air and Air-to-Water. Of these, Air-to-Water (AWHP) was suggested for its higher efficiency. In Case I, for instance, an AWHP was estimated to achieve annual savings of up to 273 €/year with a simple payback period of approx. 10 years. For solar systems, an electrical load profile was generated by considering the units consumed as per the energy bills and consumer behaviour. Solar systems have been designed and optimized in HOMER Pro for the three cases. Similarly, the systems’ economics have been analysed and recommendations have been provided to the homeowners. Moreover, grants supporting sustainable technologies have been highlighted within this report along with their eligibility criteria. Executive summary XVIII Additionally, one of the cases with the Solar PV system has been analysed for its environmental and socio-economic impact on the Loop Head community. The analysis has focussed on the carbon footprint for a PV system with a battery and another one without a battery starting from the manufacturing and throughout the 25-years utilization of the PV system. The analysis concluded that the most carbon and energy-intensive stage of the life of a PV system is the manufacturing phase. Further, the effect of introducing a PV system on CO2 reduction was investigated with results showing that a Solar PV system has the potential to directly reduce emissions by replacing electricity from the grid. On the socio-economic assessment, this research revealed that solar PV installations could serve as an income stream for Ireland with the economy gaining about 30 cents for every 1 euro spent on the system. Moreover, the potential jobs created by solar PV installations is discussed in this paper. Finally, this study documents a roadmap including steps that the Loop Head residents and community should consider in their journey towards achieving sustainable energy in their residential sector. Introduction 1 1. Introduction By Ifechukwude Chinye-Ikejiunor The international class (IC) is a compulsory module of the Master programme in Energy and Environmental Management (EEM) at the Europa Universität Flensburg (EUF), Germany. The IC gives students the opportunity to work in a team on a multidisciplinary, practical, energy- related problem in a real-life situation within a given time frame to exercise the application of appropriate scientific research methods. In practice, during the IC module, students and lecturers adopt a problem-solving approach to investigate and address an energy-related challenge in a community. An integral component is the collaboration with the community members and local partners in realizing feasible solutions to the identified challenges in the community. The IC has undertaken several community-based projects including the Loop Head Energy Action Partnership (LEAP) in Ireland since 2020. This is a collaboration between EEM department at EUF, the development organizations of Carrigaholt, Kilballyowen and Kilkee, Loop Head Tourism, the Farming Community of Loophead (Carrigaholt, Kilballyowen and Kilkee parishes), local residents, business owners, interested individuals and Astoneco Management. The LEAP partnership aims to understand the constituents of the local energy status quo in order to explore possible case studies of prospective energy balances and to help encourage the use of sustainable energy resources in the Loop Head community. The LEAP programme has been in operation since 2020. 1.1. Background IC 2020 intended to empower the community with adept information on the renewable energy potential with a strategy to involve and facilitate local sustainability. The study revealed that emigration and lack of job opportunities were among the key challenges of the community, hence, the focus was on Loop Head’s demand assessment, community-level renewable energy resources assessment and their feasibility in the transport, farms and residential sectors. According to (Astoneco Management, 2020), the findings showed that the geographical location of Loop Head gives it a good potential for wind, solar, biomass and wave energy. Farming, especially cattle rearing, was indicated to be mostly practised by the community as most of the land in Loop Head is grassland; grasses are used to feed cattle in the form of silage. The farming activities from the livestock rearing results in a good potential for the Introduction 2 biomass resource. It was estimated that the total biogas that could be produced by slurry and silage in Loop Head would have an energy content of 7,614 MWh/year, which was estimated could heat over 450 homes. The potential of wind energy at mean wind speeds of 9.2m/s and a height of 100m above ground level was estimated at 13.82 GWh of annual generation. The solar daily specific yield was found to be in the range of 2.62 kWh/kWp and 2.65 kWh/kWp, which is higher than Ireland’s average value of 2.51 kWh/kWp. Loop Head Peninsula has roughly a total electricity demand of 18.4 GWh, and a potentially available area from rooftops in residential and agriculture sectors equivalent to 0.247 km². This translates to an estimated installation of 23.52 MWp of solar photovoltaic capacity. The combined estimated energy from biomass, wind and solar would meet the demand of Loop Head. Findings also showed that a major part of heat and electricity consumption resulted from buildings meant for residence and holiday homes. Finally, pre-feasibility studies for community-owned wind, solar, and biogas plants were conducted and a simple energy model was designed to study potentials and develop a vision for an energy self-sufficient Loop Head. By modification of different parameters in the model, the share of renewable energies can be varied to attain 100 % and even more of total generation if the total demand experiences an increase on an annual basis. Seeing the outcome from the synergy with the engagement of the community, the IC2021 sustained the connection with the locals. Although it was carried out online as a result of the global pandemic, community capacity building workshops were conducted through the LEAP Energy Academy to explain relevant topics in community energy for Loop Head. A step into investigating energy efficiency measures in buildings and their effect on energy consumption in the residential and farming sectors was carried out. As a result, the building energy rating (BER) estimator tool was developed to provide recommendations on improving energy efficiency for households based on their pre-existing conditions. The study also identified several factors which influence the heat demand in the residential sector and the retrofitting measures to reduce heat demand and heat losses in buildings. Additionally, to promote and assist in the establishment process for the findings, an excel energy modelling tool was developed for Loop Head. The model was designed to aid the users to identify solutions for obtaining energy efficiency in residential buildings and attain sustainability in Loop Head Farms. Introduction 3 1.2. Scope and objectives Building on the previous work done by IC 2020 and IC2021, IC 2022 started by reviewing the previous findings and brainstorming on a possible way-forward to delivering solutions to the observed challenges. A focus on improving energy efficiency in the residential sector was established following the findings where it was observed that the bulk of the energy demand came from the residential sector in the community. To refine the scope of IC 2022, a questionnaire was shared and a feedback session with the community was organised to gather questions and interests that were later accommodated in the scope of work in order to meet the expectations of the community. Some of the questions asked were: • How much will a solar installation cost me? • What capacity of Solar PV should I go for? • How can I use less energy in my house? • What is my BER rating now? • Whom should I contact for retrofitting of my house? • Are there any grants I am eligible for to improve my house BER rating? • Are there any environmental effects caused by solar panels? 1.2.1. Scope of work The study for the IC2022 centres around developing solutions in the residential sector for the Loop Head community. The scope of work, showing the core interests, can be seen in Figure 1.1 below: Figure 1.1: Scope of the IC 2022 work Introduction 4 Source: Author The IC 2022 adopted a holistic approach focusing on four dimensions to specifically provide solutions to the Loop Head community residential sector: 1. Energy assessment for residential buildings: ascertaining and upgrading the houses to be energy efficient. 2. Feasibility study on solar technology and space heating for residential buildings: finding integrated energy-efficient technological solutions. 3. Stakeholder engagement: building and sustaining community participation in the IC2022. 4. Environmental and socio-economic impact assessments: measuring the environmental and socioeconomic impacts of installing a PV system on Loop Head using the methods of Lifecycle Assessment (LCA) and input-output analysis. 1.2.2. Objectives Therefore, the IC2022 streamlined its focus to finding solutions that could: • Strengthen community engagement and build on existing structures from the previous work done. • Provide energy efficiency recommendations including retrofitting and energy conservation measures to improve energy usage. • Select representative households as case studies to carry out energy assessments covering their energy use and saving potential and the design and modelling of real- life, feasible solutions. • Create awareness on available SEAI grants and incentives and the eligibility criteria. • Assess environmental and socio-economic impacts and benefits such as carbon footprint, value added and employment in order to ascertain the actual impact from clean energy solutions on the community. • Design space heating solutions such as heat pumps and assess the cost implications and potential resultant benefits to address the households’ heating challenges. Community engagement 5 • Design Solar Photo-voltaic (PV) systems and integration solutions to maximise the use of renewable energy at the household level and assess the cost and also benefits from this clean energy for the households in the Loop Head community. Chapter 2.3 of the IC 2022 report gives detailed information on how the action in Ireland began with an interaction at informal levels and then unto workshops with the community such as the solar deep-dive event. Furthermore, as seen in Chapter 3.2 of this report, pre-assessment surveys where students visited houses of house owners interested in energy efficiency and/or solar PV followed. A scope for each selected household was defined and an energy assessment was carried out to gather the required data for the design of solutions to meet the energy demand of the household through sustainable energy. Also, an analysis of the cost and benefits and environmental and socio-economic impacts was carried out as seen in Chapters 8.1 and 8.2. Finally, IC 2022 presented the results with recommendations, which can be found in Chapter 10 of this report, to the community. 2. Community engagement By Max Andriamanalina Community engagement is a crucial part of LEAP, a program in which EUF is partnering with local development organisations in Loop Head as well as AstonEco Management. Community engagement is used to have a better understanding of the Loop Head community’s viewpoint and preferences on the energy sector. Several engagement activities were carried out by the team to interact with the community as well as to integrate the community into the project. These activities were, therefore, an opportunity for the team to familiarize themselves with the community and to investigate the challenges they are currently facing in the energy sector. Knowing those challenges helped the IC 2022 team to identify suitable approaches to tackle these challenges. As mentioned above, several activities were organized by the students in collaboration with the local partner, Astoneco, for the purpose of bringing the team and the community together. These activities include, informal social interaction with local stakeholders and the community members at,e.g., the local pubs, cultural nights, a solar lab demonstration, and two workshops that took place at Kilkee Bay Hotel. The team also conducted a visit to the Carrigaholt National primary school in an effort to increase clean energy awareness among pupils. Additionally, the IC 2022 team participated in community-initiated Sunday walks. Participating in these walks Community engagement 6 afforded the team a platform to receive feedback from the community members about previously conducted activities. 2.1. Stakeholders’ identification By Hiram Masese Constructive community engagement (CE) has been indicated as a prerequisite for the success of projects (Clean Energy Council, 2022). The council further argues that investing time and effort in building relationships is most likely to be reciprocated by embracement and support from the community members. Consequently, CE ought to be a continuous process starting from the planning phase and lasting throughout the life cycle of a project (USAID, n.d.). Similar to other communities, Loop Head (LH) has its unique challenges and priorities as outlined in the (Loop Head Together, n.d.) 9-point strategy which the project team required to be appraised on, to enable a formulation of an appropriate interaction approach. For instance, IC 2022 learnt in a timely fashion of the lifting of the stringent COVID-19 protocols which had previously been enforced in Ireland. Consequently, this implied that the team could plan for face-to-face interactions with community members while adhering to medical safety precautions on the pandemic. Fortunately, this project’s team adopted an early initiation of CE strategy through maintaining contact and holding preliminary planning sessions with the liaison person, John Aston. This ensured that such vital information was relayed well in advance. From spearheading operations of an organization that facilitates and co-develops sustainable community programmes (Astoneco Management, n.d.) – including in LH, he has played an integral social focal point role connecting the Flensburg University’s IC to LH community for the past 3 years. IC 2022 used this goodwill, in addition to the previous 2 years’ work, to gain insights on the demographics and the existing knowledge of the community in the energy sector as well as to identify and commence the creation of key stakeholders’ database. The IC 2022 team acknowledged the diversity in perspectives that may exist in a community and sought to establish a list of community members who would either be affected by or have an interest in the project. This was a live document which was updated throughout the course of the project whenever new information and contacts arose. The identified list is as tabulated in Annex 1. Further, (Kumar, 2015) work approach was used to classify these individuals and institutions into 4 groups based on the level of interest and the influence they were likely to have on the project as highlighted in the action plan below: i. Manage – Key stakeholders, with both high influence and interest, who were actively engaged throughout the project lifecycle. Community engagement 7 ii. Satisfy – These had great influence, despite low interest, on the project. Activities undertaken were reviewed regularly against their expectations. iii. Inform – Although their direct influence on the project was on a limited scale, they had the opportunity to influence other project participants. iv. Monitor – They were watched closely, despite ranking low on the priority list by virtue of having both low interest and low influence on the project. The detailed classification of stakeholders is highlighted in Annex 2. This information was then applied to help align on expectations and to curate suitable engagement schedules and methods. For instance, contacts of the Kilkee Bay Hotel owner enabled scheduling of the workshop sessions which are discussed later in this chapter as well as the reservation of the conference hall. Moreover, a partnership with a local company that provided solar PV panels and an inverter for demonstration purposes informed the decision on the complimentary electronic measuring instruments carried by the IC team from the University Department. Whereas knowledge on the expected audience demographics enabled the choice of flip charts, powerpoint presentations and practical solar lab sessions as engagement approaches 2.2. Communication approach By Hiram Masese A communication schedule is a crucial part of CE for it outlines the plan of activities, techniques and the media through which information is relayed to the community (USAID, n.d.). The same source indicates that clear communication enhances transparency, builds trust and mitigates parties’ conflicts. IC 2022 documented and shared a road map, see Annex 3, comprising of the dates and venues for the activities planned towards enriching the stakeholders’ engagement efforts. This was yet another live document which was updated based on the dynamic needs of the project. Prior to that, an energy survey questionnaire, Annex 4, was prepared while still in Germany to establish communication with some 44 community members within an existing WhatsApp group. The design of the survey was characterised by iterations to incorporate various contributions from both internal and the community mobilization teams. Ultimately, the survey received a low participation rate and recorded only 9 responses. Nonetheless, the questionnaire was used as a tool for identifying and contacting house owners who were interested to have their house energy consumption assessed. Invitations to conduct case studies for the 3 sampled households whose findings have been discussed in later chapters Community engagement 8 of this report were a result of the survey shared. Additionally, the team documented key learning lessons from the preparation exercise as tabulated in Table 2.1below. Table 2.1: Questionnaire preparation key take-aways Source: Author based on project planning exercise Key Take-away Description Complexity of the sentences A fairly designed questionnaire uses simple language structure to reach a wider audience. Where technical terms must be used, defining them is vital for the audience’s understanding. Clear motive of questions Adequate transparency on formulation of all questions enable respondents to understand the importance and, to an extent, how the information they provide will be used. This affords them comfort when providing the answers. Length of the survey There is a risk of respondents losing focus with very long questionnaires. (Versta Research, 2011) estimates that data quality reduces significantly for surveys requiring longer than 20 minutes. Flow of questions Keeping questions related to a similar topic in the same section in a way that ensures respondents have adequate foundation knowledge of dependent questions makes the questionnaires clearer. General Data Protection Regulation Unless absolutely necessary and on respondents’ voluntary acceptance, questions which prompt for personally identifiable information ought to be avoided to maintain anonymity. Preparation Timing Iterations of the draft are inevitable as different views have to be incorporated. Preparation should, therefore, commence early. Questionnaire participation reminders Significant responses were as a result of face-to-face reminders upon arriving on the loop. Responses are likely to be more forthcoming through increased in-person interactions. In a bid to achieve familiarity with community members prior to joining them in Loop Head, the Flensburg team documented and shared bio-profiles detailing their country of origin, work experience and expectations on what they hoped to achieve during their IC. Further, the 1st week of the project was heavily invested in interactions with the locals within social gatherings. Collaboration between the visiting students and the locals enabled successful conduction of a music concert (see Annex 5). While all the attendees enjoyed the diverse songs and dance sessions, the project progress gained valuable contact persons - through the interaction - who became instrumental in the execution phase the following weeks. Community engagement 9 Communication for such and other events was achieved through various channels including: i. Social media on LEAP WhatsApp group comprising of 44 local members, IC 2022 constituting Flensburg team and 4 community mobilizing champions and Loop Head Community Facebook page. The project utilized the social media management representative to upload information on the Facebook page. ii. Radio news item where the LEAP partner, Astoneco, assisted in the dissemination of the upcoming events in the discussed roadmap to the community via a local radio station. iii. Face-to-face interactions and relaying of information throughphone calls and text messages for the already established contacts. These groups also advised on strategic places to display important communication such as the invitation to workshops. iv. Organized workshops: Planned sessions were also used for communicating further upcoming events in a face-to-face fashion. v. Posters: The project team quickly realized that this seemed to be a commonly used communication medium. Posters for upcoming events were designed through consultative meetings before draft designs being rolled out. Plenty of iterations were realized during the incorporation of suggested improvements. The general workflow of the design is as shown in Figure 2.1 below. Ultimately, the final approved posters were circulated for display around Carrigaholt, Kilkee and Kilrush at frequently visited business premises. Community engagement 10 Figure 2.1: Poster communication flow Source: Author 2.3. Conducted workshops By Hiram Masese Workshops were mainly organised to share the scope of IC2022, incorporate input from the community and later sessions were used to share findings of the study and to receive feedback from the community. Workshop 1 - Saturday, February 5th 2022 Dubbed ‘Solar Deep Dive’, this session was held in Kilkee Bay Hotel and was attended by 56 people; including 7 children. The 3-hour session as indicated in Annex 6 was a joint facilitation by Flensburg and Astoneco Management. It served the purpose of linking work done by the Community engagement 11 predecessor IC classes of 2020 and 2021 to the scope proposed by the class of 2022. Additionally, an introduction on working of a solar system was covered followed by a local community member sharing challenges and benefits of his 4.8 kWp Solar PV system. This provided diversity in the engagement approach where the audience were listening from a practical case in their neighbourhood. The participants were then introduced to physical solar panel components where they had a chance to have hands-on interaction and to pose further questions. Finally, the Flensburg team organized 3 information booths where they provided details to the workshop participants on building retrofit steps, solar and space heating sizings and their economics as well as life cycle assessment and socio-economic assessment of solar PV systems. The most important outcome of the session were the questions raised by the community members on the scope of what they were interested to know, see Annex 7. Questions which could not be answered immediately were processed and were incorporated within the scope of work. IC2022 noted from the session that nearly all questions raised would be addressed by the initial scope of the project. Workshop 2 - Saturday, February 19th 2022 A series of events were conducted on this day. First was a 3-hour session attended by 31 individuals at Kilkee Bay Hotel to present the first findings on one sampled case study from the three under review. Since the evaluation of the previously used booths yielded positive feedback, on this day, 5 stations (see Annex 8 were organized to offer detailed information, including those raised a fortnight earlier on: i. SEAI grants ii. Building Retrofits iii. Heat Pump technologies iv. Energy consumption and economics of solar PV systems v. Carbon footprint and emission reduction Solar lab demonstration Although the initial plan for this event was to be an actual installation of four 320 Wp solar panels donated by the local F4 Energy Limited Company to Keane’s Beer Garden, poor weather conditions prompted a change and the team opted to conduct a solar lab demonstration instead. The 20 participants in attendance were taken through the technical Community engagement 12 functionalities of the solar system components and their questions addressed, and the system components were connected to produce electricity to the grid. Carrigaholt National School Visit Later that afternoon, the team facilitated a clean energy awareness session at a local primary school for 12 children aged between 6 and 12 years. The 2-hour session entailed a basic explanation of solar, biomass and wind energy technologies. The children were taught how to make a home-made solar oven using aluminium foil and cardboard paper, a small biogas plants in 500 ml bottles using soil, organic waste, sugar and warm water by placing the mixture in an environment of about 40 °C and on how to collect the generated gas in a balloon covering the opening of the bottle. Further, they got to make simple propellers from hard paper. 2.3.1. Monitoring and evaluation Monitoring and Evaluation (M&E) was intended to track, understand and improve the effectiveness of the CE activities carried out during the project. The (USAID, n.d.) indicates that it is significant to have feedback continuously at every significant milestone of a project. IC 2022 collected responses during workshop sessions, through social media platforms and through participating in community-organized events such as the Sunday walks where feedback was solicited. Depending on the feedback obtained, action was taken to achieve the most appropriate level of CE as outlined in Table 2.2 below. Table 2.2: Indicative level of community engagement Source: Author based on (IAP2 Federation, 2014) CE Level Description and application Inform Providing clear information to stakeholders; walk participants who had limited knowledge of Flensburg University presence and objectives were appraised. Consult Receiving feedback from members, sometimes suggestions for alternatives; information on where to display event posters. Involve Executing tasks to address the concerns of the stakeholders; households energy assessment walkthroughs were conducted in presence of the owners. Members participated in solar panels installations at Keane’s Beer garden. Collaborate Working in synergy with the members to deliver on a task; the IC team visited a local resident with an installed solar PV system and partnered in the preparation of workshop material. Empower Members get a significant understanding and can make decisions on their own; workshop reports and household energy assessments were shared with the community members for their consideration on the solutions they would opt for. Community engagement 13 Some of the indicators monitored included: i. Number of attendees for organized sessions – Low turnout was taken as an indicator that mobilization needed to be intensified. Conversations such as rolling communication early and distributing posters to a wider target were held to improve attendance. ii. Budget – Expenses were monitored against their allocated fraction of the budget. Alternatives such as printing smaller, cheaper sizes of posters and hosting only necessary sessions were implemented. iii. Slight delay in starting the first workshop session – Measures such as early set- up for the subsequent sessions were taken to mitigate this challenge. iv. Feedback in questions format during and at the end of the sessions – Questions raised in the first workshop, for instance, were used to align on the scope and manage expectations. 2.4. Challenges and recommendation In general, the process of involving the stakeholders in the LEAP program went smoothly. The engagement activities were overall successful. The community was greatly involved, and the turnout in the events was largely satisfying with between 35 and 50 attendees in the two main sessions held in Kilkee. However, there were some challenges that made the community engagement difficult. The first challenge the team encountered was the availability of the community. The community members are mostly available during the weekend. This has significantly changed the plan prepared in advance by the team but also has changed the approaches adopted. Some planned activities were, as a result, cancelled or had to be reorganized. Another challenge the team was facing is the communication process. A lengthy internal approval process for communication caused delays in the activity preparation. In addition, the team relies mainly on a single contact person for communication which also causes delays if the person is not available. Additionally, the broad range of interests in the community became a challenge for the team since the team’s scope and available resources are limited. Moreover, the team could not cover all the topics that might be in the interest of the community due to time constraints. Based on the challenges mentioned above, the team came up with some recommendations. First, knowing the availability of the community in advance would help the team to properly Case study approach to energy efficiency and technologies 14 plan ahead of the activities. It is therefore recommended for the next batch of the IC to acquire information on the availability of the community and the different stakeholders prior to travelling to Loop Head. Delays could be avoided by having more than one member of the community as a contact person. Regarding the COVID pandemic, the measures had already been lifted by the time the team arrived in Loop Head. However, for precaution, the team was tested for COVID on a weekly basis. 3. Case study approach to energy efficiency and technologies 3.1. Common definitions used throughout the energy assessments and case studies Building Energy Rating Ireland measures the energy performance of the building based on the annual primary energy usage of the building represented in units of kWh/m2/year1. The BER rating scale is divided into categories from G to A where G represents the poorly performing building with the largest primary energy use and A represents the efficient building performance with the lowest primary energy use. The full range of categories is illustrated in Figure 3.1. To improve the BER rating of a house, SEAI recommends building fabric upgrades as the first step. Furthermore, switching from an oil boiler to a heat pump can have a significant impact on the BER. However, it should only be done when the house is very well insulated. An on-site renewable source of energy helps decrease the imported primary energy demand of the house further increasing the BER rating of the house. 1 Indicated in BER rating scale. Case study approach to energy efficiency and technologies 15 Figure 3.1: Building Energy Rating scales Source:(Energlaze, 2022) Heat Loss Indicator Heat Loss Indicator (HLI) is an indicator to assess if the fabric and ventilation loss of the dwelling is sufficiently low for a domestic heat pump system to work in lower space heating temperature and meet all or most of the water heating demand. HLI is measured by summing up the total fabric (walls, floors, roofs, doors and windows) and ventilation loss of the dwelling divided by the total floor area. It is basically a total heat loss per m2 of the dwelling. Thermal Camera A thermal camera is a measuring device that allows to see the thermal (infrared) radiation of surrounding objects and measure the temperature at any point on the surface with an accuracy of 0.1 ° C and higher. The device allowed to identify the construction defects such as missing or defective insulation, moisture spots, structural shortcomings, sources of heat losses. Sharp thermal images were created based on temperature differences. The hottest places are coloured in red, yellow and orange and the coldest in blue and black. Detail of the thermal camera assessment is available Annex 12. U-Value Thermal transmittance (U-value) defines the ability of an element of structure to transmit heat under steady-state conditions. It measures the quantity of heat that will flow through a unit area in unit time per unit difference in temperature of the individual environments where the structure Case study approach to energy efficiency and technologies 16 intervenes expressed in W/m2 K The lower the U -Values, the better is the insulation of the material. 3.2. Energy assessment of residential buildings A series of site visits were carried out between the 2nd and 8th of February, 2022. The energy assessment was carried out based on the following methodology: Figure 3.2: Energy assessment methodology Source: Author Case study approach to energy efficiency and technologies 17 Phase 1: Preliminary assessment For the energy assessment of the residential building, it was essential to sample case study households in Loop Head. For this reason, the methodology began with defining the selection criteria. These needed to be transparent and meet the expectations of the interested households. The defined selection criteria for the case study houses were: a) The house should be an occupied residential house. b) The homeowner should provide access to the annual electricity bills for at least one year. c) The construction year should be between the following years: pre or early 1900, 1950 – 1977 and from 2005 onwards, to represent the majority of dwellings in the LH Community. d) The house should be available for measurement and data collection for more than one day. e) The homeowner should allow taking pictures and be willing to be interviewed in case of more data requirements. f) In order to keep calculations more precise and representative for a more significant number of buildings, the existence of renovation and extension, the number of joints in the house should not be greater than two. Provision of blueprints and/or hand- drawings of the dwelling is preferred. g) Owners should exhibit interest in renewable energy or heat pump installation. Through the pre-survey questionnaire, a total of five house owners expressed their interest in having an energy assessment conducted. A survey of the physical characteristics of the building and data collection of annual electricity invoices was carried out by preliminary site visits of the interested households. Phase 2: Detailed energy assessment In compliance with the defined criteria, three case studies were narrowed down for the detailed energy assessment. Phase 2 was further divided into electrical and building envelope assessments. Therefore, a team of four was divided into two students each for the respective task. The set of instruments used for the detailed energy assessment were: thermal camera, Case study approach to energy efficiency and technologies 18 laser meters, energy meters and measuring tapes. A phone camera was used to capture the building’s internal and external characteristics when required. I. Electrical assessment An audit of the electrical appliances was carried out including the total equipment counts, nameplate readings, and measurements to determine the equipment load in kilowatts (kW). The hours of operation were determined for all equipment as per the homeowner’s schedule and site observations. In the case of inefficient lighting and equipment in the case study sites, energy efficiency measures were employed to identify energy-saving opportunities. Finally, the energy use calculated above was reconciled to the actual annual metered consumption for a baseline determination of the annual load profile. II. Building envelope assessment The first step to assess characteristics of the dwelling was an on-site survey, which considered dimensions of the envelope, material, space heating, type of water boiler system, among others. For each aspect, the type of information collected is described in the following: Step 1: Envelop Survey For the data collection of the envelope, it was required two different data sheets; the first one was the DEAP for New – Final and existing Home Survey Form, and the second one is an excel table to address the information of doors and windows The DEAP2 Survey Form was used to collect the information according to requirements for DEAP 4.2.0 software (see Table 3.1). This document includes a series of questions evaluating: ▪ Age or ages of dwelling, in case of joins. ▪ Years and materials for the walls ▪ Roof construction; type of roof and insulation ▪ Floor construction; type and insulation ▪ A record for each room (dimensions) (a different datasheet was used in particular for this data.) ▪ Ventilation factors ▪ Primary and secondary space heating system and the respective fuel used ▪ Heating system for hot water 2 Annex 9 Case study approach to energy efficiency and technologies 19 The second data sheet3 was required to collect the dimensions per room, including: a) Dimensions in size and height, for the room, doors and windows b) The counting of windows and doors, c) Materials and directions. d) Height of the window from the floor Step 2: Thermal Camera Analysis A thermal camera was used for the thermal inspection of the building envelope. The technical details of the used thermal camera are attached in Annex 11. Before the examination, all the case study houses were heated for at least 24 hours so that the temperature difference between outside and inside would be at least 10 degrees. Each room was examined with the infrared camera for temperature differences in the areas of floors, walls, ceilings, windows and doors. Particular attention was paid to the inspection of ceilings with built-in lighting, ceilings and walls on the attic floors, insulation at the junction of window frames and walls. During the inspection of the premises, infrared images of areas with detected low temperatures were taken. The images were later analysed to identify poor insulation and provide recommendations. Phase 3: Analysis and calculation Two software were used for analysis and calculation. Sketchup was used for calculating the area and volume of the house and DEAP Software 4.2 was used for the heat loss analysis and development of retrofit steps. • Sketchup Sketchup is a two- and three-dimensional modelling and graphic design program. Because of that, its utilities range from urban design and planning, civil engineering, architecture, industrial design, among others (SketchUp, 2022) The program has a user-friendly and intuitive interface, which allows them to perform work quickly and accurately. For this reason, SketchUp is the tool used for the building dimensions calculation of the case studies. In the absence of dwellings blueprints, a method was opted that would allow to process the information obtained during the interview and data collection phase. Necessary inputs for the 3 Annex 10 Case study approach to energy efficiency and technologies 20 DEAP were derived from SketchUp, which included total area, dwelling volume, and total perimeter. The steps followed for SketchUp is described below: 1. Draw each room based on the measurements. This process includes the sequences and distribution of the rooms and the determination of the public and bedrooms areas. 2. Based on the type of wall of the dwelling, the dimensions (thickness) of external walls and the internal walls were included 3. Once the particular areas were classified, the calculations of the area per room can be made and cross-checked with the heights. 4. The internal walls are removed in a copy of the drawing to calculate the main floor area. 5. With the clean floor area, another layer is created to project the roof, extending the floor perimeter by 0.30m to consider the eave. The extra space influences the total area of the roof and, consequently, the volume. Note: For pitched roofs, geometry calculations to consider the slope are additional to obtain the actual area and volume. 6. In the floor area layer, after calculating the average height of the rooms from the data collected, the value is input in the elevation of the walls and calculates the area. The calculations performed with SketchUp are the input to the DEAP program, a process that is described in more detail in the next section. Case study approach to energy efficiency and technologies 21 SketchUp building process Figure 3.3: Rooms distribution drawings Source: Author based on own measurements Figure 3.4: Section of a pitchpitched roof Source: Author based on own measurement Figure 3.9: Wall elevations Source: Author based on own measurementmeasurements Figure 3.5: Total floor area Source: Author based on own measurement Figure 3.8: Rooms areas Source: Author based on own measurements Case study approach to energy efficiency and technologies 22 • DEAP The DEAP 4.2.0 software web interface developed by SEAI was used to calculate and assess the heat loss and the energy required for space heating from building fabrics and to develop retrofit strategies. The calculation of the energy demand for space and water heating is based on the the dwelling dimension and doesn’t reflect the consumption behavior of the occupants. The main inputs required for the calculation are summarized in Table 3.1. Table 3.1: Summary of the main DEAP inputs Source: DEAP manual(Seai, 2020) Element Parameters Building geometry Building floor area, living room area, floor heights, volume, exposed wall areas, roof areas, floor areas, window and door area Building fabric U-Values of element Windows Orientation, type of glazing, U-Value, number of openings, overshading, blinds curtains type, number of draughts stripped openings, overhangs. Ventilation Air-tightness (infiltration rate), number of vents, fans and flues, draught stripping, type of structure (masonry or timber), ventilation method (natural or mechanical), Space heating systems System controls and responsiveness, boiler efficiency, fuel type, distribution medium, distribution losses, secondary heating system Water heating systems System controls and responsive, boiler efficiency, fuel type, distribution losses, storage losses, solar hot water systems Lighting Proportion of low-energy light fittings Renewables Photovoltaic, biomass, CHP, etc (if applicable) The retrofit strategy is defined based on the result of the DEAP Software, the thermal camera assessment, and the severity of heat loss from fabrics in the building. The combination of the upgrade suggestions aim at improving the BER rating. The suggestions are provided in the following sequence: lighting measurements, envelop insulation, windows and door improvement and continue through heating system technologies and renewable energy technologies. The upgrade of heating and electricity generation technologies depend on the feasibility. The U-Values for the upgraded fabrics are referenced from the DEAP Manual Appendix Table S (Seai, 2020) based on the recommended measures. The fabric upgrades involved in this Case study approach to energy efficiency and technologies 23 study are for doors and windows, external wall insulation and roof insulation. Due to the relative inconvenience of floor insulation and lack of penetration of floor insulation retrofits in the Irish homes as per (Ahern et al., 2013), floor insulation was considered only in Case Study III where it was necessary for the installation of Heat Pump. The U-Value of the floor, therefore, remains unchanged, Case I and II. As per the National Housing Retrofit Scheme (National Housing Retrofit Scheme - House2home, n.d.), all homes undergoing major renovations must be built to a minimum Building Energy Rating (BER) of B2. However, the maximum fabric upgrades above the advanced retrofit strategies defined under TABULA4 was not considered irrespective of the energy rating of the house. Instead, heat loss and HLI was accessed in every fabric upgrade step to calculate the energy and cost savings with the associated payback period. Under conditions when the HLI of the developed retrofit upgrade was within the range of 2 W/K/m2 and 2.3 W/k/m2, design and system sizing of a heat pump was done, as the range defines the eligibility for the Heat Pump grant. Where the HLI is between 2 and 2.3 W/km2, it may not be economically feasible to upgrade the home further (Seai, 2020) Annual energy costs for each step are calculated using DEAP by multiplying the calculated annual delivered energy (kWh by fuel type) by the relevant fuel price kWh unit costs (including 13.5 % VAT). The unit costs were obtained from Domestic Fuels, comparison of useful energy costs for space heating (SEAI, 2022b). The cost of heating oil and wood pellets bagged was used for the calculation as outlined in Table 3.2. Table 3.2: Domestic fuels prices Sources: (SEAI, 2022b) Heating Oil (€/kWh) Wood Pellets Bagged (€/kWh) Kerosene (€/kWh Electricity unit price (€/kWh) Electricity night rate (€/kWh) 0.081 0.0716 0.0791 0.2407 0.0983 3.2.1. Cost analysis • Investment cost including VAT The investment costs include all material costs referring to the recent Irish market price, construction cost and Irish VAT. The building retrofit and domestic fuels are under a reduced rate of VAT, which is 13.5 % (Irish Tax and Customs, 2022). Equation 3.1 shows the calculation of total investment cost used for building retrofits of every case studies. 4 Tabula It is a project that produces the Building Typology Brochure of Ireland in the energy performance of typical Irish dwellings. This documented work as a base for retrofit recommendations by the combination of materials. Case study approach to energy efficiency and technologies 24 Equation 3.1: Total investment cost Source:(Coyle, 2015) 𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 = (𝐼𝑛𝑖𝑡𝑖𝑎𝑙 𝑐𝑜𝑠𝑡 × 𝐼𝑟𝑖𝑠ℎ 𝑉𝐴𝑇) − 𝐺𝑟𝑎𝑛𝑡𝑠 (𝐼𝑓 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑏𝑙𝑒) • Total Net saving The total net savings were calculated by using Equation 3.2. The total energy cost savings are the energy cost difference between the base scenario and the developed scenario. Equation 3.2: Total net saving Source: (Coyle, 2015) 𝑇𝑜𝑡𝑎𝑙 𝑁𝑒𝑡 𝑆𝑎𝑣𝑖𝑛𝑔 = (𝑃𝑟𝑖𝑚𝑎𝑟𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑎𝑣𝑖𝑛𝑔 × 𝑃𝑟𝑖𝑚𝑎𝑦 𝐹𝑢𝑒𝑙 𝑐𝑜𝑠𝑡) + (𝑆𝑒𝑐𝑜𝑛𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑎𝑣𝑖𝑛𝑔 × 𝑆𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦 𝐹𝑢𝑒𝑙 𝑐𝑜𝑠𝑡) • Payback Period Another economic indicator for building retrofitting is the payback period. There are two types of Payback Period calculated to compare the benefits of each scenario’s recommendations and alternatives, namely simple and discounted Payback Period. These are estimated as per Equation 3.3 and Equation 3.4. o Simple Payback Period Equation 3.3: Simple payback period Source: (Coyle, 2015) 𝑆𝑖𝑚𝑝𝑙𝑒 𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 = 𝑇𝑜𝑡𝑎𝑙 𝑖𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑇𝑜𝑡𝑎𝑙 𝑛𝑒𝑡 𝑠𝑎𝑣𝑖𝑛𝑔 o Discounted Payback Period Equation 3.4: Discounted payback period Source: (CFI, 2022) 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑒𝑑 𝑃𝑎𝑦𝑏𝑎𝑐𝑘 𝑃𝑒𝑟𝑖𝑜𝑑 = 𝑁𝑒𝑡 𝐶𝑎𝑠ℎ 𝐹𝑙𝑜𝑤 (1 + 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒 )𝑛 ; 𝑛: 𝑝𝑒𝑟𝑖𝑜𝑑 • Cost estimate classification The cost-benefit consideration has to include the uncertainty in the calculation. Considering the accuracy range according to AACE International Recommended Practice Professional Guidance,(Borowicz et al., 2020) there are five classes for the cost estimate classification which are also able to apply to cost estimates for building retrofitting. The cost estimate class was mapped by primary and secondary characteristics of the project phases and stages as shown in Table 3.3. Each cost estimate class has a different expected accuracy range at an 80% confidence interval. Class 5 has the highest accuracy range and Class 1 has the lowest one as detailed in Table 3.3. The building retrofitting and corresponding cost estimates can be Case study approach to energy efficiency and technologies 25 defined as Class 4 which was called "Schematic design or conceptual study” by considering the available drawings, parametric models, and preliminary material. The final calculation will consider the accuracy range between a low range of -10 % and a high range of 30 %. Table 3.3: Cost estimate classification matrix for the building and general construction industries” Source: (Borowicz et al., n.d) Estimate class Primary Characteristic Secondary Characteristic Maturity Level of Project Definition Deliverables Expressed as % of complete definition End Usage Typical purpose of estimate Methodology Typical estimating method Expected Accuracy range Typical variation in low and high ranges at an 80% confidence interval Class 5 0% to 2% Functional area or concept screening SF or m2 factoring, parametric models, judgment, or analogy L: -20% to -30% H: +30 to + 50% Class 4 1% to 15% Schematic design or concept study Parametric models, assembly driven models L: -10% to -20% H: +20% to + 30% Class 3 10% to 40% Design development, budget authorization, feasibility Semi-detailed unit costs with assembly Level lines items L: -5% to -15% H: +10% to + 20% Class 2 30% to 75% Control or bid/tender, semi- detailed Detailed unit cost with forced detailed take-off L: -5% to -10% H: +5% to + 15% Class 1 65% to 100% Check estimate or prebid/tender, change order Detailed unit cost with detailed take- off L: -3% to -5% H: +3% to + 10% 3.3. Solar PV-Heat Pump System Sizing and Integration After introducing suitable retrofitting and energy efficiency improvement measures at the residential buildings in Loop Head, the feasibility of solar PV technology for electricity generation and the replacement of conventional heating solutions with heat pumps was analyzed. The output from the PV system is not uniform throughout the year since it depends on various factors, as will be described in sub-chapter 4.3.2. The generated energy exceeds the Case study approach to energy efficiency and technologies 26 household demand at a higher energy yield from the PV system. Thus, this study analyzes the system integration of battery storage and domestic hot water storage tank for maximum utilization of the generated excess energy. Further, this study aims to design a low-cost PV system with a higher energy fraction for selected dwellings in LH. Figure 3.12 shows the methodology flow diagram for designing and analyzing a residential PV system. Figure 3.12: Case study approach for PV technology Source: Author Step 1: Developing a synthetic load profile Case study approach to energy efficiency and technologies 27 It is imperative to match the erratic behavior of PV energy with the time-varying power consumption of a household. This enables the operators to optimize the cost of the system by addressing subjects of concern on how to: operate the system, size the storage, curtail excess energy. Thus, the analysis of load profiles is crucial for PV system design (IRENA, 2018). The load profile developed after energy assessments in three different households, as discussed in Chapters 5, 6, and 7 , elucidates the annual electricity consumption of the building after implementing the energy efficiency measures. However, it does not reflect the hourly consumption of the household. The limited time for IC 2022 challenged the possibility of recording the annual hourly consumption of households in Loop Head. This led to developing a synthetic load profile for residents in Loop Head from the standard national load profile developed by (Ricardo, 2020a). The national load profile considers the average demand of typical Irish households, including the seasonality factor and variation of load pattern on weekends and weekdays. However, the demand profile data is from 1997, and the annual domestic consumption was over 7,564 kWh, which might not represent the consumption of present-day Irish households. Therefore, it is essential to scale down the standard national load profile to match the actual demand of Loop head dwellings. Since many variables are considered in the development of synthetic load profiles, it requires validation for further utilization in the PV system design. Validation process A scaling factor was created by dividing the annual electricity consumption of a typical Loop Head household obtained from the pre-energy assessment and annual average residential consumption from (Ricardo, 2020a). The synthetic load profile for Loop Head households was generated by multiplying the hourly annual national load and the scaling factor. Further, the synthetic load profile pattern was compared with the monthly load profile developed after pre- energy assessment. The similar demand pattern of the two load profiles provided a scientific base to adapting the synthetic load profile for the Solar PV sizing throughout the study. Step 2: Sizing of PV system HOMER, a software developed by National Renewable Energy Laboratory (NREL), was used to design and evaluate technically and financially apt options for microgenerators. Homer Pro models the physical behavior and lifecycle cost of microgenerators. Microgenerators such as solar PV system take the input parameters as shown in Figure 3.13 and analyze the performance of the system configuration hourly to determine its technical feasibility and life- cycle cost. The three major tasks of Homer Pro include simulation, optimization, and sensitivity analysis. The software simulates various system configurations in the optimization process to Case study approach to energy efficiency and technologies 28 satisfy the technical constraints at the lowest life-cycle cost. The sensitivity analysis helps gauge the effects of changes in the model input. The oval representation of the three tasks in Figure 3.13 shows that single optimization requires multiple simulations. Similarly, single sensitivity analysis involves a series of optimizations. Figure 3.13: Operating mechanism of Homer Pro Software Source: author In the case studies, the solar panels, inverter, and battery size options were fed as input parameters in Homer Pro. Further, the optimization tool in Homer was used to find the optimal size to cater to the demand. Two optimization criteria were set for the final selection of the system, as listed below. I. Cost-optimized-system with the lowest cost for generated electricity compared to the considered electricity tariff of 24.70 cent €/kWh II. Generation optimized-maximum renewable penetration from the system selected from criteria I by conducting sensitivity analysis on the altered tilt angle The optimized design was finalized based on the market availability of the components. Also, the system design complied with the SEAI domestic solar PV code of practice. Step 3: Power diversion to hot water storage The houses that did not pass the eligibility criteria for installing a heat pump were equipped with a power diversion controller for domestic hot water purposes. In the case of a battery storage system, the excess was diverted only after charging the battery system. The hot water demand varies significantly depending on the nur of consumers throughout the day/month/year (Herrando et al., 2014). A study (DEFRA, 2008a) in 124 dwellings in England suggested that the mean household hot water requirement is 122 L/day, with a confidence interval of 95% of +/- 18L/day. Generally, a boiler is expected to provide hot water at 60 0C (Herrando et al., 2014)( . However, considering the studies from (Zondag et al., 2015) (DEFRA, Case study approach to energy efficiency and technologies 29 2008a), this study used an approximate estimate of daily consumption of 120 L at 50 °C for a family of four. In this study, a water mains temperature of 10 0C was considered (DEFRA, 2008b),. The hourly simulation result obtained from Homer was rearranged in Microsoft Excel to calculate the daily excess from the PV system, out of which the energy required for hot water tank operation was diverted, and the rest was fed into the grid. The diverted power was used only to heat water up to 50 0C from the water mains temperature. Equation 3.5 was used to calculate the required energy to be diverted for water heating. Equation 3.5: Electricity consumption of water heating Energy required = 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 ℎ𝑒𝑎𝑡 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 ∗ 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑡𝑜 ℎ𝑒𝑎𝑡 ∗ (50 − 𝑤𝑎𝑡𝑒𝑟 𝑚𝑎𝑖𝑛𝑠 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑟𝑎𝑡𝑖𝑜 Where: • Specific heat of water= 1,163 Wh/kg°C • Efficiency= 0.9 Step 4: Economic Analysis For the economic analysis, the cost of consumed energy (COCE) generated by the designed PV system was calculated using a general NPV approach as shown in Equation 3.6 The discounted payback period and cost savings from the generated PV were also economic indicators. The overall cost of the system was cumulative of individual component cost per kWp and installation cost. Equation 3.6: COCE Source: (Homer, 2021) 𝐶𝑂𝐶𝐸 = 𝐼0 + ∑ 𝐴𝑡 (1 + 𝑖)𝑛 𝑛 𝑡=1 ∑ 𝑀𝑒𝑙 (1 + 𝑖)𝑛 𝑛 𝑡=1 Where: • I0= total investment cost for PV system • At = Annual O&M cost for PV system including Grid O&M cost • Mel = Total load served by the system Case study approach to energy efficiency and technologies 30 All the parameters involved in the economic analysis, namely operation and maintenance cost, replacement cost, inflation rate, the lifetime of the components, discount rate, feed-in-tariff, and cost of grid electricity, are discussed below. Financial Calculation Assumptions The financial calculation involved various associated cost values and estimates. This section details the cost involved in the economic analysis of retrofitting, heat pump design, and residential solar PV design. Nominal Discount rate For the economic calculation, a nominal discount rate of 3.5% was considered throughout the study based on (IC, 2020). Inflation rate The inflation rate impacts the economic analysis and could lead to a misleading result if not incorporated carefully. According to the report (Statista, 2022) the inflation rate in Ireland has been in the range of 3.04 % to 1.9 % between 1986 and 2021. The report further forecasts the inflation rate of Ireland until the year 2026 to 2 %. This study assumed the 2 % per year inflation rate for the financial calculations. Figure 3.14: Inflation rate in Ireland Source: author based on (Statista, 2022) Real Discount rate A real discount rate of 1.47 % was used for the economic calculation throughout the study. Investment and O&M Cost -3.000% -2.000% -1.000% 0.000% 1.000% 2.000% 3.000% 4.000% 5.000% 6.000% 19 8 6 19 8 8 19 9 0 19 9 2 19 9 4 19 9 6 19 9 8 20 0 0 20 0 2 20 0 4 20 0 6 20 0 8 20 1 0 20 1 2 20 1 4 20 1 6 20 1 8 20 2 0 2 02 2* 2 02 4* 2 02 6* Case study approach to energy efficiency and technologies 31 The total investment cost accounts for the investment incurred from the generation technology. The individual component cost for the various solar PV system sizes is attached in Annex . For PV system economic analysis, operation and maintenance (O&M) for panels and inverters was considered to be 1 % of the investment cost. However, the battery O&M was 2 % of the investment cost. While calculating COCE, the operation and maintenance cost of the grid is also considered. Equation 3.7 gives the grid O&M cost. Equation 3.7: Grid O&M cost Source:(Homer, 2021) 𝐺𝑟𝑖𝑑 𝑂&𝑀 𝑐𝑜𝑠𝑡 = 𝑙𝑜𝑎𝑑 𝑠𝑒𝑟𝑣𝑒𝑑 𝑏𝑦 𝑔𝑟𝑖𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 ∗ 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑔𝑟𝑖𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 − 𝑖𝑛𝑐𝑜𝑚𝑒 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑠𝑎𝑙𝑒𝑠 𝑜𝑓 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑡𝑜 𝑡ℎ𝑒 𝑔𝑟𝑖𝑑 This study restricts the grid sales in the Homer Pro software. Besides, the O&M cost for the heat pump was assumed to be 311 €/year (Anders Rosenkjær Andersen, 2021) Tariff rate Different tariff rates were found in the electricity bills of the case study houses. However, none of the tariff rate reflected the the standard charge, the public service obligation levy, VAT and discounts. As a result, the customer pay more than what is mentioned as a “tariff rate” irrespective of day/night tariff or a fixed flat rate. After including all these factors for analyzing the electricity bills of all case studies, average the tariff rate of 24.70 cent €/kWh was considered throughout the study. Feed-in tariff The Clean Energy Guarantee (CEG), discussed in sub – chapter 4.6.3, enables the microgenerators to receive payments from their electricity supplier for the electricity fed into the grid. The feed-in tariff rate will be based on a competitive market rate. However, this study considered a feed-in tariff of 0.09 €/kWh (SEAI, 2020). Degradation factor The economic analysis includes the degradation factor considering that output from PV panels degrades annually. Thus, to account for it, 2 % factor was considered in the first year, and 0.05 % for later years. Project Lifetime The lifetime of the various components and equipment considered in this study are listed below. Energy solutions for residential buildings 32 Table 3.4: Lifetime of components and equipment Components Lifetime (years) Solar Panel 25 Inverter 15 Battery 10 Heat pump 16 The cost-optimized system was analyzed under two scenarios: with and without SEAI solar grant. A further cost analysis of hot water diversion was added in system integration. Sensitivity analysis was conducted by varying the cost of grid electricity to analyze how it impacts the payback period and return on investment. Critical analysis The technical and cost constraints of the designed system were discussed. Further, the effectiveness of the PV system to enhance the BER rating of the building was analysed. 4. Energy solutions for residential buildings In residential buildings, the energy consumption depends on the level of insulation present, type of space heating and the adoption of solutions to improve energy efficiency of the house in general. Energy-saving measures do not only increase the comfort level of the house but also make it possible to reduce fuel (coal, oil, gas) consumption and increase energy cost savings. Heat loss of the house is the amount of heat given off by the house per unit of time in watts per kelvin (W/K). It is affected by the temperature difference between the inside and outside the house. The house loses heat through the building envelope (walls, windows, roof, foundation), ventilation and sewerage. 60 – 90 % of all heat losses are through the building envelope, (SEAI Blog, 2019) out of which, up to 25 % is due to the poor roof insulation, 20- 35 % due to exterior walls, 25 % due to poor insulated or badly built-in windows and door, and 15 % through ground floors (SEETECH, 2022) Energy solutions for residential buildings 33 Figure 4.1: Average house heat losses Source: (SEETECH, 2022) The extent to which external walls or windows prevent heat from escaping is a measure of the resistance to heat transfer. There is an inversely proportional relationship between the resistance to heat transfer of the enclosing structures of the house and heat losses - with an increase in thermal resistance, heat losses fall. The following factors are taken into account when calculating the heat loss of a house: ▪ resistance to heat transfer of walls, floors, ceilings, windows; ▪ heat consumption for ventilation; ▪ air temperature in a particular place during the coldest period of winter; ▪ the location of the house on the cardinal points. It is possible to achieve a reduction in heat loss if the following measures are taken: ▪ Insulation of the foundation, walls, and roof. ▪ Installation of the modern multi-chamber double-glazed windows, triple-glazed windows, or replacement of the seals and fittings in old windows. ▪ Sealing gaps and cracks in walls with polyurethane-based sealant. Energy solutions for residential buildings 34 4.1. Types of wall and insulation One of the main measures to eliminate heat loss in the house is the insulation of the walls of the house. The three main wall types are cavity walls, solid walls, and hollow block walls. External insulation Internal insulation Cavity wall insulation Figure 4.2: Types of insulation. Source: (Alexey Dedulin, 2019) 4.1.1. External wall insulation The outer wall of the house performs three main functions: • A mechanical barrier to protect against penetration into the room. • Structure of the building. • A barrier to outside cold air. When the insulation is located outside, the walls are preserved from environmental impacts like rain, snow, sunlight. It also acts as an additional barrier that excludes contact of the wall with cold air, which is why internal heat is not dissipated into the atmosphere. Accordingly, the temperature of the wall rises, the dew point shifts outward. While doing external insulation different insulation materials can be used for example mineral, wool and polystyrene. The insulating material is fixed outside the building. Mounting methods depend on the choice of material. To protect the insulating material from moisture and weather conditions, various finishing materials are used, such as decorative plaster, siding, and various decorative panels. Thus, by insulating the house from the outside, it is possible to update the appearance of the building. Energy solutions for residential buildings 35 4.1.2. Internal wall insulation Insulating a house from the inside is a cheaper option compared to external wall insulation. Insulation boards are attached to the walls and covered with a vapor barrier layer and plasterboard. However, it is not recommended to insulate the house from the inside because mistakes in the choice of insulation and vapor barrier materials can lead to the constant formation of condensate, which will cause the spread of fungus and mold in the walls. In addition, it must be borne in mind that insulation of the house from the inside will lead to a decrease in the living space of the house and cause difficulties with hanging furniture and cabinets on the walls. 4.1.3. Cavity wall insulation Insulation of walls of this type lies in the fact that the insulating material is located between the outer and inner walls of the building. Various insulating materials can be used, e.g., mineral wool and polystyrene panels. Injection of insulating products from the outside is considered to be the best method for insulating this type of wall. 4.2. Types of roofs and insulation When choosing a material for insulating the roof of a house, the difference between three types of roof structures matters: • Shed roof • Pitched roof (cold attic) • Mansard roof (floor) Shed roof A pitched roof (cold attic) Mansard roof (floor) Figure 4.3: Types of roofs Source: (DD - Stroi, 2020) Energy solutions for residential buildings 36 Each of the three types of roofs requires a different approach in the choice of insulating materials. 4.2.1. Shed roof insulation The shed roof has a simple design. This allows the use of various types of insulation: • Polystyrene is a very light material and is easy to install. • Mineral wool has good thermal quality but is extremely sensitive to moisture. Mineral wool is very easy to install from inside the attic by laying mineral wool sheets in between the rafters. • Eco wool has very similar properties to mineral and cotton wool. Eco wool is made from recycled pulp and waste paper. It has excellent thermal qualities, is light, cheap, environmentally friendly. The material has a long service life and prevents noise and vibration. The method of installing the insulation will directly depend on the choice of material. Today, roof insulation is usually done quickly and easily, without the need for special tools. In the case of sprayed Eco wool, you will need a special pump through which the material will be injected into the structure. For installation of insulation in the form of tiles or layers of mineral or cotton wool, special clamps with an increased area will be required. The material will be tightly fixed on the structure. It is easiest to start the insulation of a shed roof from the inside, but the waterproof material should be installed from the outside. Rafter insulation Attic insulation Figure 4.4: Roof insulation. Source:(SEAI, 2020a) Energy solutions for residential buildings 37 4.2.2. Pitched roof It is possible to insulate a pitched roof with a cold attic on the floor. However, in some cases, insulation is also inserted between the rafters. For these purposes, soft and elastic mineral wool slabs are most often used, which are inserted into the spacer. The attic floor could be insulated with various materials, both sheets, loose and sprayed. 4.2.3. Mansard roof The mansard roof is, in fact, the walls of the room, but they are not made of concrete or brick, but of rafters and, for example, tiles. This design is more expensive than a separate, ordinary floor. Roof insulation for a mansard roof should be environmentally friendly, because, in fact, it is warming the room from the inside. There are also increased requirements for fire safety. Most often, such roofs are insulated with mineral wool. 4.3. Floor insulation Floor insulation in the house is a necessary part of the construction. Up to 15% of the heat goes through the flooring into the ground. An unheated basement under the building leads to heat losses of 5-10%. The materials used to insulate floor coverings are produced in the form of: • granules - expanded clay, foam glass, perlite, vermiculite granulated slag; • rolls with and without a reflective layer - mineral wool, glass, slag, polyethylene foam, expanded polystyrene, cork; • foil and non-foil boards - expanded polystyrene, foam plastic, foam plastic; • liquids and foams - special insulating paints, polyurethane foam, Eco wool; To select the most suitable material, you need to consider the features of their installation and use. The most common floor structures are concrete and wooden floors. Insulation material is different for different floor types. Wooden floors are most often insulated with mineral wool or eco wool, which fit in the gaps between the floor joists as seen in Figure 4.5 Energy solutions for residential buildings 38 Figure 4.5 Wooden floor insulation Source: (Dave Judd, 2021) For the concrete floor, it is better to insulate during the construction phase. The concrete floor is one of the most practical options for a house due to its strength and durability. But concrete is a cold material, and without high-quality thermal insulation in winter, high heat losses and the problem with condensate due to the large temperature difference on the inside and outside of the concrete base can appear. When constructing a concrete floor, it is necessary to pay special attention to the waterproofing of the lower layer in order to avoid moisture getting into the concrete from the ground. In order to avoid the moisture base of the foundation of the floor, sand is laid first since it does not retain water in itself. The next step is laying the damp-proof membrane and the insulation materials on top of it. It is better to use materials with a low water absorption capacity, such as extruded polystyrene for concrete floor insulation. In the case of installation of underfloor heating, heating pipes should be laid after this process. The concrete mortar is poured on top of all with final floor covering after the concrete has hardened as seen in Figure 4.6 Energy solutions for residential buildings 39 Source: (Insulation Superstore, 2020) 4.4. Types of windows Today, in comparison with former times, the total area of glazing of buildings has increased significantly. Therefore, the problem of preserving heat in the cold season and its removal in the warm season is currently particularly acute. Therefore, to save heat and reduce energy consumption, first of all, it is necessary to take care of glazing of the building with energy- saving double-glazed or triple-glazed windows and windows with a special coating on the glass. Figure 4.7: Energy-efficient window. Source: (Window Efficiency, 2020) Figure 4.6: Concrete floor insulation Energy solutions for residential buildings 40 Special coating divides all the waves of the spectrum into long and short thermal waves. It is applied to the surface of ordinary polished float glass and can reflect exactly long thermal waves. At the same time, short rays of the visible spectrum passing through such a coating almost unhindered. The inner surface of the double-glazed window independently returns the heat to the room and at the same time freely transmits light. Such coating is called selective or low-emissive. As outlined, there is a big range of energy saving measures available. Step by step, we can make house more energy efficient, more comfortable and more environmentally friendly. Analysis energy consumption of the building and improving building envelope, replacing light bulbs with energy efficient ones, insulating the windows and doors, analyzing the characteristics of the heating system in house and improving it will help to reduce energy consumption. If the house needs a radical modernization consultation, SEAI provides support programs and grants as summarized in Sub-chapter 4.4 appliable to the house. However, the “The cost of family retrofit is as individual as family itself”. The decision to upgrade the house is individual to each household and depends upon the desired comfort and building efficiency. 4.1. Heating technologies Heat pumps are based on the second law of thermodynamics that states “There exists a useful thermodynamic variable called entropy (S). A natural process that starts in one equilibrium state and ends in another will go in the direction that causes entropy of the system plus the environment to increase for an irreversible process and to remain constant for a reversible process”(NASA, n.d.); in simpler words, “Heat will flow naturally from a hot source to the colder sink, and as per the first law, heat energy cannot be created nor destroyed but can be transformed from one form to another”(NASA, n.d.). Therefore, Equation 4.1: Change Of Entropy 𝐶ℎ𝑎𝑛𝑔𝑒 𝑜𝑓 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (∆𝑆) = 𝐴𝑚𝑜𝑢𝑛𝑡𝑜𝑓 𝐻𝑒𝑎𝑡 𝑇𝑟𝑎𝑛𝑠𝑓𝑒𝑟𝑒𝑑 (∆𝑄) 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (∆𝑇) Hence, for a reversible system: Energy solutions for residential buildings 41 Equation 4.2: Reversible System Entropy 𝑆𝑆𝑜𝑢𝑟𝑐𝑒 − 𝑆𝑆𝑖𝑛𝑘 = 0, while for an irreversible system: Equation 4.3: Irreversible System Entropy 𝑆𝑆𝑖𝑛𝑘 > 𝑆𝑆𝑜𝑢𝑟𝑐𝑒 . However, an ideal reversible system is impossible to achieve in nature, and that can be concluded according to Clausius Statement of the Second Law “It is impossible to construct a device which operates on a cycle and produces no other effect than the transfer of heat from a cooler body to a hotter body”, and Carnot’s Theorem of the second law that states “No heat engine operating between two reservoirs can be more efficient than a reversible heat engine operating between the same two reservoirs” (Israel Urieli, 2014). Figure 4.8: Work Has to Be Done to Transfer Heat Source: (Israel Urieli, 2014) Therefore, to transfer heat between two independent systems, work has to be done by a heat pump. In this case, Equation 4.4: Work of A Heat Pump Work (𝑊) = 𝑄𝐻 – 𝑄𝐿 or 𝑊 = 𝑇𝐻 − 𝑇𝐿 and the Coefficient of Performance (COP) of the Heat Pump: Equation 4.5: Heat Pump COP 𝐶𝑂𝑃 = ∆𝑄/𝑊 Energy solutions for residential buildings 42 Where, • QH: Amount of Heat added from the Heat Source. • QL: Amount of Heat available at the Heat Sink. • ∆Q: Absolute Amount of Heat transferred from Source to Sink. • TH: Temperature of the Heat Source. • TL: Temperature of the Heat Sink. Modern heat pumps harness heat from the ambient temperature to supply houses with the demanded heat and utilize the free units of heat of the ambient temperature in the form of the difference in temperature between the source and the sink. They employ a thermodynamic cycle that is called a reversed Carnot cycle since it is the reverse of the cycle adapted by refrigerators and air conditioners. To illustrate, instead of absorbing heat by the condenser and releasing cold air by the evaporator and blower as in air conditioners and fridges, modern heat pumps used for heating purposes absorb heat from the source by the evaporator and reject heat to house spaces by radiators. The beauty of modern heat pumps is that, even with the low ambient air temperature, heat can still multiply in the refrigerant by the means of the work done by the compressor, which boosts the refrigerant pressure and temperature. Modern heat pumps generate heat in multiples of units of electricity consumed and confirm the aforementioned laws of thermodynamics. Their coefficient of performance is calculated according to Equation 4.6 below. Equation 4.6: Heat Pump COP 𝐶𝑂𝑃 = 𝑇𝐻/(𝑇𝐻 − 𝑇𝐶) where, • TH: Ambient Temperature • TC: Condenser Temperature and work in this case is the electrical energy consumed by the compressor. Thus, heat pumps are often able to generate 3-5 multiples of electricity consumed, whereas boilers and storage heaters are not able to provide more units of energy than consumed (SEAI, n.d.-b). Energy solutions for residential buildings 43 Figure 4.9: Heat Pump Components Source: (Bord Gais, 2021) A typical heat pump consists of five main components, namely an evaporator, a compressor, a condenser, a refrigerant, and an expansion valve as depicted on Figure 4.9. The evaporator acts as a heat exchanger to allow the circulating refrigerant to absorb the heat from the ambient air, changing the liquid refrigerant to gas. The compressor increases the temperature and pressure of the gas state refrigerant to super-heated gas at constant entropy. While the condenser receives the pressurized high-temperature refrigerant, releases the units of heat to the surroundings, and condenses the refrigerant back to a liquid state; the latent heat released is the result of the transformation of the refrigerant gas to liquid. Lastly, the expansion valve relieves the exceeded pressure in the refrigerant to maintain the pressure of the fluid in the cycle (Bord Gais, 2021; R Nave, 2014). Heat pumps do not only utilize free units of heat of the environment, but they also escalate the free energy using electricity. To illustrate, for every kWh of electrical energy consumed by heat pumps, around 4 kWh of heat can be generated, which corresponds to a system efficiency of 400 %. Consequently, it can be interpreted that around 75 % of the energy used is renewable, while 25% of the energy is introduced to the system by electricity. So, if the electricity used to supply heat pumps are to be produced solely by means of PV panels, it can be ascertained that heat pumps are 100% renewable energy powered. Further, according to the International Energy Agency (IEA), heat pumps can easily provide 90 % of the global space heating and DHW heat demands while the potential of CO2 emissions reduction in Ireland if the most Energy solutions for residential buildings 44 efficient condensing gas boilers are replaced with Air-source heat pumps is 69 % (IEA, 2020). Moreover, heat pumps have an economic positive impact since the majority of heat pumps mounted in Europe are of European origin. Solely based on the sales levels and needed man- hours for type-specific installation of different heat pumps in the European heat pumps market, 40,358 European workers are employed on full-time basis, which in return foster employment in the European Union(European Heat Pump Association, 2021). Heat pumps are designated after the medium from which heat is absorbed from, namely ambient air and ground(SEAI, n.d.-c). 4.1.1. Brine-to-Water Heat Pumps Technology These heat pumps are also called shallow Geo-thermal, ground Source, or ground-coupled heat pumps. Geothermal heat pumps have the highest thermal performance amongst the three different types depicted in this report. As shown in Figure 4.10, typically this type of heat pump consist of a combination of two thermodynamic circuits for heat transfer. Firstly, a circuit retrieving the heat from an earth depth to the heat pump evaporator; secondly, the main reverse Carnot cycle transferring heat to the house heat emitters (Kharseh & Luleå tekniska universitet. Institutionen för samhällsbyggnad och naturresurser., 2011). In the first circuit, this type of heat of heat pump normally employs water-antifreeze mixture as a circulating fluid to acquire the heat from the depth of earth; on the other hand, common refrigerants are utilized in the second circuit due to their high ability to gain thermal units and their low boiling temperature compared to water. The Ground Heat Exchanger buried (GHE) preferably of a plastic-based material is either cored vertically or horizontally into the earth under the ground surface at an appropriate depth that best balances the thermal efficiency gains against the costs of cored earth, used materials, and employed manpower hours. Energy solutions for residential buildings 45 Figure 4.10: Geothermal heat pump schematic Source:(Lind, 2012) 4.1.2. Air-to-Air Heat Pumps Technology This type of heat pump has the lowest capital investment amongst the 3 types of heat pumps depicted in this report. They acquire heat from the circulating ambient temperature and provide the desired heat units to the indoor spaces via an air heat exchanger. This type of heat pump is widespread in the global south from the far east to the middle east as they are very convenient to employ for cooling cycles. This type of heat pump still employs a circulating refrigerant within a reverse Carnot cycle, but its main trait is having air as the source and the medium of heat transfer to the house; hence, they were designated as Air-to-Air. To illustrate, this type of heat pump is also referred as “Split Units” since the noisy equipment of the machine, namely the evaporator (Outdoor Coil) is mounted outdoors while the condenser (Indoor Coil) is mounted indoors in case of a heating cycle as shown in Figure 4.11 (Anders Rosenkjær Andersen, 2021). Therefore, one of their disadvantages is their inability to deliver conditioned air to several spaces of the house unless supplemented with an air circulation system. In such cases, these may be referred to as multi-split units; however, the investment cost will increase proportionally with the number of air handling units to be added. Another disadvantage of Air-to-Air heat pumps is their high dependency on their installation locations in the building and the design of that building. To illustrate, natural and artificial air circulation here play an immense role so if the pumps are to be installed constraint with air circulation barriers, such as walls, corners, or separations, their performance will drop significantly (Anders Rosenkjær Andersen, 2021). For this reason, besides the appropriate system sizing, Energy solutions for residential buildings 46 performance analysis must be calculated by experienced engineers, qualified plumbers or technicians are highly recommended for their installation works. Further, to isolate the heated spaces and maintain the heat supplied by this type of heat pump; windows and openings are always advised to be closed, otherwise, the compressor will be overloaded to catch up with the extended heat demand of the desired temperature due to hot air leakage via openings. Therefore, they are capable to supply 60–80 % of large spaces with good air circulation (Anders Rosenkjær Andersen, 2021). On the other hand, if windows and openings are to be closed all the time to maintain the heat supplied by the heat pumps, new fresh air will not be supplied to the air-conditioned spaces, which in return will increase health risks and the spread of viruses. Moreover, Air-to-Air heat pumps can not independently provide hot water for domestic usage; thus, they are usually supplemented with an in-parallel heating system. However, regardless of their several disadvantages; they can still be a convenient choice for retrofitting houses especially summer houses with an existing heating system due to their lower capital costs, their installations practicality, and their usage flexibility (Anders Rosenkjær Andersen, 2021). Figure 4.11: Air to Air Heat Pump Schematic Source:(MB services, 2019) 4.1.3. Air-to-Water Heat Pumps (AWHP) technology Alike, the Brine-to-Water heat pumps this type of heat pump depend on two heat transfer circuits; firstly, these pumps are typically installed outdoors to withdraw the heat units available in the ambient air via the refrigerant circuit having the heat pump major components, such as the evaporator, the compressor, the expansion valve, and the condenser. Secondly, the water- Energy solutions for residential buildings 47 based distribution circuit that receives the heat from the condenser and provide it to indoor heat emitters, such as radiators or under floor heating (Anders Rosenkjær Andersen, 2021). On the contrary to Air-to-Air heat pumps, AWHP are capable of providing 95-98 % of space heating demand as well as supplying the full Domestic Hot Water (DHW) heat demand. In addition, the older models of these pumps are usually supplemented by a direct heating coil in their DHW tanks, while newer models are independently able to heat domestic water to the aimed 55-65°C that assures Legionella bacteria elimination(Anders Rosenkjær Andersen, 2021). Nevertheless, Heat pumps are not typically capable of providing instantaneous DHW; hence, a four occupants’ dwelling requires an insulated tank of 200 liters to be attached to the system, allowing dwelling’s occupants 24 hours of hot water consumption(Anders Rosenkjær Andersen, 2021). An integrated system of DHW and space heating supplies the DHW heat demand as a priority in its operational logic before catering for the space heating demand (SEAI, n.d.-a). Even though during the summer months DHW is the only heat demand for a dwelling in Loop Head, it is still a valuable asset to consider owning the newer AWHP models that employ direct heating coils in their DHW tanks for stand by operation. To illustrate, the direct heating coil will not only reduce the number of compressor start-ups in case of slight heat demand in the domestic tank, but it will also be able to heat the water in the summer season independently (Anders Rosenkjær Andersen, 2021). The compressor start-ups can also be reduced by either adding a buffer hot water tank to increase the volume of hot water available or by obtaining the newer models having an inverter- controlled compressor that is capable of 20-30 % nominal load operation (Anders Rosenkjær Andersen, 2021). Since this type of heat pump depend on two heat transfer circuits, they can be constructed as monobloc or split units. The monobloc arrangement is normally situated outdoors including the refrigerant circuit components, while the antifreeze water distributing circuit can be connected at the condenser interface of the first circuit. While the expansion tank and DHW tank are located inside the dwelling as depicted in Figure 4.12. On the other hand, the split type splits the noisy components of the main refrigerant circuit of the heat pump outside the dwelling, including the evaporator, and the compressor, whereas the condenser exchanges heat units with the antifreeze water circuit and it is located indoors (Anders Rosenkjær Andersen, 2021). Besides, the split configuration needs a higher technician’s expertise as well as more plumbing works at installation, monobloc configurations are mobilized to sites fully refrigerant pressurized. A vital advantage of split configuration is their ability to cater higher heat demand for large buildings as cascade arrangements having the indoor units and the DHW tanks serving each floor independently, which is cheaper than Energy solutions for residential buildings 48 customizing a single high-capacity unit. Hence, monobloc heat pump arrangement is usually preferred for one family houses even though the presence of the condenser outdoors might possess a slight risk of freezing if the pipes are jammed and circulation stops. However, the likelihood of this risk occurring is minimal (Anders Rosenkjær Andersen, 2021). Figure 4.12: Air to Water Heat Pump Schematic Source: (Asset Heating & Solar, 2020) 4.2. Heat emitters There are several types of heat emitters that can serve heat pumps as the last point of transmitting the heat transferred of the antifreeze water circuit, Such as: 4.2.1. Steel radiators Standard radiators transmit heat by convection, and they are constructed of steel fins. They are used as an emitting technology for Air-to-Water heat pumps, but they must be oversized to compensate for the lower forward temperature5 generated by AWHPs compared with conventional oil boilers. 5 Forward Temperature: the temperature at which water enters the heat emitter. Energy solutions for residential buildings 49 4.2.2. Low-temperature fan assisted radiators This type of radiator can supplement AWHP especially to cater for spaces with higher heat demand, and they utilize lower forward temperature than convection radiators. They are designed to permit higher heat output than convection radiators since they are constructed with aluminium fins forced with fan blowers. This type of radiator has a higher capital cost than convection radiators, and they can be installed in specific rooms with high thermal loads. 4.2.3. Underfloor heating The most detaining aspect of this technology is that it has to be considered as part of a major refurbishment or in newly constructed dwellings due to its excessive under floor works. The temperatures required and the quantity of heat emitted utilizing this technology is dependent on the material of flooring and floor coverings. Solid floors covered with tiles generate the most thermal mass. Highly insulated floor coverings, for example, thick carpet or underlay, hinders heat delivery via the floor and are not recommended with underfloor emitters. This technology tends to be the highest cost of all emitting technologies; however, it can deliver high thermal mass and operate at low forward temperature. As a general rule when designing a heat-emitting system the forward temperature required for emitters has to conform with the heat pump forward temperature. Also, if considering more than one heat emitting technology the heat pump forward temperature must align with the highest forward temperature required of all emitters. Therefore, if considering steel radiators for installation, it is of no sense to consider other technologies in parallel unless they are not able to suffice the heat demand at specific rooms. That’s due to their lowest investment costs and highest forward temperature required, so the heat pump will be sized to supply the high forward temperature anyways(Heat Pumps Implementation Guide, 2020). Heat pumps performance is inversely proportional with the heat emitters' required forward temperatures. The lower the forward temperature required, the higher the performance of a heat pump. 4.3. Solar PV 4.3.1. Introduction to solar technology When light hits a semiconductor material, the electrically charged particles are released from or within a material. This phenomenon is referred to as the Photoelectric effect. Solar Photovoltaic (PV) cells are based on this phenomenon to convert sunlight into electricity. Sunlight is composed of photons with various amounts of energy having different wavelengths. Energy solutions for residential buildings 50 When sunlight hits the PV cells mostly made up of silicon, the photons may be absorbed or reflected. The absorbed photons transfer the energy into an electron in an atom of the cell and generate DC electricity. Several cells that generate a small amount of electricity are combined to form a module that can then be arranged into arrays to increase electric energy production. Additional equipment is required to convert the generated DC electricity into useable form Alternating Current (AC) electricity and to store the excess DC electricity for later use. The generated electricity can be used to power electrical devices such as lights, electric vehicles in the house. The solar PV system is one of the feasible solutions for generating electricity for domestic use. According to the Code of Practice of Ireland, the system should be sized for the self- consumption of the energy within the home. Therefore, it is necessary to size the solar PV sizing depending upon the individual household electricity demand. In the Irish market, different varieties of solar PV technologies are available. The most commonly used PV technologies are Monocrystalline, Polycrystalline, Multi-crystalline and Thin film panels. Moreover, the performance and the cost of the Panels vary widely. Table 4.1 shows the comparision of different types of solar panels. Table 4.1: Summary of different types of solar panel Source:(Aurora, 2021) Panels Monocrystalline Polycrystalline Thin Film Materials Made from a single pure silicon crystal Made from different fragments of silicon crystals instead of one Made from Cadmium telluride (CdTe), Amorphous silicon (a-Si), and Copper indium gallium selenide (CIGS) not from silicon wafers Appearance Dark Black Colour with rounded edges Blue with Square Edges It comes in both black and blue depending on the type of thin-film variant Efficiency Over 20% Between 15% -17% CIGS 13%-15% CdTe 9%-11% a-Si 6%-8% Initial Cost High Middle Highest to Lowest CIGS Energy solutions for residential buildings 51 Panels Monocrystalline Polycrystalline Thin Film CdTe a-Si Lifespan Around 40 years 20-35 years 10-20 years Advantages Highest efficiency Highest power capacity per square meter Most space-efficient Long-lasting panel Slightly less affected by high temperature in comparison to polycrystalline Mid-range option in terms of cost, efficiency, and power capacity Flexible and light- weighted solar panel Easier to install and less labour intensive Lowest Cost Less affected by high temperature Disadvantage High Initial Investment Not recommended for hot environments because of low heat tolerance capacity Shorter lifespan Least Efficient Requires more space Bifacial solar panels absorb sunlight from both the front and back of the PV panels producing more electricity by utilizing the same space than the traditional PV technology. As the panels are provided with a transparent back, the sunlight goes through the panel and reflects off the ground surface towards the back of the solar cell of the panel. Bifacial panels are used primarily in large commercial, utility sectors but can also be used in residential. However, the type of solar panel appropriate for the installation depends upon the individual’s property specifications, budget, preference, specific situation, and the type of PV system (ground- mounted or rooftop) installation. If an individual is going for a bifacial PV system, the roof must have a light-coloured surface for optimal performance. 4.3.2. Factors Affecting Solar Energy Generation All the sunlight that reaches the PV module is not converted into electricity. The output from the PV system installation is the PV output minus the losses through the rest of the system. Several factors to be considered while designing a Solar PV System that determines the maximum annual yield are discussed below. Energy solutions for residential buildings 52 PV Module Orientation and Tilt Angle The solar radiation at the specific location or at the building in that site is important. The Loop head, Peninsula lies in the northern hemisphere. To obtain maximum annual incident solar radiation, the orientation of PV modules must be towards the south with an optimum tilt angle from the horizontal of 37°(PVGIS, 2019). A slight deviation from the optimum tilt angle will not significantly affect solar availability (SEAI, 2017).However, depending upon the geographical location and the consumption pattern of the consumer, the tilt angle can be adjusted for maximizing the self-sufficiency of the system. Solar Irradiance Irradiance is an instantaneous measurement of solar power over some area. The units of irradiance are watts per square meter (w/m2). The Global Horizontal Irradiance (GHI) s the total irradiance from the sun on a horizontal earth surface. The GHI is the sum of the solar radiation that directly reaches the earth’s surface (Direct Normal Irradiance, DNI), and the radiation that is scattered, diffused or reflected by the molecules or particulate in the atmosphere (Diffuse Horizontal Irradiance, DHI). Figure 4.13 shows the solar irradiance on the solar panel. Figure 4.13: Solar Irradiance Source:(SpringerNature, 2021) The Loop Head Peninsula is located at Latitude 52.6°N and Longitude 9.6°W. The average GHI is 985 kWh/m2 per year, with a maximum of 1000 kWh/m2(Global Solar Atlas, 2021). The average GHI of Loop Head is higher than that of Irelandx (924 kWh/m2 )(Global Solar Atlas, 2021). That implies that, on average, Loop Head is sunnier than many other parts of Ireland. A PV plant of 1 kWp installed in Loop Head will produce between 934 and 960 kWh per year (Global Solar Atlas, 2021). Figure 4.14 shows the generation profile of a 1 kWp PV system. It Energy solutions for residential buildings 53 is clear that Loop Head receives the highest solar irradiation during May whereas the lowest irradiation is during December. Figure 4.14: Monthly PV generation of 1kWp system Source: Author based on simulations using Homer Pro The monthly data of GHI for the location has been retrieved from the European Solar Energy Platform PVGIS (PVGIS, 2019). The solar irradiation data for each month from 2006 to 2016 was used to calculate the average monthly PV production. Figure 4.15 shows the average monthly global horizontal irradiation and global irradiation at an optimal angle of 39°. The maximum irradiation at an optimum angle is in May and June, around 144 kWh/m2, and the minimum is in December, around 31 kWh/m2 (PVGIS, 2019) Energy solutions for residential buildings 54 Figure 4.15: Average monthly solar irradiation Source: (PVGIS, 2019) Sun Path The sun path diagram reads the solar azimuth and altitude for a given location. It provides information about how the sun’s position will impact the site throughout the year. Figure 4.16 illustrates the sun path and sun elevation angle of Clare County and provides information about the daylight length. In June, the summer sun rises roughly at around 47° and reaches the maximum elevation angle slightly above 60° at solar noon, and sets at an angle just close to 315° (UO SRML, 2007). During summer, Clare County receives long daytime hours from 4 AM to 54pprox.. 9 PM. It is in contrast to the winter sun path. During December, the sun rises at roughly around 130° (8.30 AM) and sets at approximately 230° (3:30 PM) with a maximum elevation angle of 13° (UO SRML, 2007). . Energy solutions for residential buildings 55 Figure 4.16: Sun path chart of Loop Head, Peninsula Source: (UO SRML, 2007) Temperature Irradiation and temperature share a strong correlation. The temperature has a significant impact on the efficiency of the Solar panel. The solar panel is tested at a standard temperature of 25°. Moreover, every panel is provided with temperature coefficient Pmax which illustrates the change in efficiency with rising and fall by every degree. So, for high efficiency, cooler sunny weather is suitable. In order to appropriately design the PV system, it is therefore essential to analyze the weather condition of Loop Head. The warmest month is July, with an average daily high temperature of around 19° C and a daily low temperature of 13° C (WeatherSpark, 2021). The coldest month is January, with an average daily high temperature of 9° C and a daily low temperature of 4° C (WeatherSpark, 2021). Precipitation In Loop Head, the chance of wet days differs significantly throughout the year. The wet season lasts from October to February, with a more than 40% chance of being a wet day (WeatherSpark, 2021). Moreover, April has the fewest wet days of precipitation from rain. There is a strong correlation between cloud cover and precipitation. Changes in precipitation influence the cloud cover, and an increase in the cloud cover reduces the output power. Energy solutions for residential buildings 56 Shading The cells in each PV module are connected in series to increase the voltage. As the cells are in series, when one cell is shaded, the output current from the whole string is affected. Regardless of the number of cells in the string, the shading of one cell causes the output power of the module to fall to the level of the shaded cell. Shading can be caused by neighbouring buildings, trees, poles, or self-shading by the array itself. During the system design, the overshading factors need to be considered. Soiling Another factor that should be considered while designing a PV system is the soiling effect. The PV modules are placed outdoors and are subjected to soiling. Small dust particles on the module’s surface act as an obstacle to the solar irradiation hitting the cell’s surface, ultimately reducing the module output power. The bigger dust particles are cleaned by the rainfall, whereas the rainfall has a very minimum cleaning effect on smaller dust particles. The soiling loss is indirectly proportional to the tilt angle of the PV module. 4.4. Solar PV System integration Domestic solar PV systems can be designed as grid-connected systems with or without energy storage. Proper integration of domestic solar PV systems with household electric appliances, energy storage systems, and the national grid helps maximize the electricity consumption generated by the solar PV system. In this case, the priorities can be given for different systems, as shown in Figure 4.17. Using the electricity generated by solar PV for household electrical appliances is the primary requirement. Then, surplus electricity can be used to charge the battery storage system. If there is no battery storage system or if there is further surplus energy after charging the battery, it can be directed to an immersion heater to generate hot water for domestic consumption. Finally, the excess electricity can be fed to the grid. Currently, the energy suppliers are not obliged to pay for the electricity export from the domestic solar PV system. Hence, homeowners’ intelligent management of day-to-day energy consumption will help to minimize surplus energy export to the grid. Energy solutions for residential buildings 57 Figure 4.17: Domestic solar PV system integration priority Source:(Leonard, 2022a) 4.4.1. Battery storage system A rechargeable battery helps to store surplus electricity without exporting it to the grid. The stored energy drains during the cloudy or dark hours of the day. The battery storage can increase the solar PV-based electricity penetration, and also it can be used as a standby system for power outages. Even though adding battery storage to the solar PV system increases capital investment by a considerable amount, it allows storing electricity generated by solar PV at a lower cost compared to the grid tariff. Further, the system can be programmed to charge batteries during the lower night rate times. James Frith,(2021) reports that the higher demand in Battery storage due to the booming Electric Vehicles (Evs) and the solar market has driven massive technological improvements in battery storage, such as a longer life span, higher charging and discharging rates, higher efficiencies, and energy densities. The report further emphasizes that high demand and rapid technological improvements make the prices of batteries low, with the prediction of a 50% further drop in Lithium-Ion battery price by 2023. 4.4.2. Store energy as hot water The discussion with the community people and energy assessment at selected houses revealed that typical houses in Loop Head peninsula use 200 Litres insulated cylinders to store hot water for their daily consumption. An immersion type heater is fitted to the cylinder, and the heater rating is 3 kW. The cold water preheats up 35 - 40 ºC using the oil or gas-fired boiler Energy solutions for residential buildings 58 or heat pump and stored in the cylinder. The cylinder is well-insulated with minor heat losses. If water temperature goes down drastically, the immersion heater is turned on to increase the temperature. As (Energyd, 2022) explains, the hot water inside the cylinder must be heated up above 65ºC at least once per week to avoid forming "Legionella” bacteria growing in the lukewarm water. Surplus electricity of the solar PV system can be used for domestic hot water heating by installing a simple power diversion controller. The immersion controller automatically detects and throttle the surplus electricity to the immersion heater without export to the grid. 4.4.3. Grid connection As per (ESBN, 2021b), domestic solar PV systems can be connected to the distribution network under the condition of peak export from the system inverter not exceeding 6 kVA for single-phase systems and 11 kVA for three-phase systems. The ESBN further explained that the domestic solar PV systems are categorized under the micro-generation scheme covered under IS EN 50549-1 standard, and the owners can export surplus electricity to the national grid after obtaining approval from the ESBN using N6 form. The applicable conditions for connecting a microgenerator to the ESBN are given in (ESBN, 2021a) publication on “Conditions governing the connection and operation of Micro Generation Policy”. The owner of the PV system shall make sure to wire the system to comply with the ESBN published technical and safety rules and regulations while assuring the acceptable quality of the electricity fed to the grid. For example, the solar PV system must be equipped with protective devices to automatically close down the micro-generator when the grid’s loss of power gives non-live systems in the grid to carry out the breakdowns repairs safely. If a microgenerator is used with a battery storage system as standby, it is required to be wired according to ESBN recommended safety requirements. At the end of the installation, the solar PV system owner shall submit the Microgeneration protection setting confirmation certificate (ESBN, 2021a) issued by a safe electric electrician and type test certificates with the N6 application form to get approval for connecting to the ESBN. f.4.4 Household appliances load management. The residential solar PV system is a grid-dependent system. When the grid power is lost, the PV system should be isolated from the AC side. During this situation, even if the PV system generates power, it cannot be utilized to cater the household demand. The battery storage system can be used as the backup for the house while storing solar PV-generated electricity Energy solutions for residential buildings 59 during grid power outages. However, installing a battery backup system requires extra investment, maintenance, replacement, and extra space within the house. In this case, the homeowner can decide to use a battery storage system for a whole-home backup or a critical load-only backup (Florida solar design group, 2020). Moreover, changing the electricity consumption pattern to match the availability of solar irradiance in the location would help maximize the solar PV electricity utilization during the daytime. For example, operating the washing machine and oven in two different periods during high solar irradiance hours of the day helps get done both the works only using solar PV electricity without taking extra electricity from the grid. Solar PV systems come with a monitoring platform as a mobile app and web portal. This platform provides real-time data to the user to monitor their electricity generation, consumption, storage, and feeding to the grid. This application helps the user manage their daily electricity consumption to maximize benefit from their solar PV system. Figure 4.18 shows the solar PV monitoring platform used by one solar PV system installed in Loop Head area. Figure 4.18: Mobile app for solar PV system performance monitoring Source:(Leonard, 2022a) 4.5. Solar PV system component specifications The components of the solar PV system are shown in Figure 4.19. The solar PV panels, inverter and battery are the major components. The DC and AC isolators, optimizers, energy meter, and fire safety help the safe and reliable operation of the system. The specification and performance of each component are discussed below. Energy solutions for residential buildings 60 Figure 4.19: Solar PV system configuration Source: Author based on(Koones, 2020) 4.5.1. Solar PV panels Monocrystalline solar PV panels are commonly used for residential solar PV installations and, currently, the panels are available in blacked framed panels for giving a better aesthetic appearance for the house. As per (SEAI, 2022c) standards, solar PV panels shall comply with EN61215 and 61730 standards. The minimum peak output (Wp) requirement is 170 Wp/m2 at Standards Test Condition (STC). During the visits to solar domestic PV system owners and solar PV installers, it was recognized that Longi, Qcells, and LG None are commonly used brands of solar PV panels used in the Loop Head area. Based on that, the Longi PV panel was selected for the PV system design of all three case studies. Important specifications of the selected PV panel are given below and the data-sheet is attached in Annex 18: Existing Geothermal Heat Pump Commissioning Sheet Energy solutions for residential buildings 61 Annex 19 a. Maximum Power at STC of 25°C cell temperature and 1000 W/m2– 370 W b. Module efficiency – 20.3% c. Voltage at Maximum Power – 32.8V d. Current at Maximum Power – 8.43 A e. Panel dimension – 1755 x 1038 x 35mm f. Panel Weight – 19.5kg Energy solutions for residential buildings 62 4.5.2. Single-phase Inverter The inverter is used to convert the DC electricity generated by solar PV panel into AC current with 230 V voltage fore use in household appliances. As per (SEAI, 2022c), the selected inverter for the solar PV system is required to meet the EN62109 and the Irish protection setting standard of EN50549. Further, the rated efficiency of the inverter shall be greater than 95% and the DC power rating of the inverter shall be a minimum of the DC peak power of the solar PV array. If the solar PV system is designed with a DC-coupled battery storage system, the inverter rating can be lower than the DC peak power of the solar system. However, a commonly used solar PV array to inverter ratio is 1.15 to 1.2 and the inverter manufacturers and solar system designers do not recommend this ratio greater than 1.5. In the case studies, the inverters for solar PV systems without battery storage are selected considering DC peak power and for the system with battery storage, the inverter is sized assuming a 1.2 arrays to inverter ratio. An internet survey about the residential solar PV inverters available in the Irish market found that these are single string inverters with a capacity of 1kW to 3kW and dual string inverters with capacity ranging from 2.5kW to 6kW. Those modern inverters come with in-built charge controllers for the batteries and there is no requirement to install a charge controller. Moreover, there are hybrid inverters that include both inverter and battery storage in one single unit. The capacity of the hybrid inverter ranges from 3.6kW to 6kW. Microinverters can be used if the solar system is designed with a capacity of less than 1 kW. Also, individual panel optimizers are the effective solution not only for 62malller capacity PV systems (less than 1kW) but also for the system having the risk of uneven electric output from each and every panel in the array due to shading on panels, roof facing different directions and different angle of installations (SEAI, 2022c). In the following three case studies, single string or dual string inverters are selected based on the solar PV system capacity. Solis, Solax and Hypontech are the single- phase inverter brands used in solar PV systems installed in the Loop Head area. The data sheets of the selected single-phase inverters are attached as Annex 20 and Annex 21 4.5.3. DC Battery The solar PV system with battery storage has been designed by connecting the battery to the DC side of the inverter. The lithium-ion battery is the available DC battery type for residential solar PV systems in Ireland’s solar PV component selling websites. Pylontech, Triple power, and Weco are Lithium-ion battery brands used in the Loop Head area. The capacities of batteries available in the Irish market are 2.4 kWh, 3.6 kWh, 4.5 kWh, 5.3 kWh, and 6.3 kWh. The selected Lithium-Ion battery shall comply with EN 62133 or EN 62619 standards (SEAI, 2022c). The data sheets of the selected batteries are attached Annex 22 and Annex 23 Energy solutions for residential buildings 63 4.5.4. Mounting system (SEAI, 2022c) accepted mounting systems are pitched or flat roof, in-roof of integrated, cantilever to the wall or Brise Soleil, carport or ground mounting systems. The three selected houses are not suitable for rooftop mounting systems because of the unavailability of suitable roof area towards the south. In addition, the visits to the existing solar PV system revealed the following drawbacks of the roof mounting systems. a. Shallow roof pitch of less than 30o angle b. Difficulties in access to solar PV arrays for cleaning c. Insufficient roof strengths for installing solar PV system under the stormy weather conditions in Loop Head. More importantly, the market survey about the cost of solar PV system installation revealed that the rooftop mounting is more expensive than the ground mounting system. This is especially true for the two-story house with slate roofs. The drivers of the high cost of the roof mounting system are the cost of hiring scaffolding and more labour hours required for a system installation on the roof. The solar PV system design of the three case studies has been proposed with a ground mounting system considering the constraints and drawbacks of the rooftop mounting system. Further, the houses are located in large land areas with shadow-free locations for solar PV panels. The proposed ground-mounted system is shown in Figure 4.20. The mounting method is called a "Tree system" that uses an anchoring device without ground excavation (Solartricity, 2022).. The cabling from the solar PV system to the house has been planned to pass through underground using cable conduits. Energy solutions for residential buildings 64 Figure 4.20: Ground mounting system solar PV panels Source: (Solartricity, 2022) 4.5.5. Balance of system components (SEAI, 2022c) has recommended the following balance of system components for assuring the safe operation of residential solar PV systems. ▪ Two-pole DC isolator/s at the connection of string to the inverter ▪ Two-pole DC isolators at the connection point of the DC power line to the battery ▪ Two-pole AC isolators in the AC power line between the inverter and consumer unit (distribution board) ▪ Firefighter safety switches within 1.5m distance from the solar PV array ▪ Energy meter in the AC power side of the inverter to measure the amount of solar PV electricity generation ▪ Labelling the system including warning signs (SEAI recommended Solar PV system Labile sets are available in Irish market) The (SEAI, 2022c) guideline gives the standards, product warranty,performance requirements and installation procedure of the complete solar PV system in detail. Designing, purchasing and installation of solar PV systems complying with the given guideline helps to obtain the SEAI grant. 4.5.6. Hot water diverter The hot water diverter comes with two parts, namely the CT sensor and power diverter controller. The CT sensor is installed near the energy meter on the grid side to detect the surplus electricity export to the grid. The controller is connected to the immersion heater. The two parts are connected through wired or wireless communication mode. When the CT sensor detects any surplus energy to the grid, it communicates to the controller to use this energy for water heating. After the water reaches the set temperature of the storage tank, the surplus electricity diversion stops and allows to export surplus electricity to the grid. This controller allows using grid electricity when required for water heating. There are two types of water heater diverters in the Irish market. They are diversion controller "Zero-export" diverter and "Battery-compatible" diverter. The "Zero-export" diverter is used for diverting surplus electricity of the solar PV system without battery storage. Its operation is simple as discussed above. However, the drawback of this type of diverter is that sometimes it takes battery electricity for a short period when the surplus electricity generation stops. Energy solutions for residential buildings 65 The recommended diverter for the solar PV system with battery storage is the battery compatible power diverter. The CT sensor does not trigger the controller to drain electricity to the heater until the surplus electricity reaches a set point. Normally, the set point ranges from 50-100W (Energyd, 2022). The most commonly used hot water diverters in Ireland are “Solar i-Boost +”, “Solic 200” and my Energy Eddi. The functions of each type are given in Annex 25. Figure 4.21: Hot water diverter configuration Source: Author based on (Marlec, 2016) 4.6. SEAI grants 4.6.1. Home energy upgrade SEAI provides many ways for a homeowner to upgrade their home for more energy efficiency. Each upgrade option has different criteria and services that can be summarized in Table 4.2. Table 4.2:Home Energy Grants options Source:(SEAI, 2022g) Upgrade options Service Criteria Upgrades offered Free Energy Upgrade • Home survey • Contractor selection • Contractor works • The homeowner must own and live as the main residence • Attic insulation • Cavity wall insulation • External wall insulation Energy solutions for residential buildings 66 Upgrade options Service Criteria Upgrades offered • Follow up BER • 2006 for insulation and heating systems A homeowner receives one of the following welfare payments. • Fuel Allowance • Job Seeks Allowance • Working Family Payment • One-Parent Family Payment • Domiciliary Care Allowance • Carers Allowance • Disability Allowance for over six months with a child under seven • Internal wall insulation • Secondary work such as lagging jackets, draught proofing and energy efficient lighting • New heating systems and windows are occasionally recommended One-Stop- Shop Service • Home energy assessment • Grant application • Project management • Contractor works • Follow up BER • 2011 for insulation and heating controls • 2011 for renewable systems • All homes must complete a minimum level of energy upgrades and achieve a minimum BER rating of B2 • Has not previously received grants for the same home energy upgrades • Multiple energy upgrades • Upgrade to a minimum B2 BER • A fully managed solution including grant applications and project management • Pay for the works net of eligible grant • Assigned a contractor • Complete the post-work by BER Assessor and publish the certificate Individual Energy Upgrade Grants • Contractor selection • Grant application • Contractor works • Follow up BER • 2011 for insulation and heating controls • 2021 for heat pumps and renewable systems • Individual energy upgrades • To manage their own project • To apply for the grant themselves • To pay for the full cost of works and claim grants afterwards • Attic and walls insulation • Heating controls • Heat pump system • Solar PV (electricity) • Solar thermal (water heating) Energy solutions for residential buildings 67 The qualifying homeowner for the free energy upgrade option is to receive certain welfare payments. The changes as of 8th Feb 2022 will prioritize new applications of homes built before 1993 that have a pre-works BER of E, F, G. This option is funded by the Irish Government and the European Union Regional Development Fund (SEAI, 2022h). The option of One-Stop-Shop Service suits a homeowner who is seeking a wider range of grants and full-service including project management for a complete home energy upgrade. The highlighted point from this option is that grant values can be deducted from the cost of work upfront (SEAI, 2022j). On the other hand, the homeowner or landlord who chooses the individual energy upgrade grants option has to pay for the full cost of the works and claim the grants afterwards. Also, the homeowner or landlord must get the approval for grant before proceeding with any work (SEAI, 2022i). The payment of the grant timeframe is limited to four to six weeks (SEAI, 2022i). In addition, an applicant who applies for this grant option has to read the important notes carefully to avoid any uneligibility issues. Table 4.3 shows the grant amounts for the different building retrofitting options. Table 4.3: SEAI Grant amounts of Building retrofits Source:(SEAI, 2022j) Grant name Types of home Grant Value (Max.) Free Energy Upgrade One- Stop- Shop Service Individual Energy Upgrade Grants Attic insulation Apartment (any) € 800 ✓ ✓ ✓ Mid-Terrace € 1,200 Semi-detached or end of terrace € 1,300 Detached house € 1,500 Rafter insulation Apartment (any) € 1,500 ✓ Mid-Terrace € 2,000 Semi-detached or end of terrace € 3,000 Detached house € 3,000 Cavity wall insulation Apartment (any) € 700 ✓ ✓ ✓ Mid-Terrace € 800 Semi-detached or end of terrace € 1,200 Detached house € 1,700 Internal Insulation (Dry Lining) Apartment (any) € 1,500 ✓ ✓ ✓ Mid-Terrace € 2,000 Semi-detached or end of terrace € 3,500 Energy solutions for residential buildings 68 Grant name Types of home Grant Value (Max.) Free Energy Upgrade One- Stop- Shop Service Individual Energy Upgrade Grants Detached house € 4,500 External Wall Insulation (The Wrap) Apartment (any) € 3,000 ✓ ✓ ✓ Mid-Terrace € 3,500 Semi-detached or end of terrace € 6,000 Detached house € 8,000 Windows (Complete Upgrade) Apartment (any) € 1,500 ✓ ✓ Mid-Terrace € 1,800 Semi-detached or end of terrace € 3,000 Detached house € 4,000 External Doors Remake: max. 2 €800 per door ✓ Floor Insulation € 3,500 ✓ Mechanical Ventilation € 1,500 ✓ ✓ Air Tightness € 1,000 ✓ Home Energy Assessment € 350 ✓ ✓ Project Management Apartment (any) € 800 ✓ Mid-Terrace € 1,200 Semi-detached or end of terrace € 1,600 Detached house € 2,000 4.6.2. Heat pump grant Homes built and occupied before 2021 are eligible to apply for Heat Pump Grants. An individual can apply for heat pump grants if the home heat loss is within the range as mentioned in sub- chapter 3.2. An independent SEAI registered technical advisor must be selected before applying for the grants to carry out the technical assessment of the home. SEAI provides 200 € grants for the technical assessment which is paid with heat pump system grants. The heat pump grant support has been increased from 3,500 € to 6,500 € under Better Energy Homes Schemes (Government of Ireland, 2022). Energy solutions for residential buildings 69 Table 4.4: Heat Pump Grants Source: (SEAI, 2022a) System Dwelling Type Grant Air to Water Heat Pump Apartment 4,500 € Semi-Detached/ Detached/ Terrace/ Mid- Terrace 6,500 € Ground Source to Water Heat Pump Apartment 4,500 € Semi-Detached/ Detached/ Terrace/ Mid- Terrace 6,500 € Exhaust Air to Water Heat Pump Apartment 4,500 € Semi-Detached/ Detached/ Terrace/ Mid- Terrace 6,500 € Water to Water Heat Pump Apartment 4,500 € Semi-Detached/ Detached/ Terrace/ Mid- Terrace 6,500 € Air to Air Heat Pump Apartment/ Semi-Detached/ End of Terrace/ Detached/ Mid Terrace 3,500 € 4.6.3. Grants for Solar Electricity The government of Ireland, in July 2018, launched a support scheme to assist homeowners in installing microgeneration systems in their homes, which resulted in rapid growth of solar PV throughout the country. Homeowners can apply for grants from SEAI to purchase and install solar PV systems and battery energy storage systems. According to the updated regulations, since February 2021, the grant rate and minimum BER rating requirement went through some changes. The recently updated solar electricity scheme removed the grants of € 600 for the battery storage system. In addition, as per the Code of Practice published by SEAI, the system must be sized for self-consumption of the energy within the home. The new supporting solar scheme levels are as mentioned in Table 4.5. Table 4.5: Solar PV Grants Available Source: (SEAI, 2020) Solar PV Installations Up to 2 kWp Solar PV System 900 € per kWp installed 2-4 kWp Solar PV System 300 € per kWp installed Energy solutions for residential buildings 70 For example, a house with an estimated solar PV system of 3 kWp system will receive a grant of 2100. € In 2022, the solar PV grant scheme will be at the same level per KW as the current grant, which is capped at a maximum of 2400. € How to apply for a Solar PV Grant? The landlords or the homeowners of buildings built and occupied before 2021, defined through the date of electric meter installation, are eligible to send an application to SEAI. Before sending the application to SEAI, the applicants should choose a registered SEAI solar PV installer company and request a quotation. The applicants must receive a grant offer from SEAI before starting any work. Once the grant is approved, the offer will be valid for eight months to complete the work and submit the required documents. In order to connect the solar PV system to the grid network, the chosen installer should apply for the ESB Network NC6 form before installation, which usually takes a minimum of 4 weeks. Until now, once the installation of the PV system is complete, post-work BER certificate assessment is required with a BER rating of C or better, and the cost to carry out the assessment is included in the grant amount. Then, once the solar company submits the necessary documents to the user and SEAI, the SEAI processes the claim (SEAI, 2017c). Planning Permission required for Grants There are certain planning permission requirements that need to be taken into account while applying for SEAI solar grants for installing residential solar panels under Planning and Development Regulation, 2007 (SEAI, 2020). If the PV system in the domestic roof covers more than 50% of the total roof area, then the homeowner requires planning permission; otherwise, it is not required (SEAI, 2020) . The height of the ground-mounted solar array should not exceed 2 meters above the ground level and the PV system should not be placed in front of the front wall of the house (SEAI, 2020) . In addition, the distance between the pitched roof and the PV panel should not exceed more than 15 cm (SEAI, 2020) . Microgeneration Support Scheme In January 2021, a public consultation on the new Micro-generation Support Scheme (MSS) was launched by the Department of Communication, Climate Action and Environment (DCCAE), Ireland. The Climate Action Plan 2021 introduced an MSS with a target to support the deployment of an expected 260 MW of new micro-generation by 2030 (Government of Ireland, 2021). The DCCAE and other key members led the Micro-Generation working group to deliver the six Micro-Generation Support Schemes. One of the schemes is the “Scheme for Rooftop Solar PV panel support.” The MSS provides a market for homes, businesses, farms, Energy solutions for residential buildings 71 and communities to produce their own renewable energy and receive competitive tariffs for exported electricity. In response to the public consultation in February 2021, the Irish Solar Energy Association (ISEA) provided elaborated suggestions regarding the pricing structure, planning requirement, BER requirement, no export requirement, and new building inclusion to increase the rollout of solar PV. The MSS received final approval from the government on 21 December 2021, and the final scheme design will be published on Q1 2022. The capital grants of rooftop solar PV MSS will be a continuation of the existing grants of SEAI for domestic applicants. The transition from existing domestic solar PV to MSS grant scheme will be from Q2 2022 (Government of Ireland, 2021). According to this newly approved MSS, residential buildings built pre-2021 can apply for grants. Further, the residential buildings, even after installing new efficient equipment, do not have to meet a minimum BER standard. The grant will be the same per kW as the current solar PV grant scheme. However, taking into account the declining PV system cost, from 2024, the MSS support will be deducted gradually over time (Government of Ireland, 2021) After public consultation, the Commission for Regulation of Utilities (CRU) published a Clean Export Guarantee (CEG) in December 2021. Under the micro-generation enabling framework, the homeowners will be eligible for a CEG tariff for all surplus energy exported to the grid at a competitive market rate from different electricity suppliers with a minimum price of 0 €/kWh (CRU, 2021). The individual suppliers will set their own CEG tariff rates, providing them with the flexibility of dynamic pricing. Moreover, the customer can benefit by exporting significant energy during peak periods when the CEG tariff is higher than average (CRU, 2021). The CRU has not yet fixed the date for the first payment or credit from the suppliers. But it is announced by the government that the first export payment to the customer is expected to start by 31 August 2022 at the latest (CRU, 2021, CRU 2022). The CRU expects the supplier to provide back-payments or back-credits for the exported electricity to the customers from the very first day after the micro-generation has been installed and registered with ESBN. (CRU, 2022). A smart meter measures the energy demand profile, assists micro-generators in increasing their self-consumption and measures the surplus exported to the grid. The country has the National Smart Metering Program (NSMP) that aims to install over two million smart meters in Irish homes for six years from 2019 to 2025 in three phases (CRU, 2022). The CRU and ESBN have collaborated to deliver the electricity meter throughout the country on a phased basis. The use of a smart meter offers the consumer the Time of Use tariff which means the cost of electricity changes depending on the time of the day. This provides the consumer with the flexibility to use energy at a cheaper price. The rate of electricity at peak hours, during the day Energy solutions for residential buildings 72 time and during the night time differs. However, the suppliers can provide a customized tariff rate that fits best for the user. The micro-generators will be eligible for remuneration by CEG based on smart metered export data or deemed export quantity. According to the updated Interim CEG Decision Paper (CRU, 2022), CRU expects the grid electricity suppliers to provide clear information to their customers (micro-generators) on whether they are currently eligible for metered or deemed payments or when the customer will be eligible. The customer who has installed micro-generation and is eligible or wants to upgrade their meter to a smart meter to receive the remuneration has to submit the NC6 form to ESBN. Customers with smart meters will receive remuneration from the supplier companies based on the metered quantity of electricity that is being exported to the grid generated from the PV system. CRU expects smart meters to be installed by ESBN within four months of the request from the customer. Currently, ESBN is installing smart meters for MCC01 customers based in the current deployment planning area (Government of Ireland, 2021) The MCC02 customers with dual or day & night tariffs have to contact their electricity supplier and agree to the new smart tariff. As the customer agrees to the new tariff, the suppliers on behalf of the customer request ESBN for a prioritized smart meter exchange. Moreover, if the customers are not eligible for a smart meter installation under the ESBN-led deployment approach under NSMP at that particular time or are eligible but cannot get it at the moment, they will receive the payment based on the calculation of deemed export quantities as a short-term interim measure (CRU, 2021). The customers who have avoided, rejected or delayed the installation of a smart meter offered by ESBN under NSMP are ineligible to apply for deemed export quantity remuneration (CRU, 2021). CRU has considered 35% as an export factor for the calculation of the deemed export quantity (CRU, 2021). The deemed export quantity is calculated based on Equation 4.7 Equation 4.7: Deemed export quantity calculation Source: (CRU, 2021) 𝐷𝑒𝑒𝑚𝑒𝑑 𝐸𝑥𝑝𝑜𝑟𝑡 𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦 = 𝑀𝐸𝐶 ∗ 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝐹𝑎𝑐𝑡𝑜𝑟 ∗ 𝐸𝑥𝑝𝑜𝑟𝑡 𝐹𝑎𝑐𝑡𝑜𝑟 ∗ 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛 𝐼𝑛𝑡𝑒𝑟𝑣𝑎𝑙 Where: • Deemed Export Quantity: Quantity of electricity in kWh • MEC: Generation Capacity of the installed generation equipment • Capacity Factor: Ratio of average electricity produced to the theoretical maximum possible if the installed capacity was generating at a maximum for a full year • Export Factor: Amount of electricity deemed to be exported • Provision Interval: Number of hours for which the cumulative export quantities are to be calculated Case study I 73 Further, the single-phase grid connection for domestic micro-generation projects up to 6 kW is done through ESB Network’s “Inform, Fit and Forget” notification process (Government of Ireland, 2021) 5. Case study I 5.1. Status quo 5.1.1. Building Envelope The first case study building is a typical detached dwelling initially constructed in 1910, with a total floor area of 107 m2. The dwelling has a 500 mm stone wall from the original construction. In 2011, the owner retrofitted the house with partial insulation of the walls and turned the second floor into a “Mezzanine floor6”. Therefore, the house is a mixture of two wall types, concrete block and stone wall. Two kinds of space heating systems are present in the case study dwelling; an oil condensing boiler and a wood pellet stove. The condensing oil boiler is also for domestic water heating purposes. It has thermostatic temperature control for the heating and hot water systems. Table 5.1 summarizes the main inputs for the DEAP analysis mentioned in subchapter 3.2. Table 5.1: Status quo of Case study I Description Value Unit Dwelling Floor Area 82.95 m2 Total Door Area 7 3.52 m2 Total window Area 9.92 m2 Number of occupants 1 Number of rooms 9 Fabric U-Values Wall 1(Stone walls, 500 mm uninsulated) 2.1 W/m2 ∙K Wall 2 (Stone walls, unknown insulation) 1.41 W/m2 ∙K Roof (100 mm mineral wool insulation) 0.22 W/m2 ∙K Ground floor (uninsulated, solid) 0.84 W/m2 ∙K Mezzanine floor (uninsulated, solid) 0.28 W/m2 ∙K Windows (double-glazed) 2.2 W/m2 ∙K External Doors (double-glazed) 3.1 W/m2 ∙K Appropriate default U values were used as per the year of construction values are labelled as d as per 6 Mezzanine or interior balcony, is an area of floor in a dwelling which overhangs the storey below. 7 Area of door that are expose to the exterior Case study I 74 DEAP manual. Although the house was renovated in 2011, it is only partially insulated. During the on-site thermal inspection of the building, the following deficiencies were found in the insulation of the building • Inadequate Attic insulation Cold zones were detected on the attic walls and roof as seen in Figure 5.1. In the under-roof space, a non-uniform decomposed insulation material was seen, as a result of which cold air can penetrate and cool down the under-roof space. It is recommended to adequately insulate the walls of the attic floor and the roof. There are multiple insulation materials available for this purpose with varying costs as explained in sub-chapter 4.2. Among all the options, mineral wool insulation is considered for the cost analysis. Figure 5.1 Infrared and digital image of the attic walls Source: Thermal camera • Partial external wall insulation During the inspection of the house walls in infrared radiation, the temperature difference between the insulated and not insulated external walls of the house is visible. Since the walls of the house are made of natural stone, they have a high thermal conductivity which causes significant heat loss. In order to reduce heat loss at home, it is recommended to insulate the outer walls of the house, as was recommended in sub-chapter 4.1. Case study I 75 Figure 5.2: Infrared and digital image of the external walls. Source: Thermal camera • Windows and Doors The house has 11 windows, out of which four are roof windows. All the windows are double glazed and do not manifest urgent change requirement. The two exposed doors, however, on replacement can provide an energy-saving opportunity. 5.1.2. Electricity usage pattern The annual electricity consumption of the building in the year 2021 was summed up to be 1571 kWh as per the provision of monthly electricity bills. The billing units and the amount was not the actual one and was often estimated. This is a common occurrence in Loop Head as was observed in all three case studies. Moreover, the billing period was mostly two months. Hence, the average daily consumption for the billing period was multiplied by the number of days to estimate the monthly electricity consumption. The resulted annual load profile is as seen in Figure 5.3. The highest electricity consumption was observed in December and January corresponding to the coldest months of the year. Since the number of occupants in this particular case study is only one and electricity is not used for space heating, the electricity demand is seen to be fairly constant throughout the year. To minimize the uncertainty in the accuracy of load profile, synthetic load profiles as defined in sub-chapter 5.1.1 have been developed for PV system sizing. Case study I 76 Figure 5.3: Annual estimated electricity demand profile As per the electricity assessment conducted during the site visit, the end-use breakdown of electricity has been done for Case Study 1 to categorize what appliance consumes the most electricity as illustrated in Figure 5.4. Lighting accounts for 6% of the total electricity demand and all of them are either LED or CFL. Since electricity is not used for space heating purposes, the remaining 94% is consumed by the appliances in the house No energy efficiency measures were applied in this case study as the house has efficient appliances. Figure 5.4 End-Use Breakdown of electrical appliances Source: Author 110.0 115.0 120.0 125.0 130.0 135.0 140.0 145.0 El ec tr ic it y C o n su m p ti o n in k w h Months 6% 94% Lighting Electrical Appliances Case study I 77 5.1.3. BER Rating A BER assessment of the original dwelling was calculated utilising an online version of DEAP 4.2.0 software developed by SEAI. The building geometry and fabric U-values referred to in Table 5.1 have been considered to calculate the BER rating of the building. The primary energy use of the building is 284 kWh/m2/yr and HLI is 4.89 W/K/m2 corresponding to a BER Rating of “D2”. 5.2. Identified energy-saving measures 5.2.1. Building envelope Based on the thermal camera inspection, we saw the need for the following fabric insulation: a) Roof insulation b) External wall insulation c) Exposed door replacement The objective of the retrofit strategy applied was not only to minimize heat loss but also to increase the rating of the house to a minimum of B2 or comply with the HLI criteria. Hence, the recommendations have been presented in the form of steps to present alternatives for the homeowner. The homeowner thus will have a complete picture of where he/she is at and how will the insulation impact him/her. Individual energy upgrade grant does not necessitate reaching a B2 BER rating. The one-stop- shop, on the other hand, mandates a BER rating of B2. Therefore, two scenarios are developed to address each scheme. Individual energy upgrade grant Insulation of attic and walls have been considered in this case. a) Wall insulation: Although the wall is partially insulated, it is assumed that all the walls are insulated with 100 mm of Rockwool insulation to lower the U-value to 0.27. It is the standard refurbishment step as defined by TABULA (Tabula, 2014). With this step, the HLI is reduced to 3.52 W/K/m2 and total heat loss is reduced by 44.85% leading to a BER rating of “C1”. b) Roof insulation: The current roof insulation comprises around 100- 150 mm of fibreglass insulation. Adding an insulation layer to make it to 300 mm insulation will Case study I 78 reduce the heat loss by 4.86 %. The HLI is 4.65 W/K/m2. With this upgrade, the BER rating remains at “D1” as base case. Table 5.2: Energy cost savings and payback period of insulation Type of insulation Grants Total Investment Cost (Euro) Total energy saving* (kWh/y) Estimated annual savings (Euro/y) BER rate Simple Payback Period (Years) Wall insulation No 14,256 6,475 501 C1 28 Yes 6,255 13 Roof insulation No 3,662 1,064 82 D1 44 Yes 2,162 26 * Total energy saving is delivered energy saving from primary and secondary space heating energy systems. **Replacement of doors and windows is not recommended under this scheme as there is no available grant and the payback period is more than 90 years. From these two options presented in Table 5.2 it was observed that although the initial cost of wall insulation is higher, the payback period is less as compared to that of roof insulation. Hence, the homeowner could opt for higher savings (wall insulation) or lower investment costs (roof insulation). One-Stop-Shop Service The One-Stop-Shop Service mandates a post-refurbishment BER rating of B2. The above- recommended measures are not enough to achieve that level. Therefore, to assess the reduction in heat loss and increment in the dwelling rating, step-by-step fabric improvements were investigated. Base Case: “Do Nothing” Scenario: No fabric upgrades have been considered in this scenario. Step 1: Roof and External wall insulation The recommendation as summarized in Table 5.2 was combined in this step. The heat loss was decreased by 49.6 %. The HLI of this step was reduce to 2.43 W/K/m2. The BER rating of the house could be improved to “C1”. Step 2: Doors and windows replaced in addition to roof and external wall insulation Case study I 79 The dwelling presently has eleven double glazed windows. Therefore, replacing them with triple glazed windows should be of last consideration. However, for the attainment of a heat pump grant, the homeowner should consider this step. Further, the two exposed solid doors should be replaced with the door having the material with better U-value of 0.8 W/K/m2. This reduces HLI to 2.6 W/K/m2, only a 2.72 % decrement from the previous step. The rating of the house remains at “C1”. The HLI indicator after Step 2 was 2.4 W/K/m2, which is not in the range of eligibility for heat pump grant. Therefore, as a final step, the walls were recommended to follow an advanced refurbishment step as suggested by TABULA (Tabula, 2014). Advanced Refurbishment: Roof insulation, advanced external wall insulation with doors and windows replaced. In this case, the external wall insulation could be change to 200 mm with the better material with lower U-value of 0.15 W/K/m2. The final rating of the house after this step could be B3. It was not possible to achieve the desired B2 rating as outlined by the National Retrofit Scheme without reducing the U-values of the floor. However, with the advanced refurbishment step, the HLI was within the desired range for heat pump grant. The calculation of the heat pump sizing for the particular case study is explained in sub-chapter 5.3. The recommended changes with the upgraded U-values are listed in Table 5.3. Table 5.3: Upgrade recommendation with their U-Values of case study I Source: (Tabula,2014) Upgrade Recommendation Upgraded U value (W/K/m2) 300 mm of mineral wool insulation in the roof 0.13 100 mm of Grey EPS external wall insulation 0.27 200 mm of Silver EPS external wall insulation 0.15 Triple glazed Argon Low E (0.15, hard)- Wood/PVC – 16mm Gap for window 1.4 Munster Joinery GRP Joinery door 0.8 Appropriate default U values were used as per the year of construction values are labelled as d For the upgraded value of the roof, table S4 of the DEAP manual (Seai, 2020) is referred, based on the recommended insulation. 5.2.2. Cost analysis of building retrofit The range of retrofit measures chosen for the retrofit program is in line with available grants at the time of analysis. The calculation was nevertheless done with and without a grant. The Case study I 80 estimated annual savings after the fabric upgrades along with their cost breakdown and payback period is set out in Table 5.4. Table 5.4: Cost Analysis for different retrofitted steps of case study I Source: Author Steps Grants Estimated Investment Cost (Euro) Total energy saving* (kWh/y) Estimated annual savings (Euro/y) BER rate Simple Payback Period (Years) Step1 No 32,037 12,407 960 C1 33 Yes 22,537 23 Step2 No 44,403 12,571 973 C1 46 Yes 27,803 29 Advanced Refurbishment1 No 46,907 13,558 1049 B3 45 Yes 30,307 29 The estimated investment cost breakdown is in Annex 13 As mentioned in sub-chapter 3.2, the uncertainty of the cost analysis was included by using the accuracy range between a low range of -10% and a high range of 30%. Figure 5.5 shows the total investment costs and discounted payback periods, both with and without grants at each step. Giving an example, the total investment cost without grant of advanced refurbishment is 46.91 thoudsand euro with an uncertainty range between 42.22 to 60.98 thousand euro. The discounted payback period of this case is 77 years with an uncertainty range between 69 to 100 years. On the other hand, the applicable grants can reduce both investment costs and discounted payback period significantly. The total investment cost with “One-Stop-Shop Service” grant for advanced refurbishment could be reduced to 30.31 thoudsand euro with an uncertainty range between 27.28 to 39.40 thousand euro. The discounted payback period of this case would be 38 years with an uncertainty range between 34 to 49 years. Case study I 81 Figure 5.5: Total investment cost and Discounted Payback Period of case study 1 Source: Author 5.3. Domestic Hot Water (DHW) and Space Heating Supply 5.3.1. Space Heating To identify a feasible and renewable energy solution to supply the case study house with the sufficient heat demanded, several aspects have to be considered and analyzed. Firstly, the house’s current heating system and expenditure on heating. Secondly, the behavioral patterns of the house owners in running the system since this will affect the system under development. Thirdly, the house’s current insulation, heat losses, and heat demand status. Fourthly, the heat energy efficiency level of the dwelling that can be reached by the proposed renovations. Consequently, an onsite inspection was carried out to find that two heating systems are employed to complement each other, namely a condensing gas boiler and a traditional stove system. The condensing oil boiler is manufactured by “Grant”, this brand and boiler type was found to be used in 2 case studies out of 3 analyzed in Loop Head, fuelled by class 2 kerosene and installed in 2011. The installed machine was found to be working in the heat output range of 26-36 kW, heating the water distribution circuit to 75°C according to the machine nameplate as depicted in Figure 5.6. Case study I 82 Figure 5.6: Condensing oil boiler name plate Source: Author On the house owner interview, it was reported that he/she operates the condensing oil boiler for 1 hour in the morning and 1-2 hours in the evening, then he/she would most likely start the stove heating system to maintain the heat inside the house. According to the house owner, he/she uses 1300 -1500 Litres of gas oil per year at a cost of 70 cents per litre excluding the value-added taxes; on the other hand, he/she uses wood or coal for firing the stove system at a cost of 500 Euros per year. Since analyzing the dwelling’s current insulation, heat losses, and heat demand include several sub-activities and information that have to be provided by the house owner, which might not be available. A reverse engineering exercise was undertaken to measure the current dimensions of the radiators in the house. Through this method, the heat emitted from each emitter per heated space can be roughly estimated, especially since it was confirmed by the owner that the heating system can easily reach his/her desired room temperature and that is the reason why the system only operates for 2 hours a day. In addition, the owner reported using the stove system only to keep the house warm for the rest of the day. Case study I 83 The house consisted of a total of eight heated spaces: a kitchen, 2 living rooms, 3 bedrooms, and 2 toilets. The heat emitted per radiator was estimated based on a temperature drop across the radiator surface area (∆T) of 50°C, which is the typical ∆T generated by a condensing oil boiler. Hence, the dimensions of the existing radiator were measured and their equivalent heat emitted were calculated as shown in Table 5.5. Table 5.5: Existing Radiators' Sizes and Heat Emittance Source: Author Room Existing Rad Size (m) Existing Heat Emitted (W) Kitchen 0.9*0.5 480 Living Room 1 1.4*0.5 774 Living Room 2 0.9*0.5 480 Bedroom 2 1*0.5 578 Toilet 2 0.34*0.5 382 Bedroom 1 1.16*0.5 774 Toilet 1 0.34*0.5 382 Home office 0.9*0.5 480 Bedroom 3 1.16*0.5 774 Accordingly, the total heat emitted by all radiators was found to be 5104 Watts (W), which is very sensible for a house with a total area of 107.55 m2. As for now, an estimate of the heat demand is available, thus the next step was to estimate the heat losses per each room, and they were found for the base case scenario as shown in Table 5.6. Table 5.6: Base Case Scenario Room Wise Heat Loss Source: Author Room Heat Loss (W) Kitchen 2293 Living Room 1 3520.4 Living Room 2 2063 Bedroom 2 651.4 Toilet 2 594.1 Bedroom 1 1010.9 Toilet 1 845 Home office 888.9 Bedroom 3 811.8 Case study I 84 The sum of the heat losses from all spaces was found to be 12,678.5 W, which is much higher than the estimated heat emitted by the existing radiators. This may suggest that the existing heating system is undersized and that is why the house owner has to complement the condensing oil boiler with the stove system. Alternatively, this may be due to the non- availability of exact thermal conductivity values of the house’s different fabrics. Regardless, the most important aspect for designing an effective heating system will be the heat demand after retrofitting the house by considering an advanced level of insulative material irrespective of the house's current losses. In other words, the house must reach a high thermal efficiency by upgrading the fabrics’ thermal conductivity utilizing the available SEAI insulation grants, so it can reach a BER rating of B2. Based on that, the household will not only be eligible for heat pump grant but also a smaller heat pump size will be ascertained reducing the whole cost of introducing energy sufficiency and energy efficiency for the household. On this basis, the economically feasible refurbishments advised sub – chapter 5.2 can upgrade the household to a BER B2 rating with the heat losses as shown in Table 5.7. Table 5.7: Refurbished house room wise heat loss Source: Author Room Heat Loss (W) Kitchen 1189.6 Living Room 1 1406.8 Living Room 2 787.6 Bedroom 2 308.2 Toilet 2 252.9 Bedroom 1 531.3 Toilet 1 429.1 Home office 431.6 Bedroom 3 338 These thermal losses sum up to 5,675.1 W. Accordingly, the Heat loss indicator of the dwelling was checked to be 2.21 W/K/m2. Hence, it lies in the range of eligibility for Heat pump grants by SEAI after conforming to the 4 special criteria mentioned in sub–chapter 4.6.2. As of now the demanded heat per each room is known in the case of upgrading the house to BER B2, thus a new space heating system can be sized in two concurrent steps after selecting the proper heat pump and heat emittance technologies. Air-to-Water heat pump was selected since Air-to-Air heat pumps only caters 60 – 80 % of the load, while Ground Source heat pumps require excavation works that increase the investment cost. The decision was made to select Case study I 85 radiators as the appropriate heat emittance technology since they are relatively cheaper than underfloor emitters, which would require excessive construction works on top of their cost. Also, due to the fact that the thermal mass demanded is manageable by acceptable dimensions of radiators that are cheaper than other alternatives available on the market while still being effective. The space heating design methodology was selecting an equivalent heat pump size that is able to overcome the heat demand of the house from the variety available in the market; concurrently, the radiators were sized according to the heat pump forward water temperature, and the heat emitted per each radiator is equal to the amount of heat lost due to temperature drop across its surface area (∆T). The dwelling’s existing radiators were also checked for their compatibility with the new system. However, it shall be noted that the ∆T generated by Air-to-Water heat pumps is lower than that of the condensing oil boiler at 30°C and 50°C respectively since an Air-to-Water Heat Pump can heat the water circulating to the radiators in the range of 15-65°C, while an oil boiler heats water in the water distribution circuits to 75°C. Therefore, it was found that even if it will be decided to utilize the existing radiators; it is impossible to assure their efficiency under the new working conditions of a lower ∆T in the absence of their datasheets. Nonetheless, for the sake of other households in the community which might still have the datasheets of the installed radiators, some radiators might not need to be changed. Suppliers of radiators usually quote radiators' capacities of heat emitted for different levels of ∆T since not all space heating technologies can elevate water temperature to the same level. To illustrate, a radiator size 1 X 0.5 m of a brand X is capable of emitting 722 W of heat at ∆T of 30°C, whereas the same radiator size emitted heat can increase to 1,048 W at ∆T of 40°C. Hence, a ∆T of 30°C was decided based on the specifications of the prospect heat pump under study since this heat pump can heat water in the range of 15 - 65°C. Another method adopted by other radiators suppliers would be providing heat emittance of different radiators sizes at ∆T of 50°C accompanied with conversion factors for other ∆T values. A brand X was selected based on their acceptable dimensions at low ∆T so that the total heat demanded from the heat pump is 5,917 W as shown in Table 5.8. For more information see Annex 17. Table 5.8: New Radiators Sizes and Heat Emittance Source: Author Room Radiator Size (m) Heat Emitted (W) Kitchen 1.8*0.5 1299 Case study I 86 Living Room 1 2*0.5 1443 Living Room 2 1*0.5 722 Bedroom 2 0.5*0.5 361 Toilet 2 0.4*0.5 288 Bedroom 1 0.8*0.5 577 Toilet 1 0.6*0.5 433 Home office 0.6*0.5 433 Bedroom 3 0.5*0.5 361 An LG model HM051M.U43 monobloc AWHP heat pump of a rated heat output of 6 KW was selected, so it can overcome the heat load of the whole system and provide the desired room temperature to all heated spaces. Not only the selected AWHP can provide heat emitters a forward temperature up to 65°C, but it also elevates the DHW temperature up to 80°C. The selected machine employs R32 refrigerant in its reverse Carnot cycle circuit, which is considered a replacement refrigerant for lower forward temperature heat pumps (Agas, 2022). R32 has a low Global Warming Potential (GWP) of 675 and a lower carbon footprint than other common hydrofluorocarbon refrigerants (BOC Online, 2022) . For more information about the selected AWHP see Annex 14 5.3.2. The Space Heating and DHW Excel Model To further analyze the selected Air-to-Water heat pump performance, an hourly ambient temperature data set was collected for Shannon Airport weather station for the year 2021 (Met Eireann, 2022). The data set was principally collected to calculate the hourly COP, heat load, and electrical load of the selected AWHP at different hourly ambient temperatures, so Loop Head’s house owners can estimate their annual electrical consumption, AWHP’s annual COP, and the total annual heat supplied in case of transforming their dwellings towards energy efficiency and sufficiency. In order to achieve these objectives, an Excel model was constructed such that a homeowner can input the heat pump size, and the total heat demanded of the dwelling according to the radiator sizes based on “SEAI Room Heat Loss and Radiator Sizing Guidance”, “SEAI Designer installer Sign Off Form”, and “DEAP” as executed to design the system in sub-chapter “5.3.1”(SEAI, 2021b). The “SEAI Room Heat Loss and Radiator Sizing Guidance” will allow the house owner to calculate the rooms’ heat losses as in Table 5.7, integrating these losses to size a heat pump and emittance technologies as concluded from Table 5.8. Then, the homeowner can use the “SEAI Designer installer Sign Off Form” to validate the results, specifically the Heat Loss Indicator (HLI) being in the range of an eligible Case study I 87 heat pump grant. Finally, from the DEAP online software, a house owner can retrieve the total annual heat demand of the dwelling after renovations. Implementing the developed Excel model for their dwellings’ refurbishments process, Loop Head’s house owners will be capable of figuring out, the AWHP’s annual COP, total annual heat supplied, and annual electrical consumption. For more information, see Annex 16 Utilizing the aforementioned constructed model, it was found that the “Total Annual Heat Supplied to Dwelling” is 10,818 kWhthermal compromised of 9,622 kWhthermal as “Total Heat Demand for Space Heating” and 1,196.84 kWhthermal as “Annual Domestic Hot Water Heat Demand” in case of the dwelling being occupied by 2 residents. The “Yearly COP” was found to be 3.63; in other words, the heating system generates 3.63 units of heat for every electrical unit consumed. The AWHP monthly thermal generation and electricity consumption can be automatically displayed on the second sheet of the model “HP Monthly Energy Profile” as per the given input required in the first sheet. Figure 5.7: Heat Pump Monthly Heat Generation Vs Electrical Consumption Source: Author based on (Met Eireann, 2022) In the case of the dwelling under analysis, as shown in Figure 5.7, it was found that the highest heat generation, gains, and transfer to the heated spaces occur during the coldest month of the year, January, at 1,811.95 kWhthermal due to the fact that the AWHP will be able to exploit the higher difference in temperature between indoors and outdoors. As the mid-summer season approaches, the heat pump will not generate much heat for spaces but rather solely for DHW. It can be noticed that the COP of the AWHP varies according to the ambient 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00 1600.00 1800.00 2000.00 Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. E n e rg y ( k W h ) Heat Supplied (kWh) Electricity Consumed (kWh) Case study I 88 temperature, while the lowest electrical consumption and highest COP ocurr in July at 44.74 kWhe and 4.22 respectively. Even though the ambient temperature will reach -4.5°C on Jan 9th at 6:00 am, the AWHP will still be capable of generating 2.81 thermal units for every electrical unit it consumes. To illustrate, the AWHP will generate the maximum thermal units per hour at 4.16 kWh thermal, consume the maximum electrical units per hour at 1.48 kWhe, and it will have the lowest COP in the year at 2.81 as shown in Figure 5.8. Figure 5.8: Heat pump performance on the coldest day of the year Source: Author based on (Met Eireann, 2022) To conclude, the space heating and DHW systems were found of significantly high performance at Loop Head ambient weather conditions since the median COP is 3.95. In other words, at 77 % of the total hours of the year, namely 6,800 hrs, the space heating and DHW system is able to supply 3.63 units of thermal heat against every unit of electricity consumed. In addition, at 59 % of the total hours of the year, namely 5,200 hrs, the space heating and DHW system is capable of supplying more than 3.84 units of heat to the dwelling at the cost of a single electrical unit, a COP of 4.45 is achieved for 13 % of the total hours of the year for 1,200 hrs as depicted on Figure 5.9.This suggests that the heat pump operates efficiently throughout the whole year, including in the summer. -5.00 -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 5.00 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Heat Generated (kWh) Electrical Consumption (kWh) COP Ambient Temperature (°C) Case study I 89 Figure 5.9: Selected AWHP COP uration Curve Source: Author based on (Met Eireann, 2022) 5.3.3. Space Heating and DHW economic analysis In order to analyze the economic feasibility of the space and DHW heating system, a nominal investment cost and an annual operational & maintenance cost of €9,385 and €311 were assumed respectively to calculate the cost of annual energy savings, simple payback period, and discounted payback period (Anders Rosenkjær Andersen, 2021). The selected AWHP is a monobloc type that only requires four spring mounts, an electrical connection, and a water connection. In addition, the nominal investment and maintenance costs selected were noticed to exceed the prices on the Irish market since they are extracted from Danish Energy Agency a data set (Anders Rosenkjær Andersen, 2021). Further, the nominal investment and operational costs include spare parts, DHW tank, and auxiliary equipment. Exploiting the SEAI heat pump grant of €6,500 and assuming an interest rate of 1.47 %, the total initial investment was found at €2,885 (SEAI, 2022b) Calculating the annual energy savings, the selected AWHP generates 10,819 kWh thermal while it consumes 2979 kWhe. Thus, at a levelized cost of energy of 0.2209 €cents/kwh generated from the integrated energy solution utilizing solar PV, the AWHP electrical consumption is 658.11 €/year. The annual maintenance cost of the existing oil condensing boiler was reported during the house owner interview to be 100 €/year, while the fuel consumption was reported to be 1500 L/year and the delivered energy cost of gas oil is 8.09 €cents/kwh (SEAI). Therefore, the same annual thermal units using the existing boiler will cost 931 €/year. Hence, the annual energy savings is 273 €/year. 4.45 4.25 4.12 3.84 3.63 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 0 400 800 1200 1600 2000 2400 2800 3200 3600 4000 4400 4800 5200 5600 6000 6400 6800 7200 7600 8000 8400 8800 Case study I 90 For calculating the simple payback period, the initial investment of installing AWHP is divided over the annual energy savings resulting in 10.56 years to pay back the initial investment. Nevertheless, in order to calculate the discounted payback period, the investment cost and the operational cost of the existing oil condensing boiler have to be considered since the brand of the boiler installed has a lifetime of 12 years, and it has already been installed since 2011 as reported in the interview (Grant Engineering, 2022). €1,905 has been estimated as the investment cost of the condensing boiler as of today's prices, while it will cost €1,933 and €2,302.95 in 1 year and 13 years respectively (My Building Supplies, 2022). A project lifetime of 16 years was assumed, the same as the economic lifetime of AWHP. In other words, even if the existing boiler is to be replaced next year, it will need to be replaced one more time during the lifetime of the AWHP. Therefore, the AWHP initial investment will pay back the house owner after 12 years, considering lifetime, discounted maintenance costs, discounted replacement costs, and discounted annual energy savings. 5.3.4. Domestic Hot Water Heat Demand excel model update The heat demand of the Domestic Hot Water (DHW) cannot be assumed to be catered within the annual total heat demand retrieved from DEAP. The system will serve the DHW heat demand first, but there might be a shortage in the total units of heat to be supplied to the dwelling. Therefore, the space heating and domestic hot water model has an input cell for the number of occupants per dwelling, and an output cell that will show the annual DHW heat demand. Subsequently, the output cell for the “total annual heat supplied to the dwelling” is updated to include both the heat demand for space heating and DHW. 5.4. Solar PV 5.4.1. Residential Demand The energy assessment for case I revealed the annual electrical demand to be 1571 kWh/year. Figure 5.10 shows the monthly load profile. As discussed in sub-chapter 3.3, the hourly synthetic load profile was developed from the standard national load profile. Figure 5.10 shows the comparison of synthetic and actual monthly load profiles. The similar load pattern of the two load profiles validates the synthetic load profile for further sizing of the PV system. Case study I 91 Figure 5.10: Comparison of Synthetic and actual monthly load profile Source: author based on (Ricardo, 2020a) 5.4.2. System Design The PV system was designed to cater for the electrical demand under two scenarios: with and without battery storage system, using Homer Pro software. The best suitable PV system was selected based on the optimization criteria defined in sub-chapter 3.3 Different battery capacities available in the market were kept as input parameters in Homer Pro for simulating the system. All the possible PV system configurations with the cost of consumed electricity (COCE) below the considered tariff rate are tabulated in Table 5.9. As per the least cost optimization result, a 1.2 kWp capacity PV system with 2.4 kWh storage capacity results in the lowest COCE of 22.98 cent €/kWh. This system served 58.2 % of the total electricity demand from generated PV electricity. However, with a 0.729 kWp system with no battery storage, the system served only 29 % of the total electricity demand and the COCE is closer to the cost of grid electricity. 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 E le c tr ic a l C o n s u m p ti o n ( k W h ) Month Energy assessment data Synthetic data Case study I 92 Table 5.9: Least cost PV system configurations- Case study I Source: Author using Homer Pro optimization tool System PV capacity kWp Battery capacity kWh Initial investment € COCE cent €/kWh RE fraction (%) 1 0.729 - 1,304 24.07 29 2 1.2 2.4 2,865 22.98 58.2 The 1.2 kWp system with 2.4 kWh battery storage fitted the cost-optimized criteria for the PV system. However, the PV system was resized to 1.48 kWp for installing 4 “Longi” solar panels with 370 Wp capacity each, and a 2.4 kWh Pylontech Lithium-ion battery. The peak DC power of the designed system was 1.317 KW on 21st August. It means that the selected inverter’s DC peak power should be more than 1.317 kW. Subsequently, the ‘Solis’ two-string MPPT inverter with a capacity of 2.4 KW was selected with a peak DC power rating of 3KW based on the market available inverter size. The technical specifications for the panels, inverters and battery are attached in Annex 26,. The designed system catered to 70.7% of the total electricity demand of the household. Moreover, the total area required for this system was calculated to be 8.6 m2. Table 5.10 elucidates the performance of the designed PV system. Table 5.10: PV system performance of cost optimized system- Case study I Source: author based on Homer Pro simulation Electricity demand of household kWh/year Electricity generated from PV kWh/year Demand met by PV generation kWh/year Grid purchase kWh/year Surplus electricity to the grid kWh/year RE fraction (%) 1571 1547 1086 461 1113 70.7 Case study I 93 Figure 5.11: Monthly PV performance for cost optimized system- Case I Source: Author based on Homer Pro simulation The generation from the PV system was maximum at 162.4 kWh during May, and minimum at 41.5 kWh during December as shown in Figure 5.11. To increase the renewable penetration of the system, there should be more generation during winter and less excess during summer times. This requirement was attempted to be met by adjusting the tilt angle to improve the performance of solar PV panels during winter. Since the location of the case study is in the northern hemisphere, the sun is low with respect to the horizon in winter. Thus, placing the panels at a deep tilt angle might help the sun rays to hit the panel perpendicularly, thereby, increasing the energy generation. A sensitivity analysis was conducted by changing the tilt angle of the panel and the panel azimuth to analyze the variation in power generation from the PV system. The sensitivity analysis result would help to fulfil the second criteria of the optimized system. Table 5.11 shows the energy generation of the cost-optimized system with varying tilt angles. Table 5.11: Sensitivity analysis by varying tilt angle- case study I Source: Author based on Homer Pro simulation Tilt angle Azimuth angle, south to west PV generation (kWh/year) Surplus to grid (kWh/year) Grid Purchase (kWh/year) Renewable fraction (%) 370 00 1547 490 581 63.1 370 100 1539 481 581 63.1 Case study I 94 Tilt angle Azimuth angle, south to west PV generation (kWh/year) Surplus to grid (kWh/year) Grid Purchase (kWh/year) Renewable fraction (%) 450 00 1545 490 583 62.9 450 100 1536 480 581 62.9 The renewable fraction is maximum with a tilt angle of 370 and it is the optimal tilt angle for Loop Head. It can be concluded that the PV energy yield decreases with the deviation from the optimum tilt angle and 00 azimuth angle, i.e., south faced. Therefore, the 1.48 kW system integrated with a 2.4 kWh battery placed south faced at a tilt angle of 370 is finalized as the optimized system for Case I. 5.5. System integration 5.5.1. Domestic hot water Case I is a single resident household where condensing oil boiler is used to meet its hot water requirement. As per the interview with the house owner, the boiler is operated one hour in the morning and two hours in the evening for space heating and hot water purposes. In addition, the water cylinder tank is equipped with a 3 kW rated immersion heater, and the immersion heater is used once or twice a week for 30 minutes to increase the temperature up to the set point. . According to this consumption pattern, the annual electricity demand for water heating is 117 kWh/year.The integration of a power diverter controller in the PV system could be an effective solution for meeting the hot water demand during the excess electricity generation time. This increases the self-consumption of the PV system and with the existing uncertainty of feed-in tariff, it is better to increase the self-consumption. As discussed in methodology sub–chapter 3.3, a typical family of four people consumes 120 liters of hot water daily heated up to 50 0C from the water mains temperature. Therefore, a hot water requirement of 30 liters was considered for this case study. The power required to meet the hot water demand was calculated to be 566 kWh/year, out of which 220 kWh/year can be catered by diverting the excess PV power. The remaining excess of 284 kWh/year power can be diverted to the grid. Table 5.12: PV system performance with battery storage and power diversion to hot water tank Source: Author based on Homer Pro simulation Case study I 95 Electricity demand of household (kWh/year) Electricity generated from PV (kWh/year) Demand met by PV generation (kWh/year) Grid purchase (kWh/year) Surplus PV energy diverted to grid (kWh/year) Surplus PV energy diverted to hot water storage tank (kWh/year) Renewable fraction (%) 1571 1547 1086 461 270 191 70.7 PV system considering electrical demand from heat pump The energy assessment for Case I suggests the installation of heat pumps for more efficient use of energy. In sub-chapter 5.3, a 6 KW heat pump was designed to cater to case I’s heating demand for space heating and hot water purposes. After incorporating the annual electrical consumption of the heat pump, the electrical demand of the house increases to 4550 kWh. The previously optimized system was further analyzed to meet the new additional demand. An additional electrical load profile was fed as input to Homer. The same approach of determining the cost-optimal system as in sub-chapter 5.4.2 was followed. All the possible system configurations with the cost of consumed electricity (COCE) below the considered tariff rate are tabulated in Table 5.13. Although the system with 2.71 kWp with battery storage systems have higher RE fraction, the COCE is closer to the cost of grid electricity. Therefore, these systems were not considered. Table 5.13: Least cost PV system configuration for the system with heat pump electrical demand- Case study I Source: Author based on Homer Pro simulation System # PV capacity kWp Battery capacity kWh Initial investment € COCE cent €/kWh RE fraction (%) 1 2.17 2.4 3,917 22.5 32.5 2 1.63 - 2,277 23.1 20.6 3 2.71 2.4 4,877 24.2 38.8 4 2.71 3.6 4,927 24.3 38.8 Case study I 96 Figure 5.12:Perfomence comparision for the lowest COE system Source: Author based on Homer Pro simulation The 2.17 kWp system with 2.4 kWh battery storage was the least costsolution. Considering 6 ‘Longi’ solar panels with 370 kWp capacity each, the solar PV system was fixed to 2.2 KW. The ‘Solis Dual MPPT’ 2.5 kW inverter was selected based on the market availability. The selected inverter has a peak DC rating of 3 kW. The Pylontech lithium-ion battery of 2.4 kWh capacity was selected. Finally, the 2.2 kWp system with 2.4 kWh at an angle of 370 was selected as the optimized system for Case I to cater for the electrical demand including the electrical consumption from the heat pump. The total required area for this system was calculated to be 12.7 m2. The solar penetration rate for the optimized system was 32.7% while the excess generation was 13.7% and the system meets most of its demand from grid purchased electricity. The behavior of the system is as such because of the higher consumption and demand pattern of the heat pump. Although the heat pump is operated throughout the year, the consumption is highest during the winter season and lowest during summer. However, the generation from the PV system is low during winter and high during summer. Thus, installing a higher capacity system to cater to most of the electrical consumption from the heat pump demands a larger battery storage, which drastically increases the system’s investment cost. Therefore, it is economically feasible to go for a lower system size and cater to the high consumption of the heat pump from grid electricity during the winter season. Table 5.14 shows the details on the performance of the PV system. Table 5.14: Performance of PV system including electrical demand from heat pump- Case I Source: Author based on Homer simulation 21.5 22 22.5 23 23.5 24 24.5 0 500 1000 1500 2000 2500 3000 3500 4000 1 2 3 4 C O C E (e u ro /k W h ) El ec tr ic it y u sa ge ( kW h /y ea r) System Useful PV electricity (kWh/year) Grid Purchased electricity (kWh/year) Surplus PV energy to grid (kWh/year) Cost of consumed electricity (euro/kWh) Case study I 97 Electricity demand of household (kWh/year) Electricity generated from PV (kWh/year) Demand met by PV generation (kWh/year) Grid purchase (kWh/year) Surplus PV energy diverted to grid (kWh/year) RE fraction (%) 4594 2300 1532 3062 733 32.7 Figure 5.13 shows the time series plot of the system during low irradiance. These days, almost all the demand is catered from grid purchased electricity as there is not enough solar irradiation and the battery is also fully discharged most of the time. However, during the high solar irradiance, as shown in Figure 5.14 the load during the daytime is fully served by PV generated electricity, only nighttime load is served from grid electricity. The battery is fully functional with constant charging and discharging. Figure 5.13: Time-series plot of PV system during lowest irradiation- case study I Source: Author based on Homer Pro simulation Case study I 98 Figure 5.14: Time-series plot of PV system during highest irradiation- case study I Source: Author based on Homer Pro simulation Figure 5.15 depicts the monthly performance of the PV system integrated with the heat pump. The high demand during winter and low generation from the PV system results in high grid electricity purchase. Similarly, during summer, most of the demand is served by the PV system along with excess electricity sales to the grid. Figure 5.15: System performance throughout the year Source: Author 0 100 200 300 400 500 600 700 800 E le c tr ic it y g e n e ra ti o n ( k W h ) Month Load served by PV Grid purchase Surplus to grid Case study I 99 5.5.2. Cost-effectiveness analysis The cost analysis for the above-mentioned systems was carried out without grants and with grants. All the economic parameters used for the calculation is as mentioned in sub–chapter 3.3. The COCE of the proposed solar PV system varies from 20.6 cent €/kWh to 23.54 cent €/kWh, which are less than the considered grid tariff rate. The summary of the cost analysis is presented in Table 5.15. Table 5.15: Cost analysis summary for Case I Source: Author based on Homer Pro Simulation PV System Grants (€) Capital Cost (€) COCE (cent €/kWh) 1.48 kW PV system with battery storage - 3,719 € 23.54 900 € 2,819 € 20.79 2.2 kW PV system with battery storage (considering electrical demand of heat pump) - 3,953 € 22.5 1800 € 2,153 € 20.6 Taking into account the annual savings from grid electricity consumption, degradation factor of panels, and discount rate, the simple and discounted payback period and return on investment for the designed PV system was calculated. Based on the result tabulated in Table 5.16, the 2.2 KW system with grants has the least payback period with the highest return on investment. Table 5.16: Payback period and ROI for case study I Source: Author based on Excel calculation PV System Grants (€) Simple Payback Period (Years) Discounted Payback Period (Years) Return on Investment (ROI) 1.48 kW PV system without battery storage No 20 23.1 1.3% Yes 13.6 17.2 3 % 2.2 kW PV system with battery storage (including electrical No 14 15.4 3.4% Yes 6.7 7.1 9.5 % Case study I 100 PV System Grants (€) Simple Payback Period (Years) Discounted Payback Period (Years) Return on Investment (ROI) demand of heat pump) The COCE of the optimized system is sensitive to the cost of grid electricity. In the case study, it is assumed that the grid electricity tariff is 24.70 cent €/kWh referring to the electricity bills of 2020 and 2021. However, the tariff rate changes monthly based on the generation energy mix. Thus, it is relevant to analyse how the cost of the generated electricity and the payback period of the system deviates when the cost of grid electricity is varied. Therefore, for the 2.2 KW system, a sensitivity analysis was conducted. The low-end rate (15 cent €/kWh), the middle range (24.7 cent €/kWh) and the high-end rate (30 cent €/kWh) were considered based on the electricity bill provided by the case study representatives. The result of the sensitivity analysis is presented in Table 5.17. Table 5.17: Cost sensitivity analysis- Case I Source: Author based on Homer Simulation Scenarios Grants COCE (cent €/kWh) Discounted Payback period (years) ROI (%) Scenario 1- low end tariff No 15.97 n/a -0.3% Yes 14.07 16.6 4.6% Scenario 2- middle range tariff No 22.5 15.4 3.4% Yes 20.6 7.12 9.5% Scenario 3- high end tariff No 26.07 12.08 5.3% Yes 24.16 12.08 13.2% For the low-end tariff of 15 cent €/kWh, the investment is not recovered throughout the lifetime of the PV system. Among the scenarios tabulated in Table 5.17, the high-end tariff scenario has the highest return on investment. However, the COCE is also relatively high. A 10 eurocent/kWh decrease from 24.70 cent € /kWh with grants leads to a decrease in COCE by 31.69% and an increase in payback period by 9.4 years. Similarly, a 5 Eurocents/kWh increase in the cost of grid electricity from 24.70 cent € leads to an increase in the COCE by 14.7% and an increase in the payback period by 5 years. With a higher cost of grid electricity, the return Case study I 101 on investment is also increased and the COCE also increases. In the COCE calculation, the cost of grid electricity is proportional to the O&M cost. Therefore, increasing grid electricity price leads to higher O&M costs for grid electricity, subsequently leading to higher COCE. For the 2.2 KW system, a further cost analysis was done considering the annual income from excess electricity sales. The feed-in tariff was considered for 15 years. The regulations on feed-in tariff are described in sub-chapter 4.6.3. Considering a feed-in tariff of 0.09 cent €/kWh, a cost of grid electricity of 0.2407 €/kWh and SEAI grants of 1800 €, the discounted payback period of the system was reduced to 3 years from 7 years and 1 month. Figure 5.16: Cost savings for case study I Source: Author based on Excel calculation The net cost savings due to PV energy for the 2.2 KW system over the lifetime of 25 years was calculated to be around € 5712 after deducting the investment and annual O&M cost of the PV system. The net-saving is sensitive to the cost of grid electricity and feed in tariff. Therefore the change in these parameters lead to change in the net savings. Summarized key findings The overall PV sizing of case I is done to cater to the maximum household demand at the lowest possible cost. However, due to the high electricity consumption of the heat pump during low irradiance hours, the cost-optimized system could not serve most of the electricity demand. Nonetheless, the cost-optimized system provides considerable renewable penetration. If the homeowner wants a system with a significantly high renewable fraction then the COCE goes higher. Therefore, there would be either technical or cost constraints while system designing. 0 200 400 600 800 1000 1200 1400 1600 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 E u ro Year Electricity cost with PV Electricity cost without PV Savings Case study II 102 The designed capacity of the heat pump and the optimised Solar PV system as mentioned in sub-chapter 5.3 and 5.1.1 respectively were put to the step of the advanced refurbishment as mentioned in section 5.2.1 to find the potential BER rate via DEAP interface. The analysis of the results from the DEAP interface provided the BER rating of B3. 5.6. Summarized key findings The overall PV sizing of case I is done to cater to the maximum household demand at the lowest possible cost. However, due to the high electricity consumption of the heat pump during low irradiance hours, the cost-optimized system could not serve most of the electricity demand. Nonetheless, the cost-optimized system provides considerable renewable penetration. If the homeowner wants a system with a significantly high renewable fraction then the COCE goes higher. Therefore, there would be either technical or cost constraints while system designing. The designed capacity of the heat pump and the optimised Solar PV system as mentioned in sub-chapter 5.3 and 5.1.1 respectively were put to the step of the advanced refurbishment as mentioned in section 5.2.1 to find the potential BER rate via DEAP interface. The analysis of the results from the DEAP interface provided the BER rating of B3. 6. Case study II 6.1. Status quo 6.1.1. Building Envelope The second case study building is a two-story building constructed in 2005 with a total floor area of 215 m2 . It has 305 mm concrete block walls with 50 mm of external wall insulation and 50 mm of internal wall insulation. The building has two pitched roofs of 97 m2 and 40 m2 respectively including one chimney. A central space heating system is supplied by a geothermal heat pump of 12 KW connected to the underfloor heating. The system also meets the requirement of hot water. As per the interview with the homeowner, the heat pump operates two hours in the morning and three hours in the evening as per the household’s preferences. The domestic hot water requirement is provided by the same heat pump and the water is stored in a 200 liter capacity factory insulated hot water cylinder. The water cylinder is equipped with a 3 kW rated immersion heater. The geothermal heat pump stores 35–40-degree Celsius temperature hot water in the cylinder during the morning operating hours. Then the immersion heater is used to increase the temperature up to the set point of 60 0C. During the hot summer days, the heat Case study II 103 pump is not operated and the domestic hot water is provided by electricity from the grid.There is also the secondary space heating system which is a traditional wood pellet stove. The dwelling has four bedrooms, three and a half bathrooms, and one bathtub. The common areas include four rooms (kitchen, living room, study, office) and one utility room. The number of occupants in the case study is four. The status quo of building envelope along with the fabric material and the associated U-Value is explained in Table 6.1 Table 6.1: Status quo of case study II Source: Author Description Value Unit Floor Area 2157 m2 Total Door Area 1.54 m2 Total window Area 29.41 m2 Number of occupants 4 Fabric U-Values 305 mm concrete block wall :50 mm internal insulation and 50mm external insulation( Insulation material unknown) 0.27 W/m2 ∙K First floor Pitched roof: insulated on rafter 0.49d W/m2 ∙K Second floor Pitched roof: insulated on rafter 0.25d W/m2 ∙K Ground floor (solid) 0.34d W/m2 ∙K Second floor (solid) 0.37d W/m2 ∙K Windows (double-glazed low -E/ Wood) 2 W/m2 ∙K Door between heated and unheated space (semi-exposed) 1.71 W/m2 ∙K Door between two unheated space (exposed door) 3 W/m2 ∙K Appropriate default U-values were used as per the year of construction and as per DEAP Manual are labelled as d. The house is well insulated and only few areas with exceptional heat loss as described were observed during the thermal inspection. • Improper sealing of windows and doors The windows of the house are made of PVC with double glazing; however, cold air from the outside is observed around all the windows of the house due to insufficient insulation between the window frame and the wall as observed in Figure 6.1 In order to reduce heat loss, it is Case study II 104 recommended to insulate the space between window and wall. Insulating materials like polyurethane-based sealant. Figure 6.1: Infrared and digital image of the windows. Souce: Thermal camera A significant heat loss was also observed from the external doors as seen in Figure 6.2 Figure 6.2: Infrared and digital image of the windows. Souce: Thermal camera • Attic and roof insulation During the inspection of the kitchen ceiling, heat losses were identified in the areas of built-in light fittings as seen in Figure 6.3 . Cold air was felt penetrating into the room, perhaps because Case study II 105 the installation recess of the light fitting is not air tight. It is recommended to check the insulation and improve it. Figure 6.3: Infrared and digital image of the external walls. Souce: Thermal cemera • Uninsulated porch The case study house has an attached porch. The walls of the porch are made of stone and are not insulated. Warm air escapes from the house into the porch as a result of an open or insufficiently insulated door between the porch and the corridor of the house. The infrared image of the house as seen in Figure 6.4 shows heat loss through the uninsulated stone walls of the porch. Case study II 106 Figure 6.4: Infrared image of the porch. Souce: Thermal camera It is recommended to insulate the walls of the porch or, if the porch is not considered as part of the heated area of the house, to separate this space with an energy-efficient front door. 6.1.2. Electricity usage pattern The annual electricity consumption of Case Study II in 2021 is 17,038 kWh. The Case Study II has high electricity consuming loads such as an electric car and a heat pump. Similar to case study I, case study II pays a fixed electricity bill every month so montly payments may not reflect the variability of electricity consumption throughout the year. The electricity demand profile is illustrated in Figure 6.5. Case study II 107 Figure 6.5: Annual estimatedl electricity consumption Source: author based on own calculation As per the electricity assessment conducted during the site vist, the end-use breakdown as seen in Figure 6.5 is done to categorize what contributes in the total electricity consumption. As expected, the electric car consumed the maximum amount of electricity, followed by the heat pump.Water heating consumes 9 % and ligting consumes 3 % of the total electricity consumption. A plumbing issue was observed in the house that connects a booster pump with the toilet flush. This configuration operates the pump every time a toilet is flushed and consumes electricity. A data logger was used for the span of 24 hours and 1.09 kWh of consumption was measured. It should be noted that this is not the maximum energy used by the flush itself as the energy is consumed every time hot water is being used, for example, during a shower. The measured power of the pump during the operation was 662.5 W. It was assumed that the flush is used for 30 minutes every day to calculate the annual energy savings of 120 kWh. Further, if the cumulative of 340 W of halogen light present in the dwelling is replaced with an equivalent 60 W of LED, 40 kWh of annual saving is achieved. The saving is very insignificant because the lights are not used every day and only for short intervals. The combined annual energy savings then would be 160 kWh resulting in the reduced electricity consumption of 16,918 kWh. The efficiency measures have only considered lighting in this case study as the appliances were found to be efficient. Moreover, the homeowner was wary of the consumption Case study II 108 of high energy usage appliances like a dryer (rated 2600 W) and reduced the use of such appliances when possible. Figure 6.6 End-Use Breakdown of electrical appliances Source: Author 6.1.3. BER Rating A BER assessment of the original dwelling was calculated using an online version of DEAP 4.2.0 Online software by SEAI. The building geometry and fabric U-values referred to The second case study building is a two-story building constructed in 2005 with a total floor area of 215 m2 . It has 305 mm concrete block walls with 50 mm of external wall insulation and 50 mm of internal wall insulation. The building has two pitched roofs of 97 m2 and 40 m2 respectively including one chimney. A central space heating system is supplied by a geothermal heat pump of 12 KW connected to the underfloor heating. The system also meets the requirement of hot water. As per the interview with the homeowner, the heat pump operates two hours in the morning and three hours in the evening as per the household’s preferences. The domestic hot water requirement is provided by the same heat pump and the water is stored in a 200 liter capacity factory insulated hot water cylinder. The water cylinder is equipped with a 3 kW rated immersion heater. The geothermal heat pump stores 35–40-degree Celsius temperature hot water in the cylinder during the morning operating hours. Then the immersion heater is used to increase the temperature up to the set point of 60 0C. During the hot summer days, the heat pump is not operated and the domestic hot water is provided by electricity from the grid.There is also the secondary space heating system which is a traditional wood pellet stove. The dwelling has four bedrooms, three and a half bathrooms, and one bathtub. The common areas include four rooms (kitchen, living room, study, office) and one utility room. The number 9% 3% 30% 34% 24% Water heating Lighting Heat pump Electric Car Other appliances Case study II 109 of occupants in the case study is four. The status quo of building envelope along with the fabric material and the associated U-Value is explained in Table 6.1 Table 6.1 have been considered to calculate the BER rating of the building. The primary energy use of the building is 161.94 kWh/m2/yr corresponding to a BER Rating of “C1”. The Heat loss of the house is 415.97 W/k with a HLI of 1.94 W/k/m2. 6.2. Identified energy saving measures 6.2.1. Building envelope There wasn’t a need for the major fabric upgrade of the building. However, based on the thermal camera inspection, the impact of following fabric insulation was assessed. a) Roof insulation b) Exposed door replacement For the added comfort the houseowner is recommended to replace the exposed doors and insulate the kitchen roof. However, this doesn’t impact significantly on the energy consumption by the dwelling. The recommended changes with the upgraded U-values are listed in Table 6.2 Table 6.2: Upgrade Recommendations with their U-Values for case study 2 Source: Based on (Tabula,2014) Upgrade Recommendation Upgraded U value Pitched roof, 300 mm of mineral wool between and over the ceiling rafters and installation of required roof vents 0.13d Munster Joinery GRP premium door 0.8 After employing the recommended changes, the primary energy use was reduced to kWh/m2/year from 150.32 kWh/m2/year, i.e., 7 % reduction in the primary energy. The rating of the house remains unaltered as C1. The HLI after this step is 1.72 W/k/m2 Therefore, in case the houseowner wish to upgrade his BER Rating to B2 or higher, installation of renewable energy technology is recommended. In this case study, a detailed design and system sizing of Solar PV technology is done. The BER rating is accessed at the end considering the impact of Solar PV technology in the dwelling. Case study II 110 6.2.2. Cost analysis of Building retrofit The range of retrofit measures chosen for the retrofit program is in line with Individual Energy Upgrade grants at the time of analysis. The estimated annual savings after the fabric upgrades along with their cost breakdown is set out in Table 6.3 Table 6.3: Cost Analysis for upgrade recommendation Source: Author Upgrade Recommendation Grant Estimated Investment Cost (Euro) Estimated annual savings (Euro/year) Simple Payback Period (Years) Roof insulation and doors replacement Yes 5313 167.415 32 No 6813 41 he estimated investment cost breakdown is in Annex 13 Refering the cost estimate classification in sub-chapter 3.2, the uncertainty of the cost analysis was considered by using the accuracy range between a low range of -10 % and a high range of 30 %. Figure 6.7 shows the total investment costs and discounted payback periods, both with and without grant of each step. Giving an example, the total investment cost without grant of is 6,813 euros with the accuracy range between 6,132 euros to 8,857 euros. The discounted payback period of this case without grant is 63 yearswith the accuracy range between 57 to 82year. On the other hand, the applicable “Individual Energy Upgrade” grant for the attic roof insulation of 1,500 euro can reduce both investment cost and discounted payback period. The total investment cost with the applicable grant of roof insulation would be reduced to 5313 euros with the accuracy range between 4,782 and 6,907 euros and the discounted payback period of this case would be 44 years with the accuracy range between 40 to 57 years. Case study II 111 Figure 6.7: Total investment cost and Discounted Payback Period of case study 2 Source: Author 6.3. Existing Space Heating and DHW System Inspecting the existing space heating and DHW system, it was found that the system depend on a ground source heat pump of 12 kW rated heat output capacity. The system was constructed in 2007; it has 2 vertical GHEs cored into an earth depth absorbing heat units with a total length of 200m, it employs an antifreeze water mixture of Ethylene Glycol at 40% concentration in the first heat transfer circuit. On the other hand, the reverse carnot cicuit employs 2.2 Kg of R-407C refrigerant that transmits heat to the underfloor heat emitters installed at the ground and first floors. The existing space and DHW system depend on 3 time and temperature controls including the 7 rooms in the ground floor, and the 7 rooms in the first floor, and a prioritized DHW tank. The DHW tank is of 500 liters capacity with a 3 kW immersion suplementary heater. The heat pump provides the underfloor heat emitters and DHW tank forward temperature of 44°C, then the immersion coil raise the water temperature to 60°C to avoid bacterial growth. According to the house owner interview, he/she operates the system from 4:00 – 5:00 am and 7:00 – 8:30 pm everyday whether with the timer control or manually. For more information, see Annex 18. Case study II 112 6.3.1. Existing Space Heating and DHW System Analysis Based on the house owner behavioural operation pattern noticed, it was not clear why he/she only operate the system in winter and during fixed hours since depending on the emersion coil to heat the DHW increases his energy costs. Accordingly, it was decided to implement the previously developed excel model in sub-chapter 5.3.2 in order to confirm that allowing the existing automatic temperature control could save the house owner more energy than force stopping the system for prolonged periods. Utilizing the system data as input in the excel model it was found that the automatic temperature control with no human interefence will result in a total of 5752.72 kWhs of electricity consumption at the current thermal mass base scenario, while if the house is further upgraded to a higher energy efficiency level the electricity consumption will be 5,499.82 kWhs. Comparing the 5,752.72 kWhs against the current 6,830 KWhs consumed by the heat pump due to his behavioral patterns; it can be concluded that due to the emersion heater limited efficiency against the high annual average COP of the heat pump at 3, the electricity consumption increases. As a result, it is highly advisable to allow the temperature automatic control all the time without force stopping it in the summer. As shown in Figure 6.8 even if the house owner fully allow his existing thermostatic control to take charge all year round, his electrical consumption in the summer will be just limited to heating DHW at 311 % efficiency. In other words, the heat supplied will be 3.11 times of the electiricty consumed in July. Figure 6.8: Existing geothermal heat pump heat generated against electricity consumed Source: Author based on (Met Eireann, 2022) 0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 3500.00 Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. E n e rg y ( k W h ) Heat Supplied (kWh) Electricity Consumed (kWh) Case study II 113 6.4. Solar PV system design 6.4.1. Residential electricity demand The case study II house uses electricity for geothermal heat pump, Electric Vehicle charging, lighting and household appliances, mentioned in sub – chapter 6.1.2. The monthly electricity consumption of the Case study II house according to 2020 electricity bills is 11,143 kWh/year and according to 2021 electricity bills is 17038 kWh/year. The difference between these two years electricity demand is 5,735 kWh/year. According to the conversation with homeowner, the electric vehicle (EV) was purchased in the end of December 2020. And this difference of 5735 kWh/year reflects the presence of EV since 2021. As mentioned in sub - chapter 3.3. the standard national load profile is from the year 1997 that reflelects only the lighting and household appliance electricity consumption only. Therefore it doenot reflect electric vehicle consumption and electrical demand from heat pump in its consumption pattern. Considering this reason separate hourly load profiles developed for electric vehicle charging and electric consumption from heat pump.However for the remaining household electrical load, the synthetic load profile was developed in the similar manner as described in sub - chapter 3.3. The house owner uses a Nissan Leaf electric vehicle with a 30 kWh battery. The running mileage of the vehicle is 170 km per complete charging of the battery (Electric Vehicle Database, 2021). The owner uses this car during weekdays to commute to his workplace located 78km away from his house. Hence, the total traveling distance is 160km which requires approximately one charge per day after considering battery capacity degradation. As the homeowner explains, the vehicle connects to the home charging port for approximately 10 hours in the evening from around 18:00 every Monday, Wednesday, and Thursday. However the charging of the vehicle happens in day time on Saturday. It was assumed the EV charging on Sunday start from 12 pm noon for consuming PV electrcity. After looking at this EV charging pattern, estimated electricity consumption is 5,790 kWh for EV charging, the vehicle goes for upto 16 full charge per month. In addition, hourly load profile for electrical consumption from heat pump developed in sub-chapter 6.3 was considered for the further calculation. The total annual electrical consumption from heat pump is 5,753 kWh/year. The light and household appliance electricity consumption was estimated as 5,335 kWh/year after introducing energy efficient measures. The estimated total electricity of the case study house is 16,878 kWh/year. Figure 6.9 shows the montly consumption pattern of the house. Case study II 114 Figure 6.9: Electricity consumption pattern Source: Author based on (Ricardo, 2020a) 6.4.2. Solar electricity generation The Global Horizontal Irradiance of the case study II house location is 983.7 𝑘𝑊ℎ/𝑚2/𝑦𝑒𝑎𝑟, and the total electric energy generated by a 1𝑘𝑊𝑝 solar PV at 360 optimal tilt angle is 943 𝑘𝑊ℎ/𝑦𝑒𝑎𝑟 6.4.3. Solar PV System Design The solar PV system for the case study II house was designed without a battery storage system and with a battery storage system. The best suitable PV system for the case study house II was selected using the Homer Pro optimization tool. The system was optimized based on two criteria. The system is sized based on the lowest Cost of Consumed Electricity (COCE) compared to the considered electricity tariff of 24.70 cent €/kWh. Then, system integration is considered to obtain maximum renewable penetration for house electricity consumption. All possible system configurations based on the market available battery storage capacities are tabulated in Table 6.4 The performance of each system is illustrated in Figure 6.10 As per the optimization results, a 5.63 kW capacity solar PV system with 2.4 kWh battery storage gives the lowest COCE at 22.54 cent €/kWh. This system can cater 21.3 % of annual electricity consumption using PV electricity. Thus, this system fulfils the cost-optimization and high renewable penetration criteria. Table 6.4: Least cost PV system configurations 0 200 400 600 800 1000 1200 E le ct ri ci ty c o n su m p ti o n / kW h Month Heat pump EV Actual Household Appliances Synthetic-Household Appliances Case study II 115 Source: Author using Homer Pro optimization tool System PV capacity kWp Battery capacity kWh Initial investment € COCE Cent €/kWh Renewable fraction 1 5.16 - 6,748.00 22.79 17.7% 2 5.63 2.4 8,342.00 22.54 21.3% 3 6.48 3.6 9,690.00 22.90 23.8% 4 6.85 4.5 10,986 23.30 25.4% 5 6.85 6.3 11,658.00 23.40 27% Figure 6.10: Performance comparison for the lowest COE systems Source: Author based on Homer Pro optimization results The system can be resized to a 5.92 kWp PV system, allowing 16 “Longi” solar PV panels with 370Wp capacity each. The peak DC power output of the system is 6.5kW on the 21st of August. All other days of the year, DC peak power output is lower than 6.5kW. Hence- the “Solis” two- string MPPT inverter with a capacity of 6kW was selected for the design with a peak DC power rating of 8kW. Table 6.5: Electricity generation from solar PV system Source: Author base on Homer Pro results 22 22.2 22.4 22.6 22.8 23 23.2 23.4 23.6 0 2000 4000 6000 8000 10000 12000 14000 16000 1 2 3 4 5 C O C E / C en ts €/ kW h E le ct rc it y u sa ge / k W h System # Useful PV electricity / kWh Grid purchase electricty /kWh Surplus electricty export to grid / kWh Cost of electricity / (Euro/kWh) Case study II 116 System Configuration Demand met by PV electricity (kWh/year) Grid purchase electricity (kWh/year) Surplus electricity to the grid (kWh/year) 5.16 kWp solar PV without battery 3103 13889 2312 5.92 kWp solar PV with 2.4kWh battery 3834 13195 2354 Figure 6.11: Monthly electricity generation by 5.92 kWp solar PV system with battery Source: Author based on Homer Pro results As per Figure 6.11 , the highest solar PV electricity penetration is May, with 475 kWh. The lowest PV electricity penetration happens in December in a total of 180kWh. Simultaneously, more than 3 kWh/month of surplus electricity generation happens from April to September..If the system can generate more electricity in winter while reducing surplus energy generation during summer would help to increase more renewable penetration. This requirement can be achieved by fixing the solar panel in deep tilt angle to get the best performance during winter season. Because in winter, the sun is low with respect to the the horizon for the northern hemisphere. In this case, putting the solar panel at deep tilt angle help to incidents the sun rays perpendicularly by increasing energy generation. However, the sensitivity analysis by changing tilt angle upto 450 carried out in sub-chapter 5.4.2 reflects that the PV energy yield decreases with the increased deviation from the optimum tilt angle of 370 in Loop head. 0 500 1000 1500 2000 2500 El ec tr ci ty / k W h Months Useful PV electrcity Grid Purchase Excess Case study II 117 Therefore, it is decided to go with the initially selected panel orientation of the tilt angle of 37º and azimuth angle of 0º from south to west. Figure 6.12 and Figure 6.13Error! Reference source not found. show the time series graphs of electricity generation by solar PV array, state of charge of the battery, grid electricity purchase and electricity demand of two selected time periods of the year. Figure 6.12 show low irradiance days of the year and Figure 6.13Error! Reference source not found. show the high irradiance days of the year. During the low irradiance days, all the electr icity demand is catered by the grid. During the high irradiance days, the battery is charged fully and start to discharge when the absence of irradiance. The battery can supply electricity to the home during the peak time until 21 hour and then grid purchase starts. As the Figure 6.13Error! Reference source not found. show the battery starts to drain faster because of during this time period both the heate pump operation and the EV charging happens. Figure 6.12: Time-series plot of PV system during low irradiation days Source: Author based on Homer Pro simulation Case study II 118 Figure 6.13: Time-series plot of PV system during high irradiation Source: Author based on Homer Pro simulation 6.4.4. Cost-effectiveness analysis The detailed cost estimates of the designed solar PV system with battery and without battery are presented in Annex 30 and Annex 31 The capital investment and cost of electricity produced by the design system are summarized in Table 6.6 The calculated tariff rate of the house considering the 2020 and 2021 electricity bills is 24.70 cent €/kWh. The cost of electricity generated by the proposed solar PV system varies from 20.35 cent €/kWh to 21.17 cent €/kWh and the all costs are less than the considered tariff rate of 24.70 cent €/kWh. Table 6.6: Cost analysis summary of 5.92kWp solar PV system with 2.4kWh battery Source: Author based on Homer Pro simulation Grants (€) Capital Cost (€) COCE (cent €/kWh) - 8,675.00 22.54 € 2400 6,278.00 21.86 The payback period and Return of Investment of the proposed systems were calculated by considering degradation factors of the panel, equivalent annual revenue from PV electricity, system operation and meintence cost, replacement cost of battery and inverter and the Case study II 119 discount factor. Error! Reference source not found. illustrates the payback period and ROI of the 5.92 kWp solar PV system with battery storage. Table 6.7: Payback period and return on investment of 5.92kWp solar PV system with 2.4kWh battery Source: Author based on Homer Pro simulation Grants Simple payback period (years) Discounted payback period (years) Return on Investment No 10 11 5.2% Yes 7 8 8.7% The cost of generated electricity from the solar PV system changes concerning the cost of grid electricity. In the case study, it is considered the grid electricity cost as 24.7 cent €/kWh by refering the 2020 and 2021 electrcity bills. However the grid electricity price is changing in every month based on the national electricity generation mix. Hence it is meaningful to analyze the sensitivity of the system by changing the grid price within a range. For this sensitivity analysis, the low-end tariff (15 cent €/kWh), the middle range tariff (24.7cent €/kWh) and the high-end tariff (30 €/kWh) was considered based on the electricity bill provided by the case study representatives. The result of the sensitivity analysis is presented in Table 6.8 As per the data the cost of the generated electricity is low when the grid tariff is low and it increase when grid tariff goes up. Also the payback period is high for lower cost of electricity system and the ROI low. The COCE can be reduced by 0.04 cent €/ kWh for the low tariff without grants (worst case) while with a high-end tariff the cost can be reduced by 4 to 5 cent €/kWh. This happens because of lower renewable energy fraction in the system. Table 6.8: Cost sensitivity analysis for the solar PV system with battery- Case II Source: Author based on Homer Simulation Scenarios Grants Cost of consumed electricity (cent € /kWh) Simple Payback period (years) ROI (%) Low end tariff No 14.96 20 1% Yes 14.27 13 3% Middle range tariff No 22.54 10 5.2% Yes 21.86 7 8.7% High end tariff No 26.68 8 7.5% Yes 26.00 6 11.8% The same system was further analyzed considering the annual income from excess electricity sales. The feed- in tariff was considered for 15 years. Considering a feed-in tariff of 0.09 €/kWh, cost of grid electricity as 0.2407 €/kWh and SEAI grants of 2400 €. the discounted payback Case study II 120 period of the system was reduced to 5 years from 8 years. The net cost savings due to PV energy for 5.92 KWp system with battery storage over the lifetime of 25 years was calculated to be around 13,011 € after deducting the investment and annual O&M cost of PV system. Figure 6.14: Cost savings for case study I Source: Author based on Excel calculation The electrical demand considered for the system design includes space heating, hot water generation, and EV charging. Hence, the excess from the PV system is directed to grid. The overall PV sizing is done to cater to the maximum household demand at the lowest possible cost. However, due to the high electricity consumption of the heat pump and EV charging during low irradiance hours, the cost-optimized system could not serve most of the electricity demand. Nonetheless, the cost-optimized system provides considerable renewable penetration. If the homeowner wants a system with a significantly high renewable fraction then the COCE goes higher. 6.5. Summarized key findings A finding on the booster pump mentioned in 6.1.2, disconnecting or bypassing this booster pump from the toilet flushing system and replacing halogen light with LED light can reduce the electricity consumption for this particular case study. The designed capacity of the Solar PV system as mentioned in sub-chapter 6.3 was put to the base-case scenario with exiting geothermal heat pump to consider the possible upgraded BER rate via DEAP interface (online Case study III 121 tool). The results from the DEAP interface provided that the BER rating could be upgraded from “C1” to “B2”. In this case, the improvement of building retrofitting could be justified as an option to increase the comfort of living. According to the DEAP interface calculation, the building refurbishment provides fewer impacts to the well-insulated building with the construction year of 2005 onward. 7. Case study III 7.1. Status quo 7.1.1. Building Envelope The third case study building is a detached dwelling initially constructed in 1930, with a pitched roof without insulation, including a skylight, has two joints built around 1975 with a flat roof and no insulation. The total internal gross area is 182 m2. The house has a solid concrete floor with no underground heating and a mixture of two wall types, solid stone with 500mm thickness, concrete block with a thickness of 320 mm. The primary space heating is an oil condensing boiler with radiators in each room. It also serves domestic water heating demands with a timer control. A wood pellet stove provides secondary heating to the living room, and a small electric heater exists. As per the houseowner , they prefer to use one electric heaters for two hours in the kitchen for added warmt. Also, one heater is used in the study room sometimes during winter. Houseowner emphasizes the lack of insulation in the house to be the casue of additional heating appliance in addition to the central and secondary space heating. The design of the house is with four bedrooms, for a family of four (two adults and two children), one and a half bathrooms including bathtub, a living room, one storage room and two kitchens (one is of small size). The house has three chimneys and an extraction fan in the kitchen for the ventilation calculations. Table 7.1: Default U values Description Value Unit Dwelling Floor Area 143 m2 Total Door Area 1.85 m2 Total window Area 25.08 m2 Case study III 122 Description Value Unit Fabric U-Values Wall 1 (Stone walls, 500 mm uninsulated) 2.1d W/m2 ∙K Wall 2 (Concrete block, 320 mm insulated) 1.64d W/m2 ∙K Roof (No insulation) 2.3d W/m2 ∙K Ground floor (uninsulated, solid) 0.61d W/m2 ∙K Windows (Single glazed metal frame) 5.7d W/m2 ∙K Windows (Single glazed wood/pvc frame) 4.8d W/m2 ∙K External Doors (double-glazed) 3d W/m2 ∙K Appropriate default U values were used as per the year of construction values are labelled as d During the inspection of the building, the following deficiencies in the insulation of the building were found: • Single glazed aluminium windows and doors When examining the rooms, infrared images show heat losses through windows and doors. These losses occur due to the fact that the windows have an aluminum frame and single glazing, as a result of which they have a high thermal conductivity, which leads to heat losses. To avoid heat loss, it is recommended to replace doors and windows with energy-efficient ones, as is shown in Figure 7.1 Figure 7.1: Infrared image of single glazed window with aluminium frame. Source: Thermal camera • Uninsulated roof Case study III 123 Figure 7.3 show an increase in the temperature of the roof compared to the main walls. Heat loss occurs due to the lack of roof insulation throughout the house as Figure 7.2. Roof insulation will help to significantly reduce heat loss throughout the house. Figure 7.2 Digital image of under-roof space Source: Author Figure 7.3 Infrared image of the house roof from the outside Source: Thermal camera • Uninsulated walls The Figure 7.4 shows the temperature difference between the inner and outer wall. Insulating the outer walls will help to reduce heat losses in the house. Figure 7.4: Infrared image of an uninsulated external wall. Source: Thermal camera Case study III 124 Figure 7.5: Infrared image of the unused fireplace. Source: Thermal camera 7.1.2. Energy usage pattern The annual electricity consumption of Case Study III in the year 2021 is 6272 kWh as per the provision of monthly electric bills. The distribution of electricity demand is seen in Figure 7.6It was observed that the highest electricity consumption was seen during winter seasons, as the dwelling relies on electric heaters and electric blankets during winter for the homeowners’s comfort level as interviewed. Figure 7.6: Monthly electricity consumption of case study 3 Source: Author The end use energy breakdown is as seen as Figure 7.7 Lighting consumes 7% of the total electricity, followed by 12% of electric heater. Case study III 125 Figure 7.7: End use energy breakdown of case study 3 Source: Author Energy efficiency measures have been applied for the current electricity demand that includes replacement of 3 Fluorescent light and limiting the use of electric heater in winter, which may reduce the annual electricity demand by 627 kWh. 7.1.3. BER Rating The BER assessment of the original dwelling calculates in the online version of DEAP 4.2.0 Online software by SEAI. The building geometry and fabric U-values are considered according to Table 7.1: Default U values existing values) to estimate the BER rating of the building. With a primary energy use of 451 kWh/m2/yr., the current HLI is 8 W/k/m2 and the current BER rating of the building is “G”. 7.2. Identified energy saving measures 7.2.1. Building envelope Since the house lacks insulation of any form, the house requires a complete upgrade to improve the energy performance of the building. The objective of the retrofit strategy applied was not only to minimize heat loss but also to increase the rating of the house to a minimum of B2 or to comply with the HLI criteria. Hence the strategies has been presented in the form of steps to present alternatives to the homeowner. The homeowners thus will have a complete picture of where they are at now and how will the insulation impact their annual energy consumption. 7% 12% 81% Lighting Electric heater Appliances Case study III 126 Individual energy upgrade doesn’t necessitate to reach a B2 level BER rating. If the homeowners wish to only improve the comfort level, the insulation of roof and walls can be done with an application for a grant from SEAI. However, if the homeowers wish a deep retrofit strategy, it is recommended to apply the One Stop Solution Service grants. In this case, however, the house should reach a minimum B2 level after all the upgrades have been carried out. Both alternatives are presented for the homeowners to make an informed decision. Individual energy upgrade Insulation of attic and walls have been considered in this case. a) Wall insulation: Wall insulation with 100 mm of rockwool insulation will result in a U- value to 0.27. It is the standard refurbishment step of the wall as defined by TABULA (Tabula, 2014). With this step, the HLI is reduced to 6 W/k/m2 which is 23% of the total heat loss of the base case. The BER rating of the building after wall insulation could be improved to “E2”. b) Roof insulation: As there is zero insulation in the roof, insulating the roof has the maximum savings. There is separate insulation employed for pitched roof and flat roof. According to TABULA (Tabula, 2014), the pitched roof insulation with 300 mm of mineral wool will result in a U-value to 0.13 and the flat roof insulation with 150 mm of hardrock underlay slab will result in a U-value to 0.22. These U-values were used in each case. By insulating the roof, the HLI is reduced to 5 W/k/m2 and the heat loss is reduced by 37%. The BER rating after roof insulation could be “E1”. The summary of the investment cost with the simple payback is shown in Table 7.2 Table 7.2: Energy Cost Savings and payback period of insulation Type of insulation Grants Total Investment Cost (Euro) Total energy saving* (kWh/y) Estimated annual savings (Euro/y) BER rate Simple Payback Period (Years) Wall insulation No 31,484 11,518 891 E2 36 Yes 23,484 26 Roof insulation No 6256 19,141 1481 E1 5 Yes 3256 3 Case study III 127 * Total energy saving is delivered energy saving from primary and secondary space heating energy systems. **Replacement of doors and windows is not recommended under this scheme as there is no available grant. The annual savings is only 2,381 kWh/year with a payback period of and the payback period is more than 80 years. It was observed that investing in roof insulation is a better option for the homeowner than investing in the walls if he/she has to choose between one of the alternatives. Insulating the roof has lower investment but the highest energy cost savings. One-Stop-Shop Service The One-Stop-Shop Service mandates the post- refurbishment BER rating of B2. Hence the above-recommended measures are not enough to achieve that level. Therefore, to assess the reduction in heat loss and increase in the dwelling rating, step-by-step fabric improvement was done. Base Case: “Do Nothing” Scenario: No fabric upgrades have been considered in this scenario. Step 1: Roof and External wall insulation The recommendation as summarized in Table 7.2 was combined in this step. The heat loss was decreased by 62% from the base case scenario. Further, the two dormant chimneys are recommended to be sealed in this step. HLI was improved to 3.08 W/K/m2 and the BER rating of the house could be upgraded to “C2”. Step 2: Doors and windows replaced in addition to roof and external wall insulation The dwelling presently has thirteen single glazed windows. Therefore, it should be replaced with a windows with lower U-Values preferably highly efficient double glazed or triple glazed windows. With this step, the heat loss reduced by 67 % in total. HLI was improved to 2.68 W/K/m2 and the BER rating of this step would be upgraded to “C1”. Advanced Refurbishment 1: 200 mm wall insulation thickness with with better material grade in addition to roof insulation, doors and windows replacement Since Step 2 could not meet the criteria for getting heat pump grant, the wall insulation was changed to 200 mm with better material grade to see its impact on the heat loss. However, this step does not reflect considerable difference in the HLI. The final HLI of the advanced refurbishment 1 was 2.53 W/K/m2. The BER rating of the house is still at “C1”. Case study III 128 Further improvement was considered to recheck HLI as lowest as possble to be eligible for heat pump grant by another advanced refurbishment as follow. Advanced Refurbishment 2: 125 mm floor insulation thickness in addition to 200 mm wall insulation thickness with with better material grade, roof insulation, doors and windows replacement After improved by floor insulation, the heat loss reduced by 73% in total. HLI was improved to 2.2 W/K/m2 which now is eligible to get the heat pump grant. The BER rating of this advanced step could bring the house upgrading to “B2”. The recommended changes with the upgraded U-values are listed in Table 7.3 Table 7.3 Upgrade Recommendation with their U-Values of case study III Source: (Tabula,2014) Upgrade Recommendation Upgraded U value (W/m2 ∙K) 300 mm of mineral wool insulation for pitched roof insulation 0.13 150 mm of hardrock underlay slab for flat roof insulation 0.22 100 mm of Grey EPS external wall insulation 0.27 200 mm of Silver EPS external wall insulation 0.15 Triple glazed Argon Low E (0.15, hard) - Wood/PVC – 16mm Gap Window 1.4 Munster Joinery GRP Joinery Door 0.8 125 mm of White EPS floor insulation (Solid Concrete Ground Floors – Insulation Below the Floor Slab) (KORE, 2022) 0.21 7.2.2. Cost analysis of Building retrofit The range of retrofit measures chosen for the retrofit program is in line with available grants as of the current analysis time. The estimated annual savings after the fabric upgrades along with their cost breakdown is set out in Table 7.4 Table 7.4 Cost Analysis for different retrofitted steps of case study 3 Source: Author Steps Grants Estimated Investment Cost (Euro) Total energy saving* (kWh/y) Estimated annual savings (Euro/y) BER rate Simple Payback Period (Years) Step1 No 39,152 33,468 2,590 C2 15 Yes 28,152 11 Case study III 129 Steps Grants Estimated Investment Cost (Euro) Total energy saving* (kWh/y) Estimated annual savings (Euro/y) BER rate Simple Payback Period (Years) Step2 No 54,463 36,844 2,851 C1 19 Yes 37,863 13 Advanced Refurbishment1 No 57,035 38,021 2,942 C1 19 Yes 40,435 14 Advanced Refurbishment2 No 98,561 40,908 3,166 B2 32 Yes 78,461 25 The estimated investment cost breakdown is in Annex 13 With the advanced refurbishment 2 the dwelling can lower down the original heat demand by 76.4%. However, this holds true only when the house is actually heated to the comfort level of the house, that is have an actual demand of 53,550 kWh/year as calculated by DEAP in the base case scenario. DEAP calculation is based on standardized conditions anddoesn’t account the behaviour of occupants, hence the total energy savings might differ based on the consumption behaviour of the occupants. Considering the uncertainty by the cost estimate classification as described in sub-chapter 3.2, the uncertainty range is between a low range of -10% and a high range of 30%.Figure 7.8 Error! Reference source not found.shows the total investment costs and discounted payback periods, both with and without grant of each step. Giving an example, the total investment cost without grant of advanced refurbishment 1 is 57.04 thoudsand euro with the accuracy range between 51.33 to 74.15 thousand euro. The discounted payback period of this case is 23 years with the accuracy range between 21 to 30 year. On the other hand, the applicable “One Stop Shop Service” grant help reduce both investment cost and discounted payback period. The total investment cost with the applicable grants could be reduced to 40.44 thoudsand euro with the accuracy range between 36.39 to 52.57 thousand euro and the discounted payback period of this case would be 16 years with the uncertainty range between 14 to 21 year. Case study III 130 Figure 7.8: Total investment cost and Discounted Payback Period of case study 3 Source: Author In this case study, insulating floor is an additional option for the homeowners if they wish to replace the exising condensing oil boiler to be heat pump with applicable grant for heat pump. The estimated total investment cost with grants of the advanced refurbishment 2 is the highest one among all steps between range of 70.61 to 102 thousand euro. Also, the discounted payback period with grants is the longest period which is 42 years with the uncertainty range between 38 to 55 year. 7.3. Solar PV 7.3.1. Residential Electricity Demand After energy efficiency measures (EEM) are applied as described in sub-chapter 7.1.2 the electricity demand is reduced to 5645 kWh per year. As mentioned in methodology sub– chapter 3.3 this electricity consumption per year was used to calculate a scaling factor. Then, the scaling factor and the standard national hourly load profile based on (Ricardo, 2020b) was used to develop the synthetic hourly load profile Figure 7.9. compares the synthetic monthly load profile and actual monthly load profile. As a similar demand pattern was observed between the two load profiles, the developed synthetic load profile was used for sizing the PV system using Homer. The electricity consumption for this house is higher during the winter months from November to March at above 500 kWh per month. Case study III 131 Figure 7.9: Comparison of the synthetic and monthly demand profile Source: Author based on monthly bills and (Ricardo, 2020b) 7.3.2. Solar PV System Design Using Homer Pro Software: The solar PV system for Case study III was designed for two different scenarios using Homer Pro. One system is without a battery, and another one is with a battery. The Homer optimizer was used to optimize the system for the given electricity demand and to calculate the profitability for each of the options for the owner. The system was optimized based on the two criterias. The selection is based on lowest COCE compared to the considered electricity tariff of 24.70 cent €/kWh and system integration is obtained for maximum renewable penetration. All possible system configurations based on the market available battery storage capacities are tabulated in Table 7.5 and the performance of each system is illustrated in Figure 7.10 As per the optimization results, a 4.37 kWp capacity solar PV system with three 2.4 kWh (7.2 kWh) battery storage gives the lowest COCE at 20.3 cent €/kWh with renewable energy penetration of 56.1%. However, the initial investment cost is high and this system requires three numbers of battery storage. Therefore this system is not considered for the futher calculation. A 2.83kWp capacity solar PV system without a storage has COCE of 21.4 cent €/kWh. This system can cater to 29.9% of annual electricity consumption using PV electricity. Further, combining battery energy storage of 3.6 kWh with 4.4 kWp panel capacity, the renewable energy fraction (self-consumption) increased upto 47.1%.The integration of a battery in the system will ensure reliability during the power outage at night time and increase self-consumption. Table 7.5: Least cost PV system configuration Source: Author based on Homer Pro Optimization tool 0 100 200 300 400 500 600 700 1 2 3 4 5 6 7 8 9 10 11 12 El ec tr ic al C o n su m p ti o n k W h Month Energy Assessment Data Synthetic Data Case study III 132 System PV capacity kWp Battery capacity kWh Initial investment € COCE cent €/kWh RE Share 1 2.83 - 3,566.00 21.17 29.9% 2 4.37 7.2 8,270.00 20.3 56.1% 3 4.4 3.6 6,449.00 22.07 47.1% 4 4.4 4.5 7,311.00 23.14 50.6% 5 4.4 6.3 7,983.00 23.59 55.0% Figure 7.10: Performance comparision for the lowest COE systems Source: Author based on Homer Pro Optimization results The selection of the appropriate system depends upon the preference of homeowner. If the homeowner prefers the least COCE with the lower initial capital investment to cater the household demand then the solar PV system of 2.83 kWp capacity without battery storage and 3 kW inverter can be selected. The system was resized to a 2.96 kWp PV system, allowing 8 “Longi” solar PV panels with 370Wp capacity each. The peak DC power output of the system is 3.2kW on the 21st of August. All other days of the year, DC peak power output is lower than 3.2kW. Hence- the Solis mini inverter with a capacity of 3 kW was selected for the design with a peak DC power rating of 3.5 kW. The total area required for 2.96 kWp system is 17.15m2. Using this system, the daytime household demand is covered by the solar PV electricity generation but during nighttime, when there is no output generated from the PV system, the demand is met from electricity purchased from the grid. In this case, as a battery is not used, Case study III 133 excess electricity is being directly fed into the grid. Moreover, if homeowners want to increase renewable energy penetration and add battery backup system to the house, then 4.4 kWp solar PV system (12 solar panel with 370 watt each) with 3.6 kWh battery storage can be selected. The resized system with battery storage ensure the optimum use of all the electricity generated by the PV system. The Solis Single Phase Dual MPPT inverter of 3.6 kW was selected for the design. The selected inverter has a peak DC power rating of 7 kW. The total area required for this system is 25.48m2. The datasheet for the panel, battery and the inverter is in Annex 19, Annex 20, Annex 21, Annex 22, and Annex 23 In this case, at first, the household requirement is served, after which the excess goes to the battery. When the battery is fully charged, the remaining electricity is diverted for domestic water heating. Finally, after all this, the remaining power is fed to the grid. Table 7.6 elucidates the output of the designed PV system, and Figure 7.11 shows the monthly electricity generation by the 4.4 kWp selected system. Table 7.6: Electricity generation from selected solar PV system Source: Author based on Homer Pro Optimization results System Configuration PV generation (kWh/year) Demand met by PV electricity (kWh/year) Grid purchase electricity (kWh/year) Surplus electricity to the grid (kWh/year) 2.96 kWp solar PV system without Battery 3093 1713 3932 1335 4.4 kWp solar PV system with 3.6kWh Battery 4598 2794 2988 1804 The amount of grid-purchased electricity after including battery storage is reduced from 3,932 kWh/year to 2,988 kWh/year. The renewable fraction has increased from 30.4% to 47.1%. At the same time, excess electricity generation increased from 19% to 23.8%. Case study III 134 Figure 7.11: Monthly electricity generation by 4.4 kWp solar PV system Source: Author based on Homer Pro results As per Figure 7.11 the highest solar PV electricity penetration of 310.22 kWh is in May followed by June and July with 307 kWh and the lowest PV electricity penetration happens in December with a total of 129.3 kWh. For this case study III sensitivity analysis by changing the tilt angle was done. But as already mentioned in case study I and II the increased deviation from the optimal angle reduced the PV yield. If the system tilt angle increased from 37º to 45º, the system renewable fraction percentage decreases from 47.1% to 46.7%. And if we reduce the tilt angle from 37º to 25º, the system renewable fraction is same 47.1% but the generation from PV is less compared to 37º. Based on the sensitivity analysis concerning the tilt angle and azimuth angle, it is decided to go with the initially selected panel orientation of the tilt angle of 37º and azimuth angle of 0º from south to west. As shown in Figure 4.14, the highest irradiance is in May. Figure 7.12 shows most of the household demand is served by the PV system and during nighttime, when there is no output generated from the PV system, the demand is met from battery storage. When the battery is completely drained demand is met by electricity purchased from the grid. Figure 7.13 shows the load served by the solar PV system, grid purchases and the excess electricity generated throughout the year. The excess is high during the summer months (March to September) and low during the winter months (October to February). Case study III 135 Figure 7.12: Time series plot of electricity generation and demand during lowest irradiation day Source: Author Based on Simulation in Homer Figure 7.13: Time series plot of electricity generation and demand during highest irradiation day Source: Author based on simulation in Homer Case study III 136 Domestic hot water Currently, the house is using a condensing oil boiler and stove for space heating and domestic hot water heating purposes. The stove is on from 9 am to 10 pm 4 days a week and for approx. 5 hours for the remaining 3 days of the week throughout the year. The water cylinder tank is equipped with a 3 kW rated immersion heater and the immersion heater is used once or twice a week to increase the temperature up to the setpoint for 30 minutes. According to this behaviour the annual electricity consumption for water aheating is 117 kWh/year. But it is a good approach to store the excess electricity generation from PV in form of thermal storage in the hot water tank. Installing a hot water diversion control will be an effective solution for operating the immersion heater during the excess electricity generation time. As mentioned in sub – chapter 3.3, for a family of four members an approximate estimate of 120 L of water at 50 0C daily consumption is assumed. The diverted power was used to heat water up from water mains temperature of 100C to 500C from the water mainstemperature.This study aims to replace the fuel for the domestic hot water purpose and utilize the excess PV generation to cater the demand. The calculated electricity requirement for hot water is 2213.57 kWh/year. If the excess from the PV generation is not sufficient to cater the hot water demand then hot water storage tank draws electricity from the grid. With a PV system of 4.4 kW, 916.28 kWh/year of excess electricity can be used for hot water diversion. The remaining 888.53 kWh/year will be finally diverted to the grid. 7.3.3. Cost effectiveness analysis The cost analysis for the above-mentioned system was carried out without grants and with grants. The cost of each component and balance of system is detailed in Annex 28 and Annex 29. All the economic parameters used for the calculation are mentioned in sub-chapter 3.3. A considered tariff rate for the electricity from the grid as mentioned in sub-chapter 3.3. is assumed for the calculation. The COCE generated by the above proposed PV system varies from 19.34 cent €/kWh to 21.12 cent €/kWh and all the costs are below the considered tariff rate of 24.70 cent €/kWh. Table 7.7: Cost Analysis – Case Study III Source: Author based on simulation in Homer PV System Grants (€) Capital Cost (€) COCE) (cent €/kWh) 2.96 kW (~ 3 kW) PV system without battery - 3,706 21.12 2,100 1,606 19.34 4.4 kW PV system With Battery - 6,449 22.07 2,400 4,049 20.34 Case study III 137 Taking into consideration the annual savings from grid electricity consumption, the degradation of the panels and a discount rate of 1.47%,the payback period and return on investment for the both the PV system without battery and with battery was calculated. Table 7.8 presents the payback period and ROI for the case study III. Based on the result,the 2.96 kW PV system with grants has the least payback period with highest return on investment. Table 7.8: Payback period and ROI for case study III Source: Author Based on Excel Calculation PV System Grants (€) Simple Payback Period (years) Discounted Payback Period (Years) Return on Investment (ROI) 2.96 kW (~ 3 kW) PV system Without battery No 9 10 6.2% Yes 4 5 19.7% 4.4 kW PV system With battery No 14 16 3.1% Yes 7 7 7.3% For both the system, a sensitivity analysis was conducted to investigate the different outcomes when above-mentioned assumptions vary. The grid electricity price varies everyday based on the national electricity generation mix. Therefore, it is important to identify how dependent the COCE and payback period are on the cost of electricity. To conduct the sensitivity analysis, three different scenarios were assumed. A low-end rate (15 cent €/kWh), a middle rate (24.7 cent €/kWh) and a high-end rate (30 cent €/kWh) were considered based on the electricity bill provided by the homeowner. Table 7.9 shows the results of the sensitivity analysis for 2.96 kW system without Battery storage. The COCE goes high when the cost of grid tariff increased high. The payback period is lower and ROI is higher with the increase in tariff rate. A 5 cent €/kWh increase from 24.70 cent €/kWh with grants leads to increase in COCE by 18.8%, a decrease in payback period by one year and an increase in ROI from 15.9% to 20.7%. And a 10 cent €/kWh decrease from 24.70 cent €/kWh with grants leads to decrease in COCE by 35.5%, a increase in payback period by 4 years and decrease in ROI from 15.9% to 7.2%. Table 7.9: Sensitivity Analysis for the 2.96 kWp system Source: Author Based on Calculation with Homer Scenario Grants COCE (cent €/kWh) Payback Period (Years) ROI (%) Scenario 1 (Low-end rate) No 14.43 21 1.7% Yes 12.84 9 7.2% Case study III 138 Scenario 2 (Middle range rate) No 21.18 10 6.2% Yes 19.60 5 15.9% Scenario 3 (High-end rate) No 24.88 8 8.6% Yes 23.29 4 20.7% Table 7.10 shows the results of thesensitivity analysis for the 4.4 kW system with battery storage. Table 7.10: Sensitivity Analysis for the 4.4 kWp system Source: Author Based on Calculation with Homer Scenario Grants LCOE (cent €/kWh) Payback Period (Years) ROI (%) Scenario 1 (Low-end rate) No 16.93 n/a -0.9% Yes 14.90 25 1% Scenario 2 (Middle range rate) No 22.07 16 3.1% Yes 20.34 7 7.3% Scenario 3 (High-end rate) No 24.87 12 5.3% Yes 22.83 6 10.8% If the LCOE is higher than the cost of electricity from the grid, then the investment in the system is not viable. When the cost of electricity is low, the investment takes a longer time to be recovered whereas when the cost of electricity purchased from the grid is high, the investment takes a short period of time to be recovered. A system with a high renewable fraction for catering the demand and a low LCOE is considered as an attractive solution. The government of Ireland has announced the introduction of a feed-in tariff as mentioned in sub – chapter 4.6.3. Therefore, the solar PV system cost analysis was done with feed-in tariff consideration as well. The feed-in tariff is assumed to be applicable for 15 years.With a feed- in tariff of 9 cent €/kWh and grants, the payback period of the 2.96 kWp system is reduced from 5 years to 3 years. Figure 7.14 shows, the total cost saving for this system is 8,251 € and the net total saving for the system is 4,545 € over the life time of the PV system. Figure 7.15 shows the total cost saving for 4.4 kWp system is 12,359 € over a lifetime of 25 years. If the investment cost and operation and maintenance cost are deducted, the net savings from the 4.4 kWp PV system is 5,952 € over a 25 years period. The payback period is reduced from 7 years to 5 years. Case study III 139 Figure 7.14: Cost saving for 2.96 kWp PV system without battery case study III Source: Author Figure 7.15: Cost saving for 4.4 kWp system with battery case study III Source: Author The overall PV sizing for the case study is done to cater the maximum household demand and to increase the renewable energy penetration. The net-saving is sensitive to the cost of grid electricity and feed in tariff. Therefore the change in these parameters lead to change in the net savings. The choice PV system without and with battery energy storage depends upon the 0 200 400 600 800 1000 1200 1400 1600 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 E u ro Year Electricity cost with PV Electricity cost without PV Savings Local environmental and socio-economic impacts 140 preference of the homeowner based on cost and renewable energy penetration mentioned in the Table 7.7. 7.4. Summarized key findings The designed capacity of the Solar PV system as mentioned in section 7.3 was put to the step of the advanced refurbishment as mentioned in section 7.3.1 to consider the possible upgraded BER rate via DEAP interface (online tool). The analysis was simulated based on the existing condensing oil boiler as a space heating system. The results from the DEAP interface provided that the BER rating could be upgraded from “G” to “B2” that is from 451 kWh/m2/yr to 115.09 kWh/m2/y with addition to Solar PV System together with Step 2 fabric upgrades. With solar integrated with Advanced Refurbishment 2 the BER rating could be further upgraded to B1 with the final primary energy of 82.80kWh/m2/y. However,after the advanced refurbishment Step 2, the dwelling is eligible for heat pump installation, the integration of which might decrease the dwelling rating. However, heat pump sizing wasn’t considered for this particular Case study. 8. Local environmental and socio-economic impacts 8.1. Life cycle assessment 8.1.1. Goal and scope By Saleheh Rahimi The life cycle assessment (LCA) analysis provides a clear and specific understanding of the environmental impacts of a product throughout its life cycle (Vácha et al., 2021). This chapter investigates the environmental performance of the Solar PV technology in Loop Head by focusing on analyzing solar PV technology. The majority of life cycle assessment studies of solar PV systems show that the biggest environmental impact happens during the extraction and manufacturing phases (Ludin et al., 2018a). Therefore, a “cradle to grave” approach was set as the boundary of the system in this study. “Cradle to grave”, as a partial product life cycle, is performed on a residential solar system: starting from the extraction of the raw materials phases up until the usage phase. Therefore, environmental impacts were assessed from raw material extraction and processing (cradle), product manufacturing, distribution, the operation and usage of the solar PV system, until the end of its life cycle (grave). Local environmental and socio-economic impacts 141 In addition, the carbon footprint of electricity generated from the current Irish generation mix as per (SEAI, 2022e) statistics, 52% of electricity generation in Ireland rely on natural gas and 42 % comes from renewables energies including a high penetration of Wind energy around 37 %, however, solar technology accounts for just a small fraction of electricity mix.) compared to the electricity generated from the solar PV system (on a household level) will be investigated. According to the standard protocol of ISO 14044:2006, to define the goal and scope of the LCA, supply chain’s processes and system boundaries should be defined (ISO, 2006) as well as a description of the functional unit and other assumptions which affect the final result (Zendehdel et al., 2020). To define the goal and scope of this LCA study, the diagram in Figure 8.1 was created to illustrate the central framework of the LCA, starting from the initial stage of the module production to the manufacturing and usage of the solar system. In this case, the goal of the LCA is to evaluate different environmental impact categories of Solar PV systems and services in Loop Head on a household level. The analysis will investigate various impact categories which are global warming potential, acidification, and land use by quantifying all inputs and outputs of material and energy flows in each life cycle stage. Additionally, assessing the environmental impacts of such low-carbon technology is a pre-requisite step to prove and show its comparative sustainability. The aim is to inform the local community about the ultimate impact of their energy-related choices on the environment. The LCA will also highlight the carbon footprint of the PV system and its potential to contribute to CO2 reduction when compared to the Irish grid. In the case of manufacturing solar modules and the balance of system (BOS), the first step was to identify all the processes involved in primary and natural resources and direct and indirect energy sources. To achieve this, after the definition of the goal and perspective of the life cycle assessment, the next step was to define the scope and boundary of the system as seen in Figure 8.1. Local environmental and socio-economic impacts 142 Figure 8.1: The scope and system boundaries of the solar PV life cycle analysis Source: Author based on (Ludin et al., 2018) While defining the scope of the LCA, the materials and energy used for the production of the PV panel, inverter, cabling, mounting system, and battery were taken into account. The recycling and disposal of the panels and other components (after end-of-life) are not considered in this study. In addition, it was assumed that after the manufacturing and packaging processes of PV panels and other components, they are transported from China to Ireland as a separate process. Other marginal and subsequence transportation such as flows between factories (e.g., between inverter and mounting system) in China were not taken into account. According to the scope of the PV system, the initial process starts from the raw material acquisition. During this process, the extraction and insertion of natural resources and energy supply are analyzed. Then, within the process of manufacturing PV modules, all the raw material acquisition, the manufacturing of silicone base PV module product, and the corresponding energy consumption are considered. Other auxiliaries such as water demand are also considered. After the production process of the PV module, manufacturing of all components in the balance of system (BOS) including inverter, cables and wires, mounting system and battery that are needed to make the PV system ready to produce electricity from sun irradiation is included in the system boundaries. Local environmental and socio-economic impacts 143 Additionally, other supply chain processes including transportation of the PV products to the place of installation of the PV system, as shown in Figure 8.2, was evaluated in the final stage of the life cycle. Figure 8.2: Transportation of solar system components from China to Ireland Source: Author based on (Sea-route, 2021) The scope of this work includes the LCA study of the two recommended solar PV systems from case study III (Chapter 7) and a comparison with the Irish electricity grid. This comparison helps stakeholders to analyze and interpret the environmental impacts of conventional (grid) and renewable energy resources (solar PV systems) in a comprehensive way. The following items form the boundaries of the LCA system: ▪ Case study III as described in Chapter 7, including solar PV system (Mono-Si panel) of 2.96 kWp without battery and solar PV system (Mono-Si panel) of 4.4 kWp with 3.6 kWh battery ▪ Electricity from the Irish grid To determine the functional unit as a measurement of the system’s performance, various functional units were selected based on each process output. For example, the unit of a square meter (m²) is used to define the area of the solar module while the unit of a kilogram (kg) is used to describe the production of potteries. Also, when comparing the performance of different projects with different units, a unified unit was selected. In this case the unit of energy generation (kWh) was used for comparing the environmental impact of different configurations of the solar PV systems to the grid. Local environmental and socio-economic impacts 144 8.1.2. Methodology By Fitri Wulandari The LCA approach is standardized in ISO 14040:2006 Environmental Management – Life Cycle Assessment – Principles and Framework (ISO, 2006). The LCA carried out during the International Class 2022 follows this standardized approach. Figure 8.3. presents the overall methodology of this study. Figure 8.3: LCA methodology Source: Author The study started with a literature review to understand the concept of LCA and state of the art of LCA of a PV system. The LCA was conducted using the open-source software, OpenLCA, as it does not require an additional cost for licence. Although it has a larger database and is perceived to be more user-friendly by the author, GaBi software was not chosen as it can only be utilized without charge for 30 days. The next step was to collect qualitative data through a site visit to a household that already has a solar PV installed to understand the typical system in Loop Head. This phase was needed to build the LCA prototype while waiting for the calculation result from the case study presented in Chapter 7. Key parameters such as PV technology, type of system, the lifetime of solar PV, and the balance of system (BOS) were identified during this phase. In addition, the electricity production from the installed solar PV in one household was calculated and the expected electricity production for the 25 years of PV lifetime period was calculated. These Literature review Site visit LCA study Goal and scope definition Life cycle inventory Life cycle impact assessment Data interpretation Local environmental and socio-economic impacts 145 key parameters were later updated using the details of the proposed technology designed for Case Study III (Chapter 7) The LCA study was carried out as the last step. This step was done based on the conceptual framework of life cycle assessment defined by the International Organization for Standardization (ISO). The LCA was performed for three solar PV systems: the prototype, one system with a battery, and one system without a battery. Case study III was chosen as the representative of the systems with battery and without battery. ISO (n.d.) defines four phases for the LCA study: the goal and scope definition phase, the inventory analysis phase, the impact assessment phase, and the interpretation phase. The goal and scope definition phase defines the stages of the life cycle included in the LCA study, which would affect the system boundary (aspects that are included and excluded from the study). The level of detail of an LCA depends on its intended use. The life cycle inventory phase is very crucial as it determines the accuracy of an LCA study. In this phase, the inputs and outputs for the solar PV system throughout its life-cycle stages, as defined in Figure 8.1 were quantified. This includes all the materials and energy flows (inputs) as well as the waste discharged into the environment during the manufacturing process (output). For the input-output tables, this study used generic data (not directly measured or collected) as specified by the IEA PV Power Systems Programme (Heath et al., 2015) based on the specification of the Solar PV system identified during the site visit and case study III. The Ecoinvent Consequential LCI database was used as it complies with ISO 14040 and 14044. The database includes international industrial life cycle inventory data on agriculture, mining, manufacturing, energy supply, and transportation. The life cycle impact assessment phase aimed to understand and evaluate the significance of the impacts of the Solar PV system throughout its life cycle. This study focused on two LCA indicators, which are climate change and cumulative energy demand (CED). These two indicators will be further explained in sub-chapter 8.1.5. Two impact assessment methods were used to assess the desired indicators. The first one was ILCD 2011 Midpoint+, which corresponds to the ILCD, version 1.0.9, May 2016 (European Commission, n.d.) and focuses on environmental life cycle impact assessment. The second method used was Cumulative Energy Demand by EcoInvent version 2.0. To run the calculation, the reference flow and functional unit needed to be defined. (ISO, 2006) defined reference flow as a measure of the outputs from processes in a given product system required to fulfil the function expressed by the functional unit, whereas the functional unit specifies the quantified performance of a product system. Local environmental and socio-economic impacts 146 In the data interpretation phase, the value from the climate change and the CED indicators were compared with literature values for the two solar PV systems, with battery and without a battery. The environmental impact was analysed based on units of electricity produced over the solar PV lifetime of 25 years and the total CO2 emissions embodied in the solar PV system, measured in gCO2/kWh. The resulting two impact indicators were further processed into energy payback times (EPBT) and energy return on investment (EROI). Furthermore, this phase would also include a system comparison of the CO2eq between electricity generated by a solar PV system versus electricity generated by the Irish grid, measured in gCO2/kWh to assess the potential of carbon saving by installing solar PV at the household level. Limitation Due to time and resource constraints, the study faced limitations and undertook assumptions that will be described in the following paragraphs. This study emphasized the impact at the production stage. The construction and installation process as well as operation and mainteinance were not considered to be negligible for household systems. The main limitation of this study is related to data uncertainties. The quality of an LCA relies on the compiled data from various sources during the lifecycle inventory phase. It is not possible for the collected data to be accurately representative of the specific two cases (with battery and without battery) that were being analysed. This study assumed that all manufacturing processes of the different solar PV system components happen in one place in China, hence transportation during the manufacturing process was not considered. In addition, this study also did not include infrastructure during the manufacturing stage as an input as it is considered to be negligible. Possible replacements of the components as well as impacts related to the operation and maintenance during the lifetime of the installation, were not considered. The electricity mix used in the manufacturing process is based on the available electricity generation mix available within the database; hence, this study did not use the specific electricity generation mix from when the components were manufactured. Furthermore, the calculation of electricity produced did not include losses reported by the panel manufacturer. Losses caused by other components within the solar PV system were also not included. Some inputs, such as integrated circuit, sewage, PV cell production effluent, were defined as new flows as they were not available within the database. Sub-chapter 8.1.6 will discuss how to address these limitations in the future. 8.1.3. Life cycle inventory analysis By Philip Miltrup Local environmental and socio-economic impacts 147 To achieve the objectives of the study defined in chapter 8.1.1, a collection of all necessary data is required. This data collection is also defined as inventory, more precisely as lifecycle inventory (LCI). According to ISO 14040:2006, LCI is an inventory of input and output data related to the system studied, in this case, the PV system. For the LCA, all environmental inputs and outputs that occur during the life cycle of the PV system must be considered. These inputs/outputs can represent, for example, the energy required for the manufacture of products or the demand of raw materials for production. Furthermore, the LCI analysis requires input/output elements such as emissions. These are, for example, emissions to air, emissions to water, or emissions to soil. Additionally, other releases to the environment are considered in the inventory. To determine the PV inventory, a flowchart was created first to define the individual processes of the system and the system boundaries. A simplified flowchart of the analyzed processes and their inputs and outputs is shown in Figure 8.4 Local environmental and socio-economic impacts 148 Figure 8.4: Simplified process flow diagram of an LCA for a PV system Source: Author PV modules Case study III (Chapter 7) consists of two different system sizes. The system without a battery consists of a total of 8 monocrystalline Longi solar panels with 370 W each and has a capacity of 2.96 kWp. The system with a battery has a capacity of 4.4 kWp and consists of a total of 12 solar panels with the same rated power, from the same manufacturer. For the manufacturing of the PV modules, the process steps in Figure 8.5 were taken into account. Since the case studies conducted exclusively use monocrystalline PV modules, only the input/output data of this module type was used. Module production essentially consists of five process steps, which are carried out in China for module manufacturer Longi. However, in the OpenLCA software, the basic silicon products have been divided into further sub-processes, as these Raw material production Process of manufacturing of PV panels and balance of system (BOS) Transportation Operation Inputs Energy inputs Material inputs Outputs Emissions to air Emissions to water Emissions to land Waste Figure 8.5: Supply chain of sillicon-based PV electricity production Source: (Frischknecht et al., 2020) Local environmental and socio-economic impacts 149 products are divided into three sub-processes according to (Frischknecht et al., 2020). This includes the production of metallurgical (MG) silicon and solar grade (SG) silicon as well as the silicon production mixes for China. Figure 8.6 shows an example of the input/output data for the production of SG silicon for monocrystalline PV modules. To produce 1 kg of SG silicon, 1.13 kg of MG silicon is required from the preceding process. For the MG silicone to be processed in the example, energy in form of electricity (49 kWh) and heat (28 MJ) is used to produce the desired end product of this process with the addition of hydrochloric acid, liquid hydrogen and sodium hydroxide. It can be seen in the outputs that most of the emissions for the production of SG silicon are emitted to air and water. Based on this approach, the input/output data for all processes of the PV life cycle were defined, except the battery manufacturing. Figure 8.6: Input/output data in OpenLCA for solar grade silicon production in PV module manufacturing. Source: (Frischknecht et al., 2020) As the output product in the last module production stage (PV laminate and panel), the total module area of the respective system is finally entered as the target amount. The dimensions of the module data assumed for Case Study III (Chapter 7) are 1755 mm x 1038 mm for a 370 W module. This results in a total module area of 21.86 m2 for the system with a battery and a total module area of 14.74 m2 for the system without a battery. Local environmental and socio-economic impacts 150 Balance of System BOS includes all components of the PV system, except the PV array (Irwan et al., 2016). For the BOS, the following components were considered in the case study III (Chapter 7) and added as processes in OpenLCA: • Inverter • Battery storage • Electrical cables and wiring • Mounting system Inverter: The input/output data for the inverter is based on the respective inverter capacity. The capacities in the IEA report range from 2.5 kW to 20 kW and are thus within the scope of the inverter capacities used for case study III. The higher the capacity of the inverter, the more materials have to be used and the more emissions are released, hence the input/output values increase. Since the case study includes a PV system with a battery and without a battery, the inverter capacities in the PV lifecycle must be adjusted accordingly. For the system without a battery, an inverter with a capacity of 3 kW was assumed and for the system with a battery, an inverter capacity of 3.6 kW was assumed. Therefore, the input/output data for these inverter dimensions were used. Since the output product is an inverter unit, i.e., a complete inverter, weight and dimensions can be neglected in the result calculation. Only the number of items has to be entered, in the case study, this is one inverter. Battery storage: Since case study III (Chapter 7) also includes a PV system with a battery, the battery described in (Chapter 7) was added to the PV life cycle. This is a lithium-ion battery with a capacity of 3.6 kWh. For the battery production, a predefined process from the Ecoinvent database was used in OpenLCA. The default output product is 1 kg of a lithium-ion battery. The provided Pylontech battery weighs 32 kg. Therefore, this value was entered as the target amount when calculating the results. Electrical cables: The wiring was defined based on the system requirements of the design defined for Case Study III (Chapter 7). A cable length of around 100 m with a diameter of 4 mm was assumed for the PV systems. This takes into account the distance between the plant and the connection point between the battery and the inverter. According to (Fouad et al., 2017), the most commonly used cables for PV cabling are made of copper. Therefore, the input/output data Local environmental and socio-economic impacts 151 from (Frischknecht et al., 2020) was used for the production of copper cables in OpenLCA. However, the length of the cables can be adjusted depending on the system studied. Mounting system: The mounting system in the respective system of case study III (Chapter 7) is based on a ground mounting. This type of installation requires more materials compared to a roof installation since the stand must be considered as well as the concreting. In the case of roof mounting, the roof serves as a substructure for the aluminum strips, which saves material. The required input/output data were again used from IEA and include materials, packaging and transportation of mounting systems, and disposal of packaging materials. The inputs entered in OpenLCA subsequently result in an output of 1 m2 of mounting system. Transportation: As mentioned in chapter 8.1.1, the transportation route of the modules is from China to Ireland. The production facilities of the Longi modules are located in Jiangsu, China, a neighboring province of Shanghai. Jiangsu's production base mainly consists of five plants, Wuxi Hydrogen Production Equipment Plant, Wuxi Wafer Plant, Taizhou Cell Plant, Taizhou Module Plant, and Jiangsu Module Plant (LONGi, 2022). The Solis inverters used are produced in Xianshan, also close to Shanghai. Therefore, for the transportation inventory, it was assumed that the modules and inverters are shipped from the port of Shanghai to Dublin. According to (SEA- DISTANCES, 2022), an online tool for calculating distances between seaports, the shipping route between the two locations is approximately 19.186 km, passing through the Suez Canal. This value is used as an input parameter in the predefined container ship transport process from the Ecoinvent database. Therefore, the consumption of the container ship, as well as the inputs/outputs do not have to be calculated or entered. According to (Pérez-López et al., 2017), transportation has already been identified as a marginal contribution (maximum 5%) to the total environmental impact of PV systems in previous LCA studies. Therefore, only the transport route from China to Ireland is considered and not the transport of raw material to the production company or transport within Ireland. Electricity requirements Since the production of the PV modules and inverters are executed in China, energy in form of electricity and heat from China is required for the manufacturing process. For this purpose, the predefined process of the Ecoinvent database in OpenLCA was used. The dataset describes the electricity available at the medium voltage level of the State Grid Corporation of China for the year 2014. Therefore, the transmission of 1 kWh of electricity on the medium Local environmental and socio-economic impacts 152 voltage level is represented by default in the process. To ensure that the available electricity and the associated emissions from generation are as current as possible, China's generation and emissions data were compared using available data from the International Energy Agency (IEA). China's electricity generation in 2019 was approximately 7.505 TWh, of which nearly 65 % (4.875.5 TWh) was attributable to coal (IEA, 2019). Electricity generation from oil and gas account for only a comparatively small share of 0.15 % and 2.84 %, respectively. Chinas heat generation in 2019 was approximately 1,300 TWh, of wich 85% (1300 TWh) was attributable to coal, 11.2 % (171 TWh) to gas and just under 3 % (44TWh) to oil (IEA, 2019). According to the IEA, China's CO2 emissions from electricity and heat generation are mainly based on coal, gas, and oil emissions. In 2019, electricity and heat generation from coal emitted about 5,078.1 MtCO2, representing nearly 97.5 % of total emissions from this energy source. Therefore, CO2 emissions from oil and gas are neglected in this case. Hence, the production of 1 TWh results in around 1.04 MtCO2. According to (IEA, 2019), the electricity and heat generation from coal in 2014 was about 4.246.9 TWh and emissions from this energy source are reported as 4.272.1 MtCO2. This results in a value of 1.01 MtCO2/TWh for the year 2014. Therefore, the increase in CO2 emissions in electricity and heat generation from coal from 2014 to 2019 is only about 3.5 %. For this reason, when considering the electricity required for the processes, the Ecoinvent dataset of available electricity from the State Grid Corporation of China from 2014 is used. 8.1.4. Life cycle impact assessment By Munzer Osman According to (Kun-Mo Lee, 2004a) the Life Cycle Impact Assessment (LCIA) stage is where the potential environmental effects of all the elements and flows collected during the inventory analysis are calculated. In general, the LCIA is carried out in four steps: classification, characterization, normalization, and weighting. However, the last two elements are considered optional. In classification, as explained in (Kun-Mo Lee, 2004b), the data from the inventory analysis according to their prospective environmental impact are assigned under specific impact categories based on the cause-effect relationship (some inventory flows and elements could have multiple effects, thus classified under more than one impacts category). Based on the impact assessment used typical impact categories are defined (see Table 8.1). Local environmental and socio-economic impacts 153 The second crucial stage for the LCIA is the characterization, in this step, the pre-classified inventories under different impact categories are quantified, however, to measure the potential environmental impact from different inventory parameters a unified unit is required. This unit is identified as a “categorization factor” which measures the contribution of each inventory parameter in its assigned impact category (Kun-Mo Lee, 2004a). The equivalency principle is a typical approach to define a characterization factor, for example, the environmental impact is assessed based on CO2 equivalent, which means the effect of each of the other elements and parameters in the inventory are translated in terms of CO2 equivalent (for instance the environmental impact of 1 g of Methane is reported as equivalent to 23 gCO2) (Kun-Mo Lee, 2004a). For the modeling in OpenLCA, the International Reference Life Cycle Data System (ILCD 2011 MIDPOINT+) which was released by the European Commission and the Joint Research Centre in 2012 as a lifecycle impact assessment method was used (OpenLCA, 2022). Table 8.1 illustrates the impact categories that are covered in this method: Table 8.1: ILCD 2011 MIDPOINT+ impact categories Source: Author based on (OpenLCA, 2022) No Impact category Unit 1 Acidification molc H+ eq 2 Climate Change kg CO2 eq 3 Freshwater eutrophication kg P eq 4 Water Resource Depletion m3 water eq 5 Ecotoxicity, Freshwater CTUe 6 Terrestrial Eutrophication molc N eq 7 Human Toxicity, Cancer Effects CTUh 8 Human Toxicity, Non-Cancer Effects CTUh 9 Ionizing Irradiation, human health effects kBq U235 eq 10 Land Use kg C deficit 11 Mineral, Fossil and Renewable Resource Depletion kg Sb eq 12 Ionizing radiation E (interim) CTUe 13 Ozone Depletion kg CFC-11 eq 14 Particulate Matter, Respiratory Effects kg PM2.5 eq 15 Photochemical Ozone Formation kg NMVOC eq 16 Marine eutrophication Kg N eq Local environmental and socio-economic impacts 154 For this case study, the environmental impact for two scenarios of PV systems mentioned in section 8.1.1 will be investigated (both installed in the same location). The following impact categories will be addressed in further detail: Climate Change sometimes referred to as the “Greenhouse effect”, during the various processes of the PV lifecycle, various inventory parameters release emissions to the atmosphere. Such emissions contribute to raising the temperature of the earth's surface by trapping and reflecting the radiation emitted from the earth’s surface (capturing the heat) which eventually results in raising the planet’s temperature (Brockway et al., 2019a). The global warming potential (GWP) is expressed as kilogram equivalent of CO2 (kg CO2 eq). Figure 8.7 shows the results from the OpenLCA modeling for the 2.96 kWp system (without a battery), the total estimated CO2 emissions over its lifecycle were 3,108 kg CO2 eq. Figure 8.7: Lifecycle CO2 emissions for the 2.96 kWp PV system components Source: Author As illustrated in Figure 8.7, PV manufacturing has the highest carbon footprint with 1,792 kg CO2 eq, mainly attributed to flows of energy in the forms of coal, natural gas, and electricity required during the initial processes such as the manufacturing of the Metallurgical- grade silicon (MG silicon). According to (PV production, 2022), the MG silicon is extremely purified (98% purity) silicon that is essential in building solar cells. The process of producing MG silicon includes reaction with carbon in the forms of coal, charcoal under extreme heat in an arc furnace. The second most intensive component in terms of CO2 emissions is the mounting system with 972 kg CO2 eq mainly attributed to emissions from the upstream 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 PV panel manufacturing Inverter Wiring Mounting System ground Transportation C O 2 em is si o n s in k g C O 2 eq System components Local environmental and socio-economic impacts 155 processes required to produce the necessary materials for constructing the mounting structure such as reinforced steel, concrete, and steel sections. For the inverter, the most intensive process is the manufacturing of the integrated circuits (IC), however, in this case study, the IC flows are considered as a final product at the plant, without looking at the upstream manufacturing processes. Considering the small size of the inverter the total emissions were 161.3 kg CO2 eq. Finally, for the transportation process of the PV modules from the location of production in China to Ireland (as sea cargo), the total emissions were 182 kg CO2 eq, mainly attributed to the fossil fuels burned in the ship engine (transportation was responsible for less than 6% of the total emissions). Similarly, Figure 8.8 shows the carbon emissions for the second PV system (4.4 kWp, with a battery). The total CO2 emissions from the battery used are 65.41 kg CO2 eq, and the total emissions from the entire components of the system (including transportation) throughout its lifecycle are 4,898.37 kg CO2 eq. Figure 8.8: CO2 emissions for the 4.4 kWp PV system components Source: Author When comparing the two PV systems, a pattern could be noticed, the PV panels manufacturing process still accounts for the largest share of the carbon footprint, followed by the mounting system and then transportation and inverter, with zero emissions from the wiring and cables. Additionally, a linear relationship could be seen. With the increase in the system size and the system components, the system boundaries changes, hence more emissions are obtained (especially for the PV panels and the mounting system). Figure 8.9 depicts the total emissions from each of the two PV systems when compared to the Irish grid, for the same amount of electrical energy generated (assuming a 25-years lifecycle 0 500 1,000 1,500 2,000 2,500 3,000 PV Panel manufacturing Inverter Wiring Mounting System ground Transportation Battery C O 2 em is si o n s in k g C O 2 eq System component Local environmental and socio-economic impacts 156 for the PV system). As shown below, in the first case (2.96 kWp), the grid emissions are 10,590 CO2 eq which is higher when compared to the 3,108 kg CO2 eq resulting from the 2.96 kWp PV system. The comparison is based on the amount of emission resulting from the electricity generated by the solar PV system (a total of 33,725 kWh electricity generation over the 25 years) and the total emissions to generate the same amount from the grid. In the second case, when increasing the PV system size to 4.4 kWp with 3.6 kWh battery the total renewable electricity generated is 114,950 kWh in 25 years, then the emission from the grid equivalent to generate the same amount of energy is 15,744 kg CO2 eq compared to only 4,898 kg CO2 eq from the PV system. Figure 8.9: Total emissions comparison between the two PV systems and the Irish grid Source: Author Figure 8.9 implies that more differences in the carbon footprint between the grid and the PV system appear at larger sizes: the higher the capacity installed, the more energy obtained from the PV system, and, to produce the same amount of energy using the grid, higher emissions will be obtained. By: Philip Miltrup Land use The impact category "land use" describes in the methodology of life cycle assessment (LCA) the environmental impacts of the use, transformation, and management of land for human purposes (Frank Brentrup et al., 2002). Furthermore, the land use indicator describes the impact of land use and how it is used (Aitor P. Acero et al., 2005). Consequences of land use 3,108 10,590 4,898 15,744 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 Total PV System Electricity Grid Ireland em is si o n s in k g C O 2 eq 2.96 kWp PV system without battery 4.4 kWp with 3.6 Kwh battery Local environmental and socio-economic impacts 157 are, for example, the reduction of landscape elements, impairment of the naturalness of used areas or the decrease in the diversity of animal species. The naturalness of an area can be defined as the sum of the areas not influenced by humans and the remaining naturalness of the used areas (Frank Brentrup et al., 2002). The ILCD 2011 MIDPOINT+ methodology used in OpenLCA uses "kg C deficit" as its reference unit and describes the deficit of soil organic carbon in kg. Equation 8.4 shows the land use comparison between a 2.9 kWp PV system without a battery and the Irish grid. As illustrated Figure 8.10, the land use of the Irish power grid is about twice as high compared to the PV system. The land use of the power grid is 7.775 kg C deficit and of the PV system 3.338 kg C deficit. The highest influencing factor on land use in the Irish grid is the conversion of mineral extraction areas. This amounts to about half of the total land use and is mainly due to the gas extraction required for electricity generation, as gas-fired electricity generation accounts for the highest share in the Irish grid. Extraction degrades the naturalness of land in use. Large areas of land are crossed by pipelines, putting a long-term strain on them for agricultural use, for example. In the entire PV system, PV module manufacturing has the highest proportion of land use with a 1.152 kg C deficit followed by the ground mounting system with a 1.071 kg C deficit. In PV module manufacturing, the largest influencing factor is the electricity from China required for the process. This is based, as described in sub-chapter 8.1.3, mainly on coal. During coal mining, large land masses are moved, which leads to a reduction of landscape elements. In addition, the mining of raw materials such as aluminum and silicone, as well as the production of solar glass, has one of the greater impacts on land use. Figure 8.10: Land use comparison, 2.9 kWp without battery and Irish electricity grid Source: Author 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 PV panel manufacturing Inverter Wiring Mounting System ground Transportation Total PV System Electricity Grid Ireland La n d u se ( kg C d ef ic it ) system component Local environmental and socio-economic impacts 158 Figure 8.11 shows the land use comparison between a 4.4 kWp PV system with a battery and the Irish power grid. It can be seen that the overall PV system has more than twice the land use relative to the electricity grid in Ireland. The land use of the PV system is 26,800 kg C deficit and the power grid is about 11,600 kg C deficit. The trigger of the high land use in the PV system is the added 3.6 kWh battery. The high land use is associated with the raw material extraction for battery production. The extraction of lithium has significant environmental impacts. According to (Joseph Zacune, 2014), the extraction process leads to water pollution and depletion. Water scarcity in turn affects land use negatively, as neighboring land areas lose fertility and food production is restricted (Joseph Zacune, 2014). Figure 8.11 Land use comparison, 4.4 kWp with battery and Irish electricity grid Source: Author Acidification During the combustion of fossil fuels, emissions are released into the air. According to the National Oceanic and Atmospheric Administration (NOAA, 2020), about 30 % of these emissions are absorbed by the ocean. Through a series of chemical reactions, sulfur oxide (SOX) and CO2 cause the acidity of the oceans to increase. In addition, the release of acidifying exhaust gases leads to acidification of cloud water, causing precipitation to contain these acids and damage the soil. Millions of tons of SOX and CO2 are absorbed by the ocean and land surfaces every day. Two other main contributors to acidification are ammonia (NH3) and nitrogen dioxide (NO2). Other compounds are of minor importance and are not considered in 0 5,000 10,000 15,000 20,000 25,000 30,000 La n d u se ( kg C d ef ic it ) system component Local environmental and socio-economic impacts 159 the recommended LCIA method. Acidification potential is expressed by the ILCD 2011 Midpoint + methodology in the reference unit "molc H+ eq." and describes the ability of substances to form H+ ions (Khalid Raouz, 2018). Figure 8.12 shows a comparison between the acidification impact from a 2.9 kWp PV system without a battery and the Irish power grid. As illustrated, Ireland's power grid (17.12 molc H+ eq) has slightly lower acidification compared to the overall PV system (22.72 molc H+ eq). In the PV system, module production has the highest contribution to acidification with about 11.26 molc H+ eq. Aluminum alloys in particular cause high emissions of nitrogen oxide, ammonia and sulfur dioxide. According to (Hassellöv et al., 2013), international shipping has been identified as a significant contributor of SOX and NOX to the atmosphere at local, regional, and global levels. When sulfur-containing fuels are burned by container ships, SOX is produced, which is absorbed by the water and thus contributes to ocean acidification. Figure 8.12 Acidification comparison, 2.9 kWp without battery and Irish electricity grid Source: Author Figure 8.13 shows a comparison of acidification between a 4.4 kWp PV system with a battery and the Irish electricity grid. It can be seen that in this case study, the acidification of the entire PV system is significantly higher than the Irish electricity grid. With 72.78 molc H+ eq, the acidification caused by the PV system is almost three times higher than in the case of the power grid (25.46 molc H+ eq). The main contributor is the lithium-ion battery added in the case study. The entire battery production causes significantly high acidification which is mainly 0 5 10 15 20 25 A ci d if ic at io n ( m o lc H + e q ) system component Local environmental and socio-economic impacts 160 due to the cathode production and the copper foil which is used as current collector (Chordia et al., 2021). However, acidification from the Chinese power grid is not negligible. Since all system components of the entire PV system are produced in China, the Chinese electricity grid contributes considerably to acidification. Figure 8.13 Acidification comparison, 4.4 kWp with battery and Irish electricity grid Source: Author 8.1.5. Data interpretation By Munzer Osman As illustrated in sub-chapter 8.1.2, the fourth step of the lifecycle assessment study is the LCA data interpretation. The LCA interpretation is a comprehensive stage that considers all the three previous steps (goals & scope, lifecycle inventory, lifecycle impact assessment). According to (Zampori L., 2016), the LCA results interpretation provides answers to the questions raised during the first steps of the LCA study, questions such as which phase or stage of the life cycle of the PV system has the largest environmental footprint. This will help in identifying the hotspots of the system under investigation. The LCA data interpretation also discusses the limitations and constraints that might occur during the LCA inventory phase. It also discusses whether data collected to describe the different processes flows are accurate and comprehensive enough or not, because such limitations affect the final results of the life cycle assessment. Regarding the lifecycle impact assessment, the data interpretation also highlights the impact assessment method used, the different impact categories, and the process contribution trees. 0 10 20 30 40 50 60 70 80 A ci d if ic at io n ( m o lc H + eq ) system component Local environmental and socio-economic impacts 161 For the two PV systems investigated in this case study, four lifecycle interpretation indicators will be investigated: ▪ Cumulative Energy Demand (CED) ▪ Energy Pay Back Time (EPBT) ▪ Energy Return on Investment (EROI) ▪ Global Warming Potential (GWP) Cumulative Energy Demand (CED) The method of CED is used to assess the total primary energy consumption throughout the life cycle of the PV system (Acero et al., 2015a). Considering the stages of PV manufacturing, significant amounts of energy are utilized during the extraction and processing of raw materials, manufacturing of different components (wafer, cells, panel), transportation, and other auxiliaries related to the balance of the system (BOS). The CED method contains eight different impact categories. These categories describe the exact type (source) and amount of primary energy used, including renewable and non-renewable sources of energy to manufacture the PV system. Table 8.2 summarizes these different categories. Table 8.2: The impact Categories of Cumulative Energy Demand (CED) method Source:(Acero et al., 2015a) Method: Cumulative Energy Demand (CED) Impact category group Name of the impact category in the method Reference unit Non-renewable resources Fossil MJ-eq Nuclear MJ-eq Primary forest MJ-eq Biomass MJ-eq Renewable resources Geothermal MJ-eq Solar MJ-eq Wind MJ-eq Water MJ-eq The CED results differ according to the type of system assessed for the lifecycle, also it is affected by the location of production, the availability of energy sources, and the technology of production. By using the CED method, it is possible to define the hotspots of the supply chain for the system under assessment and highlight the processes that consume the most energy. Local environmental and socio-economic impacts 162 For this report, the focus will be on estimating the non-renewable primary energy demand for both of the introduced PV systems (the 2.96 kWp and the 4.4 kWp with a battery). For the 2.96 kWp system, Figure 8.14 shows the amount of the non-renewable primary energy consumption for each component of the PV system. Figure 8.14: Non-renewable primary energy demand for the 2.96 kWp PV system Source: Author Currently, the majority of the PV modules in the market are crystalline silicon (same as the system under study is single crystalline). Both single-crystal, and multi-crystalline silicon PV panels manufacturing require using large wafers of purified silicon. The process of purifying and crystallizing the silicon are the most energy-intensive phases of the solar-cell manufacturing process (Renfrow, 2004). To obtain 77,325 kWh of electricity from the 2.96 kWp PV system, 39,320.32 MJ-eq of non-renewable primary energy is required (about 60% of this energy is required to manufacture the PV system and 27% is consumed to manufacture the mounting system). The energy is mainly consumed during the different production phases and processes, for example, the hard coal consumption during the production of the MG silicon stage as seen in the inventory flows, also the required electricity (which comes from the Chinese energy mix) to power-up the factories for the PV production, and also the energy needed for the transportation. As a comparison, to generate the same energy from the Irish grid,195,751MJ-eq of non- renewable primary energy is required. For the 4.4 kWp PV system with a 3.6 kWh battery, the non-renewable energy demand is illustrated in Figure 8.15. Similarly, the top three energy- 0 5,000 10,000 15,000 20,000 25,000 PV Panel manufacturing Inverter PV Mounting Transportation en e rg y d em an d in ( M j- eq ) Systm component Local environmental and socio-economic impacts 163 intensive components could be identified: PV manufacturing is consuming most of the energy with 35,374 MJ-eq, followed by the mounting system with 19,068 MJ-eq then inverter and transportation as shown below, the battery production consumes only 0.25 MJ-eq of non- renewable primary energy. Figure 8.15: Non-renewable primary energy demand for the 4.4 kWp PV system Source: Author Energy Pay Back Time (EPBT) While the CED indicator illustrated that the production of PV systems requires significant amounts of energy, the term Energy Pay Back Time (EPBT) describes how much time is required for the PV system to generate energy that is equivalent to the energy utilized to produce the system in the first place (Renfrow, 2004). The energy generated from the PV system is a crucial factor in determining the energy payback time, the higher the energy generated from the PV system the less time is needed to pay back the energy invested during the production of the PV system. Another crucial factor in EPBT is grid efficiency (Tariq, 2019a). It indicates the conversion efficiency of primary energy to electricity (basically describing how efficient the Irish grid is in converting the various primary energy sources of the current energy mix as an input to useful electricity energy as an output). For Western Europe the grid efficiency is considered to be 31% in 2015 (Frischknecht, 2015), however, for Ireland, it is estimated to be 49.1% in 2020 according to (SEAI, 2022e). 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 PV Panel manufacturing Inverter PV Mounting Battery Transportation e n e rg y d e m an d M J- eq System components Local environmental and socio-economic impacts 164 By applying Equation 8.1, the EPBT could be calculated for the two PV systems investigated in this section of the report. Equation 8.1: Energy Pay Back Time Source: (Tariq, 2019b) 𝐸𝑃𝐵𝑇 = 𝑃𝐸𝑖𝑛𝑣 𝐸𝑝𝑣𝑔𝑒𝑛 𝑇 /Ƞ𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑔𝑟𝑖𝑑 Where: ▪ EPBT is the energy payback time in years ▪ PEinv is the primary energy invested in producing the PV system ▪ Epv_gen is the energy generated from the PV over its lifetime (electricity generated) ▪ T is the lifetime of the PV system ▪ Ƞnational 𝑔𝑟𝑖𝑑 is the national grid efficiency (the national power sytem for Ireland) The total electricity generated from the PV system over 25 years of a lifetime is estimated to be 77,325 kWh for the 2.96 kWp system, and 114,950 for the 4.4 kWp system, and from the OpenLCA simulation results the total required energy to build these two PV systems are 42,029 MJ-eq and 66,525 MJ-eq respectively. By applying the EPBT equation, the EPBT for the 2.96 kWp system is 1.85 years and for the 4.4 kWp system is 1.97 years (based on the LCA results, when using the CED impact assessment method negative values/numbers for certain CED impact categories were obtained, these negative results suggested an avoided impact or saving in energy rather than consumption (Carvalho et al., 2021). Thus, these values were not considered in calculating the EPBT). Energy Return on Investment (EROI) EROI is a useful way to evaluate and compare energy inputs, output, and energy yield for various technologies (world nuclear association, 2020). The EROI indicator evaluates the profitability of technologies, not in terms of monetary value, rather using energy itself as an evaluation criterion. EROI could be obtained by simply dividing the energy output from the specific technology to the energy input (energy invested to obtain useful energy from the corresponding technology). A higher value of EROI implies better technology in terms of net energy profitability. Certain technologies require a small amount of energy to extract fuels or raw materials, and to build systems that convert these fuels to useful energy, when the resulting output energy from Local environmental and socio-economic impacts 165 these fuels is significant, such technologies would have a high EROI. In general, technologies such as hydroelectric and wind power plants require relatively low energy investment to construct a complete system and facilities to produce electricity when compared to other technologies that are energy-intensive during the manufacturing or the extraction of fuels such as natural gas, or oil which usually have lower EROI (CARBON BRIEF STAFF, 2013). Equation 8.2 was applied to calculate the EROI for the PV systems under study. Equation 8.2: Energy Return On Investment Source: (Tariq, 2019a) 𝐸𝑅𝑂𝐼 = 𝐸𝑃𝑉𝑔𝑒𝑛 Ƞnational grid⁄ 𝑃𝐸𝑖𝑛𝑣 Similarly, as the total electricity produced by the 2.96 kWp PV system over its lifecycle is 77,325 kWh, the national grid efficiency is 49%, and the total primary energy invested is 42,029 MJ-eq. Then, the EROI is estimated to be 13.5. And for the 4.4 kWp, the total electricity produced over its lifetime is 114,950 kWh, total primary energy invested is 66,543 MJ-eq; then, the EROI is 12.3. In general, solar PV systems have lower EROI when compared with other renewable technologies such as wind which has a high EROI value, with a mean value of 18:1 and 20:1 (Hall et al., 2014) or even 30:1 (Brockway et al., 2019b), however, for electricity generated from fossil fuels resources such as electricity generated from coal, the typical EROI is ranging between 4 and17, and for gas, it is ranging between 6 and 14 (Brockway et al., 2019b). This places renewable energies as strong competitors for traditional fossil fuels when it comes to the net profitability of energy. Climate change potential Increasing penetration of renewables in the Irish electricity generation mix will significantly reduce the GHG emissions in this sector. Between 2005 - 2020, Ireland increased its dependency on renewable resources from 6% to 42% (SEAI, 2022f), along with the adoption of more efficient technologies and improved conversion rates in the electricity generation sector. This led to a reduction in the carbon intensity of the Irish electricity from 636 g CO2/kWh in 2005 to only 296 g CO2/kWh in 2020 (SEAI, 2022f). Similarly, each kWh generated from the solar PV system placed on Loop Head will replace kWh of electricity from the Irish grid, which will further reduce GHG emissions. For the two PV systems in this case study, and considering the different stages over the PV lifecycle, the total GHG emissions per unit generated (emission factor) could be calculated by dividing the total emissions produced over the lifecycle of the PV system (emissions released Local environmental and socio-economic impacts 166 during material extraction, processing, manufacturing, and transportation) by the total electricity produced over its lifecycle as illustrated in Equation 8.3. Equation 8.3: Emission factor Source: (Tariq, 2019a) 𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 = 𝑡𝑜𝑡𝑎𝑙 𝐺𝐻𝐺 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 𝑖𝑛 𝐶𝑂2 𝑒𝑞 𝑜𝑣𝑒𝑟 𝑖𝑡𝑠 𝑙𝑖𝑓𝑒 𝑐𝑦𝑐𝑙𝑒 𝑡𝑜𝑡𝑎𝑙 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑦 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑 𝑏𝑦 𝑡ℎ𝑒 𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 𝑜𝑣𝑒𝑟 𝑖𝑡𝑠 𝑙𝑖𝑓𝑒 𝑐𝑦𝑐𝑙𝑒 By applying Equation 8.3, considering total emissions of 3,107.8 kg CO2 eq for the 2.96 kWp system, the total CO2 intensity (emission factor) calculated is 40.2 g CO2 eq/kWh, which is fewer emissions when compared to the Irish grid. Similarly, for the 4.4 kWp system, the carbon intensity factor is 42.6 g CO2 eq/kWh. In 2020, 52 % of electricity generated in Ireland came from natural gas and about 37% from wind energy (SEAI, 2022f). By introducing the PV system into the Ireland generation mix (the republic of Ireland generation mix), it is expected to contribute to reducing the CO2 emissions in the electricity generation sector. To further illustrate the process of carbon reduction as a result of introducing solar energy in electricity generation, a sample day will be analyzed (this date is randomly selected and is not completely representative for the whole year, however, for the sake of understanding the carbon saving from PV system the general approach will be the same). Figure 8.16 shows the hourly CO2 intensity from the electricity generation sector in Ireland on February 17th, 2022. Figure 8.16: Electricity generation carbon intensity in Ireland, February 17th, 2022 Source: Author based on (EirGrid, 2022) Considering the Irish electricity generation mix on that day, these emissions are mainly attributed to natural gas and coal as illustrated in Figure 8.17, which shows the contribution of different fuels in the electricity generation on the same day. 0 50 100 150 200 250 300 350 400 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2 Hours Local environmental and socio-economic impacts 167 Figure 8.17: Irish system generation fuel mix, February 17th, 2022 Source: Author based on (EirGrid, 2022) Figure 8.18 depicts the positive impact of the 2.96 kWp PV system contribution in the generation mix (on the household level), the total electricity withdrawn from the national grid will be reduced by an amount equal to the generation of the PV system. As the generation from solar is only during the daylight hours, and considering a 2.96 kWp PV system without battery storage, the PV system contribution in CO2 emissions reduction will approximately be between 8:00 to 18:00 (slight fluctuation might occur depending on seasonality). Considering the electricity demand in this case study and the household hourly load curve, if the entire demand is covered from the electricity generated by the grid, the total CO2 emissions will be 4.3 kg, however, by introducing the PV system the total electricity purchased from the grid is reduced by the equivalent amount of electricity generated by the PV system. As a result, the total emissions are reduced to 2.2 kg CO2 (approximately a 50% reduction). Figure 8.18: The effect of the 2.96 kWp PV system on CO2 reduction, February 17th, 2022 Source: Author Coal 9% Gas 27% Others 5% Renewbles 59% 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2 Hours emissions without PV system emission with PV system Local environmental and socio-economic impacts 168 Similarly, Figure 8.19 shows the results for the 4.4 kWp system with a battery, the battery prolongs the hours of solar penetration to cover the household demand (for certain hours when there is no sun) which will further reduce the electricity purchased from the grid. The total emissions without a PV system are 4.3 kg CO2, reduced to 1.17 kg of CO2 as an effect of introducing the PV system (3.1 kg CO2 reduction). Figure 8.19: The effect of the 4.4 kWp PV system on CO2 reduction, February 17th, 2022 Source: Author According to (Renewable Ninja, 2022) statistics, in July and May Ireland has the lowest capacity factor of wind energy (approximately 25% - 26%), hence the penetration of wind energy will be low during these months and the Irish grid would have different fuel mix. Figure 8.20 shows the CO2 intensity of electricity generation on July 14th, 2021, where the wind conditions are relatively low. Figure 8.20: Electricity generation carbon intensity in Ireland, July 14th, 2021 Source: (EIRGRID, 2022) 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2 Hours emissions without PV system emission with PV system 0 50 100 150 200 250 300 350 400 450 500 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2/ kW h Hours Local environmental and socio-economic impacts 169 By following the same approach earlier (for the February 17 th case), the hourly CO2 savings for the 14th of July for both 2.96 kWp and 4.4 kWp systems are illustrated in Figure 8.21 and Figure 8.22 respectively. Figure 8.21: The effect of the 2.96 kWp PV system on CO2 reduction, July 14th, 2021 Source: Author Figure 8.22:The effect of the 4.4 kWp PV system on CO2 reduction, July 14th, 2021 Source: Author The carbon savings on July 14th is 4.4 kg CO2 (in the case of 4.4 kWp system) which are higher than the carbon savings on February 17th. This difference could be explained by the nature of the energy replaced from the grid (based on the fuel mix on that day) and the operation of the PV system as it is generating electricity since 5:00 and feeding the household up until 23:00, because of the battery and the sunny summer hours. 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2 Hours emissions without PV system emission with PV system 0 50 100 150 200 250 300 350 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 em is si o n s g C O 2 Hours emissions without PV system emission with PV system Local environmental and socio-economic impacts 170 It should be mentioned that the CO2 reduction is affected by several factors, for example, the hourly grid CO2 intensity is continuously fluctuating according to the share of each fuel type in the generation mix. Additionally, the seasonality and fluctuation in the wind generation in Ireland is an important factor as it is considered the second-largest generation source after natural gas. Ffor a windy day with high renewable penetration in the Irish grid the carbon intensity of the grid will be low, and the carbon savings by the PV system will be less. On the contrary, for a day with lower renewable penetration, the carbon intensity of the grid is high and then the carbon savings by the PV system will be higher. Moreover, the PV system size and configuration (e.g., with battery or without a battery) is also an important factor because it affects the time (day/ night) at which the electricity generated from the PV system is replacing the electricity from the grid. Finally, as introduced earlier and according to (SEAI, 2022e), the Irish electricity CO2 intensity is 296 g CO2/kWh, by applying this value over the whole year (as an average), the total annual carbon savings from the 2.96 kWp and the 4.4 kWp PV systems placed in Loop Head will be about 507 kg CO2 and 787 kg CO2 respectively. 8.1.6. Critical discussion By Philip Miltrup One of the most important elements of a successful life cycle analysis is the existence of reliable data for the product under investigation. The IEA data used for module, inverter, cable and mounting system production of PV systems can be evaluated as reliable since the individual system components can be found with their exact wording in the Ecoinvent database. However, the existing flows and processes of the database for the corresponding processes (cradle to grave PV LCA) need to be evaluated in detail before use. It is of paramount importance to understand what lies behind predefined flows or processes, otherwise, it is possible that a predefined database process includes sub-processes such as disposal or even recycling, which is not considered in this study. This not only misrepresents the results but also misinterprets them if, for example, the subsequent energy required for recycling is also included in a flow or process. The goals and scope phase also defines, among other things, the motivation for conducting a project. One of the purposes of the PV systems analyzed in this LCA is to illustrate to members of the Loop Head community that there are also emissions associated with renewable energy technologies such as PV systems. However, more importantly, these emissions are significantly lower than those associated with energy generated from conventional, non- Local environmental and socio-economic impacts 171 renewable technologies. In addition, the analysis serves to illustrate how much emissions can be saved on a household basis by a PV system. The scope describes very well that this project is limited to a certain time and space. In the scheduled project duration of five weeks, restrictions have to be accepted. Therefore, a cradle to cradle analysis would have exceeded this scope. In addition, one of the key findings of LCA is that it is iterative, meaning that it continues for years, with data quality improving as measurement improves and the scope and goals of the project are better understood. 8.2. Socio-economic impact assessment 8.2.1. Introduction By Philip Miltrup According to the IC2021 results, the total energy demand in Loop Head is composed of heat, electricity, and transportation, with the main energy sources for heating being oil, LPG, coal, peat, and electricity. Most households in Loop Head still rely on fossil fuels as their primary heating fuel. In order to initiate a transition to greener energy production in Loop Head, active citizen participation in renewable energy generation is an important success factor. According to (Claudia Fruhmann & Nina Knittel, 2016), renewable energy production offers common benefits to people and communities. These include, for example, the use of local natural resources, building social capital and counteracting energy poverty. Additionally mentioned, and considered in more detail in this chapter, is how employment can increase at the regional level through the deployment of PV systems and what impact the implementation of PV systems could have on the Loop Head economy. The consequences of these changes are examined, including the effects on total and sectoral output and on employment levels. According to (Ronald E. Miller & Peter D. Blair, 2009), an input-output analysis (IO analysis) is considered as a suitable method to be used in this context as it captures the interdependencies between different sectors of the economy and since Ireland publishes official IO tables. Sub- chapter 8.2.2 discusses the methodology of the socio-economic analysis. Sub-chapter 8.2.3 describes the data collection procedure for the analysis. Among other things, the system components in Case Study III (Chapter 7) are included in the data collection. OPEX assumptions and CAPEX breakdowns are outlined and leakage assumptions are explained. Chapter 8.2.4 presents the results of the examined topics in terms of gross value added (GVA) and job creation. The results of the GVA essentially represent the GVA of the respective (economic) sectors involved and the economic benefits of a PV system implementation. The following qualitative analysis of resulting jobs through the installation of Local environmental and socio-economic impacts 172 PV systems and the discussion of potential business models such as social enterprises or cooperatives is presented in the last part of this section. A critical discussion of the results follows in sub-chapter 8.2.5. 8.2.2. Methodology By Munzer Osman To measure the socioeconomic impact of installing a PV system on Loop Head, the method of input-output analysis is used. The approach of input-output analysis is widely utilized in understanding and measuring the direct, indirect, and induced impact of renewable resources (Acero et al., 2015b). The Input-output tables for Ireland were obtained and examined to identify the main economic sectors and their interdependencies and the different economic transactions among different sectors. In general, the implementation of the PV system in Loop Head could have three different effects. A direct effect which is the immediate expenditure associated with such a project (for example, for the process of constructing the PV system, equipment such as PV panels, inverters, and the mounting system should be spent on). The second type of effect is indirect such as the expenditure on hiring the installation engineers, technicians, and consultants to perform the job. The third effect is induced such as the benefit that reflected on the economy from the further expenditure of the working team in installing the PV system (for example, workers buying drinks and snacks from a local shop in Loop Head is an induced impact. Additionally, the positive image of a green and sustainable energy community of Loop Head would enhance the tourism activities and attract higher number of visitors, this could be considered as an induced effect as well ). After screening all potential expenditures for the PV system (cost per kW installed then calculating for the whole PV array), a unique Standard Industrial Classification (SIC) was assigned to each of the expenditures for system components. Then, all items with similar SIC were grouped under one category (to identify the main economic sectors). Then, a leakage factor was applied. A leakage factor is an indicator that could be used to describe the strengths of a certain economy, a lower leakage value indicates a strong supply chain, which means all the spending on production inputs is within the country borders and no significant imports are required. Conversely, a high leakage factor indicates a weak supply chain, and the production elements are being imported from outside the country, which means the country’s expenditure is leaving its borders (Charlotte Cochrane, 2021). This leakage describes how much of the spending remains in Ireland’s economy. Additionally, “GVA output multipliers” were calculated, and the GVA output multiplier type II was considered as a way to account for all the potential socioeconomic effects (direct, indirect, and induced). The output obtained from this process is Local environmental and socio-economic impacts 173 the Gross Value Added (GVA) per unit kW installed capacity of the PV system which reflects the economic value from this activity (installing of PV systems). 8.2.3. Data collection By Saleheh Rahimi Primary data collection is a systematic flow of gathering observations or measurements. It helps to gain first-hand knowledge and original insights into the research problem, whether it is performing research for business, governmental or academic purposes (Shanks & Bekmamedova, 2018). This section presents the collection of data in order to calculate the contribution of implementing a solar PV system to the Irish economy. Two main indicators including Gross Value Added (GVA) (which decribed in following part) and employment factor are determined to evaluate the socio-economic impacts of implementing a solar system based on characteristics of the case study III described in Chapter 7. The data collected are mainly quantitative (numeric values) in calculating the GVA but also some qualitative (words and meaning) data is used to describe some linguistic variables which are described in more detail in chapter 8.2.4. to evaluate the job creation. The methods used to gather the data for estimation of expenditures and investment cost for installing solar PV system are interviews (face-to-face) with a group of experts and engineers who were directly involved in the process of installing the solar PV system. Secondary data collection to calculate the GVA variables are based on Ireland’s Industry and Service Statistics provided by Organization for Economic Co- Operation and Development (OECD.stat, 2021) and regarding job employment creation, data collection is based on literature review and country’s statistical data (KMPG Ireland, 2015). All the data and assumptions which influence the result are explained in this section. Gross Value added (GVA) One of the main inicators to measure the economic impact of Solar PV system on the region is GVA, which is calculated through the Input Output-Tables (IOTs) method described in chapter 8.2.4. GVA represents the value generated by selling goods and services as an output in the economy. it measures the contribution of each activity in industry or sector to the Gross Domestic Product of a nation, GDP. Therefore, the summation of all GVA from different industries and services subtracting product taxes but adding subsidies is equal to the GDP of a country as the total economic output (SSE, 2018a). This indicator shows how many products (goods and services) are brought into an economy (either as a result of domestic production or imports from other countries) and how many of those same products (intermediate consumption by industry and final consumption by a household or any sector and exports) are Local environmental and socio-economic impacts 174 used in the economy (UNITED NATIONS, 2018). Many economists believe that GVA can be a better indicator for measuring the country’s economic growth than GDP since GVA does not take into account the taxes paid by people. Sometimes GDP increases solely because of growth in taxes, however, it does not necessarily mean that more goods and services are produced (The Economic Times, 2017). Each country has its own Output-Input table which has become an essential tool to inform policy and decision-makers. The table which is used to calculate the GVA output is Ireland ’s Input-Output 2018 prepared by Organization for Economic Co-operation and Development (OECD) (OECD.stat, 2021) According to Equation 8.4 industry output is calculated by summation of intermediate consumption of a product by industry (manufacturing and selling the PV system) and GVA output from final consumption by customer (households). Equation 8.4: Industry output Source: (UNITED NATIONS, 2018) 𝑂𝑢𝑡𝑝𝑢𝑡 = 𝐼𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑡𝑒 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 + 𝐺𝑉𝐴 The GVA output for an industry is defined as the total value of production in all industries of the economy that is necessary for all stages of production in order to produce one unit of the product for final use (UNITED NATIONS, 2018). Equation 8.5: GVA output Source: (SSE, 2018b) 𝐺𝑉𝐴 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝐺𝑉𝐴 𝑒𝑓𝑓𝑒𝑐𝑡𝑠/𝐺𝑉𝐴 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 In Equation 8.5, GVA effects show the total change in GVA as a result of one unit change in product demand. GVA multiplier also identifies the effects of 1 euro of GVA in each industry on the whole Irish economy which consist of two types, Type I and type II(SSE, 2018a). Type I multipliers sum together direct and indirect effects while Type II multipliers also include induced effects which are defined in section 8.2.2. In this study, GVA multiplier type II (including direct, indirect and induced impacts) is considered. Leontief methodology The methodology used for calculating the GVA multiplier is based on the Leontief inverse in a linear model from the input-output model. Then, for each industry category, GVA multipliers are calculated based on the Input-Output table. Leontief model a is methodology used for calculating the production level of each industry for the whole country or region. It defines the relative value of each specific industry for associated GVA. Basically, in this model, there are n industries with n products. It is assumed that Local environmental and socio-economic impacts 175 production is equal to consumption. In general, 𝑥1 … … … 𝑥𝑛 is the total output of the respective industry (see Equation 8.6) Equation 8.6:The total output of each industry Source: (Kansas State, 2020) 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 1: 𝑥1 = 𝑎11𝑥1 + 𝑎12𝑥1 + ⋯ + 𝑎1𝑛𝑥1 + 𝑏1 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 2: 𝑥2 = 𝑎21𝑥1 + 𝑎22𝑥1 + ⋯ + 𝑎2𝑛𝑥2 + 𝑏2 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑛 ∶ 𝑥𝑛 = 𝑎𝑛1𝑥1 + 𝑎𝑛2𝑥1 + ⋯ + 𝑎𝑛𝑛𝑥𝑛 + 𝑏𝑛 And 𝑎𝑖𝑗𝑥𝑗 is the number of units generated by industry i and consumed by industry j. Then, the input-output matrix (A) is obtained from Equation 8.7. Equation 8.7: Input-output matrix Source: (Kansas State, 2020) 𝐴 = ( 𝑎11 … 𝑎1𝑛.... 𝑎𝑛1 … 𝑎𝑛𝑛 ) , 𝐵 = ( 𝑏1 𝑏2 . . 𝑏𝑛 ) , 𝑋 = ( 𝑥1 . . 𝑥𝑛 ) In this equation, B is the external demand vector and X is the production level of each industry. Thus, Equation 8.8 is defined as follows. Equation 8.8: Linear equation in Leontief model Source: (Kansas State, 2020) 𝑋 = 𝐴𝑋 + 𝐵 To determine the X as the solution from this matrix, it can be transformed to Equation 8.9. Equation 8.9 Leontief Inverse Source: (Kansas State, 2020) 𝐼𝑛𝑋 − 𝐴𝑋 = 𝐵 (𝐼𝑛 − 𝐴)𝑋 = 𝐵, 𝑋 = (𝐼𝑛 − 𝐴)−1 𝐵 Then (𝐼𝑛 − 𝐴)−1, which is called the Leontief inverse, defines the amount of output in each industry. According to (UNITED NATIONS, 2018), International Standard Industrial Classification (SIC) is the international reference for the classification of all economic activities. The fourth revision, ISIC Rev. 4, was published by the United Nations in 2008 and it consists of 21 sections, 88 sub-section, 238 groups and 419 classes. It aims at providing a standard classification of products and services used for gathering and providing economic statistics for further economic analysis by each industry. Section level defined the main industries and businesses involved in producing the products and activities, then in the next level, sub-section, it Local environmental and socio-economic impacts 176 described the main characteristics of industry’s outputs such as products and services. At the class level, it classifies the products and services based on their associated process and technology. Table 8.3 shows the GVA multiplier Type II as a result of Leontief method used to build the model for each main SIC category assumed to be involved in the implementing of a PV system in Loop Head. Table 8.3: GVA multiplier II Source: Author based on (OECD.stat, 2021) SIC category SIC Code GVA multiplier II Manufacture of electronic industrial process control equipment 26.51/2 0.4002 Manufacture of electrical equipment 27.00 0.4419 Manufacture of machinery and equipment 28.00 0.5204 Electrical installation 43.21 0.5504 Repair and installation of machinery and equipment 33 1.0301 According to the environmental life cycle assessment of a solar PV system, the technical cost breakdown structure is identified from the first LCA stage, manufacturing of solar system including solar panels and balance of system. In this study, the assessment of socioeconomic impacts of the PV system is aligned with the supply chain stages in LCA. To calculate the economic impact of the solar PV system, the first step is to define the cost of the material and services involved in utilizing the solar panels as the final consumption by the consumer. As it is already mentioned in chapter 8.1.1, PV life cycle assessment consists of extraction of raw material, manufacturing of solar PV system, transporting the components from the country of producing the system’s components (China) to the place of the operation and usage stage, Ireland (cradle-to-grave LCA). However, to create the technical cost breakdown structure of the system, it is assumed that some expenditure categories are already considered in other categories. For instance, the expenditure of transportation from China to Ireland is not considered as any separate category in this study. It is already included in the final cost of the solar equipment. These expenditure values were split across individual technical cost breakdowns for a specific solar system (cost for solar panels, inverter, cabels, battery and other components). Table 8.4: Technical cost breakdown structure of PV solar system corresponding the SIC sectors Source: Author based on (OECD.stat, 2021) SIC sector code Technical cost center OPEX and CAPEX share (2.96 kWp without battery) OPEX and CAPEX share (4.4 kWp with battery) Local environmental and socio-economic impacts 177 C A P E X Manufacturing of Solar PV system (Solar panels and BOS’s components) 34.92% 45.31% C26 26.51/2 Manufacture of electronic industrial process control equipment 0.28% 0.19% C27 27.00 Manufacture of electrical equipment 29.67% 28.49% C28 28.00 Manufacture of machinery and equipment 4.97% 16.63% Installation of the Solar PV system 38.57% 36.12% F43 43.21 Electrical installation 38.57% 36.12% O P E X Operation and maintenance 26.52% 18.57% M71 33.00 Repair and installation of machinery and equipment 26.52% 18.57% Capital expenditure (CAPEX) and operational expenditure (OPEX) values are derived based on the technical cost breakdown structure. OPEX and CAPEX shares show the contribution of each technical cost breakdown in the GVA effect. Although Manufacturing of solar PV components has the high leakage rate (75 %), it accounted for the largest share in CAPEX, followed by installation of system and operation and maintanace sector. Leakage To describe the strength of the solar system as a final product and services on the Irish economy, variable leakage rates based on the proportianl of each activity within the economy of Ireland is defined in the model. During supply chain stages, since the factory which provides the equipment is located in Ireland, but manufacturing of PV components accures in China, it is assumed that most of the monetary values spending on production inputs is outside of the Irish borders, thus a leakage rate of 75 % is considered. Then, for the operation stage including all the installation activities, cabling and preparing mounting system, no leakage (0 %) is defined. All the mentioned activities such as engineering designs, installation works, testing, commissioning and consultancy entirely is conducted by domestic factories and labourers (leakage is 0%). Regarding repairs and maintenances of the equipment, it is assumed that machinery equipment such as inverter may need to be replaced every 10 years, and a leakage rate of 50% is defined. Employment factor The deployment of solar technology in Ireland will support commercial activities and jobs. According to the report published by the Irish Solar Energy Association in 2015 (KMPG Ireland, 2015), investment in solar technology can create up to 7,300 jobs per year between 2017 and Local environmental and socio-economic impacts 178 2030. Many of the jobs' opportunities will benefit from the construction sector. Therefore, another indicator to measure the socio-economic impact of the solar PV system is the employment factor. It gives the number of jobs per kWp for solar PV technology in construction, installation, operations and maintenance processes. The data was collected from previous studies and research and the Irish government to simulate and analyse the potential employment benefits in the area of Loop Head. These data are the input to the model to calculate the number and type of the job created as a result of installing solar PV systems with a specific installed capacity and the estimated number of households with installed solar panels. The calculation of the employment factor is described in chapter 8.2.4. 8.2.4. Result By Fitri Wulandari To analyse the socio-economic impacts of a solar PV installation on a residential level, both GVA and jobs were estimated using two different approaches. Welfare – GVA To estimate the economic productivity impact in Ireland from the solar PV industry, this study used an input-output (IO) model using the IO table of Ireland with some simplifications involved. Essentially this part of the study tried to analyse to which business sector in Ireland does the money go to everytime one household installs a solar PV system. This approach allows us to estimate how an increase in demand for solar PV can make an impact in the different sectors of an economy. Installing a solar PV on a household will generate GVA directly through preparation and installation phases and indirectly through the manufacturing phase. In this analysis, deadweight, displacement, and substitution were zero as the value would be too small that it was considered to be insignificant. The following discussion will present in detail the result of the IO model and the different impacts of installing solar PV on a residential level in the Irish economy. Figure 8.23 shows that the economic impact of an investment made when installing solar PV would not be felt uniformly across the sectors involved. The repair and installation of machinery and equipment industry received the most output from the solar PV installation. This comes from the purchase of solar PV panels, the inverter, the battery, etc. Although almost all of the components are purchased abroad, there will still be some contributions to the Irish economy assuming that the household owner will buy the components through an Irish company (that serves as an intermediate). Hence, even when Ireland doesn’t manufacture any of the solar PV components at the time being, there will still be some GVA contributed to the Irish economy Local environmental and socio-economic impacts 179 when purchasing the solar PV components. The next industry with the largest GVA benefit is the electrical installation industry, mainly because all of the money spent during the installation process will stay within the Irish economy, assuming that the solar PV installation process will be done by an Irish corporation, hence zero leakage. Figure 8.23: GVA comparison per industry, for case study III with and without battery Source: Author based on own calculation Figure 8.24 shows that most of the GVA into the Irish economy will come from the initial investment made when installing solar PV. The capital expenditures consist of the purchase of the components and the solar PV installation cost. The operational expenditure consists of the cost of inverter and its installation cost that will only happen once throughout the PV lifecyle. 342.30 250 214.67 45 558.42 418 556.68 557 - 200.00 400.00 600.00 800.00 1,000.00 1,200.00 1,400.00 1,600.00 1,800.00 with battery without battery G V A ( II ,€ ) Repair and installation of machinery and equipment Electrical installation Manufacture of machinery and equipment n.e.c. Manufacture of electrical equipment Manufacture of electronic industrial process control equipment Local environmental and socio-economic impacts 180 Figure 8.24: GVA comparison per type of expenditure, for case study III with and without battery Source: Author based on own calculation In total, the GVA from an installation of a 4.4 kWp solar PV system with battery would be 380 €/kW and the GVA from an installation of a 2.96 kWp solar PV system without battery would be 430 €/kW. Hence, referring to the investment cost mentioned in Chapter 7, it can be concluded that for every one euro invested to install a 4.4 kWp solar PV system with a battery, 30 cents will go to the Ireland economy, whereas for the installation of the 2.96 kWp solar PV system, for every one euro invested 34 cents will go to the Irish economy. Table 8.5 shows the summary of total investment and GVA to the Irish economy based on case study III. Table 8.5: Summary of total investment and GVA of case study III Source: author based on own calculation Total investment (€) Investment (€/kW) GVA (€/kW) With battery (4.4 kWp) 6,449 1465.68 380.5 Without battery (2.96 kWp) 3,707 1252.36 429.3 Another important message here is that, although installing solar PV seems to bring value to the Irish economy, it is important to note that still, more than 60 % of the capital expenditure made within Ireland go outside of Ireland. In short: Ireland is losing money. (SEAI, 2017b) stated that although the country has the opportunities to be involved earlier in the value chain, Ireland is still considered as a technology-taker within the solar PV industry as most of the manufacturing of the solar PV system components happen in China and the know-how in 1,115.4 712.0 556.7 556.7 - 200.0 400.0 600.0 800.0 1,000.0 1,200.0 1,400.0 1,600.0 1,800.0 With battery Without battery G V A ( II ,€ ) OPEX CAPEX Local environmental and socio-economic impacts 181 deploying solar PV at scale is concentrated in other European, North American, and Australasian countries. Job creation – Employment As mentioned in sub-chapter 8.1.3, most of the main components in the solar PV value chain are currently still manufactured in China. In 2019, most of the key raw materials in the solar PV supply chain, such as polysilicon, glass, and aluminium frames were produced in China (IRENA & ILO, 2021). Hence, due to its strong domestic supply of the main components, more than 90 % of the world’s wafer are manufactured in China, along with 78 % of the world’s cell production and 72 % of module output (IRENA & ILO, 2021). Therefore, as there is only very little of the solar PV value chain that happens in Ireland, most of the job creation opportunities will exist in the construction and installation phase, as well as during operation and maintenance. In addition, as in 2018, it was recorded that Ireland’s import dependence on fossil fuel was 67 % (Dineen et al., 2020), most of the jobs lost in the mining and refining sector as a result of lower demand will be incurred abroad hence was not considered further in this study. The following discussion will explore the employment opportunity in terms of job creation from solar PV installation on a residential level in Loop Head through qualitative assessment. As mentioned in sub-chapter 8.2.3, the number of jobs supported by installing solar PV was estimated by using the employment ratios based on literature, but still regional and technology- specific. This number represents the total amount of full-time equivalent jobs created by the solar PV industry. Hence, to calculate the potentials of direct and long-term employment, the key inputs needed are the employment factor and the regional multiplier. Below are the formulas used in this study to calculate the job potential: Equation 8.10: Calculation of energy supply jobs - construction Source: (Rutovitz et al., 2015) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑑𝑢𝑟𝑖𝑛𝑔 𝑐𝑜𝑛𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 = 𝑀𝑊 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 × 𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑗𝑜𝑏 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 Equation 8.11: Calculation of energy supply jobs – O&M Source: (Rutovitz et al., 2015) 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑗𝑜𝑏𝑠 𝑑𝑢𝑟𝑖𝑛𝑔 𝑂&𝑀 = 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 × 𝑂&𝑀 𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙 𝑗𝑜𝑏 𝑚𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 Local environmental and socio-economic impacts 182 The regional job multiplier used in this study is based on the regional job multiplier for the OECD countries which is 1 (Rutovitz et al., 2015). (KMPG Ireland, 2015) estimated the job employment factor for rooftop installation in Ireland is 13 job years/MW, 4.5 job years/MW for ground mount installation, and 0.3 jobs/MW for O&M for the year of 2017. These numbers were then compared with the employment factors from another literature (Rutovitz et al., 2015), where the global employment factor for solar PV construction is 13 job years/MW and 0.7 jobs/MW for O&M. Based on the employment factors for Ireland, the job creation potential for Loop Head was first estimated by assuming that in one year, 1,100 households will install solar PV with capacities as presented in sub-chapter 8.2.3. The resulting job creation opportunities are presented in Table 8.6. Table 8.6: Potential of job creation in Loop Head for 1,100 households Source: own calculation Case study III - 2.96 kWp capacity Case study III - 4.4 kWp capacity 1 household 1,100 households 1 household 1,100 households Roof top installation (job year) 0.04 42 0.1 63 Ground mount installation (job year) 0.013 15 0.02 22 Operations & maintenance (job year) 0.001 1 0.001 1 The result in this study was presented in one job year, which essentially means one job in one year. It is important to note that this doesn’t simply mean one new employment in every year. However, the idea that 1,100 households will install solar PV within only one year period is difficult to imagine seems to be very optimistic. Hence, another way to present this was to assume that every year, 100 households would install solar PV for a period of 11 years. The resulting job creation opportunities for this approach are presented in Table 8.7. Table 8.7: Potential of job creation in Loop Head for 100 households Source: own calculation Case study III - 2.96 kWp capacity Case study III - 4.4 kWp capacity Roof top installation (job year) 4 6 Local environmental and socio-economic impacts 183 Case study III - 2.96 kWp capacity Case study III - 4.4 kWp capacity Ground mount installation (job year) 1 2 Operations & maintenance (job year) 0.1 0.1 Based on this approach, if 100 households in Loop Head would install roof top Solar PV with 2.96 kWp capacity, four people in Loop Head would be employed for at least 11 years and 6 people would be employed if the households install roof top solar PV with 4.4 kWp capacity. For ground mounting installations, one person in Loop Head would be employed for at least 11 years and 2 people would be employed if the households install ground-mounted solar PV with 4.4 kWp capacity. These numbers were then compared with the qualitative assessment done through an interview with Kenneth Thomas O’Connell, one of the engineers who work for a company that offers a solar PV installation service. During the interview, it was noted that to do a ground- mount installation for one household, two workers would be required for one work day. Roof top installation requires more labour, two workers would be required for almost three work days. However, for operational and maintenance, the labour required is so minimal if not zero as solar PV doesn’t require any particular maintenance other than the replacement of inverter that would only happen once throughout the PV lifecycle. Another interview was done with one household that has already installed solar PV in Loop Head, in which the household owner stated that no additional service was required ever since he installed the solar PV. However, it is important to note that the employment factor used in this study is the employment factor in 2017. This study did not further consider the learning adjustment rates or the declie factors for the solar PV technology that would take into account the reduction in employment per MW as the solar PV technology improves (Rutovitz et al., 2015). Furthermore, numerous changes can happen in the solar PV insutry over a period of 11 years. More people can be employed earlier in the value chain as Ireland has the capacity to capture the opportunity from the growing solar PV market. (SEAI, 2017b) identified that Ireland has the capability to play bigger role in the design and optimisation of solar PV and building integrated PV. Furthermore, (SEAI, 2017b) also stated that the country has potential to invent new solar PV technology or applications related to storage and monitoring, in which University of Limerick was mentioned as one of the institutions with know-how in electrochemistry. Local environmental and socio-economic impacts 184 In terms of occupation, (IRENA & ILO, 2021) provides an estimation of the proportion of human resource requirements in a Solar PV installation project, as presented in Figure 8.25. Figure 8.25: Human resource requirements for workers in solar PV Source: author based on (IRENA & ILO, 2021) According to (IRENA & ILO, 2021), 64 % of the human resources required during the installation of solar PV would be workers with lower certifications. The type of occupations could be electricians or roofers specializing in solar with skills that can be obtained through formal training and will work mostly during the construction phase. The second largest proportion, with 31 % of the human resource required, would be STEM (Science, Technology, Engineering, and Mathematics) professionals. These would be highly skilled workers in professional and managerial positions. The type of occupation would be system engineers who would be in charge of solar system design and site assessment for shadow and solar radiation. In general, (IRENA & ILO, 2021) stated that solar PV is the largest driver of job growth in the renewable energy sector, mainly because compared with the other renewable energy technologies, solar PV draws the most investors and has greater labour intensity. Hence, there is no doubt that solar PV installation on the residential level in Loop Head will lead to more job creation and employment opportunities for the community. (Stainforth, 2021) stated that one of the key factors that enable the community to fully harness the economic opportunities through renewable energy projects is the availability of local skilled labour. However, currently, there is a particular problem in Loop Head where many young people and potentially skilled workers move out. This needs to be addressed through education, skills, training, and retraining. An integrated approach between labour requirements in terms of education and skills needed in the solar PV sector and the gap in between needs Lower certification 64% STEM professionals 31% Non-STEM professionals 4% Administrative 1% Local environmental and socio-economic impacts 185 to be addressed. This will also be further discussed in Chapter 9 for the opportunity within a social enterprise, focusing on different skill delivery pathways. 8.2.5. Critical discussion By Saleheh Rahimi The model was created based on some assumptions such as leakage which needs to be more precise to achieve more accurate results. Defining different scenarios based on high or low leakage also provides a supplement comparison to analyse the accuracy of the result. According to (SEAI, 2017b), the solar market has a total potential value of around €42-€216 million per year, thus solar technology is a fast-growing market in Ireland.The data for calculating the GVA multiplier for socioeconomic impacts derived from the references provided in the year 2018 (The Ireland Input-Output Table derived from OECD in 2018) so updated data was not available for 2022, however, the data for calculating the cost for CAPEX and OPEX was collected from market price resources in 2022. This disparity may have overlooked recent changes in the dynamics of both the solar PV industry and the Irish economy. In addition, to define the proper SIC class for each category, there was some uncertainties and doubts to selecting the correct and appropriate category associated with each component. For instance, for a smart meter, the recognition between “Manufacture of electronic industrial process control equipment” and “Manufacture of electrical equipment” and also other categories defined in the SIC list was not so specific and clear. Thus, it was attempted to select the most appropriate SIC category for the individual category. Ideally, more technical and economic analysis needs to be investigated. Besides, the transportation expenditure has not been addressed as a separate cost in the technical breakdown structure. it is already included in the final cost of products which needs to be defined in more detail but due to uncertain data, a general estimation was identified. Although employment creation could be calculated through the same approach as the GVA model from the Input-Output table, due to lack of sufficient data and limited time available, another approach was performed to estimate the job creation factor depending on qualitative data and based on literature review (for Ireland as a whole, not Loop Head). Alternatively, it would have been better to accurately calculate the job effects resulting from implementing solar PV systems in Loop Head using data only for Loop Head. Next step to be taken for house owners 186 9. Next step to be taken for house owners By Max Andriamanalina After the analyses have been completed on the 3 Households case study and recommendations have been made, the next step is to find out how to implement the results of the study. Figure 9.1 below shows what could be the next step of the IC 2022 project. Figure 9.1: Way forward LEAP project Source: Author Information about grants for both solar electricity and building retrofitting are stated in Chapter 4. More in-depth information about grants is found on the SEAI website. Additionally, the SEAI website has information about where to find solar PV system installers, energy auditors as well as BER assessors. Another alternative for skilled labour as described in sub-chapter 9.1 is from capacity building of the community which could lead to the creation of social enterprise locally. There are numbers of social enterprise development funds in Ireland that the Loop Head community can also benefit from. Rethink Ireland, for example, provide € 9 million in cash grants and business support to 71 social enterprises in every county (Rethink Ireland, 2022). An initiative of creating a social enterprise in Loop Head could be proposed via the LT LEAP group. Next step to be taken for house owners 187 9.1. Community energy capacity building The Loop Head community is facing several challenges. One of the challenges is emigration. According to (Euromonitor International, 2016). Loop Head has experienced emigration during the recent economic growth period of Ireland which led to a significant decrease in the size of the population. According to (EEM IC, 2020) for instance, the population of Kilkee, the main village of Loop Head, decreased by 30% in a period of 10 years. Another challenge in Loop Head is the limited job opportunities within the peninsula, which is also a factor driving the migration of the residents to other locations. The community members also stated that there is not enough skilled labour in Loop Head who can assist them on matters such as installation of a solar PV system and building retrofitting for their homes. Thus, they are compelled to look for assistance from outside of Loop Head, which sometimes can be challenging. To address these challenges, the community members should have access to training to gain technical skills that allow them to work on the installation by themselves. After the training, certificates should be delivered to the community to enable them to apply for job opportunities within the same field. In this regard, there should be a collaboration between trade associations and the Loop Head community. The association can offer training related to solar PV installation and building retrofitting to the community. This will address the issue related to the lack of skilled labour in the local area as described in sub-chapter 8.2.4. This will also create job opportunities for the community and subsequently attract residents to come back to Loop Head. Overall, socio-economic benefits could be induced to the community from capacity building. 9.2. Community ownership of renewable energy projects According to (IRENA, 2020), “Community-ownership structures, in the context of the global energy transition and the decentralization of power systems, refer to the collective ownership and management of energy-related assets, usually distributed energy resources”. Energy community is a movement that brings the community together into the heart of the energy system. Energy community harnesses the power of renewable resources and provides the local community with clean and sustainable energy. It has recently experienced rapid growth given the opportunities it offers in producing energy from renewable sources and in reducing dependence on fossil fuels. Energy community is one of the tools used by the EU to drive the green transition regionally (Boulanger et al., 2021). Local energy communities involve citizens, public and private actors who both produce, sell and consume sustainable energy. Sustainable energy is then shared within the community and additional energy generated is sold to the grid. An example of community energy is the collective purchase of solar modules by the community members living in one area or a group of community members who collectively invest in a wind Next step to be taken for house owners 188 turbine and sell the output energy to the consumers living nearby or to the grid. (Gall J., 2018) reports that community-ownership projects vary in size but are often between 5 kW and 5 MW, depending on where they are being implemented. There are different energy community ownership models according to (IRENA, 2020) including community-owned electricity generation plants such as solar PV plants, wind power plants and biomass plants, community- owned district heating systems and community energy storage systems. Figure 9.2 depicts an energy system based on the community-ownership business model. Figure 9.2: S Schematic of energy system based on the community-ownership business model Source: (IRENA, 2020) (Caramizaru, 2020) reports that 21 % of the solar installed in Europe could be owned by energy communities by 2030 and in the nine European countries that he investigated, there are 3,500 renewable community energy projects. Those projects, as shown in Figure 9.3 below, are mostly based in North-Western Europe. Community energy is not something new in Ireland, 500 communities are part of the Sustainable Energy Authority of Ireland’s (SEAI’s) community energy network and more than 25,000 citizens are engaged with it (SEAI, 2017d). An example of this is the Templederry Community Wind Farm in Co Tipperary which is now supplying electricity to homes and businesses all over Ireland and generating local jobs in rural communities. Community energy can be a potential opportunities for the community in Loop Head given the socio-economic benefit described in sub-chapter 9.5 it offers. Next step to be taken for house owners 189 Figure 9.3: Number of community energy initiatives from the nine European countries Source: (Caramizaru, 2020) 9.3. Solar community Solar energy is one of the cheapest forms of clean energy. Its cost has steadily declined due to the fall in the cost of solar panels. This cost decline has turned solar into a booming industry in recent years. Community solar, which is a term used to describe solar farms owned by the members of the community, is a fast-growing part of the solar industry. According to (SEAI, 2021a), the US community solar market has grown from 66 MW to 1.8 GW since 2014, and currently, 25 states have at least one functional community solar project. Concerning Europe, (Caramizaru, 2020) reports that solar energy has the highest share of energy community projects in Europe. As shown is in Figure 9.4 below, solar represents 38 % of the total community energy projects in Europe. Figure 9.4: Energy community share per technology in Europe Source: : (Caramizaru, 2020) Next step to be taken for house owners 190 9.4. Community solar participation models There are two community solar participation models: ownership-based and subscription-based community solar (Etelson E., 2022). In the ownership-based model, the participants own a share in the project (own some of the solar panels for instance), then receive credit for all the electricity their share generates. In subscription-based community solar, on the other hand, the community members do not own the solar farm, it is owned by a solar developer. However, each of the community members can buy part of the electricity generated from the solar farm at a lower price than the grid rate. For both the subscription-based and the ownership-based models, the electricity produced at the farm is injected into the national grid. In the former, the members of the community solar (or subscribers) continue to receive their electricity bill from the utility. The share of electricity from the solar farm will then appear as a credit in the bill, which will lower the monthly electricity bill of the subscribers. Additionally, the subscriber will also receive a separate bill from the solar developer (Etelson E., 2022). During summer, when the solar panels generate more electricity, the subscribers will earn more credits. The surplus of the credit earned during the previous months will be added to the credit in the following months. In the latter, i.e., the case of the ownership-based model, the difference is that the community members do not pay any money for the electricity from the solar farm. However, if their electricity consumption is higher than the electricity produced from their share, they have to pay for the extra electricity to the utility. Figure 9.5 illustrates the electricity billing in community solar. Figure 9.5: Community solar billing system Source: (Etelson E., 2022) Next step to be taken for house owners 191 9.5. Benefit of solar community According to (Melius et al., 2013) only 22 % to 27 % of the residential rooftop area is suitable for hosting an on-site PV system. There are many reasons for which rooftop is not suitable for solar panels such as the roof size being too small to accommodate solar panels, the roof being too shaded, or restrictions from the landlords. Community solar is the solution for those who cannot have a traditional rooftop solar system in their own home. Another benefit of community solar is scalability, contrary to a solar rooftop system in which roof area is limited, community solar has the advantage of expanding the system, which makes it more flexible. Additionally, community solar is an approach that allows everybody to participate in the benefits of solar (Maria G., 2019) It facilitates the process for low-income community members who do not have the necessary financial means to install a rooftop solar system, to benefit from solar energy and reduce their carbon footprint as described in sub-chapter 8.1.5 and 8.2.4. The goal of community solar is to bring solar to every household in the community. With community solar, a wide range of the community is engaged in planning, operating and maintaining the clean energy solution. To bring solar to all households in the Loop Head community is feasible through a solar community project. From the economic point of view, community solar projects create local job opportunities such as during the construction and installation process of the PV system. After capacity building in collaboration with local institutions, as mentioned earlier, the community members can occupy these job opportunities themselves. Therefore, in response to the limited employment in Loop Head, community solar could be one of the solutions. But for the community solar to work, the community should be aware of the benefit it can bring so that the community is willing to get involved in it. At a household level, Loop head community can also benefit from peer to peer (P2P) trading model as shown in Figure 9.6 below. Peer to peer electricity trading is a business model where households in a community that own rooftop solar home system can trade electricity among themselves at a desired price (IRENA, 2020). The benefit of P2P is that a household can sell the surplus energy from their solar PV system but also can buy surplus of energy at lower price compared to grid price from other households. With P2P, all households can benefit from the energy trade. Conclusion and recommendation 192 Figure 9.6: Structure of P2P electricity trading model Source: (IRENA, 2020) 10. Conclusion and recommendation Hiram Masese IC 2022 team received a warm welcome back in Loop Head, especially after their predecessors had conducted IC 2021 virtually due to COVID-19 travelling restrictions at the time of their project execution. The community’s interest in the project and the willingness to offer support was evident from when the Flensburg team arrived in Loop Head. On their part, the IC 2022 team planned a number of activities, such as solar lab demonstrations, to continuously engaging with the community members. This resulted in a highly hands-on project. This study focused on the residential sector by building on previous years’ work. At a community level, the findings indicated that a short-term strategy is needed for capacity building where a dedicated group of energy enthusiasts could be trained further and in turn, share the knowledge with the larger community. This approach will benefit the house owners with basic knowledge on efficient use of energy as well as appraise them on a roadmap towards the use of sustainable energy in their homes. The residents will enhance their knowledge on how energy matters and the energy-political framework that defines the space for action, and learn to differentiate facts from propaganda. The formulated group will be energy champions offering energy-related services and advice to the community. In the long- run, the group could also offer support on energy-related entrepreneurial ventures within the community. The community could aim to utilize the expertise in designing, procuring and installing Solar PV systems in the region. Provision of such services locally will ensure potential savings for residents who currently pay expensive costs to import labour and, in turn, economic growth for Loop Head when the savings are invested in other enterprises. Further, their training and facilitation services could extend to neighbouring communities hence earning Loop Head Conclusion and recommendation 193 residents some additional income. Borrowing from community-level energy schemes from other regions, IC 2022 recommended that Loop Head members could consider trading energy among themselves in their quest to become energy sufficient, in a peer-to-peer energy network. At a household level, the study revealed that most of the heat losses from households occurred through poorly insulated walls and roofs and that retrofitting of poorly insulated roofs should particularly be prioritized. Whereas the recommendations on retrofits are dependent on the results of a detailed energy audit of the building, the study indicated that the replacement of windows and doors, as part of the building’s envelope, generally have the least impact on energy savings. Additionally, the installation of efficient heat pumps and solar PV systems after envelope retrofits was noted to significantly improve the energy efficiency and, hence the comfort levels of homes as well as improved living standards, by creating warm and comfortable homes. The interaction with the local residents revealed that a significant number operated their heat systems twice a day, in morning and evening cycles. However, this study indicated that such a practice leads to rapid cooling occurring in buildings which necessitates significantly higher energy for each heating cycle, as well as secondary heating from electricity and solid fuels. In contrast, IC 2022 estimated that lower energy would be needed in the case of a 24-hour thermostat-controlled system in households. IC 2022 highlighted a roadmap towards sustainable energy in residential buildings at Loop Head to guide the residents in the projects. The residents were advised to: i. Seek support from an energy expert in understanding their consumption pattern as a crucial first step when planning for energy efficiency measures in their household. ii. Eliminate unnecessary electricity demand. This, together with knowledge of their consumption patterns, helps in preventing either over expenditure or under expenditure in investment decisions as the capacity of either solar PV or space heating units is dependent on the energy demand. iii. Retrofit their building to eliminate heat losses in cases of poor insulation. However, IC 2022 advised that an individual that needs to carry out all home energy upgrades at once, an individual doesn't need to carry out all upgrades at once. On the contrary, building efficiency can be improved through a series of steps towards the desired state. Conclusion and recommendation 194 iv. Hold conversations with individuals who have already installed the desired energy technology to gain a basic understanding of how the system works. Residents were encouraged to learn from the 1.28 kWp solar PV system being installed at Keane’s Beer Garden, at the time of writing this report. v. Check on available grants they were eligible for which may reduce the capital expenditure of the intended projects. To be eligible for grants, one has to comply with the set standards which require that technology installations must be conducted by installers certified by SEAI. vi. Seek professional support in the purchasing and installation of the energy system desired. The 3 sampled households under the IC 2022 study were approximated to be representative of residential buildings in Loop Head. The project was estimated to give a general overview of the measures and costs which would be incurred by comparable household types in Loop Head. On the environmental impact assessment, the study of the life cycle from raw materials and manufacturing, transport and use of solar PV components revealed that the use of solar energy is associated with CO2 emissions, especially from the manufacturing phase of the solar modules. However, the findings indicated that this share is significantly lower as compared to the emissions resulting from the current Irish electricity mix. Consequently, the level of emissions resulting from the use of solar energy was considered to be manageable. Additionally, the results from the land use analysis suggest that the use of solar energy has a lower negative impact on land areas than that caused by the current Irish electricity grid. Since the majority of residents in Loop Head are farmers while others are employed in the agricultural sector, the adoption of solar energy implies lesser constraints to the farming sector in Loop Head. The socio-economic impact assessment of the cases studied in this research showed positive indicators. For instance, installing solar PV on a household level would benefit both the Irish economy (through the generation of gross value added) as well as support job creation within Ireland. The findings revealed, that if one household in Loop Head installed a solar PV system, for every one euro spent by the household owner the Irish economy would gain approximately 30 cents in gross value added. 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Annex Annex 1: Stakeholder identification S/N Stakeholder Stake in the project 1 Community Members Target audience most affected by the project 2 Flensburg Students Team Involved in the research and the daily execution of the project 3 Astoneco Management Facilitated the development project of the IC within Loop Head Community 4 Loop Head Together Programme Community-based group spearheading the overall development projects in Loop Head 5 F4 Energy Limited Supplied solar panels used in the demonstration workshops and later installed at Keane's Beer Garden. They also offered technical input for conducted solar workshops. 6 Building Energy Rating Specialist Offered technical input for households' energy assessment 7 Local Community Member with installed solar PV system Offered input on benefits realized and challenges experienced from using the system during organized workshop 8 Sustainable Energy Authority of Ireland (SEA) Source of information on regulation and policies of the energy sector in Ireland 9 Department of Immigration Clearance for travel into Ireland 10 Department of Health Offered updates on local Corona pandemic protocols 11 Noel O'shea Coaches Local transport services Annex 209 S/N Stakeholder Stake in the project 12 Owners of sampled households for the case study Offered their houses for case study purposes 13 Kilkee Bay Hotel Owner Conference facility used for the workshops 14 Carmody's Bar Owner Social Meet up venue for community interaction 15 Keane's Bar Owner Social Meet up venue for community interaction 16 Other Business owners Assisted in the dissemination of information through display of project activities' posters 17 Community Social Media Management Team Assisted in the dissemination of information through uploads in the available social media platforms 18 Carrigaholt National School Principal Approval for school visit to conduct energy awareness workshop 19 Accommodation Premises Landlords Provided renting pace for accommodation of the Flensburg team on the Loop 20 North Clare Municipal visiting representatives Benchmarking with ongoing project and knowledge sharing on progress in the neighboring community. Annex 210 Annex 2: Stakeholders mapping Stakeholders High Influence Low Influence Manage Inform High Interest -Community Members -Loop Head Together Programme -Flensburg University Team -Astoneco Management -Owners of sampled households for the case study -F4 Energy Limited -Building Energy Rating Specialist -Local Community Member with installed solar PV system -Kilkee Bay Hotel Owner Bar Owner -Keane's Bar Owner -Other Business Owners -Community Social Media Management Team -Carrigaholt National School Principal Satisfy Monitor Low Interest -Sustainable Energy Authority of Ireland (SEA) -Department of Immigration -Department of Health -Accommodation premises Landlords -Noel O'shea Coaches -North Clare Municipal visiting representative Annex 211 Annex 3: Community engagement activities roadmap Annex 212 Annex 4: Pre-survey questionnaire Annex 213 Annex 214 Annex 215 Annex 216 Annex 217 Annex 218 Annex 5: Music gala Annex 219 Annex 6: Solar deep dive Annex 220 Annex 7: Solar deep dive arising questions S/N Question Scope Check 1 Do I need to have certified installer of solar in order to receive grants? Part of the IC2022 scope of work 2 How can I insulate cavity walls and what grants are available for this? Part of the IC2022 scope of work 3 Should I go for solar or for a wind? Wind is strong in the region will it break the wind turbine? Out of IC2022 scope 4 What are the social and economic benefit of installing solar PV? Part of the IC2022 scope of work 5 How can change in behavior affect the household electricity consumption? Part of the IC2022 scope of work 6 What are the changes that can be done daily behavior that can lower the electricity bill? Part of the IC2022 scope of work 7 How can installing solar PV benefit our community in Loop Head? Part of the IC2022 scope of work 8 How can I know the BER rating of my house now? Part of the IC2022 scope of work 9 Are there grants I am eligible for to improve the BER rating of my house? Part of the IC2022 scope of work 10 Who should be contracted for house retrofitting? Part of the IC2022 scope of work 11 What capacity of solar PV should I go for? Part of the IC2022 scope of work 12 How much will solar PV installation cost me? Part of the IC2022 scope of work Annex 221 Annex 8: Workshop 2 Annex 222 Annex 9: DEAP for New – Final and existing Home Survey Form Annex 223 Annex 224 Annex 225 Source: Author based on Johnny Redmond Ref Room Open- Notes Front/Rear H L ings Lighting Constr. etc. W1 W2 W3 Total Area Floor Level Orient.. Over- Shadin Flue Chimn Fans Vents Address: Window Schedule Dimensions Annex 10: Excel Data collection sheet Annex 226 Thermocamara configurations Data was An infrared camera FLIR b40 with the instrument's high accuracy of 2%, thermal sensitivity of 0.1ºC and boasting a temperature range of between -20ºC to +120ºC was used to survey the case study buildings. Annex 11: FLIR B40 Technical Specifications Source: Imaging and optical data Field of view (FOV) 25° × 25° Thermal sensitivity/NETD < 0.1°C (0.25°C) / 100mK Image frequency 9 Hz Detector data Detector type Uncooled microbolometer Spectral range 7.5–13 μm IR resolution 120 x 120 Measurement Object temperature range -20°C to +120°C Accuracy ±2% of reading Area (Max/Min) Isotherm (above/below selected temperature interval) Emissivity Emissivity Table 0.1 to 1.0 adjustable or selected from list of materials Annex 12: Link to thermographer inspection report – case study I, II, and III https://eufbox.uni-flensburg.de/index.php/s/6CAxZsDKcSqbWzD/download Annex 13: Cost estimation for building retrofiting in Ireland Description (Euro/m2) Unit price (Euro) Pitch Roof insulation – 300 mm Mineral Wool 22.63 Flat Roof insulation - HARDROCK Underlay slab 150mm 41.65 External wall insulation - 100 mm Gray EPS 167.67 External wall insulation - 200 mm Gray EPS 181.37 Floor Insulation – 125 mm White EPS 256 Windows (Triple glaze, U-Value 1.4) 815 GRP Composite doors, U-Value 0.8 965 *All costs include the cost of material plus the installation cost. https://eufbox.uni-flensburg.de/index.php/s/6CAxZsDKcSqbWzD/download Annex 227 *The costs are subjected to 13.5% VAT Annex 14: AWHP Annex 228 Annex 15: AWHP Auxilary Equipment Annex 229 Annex 230 Annex 16: Space Heating and DHW Excel Model Interface Annex 17: Radiators Annex 231 Annex 18: Existing Geothermal Heat Pump Commissioning Sheet Annex 19: Solar Panel Annex 232 Annex 233 Annex 234 Annex 20: Solis Mini Inverter Annex 235 Annex 21: Solis Single Phase Dual MPPT Annex 236 Annex 22: US2000B PYLONTECH Annex 237 Annex 23: US3000 Pylontech Annex 238 Annex 24: Triple Power Annex 239 Annex 25: Hot water power diverters Annex 26: Solar PV system with 2.2 kWp capacity (bill of quantity) Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 6 174.00 1044.00 Inverter – Solis Dual MPPT, 2.5 kW Nos. 1 511.00 511.00 Battery- Pylontech 3.6 kwh, Lithium Ion Nos. 1 1052.00 1052.00 BOS Cost Nos. 1 681.00 681.00 The total cost of Material (Including 23% VAT) Annex 240 Cost of material Item description with specifications UOM Quantity Unit Price Total price Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 4 1 1 340.00 Electrical wiring 4 - 2 200.00 Transport 125.00 The total cost of installation 665.00 The total cost of the PV system 3953 Annex 27: Solar PV system with 1.48 kWp capacity Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 4 174.00 696.00 Inverter – Solis Mini 2000 4G, 1.5kW Nos. 1 375 375 Battery- Pylontech 2.4 kwh, Lithium Ion Nos. 1 1064 1064 Firefighter safety switch – 2pole Nos. 1 200.00 200.00 AC isolator – 20/25 A per pole Nos. 1 40.00 40.00 DC isolator 2 Pole, 1 string Nos. 3 100.00 300.00 Wiring cable – 4 sq. mm solar cable Meter 100 1.10 110.00 Cable connector (MC4 solar connector) Pair 2 6.50 13.00 Annex 241 Cost of material Item description with specifications UOM Quantity Unit Price Total price Energy meter – single phase Nos. 1 23.00 23.00 Mounting system – ground mount Set. 1 223.00 223.00 System labeling sticker Nos. 1 10.00 10.00 The total cost of Material (Including 23% VAT) 3054 Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 4 1 1 340.00 Electrical wiring 4 - 2 200.00 Transport 125.00 The total cost of installation 665.00 The total cost of the PV system 3719 Annex 28: Solar PV system with 2.96 kWp capacity Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 8 174.00 1392.00 Inverter – Solis Mini 2000 4G, 3kW Nos. 1 507.00 507.00 Firefighter safety switch – 2pole Nos. 1 200.00 200.00 AC isolator – 20/25 A per pole Nos. 1 40.00 40.00 Annex 242 Cost of material Item description with specifications UOM Quantity Unit Price Total price DC isolator 2 Pole, 1 string Nos. 2 100.00 200.00 Wiring cable – 4 sq. mm solar cable Meter 100 1.10 110.00 Cable connector (MC4 solar connector) Pair 2 6.50 13.00 Energy meter – single phase Nos. 1 23.00 23.00 Mounting system – ground mount Set. 1 412.00 412.00 System labeling sticker Nos. 1 10.00 10.00 The total cost of Material (Including 23% VAT) 2907.00 Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 5 1 1 425.00 Electrical wiring 5 - 2 250.00 Transport 125.00 The total cost of installation 800.00 The total cost of the PV system 3707.00 Annex 29: Solar PV system with 4.4 kWp capacity Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 12 174.00 2088.00 Annex 243 Cost of material Item description with specifications UOM Quantity Unit Price Total price Inverter –Solis Dual MPPT 3.6 kW Nos. 1 612.00 612.00 Battery- Pylontech 3.6 kwh, Lithium Ion Nos. 1 1422.00 1422.00 Firefighter safety switch – 2pole Nos. 1 200.00 200.00 AC isolator – 20/25 A per pole Nos. 1 40.00 40.00 DC isolator 2 Pole, 1 string Nos. 3 100.00 300.00 Wiring cable – 4 sq. mm solar cable Meter 100 1.10 110.00 Cable connector (MC4 solar connector) Pair 4 6.50 26.00 Energy meter – single phase Nos. 1 23.00 23.00 Mounting system – ground mount Set. 1 548.00 548.00 System labeling sticker Nos. 1 10.00 10.00 The total cost of Material (Including 23% VAT) 5379.00 Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 7 1 1 595.00 Electrical wiring 7 - 2 350.00 Transport 125.00 The total cost of installation 1070.00 Annex 244 Annex 30: Solar PV system with 5.18 kWp capacity without battery storage Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 14 174.00 2436.00 Inverter – Solis Dual PPT, 6kW Nos. 1 798.00 798.00 Firefighter safety switch – 2pole Nos. 1 200.00 200.00 AC isolator – 20/25 A per pole Nos. 1 40.00 40.00 DC isolator 2 Pole, 1 string Nos. 3 100.00 200.00 Wiring cable – 4 sq. mm solar cable Meter 100 1.10 110.00 Cable connector (MC4 solar connector) Pair 2 6.50 13.00 Energy meter – single phase Nos. 1 23.00 23.00 Mounting system – ground mount Set. 1 868.00 868.00 System labelling sticker Nos. 1 10.00 10.00 The total cost of Material (Including 23% VAT) 7700.00 Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 15 1 1 1275.00 Electrical wiring 13 - 2 650.00 Transport 125.00 The total cost of installation 2050.00 The total cost of the PV system 6470.00 Annex 245 Annex 31: Solar PV system with 5.92 kWp capacity with 3.6kWh battery storage Cost of material Item description with specifications UOM Quantity Unit Price Total price Solar PV panel – Longi 370W Nos. 16 174.00 2784.00 Inverter – Solis Dual MPPT ,6kW Nos. 1 798.00 798.00 Battery- Pylontech 2.4 kwh, Lithium Ion Nos. 1 1052.00 1052.00 Firefighter safety switch – 2pole Nos. 1 200.00 200.00 AC isolator – 20/25 A per pole Nos. 1 40.00 40.00 DC isolator 2 Pole, 1 string Nos. 3 100.00 300.00 Wiring cable – 4 sq. mm solar cable Meter 100 1.10 110.00 Cable connector (MC4 solar connector) Pair 2 6.50 13.00 Energy meter – single phase Nos. 1 23.00 23.00 Mounting system – ground mount Set. 1 1295.00 1295.00 System labeling sticker Nos. 1 10.00 10.00 The total cost of Material (Including 23% VAT) 6625.00 Cost of installation (Labour & transport) Work description Nos. of hours Nos. of Electrician: 60.00€/hour Nos. of Normal labour: 25€/hour Total cost € Installation of solar PV system 15 1 1 1275.00 Electrical wiring 13 - 2 650.00 Transport 125.00 The total cost of installation 2050.00 Annex 246 Cost of material Item description with specifications UOM Quantity Unit Price Total price The total cost of the PV system 8675.00 List of abbreviations List of tables List of figures List of equation Annex Executive summary 1. Introduction 1.1. Background 1.2. Scope and objectives 1.2.1. Scope of work 1.2.2. Objectives 2. Community engagement 2.1. Stakeholders’ identification 2.2. Communication approach 2.3. Conducted workshops 2.3.1. Monitoring and evaluation 2.4. Challenges and recommendation 3. Case study approach to energy efficiency and technologies 3.1. Common definitions used throughout the energy assessments and case studies 3.2. Energy assessment of residential buildings Phase 1: Preliminary assessment Phase 2: Detailed energy assessment Phase 3: Analysis and calculation 3.2.1. Cost analysis 3.3. Solar PV-Heat Pump System Sizing and Integration 4. Energy solutions for residential buildings 4.1. Types of wall and insulation 4.1.1. External wall insulation 4.1.2. Internal wall insulation 4.1.3. Cavity wall insulation 4.2. Types of roofs and insulation 4.2.1. Shed roof insulation 4.2.2. Pitched roof 4.2.3. Mansard roof 4.3. Floor insulation 4.4. Types of windows 4.1. Heating technologies 4.1.1. Brine-to-Water Heat Pumps Technology 4.1.2. Air-to-Air Heat Pumps Technology 4.1.3. Air-to-Water Heat Pumps (AWHP) technology 4.2. Heat emitters 4.2.1. Steel radiators 4.2.2. Low-temperature fan assisted radiators 4.2.3. Underfloor heating 4.3. Solar PV 4.3.1. Introduction to solar technology 4.3.2. Factors Affecting Solar Energy Generation PV Module Orientation and Tilt Angle Solar Irradiance Sun Path Temperature Precipitation Shading Soiling 4.4. Solar PV System integration 4.4.1. Battery storage system 4.4.2. Store energy as hot water 4.4.3. Grid connection 4.5. Solar PV system component specifications 4.5.1. Solar PV panels 4.5.2. Single-phase Inverter 4.5.3. DC Battery 4.5.4. Mounting system 4.5.5. Balance of system components 4.5.6. Hot water diverter 4.6. SEAI grants 4.6.1. Home energy upgrade 4.6.2. Heat pump grant 4.6.3. Grants for Solar Electricity Planning Permission required for Grants Microgeneration Support Scheme 5. Case study I 5.1. Status quo 5.1.1. Building Envelope 5.1.2. Electricity usage pattern 5.1.3. BER Rating 5.2. Identified energy-saving measures 5.2.1. Building envelope 5.2.2. Cost analysis of building retrofit 5.3. Domestic Hot Water (DHW) and Space Heating Supply 5.3.1. Space Heating 5.3.2. The Space Heating and DHW Excel Model 5.3.3. Space Heating and DHW economic analysis 5.3.4. Domestic Hot Water Heat Demand excel model update 5.4. Solar PV 5.4.1. Residential Demand 5.4.2. System Design 5.5. System integration 5.5.1. Domestic hot water PV system considering electrical demand from heat pump 5.5.2. Cost-effectiveness analysis 5.6. Summarized key findings 6. Case study II 6.1. Status quo 6.1.1. Building Envelope 6.1.2. Electricity usage pattern 6.1.3. BER Rating 6.2. Identified energy saving measures 6.2.1. Building envelope 6.2.2. Cost analysis of Building retrofit 6.3. Existing Space Heating and DHW System 6.3.1. Existing Space Heating and DHW System Analysis 6.4. Solar PV system design 6.4.1. Residential electricity demand 6.4.2. Solar electricity generation 6.4.3. Solar PV System Design 6.4.4. Cost-effectiveness analysis 6.5. Summarized key findings 7. Case study III 7.1. Status quo 7.1.1. Building Envelope 7.1.2. Energy usage pattern 7.1.3. BER Rating 7.2. Identified energy saving measures 7.2.1. Building envelope 7.2.2. Cost analysis of Building retrofit 7.3. Solar PV 7.3.1. Residential Electricity Demand 7.3.2. Solar PV System Design Using Homer Pro Software: 7.3.3. Cost effectiveness analysis 7.4. Summarized key findings 8. Local environmental and socio-economic impacts 8.1. Life cycle assessment 8.1.1. Goal and scope 8.1.2. Methodology Limitation 8.1.3. Life cycle inventory analysis PV modules Balance of System Electricity requirements 8.1.4. Life cycle impact assessment Climate Change Land use Acidification 1.1.1. 8.1.5. Data interpretation Cumulative Energy Demand (CED) Energy Pay Back Time (EPBT) Energy Return on Investment (EROI) Climate change potential 8.1.6. Critical discussion 8.2. Socio-economic impact assessment 8.2.1. Introduction 8.2.2. Methodology 8.2.3. Data collection Gross Value added (GVA) Leontief methodology Leakage Employment factor 8.2.4. Result Welfare – GVA Job creation – Employment 8.2.5. Critical discussion 9. Next step to be taken for house owners 9.1. Community energy capacity building 9.2. Community ownership of renewable energy projects 9.3. Solar community 9.4. Community solar participation models 9.5. Benefit of solar community 10. Conclusion and recommendation 11. References 12. Annex

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matthias.schmelzer@uni-flensburg.de Web: uni-flensburg.de/nec ORCID iD: 0000-0003-3987-1930 Academic positions 1 Academic formation 2 Non-academic positions and activities 3 Research projects 3 Awards, grants [...] uni-flensburg.de https://www.uni-flensburg.de/nec https://orcid.org/0000-0003-3987-1930 CV Matthias Schmelzer – 2025 2 2023 University of Vienna Teaching position in Master program “International Politics

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• Matthias Schmelzer

List of Authors: Assem Tarhini Chioma Ihuoma Adiele Geetika Chauhan Ghalia Taef Jahanzeb Tariq Jorge Alberto Caceres Viana Kathia Raquel Rocha Rodrigues Lima Verdugo Angel Ricardo Mahdi Omid Mohammad Mahesh Shrestha Mohammed Abi Afthab Olikathodi Namrata Vijay Mhaddolkar Richard Sseruwagi Sonam Tobden Valois Rodriguez Manuel Fernando Vida Khajavi. Supervised by: Prof. Dr. Bernd Moeller, Dipl.-Soz. Dorsi Germann, Dipl.-Ing. Wulf Christian Boie, M.Eng. Abalo Irene Jerry Fabiano. ii Disclaimer Although the contents were reviewed several times before being part of this report, the accuracy of the results cannot be guaranteed. The University of Flensburg as well as the authors of this document have no legal responsibility in case of any errors, omissions or misleading statements. Therefore, we recommend that expert opinion of the relevant topics should be sought before using any data presented in this report. iii Acknowledgements We recognize and thank the Deutscher Akademischer Austausch Dienst (DAAD): German Academic Exchange Service, for their financial support towards making the International Class 2019 a reality. We express our sincere gratitude to the community members in Unst Shetland Island: the participants in the stakeholder consultation meetings, the pupils and teachers of Balta Sound Junior High School, the management of Saxa Vord Resort; for making our stay on the island worthwhile, the Shetland Island Council: for providing us with necessary information to aid our pre-assessment study, and the hospitality of both the management at Nordalea Care Centre and Unst Leisure Centre, certainly one could consider aging there. We would also like to acknowledge the continuous support and guidance from the lecturers of the Energy and Environmental Management Department in the University of Flensburg: Prof. Dr. Bernd Möller, Dipl.-Ing. Wulf Boie, Dipl.-Soz. Dorsi Germann, and research assistant: M.Eng. Abalo Irene Jerry Fabiano. The International Class 2019 would not be complete without recognizing; Dr. Daniel Aklil (Ph.D.), Mrs. Elizabeth Johnson and Dr. Ross Gazey (Ph.D.) of the Pure Energy Centre, and the Unst Partnership for providing us with the case study area of Unst and supporting us with required information for analysis. iv Executive Summary This is a pre-feasibility study of incorporating renewable energy to provide building heat demand and simultaneously establish production of hydrogen and its derivates, on the island of Unst, in Shetland. Renewable sources may cover energy needs and provide a substitute for oil that is imported and used for heating buildings. This, however, would leave much of the renewable energy unused. The production of hydrogen may provide a cost-effective basis to produce Ammonia and Hydrogen Peroxide, that have a capability to supply a potential market, such as fertilizer production and fisheries. The study was implemented using a community-based approach that took into account the opinion of the community about the technologies used and their implementation through different business models. The involvement of local community was enacted through direct interaction and documentation of their concerns and perception about the project idea. The buildings identified for heat supply consist of building stock at Saxa Vord, Balta Sound Junior High School, Nordalea Care Centre, and Unst Leisure Centre Figure 2.1.1. The heat demand for these buildings was estimated, and three scenarios were further studied, two for the occupancy variation and one for efficiency improvement. The heat demand assessment of the identified buildings, was followed by a renewable energy resource and economic assessment of wind, solar, and geothermal sources. Different technology assessments were carried out for hydrogen production and the subsequent derivatives; ammonia and hydrogen peroxide. The application of hydrogen as a transport fuel was also assessed. Using a dynamic Microsoft Excel tool, a technical and financial assessment was carried out. Using diverse inputs, the tool simulates the output of the technologies involved. The tool provides essential indicators that assess the feasibility of the proposed ideas, and it allows for comparison of existing technologies and their related costs. The tool provides a basis in formulating scenarios that indicate different conditions for the technologies to work in. These scenarios vary from a more energy efficient heat demand to a selection of hydrogen and/or derivatives production priorities. This tool also allows for the alteration of capacity of the RE technologies. In addition, the tool does an economic assessment on the scenarios to form a baseline for the feasibility of the selected option. The financial v analysis and the resulting business models identify the investment opportunities along with the risks involved. To conclude, the study provides the community and other key stakeholders such as the PURE Energy Centre, Unst Community Council, Shetland Council, and the Unst Partnership, with a decision-making tool. This tool will enable them to study appropriate project options. In the end, the renewable energy resources could potentially reduce CO2 emissions and provide a green end-to-end supply chain of energy for heat demand and hydrogen, ammonia and hydrogen peroxide production. vi Table of Contents List of Authors: ........................................................................................................................... i Disclaimer .................................................................................................................................. ii Acknowledgements .................................................................................................................. iii Executive Summary .................................................................................................................. iv Table of Contents ...................................................................................................................... vi List of figures ........................................................................................................................... xii List of tables ............................................................................................................................. xv List of equations ..................................................................................................................... xvii List of Acronyms ................................................................................................................. xviii Introduction ................................................................................................ 1 Methodology ................................................................................................ 2 Project Scope ........................................................................................................... 2 Main Objective ........................................................................................................ 2 Specific Objectives .................................................................................................. 3 Methodology Overview ........................................................................................... 3 Community Activities ................................................................................. 5 Survey ...................................................................................................................... 5 Baltasound Junior High School Seminar ................................................................. 7 Community Workshop ............................................................................................ 7 3.3.1 Foreseen Concerns and Solutions on the Project Idea ..................................... 8 3.3.2 Expected Benefits of Potential Project to the Community ............................... 9 3.3.3 Community Involvement on the Potential Project ......................................... 10 3.3.4 Recommendations .......................................................................................... 11 System Architecture ................................................................................. 13 Introduction ........................................................................................................... 13 vii Heating System Layout ......................................................................................... 13 Hydrogen and Derivatives System Layout ............................................................ 15 Baltasound ............................................................................................................. 17 Saxa Vord .............................................................................................................. 17 Introduction to the Modelling Tool and Simulation of Scenarios ........ 19 Introduction to the Modelling Tool ....................................................................... 19 5.1.1 Assumptions of the Model ............................................................................. 19 Simulation of Scenarios ......................................................................................... 20 Financial Analysis ................................................................................................. 20 5.3.1 Methodology .................................................................................................. 20 5.3.2 Inputs and Assumptions ................................................................................. 23 5.3.3 Project Financing............................................................................................ 23 5.3.4 Indicators ........................................................................................................ 23 5.3.5 Uncertainties in Financial Parameters ............................................................ 25 Introduction to simulation ..................................................................................... 25 Simulation results for Baltasound location ............................................................ 25 Simulation results for Saxa Vord Resort Location ................................................ 27 Scenario 1: Heat Demand Only Scenario Saxa Vord Resort ................................. 28 Scenario 2: 900 kW Wind turbine and hydrogen production with heat as priority. 30 5.8.1 Base case demand........................................................................................... 30 5.8.2 Full Occupancy demand ................................................................................. 34 5.8.3 25 % efficiency on the full occupancy demand ............................................. 37 5.8.4 Conclusions for this scenario ......................................................................... 38 Scenario 3: Shifting Heating and Electrolyser Priorities and Ammonia Production. 38 5.9.1 Heating Supply ............................................................................................... 39 5.9.2 Hydrogen Production ..................................................................................... 40 viii 5.9.3 Production Costs and Heating Costs .............................................................. 40 5.9.4 Monthly Production........................................................................................ 41 5.9.5 Including Ammonia Production along with the hydrogen ............................. 44 Summary ................................................................................................................ 44 Heat demand assessment.......................................................................... 47 Introduction ........................................................................................................... 47 Building Stock information ................................................................................... 47 6.2.1 Unst Leisure Centre ........................................................................................ 47 6.2.2 Nordalea Care Centre ..................................................................................... 48 6.2.3 Baltasound Junior High School ...................................................................... 48 6.2.4 Saxa Vord ....................................................................................................... 51 Methodology for hourly heat demand projection .................................................. 54 6.3.1 Building heat demand assessment: Method I ................................................. 56 6.3.2 Building heating assessment: Method II ........................................................ 60 Building Occupancy Scenarios .............................................................................. 63 Energy Efficiency Methodology ........................................................................... 65 6.5.1 Energy Simulation Results for Current Scenario ........................................... 66 6.5.2 Building Energy Efficiency Study Scenarios ................................................. 70 6.5.3 Energy Efficiency Cost Scenarios .................................................................. 72 6.5.4 Wet heating System ........................................................................................ 74 Renewable Energy Analysis ..................................................................... 81 Wind ...................................................................................................................... 81 7.1.1 Methodology .................................................................................................. 81 7.1.2 Wind Turbine Selection ................................................................................. 81 7.1.3 Site Selection .................................................................................................. 82 7.1.4 Wind Resource Map ....................................................................................... 86 7.1.5 Wind Energy Project Design .......................................................................... 87 ix 7.1.6 Cost................................................................................................................. 89 Heat Pumps ............................................................................................................ 90 7.2.1 Ground Source Heat Pump (GSHP) Capacity Estimation ............................. 90 7.2.2 Ground Temperature Modelling..................................................................... 91 7.2.3 GSHP Performance Modelling....................................................................... 93 7.2.4 Air Source Heat Pump (ASHP) Performance Modelling .............................. 94 7.2.5 COSTS ........................................................................................................... 95 Solar PV and Solar Thermal .................................................................................. 97 7.3.1 Solar Resource................................................................................................ 97 7.3.2 Site Selection .................................................................................................. 98 7.3.3 Solar PV ......................................................................................................... 99 7.3.4 Solar Thermal ............................................................................................... 104 Chapter Summary ................................................................................................ 109 Hydrogen and its derivatives ................................................................. 110 Hydrogen Production ........................................................................................... 110 8.1.1 Introduction .................................................................................................. 110 8.1.2 Technology Comparison for Electrolysis ..................................................... 110 8.1.3 Alkaline Electrolyser Modelling .................................................................. 111 8.1.4 Hydrogen and Oxygen Storage .................................................................... 113 8.1.5 Costs associated with the electrolyser operation .......................................... 115 8.1.6 Electric Storage Modelling for H2 Production ............................................. 116 8.1.7 Design Possibilities ...................................................................................... 117 8.1.8 Transportation .............................................................................................. 120 8.1.9 Ferries ........................................................................................................... 121 8.1.10 Bin Lorry for Garbage .............................................................................. 121 8.1.11 Care Center Van ....................................................................................... 121 8.1.12 Public buses connecting Unst and Lerwick .............................................. 121 x 8.1.13 Public buses inside Unst ........................................................................... 121 Ammonia Production ........................................................................................... 122 8.2.1 Brief introduction to the process .................................................................. 122 8.2.2 Example of a Small-Scale Technology: Proton Venture ............................. 123 8.2.3 Chemical Properties of Ammonia ................................................................ 124 8.2.4 Inputs to the System Tool ............................................................................ 125 8.2.5 Potential Market for Ammonia in Unst Shetland. ........................................ 128 8.2.6 Ammonia Market Price ................................................................................ 131 8.2.7 Results and Recommendations..................................................................... 131 Hydrogen Peroxide .............................................................................................. 131 8.3.1 What is Hydrogen Peroxide?........................................................................ 131 8.3.2 Production of Hydrogen Peroxide ................................................................ 132 8.3.3 Mass and Energy Balance ............................................................................ 133 8.3.4 Logic for hydrogen peroxide production simulation in excel ...................... 134 8.3.5 Applications ................................................................................................. 135 8.3.6 Economics Parameters of Hydrogen Peroxide ............................................. 136 Final Remarks ...................................................................................................... 137 Conclusion and Recommendations ....................................................... 138 References .............................................................................................................................. 140 Appendix I: Survey Design and Sample ........................................................................... I-1 Appendix II: Baltasound Junior High School Seminar ..................................................... II-4 Appendix III: Community Workshop ............................................................................... III-4 Appendix IV: Potential Stakeholders and beneficiaries of the project .......................... IV-9 Appendix V: Companies Contacted during the Project ................................................... V-10 Appendix VI: Building heat demand assessment model description .......................... VI-11 Appendix VII: Pipe Sizing ............................................................................................. VII-1 Appendix VIII: NHER Certificate ................................................................................. VIII-2 xi Appendix IX: CATALYST CALCULATIONS ............................................................ IX-1 xii List of figures Figure 2.1.1 Location in Unst .................................................................................................... 2 Figure 4.1.1 Overall System Architecture ............................................................................... 13 Figure 4.2.1 General Architecture of Heating System of Buildings ........................................ 14 Figure 4.3.1: Architecture of Production System for Hydrogen, Ammonia and Hydrogen Peroxide ................................................................................................................................... 16 Figure 5.5.1 The output of the simulation showing contribution of different sources to meet the heat demand ....................................................................................................................... 27 Figure 5.7.1 Variation of LCOH and share of oil consumption with change in storage size .. 28 Figure 5.7.2 Monthly distribution of the energy mix in meeting the heat demand ................. 29 Figure 5.7.3 Annual energy consumption of heat pumps ........................................................ 29 Figure 5.9.1 Monthly Share of Annual Production of Hydrogen (Heat Priority) ................... 41 Figure.5.9.2 Monthly Share of Annual Production of hydrogen (Hydrogen Priority) ............ 42 Figure 5.9.3 Heat Supply by Technology (Heat Priority) ....................................................... 43 Figure 5.9.4 Heat Supply by Technology (Hydrogen Priority) ............................................... 43 Figure 6.2.1 Baltasound Building Stock. ................................................................................. 50 Figure 6.2.2 Building Stock at Saxa Vord. .............................................................................. 52 Figure 6.3.1 Relationship of outdoor temperature to heat demand in a building: ................... 55 Figure 6.3.2 Project adjusted heat degree hours. ..................................................................... 56 Figure 6.3.3 1Building performance line for Baltasound Junior High School and Nordalea Care Centre. ...................................................................................................................................... 58 Figure 6.3.4Baltasound Buildings heat demand ...................................................................... 60 Figure 6.3.5 Projected building demand at 100 % Occupancy at Saxa Vord .......................... 63 Figure 6.4.1 Yearly occupancy variation in tourism scenario for Saxa Vord main Buildings block and the residential blocks. .............................................................................................. 64 Figure 6.4.2 Tourism and residential scenario. ........................................................................ 64 Figure 6.5.1 schematic developed SketchUp model and plan ................................................. 66 Figure 6.5.2 Proposed system route at Saxa Vord for a wet heating System. ......................... 75 Figure 6.5.3 Specific price of hot water storage tank as function of volume. ......................... 78 Figure 7.1.1 Hermaness NNR Site ........................................................................................... 83 Figure 7.1.2 Areas of Significant Protection ........................................................................... 84 Figure 7.1.3 Unst Inland Water................................................................................................ 85 Figure 7.1.4 Unst Road ............................................................................................................ 86 xiii Figure 7.1.5Wind Resource Map ............................................................................................. 87 Figure 7.1.6 Hourly Wind Speed of Generated Saxa Vord Resort Meteo-Station .................. 88 Figure 7.2.1Unst Ground Temperature at 2m Below the Earth Surface .................................. 93 Figure 7.2.2 CRHV-P600YA-HPB GSHP Performance Modelling ....................................... 94 Figure 7.2.3 PUHZ-W85VHA2ASHP Performance Modelling.............................................. 95 Figure 7.3.1 Global Horizontal Irradiation .............................................................................. 97 Figure 7.3.2 Ambient Temperature for 2017 ........................................................................... 98 Figure 7.3.3 Land Gradient in Unst ......................................................................................... 99 Figure 7.3.4 Effect of Temperature ........................................................................................ 100 Figure 7.3.5 Thermal Gain and Loss ..................................................................................... 100 Figure 7.3.6 Monthly Energy Yield ....................................................................................... 102 Figure 7.3.7 Actual Collector yield Calculation .................................................................... 106 Figure 8.1.1 Hydrogen Production efficiency as a function of power input as percentage of rated electrolyser power rating ............................................................................................... 113 Figure 8.1.2 Power input and H2 production without battery operation. ............................... 118 Figure 8.1.3 Power input and H2 production with battery operation. .................................... 118 Figure 8.1.4 Schematic for Battery incorporation with Electrolyser operation. .................... 119 Figure 8.2.1 The Haber-Bosch Process.................................................................................. 122 Figure 8.2.2 Proton Ventures: Small scale ammonia production units.................................. 124 Figure 8.2.3 Impact ratings of Ammonia ............................................................................... 125 Figure 8.2.4 Linear Extrapolation for Capital Cost of Ammonia Production ....................... 127 Figure 8.3.1 Electrolysis Mode Operation ............................................................................. 133 Figure 8.3.2 Hydrogen Peroxide Production rate against intensity. ...................................... 134 Figure 8.3.3 Consumption of Hydrogen Peroxide in Shetland's Fisheries ............................ 136 Appendix Figure I-1 EvaSys Software Logo .......................................................................... I-1 Appendix Figure I-2 Survey Questionnaire ............................................................................ I-3 Appendix Figure II-1Evaluation Results of Baltasound School Seminar ............................. II-4 Appendix Figure III-1Results of concern group discussion ................................................ III-6 Appendix Figure III-2 Benefits Group discussions results .................................................. III-7 Appendix Figure III-3 Community Involvement Group Results ......................................... III-8 Appendix Figure VI-1 Building hourly heat demand projection methodology. ................ VI-12 file:///C:/Users/user/Desktop/Final_Report/IC_Report_2019_08.docx%23_Toc2930240 file:///C:/Users/user/Desktop/Final_Report/IC_Report_2019_08.docx%23_Toc2930244 xiv xv List of tables Table 5.3.1 The details of financial modelling at Baltasound ................................................ 21 Table 5.3.2 The details of financial modelling at Saxa Vord .................................................. 22 Table 5.5.1 Details of the simulated energy mix ..................................................................... 26 Table 5.7.1 Details of the simulated energy mix for the Heat Demand Only Scenario ........... 28 Table 5.8.1 Input parameters base case scenario 900 kW ....................................................... 31 Table 5.8.2 Heat pump capacity analysis for 900 kW turbine – Base case demand ............... 31 Table 5.8.3 Thermal storage analysis for 900 kW turbine- Base case demand ....................... 32 Table 5.8.4 Hydrogen electrolyser capacity analysis for 900 kW turbine- Base case demand33 Table 5.8.5 Hydrogen production with 900 kW turbine- basic demand scenario ................... 33 Table 5.8.6 Financial indicators for 900 kW turbine – Basic case demand ............................. 34 Table 5.8.7 Input parameter 900 kW -Full occupancy demand ............................................... 34 Table 5.8.8 Heat pump capacity analysis for 900 kW turbine – Full occupancy demand....... 35 Table 5.8.9 Thermal storage analysis for 900 kW turbine- Full occupancy demand scenario 36 Table 5.8.10 Hydrogen production with 900 kW turbine- Full occupancy demand scenario . 36 Table 5.8.11 Financial indicators for 900 kW turbine – Full occupancy demand ................... 37 Table 5.9.1 Fixed Inputs for Priority Shifting Scenarios ......................................................... 39 Table 5.9.2 Thermal Energy Balance for Saxa Vord Heat Demand ........................................ 39 Table 5.9.3 Hydrogen and Derivatives Production Performance ............................................ 40 Table 5.9.4 Cost for Different Scenarios ................................................................................. 40 Table 5.9.5 Effect of Ammonia Production and Financial Results ......................................... 44 Table 5.10.1 Summary of financial indicator of the Saxa Vord scenario 1 ............................. 45 Table 5.10.2 Summary of financial indicators of Saxa Vord Scenario 2 ................................ 45 Table 5.10.3 Summary of financial indicator of Saxa Vord Scenario 3 .................................. 46 Table 6.2.1 Building Stock Summary at Baltasound. .............................................................. 50 Table 6.2.2 Building Stock Summary ...................................................................................... 53 Table 6.5.1 Energy simulation results from NHER software for sample building unit........... 67 Table 6.5.2 TRNSYS Inputs for Current Scenario energy simulation .................................... 69 Table 6.5.3 All window improvement scenario. ...................................................................... 70 Table 6.5.4 External Wall improvement TRNSYS simulation cost. ....................................... 70 Table 6.5.5 TRNSYS Roof improvement scenario. ................................................................ 71 Table 6.5.6 TRNSYS 30% energy efficiency scenario. ........................................................... 72 Table 6.5.7 Costs of different energy improvement measures in relation to energy savings .. 73 xvi Table 6.5.8 Annualised capital cost for 20 years for Saxa Vord piping works for mini district heating system. ......................................................................................................................... 76 Table 6.5.9 Economic Indicators for thermal storage .............................................................. 79 Table 6.5.10 Economic & Technical Indicators for Electric & Oil Boilers ............................ 80 Table 7.1.1 Wind Turbine Specification .................................................................................. 82 Table 7.1.2 Saxa Proposed Vord Wind Energyy Project ......................................................... 89 Table 7.1.3 Baltasound Proposed Wind Energy Project .......................................................... 89 Table 7.1.4 CAPEX and OPEX of Wind Turbines .................................................................. 90 Table 7.2.1 GSHP Performance at Different Water Outlet Temperature and Output Heating Power ....................................................................................................................................... 94 Table 7.2.2 ASHPs Performance at Different Water Outlet Temperature .............................. 95 Table 7.2.3 CAPEX and OPEX for GSHP and ASHP in the UK ........................................... 96 Table 7.3.1 Panel Comparison for Specific Yield in Unst ..................................................... 101 Table 7.3.2 Results ................................................................................................................. 103 Table 7.3.3 CAPEX and OPEX ............................................................................................. 104 Table 7.3.4 CAPEX and OPEX of flat plate collector ........................................................... 108 Table 7.3.5. Cost comparison for evacuated and flat plat collector ....................................... 108 Table 8.1.1 Comparison of Electrolyser Technologies .......................................................... 111 Table 8.1.2 Cost of Electrolyser System. ............................................................................... 115 Table 8.2.1 Selected Properties of Ammonia ........................................................................ 124 Table 8.2.2 Properties of ammonia and hydrogen densities for storage ................................ 130 Table 8.3.1 Characteristics of Hydrogen Peroxide at Ambient Temperature ........................ 132 Appendix Table IV-1Potential stakeholders of the project.................................................. IV-9 Appendix Table V-1 Companies contacted during project assessment ............................... V-10 Appendix Table VII-1Pipe sizing and costing at Saxa Vord .............................................. VII-1 xvii List of equations Equation 6.3.1 Heat energy flow out of a building and indoor and outdoor temperature relationship:.............................................................................................................................. 54 Equation 6.3.2 Area indexed degree days ................................................................................ 59 Equation 6.3.3 Swimming pool heating demand ..................................................................... 59 Equation 6.3.4 heating demand for the rest of the building ..................................................... 59 Equation 6.3.5 kWh per degree hour constant ......................................................................... 59 Equation 6.3.6 Building section heat demand ......................................................................... 59 Equation 6.3.7 total hourly heat demand ................................................................................. 59 Equation 6.3.8 heat intensity benchmark relationship to gross internal area and building heat demand ..................................................................................................................................... 61 Equation 6.3.9 Percentage share of base heat load in total demand ........................................ 61 Equation 6.3.10Equation used to predict the building heat demand ........................................ 61 Equation 6.3.11 Hourly base load computation for method II ................................................ 62 Equation 6.3.12 hourly temperature correlated demand computation for method II ............... 62 Equation 6.3.13 Method II hourly heat demand formula ......................................................... 62 Equation 6.5.1 Equivalent annual cost of an asset ................................................................... 76 Equation 6.5.2 Sizing of Thermal Storage ............................................................................... 77 Equation 7.2.1 Shallow Earth temperature as a function of depth and day of the year ........... 92 Equation 7.3.1 Overall efficiency calculation........................................................................ 105 Equation 7.3.2 Cost calculation of flat solar thermal ............................................................. 107 Equation 8.1.1 Adjusted ideal gas equation ........................................................................... 114 Equation 8.2.1 The Haber-Bosch Equation ........................................................................... 122 Equation 8.2.2 Decomposition of Ammonia ......................................................................... 130 Equation 8.3.1 Anode Reaction for Hydrogen Peroxide Production. .................................... 133 Equation 8.3.2 Cathode Reaction for Hydrogen Peroxide Production. ................................. 133 Equation 8.3.3 Linear regression Hydrogen Peroxide production rate. ................................. 135 xviii List of Acronyms AM Air Mass ASHP Air Source Heat Pump CAPEX Capital Expenditure CAS Chemical Abstracts Service CIBSE Chartered Institution of Building Services Engineers CO2 Carbon dioxide COP Coefficient of Performance EPC Energy Performance Certificate FISE Fraunhofer Institute of Solar Energy GBP Great British Pound GBP/EURO Great Britain Pound to Euro Exchange Rate GCRS Geological Conservation Review Sites GHE Ground Heat Exchanger GHI Global Horizontal Irradiation GIA Gross Internal Area GSHP Ground Source Heat Pump H2 Hydrogen HHV Higher heating value HP Heat Pump IRENA International Renewable Energy Agency kg kilogram kJ kilojoules kWh kilowatt hour xix LHV Lower heating value m3 cubic meter MCS Microgeneration Certificate Scheme MJ megajoules MOD Ministry of Defence MSDS Material Safety Datasheet N2 Nitrogen NASA National Aeronautics and Space Administration NCMPA Nature Conservation of Marine Protected Area NH3 Ammonia NNR National Nature Reserve NTP Normal Temperature and Pressure O&M Operations and Maintenance OPEX Operational Expenditure PEC Pure Energy Centre PSA Pressure Swing Adsorption PV Photovoltaics QGIS Quantum Geographical Information System RE Renewable Energy SAC Special Area of Conservation SAP Standard Assessment Procedure SCOP Seasonal Coefficient of Performance SLDP Shetland Local Development Plan SNH Scottish National Heritage xx SPA Special Protection Area SSSI Sites of Special Scientific Interest STC Standard Testing Conditions STP Standard Temperature and Pressure UK United Kingdom USD United States Dollar WRF Weather Research and Forecasting 1 Introduction The sixteen students from the University of Flensburg (Germany) studying Energy and Environmental Management and their three lecturers along with a research associate made a resident research visit on Unst island, Shetland in February to March 2019. This visit was a part of the International Class (IC) module, in which the students explore the energy related opportunities for rural communities. The study was undertaken in accordance with the recommendations of PURE Energy Centre and Unst Partnership, who were the key partners of this case study. The students studied the previous project reports conducted by PURE Energy Centre and Unst Partnership. These reports suggested solutions for deploying RE through off grid systems using energy storage mechanisms (Estate and Islands, no date). Furthermore, the reports emphasized the role of hydrogen produced with renewable energy sources like wind turbines in a green supply chain. The students then worked on developing the preliminary ideas presented by the PURE Energy Centre, which is discussed in this report. 2 Methodology Project Scope The study aims at assessing the heat demand of the building stock at Saxa Vord, Balta Sound Junior High School, Nordalea Care Centre, and Unst Leisure Centre as shown in Figure 2.1.1 Figure 2.1.1 Location in Unst Main Objective The main objective of this study is to develop an integrated tool, which would make it possible to analyze the feasibility of incorporating renewable energy to provide building heat demand for the building stock at Saxa Vord, Balta Sound Junior High School, Nordalea Care Centre, and Unst 3 Leisure Centre and also assess the market potential of hydrogen and its derivates on the island of Unst, in Scotland. Specific Objectives Furthermore, the main objective was divided into following specific objectives: • To identify stakeholders and study the perception of the community about the deployment of renewable technologies in Unst. • To determine and analyze the current heat energy use and building efficiency of the project buildings. • To assess the potential of the island for the production of electricity and heat from RE (wind, solar PV, solar thermal, etc.). • To assess the potential of hydrogen production and the market for derivatives in Unst. • To analyse and integrate the outcomes of all workgroups by designing a transparent and flexible tool. Methodology Overview The methodology adopted for meeting the above objectives is described briefly as under: Three community involvement activities were planned before the students came to Unst, as their final implementation of their project. A survey questionnaire was designed and circulated using 4 EvaSys, a web-based software. Two workshops were conducted, one with the students of the Baltasound Junior High School and the other with the community. The building heat demand assessment was done using historical energy consumption data from the relevant stakeholders, site visits and the use of heat intensity benchmarks. TRNSYS and NHER software were used to model the energy performance of a sample unit. The renewable energy resource assessment was undertaken for solar thermal, solar photovoltaic, wind and geothermal heat-pumps. Software used for the assessment of solar photovoltaic and wind were PVSOL and WindPRO. Hydrogen production, based on the available residual energy for the process, was simulated in a separate excel tool. The production of ammonia and hydrogen peroxide was also simulated in the same tool. These inputs were then integrated in a combined excel tool. 5 Community Activities It can be argued that the effective implementation of renewable energy projects at the community level requires the acceptance and support of community members. Besides the technical and economic aspects, social aspects such as the existing knowledge and attitudes of the community on Renewable Energy (RE) technologies, and community involvement should be taken into consideration to enhance decision making. While several stakeholders will be involved in the future realization of RE projects, the community was identified as a very important stakeholder for the potential project in Unst. Therefore, the students from the University of Flensburg planned to interact with the community to include their ideas and opinions in the early stage of assessment and planning for a renewable project in Unst. Thus, the strategy was to use the methods listed below to collate these opinions: 1. Survey; to understand the community’s attitudes towards renewable energy projects 2. Seminar at Junior School; to introduce the project idea to the future generations of Unst 3. Community Workshop; to interact with community members and gather their opinions and suggestions on the project idea. The sections below summarize the outcome of these activities with the community members. Survey The purpose of the survey was to acquire a primary overview of opinions from the community members of Unst, to assess their acceptance, attitudes, and concerns towards the development of RE projects on the island. The survey essence derived from an article of the Climate Policy Info Hub “Social Acceptance of Renewable Energy” (Climate Policy Info Hub, 2015) focusing on five areas to discover the social aspects influencing the acceptance of renewable energy technologies mentioning: ● Awareness of climate change and knowledge of technology, which motivates the collective willingness to act and/or accept renewable energy projects. ● Fairness of decision-making process that is evidenced by open and transparent communication between the public, stakeholders, and decision makers, which allows the public to evaluate openly the projects. 6 ● Overall evaluation and assessment of costs, risks, and benefits: - by taking into consideration all sorts of impacts as economic, environmental, and social. ● Link Local context in order to examine the community reactions and probable resistance towards projects implemented in their vicinity, and stresses on sharing benefits as a strategy to impede the probable resistance. ● Trust in decision-makers elaborating the perception of the local communities towards developers and decision makers and plays an integral role in their wholesome acceptance. The survey design included 10 statements with a provision for only four answer categories (Agree, Strongly Agree, Disagree and Strongly Disagree) to motivate the respondents to take a position. The Survey was disseminated through an online platform and hard copies were placed in the local shops in Unst. There were 25 responses, and the main findings were: ● Respondents were aware of the climate change issue and try to behave in an environmentally friendly way on a personal level. ● Respondents had a positive attitude towards development of RE projects, although two people were not in favour of RE project development even if their concerns were addressed. ● Respondents believed that community-owned projects could bring more benefit to the community. ● Respondents had their doubts that decision makers and developers take the community interests as a priority. ● Respondents emphasized transparency and community involvement as a precondition for acceptance and support for the development projects. 25 responses out of a population about 600 people in Unst (Gall, 2017) is equivalent to only 4.1 % of the island’s community. This means by far that the sample is not representative of the 7 population of Unst, thus the results obtained cannot summarize the collective attitudes and opinions of the population and it requires to be conducted again. For more information on the Survey design with statements, refer to Appendix I: Baltasound Junior High School Seminar The main objective of the school seminar was to introduce a simplified idea of the project to the high school students; who are the future generation of the island. In addition, it was an opportunity to know about their familiarity with renewable energy and environmental related topics like climate change. The seminar resulted in forming very good communication with the students and some of the school staff, as the Baltasound Junior High School is one of the suggested beneficiaries of the project idea. Furthermore, it was obvious that the school students had some knowledge of the benefits of renewable energies to the environment and they are aware of the importance of alleviating environmental issues such as global warming and climate change. For more details of the seminar, refer to Appendix II: . Community Workshop The objectives of this workshop were: • To provide feedback on the survey results • To present information on the potential RE project idea for Unst by the students of University of Flensburg. • To collect ideas and suggestions of community members on how to make RE projects acceptable through community participation and for the benefit of the community The students from the University of Flensburg presented the survey results, the RE project idea, and then facilitated three group discussions on the: (1) foreseen concerns and potential solutions, 8 (2) expected benefits to the community, and (3) ways in which the community can be involved in the future project. There was a total of 10 community members in attendance for this meeting. The opinions and suggestions generated during the community workshop will be considered and presented to the decision-makers, as a guideline of the social options for future projects. The sections below provide a summary of the discussion outcomes in the three groups: 3.3.1 Foreseen Concerns and Solutions on the Project Idea The concerns of the participants were classified into the below listed factors: i. Economic - Defining the long-term benefits to the community, amidst fears of profit centric investors. - The aesthetic impact of wind turbines on the tourism on the island, as these could reduce the number of tourists that come to the island for its scenic landscape. - Utilization of local human skills and service during project implementation - Existence of fuel poverty because of the need for longer heating hours, high costs of electricity charge by monopolies and transport services by ferries Participants suggested developing a financial structure for the project to benefit community instead of individuals, financing instruments for technologies such as heat pumps, and allowing the export of electricity to the grid. Furthermore, it was proposed to explore the use of oxygen for fish hatcheries on the island as an added value of the project. ii. Environmental: - Avoiding installations on the island’s nature reserves and other protected areas. iii. Social - Emphasis that land usage benefits may not accrue to the community, but only land owners. - The consistent development of the island’s local skills and capacity - Efficiency measures for old buildings that ensure that their ancestral value is not destroyed. 9 The solutions suggested were initiating apprenticeships programs on the island to create long term job creation and sharing success stories between community members. iv. Technical - Providing information on the size, quantity, and site for potential wind installations - Providing more information on the other technologies proposed for the project. - Limitations of wind installations that may arise due to potential satellite radar site - Elaboration of building insulation and better building heating technologies to the community members. At the end of the discussion, participants placed higher priorities on the economic and social concerns over the technical and environmental factors. This indicates that the concept of “social sustainability” is important and the proper organization of a RE project is an enabling factor for any RE activity. 3.3.2 Expected Benefits of Potential Project to the Community The potential benefits raised in the group discussion were based on the future deployment of RE technologies and hydrogen production on the island. The main findings are as listed below: i. Expected economic benefits; - The main benefits discussed were job creation and stimulation of the local economy, in which local industries such as fish farms would benefit from locally produced hydrogen peroxide, which could be provided at lower cost. - The possibility of producing fertilizer to meet internal demands and then considering exporting any excess was also a benefit which the participants suggested. - The participants also expressed the idea of creating a Community Benefit Fund, which would channel the revenue from the new installations to community benefits. The main point was that the money would be circulated within the island’s economy. ii. Expected social and socio-economic benefits: 10 - Some of the major social benefits highlighted by the participants were stimulating population growth, mitigating energy poverty and clean energy, and safeguarding the services. - They also suggested that developing RE projects could serve as a knowledge-hub in Unst, which could serve as examples to other islands as well as inspire the future generation. - The idea of improving the fabric of buildings was raised, as the present condition of the building materials is degrading owing to the discontinuous supply of expensive available energy for heating. iii. The environmental benefits pointed towards decreasing the environmental impact in the form of CO2 emissions. The participants prioritized the economic and social benefits, followed by the socio-economic benefits, while the environmental benefits were considered as a by-product of the economic benefits. 3.3.3 Community Involvement on the Potential Project The main findings from this discussion with the community members were: The emphasis by participants on ensuring that the local community should have a role in the process of decision making of renewable energy projects, also suggesting the formation of decision-making sub – committees. i. The participants also claimed that they are interested on a personal level to participate in the planning and implementation process for renewable energy projects. ii. The participants also highlighted the importance of introducing renewable energies, along with raising awareness about the specific benefits of RE projects to the community level, through meetings and awareness sessions, case studies of conducted projects in other communities, and facilitating visits to RE projects sites. iii. The participants also expressed the possibility of providing project equity and the ability of some community members to contribute financially in implementing RE projects. 11 However, they insisted on the importance of raising awareness and addressing the concerns of the community before investing in these projects. iv. Another point was the involvement of the community through apprenticeships and employment, with the possibility of job creation after implementing RE projects. The participants have also pointed to the benefit of using local skills and expertise within the community and involving them through participating in online publicity of RE projects. v. The participants expressed their personal willingness to contribute to RE projects, based on their interests and abilities, through attending workshops and awareness meetings, spreading this knowledge and encouraging others to attend or even by facilitating such meetings. They also showed interest in giving publicity through media channels like the local newspaper and radio stations. For more details on the activities of the Community Workshop, refer to Appendix III: 3.3.4 Recommendations There were only 10 community members in attendance for the workshop, which does not seem representative of the views of all the adult population residing in the community. However, our recommendations are that the above points raised during the activities are vital and should be highly considered before project implementation phase by addressing the key areas on: • Creating awareness to the community by providing more consistent information on the proposed RE technologies and any other auxiliary productions (such as hydrogen production) through workshops, newspaper articles or the media. • Job creation by utilizing local skills / expertise, initiating apprenticeship programs, and highlighting any other value addition to the community as a result of the RE project. • The formation of a community sub-committee for inclusion in planning and decision making, and a Community Benefit Fund to manage funds accrued from any RE project to develop Unst. • Providing solutions for the high heating requirement in buildings on the island and advising on improvements that can be applied to materials in old buildings. • Minimizing the capacity and quantity of wind turbine installations, while taking care to avoid installation on protected areas and not affecting the aesthetic landscape view 12 • Clarity on the financing of the RE project and providing for the incorporation of co- ownership by the community, in order to ensure that community benefits is a priority. 13 System Architecture Introduction The system is divided in two main branches. The first branch integrates different technologies for heating the buildings and supply the heat demand while the second branch utilizes the energy for producing hydrogen, oxygen, ammonia and hydrogen peroxide in the priority that the user defines. This framework is translated to two tools, in Microsoft Excel, separately for two locations: ‘Baltasound’ and ‘Saxa Vord’. This section details the relationship between the system components in this framework, and after that briefly introduces the two excel tools. The broad architecture of the entire system is demonstrated in Figure 4.1.1 Figure 4.1.1 Overall System Architecture Source: Author Heating System Layout Within the scope of our design, the energy supplied from RE technologies is utilized to meet the heat demand. The heating system integrates different components aiming to deliver hot water for 14 space heating and for domestic use at a temperature of 70 ̊ C. The heat pump, electric boiler, thermal storage and the backup oil boiler enable the system to achieve a reliable heat supply. Further, into the operation, the system divides into two blocks; electricity and heat. Figure 4.2.1 summarizes the basic heating system architecture: Figure 4.2.1 General Architecture of Heating System of Buildings Source: Authors The electric block comprises of two components ground source heat pump and electric boiler, both utilizing electricity acquired from RE. The system also includes a provision of electricity supply from grid only to the heat pump allowing it to serve as a backup in case of shortage of supply due to the fluctuation and intermittency of RE. As per the heat block, the (heat pump/solar thermal) acts as a base load supplying hot water at a temperature of 45 ̊ C. The electric boiler works in parallel supplying hot water to the thermal storage at a temperature 90 ̊ C. In addition, an oil boiler is available also as a backup in case of shortages replacing the supply from hot water storage. The modelling assumption is that the water 15 temperature coming from thermal storage is adjusted by changing the water flow rate in order to attain the desired temperature for the wet heating system. The heating system layout adopts to the various loads, considering their geographical locations, area of the property, and the aggregated demand. Considering loads, for Saxa Vord Resort the demand accounted as a bulk delivering it via a mini district-heating scheme, assuming that all electrical heaters will be replaced by a wet heating system. This is required by the system since existing electrical storage heaters do not provide enough storage capacity to balance the intermittent supply of the wind resource. In Baltasound, each building has its own central heating system and electricity from the wind turbine is distributed to the properties. Hydrogen and Derivatives System Layout As can be seen in the diagram, the process is divided in four main blocks: hydrogen and oxygen production, hydrogen peroxide production, hydrogen for transportation and ammonia production. Figure 4.3.1 shows the flow of the products, from an initial state of water, until the final states. Refer to Chapter 8:for a detailed explanation of the process. The system compares the energy input and the energy required for the process with first priority, if the process cannot be completed because there is not enough energy or additional inputs are not available, the energy goes to the process with second priority. If the first process is executed the remaining energy goes for the process with second priority. This logic will be repeated between the second and third process. Ammonia and hydrogen peroxide production require the confirmation of two inputs. In the case of ammonia, it requires energy and hydrogen while in the case of hydrogen peroxide it requires energy and oxygen. 16 Electricity from RE Electrolyser 1 100 kW Oxygen Low Pressure Storage Hydrogen Low Pressure Storage H2O Deionized Water 12 bar, 0.8 m3 12 bar, 1.8 m3 Electrolyser 2 100 kW H2O O2O2 O2 Oxygen H2 Hydrogen H2 H2 Hydrogen and Oxygen Production H2O2 Hydrogen Peroxide Hydrogen Peroxide Production Hydrogen High Pressure Storage Hydrogen Compressor 10kW 70 bar, 1.5 m3 H2 H2 Refueling Station H2 Electricity Electricity Hydrogen for Transportation Electricity H2 Compressor 10kW N2 Air H2 + N2 Nitrogen Extractor Catalyst Chamber 10kW NH3 Ammonia Ammonia Production Figure 4.3.1: Architecture of Production System for Hydrogen, Ammonia and Hydrogen Peroxide Source: Authors 17 Baltasound The three demand centres in Baltasound are the Unst Leisure Centre, Nordalea Care Centre and Baltasound Junior High School. The current situation is that each building has its own heating system which uses Oil boilers as the heating source. An hourly heating demand assessment of each of these buildings was conducted and this is explained in Chapter 6. Considering that current consumption is from oil, the study explores alternate cleaner heating systems for meeting this demand. This could technically include heat pumps from geothermal energy (ground source) and/or air sourced technology, to be electrically powered by a renewable energy system. Solar thermal collectors were also considered as an option as alternative to heat pumps. The renewable energy system consisting of wind turbine(s) and / or solar PV system provides intermittent energy, which doesn’t always coincide with demand. Therefore, it was prudent to explore using a thermal storage for the buildings which recharges from an electric boiler when there is excess energy produced by the RE system. This interaction within the components of each of the three buildings’ heating system is modelled into Ms. Excel by adding parameters derived from the technical assessment. This is detailed in Chapter 7:for renewables and in Chapter 6 for thermal storage. To arrive at a cost of heating due to new investments in the buildings’ heating system and RE system, costs for investment in capital, operations and fuel expenditure were conducted along with technical assessment. These financial parameters are used in the tool to estimate cost of heating from the combined system (per building) and compare with current or future expected expenditure by the leisure centre, school and care centre on heating. Saxa Vord The Saxa Vord buildings are considerably far from Baltasound, which required the modelling of a separate RE system, and thus a separate excel tool. The heat demand assessment along with the possibility of different occupancy patterns, seasonal variation, increased building efficiency was conducted (refer to Chapter 6) and this is modelled into the tool for Saxa Vord. The Saxa Vord resort has both wet heating systems as well as dry heating systems, which source the heat from either the oil boiler or grid electricity. Similar to Baltasound, the Saxa Vord buildings’ heating system is also assessed for improvement by introducing heat pumps (or solar thermal collectors), thermal storage (supplied by electric boiler). This would be 18 supplied by a renewable energy system with wind turbines and/or solar PV to supply the heating system’s electric demand. The collective ownership of the buildings by Saxa Vord provides the opportunity for a combined district heating system. Thus, thermal storage and pipeline layout feasibility assessment was conducted for Saxa Vord. Finally, the payback assessment of investing in the new heating system is an output for the building owners in the model. It was agreed that an important component of the whole feasibility study is production of Hydrogen, Ammonia and Hydrogen Peroxide (Hydrogen and its derivatives). Saxa Vord was chosen for simulating the production of these derivatives because of the high possibility of a larger project that could come up in its vicinity. This is because viability of the relatively new market expected for the chemical production project is still volatile. Therefore, it should be possible to simulate scenarios assuming that it can be subsumed within a larger company (if so decided by the community), diversifying the portfolio and thus diluting risk. The presence of a school and more residential complexes in Baltasound than Saxa Vord was also a factor that contributed to this selection. Unlike the institutional buildings selected for the study in Baltasound, the hourly heat demand of any given year from Saxa Vord is more susceptible to variation in the future. But within a given year, the load is fixed and cannot be varied. On the other hand, electricity demand for the Chemical Production Project is more flexible. It can be run on residual load from the Renewable Energy system after meeting heating demand of Saxa Vord buildings. This is link between two projects. The model treats production of the chemical derivatives as a separately owned project. However, it may be noted that for aforementioned feasibility reasons, it is possible to simulate co-ownership with the ESCO handing renewable energy production (combined financial indicators). The ownership possibilities are left flexible in the model and this is further clarified in the description of the excel model in the Chapter 5:. 19 Introduction to the Modelling Tool and Simulation of Scenarios Introduction to the Modelling Tool The system architecture described in the previous section was translated to MS Excel tools for Baltasound and Saxa Vord, which are flexible and at the same time transparent for the user. The tool is developed in order to check the feasibility of the renewable energy production and hydrogen and hydrogen derivatives production in Unst. Hence, it simulates the operation of the energy production and demand centres on an 8760 h time series. The model also allows to modify various parameters in order to arrive at different user-defined scenarios. It must be considered that exact real-world situations cannot be simulated in the model and this should be considered while interpreting results. In order to make the modelling simpler, a number of assumptions were made. 5.1.1 Assumptions of the Model • The return temperature of the wet heating systems is assumed to be 40 ° C. Therefore, the inlet water temperature to heat pumps and thermal storage is at this temperature. • The temperature of water stored in the thermal storage is assumed to be 90 °C. • The outlet water from the thermal storage and heat pump is assumed to be mixed in order to attain the required inlet temperature for the wet water heating system. • The demand for space heating and domestic water heating is treated together in the model in energy terms. However, in real case the temperature requirement varies. It is assumed that water to domestic water heating gets separated and gets heated further according to the required temperature. • The outlet temperature of the heat pumps is 45 ° C. • The heat pumps are modelled in such a way that they adjust their output according to the demand capped by their capacity. • A 60-kW ground source heat pump and an 8-kW air source heat pump are included in the model. The ground source heat pump may be scaled from 40 kW to 80 kW while it is assumed to have the characteristics of a 60-kW heat pump. Similarly, air source heat pumps from 5 to 15 kW are assumed to have the same characteristics of the 8-kW air source heat pump. 20 Simulation of Scenarios Nothing in this world is constant. Everything is subject to change and has their own sensitivities. Considering this, various demand scenarios for buildings Chapter 6 and different priorities to hydrogen derivatives were considered while performing the simulation. Results of following scenarios have been described in this report: • Scenario 1: Heat Demand only scenario/Saxa Vord Resort • Scenario 2: 900 kW wind, heat priority and hydrogen production scenario/Saxa Vord Resort • Scenario 3: Shifting heating and electrolyser priorities and Hydrogen Storage /Saxa Vord Resort Some of the scenarios have sub scenarios as base case1 demand scenario, full occupancy 2demand scenario and 25 % efficiency on full occupancy demand scenario. The technical outcomes along with financial indicators are mentioned in the results of each scenario. Before going to the results of each scenario, a brief introduction to the financial analysis has been presented in the following section. Financial Analysis 5.3.1 Methodology In order to estimate economic impact, the approach adopted was to evaluate from the ownership perspective. Types of owners: • Building owner: This refers to owners of buildings of Saxa Vord and Baltasound. Each set of buildings has its own inputs and outputs and they are separately visible in Baltasound. However, this is combined under one building ownership in Saxa Vord. • Energy Service Company (ESCO): The ESCO is an incorporated as a society, company or cooperative which may be owned privately, publicly, in joint ownership as public- private ownership or collective by the community as a cooperative or trust. • Entity producing Hydrogen, Ammonia and Hydrogen Peroxide: The chemicals’ production is treated separately in the model. However, the financials are represented with separate ownership as well as ownership by the ESCO itself. 1 “Base Case” refers to a use of the buildings entirely for tourism 2 “full occupancy” refers to a use of the buildings as residences and for tourism 21 Keeping the above ownerships in mind, the model structure produces results for following projects: a) Baltasound: Table 5.3.1 The details of financial modelling at Baltasound Component / Project Owner Consumer Parameters Fin. Analysis Link to other components/projects Heating System Leisure Centre Unst Leisure Centre Self- Consumption Payback Period, Cash Flow (NPV) Tariff paid to ESCO Heating System School Baltasound Junior High School Self- Consumption Payback Period, Cash Flow (NPV) Tariff paid to ESCO Heating System Care Centre Nordalea Care Centre Self- Consumption Payback Period, Cash Flow (NPV) Tariff paid to ESCO Renewable Energy (RE) System ESCO Buildings (Leisure Centre, School and Care Centre) NPV, IRR, Debt Service Coverage Ratio (DSCR Income from Buildings (Leisure Centre, School and Care Centre) Source: Authors 22 b) Saxa Vord Table 5.3.2 The details of financial modelling at Saxa Vord Component / Project Owner Consumer Parameters Fin. Analysis Link to other components/projects Project 1: Heating System Saxa Vord owned by ESCO ESCO Saxa Vord Buildings (through Heating system management company) Saxa Vord owners Directly NPV, IRR, Debt Service Coverage Ratio (DSCR) Income from Saxa Vord Buildings (directly or through heating system management company) Project 2: Chemicals Production System (H2, NH3 and H2O2) ESCO; or Other company Businesses in Unst that may have a demand for H2, NH3 and H2O2) NPV, IRR, Debt Service Coverage Ratio (DSCR) Cost visible to ESCO; or Tariff paid to ESCO Combined: (Combined of projects 1 and 2) Renewable Energy (RE) System ESCO Saxa Vord Buildings Chemicals Production System NPV, IRR, Debt Service Coverage Ratio (DSCR) Income from Saxa Vord Buildings; And/or Income from Chemical Productions System Project 3: Heating System Saxa Vord Saxa Vord Owners; Heating System Management Company Self Consumption; Saxa Vord Buildings Payback Period, Cash Flow (NPV) Cost of heating visible to Heating System Management Company; or Tariff paid to ESCO Source: Author It is considered that the building owners in Saxa Vord and Baltasound would invest in their own heating system or through another energy management company. This investment 23 constitutes the heat pumps or solar thermal collectors, thermal storage, electric boiler, heat distribution system and backup from oil boilers. 5.3.2 Inputs and Assumptions The projects feasibility for different scenarios was studied by generating the cashflow statement of Projects 1 and 2, as well as for the installation of RE sourced heating system by buildings. Costs and revenue were adjusted to an inflation rate of 1.4% estimated from historical figures (TradingEconomics, 2019). To better reflect future cashflow, discount rate was set to Weighted Average Cost of Capital (WACC) calculated at 3.72% for Project 1 and 3.77% for Project 2 in the United Kingdom (WACC Expert, 2019), reflecting marginally higher risk for the derivatives project3. The input for investment costs, operational expenses and fuel rates were calculated individually for all components of the project as mentioned in Chapter 8:. Currency conversion selected for all values is that One GBP equals EUR 1.15 or USD 1.32 as of 25th February 2019 (XECorp., 2019). 5.3.3 Project Financing Parameters for financing chosen to simulate scenarios included constant repayment structure over a maturity period of 15 years and interest rate of 3.75 %. The interest rate assumed was significantly lower than business loan figures by commercial banks. However it is marked up higher than rates offered for SMEs by cooperative / regional banks (Barclays, 2019; MoneyFacts, 2019). Assets have been capitalized over lifetime of assessment by straight line depreciation of assets for 10 years. It must be noted that losses were carried forward to defer tax liability, subject to regulatory restrictions (GOV.UK, 2018). 5.3.4 Indicators One of the important indicators of output costs was the levelized cost of electricity, heat and derivatives which was calculated to estimate pricing of the products in the market. The method to calculate LCOE was referred to from the Simple Levelized Cost of Energy (LCOE) Calculator by National Renewable Energy Laboratory (NREL, 2010). The tool includes the 3 WACCs of 3.72% and 3.77% were calculated using WACC Expert for United Kingdom, drawing an analogy between Project 1 with Utilities Sector and Project 2 with the Chemicals sector. Corporate tax of 19% for UK (GOV.UK, 2019) was incorporated as a detailed assumption while calculating weighted cost of debt in the calculation. 24 option to mark up this value and establish a profit-based tariff flexible to subsidy or price premiums. The bankability of Projects 1 and 2 for the ESCO was measured in terms of Debt Service Coverage Ratio (DSCR), which is required to above market benchmarks for financing. Since these projects are technically high risk due to uncertain demand, especially the derivatives, the financing institution would require values as high as 1.4, but this can be reduced to lower ranges like 1.1-1.2 if there is assured government support to meet some costs as operational premiums or capital subsidy (Barclays, 2019). The value for investors and shareholders is visible in terms of the Net Present Value (NPV) that represents discounted cashflow over the 25-year period chosen for lifetime analysis. For the ESCO, combined value of NPV is a significant indicator of overall viability, where the high risk of one project can be offset by the other, as is demonstrated in the tool. This can be achieved by methods like cross-subsidizing between the projects, proportionate to value for the buildings or derivatives and the consistency of demand. In case of a positive NPV, the Internal Rate of Return (IRR) of the project is an indicator of good investment, when exceeding the WACC of company. For the assessment of the investors, the IRR Shareholders indicator presents the estimated return on their holding in the enterprise which could be the ESCO or the Energy management company for the Buildings. It is here that opportunity for local investors exist to collaboratively generate a sustainable and clean energy driven economy. In order to evaluate the return on investment of the buildings, their payback period is calculated vis a vis existing scenario, which accounts for a cashflow from savings on oil consumption or electric consumption from existing grid. This indicator is critical in perceiving the choice for 25 the building owner between existing dependence (cost of heating) on the current heating system or a purely RE driven system with heating system design improvement. 5.3.5 Uncertainties in Financial Parameters The flexibility in the model is important considering the high variability in inputs which are possible. Considering this, there are some factors to consider while interpreting results from the scenarios simulated in following sections: • Demand Assessment approximation is affected by the sporadic occupancy pattern in case of Saxa Vord, unbalanced between seasons and future plans, thus affecting agreement on tariff and therefore revenue. • Variability of wind energy is also subject to volatility due evident by studied wind patterns, and this directly affects revenue stream. • The price of oil is a constant assuming value available from 2018, but it is an extremely volatile component of the system given its high dependence on global markets, especially over long period of time. Introduction to simulation The model was simulated for the two project locations: Saxa Vord and Baltasound (Leisure Centre, School, Nordalea Care Centre). The energy production for Baltasound was simulated initially considering the low demand of this location. The hydrogen production centre was not simulated for this location, considering the lower wind speeds at this location and the proximity of the school. Following sections describe the simulation results for both the locations. Simulation results for Baltasound location The target of the simulation was to obtain an optimum mix of the renewable energy technologies to meet the heat demand and minimize the oil consumption for all the three 26 locations in Baltasound. Considering the annual demand of 922 MWh for the location, one 100 kW turbine along with other technologies were simulated. Table 5.5.1 Details of the simulated energy mix Energy Technology Capacity simulated Wind 100 kW Heat Pump 2x10 kW for three locations Thermal Storage 1000 kWh (17 m3) for 3 locations Electric Boiler 100 kW for 3 locations Source: Authors The oil consumption percentage came out to be 18 % of the total demand and 17.5 % of the heat pump consumption was supplied from the grid Table 5.5.1. The Levelized cost of heating obtained was 0.10 to 0.12 GBP/kWh for the three locations and the levelized cost of renewable energy was 0.068 GBP/kWh. The buildings receive no payback during the 25 years life time with this configuration. Reducing the number of heat pumps increases the oil consumption. However, the buildings still receive no payback due to the high initial investment cost of the turbine and the low demand of the location. The RE heat supplying project to the buildings also ends up with negative NPV due to a low demand of the location. Using a cheaper wind turbine will make the project feasible. Also, this situation should be regarded as a potential for integrating the buildings demand with the green hydrogen production by using a larger capacity wind turbine. A similar opportunity exists for the Saxa Vord Resort project location and this will be simulated for the Saxa Vord Resort location. 27 Figure 5.5.1 The output of the simulation showing contribution of different sources to meet the heat demand Source: Authors Simulation results for Saxa Vord Resort Location The Saxa Vord resort has a higher demand than the buildings considered in Baltasound location. In the base case demand scenario section 6.4 , the annual demand is 1527 MWh which is 1.65 times the annual demand of Baltasound location. Hence, the scenario without hydrogen 28 production (Heat Demand only scenario) just to meet the heat demand of the location was simulated. Scenario 1: Heat Demand Only Scenario Saxa Vord Resort The simulation was performed to meet the heat demand with renewables with minimum oil consumption. In the simulation tool, the electrolyser capacity was set to zero. Table 5.7.1 Details of the simulated energy mix for the Heat Demand Only Scenario Energy Technology Capacity simulated Wind 100 kW Heat Pump 2x40 kW Thermal Storage 1000 kWh (17 m3) Electric Boiler 100 kW Source: Authors The thermal storage size was chosen by observing the levelized cost of heat and the oil consumption percentage of the buildings. The storage size was chosen as 17 m3 based on Figure 5.5.1. After 17 m3, the decrease in LCOH was considerably low. For selecting the storage size, the same approach has been used in the simulation of all scenarios. Figure 5.7.1 Variation of LCOH and share of oil consumption with change in storage size Source: Authors 28 28.2 28.4 28.6 28.8 29 29.2 29.4 29.6 12.36 12.37 12.38 12.39 12.4 12.41 12.42 12.43 12.44 12.45 5 9 17 52 S h ar e o f O il C o n su m p ti o n (% ) L C O H ( G B P /k W h ) Thermal storage size (m3) Variation of Levelised cost of heat and share of oil in the energy mix LCOheat (GBP/kWh) Share of oil (%) 29 The monthly distribution of energy mix and the RE and grid contribution to heat pump power requirement obtained are shown in Figure 5.7.2 and Figure 5.7.3. Figure 5.7.2 Monthly distribution of the energy mix in meeting the heat demand Source: Authors Figure 5.7.3 Annual energy consumption of heat pumps Source: Authors The Levelized cost of heat for this scenario was 0.12 GBP/kWh and the levelized cost of RE electricity was 0.15 GBP/kWh which is relatively higher cost. A payback calculation of investing in building heating system gives a payback in 10 years. However, due to the lower demand of buildings the IRR of the RE heat supplying project was found to be 1 %. This again 159527 kWh, 89% 19237 kWh, 11% Annual Energy Consumption of Heat Pumps Consumption from RE Consumption from Grid 30 highlights the opportunity to integrate a hydrogen production centre with a large capacity wind turbine. This scenario was simulated, and results are presented in the coming sections. Scenario 2: 900 kW Wind turbine and hydrogen production with heat as priority. In this scenario are going to be evaluated the impacts in the energy mix and heating technologies required to reach the heat demand as a priority and to produce hydrogen with the remaining energy in Saxa Vord, considering a variation in the demand according to a: • Base case demand in section 6.4 • Full Occupancy demand in section 6.4 • 25% Efficiency on full occupancy demand in section 6.5.2.1 The production of ammonia and hydrogen peroxide is not simulated in this scenario. The control philosophy is defined as “Heat priority” and the indicators that are going to be compared are the IRR, IRR Stakeholders, NPV, DSCR and the Levelized Cost of Electricity, levelized cost of heat, levelized cost of hydrogen and the adequate technology configuration for the heating system Saxa Vord project, chemical production system project and ESCO project. 5.8.1 Base case demand Inputs In order to fulfill the heat demand of the base case, several simulations were performed in order to understand the behavior of the parameters and their impacts in the outputs. As a result, it was found the best configuration in order to cover the demand and production of hydrogen. The energy mix and heating technologies were selected according to the Table 5.8.1 below. 31 Table 5.8.1 Input parameters base case scenario 900 kW Technology Installed Capacity Units Wind capacity 900 kW Heat pumps 100 kW Thermal storage 1000 kWh Hydrogen electrolyser 800 kW Source: Authors As described in the previous section, there is no possibility of producing hydrogen with a 100 kW turbine, increasing the number of turbines increases also the costs and is not recommended. According to this a 900 kW turbine is selected due to its advantage in cost and energy production. In order to define the configuration, the impacts of the heat pump were evaluated as can be seen in the Table 5.8.2 below. According to this is was found that using 2 heat pumps of 50 kW obtains adequate and positive output parameters. Table 5.8.2 Heat pump capacity analysis for 900 kW turbine – Base case demand Heat pump Capacity kW Payback Years for heating system Saxa Vord IRR for ESCO project LCOE GBP/kW LCOH GBP/kW % of oil used % of Share of heating from thermal storage 40 3 10 0.045 0.088 27.52 49.55 60 3 10 0.045 0.087 18.24 47.36 80 2 10 0.045 0.085 11.05 43.08 90 2 10 0.045 0.085 8.157 40.25 100 2 10 0.045 0.085 5.864 36.87 120 3 10 0.045 0.085 2.671 28.93 Source: Authors Same procedure was developed to define the optimize the thermal storage size, as a result a 1000 kW was selected as the best option. 32 Table 5.8.3 Thermal storage analysis for 900 kW turbine- Base case demand Thermal storage kW Payback Years for heating system Saxa Vord IRR for ESCO project LCOE GBP/kW % of oil used % of Share of heating from thermal storage 500 3 10 0.045 6.681 36.03 1000 2 10 0.045 5.864 36.87 1500 3 10 0.045 5.312 37.40 Source: Authors The residual Load after covering the heating demand is used for the Hydrogen production in order to take advantage of the excess energy in the system. To obtain the best configuration, it was analyzed the production of hydrogen to obtain positive indicators. Nevertheless, more variations in the control philosophy and priorities were analyzed and are explained in the later sections. This analysis considers heat has priority over the use of high-pressure storage of hydrogen. According to this configuration different sizes of electrolyser were analyzed as shown in the Table 5.8.4 below, the size selected is 800 kW were the LCOE and cost of hydrogen obtain good results. 33 Table 5.8.4 Hydrogen electrolyser capacity analysis for 900 kW turbine- Base case demand Hydrogen electrolyser kW Payback Years for heating system Saxa Vord IRR for ESCO project (%) LCOE GBP/Kw Cost hydrogen GBP/ kg 650 3 10 0.048 2.25 700 3 10 0.046 2.33 750 3 10 0.045 2.43 800 3 10 0.045 2.55 850 3 10 0.045 2.69 Source: Authors Outputs The energy demand is covered with a total wind generation of 4101 MWh with a capacity factor of 52.02% and a total excess energy of 274.1 MWh. The Ground Source Heat Pump contributed 875.2 MWh to a total demand of 1527.76 MWh with an annual unmet demand of 81.15 MWh that is being covered with the oil boiler. The electricity consumed by the electric boiler is 618 MWh which is powered by wind energy. The hydrogen production outputs can be seen in the Table 5.8.5 below. Table 5.8.5 Hydrogen production with 900 kW turbine- basic demand scenario Hydrogen Production Produced Hydrogen: 51948.4 Kg 51.95 Tonnes Produced Oxygen: 205736.4 Kg 205.7 Tonnes Utilized Energy: 2861.0 MWh Capacity Factor: 46.57% LPS Hydrogen 108.50 Kg Pressurized Hydrogen 5183993 Kg 51.84 Tonnes Excess Oxygen 20573638 Kg 205.7 Tonnes Source: Excel tool Financial Indicators The LCOE obtained is 0.045 GBP/kWh. 34 The LCOH obtained is 0.085 GBP/kWh. The cost of hydrogen obtained is 2.55 GBP/kg. As can be seen in the Table 5.8.6 below the indicator involved with hydrogen production, Hydrogen Production project and ESCO project are positive. Table 5.8.6 Financial indicators for 900 kW turbine – Basic case demand Outputs Indicator ESCO Project units IRR 10% % IRR Shareholders 17% % NPV 3,653.296.12 £ DSCR (min) 1.367256831 - ADSCR 1.755640863 - Source: Excel tool The Saxa Vord heating system project has a payback period of 2 years in the heating system investments. 5.8.2 Full Occupancy demand Now a sub scenario when the residential buildings are permanently occupied was simulated in order to identify the sensitivity of the project towards the demand. Inputs The configuration simulated can be seen in Table 5.8.7 below. Table 5.8.7 Input parameter 900 kW -Full occupancy demand Technology Installed Capacity Units Wind capacity 900 kW Heat pumps 100 kW Thermal storage 1000 kWh Hydrogen electrolyser 800 kW Source: Authors The same methodology was utilized to define the heat pump capacity as can be seen in Table 5.8.8 below 35 Table 5.8.8 Heat pump capacity analysis for 900 kW turbine – Full occupancy demand Heat pump Capacity kW Payback Years for Saxa Vord heating system project IRR for ESCO project LCOE GBP/Kw % of oil used % Share of heating from thermal storage 40 3 10 0.044 48.12 35.59 80 3 10 0.044 33.61 33.81 100 3 10 0.044 27.11 32.18 120 3 10 0.044 21.32 29.92 Source: Authors Even though increasing the heat pump capacity decreases the oil consumption, in reality in order to keep the heat pump efficient it is run at a lower output (45 oC in the modelling assumption) and inlet temperature. Therefore, the heat pump should not be allowed to cover the entire heat demand throughout the year. Especially in winter, when the inlet temperature to wet heating system is required to be around 60 oC. Therefore, energy from heat pump has to be complemented with the energy from thermal storage to achieve higher temperature. In this case, we do not increase the heat pump capacity beyond 100 kW. Same procedure was developed to define the adequate thermal storage size, In the table below it can be seen that there is no considerable change in the % of oil used and the % Share of heating from thermal storage. As a result, it was decided to keep 1000 kW as was selected in the basic demand scenario. 36 Table 5.8.9 Thermal storage analysis for 900 kW turbine- Full occupancy demand scenario Thermal storage kW Payback Years IRR LCOE GBP/Kw % of oil used % Share of heating from thermal storage 500 3 10 0.044 27.38 31.92 1000 2 10 0.044 27.11 32.18 1500 3 10 0.044 26.94 32.38 Source: Authors Outputs The energy demand is covered with a total wind generation of 4101 MWh with a capacity factor of 52,02% and a total excess energy of 274.1 MWh as in the basic demand scenario. The Ground Source Heat Pump 875.5 MWh from a total demand of 2151 MWh with an annual unmet demand of 589.0 MWh that is being covered with the oil boiler. The electricity consumed by the electric boiler is a total of 695.8 MWh which is powered by wind. The hydrogen production outputs can be seen in Table 5.8.10 below. Table 5.8.10 Hydrogen production with 900 kW turbine- Full occupancy demand scenario Hydrogen Production Produced Hydrogen: 53435.8 Kg 53.44 Tones Produced Oxygen: 21162.8 Kg 211.6 Tones Utilized Energy: 2936.8 MWh Capacity Factor: 41.91% LPS Hydrogen 108.17 Kg Pressurized Hydrogen 53327.59 Kg 53.33 Tones Excess Oxygen 211626.80 Kg 211.6 Tones Source: Excel tool Financial Indicators The LCOE obtained is 0.044 GBP/kWh. The LCOH obtained is 0.085 GBP/kWh. The cost of hydrogen obtained is 2.59 GBP/kg. 37 As can be seen in Table 5.8.11 below the indicator involved with hydrogen production, Hydrogen Production project and ESCO project are positive. Table 5.8.11 Financial indicators for 900 kW turbine – Full occupancy demand Outputs Indicator ESCO project Units IRR 10% % IRR Shareholders 17% % NPV 3,596.362.58 £ DSCR (min) 1.358206064 - ADSCR 1.743334864 - Source: Excel tool It could be seen that in the case of full occupancy demand the payback period of building heating system investment has increased to 3 years. Therefore, a scenario with better efficiency of the buildings was further simulated to see the effect in financial parameters. 5.8.3 25 % efficiency on the full occupancy demand The inputs remain the same as in the full occupancy scenario in order to see the effect of increasing the efficiency in the buildings. Outputs Total excess energy of 118.2 MWh as in the basic demand scenario. The Ground Source Heat Pump 871.2 MWh from a total demand of 1613 MWh with an annual unmet demand of 188.38 MWh that is being covered with the oil boiler. The electricity consumed by the electric boiler is a total of 568.9 MWh. It can be seen an increase on the hydrogen production from 53.44 to 55.13 tons implementation of the efficiency in comparison with the full occupancy demand and around 4 tons from the basic case demand from 51.95 to 55.13 tons of hydrogen production. Financial Indicators The LCOE obtained is 0.045 GBP/kWh. 38 The LCOH obtained is 0.085 GBP/kWh. The cost of hydrogen obtained is 2.51 GBP/kg. When implementing 25 % efficiency in the buildings, the financial indicator IRR for the ESCO project does not change but it could be seen that the payback period of heating systems Saxa Vord project improved to 2 years. 5.8.4 Conclusions for this scenario • The LCOE, LCOH and cost of hydrogen remain similar with the variation on the demand. • The oil consumption helps to maintain the levelized cost prices almost constant, nevertheless, is possible to observe a considerable increase of share considerable from base case scenario to full occupancy scenario. • The production of hydrogen varies in around 2 to 4 tons with the variation of the demand. • Payback period for Saxa Vord heating systems project increases with increase in heat demand. Scenario 3: Shifting Heating and Electrolyser Priorities and Ammonia Production. As per the previous section, the best performance of the project was reached when only hydrogen is produced as the result for the price of ammonia and hydrogen peroxide was above the market price. In this section, it will be analysed the effect of shifting the control priority from heat demand to hydrogen and the profitability of the project with an increased high- pressure hydrogen storage. With the shift of priority, an increase in the capacity factor of the hydrogen electrolyser is expected and a decrease on the cost of production. Nonetheless, an increased cost for heating the buildings is also expected. Both conditions will influence the overall performance of the projects which will be evaluated. In order to evaluate such scenarios; hydrogen peroxide production and Electric Batteries were disabled for all the simulations and the rest of the parameters were fixed as shown in the Table 5.9.1. 39 Table 5.9.1 Fixed Inputs for Priority Shifting Scenarios Scenario Base Scenario Full Occupancy Wind Turbine Enercon (900 kW) 1 Enercon (900 kW) 1 Heat Pumps 50 kW 2 50 kW 2 Electric Boiler 100 kW 1 100 kW 1 Heat Storage 1000 kWh 1 1000 kWh 1 Electrolyser 750 kW 1 750 kW 1 LPHS capacity 150 kg 1 150 kg 1 Source: Authors 5.9.1 Heating Supply The heating supply has the advantage of having a back-up system for supplying the demand. This flexibility can be used for taking advantage of the peaks of wind production while providing uninterrupted heat supply for the systems. However, an increase in the oil consumption may negatively affect the Levelized Cost of Heating the buildings. As it was expected, the change in control philosophies generated a different share of energy in the buildings. The different shares are shown in the Table 5.9.2. Table 5.9.2 Thermal Energy Balance for Saxa Vord Heat Demand Fuel Base Case Full Occupancy Heat Priority H2 Priority Heat Priority H2 Priority Heat Pump (MWh) 875 875 876 876 Electric Boiler from RE (MWh) 563 264 692 283 Oil Boiler (MWh) 89 388 583 992 Total (MWh) 1527 1527 2151 2151 Source: Authors In the base case scenario, it is important to emphasize that when heat is priority 94 % of the energy utilized by the heat pump was coming from the wind turbine, if the priority is shifted to 40 hydrogen, only 42 % of the energy input for the heat pump comes from the wind turbine and the remaining 68 % is coming from the grid. The different shares of energy affect the cost of heating the buildings and the LCOE, this will be shown in the next sections. 5.9.2 Hydrogen Production The shift of the priority to hydrogen and derivatives will have a positive effect in the capacity factor and production. This is shown in the Table 5.9.3. Table 5.9.3 Hydrogen and Derivatives Production Performance Base Case Full Occupancy Heat Priority H2 Priority Heat Priority H2 Priority H2 CF (%) 45% 52% 44 52% Pressurize Hydrogen (Ton) 53.6 61.8 53.2 61.7 H2 Utilized Energy (MWh) 2956 3407 2896 3407 H2 Compressor Energy (MWh) 614 707 602 707 Source: Authors As expected, the capacity factor increased a 7 % while the hydrogen production and the utilized energy increased a 15 % compared with the heat priority scenario. 5.9.3 Production Costs and Heating Costs The differences in capacity factors, energy sources, production rates and stored fuels and the total utilized energy of the scenarios should not affect the LCOE because the same amount of total energy is being utilized. However, the cost of heating buildings and cost of hydrogen and its derivatives will be affected. The results of the simulation are detailed in Table 5.9.4 Table 5.9.4 Cost for Different Scenarios Cost Base Case Full Occupancy Heat Priority H2 Priority Heat Priority H2 Priority LCOE (GBP/kWh) 0.04 0.04 0.04 0.039 Cost of Heat (GBP/kWh) 0.072 0.092 0.076 0.093 Cost of H2 (GBP/kg) 4.94 4.53 4.94 4.52 Source: Authors As it was expected, with the base case demand the priority is a determinant factor for achieving lower costs. When hydrogen was set as a priority High Pressure Hydrogen decreases its production cost by 8 % due to the increase of the capacity factor while the cost of heat the 41 buildings increased an 27 % due to the use of oil and grid electricity in the heat pump. In the same way in the full occupancy scenario the cost of heating buildings increased by a 22 % and the cost of producing hydrogen decreased by 8.5 %. 5.9.4 Monthly Production The integration of non-dispatchable power plants with more than one energy demands, as is heat for buildings and electricity for electrolysers, allows the system to install extra energy capacity in case this can be used by the demand with less priority. The winner of this integration is the participant with first priority because the extra capacity installed will allow the system to supply more reliable energy at a relatively lower price. Figure 5.9.1 Monthly Share of Annual Production of Hydrogen (Heat Priority) Source: Authors 42 Figure.5.9.2 Monthly Share of Annual Production of hydrogen (Hydrogen Priority) Source: Authors As shown in Figure 5.9.1 and Figure.5.9.2, when hydrogen is a priority the production is less dependent of the peak months (October, to January) as they have decreased their share of the total production. Spring and Summer months have increased their share when hydrogen is first priority. Due to the overall increase of production, August and September have a lower share of the annual production. The same pattern can be observed in the following figures. In Figure 5.9.3 the heat supply is regularly working with the heat pump and electric boiler. The share of the oil boiler is very less in all the months. On the other hand, in Figure 5.9.4 can be seen that the share of energy from renewables to the heat supply, depends on the peak of production that are available in the winter months. It can also be seen that the heat pump is utilizing with a higher input of the grid. 43 Figure 5.9.3 Heat Supply by Technology (Heat Priority) Source: Authors Figure 5.9.4 Heat Supply by Technology (Hydrogen Priority) Source: Authors For the scenarios with Base Case demand, the IRR improved from 6 % to 7 % when the priority was shifted from Heat Demand to Hydrogen Priority. In the same way, the NPV increased by 674,000 GBP going from 1.256 million GBP to 1.93 million GBP. In the same way, there was 44 an improvement for the Full Occupancy scenarios when priority was shifted from heat demand to hydrogen production, the IRR improved from 6 % to 7 % while the NPV changed from 1.22 million GBP to 1.95 million GBP. 5.9.5 Including Ammonia Production along with the hydrogen Under this sub-scenario, the effect of including the ammonia production is analyzed. The produced ammonia in the model has a higher cost than the price found in the market. However, the ability to produce ammonia represents an advantage to the island because there are more applications for the products. In order to evaluate up to what point ammonia can be produced and the whole project would be profitable, the ammonia production will be included and increased from 27 annual tons up to 105 tons. Therefore, in the heat priority scenario, ammonia is prioritized over hydrogen production. Table 5.9.5 Effect of Ammonia Production and Financial Results NH3 Produced (Tons) 27 54 80 105 IRR 5% 4% 3% 2% IRR Shareholders 7% 5% 3% 1% NPV (GBP) 779 600 312 350 (153 500) (615 500) Cost of NH3 (GBP/kg) 3.95 2.47 1.97 1.72 CF 82% 82% 81% 80% Source: Author As shown in the Table 5.9.5, the result is that the profit earned by the produced pressurized hydrogen compensates for the gap of ammonia price and makes the whole project sustainable. Summary The simulation of the Baltasound location shows that there is no payback for investing in building heating systems and not attractive for the renewable energy production entity also. However, this is an opportunity for a higher demand center like renewable hydrogen production center to be integrated with a large capacity wind turbine. The outcomes on financial indicators 45 of the Saxa Vord heating system project (project 3) and the ESCO project (project 1 and project 2 combined) are summarized in Table 5.10.1, Table 5.10.2 and Table 5.10.3 Table 5.10.1 Summary of financial indicator of the Saxa Vord scenario 1 Scenarios vs Impacts Building Heat Demand (Project 3) ESCO (Project 1 and Project 2 combined) Scenario 1 The cost of wind electricity production is higher. However, the buildings have a payback period of 10 years. The lower demand of buildings makes the project less attractive for RE- Buildings The lower demand for buildings could be regarded as an opportunity to integrate renewable hydrogen production centre with a larger wind turbine. Source: Author Table 5.10.2 Summary of financial indicators of Saxa Vord Scenario 2 Scenarios vs Impacts Building Heat Demand (Project 3) ESCO (project 1 and project 2 combined) Scenario 2 (900 kW turbine with heat demand priority and hydrogen production) The payback period for the building heating system investment increases with increase in demand The ESCO project returns positive IRR of around 10 % for all the demand scenarios For the heating system, the payback period is 2 years for base case demand and 3 years for the full occupancy scenario. Investing in efficiency improves the payback period - Source: Author 46 Table 5.10.3 Summary of financial indicator of Saxa Vord Scenario 3 Scenarios vs Impacts Building Heat Demand (Project 3) ESCO (Project 1 and Project 2) Scenario 3 When Derivatives are priority, less energy for heating is available, so share of grid electricity increases. This increases heating cost for the building If derivatives are priority, the electricity available to buildings is unreliable, which increases gap between LCOE of RE and acceptable tariff agreement with building . When there is a heat priority, production of hydrogen is lower. This leads to higher cost of production. The financial indicators were not affected when the heat and electrolyser priority was modified. Derivatives priority creates an expectation gap between cost of producing RE based electricity and expected building tariff. The ESCO’s interest would seem to conflict with the energy management company of the building heating system. The production cost of both the derivatives were higher than the market price. However, small shares of ammonia (54 tonnes) can be produced and the project is still profitable due to the profit margin from hydrogen production. Source: Author 47 Heat demand assessment Introduction This chapter covers not only the methodology used to determine and analyse the current heat energy use and efficiency of the project buildings, but also the results of these assessments. It also highlights the state of the current building stock and the emissions that could be avoided if renewable heating is implemented at 100 % or a techno-economically feasible level. It also includes a valuation for the implementation of a mini district wet heating system and hot water storage tank at Saxa Vord resort. Building Stock information Geographically the buildings are located on Unst island, Shetland, Scotland, United Kingdom. The project buildings are the Nordalea Care Centre, Unst Leisure Centre, Baltasound Junior High School and buildings at Saxa Vord. Table 6.2.1 and Table 6.2.2 represent the project building stock with their gross internal area, methodology used, and building base load for Baltasound and Saxa Vord respectively. The corresponding building ID and building location can be paired using the maps in Figure 6.2.1 and Figure 6.2.2. UK energy benchmarks are only computed with gross internal area (Communities and Local Goverment, 2008, p. 28) . The BBRI (2005, p. 16) defines the gross internal floor area as the: ‘area of a building measured to the internal face of the perimeter wall at floor level. It includes areas occupied by internal walls and partitions, columns, piers and other internal projections, internal balconies, stairwells, toilets, life lobbies, fire corridors, atria, measured at base level only, and covered plant rooms. It excludes the perimeter wall thickness and external projections, external balconies and external fire escapes.’ The gross internal area was therefore estimated from building plans provided by the different stakeholders. 6.2.1 Unst Leisure Centre Managed by the Shetland Recreational Trust, Unst Leisure Centre is open for all people of all ages and abilities. The centre has a 12.5 m x 5 m pool with a lagoon, games hall, squash court, 48 gym, community room, viewing balcony with two pool tables, football table and several vending facilities (Shetland Recreational Trust, no date). According to a site visit in February 2019, the leisure centre is currently oil heated by two Clyde Combustion 505-9 boilers each 305 kW 6 bars 90oC boilers and domestic hot water supplied a Hoval CT 180 calorifier. An air handling unit running through a heat exchanger from the boiler ensures the air temperature and humidity in all parts of the buildings is comfortable. The pool water is kept at 28 oC all year round. An Energy Performance Certificate done by Green Tourism indicated the area of the building is 1370.6 m2 and the energy intensity is 256.86 kWh/ m2. The heating oil used, kerosene 28 second or burning oil, has a net calorific value of 43.69 ± 0.51 MJ/kg and an emission factor of 72 ± 1.8 t CO2/ TJ (Staffell, 2011, p. 2). 6.2.2 Nordalea Care Centre According to the Community Care Services of the Shetland Islands Council (2013a), the care centre is a resource centre for adults built to accommodate up to 7 residents. There are 6 single rooms and one double room. Each of the rooms has an en suite toilet and shower. There is a lounge in the residential area of the building and large lounge, dining room and conservatory (Community Care Services Shetland Islands Council, 2013b). The Energy Performance Certificate in the building issued in 2009 indicated that the building was constructed in 2002, has a gross internal area of 1082.9 m2 and energy intensity of 231 kWh/m2. Further analysis of the oil consumption data provided by the Shetland Island Council indicated that the heat energy intensity in 2011-12, 2012-2013, 2013-14 and 2014-15 as 316.34 kWh/m2, 315.02 kWh/m2, 274.83 kWh/m2 and 290.29 kWh/m2 respectively with the years starting from April to March. The building is primarily floor heated and kept at around 20 oC by two Falcon GTE 7 oil boilers each rated 92 kW 4 bars 110 oC and domestic hot water supplied using a 44.8 kW calorifier. A Perkins P100 kerosene 28 fuelled 100 kVA three phase generator is also used for backup during period of power blackout and runs from the same kerosene storage as the boilers. 6.2.3 Baltasound Junior High School Baltasound Junior high school is UK’s most northern education institution. The school currently provides nursery, primary and secondary education for the whole island of Unst. Some of the buildings are reported to have been built as late as 1879 (Baltasound Junior High 49 School, no date). By building ID, 8 is currently not used and left in a dilapidated stated. The school is open from 8:55 to 15:40 Monday to Thursday and only 8:55 to 14:00 on Friday. The school library is open Monday to Thursday 9:30 to 15:00 (BaltaSound Junior High School, no date). The school is currently all air heated using two Clyde Combustion 351-8 boilers each 261.872 kW 60 psi 100oC boilers and domestic hot water is supplied by a Hoval CT 180 calorifier. The hot water from the boiler is fed into a central air heating system that circulates hot air to the whole building simultaneously. 50 Table 6.2.1 Building Stock Summary at Baltasound. Building ID Methodology Building by use GIA4/m2 Heat Intensity kWh/m2/year 1 I Nordalea Care Centre 1082.95 290.296 2 I Unst Leisure Centre 13707 256.868 3 I Old School Building 131 Not required 4 I Main School Building 4291 5 I School Oil Store 57 6 I School Building-Part of music room 40 7 I School Building - Music Room 312 17 I School Old Building 1216 Data Sources: Authors’ analysis in QGIS of (OpenStreetMap Contributors, 2018) and Oil consumption data from Shetland Island Council or Shetland Recreational Trust Figure 6.2.1 Baltasound Building Stock. Source: Authors’ analysis in QGIS of (OpenStreetMap Contributors, 2018) 51 6.2.4 Saxa Vord Saxa Vord project building stock was constructed between 1989 and 1992. It has a mixed building usage and also different ownerships. Table 6.2.2 and Figure 6.2.2 show the related building usage and location. An information meeting with site care taker indicated that the buildings were built under the Ministry of Defence specifications. From a site visit in February 2019, by building ID, buildings 29 to 34 are linked with a wet heating system using a 450 kW Ideal Viceroy GT400 oil boiler. The boiler supplies both domestic hot water and space heating. Buildings 26 to 28 are going to be demolished and therefore are not included in the analysis, however the model allows for their incorporation in case they are simply renovated. Buildings 11,14-16 and 18-23 use electricity for both space heating and domestic hot water. The Nordabrake houses, building IDs 21 and 22, are both fitted with 4 kW insulated domestic hot water heaters as well as electric panel heaters and electric storage heaters. The mini-district heating system is old and requires reinvestment to improve its efficiency. There is evidence of degraded insulation and poorly insulated joints and flanges. 52 Figure 6.2.2 Building Stock at Saxa Vord. Source: Author based on analysis in QGIS of (OpenStreetMap Contributors, 2018) 53 Table 6.2.2 Building Stock Summary Building ID9 Methodolo gy10 Building by use GIA11/ m2 Heat Intensity kWh12/m2/y ear Baseload 13/ % of yearly demand 11 II taftens Housings 1190 181 14.9% 12 II Old Power house to be demolished 969 Not required 14 II Brewery 680 180 15 II Demolished 122 Not required 16 II Demolished 99 Not required 18 II Machinery Store (not heated) 816 Not required 19 II Guard room 114 181 14.9% 20 II Medical Centre 673 200 16.7% 21 II Nordabrake Housing 1 1200 181 14.9% 22 II Nordabrake Housing 2 960 181 14.9% 23 II Sheckleton Block 383 181 14.9% 24 II Unst Distillery ltd 34 180 16.7% 25 II Store 34 120 55.0% 26 II Accommodation Blocks 12a 2652 181 16.7% 27 II NAAFI Shop & Thrift shop (Retail plus office &Stores) 425 170 55.0% 28 II Station HQ (Offices & Stores) 408 120 55.0% 29 II Gymnasium (Indoor Recreation facilities) 332 440 55.0% 30 II Kitchen area (Kitchen, offices &Stores) 859 370 55.0% 31 II Amenity Centre (Lounge, coffee shop) 583 350 55.0% 32 II Accommodation Blocks 12b 734 181 16.7% 33 II Combined mess (Dining, bar & lounge areas) 503 350 16.7% 34 II Accommodation Blocks 12c 808 181 16.7% Data Sources: Authors’ analysis in QGIS of (OpenStreetMap Contributors, 2018) and (Department of Energy& Climate Change UK, 2014, pp. 5–9) 9Linked to Figure 6.2.2 10 Method II is explained in Section 6.3 11Gross internal area obtained by adjusting gross external area obtained by analysing (OpenStreetMap Contributors, 2018) with QGIS by 85 % (space management group, 2006) or obtained from documentation provided by Saxa Vord management. 54 Methodology for hourly heat demand projection Two main methodologies, Method I and Method II, as indicated in Table 6.2.1 and Table 6.2.2 were used to develop the distribution of heat demand with hours for the project buildings. At the core of these is the concept of heat degree days or hours. According to Tony (2006, p. 1) , ‘Degree-days are essentially the summation of temperature differences over time, and hence they capture both extremity and duration of outdoor temperatures. The temperature difference is between a reference temperature and the outdoor air temperature. The reference temperature is known as the base temperature which, for buildings, is a balance point temperature, i.e. the outdoor temperature at which the heating (or cooling) systems do not need to run in order to maintain comfort conditions.’ Primarily a reference temperature of 15.5oC (Field, 2008a, p. 12) was used. difference between the reference temperature and average temperature of the day as long as its positive. The prediction of building heat demand is based on the notion that heat loss from a building is directly proportional to the difference between the indoor and outdoor temperatures (Tony, 2006, p. 6), with time the energy becomes an equivalent of power, since power is a product of energy and time, such that the equation in Equation 6.3.1 becomes valid. The constant of proportionality reflects the resistance to heat flow or the cumulative U values of the materials between the building indoor and outdoor. It’s called the overall heat loss coefficient Equation 6.3.1 Heat energy flow out of a building and indoor and outdoor temperature relationship: Source: (Tony, 2006) There is a limit to the proportionality of outdoor temperature to the building heat use as expressed in Figure 6.3.1 and when monthly building heat demand and monthly heat degree 12 Heat intensities obtained from (The Scottish Government, 2013, p. 36; Department of Energy& Climate Change UK, 2014, pp. 5–9; Ramboll Energy and The Carbon Trust, 2014) 13 Base load share of heat demand computed from share of domestic hot water demand as illustrated in Section 4.3.2 55 days is plotted, a region where linearity is lost is observed. The plot is called the performance line of a building. Figure 6.3.1 Relationship of outdoor temperature to heat demand in a building: Source: (Meng and Mourshed, 2017, p. 261) The non-weather-related heating of the building which is termed as the based load is not proportional to the heat degree day and had to be first deducted from the monthly heat demand of each building type before projections were made for temperature correlated demand. Generating heat degree hours from the actual hourly temperature distribution, would show large variation in heat demand from hour to hour, which would be un-realistic because of building thermal storage and the user profiles of the buildings. Therefore, the daily average heat degree day was assumed to be overall heat degree day across the 24 hours that day. Figure 6.3.2 shows the generated heat degree hours for the projection location. 56 Figure 6.3.2 Project adjusted heat degree hours. Source: (Meteoblue, 2019a) The distribution shows that mid-year in the summer a few days were warm enough to have the heating systems off or supplying domestic hot water demand in since values close to zero are observed. Below are the methods discussed in detail; a) Method I based on historical oil and or electricity consumption for project buildings b) Method II based on heat intensity benchmarks with adjustments using occupancy factors, wind factor and Energy Performance Certificate found in project buildings. The limitations of the methodology used to determine the building heat demand by degrees days are the human factor like flipping the thermostat off on cold days, it ignores variation in occupancy and activity over the weekend and public holidays, and accuracy is affected by energy efficiency improvements. 6.3.1 Building heat demand assessment: Method I For Baltasound Junior High School and Nordalea Care Centre (NCC) historical data of oil and electricity consumption from April 2014 to May 2015 was availed by Shetland Island Council. Basing on interviews and site visits at the respective premises in February 2019, it was devised that oil is the primary fuel used for heating. An overall boiler operational efficiency of 80 % was assumed and the heat demand obtained by applying this efficiency to the oil consumption data. Monthly Modern-Era Retrospective analysis for Research and Applications (MERRA) 57 temperature data from April 2014 to May 2015 was downloaded from a NASA website14 and heat degree day month generated to ensure approximate coincidence with the oil consumption data (Paul W. Stackhouse et al., no date). The generated heat degree days at a base temperature of 15.5 oC for the Baltasound Junior High School (Field, 2008a, p. 12) and at 20 oC for Nordalea Care Centre were plotted against the adjusted oil consumption. The resulting plots called the building performance lines, as shown in Figure 6.3.3, were used to establish the buildings base temperature and temperature correlated heat demand (Rodrigues et al., 2002, p. 8). Figure 6.3.3 shows that the monthly base load of the care centre is 19,996 kWh while that of the school is less at 9,070 kWh. The temperature correlated demands are 3.8368 kWh/oC day and 0.6762 kWh/oC day for the school and care centre respectively. The regression correlation coefficient of the care centre at 0.1796 indicates there is less correlation with the outdoor temperature to the building internal temperature since its heated to a high temperature year-round. Furthermore, using a similar approach for the Unst leisure centre, the energy demand was established from the Energy Performance Certificate. The share of heating oil was established by relating the total CO2 emissions to the respective CO2 emission factor and hence the respective energy required to deliver the emissions. This was done because it was hard to deduce the monthly oil consumption from the data provided by the stake holder. Heat degree hours were computed and realised from 2017 temperature data for a base temperature of 28 oC and 15.5 oC (Field, 2008b). The share of energy from the swimming pool whose room 14 https://power.larc.nasa.gov/data-access-viewer/ 58 temperature is maintained at 28 oC was computed basing on the share of the total building area by the swimming pool and weighted heat degree days from both parts of the building. Figure 6.3.3 1Building performance line for Baltasound Junior High School and Nordalea Care Centre. Source: Author based on analysis of Oil consumption data from Shetland Island Council and MERRA temperature data. This was based on the fact that an interview with the Leisure Centre manager justified these operational characteristics of the building and that heating oil is the main fuel used for heating the building. The corresponding heat demand by the different parts of the building were then divided by their corresponding heat degree hours. The result, a constant for each building zone, was used with heat degree day distribution to realise the effect hourly heat demand as illustrated in Equation 6.3.2 to Equation 6.3.7. 59 𝐴𝑟𝑒𝑎 𝑖𝑛𝑑𝑒𝑥𝑒𝑑 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 = (𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑎𝑟𝑒𝑎 − 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑤𝑖𝑚𝑚𝑖𝑛𝑔 𝑝𝑜𝑜𝑙) 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 𝑎𝑡 15.50 𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑎𝑟𝑒𝑎 + 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑤𝑖𝑚𝑚𝑖𝑛𝑔 𝑝𝑜𝑜𝑙 × 𝐷𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 𝑎𝑡 28𝑜 𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑎𝑟𝑒𝑎 Equation 6.3.2 Area indexed degree days 𝑆𝑤𝑖𝑚𝑚𝑖𝑛𝑔 𝑝𝑜𝑜𝑙 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 𝑎𝑡 280 𝐴𝑟𝑒𝑎 𝑖𝑛𝑑𝑒𝑥𝑒𝑑 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 ∗ 𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 Equation 6.3.3 Swimming pool heating demand 𝑅𝑒𝑠𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 𝑎𝑡 15.50 𝐴𝑟𝑒𝑎 𝑖𝑛𝑑𝑒𝑥𝑒𝑑 𝑑𝑒𝑔𝑟𝑒𝑒 𝑑𝑎𝑦𝑠 ∗ 𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 Equation 6.3.4 heating demand for the rest of the building 𝑘𝑊ℎ/ ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟 = 𝐻𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 𝑓𝑜𝑟 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑠𝑒𝑐𝑡𝑖𝑜𝑛 𝐷𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟𝑠 𝑓𝑜𝑟 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑠𝑒𝑐𝑡𝑖𝑜𝑛 Equation 6.3.5 kWh per degree hour constant 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑠𝑒𝑐𝑡𝑖𝑜𝑛 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = (𝑘𝑊ℎ/ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟) ∗ 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑 ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟 Equation 6.3.6 Building section heat demand 𝑇𝑜𝑡𝑎𝑙 ℎ𝑜𝑢𝑟𝑙𝑦 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = ℎ𝑜𝑢𝑟𝑙𝑦 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 𝑓𝑟𝑜𝑚 𝑠𝑤𝑖𝑚𝑚𝑖𝑛𝑔 𝑝𝑜𝑜𝑙 + ℎ𝑜𝑢𝑟𝑙𝑦 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑟𝑒𝑠𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 Equation 6.3.7 total hourly heat demand Figure 6.3.4 then shows the resulting heat demand projections by buildings. It shows that the demand of the care centre is pretty much constant throughout the year with a minimal impact of the variation in temperature. This is anticipated since the building is kept at higher constant 60 temperature, while for the leisure centre and school the demand varies by season due to adjustment of the indoor temperature in the building with changes in the outdoor temperature. Figure 6.3.4Baltasound Buildings heat demand Source: Author based on analysis in Ms. Excel 6.3.2 Building heating assessment: Method II For method II, heat intensity benchmarks from a publication by the Department of Energy & Climate Change and the Scottish Heat map were used to determine the yearly heat energy use of the buildings with an adjustment of 12 % due to the wind factor and (The Scottish Government, 2013, p. 36; Department of Energy & Climate Change, 2014, pp. 5–9; Ramboll Energy and The Carbon Trust, 2014). Furthermore, an occupancy factor was applied to the demand after distributing by the hour. This methodology was primarily applied in Saxa Vord 61 due to the fact the that consumption data provided by the Saxa Vord management didn’t have an occupancy pattern availed as well for the different buildings. 𝐻𝑒𝑎𝑡 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑏𝑒𝑛𝑐ℎ𝑚𝑎𝑟𝑘 (𝑘𝑊ℎ/𝑚2/𝑦𝑒𝑎𝑟) = 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑎𝑛𝑛𝑢𝑎𝑙 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝐺𝑟𝑜𝑠𝑠 𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝐴𝑟𝑒𝑎 Equation 6.3.8 heat intensity benchmark relationship to gross internal area and building heat demand The building heat demand includes both domestic hot water demand and space heating. Since most residential buildings in Saxa Vord were constructed between after 1983, an energy intensity of 181 kWh/m2/year (The Scottish Government, 2013, p. 36) was considered for the buildings classified as dwellings as shown in Table 6.2.2. The building’s base load heat demand was assumed to be equal to the share of domestic hot water demand from the total heat demand. The share of space heating and domestic hot water by building type were assumed from literature as discussed below. Scottish dwellings consume 74 % and 13 % of their energy for space heating and water heating respectively (Clarkson, Palombi and Lloyd, 2017, p. 62). In the UK services sector 9 % of the energy supplied is used for water heating and 45 % for space heating (The Open University, 2018, pp. 8–9). For Leisure Centres, the Chartered Institution of Building Services Engineers (no date, p. 4) estimates the heating energy demand to be 74 % (56.5 % space heating and 17.5 % hot water) of the total building energy use. Furthermore the Carbon Trust (Carbon Trust, no date) estimates that schools consume a total of 60 % of their total energy in space heating (2 % of this from electricity and 58 % from fossil fuel) and 16 % for hot water demand ( 1 % of this being electric and 15 % being from fossil fuels). The computed proportions of the base load computed using Equation 6.3.9 are illustrated % 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑏𝑎𝑠𝑒 ℎ𝑒𝑎𝑡 𝑙𝑜𝑎𝑑 = 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ℎ𝑜𝑡 𝑤𝑎𝑡𝑒𝑟 𝑑𝑒𝑚𝑎𝑛𝑑 𝑠𝑢𝑚 𝑜𝑓 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑠𝑝𝑎𝑐𝑒 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑎𝑛𝑑 𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 ℎ𝑜𝑡 𝑤𝑎𝑡𝑒𝑟 Equation 6.3.9 Percentage share of base heat load in total demand 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = 𝑏𝑎𝑠𝑒 𝑙𝑜𝑎𝑑 + 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 Equation 6.3.10Equation used to predict the building heat demand 62 The hourly temperature-correlated part of the demand coefficient was determined by dividing the difference between the projected building heat demand and the base load with the total number of degree hours as shown in Equation 6.3.12. The hourly base load was determined by dividing the yearly base load, which was computed from the share of base load for projected demand, with the total number of hours in a year 8760 as shown in Equation 6.3.10. The hourly temperature correlated part of the demand obtained by multiplying the hourly observed heat degree hour with the proportionality constant was established as discussed from Equation 6.3.12. Equation 6.3.13 illustrates how the hourly heat demand was estimated from the sum of the hourly base load and hourly temperature correlated demand. ℎ𝑜𝑢𝑟𝑙𝑦 𝑏𝑎𝑠𝑒 𝑙𝑜𝑎𝑑 = 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 𝑥 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑏𝑎𝑠𝑒 𝑙𝑜𝑎𝑑 𝑓𝑜𝑟 𝑝𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 8760 Equation 6.3.11 Hourly base load computation for method II ℎ𝑜𝑢𝑟𝑙𝑦 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 = (𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 − 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 𝑥 𝑠ℎ𝑎𝑟𝑒 𝑜𝑓 𝑏𝑎𝑠𝑒 𝑙𝑜𝑎𝑑 𝑖𝑛 𝑡𝑜𝑡𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 𝑇𝑜𝑡𝑎𝑙 ℎ𝑒𝑎𝑡 𝑑𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟𝑠 Equation 6.3.12 hourly temperature correlated demand computation for method II ℎ𝑜𝑢𝑟𝑙𝑦 ℎ𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 = ℎ𝑜𝑢𝑟𝑙𝑦 𝑏𝑎𝑠𝑒 𝑙𝑜𝑎𝑑 + 𝑑𝑒𝑔𝑟𝑒𝑒 ℎ𝑜𝑢𝑟 ∗ ℎ𝑜𝑢𝑟𝑙𝑦 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑒𝑑 𝑑𝑒𝑚𝑎𝑛𝑑 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 Equation 6.3.13 Method II hourly heat demand formula Figure 6.3.5 shows the projected total building heat demand at Saxa Vord at 100 % occupancy implying that all buildings are occupied throughout the year. 63 Figure 6.3.5 Projected building demand at 100 % Occupancy at Saxa Vord Building Occupancy Scenarios To model for the impact of tourism on room occupancy, the Saxa Vord main buildings blocks were assumed to have an occupancy similar to that of Scottish accommodation buildings as estimated by J. John et al. (2017, p. 11 Table 3) in the tourism scenario. On the other hand, the occupancy of the residential blocks was assumed to peak in summer and reduce in winter to 15 %, primarily to heat the rooms to ensure the buildings retain their quality as shown in Figure 6.4.1. Figure 6.4.2 shows the tourism plus residential occupancy scenario in which the residential blocks are assumed to be fully occupied year-round due to the anticipated development of a space centre in Saxa Vord. 64 Figure 6.4.1 Yearly occupancy variation in tourism scenario for Saxa Vord main Buildings block and the residential blocks. Source: Authors’ analysis Figure 6.4.2 Tourism and residential scenario. Source: Authors’ Analysis 65 Energy Efficiency Methodology Energy efficiency assessment of a building’s thermal system can be conducted with observations of the three elements below: i. Existing building envelope. ii. Existing heat distribution system, e.g. pipes for wet heating system. iii. Existing heat generation technologies, i.e. boiler. One residential unit from Nordabrake building in Saxa Vord was selected for assessment. The reason for selecting Nordabrake is because a residential example allows to utilise the UK Standard Assessment Procedure (SAP), and NHER Software (National Home Energy Rating)15 for energy efficiency assessment, which are both specified for dwellings. . It is assumed that the results can be transferred to all the buildings considered in the project because they are approximately built at the same location and the same time and have the same construction. Space heating demand was the major focus for energy efficiency assessment and energy demand. Space heating was observed to take up most of the building energy and more energy savings potential could therefore be realised in this area. For assessing the existing building and its components, a unit from Nordabrake was selected, and TRNSYS16 software and NHER software were both used to determine the energy performance and SAP rating for the respective unit. Using these, two different software for building energy consumption assessment was important because of their methodology of energy simulations and output reports. The NHER provides SAP rating and CO2 emissions based on the age of the building and its location, with less detailed input data. The TRNSYS software requires more detailed input and allows one to perform energy efficiency improvements to assess the energy savings potential and helps in establishing different scenarios for assessment. During the calculation process constructional data was not available due to many limitations so in that case assumptions were made to move forward. The results of the software used were assessed to understand the energy efficiency potential of the model unit and the rest of the Nordabrake housing. The unit building was measured and modelled 15 An energy efficiency assessing software for new build house designed by UK government- authorities(National Energy Services, 2010) 16 Transient System simulation software (Caram, Ph and Labaki, 1997) 66 using AutoCAD and SketchUp. The actual orientation of the building considered for simulation was 313°. Figure 6.5.1 schematic developed SketchUp model and plan Source: Author using Sketch UP and AutoCAD The results of the software used were assessed to understand the energy efficiency potential of the model unit and the rest of the Nordabrake housing. 6.5.1 Energy Simulation Results for Current Scenario The simulation results, from NHER software, for one unit of Nordabrake resort for space heating total to 25,318 kWh/year and total CO2 emission of 14,816 kg/year from an emission factor of 0.519 kg CO2/kWh. The SAP rating for the unit is 1 and the SAP band is G. This implies the building has a poor energy performance. The SAP report from the simulation is attached in Appendix VIII: In comparison, the excel model generates an estimated annual heat demand of 20,691.4 kWh/ year with an intensity of 172.48 kWh/m2. The summary of the results is shown in Table 6.5.1 below. The table also shows the intensity of energy consumption and CO2 emission per unit area. 67 Table 6.5.1 Energy simulation results from NHER software for sample building unit. Parameter Energy Consumption (kWh/year) CO2 emissions (kg/year) Energy Rate (kWh/m2/year) CO2 emissions (kg/m2/year) Space Heating 25,318 13,140 184.80 95.91 Water Heating 2,175 1,129 15.88 8.24 Total 28,547 14,816 208.37 108.15 Source: NHER output As a comparison, the TRNSYS energy simulation results show that the space heating for the building consumes 21,428 kWh/year. The energy intensity for space heating for the model building is 158.73 kWh/m2/year. The values are approximately equal to the building space heating excel model. However, it should be mentioned that the area input for TRNSYS includes garage area of 15 m2 which is not included in excel model. The assumptions made during the TRNSYS software energy simulation include that the building is occupied by 2.5 people and the heating system runs continuously throughout the year. These inputs are not required by the NHER software. Air change of infiltration and ventilation has been assumed as 0.27 1/h and 0.6 1/h respectively. Some assumption other of current scenario with significant impact on energy consumption of building is shown in Table 6.5.2 below. Some of the material selection, such as mineral wool, polystyrene for walls and floors and types of windows, has been made based on assumptions while the thermal properties’ values have been taken from TRNSYS library. A site visit to the Medical Centre in Saxa Vord, Building ID 20, from Figure 6.2.2 in February 2019 indicated the loft was insulated using mineral wool therefore it was assumed that the same insulation was applied to the Nordabrake house that was modelled.. However, because the mineral wool was not in good condition, a mineral wool with high conductivity value of 0.54 kJ/hm K from the TRNSYS library was selected. The difference in the space heating energy results of NHER with two other results can be due to the reasons that the NHER takes the sample unit as an aged building as per its construction 68 date and does not allow changes while TRNSYS software required the entry of the current building state manually basing on the observation of the different materials used. The simulation results for the current scenario provide basis for calculating energy efficiency potentials for the unit building. The TRNSYS software allows inputs for energy efficiency improvements such as manipulating building component layers, constructional and insulation materials. That’s why the results of TRNSYS simulation have been used to compare the energy efficiency scenarios with the current scenario results 69 Table 6.5.2 TRNSYS Inputs for Current Scenario energy simulation Current Scenario Material Place of Use Used Area (m^2) 1 Double-Glazed Windows All windows 6.9 U-Vlaue 2.82 W/m^2K G-Vlaue 0.64 100% Single Glazed Windows 12.69 U-Vlaue 5.69 W/m^2K G-Vlaue 0.89 100% 2 Mineral wool Roof 123.58 Conductivity 0.54 kJ/hmK Capacity 0.015 kJ/kgK Density 570 kg/m^3 Thickness 100 mm 3 Polystyrene' Ext-Walls 92 Conductivity 0.14 kJ/hmK Capacity 1.25 kJ/kgK Density 30 kg/m^3 Thickness 100 mm 3 Polystyrene Floor 92 Conductivity 0.14 kJ/hmK Capacity 1.25 kJ/kgK Density 30 kg/m^3 Thickness 3 mm 4 Cork Floor 92 Conductivity 0.16 kJ/hmK Capacity 0.18 kJ/kgK Density 100 kg/m^3 Thickness 4 mm . 70 6.5.2 Building Energy Efficiency Study Scenarios 6.5.2.1 Energy Efficiency Scenarios Definition After assessment of the results of the current scenario, three energy efficiency scenarios were created each targeting an effective building component for efficiency improvement, to estimate the energy savings potential of each individual component for the unit building. Table 6.5.3 to Table 6.5.5 below shows the three scenarios. Table 6.5.3 All window improvement scenario. all windows Improvement material place of use used area (m^2) average window size Material + labour cost per window (£) life time (year) Total price/£ window double glazed all windows 19.59 0.9 21766.67 1000 25 21766.67 u value 1.07 w/m^2K g value 0.62 100% - - - - Total 21766.67 Source of costs (Palmer, Livingstone and Angela, 2017) Table 6.5.4 External Wall improvement TRNSYS simulation cost. Ext Wall Improvement Scenario material place of use used area (m^2) Material + labour (£/m2) life time (year) Total price/£ Mineral wool 100 mm Ext-Walls 52.51 180 25 9451.80 conductivity 0.13 kJ/hmK capacity 0.9 kJ/kgK density 80 kg/m^3 Total 9451.80 Source of costs (Palmer, Livingstone and Angela, 2017) 71 Table 6.5.5 TRNSYS Roof improvement scenario. Roof Improvement Scenario Material place of use used area (m^2) Material + labour (£/m2) life time (year) Total price/£ Mineral wool 10 0 mm Roof 123.58 40 25 4943.20 conductivi ty 0.1 3 kJ/hm K capacity 0.9 kJ/kg K - density 80 kg/m^ 3 - Total 4943.20 Source of costs (Palmer, Livingstone and Angela, 2017) In addition, another scenario which is a mixture of the other scenarios, except window scenario where only greenhouse window has been taken for improvement. 72 Table 6.5.6 TRNSYS 30% energy efficiency scenario. Energy Efficiency Scenario _30% material place of use used area (m^2) averag e windo w size material +labour cost per window / £ life time (year ) Total price /£ 1.window double glazed greenhouse 12.69 0.9 14100.0 0 1000 25 14100.0 0 u value 1.0 7 w/m^2 K g value 0.6 2 100% - - - - 2. Mineral wool Thickness 100 mm Ext-Walls 52.51 180 £/m2 25 9451.80 conductivit y 0.1 3 kJ/hmK capacity 0.9 kJ/kgK - - - - density 80 kg/m^3 - - - - Thickness 100 mm Roof 123.5 8 40 £/m2 4943.2 Total 28495 Source of cost data (Palmer, Livingstone and Angela, 2017) The idea to develop different scenarios was to get the most cost-effective and energy saving improvement to the building unit. 6.5.3 Energy Efficiency Cost Scenarios For each energy efficiency scenario and its measures, annual costs were calculated to assess the investment against the annual energy savings potential. This is important to analyse so that an informed decision can be made about which measure could provide with the most benefit. Table 6.5.7 below shows the energy efficiency improvements and the related costs to the measures, for different scenarios. 73 Table 6.5.7 Costs of different energy improvement measures in relation to energy savings Scenarios Capex /£ Energy Demand/kWh Energy Reduction percentage per £ Annualised CAPEX17/£ Cost of measure £/kWh 0 current 0 21433.83 0.00% 0.00 0.0000 1 Roof Improvement 4943.2 19841.38 7.43% 307.13 0.1929 2 All Windows Improvement 21767 16036.11 25.18% 1352.40 0.2505 3 Ext-Wall Improvement 9451.8 21071.36 1.69% 587.26 1.62 4 scenario 30% 28495 14981.20 30.10% 1770.44 0.2744 The cost of measure/£ indicates the investment per each kWh of energy saved for each scenario. This indicator helps to identify the most suitable scenarios taking into count the energy efficiency and financials. Based on the indicator, the scenario Roof Improvement and scenario All Windows Improvement prove to be the most feasible among the four scenarios created. Therefore, the respected scenarios have been selected as project energy efficiency scenarios. Their energy demand reductions are lying above 7% and 25%. The External Wall Improvement scenario shows the highest cost of measure/£ which is 1.62 £/kWh. This is because the external walls of the buildings have a good insulation in their existing condition. So, the additional energy efficiency measures do not improve the energy efficiency with a greater difference as compared to the other scenarios, and the investment cost does not return a lot of value. 17 Annualised for 25 years 74 6.5.4 Wet heating System Furthermore, to determine if there are higher carbon emission avoidance benefits and economic benefits from the conversion of the existing system to a wet heating system, a cost benefit analysis was added to the overall project study. The proposed system layouts are discussed within this section. To estimate the costs of installing a wet heating system, the buildings were linked with hot water distribution pipes in QGIS as shown in Figure 6.5.2 below, and the location of the hot water tank was estimated as well by ensuring that the tank is closest to the building with the highest heat demand. The costs for installing radiators and plumbing within the buildings are however excluded in the analysis. 6.5.4.1 Wet Heating System Pipework The main methodology used was to size the maximum demand of the buildings on a design day when the temperature is -5 oC. The corresponding adjustments using the building base load and the temperature correlated component of the head demand would yield the maximum building heat demand at this temperature using heat degree day at -5 oC. This demand was then adjusted for an assumed loss at a rate of 5 %, considering the transmission distances are small, to yield a peak heat demand. i. Saxa Vord To size the pipes at Saxa Vord a proposed route was drawn in QGIS as shown in Figure 6.5.2. 75 Figure 6.5.2 Proposed system route at Saxa Vord for a wet heating System. Source: Author based on Analysis in QGIS of (OpenStreetMap Contributors, 2018) Based on the peak heat demand of each building and the temperature difference between supply and return water of 35 oC, the mass flow rate was calculated to choose the convenient pipe diameter for each section of the pipe line. A steel twin pipe system is suggested, which has a polyurethane foam insulation and aluminium foil as a diffusion barrier. Such a flexible pipe system has approximately 30 years of continuous operating life and at 140oC is rated for 25 bars of maximum pressure. Some other parameters and assumptions taken into consideration are a 10 % heat loss through the pipes, a water velocity less than 3 m/s (Jones et al., no date, p. 36) and fittings resistance of 33 %. Furthermore, the pressure loss per meter of pipe was based on D'Arcy-Weisbach Equation friction loss chart (Steel and Friction, no date). The proposed pipe sizes of pipes vary between 32 mm for the main pipe and 20-25 mm for the branches connecting the Boiler house, brewery, distillery and Saxa Vord self-catering resort 76 buildings. According to a publication by the IEA ETSAP (2013, p. 5 Table 4), the total piping costs could vary from 300 to 350 €/m which is equivalent to 260-303.45 £/m at an exchange rate of 0.86 €/£. The total pipe length from Table 1.7 is 565 m, therefore the investment cost is £ 171,195.0. It is also important to consider a water pump to pump the water through the most resisting circuit (index circuit) with a pipe loss of 22.7 m head, delivering a flow rate of 1.706 l/s, considering 2 % pump energy. This pump is estimated with 45.08 m of water Column which is equivalent to 4.508 bar. The sizing and calculations of the estimated pipes can be found in Appendix VII: in the appendix. 𝐸𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 𝑎𝑛𝑛𝑢𝑎𝑙 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 = 𝐴𝑠𝑠𝑒𝑡 𝑃𝑟𝑖𝑐𝑒 𝑥 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒 1 − (1 + 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒)−𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑒𝑟𝑖𝑜𝑑𝑠 Equation 6.5.1 Equivalent annual cost of an asset Assuming annual service and maintenance cost of 1 % of CAPEX (IEA ETSAP, 2013, p. 5), a 20-year period and a discount rate of 3.72 % the annualised capital cost computed based on Equation 6.5.1 for the project locations is as shown below in Table 6.5.8. Table 6.5.8 Annualised capital cost for 20 years for Saxa Vord piping works for mini district heating system. Indicator Saxa Vord Units Pipe runs 565 m Capital Cost 171,195.0 £ Annual OPEX 1711.95 £ Annualised CAPEX 3300 £ Total annual cost 5011.95 £ per annum Source: Author (s) 6.5.4.2 Thermal Storage Thermal storage came into picture as an important strategy to balance the system, using insulated thermal storage tank allows storing the surplus of electricity as heat and delivering it back to the system whenever there is a shortage of supply. The recommended thermal storage 77 technology is a welded steel hot water tank. This allows using the high specific heat content per volume of water (Danish Energy Agency, 2018b). Considering the heat capacity of water as 1.16 kWh/m3K (AEE – Institute for Sustainable Technologies, 2010) and a temperature difference between inlet and outlet as 45 ̊ C, the basic requirement to store 1 kWh of energy is approximately 19 litres. Calculations conducted using the following formula: 𝑸𝒔 = (𝒎 𝑪𝒑)∆𝑻 Equation 6.5.2 Sizing of Thermal Storage Source: (AEE – Institute for Sustainable Technologies, 2010, p. 3) Having Qs Total heat capacity of the storage tank [kWh] m Volume of storage tank [m3] Cp Heat Capacity of Water [1.16 kWh/m3K] ∆T Temperature difference [K] The (Danish Energy Agency, 2018b, sec. 54) recommends these guidelines for commissioning the hot water tank ; 1. Steel structure with concrete base allowing more flexibility and lower CAPEX cost, 2. In order to impede corrosion, keep water at pH 9.8, or use additional heat exchanger and pasteurize the tank, 3. Use 300 mm insulation mineral wool and increase to 450 mm for long-term storage. 4. The height/diameter factor influences heat losses, achieving a 1.8 ratio limits the heat losses to a maximum of 2.1 % per week at 90 ̊ C. 78 Figure 6.5.3 Specific price of hot water storage tank as function of volume. Source: (Sørensen et al., 2013) A report published by the Department for Business Energy & Industrial Strategy states that within the UK market 11 million tanks for thermal energy storage are implemented on domestic scale for intra-day applications (BEIS, 2016, p. 33). The economics of scale plays an integral role when implementing a thermal storage system as the CAPEX shows a significant drop when increasing the plant size, offering a valuable indicator for developers when planning the layout of the heating systems. The figure above provides a reference for the variation of CAPEX as a function of plant size. For the UK market, the same relation presented in the figure above is developed. As per small scale integrated hot water and space heating tanks up to size 1.5 m3 the average prices is 3,950 £/m³, while for large scale tanks until a size of 300 m³ it costs 360 £/m³, the costs drop significantly to 131 and 105 £/m³ for 4,300 m³ and 12,000 m³ respectively (BEIS, 2016, pp. 38–39). Furthermore, into economics the table below summarizes the economic indicators related to hot water steel storage tanks. 79 Table 6.5.9 Economic Indicators for thermal storage Indicator Value Source Capex (£ / kWh) 3 (Danish Energy Agency, 2018b, p. 59, Technology Data for Energy Storage) Fixed O&M (£ / kWh / a) 1 Round Trip Efficiency (%) 98 Technical Life time (years) 40 Economic Life time (years) 20 (IEA-ETSAP and International Renewable Energy Agency (IRENA), 2013, ETSAP - Thermal Energy Storage) 6.5.4.3 Electric and Oil Boiler As a supplementary to the heat pump, the energy supplied from RE is utilized to heat the water using also an electric boiler in order to reach the desired temperature of 90 ̊ C, in addition the system includes a supplementary oil boiler for backup. As per technology specifications, Table below summarizes the data related to both boilers: 80 Table 6.5.10 Economic & Technical Indicators for Electric & Oil Boilers Indicator Value References Electric Boiler Capex Capacity 1-5 MW (£ / kW) 130.5 (Danish Energy Agency, 2018a, p. 227, Technology Data for Energy Plants for Electricity and District Heating Generation.) Fixed O&M (£ / kW / a) 930.9 Variable O&M (£ / kWh output) 0.000783 Efficiency (%) 98 Technical Life Time (years) 20 Minimum Load (% of full load) 5 Oil Boiler Rating (kW) 100 1000 (Ernst & Young, 2007, sec. Appendix D p.47, Renewable Heat Initial Business Case) Capex (£/kW) 50 Opex (£/a) 330 3,400 Life time (years) 20 Efficiency (%) 85 Average price of heating oil (£/l) – Base Case 0.7388 Saxa Vord Bills 81 Renewable Energy Analysis Wind 7.1.1 Methodology This study was conducted to assess the wind potential of proposed sites by using the ArcGIS®10.4.1 (Esri, 2019) software for site assessment and WindPRO®3.2 software (EMD International, 2019) for wind project. In the initial phase, the site assessment was conducted based on the Shetland local development plan (The Highland Council, 2016). Protected areas in the vicinity of the proposed sites, which are sensitive to a wind turbine, were defined and then excluded. Safety measure of road and separation distance from residential areas were also studied and region within those areas were excluded. WindPRO®3.2 software was used to generate the wind resource map of Unst Island. The remaining areas which are located close to proposed sites and have high wind speed, were selected for this study. Finally, for one site near Saxa Vord Resort and another near Baltasound the wind potential of several potential wind turbines was assessed. 7.1.2 Wind Turbine Selection The Scottish wind turbine market was studied in terms of size, availability and services provided by manufacturers. It was found that seven wind turbine suppliers, supply wind turbines in all areas of UK (The Renewable Energy Centre, 2019). Due to the weather condition in Unst, wind class 1 turbines, which operate and withstand extreme weather conditions were selected for this study (Renewable First, 2015).Wind class 1 turbines are specially design for tough operating conditions and they are equipped with smaller rotors and hub heights (Renewable First, 2015). Two wind turbines class1 were selected for this study (100 kW and 82 900 kW), because of the load and system scenario variation. The specification for the same is shown in Table 7.1.1. Table 7.1.1 Wind Turbine Specification N o Brand/Na me Rated Power/kW Rotor diameter /m Default Hub- Height/m Surviv al Wind Speed/ m/s Country of Origin 1 Xant M- 21 100 21 38 70 Belgium 2 Enercon E-44 900 44 45 59.9 Germany Source: Author based (Bauer & Matysik, 2019 & WindPro, 2019) Enercon E-44 wind turbine is also used in Garth wind farm in Yell (EMN Plant Ltd., 2019), which is the closest wind farm to Unst Island. 7.1.3 Site Selection To identify the suitable sites for an onshore wind project in the vicinity of the proposed load centres, the entire island was assessed to consider all important planning issues. The supplementary guidance for onshore wind by Shetland Island Council, provides spatial planning policies for onshore wind energy. This guideline classifies the regions of Shetland in three different categories (The Highland Council, 2016, p. 8). 1. Spatial Planning Policy One: This planning policy states that a wind turbine is not acceptable within these areas, which contain the national parks and areas with national importance called National Nature Reserve (NNR) (The Highland Council, 2016, p. 8). Hermaness NNR is within this category and is located on northern west of island. It is excluded for this study. 83 Figure 7.1.1 Hermaness NNR Site Source: Author based (Scottish Natural Heritage, 2016; McGarva, 2017) 2. Spatial Planning Policy Two: The areas included in this category, are recognized to be sensitive to large scale wind energy because of their national and international natural heritage value. Areas defined in this category include wild land, Scottish Natural heritage Natural Reserve (SNH NR), gardens & designed landscapes, RAMSAR or wetland protected areas, Sites of Special Scientific Interest (SSSI), Special Protection Areas (SPA) and Special Areas of Conservation (SAC) (The Highland Council, 2016, p. 8). Due to national and international value of marine and geological protection area, this study includes the Nature Conservation of Marine Protected Areas (NCMPA) and Geological Conservation Review Sites (GCRS). Development of large scale wind farm is permitted in this area, but on a condition that all the environmental considerations must be carefully considered during the planning, site selection and design phase of the wind projects (The Highland Council, 2016, p. 8). These regions represent some of the valuable natural heritage of Unst such as flora, fauna, geology and geomorphology. These areas were excluded for this study because of time constraints and unavailability of the exact data to study and survey the impact of wind turbine (Scottish Natural Heritage, 2017). 84 Figure 7.1.2 Areas of Significant Protection Source: Author based (Scottish Natural Heritage, 2016; McGarva, 2017) 3. Spatial Planning Policy Three: The remaining areas contain the regions where installation of large scale wind energy (20MW to 50MW) is allowed, but potential development must satisfy the development criteria according to the Shetland Local Development Plan (SLDP) (The Highland Council, 2016, p. 8). The regions with potential development of large-scale wind energy is shown in Figure 7.1.3. Areas around the proposed load centres also come under Ministry of Defence (MOD) areas, which mostly cover all of the Saxa Vord Resort and Baltasound (The Highland Council, 2016, p. 11). During the initial planning phase of specific sites, this issue should be shared with MOD. Other environmental impacts of wind turbines are related to aquatic environment. Development of wind turbines can exert a potential threat to groundwater flow regime and quality (Northern 85 Ireland Environmental Agency, 2015, p. 4). Therefore, a buffer of 50 meters was created around all regions, which contain water in the island, and it was excluded. Figure 7.1.3 Unst Inland Water Source: Author based (McGarva, 2017) The safety clearance with respect to roads is also considered for this study. A buffer of 50 meters (10% of hub height plus the hub height of wind turbine) was created around the roads and the area within the buffered regions was excluded which is shown in Figure 7.1.3. Since the hub height of both wind turbines do not have too much differences in length, only the safety clearance for Enercon E-44 wind turbine was considered. 86 Figure 7.1.4 Unst Road Source: Author based (McGarva, 2017) The separation distance of wind turbine from residential areas (most of buildings are residential area) in Shetland island, is based on noise limitation of wind turbine. According to local development plan (The Highland Council, 2016, p. 23) for onshore wind energy in Shetland island, the noise limitation of wind turbine ranges between 38.6dB(A) during the day to 34.6dB(A) during the night. By increasing the distance from wind turbine the noise level will decline and with to some extend it follows the rule of inverse square law (Salt and Hullar, 2010). The remaining area from Figure 7.1.4 was considered to find the areas where the noise is limited by a value of 35dB (A). 7.1.4 Wind Resource Map A wind resource map was generated by WindPRO®3.2 software based on mesoscale data purchased from EMD with the hub height of 50m, the location is shown in Figure 7.1.5. The closest areas to proposed load centres with high wind potential was studied to generate the noise map by spotting both wind turbines. The closest area by considering all onshore wind 87 turbine-planning issues near Saxa Vord Resort has a distance of almost 1.5 km from the proposed load centre. Other closest area for Baltasound is located almost 1km away from the proposed load center. These two areas were selected for this study. Figure 7.1.5Wind Resource Map Source: WindPro 7.1.5 Wind Energy Project Design WindPRO®3.2 software was used to design the wind project and all layers such as land’s roughness area, roughness line, elevation data grid and elevation contour lines were downloaded online by WindPRO®3.2 software (EMD International, 2019). Buildings in the vicinity of wind turbine act like obstacles and affect the wind speed and directions. Therefore, they are taken into consideration for the simulation. 7.1.5.1 Wind Data Mesoscale wind data was purchased from EMD (EMD International, 2019) and was used to design the wind project in both locations. The mesoscale wind data is based on Weather Research and Forecasting (WRF) on hourly bases with small spatial resolution and is provided for three years starting from October 2015 to October 2018 (EMD International, 2019). The location of the generated metronomic station is shown in Figure 7.1.5. Wind Resource Map 88 and also the generated Saxa Vord meteo-station data was used to generate the wind resource map. Figure 7.1.6 Hourly Wind Speed of Generated Saxa Vord Resort Meteo-Station Source: WindPRO Figure 7.1.6 shows the hourly wind speed variation for 2017, the reference year (EMD International, 2019). 7.1.5.2 Saxa Vord Proposed Wind Project For the Saxa Vord wind project both Enercon and Xant wind turbine were studied. Since the wind direction is quite scattered throughout the months, a 5 times rotor diameter separation distance for both wind turbines were assigned during the simulation. The location of wind turbines in proposed project site were optimized and wind turbines were spotted where the wake effect was minimised by WindPRO®3.2 software. The mesoscale data generated in the vicinity of Saxa Vord Resort was used to generate the annual energy profile of wind turbines which is shown in table below. 89 Table 7.1.2 Saxa Proposed Vord Wind Energyy Project No Wind Turbine / Rating QTY Annual Energy (MWh/y) Capacity Factor (%) 1 Xant (100kW) 1 562.7 57.8 2 Enercon (900kW) 1 4560.2 52 Source: Author based on WindPro 7.1.5.3 Baltasound Proposed Wind Energy Project The separation distance between wind turbines is five times the rotor diameter. The generated Baltasound meteo-station wind data was used to generate the annual energy profile of wind turbines in this location. Table 7.1.3 Baltasound Proposed Wind Energy Project No Wind Turbine / Rating QTY Annual Energy (MWh/y) Capacity Factor (%) 1 Xant (100kW) 1 453 46.5 2 Enercon (900kW) 1 3225.7 36.8 Source: WindPro 7.1.6 Cost Table 7.1.4 shows the cost breakdown of both types of wind turbines based on study by University of Flensburg students in Durness (Adensanya et al., 2017). The connection cost of wind turbine from the proposed wind project site to the load centre is estimated based on Scottish Hydro Electric Power Distribution plc (Scottish and Southern Energy Power Distribution, 2014). The capital, operational and maintenance cost of grid connection is added to the total project’s cost. The capital, operational and maintenance cost are all adjusted to current prices (International Renewable Energy Agency (IRENA), 2018; The Statistics Portal, 2019). 90 Table 7.1.4 CAPEX and OPEX of Wind Turbines Saxa Vord Description 900 kW Enercon 100 kW Xant Unit Total Capex 2,097,780 614,986 GBP O&M 47,561 11,256 GBP/year Baltasound Description 900 kW Enercon 100 kW Xant Unit Total Capex 2,015,307 475,090 GBP O&M 46,731 9,848 GBP/year Source: Author based (Adensanya et al., 2017; Cirincione, Wolfsthal and Rajkumar, 2018; The Statistics Portal, 2019; WindPro, 2019) Some uncertainties due to remoteness of Unst Island will affect the cost of the proposed project such as the transportation cost of wind turbine to the site and service cost for operation and maintenance of wind turbine would be higher. Heat Pumps Heat Pumps (HP) are a sort of renewable energy technologies which extract heat from the air (Air Source HP) or from ground (Ground Source HP) in order to supply buildings’ heating demand. The main advantage of implementing this technology is its reduced electricity consumption inasmuch as its design efficiency rounds the 400% (Mitsubishi Electric, 2014b, p. 13) ; it means that for producing four units of heat power, a HP will only need one unit of electricity power . Modern oil-fired boilers and electric radiators have an annual average efficiency of 92 % and 100 % respectively (Danish Energy Agency, 2018, p. 30&142 ). 7.2.1 Ground Source Heat Pump (GSHP) Capacity Estimation The capacity of the GSHPs to be installed is limited by the area available for the installation of the horizontal ground heat exchanger. The method presented in (Department of Energy and Climate Change (DECC), 2013) were used to estimate the maximum size of the heat pump for the different locations. The first step is to calculate the length of the heat exchanger according to the area available, and the recommended separation between trenches, as follows. 91 (Lb) - Length of the Ground Heat Exchange (GHE) (m) Lb = A d⁄ (A) - GHE area (m2) (d) - Minimum centre-to-centre spacing of the slinky GHE (m) Once the length of the GHE is obtained, the maximum power that can be extracted from the ground (i.e. the heat pump evaporator capacity) can be obtained by using the following calculation. (G)- Maximum power extracted from the ground (W) G = g ∗ Lb (g)- Specific heat power extraction from the ground (W/m) Finally, the HP capacity can be estimated as shown next. (SCOP)- Seasonal Coefficient of Performance of the HP H = G (1 − 1 SCOP )⁄ (H)- HP heating capacity at 0°C ground return temperature and design emitter temperature. (W) The specific heat power extraction from the ground (g) will depend on the thermal conductivity, the mean ground temperature, and the HP’s full load equivalent run hours per year, among other variables. The value of (g) for different locations across the UK, as well as the recommended separation between trenches, can be taken from (Department of Energy and Climate Change (DECC), 2008). In case the specific data for the locations is not available, the thermal conductivity in Unst can be assumed to be similar to the one in the Fair Isle, 1.42 (W/m/K) (Busby, 2016, Table 2); while the mean ground temperature in Unst is equal to 8.6 °C as can be evidenced in (Busby, 2015). 7.2.2 Ground Temperature Modelling The performance of a GSHP depend intrinsically on the ground temperature at the depth at which the loops are placed; typically, 2 m below the earth surface. The equation given by Kasuda and Achenbach in 1965 for the calculation of the seasonal variation of the shallow ground temperature was used in our approach (Chiasson, 2017, p. 224). 92 T(d, t) = TM − As. e [−d( π 365α ) 1 2⁄ ] . cos [ 2π 365 (t − t0 − d 2 ( 365 πα ) 1 2⁄ )] Equation 7.2.1 Shallow Earth temperature as a function of depth and day of the year Source: Kasuda & Achenbach, 1965 Where, 𝑇(𝑑, 𝑡) −is the ground temperature at a depth (d), and after (t) days after the 1st of January. (°F) 𝑇𝑀 − is the mean (average) surface temperature of the ground during the year; typically, the average annual air temperature for the given location (°F) 𝐴𝑠 − is the amplitude of the surface temperature function throughout the year (°F) 𝑑 − is the depth at which we want to know its temperature (ft) 𝛼 − is the soil thermal diffusivity (ft2/day) 𝑡0 − is the time difference (in days) between the beginning of the calendar year and the occurrence of the minimum surface temperature days As reported in (Busby, 2015), the weather station Baltasound N°2, located in Unst, registered between 2002 and 2009 a mean ground temperature of 8.6°C at a depth of 0.3 m, with an amplitude of 16.2°C. The coldest day in the period from 1980 to 2016 was February 17th according to the analysis conducted by (Weather Spark, 2019). Similarly, in (Busby, 2016) the thermal diffusivity across the UK have been measured through weather stations; the thermal diffusivity in Unst was assumed the same as in the Fair Isle, 0.9003x10-6 m2/s (0,8372 ft2/day). By using the previous values, the computation of the yearly ground temperature at a depth of 2 m resulted in the following outcome. 93 Figure 7.2.1Unst Ground Temperature at 2m Below the Earth Surface Source: Author 7.2.3 GSHP Performance Modelling Two GSHPs were included in the tool; from the manufacturer MITSUBISHI the model CRHV- P600YA-HPB with a capacity of 60 kW (certified for the Microgeneration Certificate Scheme- MCS from UK the Government according to (Mitsubishi Electric, 2016, p. 3)); and from the manufacturer VIESMANN the model Vitocal 300-G-Type129 with a capacity of 28.8 kW. In order to model their performance, the data books from the manufacturers were used (for more details see (Mitsubishi Electric, 2014b, p. 13) and (VIESSMANN, 2010, p. 9) ). The relationships among the brine temperature (brine temperature was assumed as 95% of the ground temperature), the outlet water temperature, the heat production and the coefficient of performance (COP), which are provided by the manufactures’ data book, were used to estimate the maximum heating output across the year and the SCOP. The results for the Mitsubishi CRHV-P600YA-HPB at different capacities and outlet water temperatures are summarized in Table 7.2.1. 94 Table 7.2.1 GSHP Performance at Different Water Outlet Temperature and Output Heating Power Source: Author based on (Mitsubishi Electric, 2014b, p. 13) Figure 7.2.2 illustrates a one-year performance for the Mitsubishi CRHV-P600YA-HPB when running at 100 % of its capacity and with an outlet water temperature of 45 °C. Figure 7.2.2 CRHV-P600YA-HPB GSHP Performance Modelling Source: Author based on (Mitsubishi Electric, 2014b, p. 13) 7.2.4 Air Source Heat Pump (ASHP) Performance Modelling Similarly, two ASHPs from the manufactures MITSUBISHI were added to the tool; models CAHV-P500YA with a capacity of 45 kW and PUHZ-W85VHA2-(BS) with a capacity of 8.5 kW were selected. Both are certified for the Microgeneration Certificate Scheme (MCS) from the UK Government. For the performance modelling of CAHV-P500YA, its efficiency priority mode was included in the tool (for more details see (Mitsubishi Electric, no date, p. 10)), while for the PUHZ- W85VHA2-(BS) its nominal performance was selected (for more details see (Mitsubishi Electric, 2015, p. A63)). The hourly air temperature in Unst in 2017 was obtained from (Meteoblue, 2019c); by using this time series in combination with the datasheets from the Mitsubishi CRHV-P600YA-HPB Outlet Water Temperature °C (Spread 5°C) 30 35 40 45 50 55 Yearly Heat Production (kWh) –at 100% of its capacity 525,600 SCOP–at 100% of its capacity 5.27 4.73 4.30 3.85 3.43 3.23 Yearly Heat Production (kWh) –at 75% of its capacity 394,200 SCOP–at 75% of its 5.32 4.74 4.29 3.84 3.42 3.04 Yearly Heat Production (kWh) – at 50% of its capacity 262,800 SCOP- at 50% of its 5.07 4.53 4.14 3.73 3.33 2.98 95 manufactures, which relate the intake air temperature of the HP with its outlet water temperature, the heating output and the electricity consumption, it was possible to estimate the hourly maximum heat production across the year. The results at different outlet water temperatures are condensed in Table 7.2.2. Table 7.2.2 ASHPs Performance at Different Water Outlet Temperature Mitsubishi CAHV-P500YA Outlet Water Temperature °C (Spread 5°C) Yearly Heat Production (kWh) SCOP 35 387,778 3.97 45 388,140 3.33 55 388,661 2.66 60 389,001 2.44 65 389,182 2.09 70 389,363 1.76 Mitsubishi PUHZ-W85VHA2-(BS) Outlet Water Temperature °C (Spread 5°C) Yearly Heat Production (kWh) SCOP 35 77,092 3.87 40 77,321 3.46 45 77,548 3.04 50 77,279 2.71 55 77,024 2.37 Figure 7.2.3 illustrates the hourly performance of PUHZ-W85VHA2-(BS) at an outlet water temperature of 45°C. Figure 7.2.3 PUHZ-W85VHA2ASHP Performance Modelling Source: Author based on (Mitsubishi Electric, 2015, A63; Meteoblue, 2019a) 7.2.5 COSTS The investment and maintenance of a heat pump heating system, either ground or air source, vary depending of its size and location. According to (Generating Renewable Energy Business 96 Enterprise (GREBE), 2017), the investment cost (including the HP and installation, horizontal groundwork , machinery, equipment, design, engineering and civil works) for a 55 kW horizontal GSHP in Scotland ranges between £900-£1,050/kW, whereas a 6-11kW GSHP varies from £940-£1,830/kW. Similarly, an ASHP requires an investment of £800-£1,200/kW. These costs do not include the cost of the heat distribution system, gets lower as the capacity increase, and are aligned with the costs presented in (Danish Energy Agency, 2018, p. 91) . It is important to point out that the information presented by (Generating Renewable Energy Business Enterprise (GREBE), 2017, p. 11) shows that vertical heat exchangers costs around three times more compared to a horizontal heat exchanger; this difference gets higher as the rated power is the system increases. This report also evidences that in the case of horizontal GSHP, the ground loops account for approximately 19 % of the investment and increase along with the capacity of the system. Regarding the operational costs, the values for ASHPs and GSHPs of different capacities in the UK are summarized in (Sweett Group, 2013, p. 16, 25). In order to estimate the costs for the year 2017, these numbers were affected by the inflation in the UK as found in (The Statistics Portal, 2019). The average value of the costs discussed above are show in Table 7.2.3. Table 7.2.3 CAPEX and OPEX for GSHP and ASHP in the UK Ground Source Heat Pump (GSHP) Capacity (kW) 6 - 11 55 Source Capex (2017) (£/kW) 1,385 975 (Generating Renewable Energy Business Enterprise (GREBE), 2017, p. 11) Average Opex (£/kWh) including electricity costs @ 2017 (£/kWh) 0.0538810 (Sweett Group, 2013, p. 25) Lifespan GSHP (years) 25 (Generating Renewable Energy Business Enterprise (GREBE), 2017, p. 8) Lifespan Ground loops (years) 50 Air Source Heat Pump (ASHP) Capacity (kW) 5 10 15 Source Capex (2017) (£/kW) 8,620 11,494 13,793 (Danish Energy Agency, 2018, p. 91) Average Opex (£/kWh) including electricity costs @ 2017 (£/kWh) 0.0754334 (Sweett Group, 2013, p. 15) Lifespan ASHP (years) 20 (Mitsubishi Electric, 2016, p. 13) 97 Source: Author(s) Solar PV and Solar Thermal 7.3.1 Solar Resource 7.3.1.1 Solar Irradiation The solar resource in Unst is very low in comparison to most places in the world. The average annual Global Horizontal Irradiation for reference year 2017 is 742 kWh/m2 (Meteoblue, 2019) as presented in Figure 7.3.1. The intra-annual variation is significant with winter months receiving minimum irradiation. About 282 kWh/m2 is received as direct irradiation while 460 kWh/m2 insolates as diffused irradiation (PVSOL) Figure 7.3.1 Global Horizontal Irradiation Source: Illustration by author based on (Meteoblue, 2019c) 98 7.3.1.2 Ambient Temperature Figure 7.3.2 Ambient Temperature for 2017 Source: (Meteoblue, 2019) The temperature data was obtained from Meteoblue (2019) for 2 m above the ground for the base year 2017. Figure 7.3.2 above shows that the temperature is highest from April to September. The maximum temperature did not exceed 18 ˚C whereas the minimum was -1.08 ˚C. 7.3.2 Site Selection The roof PV and solar thermal installation is constrained with respect to aspect and tilt angle considerations, and to optimize the generation, ground mount installation is preferred. Land in Unst is mostly flat with gentle slopes and in the flat areas; the effect of aspect is minimal if any. Generally, south-facing aspects of land area is considered while selecting a site for the PV and solar thermal. In Baltasound and Saxa Vord there are plenty of suitable land areas for PV and solar thermal installations. -5 0 5 10 15 20 1 2 9 3 5 8 5 8 7 7 1 1 6 9 1 4 6 1 1 7 5 3 2 0 4 5 2 3 3 7 2 6 2 9 2 9 2 1 3 2 1 3 3 5 0 5 3 7 9 7 4 0 8 9 4 3 8 1 4 6 7 3 4 9 6 5 5 2 5 7 5 5 4 9 5 8 4 1 6 1 3 3 6 4 2 5 6 7 1 7 7 0 0 9 7 3 0 1 7 5 9 3 7 8 8 5 8 1 7 7 8 4 6 9 T em p er at u re (˚ C ) 8760 Hours Ambient Temperature 99 Figure 7.3.3 Land Gradient in Unst Source: Illustration by Author based on (Scottish Natural Heritage, 2016) 7.3.3 Solar PV 7.3.3.1 Panel Selection Photovoltaic panels of three manufacturers namely Jinko Solar, Trina Solar and Sun Power are proposed for Unst as per the discussion held with Pure Energy Centre. The selection of panel is based on the results from PVSOL considering specific yield and thermal gain/loss. 7.3.3.2 Thermal Gain/Loss Panels are tested at a Standard Testing Condition (STC) temperature of 25 °C, irradiance of 1000 W/m2 and at AM 1.5 (Fraunhofer ISE, n.d). A higher ambient temperature reduces the voltage with a minimal increase in current, hence reducing the overall output. In opposite, 100 lower temperature positively increases the voltage with a minimal decrease in current, thereby resulting in a gain in output as shown in Figure 7.3.4. Figure 7.3.4 Effect of Temperature Source: (Seaward Group USA, 2018) Figure 7.3.5 Thermal Gain and Loss Source: Illustration by Author based on results from PVSOL Most poly-crystalline panels tend to have a higher thermal coefficient as compared to the mono-crystalline panels whereas mono-crystalline are more efficient than poly-crystalline. 101 Higher thermal coefficient results in higher thermal gain in colder regions as depicted by Figure 7.3.5 7.3.3.3 Specific Yield Specific yield can be defined as energy produced by a unit capacity of a system. In solar PV it is usually expressed in kWh/kWp. The higher the specific yield better the performance and vice-versa. The specific yield may depend on multifarious factors such as design, location, irradiation, panel efficiency, thermal coefficient, losses etc. In essence, higher thermal coefficient and higher efficiencies are more appropriate for places like Unst. The Sun-Power monocrystalline panel (SPR-X21-327-BLK) performs slightly better than other two panels. Table 7.3.1 Panel Comparison for Specific Yield in Unst Manufacturer Panel Type Model Thermal Coefficient (%/°C) Efficiency (%) Specific Yield (kWh/kWp) Jinko Solar Poly- Crystalline JKM330P-72 -0.41 17.10 793.14 Trina Solar Poly- Crystalline Trina TSM-280- PD05 -0.41 17.10 781.67 Sun Power Mono- Crystalline SPR-X21-327- BLK -0.30 23.30 797.11 Source: Illustration by Author based on results from PVSOL 7.3.3.4 Mechanical Design 7.3.3.4.1 Optimized Tilt Angle The tilt angle was optimized using PVSOL software by comparing specific yield (kWh/kWp) for different possible tilt angle. The tilt angle with the highest specific yield is the optimized tilt angle for the module array. In summer when the sun elevation angle is higher, the tilt angle should be lower to optimize the production and on the other hand, tilt angle should be higher when the sun elevation angle is lower during winter days. However, in a fixed rack system, sunrays are not perpendicular throughout the year. The optimized tilt angle from PVSOL is found to be 43° in Unst. 7.3.3.4.2 Inter-Row Distance Considering the Sun Power panel (SPR-X21-327-BLK) as discussed in the earlier section, the designed inter-row distance between the panels is 2.50 meters. The sun elevation considered for the inter-row spacing is 9 am and 3 pm of 20th March and 20th October, wherein the sun angle is at 20°. This particular date and time are considered as the worst-case sun angle for a 102 shadow free inter row distance. 7.3.3.5 Prospective Performance For 4.91 kWp installation, the prospective yield is 3,909.80 kWh and the corresponding capacity factor is 8.93 after accounting for all the losses (viz. inverter loss, mismatch loss, cable loss etc.) as shown in Table 7.3.2. The energy yield is highest for the month of May and lowest in December in line with the irradiation as illustrated by the figure below. Due to the weak solar resource, the capacity factor is substantially less than the world average of 17.1 percent for the year 2017 (IRENA, 2018,p.66). The inference that can be drawn from the lower capacity factor is that any endeavour inclining towards the deployment of solar PV will come at a higher cost. For the excel model 4.91 kWp of a system is simulated in PVSOL for a more comprehensive and credible results. This particular capacity is the reference capacity and for capacity larger than 4.91 kWp a multiplicative factor is applied. Figure 7.3.6 Monthly Energy Yield Source: PVSOL 103 Table 7.3.2 Results Source: PVSOL 7.3.3.6 Cost A paper by Tjengdrawira et al., (2016, Table 2) categorizes the cost of PV under three category namely, low scenario, medium scenario and high scenario, with low scenario based on places with high solar resource and high scenario based on places with low solar resource. The low scenario applies to country like Italy and high scenarios applies to country like Netherland and the UK. As per Tjengdrawira et al., CAPEX and OPEX information is collected from multiple sources like “project partner, project advisory board as well as recent publications on PV system pricing” (2016, p.18). 104 Table 7.3.3 CAPEX and OPEX Source: (Tjengdrawira, Richter and Theologitis, 2016,Table 2) A CAPEX of 1,218 £/kWp (1400€/kWp from the table above) and correspondingly OPEX of 16 £/kWp (18€/kWp from Table 7.3.3) is considered for further calculations. 7.3.4 Solar Thermal Non-concentrating collectors (flat plate and evacuated tube solar collectors) were considered for the project because of the low solar irradiance in Unst. Some important aspects of non- concentrating collectors are listed below: i. Evacuated Tube Collectors • The sunrays are always perpendicular to the circular surface of the evacuated tube collector (like Thermomax DF400 model). • The investment cost of the evacuated tube collector is about 20-40 % higher than flat plate collectors (Solar Panel Plus, 2014, p. 1). ii. Flat plate Collectors • Flat plate collectors are easy to install and widely proven. • They are cheap than the evacuated tube collector as illustrated in Table 7.3.5. 105 7.3.4.1 Efficiency Calculation of the collector The following equation was used to determine the overall efficiency of the collector. 𝜂 = 𝜂𝑜 − 𝑎1(𝑡𝑚 − 𝑡𝑎) 𝐼 − 𝑎2(𝑡𝑚 − 𝑡𝑎)2 𝐼 Equation 7.3.1 Overall efficiency calculation Source: (ESTIF, 2007, p. 1) η=overall collector efficiency η𝑜= zero loss efficiency=optical efficiency a1=First order heat loss coefficient (W/m2.K) 𝑎2=Second order heat Loss Coefficient (W/m2.K) I=Global Radiance on the Collector (W/m2) 𝑡𝑚 =Collector Mean Temperature (˚C) 𝑡𝑎=Ambient Temperature (˚C) The values of ηo ,a1 and a2 were obtained from the Kingspan Company collector catalogue. The collector mean temperature was calculated as the mean temperature of both the incoming and outflowing fluid temperature. Global Radiance on the collector was calculated with the optimum tilt angle for the optimum energy production throughout the year. The ambient temperature was obtained from Meteoblue (Meteoblue, 2019c). The 8760-hour efficiencies were calculated to find the hourly energy output from the collector. 7.3.4.2 Indirect Water Heating System In this system, non-toxic and non-freezing heat transfer fluid is used. The heat transfer fluid has higher boiling point and lower freezing point when compared to water. It circulates in a closed loop. The fluid transfers the heat from solar collector to the water in the storage tank. Ethylene glycol and Propylene glycol solutions are commonly used heat transfer fluids (Lanru Jing, 2007). Kingspan uses propylene glycol solution for Thermomax DF400 and FPW flat plate collectors and is considered for further analysis (Kingspan, 2019, p. 1) Volker Quaschning (2004, p. 1) discusses the operation of the solar collector system, stating that temperature sensors are used to monitor the temperature of water in the storage tank and fluid temperature in the solar collector. When the temperature of the collector is higher than 106 the tank temperature, the controller starts the pump and pushes the heat transfer fluid through the collector. The heated fluid flows down the heat exchanger and transfers the heat to the water in the storage tank. If the temperature is lower in the collector, the controller switches off the pump. The process continues, transferring heat to the water in the storage tank. 7.3.4.3 Annual yield of collector Figure 7.3.7 Actual Collector yield Calculation Source: Author based (ESTIF, 2007; Meteoblue, 2019a) Figure 7.3.7 above shows the actual yield from the collector. The actual yield of the collector is calculated by deducting the pipe losses. The heat gained on the collector by the solar radiation needs to be transferred to the storage with the minimum heat losses. In order to prevent substantial losses, pipe insulation plays a vital role. The insulation reduces the heat loss by convection and radiation. Pipe loss of 5 % has been considered for the project basing on ESTIF (2007, p. 4) . 0 50000 100000 150000 200000 250000 300000 Jan Feb March April May June July Aug Sept Oct Nov Dec M o n th ly c o ll ec to r y ie ld / W h Months Theoritical Collector Output(Wh) Sum of Pipe losses(Wh) Actual yield(Wh) 107 7.3.4.4 Mechanical Design 7.3.4.4.1 Optimum Tilt Angle The optimum tilt angle of 43o was calculated using PVSOL to produce the maximum energy output from the system. 7.3.4.4.2 Inter Row Distance The system was designed to supplement the heat supply when the wind speed is low i.e. April to September. The sun angle of 30˚was considered for the month between April 20 to September (9 am to 3 pm). The inter row distance for selected collectors viz. Thermomax DF400 Collector and FPW Flat Plate (Kingspan) was calculated as 3.74 m and 2.34 m respectively. The different size of the collector resulted in different row distance(Kingspan, 2019, p. 1) 7.3.4.5 Cost The following formula was used to calculate the unit area cost of flat solar collector. 𝑃𝑟𝑖𝑐𝑒[ € 𝑚2 𝑔𝑟𝑜𝑠𝑠 ] = 1000 𝐴𝑐 −0.16 Equation 7.3.2 Cost calculation of flat solar thermal Source: (Trier, 2018, p. 3) Ac=Gross collector area Basing on Trier (2018,p.3), the price of the system is given by the Equation 7.3.2 above and it includes the following parameters : • Cost for the collector panels • Cost of piping, mounting and the installation work of the collector • Cost of heat exchanger, pumps, control and regulatory system for the system • Cost of heat transfer fluid • Heat exchanger substation. 108 The calculated cost is validated with the capital cost from Finland as shown in Table 7.3.4. Table 7.3.4 CAPEX and OPEX of flat plate collector Reference for cost of collector (Flat Plate) Collector area ( m2) CAPEX (€/𝒎𝟐) OPEX (€/𝒎𝟐) OPEX ( in %) 4-20 500-1,000 50-100 10% 20-100 500-750 40-60 8% 100-1,000 450-500 20-25 5% 15,000 280-340 20 Source: (Entreprises, n.d, p. 16) For solar thermal system installed with flat plate collectors, the latter accounts for 45 % of the total capital system cost, 20 % is for storage, 25 % is for installation and commissioning, and the remaining 10% is small system component costs (Entreprises, n.d., p. 6). Normally, the investment cost of evacuated tube collectors are 20 to 40 % more expensive than flat plate collectors (Solar Panel Plus, 2014, p. 1). However, 20% was assumed keeping in mind the economies of scale for large heating systems. The rest factors affecting the capital cost discussed earlier are considered to remain constant. The cost of evacuated tube system increased by the factor of 1.09 as per the assumption mentioned earlier. The operational cost is considered as percentage of the capital cost as detailed in Table 7.3.4 (Entreprises, n.d., p. 16). Based on the product datasheet of the Thermomax DF400 and FPW, a life of span of 25 years was considered (Kingspan, 2019, p. 1). 7.3.4.6 Cost Comparison for Evacuated and Flat Plate Collector Table 7.3.5. Cost comparison for evacuated and flat plat collector Type of collector Number of Collector (m2) Area of collector (m2) land required (m2) Annual yield (MWh/yr) Cost/m 2 (£/m2) Total Cost (£) Specific cost (£/MW h/yr) Investment Cost Difference /£ Evacuated Tube 203 609 1,774 374 340 207,026 553 -18,929 Flat Plate 270 602 1,039 376 312 188,096 501 Source: Author based on (Kingspan, 2019; Meteoblue, 2019; Trier, 2018) Table 7.3.5 shows the cost comparison between the Thermomax DF400 and FPW flat plate collector. The calculation was done with the assumption that all thermal energy was used and 109 with constant fluid inflow and outflow temperatures of 40o and 50o respectively. The cost comparison in Table 7.3.5 was done exclusively considering the investment cost of collectors. The above calculation in Table 7.3.5 shows that Flat Plate (FPW) collectors are more affordable than the evacuated tube (Thermomax DF400) collector. Chapter Summary Wind, solar PV, solar thermal and heat pump renewable energy technologies have been assessed in the context of Unst. The results indicate a high potential for electricity generation from wind turbines near the Saxa Vord Resort and Baltasound. Similarly, both ground and air source heat pumps, have shown a promising potential if used for supplying heat to the buildings within the scope of the present study. Due to the low solar irradiation throughout the year, the implementation of solar PV and solar thermal technologies in Unst, although feasible, require a high investment which seem to be less attractive when compared to the yearly energy yield. 110 Hydrogen and its derivatives Hydrogen Production 8.1.1 Introduction Hydrogen as a green fuel is a possibility because of its high energy content (1 kg of Hydrogen has three times the energy of 1 kg of gasoline (Koponen, 2016)), its capacity to be stored for long periods without degradation (Gazey, 2014, p. 21), existing technology for hydrogen based vehicles and the technology to produce it without the use of fossil fuel: Water Electrolysis. However, improvements still have to be achieved in hydrogen storage with high volumetric density that is required in transport applications. Currently, technologies depend on high compression rates that consume a considerable portion of the energy content in the gas (Gazey, 2014, p. 23). Electrolysis is frequently used to produce green hydrogen in situations where electricity is produced using VRE, such as Wind and Solar. The dependency of VREs on climate conditions results in certain moments where this energy is unused. This unused energy can be diverted to produce hydrogen by water electrolysis. Although Hydrogen by its self is a valuable gas, many other valuable chemicals can be produced by using Hydrogen as a prime resource. These chemicals are called hydrogen derivatives, and the two analyzed during this chapter because of their applications are: Ammonia and Hydrogen Peroxide. A major application of hydrogen is its use in the transport sector, to power a wide arrange of vehicles: from small cars, to large ferries. 8.1.2 Technology Comparison for Electrolysis Two electrolyser technologies were considered and compared to propose the most suitable for the present system. These technologies were selected because of their commercial availability 111 and operating conditions. Each one has certain advantages and disadvantages when compared to the other, as seen in Table 8.1.1 below. Table 8.1.1 Comparison of Electrolyser Technologies Description Alkaline PEM Sources Purity level of hydrogen produced 99.5-99.9998% 99.9-99.9999% (Koponen, 2016) Investment 1,250 €/kW 1,800 €/kW (Pure Energy Centre, 2019b) Partial load operation limits 20-100% of full capacity 5-100% of full capacity (Koponen, 2016) Ramp up speed 0.13%-10% full load/s 10%-100% full load/s (Koponen, 2016) Lifetime of stack 60,000-90,000 h 20,000-90,000 h (Koponen, 2016) PEM allows for a high level of purity of hydrogen and a faster response to an intermittent energy supply, which would allow it to adapt to the variations that occur when supplying energy with wind or solar technologies. For the production of derivatives, a high-level purity of hydrogen and oxygen is not required, relative to fuel cell applications. Alkaline is a more mature technology, which allows it to have a lower cost and a higher degree of confidence in the reported lifetimes from manufacturers (Pure Energy Centre, 2019b). Of the two technologies only alkaline was modelled and considered in the tool because of the data and methodology available and the local existence of a supplier with the know-how to maintain the equipment (Pure Energy Centre). 8.1.3 Alkaline Electrolyser Modelling To model the Alkaline Electrolyser, three major and unique divisions are considered to structure the model. The model is based on the PhD dissertation by Ross Gazey (Gazey, 2014). The first division is the Polarization curve (I-V curve) that represents the required cell voltage for the desired current density for the electrolyser cell. This, in turn, depicts the losses such as: activation potential losses, ohmic losses, and concentration overpotential losses, which need to be overcome for the electrolyser to function in the real world. The second division is the Faraday efficiency that compares the theoretical production of the Hydrogen gas to the actual 112 production, in essence taking into account the parasitic current losses for the electrolyser. The parasitic currents refer to the electrons that do not contribute to Hydrogen gas formation from the H+ ions on that cathode. The third division is the thermal model that takes into account the heat generated by the electrolyser due to a higher cell voltage required that is needed to overcome the aforementioned losses. The thermal model from (Gazey, 2014) takes into count the thermal transients that have an effect on the production of hydrogen when there is an intermittent power input to the electrolyser such as from Wind and/or Solar PV. The electrolyser needs to reach an optimum temperature to function at high efficiencies. Due to the intermittent power input, the electrolyser has a susceptibility of operating as a partial-load and may be switched on and off several times, depending on the input. This hinders the electrolyser to reach its optimum temperature of operation. Hence, there is a loss in efficiency of hydrogen production and compared to the ideal (full-load) operation of the electrolyser, the hydrogen production becomes expensive owing to lower capacity factor. The effect of thermal transients is more prominent in large-scale electrolysers; scaling in MW ranges. The effect of thermal transients, between 20°C to 60°C, has an efficiency increase of around 3% - 4% on a 100% load operation of the electrolyser (Gazey, 2014, p. 72). The size range of the electrolyser required for hydrogen production for this project and the small efficiency change over a range of temperatures, the operation of the electrolyser has been considered at a constant temperature; 60 °C. This consideration is, further made, on basis of literature; (Sanath, Silva and Middleton, 2017), (Spyros Voutetakis, Fotis Stergiopoulos, Panos Seferlis, 2010), and (Salami et al., 2014). Another rationale for choosing the operation of the electrolyser at a constant temperature of 60°C is the possibility of installing the electrolyser right next to the thermal storage tank where the exchange of the heat from the electrolyser operation and the thermal storage can make the electrolyser work at a rather constant and higher temperature. This also adds the possibility of using lesser electrical energy in total, as the heat energy A simplified approach was taken into account where the hydrogen production efficiency curve from (Gazey, 2014), at 60 °C, was re-modelled in excel and a 5th degree polynomial was developed to model the operation of the electrolyser as a function of the power input. The re- modelled curve is shown in Figure 8.1.1. Hydrogen Production efficiency as a function of power input as percentage of rated electrolyser power rating. 113 Figure 8.1.1 Hydrogen Production efficiency as a function of power input as percentage of rated electrolyser power rating Source: Author based on (Gazey, 2014, p. 72) The polynomial gives the H2 production as efficiency percentage of the input energy the electrolyser. The input to the polynomial is the power input as percentage of the electrolyser maximum power rating. The H2 production efficiency is then translated to the energy content of the H2 quantity in kWh, and then converted to mol/hour values using the lower heating value of the hydrogen gas. The oxygen mol/hour values are calculated using simple stoichiometric equation of breaking the water molecule into H2 and O2. The values of H2 and O2 production in mol/hour are then converted to kg/hour using molar masses. The electrolyser model then returns annualized output indicators such as electrical energy required to produce 1 kg of H2 (kWh/kg), the total H2 gas produced in kg, and the residual energy left after the electrolyser operation; both with and without battery operation. 8.1.4 Hydrogen and Oxygen Storage In order to supply the necessary hydrogen and oxygen for the production of derivatives and for a refuelling station for a hydrogen-based transportation system, storage vessels at different y = 9.8948359965x5 - 32.8304323741x4 + 42.1205051057x3 - 26.0988141200x2 + 7.7919912386x - 0.2730393401 R² = 0.9981144411 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00% H 2 P ro d u ct io n E ff ic ie n cy ( % ) Power input % of rated capacity(%) Production System Efficiency Curve 114 pressures are required. The use of storage allows for a production of derivatives that is independent of the operation and current production rate of hydrogen. In the proposed tool both hydrogen and oxygen are stored at the output pressure of the (12 bars) (Pure Energy Centre, 2019a) before being utilized for production of derivatives. In the case of hydrogen for use in transportation a higher pressure (700 bar) is required to allow for a higher volumetric energy density (Pure Energy Centre, 2019b). In order to model the storage of hydrogen and oxygen, the use of a compressibility factor was required. This factor allows describing the behaviour of non-ideal gases at high pressure in an accurate way. The Z factor is used to adjust the ideal gas equation in order to accommodate the additional compression energy, as can be seen in Equation 8.1.1 below (Gazey, 2014, p. 134). 𝑃 = 𝑍𝜌𝑅𝑇 Equation 8.1.1 Adjusted ideal gas equation Source: (Gazey, 2014, p. 134) Where: P: Absolute Pressure (Pascal) Z: Compressibility factor ρ: Molar density (mol/m3) T: Absolute Temperature (Kelvin) R: Universal gas constant 8.31434 (Nm/mol K) Z values for hydrogen were calculated using a method developed by (Lemmon, Huber and Leachman, 2008) which uses an equation based on absolute pressure and temperature. Z values for oxygen were calculated utilizing a table with reported values for given temperatures and pressures and using linear interpolation to obtain the approximate value (Perry and Green, 2008). To account for the electricity demand of compression of hydrogen a curve was used that plots the energy requirement against the final pressure of the gas (Makridis, 2016). For an initial pressure of 12 bars and a final pressure of 700 bar the compression energy was calculated as 115 11.454 kWh/kg-H2, which is one third of the energy content of hydrogen when utilizing the LHV. 8.1.5 Costs associated with the electrolyser operation The cost of the Electrolyser is broken down into capital cost, operational cost and cost of auxiliary equipment. The costs are presented in Table 8.1.2: Table 8.1.2 Cost of Electrolyser System. Description Value used in tool Reference information Source Capital cost for Alkaline Electrolyser 1,087.5 £/kW 1 MW Alkaline Electrolyser, 1.250 million € (Pure Energy Centre, 2019b) Operational cost for Alkaline Electrolyser 3 % of Capital Cost per year 3 % of Capital Cost per year (Pure Energy Centre, 2019b) Hydrogen Low Pressure Storage 24,650 £/m3 A 600 litre vessel , 17,000 € (Pure Energy Centre, 2019b) Hydrogen High Pressure Storage 243,600 £/m3 50 litre cylinder, 14,000 € (Pure Energy Centre, 2019b) Water Purification System: 71 £/kW of Capacity Mechanical vapor compression system with a max flow rate of 200 kg of H2O/h, £47,000. (Pure Energy Centre, 2019b) Hydrogen Compressor 263 £/kW of Capacity Compressor of a capacity of 350 Nm3/h from 30 to 200 bars, €250,000. Compressor of a capacity of 350 Nm3/h from 200 to 700 bars, €350,000. (Pure Energy Centre, 2019b) Lithium-Ion Batteries 457.80 £/kWh 600 US$/kWh (IRENA, 2017) 116 The life time of the electrolyser, compressor and water purification system was considered to be 20 years (Pure Energy Centre, 2019b). The stack of the electrolyser is to be replaced every seven years with a cost of 41% of the Capital Cost of the Alkaline Electrolyser. All other components are considered to have a lifetime of 25 years (Pure Energy Centre, 2019b). 8.1.6 Electric Storage Modelling for H2 Production The Electric Storage, for Hydrogen production specifically, has been considered to increase the capacity factor of operation of the alkaline electrolyzer when it is susceptible to an intermittent power input. The battery acts as a buffer in between the Wind turbine/Solar PV plant output and the electrolyzer input and stabilizes the operation of the electrolyzer for Hydrogen Production. A battery model has been incorporated to the system model which is based on energy flows, in kWh, in and out of the battery for simplicity purposes. The battery is modelled to take in industry standard inputs from the user such as the Battery/Cell rating in Ah, the battery system voltage, the minimum Depth of Discharge, and the maximum discharge current. The charging and discharging efficiencies are also to be provided by the user and are easily available from the data sheets of the different type of battery manufacturers. The model also allows the user to change the charging and discharging currents in and out of the battery respectively as a percentage of the maximum charging and discharging currents. The rating of the battery bank is then translated to energy values in kWh to perform energy calculations. These inputs provide flexibility to the user to model different chemistries of batteries and analyze which chemistry is suitable for the operation and the specific application that the electrolyzer is intended to be used. The operation of the battery takes into account a simple charging and discharging strategy. The discharge operation performs checks whether a discharge is required or not to maximize the power input to the electrolyzer and whether the battery has enough energy left to discharge with the intended discharge rate. The discharge of the battery bears the discharge efficiency. The charging regime, similarly, performs checks such as the battery State of Charge in the previous hour; whether there is room to charge the battery or not to prevent overcharging, and the excess energy available after the electrolyzer operation; to identify if there is energy available for charging. Another check of battery discharging is required for the charging operation for the current specific hour. If the battery is discharging in that hour, the charging operation cannot occur. The charging of the battery also bears the charging efficiency. To 117 calculate the state of the charge of the battery for the current hour, the previous hour is looked upon for the battery operation of either charging or discharging. 8.1.7 Design Possibilities For the hydrogen production system, several design arrangements are possible depending on the user preferences and the economics involved in the project. The selection of the electrolyser technology and the associated costs play a major role in the technical robustness of the system as well as in producing low-cost hydrogen and oxygen. The Polymer Electrolyte Membrane (PEM) electrolyser, as discussed before, is more receptive to the intermittent power inputs and has a better switch on and switch off operation capability. This results in a more robust partial load operation as it takes less effect on the electrolyser stack life. In comparison, the alkaline electrolyser partial load operation has a minimum limit between 15-20 %, as discussed in 8 .1.2. This in turn decreases the capacity factor of operation even more when compared to the PEM electrolyser stack. To enhance the stack life and for a smoother operation of the electrolyser stacks, either PEM or Alkaline, a battery buffer smoothens the variable input power to a great extent. This smoother operation not only adds value to the life of the electrolyser stacks but also causes an increase in the capacity factor of operation of the electrolyser. This translates to more kg of H2 produced from the same amount of energy input. The battery absorbs the excess the excess energy when available from the intermittent renewable sources and then discharges energy when required. This means a cheaper production of H2 from the given production centres. Figure 8.1.2 and Figure 8.1.3 display the power input to the electrolyzer and the subsequent H2 production without the battery operation and with the battery operation, respectively. 118 Figure 8.1.2 Power input and H2 production without battery operation. Source: Author Figure 8.1.3 Power input and H2 production with battery operation. Source: Author -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 10 20 30 40 50 60 70 80 90 1 7 1 3 1 9 2 5 3 1 3 7 4 3 4 9 5 5 6 1 6 7 7 3 7 9 8 5 9 1 9 7 1 0 3 1 0 9 1 1 5 1 2 1 1 2 7 1 3 3 1 3 9 1 4 5 1 5 1 1 5 7 1 6 3 1 6 9 1 7 5 1 8 1 1 8 7 1 9 3 1 9 9 H 2 P ro d u ct io n ( k g ) E le ct ro ly se r In p u t (k W ) Hour Power Input & H2 Production Electrolyzer input power (kW) H2 Production (kg/hr) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 0 10 20 30 40 50 60 70 80 90 1 7 1 3 1 9 2 5 3 1 3 7 4 3 4 9 5 5 6 1 6 7 7 3 7 9 8 5 9 1 9 7 1 0 3 1 0 9 1 1 5 1 2 1 1 2 7 1 3 3 1 3 9 1 4 5 1 5 1 1 5 7 1 6 3 1 6 9 1 7 5 1 8 1 1 8 7 1 9 3 1 9 9 H 2 P ro d u ct io n ( k g ) E le ct ro ly se r In p u t (k W ) Hour Power Input & H2 Production Electrolyzer input power (kW) H2 Production (kg/hr) 119 The area under the curve of Electrolyser input, in both Figure 8.1.2 and Figure 8.1.3 depict the electrical energy used in that time frame to produce H2 gas. Figure 8.1.3 shows electrolyser operation with battery operation, displays more electrical energy used, interpreting from the area under the graph and hence more H2 produced. The curves, when Figure 8.1.2 and Figure 8.1.3 are compared, also show more smoothness of power input and H2 production that indicates and optimized operation from the durability of the electrolyser stack perspective. The battery operation in conjunction with the electrolyser is shown in the schematic shown in Figure 8.1.4 (Adapted from Spyros Voutetakis, Fotis Stergiopoulos, Panos Seferlis, 2010), depicting the battery placement. Figure 8.1.4 Schematic for Battery incorporation with Electrolyser operation. Source: (Spyros Voutetakis, Fotis Stergiopoulos, Panos Seferlis, 2010) The suitable battery chemistry for this operation is the lithium-ion technology. Due to its high specific power capacity, high discharge rate, and lower depth of discharge, the technology is well suited for the operation required with electrolyser (Www.batteryuniversity.com, no date). Integrated with a good battery management system, the lifetime of the battery can be prolonged 120 and worked more efficiently. Due to these reasons, the model provides the financial evaluation of the lithium-ion technology only, as technically and commercially it is the optimal solution for the operation. In a scenario where the electrolyser operation is the priority over the building heat demand, of taking electrical power from the 900 kW Wind turbine, the battery is operated to analyse the effect on the operation of the electrolyser and the hydrogen production. The capacity factor of the electrolyser is observed to increase when it has a low-scale rating in kW. For example, a 100 kW rated electrolyser operates at a capacity factor of 82.38 % and with 1 MWh/120kW battery storage, the capacity factor increases to 90.93 %. The hydrogen production varies from 13.15 tonnes to 14.5 tonnes. For 650 kW electrolyser, which is a realisable size for the project, the capacity factor is 47.61 % and with 1 MWh/700 kW the capacity factor increases to 48.28%. The hydrogen production, in this case, varies from 49.6 tonnes to 50.26 tonnes. So, for the same energy storage capacity, at different discharge rates, given the size ranges of the electrolyser the battery does not provide a significant increase in production and does not make a financially viable investment as well, for a minimal gain, at high cost of 600 USD/kWh of storage, totalling up to 720,000 USD, as mentioned in Table 8.1.2. 8.1.8 Transportation With a price of 133 pence per liter (Rogers, 2018), Unst is in the 7th place for most expensive place to purchase diesel in the UK. This high cost and the prospect of producing Hydrogen through Renewable Energies creates the window of opportunity for a demand substitution. Low cost solutions presently exist in the UK that allow users to partially convert their vehicles to hydrogen, reducing fuel consumption in up to 50% (Autogaspol, 2014).The diesel demand of ferries, public buses and vehicles for community service is estimated in order to compare to the Hydrogen production and determine what percentage of the demand can be covered in a given scenario. 121 8.1.9 Ferries Currently two ferries, Bigga and Geira, are in operation, connecting the isles of Unst, Fetlar & Yell. The hydrogen demand considering they were completely converted to hydrogen was reported as 165,543 kg of hydrogen per year (Unst Partnership Ltd, 2016). 8.1.10 Bin Lorry for Garbage A bin lorry for garbage operating in Unst reportedly covers a weekly distance of 80 miles. Considering a fuel economy of 4.4 miles per gallon of diesel (Sandhu et al., 2015) this would be equivalent to 20.65 kg of hydrogen per week or 1,073.68 kg per year. 8.1.11 Care Center Van The vehicle owned by Nordalea Care Center in Unst is reported to travel 12 days per month (Pure Energy Centre, 2019b). Considering an average distance of travel of 12 miles (round trip between Baltasound and Uyeasound) and a fuel economy of 54 miles per gallon of diesel (Parkers, 2018) this would be equivalent to 3.03 kg of Hydrogen per month or 36.34 kg per year. 8.1.12 Public buses connecting Unst and Lerwick According to Zettrans (2019), one bus travels between Unst and Lerwick (round trip) every day between Monday and Saturday. Considering a distance of 65 miles between the two locations and an efficiency of a hydrogen bus of 6.25 miles/kg of Hydrogen (Unst Partnership Ltd, 2016) the total demand of hydrogen would be of 124.8 kg Hydrogen per week or 6,489.6 kg per year. 8.1.13 Public buses inside Unst According to Zettrans (2019), buses travel between Haroldswick and Belmont (round trip) seven times a day between Monday and Saturday. Considering a distance of 15 miles between the two locations and an efficiency of a hydrogen bus of 6.25 miles/kg of Hydrogen (Unst Partnership Ltd, 2016) the total demand of hydrogen would be of 201.6 kg Hydrogen per week or 10,483.2 kg per year. 122 Ammonia Production The main objective of this section is to assess the viability of using renewable technologies for a small-scale production (less than 1,000 tonnes per annum) of ammonia on Unst: • to meet the local demand or a portion of the demand for ammonia by the community. • to study other uses of ammonia which could be of added value (revenue) to the community 8.2.1 Brief introduction to the process The synthesis to produce ammonia is through the Haber-Bosch process (Ababio, 1980, p. 409). This process involves the combination of nitrogen and hydrogen gases in a proportion of 1:3 respectively, under pressure conditions of 200 atmospheres, and temperature ranging between 400 oC – 500 oC in the presence of a catalyst (Chemistry Libre Texts, 2017) as shown in Figure 8.2.1. This is an exothermic reaction that yields ammonia (NH3), and heat as a by-product as indicated in Equation 8.2.1 below. Equation 8.2.1 The Haber-Bosch Equation Source: (Chemistry Libre Texts, 2017) Figure 8.2.1 The Haber-Bosch Process Source: Author based on (BBC Bitesize GCSE Chemistry, no date). 123 The expected yield of ammonia is about 15 % after each pass of the gases through the catalyst chamber (Ababio, 1980, p. 278). However, the further recycling of the unreacted gases produces achieves an overall conversion of about 98 % (Chemistry Libre Texts, 2017). Furthermore, studies by Brightling (2018) indicate that; ‘In reality, ammonia synthesis is coupled with hydrogen production to improve efficiency. Hence, the energy consumed, and costs of ammonia production depends greatly on the selection of feedstock used to produce hydrogen. This also plays a role in the amount of carbon dioxide (CO2) emitted, which ranges from about 1.6 ton per ton NH3 produced to 3.8 ton per ton NH3 produced when hydrogen is produced from fossil fuels such as natural gas and coal respectively.’ In the case of Unst, hydrogen production through water electrolysis and powered by renewable energy is being proposed. Thus, eliminating the release of CO2 to the environment for ammonia produced, while catering for the variability and intermittency of the renewable source. Also, the catalyst studied for this case is a fused iron oxide (Fe3O4), some offering advantages of low cost and reduced susceptive nature to H2 poisoning over Ruthenium and Cobalt catalyst, even though its efficiency for the reaction is as low as 70 % (Bañares-Alcántara et al., 2014, p. 48). 8.2.2 Example of a Small-Scale Technology: Proton Venture The main challenge in this study was to consider a small-scale ammonia production plant for which Proton Ventures was found to be a suitable choice. It is based in Netherlands and is a pioneer in manufacturing small scale ammonia plants with the lowest being 1000 tonnes annually and 3 tonnes per day as shown in Figure 8.2.2 (Proton Ventures, no date). The company was contacted with a request for important information, however no related information could be received from them. However, after doing literature review, important 124 information regarding the energy requirement and cost was obtained, which helped to estimate the important parameters of the ammonia plant for the study. Figure 8.2.2 Proton Ventures: Small scale ammonia production units Source: (Proton Ventures, no date) 8.2.3 Chemical Properties of Ammonia According to the Pub Chem Open Chemistry Database (no date) and (Ababio, 1980, pp. 409 - 410), ammonia also known as “Azane” or CAS number “7664-41-7” has some of the properties listed below in Table 8.2.1: Table 8.2.1 Selected Properties of Ammonia PROPERTY VALUE Appearance At room temperature, it is a colourless gas Odour It has a characteristic pungent or choking smell Type of compound It is an alkaline gas Molecular weight 17.031 grams per mole Boiling point - 33.3 oC Melting point - 77.7 oC Energy Content 18.6 kJ g-1 (LHV), 22.5 kJ g-1 (HHV) Density 0.763 grams per litre at STP (0oC and 1 bar) Thermal Decomposition It can be decomposed at temperatures above 500oC Source: (Pub Chem Open Chemistry Database, no date; Ababio, 1980) In addition, the National Fire Protection Association provides a guide on ammonia’s hazard ratings with respect to flammability, health, and stability as shown in Figure 8.2.3. 125 Figure 8.2.3 Impact ratings of Ammonia Source: (My Safety Signs, no date) Table 8.2.1 only indicates some selected properties of ammonia, while a material safety datasheet (MSDS) provides detailed information on its properties, impacts on health and environment, and conditions for handling ammonia. A sample of a MSDS by BOC (a provider for industrial gases in UK and Ireland) can be assessed at https://www.boconline.co.uk/en/images/sg-002-ammonia-v1.44_tcm410-39616.pdf. 8.2.4 Inputs to the System Tool In order to provide the input to the Excel Tool, three main calculations were conducted, namely: mass balance, energy balance and financial parameters, which are explained as under. 8.2.4.1 Mass Balance https://www.boconline.co.uk/en/images/sg-002-ammonia-v1.44_tcm410-39616.pdf 126 To estimate the amount of ammonia produced based on the hydrogen production, the mass balance calculation was conducted for Equation 8.2.1 as under. N2 (g) + 3H2 (g) ⇌ 2NH3 (g) Atomic weight of N2 is 14.007 g and H2 is 1.008 g. Thus, (2 x 14.007) + (3 x 1.008) ⇌ (2 x 14.007 + 3 x 1.008) 28.014 + 3.024 ⇌ 31.038 Thus, to produce 31.038 g of ammonia, 28.014 g of nitrogen and 3.024 g of hydrogen is required. Therefore, it was found after calculation that for 1 kg of hydrogen produced 5.62655 kg of ammonia, which in turn required 4.63194 kg of nitrogen. These values were used for adding a logic in the System Tool, for calculating the ammonia produced with respect to the available hydrogen and required nitrogen. Also, there is a provision in the tool to input share of the hydrogen available for the ammonia production. The unreacted hydrogen and nitrogen are continuously circulated in the ammonia reactor, until about 98 % of these gases are utilized (Chemistry Libre Texts, 2017). This was taken into consideration in the calculations. 8.2.4.2 Energy Balance The energy requirements for ammonia production in literature considers the coupled unit of the electrolyser, the nitrogen generator unit, and the ammonia reactor. From the report by (McNaught et al., 2014, p. 19), the estimated energy required for ammonia production by Proton Ventures is 13 kWh per kg ammonia produced. This includes energy utilized for both hydrogen production and the nitrogen generator unit. In addition, from Wiessner (1988), a Pressure Swing Adsorption nitrogen generator unit requires an average of 0.29 kWh/kg N2 produced, and offers an advantage of a compact solution, requires little maintenance, and short start up times with less risk of apparatus clogging when compared to the cryogenic or fractional distillation method. As per Brown (2018), considering that about 83 to 92 % of the energy is used for hydrogen production from electrolysis. Hence, considering 83 % for hydrogen production and the energy 127 required for nitrogen production, it was calculated that the Haber-Bosch synthesis will require about 1.92 kWh/kg NH3 produced. The energy required for the heating the catalyst, magnetite (Fe3O4) to about 400oC for the first hour was estimated as 0.47 kWh (refer to Appendix IX: for calculation details). Equation 8.2.1 indicates that ammonia synthesis releases 92.4 kJ for the 2 moles of NH3 produced, and the heat of reaction generated by the production of ammonia is sufficient to maintain the temperature level in the catalyst chamber. 8.2.4.3 Cost Owing to the commercial unavailability of a smaller scale ammonia production plant than 1000 tonnes in market, the cost calculation was done with the help of literature review. According to a study done by McNaught et al. (2014, p. 27), the capital cost for a 50 tonne ammonia plant was stated to be in the range of 1.2 to 2 million £. Thus, for the present study, an average of 1.6 million £ was considered for a 50-tonne plant. And for a 100 tonne per annum plant, the capital cost was said to be 2.25 million £. Thus, with these two values, a linear forecast Figure 8.2.4 was obtained to determine the CAPEX of the plant. It is important to mention here that the size of the ammonia plant was estimated based on the actual ammonia which can be produced annually when it is working at 100 % for 340 days for the selected configuration in the excel tool. The CAPEX was then calculated with the help of this estimated size with the help of the linear forecast shown in Figure 8.2.4. Source: Author based on (McNaught et al., 2014, p. 27) As reported by Vrijenhof (2010), about 2 % of the CAPEX is taken as OPEX for the ammonia plant. y = 0.013x + 0.95 R² = 1 0 0.5 1 1.5 2 2.5 0 20 40 60 80 100 120 C A P E X i n m il li o n £ Ammonia Production in tonnes Ammonia Production Capital Cost Figure 8.2.4 Linear Extrapolation for Capital Cost of Ammonia Production 128 As per (Bartels, 2008, p. 52; Patil, Laumans and Vrijenhoef, 2014, p. 325), the life time of the ammonia production unit is 30 years and it is comprised of compressors and the ammonia reactor. The nitrogen production unit has a life time of 20 years (Patil, Laumans and Vrijenhoef, 2014, p. 325). (Patil, Laumans and Vrijenhoef, 2014, p. 326) state that the catalyst unit has a guaranteed operation of 15 years. Since the lifetime of nitrogen production unit and catalyst is shorter than that of the ammonia plant, the initial investment cost for these components was computed as a percentage of total CAPEX. This value was used as the re-investment cost for the components whose lifetime ends before 30 years. Some information received from Pure Energy Centre regarding the cost of the nitrogen units during one of the sessions was used for the cost estimation. With the help of this data and calculating the corresponding size of the plant required for a 1,000 tonne plant, it was estimated that 2.16 % of the CAPEX of the ammonia plant is the nitrogen production unit cost. The cost of the catalyst was estimated as 0.037 % of total CAPEX, using the cost data from (Alibaba.com, no date) for 1,000 tonnes of ammonia plant. 8.2.5 Potential Market for Ammonia in Unst Shetland. In this section the potential market for ammonia in Shetland would be described. 8.2.5.1 Fertilizer About 80 % of the ammonia produced globally is used for fertilizer production (Bañares- Alcántara et al., 2014). As per the description of the Shetland economy on their website (Shetland.org, no date), the main agricultural activity is sheep raring (hence grazing), but they also have herds of cattle for dairy and beef. Even though most of the fruits and vegetables in Shetland are imported, there is also some local produce. A preliminary demand calculation was done, because the actual demand for fertilizer was unavailable. As per Department of Environment & Rural Affairs (2018), in the year 2017 the annual overall application of Nitrogen to tillage crops and grassland together was 91 kg N/ ha (0.091 t / ha). The Shetland Island Council (2018) estimates that the area of land with crops 129 excluding fallow land was 211 hectares as of 2017. Thus, after calculating the total nitrogen fertilizer demand in Shetland in the year 2017 was 19.20 tonnes. This is the approximate demand that was calculated. Considering a possibility that the project produces more ammonia in one of the scenarios, which leads to increase in fertilizer production, the excess could be suggested to be exported to the other neighbouring island of Orkney. This in turn would yield extra revenue. 8.2.5.2 Transportation Although use of ammonia for transportation is quite old, the new technology to adapt to the ammonia as a fuel is still under development. For Unst, use of ammonia as a fuel was studied for bin lorry used to collect garbage. Starkman, James and Newhall (1967) state that modifications are required to be done in the existing diesel engines to be able to use ammonia as a fuel. Also, the possibility of operating on dual-fuel (95 % ammonia and 5 % diesel) could also be considered. Bin lorry for garbage Coren (2016) stated that garbage trucks are amongst the most inefficient vehicles on the road, working on diesel with an average of 4.4 miles per US gallons of diesel (Sandhu et al., 2015). As per the available information, the bin lorry covers a distance of 80 miles per week; thus, using about 18.18 US gallons (68.82 litres) of diesel per week. (NH3 Fuel Association, 2018) states that for a 300 miles range, the tank size for a diesel fuel is 8.8 US gallons (33.31 litres), whereas for ammonia fuel is 27.4 US gallons (103.72 litres). This means that the fuel requirement for ammonia is 3.11 times that of diesel. Thus, to cover a distance of 80 miles per week 56.54 US gallons (214.03 litres) of ammonia will be required. Translating to weight (Greenwood and Earnshaw, 1997), it’s 146.09 kg or 0.146 tonnes per week (7.59 tonnes annually). 8.2.5.3 Storage The use of ammonia as a hydrogen carrier or “storage” arises from the low volumetric density and high-pressure storage requirements for hydrogen at 200 bar and 350 bars, which in turn leads to a higher cost for storage. Ammonia (NH3) has advantages of a higher volumetric density at storage pressure of 10 bar, no carbon footprint, and an established infrastructure and 130 knowledge for its handling, storage and distribution over the years (Bartels and Pate, 2008, p. 2). From Equation 8.2.1 it can be deduced that 1 mole of NH3 contains 1.5 moles of H2, which is about 17.8 % of its atomic mass. Table 8.2.2 outlines the volumetric densities of hydrogen and ammonia, considering their boiling points and pressure requirements. Table 8.2.2 Properties of ammonia and hydrogen densities for storage Parameters Value H2 content Liquid ammonia at -33 oC and 1 bar (kg/m3) 682.6 121.5 Liquid ammonia at 25 oC and 10 bar (kg/m3) 610.3 108.6 Liquid hydrogen at – 252.9 oC and 1 bar (kg/m3) 70.8 70.8 Liquid hydrogen at 25 oC and 150/200 bar -LP (kg/m3) 10.9 10.9 Liquid hydrogen at 25 oC and 350 bar -HP (kg/m3) 23 23 Source: (Aqua-Calc, no date; Bruce et al., 2018) From Table 8.2.2, at 25oC and 10 bar, ammonia stores considerably more hydrogen content than hydrogen stored at 200 bar (low pressure) or 350 bar (high pressure) where the boiling point for ammonia and hydrogen is – 33 o C and – 253 o C at 1 bar respectively as was also deduced from (Bañares-alcántara and Nayak-Luke, 2017). The cost for storage of ammonia was estimated at a factor 10 of the cost of hydrogen storage at 2,645£ /m3 (Pure Energy Centre, 2019b). Hydrogen is derived from the reversible ammonia synthesis as in Equation 8.2.1. This decomposition of ammonia also yields nitrogen and is free of greenhouse gas emissions. Equation 8.2.2 Decomposition of Ammonia Source: (Hacker and Kordesch, 2003, p. 122) 131 However, the efficiency and energy consumption of hydrogen extracted from ammonia needs to be considered as this will also contribute to part of the cost. 8.2.6 Ammonia Market Price The market price for ammonia is closely dependent on the price for the feedstock commonly used for hydrogen production such as natural gas or coal, hence there are large uncertainties and variations in market price structure, even geographically. Hence, a market value of £650/tonne of NH3 quoted by McNaught et al (2014, p. 9). was considered as this is a similar study focused on using wind energy for ammonia production in rural Scotland. 8.2.7 Results and Recommendations As seen in the above sub-chapters, the small-scale ammonia production is costlier as compared to the large scale. Although there could be potential application for ammonia in Unst, the exact amount of production is dependent on the size of the electrolyser plant, which in turn depends on the scenario. Hydrogen Peroxide 8.3.1 What is Hydrogen Peroxide? Hydrogen peroxide is a chemical compound from two atoms of hydrogen and two atoms of oxygen. It has strong oxidizing properties. Therefore, it is utilized as a bleaching (whitening) agent in different industries. In this study case, hydrogen peroxide is particularly important, because it is utilized as antibiotic for fish farms and hatcheries (Pert et al., 2015). Under ambient conditions, hydrogen peroxide has the following characteristics: 132 Table 8.3.1 Characteristics of Hydrogen Peroxide at Ambient Temperature State Liquid Colour Transparent, sometimes a pale blue may be noted Smell Odourless pH 3.7 Freezing point -33 °C Boiling Point 108 °C Density 1.13 g/cm3 Source: (George and Vns, 2015) According to George and Vns (2015), hydrogen peroxide is considered a hazardous chemical because of its toxicity in case of oral ingestion or inhalation of vapours. In case of contact, it may provoke skin corrosion/irritation and serious eye damage/irritation. 8.3.2 Production of Hydrogen Peroxide Worldwide, 99% of hydrogen peroxide is produced through an anthraquinone process (Intratec, 2016). However, this process requires annual productions of at least 3,000 tons due to its high investment cost. For that reason, we will consider an electrochemical method which can be scaled down and its economic viability depends on the price of electricity (Gonzales, 2012). The redox reaction of hydrogen and oxygen is the basic process to generate hydrogen peroxide applying electrolysis. This process requires energy that may be provided by an external DC supply or using hydrogen and the electrolyser as a fuel cell. For this study, the first option has been considered as the production of hydrogen peroxide will work as a flexible load for a better integration of non-dispatchable energy technologies. This process takes place in the cathode as it is shown in Figure 8.3.1. It can also be noted that the anode requires H2O from which the hydrogen ions will be obtained. These hydrogen ions will react with the oxygen in the cathode in order to form hydrogen peroxide. As it is shown in Figure 8.3.1 the pathway of two electrons is required for the reaction (Li, 2017). 133 Figure 8.3.1 Electrolysis Mode Operation Image source: (Li, 2017). According to Figure 8.3.1, the reaction that takes place in the anode is: 2 𝐻2𝑂 ↔ 𝑂2 + 4𝐻+ + 4𝑒− Equation 8.3.1 Anode Reaction for Hydrogen Peroxide Production. Source: (Li, 2017) While in the cathode, where the formation of hydrogen peroxide takes place, we have the following chemical reaction. 𝑂2 + 2𝐻+ + 2𝑒− ↔ 𝐻2𝑂2 Equation 8.3.2 Cathode Reaction for Hydrogen Peroxide Production. Source: (Li, 2017) 8.3.3 Mass and Energy Balance In order to evaluate how much hydrogen peroxide could be obtained from the oxygen provided by the first electrolyser, the mass and energy balance of the reaction in the cathode was calculated from the Equation 8.3.2 and the following results were obtained. 𝑂2 + 2𝐻+ + 2𝑒− ↔ 𝐻2𝑂2 𝑂2𝑀𝑂𝐿 + 2𝐻+ 𝑀𝑂𝐿 + 2𝑒− ↔ 𝐻2𝑂2𝑀𝑂𝐿 𝐴𝑡𝑜𝑚𝑖𝑐 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑂𝑥𝑦𝑔𝑒𝑛: 15.999 𝑢 𝑎𝑛𝑑 𝐴𝑡𝑜𝑚𝑖𝑐 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛: 1.008 𝑢 134 2 × 15.999 𝑂2 𝑘𝑔 + 2 × 1.008 𝐻2 𝑘𝑔 + 2𝑒− ↔ (2 × 15.999 + 2 × 1.008) 𝐻2𝑂2 𝑘𝑔 31.998 𝑂2 𝑘𝑔 + 2.016 𝐻2 𝑘𝑔 + 2𝑒− ↔ 34.014 𝐻2𝑂2 𝑘𝑔 34.014 𝐻2𝑂2 𝑘𝑔 31.998 𝑂2 𝑘𝑔 = 1.06 𝐹𝑜𝑟 𝑒𝑎𝑐ℎ 𝑘𝑖𝑙𝑜𝑔𝑟𝑎𝑚 𝑜𝑓 𝑂𝑥𝑦𝑔𝑒𝑛, 1.06 𝑘𝑖𝑙𝑜𝑔𝑟𝑎𝑚𝑠 𝑜𝑓 𝐻𝑦𝑑𝑟𝑜𝑔𝑒𝑛 𝑃𝑒𝑟𝑜𝑥𝑖𝑑𝑒 𝑤𝑖𝑙𝑙 𝑏𝑒 𝑝𝑟𝑜𝑑𝑢𝑐𝑒𝑑. As can be seen in Equation 8.3.1, per each mol of oxygen, two mols of hydrogen are produced. In order to maintain a balance in the cathode’s reaction, is required to inject the extra oxygen that comes from the hydrogen electrolyser. 8.3.4 Logic for hydrogen peroxide production simulation in excel Inputs required for the production of hydrogen peroxide via electrolysis are water, energy and oxygen. The oxygen is injected in the cathode to obtain hydrogen peroxide instead of hydrogen as in a regular electrolysis process, allowing the production of hydrogen peroxide. According to Li (2017), the production rate of the hydrogen peroxide is proportional to the current intensity when there is no limitation of the oxygen supply as it is shown in Figure 8.3.2 below. Figure 8.3.2 Hydrogen Peroxide Production rate against intensity. Source: Author based on (Li, 2017) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.5 1 1.5 2 2.5P ro d u ct io n r a te H 2 O 2 ( m g d m 3 m in - 1 ) Current (A) Hydrogen Peroxide Production Rate 135 In order to simulate this behaviour, according to the data of Figure 8.3.2 below the linear regression equation was obtained Equation 8.3.3 where “y” is the quantity of Hydrogen produced and “x” the current. y = 0.2866x + 0.0324 Equation 8.3.3 Linear regression Hydrogen Peroxide production rate. Source: Author Therefore, the current to be used in the Equation 6.3.3 is calculated according the electrolyser capacity and available energy for the production, for this is also considered that the electrolyser can work in a minimum partial load of 20 % and that the efficiency of the reaction is already been considered in the production rate. The electrolyser voltage is 400 V (Pure Energy Centre, 2019b). Finally, to fulfil the specific supplies for the production of hydrogen peroxide, it was important to ensure the requirement of oxygen in the simulation. Therefore, according to the mass balance calculations discussed above, it was observed that for each kg of H2O2 0.94 kg of oxygen is required. As a result, the simulation of hydrogen peroxide production has a dual dependency on the energy available for the electrolyser and the available oxygen in the low-pressure storage of oxygen. The outputs obtained are the hydrogen peroxide produced in kg, oxygen utilized in kg and the energy utilized in kW in an hourly and annually basis. According to Drogui et .al (2001, p. 876) the concentration obtained after electrolysis generation of Hydrogen peroxide according to this production rate is 15 mg/l, what represents 1% w/w. This is a really low concentration in comparison with the concentration that can be founded in the market but is the necessary concentration for the application in fisheries as is going to be explained below. 8.3.5 Applications Hydrogen peroxide is utilized in aquaculture for veterinarian purposes, in the case of the United States the FDA (Food and Drugs Administration) approves a concentration of 35 % w/w (Yanong, 2018) while in the UK, concentrations of 35 % w/w and 50 % w/w are allowed. These concentrations must be diluted for getting concentrations of 1500 ppm (2.1 mg/l) in the fish environment according to the recommendation of the manufacturers in Scotland (Pert et al., 136 2015). Hydrogen peroxide works as an antibiotic for certain infections. It has been found that it has secondary effects in the fish and it may endanger the health of fish for up to two weeks, this is due to the fact that it has damaging effects for the mucous18 of the gill (OneKind, 2018). According to a publication by the Scottish Government (2018), Shetland’s fisheries have increased the demand of hydrogen peroxide in the last few years and by 2017 the demand of hydrogen peroxide in Shetland was 625,000 of litres of hydrogen peroxide with a concentration of at least 35 %w/w, as can be seen in the Figure 8.3.3. Figure 8.3.3 Consumption of Hydrogen Peroxide in Shetland's Fisheries Source: (Scottish Government, 2018) 8.3.6 Economics Parameters of Hydrogen Peroxide The economic parameters Capital cost and Operational cost were calculated for the Hydrogen Peroxide production. The CAPEX takes into account the alkaline electrolyser Cost (1,087.5 GBP/kW) and the water purification system cost (71 GBP/KW), same prices as presented before in the hydrogen 18 The mucous of the gill protects fish against infections and hydrogen peroxide has a temporary negative effect over the mucous. 137 production sector, considering also the reinvestment of the replacement of its components of the electrolyser. The OPEX considers 3% of CAPEX for the electrolyser as mentioned before and cost of electricity utilized for the production, which changes according to the configuration of the renewable energies in the simulation in the excel tool. Generally, the production of H2O2 by electrochemical process is considered expensive due to the high consumption of energy and the cost that it incurs, as was mentioned before in the section 8.3.2 . Therefore, the economic viability of the hydrogen peroxide in the present study highly depends in the low costs of energy that are expected from the renewable sources. Due to the low concentration that can be obtained in the production of hydrogen peroxide, the price that is going to be calculated in the system is considerable high compare with the market price (2.52 GBP/l at 35% w/w (ReAgent, no date)). Nevertheless, it will be a clean hydrogen peroxide with a low ecological footprint and will create income flows that remain in the island. Final Remarks Hydrogen, ammonia and hydrogen peroxide allow the proposed energy system to go beyond supplying heat, as they produce an economic impact in other sectors of society. Transportation, fisheries and agriculture could all benefit from locally produced resources that increase their competitiveness and sustainability. 138 Conclusion and Recommendations The completion of the case study is marked by the formation of the excel tool and the case study report that would be submitted to the key partners. The demand assessment of buildings in the project locations: Saxa Vord Resort and Baltasound were performed in order to assess the feasibility of the renewable energy production and hydrogen production centre near the project locations. The renewable energy production simulation in the Baltasound location shows that the demand of the location is low for the renewable energy project to be attractive. However, this should be seen as an opportunity to integrate a renewable hydrogen and derivatives production centre in the location. At the Saxa Vord Resort location, the renewable energy production along with the hydrogen production were simulated. In order to assess the feasibility, various scenarios were simulated in the modelling tool considering various project structures with ESCO (Energy service company) ownership. The financial calculations were performed treating the ownership of heating systems and hydrogen production centre as different entities. The results show that integrating the buildings heat demand with the hydrogen production centre is a promising project. According to the simulations, hydrogen was the only product that was possible to produce at a lower price than the market cost. Nonetheless, it is possible to include small share of ammonia production and the overall project is still profitable. This opportunity increases the utility of the hydrogen produced in the island as ammonia may be used as fuel, for producing fertilizers or as a hydrogen storage. Therefore, there is the possibility to have a diverse portfolio and participate in different markets. In terms of renewable technologies, wind turbines, air and ground source heat pump, solar photovoltaic and solar thermal technologies have been assessed. The results of the simulations showed that wind and heat pump technologies are suitable for the supply of heat for the building within the scope of the project, both from a technical and economical point of view. Solar PV and solar thermal technologies seem to be less promising for the island. Overall, the study shows that the renewable energy potential of the island is underutilised and there are ample opportunities for the island to harness its rich renewable energy potential, especially with wind. The community can benefit from the project in many ways. A hydrogen and derivatives production centre for supplying the local demand will help the money flow to stay within the community. The interaction with the stakeholders, although a sample size, 139 reveals that the community has expectations and concerns about the project. 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Accordingly, it collects and clusters the data acquired from respondents, and finally delivers to the developers an evaluation of results obtained by quantifying statistical indicators (Mean, Median, Mode, and Standard Deviation) and by plotting trends and presenting it in an interactive display. The EvaSys Team of the University of Flensburg offered an instructor account, which allows designing a personalized questionnaire, and later publishing a survey having the University logo within the heading for validation. The survey questionnaire was prepared as part of the activities of the stakeholders’ group for the international class of the M.Eng. Energy and Environmental Management in Developing Countries 2019. All data acquired was directed only to the server of the University of Flensburg. The identity of participants and contact information treated according to the EU General Data Protection Regulation (GDPR), was kept secret and not shared with any third party. The survey named Unst Shetland 2019 was distributed in two forms: one as web-based open link (https://evasys.uni-flensburg.de/evasys/online/) with one password (unst2019), and the Appendix Figure I-1 EvaSys Software Logo https://evasys.uni-flensburg.de/evasys/online/ I-2 other as a paper-based version distributed by Mrs. Elizabeth Johnson from Pure Energy Centre, which was later collected by the program directors upon arrival to Unst. In addition, an email eum.fl.2019@gmail.com was created to receive further comments and feedback from respondents since there was no provision for further comments on the survey sheet or the web-based interface. A sample of the survey is attached below in Appendix Figure I-2: mailto:eum.fl.2019@gmail.com I-3 Appendix Figure I-2 Survey Questionnaire III-4 Appendix II: Baltasound Junior High School Seminar The seminar was held on 12th February at Baltasound Junior High School from 9:15 to 10:35 in the morning. 15 pupils, the head teacher of the school (Mr. Paul Thomson) and two of the teachers were in attendance. The seminar started with an introduction of the pupils. The students from University of Flensburg were introduced through a game in which the pupils had to guess the country of origin of the students. A simplified project idea was presented, within which the familiarity of RE, the importance of using RE technologies and some environmental related topics were discussed. As part of the seminar's agenda, the pupils presented varied topics about Unst. The presentations gave information about the location of Unst, services in the island, events and festivals, and lifestyle in Unst. The seminar ended with a brief evaluation conducted by University of Flensburg students. Appendix Figure II-1Evaluation Results of Baltasound School Seminar Appendix III: Community Workshop The community workshop was held on the 12th of February 2019 at Balta Light Function Room, Hagdale in Unst, Shetland from 18.30 – 20.00. The focus of the community workshop was to present the survey results, introduce the project idea and to hold group discussions with the III-5 community members on their opinions to enhance the project idea. The group discussion was divided into three sub-groups namely: 1. Concerns and Solutions Group: to discuss any foreseen concerns about the project idea 2. Benefits Group: to collect ideas on the benefits which the community would expect from such type of project. 3. Community Involvement Group: to hold discussions about the ways that the community thinks that they can be involved in RE projects for their community ‘s benefits There was a total of 10 attendees and each of them was given the option to choose one out of the three sub-groups to participate in, according to personal interest. The activity in the three sub-groups took place simultaneously. A. Group Discussion on Foreseen Concerns and Solutions This group’s discussion was centred around the community members’ foreseen concerns on the presented RE project idea by the University of Flensburg students, and was also intended to collect their opinion on ways to address all concerns mentioned. There were five community members present for this. Activity 1 The Participants were asked to discuss the question: “What are the Community’s concerns about the project idea?”, with a supplementary request to also determine if the concerns raised should be classified under the economic, technical, social and environmental aspects. All their responses were noted down on a flip chart. This was followed by the next activity. Activity 2 After stating the concerns, the participants were asked to propose suggestions to any of the raised concerns which could be addressed or solved. This was done by asking the next question: “Are there any solutions to these challenges?”. Their responses were further noted on the flip charts beside the earlier mentioned concerns in Activity 1. Activity 3 Then participants were then asked to prioritize the classified concerns (such as economic, social, environmental or technical), for which each participant was given three sticky dots. III-6 Each participant was given the choice to place all their dots on one aspect, or distribute it between three aspects, or otherwise reflecting his or her order of priority. Appendix Figure III-1Results of concern group discussion B. Group Discussion on Benefits The first activity involved gathering answers and the participants’ view to the question: “How would you like the community to benefit from the implementation of RE and hydrogen project in Unst?” Their answers were written on cards and arranged in the four clusters: economic benefits, social benefits, socio-economic benefits and environmental benefits. Activity 2 So, the four clusters were arranged on another flipchart and the participants were given three votes and asked to vote the clusters of their choice. So out of the total of nine votes, five votes were obtained by the economic cluster, three were obtained by the social cluster and one was III-7 obtained by socio-economic cluster. The environmental cluster received no votes, because it was considered as a by-product of the economic benefit. The main conclusion was that everything is linked with the economics. Once economic benefits for the community is higher, it’ll lead to the social, and environmental benefit. Appendix Figure III-2 Benefits Group discussions results C. Group Discussion on Community Involvement Two participants with two children chose the community involvement group to discuss the potentials of engaging the local community in planning and implementing RE projects. The activity contained two exercises and lasted for 30 minutes. The first exercise had a table of two questions to measure the importance and motivation of community involvement in planning and implementing RE projects. The second exercise was an open discussion about "How the community can be involved in planning and implementing RE projects?" and " III-8 How would you like to participate and contribute personally in implementing RE projects?" Appendix Figure III-3 Community Involvement Group Results IV-9 Appendix IV: Potential Stakeholders and beneficiaries of the project Appendix Table IV-1Potential stakeholders of the project Potential Stakeholders Field of work Pure Energy Centre The main technical partner working in the fields of renewable energies, hydrogen, compact natural gas, oxygen, nitrogen and Fuel Cell technologies Unst Partnership Limited A local development organization located in the island of Unst, engaging in all community development projects and activities on the island. Shetland Islands Council The local authority and owner of some of the governmental buildings such as Baltasound Junior High School, and Nordalea Care Centre. The Highlands and Islands Enterprise The Scottish government development agency. Potential beneficiaries of the project: • Saxa Vord Resort • Baltasound Junior High School • Unst Leisure Centre • Nordalea Care Centre V-10 Appendix V: Companies Contacted during the Project Appendix Table V-1 Companies contacted during project assessment Company Country Reason for Communication Website Xant Belgium Wind Turbines Datasheet http://xant.com/ Enercon Germany Wind Turbines Datasheet https://www.enercon.de/en/home/ Jinko Solar China Solar PV Datasheet https://www.jinkosolar.com/ Trina Solar LTD China Solar PV Datasheet https://www.trinasolar.com/en-uk Sunpower Corporation USA Solar PV Datasheet https://us.sunpower.com/ E&H Building Contractors UK Referred to Kingspan https://www.ehbuildingcontractors.com/ Kingspan UK Solar Collectors Datasheet https://www.kingspan.com/group/ Mitsubishi Japan Heat Pumps Datasheet https://les.mitsubishielectric.co.uk Viessmann Group Germany Vitocal Heat Pumps Datasheet https://www.viessmann.co.uk Northman Group UK Japsi Electric Boilers http://www.northmangroup.co.uk/ Precolor UK Hot Water Storage Tank https://www.precolortankdivision.co.uk/ Tanks Direct UK Hot Water Storage Tank https://www.tanks-direct.co.uk/ Pure Energy Centre UK Hydrogen Electrolyser https://pureenergycentre.com/ Proton ventures Netherlands Ammonia Electrolyser https://www.protonventures.com/ http://xant.com/ https://www.enercon.de/en/home/ https://www.jinkosolar.com/ https://www.trinasolar.com/en-uk https://us.sunpower.com/ https://www.ehbuildingcontractors.com/ https://www.kingspan.com/group/ https://les.mitsubishielectric.co.uk/ https://www.viessmann.co.uk/ http://www.northmangroup.co.uk/ https://www.precolortankdivision.co.uk/ https://www.tanks-direct.co.uk/ https://pureenergycentre.com/ https://www.protonventures.com/ VI-11 Appendix VI: Building heat demand assessment model description As illustrated in Appendix Figure VI-1, the building heat demand projection model requires data on building area, heat intensity benchmarks and historical oil consumption. If there is historical oil consumption, method I is used else method II is used. In method I, historical oil consumption data is adjusted with a boiler conversion efficiency and this when plotted by the generated heat degree days or hours enables one to realise a building performance line. From the performance line the base load is the intercept and the gradient the correlation between heat demand and heat degree days. These are then adjusted to develop hourly figures and used to project the building heat demand with the observed heat degree hours. For method II, the model reads heat intensity benchmark and multiplies it with the building area to determine the total annual heat demand. This is further adjusted with weather and occupancy factors. From historical statistics by type of building or use, a base load is assumed to be the share of domestic hot water since its pretty much constant year-round. The temperature correlated part of the demand is then the remaining share. This is divided by the cumulative degree days to determine the constant of proportionality. The hourly base load and constant of proportionality are then applied to the heat degree hours to determine the hourly head demand. Further adjustment like improvements in efficiency can be made to the total to determine the final heat energy demand of the building. The Heat_Demand_Assessment_Model Ms. Excel model is made up of 22 sheets. First is a sheet which gives the usage insights on the role of each of the sheets. The input sheet enables one to select a simulation scenario either the base case scenario or energy efficiency scenarios, view the carbon emissions from the simulated scenario, The base year used in the model is 2017 and 2017 weather statistics were used to realise the hourly head demand. The SV_Heat_Demand_15.5HDH_2017 sheet relates the changes in energy efficiency, building occupancy and the building heat demand at Saxa Vord. The BS_JS_NCC_ULC_2017 sheet generates the heat demand for the building stock at Baltasound. The ULC_Demand_2017 sheet generates the Unst Leisure Centre heat demand since for it a unique 28oC heating day is required for the swimming pool heating. BS_JS_NCC_Demand_2014_2015 generates the heat demand for Baltasound Junior high VI-12 school and Nordalea Care centre from the historical 2014-15 oil consumption statistic. The Occupancy_Scenario sheet is a display of the simulated occupancy scenario. Appendix Figure VI-1 Building hourly heat demand projection methodology. Source: Illustration by Author The Financial Analysis sheet is an analysis primarily of energy efficiency measures and the investment in wet heating system. The radiator_sizing sheet provides benchmarks for the capacity of the existing or required radiator capacitors for the wet heating system at either building location. The BS_JS&NCC _Building_P_Lines1314 sheet generates a building performance line to realise the base load and temperature correlated coefficients for Baltasound Junior high school and Nordalea Care Centre using the 2014-15 historical data and heat degree days. April_2014_May_2015_MERRA generates heat degree days from MERRA data. VI-13 Method_II_Benchmark&Area is a summary of the benchmarks and the corresponding building area and also generates base load and temperature correlated figures using the share of domestic hot water by building usage. Energy_Efficiency Matrix enables the application of the assumed energy efficiency to each type of building in the model. Existing_Energy_Use_Data is the historical oil and electricity usage data from the relevant stake holder. 2017_15.5HDH_NEMS12 is the 15.5 heat degree day generating sheet from the raw 2017_NEMS12_DataSet sheet. 2017_28HDH_NEMS12 generates heat degree days at 28 degrees. HDD_042014-052015 generates heat degree days for April 2014 to May 2015. 8760_HDH_Datasets is a summary of all the 8760 heat degree days in the model. Energy_Scenario _Simulations is a display of the simulation results from TRNSYS and TRNSYS_Simulation_Scenario is an analysis of the capital cost for energy efficiency improvement by scenario. VII-1 Appendix VII: Pipe Sizing Appendix Table VII-1Pipe sizing and costing at Saxa Vord Pipe Name Peak Demand /kW Main Loss 10 % Flow Rate/ gpm Velocity (m/s) Pipe size (mm) Friction Loss per 100ft (psi) Pressure Loss per meter of pipe (pas/m) Actual Pipe Length (m) Fittings Resistance 33% Effective Pipe Length/ m Branch Head Loss (Meters Head) Main Saxa Vord Pipe from Storage to compound 114.00 1.10 11.25 0.91 32.00 2.71 613.47 200.00 1.33 266.00 16.64 Saxa Vord Self Catering Compound 1 51.00 1.10 5.03 0.81 25.00 3.07 695.13 29.00 1.33 38.57 2.73 Saxa Vord Self Catering Compound 2 63.00 1.10 6.22 0.95 25.00 4.08 922.47 27.10 1.33 36.04 3.39 Taftens Houses 63.00 1.10 6.22 0.95 25.00 4.08 922.47 13.20 1.33 17.56 1.65 Main Distillery and Brewery Pipe 88.00 1.10 8.69 0.84 32.00 2.31 522.31 237.00 1.33 315.21 16.79 Distillery 52.00 1.10 5.13 0.81 25.00 3.07 695.13 37.00 1.33 49.21 3.49 Brewery 36.00 1.10 3.55 0.90 20.00 5.01 1132.39 21.70 1.33 28.86 3.33 Source: Author URN: Nordabrake15 version 1 NHER Plan Assessor version 6.3.4 SAP version 9.92 Page VIII-2 Appendix VIII: NHER Certificate SAP Worksheet Design - Draft This design submission has been carried out using Approved SAP software. It has been prepared from plans and specifications and may not reflect the property as constructed. Assessor name building-IC-2019 Assessor number 1 Client shetland stokholders Last modified 01/03/2019 Address 18 Nordabrake 18 Haroldswick, Saxa Vord, Unst, Schotland, ZE2 9TP 10a. Fuel costs - individual heating systems including micro-CHP Fuel kWh/year Fuel price Fuel cost £/year Space heating - main system 1 high-rate cost 25318.82 x 15.29 x 0.01 = 3871.25 (240) low-rate cost Water heating 0.00 x 5.50 x 0.01 = 0.00 (240) high-rate fraction 0.31 (243) low-rate fraction 0.69 (244) high-rate cost 678.24 x 15.29 x 0.01 = 103.70 (245) low-rate cost 1496.34 x 5.50 x 0.01 = 82.30 (246) Electricity for lighting high-rate cost 948.39 x 15.29 x 0.01 = 145.01 (250) low-rate cost 105.38 x 5.50 x 0.01 = 5.80 (250) Additional standing charges 24.00 (251) Total energy cost (240)...(242) + (245)...(254) = 4232.05 (255) 11a. SAP rating - individual heating systems including micro-CHP URN: Nordabrake15 version 1 NHER Plan Assessor version 6.3.4 SAP version 9.92 Page VIII-3 Energy cost deflator (Table 12) 0.42 (256) Energy cost factor (ECF) 9.77 (257) SAP value -2.76 SAP rating (section 13) 1 (258) SAP band G 12a. CO₂ emissions - individual heating systems including micro- CHP Energy kWh/year Emission factor kg CO₂/kWh Emissions kg CO₂/year Space heating - main system 1 25318.82 x 0.519 = 13140.47 (261) Water heating 2174.59 x 0.519 = 1128.61 (264) Space and water heating (261) + (262) + (263) + ( 264) = 14269.08 (265) Electricity for lighting 1053.77 x 0.519 = 546.91 (268) Total CO₂, kg/year (265)...(271) = 14815.98 (272) Dwelling CO₂ emission rate (272) ÷ (4) = 108.15 (273) EI value 18.49 URN: Nordabrake15 version 1 NHER Plan Assessor version 6.3.4 SAP version 9.92 Page 7.3.4.4.2-1 EI rating (section 14) 18 (274) EI band G 13a. Primary energy - individual heating systems including micr -CHP Energy kWh/year Primary factor Primary Energy kWh/year Space heating - main system 1 25318.82 x 3.07 = 77728.78 (261) Water heating 2174.59 x 3.07 = 6675.98 (264) Space and water heating (261) + (262) + (263) + ( 264) = 84404.76 (265) Electricity for lighting 1053.77 x 3.07 = 3235.06 (268) Primary energy kWh/year 87639.83 (272) Dwelling primary energy rate kWh/m2/year 639.71 (273) o Appendix IX: CATALYST CALCULATIONS The catalyst being considered is Magnetite Fe3O4. From , the mass of Iron catalysts used should comprise pelleted iron oxide with density of at least 2.6 g cm-3 (i.e. 2600 kg m-3) (Jennings, 1985). As per a case study report by (Cheema and Krewer, 2018) for ammonia production of 3 tons per day ( i.e. 3000 kg per day), the total volume of catalyst bed required was 0.076 m3. Volume of catalyst required In this case, a production capacity of 4.5 kg NH3 per hour (i.e. 108 kg per day) is considered. If, 3000 kg requires a total catalyst volume of 0.076 m3 Then, 108 kg requires a total catalyst volume of x m3 Hence, volume of catalyst required = 108∗0.076 3000 = 0.00274 m3 Mass of catalyst required mass = volume * density Hence, mass of catalyst required = 0.00274 * 2600 = 7.124 kg Heat energy required to heat catalyst to 400oC Q (heat energy) = m * cp * ΔT Where m = mass of catalyst Cp = specific heat capacity of catalyst at 25 oC ΔT = change in temperature (difference between temperature at start of reaction and desired end temperature i.e. 400oC). To estimate number of moles that contain 7.1 kg; 1 mole of Fe3O4 contains 231.55 g, or 0.232 kg. Hence, 30 moles of Fe3O4 contains 7.124 kg. The specific heat capacity (cp) of Fe3O4 at 25oC = 143.5 J/mol oC (Chemistry Libre Texts, 2016) ΔT = 400oC – ambient temperature in Unst (7oC) = 393 oC Therefore Q (heat energy required) = 30 moles * 143.5 J / mol oC * 393oC = 1,691,865J = 1691.87 kJ = 0.47 kWh

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and other detailed influences. 0 0.2 0.4 0.6 0.8 1 1.2 01/01/2023 00:00 01/04/2023 00:00 01/07/2023 00:00 01/10/2023 00:00 01/01/2024 00:00 Lo ad (M W ) Timestamp 18 Which suggests that total electricity

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Transition Pathways For Decarbonization And Self-Sufficiency On The Isle Of Eigg 1 Año | De: Nombre Transition Pathways for Decarbonisation and Self Sufficiency on the Isle of Eigg International Class 2024 dfn erget MASTER IN ENGINEERING ENERGY AND ENVIRONMENTAL MANAGEMENT Supported by the DAAD, through the funds of the Federal Ministry of Economic Cooperation and Development - BMZ 2 INTERNATIONAL CLASS OF 2024 Ashish Rawat Barbara Monteiro Dzifa Ahadzi Francis Eustinah Tatenda Sithole Hareem Taha Ivy Njoki Maharani Dyah Alfiana Mohamed Bahmani Mohsen Mansouri Paul Mendoza Ruqaiya Chhipa Samantha Lopez Sayed Mohammed Sadat Taha Ahmed Siddiqui DISCLAIMER Although the contents were reviewed several times before being included in this report, the accuracy of the results cannot be guaranteed. The Europa Universität Flensburg as well as the authors of the present document have no legal responsibility in case of any errors, omissions, or misleading statements. We recommend seeking an expert opinion before using any results, information or data presented in this report. Cover picture: Authors. 3 Acknowledgements The Energy and Environmental Management class of 2024 visited the Isle of Eigg as part of the International Class module. The International Class is a long-standing tradition of the EEM Master program, and it is part of its core identity. This report was prepared by the students during their stay on the Isle. We would like to thank the Eigg Heritage Trust, Eigg Electric, the Isle of Eigg Shop and Community Energy Scotland for their insights and support during our stay. We would like to thank Sue Hollands, Bob Wallace, Jenny MacLaren, Labhaoise McKenna, Maggie Fyffe, Norah Barnes, Christine Booth, Camille Dressler, Charlie Galli, and Tamsin McCarthy for their invaluable support during the project. Thank you for answering our many questions, and for always being willing to share your knowledge with us. We would also like to thank all the residents of Eigg for welcoming us, especially those who took the time to respond to our survey and those that participated in the interviews. Special thanks go to our supervisors from the University of Flensburg, Prof.Dr. Bernd Möller, and M. Eng. ASM Mominul Hasan. Without their constant support and guidance, this study would not have been possible. Finally, we would like to thank the Deutscher Akademischer Austauschdienst (DAAD, German Academic Exchange Service) for their financial support, which made the International Class of 2024 possible. 4 Executive Summary The electricity system on the Isle of Eigg has attained an exemplary status being the fore-runner in off- grid renewable energy systems. With an annual renewable penetration up to 95%, the power system is an ideal for what self-sufficient energy systems can look like. However, other sectors of energy, including heating, cooking, and mobility, are dependent on conventional fossils, some of which are imported on the island. A multi-disciplinary approach of qualitative and quantitative methods was used to develop transition pathways that were in line with Eigg’s 2030 decarbonization goals. Data was gathered through community engagement activities, site-specific studies for the potential of renewable energy technologies, and through analysis of demand across all sectors. Workshops and surveys were also used to draw a representative image of the socioeconomic context of the Isle of Eigg community. Assumptions on population growth, tourism, and change in behavioural patterns were made to produce data that could be used for the transition pathways. A modelling framework was developed to compare transition pathways to net zero on Eigg. Three long term scenarios were modelled. The Business-As-Usual (BAU) scenario sets the benchmarks for the other future scenarios. In the BAU scenario, everything remains as is until 2030. The community continues to be as reliant on conventional fuels. However, emissions increase due to increasing demand. In the sector coupling scenario, decarbonization is achieved through the electrification of heating, cooking, and mobility. In order to electrify these sectors, power system expansion will have to be made through tidal energy and increasing battery storage. The third scenario was called Sector-Coupling with Energy Efficiency scenario, which built upon last scenario and utilised the anticipated gains of undertaking community-wide energy efficiency measures. This scenario indicated a similar outcome as last scenario, with the only difference being in the capacities of tidal and storage that were needed for expansion. The new capacities that were needed were smaller than last scenario, mainly because of shrunken demands due to energy efficiency gains. Also, while other sectors were fully electrified, heating sector could be electrified roughly 60%, which is higher than the electrification rate attained in last scenario. Realizing that both future scenarios suggest expansion with tidal energy, which is a technology that is relatively lesser commercial in comparison to solar PV and wind, an additional scenario was modelled that did not have tidal as an option. This was attempted to give the community insights into the alternative they would have if they decided other than tidal energy. This scenario indicated large expansions in solar PV, wind, and battery storage capacities, and these expansions were larger than the two scenarios modelled earlier. Even with these large investments, the heating sector could only be electrified half-way 5 through. This was an indication that while it is possible to have solar PV and wind expand to meet future demands, a diversity in technology is needed to keep the system size reasonably small for an off-grid community. In all modelled scenario for the future, excluding the Business-As-Usual scenario, achievement of a net-zero cross-sectoral energy system is possible. The costs of this however vary by a lot, where the most economical transition could be undertaken through implementation of energy efficiency measures while the most expensive would be in the scenario where we do not have any tidal energy. Across the modelled scenarios, energy efficiency appears the most promising to achieve the least-cost carbon neutrality across all sectors. This study and its results are intended to serve as an analysis for future transition pathways that the community on Eigg can refer to; for their energy system expansion plans, for their decarbonisation plans, or to gain maximum benefit through their behavioural adjustments. 6 Table of Contents Acknowledgements ................................................................................................................................. 3 Executive Summary ................................................................................................................................. 4 List of Tables .......................................................................................................................................... 10 List of Figures ........................................................................................................................................ 12 1. Introduction .................................................................................................................................. 17 1.1. Background ........................................................................................................................... 18 1.2. Problem Definition ................................................................................................................ 19 1.3. Scope and Objectives ............................................................................................................ 20 2. Methodology ................................................................................................................................. 20 2.1. Interactive Workshop ............................................................................................................ 21 2.2. Interviews .............................................................................................................................. 21 2.3. Scenarios ............................................................................................................................... 22 2.4. Model Selection and Development ....................................................................................... 26 2.5. Scenario Indicators ................................................................................................................ 26 3. Modelling Framework Design ....................................................................................................... 28 3.1. OSeMOSYS: GENeSYS-MOD .................................................................................................. 28 3.2. Input Collection ..................................................................................................................... 29 3.2.1. Demand ......................................................................................................................... 29 3.2.2. Technologies .................................................................................................................. 29 3.2.3. Fuels .............................................................................................................................. 30 3.2.4. Storages ......................................................................................................................... 30 3.2.5. Emission ratio and Emission limit.................................................................................. 30 3.2.6. Investment and Variable costs ...................................................................................... 30 3.3. Model Design Attributes ....................................................................................................... 31 3.3.1. Sets ................................................................................................................................ 31 3.3.2. Variables ........................................................................................................................ 31 3.3.3. Objective Function ........................................................................................................ 32 7 3.3.4. Constraints .................................................................................................................... 33 4. Insights into Key Demand Sectors ................................................................................................. 35 4.1. Electricity ............................................................................................................................... 35 4.1.1. Calculation of electricity demand and results ............................................................... 36 4.1.2. Analysis ......................................................................................................................... 39 4.2. Heating .................................................................................................................................. 39 4.2.1. Space heating ................................................................................................................ 40 4.2.2. Domestic hot water ....................................................................................................... 42 4.2.3. Total Heat Demand........................................................................................................ 43 4.2.4. Technology recommendations ...................................................................................... 44 4.3. Cooking ................................................................................................................................. 45 4.3.1. Cooking Demand in Households ................................................................................... 47 4.3.2. Cooking Demand in Businesses ..................................................................................... 48 4.3.3. Cooking Demand for Scenarios ..................................................................................... 50 4.4. Transport ............................................................................................................................... 51 4.4.1. Electric Vehicles ............................................................................................................. 53 4.5. Energy Efficiency in Buildings ................................................................................................ 60 4.5.1. Energy Efficiency in Building Scenario ........................................................................... 61 4.5.2. Energy Savings in Buildings ........................................................................................... 64 4.6. Energy Efficiency in Appliances ............................................................................................. 65 4.6.1. Calculation of energy efficiency potential in Appliances .............................................. 66 4.6.2. Analysis of Energy Efficient Appliances ......................................................................... 67 5. Renewable Energy Technologies for the Isle of Eigg ..................................................................... 70 5.1. Solar Energy .......................................................................................................................... 70 5.1.1. Current Solar System ..................................................................................................... 70 5.1.2. Future Solar Potential on the Isle of Eigg ...................................................................... 71 5.1.3. Site Assessment............................................................................................................. 73 5.1.4. Financial Parameters ..................................................................................................... 75 8 5.1.5. Solar Rooftop Systems Potential ................................................................................... 76 5.2. Wind Energy .......................................................................................................................... 79 5.2.1. Explore the Generation by Existing Wind Turbines on the Island ................................. 80 5.2.2. Potential for the Expansion of Wind Energy ................................................................. 81 5.3. Tidal Energy ........................................................................................................................... 87 5.3.1. Tidal Energy Potential ................................................................................................... 88 5.4. Hydropower System .............................................................................................................. 91 5.4.1. Overview of the current hydropower system ............................................................... 91 5.4.2. Hydropower System Upgrade ....................................................................................... 93 5.5. Energy Storage System .......................................................................................................... 95 5.5.2. Storage System Upgrade ............................................................................................... 96 6. Results and Analysis ...................................................................................................................... 99 6.1. Insights from the Survey and Community Workshop ........................................................... 99 6.2. Modelling Results ................................................................................................................ 100 6.2.1. Current Energy System ................................................................................................ 100 6.2.2. Business-As-Usual Scenario ........................................................................................ 103 6.2.3. Sector Coupling Scenario ............................................................................................ 106 6.2.4. Sector Coupling with Energy Efficiency ....................................................................... 110 6.2.5. Alternative Scenario – Without Tidal Energy .............................................................. 114 6.3. Comparison of scenarios using Indicators .......................................................................... 117 6.3.1. Emissions ..................................................................................................................... 117 6.3.2. Annual Capital Costs.................................................................................................... 118 6.3.3. Sectoral Renewable Fraction ....................................................................................... 119 6.3.4. Surplus or Potential for Excess Generation ................................................................. 120 6.4. Discussion and Socioeconomic Insights into the Modelled Scenarios ................................ 121 6.4.1. Current Energy System ................................................................................................ 121 6.4.2. Business-As-Usual Scenario ........................................................................................ 121 6.4.3. Sector Coupling Scenario ............................................................................................ 122 9 6.4.4. Sector Coupling with Energy Efficiency ....................................................................... 124 6.4.5. Demand Shifting .......................................................................................................... 127 6.5. Energy Poverty in the Context of the Isle of Eigg ................................................................ 128 6.5.1. Measures of Fuel Poverty on the Isle of Eigg .............................................................. 130 6.5.2. Interpretation of the Findings on Fuel Poverty ........................................................... 132 7. The Role of the Eigg Community in the Energy Transition .......................................................... 134 7.1. The Community Role in the Eigg Carbon Neutral Target .................................................... 134 7.2. Eigg Electric and Community Involvement ......................................................................... 135 8. Conclusion and Recommendations ............................................................................................. 136 References ........................................................................................................................................... 139 Annex .................................................................................................................................................. 150 A. Modifications and Additions to GENeSYS-MOD ...................................................................... 150 B. Simplifications and Generalisations of the Model .................................................................. 152 C. Eigg Electric Monthly Report Layout Suggestion ........................................................................ 154 D. Isle of Eigg Renewable Energy Transition Survey ........................................................................ 155 10 List of Tables Table 2-1: Interviewee categories ......................................................................................................... 22 Table 2-2: Definition of Scenarios ......................................................................................................... 23 Table 3-1: Definition of Model Sets ....................................................................................................... 31 Table 3-2: Definition of Model Variables ............................................................................................... 32 Table 3-3: Definition of Model Constraints............................................................................................ 33 Table 4-1: Demand forecasting assumptions ........................................................................................ 35 Table 4-2: Electricity consumption in a year for households and businesses (2023) ........................... 37 Table 4-3: Projected Electricity consumption ....................................................................................... 39 Table 4-4: Heating Fuels costs comparison ........................................................................................... 40 Table 4-5: Housing Categories ............................................................................................................... 40 Table 4-6: Heating degree days source ................................................................................................. 42 Table 4-7: Domestic hot water demand inputs ..................................................................................... 42 Table 4-8: Average monthly water inlet temperatures ......................................................................... 43 Table 4-9: Total heat demand BAU ........................................................................................................ 43 Table 4-10: Demand distribution per meal - HH ................................................................................... 47 Table 4-11: Demand distribution per hour - HH ................................................................................... 47 Table 4-12: Business Unit Demand per season ..................................................................................... 49 Table 4-13: Daily Variation BU ............................................................................................................... 49 Table 4-14: Number of cars ................................................................................................................... 52 Table 4-15: EVs on the island ................................................................................................................ 54 Table 4-16: Percentage of EVs compared to the population. ................................................................ 55 Table 4-17: EV Van characteristics ......................................................................................................... 55 Table 4-18: Availability at home ............................................................................................................ 56 Table 4-19: Categories of Houses on the Isle of Eigg ............................................................................ 62 Table 4-20: Comparison of Building Materials in the Bungalow and Old Category and New Building Category ................................................................................................................................................ 63 Table 4-21: Comparison of Building Materials in the Renovated Category and New Building Category .............................................................................................................................................................. 64 Table 4-22: Total Energy Heating Demand before and after Energy Efficiency ..................................... 64 Table 4-23: Total yearly consumption (in kWh) by a household .......................................................... 67 Table 4-24: Annual Energy Saving by the Island of Eigg ........................................................................ 68 Table 5-1. Solar PV plant system characteristics ................................................................................... 71 Table 5-2: Criteria for PV site suitability ................................................................................................ 73 11 Table 5-4. Financial parameters for solar PV plant on the Isle of Eigg .................................................. 76 Table 5-5. Technical specification of the selected system ..................................................................... 77 Table 5-6. Comparison of existing Wind Turbines generation in different terrain ................................ 81 Table 5-7. Selected Wind Turbine Characteristics ................................................................................. 83 Table 5-8. Cost analysis of selected wind turbine ................................................................................. 87 Table 5-9. Financial Parameters of Tidal Energy on the Isle of Eigg ...................................................... 90 Table 5-10. Characteristic of the current Micro hydro plant ................................................................. 92 Table 5-11. Current Battery system specification.................................................................................. 96 Table 5-12. LiFePO4 batteries specification .......................................................................................... 97 Table 5-13. Hydrogen storage system specification .............................................................................. 98 Table 0-1: Model Attributes ................................................................................................................ 153 12 List of Figures Figure 1-1. Isle of Eigg location. ............................................................................................................ 17 Figure 1-2. Isle of Eigg. .......................................................................................................................... 17 Figure 1-3. Eigg Heritage Trust Organigram .......................................................................................... 19 Figure 2-1: Current and BAU Scenarios – Schematic ............................................................................ 25 Figure 2-2: Sector Coupling and Energy Efficiency Scenarios - Schematic ............................................ 25 Figure 4-1: Methodology for estimation of electricity demand (2021) ................................................ 36 Figure 4-2: Total and hydropower generation (2023) ........................................................................... 37 Figure 4-3: Electricity demand (2023) ................................................................................................... 38 Figure 4-4: 2030 BAU heat demand load profile ................................................................................... 44 Figure 4-5: Type of fuel mostly used for cooking .................................................................................. 45 Figure 4-6: Community likeliness to accept new cooking habits. ......................................................... 46 Figure 4-7: Cooking Daily Curve Households ........................................................................................ 48 Figure 4-8: Saturday Cooking Demand for Business Unit ..................................................................... 50 Figure 4-9: Cooking Demand Scenarios ................................................................................................ 51 Figure 4-10: Cooking Load Curve 2030 ................................................................................................. 51 Figure 4-11: Diesel Consumption .......................................................................................................... 53 Figure 4-12: Charging option for the LDV EV8 ...................................................................................... 56 Figure 4-13 Current charging of EV through the day ............................................................................ 57 Figure 4-14: Storage capacity and the energy that can be reinjected into the grid ............................. 58 Figure 4-15 DCW40 Wallbox Bidirectional DC Charger ......................................................................... 60 Figure 4-16: Example of EPC rating level ............................................................................................... 61 Figure 4-17: Scenario of Energy Efficiency in Building .......................................................................... 62 Figure 4-18: Average domestic electricity consumption in Scotland (kWh per household) ................. 65 Figure 4-19: Energy Efficiency Logo, UK ................................................................................................ 66 Figure 4-20: Electricity Consumption after Energy Efficiency Measures .............................................. 68 Figure 5-1 The location of solar PV plant on Eigg ................................................................................. 70 Figure 5-2:Monthly solar PV electricity production for a 1kW capacity plant ...................................... 72 Figure 5-3 electricity generation from existing four wind turbines for 2021 ........................................ 80 Figure 5-4. wind energy distribution and elevation characteristics of the Isle of Eigg ......................... 82 Figure 5-5. wind energy distribution and elevation characteristics of the Isle of Eigg ......................... 84 Figure 5-6. Arrangement of the wind turbines (including the altitude and roughness data map) ....... 85 Figure 5-7. Monthly hourly average wind energy generation from 1999-2022 for Isle of Eigg ............ 85 Figure 5-8. Annual wind energy generation from 1999 to 2022 ........................................................... 86 13 Figure 5-9. Monthly wind energy generation for 2021 ......................................................................... 86 Figure 5-11 Average Tidal Energy Power Maps ..................................................................................... 88 Figure 5-10. Average Tidal Energy Power Maps on The Isle of Eigg...................................................... 88 Figure 5-12. Tidal potential for a single stream turbine........................................................................ 90 Figure 5-13 Hydropower generation and the energy generation from the other sources ................... 92 Figure 5-14. Yearly energy generation from Hydro in 2020 .................................................................. 92 Figure 5-15. Yearly energy generation from Hydro in 2021 .................................................................. 93 Figure 5-16. Yearly Precipitation in 2021 .............................................................................................. 93 Figure 5-17. connecting the lake Beinn Tighe with the hydro generator.............................................. 94 Figure 5-18. Yearly energy generation from Hydro in 2030 .................................................................. 95 Figure 6-1: Community level of satisfaction with energy provision with energy provision based on 33 respondents .......................................................................................................................................... 99 Figure 6-2: Current Scenario - Generation and Storage Capacities .................................................... 101 Figure 6-3: Current Scenario – CO2 Emissions across Sectors ............................................................. 101 Figure 6-4: Current Scenario - Winter-days Generation and Consumption Profile............................. 102 Figure 6-5: Summer-days - Generation and Consumption Profile ...................................................... 103 Figure 6-6: BAU Scenario - Generation and Storage Capacities .......................................................... 104 Figure 6-7: BAU Scenario – CO2 Emissions across Sectors .................................................................. 104 Figure 6-8: BAU Scenario – Winter-days Generation and Consumption Profile - 2030 ...................... 105 Figure 6-9: BAU Scenario – Summer-days Generation and Consumption Profile – 2030 ................... 106 Figure 6-10: Sector Coupling Scenario - Generation and Storage Capacities ..................................... 107 Figure 6-11: Sector Coupling Scenario - CO2 Emissions across Sectors ............................................... 108 Figure 6-12: Sector Coupling Scenario - Winter-days Generation and Consumption Profile – 2030 .. 109 Figure 6-13: Sector Coupling - Summer-days Generation and Consumption Profile – 2030 .............. 110 Figure 6-14: Energy Efficiency Scenario - Generation and Storage Capacities .................................... 111 Figure 6-15: Energy Efficiency Scenario - CO2 Emissions across Sectors ............................................. 112 Figure 6-16: Energy Efficiency - Winter-days Generation and Consumption Profile - 2030 ............... 113 Figure 6-17: Energy Efficiency - Summer-days Generation and Consumption Profile - 2030 ............. 114 Figure 6-18: No-Tidal Scenario - Generation and Storage Capacity .................................................... 115 Figure 6-19: No-Tidal Scenario - Winter-days Generation and Consumption Profile – 2030 ............. 116 Figure 6-20: No-Tidal Scenario - Summer-days Generation and Consumption Profile – 2030 ........... 117 Figure 6-21: CO2 Emissions across Scenarios ...................................................................................... 118 Figure 6-22: Capital Costs (US $) Comparison across Scenarios ......................................................... 119 Figure 6-23: Comparison of Renewable Fraction across Sectors and Scenarios, 2030 ....................... 120 14 Figure 6-24: Comparison of Surplus or Potential of Excess Generation ............................................. 120 Figure 6-25: Fuel poverty in Scotland ................................................................................................. 129 Figure 6-26: Key factors affecting fuel poverty ................................................................................... 130 Figure 6-27: Annual fuel poverty on the Isle of Eigg ........................................................................... 130 15 Abbreviations AC Alternative Current BAU Business-As-Usual BU Business Units B&B Bed and Breakfast CCS Combined Charging Systems CFD Computational Fluid Dynamics CIBSE Chartered Institution of Building Services Engineers CNI Carbon Neutral Islands DC Direct Current E East E2P Energy to Power Ratio EPC Energy Performance Certificate EV Electric Vehicle FEE Fabric Energy Efficiency FiT Feed in Tariff GHI Global Horizontal Irradiance HDD Heating Degree Days HH Households HP Heat Pumps ICE Internal Combustion Engine IEA International Energy Agency IEC International Electrotechnical Commission IRENA International Renewable Energy Agency kVA kilovolts-amperes kWh kilo-Watt hours kW kilo-Watt LABattery Lead-Acid Battery LiFePO4, LFP Lithium Iron Phosphate Battery MJ Megajoules N North NE Northeast NW Northwest PV Photovoltaic 16 PVOUT Photovoltaic Power Output RdSAP Reduced Data Standard Assessment Procedure ROC Renewable Obligation Certificates SAP Standard Assessment Procedure SE Southeast S South SW Southwest USD US Dollars V2G Vehicle to grid W West 17 1. Introduction Community engagement is the force that drives development and cohesion in many communities around the world. In Eigg, this is even more evident. Different from other places where community involvement is an abstract idea and has much interference from the government, Eigg took control of their destiny as residents purchased the island. This milestone not only revitalized hope in the community that suffered from a lack of basic infrastructure and depopulation, but also sparked a movement of local empowerment. Since then, Eigg residents have worked together to develop innovative projects that promote environmental, economic, and social sustainability. Despite the impressive achievements made by the Eigg community, the journey towards energy self-sufficiency has been one of the main challenges for them. Like many rural and remote communities, Eigg has faced significant obstacles in providing and managing electricity, and it is only through the collective involvement and determination of residents that these challenges have been overcome. The Isle of Eigg is located in the West Coast of Scotland. As one of the four islands known as The Small Isles, Eigg is home to around 100 people. With more than 15 km separating it from the mainland, the island is around 9 km long and 5 km wide. Reachable only by ferry, Eigg gained fame when the isle managed to provide 24/7 electricity to its residents from virtually 100% renewables in February 2008 (Eigg 2023). Figure 1-1. Isle of Eigg location. Source: Google Maps Figure 1-2. Isle of Eigg. Source: OpenStreetMapScotland Eigg is part of the Lochaber Geopark and is mostly made up of volcanic rocks (Lochaber Geopark). The weather is similar to the rest of the Isles from the Inner Hebrides; lots of rainfall in mountainous areas, year-round humidity, and strong winds coming from the Atlantic (southwest). East winds in winter tend to be drier and colder (Encyclopaedia Britannica 2024). The Isle’s main economic activity is tourism. Other important activities are agriculture and public services. 1.1. Background In 1997, the residents of Eigg bought the island from its previous owner. It was a collective effort that included donations from across the world, anonymous donors, and a grant. However, what is important here is the community spirit and mobilisation that gave way to the buyout. This became the core of the Isle’s identity and is deeply embedded in the collective mind of its residents. This is not surprising. Land ownership has been an issue in Scotland for a long time. A 2019 report from the Scottish Land Commission states: “Land is vital for so many aspects of life in a rural community: housing development, community facilities, recreation, growing food and business expansion. If the supply of land is controlled by a single individual, then that individual has a huge degree of control over almost every aspect of life.” (Glenn, et.al., 2019). A Reuters special investigation from 2022 mentions that “fewer than 500 people own more than half of Scotland’s private land, and many of them are foreigners.” (Marshall, 2022). Hence the importance of Eigg’s success in buying the land and making it a community-owned property. Before the buyout, living conditions on the island were less than ideal; there were almost no job opportunities, houses were in terrible conditions, and there was no infrastructure for public services. Each house used to have a small diesel generator to generate electricity. In the words of the residents, they were noisy, smelly, and they would “hate to see Eigg go back to that.” After the buyout, the Eigg Heritage Trust now manages the island. The goal of the trust is to “provide and create opportunity for economic development, housing and infrastructure, whilst conserving our natural and cultural heritage to ensure that development takes place in a sustainable way.” (Eigg 2024a). It is formed by members residents of Eigg (through The Isle of Eigg Residents’ Association), The Highland Council and the Scottish Wildlife Trust. The Residents’ Association has 4 directors elected by the community, and the Highland Council and the Wildlife Trust appoint one director each. The board meets quarterly, and the island directors hold monthly meetings Figure 1-3. Eigg Heritage Trust Organigram The illustration above represents the community organization throughout Eigg. All the agencies are formed by community members and play a vital role in providing a better organization within Eigg’s administration. Eigg’s electricity is managed by Eigg Electric, one of the three subsidiaries of Eigg’s Heritage Trust. It also has a Board of Directors that is composed of members of the community, who work on a voluntary basis. Eigg Electric employees’ salary comes from the pre-paid electricity cards users purchase, and from a daily charge for capacity. Additionally, Eigg Electric receives payments from Renewable Obligation Certificates (ROC) and a Feed in Tariff (FiT) scheme. The existing energy system on Eigg relies on a combination of wind, hydro, and solar power, with intermittent support from diesel generators and battery storage to meet the electricity demand of the island. The output from diesel generators and storage is mainly used to manage the variability of the renewable resources, which cover more than 90% of the annual demand. 1.2. Problem Definition How can Eigg transition away from conventional fuels without risking satisfying its energy needs? Even though Eigg’s electricity comes from renewable sources, it still uses conventional fuels to fulfil the rest of its energy needs for transportation, cooking, and heating. It also faces the challenges of an aging power system, where replacement of some equipment is imminent. The current capacity of the grid does not allow for sector coupling, with the grid unable to cover the electricity demand from the new sectors. Achieving self-sufficiency while reducing the carbon footprint of the island presents its own set of challenges. Economic factors associated with the adoption of non-conventional alternatives and reducing the environmental impact across different facets of energy consumption, are things that must be considered when making decisions. In addition, having community buy-in is crucial if the new system is to be successful. 1.3. Scope and Objectives The Isle of Eigg’s desire to reach Net Zero Emissions posed an opportunity for us to model how that transition could look like. Therefore, the aim of the study is to identify the needs of renewable energy capacity enhancement on the grid, considering the future energy needs of the island across all energy sectors, including power, cooking, heating, and mobility, considering existing transmission limitations. To achieve this, the following objectives were established: 1. Assess the capability and current situation of five distinct energy technologies on the Isle of Eigg, including solar, wind, hydro, tidal, along with potential storage solutions each technology. Identify optimal sites for expanding existing capacities and provide critical technical and financial data. 2. Assess and project potential electricity demand at sector level for electricity, transport, heating, and cooking, whilst also considering the impacts of energy efficiency improvements on the demand for heating and electricity. Calculate new demand needed to achieve sector coupling. 3. Select modelling tool to assess and identify a set of pathways to reduce carbon emissions and achieve self-sufficiency in electricity generation to meet future demands across all energy sectors. 4. Understand the social, cultural, and economic context under which this transition scenarios could take place. 2. Methodology Energy communities tend to be very heterogenous in terms of organisational models. For this reason, it is crucial to understand the socio-economic and political drivers that shaped the emergence and success of the Eigg energy community. Doing so would allow for a much-improved community energy system to be proposed. To perform this study, we adopted a mixed method approach combining both quantitative and qualitative research approaches. A survey was conducted to gather preliminary data on energy sources used, energy consumption habits, satisfaction levels with the current system, and openness to future changes to the energy system. According to Ribeiro et al (2011), quantitative methods such as these are useful in creating snapshots of public views, given sample size and statistical significance of a survey. However, their disadvantage lies in the fact the details behind respondents’ answers remain obscure. With this in mind, we conducted an interactive workshop session and a series of interviews to understand local attitudes towards the current energy system, identify areas of improvement and discuss the community’s aspirations towards a net zero future. The purpose of combining structured interviews with an interactive workshop session was to capture a wide range of data – from individual experiences and expectations to collective attitudes and ideas for future energy solutions. For the modelling framework, the methodology entailed the definition of scenarios that were identified to be important for the future transition pathways. This was accompanied by the selection of a modelling framework and its further development. Lastly, keeping in view the interests of the local community and the key attributes of a transition pathway, certain indicators were selected that formed the model results. 2.1. Interactive Workshop The workshop component was meant to actively engage community members in shaping their energy future by gathering any worries and concerns they might have. The intention was to integrate these concerns in the scenarios to be modelled. This approach would ensure that the designed energy system would not only be technically feasible but would also align with community expectations and address their specific concerns. Due to the unique nature of the energy system, the workshop also aimed to evaluate what it meant to be energy poor on the Isle of Eigg. Voluntary sampling, a non-probability sampling design, was used to facilitate the workshop. With the aid of Eigg Electric, an email was sent out to community members explaining the aim of the workshop, allowing community members to volunteer to participate based on their interest in the topic (Murairwa, 2015). 2.2. Interviews Furthermore, insights gained during the workshop session were then used to create targeted interview questions. The interviews were intended to uncover perspectives that might not emerge in a group setting, thereby complimenting and validating the insights gathered from the survey and the workshop session. In this project, we opted to use semi-structured interviews because of the usefulness of the method in gaining a deeper understanding of human experiences and behaviours. For this present research, the approach allowed us to capture the nuanced and context-specific aspects of the topic we researched. The interview questions were designed to be open-ended, focusing on the four identified themes: fuel poverty, dynamic tariffing, community engagement and social acceptance. For example, we set out to understand the community members’ level of involvement in energy decisions and whether they felt limited in their energy use due to the 5 kW cap and if they would be open to a different electricity pricing regime. It is worth noting that, not every question was asked to every participant in an effort to foster a “dialogic space,” through which 1) participants would be better able to express their views and 2) members of the socioeconomic team would be able to unearth issues and perspectives that our preliminary research had not uncovered (Martin, 2020). A total of 8 interviews were conducted, the names and identifiers of participants are anonymized to ensure their privacy. Purposive sampling, a non-probability sampling technique was used to select participants based on specific criteria and the researcher's judgment (Ames et al., 2019; Campbell et al., 2020). The aim was to deliberately select individuals who possess experiences relevant to the research objectives. Interviewees were recruited through participation in social activities, including the community lunch, singing events, and the interactive workshop. The main selection criteria for the interviewees were based on the following: • All interviewees must be members of the local community and have lived on the island for at least two years. • At least one of the interviewees should be directly involved in the running of Eigg Electric. • The interviewees must be a mix of people who were born and raised on the island and those who moved to the island at a certain point. Table 2-1: Interviewee categories Type of Interviewee No. of interviewees Description Residential 4 In person interviews Eigg Electric affiliate 2 In person interview and virtual call Business Operator 2 In person interviews The workshop and interviews methods were used to build a comprehensive story of local attitudes about the current and future of the Eigg energy system. 2.3. Scenarios An iterative process was undertaken to finalise the scenarios that were to be modelled for transition pathways towards decarbonisation. The process involved referrals to the commonly developed transition scenarios by renowned international energy agencies such as the International Energy Agency (IEA), while Scottish ambition for Carbon Neutral Islands (CNI). The pressing needs and expectations of the residents and stakeholders of Eigg were considered while developing the scenarios. Four scenarios were developed with a target of achieving net-zero or near net-zero by 2030. Table 2-2 describes these scenarios and provides the timeframe of transition. Table 2-2: Definition of Scenarios Scenario Title Definition Time frame Current Scenario Although titled as a scenario, it is a reproduction of the current state of the energy system. In this scenario, the performance of the system was simulated as it stands today so it could be used as a benchmark for other scenarios. As such, there is no cross-sectoral coupling, and the energy system fulfils its demands from the power sector. Note: Cross-sectoral energy coupling refers to the shift of heating, cooking, and mobility sectors to technology that consumes power instead of conventional fuels. This scenario runs for one year across hourly timesteps. Business-As-Usual (BAU) Scenario The Business-As-Usual or BAU scenario is a future extension of the current scenario with the underlying assumption that no transformative measures have been undertaken and everything proceeds as it stands. This imminently means that no cross-sectoral energy coupling takes place even though the demands evolve in the longer run. This scenario forms the grounds to compare with other scenarios. This scenario runs for 6 years from 2024 until 2030. 2024 serves as the starting year, and with 2-year long time- skips the modelled years are 2026, 2028, and 2030. Each of the modelled year runs across all hourly timesteps within the year. Sector Coupling Scenario This scenario is in direct contrast to the BAU scenario. The underlying assumption has changed, and transformative measures will be undertaken across the demand sectors. This imminently means that sectors like heating, cooking, and mobility will get electrified with the goal of achieving This scenario runs for 6 years from 2024 until 2030. 2024 serves as the starting year, and with 2-year long time- skips the modelled years are 2026, 2028, and 2030. Each of the modelled year runs decarbonisation to the highest extent. The targets are set at a 100% decarbonisation across all sectors, but the outcome shall be a result of the available capacity in the transmission network and therefore could be lesser than 100%. across all hourly timesteps within the year. Sector Coupling with Energy Efficiency Scenario This scenario is an extension of the Sector Coupling scenario with the underlying assumption that significant measures to tap energy efficiency potential have been deployed across all sectors. This scenario is designed to deliver insights into the potential benefits that can be attained through energy efficiency. Preliminary assessment indicates energy efficiency gains between 15-30% through a reduction of energy consumption. Further insights on these gains will be analysed through the modelling of the scenario. This scenario runs for 6 years from 2024 until 2030. 2024 serves as the starting year, and with 2-year long time- skips the modelled years are 2026, 2028, and 2030. Each of the modelled year runs across all hourly timesteps within the year. A more direct understanding of the scenarios can be derived from the schematics in figure 2-1 and 2- 2, where figure 2-1 is the schematic representing current and BAU scenario, and figure 2-2 is the schematic representing sector coupling and sector coupling with energy efficiency scenarios. Figure 2-1: Current and BAU Scenarios – Schematic Figure 2-2: Sector Coupling and Energy Efficiency Scenarios - Schematic 2.4. Model Selection and Development To analyse different scenarios, it was necessary to use a modelling tool. Options for scenarios development included commercial software like LEAP. However, we opted for an Open-Source Modelling Framework for the following reasons: - We wanted to promote the use of open-source software as it brings innovation and contributes to the testing and development of scientific approaches. It also establishes that development of transition pathways shall not be restricted due to commercial ownership of the tools. - Most of the commercial software are a black-box design, which take user inputs, process them, and provide outputs. The process behind the graphical user interface is hidden and hard to analyse. On the contrary, an open-source tool provides transparency and deeper understanding of the software’s operation. This transparency gives developers insights of the working behind the graphic-interface and allows for an understanding of the results alongside the possibility of troubleshooting design problems. - Open-source software is suitable for scenario development due to its flexibility and modularity. It provides the possibility of customising the framework and gives developers full liberty to change existing features to suit their specific requirements. The model deployed for the study on the Isle of Eigg is the adaptation of the open-source energy modelling framework OSeMOSYS (Howell et al., 2011) and its extension GENeSYS-MOD (Löffler et al., 2017), are both licensed under Open-Source MIT License. OSeMOSYS is a tool that can be used to analyse decarbonisation scenarios at the global level, while keeping the focus on interdependencies between sectors like electricity, heating, mobility, and cooking. These models come with detailed documentation on the modelling of complex energy systems and the interplay between various components and can be accessed through the online GitHub repository. Details of the modelling framework OSeMOSYS and its extension GENeSYS-MOD are discussed in Chapter 3. 2.5. Scenario Indicators To assess the future energy system across transition pathways, certain indicators were chosen for analysis. The chosen indicators were based on the common interest of the community and Eigg Electric. The following are the key indicators, aligning with the targets set by Eigg Electric for 2030, that will be analysed and visualised to further produce recommendations: - Carbon Emissions: By keeping in view Eigg Electric’s goal of having a net-zero energy system by 2030, this indicator holds significant importance. Annual carbon Emissions in tons of CO2 at https://git.tu-berlin.de/genesysmod/genesys-mod-public the end of 2030 will be analysed to see if the target has been achieved. By becoming carbon- neutral, the community contributes to greener future and helps the world in achieving global environmental goals. - Capacity Of Generation and Storage Technology: As the overall demand for power increases with the electrification of other sectors like heating, cooking, and mobility, it is important to look at the overall increase in size of the energy system and its ability to meet the demand across all sectors, especially during peak times. - Investment Costs: Increasing demand across all sectors necessitates the expansion of the energy system. This expansion will require investment in new renewable technologies and storage systems. It is important to note that these investment costs are only indicative of the investments needed for the expansion of the generation and storage technologies. Therefore, investment costs are an important indicator for each of the pathways, as they provide a means of comparison when looking at the economic feasibility of these expansions. Moreover, we will also analyse other secondary indicators that will help us develop recommendations. These include the percentage of electrification and the renewable fraction across each sector, as these two factors are highly dependent on the available capacity of the existing transmission network. Additionally, the potential of surplus or excess generation is another important indicator. Surplus generation indicates the potential of the future systems to cater for new demand sectors or new opportunities for revenue generation. The relative timing of investment or the commissioning of the technology is also another factor as it informs about the need of the expansion across time. This may give the understanding of the financial planning of the project. 3. Modelling Framework Design 3.1. OSeMOSYS: GENeSYS-MOD OSeMOSYS is an open-source energy system modelling framework for long-term integrated assessment and energy planning (Howells et al., 2011). It serves as an effective tool for designing energy systems across various scales, ranging from villages and regions to countries, continents, and even at a global level. This model is freely accessible and offers ease of updating and modification, catering to the specific requirements of energy system modelling analyses. To be able to model an energy system with OSeMOSYS, one must be familiar with the use of a spreadsheet tool (like Microsoft Excel) and should be able to follow the instructions in the online documentation of the model. No prior programming experience is necessary as the tutorials are very elaborate. Computation power can be a limitation for complex models however, and therefore will depend on the attributes of the model under study. In modelling practice, it is always recommended to start with simpler systems, followed by adding complexity to the model in steps so that the computation power can be assessed and controlled. The model comprises of seven functional components, namely costs, storage, capacity, adequacy, energy balance, constraints, and emissions. Additionally, it adopts a four-level abstraction approach. The first level involves describing the model's sets, parameters, variables, constraints, and objectives. The second level focuses on their algebraic formulation. The third level entails the practical implementation in a programming language. Finally, the fourth level involves the application of the model in a study. For a user with no experience in algebraic formulation or programming, the second and third level remain inaccessible, and it is recommended that these levels are skipped. The model can still be operated at its full capacity while only accessing first and fourth levels, however, the customisation of the model design is then not possible as algebraic formulation and programming knowledge is a pre-requisite for that. The primary objective of OSeMOSYS is to calculate the lowest net present cost (NPC) of an energy system that fulfils a specified demand for energy carriers, energy services, or their proxies. Achieving this objective involves adhering to a set of constraints and defined rules. For instance, constraints may include specifying minimum and maximum capacity limits for technologies within the energy system. These constraints and rules are integral components guiding the optimisation process toward minimising the net present cost while meeting the required energy demand. Derived from the strategic framework of OSeMOSYS, a model-extension called GENeSYS-MOD was developed by Löffler et al. (2017). This model enables a disaggregated analysis of energy and https://git.tu-berlin.de/genesysmod/genesys-mod-public/-/tree/main/Docs?ref_type=heads emissions, where we could see the interactions between energy and emissions at granular level, employing a system of linear equations to explore least-cost solutions while adhering to externally defined constraints, primarily in terms of CO2 emissions. GENeSYS-MOD conducts an in-depth analysis of decarbonisation scenarios at the global level, delineated into 10 pre-defined geographical regions. The primary emphasis is on understanding the interdependencies among electricity, transportation, and heating systems within these regions. For Eigg, our objective is to model energy pathways for a decarbonisation plan extending until 2030, encompassing all energy sectors, including heating, cooking, and mobility. We have adopted and customised GENeSYS-MOD to align with the specific requirements on Eigg, aiming to derive a cost- effective solution with a predominant focus on reduction of CO2 emissions and demand fulfilment while retaining self-sufficiency. 3.2. Input Collection The optimisation of the energy system model involves choosing specific inputs to achieve a desired output which is optimal within the set of constraints. In our scenario, we optimise the energy system model for Eigg by prioritising low total costs for technologies; this includes capital and variable costs of the technologies. Input values were sourced from the analysis derived in Chapters 4 and 5. These include demand time-series data for cooking, heating, mobility, and power for the current year and for the future years. Also, technology specific details such as investment and variable costs, lifetime, minimum and maximum capacities, emission ratios, and capacity factors also formed a part of the model inputs. These inputs can be categorised into six broader categories. These are accessible in the input folder of the Isle of Eigg model available on GitHub repository. 3.2.1. Demand The total demand and hourly demand time series across each sector i.e. heat, mobility, cooking and power are provided as an input to the model. 3.2.2. Technologies The input parameters used to evaluate the capacity requirements of technologies for power generation include potential technologies, their minimum and maximum capacities, residual (existing) capacities, lifetime, and capacity factors. In the context of Eigg, the technologies subjected to optimisation for power generation include solar PV, hydropower, onshore wind turbines, tidal energy, and diesel generators. Additionally, gas stoves, internal combustion engine (ICE) vehicles, wood burners, oil heaters, electric stoves, electric vehicles, https://github.com/TahaAhmedSiddiqui/Energy-Modelling---Isle-of-Eigg and electric heat pumps are considered as generic system components to aid the conversion of one form of energy to another. An important consideration in the input parameters is the residual capacity of the existing technologies. This ensures that the model does not initiate investments in capacities starting from zero, recognising and accounting for the existing capacity and the remaining life of technologies in place. 3.2.3. Fuels The necessary inputs for the operation of technologies as well as the outcomes produced by the technologies are specified as ‘fuels. This results in a total of 7 fuels, including power, heat, mobility, and cooking, which are also the demand sectors, and diesel, propane, and firewood. The interconnection between fuels and technologies is established through input and output ratios, which typically indicate the amount of consumption of input fuel that produces one unit of output fuel after conversion by the relevant technology. 3.2.4. Storages To optimise capacity for storages, various input parameters are considered. These include the type of storages, their efficiency, energy-to-power (E2P) ratio, lifetime, maximum storage capacity, and residual (existing) storage capacity. 3.2.5. Emission ratio and Emission limit The emission ratio for each technology is specified, and annual emission limits are established for each year. This setup enables the model to make investment decisions in technologies based on the annual emission limits for the modelled years. 3.2.6. Investment and Variable costs The investment and variable costs for each technology are specified, allowing the model to evaluate and consider cost-optimal solutions when determining capacities. Discount rate, referred to as Bank Rate in Scotland, is pre-defined at 3%, which is higher than the current rate of 1% (BOS, 2024) to account for the associated risks in investment in off-grid communities (Rahman et al., 2016). Additional input parameters include the designation of dispatchable technologies, specifying that the diesel generator is dispatchable in our case while the renewables are not. Moreover, in addressing power demand, the model considers both technologies that can and cannot be connected to the grid, focusing on the capacity of only grid-tagged technologies while considering grid capacity constraints. For example, all power producing technologies are tagged as grid-tagged technologies, but conversion technologies like wood burners or oil heaters, gas stoves, or ICE vehicles are not as they do not depend on the power from or to the grid and therefore the transmission constraint does not apply to them. For long-term planning scenarios, various parameters are defined for each modelling year. These include the maximum capacity of technology and storage that depend on the theoretical and geographical potentials, annual emission limits that attempt to be close to 0 to achieve decarbonisation goals, capacity factors that have been assumed to stay the same across these years and demands that have been adjusted with the predicted growths in population or technology adoption rates. This comprehensive set of parameters allows the model to account for and optimise the planning of various aspects over the specified modelling years. 3.3. Model Design Attributes Formulation of the model is based on multiple attributes. These attributes are distinct in their function and each attribute plays a key role in the optimisation process. Tables 3-1, 3-2, and 3-3 show the sets, variables, and constraints of the model, respectively. 3.3.1. Sets Sets are the dimensions across which the model iterates while finding the optimal solution. Table 3-1: Definition of Model Sets Name Description Technology Everything that processes energy in any form is considered a technology Fuel The consumable of a technology is defined as a fuel Storage The component that can store fuel and deliver when needed Hours The set of hours across each year Year The set of years for the modelled period 3.3.2. Variables All variables in the model are unknown functions that the model attempts to find. These variables are all ranged domains that can assume any value within that domain. The model iterates over all the possible combinations of each value of all the variables to find the values that bring the least possible value of the Objective function. Some of these variables later directly or indirectly also form output solutions while the model pursues the optimal solution. Table 3-2: Definition of Model Variables Name Description TotalCost Total cost of the system comprising of capital and variable costs FuelProductionByTechnology Output produced from the generators and conversion technologies NewCapacity Capacities of generators introduced at each year TotalCapacity Final capacity of the generators at the end of each year FuelUseByTechnology Input used by the Generators and conversion technologies AnnualTechnologyEmissions Total amount of emissions per Technology at the end of each year Surplus Energy that is overproduced or potential for excess production NewStorageEnergyCapacity Capacities of storages introduced at each year TotalStorageCapacity Final capacity of the storages at the end of each year StorageCharge Amount of energy being charged in the storage at each hour StorageDisCharge Amount of energy being discharged from the storage at each hour StorageLevel The state of storage at each hour TotalStorageCost Total cost of storage comprising of capital and variable costs SalvageValue End of life value of the generation technology CapacityUnits Number of units of the minimum available technology size TotalCapitalCost Amount of capital cost of the generators at the end of each year TotalVariableCost Amount of variable cost of the generators at the end of each year 3.3.3. Objective Function The objective function is another important attribute of the model. It was set to minimise the net present cost of an energy system that meets the given demands. In our modelling framework, this is done by summing up the total discounted costs associated with each technology and storage across each year. By optimising the objective function, we will get the least cost combination of technologies and storages until 2030, while keeping in view the constraints of the systems programmed in the model. An effect of incorporation of discounting in the model is the delayed investments, therefore the model tends to delay any investments to gain the maximum benefit from the discounting while minimising the objective function. Below is the general algebraic representation of the objective function for our model: min 𝑧 = ∑ 𝑇𝑜𝑡𝑎𝑙𝐶𝑜𝑠𝑡𝑡 𝑡 3.3.4. Constraints The model constraints function as the governing functions that limit the ranges of the solution domain for the optimisation, making finding of a solution possible. Table 3-3 shows the list of constraints used in the model. Table 3-3: Definition of Model Constraints Name Description demand_adequacy Energy balance equation that balances supply and demand productioncost Governs the cost of production totalcapitalcostfunction Governs the capital costs of investments totalvariablecostfunction Governs the variables costs of production Productionfunction Governs the production from technologies usefunction Governs the use of fuel for technologies maxcapacityconstraint Governs the maximum capacity limits for technologies totalcapacityaccounting Governs the addition of new capacity into existing gridcapacity Governs the sum of production to be within grid limits capacityunitfunction Governs the identification of number of units to be installed technologyemissionfunction Governs the total emissions from each technology annualemissionlimitconstraint Governs the sum of total emissions against annual limits storagechargefunction Governs the magnitude of energy charged to the storage storagedischargefunction Governs the magnitude of energy discharge to the storage storagelevelfunction Governs the logging of storage level for all instances storagelevelstartfunction Governs the starting level for storage maxstoragelevelfunction Governs the storage level against the max storage levels storagecostfunction Governs the costs of investment and operation of storages storageannualbalancefunction Governs that energy charged and discharged is balanced totalstoragecapacityaccounting Governs the addition of new capacity into existing storagemaxcapacityconstraint Governs the capacity against the max capacity salvagevalue_endoflife Governs the use of salvage value if life runs out within years salvagevalue_duringlife Governs the use of salvage value if life extends beyond years The model used for the analysis of the energy system on the Isle of Eigg required the reformulation at all four levels of abstraction. The modularity of the modelling framework eased the process of customisation, and ultimately several modifications and additions could be made to the model. These additions and modifications in the GENeSYS modelling framework can be found in Annex A. Alongside new additions or modifications, some simplifications and generalisations were also made in the model; these are also available in Annex B. This information can furthermore be accessed in the public repository on GitHub. https://github.com/TahaAhmedSiddiqui/Energy-Modelling---Isle-of-Eigg 4. Insights into Key Demand Sectors The demand was calculated at sector level for electricity, transport, heating, and cooking, whilst also considering the impacts of energy efficiency improvements on the demand for heating and electricity. Assumptions were made for projecting future demand based on the community’s growth projections and they are as shown in the table below. 2024 is considered as the point of departure. Projections were made until 2040 to guarantee coverage of future demand. However, the model only considered projections until 2030. Table 4-1: Demand forecasting assumptions Added Households Total households Added Population Total population 2024 - 64 - 110 2026 2 66 4 114 2028 3 69 6 120 2030 3 72 6 126 It is important to note that most of the emissions and energy consumption from the transport sector come from the ferry that comes from Mallaig. However, this was not considered given that this falls under the Scope 3 of the GHG Protocol, and this project will only consider Scope 1 emissions. There is also no petrol station on the island, and no single importer of fuel in the island, so there are no official numbers on the amount of fuel purchased, consumed, or imported into Eigg. Therefore, a series of assumptions were made to calculate the emissions and fuel consumption from transport. 4.1. Electricity Currently, about 95% of electricity generation comes from renewable energy sources, and only 5% from diesel (Eigg Electric, 2024). Fossil fuels are still used for heating, cooking, and transportation. The project not only aims to understand how the island can meet its electricity generation and demand, but also how the heating, cooking, and transportation sectors can be electrified, including the analysis of energy efficiency measures for buildings and appliances. Heating, cooking, and transportation sectors represent an almost negligible electricity consumption due to grid capacity and infrastructure limitations and use preferences by population. Eigg Electric has set an electrical consumption cap of 5 kW for homes and 10 kW for businesses, and its maximum value (lower than the cap) can vary from one household or business to another. A BAU scenario was analysed. This required a data set for each type of demand. They are used as input data in the model together with renewable energy generation datasets, including constraints. The electricity generation dataset is provided at hourly resolution for 2023 (base year). This year is as deemed representative for the analysis of projected demand (2026, 2028 and 2030). By the time of the visit no electricity meters had been installed at the transformers, so it was not possible to directly obtain the total electricity consumption at distribution level; therefore, the challenge here was to estimate it using the data available considering a time series from Sunny Portal as starting point. The baseline time series was extracted from this platform, which is an interactive dashboard that allows monitoring, system configuration, visualization, and logging of system data for the Eigg Electric generators, with the exception of the small hydropower plants, the wind turbines, and the diesel generators. The devices for measuring the energy generated and consumed are necessary to properly track the energy balance of a particular system. This platform also allows downloading a csv file of the energy balance in daily, weekly, monthly, and yearly format for the period 2008-2023 (Sunny Portal, 2023). As the generation data from the portal included the electricity used in the dump loads and the grid losses, the demand profile had to be adjusted by subtracting an average of 8kW from the production figures, considering that the load-following hydropower generation led to overproduction when close to the load. Then, this information was compared to the electricity income to verify the calculated demand. Figure 4-1: Methodology for estimation of electricity demand (2021) 4.1.1. Calculation of electricity demand and results Eigg Electric provided monthly reports of its electricity generation that allowed for an approximation of electricity demand. The number of electricity cards sold annually plus the amount businesses were invoiced for their electricity consumption, in the 2022-2023 fiscal year, was also used in the calculation of electricity demand. It was assumed that there was not a significant change between years 2021 and 2023. The invoiced amount of electricity was divided with the electric tariff and resulted in the approximate annual electricity demand. The following table was then constructed: Table 4-2: Electricity consumption in a year for households and businesses (2023) Item Consumption in a year (MWh) Share Number of buildings Average consumption per building (MWh) Households 164 65% 64 2.5 Business 89 35% 26 3.4 TOTAL 253 100% 90 5.9 In order to obtain the estimated demand, it was necessary to check the year that provided the most consistent information in Sunny Portal. The yearly data timeseries of 2021, 2022 and 2023 were observed and it was perceived that year 2021 had the least gaps and provided the most consistent data. However, it is important to mention that there was not significant variation between the electricity demand of the years 2021 and 2023. Consequently, the latter year was selected. Years prior to 2020 were not taken into account because gaps in the data were even greater. The data was then cleaned up and adapted to the requirements of the modelling team. As mentioned, there was some missing information in the data acquired from Sunny Portal. Some cells had no values and the reasons for this could be portal downtime, power plant failure, sensor failure, maintenance, and so on. Since the model needs values at an hourly resolution, an average of the previous hours was applied when a blank cell was found. Moreover, a minimum threshold of 10 kW was also applied to the curve. Next, hydropower generation was also evaluated as it follows the electric load. When there was insufficient generation from this source, diesel generators were used. Figure 4-2: Total and hydropower generation (2023) In the graph there are some regions where, indeed, generation from the hydroelectric power plant at Laig (in blue) follows total generation (in orange) and in others it reaches zero. It is necessary to mention that the other small hydropower plants are not separately visible in Sunny Portal. The latter occurs mainly due to the lack of rainfall, which is more frequent in summer. Dump loads are installed in places such as churches, community halls, tea rooms and other places on the island, and are mainly used for heating. They are only turned on when there is excess electricity generation from renewable energy sources. Then, to calculate the demand, a threshold of 15 kW for the hydropower plant was set. The assumption used to obtain this parameter was as follows: whenever the generation of the hydroelectric power plant was less than 15 kW, no energy was used in the dump loads. Otherwise, the excess energy was used to power them up. Therefore, the dump load was subtracted from the hydropower generation, resulting in the demand. As a result, the total estimated annual demand was 254 MWh, and the total electricity used to power the dump loads up was 40 MWh in same period. The estimated annual demand is as shown in the graph in figure 4-3. Figure 4-3: Electricity demand (2023) The total consumption projections shown in the Table 4-3 represent the total electricity consumption of households and businesses without losses. Moreover, it can be seen that this number is similar to the one that was estimated using the timeseries from the dashboard (254 MWh). To calculate the demand including system loss, it was assumed that losses were 7% of the demand. In total, the sum of all the demand and losses is 272 MWh. The projected electricity demand for the years 2026, 2028 and 2030 in the BAU scenario was calculated using the population growth rate of 4%, 10% and 15%, respectively, compared to the year 2011. In addition, the consumption of a 10-kW antenna was included in the forecasted demand. Table 4-3: Projected Electricity consumption Projected demand in 2026 (MWh) Projected demand in 2028 (MWh) Projected demand in 2030 (MWh) 352 366 380 4.1.2. Analysis Figure 4-3 shows very little seasonality in the annual distribution of electricity demands, which could be explained by an absence of electric heating and electric hot water boilers. Additionally, this can also be explained by the electricity caps of 5 kW and 10 kW for homes and businesses, respectively. It would be expected that electricity consumption would be higher in summer, since many tourists come to the island; however, it may be that owners of holiday houses inform visitors of the electricity consumption limits, so the tourists are more aware of it. Figure 4-3 shows that the maximum value of electricity demand is 53 kW, and the average value is 29 kW for the year 2023. 4.2. Heating Heating represents the biggest energy demand on the island and is also one of the key sectors to be decarbonized. The fuels currently being used include wood, kerosene and coal with the greatest consumption coming from wood. Some houses rely on just one fuel source, others use a mix two or all three. Some households also have solar thermal for hot water production. According to the local wood supplier data, households in Eigg consumed about 189m3 of firewood in 2023. The businesses are predominantly heated with wood, with an approximated consumption of 23 tonnes per year for the businesses at the Pier alone. Some of the remaining businesses use kerosene while other businesses have no heating at all. Moving to lower emission heating fuels will mean moving to electricity, or wood which fortunately is available on the island, and this may result in potential cost savings as well as the reduction of energy poverty. The pricing data was sourced from Eigg electric personnel and a comparison on the basis of calorific values for the different heating fuels is a shown in table 4-4 below (engineeringtoolbox, 2014). Due to the limitations of the grid, electrification of heating will only be considered for the residential sector including holiday homes. Table 4-4: Heating Fuels costs comparison Unit Cost/unit Energy content Cost/kWh Electricity kWh ₤0.27 COP=3.5 ₤0.077 Wood kg ₤0.25 4.50kWh/kg ₤0.056 Kerosene litres ₤0.86 10.35kWh/l ₤0.083 Coal kg ₤0.66 8.39kWh/kg ₤0.079 The approaches to calculating heat demand and generating load profiles are as explained below. The total demand was calculated separately for space heating and for domestic hot water. 4.2.1. Space heating The heat required in a building not only relies on heat provided by a heating system, but also internal gains as well as solar gains. Heat demand is impacted by heat losses which are dependent on temperature difference as well as the design, orientation, and construction of a building. The heat loss is a combination of sensible heat loss through conduction, infiltration, and ventilation loads (Burdick, 2011). These losses are also referred to as air infiltration losses and fabric losses both of which were considered in the calculation of heat demand. The island has a residential building stock of 64 which was divided into categories based on energy efficiency levels guided by the EPC certificates. This will be discussed further in the energy efficiency section of the report. Over 50% of the existing building stock has poor insulation according to EPC ratings, which would significantly increase the heat demand. Five categories were identified, and an additional sixth category was added for holiday homes to account for lower occupancy, which is presumed to be 70% of the year based on the information provided as well as on literature (CIBSE, 2008). The categories are as shown in the table below and an increase in population at each time step will be considered as an addition in housing in the “super” category. Table 4-5: Housing Categories Category Quantity Old houses 10 Bungalows 23 Renovated homes 13 New builds 6 Super 2 Holiday homes 10 Total 64 A bottom-up approach for calculating heat demand was used which considers estimated energy consumption of a representative set of individual houses based on the categories as identified above and applied to the rest of the houses that fall within the same category across the island (Ahern & Norton, 2020). The heating degree days (HDD) method was then used for analysing energy consumption in buildings (Bizee, 2024). HDDs are essentially a measure of how much (in degrees), and for how long (in days), the outside air temperature was below a certain base level; and applying the HDD method can help estimate the energy demand and, consequently, carbon emissions from space heating for buildings. The limitation with this method is the definition of the base temperature, which refers to the energy balance between the building and the heating system, and the rough assumptions made for the annual internal heat gains. The base temperature for the UK is 15.5 o C (CIBSE, 2006). The formula used to calculate heat demand according to the HDD method is as shown below. 𝑆𝑝𝑎𝑐𝑒 𝐻𝑒𝑎𝑡 𝑑𝑒𝑚𝑎𝑛𝑑 (𝑘𝑊ℎ) = 𝑂𝑣𝑒𝑟𝑎𝑙𝑙 ℎ𝑒𝑎𝑡 𝑙𝑜𝑠𝑠 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 ∗ 𝐻𝐷𝐷 ∗ 24(ℎ/𝑑𝑎𝑦) Equation 4-1: Space heating demand equation The overall heat loss coefficient is a summation of both fabric losses as well as air infiltration losses, both of which can be calculated as shown in the equations below. U-values were assumed for the elements of each building taking in consideration the insulation levels of the building and the associated area (A) was identified by looking at houses of a similar size on the Tabula webtool, where national residential building typologies from European countries can be found (Tabula, 2024). The ventilation losses were calculated based on the air change method with the air exchange rate (ACH) for the houses being determined on the basis of the building design, while the volume (V) was calculated based on the given household data (Bhatia, 2020). The factor 0.33 is the product of the specific heat and density of air under typical conditions which is the energy required to raise one cubic metre of air by one Kelvin (BRE, 2023). 𝐹𝑎𝑏𝑟𝑖𝑐 ℎ𝑒𝑎𝑡 𝑙𝑜𝑠𝑠 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 = 𝛴𝑈 ∗ 𝐴 (𝑊/𝐾) Equation 4-2: Fabric losses 𝐴𝑖𝑟 𝑖𝑛𝑓𝑖𝑙𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑙𝑜𝑠𝑠 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 = 0.33 ∗ 𝐴𝐶𝐻 ∗ 𝑉 (𝑊/𝐾) Equation 4-3: Air infiltration losses The heating degree days (HDD) were sourced for the year 2023 on the Bizee website through selecting the closest weather station to the island with high accuracy data the summary of which is as shown in the table below. As with electricity demand, 2023 was identified as the representative year and it is also important to note based on observations from the year 2021 that temperatures on the island do not fluctuate much from year to year. The total degrees days for the year are 1996 (Bizee, 2024). Table 4-6: Heating degree days source Description: Celsius-based heating degree days with a base temperature of 15.5 C Source: www.degreedays.net Accuracy: Estimates were made to account for missing data (0.9% estimated) Station: Islay (6.26W, 55.68N) Station ID: EGPI 4.2.2. Domestic hot water The calculations for domestic hot water were made based on the CIBSE guidelines. This approach gives the energy required to increase the temperature of the water to the defined set-point whilst also taking into account heat losses. For the sake of simplicity, the heat loss was considered homogeneous throughout the year which of course may not be the case practically but may suffice for a desktop approach for the heat losses and helps the evaluation of the hot water needs of the island (Syse, et al., 2016). Due to the different average inlet temperatures from month to month, the domestic hot water requirements were calculated from month to using the equation below. The inputs for the equation are as describes in the two tables below the equation. The inlet temperatures were sourced from the manual which describes the Government’s Standard Assessment Procedure (SAP) for assessing the energy performance of dwellings (UK Government, 2019). 𝐸𝑟𝑒𝑞/𝑚𝑜𝑛𝑡ℎ = [(( 𝐶 ∗ (𝑇𝑠 – 𝑇𝑖) ∗ 𝑛) / 3600) + (𝑞𝑖 / 12) ] ∗ 𝑉𝑐 ∗ 𝑝𝑜𝑝 Equation 4-4: Monthly domestic water requirements Table 4-7: Domestic hot water demand inputs Description Symbol Value Unit Source Population pop 110 people Eigg decarbonisation feasibility study Consumption/person/day Vc 50 litres Energy Savings Trust survey report Water inlet temp Ti Weather dependent °C UK SAP Guidelines Water set point temp Tset 60 °C UK Health and Safety Executive Water tank losses/year Qi 4.36 kWh/l University of Strathclyde Water specific heat capacity C 4.18 kj/kg/k Table 4-8: Average monthly water inlet temperatures Jan Feb March April May June July Aug Sept Oct Nov Dec 9.5 10.4 11.5 12.9 15.3 18.4 19.6 18.5 17.2 14.9 12.1 8.9 4.2.3. Total Heat Demand The overall heat demand is as shown below for the different time steps with an increment in demand reflected in the “super” category based on the assumption that all new houses to be built will be at this level of energy efficiency as has already been mentioned in the space heating section. Table 4-9: Total heat demand BAU Heat demand 2026 (GWh) Heat demand 2028 (GWh) Heat demand 2030 (GWh) Old 0.246 0.246 0.246 Bungalow 0.369 0.369 0.369 Standard 0.170 0.170 0.170 New build 0.072 0.072 0.072 Super 0.129 0.170 0.211 Holiday homes 0.111 0.111 0.111 Total 1.096 1.137 1.178 Using the above formulas and data, an annual load profile was generated for each of the time steps as an input for modelling. The daily degree days data was broken down to an hourly average and with an adjustment for peak heating in the mornings and evening. The domestic hot water totals were averaged for each day then distributed evenly over a 6-hour time period in a day. The hourly load profile for 2030 is as shown below. Figure 4-4: 2030 BAU heat demand load profile 4.2.4. Technology recommendations Suggestions in the documentation on the methodology for mapping from the Heat Roadmap Europe project (www.heatroadmap.eu) are that individual heat pumps can be a solution for low-carbon residential heating in rural circumstances with air-source HPs being the most deployable type at a residential level as they are comparable in size to other domestic appliances (Persson, et al., 2017). Reduced performance due to temperature drops below zero is a concern with air source heat pumps, Eigg however has stable temperatures rarely below 0 degrees and not below 10 degrees. Air-to-water heat pumps are quite prevalent in the UK and may achieve very good seasonal coefficient of performance values, which are a measure of the efficiency of the heat pumps. However, taking into consideration the lower installation costs as well as the ease of installation, an air to air, air source configuration is best to consider for the Island. In the model, a heat pump with a conservative estimate of a seasonal coefficient of performance value of 3.5 was considered as guided by some average given ranges for the UK (Burns & Strachan, 2021). Even though storage heaters are currently being explored due to their low cost, they may not be the best solution not only due to their lower efficiency but there may potentially be an increase in airborne dust when using fans to circulate heat (Judson, et al., 2015). Also, surveys done in Scotland identified better overall performance with air source heat pumps resulting in warmer homes as compared to storage heaters. The Scottish government is offering support for not only building improvements but also for the procurement of heat pumps through the Boiler Upgrade scheme, with rural households 0 50 100 150 200 250 300 350 400 450 1 2 5 2 5 0 3 7 5 4 1 0 0 5 1 2 5 6 1 5 0 7 1 7 5 8 2 0 0 9 2 2 6 0 2 5 1 1 2 7 6 2 3 0 1 3 3 2 6 4 3 5 1 5 3 7 6 6 4 0 1 7 4 2 6 8 4 5 1 9 4 7 7 0 5 0 2 1 5 2 7 2 5 5 2 3 5 7 7 4 6 0 2 5 6 2 7 6 6 5 2 7 6 7 7 8 7 0 2 9 7 2 8 0 7 5 3 1 7 7 8 2 8 0 3 3 8 2 8 4 8 5 3 5 H e at d em an d ( kW ) Time (hours 2030 BAU Heat Demand load profile eligible to receive up to £9000. This may be considered for further exploration for the island, for supporting the transition process (Gov.scot, 2024). 4.3. Cooking For Eigg to fulfil its carbon neutrality goals, one key step is converting conventional fuel stoves into electric cookers. For this to be done, it is essential to ensure that Eigg Electric can meet the additional electricity demand from the cooking sector. The purpose of this section is to estimate the current cooking demand and use it to estimate future electricity demand to provide inputs for the energy model. To gather detailed information on fuel types, cooking habits, and the community's willingness to adopt renewable energy-powered cooking appliances, a questionnaire was distributed online and responses from households and businesses were used. In total, 5 questions were used to capture the cooking habits of the island, see below. Q1: Which fuel do you mostly use for cooking? Q2: How much of this fuel do you consume on an annual basis? Q3: At what time of day do you usually cook? Q4: Approximately, how much time do you typically spend cooking your meal? Q5: How likely are you willing to shift your cooking habits from conventional fuels to renewable by using electric appliances? Among the answers, the survey indicated that most residents on Eigg use gas for cooking, therefore the number of gas cylinders will be counted for this study. After preliminary data analysis, 27 responses were considered representative for further analysis. Figure 4-5: Type of fuel mostly used for cooking Also, the residents were asked about their willingness to use electric cooking appliances and shift from conventional to renewable cooking methods. The responses indicate a high likelihood of implementing new cooking habits as shown in the figure below. These positive responses confirm that a shift to electric cooking may have high acceptance in the community. Figure 4-6: Community likeliness to accept new cooking habits. From the survey questions, it was possible to determine the type and amount of fuel used in cooking as well as the amount of time spent cooking. It was determined that there was an average propane consumption of 100 kg per year per household and 900 kg for the most consumption-relevant business units, the Galmisdale Bay Café and Bar. It is important to note that there are other business units on the island, such as B&Bs and accommodation houses, however, from the survey, their fuel consumption does not differ significantly from that of a household. For this reason, for the cooking sector, the business unit refers to “food establishments.” Due to the differences in seasonal behaviour between domestic consumers and businesses, the load profiles were developed separately differently and summed up at the end. Fieldwork was used to verify cylinder size from the supplier. The island is mainly supplied with 47 kg propane cylinders. Because gas cookers have lower efficiencies than electric cookers, the following formula and conversion rates were adopted to transform propane gas consumption into electricity demand: 𝐸𝑛𝑒𝑟𝑔𝑦 𝑖𝑛 𝑘𝑊ℎ = 𝐻𝑒𝑎𝑡 𝑉𝑎𝑙𝑢𝑒 3,6 ∗ 𝜂 𝑔𝑎𝑠 𝜂 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 Equation 4-5: Formula conversion kg of propane to kWh of electricity Parameters used: Propane heat value: 46,4 MJ/kg. (Linstrom, 2021) Gas stove efficiency: 50% Electric stove efficiency: 80% Conversion factor 3.6 MJ / kWh Applying above in the formula, the value to generate the same amount of heat from 1 kg of propane is 8,05 kWh of electricity. 46,4 𝑀𝐽 𝑘𝑔 ∗ 1 𝑘𝑊ℎ 3,6 𝑀𝐽 ∗ 0,5 0,8 1𝑘𝑔 𝑃𝑟𝑜𝑝𝑎𝑛𝑒 = 8,05 𝑘𝑊ℎ 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 4.3.1. Cooking Demand in Households For households, the cooking hours distribution was assumed according to the survey answers. ¡Error! No se encuentra el origen de la referencia.4-10 shows how the daily electricity consumption could be distributed between breakfast, lunch, and dinner hours. Subsequently, the proportion for each meal was distributed according to the percentage within the hours as shown on table below. Table 4-10: Demand distribution per meal - HH Meal Cooking Hours Breakfast 26% Lunch 26% Dinner 48% Table 4-11: Demand distribution per hour - HH Breakfast 05:00-08:00 08:00-11:00 14% 86% Lunch 11:00-14:00 14:00-17:00 94% 6% Dinner 17:00-19:00 19:00-22:00 83% 17% The logic can be read as that 26% of daily cooking demand is destined for breakfast. Of that 26%, 14% will be distributed between 05:00 and 8:00, and 86% between 08:00 and 11:00. A similar logic can be applied for the other meals. The time between 23:00 and 05:00 was considered to be without cooking demand. Then a daily curve demand for households was then created and will repeat throughout the year. Figure 4-7 shows the demand in kg of propane for the surveyed number of 27 houses (2024) versus the future demand for 72 households projected to 2030. Figure 4-7: Cooking Daily Curve Households 4.3.2. Cooking Demand in Businesses For business units, a more complex variation was defined, considering seasonality between months, weeks, and days, and using information such as ferry frequency, and opening hours of the business establishment. The first categorization of monthly seasonality is based on the ferry frequency. According to the CalMac timetable, the ferry operates 3 times per week between October and March, and at daily occurrence from April to September (CalMac, 2023). This information was used to define the low-season months. The remaining months were divided into two categories: shoulder-season and high season. Based on the climate and holidays calendar, April, May, and September are the middle season, while June, July and August are high season. Those times were also confirmed by business owners during the field study. In short: • Low season: October, November, December, January, February, and March • Middle season: April, May, and September • High season: June, July, and August During summer, the activity on the island is at least 3 times more intense, taking into consideration ferry frequency, tourism intensity and working hours. Table 4-12 provides a detailed overview of seasonality for a business unit in 2030. It was considered a growth on the business unit sector of 1.5 and a total of 1350 kg of propane per year. Table 4-12: Business Unit Demand per season LOW MED HIGH Months 6 3 3 Open Days 103 78 92 Propane (kg) 167 417 769 Average Daily (kg) 1.59 5.35 8.36 Analysing the table above, we can see that despite the low season being twice as long, considering the working days, the average working days through each of the seasons are around the same. Considering the working hours, the cafe opens daily and for longer periods in high season months, resulting in a considerably higher demand. Next step is to distribute the proportional demand accordingly to a daily profile. For low seasons months, there are two profiles, when the business is open and closed. During winter, the business opens 4 days a week and the kitchen is open for around 2 hours. For shoulder and high season months, the opening days increase gradually, until they are open for 6 hours per day. 3 profiles were adopted, which follow the same logic. Fridays and Saturdays high, Sundays and Thursday’s medium and Monday to Wednesday low. The table below compiles the logic into parameters. Table 4-13: Daily Variation BU LOW MEDIUM HIGH % Daily (kg) % Daily (kg) % Daily (kg) Sunday 0% 0 0% 0 15% 8.78 Monday 25% 2.8 15% 5.61 12% 7.02 Tuesday 0% 0 15% 5.61 12% 7.02 Wednesday 25% 2.8 15% 5.61 12% 7.02 Thursday 0% 0 18.3% 6.86 15% 8.78 Friday 25% 2.8 18.3% 6.86 17% 9.95 Saturday 25% 2.8 18.3% 6.86 17% 9.95 This logic is applied to differentiate a Saturday in January, from one in April and one in July, for example. The final analysis for the curve is done by following the steps described: I. Check month season: Low / Medium / High II. Check weekday: Sun / Mon / Tue / Wed / Thu / Fri / Sat III. Allocate the shared amount within working hours of the day. Different profiles will be drawn according to month and weekday. For better visualization, ¡Error! No se encuentra el origen de la referencia.-8 illustrates how a Saturday demand behaves in different months. Figure 4-8: Saturday Cooking Demand for Business Unit 4.3.3. Cooking Demand for Scenarios After creating the daily profiles for both household and business units, where business units have different profiles according to the season and day of the week, the data was combined and extrapolated for a whole year. Figure 4-9 illustrates the designed scenarios for cooking demand. The BAU scenario stands for the current situation, with no relevant use of electric stoves. Aligned with Eigg Electric's plan to carbon zero, the premise is that all 100% of households will transition to electric cooking by 2030. This shift is assumed to be conducted in two steps before full electrification is achieved in 2030. The first phase is projected to change 40% of households to electric stoves, considering a total of 66 households in 2026. At this point, old and inefficient stoves should be prioritized. In 2028, it is considered that 70% of households will transition to electric cooking, considering a total 69 households (HH). In the last phase, households on Eigg and also the business units will be fully reliant on electric cooking appliances, and projection of 72 HH and an expansion by 1.5 business units (BU) was also considered. Figure 4-9: Cooking Demand Scenarios The hourly load profile for the carbon zero scenario is as shown in figure 4.10. Figure 4-10: Cooking Load Curve 2030 4.4. Transport The roads on Eigg are easily travelled by foot or car. Most (if not all) of the residents possess at least one car to move around the island, to go shopping when the ferry arrives, or to go to the pier to pick up packages. Bikes are also available on the island but are not widely used. The last study conducted on Eigg regarding transport was done in 2014. Since there is no official data and the island is small, national data cannot be scaled down to fit the consumption of the Isle. Furthermore, there is neither public road transport nor railways. However, there is one minibus taxi that can be hired for travel around the island, and at least one lorry and some tractors mainly used to transport fuels across the Island. However, given the limited data and short time to conduct field research, private transport (mainly cars) will be the focus of the transport sector. To comply with Eigg’s vision of being a Net Zero island, we take a look into the full adoption of electric vehicles. BAU • No use of electric stoves 2026 • 40% of HH with electric stove 2028 • 70% of HH with electric stove 2030 • 100% electric stove + business units 0.000 10.000 20.000 30.000 40.000 1 2 4 5 4 8 9 7 3 3 9 7 7 1 2 2 1 1 4 6 5 1 7 0 9 1 9 5 3 2 1 9 7 2 4 4 1 2 6 8 5 2 9 2 9 3 1 7 3 3 4 1 7 3 6 6 1 3 9 0 5 4 1 4 9 4 3 9 3 4 6 3 7 4 8 8 1 5 1 2 5 5 3 6 9 5 6 1 3 5 8 5 7 6 1 0 1 6 3 4 5 6 5 8 9 6 8 3 3 7 0 7 7 7 3 2 1 7 5 6 5 7 8 0 9 8 0 5 3 8 2 9 7 8 5 4 1 2030 Cooking Demand (kWh) - 100% HH + BU Although cars are the main mobility source in the Isle of Eigg, tourists are not allowed to bring or drive cars on the island, unless they are registered as disabled persons. Therefore, it is only the residents who can drive and own cars, tractors, and electric buggies. Visitors can rent bikes and electric bikes. According to the Scottish government transport statistics, there is one car for every 2 persons over the age of 17 in the Highlands (Scottish Transport, 2023). This would mean there are around 55 cars on the island. From observations made on the ground, it appears as if there are more cars than that. However, it was not possible to know whether some of the visible cars were in fact fit for driving. Therefore, the 55 figure was kept and used for the calculations. There are around 9 hybrid and electric cars on the island. However, their electricity consumption was considered as part of the power demand given the lack of time to conduct an origin-destination survey. The island is around 10 km long from one end to the other. The main road connects one extreme of the island to the other (North to South). After a series of inquiries with the locals, an average daily round trip of 4 km was assumed for each car. This means that each car covers around 56 km per week. No seasonality factor was considered given the ban on tourist cars. The only vehicle that was considered to double its trips during summer was the Isle’s only private Taxi Service. A mix of sedan cars, pickups and SUVs was considered to calculate the fuel required. The models were selected from both interviews with the locals and from observations made on the ground. There is one petrol car in the Isle, and the rest are powered by diesel. For the fuel efficiency of the cars, the city driving factors from the technical sheets of each model were considered, given that the residents do not drive long distances and cold starts are frequent. For the EV Scenario, it is assumed that all cars will be electric by 2030. The rate of adoption is an assumption. The table below shows the number of cars for the Business-as-Usual Scenario and the EV Scenario. Table 4-14: Number of cars # of cars EV Scenario Baseline 55 55 Increase 2026 62 46 Increase 2028 65 35 Increase 2030 68 0 A seasonality factor was considered for the taxi-van that operates on the island, given that in summer, it can do up to 30 drives per day for three months in Summer (June, July, August). As we can see, consumption of diesel plummets under the EV Scenario, assuming a fast rate of adoption of electric vehicles. The annual fuel consumption is the following: Figure 4-11: Diesel Consumption Source: Authors An Origin-Destiny Survey could help shed light on the driving habits and fuel consumption of private cars on the island, allowing for better planning of where to install EVs, the number of stations, and potential charging times. It is also possible that the uptake of EVs will be slower than the rate proposed in this study. However, if Eigg is to become a Net Zero Island, it needs to start thinking of ways to phase out diesel cars. 4.4.1. Electric Vehicles Electric vehicles are set to dominate the global car market, comprising two-thirds of all vehicle sales by the year 2030. This surge in popularity follows a distinct trajectory known as the "S-curve," a pattern already evident in leading EV markets such as northern Europe and China. According to projections from the University of Exeter in 2023, this trend suggests that EV sales will experience a staggering sixfold increase by 2030, capturing a substantial market share ranging from 62% to 86% of total sales. (Alex Morrison, 2023) 4.4.1.1. Overview of the existing system While electric vehicles typically come with a higher initial cost compared to traditional cars, they may lead to significantly lower operating expenses over their lifetime, primarily due to reduced fuel and maintenance costs. Additionally, the reduced maintenance requirements of EVs not only decrease ongoing expenses for owners but also lessen the dependence on local garages, which can have implications for the overall economy, especially on islands where resources may be limited. (Autovista24, 2023) Despite the domination of traditional fossil fuel-based cars with internal combustion engines on the island, there is a shift towards electric vehicles where currently, there are reports of nine fully electric, hybrid, or buggy cars in operation (where the percentage of EVs compared to the population is 8% and it’s expected to increase to reach 51% by 2030). The list of the electric vehicles currently available on the island is as follows: Table 4-15: EVs on the island Vehicle Model Type EVs Battery Capacity (kWh) Buggy Renault Twizy 7 John Deere Gator TE 12 Ezgo Electric Golf buggy 3.3 Cushman Hauler 800 12 Sector Lithium-Ion 16.5 Polaris Ranger 14.9 Hybrid Mitsubishi Outlander 20 Fully electric Nissan Leaf 40 Citroen Ev'ie 30 Over the next 5 years, we expect the population to grow, leading to a rise in the number of electric vehicles (EVs) on the island. This increase in EVs will be driven by two main factors: the declining cost of these vehicles and the expansion of electrical infrastructure. As the cost of EVs becomes more affordable where It's been estimated that the price of an EV could match that of conventional motors as soon as 2025 or 2027, with EVs predicted to be even cheaper than petrol cars as the 2030 approaches (The Energyst, 2023). In addition to that the island's capacity for electrical sources grows, more people are likely to adopt electric vehicles as their mode of transportation. The following table will monitor the growth of electric vehicles alongside the population increase. Table 4-16: Percentage of EVs compared to the population. Year Population N° of EVs % of EVs / population Storage capacity in EVs (kWh) 2023 110 9 8% 133 2026 114 16 14% 420 2028 120 29 24% 715 2030 126 63 51% 1487 4.4.1.2. Upgrading the transport system The Prospect of a common EV Minibus or VAN In response to the growing need for efficient and sustainable transportation options on the island, the idea of obtaining a shared electric van emerges as a good solution and that by introducing a common mode of transportation. One promising option for this initiative is the LDV EV80 (Leyland DAF Vans an all-electric version of the LDV V80 van). This electric van offers a combination of environmental friendliness, and good battery range. By opting for the LDV EV80 as a common electric van, its spacious interior can be adjusted to accommodate the diverse transportation needs of island either residents or tourists. Furthermore, the LDV EV80's robust battery range ensures that it can manage the demands of daily operations without frequent recharging, enhancing its efficiency and convenience. With a lithium-ion battery pack, the LDV EV80 can achieves an electric range of up to 120 miles or 200 km on a single charge, making it well-suited for both short trips and excursions across the island. In terms of charging, the LDV EV80 supports both standard AC charging and DC rapid charging options, offering flexibility and convenience to users. With DC rapid charging, the van can be recharged from flat to full in just 90 minutes, minimizing downtime and maximizing operational efficiency, granted that charger capacity is available. Prices for the LDV EV80 start at around £54,000. Table 4-17: EV Van characteristics Vehicle Type Battery Capacity (kWh) Chargin time (h) Electric range (km) LDV EV80 56 1.5 200 Source: (Auto Express, 2020) Incorporating the LDV EV80 into the transportation infrastructure aligns with the commitment to sustainability and net zero emission goal. Figure 4-12: Charging option for the LDV EV8 Charging features on the island A recent survey conducted on the Island of Eigg aimed to understand residence habits in their homes and outside. The survey targeted 32 households across various regions of the island, focusing on the presence of residents at different times of the day. The survey as shown in the table below collected data on the presence of residents at home during four distinct time periods. Table 4-18: Availability at home Time slots Morning (06:00-12:00) Afternoon (12:00-16:00) Evening (16:00-21:00) Night (21:00-06:00) % of availability at home 70% 27% 73% 93% Based on a storage capacity of 133 kWh in 2023 derived from nine electric vehicles, in addition to the findings of a survey of home occupancy, the below charging trend chart has been created. This chart considers the collective charging capacity across the entire island in 2023. it aims to provide an insightful view of the charging patterns. Figure 4-13 Current charging of EV through the day These availability patterns have implications for EV charging habits on the island. Residents who are typically available at home during the morning and night periods may prefer to charge their electric cars during these times. This aligns with the concept of smart charging, where EV owners take advantage of off-peak hours to charge their vehicles, reducing strain on the grid and potentially benefiting from lower electricity rates. Furthermore, the lower availability during the afternoon period suggests that fewer residents may be present to charge their EVs during this time. The Prospect of Vehicle to Grid (V2G) EV’ owners on the island of Eigg currently, rely totally on domestic charging systems to recharge their vehicles since there are no public charging points on the island. As part of the ongoing efforts to integrate sustainable technologies into the transportation infrastructure, Vehicle-to-Grid (V2G) technology may be developed on the island. This allows electric vehicles (EVs) to not only draw power from the grid for charging but also to discharge power back to the grid when needed, effectively turning EVs into mobile energy storage units. At its core, V2G technology enables bidirectional energy flow between EVs and the grid, unlocking a range of potential benefits for both vehicle owners and grid operators. By leveraging the energy stored in EV batteries during periods of high demand, V2G systems can help stabilize the grid, alleviate strain on power generation facilities, and reduce the need for costly infrastructure upgrades. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 En e rg y kW h Hours Charging habit of EV through the day Based on an average capacity of 23kWh and according to the table in the previous section where the number of electric vehicles has been defined according to the growth in population at the island, a progressive increase in storage capacity within electric vehicles (EVs) over the coming decades is expected, where starting in 2023 with 133 kWh, the capacity is expected to nearly triple by 2026, reaching 420 kWh. By 2028, it is forecasted to reach 715 kWh and exceed 1,487 kWh by 2030. The table below illustrates this trend. The integration of electric vehicles (EVs) into the grid through Vehicle-to-Grid (V2G) technology offers a promising avenue for enhancing energy flexibility and sustainability. However, the extent to which an EV can inject its stored energy back into the grid is subject to various factors. One critical factor is the manufacturer’s safety measures to protect the battery, which may impose limits on discharge rates to prevent potential risks such as overheating or damage. (Ana Simarro-García, 2023) A common estimation suggests that V2G systems allow for the injection of 50-70% of a vehicle's total energy capacity into the grid (Virta Global, 2023). The energy that can inject back into the grid can exceed 800 KWh in 2030. Figure 4-14: Storage capacity and the energy that can be reinjected into the grid Connectors for the Vehicle-to-Grid (V2G) Integration One of the key components of V2G technology is the charging infrastructure, which includes various types of charging plugs and connectors that facilitate communication and power transfer between EVs and the grid (Lee Goldberg, 2023). These include: 0 200 400 600 800 1000 1200 1400 1600 2023 2026 2028 2030 C ap ac it y kW h Years Storage capacity and injected energy Storage capacity Energy into the grid • Type 1 (SAE J1772): Commonly used in North America and Japan, Type 1 connectors feature a single-phase AC charging capability and are suitable for residential and light commercial applications. • Type 2 (IEC 62196): Widely adopted in Europe, Type 2 connectors support both single-phase and three-phase AC charging, making them versatile and well-suited for a range of charging scenarios. • CHAdeMO: CHAdeMO connectors enable DC fast charging and are commonly used in electric vehicles with high-capacity batteries, offering rapid charging speeds and convenience for drivers on the go. • Combined Charging System (CCS): Developed as a universal charging standard, CCS connectors integrate both AC and DC charging capabilities into a single plug, providing flexibility and interoperability across different EV models and charging infrastructure. Of the charger types mentioned, the Combined Charging System (CCS) emerges as the most suitable for Vehicle-to-Grid (V2G) applications due to its unique capabilities. Unlike other connectors, CCS integrates both AC and DC charging functionalities into a single plug. This integration empowers CCS to facilitate bidirectional power flow, enabling not only the drawing of energy from the grid to charge the vehicle but also the discharge of energy back into the grid for V2G purposes. In contrast, while CHAdeMO connectors offer DC fast charging capabilities, they typically lack support for bidirectional power flow, which makes them unsuitable for V2G applications. The same case applies with Type 1 and Type 2 connectors where are primarily designed for one-way charging from the grid to the vehicle and do not have features essential for V2G functionality. By providing charging possibility of AC and DC, CCS ensures the necessary flexibility required for V2G applications across diverse electric vehicle models and charging infrastructure, making it the optimal choice. Bidirectional DC Charging Solution for the Island of Eigg EnerCharge’s 40kW V2G Bidirectional DC Charger, is a solution for electric vehicle (EV) charging and grid interaction. With its advanced bidirectional charging capabilities, this charger enables vehicles not only to draw power from the grid but also to discharge energy back into it. Figure 4-15 DCW40 Wallbox Bidirectional DC Charger Designed to meet the evolving needs of electric mobility and smart grid solutions, EnerCharge's charger offers high power output and seamless bidirectional communication, unlocking new opportunities for grid stability and renewable energy integration. (EnerCharge, 2024) The EnerCharge’s 40kW V2G Bidirectional DC Charger price is: $15,610 (Solarics GmbH, 2024) In summary, the potential of electric vehicles (EVs) in shaping the island's mobility is clear. And the path is open towards increased EV adoption, driven by factors such as declining costs and expanding infrastructure. Despite initial cost barriers, the long-term economic and environmental benefits of EVs make them an attractive choice, especially for a community with limited resources. The proposed solutions, such as shared electric vans and Vehicle-to-Grid (V2G) technology, demonstrate a strategic approach towards sustainability and grid resilience. In addition to that by the usage of innovative solutions like the Nuvve Bidirectional DC Charger, the island stands to not only reduce carbon emissions but also enhance energy flexibility and efficiency. 4.5. Energy Efficiency in Buildings Energy efficiency has been designated as a national priority in Scotland. According to the Eigg Electric 2030 Feasibility Study, energy efficiency is also a part of strategic priority by improving building performance for the Isle of Eigg (Eigg Electric, 2023). Based on the survey we conducted in 2024 (see the Annex D, question 13 to 16), at least eight houses still have no wall insulation, eight houses have no roof insulation, 18 houses have no floor insulation, and three houses have single glazed windows. Considering these numbers, the improvement of building insulation fabric is suggested to reduce energy demand, resulting in homes that are warmer and easier to heat. Improving the building fabric insulation can also affect long-term energy bills. However, building retrofit can be challenging considering the economic, social, and technical situation on the Isle of Eigg. 4.5.1. Energy Efficiency in Building Scenario As previously mentioned, there are 64 residential buildings on the island, with 37 houses being EPC registered with the Energy Saving Trust. Energy Performance Certificate (EPC) are required for houses, especially for rentals, new builds, and those that are up for sale in Scotland. The EPC provides information on how energy efficient a building is and how to improve energy efficiency (Scotland Government, 2020). The certificate also describes energy efficiency indicators, such as energy performance of Fabric Energy efficiency (FEE), primary energy per unit floor area, energy cost rating, environmental impact base on CO2 emission and dwelling CO2 emission rate (The Building Research Establishment, 2021). EPC has seven levels to define the energy efficiency rating of buildings, A to G based on the points generated from Standard Assessment Procedure (SAP) for new buildings and RdSAP for existing buildings. Figure 4-16: Example of EPC rating level Source: Energy Saving Trust, 2024 Interviews with The Isle of Eigg Heritage Trust's Warm Homes Manager were conducted to fill the data gap of 27 houses without an EPC. Assumptions were used to categorize houses. Similar EPC rates were assumed for homes that have similarities in building fabric. It is important to note that this was done despite these houses being of different sizes and having similar floor areas, resulting in less accurate heating calculations. Table 4-19: Categories of Houses on the Isle of Eigg Categories EPC Level No. of House Old E, F, G 10 Bungalow E, F, G 23 Renovated D 13 New Build C 6 Super B 2 Holiday Homes C, D, E, F, G 10 Total (Houses) 64 The “Renovated” house, with EPC band “D,” is considered the average rating of EPCs in Scotland. Because the rate at which a building loses heat is relative to its size, detached houses with an EPC below D are classified as 'Old', while detached bungalows with an EPC below D are classified as 'Bungalow'. The “Super” category is for houses that are highly efficient and have lower thermal transmittance of fabric insulation than other categories. The Super category has EPC band B. Additionally, there are 10 Holiday Homes with seasonal occupancy, which influences heating demand, thus we assigned them to different categories. Based on these six categories, 51 houses (Old, Bungalow, Renovated, and 5 Holiday Homes) on the Isle of Eigg are still below EPC Band C. Therefore, to reduce energy demand, basic or traditional building retrofit is necessary. This requires the alteration of the existing building fabric to increase its energy efficiency (Alabid, 2022). Figure 4-17: Scenario of Energy Efficiency in Building In 2019, the Scottish Government announced the Energy Efficiency Scotland Route Map, for all homes in fuel poverty (those households which spend 10% of their income on fuel costs (Scottish Government, 2019). It is assumed that energy efficiency in buildings will be achieved in three stages by 2030, bringing all homes on Eigg to EPC band C. In the first stage, homes categorized as “Old” will be the first to be retrofitted, starting 2024. In 2026, the Bungalow and Holiday Homes categories will be retrofitted, and in the final stage (until 2030) all the renovated houses will be renovated to new build status which is EPC band C. On top of that, as mentioned in the previous section, there will be additional houses built by 2030. It is projected that in 2026 there will be two passive houses in the super category, three more passive houses in 2028, and three more passive houses in 2030. EPC Band C is equivalent to a space heating demand of about 71- 120 kWh/m2/year (Scottish Government, 2023). Furthermore, each category of houses on the Isle of Eigg has different characteristics of fabric material. The EPC ratings also define building fabric for each category, which can be used to determine the u-value. Bungalow and Old category’s fabric characteristics are as listed in table 4-20. Table 4-20: Comparison of Building Materials in the Bungalow and Old Category and New Building Category Insulation Bungalow and Old U-value (W/m2K) New Building U-value (W/m2K) Wall Granite or Whinstone (no insulation) 2.1 Wood Fiber Internal insulation 0.4 Roof Pitched 150mm 0.35 Pitched 280mm 0.16 Floor Solid (no insulation) 0.59 Solid (insulation) 0.23 Window Single Glazing 4.8 Double Glazing 2.2 Door Wood 3 PVC 2.2 Source: (Tabula, 2024 and Historic Scotland, 2013) The table shows that the house types “Bungalow” and “Old” have poor insulation, indicated by the higher u-values, which result in higher heat demands. As a result, recommendations are made to insulate walls, roof, and floor, and to replace windows and doors, which will reduce heat loss by almost 45% in old and bungalow categories. Historical or traditional houses in Scotland typically have breathable construction that can absorb and release moisture. Selecting an appropriate water vapor permeable material to avoid a vapor barrier is important when deciding to improve insulation. Wood fibre internal insulation for wall insulation is suggested since it is cheaper to refurbish and can be safely applied directly to granite walls without a vapor barrier. Furthermore, roof loft insulation is proposed for at least 270mm of insulation installed (Historic Scotland, 2013). Double glazing installation and improvements to panelled doors may also be practical for the old and bungalow categories. For the renovated category, the suggestion is to install thicker roof insulation, this is a feasible option that can reduce heat loss of a house by about 10%. Some of buildings insulation are qualified in Renovated category in standard of band C of EPC such as wall, floor, window, and door already insulated. Table 4-21: Comparison of Building Materials in the Renovated Category and New Building Category Insulation Renovated category U-value (W/m2K) New Building category U-value (W/m2K) Wall Granite or Whinstone (Insulated) 0.26 Granite or Whinstone (Insulated) 0.26 Roof Pitched (loft Insulation) 0.2 Pitched 280mm loft insulation 0.16 Floor Solid (insulated) 0.23 Solid (insulated) 0.23 Window Double glazing 2.2 Double glazing 2.2 Door PVC 2.2 PVC 2.2 Source: (Tabula, 2024 and Historic Scotland, 2013) 4.5.2. Energy Savings in Buildings By applying energy efficiency throughout the following years until 2030, the energy demand for heating can be reduced. After completing the first stage of the energy efficiency measures to retrofit all homes in the old house category, around 118,000 kWh can be saved within 2 years after 2024, corresponding to energy savings of 11 %. In the next 4 years after the starting year, the retrofit of 10 Bungalow category buildings and 5 Housing homes can reduce 208,000 kWh, equivalent to 11% of savings by 2026. The last stage is in 2030, which will reduce 315,000 kWh with a percentage of savings achieved of 14% compared to BAU. Table 4-22: Total Energy Heating Demand before and after Energy Efficiency Upgrading of the building fabric of Old, Bungalow, and Renovated categories results in an improvement of thermal performance. Energy efficiency in buildings would be an option for improving housing BAU 2026 EE 2026 BAU 2028 EE 2028 BAU 2030 EE 2030 Total Energy for Heating (MWh) 1,096 978 1,137 929 1,178 0.863 Total Energy Saving Potential (%) 11 22 36 comfort and reducing the electrical system upgrades to electrify heating. Retrofitting houses to their specific EPC categories by 2030 may be feasible, although there will be an additional cost that should be considered. (Palmer, 2017) collected data from interviewing households in the UK to find the cost of retrofitting houses based on the types of houses and the fabric material of the building. For example, improving internal wall insulation in a small, detached house ( < 117m2) will cost around £6,600 to £8,000, and changing into double glazing will cost around £5,000 to £7,000 for a small, detached house. However, it cannot be denied that cost can be an issue when developing a recommendation for retrofitting the houses on the island. 4.6. Energy Efficiency in Appliances Energy efficiency measures in electrical appliances have been progressively improving access to energy services and reducing average domestic electricity consumption in Scotland. It was estimated that average domestic electricity consumption (kWh per household) has been reduced by 27% in 2021 (Fig 4-18) compared to 2005 (Hub, 2021). The survey conducted by our team on the of Isle of Eigg showed that the most used appliances in the houses are refrigerators, washing machines, dishwashers, microwave ovens, and water heating systems. As per the discussions with residents, some of these appliances are old and on the verge of their end of life. Some of the residents find it difficult to replace them with energy efficient appliances due to high cost of A level energy efficient appliances, compatibility with grid frequency fluctuations, and transportation from the mainland to the island. Figure 4-18: Average domestic electricity consumption in Scotland (kWh per household) 0 1,000 2,000 3,000 4,000 5,000 6,000 2006 2008 2010 2012 2014 2016 2018 2020 2022 El e ct ri ci ty C o n su m p ti o n ( kW h ) Year Moreover, it is also important to mention that some of the people live in small dwellings such as caravans and share major appliances with other residents of the island. Aside from this, our survey showed the behavioural pattern of people living in household and working hours. Most people stay home almost 75% during morning (06:00 to 12:00) and most people (almost 72%) are at work during afternoon (12:00 to 16:00) and come home by night (18:00 to 21:00). This shows that most of the peak consumption of electrical appliances happens during early morning (07:00 to 10:00) and nighttime (17:00 to 20:00). 4.6.1. Calculation of energy efficiency potential in Appliances Energy efficiency measures in electrical appliances can be a significant step towards reducing the energy bills of households and businesses. As stated earlier, old appliances nearing the end of their lifetime may need to be replaced by new A grade appliances (UK, 2020) as they might be consuming more electricity than more energy efficient appliances. Also, replacing old electrical appliances with new energy efficient appliances would require a higher initial capital investment for households and businesses. However, replacement of these appliances will conserve energy, resulting in cost savings during operation of these appliances throughout their lifetime period. Moreover, efficiency of most of the electrical appliances varies from one grade value to another as per the energy efficiency scale given by UK Energy Label. Figure 4-19: Energy Efficiency Logo, UK As such, we have come up with a top to bottom approach where we have modelled one house with all the energy efficient appliances, with most of the appliances having A grade energy efficiency (Kaplan, 2018) and the current electrical appliances are considered to be C rated as some of the appliances become less energy efficient (as per the one-on-one discussions with some individuals). This gives us an estimation of the amount of energy saved by each appliance, including a percentage of the energy share of these appliances) (Trust, Energy Saving Trust, 2022). Although the total energy consumption of an average household in Isle of Eigg comes out to approximately 2.5 MWh per year. Yet the total energy consumption of a household can be reduced by using energy efficient appliances. In the table below we estimated total yearly energy consumption for an energy efficient household which comes out be around 1.9 MWh per year in an ideal scenario. Table 4-23: Total yearly consumption (in kWh) by a household In the next step, based on the data from the Sunny Island Portal we have estimated that a total consumption of energy (after removing the grid losses and other factors) comes out to be around 254 MWh (this was calculated based on the summation of energy in kWh for one-year 2021 as base year) and it change respectively with the increase in population and business as per the Eigg electric plan 2030. As per the percentage distribution of energy share among households and businesses on the island, 65% and 35%, the total energy consumption comes out to be 164 MWh and 89 MWh respectively (these estimations are taken from the table 4-2). Moreover, we have estimated the number of appliances in each household for 2026, 2028, and 2030 based on the assumption that ownership of major energy consuming appliances such as washing machines, refrigerators, and dishwashers and estimated their share in monthly energy bills (Trust, Energy Saving Trust, 2024). Finally, the electricity demand pattern using probability distribution curve was designed to estimate the distribution of electricity demand throughout a year. This results in providing energy saving on the based on total appliances used by the people of island of Eigg in case they shift towards energy efficient appliances by 2030. 4.6.2. Analysis of Energy Efficient Appliances Based on the electricity demand pattern, a probability distribution curve was designed to estimate the distribution of electrical load throughout a year. In next step, taking this probability distribution in consideration and load factor of each year 2026, 2028, and 2030, new electricity demand pattern has been generated for 8760 hours of the year for all three years, respectively. House (A rated Appliances)No. ApplianceUsage Density (hoursper week)Total Power Run Monthly Energy Cosnumption Yearly Energy Consumption (kWh) Lighting 5 40 50 60000 720 Washing Machine 1 3 cycle 1064.21 8005.48 96.06576 Dishwasher 1 3 cycle 871.97 5231.82 62.78184 Refrigerator 1 All Time 782.83 26,400 316.8 Oven 1 3 cycle 1052.45 16,364 196 TV 1 14 100 8413 100.956 Computer 1 14 65 14946 179.352 Kattle 1 2 104.84 6864.2 82.3704 Other Appliances (Hair Dryer, toast machine etc)2 2 8000 12000 144 Total Energy Consumption 1898.69 Figure 4-20: Electricity Consumption after Energy Efficiency Measures Finally, the total energy consumption of the years 2026, 2028, and 2030 using energy efficient electrical appliances showed that energy efficiency helps in reduction in total energy consumption and peak demand for the island of Eigg. Table 4-24: Annual Energy Saving by the Island of Eigg Year 2026 2028 2030 Annual Energy Savings 40,880 42,793 44,459 Percentage Savings (%) 11% 13% 14% Annual Energy Consumption (MWh) 352 366 380 Reduction in Peak load 7% 11% 14% In conclusion, addressing energy efficiency in electrical appliances for the community on the island of Eigg requires a comprehensive approach. Although our results show significant savings in overall energy consumption of the island throughout the year using a top to bottom approach. But a detailed approach would also require studying the electrical appliances on household level and their usage pattern. By evaluating energy consumption comparing the efficiency of old and new appliances, and considering long-term costs, households on Eigg can make informed decisions about the necessity of upgrades. Additionally, exploring financial options, raising community awareness, and collaborating with local authorities and organizations can help overcoming challenges such as high upfront cost and transportation limitations of the major electrical appliances. Using proactive measures and constant monitoring, the community on the Isle of Eigg can develop towards greater energy efficiency while saving costs of expanding the power system and ultimately reducing energy poverty. 5. Renewable Energy Technologies for the Isle of Eigg This chapter conducts a comprehensive evaluation of five distinct energy technologies on the Isle of Eigg, including solar, wind, hydro, tidal, along with potential storage solutions. Through in-depth analysis, the objective is to assess the current utilisation and the perspective for future use of each technology. We will also identify suitable sites for expanding existing capacities and provide critical technical and financial data. This information will serve as crucial input for the various scenarios and assist in the strategic planning of future energy pathways for the Isle of Eigg. 5.1. Solar Energy Solar energy is one of the key renewable energy sources that can significantly contribute to meeting energy needs. The average levelized cost of solar PV electricity generation has decreased by 90% between 2010 and 2022 due to technology advancement and production improvements (IEA 2023). As a result, solar PV is now one of the most cost-effective electricity generation technologies in many regions. In this section we will evaluate the solar PV potential in Isle of Eigg and evaluate the possible strategies to effectively use the solar energy within this area. 5.1.1. Current Solar System Presently on the Isle of Eigg, there is a solar PV power plant located at the Glebe and close to Glebe powerhouse with a total capacity of around 181 kW. The first series of PV modules was installed in 2008 with subsequent arrays installed in 2011, 2013, and 2023. Figure 5-1 shows the location of solar PV plant on the Isle of Eigg along with an image of the plant, and Table 5-1 demonstrates the characteristics of the PV modules and inverters. Figure 5-1 The location of solar PV plant on Eigg Source: Authors, Google Earth. Table 5-1. Solar PV plant system characteristics Year of Installation Modules Inverters Capacity (kW) 2008 BP Solar BP3165S 165Wp SMA Sunny Boy SB-3000 9.9 2011 BP Solar BP4180 180Wp SMA SMC-7000HV 22.6 2013 REC Solar REC 250 PE 250Wp SMA SMC-7000HV 22.5 2023 REC Solar REC 440AA 72 XV 440Wp SMA STP 20000TL-30 126.72 5.1.2. Future Solar Potential on the Isle of Eigg Figure 5-2 displays the maps of Global Horizontal Irradiation (GHI) and Photovoltaic Power Potential (PVOUT) in the Isle of Eigg. Global Horizontal Irradiance (GHI) is the total amount of solar radiation received per unit area by a surface horizontal to the ground including both direct sunlight and diffuse sunlight scattered by the atmosphere and clouds while PVOUT estimates the electricity generation from a solar photovoltaic system, considering solar radiation levels, system specifications, and other relevant factors specific to the region (Yang et al., 2015). Both PVOUT and GHI are higher in southern parts of the Island. The average values for GHI and PVOUT are 855.1 kWh/m2 per year and 903.6 kWh/kWp per year, respectively (Solargis 2024). Figure 5-2: Solar photovoltaic power potential (PVOUT) and Global Horizontal Irradiation (GHI) Source: (solargis, n.d.) One crucial factor to consider when assessing solar potential is the average yearly temperature and its fluctuations. Figure 5-3 illustrates the average hourly temperature and precipitation in Isle of Eigg throughout the year, revealing an average temperature range of 2°C to 15°C. Since the average temperature is not too high throughout the year, the PV panels can work properly and are not negatively impacted by high temperatures. Precipitation peaks in winter, potentially reducing solar panel output due to cloud coverage, while the summer months experience lower precipitation, suggesting higher solar irradiance and improved solar panel performance. Figure 5-3. Average hourly temperature and precipitation Source: (Meteoblue, 2024) Figure 5-4 shows the monthly solar PV electricity production for a 1kW capacity plant with tilt angle of 30 degree on the Isle of Eigg. The data are derived from ‘renewables.ninja’ website (Pfenninger & Staffell, 2016) for the year 2021. The highest electricity output from solar panels occurs in the summer, with peaks from April to July. Winter months, from November to February, have the lowest solar electricity production, which aligns with the higher precipitation reducing efficiency during these months. Overall, the solar panels perform best in the summer and worst in the winter. This data with hourly resolution will be considered as an input for modelling section of this report. Figure 5-2:Monthly solar PV electricity production for a 1kW capacity plant Source: renewables.ninja 0 0.05 0.1 0.15 0.2 0.25 El ec tr ic ty G en er at io n ( kW ) 5.1.3. Site Assessment Several factors including economic, geographic, technical, social, and environmental aspects should be considered while conducting a detailed site assessment for solar PV power plant. For the case of Isle of Eigg, we have utilized ‘QGIS’ software and field research to review and analyse the suitability of potential locations. For this purpose, we have considered GHI (solargis, 2024), terrain slope (Pope, 2017), terrain aspect (Pope, 2017), and land cover (Space Intelligence and NatureScot, 2021) layers which have been depicted in Figures 5-2, 5-5, 5-6, and 5-7, respectively. Table 5-2: Criteria for PV site suitability Factor Unit Criteria GHI kWh/m2/year Greater than 860 Terrain Slope ° Less than 5 Terrain Aspect ° South or South-East or South-West facing Land Cover - Different types of grasslands Source: Almasad et. al, 2023 & Nebey et. Al, 2020 Figure 5-5: Terrain slope in Isle of Eigg. Figure 5-6: Terrain aspect in Isle of Eigg. Figure 5-7: Land Cover map of Isle of Eigg Each of these layers have been processed and refined in QGIS software for better visualization and further calculation to obtain solar suitability map. Table 5-2 shows the criteria for each of these parameters that will be considered to generate suitability map. The final suitability map has been generated in QGIS using raster calculator and considering all criteria (Figure 5-8). Figure 5-8: Solar PV suitability map in the Isle of Eigg As can be seen, we have added cable and transformer layers to the suitability map and displayed them on the OpenStreetMap for enhanced visualization. The suitable areas are depicted in yellow. Additionally, due to technical and economic considerations, areas close to the powerhouse are considered as the best options for installing new solar PV plants, as illustrated with the zoom tool in the Figure 5-8. 5.1.4. Financial Parameters Table 5-4 shows the most important financial parameters of a solar PV plant on the Isle of Eigg to conduct further examination of pathway scenarios. The investment cost value is derived from discussion with Eigg Electric based on the values of recent real project in 2023 as described in Table 5- 1. Based on the reports the fixed operation and maintenance costs for solar plants in the UK is 6000 £/MW/year (Department for Business, Energy & Industrial Strategy, 2023). According to this value and considering the yearly PV electricity generation on the Isle of Eigg, the variable costs being calculated. Table 5-3. Financial parameters for solar PV plant on the Isle of Eigg Parameter Value Unit Investment Cost 510 $/KW Lifetime of New Capacity 25 Years Variable Cost 0.008 $/kWh 5.1.5. Solar Rooftop Systems Potential Solar rooftop systems are one of the suitable options for households to generate electricity for either own use or grid export, and can assist them to be more self-sufficient, especially in remote areas. Currently, residents of the Isle of Eigg are not encouraged to install grid-connected roof-top solar panels; however, ongoing discussions indicate that plans and financing for the development of rooftop solar systems in this region might be applicable soon. Therefore, conducting a simulation for such a system can play a useful role in the decision-making. Presently, there are around 64 households on Eigg which are not similar in terms of house area, roofing material, and orientation. As previously discussed, the orientation of solar panels plays a crucial role in absorbing maximum solar radiation. Based on the reviewing aerial photographs on Eigg (Bing Maps, n.d.), roughly 33% of homes face south, 52% face east or west, and the rest are in between. As it is not feasible to simulate all possible configurations for houses, we will first simulate a 3.15kW solar rooftop system on a south facing house with a tilt angle of 50° as modules will be installed parallel to a sample roof with the slope of 50°. The technical specification of the selected system is presented in Table 5-5, and a schematic of system along with electricity flow diagram has been shown in Figure 5-12. The average annual electricity demand for a single household is 2579 kWh per year based on the data and assumptions which are provided in the demand section of this report. Figure 5-9. Annual electricity flow diagram of the sample solar rooftop system on Eigg All values are in kWh. Source: PVSOL simulation result Table 5-4. Technical specification of the selected system Item Specification PV Modules 7 x Sample PV Module (450 W) Inverter 1 x Sample Inverter (3 kW) Configuration MPP 1: 1 x 7 Battery 1 x 4.4 kWh Sample Battery PV Generator Surface 15.2 m² Battery Voltage 48 V As depicted in Figure 5-9 around 61 % of total annual consumption can be supplied by solar PV as the battery is also completely charged by solar panels and does not receive any electricity from grid. Also, approximately 30% of PV generation could be exported to the grid. However, the PV performance is subjected to weather conditions and changes significantly throughout the year. Figure 5-10 demonstrates the PV system performance and its contribution to meet demand by considering battery storage and interactions with the grid. The annual specific yield of such a system is 808.40 kWh/kWp. Figure 5-10. Monthly performance of the sample solar rooftop system on Eigg Source: PVSOL simulation result The chart shows a fluctuating pattern of energy production throughout the year. In months like April, May, June, and July, solar production is high, resulting in a significant amount of solar energy being consumed in household and some share of grid exporting as indicated by the light blue sections at the bottom of the columns. In contrast, during the months of November, December, January, and February, solar energy production is lower, and consumption from the grid is higher as indicated by the dark blue sections. It is important to note that some of the energy produced by the solar panels is stored in batteries as depicted by the light green sections, which can then be used to cover consumption needs as shown in dark green sections. As shown in the chart, the battery system is more active during summer. As previously discussed, the annual yield of PV system is highly dependent on modules orientation. To compare performance of PV systems in different house orientations, we altered panel facing towards different directions and compared the general annual results in Figure 5-11. -500 -400 -300 -200 -100 0 100 200 300 400 500 En e rg y (k W h ) PV Battery Discharge Grid Import Own Use Battery Charge Grid Export Figure 5-11. Comparison between different house direction in terms of source of energy consumption. As can be observed, maximum PV generation is achieved with south-facing panels. There is minimal difference between panels facing south, southeast, or southwest. For east and west-facing panels, it is advisable to install modules on both sides of the rooftop to maximize energy absorption year-round. In this case, an inverter with 2 MPPTs is required. The analysis suggests that installing rooftop solar systems could offer advantages and challenges. On one hand, such installations could significantly enhance self-sufficiency for households on Eigg, especially during the summer. However, in winter, when energy demand also increases, the potential for solar to supply household energy needs decreases markedly. Additionally, the possibility of exporting electricity to the grid, typically during summer, may lessen with decreasing the demand of whole Island which means the grid might not require as much supply from rooftop solar systems. Another challenge is the varying directions of houses, which lead to different electricity generation levels for each household. Therefore, these factors should all be considered when considering the installation of such systems. 5.2. Wind Energy This part is dedicated to exploring the existing wind generation on the island and evaluating the potential for expansion through the installation of new wind turbines. The current generation profile will be generated utilising specifications from the existing turbines and weather data. Additionally, the geographical data and wind resources will be analysed to assess the viability of installing new turbines. This assessment will include the identification of a suitable location for new turbines considering the technical aspects of connecting them to the current power system. It is essential to note that Eigg Electric has already chosen a specific type of wind turbine for their power system. Considering this predetermined turbine a generation profile was calculated. The profile 805 863 910 904 892 444 595 683 556 520 1355 1145 1011 1144 1191 0 500 1000 1500 2000 2500 3000 E SE S SW W En e rg y (k W h ) House Facing PV Battery Grid aims to serve as the basis for optimization in the energy model, allowing us to determine the required number of wind turbines in alignment with the power system demand. 5.2.1. Explore the Generation by Existing Wind Turbines on the Island Since 2008, the Eigg power system has been using four identical wind turbines for electricity generation, each with a capacity of 6kW , a hub height of 15m, and a rotor diameter of 5.5m (Eigg Electric 2008). Due to the unavailability of annual hourly generation data from the wind turbines through the sunny portal system, WindPro software was employed to simulate the existing wind turbines' generation. To facilitate this simulation, wind data encompassing wind speed, turbulence, temperature, and pressure for the designated turbine area in 2021 was imported. Additionally, landscape data, incorporating roughness area and elevation, was imported, and subsequently modified for the site. The figure below illustrates the electricity generation from the four wind turbines in 2021. Figure 5-3 electricity generation from existing four wind turbines for 2021 Source: WindPro data The figure reveals a high fluctuation in generation, higher generation in winter and spring, and lower in summer and autumn. The profile also shows 5% annually higher generation from WTG-E1 (west WT) compared to the WTG-E4 (east WT), see the table below. Site visits from the control room of wind turbines have been conducted, and the hourly differences between the turbines were higher than the annual output. Part of the difference in generation could be explained by the influence of the wake effect or the complexity of the terrain on the wind resource. Since the turbines are located in one row, perpendicular to the prevailing wind direction, as well as adequately distanced at 6 times rotor diameter, the reason might be due to the existence of a small hill in the north area of WTG-E1. Since a detailed analysis of the wind turbine performance in such a complex terrain necessitates a sophisticated numerical analysis like Computational Fluid Dynamics (CFD), which is beyond the scope of this project, a simplified approach was adopted. Using the linear flow model of the WindPro software, the existing hill was removed to demonstrate the effect of the hill on the wind turbines' performance. The results of this comparison are presented in the table below. Table 5-5. Comparison of existing Wind Turbines generation in different terrain Total WTG-1 WTG-2 WTG-3 WTG-4 Terrain type MWh/year MWh/year MWh/year MWh/year MWh/year With Hill 72.6 19.0 18.5 17.2 17.9 No Hill 71.5 19.0 18.3 16.8 17.5 As evident from the data, the presence of the hill in front of WTG-1 has a positive impact on the performance of the three turbines, while it does not affect WTG-1, which is situated in front of the hill. Higher accuracy of these findings requires a Computational Fluid Dynamics (CFD) analysis. An in-depth analysis could not only validate the observed outcomes but also provide insights into the reasons behind this discrepancy, necessitating a comprehensive assessment. Many individual studies conducted to explore the effect of complex terrain on wind turbines performance, for instance; Ann Hyvärinen and Antonio Segalini mentioned in their article that “the introduced hills have a positive impact on the wind-turbine performance and that wake-interaction effects are significantly reduced during turbulent inflow conditions” (Hyvärinen and Segalini 2017). Furthermore, regarding the technical aspects of the existing wind turbines, each turbine is connected to a 6kW inverter, and collectively connected to a 75kVA transformer. Additionally, given the wind turbines' nearly 14 years of operation, the remaining life is assumed to be no more than six years, after which parts of the turbines will have to be replaced. 5.2.2. Potential for the Expansion of Wind Energy 5.2.2.1. Geographical wind data analysis To analyse the wind energy potential across the Isle of Eigg, an assessment of the wind power density maps is essential. A wind power density map gives a reasonably accurate indication of the available wind resource (GWA 2024). This data illustrates the distribution of wind energy potential across the island, with a selected hub height of 50m, which is near the predetermined hub height of the wind turbine at 38m. Additionally, high-resolution terrain data from Great Britain’s Geospatial Data platform, offering an elevation layer with 10m equidistance for each line, is utilized to gain a clear understanding of the Isle's topography (Ordnance Survey 2024). Figure below displays the wind energy distribution and elevation characteristics for Eigg Island. Figure 5-4. wind energy distribution and elevation characteristics of the Isle of Eigg Data Sources: GWA; Ordnance Survey; Eigg Electric The map highlights the higher potential of wind energy in the north, west, and south regions, characterized by mountainous terrain and higher altitudes, consequently featuring a greater wind energy potential. In the north and west, the high wind potential is situated at the mountain's peak, posing challenges for installation, maintenance, and operation due to unavailability of access roads. Moreover, these areas are far from the current power infrastructure. In the south region, where the existing wind turbines are located, there is already a demonstrated wind energy potential of around 1000 W/m2 and a mean wind speed of approximately 8.5 m/s. The area is already near the power system infrastructure. Additionally, since the area is occupied by wind turbines, it will be more acceptable to the island's community. As observed, the roughness type of the area is open terrain covered with grass. This characteristic defines the roughness class of the area as 3 with a length of 0.03m (Linacre & Geerts 2023). Since the nearest building is located 430m away from the selected turbine location, WindPro was employed for shadow and noise analysis. The conducted analysis revealed that the house is not affected by either the shadow or the noise generated by the turbines. 5.2.2.2. Wind Time series data analysis: The next step involves the utilization of WindPro software to generate time series of wind resource data and model the energy output. For this purpose, mesoscale data from the EMD-WRF dataset, specifically generated for the southern part of the isle, was selected. The mesoscale model data offer a numerical weather prediction model that simulates atmospheric conditions on a mesoscale, typically covering a regional or local area, providing information on wind speed, wind direction, temperature, and turbulence intensity. The authoritative data for this project spans over the past 24 years, with the data from 2021 being utilized to align the time series with demand and other technologies. The data presents a spatial resolution of 3x3 km with an hourly temporal resolution (EMD 2023). Analysing the past 24 years' wind speed data reveals an average wind speed of 8.6 m/s at a hub height of 38m, along with an average turbulence intensity of 9%. According to the International Electrotechnical Commission (IEC) 61400 standard, wind speeds of 8.5 m/s and turbulence intensity below 16% classify the area as IEC class II/b (IEC 2008). This classification is crucial for wind turbine selection. It seems that the characteristics of the NPS Northern Power wind turbine selected by Eigg Electric, as shown in the table below, meet the requirements of the wind class of the selected area. Table 5-6. Selected Wind Turbine Characteristics Description Rated Power Rotor Diameter Hub Hight WT Class Cut-in/out/rated /extreme Noise level kW m m IEC 61400-1 m/s dBa NPS 100C-21 100 21 38 II/A 3/25/12-15/59.5 50 To strategically position the wind turbines, it is essential to analyse the wind rose diagram. Figure 3 shows an hourly rose diagram illustrating the frequency and energy distribution of wind in various directions. Figure 5-5. wind energy distribution and elevation characteristics of the Isle of Eigg Source: WindPro The rose distribution diagram reveals that the highest wind energy mostly comes from the range of south to west by higher potential from southwest. Although the blade face automatically aligns with the wind direction, understanding the wind direction is crucial for arranging the wind turbines at the correct angle and considering required distances between wind turbines in a row as a group. This helps to minimize the wake effect and optimizes energy production. The distance between wind turbines may vary depending on the wind direction. If the wind is perpendicular to the row of turbines, the distance could be reduced to three to five times the rotor diameter. However, if turbines are installed downstream, this distance may increase to ten times (Daphne Schwanz 2012). In further step the new wind turbines arranged in a group of three wind turbines to generate per unit generation profile. The number of turbines is subject to the on-going decision process of Eigg Electric and subject to the conducted Feasibility Study for Phase 2 of Eigg’s Decarbonization by 2030 report (Russet Engineering 2023). The final number of wind turbine will be modified considering the optimization result. Different arrangements have been examined to find out the most suitable location by having higher annual generation for the wind turbines. Below the most suitable location for three wind turbines according to the landscape and wind data resources. In the selected area for wind turbines, the focus is on choosing a location with more open space, minimal terrain impact, and minimal interference with the performance of existing wind turbines. With the necessary location and the wind resource data, the average generation profiles for each month over the past 22 years has been generated and illustrated in the figure below. Figure 5-6. Arrangement of the wind turbines (including the altitude and roughness data map) Source: WindPro Figure 5-7. Monthly hourly average wind energy generation from 1999-2022 for Isle of Eigg Source: WindPro data The graph depicts a consistent pattern of wind energy generation over the years, with higher output during winter and spring and lower output in summer. There are also variations in total annual generation, with the lowest recorded in 2010 and the highest in 2022 which has been shown in below graph. 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec M W h Months 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2011 2021 2021 2010 2011 2002 2013 2023 2012 2014 2010 Elevation Line Existing Wind Turbine New Wind Turbine Figure 5-8. Annual wind energy generation from 1999 to 2022 Data source: WindPro To ensure consistency with other renewable energy sources and demand, the data from the year 2021 has been chosen for further optimization. The annual generation for the selected data is 1.164 GWh, which is 8% lower than the total annual average generation for past 22 years, which is 1.2 GWh. This decision is based on its representation of the average generation across all available data and its alignment with the variation profile of solar generation since It is crucial to consider the relationship between wind and solar energy when selecting the year to maintain practicality and coherence in the data. The below figure shows the monthly generation of single selected wind turbine for 2021. Figure 5-9. Monthly wind energy generation for 2021 It appears that the generation during April, May, July, and August in 2021 is less compared to the other months. This lack of generation could be covered by production from solar energy, which is higher for this year during these months. The annual generation from the selected wind turbine for the year 2021 is 387 MWh, and the capacity factor calculated is 42%. 5.2.3. Financial Analysis To analyse the financial aspects of the wind turbine, the investment cost, including the turbine and its installation, is required. According to the IRENA Renewable Power Generation Cost in 2022 (IRENA, 2022), the average cost of an onshore wind turbine with a capacity range of capacity of 2MW to 4.8 1000.0 1100.0 1200.0 1300.0 1400.0 1 9 9 9 2 0 0 0 2 0 0 1 2 0 0 2 2 0 0 3 2 0 0 4 2 0 0 5 2 0 0 6 2 0 0 7 2 0 0 8 2 0 0 9 2 0 1 0 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 0 2 0 2 1 2 0 2 2 M W h Year 0 10 20 30 40 50 60 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec M W h Month (2021) MW in class II is between 840 to 1175 USD/kW. The average total installed cost for the United Kingdom is 1274 USD/kW. Additionally, a 10% extra cost has been assumed due to the selected wind turbine having a lower capacity, resulting in a higher cost compared to larger wind turbines. This leads to a total cost of 2693 USD/kW. Additionally, as per the Electricity Generation Cost 2023 report from the Department for Energy Security & Net Zero, the variable operation cost of the wind turbine is stated as zero (Department for Energy Security and Net Zero). However, to account for fixed annual costs, including maintenance and operation costs, these have been converted to variable costs, with a fixed cost of $32.3 per kW per year. This has been done using the formula below: 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑐𝑜𝑠𝑡 [ $ 𝑘𝑊ℎ ] = 𝑓𝑖𝑥𝑒𝑑 𝑐𝑜𝑠𝑡 [ $ 𝑘𝑊 ∗ 𝑦𝑒𝑎𝑟 ] ∗ 𝑊𝑖𝑛𝑑 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦[𝑘𝑊]/ 𝐴𝑛𝑛𝑢𝑎𝑙 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 [𝑘𝑊ℎ/𝑦𝑒𝑎𝑟] Equation 5-1. Variable cost calculation formula Considering this information and the wind turbine specifications, the table below illustrates the cost analysis of the selected wind turbine (IRENA 2022; Department for Energy Security and Net Zero). Table 5-7. Cost analysis of selected wind turbine Total Investment Cost Wind Turbine Lifetime Annual Generation (2021) Fixed O&M cost Variable Cost USD/kW Year kWh/Year USD/kW/year USD/kWh 2693.9 20 390000 32.258 0.008 5.3. Tidal Energy Tidal energy has been identified as a renewable resource that follows a distinct pattern and therefore is easier to predict, unlike solar and wind energy. In comparison to wind energy, tidal flow has a higher density and can produce more power with the same rotor size. Tidal energy is the energy generated by the to-and-fro motion of the tides. These flows are caused by the centrifugal and gravitational forces acting on the Earth, Moon, and Sun, which cause periodic motions of water known as tides (Shetty and Priyam 2022). Technologies commonly employed for ocean or tidal energy include tidal ranges, tidal currents or streams, and waves. Firstly, a tidal barrage, is a dam built in a bay with a tidal range typically in excess of 5 m (Etemadi et al. 2011). Secondly, tidal current and tidal stream technologies use kinetic energy to generate usable energy. Tidal stream technologies also come in a variety of designs, including hydrofoil-shaped, enclosed turbines, and horizontal or vertical axis cross-flow turbines. Lastly, wave technology uses a wave energy converter (WEC) machine to capture and convert electricity (IRENA 2021b). 5.3.1. Tidal Energy Potential Scotland is at the forefront of tidal energy potential, with 10% of Europe's tide resources located here (Lizzie Stevens 2021). The opportunity to develop tidal energy in the UK is high. In 2020, the UK planned six tidal stream projects between 2021 to 2026. The map below shows Scotland's average tidal energy power potential. Figure 5-11 Average Tidal Energy Power Maps Source: UK Renewable Atlas, 2024 Source: UK Renewable Atlas, 2024 The figure above shows the average tidal power potential on the Isle of Eigg with the highest tidal power on the island is 0.11 to 0.25 kW/m2. Due to high environmental impact, we are excluding the tidal barrage technology and will focus on tidal stream only. Tidal barrage remains the most widely used technology in the UK, but tidal stream and wave technology is starting to take the lead (IRENA 2021a). Considering spring (half-daily of the largest range) and neap (half-daily of the smallest range) peak flow for determining the potential location of tidal energy, especially for tidal streams, the highest mean Figure 5-10. Average Tidal Energy Power Maps on The Isle of Eigg spring peak flow accounted of 0.84 m/s on the southwest of Eigg. In the same location, the highest mean neap flow showed 0.36 m/s and the average water depth is 10 m (UK Renewable Atlas 2024). 5.3.2. Estimation of Tidal Potential According to a study conducted by the University of Strathclyde, there are some specific locations on the island that could be analysed. The tidal types featured in this study are streams and barrages (Mundie, et.al., 2016). Tidal energy has received more attention in recent years because there are many places in the world with high potential. Barrages often involve the construction of a dam and, consequently, an alteration of the ecosystem. Therefore, only the latter type will be evaluated in this report. The stream turbine is quite like a wind turbine and one advantage of extracting energy from tidal is that since water has a higher density than air, the energy that can be generated from the tide is also higher per unit of rotor area. To calculate the potential, some parameters are needed. There are approximately two tidal periods per day (12.40 hours each) and there are about twelve lunar phases during the year. In addition, the amplitude and speed of the tidal current are also needed. To estimate the power generation (P) the following formula will be used. 𝑃 = 0.5 ∗ 𝐶𝑝 ∗ 𝜌 ∗ 𝐴 ∗ 𝑣^3 Equation 5-2 Power generation formula for tidal energy potential From the formula, current speed v has a significant effect on power generation P as it is expressed to the power of 3. For a current turbine, typical values of the Power Coefficient (Cp) are between 0.2 and 0.35, for this specific calculation the lowest value will be considered (Ocean Energy Council 2018) The density of seawater (ρ) is 1027 kg/m3 and the area (A) corresponds to the dimensions of the selected turbine passage area. In the study conducted by the University of Strathclyde, after comparing different types of steam turbines, the "Oryon Watermill" was considered to be one of the most suitable. It has a nominal power of 50 kW, a vertical shaft, a diameter of 6 m and a height of 3 m. With these values, to facilitate the estimations, a cross-sectional area of 18 m2 is considered. The current velocity (v) is estimated considering the tidal period, the wave amplitude, and the specific time of day. As mentioned before, once the calculations for the first 28 days have been made, the results will be repeated until a full year's data set has been completed in an hourly resolution. Here it is assumed that there are no significant differences when there is spring and neap tide, which occur twice each month. Using the calculated values of current velocity from the study of the University of Strathclyde, the maximum value of this parameter is 1.2 m/s (Mundie, A., Haughey, S., Marshall J., Berbass, S 2016). However, the minimum value for a current to be considered as having adequate potential is 2.5 m/s. From this speed, turbines become economically feasible (Great Western Power Barrage 2024). The maximum stream speed needs to be twice as much to reach the minimum recommended, and it would also suppose an increase of around 9 times the amount of power generated. Figure 5-12. Tidal potential for a single stream turbine The graph shows that the peak tidal power is 3.17 kW, which represents only 6% of the rated capacity. Even if there is a pattern and predictability of generation, its adequacy will be determined in the economic evaluation. 5.3.3. Financial Parameters It is important to mention that the industrial development of tidal energy has been relatively slow compared to other renewable energy technologies. Therefore, the estimation of investment, operation, and maintenance costs of the ocean technologies is highly uncertain (IEA 2010). The current investment cost of tidal energy is about 3600 $/kW with a lifetime of 25 years (Energy Data Expert 2023). The table below shows the financial parameters of tidal energy. Table 5-8. Financial Parameters of Tidal Energy on the Isle of Eigg Parameters Value Unit Stream Turbine “Oryon Watermill” 50 kW Investment cost 3600 $/kW Lifetime of new capacity 25 Years Variable cost 0.0645 $/kWh One of the reasons why tidal power has not been deployed at many potential sites is that, unlike solar or wind, tidal turbines are not manufactured on an industrial scale and there is not much research conducted about this topic. This means that costs remain high and are not expected to decrease significantly in the coming years. In addition, maintenance costs are higher than those of a wind turbine due to rust or debris (Ross Jennings 2015). Certain specific conditions must also be considered for the realization of tidal projects. Particularly, in the case of the Isle of Eigg, the potential generation is constrained by the slow current speed, consequently, decreasing the energy generation. 5.4. Hydropower System Hydropower is an important source of renewable energy and a reliable generation technology. As of 2022, it held the title of the most widely deployed renewable energy source globally, as reported by the International Renewable Energy Agency (IRENA 2022). Beyond its renewable status, hydropower offers a dual advantage: it provides electricity at a comparatively low cost while concurrently providing a valuable source of flexibility to energy grids. Hydropower plants generate energy by the usage of the flow of water, by utilizing turbines to convert the kinetic energy of flowing water and the potential energy from differences in height into electricity. 5.4.1. Overview of the current hydropower system On the island, the hydroelectric infrastructure uses three generators, contributing to an average of 66% of the region's energy production during the period from 2009 to 2023. Among these, the largest hydro generator, stationed in Laig, has a capacity of 100 kW. In addition, the Kildonan hydro and Pier hydro generators each possess a capacity of 6 kW (Eigg Electric line drawing), These hydro generators play a vital role in meeting the daily electricity needs of the island's residents and businesses. From 2009 to 2023, hydropower consistently played a significant role in energy production within the region, during this period the data indicates varying levels of hydropower generation each year, with percentages ranging from 50% to 86%. With a yearly generation ranging from 137,962 kWh in 2023 and 494,911 kWh in 2016. (Eigg Electric Generation 2007-2024) Despite these fluctuations, hydropower remains an important source of energy, contributing to the energy mix of the island. Figure 5-13 Hydropower generation and the energy generation from the other sources Table 5-9. Characteristic of the current Micro hydro plant Capacity Lifetime Investment Cost kW Year USD/kW 100 60 5629.46 The hydropower generation system on the island has a projected lifetime of 60 years, this system offers long-term sustainability and resilience in energy production. The investment costs for this hydro system amount to $5,629.46 per kW (IRENA 2022, 2022). Figure 5-14. Yearly energy generation from Hydro in 2020 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% - 100.00 200.00 300.00 400.00 500.00 600.00 700.00 800.00 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 '% o f H yd ro in t o ta l g e e n e ra ti o n Y e ar ly g e n er at io n M W h Years Hydropower and the generation from all sources Energy generated by hydropower [MWh] Total generation [MWh] % of Hydropower 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 1 2 5 2 5 0 3 7 5 4 1 0 0 5 1 2 5 6 1 5 0 7 1 7 5 8 2 0 0 9 2 2 6 0 2 5 1 1 2 7 6 2 3 0 1 3 3 2 6 4 3 5 1 5 3 7 6 6 4 0 1 7 4 2 6 8 4 5 1 9 4 7 7 0 5 0 2 1 5 2 7 2 5 5 2 3 5 7 7 4 6 0 2 5 6 2 7 6 6 5 2 7 6 7 7 8 7 0 2 9 7 2 8 0 7 5 3 1 7 7 8 2 8 0 3 3 8 2 8 4 8 5 3 5 En e rg y kW h HoursEnergy generated by hydropower [kW] Figure 5-15. Yearly energy generation from Hydro in 2021 Analysing the data from 2020 and 2021, a noticeable seasonal effect is shown, with hydropower generation exhibiting more stability during the autumn and winter periods, in opposite to the spring and summer seasons where an increased fluctuations can be noticed, with the possibility of no energy output from hydro during extended periods. 5.4.2. Hydropower System Upgrade Loch Beinn Tighe, a lake spanning 8 hectares and 15 hectares in total with the surrounding areas, can be connected to the 100-kW hydro generator located in Laig. By connecting this lake, the annual energy production may be expanded by roughly 10% by the increase in water flow. Water flow on the island is closely tied to precipitation levels. The chart below illustrates the hourly precipitation fluctuations throughout the year 2021, with the highest recorded value being 5mm in a single hour (Renewable.ninja 2024). Figure 5-16. Yearly Precipitation in 2021 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00 110.00 1 2 5 9 5 1 7 7 7 5 1 0 3 3 1 2 9 1 1 5 4 9 1 8 0 7 2 0 6 5 2 3 2 3 2 5 8 1 2 8 3 9 3 0 9 7 3 3 5 5 3 6 1 3 3 8 7 1 4 1 2 9 4 3 8 7 4 6 4 5 4 9 0 3 5 1 6 1 5 4 1 9 5 6 7 7 5 9 3 5 6 1 9 3 6 4 5 1 6 7 0 9 6 9 6 7 7 2 2 5 7 4 8 3 7 7 4 1 7 9 9 9 8 2 5 7 8 5 1 5 En er gy k W h HoursEnergy generated by hydropower [kW] 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 1 2 4 5 4 8 9 7 3 3 9 7 7 1 2 2 1 1 4 6 5 1 7 0 9 1 9 5 3 2 1 9 7 2 4 4 1 2 6 8 5 2 9 2 9 3 1 7 3 3 4 1 7 3 6 6 1 3 9 0 5 4 1 4 9 4 3 9 3 4 6 3 7 4 8 8 1 5 1 2 5 5 3 6 9 5 6 1 3 5 8 5 7 6 1 0 1 6 3 4 5 6 5 8 9 6 8 3 3 7 0 7 7 7 3 2 1 7 5 6 5 7 8 0 9 8 0 5 3 8 2 9 7 8 5 4 1 P re ci p it at io n ( m m ) Hours Taking into consideration the previous chart, the annual volume of water in 2021 has been calculated using the following formula: 𝑊𝑎𝑡𝑒𝑟 𝑟𝑢𝑛𝑜𝑓𝑓 𝑖𝑛 𝐿𝑜𝑐ℎ 𝑏𝑒𝑖𝑛𝑛 𝑡𝑖𝑔ℎ𝑒 (𝑚3) = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 (𝑚𝑚 / ℎ𝑜𝑢𝑟) 1000 ∗ 𝑇ℎ𝑒 𝑡𝑜𝑡𝑎𝑙 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 (𝑚²) Equation 5-3. formula for Annual volume of water Considering a total additional catchment area of 0.184 km² which represents the surface of the lake in addition to the surrounding surfaces, the total additional water runoff in 2021 was 266,211 m3. Figure 5-17. connecting the lake Beinn Tighe with the hydro generator Figure 5-30 The additional catchment area highlighted on the map As shown in the figures above, the lake of Loch Beinn Tighe can be connected with the hydro generator in Laig through a channel for a distance of 400 to 500m that connects the lake to the stream Abhainn Gleann; By connecting the lake with the hydro generator at Laig, an increase in energy generation by 10% is expected according to the increase in water runoff and the flow that goes through Laig hydro system. Translated to, the numbers generated for the year 2021, yearly energy generation of 311 MWh/year may be achieved. The chart below shows the forecast of the hourly power generation. Figure 5-18. Yearly energy generation from Hydro in 2030 5.5. Energy Storage System In renewable energy and sustainable power systems, the utilization of battery storage systems plays an important role. These systems not only provide a means for storing excess energy generated but also offer crucial support in stabilizing grids, managing peak demand, and enhancing energy reliability. 5.5.1. Overview of the existing storage system The existing energy storage system has a total capacity of 1.3 MWh where the installed batteries are all lead-acid type in addition to taking into consideration the new batteries that will be added where Eigg Electric is adding further batteries and inverters to the existing energy storage system. This system is comprised of 12 clusters and a total number of batteries of 288 ROLLS 25P SERIES 5000 4v battery. The maximum possible Sunny Island power rating that would be achieved with 36 Sunny Island 8.0H inverters with 6kW rated output power for each is 216kW (Voltaico 2024). The depth of discharge of the system is maintained at 40%, ensuring that the batteries are utilized within the optimal range of 100% to 60%. This strategic utilization policy helped in prolonging the operational lifespan of the system, with certain batteries boasting an impressive lifetime of up to 17 years. In addition to strategic discharge management, predetermined maintenance for the batteries is conducted regularly. This regimen involves the watering of the batteries with distilled or de-ionized water. Deionized water in particular serves a crucial role in mitigating corrosion within the battery cells. By eliminating mineral ions, deionized water prevents the conduction of electricity, thereby - 20.00 40.00 60.00 80.00 100.00 120.00 1 2 6 7 5 3 3 7 9 9 1 0 6 5 1 3 3 1 1 5 9 7 1 8 6 3 2 1 2 9 2 3 9 5 2 6 6 1 2 9 2 7 3 1 9 3 3 4 5 9 3 7 2 5 3 9 9 1 4 2 5 7 4 5 2 3 4 7 8 9 5 0 5 5 5 3 2 1 5 5 8 7 5 8 5 3 6 1 1 9 6 3 8 5 6 6 5 1 6 9 1 7 7 1 8 3 7 4 4 9 7 7 1 5 7 9 8 1 8 2 4 7 8 5 1 3 En e rg y kW h Hours Yearly Energy Generation 2030 safeguarding against any potential drop to battery performance. It is important to highlight the multifaceted benefits of regular maintenance practices. Not just by extending battery lifespan but also routine maintenance causes optimal performance, mitigates the risk of malfunction, and enhances overall system reliability. Moreover, by employing de-ionized water, the maintenance protocol not only addresses immediate concerns related to corrosion but also contributes to the long-term sustainability of the system. Table 5-10. Current Battery system specification Storage Capacity Storage Lifetime Storage Charge Efficiency Investments Costs SOC Max. and Min kWh Year % $/kWh % 1291.4 15 66 341.86 100 - 60 Source: Rolls Battery User Manual, 2020 (VOLTAICO, 2024) 5.5.2. Storage System Upgrade Although Lithium-ion batteries offer a wide range of advantages from the capacity, efficiency and charge and discharge cycles compared to lead-acid batteries. However, a new type of batteries can also be more efficient and cost competitive than traditional Lithium-ion batteries. Lithium iron phosphate (LiFePO4) batteries have more advantages than traditional lithium-ion batteries. Where Lithium iron phosphate (LiFePO4) batteries marked an advancement in battery technology, (Elfa UK, 2023). One of their features is their safety profile. Due to their stable chemical structure, LiFePO4 batteries are less prone to failures such as thermal runaway, which can lead to overheating and even fire in lithium-ion batteries. This safety feature makes LiFePO4 batteries an attractive choice for applications where reliability and risk mitigation are necessary, such as grid-scale energy storage systems or electric vehicle powertrains. LiFePO4 batteries also have a much higher lifespan. Unlike lithium-ion batteries, which can experience capacity degradation over time and with repeated charge-discharge cycles, LiFePO4 batteries have remarkable durability, maintaining their capacity through thousands of cycles. This longevity not only ensures consistent performance over the battery's lifespan but also translates to significant cost savings in terms of reduced maintenance and replacement costs, particularly in applications requiring frequent use. Another key advantage of LiFePO4 batteries is their ability to maintain their performance and reliability. This resilience ensures uninterrupted operation and makes LiFePO4 batteries an excellent choice for off-grid or remote applications where extreme conditions are in place (Murden 15- Mar-22). Furthermore, LiFePO4 batteries have a quick response time. This rapid energy discharge capability makes them well-suited for applications such as uninterruptible power supplies or emergency backup systems. Whether providing backup power during grid outages or supporting critical operations in industrial settings, LiFePO4 batteries offer reliable and continuous energy delivery. An additional point is the environmental benefits of LiFePO4 batteries, where unlike traditional lithium-ion batteries, which often contain environmentally harmful materials such as cobalt and nickel, LiFePO4 batteries are completely recyclable (Dave Murden, 2023). This not only reduces their environmental footprint but also simplifies the recycling process, contributing to a more circular and sustainable battery ecosystem (Anker 2023). In our case a study has been conducted to study the possibility of using LiFePO4 batteries to cover the needs for a system. The chosen batteries are of the type of Roamer 460SMART3 LiFePO4 battery with characteristics 12V 460AH. This system offers a lifetime of 10 years with depth of discharge of 80%. The investment cost of the system is estimated to be 290$/kWh (Roamer 2024). Table 5-11. LiFePO4 batteries specification Storage Lifetime Storage Charge Efficiency Investments Costs SOC Max. and Min Year % USD/kWh % 10 90 290 100 - 20 5.5.3. Hydrogen as an alternative backup of energy Green hydrogen, despite its high initial investment cost compared to other storage technologies such as batteries, is gaining traction as a promising energy storage solution. This fuel is produced through electrolysis, using renewable sources like wind or solar power. And then a fuel cell can be used to convert hydrogen into electricity whenever is needed. Its advantage lies in its ability to store excess renewable energy generated during periods of low demand for use during peak demand times. (Siemens-Energy 2023). When replacing electricity generation from e.g. a diesel generator with hydrogen, this offers a clean and renewable alternative, reducing greenhouse gas emissions and mitigating climate change. Moreover, its storage capabilities help stabilize the grid and enhance energy resilience, particularly in regions with intermittent renewable energy sources. In our system a Hydrogen generation system of HOGEN NEL H Series has been chosen according to the size of the electricity system of the island. At full load, the system may generate 12.94kg of Hydrogen per day where one kg of Hydrogen is the equivalent of 34 kWh, the energy content of this amount is 440kWh, which can be used in Hydrogen power generators or transferred into electricity using fuel cells (Nel Hydrogen 2018). The power required for the system is 55 kVA with a lifetime of more than 50,000 hours (NEL H Series Spec Sheet, 2024). The total investment cost in the system is set to be 1250$/kW, in addition to that a storage system will be used to store 50l of pressurized hydrogen (50Mpa/500 bar). Table 5-12. Hydrogen storage system specification Storage Capacity Storage Lifetime System Efficiency Investments Costs kg/24 h Year % USD/kWh 12.94 7 40 1250 6. Results and Analysis 6.1. Insights from the Survey and Community Workshop The initial survey carried out started with a question to gauge the community level of satisfaction with the current energy system. All 33 of the respondents were either somewhat satisfied or very satisfied with the current energy system, as show in the diagram below: Figure 6-1: Community level of satisfaction with energy provision with energy provision based on 33 respondents A few reasons were given for this high level of satisfaction during the workshop session: 1. Before the current system was installed, each household relied on individual diesel generators and in a small number of cases micro hydro or wind turbines. As people were overly dependent on diesel generators, respondents described this system as dirty, noisy, and unreliable. As such, the transition to a centralized system operated by Eigg Electric came as a relief, offering a cleaner, quieter, and more reliable energy solution. 2. Residents expressed a general satisfaction with the current system, noting that the imposed energy cap did not feel restrictive. Despite some limitations in appliance usage, for example, absence of electric showers in some households, the residents have adapted to live comfortably within the cap. Because of this, it may be necessary to reframe what it means to be energy poor within the context of the Isle of Eigg. 3. Another reason for satisfaction with the current system is that it is fair as everyone living on the island gets charged the same for electricity and is subjected to the same cap. Even though community members were satisfied with the energy system as it is, some concerns were raised about its future capacity, particularly in the context of a growing population. Moreover, the idea of transitioning to a fully electric system was met with some apprehensions. For one, there was a fear of prolonged system outages that would require external support. System management of a complex power system is already a challenge due to a limited island population and difficulty accessing support. Currently, local staff on Eigg have been trained to provide frontline support and maintenance to ensure a reliable power supply. Financial constraints were also a significant concern. A fully electric system is capital intensive. Even though funding is readily available for the initial infrastructure investment, it is difficult to attain maintenance funding. There were challenges in staffing and training for specialized maintenance, as they needed to outsource from the mainland. Its hard-to-find people that understand the Eigg system. Despite the concerns raised, community members were open to change, as long as the basic principles of what makes the current system so successful were kept in place. These insights were used to inform the modelled scenarios so that the suggested transition pathways would align with community needs and expectations. 6.2. Modelling Results 6.2.1. Current Energy System This scenario is to confirm and replicate the current energy system, its operation, its potential, and its impact. It will give a clearer image of how the current energy system on the Isle of Eigg performs in terms of the supply and demand balance. By visualising the current energy system, insights on how the system has been delivering throughout the year will be achieved and the amount of carbon emissions being emitted annually across all the sectors could be seen. This short-term scenario has also created the foundation for the other three long-term future-based scenarios. The power system on the Isle of Eigg is a combination of solar PV, onshore wind turbines, hydro turbines, lead-acid battery storage, and standby diesel generators. Figure 6-2 shows the capacities of each of the generation and storage technologies. Figure 6-2: Current Scenario - Generation and Storage Capacities Figure 6-3: Current Scenario – CO2 Emissions across Sectors The current energy system only serves power demand. Most of the heating, cooking, and mobility is served through alternate fuels as the sectors are decoupled. The usage of conventional fossils leads to carbon emissions into the atmosphere. It is important that we estimate these emissions while we consider decarbonisation pathways to set benchmarks for the future scenarios. Figure 6-3 shows the sum of emissions annually across each sector. It is to be noted here that the emissions from the heating sector are the highest due to usage of kerosene alongside firewood. Depending upon how the forest resource is managed, firewood can be considered carbon neutral. This consideration will remove the emissions by about 60% from Eigg’s heating sector. In our analysis, the emissions from the heating sector account for the emissions from the burning of the firewood. Renewable energy generation is complemented by a lead-acid battery storage of 1,291 kWh capacity. On top of it, to maintain system availability and reliability, a standby diesel generator of 76 kW is also available with another of 64 kW serving as its back-up. While the system balances demand and supply at every instant, an hourly energy balance can be established for modelling purposes. However, an hourly energy balance is devoid of the intermittent variations due to the real-time variations of the renewable resource, and therefore the hourly energy balance is only a representation of the actual balance at best and not a true imitation. Figures 6-4 and 6-5 show the hourly generation profiles of two successive winter days and two successive summer days in the simulated year that shows the interplay of all generation and storage discharging on the top half of the graph. The demand, battery charging, and the surplus energy or potential of excess generation can be seen in the bottom half of the graph. The x-axis shows the number of hours. In the winter-days profile, we see a period when hydro power is not at its full potential, the sun does not shine, and the wind also does not blow, but the demand is there to be served. In such a scenario when the system cannot meet the demand from its renewables, it is supported by its battery storage and the diesel generator. We see here very significant battery activity and very frequent successive operational hours of diesel generators. Shortly after, hydro generation recovers and the system is restored to renewable generation, even showing potential of excess generation during the later hours of the selected winter days (Figure 6-4). Figure 6-4: Current Scenario - Winter-days Generation and Consumption Profile A total contrast can be seen in the summer-days profile for current scenario. We see that all three renewable technologies produce at their maximum potential. The demand is however little in comparison and therefore not all the potential of the currently installed renewable capacity could be utilised and therefore is reflected here as a surplus or potential to produce surplus. The current system has the potential to produce 144 MWh more than its current generation. This is, though, an estimation based on the capacity factors of the technologies from the selected reference year and could vary depending on the representativeness of the reference year chosen. Figure 6-5: Summer-days - Generation and Consumption Profile Modelling and simulating the current configuration provided us with insights about the periods where the current system falls short of sufficiency through renewables, but it also showed us the tremendous potential for many periods across a year, where the potential was unutilised due to absence of a bigger load. This attempt of simulating the current generation and storage activity may be challenged to offer little utility, as the hourly profiles we have simulated rarely match with the sub-hourly changes that happen stochastically. However, this has helped us to get an estimation of carbon emissions and surplus energy while providing the foundation for the long-term scenarios modelling as the model for future transition pathways was built upon the model-state that produced this current simulation. 6.2.2. Business-As-Usual Scenario The current scenario was reconfigured to optimise the Business-As-Usual (BAU) scenario, and therefore an iterative dimension for modelling years was added to the model that extended the model until 2030. This scenario however is not optimised with the intention of proposing a solution to the community but to use it as a benchmark for the long-term scenarios, and to already produce a foresight of where the community and its energy system would be if no transformative measures are taken. The underlying assumption of this scenario is that the energy system will keep the current state of the decoupled sectors until 2030. This means that with the increase in demand, the capacities of technologies and storage might increase due to increasing power demand but the sectors that are not electrified will still be powered by conventional fossils. This scenario will give us the opportunity to estimate the amount of carbon emissions, if the energy system does not move towards the decarbonisation across all sectors, and the expansion still needed in the capacity to cater growing power demand. For this scenario, the model returned no new investments in expansion of generation capacities. The model suggested an investment in only 20 kWh of Lithium-Iron-Phosphate (LFP) Battery as can be seen in Figure 6-6. This is an indication that the current system is sufficiently sized to cater the demands of the future if no cross-sectoral coupling happens. This little investment in LFP battery could be to cover up for a brief event where renewables are not available, and the existing storage capacity is close to depletion. This investment can also be neglected as it is too small to bring a big impact in the system and also in event where this was required, the diesel generator could run for longer and provide support. Figure 6-6: BAU Scenario - Generation and Storage Capacities Figure 6-7: BAU Scenario – CO2 Emissions across Sectors Until 2030, the demand for energy across all sectors will have slightly increased. This has direct implications for the emissions from the energy system as no sector is getting electrified in the BAU scenario. Figure 6-7 shows the emissions across the modelling years for the BAU scenario. The emissions keep a similar share as seen in the current scenario, with the only change being the slight increase across all that is there because of slight increase in demands for energy from all four sectors. Figure 6-8 and 6-9 show the generation and storage profiles for the same two winter-days and summer- days, for the year 2030. In the winter-days, we see that the generator now runs more than it ran in the current scenario, to serve the increasing demand in absence of solar, wind, and hydro. We see more frequent exchange of energy between the batteries with the new LFP battery capacity playing its part to keep the system balanced. Figure 6-8: BAU Scenario – Winter-days Generation and Consumption Profile - 2030 On the other hand, during the same summer-days, the behaviour is not any different than what was seen during the current scenario, as against the high renewable availability, the demand is relatively lower, indicating a significantly high potential of surplus generation (Figure 6-9). The surplus or potential of excess generation in BAU scenario for the year 2030 amounts to 122 MWh, which is 22 MWh lower than the potential discussed in current scenario, mainly due to the increased demand and the energy that was additionally stored by the LFP battery through the year. Figure 6-9: BAU Scenario – Summer-days Generation and Consumption Profile – 2030 6.2.3. Sector Coupling Scenario The model was reconfigured to optimise the Sector Coupling scenario. This scenario follows the 2030 decarbonisation plans and goes with the underlying assumption that by 2030 all the sectors will be able to electrify to the highest extent, ideally 100%. When heating, cooking, and mobility sectors are coupled with the power sector, the overall increase in the demand for power is significant, between 2-3 times depending upon the extent of electrification across the new sectors. Unlike the earlier two scenarios, it was inevitable for the system this time to expand its generation capacity and/or storage capacity to meet the increased demand. The model suggested expansion of generation capacity through 300 kW of tidal energy. This raises a question about the selection of tidal energy over wind or solar, as it is costlier than wind or solar in terms of capital cost per kW. This is an important indication that there are some instances where the demand is high but the potential of solar, wind, and hydro is poor, it would be more feasible economically to have tidal installed over additional capacities of solar and wind as it would have ultimately costed more due to their poor generation potentials during such instances. The expansion in generation capacity is also accompanied by the expansion of storage capacity where 153 kWh of LFP battery and an additional 530 kWh of Lead-Acid battery storage was suggested. Figure 6-10 shows the total capacities of generation technology and storages after expansion. Figure 6-10: Sector Coupling Scenario - Generation and Storage Capacities Figure 6-11 shows the emissions in tons of CO2 across the modelling period for each modelling year for the sector coupling scenario. It can be seen overall that there is a constant decline in the total emissions across the sectors due to sector coupling. The emissions are reduced to a minimum by 2030, where the last remaining bit is due to heat demand that cannot be electrified due to limited transmission capacity. Only 48% of the total heat demand could be served through electric power and therefore, the other 52% will have to be served through conventional means. These last emissions, however, can be removed as well if the unelectrified heat demand is served through wood fuel, which is considered carbon neutral if it comes from a properly managed forest. Therefore, in an ideal scenario, given that heating practices are strictly shifted to firewood burning, Eigg can be fully decarbonised across all sectors by 2030. Figure 6-11: Sector Coupling Scenario - CO2 Emissions across Sectors With the new proposed system sizes and the new demand sectors, we look at the same two winter- days and summer-days in the year 2030 to compare. Only this time we also look at the heat demand profile to see the proportion of heat covered through an electric heat pump or through a conventional method like a wood burner. Figure 6-12 shows the two successive winter-days. We already see that the axes have expanded because the magnitude of the total demand has changed due to coupling of other sectors. The top half of the figure represents energy balance for power and the bottom half represents energy balance for heat. We now see that the periods where earlier there was no solar, wind, and hydro, and where the generator and the battery had to play their part, it is covered sufficiently through tidal energy which at some instances was also high enough to serve the heat demand through electric heat pumps. Since these days are also coincidental with the low-tide periods, we see some battery activity as well. Figure 6-12: Sector Coupling Scenario - Winter-days Generation and Consumption Profile – 2030 In a similar manner, we also look at the same two summer days where we earlier had no large demand, and the renewables were producing surplus (Figure 6-13). These two days are also coincidental with high-tide periods and therefore the surplus generated appears much larger than before, even while the system served all the little heat demand that there is. No significant battery activity is seen, also for the same reason. This surplus energy for the year 2030 amounts to 113 MWh, which is 9 MWh lesser than BAU scenario. While surplus is expected to increase due to increasing capacities, the consumption has also increased due to the coupling of the other three sectors with power and therefore the net effect is a reduction in the surplus for Sector Coupling scenario, in comparison to the BAU scenario. Figure 6-13: Sector Coupling - Summer-days Generation and Consumption Profile – 2030 6.2.4. Sector Coupling with Energy Efficiency The implementation of energy efficiency measures plays a significant role in reducing the overall energy consumption. Consequently, a system with lower capacity is required, contrary to the scenario where energy efficiency was not considered in the design. To analyse how significant is the impact of energy-efficient measures on the overall system design, the model was executed using demand profiles that explicitly incorporated energy efficiency measures, particularly in the heating and power sectors. For energy efficiency analysis, the mobility and cooking sectors are excluded from consideration. This exclusion assumes that these sectors will undergo a complete transition to new technologies, and it is expected that the newly acquired technologies will already account for energy saving measures. Modelling of this scenario also suggests expansion of both generation and storage technologies. Figure 6-14 illustrates the suggested generation and storage capacities for the sector coupling with energy efficiency scenario. A 10kW addition in solar PV capacity is needed alongside a major expansion through 250 kW tidal. Additionally, the storage expands by 312 kWh of Lead-Acid battery and 156 kWh of LFP battery. It is important to highlight that while the model recommends an increase in capacity, the addition in capacity and storage in this scenario is around 15% lower as compared to the sector coupling scenario where energy-efficient measures are not considered. Figure 6-14: Energy Efficiency Scenario - Generation and Storage Capacities As technology transitions to renewable alternatives through the modelling period, a noticeable reduction in carbon dioxide emissions is observed due to the electrification of carbon intensives sectors of heating, cooking, and mobility. Figure 6-15 indicates that the emissions were reduced significantly by more than 60% from 439 tons of CO2 in 2026 to 180 tons of CO2 in 2030. The reduction in emissions for each modelling year is considerably higher as compared to the scenario when energy efficiency measures are not considered, mainly due to the reducing demands. Therefore, it indicates that the journey towards achieving carbon neutrality can be expedited by giving due consideration to energy efficiency. The remaining bit of emissions are associated with the unelectrified heat demand. In this scenario, 58% of the heat demand could be served through electric power, leaving behind about 40% to be served through conventional methods. If all is served through firewood, the remaining demand can as well be displaced to zero. Figure 6-15: Energy Efficiency Scenario - CO2 Emissions across Sectors We yet again analysed the generation profile across the same two successive winter and summer days. Top of Form Figure 6-16 shows a behaviour of winter-days that is identical to the one seen in the sector coupling scenario. In the absence of solar, wind, and hydro, it is tidal that serves not just the power demand, but also the heat demand to the highest possible extent. The remaining heat demand is served by wood burner. During the later periods, when hydro power recovers, the overall supply is sufficiently large to cover most of the heat demand as well as all of the power demand. Figure 6-16: Energy Efficiency - Winter-days Generation and Consumption Profile - 2030 When observing the same summer-days, it can be seen in Figure 6-17 that all four generation technologies are active and producing enough power to supply the power and heat demand, with minimal activity observed in battery. Additionally, it is prominent here that in summer seasons the generation is exceeding the demand by a significant margin. This surplus is mainly generated during periods of reduced heating demand in summer, coupled with an abundance of available solar and tidal energy, amounting to 133MWh in 2030. Figure 6-17: Energy Efficiency - Summer-days Generation and Consumption Profile - 2030 6.2.5. Alternative Scenario – Without Tidal Energy Assessing the results of the sector coupling and sector coupling with energy efficiency scenario, a new alternative scenario was conceived that enforced no presence of tidal in the potential technologies for the future energy system. Identifying that both the scenarios suggested investment in or expansion of the energy system through tidal energy, and the fact that tidal energy at a small scale is still a technology that is not commercially competitive, it was decided to model a scenario where tidal was never an option to see the impact of it on the model’s decision-making. This scenario is also intended to serve as another choice to refer to for the local community at Eigg. In absence of tidal energy, the model found it difficult locating the least-cost optimal solution as it the processing took 1.5 times longer than the other two long-term scenarios that suggested tidal energy. Nevertheless, the optimal solution suggests capacity expansion much larger than what was suggested in the last two scenarios. The model suggests additional 330 kW of solar PV, 100 kW of Wind energy. The model also suggests expansion of battery storage by around 3550 kWh which is around three times of the current battery storage capacity. This is understandable as the model always picked tidal as the events that were the most critical and enforced expansion had little to no solar, wind, or hydro potential, all coincidentally. Now with tidal not being an option, the model is forced to choose from solar and wind. With no good solar and wind potential in the most critical events, the only solution is expansion of battery storage such that it stores the excess generated by the expanded solar and wind until the critical moments appear. Figure 6-18 shows the system capacities for the no-tidal scenario. Figure 6-18: No-Tidal Scenario - Generation and Storage Capacity Figures 6-19 and 6-20 show the winter-days and summer-days generation profiles. From winter-days profile it is evident that the expansion of solar PV and wind energy capacities are still not productive enough as we see very frequent and constant battery activity. Also, even with such large capacity, the system is only meeting a fraction of the heat demand through power and most of it is being covered through conventional means. Figure 6-19: No-Tidal Scenario - Winter-days Generation and Consumption Profile – 2030 The summer-days profile follows a pattern similar to the ones seen earlier across the other scenarios, where the demand is not high enough as compared to the generation potential and therefore the potential to produce a surplus is high. Battery activity is also very limited during this time which is an indication that batteries do not need to act during these periods, and they remain with a high storage level. The surplus is in fact a total of 107 MWh for the year 2030, and this surplus is close to or even less than the surplus seen in other scenarios. Figure 6-20: No-Tidal Scenario - Summer-days Generation and Consumption Profile – 2030 6.3. Comparison of scenarios using Indicators 6.3.1. Emissions Figure 6-21 shows the emissions across each modelling year for each of three long-term scenarios alongside the alternative scenario that we modelled. For BAU, emissions follow along the increasing trend as the demand grows over the years. This is however not replicated by the other three scenarios as all three scenarios see a decline due to coupling of the sectors and subsequent electrification of the demands. In these scenarios, the emissions still do not reach zero because the heating sector cannot be fully electrified with the existing transmission capacity. For the sector coupling scenario and no- tidal scenario, the emissions are a bit higher than sector coupling scenario with energy efficiency, mainly because the overall demands were lower in latter that allowed more of the heat demand to be electrified. Nevertheless, these emissions can be substituted through use of firewood for remaining heat demand as we consider firewood to be carbon neutral. This will eventually make the three scenarios zero emissions pathways. Figure 6-21: CO2 Emissions across Scenarios 6.3.2. Annual Capital Costs Figure 6-22 gives the schedule of the investments for the three scenarios alongside the alternate scenario. It is to be noted here that the schedules are only an indication of when the technology has to be ready and not necessarily when the investment has to be made. This should be used only as an indication and planning for the investment should be done in advance. In the figure, we see that the BAU scenario only needs an investment of 6,000 US$ as there are no major expansions in the system. However, the situation for the other three scenarios is different due to major reinvestments. First instalment of the investment is highest in no-tidal scenario, followed by Sector Coupling scenario and then Energy Efficiency scenario. The next instalment of the investment is only applicable to the no- tidal scenario, whereas for the other three scenarios no investments need to be made until the last model year. In the last model year, the last instalment of the investment is the most decisive in the total costs. The highest investment is seen by the sector coupling and energy efficiency scenario as both see expansion of the same technology and by the same capacity. The next largest is no-tidal, followed by BAU. While no-tidal sees lower investment than the other two scenarios in the last modelling year, the Net Present Cost (NPC) of the total investments across all modelling periods is the largest in no-tidal scenario (around 1.3 million US$) than Sector Coupling (1.25 million US$) and Energy Efficiency (1 million US$). Figure 6-22: Capital Costs (US $) Comparison across Scenarios 6.3.3. Sectoral Renewable Fraction Here we look at how the four scenarios compare in terms of their renewable (electricity) fraction across all sectors. The current energy system at Eigg has a high renewable electricity fraction (> 90%), but this applies only to its power system as the other sectors are not powered through renewable energy systems. We also refer to renewable fraction as renewable electricity fraction because this makes a distinction with renewable fuels that are not powered by electricity, like firewood. Looking at the renewable (electricity) fraction could be an indication of the extent to which cross-sectoral coupling can be achieved while moving towards decarbonisation. Figure 6-23 shows a comparison where green marks the highest renewable (electricity) fraction across the scenarios for each sector and red marks the lowest renewable (electricity) fraction across the scenarios for each sector, with yellow indicating a moderate fraction across the scenarios for each sector. The figure also indicates the average renewable (electricity) fraction for each scenario, indicating the progress that can be made while making a choice between one scenario or the other. Note that for sector coupling, energy efficiency, and no-tidal scenarios, renewable (electricity) fraction can directly correspond to the electrification rate across a sector as these scenarios do not see any power generation from the diesel generator. BAU sees the lowest average fraction as three of the four sectors remain unelectrified. Sector coupling and no-tidal scenarios share the same average fraction as coincidentally both could only power 48% of the heat demand. The best average could be achieved through energy efficiency scenario, where 59% of the heat demand could be powered, taking the average electrification rate across the sectors, and also the renewable electricity fraction to 90%. Scenario Power Mobility Cooking Heating Average BAU 95% 7% 0% 0% 26% Sector Coupling 100% 100% 100% 48% 87% Sector Coupling with Energy Efficiency 100% 100% 100% 59% 90% No Tidal 100% 100% 100% 48% 87% Figure 6-23: Comparison of Renewable Fraction across Sectors and Scenarios, 2030 6.3.4. Surplus or Potential for Excess Generation The last metric we analyse is the surplus generation or the potential for excess generation in each scenario. Figure 6-24 shows the surplus generation in MWh during the last model year, 2030, in each of the four scenarios, alongside the current scenario. There is little to separate these scenarios in terms of surplus generation, as all present the potential of producing surplus between 100-130 MWh annually. This is almost half of the total energy demand from the power sector in current scenario. This presents a possibility of using this surplus for innovative technologies like hydrogen or ammonia generation, bitcoin mining, or other economic activities that could increase the utility of the technologies installed while also bringing in more revenue on the island. The figure also shows that the current and BAU scenarios have larger proportion of surplus, indicating the system to be having sufficient capacity, while the other three future scenarios to have much lesser surplus as a % of total generation, indicating that the suggested capacities are much better optimised as surplus is less than 15% of the total generation. Figure 6-24: Comparison of Surplus or Potential of Excess Generation 6.4. Discussion and Socioeconomic Insights into the Modelled Scenarios 6.4.1. Current Energy System Energy communities play two roles. As decentralized, renewable energy based projects, they are key in promoting sustainable energy production and consumption practices. As community driven- initiatives, they represent a shift where a traditionally passive consumer becomes a co-owner of renewable energy resources, taking an active role in the production and management of energy. Furthermore, the primary purpose of energy communities like Eigg is to generate social and environmental benefits rather than financial profits (Caramizaru, & Uihlein, 2020). We set out to understand the social, cultural, economic and to some extent political factors within which community energy on the Isle of Eigg operates. From the workshop and interviews, a few reasons emerge as to why community energy has been so successful on Eigg: 1. It is evident that community members have an intimate understanding of how they use energy so as not to exceed the 5 kW cap. There also exists a shared sense of responsibility where residents understand the role they play in the energy system. 2. The desire to be self-sufficient and promote a sense of community is particularly prevalent among residents. Their motivations go beyond simple electricity production as community members are more likely to take an active role in communal energy management. 3. Decision-making is placed into the hands of the community, creating a more socially fair model of ownership and responsibility. In terms of the political context, higher-income countries with a strong tradition of social enterprises and community ownership have a higher concentration of community energy initiatives (Caramizaru & Uihlein, 2020). Funding programs have been key to the establishment and expansion of community energy on the Isle. In 2008, the project cost for developing a renewable based off-grid electricity system on Eigg was €1.94 million. Funding the investment was a significant challenge and was secured through a number of sources including grants and donations (Chmiel, & Bhattacharyya, 2015). Because of these reasons, any transitions in the current energy system must take place within the socioeconomic context of the local community to deliver value to all residents. 6.4.2. Business-As-Usual Scenario In the business-as-usual scenario, no sector coupling takes place, and the energy system runs as it does today. The heating, cooking and transport sectors continue to run on fossil fuels resulting in increasing carbon emissions every year. Even though community members are satisfied with their current energy system and a BAU scenario would be a continuation of the same, this scenario would not be acceptable to the community in the long run as it is not in line with the community’s decarbonization goals by 2030. 6.4.3. Sector Coupling Scenario The Sector Coupling Scenario follows the existing 2030 decarbonization plans. Under this scenario, the island needs to transition from fossil fuel use to low carbon energy, which would necessitate the electrification of heating, cooking, and transport on the island. For the Isle of Eigg, this would represent a significant increase in electrical demand, which would have to be managed carefully and cost-effectively. Furthermore, it is important to deliver these changes in a manner that gains the acceptance and support of the local community. The community workshop and interviews revealed a complex set of concerns about the electrification of heating and cooking. Given the dissatisfaction with the initial system where each household provided their own electricity through generators, electrification could be seen as additional improvement that would further improve living standards on the island. For those particularly concerned with environmental sustainability, electrification of heating might be viewed as a necessary evolution. As such, they would likely support electrification that aligns with broader renewable energy adoptions. With that said, electrification of the heating and cooking sectors brings also brings out some concerns. Islanders place a high value on self-sufficiency, a theme that is consistently highlighted in interviews. There is a desire to maintain a degree of autonomy from mainland assistance and expertise. Eigg Electric made every effort in the design of the current system to ensure that it would be able to maintain and operate the system independently (Rae & Bradley, 2013). This proactive, firsthand approach to the island’s energy system also extends to its residents who would like a system they can fix and maintain themselves that also incorporates backup solutions. When implementing the electrified future scenario, it is important to take into account the following: • In interviews, most residents seemed to favour electricity reliability and availability over how green it was. In the author’s opinion, it is more important to residents to have a reliable electricity supply that is easily managed rather than a system that is perfectly optimized for reduction of carbon emissions. • Diesel generators, which allow for a readily available and long-lasting form of energy, are an important component of the current energy system and the BAU scenario as well. The community's resistance to a fully electric system due to the potential for system failures indicates that any electrification plan must prioritize resilience and community self-sufficiency. The presence of diesel generation in the BAU scenario would alleviate community concerns about having a backup system in place. Still, the continued investment in multiple renewable energy sources helps to balance seasonal fluctuations in output from each source, which in turn helps to reduce diesel fuel usage over the year. • The opposition to electric heating, despite struggles with keeping their homes warm, requires a hybrid heating solution in order to respect the community’s preferences. A combination of electric and wood heating can offer flexibility, allowing residents from the convenience of electric heating while also having the option to use wood, which may be more accessible and culturally preferred. A hybrid approach can also provide a backup. It is worth noting that concerns about heating are further addressed further in the energy efficiency scenario that takes into account retrofitting of homes for better insulation. The acceptance of this scenario is underpinned by the community’s positive experiences with the current energy system. In 2008, the island of Eigg became the first community to launch an off-grid system based on renewables. Being self-sufficient is a source of pride for residents on the island, making it important to retain these principles even as the energy system undergoes changes. While on the subject of self-sufficiency, it is also important to discuss another idea that is equally relevant to the Isle of Eigg: sufficiency, the state of having enough. Dawby and Fawcett (2018) define energy sufficiency as “a state in which humanity would only consume energy services equitably and in quantities compatible with sustainability and ecological limits.” Sufficiency, in this context, goes beyond the mere idea of supplying adequate energy. It encapsulates the idea of meeting needs without excess, promoting energy conservation alongside the shift to renewables. It is a principal hat challenges conventional growth-oriented models of energy use and supply. An interesting perspective that was expressed in the workshop was “perhaps the mainland has it wrong” in its approach towards energy use. The case of Eigg serves as a compelling example of how the principles of sufficiency are being applied and can continue to be applied in practice. Sufficiency involves significant reduction of an energy- intensive behaviour and advocates for a model where less energy is used in a more efficient manner (Zell-Ziegler et. al., 2021). For the Isle of Eigg, the 5 kW cap already proves to be sufficient, as none of the interview or workshop respondents expressed dissatisfaction with this limit. Furthermore, with an average annual household consumption of about 2560 kWh per year, the Isle of Eigg does not fall behind the Scottish average household electricity demand of 3635 kWh per year (Martin, 2020). It can be argued that not only are the residents of Eigg satisfied with the electricity supply on the Island, but the supply is also adequate to meet their needs. 6.4.4. Sector Coupling with Energy Efficiency Building on the Sector Coupling Scenario, it is assumed that electricity becomes the primary source of energy in the island. Transport, cooking and heating sectors get electrified by 2030 and wood, diesel, kerosene, and gas get only used as back-ups. Self-sufficiency is very important for the residents in Eigg. Two interviewees mentioned that even if they had the option to switch to electric heating, they would still like to keep another source of heat as back up. The fact that the island is so isolated makes it difficult for emergency services and technical operators to reach Eigg. Therefore, residents prefer appliances and technologies that make them independent. “We like things that we can repair ourselves” was mentioned by one interviewee. For this scenario to work, support from the community is vital. The change in habits, the adjustments to new appliances and technologies, and its adoption, are all key in the success of this scenario. The survey results show that residents on Eigg are very open to adjusting to changes. On it, more than 70% of the respondents answered they would be likely to shift to electric appliances for cooking, and 70% would also adjust their cooking schedule to align with the availability of renewable energy. The easy adoption of the 5 kW cap, and the willingness to shift times and fuels show that the Energy Efficiency Scenario could be one of the most successful scenarios for the island. It is the one that addresses most of the concerns mentioned by the interviewees, and that could fit the behaviours, preferences, and current knowledge of the residents. Two interviewees also mentioned they would rather have their house insulated before considering purchasing an electric heat pump. Social acceptance of energy projects in general, is closely linked to how fair the community members perceive the proposed solutions. Two concepts are identified in literature: distributive justice and procedural justice. A third uncommon one is also mentioned in a few studies; recognition justice, which is particularly important in multicultural communities. Recognition justice refers to the concept of having a fair representation in a group, “recognising that some groups are at a disadvantage within formal participation processes. Within the field of energy, recognition justice draws attention to the dominance of certain demographics within energy decision-making processes, and the need to recognise and integrate the perspectives of less powerful stakeholders.” (Roddis et al. 2018). In the case of Eigg, and given its demographics, one could argue that the recognition justice is addressed. Given the community-owned nature of the island, each resident can participate in the monthly meetings, where decisions are made as a group. Part-time residents can also participate in those meetings but cannot vote on decisions. They are also included in the mailing list to stay informed about what is going on in the island. Therefore, the focus of this section will be on distributive and procedural justice. Distributive justice refers to how the costs and benefits of a project are distributed across stakeholders (Mundaca et al. 2018). Eigg easily addresses this both with the 5 kW cap, and the fixed tariff price for the residents, and the higher tariff for holiday houses. Both solutions (the tariff and the cap) was something that most interviewees mentioned as positive and something that they liked about the system. That it is fair. Even when asked about the limitations that a cap like that could bring to a household, the general response was that it is not a problem at all. “We have enough for our needs. I do not even think about the 5 KW cap. I have been able to work within this capacity over the years, so I am okay.” With the energy efficiency scenario, keeping that cap would not be a problem, since residents are used to monitoring their energy consumption and lowering it whenever is necessary. However, this begs the question of what would happen if a household needed more energy. Most households on Eigg have 1 or 2 people living per house. Families may have two children. But for families with many children, or a family member with special needs, this cap could be seen as anything but fair. Still, the fixed tariff, the transparency on the costs from Eigg Electric, and clearly establishing the reasons for raising the tariff are good for an energy community project. It keeps the community informed and on even ground. According to Li, Hakvoort and Lukszo, community energy systems are innovative precisely because of how they involve local community members. “They play an important role in the energy system by actively involving in the planning, development, and administration of the energy system as well as the allocation of its costs and benefit.” (Li, et. al., 2021). This perceived allocation of cost and benefits is closely linked to how the people in Eigg make decisions when it comes to the electricity system. These costs and benefits are not always monetary, and not always individual. As mentioned before, a common worry among the interviewees was the future capacity of Eigg as a whole (enough to satisfy the growing demand as a community), but not for personal consumption. This sense of shared responsibility was mentioned by all interviewees, and some even made a point on wishing to receive more information to adapt their behaviours. Two interviewees said they know when they should lower their electricity consumption, but they would also like to know when it is okay to use more because the renewable generation is high. The stoplight system was mentioned as being useful. However, currently it feels like only the red light works, limiting the amount of information residents have to make decisions. Another reason that supports the hypothesis of the energy efficiency scenario’s easy implementation is the deep understanding residents have of how the island’s grid works. This was observed both from the conducted interviews and a paper published in 2020, where it was concluded that “residents draw on heat-based and weather-based forms of sensory feedback, which help them to understand domestic energy consumption, and renewable generation, respectively” and that this “enables them to understand domestic energy consumption and renewable generation in a meaningful way; such understandings are a key first step in householders’ ability to engage in energy shedding and shifting practices” (Martin 2020). This was mentioned by some interviewees that seem to know which appliances consume the most energy: “I am always under 1 kW, but if I received the email alert, I would not use the toaster for example. And my number one priority would be to buy efficient appliances if I needed new ones. Always look for the highest grade.” When implementing this scenario, it is important to highlight that no regulator is involved in Eigg’s energy system. “The community itself needs to agree on the cost allocation method themselves. It, therefore, requires that the selected […] method be socially acceptable to local stakeholders.” (Li, et. al., 2021). Therefore, consensus needs to be reached to ensure its success. Said consensus must also be conducted in a way that is perceived as fair by all parties involved. Procedural justice refers to this agreement process. While distributed justice means to ensure a fair result, procedural justice is concerned with how fair the process to reach this result is. (Roddis et al. 2018). Mundaca mentions that “the perceived fairness of procedures seemed to increase the perceived legitimacy of outcomes” (Mundaca et al. 2018). This seems to be true for Eigg as well. The Big Green Challenge is an example of how when the whole community engages in the planning process, the buy-in is greater. The Big Green Challenge was a competition sponsored by Nesta, the UK’s innovation agency. With a £1 million prize, it aimed to “stimulate and support radical new community-led responses to climate change in the UK.” (ChallengeWorks). The Isle of Eigg was one of four winners. The competition looked at four criteria to decide on the winners: CO2 reductions; innovation of the initiatives; longevity and scalability of the proposed projects, and level of community engagement. Eigg managed to reduce their emissions by 32% in one year (Webster, 2010). An interviewee mentioned that “For the Big Green Challenge we formed working groups, a lot of community involvement. It was a big success, got lots of people involved and interested, particularly because it was about improving the living conditions in the island. There were lots of community meetings, where priorities were collectively decided. Practical things.” Therefore, the same process should be followed to brainstorm ideas for energy efficiency strategies in the island, or ideas for educating tourists that visit the island in summer. This was also something mentioned by two interviewees; they know when they should lower their electricity consumption, but they would also like to know when it is okay to use more because the renewable generation is high. The stoplight system was mentioned as being useful. However, currently it feels like only the red light works, limiting the amount of information residents have to make decisions. Finally, it is important to show the benefits that could be reaped from implementing energy efficiency measures, particularly those that are not related to consumption reduction but to increasing thermal comfort, the reduction of local emissions from replacing thermal generation (like conventional cars), and better natural lightning. 6.4.5. Demand Shifting While a demand shifting scenario was not modelled, a qualitative analysis was conducted to determine how applicable it would be to the Isle of Eigg. In electricity grids, supply and demand must be balanced at all times, as is evident on the Isle of Eigg. User behaviour and energy consumption patterns have a tangible impact on off-grid systems (Rae & Bradley, 2013). In an energy system like Eigg a single user of electricity represents a much greater fraction of the total demand, which means individual behaviour has a much greater effect on the energy balance of the system. On Eigg, to ensure that the electricity supply is constant and sustainable, household consumption is capped at 5 kW and 10 kW for businesses. While islanders do not feel restricted by this cap, there are plans in place to increase the cap to 10 kW for households to allow for electrification of heating. The island also utilizes a traffic light system, set up at the pier, to communicate when renewable energy is low: the red light signals to community members to limit energy usage while green indicates that they can use electricity as normal (Chmiel, & Bhattacharyya, 2015). Despite having to decrease their energy consumption well below the average standard, this system works well for the residents of Eigg as there is a general consensus on the island that everyone does their part not to burden the grid. As such, it can be argued that the elements that enable successful application of dynamic tariffs are present on Eigg. Demand shifting involves incentivizing consumers to adapt their energy use patterns to better align with the available supply of renewable energy resources, a system already in use on the Isle of Eigg. Implementation of a dynamic tariff customized to the island may create a win-win situation for both Eigg Electric and the residents of Eigg. As the producer of electricity, Eigg Electric can long- term average costs while consumers can save on electricity bills. But the Isle of Eigg continues to be an interesting case study. Responses to the idea of implementing a dynamic tariff were mixed among both workshop and interview participants. On one hand, some expressed interest in dynamic tariffing, valuing the visibility it would provide into their energy system as well as their household consumption. On the other hand, having a tariff that changed even twice a year would be undesirable for the islanders. They expressed a desire for stability, predictability, and transparency. Even though respondents also had some reservations about the efficacy of current traffic light system in guiding energy use, the idea of integrating smart meters was also met with some concerns about privacy and information security. There are multiple ways in which an electricity tariff can be designed with regard to how costs are allocated to users and how users react to tariff signals. Traditionally, tariffs are designed in a static way, focusing on the recovery of costs. Currently, the Isle of Eigg operates on a fixed rate electricity tariff. The tariff is designed to provide sufficient funds for system operation, staffing, maintenance, and replacement of components (Chmiel, & Bhattacharyya, 2015). On Eigg, a dynamic tariff would be designed in such a way that it would be cheaper to use electricity during the times when there is less demand on the grid. However, there are a number of reasons why a peak shifting scenario would prove difficult to apply in the Eigg energy community. 1. While community members are open to the cost saving aspects of dynamic tariffing, the current tariff scheme in place on Eigg is viewed to be fair and reasonably-priced. As such, the designed dynamic tariff scheme would have to create cost savings sufficient enough to over adoption barriers. 2. The limited use of energy monitors. While all households on Eigg are provided with an OWL brand energy monitor, the extent to which they are used in understanding individual household consumption is still unclear. Also, the hesitation among some to move to a smart metering system would hinder the effectiveness of a dynamic tariff (Ma et. al., 2018). Furthermore, changing the entire metering infrastructure already in place could result in significant costs. 3. Any changes to the pricing model must be carefully aligned with the community’s desire for stability and transparency, the introduction of a dynamic tariff scheme on the isle would therefore require full community buy-in so as to preserve the principles of fairness that characterize the current system. 6.5. Energy Poverty in the Context of the Isle of Eigg Generally, fuel poverty also called energy poverty is associated with households that spend a high proportion of their household income to keep their homes warm (Bolton et al., 2024). Fuel poverty is a critical consideration for this current study. Given that our study found that 21% of the islanders are fuel-poor and 12% are extremely fuel-poor, we believe addressing the issue of fuel-poverty is paramount in achieving sustainable and equitable energy transitions. Our findings indicate that a significant percentage of the respondents fall within the fuel-poor and extremely fuel-poor bracket (a total of 33%), this underscores the need to design de-carbonization strategies that will not only reduce carbon emissions but also prioritize affordability, accessibility, and resilience. Failure to adequately address fuel poverty could exacerbate social inequalities and hinder the effectiveness of transition efforts. In Scotland, the Fuel Poverty Act (Act 2019) defines a household to be fuel-poor if: • after housing costs have been deducted, more than 10% (20% for extreme fuel poverty) of their net income is required to pay for their reasonable fuel needs; and • after further adjustments are made to deduct childcare costs and any benefits received for a disability or care need, their remaining income is insufficient to maintain an acceptable standard of living, defined as being at least 90% of the UK Minimum Income Standard (MIS) (Bolton et al., 2024). Due to the different methodologies used in measuring fuel poverty in nations of the UK, the poverty rates cannot be directly compared. Latest estimates show that England has the lowest fuel poverty rate of 13%, followed by Wales at 14%, Northern Ireland at 24%, and Scotland being the highest at 25% (The Scottish Parliament, 2024). Scotland targets to have not more than 5% of its citizens in fuel poverty and not more than 1% in extreme fuel poverty by 2040 (The Scottish Parliament, 2024). Figure 6-25: Fuel poverty in Scotland Source: UK House of Commons, 2023 Figure 6-25 indicates that from 2003 to 2011, fuel poverty was measured and defined differently, the original methodology was used to measure fuel poverty from 2003 to 2011. In the original methodology, a household is considered to be fuel-poor if: before housing costs have been deducted, more than 10% (more than 20% for extreme fuel poverty) of their net income is required to pay for their reasonable fuel needs. Figure 6-26: Key factors affecting fuel poverty The three factors that affect fuel poverty are household income, fuel cost, and energy consumption (Belaid, 2019; Charlier and Legendre, 2021; Longhurst and Hargreaves, 2019). When households have limited financial resources, they may struggle to afford adequate heating and energy services. Likewise, if a home is poorly insulated with high energy-consuming appliances this can adversely affect the energy efficiency of the house and lead to fuel poverty. Higher fuel prices make it more difficult for low-income households to afford heating and electricity, leading to fuel poverty. Fuel poverty also deepens the vulnerability of people and adversely impacts health, education, and overall quality of life. 6.5.1. Measures of Fuel Poverty on the Isle of Eigg Figure 6-27: Annual fuel poverty on the Isle of Eigg Source: International Class Survey, 2024 Figure 6-27 shows the percentage of annual income respondents on the Isle of Eigg spend on electricity, heating, and cooking. Out of thirty-three participants who took part in this quantitative survey, 22 representing 67% of the respondents were not fuel poor. However, 7 participants representing 21% of the respondents fell in the fuel poverty bracket. These individuals indicated that they spend more than 10% of their monthly household income on energy needs. The Isle of Eigg has some residents who are extremely fuel-poor. According to this survey, 4 persons on the Isle of Eigg are in the extreme fuel poverty category. This figure represents 12% of the total number of residents that took part in the survey. This implies that these individuals spend more than 20% of their monthly income on energy bills. Results from the survey show that majority of the islanders are not experiencing fuel poverty. Even though majority of the people are not experiencing fuel poverty, quite a significant percentage of the islanders are in the energy poverty and extreme poverty bracket (33%). This is why there is the need to design de-carbonization strategies that will prioritize affordability for the islanders. 6.5.1.1. Expenditure on energy bills A key theme that emerged during the data analysis was expenditure on energy bills by households and businesses. Regarding expenditure on energy bills, all the households interviewed indicated that they spend less than 10% of their household income on energy bills. One of the interviewees said: “averagely I spend less than 10% of my income on energy bills monthly”. Another householder responded: “I can tell you that I spend less than 10% of my monthly income on energy bills”. 6.5.1.2. Keeping homes warm in the winter All but one interviewee struggled to keep the home warm during winter, responding: “I’ve never had challenges heating my home in the winter.” However, most of the interviewees indicated that due to no insulation, it is always a challenge to heat the home in the winter months. This is what a participant said: “yes, I do, there is no insulation in the house I live in.” 6.5.1.3. Effect of inadequate heating on the health of residents Regarding the consequences of inadequate heating on the health or well-being of residents, all the interviewees indicated that they have not become unwell due to lack of access to adequate heating, at least none that readily comes to mind. When one of the interviewees was asked about the impact of inadequate heating on health or well-being, the response was: “none that I can remember, so I will say no.” Other interviewees believed that even though they have not been unwell due to inadequate heating, they appreciated that lack of access to adequate heating can have detrimental consequences on the well-being and health of people. This is what one of them said: “I have not become unwell, but sometimes life can be more stressful if there is no adequate heating.” 6.5.1.4. Sacrificing other essentials for energy The interviewees were unanimous in their responses when they were asked if they had sacrificed other essentials like food, and medical expenses to cover their energy costs in the past year. All the interviewees said they did not have to sacrifice food or medical expenses to cover energy bills. It was evident that the interviewees did not have affordability issues regarding energy. This was confirmed by one of the participants who stated that: “no, I’ve never experienced that, remember I spend less than 10% of my income on energy bills”. 6.5.2. Interpretation of the Findings on Fuel Poverty The findings of this current study show that majority of the people living on the Isle of Eigg are not fuel-poor. All the householders that were qualitatively interviewed indicated that they were not experiencing fuel poverty. However, our results show that businesses on the island spend more than 10% of their monthly profit on energy bills. A comparison between the qualitative results of this study and the quantitative results produced by the International Class 2024 survey shows some similarities. Both results indicated that majority of the respondents were not fuel-poor. When it comes to heating, several studies have established that inadequate heating in the home can have detrimental impacts on the health of occupants (Awaworyi Churchill and Smyth, 2021; Boemi and Papadopoulos, 2019). Inadequate house heating can lead to a range of detrimental health conditions. These include respiratory problems such as asthma and bronchitis due to exposure to colds. Additionally, cold homes can exacerbate cardiovascular issues, increase susceptibility to infections, and worsen mental health conditions such as depression and anxiety. Ballesteros-Arjona et al., (2022) found that inadequate heating as a consequence of fuel poverty exposes individuals to health risks such as poor mental health and respiratory health. In this study, we found that the health of residents on the Isle of Eigg have not been negatively impacted. However, this is not to say that residents on the island do not struggle to keep their houses warm. Majority of the houses are not well-insulated, which is why when given the option, respondents would choose to invest in building insulation rather than electric heating. A significant observation that was made during our interactions with the participants was that residents of Eigg are frugal with energy consumption. This is probably one of the major reasons why they have seemingly sufficient energy even with the 5kW cap placed on consumption. Indeed, fuel poverty often forces individuals to make difficult choices, sometimes sacrificing essentials like food and medical expenses to cover energy bills. People in the fuel poverty bracket are sometimes faced with tough choices, risking their well-being to keep the lights on and heating their homes. Some people due to fuel poverty divert funds set aside for essentials like groceries and medical emergency purposes to cater for their energy needs (Longhurst and Hargreaves, 2019). Our findings revealed that residents on the Isle of Eigg do not have to sacrifice money meant for essentials such as food and medical expenses to pay for their energy needs. The reason could that majority of the residents were not having affordability challenges. When people are able to comfortably afford their energy needs, the issue of sacrificing other essentials for energy needs does not come up. Our finding is consistent with what Brown et al., (2020) found in their study. In conclusion, our findings revealed that majority of the households were not energy poor, as per the Scottish definition for fuel poverty, indicating a favourable situation. Our results also confirmed that residents have adapted to live comfortably within the restrictions placed on energy access, suggesting that the energy provided on the island is enough for the current population. We also found that the islanders are frugal with energy usage, a key reason for the islands' energy management successes. It is not surprising that Eigg has consistently remained a step ahead of the other small islands in Scotland when it comes to sustainable energy promotion. Further studies may be needed to explore the specific factors contributing to the successful management of the energy needs of the island. Understanding these factors could offer valuable insights for other island communities that wish to emulate Isle of Eigg’s sustainable energy success story. 7. The Role of the Eigg Community in the Energy Transition A commitment to self-sufficiency and environmental preservation are a source of pride for the Eigg community. This sense of pride also shapes their behaviour toward energy consumption. The community is aware of the system’s constraints, and they have adapted to live comfortably within it. They are open to change when necessary. Eigg's energy system is sustained and strengthened by the active participation and commitment of the local community. They recognize the importance of energy education and awareness of sustainable practices. This collective awareness is essential to promote a culture of energy sustainability on the island. Eigg residents play an active role in energy conservation by adopting daily practices that reduce unnecessary consumption. Community engagement in energy conservation and being responsible with the energy cap is critical to ensuring the effectiveness and stability of the island's energy system. When it comes to decisions and planning, the community actively participates in the island's decision- making process and energy planning. Even though there are pre-requisites, determined by the grants and programs that Eigg participates in, when it comes to an open decision, residents' opinions and concerns are considered in discussions about energy policies, infrastructure investments, and future project development. This inclusive participation strengthens the sense of ownership and shared responsibility towards the island's energy system. Overall, the community is satisfied with the service and the current scenario. But there are concerns about the future capacity, due to the advance of demand in the upcoming years. Besides the population's natural growth, the biggest impact would be the changes necessary to meet Eigg’s goal of becoming carbon neutral by 2030. 7.1. The Community Role in the Eigg Carbon Neutral Target To meet the target the population will need to shift from fuel-consuming habits to electric solutions. At this point, the following question arises, “Are we going to have enough energy for electric heating or cooking?.” This is a valid thought behind the concern that the current scenario is temporary and that changes will be necessary in the near future. And as with any change it takes time to digest, understand and adapt. The process of change in communities is distributed by several distinct phases, which include significant adaptations. As developed by Kurt Lewin, a pioneer in social psychology best known for his field theory and work in group dynamics, Lewin’s change model involves three steps. Often change begins with a phase of discomfort and disequilibrium, where the need for change is recognized but not yet fully understood or accepted by the community. It is common to encounter resistance to change, due to fear of the unknown, concerns about loss of control, or simply the desire to maintain the familiar status quo. To overcome this stage successfully the community will need to take advantage during meetings and social events to intensively work on social awareness. Open talks, debates, and information spread through different ways can be game changers. Later on, the goal is to see the needed changes incorporated in Eigg, where new practices become part of the cultural and social fabric. During this period of transition, it is critical to cultivate a culture of openness and continuous learning, where mistakes are visible as opportunities for growth and innovation. 7.2. Eigg Electric and Community Involvement Eigg Electric is a community-owned company, meaning it is controlled and managed by the island's members. Despite being conceived by residents, the business model creates a “supply vs client” relationship ensuring that community interests are prioritized and transparency is promoted. Eigg Electric serves as the operational and organizational body that facilitates energy management and distribution on the island. But goes beyond simply providing electrical services, Eigg Electric promotes local empowerment by empowering residents to actively participate in the management and operation of the electrical system. Residents have the opportunity to acquire technical skills, gain business management experience, and exert influence over decisions that affect their daily lives. This speaks for Eigg Electric's social responsibility, as job creation in rural areas is typically an issue hard to assist. Today Eigg’s Electric team counts on 6 part-time workers and 5 volunteers on the director board. In addition to implementing renewable energy sources, the Eigg community also faces technical and logistical challenges in managing and integrating these systems. Ongoing maintenance and monitoring of the wind turbines and solar panels requires specialized skills and constant commitment from residents. Most of the Eigg workforce had no experience with energy before, however, through training and cooperation programs, the inhabitants of Eigg have acquired the skills necessary to operate and maintain these systems efficiently and reliably. They are building a polyvalent team, to sum up their skills. Eigg Electric has an open channel to the community and due to its environment is easy to access one of the members. However, there are also official communications. Every month a status report including the latest updates of the energy system, such as improvements or announcements is sent out via e-mail to the whole community. The community works efficiently with grants and Eigg Electric collaborates with other organizations, institutions, and governments to promote sustainable development and innovation in energy. These partnerships are extremely important to allow the island to access additional resources, expertise, and financing to support its energy and community goals. In sum, Eigg Electric is a partnership with the community itself, but as with any relationship have matters to solve every once in a while. Those topics are open for debate during resident meetings and some decisions are made by voting, during those moments it is possible to clarify doubts and endorse the discussion. This dynamic holds a relevant part for Eigg Electric as a facilitator in the transition to a carbon-neutral island, and as mentioned before, they are still working on community awareness about the strategy throughout those channels. 8. Conclusion and Recommendations A model was developed from an Open-Source modelling framework to model the future transition scenarios for the Isle of Eigg. These scenarios were current scenario, a Business-As-Usual (BAU) scenario that extended the current scenario until 2030, a sector coupling scenario that electrified the sectors, a sector coupling with energy efficiency scenario that incorporated the utility of energy efficiency, and a scenario that was conceived to provide another perception and was called No-Tidal scenario where investment in tidal energy was never a choice. Below are some of the key takeaways from the scenarios modelled. - Current system size is sufficient to serve the power demand of the future if no sectors get coupled. - If all sectors get coupled, the most economical solution is through the expansion of the installed capacity through investment in tidal energy of at least 250 kW. - If all sectors get coupled, current battery storage will have to be expanded by at least 500 kWh. - The highest renewable fraction or cross-sectoral electrification rate can be attained through sector coupling with energy efficiency scenario, taking the overall renewable fraction to 90% across all four sectors. - If tidal cannot be pursued as an option, new investments in solar PV and wind expansion can also meet the future demand. But the investment costs in this case could be more than 30% higher. - All scenarios sufficiently bring the emissions down to less than 50%. Remaining emissions are only from heating sector and constrained by the limited power transmission capacity but can be displaced totally using only carbon-neutral firewood for heating. Therefore, decarbonisation is achievable through all three long-term scenarios involving. It is also important to address concerns. Each of these topics covers an opportunity which then can be developed into a plan. 1) Knowledge Handover and Records A lot of the knowledge comes from firsthand experience and not from an established procedure. Concentration of data can create bottlenecks or loss of information. A first step could be to run a process mapping and registration of the procedures conducted regularly. The creation of a task force can be an opportunity to promote training and professional development for the community, besides the creation of an archive that preserves the knowledge for the future. 2) Report Layout and Topics As mentioned before, the monthly report allows the residents to stay informed. It is a way to maintain transparent and open communication between stakeholders. The monthly report could be improved by establishing a standard layout with topics that the community is interested in the most. For example, key financial aspects, risks, and highlights. Such that the community would know what to expect each month and where to look for specific information. This could also help to catch the attention of the audience. See a proposed model in the appendix A. 3) Use of Different Media and Channel There are different ways to communicate a message, and people also receive information differently depending on the channel. The use of different media to diffuse the information could bring effective spread. So far there has been face-to-face communication during meetings and written correspondence. This is good for fast and efficient communication. However, for bigger projects, videos and media can be an ally to mobilise the community. Thus, a video talking about the carbon neutral plan, could motivate the inhabitants to know more about what they need to do to reach it, how could the living conditions on the island be improved or create empathy about the topic and prepare for when the changes come. 4) Educational Programs Workshops and campaigns can be a way to get the community involved in a desired goal and give extra value to it. Different from courses and training, those moments should be fun and count on moderation tools to keep the public interested. A day where the island stops and dedicates time to a certain topic, engagement approaches such as the use of the same t-shirt, colours, signs, and slogans, create a visual identity and impact. It can also be a section to refresh good practices about the importance of energy conservation and efficient use of resources. This collective awareness is essential to promote a culture of energy sustainability, as well as reinforce the community bonds. 5) Continue to Engage on Eigg Energy System and support Eigg Electric The community already participates in decision-making processes and public consultations related to energy and island development. Maintaining this engagement is essential to ensure that community interests and concerns are represented and considered. This could be volunteering or providing constructive feedback. The residents and Eigg Electric must support and collaborate with each other to provide clean, reliable, and affordable energy to the community. 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(n.d.). Retrieved from https://scotland.shinyapps.io/Energy/?Section=EnergyEfficiency&Subsection=DemandReduction&Ch art=ElecConsumption Syse, H., Stevenson, F., Byres, J., Crum, A., & Ban, P. (2016). The Thermal Energy Challenge:Sustainable Heating on the Isle of Eigg. University of Strathclyde. Tabula. (2024, January 26). United Kingdom building typology. Retrieved from Tabula Webtool: https://webtool.building-typology.eu/#bm The Building Research Establishment. (2021). The Government’s Standard Assessment Procedure for Energy Rating of Dwellings. Watford: BRE Group. The Energyst. (2023). When will EVs become more affordable? Retrieved from https://theenergyst.com/when-will-evs-become-more- affordable/#:~:text=It's%20been%20estimated%20that%20the,as%20the%202030%20deadline%20a pproaches. Trust, E. S. (2022, Jan). Energy Saving Trust. Retrieved from Energy Saving Trust: https://energysavingtrust.org.uk/top-five-energy-consuming-home-appliances/ Trust, E. S. (2024, Jan). 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Energy Policy, 157, 112483. https://www.sharonmwasco.com/home/three-stages-of-community-change https://www.sharonmwasco.com/home/three-stages-of-community-change Annex A. Modifications and Additions to GENeSYS-MOD OSeMOSYS and its extension GENeSYS-MOD provide detailed documentation on the modelling of the energy system and the interplay between various components. Open-Source access of the modelling framework laid the foundation of the model that was used for the energy system at Eigg. The modularity of the framework allows changes that are suited to each unique application. In this context, to fit the energy system installed on Eigg and to attain the desired outputs, several modifications and additions were made to the GENeSYS-MOD framework. Ultimately, the working framework exclusive to the application at Eigg has around 12 parameters, 14 variables, and 11 constraint functions that were components of GENeSYS-MOD and 5 parameters, 6 variables, and 5 constraint functions that were either modified or newly added as a necessary requirement to suit the model for the application at Eigg. The model derived from GENeSYS-MOD is referred to here as the ‘adapted model’ or just the ‘model,’ a reference to the original parent modelling framework will always be explicitly made as ‘GENeSYS-MOD.’ These modifications and additions are enlisted below. - For long term energy modelling, GENeSYS-MOD aggregates the demand and supply profiles to reduce the timesteps worked upon. Such aggregation methods are a common practice in long- term energy modelling where micro-level interactions of components is not a requirement. In our application, aggregation of the timesteps and reducing them from 8760 hours to aggregated timesteps led to a variation of +/- 40% in the results, and therefore no compromise was made on the number of timesteps involved. This was an unavoidable decision as aggregation would have led to inconsistencies in the calculation of battery activity, variable costs, and estimation of surplus potential, on top of the usual loss of representation of extreme events that occur in between aggregated hours and are consequently lost due to aggregation (Kotzur et al., 2018). A major trade-off of this change has been the long optimisation run-times, that varied from 20 min to 7 hours across different phases of framework development. - GENeSYS-MOD is a capacity expansion modelling framework that builds new generators to meet a new demand. The modelling framework did not contain the provision of modelling of existing capacities. In our application, the energy system at Eigg already had sufficient capacities of renewable and non-renewable energy generators and storage, all well in their lifetimes, and therefore these had to be modelled. A new parameter-variable pair was modelled that fulfilled this limitation. The adapted model builds upon the existing capacities only while it optimises and finds the new capacities to install. - GENeSYS-MOD expands capacity with no consideration of the minimum feasible technology size and produces results as real numbers that are the most optimum to meet the demand. It will not be unusual for an optimisation in GENeSYS-MOD to suggest a hydro powerplant of 1.85kW while the minimum feasible technology size is 5kW available in the market (hypothetically drafted situation created as an example- these can vary). Due to the size of the community, magnitude of the demand, and location of the island with its associated logistical constraints, it was essential that the minimum feasible sizes of the generators were specified so that the model could only pick from a selection of these. With this addition in the model, the model was also configured to only operate between integer ranges while selecting the number of units. Reconfiguring the selection to integer from real numbers led to added complexity as the problem turned into a Mixed-Integer Linear Programming (MILP) optimisation problem, but also a reduction in processing time could be seen as the model now only iterated between integers over infinite combinations of real numbers. - GENeSYS-MOD gives default values to certain parameters that ought to be very large as long integers (for example: maximum hydro capacity = 999999 kW). While this operates without a problem in most cases, such large values become a problem when a parameter also has a very small value (for example: variable costs per kWh of solar = 0.0008), as this leads to ‘Out of Memory’ run-time errors. Academic license of the solver used (Gurobi) recommends the matrix range to be lesser than 1E08 (Parzen et al., 2022). This was reprogrammed to be an infinite function which instead of assuming a large number to begin with starts small and only grows as large as needed to produce optimal solution. Through such downscaling across some parameters, run-time was significantly reduced from 7 hours to below 1 hour. Gurobi is a commercial solver and provides accelerated processing of MILP models. While Gurobi can be freely accessed through an academic license, other open-source solvers also exist that perform functions. These are GLPK, CBC, and IPOPT. The relative performance of these solvers deviates hugely depending on the complexity of the model. - The energy system at Eigg has a physical constraint in its transmission network. GENeSYS-MOD offers no provision of modelling a physical transmission or distribution grid congestion. OSeMOSYS offers a module for load flow analysis of the network but that could not be modelled in our application due to complexity and detachment of the scope. To still have the transmission constraint represented, the model was adapted such that it gathered all power flows at each hour and limited it against the capacity at the transformer level in the transmission network. This addition allowed the model to see the maximum electrification rates that can be achieved with the same power transmission network against all sectors in the future scenarios. - GENeSYS-MOD has no direct provision of attaining the total capital costs or variable costs across different dimensions of years, technology, and hours. In order to attain these values, dual suffixes or shadow variables are to be used, making the processing and post-processing tedious. Two new constraints solely calculating and storing the total capital costs per technology per year and variable cost per hour of energy production per fuel per year, respectively, were created that provided direct insights into these figures. A trade-off of this has been the extension of optimisation run-time. While these modifications and additions made the model more suited to the application at Eigg, the model kept some simplifications and generalisations, a few of them carried over from GENeSYS-MOD. B. Simplifications and Generalisations of the Model Neither GENeSYS-MOD nor the adapted model are devoid of simplifications and generalisations. These are sometimes essential to aid the discovery of the optimal solution, to reduce the complexity of the problem thereby reducing the run-time, and to aid modularity of the modelling framework (Subramanian et al., 2018). Enlisted below are some of such simplifications or generalisations that were either in GENeSYS-MOD and got carried over in the adapted model or were created newly in the adapted model. - The hourly resolution although gives an accurate reflection of the interplay of the supply and demand as compared to the aggregated schedules, it fails to capture the sub-hourly intermittence induced by Variable Renewable Energy (VRE) and therefore the reliability of the model output on micro-transactions remains low (de Boer and can Vuuren, 2017). - The time-skip of 2-years between modelling years marks all activities as dormant during the years between the modelling years. This has implications especially in terms of the scheduling of the investment as capacities only expand during the modelling years and it gives no information separately on when the investments should be made (Prina et al., 2020). In a similar context, the model fails to capture any intra-modelling period variations that would otherwise be seen in real life (for example, change of prices, discount rates, seasons, and associated resource availability) (Prina et al., 2020). - Modelling of the diesel generator in a Linear Programming (LP) problem or in a MILP problem is always generalised as a component that operates with linear profiles (Kusakana and Vermaak, 2014). This is in total contrast to the actual reality as the power curve or fuel consumption rates per unit of power produced and the emissions rates vary with the load on the generator and therefore cannot be linear. However, generalisation of diesel generators as a linear component is a common modelling practice as most transition pathways are for decarbonisation and diesel generators play an insignificant role in most (Ameen et al., 2015). This is a generalisation that has been carried over in the adapted model from GENeSYS-MOD. - Modelling of battery SOC or battery level in a capacity expansion model is a non-linear function as the battery charge or discharge at any hour is a variable for the model alongside its capacity (Ameen et al., 2015). The model cannot take the existing capacities and expand on top of them while taking in account the depths of discharge (DOD). To achieve the battery level or SOC indications, a way around was determined where the existing battery DOD was re-referenced such that minimum DOD indicated 0 kWh of storage and maximum DOD indicated the difference maximum storage capacity and minimum DOD. - To incentivise investments in the later years of the modelling period, a common modelling practice is to encode a salvage value such that during optimisation the model can see the benefit of deferring the investment until later (Howells et al., 2011). This is a helpful tool, as without this provision the model will tend to invest right away and this could lead to excessive new capacities or unutilised capacities in case regulations evolve in the years to come against the installed technology or if the demands reduce further for some reason (for example: due to energy efficiency) (Howells et al., 2011). Usually, a non-linear curve for depreciation of renewable energy assets is needed for a true representation of the depreciation rates. For simplification, a straight-line depreciation is modelled that serves the purpose of incentivising investments during the later parts of the model (Howells et al., 2011). This approach is commonly used in applications where salvage values are not explicitly required. With the defined modifications and additions, and simplifications and generalisations, the model simulates the 4 scenarios and produces optimal results. The model is publicly available on GitHub and can be accessed and run following the instructions on the GitHub repository. The 4 scenarios have each a folder of their own with defined and pre-set inputs and configured output sheets. Below in table 4-4 are the model attributes for the future scenarios. Next section looks at the results and analysis of the 4 scenarios as optimised by the model. Table 0-1: Model Attributes Model Attributes Value Number of Constraints 5,203,719 Number of Variables 5,019,768 Number of Binary Variables 0 Number of Integers Variables 33 https://github.com/TahaAhmedSiddiqui/Energy-Modelling---Isle-of-Eigg Number of Continuous Variables 5,019,735 Number of Non-Zeros 15,825,921 C. Eigg Electric Monthly Report Layout Suggestion D. Isle of Eigg Renewable Energy Transition Survey The survey form aims to gather information from the community in Isle of Eigg, Scotland to develop a renewable energy plan. This plan targets the transition from traditional fuel-based cooking and heating methods to renewable alternatives and includes energy-efficient measures to ensure an efficient and reliable energy system. There are a total of 19 questions, and it is expected to take maximum 10 minutes to complete the survey. The survey is anonymous and will be solely utilized to refine our findings and conduct research activities for the design of a renewable energy system for the Scottish community. 1.Are you participating in this survey as a household or business consumer? Single choice. o Household o Business o Other 2.How many people are living in your household? Single choice. o 1-2 o 3-4 o 4-5 o N/A o Other 3.What type of business do you own? Mention N/A if household. Single line text. Enter your answer 4.How much are you satisfied with the energy supply on the island? Single choice. o Very satisfied o Somewhat satisfied o Unsatisfied o Very dissatisfied o Somewhat dissatisfied 5.What approximate percentage of your annual income goes towards your yearly energy expenditure, covering electricity, heating, and cooking costs? Single choice. o 5% o 10% o 15% o 20% o 25% o 30% o More than 30% 6.During which times of the day are you typically at home or away? Likert 7.Which fuel do you mostly use for cooking? Single choice. o Kerosene (Litres) o Fuel Wood (Kg) o Gas (No. of cylinders or m3) 8.How much do you consume this fuel on annual basis? Mention your answer in relevant unit. Single line text. Enter your answer 9.At what time of day do you usually cook? Likert. o Breakfast – 05:00-0:800, 08:00-11:00, 11:00-14:00, 14:00-17:00, 17:00-19:00, 19:00-22:00 o Lunch – 05:00-0:800, 08:00-11:00, 11:00-14:00, 14:00-17:00, 17:00-19:00, 19:00-22:00 o Dinner – 05:00-0:800, 08:00-11:00, 11:00-14:00, 14:00-17:00, 17:00-19:00, 19:00-22:00 10.Approximately, how much time do you typically spend cooking your meal? Likert. o Breakfast – 1h, 2h, 3h, 4h o Lunch – 1h, 2h, 3h, 4h o Dinner – 1h, 2h, 3h, 4h 11.Which are the most important electrical appliances which you use in your daily routine? You can select more than one option. Multiple choice. o Refrigerator o Washing machine o Dishwasher o Cloth dryer o Microwave o Water heating system o Other 12.How old is your building? Single choice. o old - 1980 o 1981 - 1990 o 1991 - 2003 o 2004 - 2009 o 2010 - 2016 o 2016 - 2023 13.What type of insulation is used for Walls? Single choice. o No insulation o Timber Frame o Granite or whinstone o Solid Brick o Cavity Wall o Sandstone or limestone o Other 14.What type of insulation is used for Roof? Single choice. o No Insulation o Pitched, loft insulation o Roof Room, ceiling insulated o Flat, Insulated o Other 15.What type of insulation is used for Floor? Single choice. o No Insulation o Solid, Insulated o Suspended, limited insulated o Other 16.What type of insulation is used for Windows? Single choice. o Single Glazed o Double Glazed o Other 17.How likely are you willing to shift your cooking habits from conventional fuels to renewable by using electric appliances? Likert. 18.How likely would you adjust your cooking schedule to align with the availability of renewable energy? Likert. 19.What time would you prefer to cook if you consider changing your cooking routine? Likert.

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The assessment of renewable energy projects for community benefit in West Harris (2016)

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(Pearsons r) betragen (in abnehmender Reihenfolge) r = 0,55 (p = 0,000, n = 82) für die Gewerkschaftszugehörigkeit des Arbeitsdirektors, r = 0,38 (p = 0,001, n = 82) für die paritätische Besetzung des

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• Publications

EXPLORING SUSTAINABLE ENERGY OPTIONS FOR LOOP HEAD IC 2021 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 1 ACKNOWLEDGEMENT This report was prepared as a part of International Class 2021 by the students of Energy and Environmental Management (EEM) at Europa-Universität Flensburg. The digital International Class of 2021 was an unique experience for all the students, faculty members and Loop Head Community. The students are greatly thankful to Mr. John Aston and the entire Loop Head community for their active participation, valuable feedbacks and for immense support to complete IC 2021 successfuly in this challenging situation. The EEM students and the Loop Head community benefitted greatly from valuable experiences of Ms. Alison Macleod Ms. Elizabeth Johnson and Mr. Ross Gazey. The authors are greatly indebted to the EUF faculty Prof. Bernd Möller, Ms. Tabitha Karanja, and Mr. Mominul Hasan for their invaluable support and guidance. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 2 Contents LIST OF FIGURES ........................................................................................................................ 5 LIST OF TABLES .......................................................................................................................... 6 LIST OF ACRONYMS .................................................................................................................. 7 EXECUTIVE SUMMARY ............................................................................................................ 8 1. Background ............................................................................................................................ 10 1.1. Community Energy Planning ........................................................................................ 10 1.2. Community Energy in Ireland ...................................................................................... 11 1.3. Community Energy in Loop Head ................................................................................ 13 1.4. Loop Head Community Energy Engagement Study by IC 2020 .................................. 14 2. Objectives .............................................................................................................................. 16 3. Scope of the Work ................................................................................................................. 16 4. Residential Sector .................................................................................................................. 17 4.1. Energy Efficiency in Residential Sectors ..................................................................... 17 4.1.1. Background ........................................................................................................... 17 4.1.2. Residential Sector Heat Demand .......................................................................... 18 4.1.3. Housing Stock Characteristics .............................................................................. 21 4.1.4. Energy Efficiency Potential in Buildings ............................................................. 22 4.1.5. Case Study of Energy Refurbishment for a Selected Building Type.................... 24 4.1.6 Conclusion .................................................................................................................... 28 4.2. PV System Design for Residential Buildings ............................................................... 28 4.2.1. Residential Demand .............................................................................................. 28 4.2.2. Roof Top Solar for Residential Sector .................................................................. 30 5 Farming Sector ...................................................................................................................... 31 5.1 Circular Economy in Farms .......................................................................................... 31 5.2 Implementing Circular Economy in Loop Head Farms ................................................ 34 5.2.1 Energy Auditing ........................................................................................................ 34 5.2.2 PV System design for Farms..................................................................................... 39 5.2.3 Anaerobic Digester Design for Farms ...................................................................... 49 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 3 5.3 Conclusion .................................................................................................................... 56 6 Community Energy Modelling .............................................................................................. 58 6.1 Energy Modelling for Loop Head Community ............................................................. 59 6.2 Excel Modelling Tool Development ............................................................................. 61 6.3 Methodology for Overall Community Energy Model Development ............................ 63 6.3.1 Identification of Major Energy Consuming Sectors and Demand Estimation ......... 63 6.3.2 Hourly Electricity Demand Profile of Different Sectors .......................................... 67 6.3.3 Generation Technologies .......................................................................................... 70 6.3.4 CO2 Emission Calculation from Imported Electricity.............................................. 72 6.3.5 Financial Calculation Assumptions .......................................................................... 73 6.4 Integration of Individual Single-User Modelling Section in IC Model 2021 ............... 76 6.5 Challenges of Modelling ............................................................................................... 76 6.6 Model Limitations ......................................................................................................... 77 7 Community Engagement ....................................................................................................... 79 7.1 Community Workshops ................................................................................................ 79 7.2 Workshop Sessions and Discussion .............................................................................. 81 Session 1 ................................................................................................................................ 81 Session 2 ................................................................................................................................ 84 Session 3 ................................................................................................................................ 88 Session 4 ................................................................................................................................ 91 8 Conclusion ............................................................................................................................. 94 REFERENCES ............................................................................................................................. 97 APPENDIX ................................................................................................................................. 103 A. Residential Demand ........................................................................................................ 103 A.1. The results of poll conducted at the 1st Workshop to Community in 8th of February 2021. 103 A.2. The results of poll conducted at the Workshop to Community in 22 February 2021. 103 B. Theory on Circular Economy.......................................................................................... 105 C. Energy Audit ................................................................................................................... 108 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 4 C.1. Levels of energy audit ............................................................................................. 109 C.2. Process to conduct an energy audit ......................................................................... 110 D. Biogas for waste management ........................................................................................ 111 E. Solar PV systems ............................................................................................................ 114 F. Policies and Regulations ................................................................................................. 115 F.1. Circular Economy ................................................................................................... 115 F.2. 5.1.2.2 Solar PV ...................................................................................................... 117 G. Energy Modelling ........................................................................................................... 121 G.1. Purpose of Energy Modelling ................................................................................. 122 G.2. Approach to Energy Modelling .............................................................................. 123 H. Community Engament Workshops-Discussions............................................................. 124 H.1. Session 1 ................................................................................................................. 124 H.2. Session 2 ................................................................................................................. 126 H.3. Session 3 ................................................................................................................. 128 H.4. Session 4 ................................................................................................................. 131 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 5 LIST OF FIGURES Figure 1-1: Summary of SEAI's Guidelines for a Sustainable Energy Community ..................... 12 Figure 4-1:Share of Dwellings by Occupancy Status in Loop Head in 2016 ............................... 18 Figure 4-2:HDD and Monthly Heat Demand for Kilkee (code: 037077001/037077002/037077003) ............................................................................................. 19 Figure 4-3:Total Heat Demand in Cold and Hot Season by District in 2016, MWh. ................... 20 Figure 4-4:The Average Total, Space, Water Heat Demand per Household. ............................... 21 Figure 4-5: Cumulative Energy Cost Curve ................................................................................. 23 Figure 5-1: Circular Dairy Vision ................................................................................................. 31 Figure 5-2: Example of electricity consumption in Irish Farms ................................................... 40 Figure 5-3: Total Hourly Demand Farming Sector....................................................................... 41 Figure 5-4: Inputs rooftop solar systems tool. .............................................................................. 43 Figure 5-5: Inflation in Ireland. .................................................................................................... 44 Figure 5-6: Electricity rated in households ................................................................................... 45 Figure 5-7: Outputs rooftop solar systems too .............................................................................. 46 Figure 5-8: July - Energy balance Typical weekday and weekend day ........................................ 47 Figure 5-9: Annual electricity bill value. ...................................................................................... 48 Figure 6-1: Process of Community Energy Modelling ................................................................. 58 Figure 6-2: The purpose of Loop Head Energy Model ................................................................. 59 Figure 6-3: Sections of IC Model 2021 ........................................................................................ 61 Figure 6-4: Conceptual Framework for Community Energy Model Development ...................... 63 Figure 6-5: Sectoral Energy Demand in Loop Head .................................................................... 64 Figure 6-6: Hourly Electricity Demand Profile for Residential Sector for a Week ...................... 68 Figure 6-7: Hourly Electricity demand Profile for Farming for a Week ...................................... 69 Figure 6-8: Hourly Electricity Demand Profile for Commercial sector for a Week .................... 69 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 6 LIST OF TABLES Table 4-1. The Classification of the Building Characteristics in Loop Head ............................... 21 Table 4-2. Pre-refurbishment Parameters of the Selected House ................................................. 24 Table 4-3. Investments Cost and Related Energy/Cost Savings for 1 Scenario ........................... 25 Table 4-4. Financial and Energy Calculations for Heating Technologies for Selected House ..... 26 Table 4-5: Values for Residential Demand Profile ....................................................................... 29 Table 5-1: Results of interviews on electricity demand................................................................ 36 Table 5-2: Results of interviews on diesel demand ...................................................................... 38 Table 5-3: Feed-In Tariff Used in the Model................................................................................ 46 Table 5-4: Comparison of AD operational parameters ................................................................. 51 Table 5-5: Thermal end-use .......................................................................................................... 52 Table 5-6: Electrical end-use ........................................................................................................ 53 Table 5-7: Biomethane Upgradation end-use ............................................................................... 54 Table 6-1: Additional features IC 2021 Model ............................................................................. 60 Table 6-2: Technical Parameters for PV system ........................................................................... 70 Table 6-3: Financial Parameters for PV system ........................................................................... 71 Table 6-4: Technical Parameters for Wind Farm ......................................................................... 72 Table 6-5: Financial Parameters for Wind Farm .......................................................................... 72 Table 6-6 : Fuel Cost considered in IC Model 2021 ..................................................................... 74 Table 6-7: SEAI Solar Grants considered in IC Model 2021 ....................................................... 74 Table 7-1: Workshop content........................................................................................................ 80 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 7 LIST OF ACRONYMS AD Anaerobic Digester BEH Better Energy Homes BER Building Energy Rate CAPEX CAPital EXpenditure CAT Clare Accessible Transport COD Chemical Oxygen Demand CSO Central Statistics Office EUF Europa Universität Felnsburg GHG Green House Gas HDD Heating Degree Days IC International Class IRENA International Renewable Energy Agency IRR Internal Rate of Return LCOE Levelized Cost of Electricity LEAP Loop Head Energy Action Partnership LED Light Emitting Diodes NPV Net Present Value OPEX Operating Expenses PV Photo Voltaic RE Renewable Energy RNG Renewable Natural Gas SEAI Sustainable Energy Authority of Ireland SEC Sustainable Energy Community VAT Value Added Tax University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 8 EXECUTIVE SUMMARY Community energy planning provides a high degree of ownership and control to a community aiding towards the development of a sustainable energy sector. Community engagement is a crucial aspect of the energy planning process. Thus, the following study conducted by the international class 2021 emphasized on involving and interacting with the local community of the peninsula to get a better understanding of their perspective of the energy sector and their preferences, on which this study is based. Also, inputs were taken from the previous study of IC 2020 to consolidate the working sectors of residential, farming, energy modelling and community energy training. The students working in the residential sector focused on energy efficiency measures in buildings and their effect on energy consumption in residential sector. The report provides insight on composition of heat consumption in Loop Head and effect of energy efficient interventions towards cost savings. A BER estimator tool was developed to provide recommendations on improving energy efficiency for an individual household based on the pre-existing conditions of the household. The study also identified several factors which influence the heat demand in residential sector and the retrofitting measures to reduce heat demand and heat losses in buildings. The results of the case study showed the implementation of retrofitting measures and installation of air-to-air heat pumps had potential with regards to cost savings. The cheapest option for the heating technology was identified as biomass boiler with investment cost of about 5,000 Euros with cost savings of 2,675 Euros per annum. The cheapest heat pump option of 11,790 Euros were identified with 3,241 Euros of annual reduction in expenditure. The students introduced the farming sector to the concept of a circular economy and identified possible areas of intervention of the concept in the sector. Three main working areas were identified to implement circularity in individual farms. First, energy efficiency measures were identified and potential improvement measures for the farms were recommended. Secondly to meet the energy self-sufficiency solar PV systems were investigated. An open interactive solar technical design and financial estimation tool was developed. Finally, to address the issues of waste management in the farms the anaerobic digestion technology was analysed, and a similar University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 9 interactive tool was developed. Running simulations of the tools on sample farm inputs showed good technical and financial feasibility of solar PV systems thus ensuring potential for self-energy generation. The results form the AD tool showed potential with regards to technical feasibility and waste management but did show attractive financial feasibility in the absence of financial incentives. To promote and assist in the establishment process of Sustainable energy system, an excel energy modelling tool was developed for Loop Head. This tool will provide the basis for investment decisions, support operational decisions, and perform scenario analysis. It is designed to assist the potential users from Loop Head to analyse overall system performance. Besides, the model is also designed to aid the users to identify solutions for obtaining energy efficiency in residential buildings and attain sustainability in Loop Head Farms. In addition, community capacity building workshops were conducted through the LEAP Energy Academy to inform the community members of the work of IC2021, and to get their feedback as the work is being done. The workshops allowed IC2021 to familiarize with relevant concepts and terminologies in community energy with focus on Loop Head. Speakers from Scottish communities, whom in the past hosted international class we also invited to share experiences with the Loop Head community. Since this year’s international class is virtual, the workshops also allowed the Flensburg students to interact with the Loop Head community. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 10 1. Background 1.1. Community Energy Planning Community energy provides ‘Power to the people’. (Seyfang et al., 2013) defined community energy as “those projects where communities (of place or interest) exhibit a high degree of ownership and control, as well as benefiting collectively from the outcomes, and we include both supply- and demand-side sustainable energy initiatives”. The idea is simple, communities generate energy in the community to be consumed by the community, but how it is done depends on local and national legislation/ regulations at the time. In Europe, Community energy refers to projects where citizens own or participate, form a legal structure to collectively finance and establish energy projects, to generate electricity, heat from renewable energy projects (Claudia Fruhmann and Nina Knittel, 2016). Renewable energy generated by such projects is collectively sold, to members of community, and local energy utilities, and the profit is split among the participating members. The two approaches of community energy projects are: the bottom-up approach; where citizens establish and own renewable energy projects, and top-down approach; citizens participation is realized through buying shares of renewable energy projects of already established or planned energy projects. The latter approach allows renewable energy projects to be co-owned by citizens, thus facilitating large-scale projects. While there does not exist a magna carta for community energy planning, which has seen diverse processes in different parts of the world, the importance of community involvement is never understated. Findings by (Heaslip et al., 2016) indicate that, despite a lack of a specific implementation framework, the tools and methodologies used by the sustainable energy communities they studied were similar. (Heaslip et al., 2016)’s research studied the sustainable energy journeys of Marstal and Samsø in Denmark and Cloughjordan in Ireland. Their research also found that the Sustainable Energy Communities (SECs) studied were financially motivated, looking to bolster local economy and reduce energy cost. In addition to that (Seyfang et al., 2013) found that saving money on energy bills is the single most cited objective for SECs in the UK. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 11 Other objectives included: reducing carbon emissions, community empowerment, improve local environment and influence sustainability and climate change policy. Information and incentives from larger organizations is also cited by a number of communities studied by (St. Denis and Parker, 2009) in Canada. One of the most important characteristics of community-owned (or co-owned) energy projects is the active participation of citizens in renewable energy generation, thus, significantly increasing the public acceptance of renewable energy production. This is because community-owned renewable energy projects provide co-benefits to the participants by harnessing local natural resources, building social capital, counteracting on fuel poverty and increasing employment in regional level (Claudia Fruhmann and Nina Knittel, 2016). These close ties to the community increase public awareness, decrease local opposition to renewable energy projects and support the transition to low carbon energy. Due to their heavy financial commitments and the relatively low risk-return ratios of Renewable Energy (RE) projects, established energy companies are locked into fossil fuel-based infrastructures (Lowitzsch, 2018). On the other hand, community ownership generally operates with small capacities despite relatively high capital costs per kW of installed power compared to large central plant and does not have to worry much about their quarterly returns as well. This approach is changing energy supply infrastructure in many countries and driving the energy transition with small-scale Renewable Energy Technologies (RETs) (Lowitzsch, 2018). 1.2. Community Energy in Ireland In Ireland, the Energy White Paper launched in 2015 attaches a lot of weight to communities’ contributions to achieving a low carbon energy system by 2050. According to SEAI, there are over 400 communities which are working together in the sustainable energy network (SEAI, 2021c). The common goals of these sustainable energy communities are energy-efficiency, use renewable energy, and consider smart energy solutions. The activities of these communities are related to: • Improving Sustainability and environment by changing energy use through collective effort. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 12 • Reducing energy cost by investing on local renewable energy projects and in long term free up money, that otherwise spent on fossil fuels, to be spent in the community. • Improving the living conditions in houses and community buildings, by controlling how houses are heated, lighting systems. • Bringing the community closer and contribute to goals such as green tourism, eco- congregation, and tidy town activities. The SEC handbook also maps out a framework for communities in Ireland to become Sustainable Energy Communities under the SEC Network, which is designed to enable SECs engage and learn from each other through site visits, seminars, events, and case studies. The framework described in the SEC handbook is a welcomed simplification of the Guidelines for a Sustainable Energy Community, released in 2010. The guidelines outlined the following steps: Source: (Heaslip et al., 2016) Figure 1-1: Summary of SEAI's Guidelines for a Sustainable Energy Community The current SEC Handbook from SEAI simplifies the process to include the following three stages (SEAI, 2018d): i. Learn ii. Plan iii. Do The original guidelines limits community engagement, which aims to involve the community to achieve long-term and sustainable outcomes, processes, decision-making, or implementation. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 13 SEAI recommended community involvement only in the final stages of the planning process and in the implementation (Heaslip et al., 2016). This position is revised in the SEC Handbook, where community involvement is recommended from the very beginning. However, community involvement can be seen to vary in the initial stages of the planning process. With communication between the lay-community and experts usually a challenge, communication in the form of public consultations or meetings were realized in the SECs reviewed in both (Heaslip et al., 2016) and (St. Denis and Parker, 2009). Bomberg and McEwen (2012), emphasize the importance of mobilization for SECs in the UK, noting that, “effective mobilization is a precursor to the energy action discussed in many existing studies of community energy” (Bomberg and McEwen, 2012). 1.3. Community Energy in Loop Head The IC 2020 report has expounded on the community energy planning process in the context of the Loop Head peninsula. In this year’s international class, the LEAP Energy Academy presents an opportunity to consolidate the work of IC 2020. In the aspect of community engagement, the LEAP Energy Academy presents an avenue to inform and familiarize the community with basic topics, concepts, terminologies relevant to community energy. This is discussed in detail in the community engagement chapter. The modelling tool developed in IC 2020, is identified as a major focus for IC 2021, to simplify and improve the IC 2020 model. The improvements made are cognizant of the realities of the Loop Head peninsula, in terms of the community’s objectives, the available renewable energy potential and the applicable SEAI grants. Furthermore, energy efficiency was identified as a major energy related problem in Loop Head and as such received significant attention. Anaerobic digestion was identified by the community members interviewed by IC 2020 as the favoured technology for a community energy scheme. As part of the activities for IC 2021, the use of anaerobic digestors for farming establishments in Loop Head was studied. This study was a part of the research conducted on the concept of circular economy and its potential applications in Loop Head, primarily in the farming sector. With the main deliverables of the IC 2021 being the workshop, model, and report, it is the hope of the students that, the outcome of IC 2021 will kickstart the next phase of the community energy journey of the Loop Head Peninsula. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 14 1.4. Loop Head Community Energy Engagement Study by IC 2020 In the previous year, the International Class of 2020 (IC 2020) acknowledged the importance of community engagement during their trip to the Loop Head community. The pre-feasibility study activities of the IC 2020 understood the challenges, ideas, and future aspirations of the community members on the energy use/sector from the community’s viewpoint. This was crucial to creating an alignment with the community objectives and the objectives of the International class. The findings of the study from the previous year helped the IC 2021 study team to better understand the status of energy sector of the peninsula. It also provided a guidance to drive forward the community energy planning in Loop Head. The IC 2020 conducted two workshops with the community to understand the status-quo of energy consumption, and the community’s potential needs going forward. The workshop had identified that the major energy-related problem in the Loop Head community was with energy efficiency and insulation in residential buildings. This was due to many of the old buildings, which have poor insulations and were hard to retrofit. The farms were facing difficulty to manage slurry, especially during the wintertime. The community also faced a problem with public transport facilities and lacks access to national gas grid. Currently, in the Loop Head area, Clare Accessible Transport (CAT) operates only 2 days a week on Wednesdays and Fridays (Clarebus, 2021). The nearest injection point is located in Ennis, 55 km from Kilkee, which requires a very high investment cost (IC, 2020). Also, they were unaware of different grants for different agencies and were concerned about the potential environmental impacts of energy projects. To this, the workshop had identified energy generation from waste or slurry as a potential solution. The community had been interested in upgrading the heating system in old buildings, developing small scale solar and wind farms, conducting a pre-feasibility study on an Anaerobic Digestor for energy generation and optimizing energy to reduce CO2 emissions by retrofitting old buildings. However, there were some challenges in the community to implement energy-related projects. One of the major issues identified was the lack of knowledge of best practice in energy efficiency and energy project. The community also lacked expertise in the financial and technical management of the project. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 15 During these workshops, it was also identified that it is necessary to have active participation of the community member to develop a successful energy project. For this to happen, the community members should be able to understand various energy technologies and terminologies so that they understand the activities being carried out. A better understanding of the technology, the background of the technologies and the interaction of individual technologies with the energy system can enable the community members to participate in community energy projects. This will encourage community members to have a discussion on the energy-related topic and provide feedback, which is important to develop a community energy project. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 16 2. Objectives As direct interaction with the community during this year’s study (IC2021) was limited, the objectives for the study were defined based on online interaction with the community and learnings from the IC2020 report. • Support the community to understand technical aspects and underlying economics of renewable energy and community opportunities via community engagement. • Identify/explore the possible area, which addresses community’s interest for achieving energy sustainability in the peninsula. • Analyse possible solutions to achieve energy sustainability in the peninsula. • Develop an interactive energy modelling tool which empowers the community to initiate a qualified dialogue on energy transition. 3. Scope of the Work The scope of IC 2021 covers the identification of measures for achieving building efficiency in the residential sectors of Loop Head along with designing of PV systems for the residential sector. The study also intends to analyse the possibility of implementing circular economy in the farming sector via integration of energy efficient technologies and self-renewable energy generation like PV system and anaerobic digesters. Besides, the study aims to interact with the community to obtain inputs to design an overall community energy modelling tool. The development of this tool will integrate the details of residential and farming sector along with the commercial sector in the peninsula. Finally, IC 2021 will conduct interactive workshop and training sessions to enhance the capacity of Loop Head community in energy sector. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 17 4. Residential Sector 4.1. Energy Efficiency in Residential Sectors 4.1.1. Background This chapter discusses the potential factors that affect energy consumption in residential sector in Loop Head peninsula of Ireland. One fourth of electricity and heat demand in Ireland belongs to residential sector (SEAI, 2020c). Meanwhile, around 80 percent of energy supply source for residential sector has been produced from fossil fuel which has been responsible for nearly one fourth of CO2 emissions (SEAI, 2018a). However, Ireland supports the European Union targets towards the reduction of CO2 emission from non- industrial sectors by 20% by 2020 and increase energy efficiency of the economy by 20% as well (Government of Ireland, 2020). In this view, the residential sector can contribute to achievement of these targets significantly. As stated in (Gianluca Trotta, 2018) energy efficiency improvement is an one of the cost-effective way to decrease the CO2 emission, that can protect environment, enhance economic growth and unleash investment opportunities. This chapter discusses the potential factors that affect energy consumption in residential sector in Loop Head peninsula of Ireland. Various institutional arrangements, relevant policies and financial schemes have been initiated to enable energy efficiency across key sectors of economy in Ireland. For instance, to promote Building Energy Rate (BER) improvement in Ireland, SEAI initiated the Better Energy Homes (BEH) scheme which provided grants to support houseowners to improve energy efficiency in terms of building elements insulation and upgrading heating system such as a heat pumps installation (SEAI, 2019). Hence, there are good opportunities for residential sector to not only reduce the dependency on fossil fuels but bring social and economic benefits for communities. Various institutional arrangements, relevant policies and financial schemes have been initiated to enable energy efficiency across key sectors of economy in Ireland. For instance, to promote Building Energy Rate (BER) improvement in Ireland, SEAI initiated the Better Energy Homes (BEH) scheme which provided grants to support houseowners to improve energy efficiency in terms of building elements insulation and upgrading heating system such as a heat pumps University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 18 installation (SEAI, 2019). Hence, there are good opportunities for residential sector to not only reduce the dependency on fossil fuels but bring social and economic benefits for communities. 4.1.2. Residential Sector Heat Demand To produce a local statistical database for the composition of building mass, data on population and households was derived and summarized from administrative statistical database (Central Statistic office, 2016). As per Central Statistics Office (CSO) in Ireland, in Loop Head there are 22 small areas under 10 electoral divisions. The estimated total population amounted to 2893 which also includes Einagh, recently added tenth electoral division (Central Statistic office, 2016). As depicted in Figure 4-1, 52% of all houses (1680 houses) in Loop Head accounted for unoccupied holiday houses, while 38% (1212 houses) are occupied houses. Source: Author elaboration based on (Central Statistic office, 2016), (SEAI, 2018c) Figure 4-1:Share of Dwellings by Occupancy Status in Loop Head in 2016 According to the interactive maps data from Heat Demand Map (SEAI, 2018c), total heat demand for each small area is used in order to identify monthly values of heating demand for space and water. The conversion from annual values to monthly values is made based on heating per capita parameters and weather data on heating degree days (HDD) in Ireland. Total heating demand and University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 19 water heating demand in residential sector for whole Ireland are 31.4 TWh and 5.9 TWh respectively (SEAI, 2018a). By dividing by the total population of Ireland, water heating demand per capita was calculated. Then annual water heating demand for each small area was calculated as water heating demand per capita multiplied to the total population of people of each small area. Space heating demand for each small was found by subtracting water heating demand from total heat demand, which was derived from Heat Demand Maps (SEAI, 2018c). Then, space heating demand per capita was divided to total heating degree days, which is 1871 in 2019 (Degree Days, 2021). Attributing space heating demand per capita/HDD for each month respective heating degree days and dividing annual water heating demand to 12, monthly values of heat demand which is the sum of water heating demand and space heating demand for each area were calculated. The Figure 4-2. shows that changes in heat consumption are in line with changes in heating degree days. Source: (Author elaboration based on (SEAI, 2018c), (Central Statistic office, 2016)) Figure 4-2:HDD and Monthly Heat Demand for Kilkee (code: 037077001/037077002/037077003) As seen from the Figure 4-3, total heat demand in December across all districts are in the order of 100 MWh to 380 MWh, while in July the heat consumption is nearly in the range between 50 0 50 100 150 200 250 300 1 2 3 4 5 6 7 8 9 10 11 12 M W h Months HDD and Monthly Head Demand for Kilkee Mothly total heat demand, MWh Monthly HDD University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 20 MWh. Kilkee is the district with highest heat consumption in Loop Head with heat demand in December stands at 1254 MWh. Source: Author elaboration based on (SEAI, 2018c), (Central Statistic office, 2016). Figure 4-3:Total Heat Demand in Cold and Hot Season by District in 2016, MWh. Estimated total heat consumption for Loop Head is 18,733 MWh/year, out of which 15,151 MWh/year heat consumption goes to space heating, and 3,582 MWh/year is consumed for water heating. Average heat consumption per household for space and water heating are depicted Figure 4-4, assuming that fuel technology are the same across all districts. 0 200 400 600 800 1000 1200 1400 M W h Districts Total heat demand in cold and hot season in Loop Head Total heating demand in December, MWh Total heating demand in July, MWh University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 21 Source: Author elaboration based on (SEAI, 2018c), (Central Statistic office, 2016) Figure 4-4:The Average Total, Space, Water Heat Demand per Household. 4.1.3. Housing Stock Characteristics The housing stock characteristics of the residential sector in Loop Head were classified according to their relevance to understanding of energy use and monitoring energy efficiency measures effect. The following eight dwelling age bands were distinguished with corresponding type of dwelling, envelope material and total number of households that belong to each age band. In 2016 the highest share which is 22% share of housing stock represents buildings constructed after 2005, and second largest share of houses, 16% represents old houses built before 1919 (Central Statistic office, 2016). According to the Table 4-1 the most common type of building which can be found in each age band category is detached house. The most common materials of envelope are cavity walls and timber frame. Table 4-1. The Classification of the Building Characteristics in Loop Head Household period of construction Type of Building Building Envelope Total number of 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 1 2 3 4 5 6 7 8 9 10 11 12 M W h /H H Months Average Heat Demand per Household Total heat demand per household, MWh/hh Space heating demand per household, MWh/hh Water heating demand per household, MWh/hh University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 22 occupied Households Pre 1919 Detached House, End of terrace house, Terraced house, Bungalow stone walls, solid brick walls 188 1930-1949 Detached bungalow, Terraced house mass concrete 133 1950-1966 Bungalow, Semidetached, Terraced house, Apartment hollow block, solid brick & concrete walls 89 1967-1977 Detached Bungalow, End of terrace house cavity walls 96 1978-1982 Detached house, Terraced house cavity walls, Hollow block 144 1983-1993 Detached bungalow, Semidetached house, Terraced house cavity walls, Hollow block 117 1994-2004 Detached bungalow, Terraced house, End of terrace house cavity walls, Timber frame 170 2005-2010 Detached bungalow, Semidetached house, Terraced house cavity walls, Timber frame 262 Source: (TABULA, 2014) and (Central Statistic office, 2016) 4.1.4. Energy Efficiency Potential in Buildings Energy efficiency in building can be gained through various options including refurbishment, advanced heating system and renewable technologies and heating/cooling control system. One of the cost-efficient ways to improve the energy performance in existing houses may be to apply retrofitting measures. In this project, a model for evaluating BER rate improvement measures was built. As a part of this model, a potential energy savings cost curve was developed based on the original data of (TABULA, 2014). The aim of this curve is to inform users and help to compare costs associated with certain amount of intended energy savings in building stock. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 23 For the analysis of the effect of retrofitting measures, data on housing stock characteristics in Ireland from European building typology Tabula database was used. Eight representative buildings, all of which are detached houses, were selected for each period of construction, assuming that they have the same gross floor area. The retrofitting measures at advanced level with associated costs of investments were compiled for each house. Primary energy demand before and after refurbishment that were further converted to useful energy, was used to quantify the energy savings related to each saving option. It was assumed that all building under each age category have the same energy demand, the same heating technology and belong to the same building type. Based on total energy savings per area of building surface and total investment cost of refurbishment activity, the cumulative cost of the last retrofitting measure and corresponding energy savings in MWh per year for one house were calculated. Figure 4-5: Cumulative Energy Cost Curve Looking at the Figure 4-5, it could be inferred that to reach the heat savings at 30% of energy demand in housing stock in Loop Head, total investment costs at 6 million Euro would be needed. Potential savings of 35 and up to 42% of energy demand are observed with relatively high costs of retrofitting measures. This could be explained by lower energy demand before introducing retrofitting. 0 2 4 6 8 10 12 14 16 0% 10% 20% 30% 40% 50% C u m u la ti ve c o st , m ill io n E u ro Heat Savings University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 24 4.1.5. Case Study of Energy Refurbishment for a Selected Building Type This study aims to describe one representative house with relevant physical characteristics to analyse the effect of energy efficient measures on heat consumption. It is important to note, that due to lack of data and access to real existing houses, in-depth analysis of thermal and energy performance was deemed infeasible. However, based on data (TABULA, 2014), house characteristics and cost of retrofitting measures was used for this study. The selected house was built before 1919 with no prior deep renovation made and connected to central oil-heating system with 70% efficiency. 4.1.5.1 Pre-refurbishment parameters Table 4-2. Pre-refurbishment Parameters of the Selected House Material Current U- value, W/m2 Surface Area, m2 Current heat losses, kWh/m² Wall Solid stone 2.1 80 168 Roof Pitched, insulation between joists 0.68 100 68 Windows Single glazed, wooden frame 4.8 40 192 Door Solid timber 3.0 20 60 The U value, which expresses how well insulated the material of envelope are, was taken as an input to calculate heat losses as well as floor are of each building components. The values of floor University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 25 areas for each element are rough assumed values based on Tabula database records (TABULA, 2014). To assess the energy efficiency measures and understand the potential savings of cost and fuels, two scenarios were developed. The first scenario sets to analyses application of retrofitting measures and their effect on heat losses. The second scenario was built as a further improvement step for the selected house with introducing new heat technology to replace the oil boiler. 4.1.5.2 Energy efficiency scenario 1- Retrofitting measures Due to a limited data on different types of retrofitting measures of different building components, for the scenario analysis, standard packages of retrofitting activities was selected from (TABULA, 2014). The heat demand before retrofitting measures amounted to 42.2 MWh/ m2/y. It was assumed that the roof would be insulated by an additional layer of 200 mm mineral wool, while walls would be insulated by applying approximately 120 mm of external insulation which brings the U-value of roof and wall to 0.13 and 0.27, respectively. Door and windows would be replaced to more efficient ones with double-glazed frame and sufficiently insulated doors. Based on these considerations, heat losses after implementing all refurbishment activities were amounted to 26.2 MWh. Consequently, energy demand was reduced to 160 kWh/m2/y that lies under the range of energy consumption of BER rate C1. Investments costs of retrofitting measures, corresponding energy savings and reduced heat losses are compiled in Table 4-3. Table 4-3. Investments Cost and Related Energy/Cost Savings for 1 Scenario Investment costs, Euro Reduced heat losses, kWh Energy savings, kWh/m2/y Energy savings, % Investment cost of savings, Euro/kWh/y Roof insulation 1,322 3,528 35.3 8 0.37 Wall + roof insulation 30,886 13,535 135.3 40 2.85 Wall + roof insulation + 36,941 26,236 262.4 52 3.26 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 26 Window and Door 4.1.5.3 Energy efficiency scenario 2 - Retrofitting measures and replacing oil-boiler with more efficient heating technology. Online workshops, open discussions and results of interview through online survey questionnaire during the Training program showed a high interest of community members about new heating technology that can reduce oil consumption for existing heating system and reduce energy bills (see Appendix A). The growing interest also can be supported by SEAI grants to refund a certain amount of investment cost. One of the requirements of eligibility for a grant is low heat losses (SEAI, 2020b). This can be achieved through well insulated building envelope and renovated windows and doors. Hence, this scenario is based on the result of 1st scenario when all retrofitting measures are implemented. As alternative options for old oil heating system three types of heat technologies were analysed. Relevant calculations of investment cost, operation and maintenance costs, fuel costs, fuel saving are compiled in excel file. The technical characteristic on efficiency, nominal capacities and coefficient of performance were derived from (Danish Energy Agency, 2016). The investment cost, operation and maintenance costs were obtained through (European Comission, 2017). Fuel prices for oil, electricity and wood pellets have been derived from (SEAI, 2021a). CO2 emission intensity factors for each type of fuel have used from (SEAI, 2016b). The Table 4-4 below contains the results of calculations of costs, CO2 emissions and fuel savings. Table 4-4. Financial and Energy Calculations for Heating Technologies for Selected House Oil boiler (current system) Heat pump air-to air Heat pump air-to water Biomass boiler CAPEX, Euro 11,790 26,270 5230 OPEX, Euro/kW/year 28.26 117.9 52.54 335 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 27 Fuels costs, Euro/year 4117 876 1051 1442 Secondary energy demand after retrofitting, kWh/year 16,054 16,054 16,054 16,054 CO2 emissions, tCO2/kWh 4.2 1.8 2.1 0.2 Grants 600 3,500 Cost savings, Euro/y 3,241 3,065 2,675 Total investment (1+2 scenario) 43,075 57,555 36,515 All three heating technologies, particularly heat pumps air-to air, heat pump air-to water and biomass boiler have lower CO2 emission value and higher energy savings in comparison with oil boiler system. The cheapest option seems to be biomass boiler with 5,230 Euro of investment cost. Also, grant for installation of heat pump air-to water and heat pump air-to air are available from SEAI (SEAI, 2020b). 4.1.5.4 Case study results This case study can help in bringing understanding of energy efficiency measures in buildings, their relevance to energy consumption and household expenditures on fuels. It can add strength to the existing knowledge about insulation of building envelope, BER rates and range of available heating technologies with efficient operation characteristics. First scenario showed that the total investment cost for the whole package of retrofitting measures is 36,941 Euro. The initial heat demand was amounted to 42.2 MWh/y. After insulating roof, and replacing windows and doors, total heat demand was decreased to 16 MWh/y. The second scenario entails combination of retrofitting measures and replacing of existing oil heating system with heat pumps. Considering the lower investment cost in comparison with heat pump air-to air, it is worth to consider wood pellets boiler with investment cost 5,230 Euro and cost savings on fuel amounted to 2,675 Euro in a year. The next cheaper option is heat pump air-to air with investment cost 11,790 Euro and University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 28 reduced expenditure on oil at 3241 Euro in a year. Considering the 1st scenario, total investment cost would be 43,075 Euro for implementing retrofitting measures and installation of heat pump air-to air. Considering that this technology uses clean way of producing energy, the scenario with this heating system has huge potential to reduce household expenditures and contribute to sustainable development of community. 4.1.6 Conclusion Based on the analysis of energy demand, marginal energy cost curve and results of simulation of scenarios for one selected house several conclusions were drawn. The energy demand, particularly heat demand in residential sector, are determined by weather conditions, winter/summer season temperature patterns, housing stock parameters such as age of building, number of buildings. The heat savings in buildings and associated costs are closely linked with building envelope material and level of insulation. Moreover, replacing oil boilers with more efficient clean heating technologies can reduce household’s expenditures on fuel and their carbon footprint. 4.2. PV System Design for Residential Buildings 4.2.1. Residential Demand To calculate the hourly residential demand, the average demand of a household in Ireland was extracted from (Ricardo, 2020). From this source, a weekday and a weekend day demand profile per season were taken. The total energy consumption obtained does not match the average electricity consumption in Loop Head calculated in (Students EUF, 2020), for this reason, the values were scaled, to sum up, a total of 4245kWh/household/year. This value is the result of dividing the total energy consumption in the residential sector (EUF Students, 2020) by the total number of occupied households (EUF Students, 2020). The final values for the residential demand can be observed in Table 4-5. To calculate the hourly residential demand, the average demand of a household in Ireland was extracted from (Ricardo, 2020). University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 29 Table 4-5: Values for Residential Demand Profile Hour Autumn weekday hourly demand (kW) Autumn weekend hourly demand (kW) Winter weekday hourly demand (kW) Winter weekend hourly demand (kW) Spring weekday hourly demand (kW) Spring weekend hourly demand (kW) Summer weekday hourly demand (kW) Summer weekend hourly demand (kW) 0 0.385 0.435 0.460 0.522 0.404 0.438 0.379 0.416 1 0.280 0.329 0.329 0.385 0.292 0.345 0.273 0.317 2 0.242 0.267 0.280 0.311 0.255 0.283 0.242 0.261 3 0.230 0.242 0.261 0.273 0.236 0.249 0.230 0.239 4 0.230 0.236 0.249 0.261 0.236 0.236 0.224 0.236 5 0.236 0.236 0.267 0.264 0.236 0.245 0.230 0.236 6 0.292 0.261 0.342 0.298 0.298 0.261 0.280 0.252 7 0.479 0.342 0.565 0.401 0.479 0.354 0.447 0.339 8 0.615 0.479 0.777 0.556 0.603 0.491 0.541 0.447 9 0.541 0.584 0.677 0.696 0.541 0.590 0.503 0.547 10 0.510 0.640 0.615 0.758 0.516 0.643 0.479 0.600 11 0.479 0.634 0.590 0.795 0.479 0.668 0.460 0.612 12 0.497 0.677 0.597 0.826 0.497 0.730 0.472 0.656 13 0.491 0.684 0.597 0.833 0.497 0.690 0.460 0.640 14 0.454 0.584 0.553 0.736 0.454 0.584 0.429 0.559 15 0.454 0.547 0.578 0.724 0.460 0.559 0.435 0.531 16 0.528 0.578 0.727 0.795 0.541 0.590 0.516 0.544 17 0.721 0.671 1.038 0.997 0.708 0.674 0.671 0.609 18 0.783 0.746 1.143 1.119 0.727 0.736 0.677 0.637 19 0.851 0.858 1.112 1.109 0.708 0.715 0.634 0.628 20 0.851 0.851 1.019 1.028 0.733 0.746 0.590 0.593 21 0.826 0.802 0.963 0.938 0.814 0.805 0.621 0.597 22 0.746 0.721 0.864 0.845 0.777 0.743 0.671 0.646 23 0.578 0.597 0.671 0.687 0.609 0.615 0.559 0.569 Total 12.297 12.999 15.273 16.159 12.098 12.990 11.023 11.709 (Source: Author) University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 30 As expected, winter is the season with the highest electricity consumption, while summer is the one with the lowest demand. Regarding the daily profile demand, in general, in all the seasons there are two daily peaks at 9 am and 7 pm approximately, where the second one is the highest. This information will be highly relevant for households with rooftop solar systems, where there are different energy tariffs for energy sold and energy consumed to/from the main electric network. 4.2.2. Roof Top Solar for Residential Sector An interactive excel based model was developed by the team which can estimate the solar roof top potential of the farm and optimize the size of the system as per need. The tool permits the sizing of PV roof top system both for standalone residents and farms with residents. The details on the tool is provided under the farming sector in Chapter 5.2.2. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 31 5 Farming Sector 5.1 Circular Economy in Farms Agriculture is a major economic activity in Loop Head. As per the interview’s conducted by IC 2020 and virtual interactions with the local farmers during this study, dairy and beef farms were identified as centre of the farming sector in the peninsula. However, both sectors, more specifically the dairy farms are highly energy intensive with most of their energy demands met from the burning of fossil fuels. Though the farms are working towards reducing their carbon footprint and implementing sustainable operational practice, the linear make and waste system in these sectors may no longer be feasible considering the limitations in the natural resources availability. Even though no specific concerns were expressed by the farmers regarding their linear operation model, a transition towards a more circular approach for these farms in Loop Head could provide opportunity for long term economic, social, and environmental benefit. Therefore, IC 2020 has explored possible interventions to ensure a shift of the sector towards a vision of more circular operational model. Source: (Circle Economy, 2016) Figure 5-1: Circular Dairy Vision The Figure 5-1 extracted from (Circle Economy, 2016), provides a visual representation of a circular operating model in a dairy farm which is envisioned by this study. The Circle Economy University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 32 report focused on implementing dairy circularity defines it as “the approach to decouple economic growth from the consumption of natural resources, to achieve regenerative net positive impact” (Circle Economy, 2016). This study is thus a catalyst towards accomplishing this vision for dairy farms in Loop Head. All the environmental aspects of circularity detailed by the Dairy Sustainability framework (Circle Economy, 2016)are covered by the following targets for Loop Head farms. • Zero or negative GHG emissions • Closed soil nutrient cycles. • Zero waste generation • Water recovery and reuse • Soil and Land preservation • Biodiversity in the business model As an approach to achieve circularity in Loop Head farms, this study will focus on three aspects: making the farms in Loop Head more energy efficient, energy self-sufficient and have sustainable waste management. These working areas were identified based on the study conducted the previous year (IC 2020) and by understanding the needs and priorities of the local farmers through virtual interactions over the period of the study. Though these three areas do not fully cover the aspects of a circular economy, these are a starting point for the farms towards the transition. The open field disposal of cattle manure in farmlands for grass cultivation for grazing is a common practice in Loop Head. This provides a degree of soil nutrition recirculation but also result in emission of large amount of methane into the atmosphere. About 0.29 tons per annum of CO2 equivalent is estimated from a cattle (Blades et al., 2017). Considering the farm size in Loop Head vary from 25 to over 300 cattle, substantial emissions are expected from each farm. Also, the soil nutrient leaks, wastewater, and large volumes of unused agro by-product account for a waste of valuable resources in the farms. The implementation of circularity thus allows the mitigation of these emissions and adds value to the unused waste providing additional revenue source for the farm. In addition to the micro economic benefits the transition of the farm towards a circular University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 33 business model contributes towards the Irish vision of long-term low carbon energy systems (SEAI, 2020a). As mentioned above, this study has identified the three-working areas for initiating circularity in dairy farms. Energy efficiency is an important point of departure for circularity with an aim of decreasing the difference between the energy used and total energy consumed. The thermal energy requirement of farms is identified as potential immediate points of intervention for improvement along with the introduction of newer technologies are a possibility. With regards to self-sufficiency in energy/electricity generation, the possibility of installation of PV system has been identified as potential technology considering high local interest. Also, numerous success stories in individual solar systems in Loop Head and expected implementation of new feed-in tariffs in the coming years has made the technology a point of high interest. Further, survey results conducted at the beginning of the study verified high interest in the technology with many respondents showing willingness to invest in solar technology amongst the farmers. The final issues regarding the management of waste in farms is planned to be addressed with the studying the feasibility of anaerobic digestion technology for individual farms. National Irish studies show the storage of 81-83% of the manure in the form of slurry (Buckley, 2020). As a result, the emission of methane is quite high from the sector. Capturing the methane, while extracting energy from this waste source and also using the nutrient in the waste to restore nutrient cycle in the farm is an approach undertaken by the study. For both PV systems and anaerobic digestion systems, an interactive stand-alone tool was developed during the study, which allows the farmer to size and estimate the energy potential and financial feasibility of the technology in their respective farms. The working of the tool and findings over the study period are discussed in detail in the following sections. Also, recommendations on an individual scale towards promotion of circularity in the dairy farms of Loop Head have been presented. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 34 Thus, over the study period the team worked on understanding the feasibility and recommending possible avenues for the farming sector in Loop Head to transition towards circularity and as a result towards sustainability. 5.2 Implementing Circular Economy in Loop Head Farms The study was initiated with a general literature review to better understand the present farming sector in Loop Head and for better understanding of the technology planned to be studied and implemented in Loop Head for circularity. Though secondary literatures were referred to for data gathering, the level of interaction with the community were kept at the maximum throughout the study to get a better understanding on ground situation and get a better perspective on their motivation towards circularity. Also, during the study period, a workshop was carried out with the community to present the team’s view and objectives of the study. 5.2.1 Energy Auditing Benchmarking is particularly relevant as starting point of an energy audit since it allows estimating the potential savings that might be expected after conducting a complete energy audit. Therefore, the International Class team has conducted research on the different types of farms in Loop Head to find the average electricity consumption per livestock for each type of farm. By doing that we aim to provide to the farm owners the parameters that allow them to compare their electricity consumption with the average values of the community. A more detailed analysis is required for each individual farm to analyse where the electricity is consumed and provide tailored energy conservation measures for each farm. A detailed questionnaire (Appendix C) was developed to evaluate the appliances that consume more electricity in dairy farms and give recommendations to the farmers; however, due to time limitations this survey could not be conducted. This questionnaire can be useful for the self- assessment of the farmers as well as future studies. Nevertheless, general recommendations from literature to the dairy sector are relevant to mention to provide insight to the Loop Head farmers and encourage them to analyse in detail how the electricity is consumed in their farm. (John Upton et al., 2010) mentions key opportunities for reducing energy consumption which include: University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 35 • Eliminate energy wastage; fix all hot water leaks, insulate all hot water piping and refrigerant gas piping and use lights only when necessary. A leak as small as one litre per hour can waste 8,500 litres of hot water and 3,800 kWh per year. • Optimize plate cooling by increasing water flow to achieve the correct water to milk flow ratios. Increasing the milk to water flow ratios from 1:1 to 1:3 can reduce power consumed by the bulk tank by over 40%. • Consider using a variable speed drive controller on vacuum pumps. This can save over 60% on vacuum pump running costs. • Use energy efficient lighting. Electricity and Diesel Consumption on Farms With the objective of find information on the electricity and diesel consumption in the farms in Loop Head Peninsula, we have surveyed through an online questionnaire asking information about the type of farm, number of livestock, electricity, and diesel consumption. Some of the farmers are living on the farm and the electricity consumption provided in the interview includes both farm and house consumption. To analyse those cases in detail, we asked in the interviews if the electricity consumption includes house consumption or whether it is exclusively for the farm. In those cases where the values include the house consumption, we have deducted 350 kWh/month that is considered in this report as the average electricity consumption for a household in Loop Head. The online questionnaire submitted to the farmers of the community has been responded by seven farm owners. Additionally, to have as much information as possible, the results of the interviews with the farmers in the IC 2020 were also taken into consideration. A total number of thirteen farms were analysed, four of them are beef producers and nine are dairy farms. Beef production farms consume significantly less electricity than dairy farms per livestock unit, that is because beef production farms have no high energy-intensive appliances and most of the electricity consumption is due to lighting and some other minor consumptions such as electric University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 36 fences. Based on the interviews, the average electricity consumption for the beef production sector is 5.61 kWh/cattle/month. Dairy farms have a higher electricity consumption per cow due to electrical appliances required for milk production. In contrast to the beef production farms, according to (John Upton et al., 2010) lighting in dairy farms consumes only 10% of the electricity while milk cooling is the largest consumer of electricity (37%), followed by water heating (31%) and vacuum pumps (19%). Other items such as wash pumps, milk pumps, feed augers, and air compressors make up the balance (3%). Based on the information collected from the interviews, the average electricity consumption for the dairy sector is 17.44 kWh/cow/month. However, as we identified in the results a significant difference between the values for large dairy farms (more than 100 cows) and small dairy farms (100 cows or less), we have clustered the farms in those two groups and calculated the consequent average. Taking into consideration the farm size, the electricity consumption for large dairy farms is 14.09 kWh/cow/month and for small dairy farms, it is 29.19 kWh/cow/month. Those values are in accordance with literature parameters, according to (John Upton et al., 2010) electricity consumption per dairy cow milked, the figures vary from 4 kWh/cow/week to 7.3 kWh/cow/week. To obtain the monthly consumption, those values are multiplied by 52 and divided by 12, consequently, we obtain a range between 17.33 kWh/cow/month to 31.63 kWh/cow/month. The results of the interviews performed during International Class 2020 and 2021 are summarized in the table below. Table 5-1: Results of interviews on electricity demand Identification Farm Type Livestock Electricity consumption kWh/month kWh/ Livestock/Month Farm 1 Beef farm 80 580 7.25 Farm 2 Beef farm 60 100 1.67 Farm 3 Beef farm 25 70 2.80 Farm 4 Beef farm 320 3429 0.72 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 37 Farm 5 Small dairy farm 85 3422 40.26 Farm 6 Small dairy farm 80 1450 18.13 Farm 7 Large dairy farm 180 4115 22.86 Farm 8 Large dairy farm 110 1650 15.00 Farm 9 Large dairy farm 162 2058 12.70 Farm 10 Large dairy farm 102 1500 14.71 Farm 11 Large dairy farm 120 1189 9.91 Farm 12 Large dairy farm 180 2632 14.62 Farm 13 Large dairy farm 102 900 8.82 Regarding the diesel consumption, as there were no significant differences because of the farm size, therefore, it was not taken into consideration for the calculation of the averages. For the beef production sector, the average monthly diesel consumption per cattle is 3.77 litre/cattle/month. According to the interviews dairy sector has a lower diesel consumption with 3.27 litre/cow/month. The results of the interviews are listed in the table below. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 38 Table 5-2: Results of interviews on diesel demand Identification Farm Type Livestock Diesel consumption litre/month Litre/Livestock/Mont h Farm 1 Beef farm 80 200 2.50 Farm 2 Beef farm 60 125 2.08 Farm 3 Beef farm 25 100 4.00 Farm 4 Beef farm 320 2083 6.51 Farm 5 Small dairy farm 85 417 4.90 Farm 6 Small dairy farm 80 150 1.88 Farm 7 Large dairy farm 180 83 0.46 Farm 8 Large dairy farm 110 400 3.64 Farm 9 Large dairy farm 162 167 1.03 Farm 10 Large dairy farm 102 533 5.23 Farm 11 Large dairy farm 120 550 4.58 Farm 12 Large dairy farm 180 500 2.78 Farm 13 Large dairy farm 102 500 4.90 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 39 5.2.2 PV System design for Farms As an approach to promote the community use of PV systems, this study aims to assist Loop Head farmers to identify suitable PV system size for their farms and understand its financial viability. The analysis was, therefore, developed considering the possible factors that people could find useful to make these decisions. The activities were divided into three different stages: i. Stage I: Definition of expected outputs: During this stage, all the possible information that could help in the decision-making process was proposed, analysed, and selected. In the end, some economic, technical, and environmental factors were selected. ii. Stage II: Collection of required inputs Once the required outputs were defined, the process of data collection started. Each one of the outputs requires different inputs to be calculated and all of them were listed and searched. During this stage, specific data from the Peninsula was the priority. For this, the previous report on the field (Students EUF, 2020) was consulted and surveys were designed, applied, and analysed. When it was not possible to find specific data from Loop Head, national data were used like in the case of prices for different configurations of solar rooftop systems (Government of Ireland, 2019). iii. Stage III: Calculation development With all the required information collected, in the last stage all the outputs defined in the first stage were calculated. Microsoft Excel was the selected software to develop the Solar Calculator, due to its high flexibility and wide range of uses. This tool was designed to be used at the individual level to provide financial and technical information about a possible investment in the mentioned technology. Dairy farms demand The farms dedicated to milk production have an electricity generation that depends directly on the number of cows. From the interviews, the next functions were calculated: When the number of cows (C) is more than 100: University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 40 Weekly electricity consumption = C * 14.09 kWh/cow When the number of cows (C) is less or equal to 100: Weekly electricity consumption = C * 29.19 kWh/cow Once the amount of energy is calculated, how that energy is consumed during the day can be presented in hourly resolution. For this purpose, the shape is shown in Figure 5-2 (J. Upton et.al, 2013) is used. This shape shows the measurements of energy consumption for 22 dairy farms for two consecutive days in Ireland (J. Upton et.al, 2013) . Source: (J. Upton et.al, 2013) Figure 5-2: Example of electricity consumption in Irish Farms According to (J. Upton et.al, 2013), the highest demand in a dairy farm is milk cooling (30-40% of the total consumption). The results make sense since it is known that milk is extracted twice per day: at 8 am and 4 pm and the two peaks of this load are present in the used shape. Cattle farms demand To calculate the hourly load profile of cattle farms, a similar procedure to the one applied to dairy farms was performed. From the surveys, it was defined that on average each cow/bull represents University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 41 an energy consumption of 5.61 kWh/week. No differences were found between big and small farms. Regarding the hourly profile, it was assumed that the load is constant in the period between 6 am and 6 pm every day, when someone is present on the farm developing daily activities related to the business. In conclusion, the calculated energy consumption is divided every day into 12 hours. Total electricity consumption farming sector Current estimations according to (Central Statistics Office, 2020) affirm that the livestock for dairy and cattle is 11,271 and 7,675, respectively. The 7,675 cows for dairy purposes were divided into two groups: 3,698 in large farms and 3,959 in small farms. Knowing the total amounts, the hourly load demand was calculated using the characteristics demands per sector developed in the previous sections of this report. The result can be observed in Figure 5-3. This demand profile is used for community-level calculations. (Source: Author) Figure 5-3: Total Hourly Demand Farming Sector. Analysing Farm Level Solar PV systems Solar PV systems are called to be the largest source of microgeneration in Ireland (Ricardo, 2020). The current investment costs allow this technology to be financially interesting for household University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 42 owners due to the potential savings that it can generate. Additionally, the Ireland government supports this technology as part of the Climate Action Plan 2019 and currently offers grants for new investors. In July 2021 is expected to be available the feed-in tariff (Government of Ireland, 2019), which will allow the owners of microgeneration to sell all their surplus of energy to the gird. Currently, that energy was just taken by the main grid without receiving any payment. The new policies will encourage more people to install rooftop systems, and those people are going to require information that is more specific. For instance, investment and annual savings are very important to make a final decision regarding this kind of investment. In order to support a well-informed decision-making process, a technical and financial model was developed for the Loop Head community at the individual level. This model will be called in this report Solar Calculator. Households and farmers owners can use this model regardless of their background or expertise on energy topics, since the required inputs are parameters that every person knows or can find easily. The interface and questions can be observed in Error! Reference s ource not found.. (Source: Author) University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 43 Figure 5-4: Inputs rooftop solar systems tool. The inputs can be divided into three categories: in the first one is the values used to calculate the electricity demand based on the demand profiles calculated at the beginning of this chapter. For this purpose, the monthly energy consumption is used, if it is a household or a farm with a household, beef livestock, and dairy livestock. In the second category are the inputs used to calculate financial parameters: price per kWh and monthly energy consumption. Finally, in the third category are the inputs that are intended for the user to change and find the most optimal system size, based on the comparison of the Solar Calculator and their resources and expectations. The tool can provide very specific and understandable financial and technical results based on the next main assumptions: Inflation: The inflation in Ireland has been in a range of 5% to -1.8% in the period between 1985 and 2020 (Statista, 2021a). This parameter has a big impact on financial calculations performed in the present report and should be carefully selected. In Figure 5-5 can be seen the historical values of inflation of the mentioned period. Additionally, the projections for the next 3 years (2021, 2022, and 2023) are available in the same figure. Based on this information, all the calculations performed will use constant inflation of 2% per year. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 44 Source: (Statista, 2021a) Figure 5-5: Inflation in Ireland. Electricity rates: In the last 10 years, the price of electricity for a household in Ireland has increased 33% approximately (Statista, 2021b). High values of electricity are an important motivation to invest in renewable energies and consider energy efficiency measures. Furthermore, expectations about the future prices of electricity are highly relevant when calculating financial models during the investment decision-making processes. In Figure 5-6 can be observed the changes in electricity rates in Ireland in the period between 2010 and 2020. It is possible to identify that there was almost a constant increment in the prices between 2010 and 2014, but between 2014 and 2020 the price has been around 0.24 EUR/kWh. Based on this, the electricity rate is going to be considered to increase only due to the inflation in the next years. -3% -2% -1% 0% 1% 2% 3% 4% 5% 6% 1 9 8 5 1 9 8 7 1 9 8 9 1 9 9 1 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1 2 0 0 3 2 0 0 5 2 0 0 7 2 0 0 9 2 0 1 1 2 0 1 3 2 0 1 5 2 0 1 7 2 0 1 9 2 0 2 1 * 2 0 2 3 * 2 0 2 5 * University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 45 Source: (Statista, 2021b) Figure 5-6: Electricity rated in households Feed-in tariff: At present, the feed-in tariff has not been implemented in Ireland, but according to (Government of Ireland, 2019), it is expected to be implemented in July 2021. In order to perform financial calculations, the average expected values for this tariff presented in the Economic and policy advice to support the design and implementation of the new microgeneration support scheme in Ireland (Ricardo, 2020) report was used. The values from 2031 to 2041 are the result of considering the same tariff from 2030 plus its inflation. The values can be observed in Table 5-3. 15 17 19 21 23 25 27 2 0 1 0 S 1 2 0 1 0 S 2 2 0 1 1 S 1 2 0 1 1 S 2 2 0 1 2 S 1 2 0 1 2 S 2 2 0 1 3 S 1 2 0 1 3 S 2 2 0 1 4 S 1 2 0 1 4 S 2 2 0 1 5 S 1 2 0 1 5 S 2 2 0 1 6 S 1 2 0 1 6 S 2 2 0 1 7 S 1 2 0 1 7 S 2 2 0 1 8 S 1 2 0 1 8 S 2 2 0 1 9 S 1 2 0 1 9 S 2 2 0 2 0 S 1 Eu ro c en ts p er k ilo w h at t - h o u r University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 46 Table 5-3: Feed-In Tariff Used in the Model (Source: Author) Since the motivations for installing a rooftop solar system can be different, the outputs of the Solar Calculator were selected to satisfy the most common questions like total cost, required roof area, payback period, percentage of energy self-supplied among others. The outputs and the interface can be observed in Figure 5-7. (Source: Author) Figure 5-7: Outputs rooftop solar systems too Year Feed in tarif model [EURc/kWh] Year Feed in tarif model [EURc/kWh] 2022 5,94 2032 7,61 2023 6,69 2033 7,76 2024 6,79 2034 7,92 2025 7,65 2035 8,08 2026 7,20 2036 8,24 2027 6,80 2037 8,40 2028 6,87 2038 8,57 2029 7,31 2039 8,74 2030 7,31 2040 8,92 2031 7,46 2041 9,09 Results Investment 9180 Euros Required roof area 42 m² Energy demand self produced 19% Energy bought from the grid 81% Payback period 9,38 Years Internal Rate of Return (IRR) 9,40% Excess Electricity Sold 1046,84 KWh NPV O&M costs 712,24 Levelized Cost of Electricity (LCOE) 0,09 Euros/KWh Total NPV of investment 4431,14 Euros University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 47 For most of the users, the values presented previously could be sufficient, but in favour of clarity extra graphical information is produced. The energy balance for a typical day in January and July is shown (Figure 5-8), giving an idea of the seasonal changes. This energy balance shows when the energy produces by the rooftop solar system is frequently sold, providing useful information regarding demand-side management. Figure 5-8: July - Energy balance Typical weekday and weekend day (Source: Author) Finally, a comparison between the annual electricity bills with and without a PV system is provided (Figure 5-9). For people who are looking for savings in terms of money, the expected investment cost and the yearly savings can be analysed in order to decide if this project reaches their financial expectations. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 48 Figure 5-9: Annual electricity bill value. Source: (Statista, 2021b) In general, based on the LCOE and the expected values for the feed-in tariff, the most beneficial installed capacity in the residential sector for average households is around 3kW, bigger systems would increase the percentage of sold energy. From a financial point of view, this is undesirable because the LCOE is higher than the feed-in tariff. In any case, the use of the tool could lead to a better understanding of every particular case. In the farming sector, it makes no sense to present an average recommended installed capacity, since the energy demand in the farms is in a big range. On the other hand, farms have bigger electricity demand in comparison with the residential sector, and in most cases, bigger roof areas. These two factors create the appropriate conditions to take advantage of bigger rooftop solar systems, but the size of the system should be carefully selected aiming to consume as much as possible the generated energy. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 49 5.2.3 Anaerobic Digester Design for Farms As mentioned, focus of the study is on farm level anaerobic digestion system. The following steps were followed by the team during the international class: • The present waste management practice in the farms were assessed in detail and the view and understanding of anaerobic digestion technologies in the farm were analysed. • A model in excel was developed to calculate different parameters of the biogas production in small scale. More details of the model are explained further in this report. • After the creation of the model three scenarios were selected to make an analysis of the results for small scale biogas. Analysing Farm level biogas systems A major attraction of AD system is the associated multiple revenue streams (World Biogas Association, 2019), the possibility of which was analysed during the course of the study in the farms of Loop Head. For this study, the possibility of three different end uses on a farm level have been analysed which include direct thermal application, electricity generation and biomethane upgradation. However, biomethane upgradation has technical and financial limitations. Gas upgradation technology require considerably higher rates of gas generation and have higher investment and operation costs. Only systems with gas generation of over 100Nm3/hr can be deemed technically feasible for an upgraded biomethane end use (IRENA, 2013). Also, to maintain a financial viability the selection of waste mixture needs to be done such that maximum biogas can be generated from the same digester volume. Small scale digester could be an appropriate technology for the management of waste generated on a farm. Thus, this study focuses on the possibility of intervention of such technologies for the farms in Loop Head. The operating temperatures Optimum anaerobic digestion process takes place in Mesophilic (35-40°C) and Thermophilic (55- 60°C) temperature ranges. Though reaction rates also occur in ambient temperature ranges, the low temperature averages make it unfeasible for the case Loop Head. Hence mandating the need to maintain the either mesophilic or thermophilic range. This requires the need of a digester heating University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 50 system to ensure that the reaction inside the digester is maintained at the required rates. The heating requirements can be supplied by the use of the self-generated biogas, consuming 10-20% (XD Consulting, 2020) of the energy generated depending on the type of operating conditions. In the mesophilic range the hydraulic retention time (HRT) is estimated at 25 days and 10 days for thermophilic (Environmental Science and Engineering Magazine, 2020). The change in the HRT reduces the digester volume by almost 60% resulting in substantial decrease in the investment costs. But a major trade-off is brought by the increased operational cost. Hence selection of the operation temperature is one of the most important part of the process and is also defined by the rate at which waste is generated and the anticipated end use. Biogas tool for pre-feasibility of farm-scale AD systems To analyse the pre-feasibility of an individual farm scale biogas digester in Loop Head, an Excel based tool was developed over the course of the study. The tool provides a platform for any farmer to assess the potential of generation of biogas and energy with inputs of the waste available in the farm. The tool also provides an estimate on the investment costs, annual operating costs, and expected earning/savings in terms of the revenue generated from the farm. The tool has been incorporated in the final model developed by the study and the manual for its use has been provided along with this report. An average cattle housing in Ireland is estimated at of 121 days in dairy farms (Buckley, 2020). Similar trend is observed in Loop Head. Assuming this and considering a factor of safety for the collection rates, only 20% of the produced manure has been assumed to be available for the generation of the biogas by the tool. The value concur with the assumptions by (Sean O’Connor et al., 2020). The biogas yield from cattle manure is comparatively lower than other sources such as agriculture residue or even household organic waste. Hence co-digestion is a common practice opted by farms to increase the biogas production even with the same plant size and is recommended by the tool. The developed model was run for three farms in Loop Head covering both small and large farms in the region. The following output were obtained under different scenarios. Farm A housing 25 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 51 cattle, farm B with 102 and farm C with 320 cattle were considered for the analysis and also for the test the tool for any bugs. Important parameters under different end use case have been presented in the Table 5-4. From the input provided regarding the number of cattle in the farm, the tool also estimates the amount of silage requirement for an optimal gas generation keeping in consideration both technical and financial parameters. The calculation is based on the use of the co-digestate. However, the user also has the option to opt for calculation with only the cattle manure. Table 5-4: Comparison of AD operational parameters Total Number of Cows 25 102 320 Total waste per day (kg/day) 375.00 1,530.00 4,800.00 Suitable silage requirement (kg/day) 650.00 (12.99 ha) 2,650.00 (55.54 ha) 8,300.00 (173.97 ha) Daily Biogas Production (m3/day) 85.99 350.82 1,100.62 Total Input Feed (kg/day) 2,738.54 11,173.00 35,053.29 Water Requirement (litre per day) 1,713.66 6,991.74 21,934.89 Compost Generation (kg/day) 370.71 1,512.49 4,745.08 Digester Volume (m3) mesophilic 68.46 279.33 876.33 Digester Volume (m3) thermophilic 27.39 111.73 350.33 Source: Author The figures illustrate the wide range of AD systems that fall under the individual-farm level biogas in Loop Head. Though the tool does not define specific technology for biogas production, a single technology will not be applicable for such large range and hence the best technology needs to be identified for each farm size or the technology needs to be optimized based on the waste volume. Also as mentioned above the decrease in the digester volume in thermophilic operation range as a result of decreased HRT can also be observed. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 52 The following tables detail the energy and financial outputs of the three sample cases. The thermal parameters are available in Table 5-5. Economies of scale play a substantial role in the investment and operations of costs of a biogas plant. Hence the limitations of the tool regarding financial estimation can be observed. The investment costs are predicted based on a cost curve derived from regional AD system prices that were acquired from regional companies. However, as only limited regional data could be acquired for limited size range of AD systems, the cost curve is not able to give exact figures on the investment. Also, these values were observed to be slightly higher than the global average values, but nevertheless regional values were prioritized over global. Also, the investment costs are specific to technology in use and the service provider. Hence the tool is only able to provide a tentative cost and further detailed financial analysis is recommended. The investment cost of 2.5 M€ Euros for the Farm C is staggeringly high and investment on it in the absence of any financial support or grant may not be feasible on an individual level. The Operations and Maintenance costs are more realistic. The thermal energy potential is calculated assuming the combustion of the gas in a boiler. The revenue in the thermal case, provide a monetary value to the gas produced rather than direct inflow of cash. Table 5-5: Thermal end-use Thermal Operational Temperature 25 102 320 Thermal Energy Potential (kWh/day) N/A 371.45 1,515.54 4,754.66 Total Investment Cost (Euros) mesophilic 243,290.73 464,560.31 2,527,345.40 Thermophilic 231,565.10 269,370.82 606,218.57 Total annual operational cost (Euros/Year) mesophilic 2,999.00 12,237.16 38,391.09 Thermophilic 4,393.35 17,924.88 56,234.90 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 53 Total Revenues (Euros/Year) N/A 7,450.19 30,396.76 95,362.38 Source: Author For electrical end use the electricity potential is calculated assuming that the produced biogas is used in a CHP engine to generate electricity and heat. The heat generated can be partly used for heating the digester or meeting the heating needs of the farm. As mentioned above the high investment costs here as well are based on regional values contributes to making the project financially unsuitable without support. The revenues are calculated based on a feed in tariff of 0.14Euros/kWh which was determined as an average cost in the region as per community discussions, but the tool provides an option to change the tariff rates. Table 5-6: Electrical end-use Electrical Operational Temperature 25 102 320 Electrical Energy Potential (kWh/day) N/A 139.30 568.33 1,783.00 Total Investment Cost (Euros) (Inclusive of CHP) mesophilic 247,585.00 482,083.83 2,582,321.17 Thermophilic 235,860.00 286,894.35 661,194.34 Total annual operational cost (Euros/Year) mesophilic 3,956.97 16,144.43 50,649.20 Thermophilic 5,351.02 21,832.15 68,493.01 Total Revenues (Euros/Year) N/A 10,500.78 42,843.20 134,410.04 Source: Author University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 54 For the biomethane upgradation end use option, the tool does not permit it for plants with less than 100 cattle due to technical restrictions of the technology. The upgradation process requires higher gas flow rates. Also, high financial investments and considerable operations and maintenance cost make the technology unfeasible for application on a farm-scale biogas plant. Even though a wide variety of end use applications are possible from upgradation to Renewable Natural Gas (RNG), the study would not recommend the technology for farms in Loop Head. Table 5-7: Biomethane Upgradation end-use Biomethane Upgradation Operational Temperature 25 102 320 RNG Energy Potential (kWh/day) N/A - 1,760.81 5,524.10 Total Investment Cost (Euros) mesophilic - 541,168.00 2,767,683.24 Thermophilic - 345,978.00 846,556.42 Total annual operational cost (Euros/Year) mesophilic - 35,999.63 112,940.00 Thermophilic - 41,687.34 130,783.82 Total Revenues (Euros/Year) N/A - 49,008.32 153,751.60 Source: Author Results and Recommendations The use of AD in developed countries has been observed to incline towards managing of waste and treatment of high COD waste as opposed to the focus on energy generation in developing countries (frontiers in Energy Research, 2014). A potential farm system for Loop Head would cover both aspects and provide a leap towards circularity and sustainability. The technology has University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 55 showed technical feasibility for varying farm sizes with potential for substantial energy generation to meet the demand of the farms. However high investment cost is seen as a primary hindrance even though the generated revenues are considerable to meet the operational costs. Though a minimum of 300 cattle threshold is estimated as a requirement for electrical end-use of biogas to be feasible on farm level (The Minnesota Project, 2014), CHP is also seen as a possibility for farms in the peninsula. The results of the three sample farm types showed significant potential in terms of energy generation in the farms. Regarding electricity produced this would be able to cover the entire electricity demand of the farm if used a base load. The avoided environmental impact through the abatement of CO2 emissions is another benefit. However, implementing on a farm level, even with reasonable revenue generation did not deem the system financially feasible due to the high investment costs. Even though the financial performance can be improved with the use of specific technology and region-specific values, obtaining a reasonable payback period seems less likely. Some suggestion and recommendations have been put together for making individual-farm scale biogas more attractive for Loop Head. • Primarily a support scheme would be required to roll out the biogas industry in the community. This could be done through the support and initiation of the authoritative body, but also with active participation of the local community (farmers) (Irish bioenerby association, 2019). • Investment costs can be further reduced if high level of interest can be generated in the community, creating a market for the service providers. • The high initial investment costs make the realization of the project highly impossible. However the Irish Bioenergy Association recommend the possibility of subsidy of up to 40-50% which would provide a more favourable financial parameters (Irish Bioenergy Association and Composting and Anaerobic Digestion of Ireland, 2019). With these subsidy rates the tool showed a possible payback period between 10-15 years. The approach of using carbon taxes to fund AD projects has been suggested. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 56 • Another approach to reduce the investment costs could be through the introduction of low interest loan schemes for potential AD owners. • The digestate post AD process provides a good opportunity for revenue generation. However the tool calculates the revenue at minimal costs of 0.025 Euros/kg (XD Consulting, 2020). Better market for the fertilizer can be identified with improved rates. 10-fold higher fertilizer price were also observed during the research of the study in other parts of the world. • In case of electricity generation improved feed in tariff or incentives for generators could be an approach that can be implemented on a community level. • Though biomethane upgradation was not deemed feasible in the earlier section, the future expansion of natural gas grid in Loop Head or the initiation of district heating could provide opportunity for gas upgrading. The tool estimates the revenue of biomethane based on natural gas prices (0.03 Euros/kWh) (Irish Bioenergy Association and Composting and Anaerobic Digestion of Ireland, 2019), but its application in the transportation sector could provide more profitable opportunities and thus can be further looked into. To make the biomethane end use more feasible the Irish Bioenergy Association has also suggested the need for an additional 0.06 Euros/kWh to bridge the gap between the market rice of NG and the cost of production of biogas (Irish bioenerby association, 2019) . The difference in the price can be better covered based on the CO2 costs in the EU certification market. 5.3 Conclusion The transition of farms in Loop Head towards a circular economic model has been identified as the way forward to address issues concerning poor energy efficiency, fossil fuel dependence and mismanagement of cattle manure. The study focused on addressing each of these issues and provides point of initiation for the community towards circularity starting with a detailed energy audit is seen as the first steps to addressing the energy efficiency issues in the farms. Considering the high local interest in the technology, Solar PV systems were identified as the ideal technology for making the farms self-dependent on their energy generation. The larger roof areas of farms make the technology even more appealing. In addition, the increase in the efficiency and decrement University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 57 in the price for solar panels during the last 10 years have allowed the economic viability of rooftop solar systems in Ireland. The tool developed over the course of the study determined an average payback period of 9 years, relatively attractive compared to general payback period of a renewable energy system. The possibility of managing the manure produced in the farms through the installation of farm scale anaerobic digesters was analysed. A tool to calculate the potential of biogas in the farms and estimate costs was developed during the study. Though the technology showed technical feasibility, the high investment costs made the technology may does not make it economically attractive. Hence without incentives implementing the technology on a farm level would be difficult. All in all, switching to a circular economic model could be beneficial for the sector and could contribute toward the Irish vision of long-term low carbo economic system. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 58 6 Community Energy Modelling A community’s initiative to promote and establish sustainable energy system necessitates thorough analysis of the local energy system - assessment of community’s energy demand and energy production potential. Besides, identification of suitable generation technologies, willingness of the community to invest in those technologies, and polices to support the development of sustainable energy system in the community play an important role to shape the community’s future energy system. Based on the requirement, community energy modelling can be performed to integrate these factors for supporting investment decision as well as assessing scenarios to plan for the strategic development of energy system. The process of community energy modelling involves number of steps starting from identification of the local problem, interest, objectives and financial resource identification. To identify the correct strategy, modelling process involves further steps like data collection, estimation, and calculation, see Figure 6-1, (PlanEnergi, 2010). Each of these steps require inputs data on the community which can be collected via interviews, questionnaires, or statistical records. The data collected are then processed to estimate the economic solution for the community energy system. Source: (PlanEnergi, 2010) Figure 6-1: Process of Community Energy Modelling Identify local problem, interest, objective, and resources Collect Data on energy consumption and fuels Collect data on available generation units and prospective generation resources Develop Input Output Matrix Develop of Business As Usual Case Conduct Calculation and Estimation Scenario Analysis Develop an Action Plan University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 59 6.1 Energy Modelling for Loop Head Community In order to support Loop Head’s initiative to establish a sustainable energy system in the peninsula, an interactive energy modelling tool, IC Model 2021, was developed in Microsoft Excel. The tool was developed with an objective to provide the basis for investment decisions, support operational decisions, and serve as a platform for different stakeholders to analyse different scenarios, see Figure 6-2. (Source: Author) Figure 6-2: The purpose of Loop Head Energy Model The model aims to provide an overall outlook of the Loop Head energy by integrating following information: i. individual energy demand of each energy consuming sectors (residential, farming, and commercial) ii. possible globally proven renewable energy generation technologies in the peninsula, associated technical parameters, generation pattern of each technology, and associated cost of each technology iii. existing policies in the peninsula Besides the possibility to analyse entire energy system in Loop Head, the model also targets to assist individual community members: residential households and farms to identify suitable University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 60 solution to achieve energy sustainability. To address this, the model includes individual worksheets on the Excel model where interested stakeholders can provide inputs and analyse the scenario. The excel model developed by IC 2021 is an expansion of the model developed by IC 2020 which was initiated to identify possible improvement opportunities and apply those improvements. IC Model 2021 has been designed for Loop Head in compliance with CRaFT model: consistency, robustness, flexibility, and transparency as suggested by Liam Bastick (Liam Bastick, 2018). Subjecting the IC 2020’s model to Craft criteria, improvement opportunities, listed in Table 6-1 were observed and addressed in 2021’s model Firstly, as promised by the CRaFT model, the consistency of the excel model has been developed such that it is easily understandable by users once they become familiar with both the model's purpose and content. Secondly, based on the mathematical accuracy and absence of errors, the model’s robustness has been ensured and verified by IC 2021 groups internally and the potential users from the Loop Head community. Further, to enable the flexibility of the model, the model is designed in such a way that it can change various assumptions or input parameters to see how the changes affect various outputs. Lastly, by including sources of data used, the transparency of the model has been ensured. Table 6-1: Additional features IC 2021 Model Topic IC 2021 Key Performance Indicators (KPI’s) Includes KPIs such as: Levelized cost of energy, cost of the specific energy system, the share of renewable energy, and financial parameters such as Internal Rate of Return (IRR), and payback period Visualization Graphical and user interactive model Flexibility Provides the flexibility to adapt new assumptions and conditions on generation, demand, tariff, grants, population, households, efficiency of technologies, investment cost, population of cattle. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 61 Data Resolution Capable to show hourly variation in the system electricity demand, generation, CO2 emission, and electricity imported from grid. Energy Storage Adapts to see the system response with incorporation of battery storage in the system Variability of Solar and Wind Energy Hourly resolution of solar and wind generation in the peninsula is incorporated Model Structure Easy Operation and visually easy to understand 6.2 Excel Modelling Tool Development The IC Model 2021, an interactive energy model designed for Loop Head community, provides a platform for the users to understand the status quo of existing energy system in Loop Head whilst providing the flexibility to integrate renewable energy technologies and study the effect of integrating such technologies to the overall energy system of the peninsula. Simultaneously, this model also provides additional interface for interested individual users from residential and farming sector to understand and analyse the methods and financial benefits of adopting energy efficiency measures in residential households and installing renewable energy technologies in farms. The development of this model, therefore, can be broadly categorized into two sections (Figure 6-3). (Source: Author) Figure 6-3: Sections of IC Model 2021 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 62 i. Overall Community Energy Modelling section The overall community energy model section focuses on providing information on the entire energy system of the peninsula. This includes information related to the sectoral energy demand, necessary energy imports, CO2 emissions, energy generation prospects from installation of renewable energy technology, energy efficiency method of residential sector, and the associated costs. Besides, it also provides the possibility to analyse the effect of implementation of policy measures in the peninsula in terms of grants and feed-in tariff. The section provides the possibility to study the hourly operation of electricity sector in the peninsula in terms of electricity demand, imports, generation, and CO2 emissions. ii. Individual Single User Modelling section The individual single user modelling section, which is also incorporated in the IC Model 2021, will provide three interfaces for the individual users to analyse on listed topics: a. energy efficiency measures in residential buildings b. PV installation in residential building and/or farms c. anaerobic digester in farms The individual users who wish to have a detailed knowledge on application of energy efficiency measures in the residential sectors can directly use the related interface in IC Model 2021 and obtain the information on the retrofitting measures, cost of building retrofitting, and percentage of energy saved from applying the retrofitting measures. Similarly, in the interface related with the PV installation, the interested users can find information for fitting optimal size of solar PV systems in their residential buildings and/or farms and calculate the costs associated with it. In the third interface, which is related only to farms, interested users can estimate the size of anaerobic digesters for their farms whilst also calculating the associated investment and operation costs for the digester system. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 63 The methodology for development of each of these individual interface and related calculation are detailed in Chapter 5.2.2. 6.3 Methodology for Overall Community Energy Model Development Figure 6-4 presents the methodology adopted for developing overall community energy model section in IC Model 2021. (Source: Author) Figure 6-4: Conceptual Framework for Community Energy Model Development 6.3.1 Identification of Major Energy Consuming Sectors and Demand Estimation Three major energy consuming sectors i) Residential Sector ii) Farming Sector, and iii) Commercial Sector were identified in Loop Head peninsula. The total annual energy demand in Loop Head, which comprises electricity and heat demand was calculated separately for each of these sectors and integrated in the model. For calculating total electricity and heat demand in Loop Head, equation 4-1 was considered. Identification of Major energy consuming sectors Sectoral Energy demand estimation Energy generation technologies Energy import and CO2 emission Calculation Financial Calculation Scenario Analysis Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 64 𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐷𝑒𝑚𝑎𝑛𝑑 = 𝐹𝑖𝑛𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ∗ 𝐴𝑐𝑡𝑖𝑣𝑖𝑡𝑦 𝐿𝑒𝑣𝑒𝑙 ( 6-1) The total annual sectoral demand of each of the sector was calculated by identifying the final energy intensity and activity level associated with each of these sectors. The total annual energy demand in the Loop Head was calculated to be 40,037.32 MWh/y, see Figure 6-5. (Source: Author) Figure 6-5: Sectoral Energy Demand in Loop Head Residential Sector Energy Demand The total residential energy demand in the Loop Head, which comprises electricity and heat demands was calculated separately and integrated in the model. i) Electricity Demand in Residential Sector: The total electricity demand by the residential sector is approximately 11.50 GWh. The estimation of total electricity demand was done using equation Error! Reference source not found., where t he average per capita residential electricity consumption of Ireland (1.72 MWh/a) was used as the University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 65 final energy intensity of the residential sector in Loop Head (SEAI, 2016a). Similarly, average household size, obtained from Central Statistics Office (CSO, 2018) for each electoral division was considered as the activity level. 𝑅𝑒𝑠𝑖𝑑𝑒𝑛𝑡𝑖𝑎𝑙 𝑆𝑒𝑐𝑡𝑜𝑟 𝐸𝑙𝑒𝑐𝑡𝑖𝑐𝑖𝑡𝑦 𝑑𝑒𝑚𝑎𝑛𝑑 = ∑(𝑎𝑣𝑒𝑟𝑎𝑔𝑒 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑡𝑦 𝑑𝑒𝑚𝑎𝑛𝑑 𝑥 ℎ𝑜𝑢𝑠𝑒ℎ𝑜𝑙𝑑 𝑠𝑖𝑧𝑒 ii) Heat Demand in Residential Sector: The total heat demand in Loop Head is about 24.4GWh. This is divided into space heating and water heating demand in residential and commercial sectors. According to the heat demand atlas of the Sustainable energy Authority of Ireland (SEAI, 2018b), the total heat demand for the residential sector comprising the 10 electoral districts making up the loop is 18.74 GWh separated into space heating and water heating. The total space heating demand estimate for the residential sector is 16.9 GWh and the total water heating demand is 1.82 GWh. According to the 2018 final energy end use estimate by SEAI, the space heating and water heating per capita for Ireland are 4.098 MWh/y and 1.29 MWh/y. This shows that the current heating demand in the loop is higher than the national average based on the resident population and the number of occupied houses on the Loop Head. Therefore, heat demand per household has been estimated based on the national average heat demand and the excess demand in the residential sector is assumed to be the demand of the 1,590 holiday homes in the Loop Head. With this assumption, the total heat demand per holiday house is 2.73 MWh/y. iii) Heat saving from retrofitting: From the cost curve, as described in Chapter 4.1.4, a best-fit third-degree polynomial equation was approximated to estimate the cumulative cost of retrofitting for every percentage of heat saved from implementing retrofitting measures. 𝑦 = 213.8𝑥3 − 80.7𝑥2 + 22.8𝑥 where y = Cumulative retrofitting cost in Euros; x = Heating saving in percentage of total heat demand in MWh. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 66 Farming Sector Electricity Demand The Loop Head’s farming sector consumes, approximately 2,770 MWh of electricity on annual basis. For calculating the Farming Sector’s electricity demand, the farming sector was divided into three subcategories: i) small dairy farms, ii) large dairy farms, and iii) beef farm. For each type of farm, the average electricity required by the cattle type in respective farm was considered as the final energy intensity and the total number of the cattle in those categories of farm in the peninsula was considered as the activity level. While the average electricity consumption was calculated based on interviews, the total number of dairy cows and beef cattle in the peninsula was based on the statistical records obtained from CSO. The detail calculation of the farming sector’s electricity demand is described in Chapter 5.2.2. Commercial Sector Energy Demand i) Electricity Demand in Commercial Sector The total commercial electricity demand on the Loop Head is about 1.3 GWh. The commercial sector electricity demand comprises of the total estimated electricity demand by the five hotels on the Loop Head with annual electricity demand of each hotel room to be 12.56 MWh/y (EUF, 2020). This annual electricity intensity of each hotel room is considered as the final energy intensity and the number of hotel rooms in the Loop Head peninsula, i.e. 106, was considered as the activity level. The value seems high for a room of a hotel, however, due to unavailability of updated data and the limitation to validate the existing data, same old information from IC 2020 report has been adopted. It is highly recommended to validate and use updated data in the model. ii) Heat Demand in Commercial Sector The total commercial heat demand on Loop Head is about 3.2 GWh (SEAI, 2018b). The commercial sector heat demand consists of the total estimated heat demand by the five hotels on the Loop Head was 0.715 GWh/y (EUF, 2020), and heat demand by Water World and other shops, offices etc. was 2.49 GWh/y. The estimated average heat demand per hotel room is estimated to be 6.75 MWh/y, this value is considered as the final energy intensity for hotel consumption and total number of hotel rooms taken as the activity level. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 67 6.3.2 Hourly Electricity Demand Profile of Different Sectors The study for the transformation to entire renewable energy-based power system requires detailed assessment of the load profile and the available renewable energy sources. Integrating the potential renewable energy sources, mostly wind and solar, introduces the challenge of intermittency. Often, for an uninterrupted supply, the intermittent behaviour of these available resources requires to be matched with the time varying power consumption of the region, i.e. the load profile. This enables the operators to increase system’s reliability and optimize the system cost by addressing subjects of concern on how to: operate the system, size the storage requirements, maintain the system stability, and curtail the surplus energy. To address these subjects, analysis of load profile and identification of following situations are crucial (IRENA, 2018, p. 10) i. Minimum level of load in the system and frequency of its occurrence. ii. Times of the day where there is sharp increase or decrease if the load. In order to assess the situation, load profile with higher resolution (containing at least the hourly variations of the load) is preferred. Higher resolution of the load profile aids better flexibility of the generation units for system operation (IRENA, 2018). Usually, the best available data on load profile are the recorded data from the system operators. However, in the absence of the real load profiles for the community, synthetic load profiles can be generated based on a standard load profile of the country. In case of the Loop Head community, the sectoral load profiles were not easily available. Besides, limited time for IC 2021 challenged the possibility to conduct a survey and synthesize a load profile for each sector in the peninsula. Therefore, synthetic load profile was developed based on literature available for Ireland, as detailed in Chapter 4.2.1 (Ricardo, 2020, pp. 124–125). Similarly, the load profile seen in Figure 6-7 for the farming sector was synthesized based on energy demand profile generated for farms in Ireland (J. Upton et.al, 2013, p. 6). The details on the hourly demand synthesis for farms is explained in Chapter 5.2.2. The hourly demand profile for the commercial sector in the Loop Head was synthetically generated with similar climatic condition as in the Loop Head. This hourly demand profile was synthesized from HOMER, a power system design and University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 68 optimization software. Due to absence of student groups working on the commercial sector in IC 2021, and relatively smaller energy demand of the commercial sector, this approach was expected to approximate the commercial hourly demand profile for the Loop Head. The hourly profile of the commercial sector can be seen in Figure 6-8. The model also provides the flexibility to change these hourly demand profile of each sector. Source: Synthesized based on (Ricardo, 2020, pp. 124–125) Figure 6-6: Hourly Electricity Demand Profile for Residential Sector for a Week 0.000% 0.005% 0.010% 0.015% 0.020% 0.025% 0.030% 1 6 1 1 1 6 2 1 2 6 3 1 3 6 4 1 4 6 5 1 5 6 6 1 6 6 7 1 7 6 8 1 8 6 9 1 9 6 1 0 1 1 0 6 1 1 1 1 1 6 1 2 1 1 2 6 1 3 1 1 3 6 1 4 1 1 4 6 1 5 1 1 5 6 1 6 1 1 6 6 Weekly Electricity Demand Profile for Residential Sector University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 69 Source: Synthesized based on (J. Upton et.al, 2013, p. 6) Figure 6-7: Hourly Electricity demand Profile for Farming for a Week Source: Synthesized from HOMER energy software Figure 6-8: Hourly Electricity Demand Profile for Commercial sector for a Week 0.000% 0.005% 0.010% 0.015% 0.020% 0.025% 0.030% 0.035% 1 6 1 1 1 6 2 1 2 6 3 1 3 6 4 1 4 6 5 1 5 6 6 1 6 6 7 1 7 6 8 1 8 6 9 1 9 6 1 0 1 1 0 6 1 1 1 1 1 6 1 2 1 1 2 6 1 3 1 1 3 6 1 4 1 1 4 6 1 5 1 1 5 6 1 6 1 1 6 6 Weekly Electricity Demand Profile for Farming Sector 0.000% 0.005% 0.010% 0.015% 0.020% 0.025% 0.030% 0.035% 0.040% 0.045% 1 6 1 1 1 6 2 1 2 6 3 1 3 6 4 1 4 6 5 1 5 6 6 1 6 6 7 1 7 6 8 1 8 6 9 1 9 6 1 0 1 1 0 6 1 1 1 1 1 6 1 2 1 1 2 6 1 3 1 1 3 6 1 4 1 1 4 6 1 5 1 1 5 6 1 6 1 1 6 6 Weekly Electricity Demand Profile for Commercial Sector University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 70 6.3.3 Generation Technologies Solar and wind are the globally proven and widely implemented technologies and requires less resources for formulation. Hence, these technologies were considered as the two renewable energy technologies which could be swiftly implemented in the Loop Head peninsula to supply the internal demand of the peninsula. In case of solar technologies, the model provides the possibility to incorporate a variety of solar systems into the Loop Head energy system, ranging from small roof top solar in the individual households and farms to large scale solar plants which could be installed by the community. In case of wind technology, the model provides the option to study the effect of incorporating relatively large-scale community wind farms to the peninsula’s energy system. In order to calculate the energy generation from each of these technologies, certain technical and financial parameters associated with the respective technologies has been assumed. These assumptions will be discussed in detail in the following sub-chapters. Solar Energy In order to calculate the solar energy generation possibilities in Loop Head, reference values for the hourly electricity production which would be made by a 1 MWp solar plant installed in Kilkee was considered. These values were based on MERRA2 dataset at a latitude of 52.620N and 9.720W longitude at a tilt angle of 390 (Pfenninger et. al, 2016). Besides, listed assumption on the technical parameter (Table 6-2) and financial parameter ( Table 6-3) were made for detailed calculation of production possibilities from each type of solar system and for calculating the total cost, which would incur for the respective type of the system. The model also provides the flexibility to change the hourly generation profile and assumptions made on the technical and financial parameters. Table 6-2: Technical Parameters for PV system Technical Parameters Ground Mounted PV System Area requirement 40 m2/kWp Roof -Top Solar System Area Requirement 10 m2/kWp Optimal Tilt Angle 390 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 71 Module Efficiency 18% Performance Ratio 80% Table 6-3: Financial Parameters for PV system Financial Parameters Type of PV System Investment cost [€/kW] O & M Cost [€/kW/a] Lifetime [Year] Discount Rate (%) PV Community 1000 10 20 3.5 % PV Farm 1200 5 20 3.5 % Rooftop PV in Residential household 1200 5 20 3.5 % Rooftop PV Hotels 1200 5 20 3.5 % Small Battery (per kWh) 500 5 10 3.5 % The details on the cost associated will be discussed in Chapter 6.3.5 Wind Energy In order to calculate the wind energy generation possibilities in Loop Head, reference values for the hourly wind speed data have been utilized at a hub height of 60 m to calculate the hourly electricity production. These hourly wind speed values were obtained from MERRA2 dataset at a latitude of 52.620N and 9.720W longitude (Staffell et.al, 2016). Besides, listed assumption on the technical parameter (Table 6-4) and financial parameter (Table 6-5) were made for detailed calculation of production possibilities from the community wind farms. The model also provides the flexibility to change the hourly generation profile and assumptions made on the technical and financial parameters. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 72 Table 6-4: Technical Parameters for Wind Farm Technical Parameters Remarks Hub Height 60 m Values can be changed in the model by the user based on detailed design Coefficient of Roughness 0.1 m Values can be changed in the model by the user based on updated resources. Wind Park Efficiency 50% Values can be changed in the model based by the user based on detailed design Land Area Requirement 0.24 km2 Values can be changed in the model by the user based on detailed design Table 6-5: Financial Parameters for Wind Farm Financial Parameters Type of PV System Investment cost [€/kW] O & M Cost [€/kW/a] Lifetime [Year] Discount Rate (%) Wind Farm 1500 35 20 3.5 % 6.3.4 CO2 Emission Calculation from Imported Electricity CO2 Emission from Imported Electricity CO2 emission caused by imported electricity in Loop Head can be estimated given there is information available on the share of fuel mix present in the imported electricity. Since, electricity import in Loop Head is made through the national grid in Ireland, share of fuel mix present in the imported electricity is difficult to calculate. Therefore, the share of fuel mix present in the imported electricity was assumed to be equal to the national hourly generation mix. Electricity generating in Ireland is based on coal, natural gas, wind, hydropower, biogas, and biomass. Out of these, coal and natural gas are the major contributors of CO2 emission in the electricity supply sector. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 73 Firstly, to calculate the share of fuel mix in the electricity demand, it was assumed that for each hour of the year the generation from base load Moneypoint Power station (915 MW), coal fired plant remains constant. For the calculation of CO2 emission, the efficiency of this plant was calculated to be 38.05%, based on the energy balance of 2015 (IEA, 2017). The remaining residual demand is then supposed to be supplied by the available hourly wind generation. In the case the demand is not supplied by the coal and wind power generation, it is considered as covered by the natural gas generation. An average efficiency of the natural gas power plant is taken as 50% for calculation of CO2 emission. Based on this assumption, the hourly contribution of each type of generation to the demand was calculated and was applied to calculate the share of fuel mix in electricity import in Loop Head. This share of fuel mix was then utilized to calculate the CO2 emissions considering emissions of 0.3406 tCO2/MWh of fuel from Coal and 0.2047 tCO2/MWh of fuel from Natural Gas (SEAI, 2021b). The result is an hourly time series of CO2-emissions from imported electricity. The benefit from exported electricity is a reduction of gas-based generation and the associated CO2-generation. 6.3.5 Financial Calculation Assumptions The financial calculation involves various associated cost values and calculations. This section details the cost, which have been assumed as inputs to the IC Model 2021 for financial calculations. However, considering the possibilities of these costs to change over the time, the model provides the possibility for the user to change these values. Investment and O&M Cost Technologies The total investment cost accounts for the investment costs incurred from all the generation technologies which are added to the energy system. Investment costs and O&M costs listed in Table 6-4 are considered for the different technologies which can be added to the energy systems of the peninsula. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 74 Fuel Cost Following retail cost of fuels have been considered for the financial calculations Table 6-6 : Fuel Cost considered in IC Model 2021 Fuel Euro/MWh Source Peat 11.3 (EUF, 2020) Oil 74.1 (EUF, 2020) Coal 57.4 (EUF, 2020) LPG 119.3 (EUF, 2020) Electricity (Retail Cost) 170 Interview Other (Wood etc.) 72.1 (EUF, 2020) Electricity Cost Calculation As an approach for simplified calculation, the retail cost of 17 Euro/MWh has been considered as the cost of electricity import in the peninsula. The various utility service providers retail the electricity at various costs. Hence, an average retail price of 0.17 Euro/kWh has been considered. However, this value can be changed in the model by users according to prevailing retail cost of electricity at the time of analysis. Specific Cost of Electricity It is the cost of per unit of electricity generated or supplied by each technology in a year. For simplicity, the cost has not been levelized over the lifetime of the powerplants. The “cost per kWh used” is average cost of electricity for Loop Head after installing renewable energy resources. Grants Following grants available from SEAI have also been considered in the model. Table 6-7: SEAI Solar Grants considered in IC Model 2021 Grant Euro/HH Source SEAI Solar Grant 1-2 kWp 900 (SEAI, 2020d) University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 75 SEAI Solar Grant 3 2100 (SEAI, 2020d) SEAI Solar Grant more than 4 kWp 2400 (SEAI, 2020d) SEAI Grant for Battery 600 (SEAI, 2020d) Average Cost of Electricity The total investment cost for each technology is a product of installed capacity and unit investment cost per unit installed capacity. 𝑇𝑜𝑡𝑎𝑙 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 = 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ∗ 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝐶𝑜𝑠𝑡 𝑝𝑒𝑟 𝑢𝑛𝑖𝑡 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 Annuity Cost is a fixed annual average breakdown cost of total investment cost spread over the project period. 𝐴𝑛𝑛𝑢𝑖𝑡𝑦 = 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒 ∗ 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 1 − (1 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑅𝑎𝑡𝑒)−𝑇𝑖𝑚𝑒 𝑃𝑒𝑟𝑖𝑜𝑑 Operation and Maintenance cost is cost associated with operating and maintaining the power plants. This cost also includes the fuel cost if applicable. The total annual cost for each technology is sum of Annuity and O&M cost. 𝑇𝑜𝑡𝑎𝑙 𝑎𝑛𝑛𝑢𝑎𝑙 𝑐𝑜𝑠𝑡𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 = 𝐴𝑛𝑛𝑢𝑖𝑡𝑦𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 + 𝑂&𝑀 𝑐𝑜𝑠𝑡𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 The Specific cost, as mentioned in the model this output panel, is cost incurred to generate unit kWh of energy from specific technology. Average cost of electricity is calculated as, 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑢𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 = ∑ 𝑇𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐶𝑜𝑠𝑡𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑇𝑜𝑡𝑎𝑙 𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑈𝑠𝑒𝑑 Net spending on Energy The net spending on Energy in Loop Head is the total estimated cost of energy resources on energy minus revenue from the export of excess electricity. This estimation is conditional to the choice selection by users depending on the option of the feed-in-tariff, whether there is a feed-in tariff or University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 76 not, and whether or not to change the feed-in tariff from 6 cents/kWh. The current estimated net spending on energy on the loop is around 7 Million Euro, comprising the cost of heating fuel, cost of electricity import, and fuel cost for transportation. 6.4 Integration of Individual Single-User Modelling Section in IC Model 2021 Integration of individual single user modelling section was done in the IC Model 2021 in order to provide single tool which would provide both the functionality for modelling entire community energy system as well as the modelling of individual sectors. Based on the work division on the residential and farming sector, the individual Single-user Modelling section were developed by responsible individual groups. The assumptions, data, and final calculations, and interface developed by the individual groups were added to the IC Model 2021 along with the overall community energy modelling section. Special attention was given to maintain consistency on data and assumptions interrelated with the overall community energy modelling section and individual single user modelling section. However, the relation between the overall community energy modelling section and individual single user modelling section is one directional and there are no possibilities to provide feedback from the overall community energy modelling section to the individual single user modelling section. 6.5 Challenges of Modelling The development of IC Model 2021 provided both enriching and challenging experiences at the same time. The collaboration process takes three forms namely: collaboration with members of the Loop Head community, with other project members working in different sectors, and collaboration within the Model group members. All collaborations and carried out online and it was done with lots of coordination efforts. The project also has a constraint of time (4 weeks), which includes scope definition, project planning, model development, and result presentation. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 77 Another major challenge faced by the model development is data collection. The online nature of the project, combined with the constraints of time and resources led to some limitations in the amount of data that can be collected, which ultimately led to more assumptions in the model. An example of this effect is the carry-over of the same assumption related to hotel room electricity demand from IC 2020. This is as a result of limited time and the number of people working on the project because there is no project team dedicated to the commercial and transport sectors. It is therefore advised that future work should focus on the validation of some of the data and assumptions in the energy model. The presentation and user -interface of the model to members of the community who have limited knowledge of energy and other technical terms related to energy constitutes a major challenge. 6.6 Model Limitations The model shows the status quo of the total energy demand in Loop Head and provides the platform to formulate scenarios related to energy demand, generation, energy efficiency, and finances. The model has been created in a flexible manner such that input parameters and assumptions that can influence output information are modifiable. Notwithstanding, some functional limitations have been identified. These identified limitations are either related to the capability of the model to fulfil its intended purpose or influence the accuracy of the result obtained. These identified limitations are listed: • The model does not consider economies of scale in the calculation of cost for renewable energy technologies. • The transport sector has not been extensively elaborated; therefore, the consumption of diesel and petrol has been lumped as fuel consumption and average fuel cost assumed. • The model does not consider a dual tariff system in the calculation of electricity imports from the grid. • Finally, there is no direct interconnection between individual and community model, as a result of that, model users cannot use a bottom-up approach in scenario formulation. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 78 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 79 7 Community Engagement As per the objectives, the workshops conducted by IC 2020 provided a platform and a starting point for this year’s study. In line to findings of the IC 2020 workshops, a seven-week course was specially designed for the Loop Head Community by LEAP. The training was led by Mr. John Aston with inputs from the University of Flensburg, Energy Academy, SEAI, Clare Local Development Company, NUI Galway, Limerick Institute of Technology, and the Bryden Centre. The course aimed to support the community to understand the terminologies, technical aspects and underlying economics of renewable energy and community opportunities. The main intention of this course was helping participants to develop their confidence in renewable energy discussion at an individual level and for the community. The intended outcomes of the workshop were: • Understand the key concepts of energy. • Know how mastering your energy supply can lead to improved local livelihoods and community. • Be aware of the energy-cost cycle and possible interventions. • Access and sharing of information and resources to affect useful change. • Develop critical thinking of the individual and community on renewable energy technology. • Enable community members to use the Loop Head Peninsula Energy Balance Model to examine the status quo and alternatives. • Enable community members to develop the home and/or organization’s energy strategy and plan. 7.1 Community Workshops The goal of the online workshop was to train the participants of the Workshop on various aspects of various aspects of community energy relevant to Loop Head. The workshop aimed to close the knowledge gap in terms of community energy and energy terminologies which was identified by IC 2020. This workshop offered a channel to facilitate the activities carried out under the IC 2021. Supporting the IC2021 team to connect their work with the community, allowing the students to University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 80 interact and gather information for the studies being performed. This will also support participants to make informed energy decisions, on energy efficiency and renewable energy technology. The IC2021 took part in four sessions on 22nd, 24th of February and 1st and 3rd of March. The details on the agenda of the workshop are listed in the Annexes. The training included the following topics: • Concepts of energy audits • Energy efficiency in buildings • Introduction to circular economy • Solar on individual farms • Biogas and waste management • Community energy planning and lessons learnt from previous IC projects. • Introduction to the energy model A brief overview of the topics discussed, and the main content presented during the sessions are shown in Table 7-1. Table 7-1: Workshop content Day Topic Discussed Main Contents 22nd February Energy Auditing Discuss where energy is consumed in the residential and farming sector. Discuss the process of energy auditing. How much does the average household consume? Energy Efficiency Describe the current situation in Loop Head. Discuss the measures relating to retrofitting, building envelopes. 24th February Circular Economy Explain the idea of Circular Economy. Examples of a circular economy. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 81 Biogas and Waste management The general concept of Biogas. What kind of waste can be used for biogas generation. How can waste be managed in individual farms? Solar in Individual Farms Introduction to Solar PV system. Present the tool to calculate the optimum size of PV for individual farms 1st March Community Energy in Loop Head Discuss the objectives, progress, challenges of the Loop Head Energy Partnership to orient the Panelists. Community Energy in Applecross Discuss the energy journey of the Applecross community. Community Energy in Shetland Discuss the energy journey of the Unst community in Shetland. 3rd March Introduction to Loop Head Energy Model Introduction to Loop Head Energy Model. Training participants to develop scenarios. The workshops were hosted by Mr. John Aston on the Zoom platform. Any interested individual from the community were able to join the meeting via a link or the meeting id number. The workshops session and discussion were jointly moderated by Rijan Shrestha and Sheikh Ahmed Tijan Bah. 7.2 Workshop Sessions and Discussion Session 1 Date: Monday, 22nd February 2021 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 82 This was the first workshop conducted by the IC 2021. The workshop was done in presentation plenary discussion format. The presentation and discussion of the workshop were focused on the topic of Energy audit and Energy efficiency. The topic of energy audit was presented by Nicolas Oviedo and Carlos Epia. The topic of Energy efficiency was presented by Nazym Temirgaliyeva and Neda Ebadi. The session was held on the Zoom meeting platform and attended by 23 participants from the Loop Head community. Energy Audit: The main objective of the presentation on Energy Audit was to orient the participants on the idea of the energy audit. The focus of the presentation was to: • Introduce the concept of Energy Audit. • Explain the type of energy audits and how energy audit is performed. • Pre-site visit: Energy consumption and benchmarking. • Present some energy conservation measures for the residential sector. • Energy conservation recommendations for the dairy sector An introduction to Energy Audit was provided by Nicolas Oviedo. Different stages of an energy audit, levels of an energy audit, and who can conduct an energy audit was explained. The time required to consume 1 kWh for general household appliances, electric shower, cooker, kettle etc. was shared along with the residential profile. This was done to visualize the daily load variations based on various residential activities. Outputs of energy audits, such as analysis and evaluation on current energy use and a list of recommendations to improve energy efficiency were shared. National Agricultural Energy Optimization Tool (https://messo.shinyapps.io/AEOP_/ ) was shared with the participants. This tool can be used to simulate an optimize system for the dairy sector. A sample optimization for a rooftop PV system for a dairy farm, based on the herd size, milking time, number of milking units, investment cost for solar PV, feed-in tariffs etc. was conducted. This generated a result with monetary analysis, electricity analysis and environmental analysis which was shared with the participants. https://messo.shinyapps.io/AEOP_/ University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 83 During the presentation, a poll was conducted among the participants asking their monthly electricity consumption. The results of the polling showed an average consumption of 300-400 kWh (29%) represents the average household electricity consumption in Ireland, which is approximately 350 kWh per month per household (Selectra, 2021). Energy Efficiency in the Residential sector: The objective of the presentation was to orient the participants on the topic of energy efficiency in the residential sector. The content of the presentation was: • Introduction to the concept of energy efficiency and its importance • Energy consumption by sector in Loop Head • Introduction to BER ratings and steps for improvement • Thermal performances of houses • The heating requirement of existing buildings • Factors not captured by BER rate During the presentation, some of the key points which were discussed were how energy efficiency can improve the building energy requirement and enhance comfort levels. The participants were informed of the Building Energy Rating (BER) standards, and the requirements to achieve different BER rating. They were informed about possible improvement on building envelope, heating improvements, renewable technologies which could improve building energy efficiency performance thus leading to higher BER ratings. Participants were also informed about the retrofitting analysis, which talks about the effect of various improvement on BER ratings. Also, an example of how updating building fabric can improve building energy performance was illustrated. Lastly, the limitations of BER ratings were also explained, which are mostly related to human behavioural aspects of the occupants. Key Takeaways The objective of the sessions on the energy audit and energy efficiency in the residential sector was to capacitate them to relate these topics to their household. Information on their energy University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 84 consumption and experiences with energy auditing was shared by participants. Household energy efficiency status, retrofitting measures undertaken in their households, and current BER ratings were also shared. The details on the key discussion are also mentioned below for further insight into the main discussions. • It was observed that participants’ knowledge is heterogeneous in the sense that some have been exposed to the energy sector and knowledge and many are less exposed, for example, two of the participants shared their experience with the installation of a solar rooftop PV system. The size of PV system was 4.76 kWp and 3.06 kWp. The information on the energy purchased, PV generation, and export to the grid was shared. Those participants were encouraged with the performance of the PV system and recommended it to others. • Participants were also aware of the use of energy-efficient appliances, replacing conventional lighting with energy-efficient LED lights, use of appliances based on the energy efficiency ratings and also turning off appliances when not in use to reduce energy consumption. • A poll was conducted after the presentation sessions, which collected the energy efficiency situation in the 20 participants households. The finding of the poll showed that the majority (75%) of the participants want to improve energy efficiency to reduce their energy bill followed by improving the comfort levels in their household (55%). Only 7 participants had done BER rating of their houses with 5 having A-B and 2 with C-D rating. The majority of participants have retrofitted or are willing to retrofit their Roof, which is followed by windows and doors and improvement in walls. • A participant expressed that there is a strong will to generate energy locally in the Community as a community energy project. • It was suggested that a future pathway for the Loop Head community could be to retrofit few households in partnership with SEAI. Which could encourage others who are lagging to follow and implement energy efficiency measures. Session 2 Date: Wednesday, 24th February 2021 University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 85 The session started with a recap on the topics discussed in the 1st workshop on 22nd February. The second workshop was also conducted in the presentation plenary discussion format. The presentation and discussion for the workshop were focused on the topic of Circular Economy, Anaerobic Digestion and Solar Photovoltaic technology. The first topic on Circular Economy and Anaerobic digestion were jointly presented by Aadit Malla and Mary Obando. The second topic was presented by Carlos Epia and Tatiana Carvalho. The Session was attended by 23 participants from the Loop Head Community. Circular Economy, Anaerobic Digestion The objective of the presentation on Circular Economy and Anaerobic Digestion was to introduce the participants to the underlying concept of Circular Economy, its principles and some technical background on Anaerobic Digestion. The main idea behind the presentation was to sensitize participants on the topic and create a dialogue on how it can be applied in Loop Head Community. The details on the content presented are listed below: • Introduction to Circular Economy and its principles • Importance of circular economy • Best practices on Global Circular Economy • Circular Economy in Dairy Farms • Introduction to Anaerobic Digestion The definition of Circular economy was presented to the participant. The difference between the Linear Economy (take-make-dispose) and Circular economy was elaborated. Principles of a circular economy; design out of waste, build resilience through diversity, energy from renewable resources, think in systems, and waste is the food of another process/product was discussed. A video on the circular economy: definition & Examples (https://www.youtube.com/watch?v=X6HDcubgxRk) was shared with the participants to better understanding the concept discussed. Some success stories on the implementation of the circular economy were shared, which includes an example on circular ocean project, VenloCity C2C https://www.youtube.com/watch?v=X6HDcubgxRk University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 86 Design, Good Food Brussels presented at joint workshop “Pathways to a circular economy in cities and regions” (ESPON, 2016). The dairy sector plays an important part in the economy of the Loop Head community. Understanding this importance, the concept of circulatory in Dairy Farms in Loop Head was covered in the presentation. The advantage such as improved resource security decreased import dependency, reduced environmental impact, economic benefits and social benefits which can be achieved with the application of circular economy in dairy farms was presented. This was done to introduce circularity to different processes and activities, feed to fertilizer, in the dairy sector. The concepts of extensive grazing, intensive-high tech and optimized grazing were elaborated. The presenter suggested the optimized grazing approach as a suitable scenario for the dairy sector as it has lower upfront costs and other improvements can be made gradually. Biogas The sources which can be used for biogas production was presented. The participants were presented with the working principle of the Anaerobic digester process, and various application of biogas. The idea of Anaerobic digester application in Farms was presented. Key Takeaway The objective of the sessions on the Circular economy was to introduce the concept and sensitize farmers to apply this to energy generation through biogas and at the same time produce fertilizer required for farms. During the presentation and discussions, participants were also informed about different wastes which could be used for biogas production. In the case of solar in individual farms participants were informed about different solar technologies. Input from the participants was collected to design an optimum size of a PV system, during the process participants were informed about the important parameters for designing a solar rooftop system. • The participants were in general interested in using an anaerobic digestor at an individual level. They were curious about the use of circular economy at a farm level, type of biomass that could be used for biogas production. Some of the participants are already conducting some market University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 87 research on farm level biogas plants. The biomass model being developed can be a useful tool for the participants to estimate the biogas yield based on the available biomass feed. • As learned from the session on 22nd February, participants are interested in the solar PV system. This session briefly presented the different PV module technology and collected important feedback from the community on the PV model being designed. Rooftop PV systems The second presentation was on the topic of Rooftop PV systems. This presentation was intended to provide the participants with basic knowledge on the principle of solar energy, its functionality, costs and introduce participants to the IC 2021 Solar PV optimization model. The details on the content presented are listed below: • Source of solar energy • Components of rooftop PV systems • Cost range of Solar PV • Benefits of installing a rooftop solar system. • Identifying Optimum size of a solar rooftop PV system. The presentation started with the fundamental of solar energy. The basic working principle of the solar photovoltaic system was presented, and different types of solar panels were introduced. A schematic view of a basic rooftop solar PV system and the range of solar PV systems were shared. Different decision criteria for PV system optimization mentioned below were discussed: ▪ Cost of the PV system ▪ Future bill costs ▪ Internal rate of return (IRR) ▪ Total NPV of the investment, ▪ The carbon footprint of household electricity University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 88 The Level of Smart export guarantee per year in the high and low scenario of the planned Feed-In tariff policy was shared with participants. The model for optimization of PV system for farms and individual household in Loop Head which was under development was introduced. Participants were also asked about their expectation from the Model. Session 3 Date: Monday, 1st March 2021 This was the third session and the first in the second week. The session was focused on community energy planning, drawing lessons from the experiences of communities in Scotland; Shetland and Applecross, hosts of International Class in 2019 and 2014, respectively. John Aston discussed the current situation in Loop Head, indicating that there are immense energy resources, a strong energy demand and a lot of money is spent to import energy to the Loop. John also highlighted that the Loop Head is starting fresh and has a lot to be done and therefore, welcome all ideas and experiences. He went on further to introduce the participants from the community to the invited panellists. Statement by Ms Alison Macleod on Community Energy in Applecross Ms. Alison Macleod spoke about Applecross but first introduced herself, detailing her roles within the Applecross community company. She discussed the community’s partnership with Community Energy Scotland, who provided financial and technical support to the community. Ms. Macleod spoke about the challenges with mobilizing interest, land ownership, and the initial plan for income generation as part of a government scheme to promote renewable energy generation and the lead up to the decision to use the energy locally, instead. Ms. Macleod also spoke extensively about financing and the challenges they faced with that before detailing how this was addressed by offering shares to community members which are to be bought back within 20years when the feed in tariffs would expire. She also discussed some of the plans for the community to develop energy- related projects such as development of energy efficient affordable housing in partnership with the housing association. There are also plans for electric vehicle charging system with a view to have a shared electric vehicle for the community. One of the plans is to develop community transport, University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 89 currently a number of locals have electric bikes and there are electric trailers to deliver local food supplies. She concluded on the ongoing development of wood biomass (timber) to be exported from a community owned woodland area. Presentation by Ms. Elizabeth Johnson MBE and Ross Gazey Ms. Elizabeth Johnson and Mr. Ross Gazey made a presentation on Successful Community Project Examples looking at the sustainable energy journey of Shetland. Ms. Johnson introduced the Pure Energy Centre and the work of the organization, citing that the success of the Pure Energy Centre is an example of how a small community initiative can leap to become a global phenomenon. Ms. Johnson also explained the journey from working on electrolysis to taking control of all the components of the hydrogen production system and refilling station. The Pure Project is the first community renewable hydrogen project in the world which was first established for wind to heat. The community also got the first hydrogen system in a container, which was trademarked HyPod which led to some legal complications with Apple for sounding like iPod. The community would win the legal battle showing what a community can do. The success of the hydrogen project led to the establishment of the Pure Energy Centre. And through the Pure Project, the Pure Energy Centre was involved in tens of millions of pounds which has benefitted the Shetland community greatly. Mr. Ross Gazey also discussed the circular economic model in the Outer Hebrides which was subject of the IC2019. The project includes the use of an anaerobic digestor combined with wind to produce hydrogen. The hydrogen is used to power refuse trucks. There is also a fuel cell which powers the salmon hatchery. The salmon hatchery uses oxygen from the hydrogen electrolyser and waste from the hatchery is used in the anaerobic digestor, making the system a completely circular economy. The Yorkshire Forward project was also described. The project provided energy for the EETC building, a technology centre which serves as an innovation incubator. The project included a wind turbine which is used to power a 30kW Electrolyser, combined with storage and fuel cell to power the building. This is evidence that the application of renewable hydrogen can be in both urban and rural settings. The presentation concluded with an overview of the community benefits listed below: • Promote H2 and potential for further projects University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 90 • Create new business ventures in a community • Inward migration • Potential for new exciting jobs • Support the local community by re-investing in it • Green technology brings technology tourism • Raise the profile of your area • New ventures attract other businesses • Do not be afraid to aim high • Always ensure community benefits are high and any project contains budgets for local management • Upskilling of workforce –stop the brain drain • Energy/fuel security • Income generation Takeaways To deduce some takeaways from the session based on the objective to familiarize the community on the planning process according to the experiences in Shetland and Applecross, a question was asked at the beginning of the session and the following were the responses elicited at the end. Question: From the presentations and discussions about the community energy projects in Scotland, what could be applicable to Loop Head? Responses: • To work with what you have and from there, comes ideas. • To choose a project that uses the resources we have • Get an overview of the available resources and used that to decide which projects to start with • Think BIG if you can! University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 91 • Check under every rock and trace every loose thread to find what has worked elsewhere and use that momentum to push through the education. Session 4 Date: Wednesday, 3rd March 2021 The final session for IC 2021 in the LEAP Energy Academy workshop looked at the model developed – The Loop Head Energy Model. Details of the model are discussed in Chapter Error! R eference source not found.. The objective of the session was to familiarize the participants with the model, starting with presentations detailing the components, dashboard, input and output parameters of IC Model 2021, followed by a breakout session allowing the participants to work with the model to develop a scenario. The presentations were made by Aastha Bhattarai, Anish Chandra Pyakurel and Michael Adepoju, who headed the development of the model. Aastha defined Energy Modelling before going into details of the Loop Head Energy Model. She discussed the objectives and the sections of the model. Anish illustrated the working of the Individual section of the model, i.e., the BER estimator and Solar Calculator, which are designed following the methodology explained in Chapter 4.1.4 and Chapter 5.2.2 respectively. The Solar calculator is used to determine an appropriate size of PV System for a household or farm while the BER estimator helps the user identify the retrofitting work needs to be done to improve the BER rating of the household and the potential associated costs. Michael on the other hand explained the community model by first describing the user interface. He informed the participants of the contents of the various worksheets, tabs, charts, and work cells. Michael also demonstrated the working of the model by developing a scenario with an objective of reducing annual net energy spending in the Loop by 2 million Euros. Breakout Sessions In the first breakout session, the discussion began with identifying the objective, which as mentioned in the Chapter 1 is to conserve energy and reduce spending on energy. A scenario was developed to this effect, focusing on generating energy locally with rooftop solar for households and farms, and community solar and wind farm. In addition, energy efficiency measures for up to University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 92 25% of households was considered in the scenario. The output of the simulation indicated that, an investment of over 5 million and 2 million Euros is required for the renewable systems and retrofitting, respectively. This will bring a saving of about 2 million to the Loop from the 6.9 million spent annually. The participants however noted that the retrofitting requirement for some of the households may not be achievable. To make this point, the BER estimator was used to estimate the cost of retrofitting for the house of one of the participants which was built pre-1919 and was a detached stone wall house. The model results showed that more than 30,000 Euros will be needed to retrofit the house. From the discussion, it was highlighted that this is the situation for many households in the Loop and as such emphasis should be put on installing PV systems for households to generate their own electricity and to develop community energy schemes to reduce the net spending on energy for the community as a whole. The second breakout session was presented by Michael Adepoju, this session started with a reiteration of various components on the Input-Output tab of the energy model. During the session, multiple cases were tested based on participants request. In the beginning, a possible case of installing a 10 MW wind power plant was tested. With this capacity of a wind turbine installed, the model generated annual total electricity of 30,805 MWh. Out of this generation, 18,890MWh was exported to the grid. A possible scenario with heat pump in 20% of residential houses was tested, the output indicated that with the installation of heat pumps, 0.5 Million Euros can be saved from annual net spending on energy, which is 6.9 million Euros. Another scenario with 25% (312) of occupied houses installing 3 kWp solar rooftop system was simulated, the results were 901 MWh of electricity generation per annum and the investment requirement was estimated at 1.1 Million Euros. The model also showed that with SEAI grants this investment requirement could be reduced to 468 thousand Euros. A case of installing a 15 MW wind power plant was tested, the output showed that with a feed-in tariff of 6 Euro cents/kWh the annual revenue from electricity export would be 2.06 million euros. Participants were also guided to update the calculation parameters, to update the model to adjust future price changes and to adjust capacities of RE technologies. A case with a 20% increase in the number of farms with less than 100 Cows were simulated, which led to increase in electricity demand by around 200 MWh per annum. Lastly, a University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 93 20% increase in an electric car was simulated, which showed a possibility to reduce fuel cost by 500 thousand Euros. Takeaways • The community members present were keen on reducing their own energy consumption and carbon footprints. The main motivation being to reduce their spending on energy. • In order to get more members of the community on-board, individual members of the community will need to be shown how much impact their individual efforts have on the overall energy balance of the community, the associated costs, and benefits. • Retrofitting households is important to reduce energy consumption, however, given the associated costs, pursuing rooftop PV installations seems to be of highest priority to the community members present according to a poll conduction during the discussion. The result of the poll is found in the Appendix H.4124. It is worth noting however, that installing PV systems without improving the household energy efficiency may not reap the desired results. • The model has great potential; despite the limitations, the community members present are keen on working with it. About two thirds of the community members present expressed a desire to master the use of the Loop Head Peninsula Energy Model with at least an in-depth understanding. • The community members present expressed great satisfaction and appreciation to the students for the workshops. The feedback given was positive and indicated that the participants have understood the theories, concepts, technologies, and terminologies discussed during the course of the workshops. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 94 8 Conclusion Under the prevailing conditions set forth because of the COVID-19 pandemic, IC 2021 had to be conducted in an unprecedented manner. Without jeopardising the aim of international class, IC 2021 was designed to focus on objectives achievable within the context of the limitation of activities to virtual interactions, literature-based desk research, online workshops, and computer simulations. The objectives, nature, and scope of IC 2021 is certainly indicative of the situation, albeit the results generated are unalloyed by the circumstances. Over the course of the sessions hosted by international class 2021 in the LEAP Energy Academy, the community has shown great interest in the energy topics discussed and have always contributed significantly to the discussions with educated questions and comments. The presentations were well received and the feedback from the community indicate that the explanations were clear and well understood. While the objective of the sessions was to familiarize the participants with some of the topics, concepts, and terminologies relevant to community energy, the participants have expressed a willingness to continue the discussions with other community members out of the sessions. This indicates that they are comfortable with the concepts and terminologies introduced during the workshops and the objectives for international class 2021 has being achieved to great extent. IC 2021 has found that, to record savings in the heating demand for the residential sector, improvements in building envelope material and level of insulation is a necessity for households. Replacing oil boilers with more efficient clean heating technologies can further reduce household expenditures on fuel and reduce carbon footprint. While many might be dissuaded by the cost of implementing energy efficiency measures, taking up such endeavours like retrofitting and installing heat pumps (air to water) could yield immense economic benefit for the community. As noted in the first session of the workshop, this may start with developing local skills and raising awareness of the grants and incentives available. In one of the discussions, it was also pointed out that some trainings are available through the SEAI for free. However, there must be a market built University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 95 in the community for such skills to motivate a few people to take up such trainings. Having local personnel to carry out these works will greatly reduce cost and encourage more up take. In addition to retrofitting and other energy efficiency measures, the Loop Head community could push for a community energy scheme. The report of IC 2020 has detailed extensively the potential for wind and solar in the Loop Head peninsula. Scenarios developed during the breakout session in the final workshop showed the Loop can drastically reduce the annual net spending on energy by implementing a community energy scheme using solar and/or wind. The community members will be motivated in this regard by the examples of the communities in Shetland and Applecross, which were presented as part of the workshop. For the farmers in the Loop Head, IC 2021 recommends adopting circular economic practices to curb dependence on fossil fuels, improve management of cattle manure and improve overall energy efficiency in the farms. Given the interest in rooftop PV within the community members present during the workshops and those interviewed during IC 2020, installing rooftop PV in farms was analysed. The results indicate that farmers will recover their investment in rooftop PV within 9 years on average. The analysis of rooftop PV potential in Loop Head also lead to the development of a solar calculator which can be used both for farms and residences. It is expected that the solar calculator in the model will be of benefit in estimating solar PV system sizes and associated costs and benefits for the community. Installing PV systems in households and farms might perhaps be the most enticing for the community members since it is relatively feasible, and the available grants make it economical. The prospect for feed-in tariffs also adds a financial incentive. In addition to the solar calculator, the model also includes a BER estimator which can be used to estimate the cost of retrofitting a household based on the year of construction, building type and desired BER rating. The solar calculator and BER estimator form the individual section of the model. The community section on the other hand allows users to simulate a scenario which could include inter alia, a community-owned energy scheme with wind or solar, adoption of electric vehicles, installation of rooftop solar, and household retrofits. 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The results of poll conducted at the 1st Workshop to Community in 8th of February 2021. Question: What type of energy interventions are you considering? The graph below summarized the number of respondents and the proposed options. A.2. The results of poll conducted at the Workshop to Community in 22 February 2021. Question: Why is improving energy efficiency is important for you? 0 2 4 6 8 10 12 14 Solar panels/ PV (alone and with combination) Energy efficiency Heat pumps Solar panels/ PV+ heat pumps Solar panels/PV+heat pumps + Energy efficiency Solar panels/PV+ Energy efficiency University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 104 0 2 4 6 8 10 12 14 16 Reducing my energy bill comfort level imrovement BER imrovement CO2 emission reduction Contributing sutainability in the community University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 105 B. Theory on Circular Economy A clear understanding of circular economy is important before diving into the implementation of the concept. This section describes the concepts and theory behind circular economy and its benefits to the local economy. Also, an in-depth technical background description on the proposed technologies for this study have been described in detail with an aim that the reader will be able to visualize the implementation of these technologies in farms in Loop Head to attain circularity. The definition of circular economy considers three dimensions of: economic, environmental, and social aspects. “CE is a sustainable development initiative with the objective of reducing the societal production-consumption systems' linear material and energy throughput flows by applying material cycles, renewable and cascade-type energy flows to the linear system. CE promotes high value material cycles alongside more traditional recycling and develops systems approaches to the cooperation of producers, consumers, and other societal actors in sustainable development work” (Korhonen et al., 2018). The “linear model” is a model that could be also called “take-make-dispose”. In this model the premise is simple: companies extract raw materials, use energy and create a product, and sell the product to an end consumer. This consumer is the one who discards the product when it no longer works or no longer serves the user’s purpose (Ellen MacArthur Foundation, 2013). The following figures shows a comparison between the two models, the linear model can be observed in the left side of the image where the flow is take-make-dump, and the energy is taken from finite resources. On the right side there is a representation of the circular model, which looks at reducing, repairing, and recycling while consuming renewable energy resources. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 106 Source: (Sustainability Guide, 2018) Figure: Linear and Circular Economy Models Need for a switch to Circularity A linear economy model incurs unnecessary resource losses in several ways, some of them are mentioned below (Ellen MacArthur Foundation, 2013): • Waste in the production chain: between mining and final manufacturing significant volume of materials are commonly lost in the chain to produce different goods. • End-of-life waste: For most materials, the conventional recovery rates after the end of their first functional life is quite low compared with primary manufacturing rates. • Energy use: In the linear model, when a product is disposed in a landfill this means that all its residual energy is lost. Circular economy a model which refers to an industrial economy that is by intention; aims to use renewable energy, minimizes, tracks, and eliminates the use of toxic chemicals; and eradicates waste through careful design. According to the Ellen MacArthur Foundation, circular economy is based on a few simple principles (Ellen MacArthur Foundation, 2013): University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 107 • Design out of waste: waste is not present when the biological and technical components (or nutrients) of a product are designed by intention to fit within a biological or technical materials cycle, designed for disassembly and refurbishment. The biological nutrients can be simply composted since they are non-toxic. • Build resilience through diversity • Rely on energy from renewable • Think in systems: In circular economy it is important to have the ability to understand how parts influence one another within a whole, and the relationship of the whole to the parts. • Waste is food: waste produced in one step of the process should be considered as food for another step of the process. A shift from linear economic model to a circular model significantly reduces negative environmental impact. The re-use and recycling of raw materials could show enormous potential in all global economic sector making efficient use of resources and avoiding waste. Bioeconomy is acknowledged as to be crucial for achieving circular economy by The Circular Economy Action Plan (European Bioeconomy, 2019). The European Bioeconomy Alliance (EUBA) mention a coherent, supportive framework and policy as primary ingredient to ensure sustainable bio-economy and also emphasize on their ambitions to synergize the idea with circular economy (European Bioeconomy, 2019). • Improved resource security: using better what is already used and using what is not yet used provide a form of resource security in terms of self-sufficiency. In the policy there is not mention a current resource insecurity but to have the availability of the resources would allow the community to be more independent, • Decreased import dependency: With the escalation in the use of the locally available products, the dependency on the imports can be observed to reduce. In 2019 Ireland imported 87.7 billion of euros in different products (Observatory of Economic Complexity, 2021). University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 108 • Reduced environmental impact: A reduction of emissions due to less transportation requirements due to locally available resources and also less dependency on burning fossils fuels. • Economic Benefits: As a result of new products from locally available resource and the markets for the same, economic benefits from these new markets can have significant impact on the local economy. Also, the reduced dependency on imported fossil fuels can result in substantial import savings. • Social Benefits: A change in consumer consumption pattern along with creation of new job opportunities is expected to have significant positive impact on the society in the long run (ESPON and INTERACT, 2016). As mentioned before, the following sections will cover technical background on the aspects considered in this study as a point of departure for the transition of the farming sector in Loop Head towards a circular economy. C. Energy Audit Energy audits are used to improve the efficiency of energy-consuming processes, to obtain an economic benefit because of a decrease in energy consumption. Nowadays, when climate change is a social concern and high cost of electricity an issue efficiecnt use of energy becomes important thus making “energy audit” absolutely necessary. In 1980 the first edition of Handbook of Energy Audits (Thumann et al., 2013) which studied the term “energy audit” in detail was published and with its following editions has served as a reference to many energy auditors until the present day. The writers state that an energy audit can be simply defined as a process of determining the types and costs of energy use in the building, evaluating where a building or plant uses energy, and identifying opportunities to reduce consumption (Thumann et al., 2013). There is a wide range of possibilities in terms of energy audits and it depends on the purpose for which it is done. Simplified energy audits can be performed economically to find energy savings in a house and detailed energy audits can be necessary for complex processes. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 109 C.1. Levels of energy audit Taking into consideration the scope of the project and level of effort necessary to meet expectations, (Thumann et al., 2013) describes four basic types or levels of energy audit. • Type 0: This audit includes performing a detailed preliminary analysis of energy use and costs, and determining benchmark indices based on utility bills. This is very cost effective. • Type I: It is called walk-though audit, and is a tour of the facility to visually inspect each of the energy using systems. It will typically include an evaluation of energy consumption data to analyze energy use quantities and patterns as well as provide comparisons to industry averages or benchmarks for similar facilities. It can yield a preliminary estimate of saving potential and provide a list of low-cost savings opportunities through improvements in operational and maintenance practices. It is the least costly audit and it is an opportunity to collect information for more detailed audit later on if the preliminary savings potential appears to warrant an expanded scope of auditing activity. • Type II: It is the standard audit and it goes on to quantify energy uses and losses through a more detailed review and analysis of equipment, systems, and operational characteristics. This analysis may also include some on-site measurement and testing to quantify energy use and efficiency of various systems. Standard energy engineering calculations are used to analyze efficiency and cost savings based on improvements and changes to each system. It also includes economic analysis. • Type III: Performed through use of computer simulation software to a more detail energy use by function and a more comprehensive evaluation of energy use patterns. The auditor will develop a computer simulation of building systems that will account for weather and other variables and predict year-round energy use. Because of the time involved in collecting detailed equipment information, operational data, and setting up an accurate computer model, this is the most expensive level of energy audit but may be warranted if the facility or systems are more complex in nature. (Thumann et al., 2013, pp. 1–2) University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 110 C.2. Process to conduct an energy audit (Thumann et al., 2013) describes a long sequence of tasks to perform an energy audit. In case of farms some of them may not be relevant, and other new tasks may be required because of the farm process. The book divides the energy audit in three phases: • Pre-site tasks o Collect and review one to two years of utility energy data. Tabulate and graph consumption and cost data. Check for seasonal patterns. Apportioning energy use among specific building systems such as heating, cooling, lighting and hot water. Pie charts of energy use and cost by fuel type can offer compelling documentation of overall energy uses and expenses. o Obtain mechanical, architectural, and electrical drawings and specifications for buildings. o Draw a floor plan of the building and calculate the gross square footage of conditioned space. o Calculate the energy use index (EUI), and compare it with EUIs of similar building types to have an indicator of the relative potential for energy savings. o Develop a list of potential energy conservation measures (ECMs) and operation and maintenance (O&M) procedures. • Site visit o Prior to go to the site, sit down with the building manager to review energy consumption profiles and discuss aspects of the facility, as operation and maintenance practices, that may have an impact on energy consumption. o Fill out data sheets and look at the systems relating to the ECMs and O&Ms on your preliminary list. o Take pictures as you walk through the building and wrote down problems or additional opportunities. o Take basic measurements of light levels, temperature, relative humidity, and voltages. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 111 • Post-site work o Review and clarify your notes o Review and revise your proposed ECM and O&M lists. Conduct preliminary research on potential conservation measures. o Organize information and write up audit report. (Thumann et al., 2013, pp. 9–14) D. Biogas for waste management Anaerobic digestion is a process through which bacteria breaks down organic matter into simpler organic compounds in the absence of oxygen (Environmental Protection Agency, 2021). Any organic source such as animal manure, wastewater, food wastes, agricultural residues can be used as input resource for the process. The principle of the short carbon cycle illustrates the primary benefit of biogas use over the use of fossil fuel sources. Unlike the later, biogas is generated from fresh organic materials. The CO2 captured by the biomass during its growth is released into the atmosphere during combustion of biogas thus just moderating the natural process over a short period, unlike the release of CO2 after millions of years, when fossil fuels are combusted. Animal farming result in large quantities of manure production which releases a significant amount of methane during storage. Stopping this methane emission is not a possibility. However, biogas provides a systematic method of capturing this high energy content gas and utilizing it as a replacement for other non-renewable energy sources (European Biogas Association, 2020). Anaerobic digestion for biogas production takes place in a sealed vessel called a reactor/digester, which is designed and constructed in various shapes and sizes and vary widely on the technology in use, is site specific and is also widely affected by the composition and combination of feedstock in use. Biogas comprises of methane (CH4), which is the same compound in natural gas, and carbon dioxide (CO2). The methane content of untreated biogas may vary from 40% to 60%, with CO2 making up most of the remainder along with small amounts of water vapor and other gases (U.S Energy Information Administration, 2020). University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 112 When combination of multiple waste resources is used, the process is termed as co-digestion (Jia Lin, 2011). Co-digestion provides a degree of flexibility to the system and in terms of daily operation but also adds complication to the system adjustment of the system parameters. Hence the optimizing the share of the ratio of the of mixture becomes crucial. The installation of an anaerobic digestor system on a micro scale can provide a diversification to the farm revenue and provide self-sufficiency for its energy needs. Also the recirculation of nutrients through the use of the digestate as the bio-fertilizer also promotes the contribution of the technology towards a circular economy (World Biogas Association, 2019). The process also contributes towards food security by reducing dependance on organic fertilizers and recirculating phosphorus into the soil. Also, sanitation and waste management are another benefit provided by the technology, sustainable management of which is a major objective of the study. In addition, the generated biogas also provides a possibility of storage which is not possible in other popular renewable energy sources. However due to financial and technical restrictions, biogas storage in large volume is not recommended on a small farm scale system and only to be done in small quantities to avoid wastage during maintenance (World Biogas Association, 2019). The social benefits are another aspect which makes the operation of the system in a rural setting even more attractive. In addition to the short-term construction employment, growth in newer enterprises contributing to growth in economic benefits can be observed. Also, improvement in crop yield and positive contribution to the agriculture sector can be anticipated. The impact of biogas and biomethane towards reduction in emissions is noteworthy as it contributes to the reduction in fossil fuel dependence, prevents methane slips (Lukehurst and Bywater, 2015) from manure, stores carbon in soils, produces green fertilizers and enables carbon re-use. This gives a possibility to create a scenario of a negative carbon footprint (European Biogas Association, 2020). The use of biogas and biomethane prevents the dependence on fossil fuels. Upgraded biogas provide the possibility for injection of natural gas grid, traditional gas application power generation and use in transport. Even though the grid injection may not be a possibility for Loop Head at University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 113 present the later 3 provides immense opportunity to reduce the dependence on both micro and macro level on imported fossil fuel. In addition, the digestate, leaving the AD after the digestion provides a high grade biological and green fertilizer which substitute and replace the use of mineral fertilizers, avoiding the emissions related to their energy-intensive production (Lukehurst and Bywater, 2015). The upgraded gas also provides a possibility for application in the industrial process which could result in the establishment of potential market for the future (European Biogas Association, 2020). Circular economy and Biogas As mentioned in this introduction circular economy is an alternative to a linear economy. In a circular economy resources are kept in use for as long as possible, with the maximum value extracted from them while in use, and materials and resources are recovered and regenerated at the end of each service life. The application of the circular economy principal is well suited to rural farming communities. As proposed by Blades et al (2017), an anaerobic digestor and biogas production could be at the center of a smart sustainable energy infrastructure. The image below shows the example presented by Blades et al, farms will supply, and slurry feedstock and local industry will supply food waste to generate electricity and biomethane. As it is observed in following image the energy produced will go back into the local community, providing fuel for the farmyard vehicles, local and industrial transport. In this image can be observed that the electricity produced can supply electricity to farms, homes, and industry. The digestate which is a coproduct is utilized as fertilizer (Blades et al., 2017). University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 114 Source: (Blades et al., 2017) Figure: Biogas for Circularity in Dairy Farms E. Solar PV systems Solar photovoltaic power (PV) offers farmers the opportunity to achieve sustainable agriculture and the ability to reduce energy bills. As part of the government policy priorities, incentives to encourage the production of renewable energy has been designed. Further analysis into policies and regulations for solar PV system installation in Ireland can be find in chapter 3. The most common solar energy systems designs are grid-connected, grid-connected with energy storage, off-grid. The grid-connected system is connected to the local electric grid, consuming energy from the grid in case the production cannot meet the demand and sending back to the grid in case the production exceed the consumption. The grid-connected with energy storage system is similar to the grid-connected design but presents an energy storage system usually in the form of University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 115 battery backup. The off-grid system is not connected to the grid and is usually installed in remote areas where it not viable the connection to the electric grid. (Solarvest, 2021) In this work, it is simulated a rooftop grid-connected system and the design is shown in the Error! R eference source not found.: Source: (TendersOnTime, 2021) Figure: Grid-Connected Rooftop Solar PV Systems The Solar PV rooftop system is basically a small power plant at the rooftop, and it consists mainly of the solar PV modules, mounting structure for the modules, an inverter, and the metering. (TendersOnTime, 2021). There are three main types of solar panels in the market: Monocrystalline - Mono-SI, Polycrystalline - Poly-SI and Thin-Film Solar Cells – TFSC. They differ in cost, efficiency rate, lifetime, flexibility on the modules to be installed and space efficiency. (Greenmatch, 2021). F. Policies and Regulations F.1. Circular Economy In 2020 the government published the “Ireland’s National Waste Policy for 2020-2025”. This document states that the government is fully committed to transition to a circular economy. As a University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 116 result the government of Ireland created the “Waste Action Plan for a Circular Economy” , the aim of this plan is that Ireland places at the vanguard of the European Union efforts to transition to a circular economy level (Government of Ireland, 2021b). In the “Ireland’s National Waste Policy for 2020-2025” some of the principal objectives of this policy are mentioned above (Government of Ireland, 2021b): • Shift the focus away from waste disposal and treatment to ensure that materials and products remain in productive use for longer thereby preventing waste. • Ensure that measures support sustainable economic models (for example by supporting the use of recycled over virgin materials). • Harness the reach and influence of all sectors including the voluntary sector, R&D, producers. manufacturers, regulatory bodies, civic society. • Support clear and robust institutional arrangements for the waste sector, including through a strengthened role for local authorities. Bioeconomy is an option to achieve a circular economy in rural and coastal areas, this model can assist these areas in meeting challenges such as regional development and employment growth by introducing new business models, driving energy sector security by boosting the supply of domestic clean energy resources and maintaining and enhancing natural capital and protecting the environment. The government of Ireland is aware on how bioeconomy could move Ireland beyond simply a target compliance and carbon mitigation, in his case bioeconomy is focused in the integration of sustainable economic development which transitions to a low carbon and circular economy (Government of Ireland, 2018). In February 2018, the government of Ireland released the National Policy Statement on the Bioeconomy. In this document the government of Ireland identified key actions for the future success of the bioeconomy. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 117 Some of the key findings stated in the “National Policy statement on the Bioeconomy “are mentioned below (Government of Ireland, 2018): • Ensure that there is coherence between all sectoral strategies which impact on the bioeconomy in Ireland. • Establish a network comprised of representatives of commercial entities operating within the bio-economy and relevant public bodies to inform the future development of the bioeconomy. • Encourage the translation of research into real world applications through promoting collaboration between research institutions (academia) and industry. • Examine how greater primary producer, public and consumer awareness of the bioeconomy and its products could be built up - through knowledge transfer, advisory, sustainable business models, public procurement, consumer awareness campaigns and product labelling initiatives etc. The policies found related to circular economy show that there is an interest from the government of Ireland in developing circular economy initiatives in the country, but more in a general scope. In case of the farming sector, it seems that the efforts to initiate a transition to circular economy started with the “National Policy statement on the Bioeconomy “but the documents shows more a starting point that a clear route on how to reach circular economy on farms. F.2. 5.1.2.2 Solar PV Currently, there are no support schemes in terms of feed-in-tariff in Ireland. However, the arrival of the SEAI solar PV grant in 2018 for existing domestic homes built before 2011 July encouraged micro-generation in private homes and result in a proliferation of solar PV and renewable energy throughout the country. A total of 4,353 homes benefits from the solar PV grant, installing 15MW of micro-generation, at a cost of €10.7m, saving 4,381 tons of CO2 emissions annually. After a scheme review in 2020, the grants rates suffer some changes, including the requirement of University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 118 minimum Building Energy Rating (BER) rating to benefit. The strategy is to ensure the main principle of the scheme is pursued, which is energy efficiency. (Government of Ireland, 2021a). A public consultation proposing a new Micro-Generation Support Scheme (MSS) was prepared by the Department of the Environment, Climate and Communications, from the Government of Ireland. It is part of the scope of the MSS to identify challenges that needed to be addressed to provide a path to market for citizens and communities to generate their own renewable energy, receive a fair and efficient price when doing so. (Government of Ireland, 2021a) In response to this public consultation, in February 2021, the Irish Solar Energy Association (ISEA) elaborated a document providing contributions to the new Micro-generation Support Scheme (MSS) in Ireland. ISEA main goal is to increase in the rollout of solar PV and they emphasize that the incentive and support scheme for rooftop solar PV needs to be revised in order to achieve the government target by 2030. (Irish Solar Association, 2021) Throughout a revision from the Micro-generation Support Scheme (MSS), the ISEA and its members provide their suggestion, as follows: (Irish Solar Association, 2021) • Simplify the pricing structure. • Reduce planning requirements. • No BER preconditions • Inclusion of new builds • No export connection requirement Regarding pricing structure, the Irish Solar Association suggested to include a Clean Export Guarantee (CEG) where more of the value of the Clean Export Premium (CEP) is reallocated upfront. For the planning permission requirements, they pointed it should ease the planning permission requirements for reasonable sized rooftop installations. Concerning BER preconditions and inclusion of new buildings, they recommended expanding the scope, including all rated houses and make new buildings eligible for the MSS scheme to ensure they engage in the solar PV rollout. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 119 For the export connection requirement, the association suggested to avoid the introduction of any connection requirement for any additional export connection. (Irish Solar Association, 2021). No substantial changes was promoted until this reported was written, although good perspectives to come in terms of new policies for Ireland. So, the current policies and regulation regarding solar PV system in the country remain the same as described in the report from International Class 2020. According to the Enerpower website, a renewable energy grant is accessible through the Targeted Agricultural Modernisation Schemes – TAMS system, with grants available for energy efficiencies and renewable energy technologies. The Renewable energy farm grant was announced in March 2019 and opened one month later. (Enerpower 2021) Under certain criterias, the Sustainable Energy Authority of Ireland (SEAI) provides grants up to €3,000 for home solar panels. The criterias are: the house must have been built before 2011, must achieve a building energy rating of C3 or better after installing the panels and must get the panels installed by an SEAI-registered contractor. The grant is based on the size of system and battery also increases the grant amount. (Energyd, 2021). As expected, to start in the summer of 2021, with the start of export payments of electricity, it is very likely that the grant scheme will end soon. It is announced by the government that the export payment should start in July 2021 and smart meters will be used to measure the surplus electricity exported to the grid. The plan for the scheme is composed by two main parts: (Energyd, 2021). • Smart Export Guarantee: The Smart Export Guarantee will be available to everybody, no matter when the solar panels have been installed. And the payment of electricity exported to the grid will be based on the market value provided by the electricity supplier, with no cap on the amount of electricity sold. • Clean Export Premium Tariff: It will be a bonus payment on top of the smart export guarantee, valid for solar panel systems installed after June 2021. It will be funded via public service obligation (PSO) levy and payments are capped at 30% of the electricity generated, with a maximum system size of 50kW. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 120 Concerning planning permission, it is required for installation of solar panels on agricultural structures, or within the curtilage of an agricultural holding, house, or any buildings. It is considered exemptions ground mounted solar and roof mounted under certain conditions. The array of the ground mounted system shall not exceed 25m2 and the height of the free-standing solar array shall not exceed 2m. For the roof mounted, it shall not exceed 50m2 or 50% of the total roof area and the solar panels shall be a minimum of 50cm from the edge of the wall or roof on which it is mounted. (Teagasc, 2018). For grid connections to the electricity distribution network operator ESB, no prior permission is required for systems up to 16A per phase that is 3.68kW single phase and 11.04kW three phase. If the system will export energy to the grid and is over 26A/16A, a pre-approval is required. The commission of the system must be in compliance with EN50438. (Teagasc, 2018). It worth mentioning that, when a farmer is purchasing a solar PV system designed to be used primarily or solely in his or her farming business, a flat rate farmer can claim back the VAT incurred on the acquisition of the system. (Teagasc, 2018). Thus, the existing policies on solar PV provide favorable grounds for the initiation of solar PV rooftop systems. Also, future iterations of the policy are expected to get favorable, further confirming a bright future for solar technology. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 121 G. Energy Modelling “Energy models are simplified versions and mathematical representations of the real energy system” (Poudyal and Paatero, 2013, p. 192). A real-world energy system may contain an enormously large number of variables, constants, functions, or situations which even cannot be represented mathematically. A model categorizes similar variables, simplifies them by aggregating, makes rational assumptions, puts limits by assigning constraints, and applies best-fit mathematical equations to depict the real-world system as precisely as possible. According to (Poudyal and Paatero, 2013), a model helps to understand the problem and to investigate the solutions. Similarly, an energy model helps to: a. Allocate resources. The resources, in general, are scarce and expensive. Resource allocation helps in the effective planning of required resources and assists to utilize resources optimally. b. Find optimal solutions. There could be more than one solution to any problems. But within any solutions, there can be just one optimal point. Models use mathematical functions to calculate the optimal point where there are no other feasible solutions. c. Explore and predict the future. The statistical functions applied in the model analyze previous trends and anticipate the future over a period. d. Visualize complex systems in a comprehensible format. With the help of information categorization, visualization techniques, and systemic design, a model can help to understand and visualize the complex system. e. Organize the data. Models allow to enter the data in the desired datatype, and tabulated format which helps in the data query. f. Provide a consistent framework for testing any hypothesis. It could be impractical or expensive to perform tests in the real energy system. Hence, a model is convenient to test the hypothesis repeatedly. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 122 g. Communicate assumptions among decision-makers and between decision-makers and stakeholders. It helps to make decisions transparently by communicating assumptions and believes between the decision-makers and other stakeholders. An energy model can be developed using programming or using any specialized software. G.1. Purpose of Energy Modelling As a model is always the simplification of a real system, it lacks certain details. It depends upon the objective and purpose of the study to identify what level of detail to include and what not to include. The more purposes we want a model to serve, the more complex it becomes. Hence, there is always a tradeoff between objectives and the simplicity of a model. (Ringkjob et al., 2018) have identified four purposes of energy models. A model can be developed to serve just one of the following purposes or to serve several or all the purposes. Source: (Ringkjob et al., 2018) Figure: Purpose of an Energy Model Purpose of an energy model Power System Analysis Operational Decision Support Investment Decision Support Scenario Development University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 123 a. Power System Analysis – In order to study the electrical power systems for power flows analysis, grid stability analysis, fault level studies, etc. in detail, such models or tools are developed. b. Operational Decision Support – To optimize the dispatch of the energy or electrical system, such models are developed. Such models work as a decision support system tool which studies the flexibility in renewable energy-based power system for stable operation of the system. c. Investment Decision Support – An energy model could have the purpose of optimizing the investment in the energy system. The energy sectors have long investment cycles; hence, such models are usually long-term analysis models. d. Scenario Development – One purpose of an energy model is to develop scenarios to evaluate the impacts of various policies. G.2. Approach to Energy Modelling (Nakata, 2004) has conferred about two approaches for examining the linkages between the economy and the energy system while developing energy models, namely; top-down models and bottom-up models. Top-down model – Such models examine a broad equilibrium framework. The framework addresses the feedback between the energy sector and other economic sectors, and between the macroeconomic impacts of policies on national, regional, or global scale. Such, top-down model has limited detail in the energy demand side of the economy. In addition, the details on specific technologies are not directly captured. Bottom-up model – The bottom-up models capture technological details from the engineering standpoint. For instance, a technique related to energy consumption, or supply, with a given technical performance and cost. The bottom-up energy models focus on the microeconomic level and detailed analysis of the technical and economic dimensions of policy. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 124 H. Community Engament Workshops-Discussions H.1. Session 1 Discussion I The first discussion of the day took place after the presentation. The discussion session aimed to address questions from the participants on the presentation content. Also, to have interaction on energy consumption in the residential and farming sector, mainly the consumption pattern and the energy consumption by different appliances. Further to explain the concept of energy auditing to the participants. Important questions: Question: What is the impact of COVID on the energy consumption pattern? Answer: The energy consumption for the residential sector during the COVID period might have increased due to increased household occupancy. But a detailed study is required to validate if there has been any change. A preliminary examination can be done by comparing electricity billings with the pre-pandemic year’s bills for the same period. Question: On the electricity bill the electricity cost is 0.1548 per kWh. How is the total cost calculated? Answer: The total billing amount for the electricity tariff can be calculated by multiplying the tariff rate 0.1548/kWh with a factor of 1.135 to calculate the billing amount. The tariff as mention in the energy bills is net tariff rates which are exclusive of VAT of 13.5%. Question: Is the energy bill inclusive of heating costs? Answer: The average of 350 kWh/month is only for electricity and does not include the cost of heating. Two participants shared their experience and the reduction in energy purchase with the installation of a solar photovoltaic system. The participants have installed a Solar rooftop PV system with a University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 125 capacity of 4.76 kWp and 3.06 kWp at €10500 (€3000 grant) and €7730(€2730 grant), respectively. Discussion II Similar to the first discussion session, a discussion was held to clarify queries on the presentation content. Important questions that were asked by the participants are shown below: Question: Do people have a feeling that the incentive available is not enough for people to make changes to their building? Answer: An incentive is an economic mechanism designed to have something started. It is difficult to develop an incentive, particularly with technology like PV, which is developing at a rapid pace. The nature of incentive is based on the gradual removal of subsidies adjusted to the learning curve of technologies. In the case of building insulation, the incentives are poorly designed, due to the complexity of these activities. Question: It is expensive to implement energy efficiency measures at Loop Head. How can this be resolved? Answer: One of the ways to reduce the cost of energy efficiency improvement is to develop local skills. Additionally, this will create job opportunities and reduce the cost of rolling out energy- efficient technologies as more households will be interested in using the local manpower. Question: What is the best way to insulate Stonewalled house? Answer: The main problem with stone-walled houses is moisture build-up when insulation is applied to the inner walls. Insulation from the outside is the best option to insulate the walls to avoid excess moisture. However, the type of insulation is dependent on the actual wall structure. Question: What are the steps involved in retrofitting a residential building? Answer: In the first step a BER assessor will examine the house for insulation level for walls, roof, and windows, installed heating system, and the total energy demand is calculated. This collected data is entered into the SEAI tool (DIPAIR) to examine the BER rating. After the University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 126 implementation of recommendation based on BER assessment, an energy audit is conducted to check the energy efficiency performance. In the case of installation of a heat pump as per the criteria, a house must reach a certain level of energy performance to maintain the coefficient of performance of the heat pump. Also, a preliminary online assessment can be conducted with SEAI online tools that provide recommendations to improve building insulation to improve the building efficiency before getting an actual BER rating. H.2. Session 2 Discussion I A discussion session was conducted to clarify the topic discussed. Some of the important questions from the participants are: Question: Is the idea of a circular economy only for a large scale or can it be scaled down for a farm? Answer: It can be applied to small farms. But this could lead to higher cost. The best scenario will be to use a combined digester where the waste from farms in proximity can be used. Connecting the farms in proximity to a pipeline could be a potential way to reduce transportation cost. Farm size biogas technologies plant is a mature technology that has been in the market for more than 30 years. The main problem with the small biogas plant is the cost of the auxiliary system which is proportionally higher for smaller biogas systems compared with larger systems. Question: Slurry is an important part of the Farming sector, how is using an anaerobic digester advantageous to farms? Answer: Anaerobic Digester can be used both as a waste management solution and a way to generate Biogas. Additionally, a co-product from the anaerobic digestion can be used as fertilizer. This fertilizer is mineralized and has higher nutrient content and can easily be absorbed by the plants. University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 127 Question: Would it be beneficial to add excess seaweed from beaches to the anaerobic process? Answer: As long as a consistent supply is available, it can be used for biogas production. The IC 2020 conducted a study of using seaweed, but the initial study showed that the biggest obstacle in this approach was the logistic cost, collection cost, and the amount of seaweed available. Seaweed directly out of the sea cannot be used in anaerobic digestion due to the salt presence and requires a cleanup before being fed to the digestor. Question: What is the breakeven size for the Biogas plant? Answer: A recent study from Ireland, "Economic and Environmental Analysis of Small-Scale Anaerobic Digestion Plants on Irish Dairy Farms" (https://www.mdpi.com/1996- 1073/13/3/637/htm) shows that Biogas plant with a herd size larger than 100 will have a payback time of 7 to 13 years. Question: What type of residue can be used? Do general garden waste and food waste / certified compostable packaging waste would be suitable and useable in the AD plant? Answer: Biomass Waste which is rich in fat and starch can be used for anaerobic digestion. It is important to maintain a balanced source of Biomass as the size of the biogas plant depends on the type of feed source. Concerning packaging waste, it can be used for the AD process if the packaging is starch-based and is made with biodegradable materials. Question: What size AD system would deliver the necessary outputs to operate the Waterworld? Answer: The AD system can be estimated using the current energy requirement. Based on IC 2020 study, there are two 500 kW oil boilers and the energy consumption for three months of operation was 147.8 MWh. It was estimated in the same study that a biofuel plant with a capacity of 150 kW will provide sufficient energy required to power and heat water. Discussion II A discussion session followed the presentation session to clarify the questions raised during the presentation. Some of the important questions are listed below: https://www.mdpi.com/1996-1073/13/3/637/htm https://www.mdpi.com/1996-1073/13/3/637/htm University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 128 Question: Do the panels degrade with age? Answer: The design lifetime of a solar panel is about 20 years on average. Annually panels degrade at a linear rate of 1% to 1.5% of their rated capacity. This degradation is high during the initial age of the panel. The degradation of panels also depends on the ambient temperature and due to the cooler temperature in Ireland, the degradation of the panel is expected to be slower than in a location with a hot climate. Question: Is there a difference in the maintenance requirement for different PV panel technology? Answer: There is no specific maintenance requirement for different panel technologies types, the common maintenance requirement is to keep the surface clean. H.3. Session 3 Discussion I Question 1: How did you galvanize the community to build interest and take ownership of the project? Answer: There were people who were never going to be interested but a majority of people had thought about it. There was a high level of awareness about energy issues, the school children were a good audience. The students were very engaged and understood what was going on from the start. They also raised money to buy shares in the project and discussed how they could make the school building more energy efficient. The presence of the International class was also valuable, they visited almost every house and spoke to the people about energy issues. The community members were more enthusiastic to listen to them. Question 2: Did you receive LEADER Funding? Was there any local authority or local government funding? University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 129 Answer: There was LEADER funding for the community toilet for solar panels to provide warm water. There was also Scottish Rural Development Program funding through the EU. Question 3: Is the land all community owned? Answer: The land was owned by the Applecross Trust which is a charitable trust by the landowners and does not include any local community members. The community is renting the land from them. Because of the land reform by the Scottish government, they have started to sell land. The plan is to buy the land from them because otherwise they will end up owning the scheme as well, which is not acceptable. Question 4: Who did the business plan? Did you have expertise in that area within the group? Answer: We had expert advice from an accountancy firm that specialize in that area, who put the figures together. We also got financing to pay another community-based group who also completed a Hydro scheme a year before, to develop a business plan around those figures and I [Alison] turned it around to the local story. Question 5: How has the Applecross Community Cooperative helped the households improve energy efficiency in their buildings? Answer: Unfortunately, that was not a very successful scheme. There were lots of funding for energy efficiency but retrofitting households in Applecross is very difficult because the houses are very small and are mostly exposed to high winds. By the time the applications were completed there were not any grants, but loans and it was difficult to convince people to take up loans to retrofit their homes. There was training for local workers to carry out the retrofits and they helped some households. Community owned buildings were retrofitted instead because there was funding for that. Discussion II Question 1: How did the community organization start in the first place? University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 130 Answer: Like any sustainable community, there has to be someone to persuade the community. One of our community leaders thought Hydrogen was worth looking at. Some wacky things might have to be done to get the attention of people. In Shetland, school kids were involved in the naming the wind turbines, the prime minister was also invited to launch the project. This grabbed the attention of the community members and encouraged them to take ownership and now the community is proud of the achievement of the Pure Project and the Pure Energy Centre. Question 2: Has the government helped? Is the limited involvement of the government a license to just get up and go? How could they have helped? Answer: The government has not helped much but that has made us more determined to stand on our own and not depend on any government for money. There was a lot of funding for demonstration projects, but this was all put in one area. This could have been moved around a bit to allow communities to get their projects moving a bit quicker. Question 3: How many local jobs has the community organization created? Answer: The community organization has been working for a long time and has employed a lot of people. They also take on students in the summer to conduct projects and provided funding to community groups from the resources they have every year. Question 4: It is better to not be reliant on any government as policy changes rapidly. The best thing for a community is to have its own income. How does this relate to the situation in Ireland? Answer: Because of regulatory framework in Ireland, it is best to work in conjunction with the local authority but with a view of the objective of the community. If the community has their own income, they have a lot more freedom, but it is great if there is a working relationship between the community and the authorities because the community will still need to work within the regulatory framework. Question 5: hat is the involvement of the county council in the development of the SEC master plan in Kilkee? Are there any communities where the SEAI are working with county councils? University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 131 Answer: At the moment there isn’t any involvement from the county council. The community applied for grant through the SEAI as a part of the community energy project. There is a mentor who comes and meets with the community to discuss what needs to be done and the objectives of the community. Then the application is filled, the company registered, and the community project identified. This is used to apply for grant aid. SEAI sent a list of energy auditors to work on the master plan. In Kilkee, the Tipperary Energy Board was selected, and the funding is now available to carry out the master plan. There are communities where the county councils play a role but that is primarily to provide bridging finance. In many communities where there are buildings, housing stocks, civic buildings owned by the local authorities, they become stakeholders. Question 6: Is their funding available to train people? Answer: There are and will be opportunities for further training. There are also training courses in SEAI which are free. H.4. Session 4 Discussion As a prologue to the discussion, there was a poll looking at the areas of interest for the community members present going forward. The results of the poll indicate a high desire for retrofitting households to improve energy efficiency and installation of rooftop PV systems to generate energy locally. This was found to align with the objectives discussed in the first breakout session. The results of the poll asking the participants for their area of highest interest is as shown: University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 132 Question 1: What is the challenge with implementing total retrofits in household? Answer: With the different types of house, of which only a small percentage are terraced and located in the villages in Carrigaholt and Cross, most of the other houses are standalone. Most of the people living the older houses are living alone and as such investing 30,000 Euro for retrofitting is a nonstarter. However smaller roof insulations may be more achievable. Question 2: What percentage of the households can be fully retrofitted? Answer: The data is not available but there could be a survey to count the single houses, older houses that need retrofitting. In the case of holiday homes, the investment required may be questionable based on the amount of time spent in it. However, if a holiday home is insulated, it may allow the owners to use it more often than usual. Question 3: Should the model focus only on occupied residence houses since the holiday homes are occupied for only short periods of time? University of Flensburg Energy & Environmental Management in Developing Countries (M. Eng.) International Class 133 Answer: In the model, the heat demand values for occupied houses and holiday homes are 1300kWh per annum and 273kWh per annum, respectively. The limited use of holiday homes is already accounted for in the model. The values considered are from census data and can be adjusted when new data is available. Question 4: Does the model also consider energy usage during non-use periods, such as energy used for heating to keep the house warm while occupants are away? Answer: A lot people having heating system commands which allows them to control the heating for an hour or two a day, these systems are cheap and minimal. In the model the energy demand is aggregated hourly data and the profile of the heat demand is representative to some extent. This a challenge with all models that work on aggregated data and the model must therefore be simplified to meet specific objectives. Question 5: To what extent would you like to master the use of the Loop Head Peninsula Energy Model? Answer: The community members express interest in mastering the Loop Head Peninsula Energy Model, with two thirds of the participants expressing an interest to get at least an in- depth understanding on the model. This is shown in the poll results below:

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*innen an der EUF eingeschrieben. 2012 2012 0 € 500.000 € 1.0000.000 € 1.5000.000 € EUBund Sonstige Mittel BundDFG DFGEU Landesmittel Stiftungen Spenden 2013 2013 2014 2014 2015 2015 2016 2016 2017

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*innen an der EUF eingeschrieben. 2012 2012 0 € 500.000 € 1.0000.000 € 1.5000.000 € EUBund Sonstige Mittel BundDFG DFGEU Landesmittel Stiftungen Spenden 2013 2013 2014 2014 2015 2015 2016 2016 2017

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