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A life cycle thinking approach for planning renewable energy systems : net-zero transformation strategies… Karunathilake, Hirushie Pramuditha 2019

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 A LIFE CYCLE THINKING APPROACH FOR PLANNING RENEWABLE ENERGY SYSTEMS: NET-ZERO TRANSFORMATION STRATEGIES FOR COMMUNITIES by  Hirushie Pramuditha Karunathilake  B.Sc. Eng. (Hons), University of Moratuwa, 2014  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES  (Mechanical Engineering)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan)  January 2019  © Hirushie Pramuditha Karunathilake, 2018 ii   The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: A life cycle thinking approach for planning renewable energy systems: Net-zero transformation strategies for communities  submitted by Hirushie Pramuditha Karunathilake   in partial fulfillment of the requirements for the degree of Doctor of Philosophy  Examining Committee: Kasun Hewage, PhD (School of Engineering) Supervisor  Rehan Sadiq, PhD (School of Engineering) Supervisory Committee Member  Joshua Brinkerhoff, PhD (School of Engineering) Supervisory Committee Member Mike Chiasson (Faculty of Management) University Examiner Faisal Khan (Process Engineering, Memorial University of Newfoundland) External Examiner  iii  Abstract In Canada, 80% of the greenhouse gas emissions has been attributed to the use of energy in various forms, and the Government of Canada has set out ambitious emissions reduction targets. Moreover, around 8% of Canadian households are suffering from energy poverty. Developing energy systems at community level allows better adaptation to the needs and conditions of the community, and allows the local populace to be in control of their resource-related decisions. In net-zero energy communities, the reduced energy demand of a community is met through locally available renewable energy resources. Although net-zero energy status is proposed as the solution towards energy security and energy independence especially among remote communities, hardly any studies have been carried out to suggest pathways for planning to attain net-zero status. The main goal of this research is to develop a decision support framework, based on life cycle thinking, to transform Canadian communities to net-zero status through renewable energy integration. Multi-criteria decision making methods were used in selecting the most suitable energy technologies at community level, and in identifying the optimal energy system mix and sizing at building and community levels. A risk-based dynamic modelling method was used in evaluating investment decisions in energy system planning, and best practices were proposed for implementing and contracting community energy projects by engaging stakeholder groups at all levels. A fuzzy logic-based approach was integrated in the decision making to account for the uncertainties and variabilities associated with energy system planning. The deliverables of this research will aid in decision making for initial community planning and continuous improvement. The developed decision support framework will assist municipalities and community developers in identifying the optimal energy choices and assessing the feasibility of net-zero energy systems during the pre-project planning stage of urban development projects. It will ensure that communities possess the necessary technical support to achieve their energy and climate action goals, and that they will not be hindered in their sustainability initiatives due to lack of expertise. The results will contribute to the development of energy secure and energy independent communities, and to alleviate energy poverty especially in remote communities.    iv  Lay Summary The study involves the development of energy sustainable communities in British Columbia, and across Canada. The main goal is to develop a decision support framework based on life cycle thinking for integrating net-zero to net positive hybrid energy systems in Canadian communities. Net-zero energy communities (NZEC) can deliver climate solutions, and ensure long term energy security and energy independence. In NZEC, the reduced energy demand of a community is met through locally available renewable energy resources. This study integrates life cycle thinking with decision making for decentralized energy systems (at building and community levels), to develop robust solutions considering environment, economy, and stakeholder needs. The results were used to compile industry-ready decision support and planning tools for energy system planning. These tools will assist municipalities and community developers in identifying the optimal energy choices and assessing the feasibility of NZE systems during pre-project planning stage of urban development projects.    v  Preface I, Hirushie Karunathilake, developed the research concepts presented in this thesis, and conducted the relevant analyses. The study originated from a journal paper published in collaboration with Dr. Rajeev Ruparathna and Mr. Piyaruwan Perera, where a scenario-based assessment was carried out for renewable energy based urban community development. In the above paper, I developed the renewable energy integration scenarios for a small residential community, carried out an economic and emissions impact assessment, and wrote most of the manuscript. Based on this study and the subsequent literature review carried out by myself on building energy systems and community-level energy planning, I developed the energy planning concepts presented in this thesis. Three journal articles and three conference articles have been published based on this study, and two more journal articles are currently under preparation based on the contents of the Chapters 5 and 6 of the thesis. One more conference article is under preparation on construction of renewable-powered green building clusters, and an abstract has been submitted to a conference. The research was conducted under the supervision of Dr. Kasun Hewage. Dr. Rehan Sadiq, who is a supervisory committee member, assisted in research concept development, and reviewed all the manuscripts and provided recommendations for improving them. Dr. Joshua Brinkerhoff (committee member) assisted by providing recommendations for one journal paper in-progress. The references of the completed and in-progress papers are provided below.  Journal Articles: 1. Karunathilake, H., Hewage, K., Mérida, W., & Sadiq, R. (2019). Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty. Renewable Energy, 130, 558–573. http://doi.org/10.1016/j.renene.2018.06.086 2. Karunathilake, H., Hewage, K., & Sadiq, R. (2017). Opportunities and challenges in energy demand reduction for Canadian residential sector: A review. Renewable and Sustainable Energy Reviews, (February), 0–1. doi: 10.1016/j.rser.2017.07.021 3. Karunathilake, H., Perera, P., Ruparathna, R. Sadiq, S., Hewage, K. (2016) Renewable energy integration into community energy systems: A case study of new urban development, Journal of Cleaner Production, doi:10.1016/j.jclepro.2016.10.067 vi  Conference Articles: 1. Karunathilake, H., Hewage, K., & Sadiq, R. (2018). Mitigating risks and overcoming barriers in Canadian renewable energy projects: A partnering approach. In 1st International Conference on New Horizons in Green Civil Engineering (NHICE-01) 2018. Victoria.  2. Karunathilake, H., Hewage, K., & Sadiq, R. (2017). Renewable energy technology selection for community energy systems: A case study for British Columbia. In CSCE Annual General Conference 2017. Vancouver 3. Karunathilake, H., Hewage, K., & Sadiq, R. (2016). A life cycle perspective of municipal solid waste: Human health risk-energy nexus. In 7th International Conference on Sustainable Built Environment 2016. Kandy.  Articles under preparation 1. Karunathilake, H., Hewage, K., Sadiq, R. (2018). Optimal renewable energy supply choices for net-zero ready buildings in Canada. Completed. (Expected to be submitted to Applied Energy (Elsevier) in December 2018.) 2. Karunathilake, H., Hewage, K., Sadiq, R. (2018). Project deployment strategy selection for net-zero energy system development: A system dynamics approach. Expected to be submitted to Energy (Elsevier) in December 2018. 3. Karunathilake, H., Hewage, K., & Sadiq, R. (2019). Costs of Green Residences in Canada: An Economic and Environmental Analysis of Developing Renewable Powered Building Clusters. In CSCE Construction Specialty Conference 2019. Montreal (Abstract accepted).    vii  Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface..............................................................................................................................................v Table of Contents .......................................................................................................................... vii List of Tables ................................................................................................................................ xii List of Figures ................................................................................................................................xv List of Abbreviations .................................................................................................................. xvii Acknowledgements .................................................................................................................... xviii Dedication ......................................................................................................................................xx Chapter 1: Introduction ................................................................................................................1  Background and pressures .............................................................................................. 1  Research gap ................................................................................................................... 5  Research motivation........................................................................................................ 7  Research objectives ......................................................................................................... 9 1.4.1 Research outcomes and deliverables .......................................................................... 9  Meta language ............................................................................................................... 10  Thesis organisation ....................................................................................................... 11 Chapter 2: Research Methodology .............................................................................................14  Phases of the research ................................................................................................... 14  Phase 1: Literature review and data collection ............................................................. 16 2.2.1 Life cycle impact assessment (LCIA) ....................................................................... 17 2.2.2 Life cycle costing (LCC) and economic feasibility analysis .................................... 18  Phase 2: Technology selection and prioritisation ......................................................... 19  Phase 3: Optimisation of the energy system ................................................................. 19  Phase 4: Project selection framework for net-zero energy system implementation ..... 20  Phase 5: Decision support tools and best practices ....................................................... 21 Chapter 3: Literature Review .....................................................................................................23  Community Energy Systems......................................................................................... 23  Energy demand reduction ............................................................................................. 24 viii   Renewable energy technologies .................................................................................... 29 3.3.1 Renewable energy supply and commercially viable technologies ........................... 32 3.3.2 Technical performance and plant characteristics ...................................................... 33  Developing net-zero energy communities .................................................................... 37  Energy planning ............................................................................................................ 38 3.5.1 Constraints and regional concerns ............................................................................ 40 3.5.2 Community energy planning methods ...................................................................... 42 3.5.2.1 Multi-criteria decision making in energy planning ........................................... 43 3.5.2.2 Uncertainties in energy planning ...................................................................... 45 3.5.2.3 Life cycle thinking in energy planning ............................................................. 46 3.5.2.4 Performance criteria and objectives for RE based energy systems .................. 48  Strategizing for deploying community renewable energy projects .............................. 50  Summary ....................................................................................................................... 54 Chapter 4: Renewable energy selection for net-zero energy communities: Decision making with life cycle thinking .................................................................................................................56  Background ................................................................................................................... 56  Methods and Procedure................................................................................................. 58 4.2.1 Assessment criteria and indicators ............................................................................ 60 4.2.2 Life cycle thinking approach .................................................................................... 62 4.2.3 Decision making under uncertainty .......................................................................... 66 4.2.4 Fuzzy TOPSIS for technology ranking ..................................................................... 68 4.2.5 Case-specific methods and analysis .......................................................................... 70 4.2.5.1 Technical feasibility assessment ....................................................................... 70 4.2.5.2 Data collection .................................................................................................. 72  Results ........................................................................................................................... 75  Discussion ..................................................................................................................... 78  Summary ....................................................................................................................... 83 Chapter 5: Optimal renewable energy supply choices for net-zero communities in Canada........................................................................................................................................................85  Background ................................................................................................................... 85 ix  5.1.1 Renewable based energy systems for buildings........................................................ 85 5.1.2 Renewable energy system planning for communities .............................................. 87  Methods and Procedure................................................................................................. 94 5.2.1 Renewable integration in building energy systems .................................................. 94 5.2.1.1 Building energy use .......................................................................................... 96 5.2.1.2 Renewable energy resources and technologies ................................................. 99 5.2.1.3 Identifying the optimal building energy mix .................................................. 103 5.2.1.4 Performance objectives and weighting for building energy model ................ 104 5.2.1.5 Impact and outcome assessment for performance objectives ......................... 106 5.2.1.6 Fuzzy-based building energy supply optimisation approach .......................... 111 5.2.1.7 Energy system performance assessment ......................................................... 113 5.2.2 Community level renewable energy integration ..................................................... 114 5.2.2.1 Renewable energy generation in centralised RE facilities .............................. 115 5.2.2.2 Technology performance of centralised facilities ........................................... 119 5.2.2.3 Optimal community energy system ................................................................ 120 5.2.3 Case-specific methods and analysis ........................................................................ 122  Results ......................................................................................................................... 131 5.3.1 Building-level optimal RE integration .................................................................... 131 5.3.1.1 Economic analysis of the optimal building energy system combination ........ 134 5.3.1.2 Environmental impact analysis of RE integration at building level ............... 136 5.3.2 Community-level optimal RE integration ............................................................... 137 5.3.2.1 Economic and environmental impact analysis for community level system .. 140  Discussion ................................................................................................................... 142 5.4.1 Optimal energy supply for residential buildings ..................................................... 142 5.4.2 Optimal community energy supply ......................................................................... 145  Summary ..................................................................................................................... 148 Chapter 6: Feasibility of Community Renewable Energy Projects: Risks and Dynamic Conditions Analysis ...................................................................................................................151  Background ................................................................................................................. 151  Methods and Procedure............................................................................................... 153 x  6.2.1 Bayesian networks for risk-based acceptance for RE systems ............................... 153 6.2.1.1 Renewable energy system parameters ............................................................ 154 6.2.1.2 Model development ........................................................................................ 157 6.2.2 System dynamics modelling ................................................................................... 162 6.2.2.1 Renewable energy project scenarios ............................................................... 163 6.2.2.2 Index development for RE project rating........................................................ 165  Results ......................................................................................................................... 170 6.3.1 Bayesian decision networks .................................................................................... 170 6.3.2 Project rating under dynamic system conditions .................................................... 174  Discussion ................................................................................................................... 177  Summary ..................................................................................................................... 181 Chapter 7: Mitigating risks and overcoming barriers in Canadian renewable energy projects: Guidance for implementation and best practices ...................................................182  Renewable energy project life cycle ........................................................................... 182  Barriers, risks, and challenges .................................................................................... 183  Mechanisms for mitigating RE project issues ............................................................ 185  Roadmap for managing RE project challenges ........................................................... 189  Best practices for community RE system management .............................................. 192  Summary ..................................................................................................................... 197 Chapter 8: Conclusions and Recommendations .....................................................................199  Summary and Conclusions ......................................................................................... 199  Originality and Contributions ..................................................................................... 202  Limitations of the Study.............................................................................................. 203  Future Research .......................................................................................................... 205 References ...................................................................................................................................207 Appendices ..................................................................................................................................238 Appendix A : Life cycle impacts of different renewable energy technologies ....................... 238 Appendix B : Waste-To-Energy Technologies: A Life Cycle Perspective of the Human Health Risk-Energy Nexus ................................................................................................................. 240 B.1 Background ............................................................................................................. 240 xi  B.2 Methodology ........................................................................................................... 243 B.3 Results ..................................................................................................................... 248 B.4 Discussion ............................................................................................................... 251 B.5 Summary ................................................................................................................. 252 Appendix C : Technology data ............................................................................................... 254 C.1 Utility cost data ....................................................................................................... 254 Appendix D : Building energy use.......................................................................................... 256 D.1 Building energy database ........................................................................................ 256 D.2 Residential end uses ................................................................................................ 256 D.3 MURB energy intensities ........................................................................................ 257 Appendix E : Energy generation potential for a unit installation of solar energy technologies................................................................................................................................................. 259 Appendix F.............................................................................................................................. 260 F.1 Costing for building level RE technologies ............................................................ 260 F.2 Costing for central level RE technologies .............................................................. 261  xii  List of Tables Table 3-1: Energy use and GHG emissions by sector .................................................................. 25 Table 3-2: Demand reduction intervention categories .................................................................. 25 Table 3-3: Sources of renewable energy ....................................................................................... 29 Table 3-4: Renewable energy technologies .................................................................................. 34 Table 3-5: Renewable energy plant characteristics ...................................................................... 35 Table 3-6: Some commonly used MCDM methods used in RE selection ................................... 44 Table 3-7: Renewable energy assessment criteria ........................................................................ 49 Table 3-8: Objectives of energy system optimisation................................................................... 50 Table 3-9: Classifying issues and solutions in RE project planning ............................................. 53 Table 4-1: Rules for RET technical feasibility assessment .......................................................... 61 Table 4-2: Weighting schemes for different decision scenarios ................................................... 68 Table 4-3: Renewable energy technologies .................................................................................. 71 Table 4-4: RE resource benchmark levels for energy generation ................................................. 72 Table 4-5: Renewable energy plant characteristics ...................................................................... 73 Table 4-6: Results of resource screening ...................................................................................... 75 Table 4-7: Technology rankings under different decision scenarios ............................................ 76 Table 5-1: Energy end uses of the residential sector in BC .......................................................... 86 Table 5-2: Details of the proposed housing units in the community ............................................ 87 Table 5-3: Energy system scenarios developed for the community ............................................. 88 Table 5-4: Community level investment and emissions reduction ............................................... 91 Table 5-5: Additional investment requirement ............................................................................. 91 Table 5-6: Percentage increase in housing prices due to RE interventions .................................. 92 Table 5-7: Lifetime reduction in recurring costs .......................................................................... 92 Table 5-8: Fuzzy weighting scheme of relative importance ....................................................... 106 Table 5-9: Renewable energy technology characteristics ........................................................... 110 Table 5-10: Renewable energy technology characteristics ......................................................... 119 Table 5-11: Monthly space heating load intensity (kWh/m2) ..................................................... 123 Table 5-12: Monthly mean daily insolation for the selected location ........................................ 124 Table 5-13: Solar thermal collector characteristics .................................................................... 124 xiii  Table 5-14: Annual residential energy intensities for BC .......................................................... 125 Table 5-15: Monthly energy demand for the community ........................................................... 126 Table 5-16: Monthly solar energy generation potential .............................................................. 126 Table 5-17: Wind speed distribution and frequency for the proposed site ................................. 127 Table 5-18: Biomass types and their characteristics ................................................................... 128 Table 5-19: Annual biomass resource availability at selected site ............................................. 128 Table 5-20: Weights assigned to performance objectives for building energy system .............. 129 Table 5-21: Weights assigned to performance objectives for community energy system ......... 130 Table 5-22: Life cycle impacts of energy generation ................................................................. 131 Table 5-23: Present value of RE system costs ............................................................................ 132 Table 5-24: Best energy system combinations ........................................................................... 132 Table 5-25: Performance of the selected solution....................................................................... 133 Table 5-26: Economic indicators for the best energy system combinations .............................. 134 Table 5-27: Per capita economic performance for building energy systems .............................. 136 Table 5-28: Life cycle impacts of community level RE technologies ........................................ 137 Table 5-29: Present value of RE system costs ............................................................................ 138 Table 5-30: Optimal energy system combinations for community level energy supply ............ 138 Table 5-31: Performance of the selected solution for community energy system ...................... 139 Table 5-32: Economic indicators for the best energy system combinations .............................. 140 Table 5-33: Per capita economic performance for community energy systems ......................... 141 Table 5-33: Investment and emissions reduction potential for optimal solution ........................ 142 Table 6-1: Decision variables in RE system implementation ..................................................... 155 Table 6-2: Network input nodes ................................................................................................. 157 Table 6-3: Community data ........................................................................................................ 164 Table 6-4: Alternative renewable energy project scenarios ........................................................ 164 Table 6-5: Decision variables of the system dynamics model .................................................... 166 Table 6-6: Weights for performance criteria categories and decision variables ......................... 169 Table 6-7: Project costs and LCOE for different project scenarios ............................................ 176 Table 7-1: Selected opportunities and challenges ....................................................................... 184 Table 7-2: Mitigation mechanisms for RE project issues ........................................................... 186 xiv  Table 7-3: Best management practice checklist for community RE project management ......... 192 Table B- 1: RDF recovery fraction in MSW .............................................................................. 243 Table B- 2: Cancer risk categorisation for contaminants ............................................................ 244 Table B- 3: Human health risk assessment parameters .............................................................. 245 Table B- 4: Emissions inventory for WtE processes .................................................................. 248 Table B- 5: Incremental cancer risk for emitted chemicals of concern ...................................... 249 Table B- 6: Non-cancer risk for emitted chemicals of concern .................................................. 249 Table B- 7: Health risks associated with 1 GWh of annual energy supply ................................ 250 Table E- 1: Solar energy generation potential ............................................................................ 259   xv  List of Figures Figure 1-1: Integration of objectives and thesis organization ....................................................... 13 Figure 2-1: Phases of the research ................................................................................................ 16 Figure 2-2: LCA system boundary for RE technologies .............................................................. 18 Figure 3-1: Mix of energy sources in Canadian RE production ................................................... 31 Figure 3-2: Net-zero energy community planning........................................................................ 37 Figure 3-3: Risks in energy system implementation ..................................................................... 52 Figure 4-1: Energy alternatives for supplying community energy demand ................................. 57 Figure 4-2: Methodology for energy technology selection ........................................................... 59 Figure 4-3: Life cycle thinking approach ...................................................................................... 65 Figure 4-4: Closeness coefficients for RET .................................................................................. 76 Figure 4-5: Change in ranking with technology rating for renewable electricity technologies .... 77 Figure 5-1: Share of fuel sources by scenario ............................................................................... 90 Figure 5-2: Community level cost and emissions forecast ........................................................... 90 Figure 5-3: Overall methodology for building energy system optimisation................................. 95 Figure 5-4: Life cycle impact assessment approach ................................................................... 107 Figure 5-5: Energy use intensity ................................................................................................. 123 Figure 5-6: Energy generation mix at building level .................................................................. 133 Figure 5-7: Depreciation of energy asset value .......................................................................... 135 Figure 5-8: Membership function for total energy system life cycle cost .................................. 135 Figure 5-9: Comparison of normalised life cycle impacts between conventional and renewable energy systems ............................................................................................................................ 136 Figure 5-10: Supply mix at community level ............................................................................. 139 Figure 5-11: Comparison of normalised life cycle impacts at community level ........................ 142 Figure 6-1: Bayesian network development ............................................................................... 153 Figure 6-2: Cognitive mapping of the model inputs ................................................................... 158 Figure 6-3: Rule-based CPT for environmental and social acceptability variable ..................... 160 Figure 6-4: Rule-based CPT for economic success variable ...................................................... 161 Figure 6-5: CPT for technical viability ....................................................................................... 161 Figure 6-6: Methodology for RE project rating index development .......................................... 163 xvi  Figure 6-7: Causal loop diagram of energy demand growth ...................................................... 167 Figure 6-8: Causal loop diagram for growth in RE funding ....................................................... 167 Figure 6-9: System dynamics model for rating RE project scenarios ........................................ 168 Figure 6-10: Conditional probability table for the utility node of acceptance decision ............. 170 Figure 6-11: Bayesian network for RE system implementation decision – Case I .................... 171 Figure 6-12: Bayesian network for RE system implementation decision – Case II ................... 172 Figure 6-13: Network under pessimistic economic conditions................................................... 173 Figure 6-14: Network under pessimistic economic conditions................................................... 174 Figure 6-15: Demand growth in the proposed community ......................................................... 175 Figure 6-16: RE funding comparison ......................................................................................... 175 Figure 6-17: Project rating simulation results ............................................................................. 177 Figure 7-1: RE project life cycle ................................................................................................. 183 Figure 7-2: Matrix of challenges and opportunities for stakeholder groups ............................... 185 Figure 7-3: Integrated roadmap for value chain management in RE projects ............................ 190 Figure 8-1: Net-zero transformation vision ................................................................................ 201 Figure A- 1: Life cycle impacts of RET ..................................................................................... 239 Figure A- 2: Life cycle impacts of RET extended ...................................................................... 239 Figure B- 1: LCA system boundary ............................................................................................ 244 Figure B- 2: Contaminants and exposure pathways and routes .................................................. 245 Figure B- 3: Comparison of health risks per GWh of annual energy supply for City of Kelowna..................................................................................................................................................... 250 Figure D- 1: Monthly building energy use database ................................................................... 256 Figure D- 2: Residential end use fractions .................................................................................. 256 Figure D- 3: Energy use intensity of building stock ................................................................... 257 Figure D- 4: Fuzzy values of energy use intensity ..................................................................... 258 Figure F- 1: Building level RET cost .......................................................................................... 260 Figure F- 2: Community level RET cost..................................................................................... 261  xvii  List of Abbreviations Acronyms AHP  Analytical Hierarchy Process  BC  British Columbia BMP  Best management practices CPT  Conditional probability table DST  Decision support tool GHG  Greenhouse gas GSHP  Ground source heat pumps LCA  Life cycle assessment LCC  Life cycle costing LCI  Life cycle inventory LCIA  Life cycle impact assessment LCOE  Levelised cost of energy MADM Multi-attribute decision making  MCDM Multi-criteria decision making MODM Multi-objective decision making MOO  Multi-objective optimisation MURB Multi-unit residential building NZE  Net-zero energy OTEC  Ocean thermal energy conversion PV  Photovoltaics RDF  Refuse derived fuel RE  Renewable energy RES  Renewable energy sources RET  Renewable energy technologies ST  Solar thermal  TBL  Triple bottom line WtE  Waste-to-energy  Abbreviations and units CAD  Canadian Dollars GJ  Gigajoule kW  Kilowatt kWh  Kilowatt-hours MWh  Megawatt hours O&M  Operations and maintenance USD  United States Dollars xviii  Acknowledgements My deepest gratitude goes to Dr. Kasun Hewage for his support and guidance throughout my graduate studies. He gave me the opportunity to carry out my graduate studies in an area that truly interested me at the University of British Columbia. The experience and exposure I gained under his supervision during my time in his research group has made me a stronger professional, and has given me the confidence to succeed in my graduate studies and my career. I am forever indebted to him for the immense support, kindness, inspiration, and motivation he gave me.  I wish to thank Dr. Rehan Sadiq for his support and mentorship throughout my time at UBC. Despite his busy schedule and numerous responsibilities, he had time to discuss my research and help me in learning and developing new concepts, and he also spared his time to guide me towards being a better professional and a person. I was inspired to become a better academic from his example.  I would like to also thank Dr. Joshua Brinkerhoff and Dr. Abbas Milani for the advice they provided. The course modules taught by Dr. Brinkerhoff and Dr. Milani were truly helpful to me in the course of my studies, and they never hesitated to provide their guidance whenever I required advice. In addition, my thanks go to Dr. Dimitry Sediako for his kindness throughout the time we worked together. Moreover, I would like to acknowledge School of Engineering administrative staff including Ms. Shannon Hohl, Ms. Stephanie Oslund, and Ms. Tanya Chartrand for their support.  The University of Moratuwa, Sri Lanka, gave me the opportunity to pursue my graduate studies by providing me with the requisite study leave from my position as a faculty member. I wish to thank the staff at the Department of Mechanical Engineering for graciously sharing my workload and allowing me the chance to pursue my higher studies. I was motivated to follow an academic career by Dr. Himan Punchihewa, and I will be forever indebted to him for his mentorship and support. He gave me confidence to follow my dreams, and his trust in me gave me the strength to successfully achieve my goals. I also wish to thank Prof. Kapila Perera, Dr. W.K. Wimalsiri, and Dr. Thusitha Sugathapala for supporting, inspiring, and guiding me.  The input from many external institutes and individuals was vital for successful completion of this study. My research was funded through the Canadian Queen Elizabeth II Diamond Jubilee xix  Scholarship and collaborative research grants of Natural Sciences and Engineering Research Council of Canada (NSERC). I would also like to acknowledge New Monaco Enterprise Corporation for providing their data and financial support.  Throughout these years, I have received tremendous support from my colleagues at the Life Cycle Management Laboratory. Dr. Rajeev Ruparathna in particular was always generous with his advice and guidance. He always encouraged me to push past my limits to achieve higher expectations. Without his support, both professional and personal, I could not have succeeded in this task, and I would like to offer my special thanks to him. My warm gratitude goes to Mr. Tharindu Prabatha for his incredible friendship and companionship throughout my undergraduate and graduate studies, as well as in our professional life. He stayed by my side and helped me cope during all the challenges, failures, and hardships in the last nine years. I consider myself fortunate to have had a work partner who supports me in every way during all stages of my academic career.  Lastly, I would like to thank my amazing parents and brother, who always believed in me. They always supported me in every endeavour I undertook, and had more faith in my success than I myself ever did. I am grateful for their love and patience during all these years, and for enduring my erratic schedules and absence away from home without any complaints. I could not have accomplished anything without them. More than anything, I thank them for giving me the freedom to be myself and to follow my own path. xx  Dedication   Dedicated to my beloved family, friends, and teachers, Who loved me, protected me,  and made me who I am…  1  Chapter 1: Introduction  Background and pressures Nearly 80% of the world’s primary energy is provided via fossil fuels such as oil, coal, and natural gas [1]. With the growth in global population and the increase in industrialisation across the world leading to a corresponding growth in the energy consumption, fossil fuel resources are being consumed at a rapid rate [2]. This situation has an adverse impact on the long term energy security of communities, and can also affect the economic growth and development [1]. The rising energy costs and the unstable nature of the fuel prices also indicate the need for alternative solutions [1][3]. Fossil fuel availability and the economics surrounding them are further subject to highly volatile political situations, and many countries are increasingly reluctant to rely on these sources for their energy needs [4]. Studies have indicated that there is annual production decline of around 4-6% in the world’s oil and gas fields [5]. Energy use carries economic burdens, impacting individuals, communities, and countries [6]. Fossil fuel prices are further subject to variations, in response to the economic conditions in the global markets. Oil extraction potential may reduce when oil prices are too low, as the fossil fuels from proven resources cannot be recovered economically [4]. Fossil fuel producers reduce output to increase prices when the global fuel prices drop [7]. Fuel price variations impact the global economic parameters such as inflation and growth in GDP, thus impacting the economy of nations and communities [4]. The use of fossil fuels in energy generation has been associated with significant environmental impacts. Even the recovery of fossil fuel sources through means such as drilling of oil wells and shale gas adds to the environmental impacts of energy generation [8]. In recent times, considerable attention has been given to studying the adverse effects of climate change caused by the release of greenhouse gas (GHG) emissions, and to finding alternatives that can reduce those negative impacts [9]. In Canada, 80% of the GHG emissions has been attributed to the use of energy in various forms [10]. The use of conventional fossil fuels is the most significant source of emissions [11][12]. In 2013, 69.7% of the global anthropogenic emissions was accounted for by fossil fuel combustion [12]. Climate change leads to undesirable impacts such as global warming, melting of polar ice caps, rising sea levels and changes in weather patterns [13][14]. The latest emission reduction target indicated by the Canadian government in alignment with the United Nations 2  climate change conference goals in Paris (COP21) aims to cut down the GHG emissions by 30% of the 2005 levels by the year 2030 [15][16].   Renewable energy (RE) resources are those which can be naturally replenished and thus are sustainable [17][18], such as wind, solar, geothermal energy, biomass, wave and tidal etc. [18][8]. As such, they are known as inexhaustible and clean sources of energy which can ultimately lead to energy independence while generating benefits in terms of environmental and social aspects [18][19]. The drive for increased use of renewables in energy generation has been influenced by the rising global energy demand, scarcity of conventional fossil-based energy sources, and environmental concerns [9]. Renewable energy technologies (RET) have long been promoted as means of achieving energy security and independence from fossil fuels, while also reducing the environmental pollution associated with energy generation [3].  In addition to the costs to the environment, the depletion of conventional non-renewable energy resources has also become a matter of concern. With the growth in global population and the increase in industrialisation across the world leading to a corresponding growth in the energy consumption, fossil fuel resources are being rapidly consumed [2]. This situation has an adverse impact on the long-term energy security of communities, and can also affect the economic growth and development [1]. The rising energy costs and the unstable nature of the fuel prices also indicate the need for alternative solutions [1][3]. Fossil fuel availability and the economics surrounding them are further subject to highly volatile political situations, and many countries are increasingly reluctant to rely on these sources for their energy needs [4]. Renewable energy technologies (RET) have long been promoted as means of achieving energy security and independence from fossil fuels, while also reducing the environmental pollution associated with energy generation [3]. In 2013, 22% of the world’s power generation was renewable-based, and this is predicted to rise to at least 26% by 2020 [20].  It is evident that in order to deliver climate solutions and ensure long-term energy security for communities in the present-day world, managing the energy systems in a sustainable manner is critical. In addition to the substitution of conventional fuel resources with renewables, efficient use of energy and reducing demand also aids in achieving the above goal [21]. By reducing the net energy consumption, supplying the entire demand through RE sources is further facilitated. Demand reduction can be achieved through the use of energy efficient technologies, and 3  implementing demand reduction measures through active and passive technologies such as insulation and shading [22][23]. In addition, behaviours focusing on energy conservation and eliminating waste can also reduce energy consumption.  As a result of the above thinking on delivering climate solutions through clean energy use, the concept of energy sustainable communities has been developed. Focusing on energy systems at the community level allows for planning and design to be better adapted to local conditions and requirements [24]. Community energy initiatives based on decentralised renewable energy generation can aid in better adaptation of energy systems for local needs [24][25]. In community energy systems, the populace is involved as active stakeholders in the decision making process [24]. Hybrid distributed energy systems are now receiving attention as the future in energy generation, as the energy needs of communities grow and diversify [26].  Net-zero communities are an extension of the net-zero building concept, where the reduced energy demand of a community is met through renewable energy sources, primarily located within the community site  [27]. The net-zero concept can be adapted to include site energy or source energy, energy costs and emissions. Through this, energy sustainable communities can be developed.  There are multiple challenges present in using renewable energy to power communities. The need for specialized infrastructure and the high capital and operational costs to accommodate RET are only a part of this. In addition to the technical viability and economic costs of RE, it is vital that the environmental and social costs are also studied in detail to assess the overall feasibility of RE powered community energy systems. The integration of variable renewable energy sources such as wind and solar power to an energy system is subject to an upper limit in order to maintain the system stability [28]. The intermittent nature of such RE sources means that alternative backup power sources need to be put in place when utilising them to supply power [3]. It is necessary to assess the local availability of a RE source in order to ascertain its potential to supply the energy needs of a given locale [3]. Due to the dependency of renewable sources on climatic and other local conditions, extensive planning is necessary for successful integration in a community energy system [26].  Net-zero or net-positive community energy systems have added importance in the context of remote communities where grid connectivity remains difficult [29][30]. Such systems can act as a step in ensuring long-term energy security and energy independence for remote communities, 4  especially in Canada. Previous studies conducted in this area have explored the potential for RE integration in the energy systems for remote off-grid communities in Canada with consideration to availability and economic factors [3][30][31]. However, further work is necessary on identifying the barriers for RE penetration and on delivering optimised solutions with consideration to technical, economic, environmental, and social parameters  [9]. Therefore, delivering an optimised community energy model requires a multi-criteria decision making (MCDM) process.  A significant potential exists for renewable energy sources within Canada [32]. However, while the use of renewable sources in energy systems is expected to reduce the associated emissions of energy generation, it is necessary to assess the overall life cycle costs and impacts of the selected renewable energy technologies [33].  Moving towards net-zero energy ready buildings has been mandated by the BC Energy Step Code with the aim of developing energy sustainable and carbon neutral communities [10]. Creating sustainable cities powered through renewable energy has been envisioned in many official community plans (OCP) across Canada, and has been recommended by the BC climate action toolkit [34][35]. With the current mandates at global, federal, provincial and municipal levels for climate change mitigation, many community energy and emissions plans (CEEP) are aiming to reduce the GHG emissions by shifting to a renewable energy supply [35][36].  While all these mandates and recommendations for community-level renewable energy planning is in place, there is yet a notable lack in the technical support and tools provides to communities in actually operationalising these conceptual energy plans. The need for planning tools for communities has been noted in various forums [36]. While there are different energy planning models proposed for community level [37], there are few practical solutions that assist the community planners in making their decisions at detailed technical level, to identify the best technologies, energy mix, system sizes, and project delivery strategy. Instead, as a comprehensive literature review revealed, most planning models address the policy level planning aspects, and a systematic approach for community energy planning is lacking [37]. While policies are important, it is also necessary that communities are equipped with scientifically evidenced methods based on mathematical logic to identify the best energy choices and implementation strategies for their renewable energy system planning. Detailed guidelines that provide step-by-step decision support 5  on achieving the prescribed climate or energy targets and policy requirements is a key need in the construction industry and urban development sector.   With global population growth, communities are also growing. New neighbourhoods are constantly being planned and developed, and energy is a vital necessity for both urban and rural communities. Decision making for these energy systems needs to be supported through rational and adaptable tools and frameworks for optimal performance and benefits in the operational stages. A life cycle project management perspective needs to be applied in developing decision making systems to create the energy sustainable communities of the future.   Research gap This research originated based on the above identified pressures and challenges in shifting towards clean energy powered communities, from the conventional fossil fuel driven model. Based on a thorough review of the existing literature and tools for energy planning, several gaps were identified in the current body of knowledge.  Life cycle thinking is lacking in energy related decision making: The triple bottom line (TBL) impacts pertaining to society, environment and economy need to be assessed when making decisions about public infrastructure, of which energy systems are a part of [38]. Life cycle sustainability assessment (LSCA) technique can be used to obtain an understanding of the environmental costs associated with the use of RE sources [39][40]. In addition to the immediate and visible impacts of renewable energy use, there are indirect impacts arising from factors such as land and water use, human health risks and public safety, alternative uses for resources, job creation and economic development, as well as effect on surrounding environment and local population [18] [38][41]. The studies conducted on the overall sustainability of renewable energy systems are limited, partly due to the difficulty in developing and quantifying indicators [40][42].While these factors are usually not studied in the course of optimising renewable-based energy systems, they are critical considerations in regional level planning and decision making. Energy planning tools have limited integration of macro-economic variations and uncertainties into decision making: Energy system planning is affected by considerable uncertainties and variability. Resource availability, supply reliability, energy demand, generation capacity, and efficiency are all subject to uncertainties [43][44]. Maintaining the balance between the energy 6  demand and supply is important, and this leads to uncertainty in forecasting, defining the energy mix, and predicting economic and environmental burdens [45]. Social risks such as land availability, political factors as well as legal and regulatory issues are difficult to quantify, and can only be assessed in qualitative form in most cases [45]. The information obtained on environmental and socio-economic factors, locational parameters such as geography and climate, as well as demand and price forecasts are usually vague, incomplete or imprecise [43]. Most of the previous studies and tools treat energy planning as a deterministic problem and neglect its stochastic nature. This issue needs to be addressed through a more comprehensive modelling process which address the uncertainty and variability, and provides better information to the decision makers in selecting their energy choices. There is a lack of urban energy planning frameworks with a systematic and holistic approach to community energy system development: Building and community developers and other decision makers involved in the construction and development project need to be provided with the decision support, tools, and best practice guidance on making energy related choices. Energy system planning with renewables should start with resource potential assessment for a given location, and extend to triple bottom line impact assessment, technology selection, and energy system optimisation with an eye on the long-term viability and risks. Most studies conducted on developing RE systems have taken a scenario-based approach in energy planning [3][30][42][46]. In scenario-based planning, the decision makers define a limited number of energy scenarios by selecting the RES, and defining the mix based on their judgement [3]. The best alternative among these scenarios is then selected through further analysis, considering different selection criteria such as economic performance and environmental impacts [42][46]. While some studies have applied multi-criteria decision making approaches to RE planning [47][48], more attention needs to be paid to the identification of all relevant factors in devising community-level energy systems. So far, there is a lack of decision tools and guidelines that treat energy system planning as a holistic, practical, and systematic process and eliminate the requirement for specialised expertise from the developers and municipal decision makers [37]. The current energy planning tools take a discretized approach to the different activities and aspects of energy planning. There is no single framework which combines the different levels and aspects of community level decentralised energy system planning. Moreover, the existing tools and studies neglect life cycle thinking and 7  uncertainty in the decision making for most part. Energy planning needs to be tackled as a multi-objective, multi-stakeholder, and multi-period problem during the project planning stage of community development and building construction project.  The project delivery and risk sharing aspects have not been considered in promoting RE deployment: Community energy deployments are ultimately projects that need careful strategizing and planning to succeed. Energy related decision making during the pre-project planning stage needs to consider the practical implications of designing and installing energy systems in building and community level applications, and should consider the socio-economic impacts and realities in the decision making. The risks and unknowns should also be carefully managed through strategic project delivery and risk management [49]. While many technological solutions and policies have been brought forth in the RE arena, there is limited consideration towards the best project delivery methods and risk sharing strategies.  Considering the above limitations in the existing body of knowledge and state-of-the-art energy planning tools, the following research questions arose in this study.  i) How can the best energy sources and technologies be selected for decentralised community level energy systems? ii) How can life cycle thinking be integrated with energy system planning for communities? iii) How can energy system planning tools be adapted to reflect the practical engineering aspects and challenges in planning community energy projects?  iv) How can uncertainties and macro-environmental variations be incorporated into energy system planning frameworks?  v) How can the optimal energy systems and implementation strategies be defined in community development projects?   Research motivation  The motivation for the research originates from the evident need for a consolidated approach in planning energy systems powered by renewable sources at community level. Decentralised energy systems are gaining attention in today’s context as the next step towards energy security. Using locally available RES for energy systems is associated with job creation, economic development and reduced pressure on urban centres, while also providing more energy options to remote 8  communities [50]. This is especially important in the Canadian context, where remote communities face many challenges in grid connectivity and economic burdens of energy generation [30].  The RES-based energy generation is expected to mitigate the adverse environmental impacts of energy use, which are commonly associated with the conventional fossil fuel use. RES are also termed as a more sustainable form of energy, particularly due to the limited availability of conventional fuels. However, the actual impact mitigation potential and sustainability of RES need to be evaluated with life cycle thinking in order to achieve the climate change mitigation and economic development goals. In developing energy systems at community level, TBL sustainability is important. The energy systems need to meet the environmental, economic and social expectation of different stakeholders, while being technically feasible and stable in the long-term.  Net-zero energy community concept leads to energy independent communities fully powered through RE with minimal environmental impact. While community level energy planning is a much discussed topic, a comprehensive approach which provides decision making support to the policy makers and developers is a key necessity for its success in practice [51]. At the present, decision making on community level RE planning is generally done on an ad hoc basis in a reactive manner. Selection of energy technologies and developing integrated energy system usually involve a scenario-based approach [42][46]. An integrated approach which combines technical, economic, environmental and social aspects to develop optimised energy systems is emphasized by many previous researchers. To plan NZE communities, developers, planners, and users need to be given tools to select the most suitable technologies to provide optimal energy planning solutions by considering the practical constraints. Providing easy-to-use tools to decision makers and developers will incentivise the use of renewables at community level [42]. Therefore, policy makers and developers need to be provided with a comprehensive decision framework and user-friendly, evidence-based, and logical decision support tools. The tools should address all the information needs and the technical implementation considerations in RE-based NZE community planning. This research attempts to fulfill the above requirement for small and medium scale residential communities in Canada.  9   Research objectives The main goal of this research is to develop life cycle thinking based net-zero transformation strategies for small-to-medium scale communities in Canada. The specific sub-objectives of the research are outlined below.  1) Identify technical, economic, environmental and key social constraints and requirements applicable to regional-level renewable energy system planning in Canada 2) Develop a life cycle thinking based approach to rank and select of renewable energy technologies for community energy systems 3) Develop a multi-objective optimisation-based decision support tool to identify optimal energy choices for new and existing communities 4) Develop a project selection framework for feasibility assessment of net-zero to net-positive energy system implementation at community level 5) Recommend best management practices for planning and continuous improvement of net-zero to net positive community energy systems 1.4.1 Research outcomes and deliverables The deliverables of this research will act as an aid in decision making for initial community planning and continuous improvement, with a focus on ensuring the long-term energy security for communities and reducing the adverse environmental, economic, and key social impacts of energy usage. The developed decision tools and best practices will assist during the planning of community RE systems, with special focus on the pre-project planning stage. The outcomes are expected to bring the concept of RE integration to community planning level, specifically for residential communities. In addition to delivering long term energy security and energy independence to communities, it will also align with the climate action goals held by government bodies across Canada. Policy makers and developers will be guided through the decision support framework without additional burdens being placed on them for in-depth technical expertise. For this, the scattered knowledge on sustainable energy planning will be brought together to develop a combined approach. The allocation of limited resources in the most effective and sustainable manner while meeting multiple conflicting objectives, will be assisted through this approach.  10  The decision support framework will aid in both selecting the most locally relevant RE technologies and integrating the selected technologies into the neighbourhood energy system with optimised TBL performance. Exploiting locally available resources for energy generation will lead to economic development and improved quality of life for the local populace of such communities. By focusing on existing communities as well as the newly developed ones, this approach creates a path for all communities in Canada to be converted to “green energy” in an effective manner. A decision support tool which considers the life cycle performance of an energy system will help the users to estimate the future outcomes due to the implementation of such a system at community level. The BMPs will aid users in managing the community energy system without having to seek extensive external expertise. Therefore, it is expected that taking a practical decision making approach to energy system planning will mitigate the negative perception of risk held by stakeholders on RE projects. Furthermore, this will be a step towards ensuring that the best investment choices are made at the planning stage of community level energy systems. The findings can also be potentially further extended and adapted for different types of distributed energy systems.  Meta language Here, the specific terms used in describing the above research objectives are explained.  Life cycle thinking: Life cycle thinking is used to incorporate economic, environmental, and social impacts of a product, process, or a system throughout its life cycle, from raw material extraction to the eventual end-of-life. This approach was used in assessing the life cycle impacts of renewable energy technologies in this study, from facility construction stage, to the disposal at the end of useful life. In assessing the economic impacts of renewable energy systems at building and community level, the system boundary was set considering the renewable energy system implementation and operational project life of buildings and communities.   Small to medium scale communities: Small to medium scale communities in Canada were defined based on the definitions provided by Statistics Canada [52]. Accordingly, the following cut-off values were used in classifying population centres.  Small population centres - population between 1,000 and 29,999 Medium population centres - population between 30,000 and 99,999 11  Large urban population centres - population of 100,000 and above. Only residential communities were considered in developing the models for community energy systems, and the energy consumption data scope was limited to residential buildings and their applications.  Community energy systems: Community energy systems considered in this study focus on supplying the residential energy demand of small to medium neighbourhoods and communities in Canada, with a focus on British Columbia. The results were demonstrated in particular for a proposed residential neighbourhood with a population of 6,500 located in the Okanagan valley.  Best management practices: Best management practices proposed through this study are aimed at contracting and project delivery for renewable energy system implementation. A partnership framework with stakeholders at all levels from municipal authorities to residents was developed as an outcome.   Thesis organisation This thesis consists of eight chapters, which focus on the literature review, methods used, findings, and the conclusions.  Chapter 1: This chapter provides an overall introduction to the background and pressures, research gaps, motivation, objectives and deliverables, main concepts, and the overall net-zero energy system planning framework proposed in the study.  Chapter 2: This is the summarised methodology followed in the thesis. The chapter provides an insight to the key research phases and the methods followed in achieving the goals of each phase.  Chapter 3: The third chapter provides a comprehensive literature review on the current state and needs of community level energy planning, state-of-the-art renewable energy technologies, and the methods used in developing the planning framework. Chapters 4 to 7 present the details on achieving the objectives and the overall goal of the study. The chapter contents are based on the deliverables of this research, including renewable energy selection, energy optimisation models, project feasibility assessment, and best practices and recommendations. 12  Chapter 4: This chapter presents the model developed to prioritise and select the most suitable renewable energy technologies at community level based on the technical viability and triple bottom performance. A fuzzy-based approach is taken to account for the data uncertainties and performance variations in the different energy technologies. The developed model was demonstrated for a community in BC, Canada as a case study.  Chapter 5: The details on the energy system optimisation model are presented under this chapter. The model was developed in two phases for the building energy component and the community energy component. The optimisation to identify the optimal energy mix and renewable facility sizing was carried out with a fuzzy-based combinatorial approach. The feasibility of reaching net-zero or net-positive status was assessed using the developed model for a proposed community in Okanagan, BC, Canada as a case study.  Chapter 6: In this chapter, a renewable energy project assessment strategy is proposed, under two approaches. A Bayesian network was developed to identify the acceptability of a RE system implementation decision considering risk factors. The concept was extended to a multi-period analysis with a system dynamics model, which can rate different RE project deployment strategies under dynamic conditions over an extended period of time.  Chapter 7: In this chapter, the recommendations and best practices for RE project management is discussed under a partnering approach. A roadmap was proposed for managing challenges and effective implementation in community level RE projects, to mitigate the risks and overcome the barriers to RE deployment.  Chapter 8: This chapter is focused on the conclusions derived from the study, recommendations and future work, originality, and avenues for future research. Figure 1-1 depicts the connection between the research objectives and the chapters, and the content in each chapter. The activities carried out to fulfill each objective, and the main outcome of the objective is displayed on the figure, under the relevant chapters.  13   Figure 1-1: Integration of objectives and thesis organization 14  Chapter 2: Research Methodology The focus of the research is on facilitating NZE communities with improved triple bottom line sustainability. In such a community, the energy use of the community is reduced through demand reduction measures, and the remainder is supplied by on-site renewable energy generation. The current approaches taken in planning and developing NZE communities face limitations as previously discussed. In this study, RE selection and implementation process in the energy system of a small-to-medium scale community is analysed with reference to the technical, environmental, economic and social parameters.  The aforementioned objectives in section 1.4 were achieved in multiple research phases, culminating in the development of an overall energy planning framework for community level applications. New and existing communities located in British Columbia, Canada were selected as case studies to demonstrate the developed models under real-world scenarios. While detailed explanations on the methods and procedures used in completing the research components under each sub-objective are provided under the relevant chapters, this chapter summarises the overall methodology followed in achieving the study goal.   Phases of the research Phase 1 – Literature review and data collection This phase lead to the identification of available RE sources and technologies, and the performance requirements and constraints for community-level energy systems. Existing literature was referred to identify the most applicable energy system planning and multi-criteria decision making methods. Performance data was collected for the identified energy technologies, and energy use data was collected for buildings and communities. Life cycle environmental, economic, and social impacts were evaluated for the identified RE technologies, using life cycle assessment (LCA), life cycle costing (LCC) and literature-based data.  Phase 2 - Technology selection and prioritisation Indicators were developed for the assessment criteria, along with their respective benchmarks for the RET. A fuzzy multi attribute decision making technique was used in ranking and prioritising RE technology alternatives for the energy system. This allows the most suitable RE technologies 15  for a community energy system to be selected based on their technical viability and TBL performance.  Phase 3 - Energy system optimisation This phase involved defining the optimal RE mix and system capacity for both communities and buildings using multi-objective optimisation with a combinatorial approach, considering economic and environmental impacts of different energy system solutions.  Phase 4 – Project selection framework for net-zero/positive community energy system implementation  A project selection framework was developed for feasibility assessment and planning of net-zero/positive energy system implementation. A risk-based model was developed to evaluate the acceptability of a RE system implementation decision considering system performance and community conditions. This was further extended as a multi-period dynamic RE project rating model with system dynamics thinking.  Phase 5 – Developing decision support tools and proposing best practices A decision support tool was developed based on the research findings of the previous phases. The deliverables are in the form of an Excel-based decision support tool (DST). Best management practices (BMP) for community energy system implementation and risk mitigation was proposed, focusing on stakeholder engagement and partnering. A roadmap was proposed for value chain management in RE projects.  Figure 2-1 depicts the connection between the various research phases. The outputs of each research phase that will be used in the next phase are also highlighted. In the coming sections of this chapter, the methods employed in each phase will be discussed in further detail.  16   Figure 2-1: Phases of the research  Phase 1: Literature review and data collection An extensive literature review was conducted to identify the innovative and proven RE technologies which can be utilised to supply the energy needs of Canadian communities. Published literature and reports compiled by governmental and non-governmental bodies in the renewable 17  energy sector were used in obtaining the information. Publications made since the year 2000 in journals with impacts factors above 3.0 were prioritised in the literature review, in order to focus on up-to-date and high quality literature as sources of information. Key RE technologies were identified through a study of literature on RE-based energy systems. The key performance criteria for energy systems were defined based on literature, and indicators and performance objectives to be used in the next phases were defined. The market maturity, expected plant service life, capacity factor, capacity of commercially available units, and other details were identified through literature and manufacturer data for all identified RE technologies.  Further data collection was done for the identified RE alternatives on their social impacts, health risks, and economic impacts such as job creation potential. Resource availability and performance related data was collected during this phase. This information was used in defining the indicator scores in the technology ranking phase. Minimum threshold levels of availability for the RES were identified based on literature. The risk factors, opportunities, challenges, and barriers for implementing RE projects at community level were also identified through the literature review. All the data collected and information generated during this phase were used as inputs in the next phases of the study for RE technology, system, and project assessment, as well as in making recommendations about the best practices for RE project management. The detailed findings of the literature review and data collection phase are presented under Chapter 3.  2.2.1 Life cycle impact assessment (LCIA) ISO 14040 standard on life cycle assessment principles states that life cycle inventory analysis (LCI) involves the compilation and quantification of inputs and outputs of a system, and that data collection and calculation procedures are employed for the above quantification [53]. Accordingly, data was collected and a life cycle inventory was developed with regards to the RE technology alternatives. The scope definition set the system boundary from construction and component manufacture for RE facilities, until their end of useful life. The life cycle emissions, and the eventual demolition and disposal were considered in the outflows. Figure 2-2 details the LCA system considered in the data collection. The goal and scope definition of LCA was done with reference to literature, and the data collection for the life cycle inventory development was planned accordingly [53].  18   Figure 2-2: LCA system boundary for RE technologies Life cycle impacts of each RE technology was characterised to analyse and compare their environmental performance. SimaPro (Version 8) software and the Ecoinvent 3.1 database were used in conducting the LCA, and the impact assessment was done using the ReCiPe midpoint impact assessment method. Calculated category indicated results in the life cycle impact assessment (LCIA) phase were used under the environmental attributes in technology ranking.  2.2.2 Life cycle costing (LCC) and economic feasibility analysis A life cycle cost model was developed in order to assess the economic impacts of RET. Similar to the LCA component, the scope of the cost model was defined to include all aspects of energy generation from the point of material extraction to demolition and disposal. The effect of system factors such as investment costs, operations and maintenance, fuel costs, plant service life, and capacity factors were considered in the model. In addition to this, the external factors impacting the system such as energy prices, interest rates and inflation, funding sources and alternatives were considered in assessing the economic impacts of RE-based energy systems.  The costing and other financial information related to RET were identified through a literature review. Forecasts made on energy demand, discount rates, and information on funding sources for RE projects were obtained from published literature from peer reviewed journals, governmental reports, and other reputed sources. The costs and other factors such as service life were fuzzified to represent data uncertainty, performance ranges, and possible variations. The developed cost models were used as inputs to the RET ranking and energy system optimisation phases.  19  Taken together, the LCA and LCC models were used to obtain the performance scores under the selected performance criteria under economic and environmental categories for individual RET and energy system as a whole. A summary of the life cycle assessment thinking process and its applications in energy planning is provided in Chapter 3 under section 3.5.2.3.  Phase 2: Technology selection and prioritisation Following the identification of potential RE technologies that can be used to power a residential community, the technologies were screened and selected based on the local characteristics of the community. The locational parameters, resource availability, and technology related parameters (maturity and dispatchability) were taken as initial screening conditions. The most relevant criteria pertaining to resource availability and feasibility in a given location are identified, along with the benchmark levels for filtering. The RE alternatives which do not meet a required minimum level in the availability and maturity related pre-screening performance criteria are eliminated from the MCDM process [54].  Multi-level MADM screening has been done in previous studies on RE planning [55]. Criteria were further defined to represent availability, reliability, and other technical parameters of the system. The first step of technology ranking was conducted for the above aspects. At the second level of MADM, environmental, economic and social criteria were considered for technology ranking and prioritisation. Performance indicators were identified to reflect the economic, environmental and social performance criteria of the RE technologies under consideration.  The values (scores) for the indicators under each RE alternative were determined through the findings of the previous phase, and a site-specific data collection process (for location dependent parameters). The data uncertainties and risks were incorporated through a fuzzy MCDM process using fuzzy TOPSIS method. The developed ranking model was used to identify the best renewable electricity and heating technologies which can be used at community level. The model was demonstrated for a case study of a community located in British Columbia, Canada. The findings of this phase are presented under Chapter 4 and Appendix B    Phase 3: Optimisation of the energy system Suitable objective functions and constraints were formulated to optimise the RE-based energy system for a residential community. Various techniques used in similar sizing and optimisation 20  problems were evaluated in defining the optimisation methodology, and a combinatorial optimisation approach was selected for the developed decision model to represent the practical realities of energy system planning, as commercial energy system capacities are discrete and indivisible. Fuzzy logic was used in this phase too, to represent the variability and uncertainties related to data in energy system planning.  In the optimisation phase, the requirements and conditions of building level energy systems and community energy systems were considered separately to define the best energy system implementation strategy for the two cases. In both cases, the technical, economic, and environmental objectives were considered with reference to the existing energy systems, and the additional costs and effort involved in replacing the current systems. . Constraints were defined to filter out the unviable solutions for a given decision scenario. Weights were defined for the performance objectives that represented economic, environmental, and social performance of the developed energy system, and an aggregate performance score was determined for each possible energy system combination. A fuzzy ranking method was used to identify the optimal energy system combination, and therefore, the best energy mix and installation capacities. A case study based approach was employed in analysing and validating the proposed optimisation model, which can aid in making optimal choices for community energy systems. In the case study, the model was demonstrated for a multi-unit residential building (MURB) energy system and a community energy system for a proposed community in the Okanagan Valley, BC, Canada. The above findings are detailed in Chapter 5 under building and community energy system optimisation.   Phase 4: Project selection framework for net-zero energy system implementation The developed decision support tool was further extended to serve as project selection framework for net-zero energy system implementation, which will act as an aid in feasibility assessment and planning. Cognitive mapping and decision modelling using Bayesian networks and system dynamics modelling was used to achieve the goals of this phase. The NETICA software was used to develop the Bayesian network models, and STELLA software was used to develop the system dynamics model.  The key objective of this phase was to develop a framework for assessing the acceptability of a renewable-based energy system for a net-zero community. The key individual risk factors for a 21  community energy system were identified during this phase, and decision variables were defined. A Bayesian network was developed to represent the RE system performance based on the identified decision variables, and the network was trained with conditional probability tables for rule-based acceptance. The outputs of Bayesian network development resulted in an “acceptability index” for a renewable energy system at a given location. The feasibility of the investment decision for a RE system was further analysed through a long-range analysis with system dynamics thinking. A project rating framework (using system dynamics modelling) was developed using the STELLA software. The optimal energy capacity mix identified in the previous phase was used to define three project deployment scenarios, and these scenarios were assessed using the developed rating system to identify the best scenario, considering the community growth, increasing need for energy, and other dynamic factors. This assessment model can be used by community developers and other decision makers in planning the investment decision of a renewable-based energy system for a newly built community. The developed models and derived conclusions are explained in further detail under Chapter 6.   Phase 5: Decision support tools and best practices The previous study phases were used to inform the formulation of a comprehensive and systematic decision support framework for RE-based energy planning in NZE communities. The deliverables of the research are in the form of a decision support tool and best management practices guideline for RE project implementation at community level. The DST has the capacity to assess whether the net-zero or net-positive scenarios are feasible for a given community based on the local context, and can be used to define the optimal energy system for the community under the given conditions.  The findings obtained through the RET selection and energy system optimisation were used in developing the DST. The DST was validated through the case studies mentioned above, conducted for communities in British Columbia. Further, the developed tool has the capacity to be used in RE project assessment at the investment stage. The findings in the previous stages regarding the replacement of conventional energy systems in existing communities, and were used to define the best strategy in introducing renewable based energy systems to this type of community.  In the developed DST, the decision makers will have the flexibility in setting the relative importance of evaluation criteria for the system, based on the project priorities and local needs. 22  The separate evaluation of the techno-economic and socio-environmental criteria can be used to identify the performance of each RET under both fronts, thereby allowing the community developer and planners to set their own priorities in making the selection. The DST is in the form of an Excel-based tool, which is suitable for convenient handling in an industrial context. The best practices on renewable energy project partnering will inform the decision makers and other community level stakeholders in funding, planning, and managing the energy system throughout its project life cycle life cycle. The findings made on the RE project planning and implementation under the project selection framework and planning stage were used to develop best management guidelines for RE project delivery. These guidelines recommend the strategies to be followed in mitigating RE project risks and harnessing opportunities through a partnering approach. A comprehensive road map was compiled to demonstrate the support activities and partnerships to be developed at each stage of the RE project value chain management. The recommendations and best practices are presented under chapter 7. Together, the developed integrated decision support framework and the best practice recommendations make up the net-zero community transformation strategies promised in the overall research objective.               23  Chapter 3: Literature Review Parts of this chapter has been published in the Elsevier journals Renewable and Sustainable Energy Reviews and Renewable Energy, as articles titled “Opportunities and Challenges in Energy Demand Reduction for Canadian Residential Sector: A Review”, “Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty”, and as conference proceedings in CSCE General Conference 2017 as “Renewable energy technology selection for community energy systems: A case study for British Columbia”, and in 1st International Conference on New Horizons in Green Civil Engineering (NHICE-01) 2018 as an article titled “Mitigating risks and overcoming barriers in Canadian renewable energy projects : A partnering approach” [56][57][58][59].   Community Energy Systems Communities are the foundation of nations, and planning energy systems and environmental impact mitigation strategies at community level can help in climate change initiatives set at various administrative levels (i.e. municipal, provincial, federal, global), and can also help in reducing the dependence on the national grid and ensure local economic development [60]. Reducing the energy demand and replacing conventional fossil fuel resources with renewable energy (RE) are the key aspects in delivering climate solutions and ensuring long term energy security for communities [21]. As a result of the need for a clean and secure energy supply, the concept of energy sustainable communities has been developed. Focusing on energy systems at the community level allows for planning and design to be better adapted to local conditions and requirements [24]. Community energy initiatives based on decentralised renewable energy generation can aid in better adaptation of energy systems for local needs [24][25]. In community energy systems, the populace is involved as active stakeholders in the decision making process [24]. Hybrid distributed energy systems are now receiving attention as the future in energy generation, as the energy needs of communities grow and diversify [26]. The development of energy sustainable communities has been gaining interest in the recent times, with the gradual shift from centralised to decentralised energy generation [61][60].  Multiple factors need to be considered by the decision makers in planning community level energy systems, including the local energy demand, resource availability, economic constraints, and 24  stakeholder acceptance at local level [62][56]. The planning problem is further complicated by the uncertain nature of system parameters such as fluctuations in RE supply and resource availability, changes in macroeconomic environment, and the possibility of future evolutions in technology leading to increased efficiencies and variations in costs [62][63][64].  Community energy systems are vital in achieving the goal of energy sustainability. The first step in developing energy sustainable communities is to reduce the energy consumption through energy efficiency and energy saving measures [65]. Energy efficiency improvements can be done by adopting improved technologies, and savings can be achieved with technological advances in building design and replacement of active energy consumption with passive means [22]. Demand reduction can be further strengthened with conservation practices which focus on positive behavioural changes in residents [22]. The reduced energy demand of a community can be met through RE, thus replacing the conventional energy supply with renewable heat and electricity, and ensuring the energy sustainability [24] [65]. Renewable powered decentralised hybrid micro-grids are increasingly being explored all over the world [61][60]. It is necessary to assess the local availability of a RE source in order to ascertain its potential to supply the energy needs of a given locale [3]. The end goal is to develop 100% renewable powered communities in the future.   Energy demand reduction In order to develop such energy sustainable communities, managing the energy consumption in an efficient manner and reducing the energy consumption are vital necessities [24]. The “4 R’s concept” developed by Robyn Wark and Jorge Marques of BC Hydro emphasises reducing demand as the first level in sustainable community energy planning, followed by reusing waste heat. The final levels of energy planning for sustainable communities focus on renewable heat and electricity generation [65]. The net energy demand of residential buildings can be reduced via the means of technical and behavioural interventions [66][67]. Technical interventions can be implemented at the design or operational stages of residential buildings, while behavioural changes focus on establishing and improving environmentally conscious occupancy patterns. The concept of energy sustainable community planning acts as a pathway to long term emission reduction, energy security and energy independence for communities [68][24]. 25  Table 3-1 provides details on the energy use and the contribution made to overall GHG emissions by each economic sector in Canada. It is evident that the residential sector accounts for a significant portion of both energy use and emissions. Therefore, it is important to focus on this sector in the effort to mitigate the environmental and economic burdens of energy use.  Table 3-1: Energy use and GHG emissions by sector Sector Fraction of Canada’s secondary energy use 1 Contribution to national GHG emissions 2 Residential 17% 14% Commercial & institutional 10% 10% Industrial 40% 36% Transportation 30% 38% Agriculture 3% 3% A multitude of interventions have been proposed and implemented all over the world to achieve the target of reducing energy demand [69][70][71][72]. In this review, these interventions have been categorised under the classification described in Table 3-2 as energy conservation, energy efficiency and energy saving measures [22].  Table 3-2: Demand reduction intervention categories Category Definition Type of intervention Energy efficiency  Maximising the useful energy output by minimising the waste, thereby reducing the required energy input [24] - Same consumption patterns may be maintained with a lower energy demand [22] Technological – using appliances and technologies with improved efficiency Regulatory – adoption of efficiency standards [22] Energy savings Replacing active energy consuming activities through passive means [69][22] Technological – materials, products or designs which replace an energy consuming service, incorporated in building design or retrofitting [23] Energy conservation Reducing the energy demand through conservation practices [66][22] Behavioural – reducing energy use through changes in occupant behaviour Technological & regulatory – interventions intended to influence conservatory practices [66]                                                  1 Based on 2013 data published by Natural Resources Canada [398] 2 Based on 2013 data published by Natural Resources Canada [398] 26  Energy efficient technologies which reduce the waste of energy in appliances in non-useful forms (such as heat and noise) have a positive influence on residential energy consumption. Improving the energy efficiency of household electrical appliances has been promoted as a strategy for reducing energy demand, thereby mitigating the harmful environmental impacts while also easing the secondary costs incurred by the consumers over the operational lifetime of the product [73][74]. There is clear evidence that energy efficiency programmes can achieve significant energy savings and emissions reductions. In Canada, the estimated energy savings from all shipped appliances amounted to over 66 PJ3 from year 1992 to 2011, with an average set of main household appliances consuming less than 2800 kWh per year (about 50% reduction from the energy consumed by the same in 1992) [75]. The energy savings which can be achieved through the use of efficient technologies are subject to an upper limit due to a number of theoretical and practical reasons [76][77]. Technical reasons include the thermodynamic limit on the maximum efficiency achievable (e.g. Carnot efficiency), as well as technical limitations on the possible efficiency with the current technology available [77]. Even if there are technological means to achieve higher efficiencies in appliances, it might not be economical or commercially viable to implement these measures in the appliances released to the market, and the market trends may also play a part in determining what technologies and appliances are accepted by the consumers [77]. Due to the above factors, the potential of efficient appliance technologies in reducing the residential energy demand may reach a maximum point of saturation, particularly as the efficient technologies reach their practical limits. However, there is yet a potential for further improvement in terms of energy efficiency of residential end uses [78][50].  Energy savings in residential buildings can be achieved via non-energy consuming (passive) interventions [69][79]. In this approach, interventions for reducing energy consumption can be implemented in the building design itself, or through the use of specific materials. These interventions attempt to reduce the net energy demand in residential buildings by facilitating natural heating, cooling, ventilation or lighting [69][79][80]. Energy saving building design has been promoted in many parts of the world at regulatory level with building energy codes (BEC)                                                  3 Peta Joules (equivalent to 1015 Joules) 27  and standards [81][82][83]. These codes cover a range of aspects including the building envelope, lighting and HVAC systems, electric power systems and water heating, and can be instrumental in long-term demand reduction for the residential sector [84]. Building energy codes have been successful in reducing the energy demand with savings of 6% in Southern European countries and up to 22% in Netherlands and Germany [85]. Furthermore, passive energy saving techniques have been incorporated to green building rating tools such as BREEAM, LEED and CASBEE as feasible instruments in mitigating environmental impacts of buildings [79][84]. Certification and incentives provided for buildings are also used as measures to promote energy savings [84][86].  A concern raised with regards to the implementation of energy efficient and energy saving measures, particularly with regards to buildings, is that the actual savings generated through such interventions are less than the expected value due to the rebound effects. Under this, improvements in energy efficiency and savings can lead to an elevation in energy use by the populace, instead of resulting in an overall reduction [87]. In other words, residents tend be more carefree in their usage patterns thus using more energy, when they operate under the perception that less energy is being consumed due to energy saving measures and high efficiency technologies. Therefore, behavioural factors are also an important aspect in residential energy demand management. The concept of conservation applies to user-centered interventions for reducing the energy use of a residence, through methods which eliminate the non-essential utilisation of energy. Demand reduction can further be achieved by means of engaging in conservation practices, which by and large fall under the category of behavioural interventions. Energy conserving behaviours can be in many forms, such as the switching-off of unnecessary lights and electronic devices, adjustment of room temperature levels at night or in unoccupied spaces, using the washers and dryers only with a full load to reduce the usage frequency, and unplugging of devices when not in use [88][89]. In addition to the behavioural aspects, technological (physical) interventions can also play a role in energy conservation. The provision of smart control systems, automatic switches and energy meters on appliances are examples of such interventions which can promote and facilitate energy conservation [89][90][91][92]. Smart meters are another facet of the information factor in conservation, where the users are allowed an opportunity to identify the details regarding their energy use. Providing direct feedback to the users on their energy consumption through means such as meters or real-time displays can generate savings around 5-15% [93]. Direct-load-control 28  systems can be used to turn off high energy consuming appliances such as A/C units for a period of time to save energy [92]. Individual choices of the consumers can largely affect how much energy is consumed in residences, and therefore perceptions and knowledge plays a great role in managing domestic energy demand [94]. Occupant characteristics also determine the overall energy performance of a residence. The mix of technical, social and economic factors which influence the management of residential demand calls for multidisciplinary studies to improve the overall residential energy demand profile [91]. Energy efficient and conserving interventions (such as the use of CFLs, cold wash of laundry and turning off of appliances when not in use) as well as building retrofits (insulation, roof and window configuration etc.) have a considerable level of penetration within the Canadian residential sector. According to 2011 data, it is estimated that, 82% of the households engaged in at least one energy efficient or conservation intervention, while 37% of homeowners with their own residence had made an energy saving retrofit [67]. The National Energy Code of Canada for Buildings 2015 provides guidelines with regards to aspects such as lighting, HVAC system, building envelope, service water heating etc. [95]. New buildings constructed as per the code are expected to be 27% more efficient in comparison to the 1997 levels [96]. The National Energy Board of Canada has projected that in the period between 2016 and 2040, the energy use per one square meter of residential floor space will reduce by 0.7% annually, due to the increased use of efficient technologies, improved building envelope construction practices, and the advent of new energy standards [97]. Moreover, it has been identified that larger houses have generally been constructed after 1980s in Canada, and these constructions are more energy efficient [98]. A noticeable trend is the rising propensity to air-condition Canadian homes. The energy consumed in space cooling increased by 68% in the period between 1990 and 2009 [99]. At the present, community-level energy planning in Canada tends to focus more on demand management through energy efficiency and conservations, with limited attention being shed on long-term renewable energy and emissions planning [24]. While a considerable amount of work has been done in Canada with regards to strategizing and implementing demand reduction interventions, there is yet much work to be done on RE integration. It is evident that the goal of net-zero energy community development cannot be achieved only via the means of demand reduction interventions, particularly as there are limitations in the effectiveness of the above 29  strategies and also because their success are dependent on a multitude of external factors. The energy demand of a building or a community varies with time, and a robust and secure energy supply is required to fulfill this demand. Alternative energy resources, particularly renewable energy, can provide the solution the issues of ever-growing energy demand of communities and the concerns associated with fossil fuel use.  In Canada, the main electricity production source is hydro, providing 62.6% of the country’s electricity generation. This is followed by nuclear energy at 13.3% [100]. However, the energy mix varies widely by province, and provinces such as Alberta have a high reliance on fossil fuels while Quebec supplies most of its energy needs through hydroelectricity. In 2013, 75% of Canada’s primary energy production (heat and electricity) was through natural gas and crude oil, with hydro and other renewables supplying only 11% [100]. With this heavy reliance on fossil fuels, it is evident that further work needs to be done on incorporating more renewables into the Canadian energy mix, in line with the previously mentioned concerns about fossil fuel use and the climate action targets. Therefore, a significant potential exists at national and regional level for further replacing conventional sources with renewable alternatives.  Renewable energy technologies There are numerous renewable energy sources (RES) which can be exploited in supplying the energy needs of communities. The main types of RES, their characteristics, and potential impacts are listed in Table 3-3. Different technologies can be used in harnessing the energy from each source. The impacts from renewable energy use can be categorised as environmental, economic, or social.  Table 3-3: Sources of renewable energy RE source Characteristics Impacts Solar Solar energy can be converted directly to electricity [101], or to thermal energy [32]. Solar is an intermittent energy source [9][102]. No direct emissions are produced in energy generation. Solar energy generation potential is location dependent, due to geographic and climatic conditions [102]. - Life cycle environmental impacts in component manufacture, system construction and disposal [18][103]  - Additional energy storage systems may be required due to fluctuating nature, resulting in high installation costs. - Land and water use for PV plants [104] - Adverse visual impacts [104] 30  RE source Characteristics Impacts Hydro Hydro electricity generation can be done in large scale facilities, and at mini and micro level plants [105]. This is considered a zero-emission technology, and results in stable energy production. Hydro is a flexible power source from the perspective of the electricity grid, and can meet fluctuations in demand or supply from other sources due to its fast response [106].  - Effect on environment and ecology (i.e. fish migration, downstream ecology and livelihood) [107] - High initial investment; low O&M costs [108][109] - Life cycle impacts due to construction of facilities - Plants may be able to function without for long durations, with service lives up to 50 years [106] Wind Wind electricity generation depends on locational parameters [110], and the economic feasibility depends on available wind speeds [110]. Energy generation results in no direct emissions [111]. This is a fluctuating and intermittent energy source, and depends on the wind speed at a given location  [111].  - High initial investment required [112] - Negative impact on values of surrounding property [110] - Noise and visual disturbances - Effect on wildlife (i.e. birds and bats) [110] - Life cycle impacts due to construction of facilities Geothermal energy Earth’s internal energy is used in heating applications [102] or for electricity generation [113]. Energy generation potential varies by location, based on the resource availability. Does not result in any direct emissions [114]. Installations can be done at building level for heating [115]. Use for electricity generation is restricted due to the requirement for high temperature geothermal reservoirs [102]. Some amount of input energy is required in operating the system [116]. - Life cycle impacts of component manufacturing and facility construction  - Drilling of earth - High initial investment costs and low O&M costs, with lifetimes around 20-25 years [117]  - High water consumption in geothermal electricity [113] - Possibility of air and water pollution [8]  - Land use – may conflict with other applications such as agriculture [102]  Waste-to-energy Heat and/or power is recovered from waste matter [118]. This can help in sustainable waste management and turning waste (i.e. industrial, agricultural, MSW, waste water) into a resource. Thermochemical and biochemical methods can be used to convert waste into energy. Results in lower emissions than conventional fossil fuels [119]. Can be considered a dispatchable energy source with a constant supply of feedstock.  - Emissions from energy generation  - Environmental and health impacts of WtE plant operations and generated residue [120] [121][122] - Reduction of environmental impacts from landfilling [123] - High initial investment; incurs additional operational, maintenance and transportation costs [124] - Life cycle environmental impacts of facility construction [121][122] Biomass Biomass is used in electricity generation, combined heat and power (CHP) plants, and production of biofuels [125]. Agricultural and forestry residue as well as specially grown energy crops can be used in this. Results in lower emissions than fossil fuels [125].  - Emissions from energy generation [9] - Conflicting needs with food production (i.e. alternative uses for crops, diverting land and water from food-related agriculture) [8] - Life cycle impacts of facility construction 31  RE source Characteristics Impacts Marine energy Wave, tidal and ocean thermal energy can be used in electricity generation [9]. These can be suitable for a coastal community [9]. Marine energy has not seen widespread commercial use so far, although a significant potential is available for exploitation.  - Adverse impacts on marine ecology [8] - Expensive – high investment costs [9] - Disruption to shipping and fishing industries [9] Canada ranks 7th in the world in renewable production, and hydro accounts for around 7% of the Canadian primary energy production, while other renewables amounted to 4% [126]. The total RE production in Canada in 2014 amounted to 2097 PJ [126]. The supply mix of energy sources which contributed to the above generation are illustrated in Figure 3-1, based on information published by Natural Resources Canada [126]. It can be noted that hydroelectricity is the most highly exploited renewable energy source in Canada. Some of the Canadian provinces such as Quebec, Manitoba, and British Columbia have high reliance on hydropower to supply their electricity needs, leading to lower grid emissions factors [56][127].   Figure 3-1: Mix of energy sources in Canadian RE production In addition to hydro, bioenergy, solar, wind, geothermal and ocean energy have been identified as renewable resources with high potential in Canada. Wind and solar in particular are experiencing high growth rates [17]. The abundant potential for renewables throughout its considerable landmass provides Canada an ideal opportunity to exploit these resources in developing sustainable communities powered with clean energy. While the hydro-heavy provinces have a relatively clean energy supply, provinces with a high reliance on fossil fuels such as Alberta have 32  very high emissions factors [56], thus severely contributing to the environmental issues associated with energy use. The off-grid remote communities, particularly in Northern Canada, depend heavily on imported fossil fuels (e.g. diesel) for their energy needs [3][24]. This leads to tremendous economic burdens for such communities due to fuel and transportation related costs [3]. Increased exploitation of renewables in the Canadian context can provide solutions to the above issues. 3.3.1 Renewable energy supply and commercially viable technologies As the availability and energy generation potential of renewables vary due to locational factors such as geography and climate conditions [128][41], it is important to identify the supply characteristics of the energy sources. Renewable sources can be classified as “dispatchable” and “non-dispatchable”. Dispatchable sources have the ability to generate power on demand, while energy generation from non-dispatchable sources is dependent on external variables, and is intermittent [129]. Solar, wind, wave, and tidal are non-dispatchable energy resources with variable output capacity [130]. In contrast, the energy generation from resources such as biomass, waste, hydro, and geothermal can be controlled and dispatched at will [113][130].  Out of the various technologies explored at the present for RE exploitation, only a select number are commercially viable. These technologies have achieved varying levels of market penetration. Table 3-4 provides details on the RET currently in the market. International Energy Agency (IEA) has classified RET as first, second, and third-generation technologies, based on their level of maturity [106]. First generation technologies are those that were developed at the end of the 19th century as a result of the industrial revolution, thus having high maturity. R&D investments made during and after the 1980s resulted in the development of second generation, which are currently entering the market as commercially viable methods for exploiting RE. Third generation RET are relatively newer technologies which are still being developed, and are not widely used at commercial scale [106]. The inputs and natural resources used in energy supply are also listed for each technology. Renewable energy can be used in generating both heat and electricity as outputs. Solar PV, Concentrated solar power (CSP), wind, hydro, geothermal, and marine technologies can all produce renewable electricity [9]. Marine energy technologies can be classified as wave, tidal, and ocean thermal energy conversion (OTEC), and these technologies yet in their early stages of deployment [131]. Geothermal (ground source) heat pumps, solar thermal, biomass boilers, and 33  biogas digesters and landfill gas are technologies which can be used in renewable heat applications [132][133]. Solid biomass, biomass, and waste can also be used in combined heat and power (CHP) plants to generate both electricity and heat [134]. In general, various technologies such as incineration, gasification, and pyrolysis are used to convert biomass and solid waste into energy forms of heat, electricity and fuels [135]. Refuse derived fuel (RDF) technology is used to extract the higher energy content fraction of solid waste [135]. The information on supply, energy output, and technology is useful in assessing the suitability of a particular RET at a given location, and thus in selecting the most viable sources and technologies for energy generation at a regional level.  3.3.2 Technical performance and plant characteristics RE generation plants and infrastructure have different characteristics depending on the technology that is being used to harness energy. Energy facility installations may be at centralised level in large scale plants, or decentralised at building level [20]. These energy-generating facilities are characterised by their service lives, efficiencies, and capacity factors. In Table 4, these characteristics of each type of plant have been identified based on the values in published literature. Reasonable assumptions were made where direct data was not available. This information is useful in comparing the performance of different RE technologies. The financial data on different RET has also been provided in Table 4. The initial investment costs for each plant type, and the recurring annual operations and maintenance (O&M) costs for each type of plant have been identified through literature. This helps in assessing the financial burden of RE implementation, and allows the life cycle costs of RE to be compared with the conventional modes of energy supply. Moreover, criteria such as job creation due to RE can be used in measuring the community level benefits and contributions to local economy [69][70]. By combining all of the above information, it is possible to compare the different RET and evaluate their effectiveness in a distributed energy generation system. Community-level energy system development can be assisted by analysing this information, to identify the best solutions based on the local needs and conditions.  A key challenge in planning and decision making is the unavailability of exact technical and cost information on RET.34  Table 3-4: Renewable energy technologies RE sources Application Technology Inputs/Reserves Energy output Technology maturity a Dispatchability Measure of resource availability (unit) Solar Solar PV Building level Solar radiation Electricity Medium Non-dispatch. Daily solar radiation c (kWh/m2/day)   Centralised plant Solar radiation Electricity Medium Non-dispatch.  CSP Parabolic trough Solar radiation Electricity Low Non-dispatch.   Fresnel reflector Solar radiation Electricity Low Non-dispatch.   Dish/engine Solar radiation Electricity Low Non-dispatch.   Power tower Solar radiation Electricity Low Non-dispatch.  Thermal Flat plate collector Solar radiation Heat Medium Non-dispatch.   Tube collector Solar radiation Heat Medium Non-dispatch. Wind Turbine Onshore Wind – kinetic energy Electricity Medium Non-dispatch. Wind speed (ms-1) c   Offshore Wind – kinetic energy Electricity Low Non-dispatch. Biomass Incineration Boiler Solid biomass Heat High Dispatchable Mass available (tons/day)   CHP Solid biomass Electricity/Heat Medium Dispatchable  Gasification Combustion engines Solid biomass Electricity Medium Dispatchable   Gas turbines Solid biomass Electricity Low Dispatchable   Pyrolysis Solid biomass Electricity Low b Dispatchable Waste Incineration Mass combustion Municipal solid waste Electricity Medium Dispatchable Mass generated (tons/day)   CHP Municipal solid waste Electricity/Heat Medium Dispatchable  RDF Combustion Municipal solid waste Electricity Medium Dispatchable  Biogas Anaerobic digestion Wastewater/organic waste Heat Medium Dispatchable   Landfill gas Municipal solid waste Heat High b Dispatchable Hydro Major Power plants – turbine Reservoirs-dams Electricity High Dispatchable Flow rate (m3s-1) & available head (m) [136]  Small Run-of-the-river River- kinetic energy Electricity High Dispatchable Geothermal Heating Heat pumps Ground temperature  Heat High Dispatchable Geothermal gradient (°C/km) [61]  Electricity Dry steam Underground fluid reservoirs Electricity High Dispatchable   Flashing Underground fluid reservoirs Electricity High Dispatchable   Binary Underground fluid reservoirs Electricity High Dispatchable   Hot dry rock (EGS4) Underground hot rocks Electricity Low Dispatchable Marine Wave Wave energy converters Ocean waves Electricity Low Non-dispatch. wave energy density (kW/m) [137]  Tidal Tidal stream (current) Ocean tides Electricity Low Non-dispatch. Speed of current (ms-1) [138]   Tidal barrage Ocean tides Electricity Low Non-dispatch. Tidal range (m) [139]  OTEC OTEC – heat engine Ocean thermal gradient Electricity Low b Dispatchable Temperature gradient (°C/m) d a The first, second, and third generation technologies are classified as high, medium, and low in maturity respectively [106].  b These technologies were classified based on their low market and technological maturity [140]. c Based on RETScreen climate data [141]           d The temperature difference between surface water and water at a depth of around 1000 m is considered in site selection for OTEC [142].                                                      4 EGS - Enhanced geothermal systems 35  Table 3-5: Renewable energy plant characteristics RE sources Technology Average efficiency Service life Capacity factor Initial investment  Annual O&M costs  Jobs created    Energy (%) Exergetic (%) Years % (USD/kW) (USD/kW-year) (jobs/MW) Solar Solar PV - Building level  6-25 % a 6-25 % a 25-40 b 20.3% c $3,897 d $21 d 20.2 m  Solar PV - Centralised plant $2,025 d $16 d  CSP - Parabolic trough 14-20% e 25.81% [143] 25 25-28% e $4,600 e $20-350 per kWh e 7.6-19.9 m  CSP – Linear Fresnel 18% e 27.71 % [144] 25 22-24% e   CSP - Power tower 23-35% e 24.48 % [145] 25 55% e $6,300-7,500 e  Solar thermal- Flat plate collector 10-30% a 10-30% a 10-25 b 20% [146] $162 per sq.ft d 0.5-1% of initial investment d 6.5-27.72 m  Solar thermal- Tube collector Wind Wind - Onshore 80-97% a 80-97% a 20 b 38% c $,2346 d $33 d 12.2 m  Wind - Offshore 20 b 39% c $4,605 f $76.1 f 18.3 m Biomass Biomass boiler 80% h 9.54% [147] 20-30 b 85% c $575 d $29 d 13.21 m  CHP 17% h 22.2% [148] 20-30 b $5,792 d $98 d  Gasification- Combustion engines ~85% g ~75-85% g 25 $2,140-5700 i 2-7% of installation costs i  Gasification- Gas turbines 25  Gasification- Pyrolysis 20 Waste Mass combustion 20% [149] 21.3% [120] 25 85% j  $5,000-6,000 i 2-7% of installation costs i ~16 m  CHP 32 l 31.1-43.7% [149] 25 $5,570-$6,545 i  Biogas - Anaerobic digestion 32 l - 20 $2,574-6,104 i  Landfill gas 2% [149] 2% [149] 15 $1,917-2,346 i ~6 m Hydro Large-scale power plants 90% a 90% a 40-80 l 44 l $1,000-3,000 l 2-2.5% of investment n 7.8 m  Run-of-the-river 40-80 l 20-95% l 1-6% of investment n 20.34-22.9 m Geothermal Heat pumps - 15.5% [147] 20 b 25-30% l $900-3,800 l Low maintenance [150] 7.32-11.1 m  Dry steam - 50-70% k 30 k 85% c  $116.12 f  Flashing - ~30-45% k 30 k $2,675-5,767 k  Binary - ~25-45% k 30 k $6,923-7,993 k  Hot dry rock (EGS) - 10.6 % k 30 k 90% c $12,519-14,338 k $148.01 [151] Marine Wave energy converters 40% [152]  20 l 25-40 % l $6,200-16,100 l $180 l 4.22-15.3 person-years/MW in CMI5; 0.1-0.32 jobs/MW in O&M [153]  Tidal stream 80% [154] 25-85% [155] 20 l 26-40% l $5,400-13,400 l $140 l  Tidal barrage 40 l  22.5-28.4% l $4,500-5,000 l $100 l  OTEC 1-2% [156] ~40% [156] 20 l - $4,200-12,300 l -                                                  5 CMI – Construction, manufacturing, installation  36  a Based on typical energy and exergy efficiency ranges for energy generation systems [157] b Based on data published by the National Renewable Energy Laboratory of the U.S. Department of Energy for typical RET facilities [158].  c Based on median plant capacity factors published by the National Renewable Energy Laboratory of the U.S. Department of Energy for typical RET facilities [146] d Based on data published by the National Renewable Energy Laboratory of the U.S. Department of Energy for technically proven and commercially viable RET (grid tied where applicable)[159]. Building level PV systems are assumed to be less than 10kW in capacity, while centralized plants are assumed to be between 1-10 MW at community level. Utility-scale wind power plants are assumed to be between 1-10 MW. For solar thermal, the costs are given on per square foot basis. e Based on the data published by the International Renewable Energy Agency (IRENA) for CSP [160]. Parabolic trough plants are assumed to have no storage, while power towers are assumed to have ~6 hours of storage. If 6 hours of storage is implemented in parabolic troughs, capacity factors will increase to 40-53%, while capitals costs will also increase by over $2,500. O&M costs are given on a per kWh basis. f  Based on the data published by the U.S. Energy Information Administration of the U.S. Department of Energy, under assumptions for Annual Energy Outlook report [161] g Energy efficiency is based on lower heating value, while energy efficiency considers chemical and physical exergy [157] h Based on (EU data) [162]. The electrical efficiency is provided for combined heat and power (CHP) plants, while the heat efficiency is ~68%.  i Based on data published by the International Renewable Energy Agency (IRENA) for biomass technologies [147]. The cost for waste CHP is assumed to be similar to general biomass.  j General value for biomass based power plants [163]. The actual value may vary based on the plant design, feedstock quality and availability, and annual costs.  k Based on data published by a European Commission Joint Research Centre report [164]. Exergetic efficiency increases with the flashing turns. Euro cost values converted to USD at a conversion rate of 1.07. l Based on a report compiled by Intergovernmental Panel on Climate Change (IPCC) [165]. The average efficiency value for biomass, MSW, and biogas is used.  m Based on a report published by the International Renewable Energy Agency (IRENA) on jobs in renewable energy [166]. Manufacturing, construction, installation, and O&M jobs are considered for the technologies. A range of values are provided where data specific to North America is not available.  n Based on the data published by the International Renewable Energy Agency (IRENA) for hydropower [167]  37   Developing net-zero energy communities  The next step in moving towards energy sustainability through decentralised systems is the development of net-zero energy (NZE) communities. In these, the energy consumption is reduced through energy savings and gains in efficiency, and the remainder of demand is met entirely through locally available renewable energy resources [27]. Under this definition, the renewable energy supply should be capable of meeting the thermal and electric energy needs of the whole community, while delivering economic, environmental, and social benefits. Figure 3-2 depicts the aspects involved in planning NZE communities.  Figure 3-2: Net-zero energy community planning The net-zero community concept has been developed from that of net-zero buildings [27]. The term “net-zero” can be defined in various ways. While net-zero site energy means that the amount used at site is produced through RE. In net-zero source energy communities, the total energy accounted for at the source is produced through RE. Here, source energy is the primary energy used in generating and delivering energy to the site of use [27]. In addition to the above, net-zero energy costs and net-zero energy emissions are other definitions used in net-zero communities. Under the net-zero cost definition, the utility provider pays the community for grid energy exports a sum that is equal or more than the cost of energy services acquired from the utility provider [27][168]. In net-zero emissions communities, the energy produced and used from emissions free RE is at least equal to the energy produced from sources with emissions [27]. According to the definition published by National Renewable Energy Laboratory (NREL) of the U.S. Department of Energy, communities can be considered “near-zero” if at the minimum 75% of its energy need 38  is met through on-site renewables [27]. Net-zero communities can be grid connected, where the mismatches in demand and supply are balanced by the central utility grid, or they can be off-grid with reliable long-term energy storage systems [169].  Net-zero or net-positive community energy systems have added importance in the context of remote communities where grid connectivity remains difficult [29][30]. Such systems can act as a step in ensuring long-term energy security and energy independence for remote communities, especially in Canada. Communities do not have to rely on external energy resources brought in from far off locations or countries [3], and can move away from depleting fossil fuels to locally available RE which is naturally replenished [9]. Previous studies conducted in this area have explored the potential for RE integration in the energy systems for remote off-grid communities in Canada with consideration to availability and economic factors [3][30][31]. However, further work is necessary on identifying the barriers for RE penetration and on delivering optimised solutions with consideration to technical, economic, environmental, and social parameters [9].  To achieve maximum benefit and true sustainability for the community, it is necessary to optimise both demand and supply sides of its energy system for the local environment under triple bottom line (TBL) objectives pertaining to economic, environmental and social aspects [38][40][39]. This optimisation of demand reduction needs to consider not only the energy use in a community, but also the costs, environmental impacts, and social welfare of energy use and potential interventions [170][171]. Small-scale residential communities are of particular importance in net-zero energy planning, as these make up the majority of Canadian communities, and the residential sector makes a significant contribution to the country’s energy use and GHG emissions as previously mentioned. Residential energy demand patterns are subject to variations based on the weather, particularly in the case of heating and cooling loads [97]. The seasonal variations in demand and RE resource availability have to be taken into account when planning community energy systems.   Energy planning The triple bottom line (TBL) impacts pertaining to society, environment and economy are need to be assessed when making decisions about public infrastructure, of which energy systems are a part 39  of [38]. These three pillars need to be considered with a multi-stakeholder perspective when assessing the sustainability of an energy system [42]. In order to economically viable, renewable energy should be competitive with the existing conventional energy sources in terms of cost [106]. In order to achieve this, it should be possible to provide unit of energy from renewable systems at the same or a lower cost as that of a unit of conventional systems. This concept of a breakeven point in energy costs is referred to as grid parity [172].  Economic indicators such as levelised cost of energy (LCOE), payback time, and net present value of the energy system are used in assessing the economic impacts and viability of an energy system.  One of the key objectives of shifting towards renewables from conventional fossil fuel based energy resources is to ensure environmental impact mitigation. Carbon emissions is one commonly used parameter in assessing the success of energy plans [173]. Renewables in general are assumed to be low or zero-emission sources of energy during their operational phase [3][174]. However, the environmental impacts of an energy system may also be assessed with a life cycle perspective [173]. This is important as even the so called “zero-emission” technologies such as solar PV and wind energy have environmental impacts due to the upstream and downstream phases such as manufacturing and disposal [18] [111]. In assessing the life cycle impacts of a RET with a cradle-to-grave approach, all stages spanning from the facility construction to the eventual demolition and disposal at the end of life are considered [18][173]. In addition to carbon emissions, several other impact categories such as damage to ecosystem (acidification, eutrophication), resource and water depletion, land use, and human health impacts (carcinogens, non-carcinogens, radiation), ecotoxicity, and ozone depletion are included in a life cycle assessment (LCA) [42]. LCA provides a more holistic view of the actual impacts associated with RE use, thereby allowing the true environmental burdens of RET to be identified and considered in net-zero community planning [33]. Minimising the overall life cycle environmental impacts of the net-zero energy system should be the end goal in planning. Besides economic and environmental factors, enhancement of local welfare under social indicators such as local economic development and job creation have been considered in evaluating renewable energy options [9][175][176]. Moreover, improving the energy security and access to energy while reducing energy poverty are key goals in developing net-zero energy systems [9][60]. 40  By taking all these aspects into account, it is possible to estimate how a net-zero energy system impacts the welfare and development of the community. 3.5.1 Constraints and regional concerns Resource availability is vital in selecting RES for a community energy systems [102][106]. Most renewable resources are spatially distributed. Moreover, non-dispatchable energy sources such as solar and wind are intermittent in nature, and the power generation from these resources fluctuates with environmental conditions [44]. Due to these characteristics, the energy which be potentially harnessed from these sources are tied with the conditions of the locality, including its geography and climate [102]. The resource availability is measured in different forms for various RES. As an example, the availability of biomass depends not on the physical mass of biomass resources available in the vicinity and its accessibility, but also the transportation, and the other applications which compete for the resources (e.g. use of agricultural residues in livestock feeding) [102]. As the objective of a NZE system is to supply the total energy demand with RE, the resource availability at the location in concern should be sufficient to achieve this goal.  A study conducted by Angelis-Dimakis et al. (2011) noted three levels of constraints regarding the availability of an energy source. Thus potential energy is how much gross energy is available at a the site; theoretical energy is the portion of the which can be collected via the conversion technology system; and exploitable energy is the amount which can be utilised considering logistic, environmental, and economic factors [102]. Accordingly, the theoretical potential of available energy is further narrowed down in practical exploitation due to technical factors such as efficiency of the conversion technology, land availability for infrastructure development, logistics in the supply chain, alternative uses for resources, and legislation [102]. This potential of harnessing energy is further reduced due to economic, environmental and social factors. These considerations can be in the form of the cost of energy production, stakeholder acceptance, as well as emissions and other environmental and health impacts [102][177].  Fluctuations in the energy supply affect the stability of the electricity grid. In order to increase the RE penetration, grids need to be increased in flexibility. Energy generation from variable RE is not completely predictable or controllable, and this can lead to power imbalances and power flow changes in the electricity grid [178]. The ramp rate of an energy source is a measure of the variation in energy generation output per unit time (e.g. MW/min), and energy generation systems can be 41  subject to maximum ramp rate constraints [179][178]. Forecasting is done to reduce the uncertainty so that the variability in generation due to fluctuations in resources can be better accommodated [180].  The impacts of variability can be reduced by integrating spatially distributed resources for cumulative reduction of variability, and by using energy storage technologies in the grid [181]. Commonly, the variation in non-dispatchable RE is accommodated by the dispatchable reserves (e.g. coal, biomass) [180]. However, the response by the reserves to potential variations in supply are constrained by the ramp rates and minimum power levels [182]. In general, there is an upper limit to the fraction of variable RE which can be accommodated in an electricity grid without energy storage. Smart grid technologies have been much discussed in the recent times as a solution to the problems associated with increased RE penetration, where supply and demand points are have intelligent connection to enable real-time communication and grid optimisation [181]. In addition, storage-equipped microgrids which can be isolated are another possible method to reduce the impact of variations on the central grid [181].   Most renewable energy systems are characterised by high investment costs, as identified in Table 3-3. In addition, these technologies require specialised physical infrastructure [183]. Funding availability is a key challenge for RE schemes, and is a determinant of whether a proposed energy system is financially viable for a community [45][56]. Therefore, the amount of funding allocated to RE development can act as a constraint to NZE system deployment.  RE too can lead to environmental and social concerns. Technologies such as waste-to-energy generation may carry human health risks due to undesirable emissions [184]. Enhanced geothermal electricity generation technologies require water in electricity generation. However, water is a limited resource which has other alternative uses, and these factors need to be accommodated in the decision making to ensure that no adverse social impacts are created due to the harnessing to RE [113]. Biomass may have competing non-energy related uses [185], and there are concerns about the growth of energy crops in light of the food crisis in different parts of the world [186]. In addition, there are aesthetic and land use concerns raised with regards to RE plants, and land availability can act as a constraint to RE facility construction [106]. Most importantly, stakeholder acceptance at both political and public levels is vital in ensuring successful integration of 42  community level NZE systems. Lack of information and perception of risk act as main barriers to the acceptance of decentralised energy systems [174].  Principal agent problem is a significant issue associated with infrastructure projects, where the cost-owners and beneficiaries are two different parties [187]. This hinders the motivation for the development of NZE energy systems, as the additional costs of development would further burden community developers. Similarly, the cost ownership of ongoing facility operation may also lead to an issue for the community. Appropriate funding mechanisms and other regulatory initiatives need to be applied to mitigate these financial concerns on the part of community developers and residents [56]. Facility management responsibilities and allocation of economic costs and benefits are key concerns of stakeholder groups in RE project strategising [56]. The ultimate objective is to distribute the benefits of NZE systems at all stakeholder levels in the community.  3.5.2 Community energy planning methods Various methods are used in doing community energy planning. At a high level, energy planning tools such as The Long-range Energy Alternatives Planning System (LEAP) software can be used for energy and emissions analysis and forecasting under different future scenarios [188][43]. Another tool developed by the Government of Canada is RETScreen, which allows the assessment of energy efficiency and RE projects for their viability considering technical and financial aspects [141]. This tool provides access to regional data related to features such as weather, cost, and benchmarks.  Scenario-based planning is one method which has been extensively used in RE planning [56]. In this mode of planning, several alternative future scenarios are established by the decision makers, who then compare the scenarios against each other. Thus, the best scenario is selected based on various factors (e.g. economic, environmental) [42].  However, scenario-based planning only allows the investigation of a limited number of development options for community energy systems. As the scenarios are defined in an ad-hoc and limited manner based on decision maker preferences and knowledge. Therefore, it does not allow much room for comparing all different renewable energy technology options and select the most suitable options. Further, scenario-based planning fails to take into account the needs of different stakeholder groups and the relative importance of energy system planning goals and performance criteria. A more in-depth analysis is required to identify the best energy technologies 43  and optimal energy systems for communities. The “in-between” scenarios are neglected in this type of analysis, thereby limiting the possibility of finding the optimal energy solution.  3.5.2.1 Multi-criteria decision making in energy planning In developing community level energy systems, it should be possible to pay attention to the different objectives and stakeholder requirements in selecting the most suitable energy technologies.  In energy planning problems, the quality of energy supply, the generation output and the cost of energy, the environmental impacts of energy generation, and the impact on the communities are all important considerations, which need to be weighed and balanced against each other based on the decision makers’ priorities. While many technologies are available for renewable energy generation, the most suitable technologies need to be selected based on the local context and planning objectives. In MCDM techniques, decisions are made with consideration to different criteria representing multiple conflicting objectives. The solution to any MCDM problem can vary based on the technique used, and it is necessary to select the most suitable method for the selection [189]. The common characteristics between MCDM methods have been identified as conflict between the criteria, units without a common standard of measurement, and difficulty in selecting alternatives [48]. Multi-attribute decision making (MADM) is a subdivision within MCDM field. Selection and prioritisation for a finite number of possible alternatives under numerous different criteria can be done with MADM techniques [48][190]. MADM problems can be represented as decision matrices [190]. Quantitative or qualitative criteria can be used in decision making through MADM techniques [191][48]. The criteria for selection should adequately represent the goals of the energy planning problem [192]. The weights assigned to attributes reflect their relative importance in the selection/prioritisation, ultimately reaching a compromise between the conflicting objectives based on decision makers’ preferences. Some MADM methods commonly used in energy planning are listed below in Table 3-6.     44  Table 3-6: Some commonly used MCDM methods used in RE selection Method Characteristics Strengths Studies  ELECTRE Outranking method –alternatives should be reasonably favourable for all criteria; concordance/discordance indices and threshold levels used in ranking alternatives [48] Can handle both quantitative and qualitative criteria [48]; gives complete ordering of alternatives [193] [194] PROMETHEE Outranking method – Pairwise comparison of alternatives for each criterion; preference index developed to determine ranking of alternatives [177] Ease of use; less complex [48]; Possible to use scores without normalising [195][47]  TOPSIS Preference determined by closeness to ideal solution, i.e. shortest distance from positive ideal and longest from negative ideal [48]. This is not an outranking method.  Best and worst scenarios are considered simultaneously; simple computation process and sound logic [196] [197][198] AHP Complex problem formed into a hierarchy with goal at top and alternatives at the bottom [48]; pairwise comparison done for components at each level to assess their preference based on elements in the level above [191]  Computes inconsistency index to evaluate consistency in decision making [191]; Can evaluate alternatives at different levels based on goals [42][199] MAUT Developed based on the utility theory – utility functions of individual attributes are used in deriving the multi-attribute utility function Allows for decision maker’s preferences to be considered in delivering solutions [200] [201] MADM techniques can address one limitation of scenario of scenario based planning, by providing means of comparing RE alternatives on the basis of multiple decision criteria and selecting the best ones.  Further, an optimization based approach could gain an optimal solution for renewable energy integration plans. With the multiple conflicting objectives of costs, energy savings and environmental impacts, this leads to a multi-criteria decision making problem. Mathematical optimisation can be used to identify the best choices in terms of supply mix and system sizing with a more continuous approach as opposed to the discrete approach used in scenario-based planning [195]. Moreover, various types of modelling has been used to represent planning problems, to address data aspect such as resource availability and economic projections [102]. Energy system modelling has been classified under power system and electricity market, simulation, optimisation, and qualitative and mixed-method models [28]. These methods are important in assessing potential RE projects, and making the optimal decisions in community-level energy systems. Achieving a balance or “trade-off” between conflicting objectives in energy planning is vital for its success. Therefore, RE project delivery requires a multi-criteria approach in order to represent the priorities and interest of different stakeholder groups, and optimise between manifold objectives. The weights assigned to attributes reflect their relative importance in the selection/prioritisation [190]. 45  Defining the optimal energy system for a community is a multi-objective decision making (MODM) problem, where conflicting criteria are optimised to deliver the best solution to a given design problem [48][190]. Energy system optimisation involves sizing, and developing the ideal “mix” of energy sources in the system [202][203][204]. With the selected RE technologies, a solution has to be obtained. Out of the feasible solutions, the optimal one is defined with respect to a number of constraints under different aspects such as technical, environmental and economic, under limited resources [205]. In tactical decision making for planning a net-zero energy system, the community’s energy demand needs to be met through RES with minimal burdens while operating under budgetary and other organisational constraints. Objective functions representing conflicting criteria in the planning problem are modelled in multi-objective optimisation (MOO) [206]. The set of alternatives in the non-dominant Pareto front are used in generating the potential solutions. Converting of technical and environmental criteria into cost form and optimising a total cost objective function is an approach used in some RE studies. However, this does not reflect the specific importance of different criteria in the energy system problem [195]. The system sizing and the energy mix (with relative contributions from each RES) need to be determined through the MOO solution. The design constraints such as total energy lost and total economic cost should be imposed while optimising (minimising or maximising) the objective functions [195]. Different techniques are used in system optimisation, in both combinatorial and non-combinatorial optimisation approaches [203][207]. 3.5.2.2 Uncertainties in energy planning Energy system planning is also subject to numerous uncertainties, which needs to be addressed through different mathematical and modelling techniques. Epistemic uncertainties are a result of limitations in available data and lack of knowledge, while aleatory uncertainties are caused by variabilities in the studied system [208]. While scenario based analysis is employed to represent possible scenarios which account for the potential variations in future outcomes, these scenarios too cannot be formulated to be fully accurate [209]. Data uncertainties are the main problem which contributes to inaccuracies in urban planning, and this issue is caused by imprecise, vague, incomplete, and qualitative data [208]. Scenario uncertainties are further caused by variations over time, especially due to the changes in external environment [209]. Due to the impreciseness and unavailability of data, it may be necessary to present the performance scores for the criteria 46  indicators as a range instead of a crisp single data point, or in linguistic terms such as “high” “medium” or “low” [210]. In addition to improvements of data collection and sampling, other measures can be taken to address uncertainty in energy planning. Probabilistic modelling and risk assessment is conducted to mitigate the impacts of uncertainty. Data uncertainty can be addressed through techniques such as Monte Carlo simulation, and fuzzy based techniques [208][192].  Fuzzy logic also has the advantage of being able to represent qualitative information and the vague or imprecise nature of human decision making, and has been commonly used in energy planning problems [211]. The possible outcomes in an uncertain future can be explored with probabilistic risk assessment tools such as Bayesian networks [212]. 3.5.2.3 Life cycle thinking in energy planning Taking a life cycle perspective in decision making will assist in obtaining a holistic view of the outcomes of implementing a NZE system. Life cycle assessment (LCA) and life cycle costing (LCC) are techniques to assess the environmental and economic impacts of system respectively [213]. Taking this holistic approach to account for the life cycle impacts delivers a clearer perspective when comparing energy sources and making selections, thus improving decision making [33].  While renewables are in general expected to be low or zero-emission in comparison to the conventional energy sources such as fossil fuel, GHG emissions are caused due to the upstream and downstream processes associated with RE facilities [111]. As per ISO 14040, the phases in a LCA study include goal & scope definition, inventory analysis, impact assessment and interpretation. When comparing different technologies through LCA, it is necessary to maintain the comparability of results on a common basis [53]. LCC techniques are used to assess the economic impacts of energy systems throughout their lifetime, including initial investment for RE implementation, operational and maintenance costs, repair and replacement costs, and disposal costs at end-of-life [46]. In addition to the direct environmental and economic impacts of energy systems, other factors such as human health risks, public safety, and social equity need to be considered in decision making [18][38]. Indirect impacts arise from factors such as land and water use, alternative uses for resources, job creation and economic development as well as effect on surrounding environment 47  and local population [18][41]. While these factors are usually not studied in the course of optimising renewable-based energy systems, they are critical considerations in regional level planning and decision making. Life cycle sustainability assessment (LCSA) is used to appraise all the favourable and adverse impacts pertaining to environmental, economic and social aspects of a product (or technology) during its life cycle [39][40]. The components of LCSA are LCA, LCC and social life cycle assessment (S-LCA) [39][40]. LCSA model is used in evaluating energy systems with regards to TBL sustainability. The studies conducted on the social sustainability of renewable energy systems is limited, partly due to the difficulty in developing and quantifying indicators [40][42]. Applying life cycle thinking in energy facility management is helpful in developing a more comprehensive approach in RE project delivery. Asset management has been identified as an important component of RE facility development [106]. Asset management practices can improve plant value and revenue, improve decision support systems, and extend asset life [106]. Asset management has been proposed as a method for enhancing the life cycle performance of infrastructure [187]. In considering the life cycle of public infrastructure systems, care should be taken to include all phases from construction of facilities, to the end-of-life scenario. Unless carefully managed, the disposal at the end-of-life can result in health and eco-risks for RE plants [104][120]. Disposal also incurs costs, and in energy system facilities managed at community level, it is important to identify mechanisms to absorb these costs, and to decide which stakeholder groups would bear the additional burden. One study has identified that the salvage value sets off the demolition cost for some energy facilities [120]. Life cycle costing for RE facilities also needs to account for the fact that the system lifespan and the components lifespans are not necessarily the same, as identified previously. A repair and replacement strategy for public assets needs to be developed at component-level to account for this. Moreover, reuse and recycling of components conserve natural resources and energy expended in the demolition process [214].  The disposal of emissions and other by-products arising from plant operations is also important. There are costs involved in this, as well as regulations governing such activities [120]. Appropriate disposal of residues, by-products, and other emissions need to be a part of the management practices for RE system facilities [104][120]. Cost optimisation of the facility needs to take the long-term costs of disposal into account with a system perspective [120]. Providing operational 48  guidance to the community in managing their energy system as best management practices (BMP) will further solidify the success of NZE community development.  3.5.2.4 Performance criteria and objectives for RE based energy systems Developing an optimal renewable energy system requires that the energy system is evaluated under multiple criteria as discussed above. The decision criteria can be under technical, economic, environmental and social categories. Indicators are used to measure the criteria under each alternative [215].  The performance assessment criteria, indicators, and optimisation objectives for energy systems all need to be carefully selected and defined based on the requirements, constraints, and stakeholder groups.  Some of the criteria identified for energy technology assessment have been listed in Table 3-7. It can be seen that while some criteria can be quantitatively measures, others can only have qualitative assessments. This is especially true with regards to social factors and stakeholder concerns. For a more comprehensive decision making process for sustainability in RE based energy systems, qualitative criteria too have to be accommodated in the decision support framework.              49  Table 3-7: Renewable energy assessment criteria Category Criteria Indicators Reference Technology Feasibility Number of times tested [216]  Risk Number of problems in a tested case [216]  Reliability Technology reliability [216][210][217]  Maturity Market maturity of technology [217][115]  Safety Accidents per energy produced [217][218][219]  Performance Efficiency [217]   Exergetic efficiency  [217]  Capacity Annual energy generation [218][176] Availability Theoretical potential Annual estimated energy generation potential [9]  Land use Land requirement [202][220]  Reliability Reliability of supply [176] Environmental Emissions Carbon/GHG emissions [218] [217]   SOx emissions [210] [217]   NOx emissions [210] [217]   Particulate matter emissions [210] [217]   Ozone depletion potential [221]  Water footprint Water depletion [202] [220]  Resource use Resource depletion (mineral/fossil fuel) [202] [220]  Effect on eco-system Habitat alteration [220][222]   Radioactive waste/ Eco-toxicity [221][202]   Acidification [221] Economic Financial feasibility Capital investment [210][223][217][217]   Operational and maintenance cost [210][218][217][217]   Availability of funding [216]  Economic feasibility Payback period [216][218][210]   Net present value [216][210][217]   Internal rate of return (IRR) [216]   Service life [210][218]   Levelised cost of electricity (LCOE) [176][115]  Economic growth Gain of GNP for the community [223] Social Community benefits Number of jobs created [216] [217] [220]   New capital generated [223]  Human health impacts Human health respiratory effects potential [18]   Carcinogens [224]  Stakeholder acceptance Social acceptability [210] [217]   Political acceptance [216] The optimisation too is done under a number of technical, economic, environmental objectives and constraints. Optimisation techniques use objective functions to define particular criteria of the system which are to be minimised or maximised [203]. Some of the factors defined as objective functions in optimising energy systems have been listed in Table 3-8 [204][62][206][203][55].   50  Table 3-8: Objectives of energy system optimisation  Technical Economic Environmental & other Minimise - Cost/efficiency ratio - Dumped energy  - Total cost of the system - Capital cost / investment - Total annual cost - Energy generation cost - Equipment cost - Levelised cost of energy - O&M costs - Life cycle cost of system (LCC) - Environmental impact  - Energy price volatility - Economic penalties due to insufficient energy generation through RE  Maximise - Electricity production  - Thermal energy production - Energy supply - Power quality  - Reliability of power supply - Revenue of system - Total benefits for a fixed investment - Benefit/cost ratio - Project lifetime economic return (PLER) - Local economic development - Job creation  Strategizing for deploying community renewable energy projects  Projects can be generalised into five different stages in their life cycle; initiation, planning, execution, control, and termination [225]. In a RE project, various stakeholders are involved in each of these phases, including the government and other authorities, community developers and planners, consultants and contractors, as well as the local community. The motivation for RE projects is established considering the baseline requirements, economics, policy issues and incentives, technology, and the consensus among stakeholders [49]. At the initiation of an energy project, the portfolio selection needs to be conducted based on the available energy resources. The selection criteria may be in the form of availability, supply reliability, maturity of the technology, and location [226]. Based on the expected costs and energy generation potential, the business case needs to be proven for the proposed energy system [49]. The economic merits of the renewable energy investment need to be considered within the project funding boundaries. Further, a technical assessment is necessary on the engineering and operational risks of the selected technologies [180]. Risks and barriers associated with RE projects can be manifold. These risks, barriers, and other challenges have a detrimental effect in the propagation of commercialised RE integration at community level. A study conducted by Angelis-Dimakis et al. in 2011 indicates that there are three levels to renewable energy potential. These are the theoretical resource availability, the technical potential influenced by technology efficiencies and accessibility, and the economic potential [102]. At a very basic level, resource availability as well as geographic and other 51  locational conditions (e.g. climatic conditions, weather fluctuations) act as constraints to RE penetration [102]. There should be sufficient availability of the resource in close proximity to the site location of the community, which can also be sourced, transported and converted to energy with minimal economic and environmental burdens [227][228]. Resource availability, energy demand, generation capacity and efficiency are all subject to uncertainties [43]. Moreover, non-dispatchable energy sources such as solar and wind are intermittent in nature, and the power generation from these resources fluctuates with environmental conditions [44]. This unreliability in resource availability needs to be taken into account in decision making for RE planning [43].  Once the theoretical and technical potential of a RE system has been ascertained, it is necessary to establish the economic and social viability of the proposed energy project. Uncertainties contribute to making RE project planning even more complex. Maintaining the balance between the energy demand and supply is important, and this leads to uncertainty in forecasting, defining the energy mix, and predicting economic and environmental burdens [45]. Social risks such as land availability, political factors as well as legal and regulatory issues are difficult to quantify, and can only be assessed in qualitative form in most cases [45]. Energy planning for communities is also subject to the influence of macro-economic factors, which undergo continuous variations. Risk management plays an integral role in making an RE project viable [229]. Data uncertainty is another critical issue faced in RE planning. The information obtained on environmental and socio-economic factors, locational parameters such as geography and climate, as well as demand and price forecasts are usually vague, incomplete or imprecise [43].  The potential variations in economic, environmental, social, legislative and technical factors as well as resource availability with time needs to be taken into account in dynamic energy planning [43]. To increase the acceptance of RE systems among community-level decision makers and other stakeholders and increase RE penetration, it is necessary to provide some means of quantifying the associated risks and estimate the viability of these systems at the planning stage [45]. Figure 3-3 summarises the risks and contributing variables factors affecting energy system implementation at community level. 52   Figure 3-3: Risks in energy system implementation Risks have implications on the viability and future stability of energy systems. Risk assessment can be used to indicate whether the system is financially and economically feasible, and whether it can meet the local energy demand as anticipated [128]. It can also provide an estimate of the suitability of installing such a system with respect to environment, human health, or other socio-cultural aspects. In addition to risks, other challenges and barriers hinder the promotion of RE penetration, including funding issues, lack of social acceptance, policy and regulatory barriers, and difficulties in operationalising. Some key challenges and opportunities in planning and delivery of RE projects are summarised in Table 3-9. Different deployment mechanisms can be used in planning and operationalising RE projects to mitigate and solve the below mentioned challenges.  The above issues lead to direct and indirect impacts on the stakeholders involved. An example of an indirect impact is how the housing prices of a newly built community with RE integration can increase due to the additional cost of RE infrastructure [56].    53  Table 3-9: Classifying issues and solutions in RE project planning Issue Challenges / Barriers Opportunities / Solutions Geographic location - Availability and feasibility of RES vary by location [128] - Lack of access to grid connectivity, infrastructure, technology or knowledge in remote regions [174] - Selection of locally suitable RE technologies  - Possibility of developing self-sustained standalone net-zero energy communities [29] Funding - Project finance gap – The development of optimised RE systems needs to varied out under the constraint of limited funding allocations [45]. - Split incentives in investment motivation (agency problems)– The party who incurs the additional costs of RE integration may not derive any direct benefit [174] - Cost sharing agreements with local governments/ other entities [230]  - Strategic alliances and agreements with financial institutions [45] - Government policies and funding mechanisms (i.e. tax breaks, grants, low interest loans) [231] - Carbon emission reduction credits [231] Risks and uncertainties in planning - Economic and technical risk [128] – Changes in external environment and technological evolution, and lack of precedents make planning difficult - Vague, incomplete and imprecise information leading to uncertainty in planning [43] - Site analysis for risks & identifying acceptance levels [232];  - System dynamics modelling and risk analysis for long-term energy planning [128]  - Decision support tools for planners [51]  Social acceptance - Socio-cultural barriers, and lack of incentive for acceptance of new RE technologies and behavioural changes [174] - Adverse perception of risk [174] - Multi-stakeholder view based RE planning - Stakeholder consultation during planning phase of energy systems [174]  - Systematic risk management approach [45] Operational - Allocating responsibility for operations and maintenance of RE facilities - Challenges in obtaining the necessary technical expertise for the communities [24] - Challenges in selling the generated power and making a business case [233][234] - Developing community level management plans [24] - Providing guidelines and decision support for energy systems management [217] - Agreements with third party contractors and suppliers, and power purchase agreements with utility providers [45] Policy and regulations - Regulatory barriers to technology penetration, and grid integration and transmission issues [165]  - Lack of coordination between stakeholder groups, especially at administrative levels [174] - Absence of consistent policies and guidelines in RE project planning and management [174] - Policy incentives and regulatory changes for promoting RE [174] - Streamlined process for RE planning to combine all levels of decision making  - Infrastructure and technology development to accommodate RE integration [9] While technical planning support is a key necessity for successful RE projects, risk management and a clear policy vision is necessary to promote RE projects at community level. The RE project life cycle needs to be managed through effective best practices in project deployment to manage 54  the above mentioned challenges and harness the opportunities. The stakeholders need to be provides with a clear vision on effective project implementation through partnering [235]. The existing body of knowledge on RE project management and partnering were thoroughly studied in order to identify the different stages of RE project deployment and thereby to propose a RE project life cycle and an effective partnering strategy at each life cycle stage. This knowledge was then used to define a roadmap for guidance on managing RE project value chains. The detailed findings on the best practices and partnering roadmap are presented under Chapter 7.   Summary Net-zero energy systems can deliver environmental and economic benefits, while easing the present concerns on climate change and other impacts of energy use. At community-level, this initiative can lead to enhanced energy security and energy independence. The goal of planning renewable-based energy systems at community level is to deliver solutions which best suit the local conditions and needs, and can make use of the resources available in that particular locality. There are a multitude of objectives, constraints, and challenges in developing NZE systems in new and existing communities. Technical, economic, environmental, and social aspects need to be considered in defining optimal energy choices for communities with life cycle thinking. The application of life cycle thinking in community level energy systems can deliver multiple benefits, ranging from economic value, to improved environmental and social acceptability. Canada in particular is blessed with significant renewable energy resources, which creates an advantageous position in developing net-zero energy communities. A key challenge in community energy planning is the lack of streamlined approaches and local expertise. A holistic approach combining the different aspect of energy planning needs to be developed to further propagate community-level NZE systems in Canada as well as in the rest of the world. Energy planning is a multifaceted problem, and a balance needs to be achieved with regards to the conflicting priorities and constraints in energy planning at community level. In this, the most viable technologies for energy systems can be selected through multi-criteria decision making techniques, and the system can be optimised for the selected local context through multi-objective optimisation. As energy planning is a dynamic problem, feasibility assessment of RE projects needs to consider the variation in the macro-economic environment with time. A system dynamics approach can be used in assessing the future outcomes of a RE project under changing environmental variables, in order to select the 55  most viable RE projects under a given context. The decision making can be further improved by incorporating uncertainty analysis. The findings of this review have been compiled to develop a vision for net-zero community planning. To exploit the RE resources in an optimal manner at community level, this vision needs to be converted to practical decision making strategies. Ultimately, communities and their decision makers need to be provided with robust tools and guidance to make net-zero energy communities a practical success.  56  Chapter 4: Renewable energy selection for net-zero energy communities: Decision making with life cycle thinking Versions of this chapter has been published in the Elsevier journal Renewable Energy and in the conference proceedings of CSCE Annual General Conference 2017, as articles titled “Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty” and “Renewable energy technology selection for community energy systems: A case study for British Columbia” [57][58].   Background In addition to being capable of fulfilling the demand, a community’s energy generation needs to be reliable, considering both supply and technical reliability [106][29]. A main challenge associated with RE based energy supply is the fluctuations and the resulting non-dispatchability associated with sources such as wind and solar [130]. In addition, the local resource availability needs to be sufficient in fulfilling the community energy demand, and the energy resource quality is an important aspect which needs to be established with regards to technical viability. To develop a dependable and technically sound energy system, the renewable energy technologies (RET) used in a community energy system need to be tested and proven technologies with sufficient market maturity to minimise the associated risk [236][45]. Moreover, as a technology become more mature, the associated costs of energy generation will decrease [105]. The technical viability of a RET needs to be first established before considering its inclusion in a community energy system based on its economic and socio-environmental performance. The term “technical criteria” is used in published literature to refer to indicators which assess the technical performance of individual energy technologies in multi-criteria ranking problems, such as maturity, reliability, efficiency, and resource availability [210][192][199]. Life cycle assessment (LCA) technique can be used to obtain an understanding of the environmental costs associated with the use of RES. LCA can study the impacts of a product of a system throughout its life cycle, from raw material extraction (cradle) to the eventual demolition and disposal (grave) [33]. Life cycle costing (LCC) is conducted to assess the economic impacts of renewable energy systems. Minimising life cycle costs of energy systems is another key objective associated with energy system planning. To evaluate the true impacts of a RET and 57  thereby to determine its suitability through a life cycle lens, the life cycle costs, environmental impacts, and other indirect impacts such as human health risk, public safety, and contribution to economic development have to be considered [18].  Energy system planning is a complex problem, where a number of conflicting priorities come into play in terms of energy use, economics, environment, and stakeholder requirements. Energy supply alternatives available for a community’s electric and thermal applications are depicted in Figure 4-1.   Figure 4-1: Energy alternatives for supplying community energy demand While RE ranking has been done in previous studies, a comprehensive and practical methodology in decision making for community energy systems which considers available options at technology level, and combines triple bottom line planning with life cycle thinking while also integrating uncertainty into decision making, is missing in published literature.  To address this gap, technical, economic, environmental and social criteria are considered in a multi-stage RET selection process that accommodates the practical realities of community level energy planning. The key objective of this study phase is to present a framework for selecting the most viable renewable energy technologies during the pre-project planning stage of community level RE-based energy system development, based on multiple decision criteria with life cycle thinking. Multi-stage selection reflects the practical realities of energy planning, as engineering 58  decision making needs to be based on technical feasibility as well as socio-economic performance. Energy planning is also fuzzy problem with data uncertainties and system variations that need to be incorporated to the decision making. Therefore, in this phase of the study, a fuzzy logic based approach is taken to account for the said uncertainties and variations.  The proposed methodology can be used in developing a robust decision support framework for planning community-level net-zero energy systems. The findings of the research will inform community developers and decision makers in energy system planning under multiple objectives and constraints based on locally available resources, while paying attention to the interests of different stakeholder groups. Community developers can use the developed method and the resulting decision support tool to select RETs based on the local needs, and use that information for the prefeasibility assessment of the energy system.  Methods and Procedure The focus of the study was to develop a life cycle thinking based decision making model which can capture the uncertainties and the practical requirements developing a community level energy system, while providing flexibility to the decision makers to integrate their own priorities. The energy selection process was approached as a multi-stage problem in the present study. Figure 4-2 provides an overview of the methodology followed in RET selection under this study. Under each assessment criteria category, different indicators were selected to represent the performance of the RET, based on the requirements of a community energy system. The first stage of the energy selection establishes the technical viability of the RET under consideration. The RET which pass the technology screening are evaluated and compared through a MADM process in the second stage based on their TBL performance. The assessment criteria and their representative indicators were defined based on the goals of energy system planning. Fuzzy TOPSIS was used in ranking the RET, and the weights for the ranking were set to reflect the decision makers’ priorities under different scenarios. Life cycle costing and life cycle impact assessment was used in combination with published literature to determine the performance scores for each RET. The life cycle thinking approach used in TBL based MCMD is explained in detail in Section 4.2.2 and Figure 4-3. All other methodologies have been described under their relevant subsections.  59   Figure 4-2: Methodology for energy technology selection  60  4.2.1 Assessment criteria and indicators  The main assessment criteria are categorised as technical (viability of technologies), economic, environmental, and social. Performance indicators were selected to represent the four criteria categories. This approach has been used in previous studies conducted on energy planning [215][217]. Out of the assessment indicators listed in Chapter 3, the most pertinent indicators were selected for the present assessment as per the rationalisation provided below. In this problem, the technical criteria are used to establish whether the RETs are viable in terms of technology and resource supply. This is especially important as an energy technology with low environmental and social impacts and low costs may not have yet reached market maturity, thereby making it a risky investment for a community [45]. On the other hand, a RET with a good TBL performance may have low resource availability, thereby rendering its inclusion in the community energy system practically unprofitable.  The technical factors are used to filter the RET based on their suitability in the present day local context. The initial screening was based on the technological maturity and the resource quality at the site. The resource assessment was done considering whether a RE resource meets the minimal thresholds levels for commercially viable energy generation. Maturity of the technology is an important parameter determining the practical viability and success of a RET, which is commonly used in RE assessment [237][175]. With improved technology maturity, the reliability, safety, and feasibility of a technology increase, along with a reduction in technical and market risk. The technologies which were deemed to be unviable due to low maturity, or whose resource supply quality was classified as low, were filtered out. After passing the initial screening, the overall technical viability of a RET in fulfilling community energy needs is measured based on the resource quality, technology maturity, and supply reliability. This was used to assign a rating to each technology based on the technical viability, in the form of a linguistic qualifier.  A rule-based method was employed in assigning the qualifiers to the RETs. This subjective linguistic rating was based on the following rules defined in Table 4-1, which were developed considering technology maturity, resource quality, and supply reliability. Rule-based methods have been used in different engineering applications in selection and decision making [238]. These rules represent the choices made by decision makers in the assessments regarding energy selection, and can vary depending on the risk and innovation preferences of individual communities. Thus, 61  the rules below were defined through subjective judgements based on previous studies and expert opinion, and can be changed for different decision environments.  Table 4-1: Rules for RET technical feasibility assessment Assessment Technology maturity Resource quality Supply reliability Very good High maturity  High resource availability Dispatchable – steady supply High maturity  High resource availability Non-dispatchable – fluctuating supply High maturity  Medium resource availability Dispatchable – steady supply Good High maturity  Medium resource availability Non-dispatchable – fluctuating supply Medium maturity  High resource availability Dispatchable – steady supply Fair Medium maturity  High resource availability Non-dispatchable – fluctuating supply Medium maturity  Medium resource availability Dispatchable – steady supply Poor Medium maturity  Medium resource availability Non-dispatchable – fluctuating supply Unviable Technology maturity is low, or low/non-existent resource availability The second step of the energy selection process is the technology ranking with MADM under economic, environmental, and social (TBL) criteria. The RET which passed the technology screening are taken forward to the energy ranking stage based on the TBL performance.  The economic assessment criteria considered in the ranking are levelised cost of energy (LCOE), payback period, and profitability index (PI) respectively. LCOE is the ratio between the lifetime costs of the energy system and the lifetime energy generation [239]. While investment and operational costs as well as service life are taken as decision attributes in previous studies on renewable energy ranking [217][210], these are included in assessing the LCOE from any RET. Therefore, LCOE was assumed to be a representative indicator of the costs associated in replacing the community energy supply with renewables. A long payback period is mentioned as a barrier to RE penetration, and therefore payback period was selected as another critical economic indicator for RET selection [174]. PI or the benefit/cost ratio provides a measure of the desirability or the relative profitability of an investment in terms of its future returns, and can be used to compare projects of unequal sizes [240]. Taken together, these three indicators provide an assessment of the cost of energy supply, the time to recap the financial expenditure, and the economic viability of the RE investment.  The overall environmental impacts of using a particular RET for energy generation from the cradle (i.e. raw material extraction and facility construction) to grave (i.e. disposal at end of life) were quantified through LCA. These impacts scores were then normalised and aggregated to identify the attribute values under the environmental criteria for each RET. LCA inventory data was also used to quantify the human health impacts (cancer and non-cancer) for RET. While there can be 62  numerous socio-economic benefits to the local community due to the use of RE such as economic diversification, stimulation of investments and business activity, and wealth distribution [233], the number of jobs created can be taken as a direct and quantifiable indicator of the societal benefits of RET, which has been used in many previous studies [217][175].  4.2.2 Life cycle thinking approach ISO 14040 specifies that life cycle assessment (LCA) can be used in quantifying the potential environmental impacts of products (including services) [53]. For this, the initial step is to define the goal and scope of LCA. Accordingly, in the scope definition, a system boundary extending from raw material extraction and facility construction to the disposal at end of life was considered for the assessment. Material, energy, and water inputs to the system are the inflows, and the solid, liquid, and airborne emissions and waste are the system outflows. The LCA was conducted for a functional unit of 1 MWh of energy generated. The ReCiPe Midpoint assessment method was used in the LCA. Midpoint assessment method was used in the life cycle assessment. Midpoint assessment methods are used to derive quantitative information on emissions and various environmental impacts [241]. LCIA methods are used to connect the life cycle inventory (LCI) results which may be in the form of GHG emissions, other toxic chemicals, or resource use, to environmental impact categories such as global warming, ecotoxicity, and human toxicity [241]. Midpoint indicators have lower uncertainty, compared to endpoint ones which require significantly higher environmental mechanism modelling [242]. The impact data obtained from the LCA were under the following impact categories. ▪ Climate change (kg CO2 eq) ▪ Ozone depletion (kg CFC-11 eq) ▪ Terrestrial acidification (kg SO2 eq) ▪ Freshwater eutrophication (kg P eq) ▪ Marine eutrophication (kg N eq) ▪ Human toxicity (kg 1,4-DB eq) ▪ Photochemical oxidant formation (kg NMVOC) ▪ Particulate matter formation (kg PM10 eq) ▪ Terrestrial ecotoxicity (kg 1,4-DB eq) ▪ Freshwater ecotoxicity (kg 1,4-DB eq) 63  ▪ Marine ecotoxicity (kg 1,4-DB eq) ▪ Ionising radiation (kBq U235 eq) ▪ Agricultural land occupation (m2a) ▪ Urban land occupation (m2a) ▪ Natural land transformation (m2) ▪ Water depletion (m3) ▪ Metal depletion (kg Fe eq) ▪ Fossil depletion (kg oil eq) After obtaining the life cycle impact data for the RET, it is necessary to normalise the results and aggregate using weights in order to employ them in the MADM for comparison of technologies. Weighting is done by multiplying the normalised scores under each impact category with a factor that represents the relative importance of that category, enabling a single score to be obtained in the end [242]. Four main indicators were defined from the selected impact categories to be used in the MADM matrix, namely climate change, damage to ecosystem, resource depletion, and human health risk [241]. Similar endpoint damage categories have been defined in previous literature in grouping LCA impacts [42][241]. The general midpoint to endpoint aggregation in ReCiPe is done under the three categories of ecosystems, human health, and resources [243]. In this assessment, the additional category of global warming was used to represent the carbon emissions and their related climate change impact, due to the importance of emissions mitigation in the Canadian climate action goals [15][16]. Figure 4-3 depicts the impact indicators aggregated under each category.  LCI data on some RET were not found in the Ecoinvent database, particularly in the case of newer technologies. For these, literature based LCA data was substituted where possible. LCI data was assumed to be fuzzy as the life cycle datasets are based on approximate values for a given technology or a material. In cases where no impact data could be obtained for a RET under a specific impact category, the fuzzy numbers were defined based on a possible range using the following method. The available impact data from other technologies under the same renewable resource category were used to define the upper and lower limits of the potential impact range, with a 25% uncertainly tolerance. The higher tolerance reflects the increased uncertainty due to data approximation. Using techniques such as data approximation from similar applications and 64  developing fuzzy numbers to represent a likely range of values are approaches used in previous studies to account for uncertain or incomplete data [244].  In order to calculate the attribute scores for the economic indicators, a life cycle cost assessment (LCCA) model was developed based on the capital expenditure (CAPEX), operational and maintenance costs (O&M), and service life of the energy generating facilities. In the case of geothermal heating, the energy saving was considered as the representative value for the energy generation. The annualised LCC was calculated for an equivalent installed plant capacity of 1 kW. While previous studies have used capital expenditure and operational costs as ranking criteria, these parameters are already included within the LCOE. The developed LCCA model was used to determine the payback periods and IRR for the RET in addition to the LCOE. The following equation was used in calculating the LCOE for a particular technology [172].  𝑳𝑪𝑶𝑬 =  ∑𝑰𝒕+ 𝑴𝒕+ 𝑭𝒕(𝟏+𝒓)𝒕𝒏𝒕=𝟏∑𝑬𝒕 (𝟏 +𝒓)𝒕𝒏𝒕=𝟏⁄       Equation 1 It  = Investment expenditure for year t;  Mt  = O&M expenditure for year t; Ft  = Fuel expenditure for year t; Et  = Electricity generated in year t; r  = Discount rate; n  = Facility lifetime (service life) 65   Figure 4-3: Life cycle thinking approach  The present value (PV) of a future one time cash flow at time t, and the PV of annually recurring equal cash flows for t periods (PVA) are respectively represented by following formulas [208]. 𝑷𝑽 =  𝑭𝑽(𝟏+𝒓)𝒕;  where FV is the future value, and r is the discount rate   Equation 2 𝑷𝑽𝑨 =  𝑨[(𝟏+𝒓)𝒕−𝟏]𝒓(𝟏+𝒓)𝒕; where A is the annually recurring cash flow   Equation 3 Thus, the simple payback (SP) and discounted payback period (DPP) in which the initial investment can be recovered is given by the following equations. 66  𝑺𝑷 =  𝑰𝑪𝑬          Equation 4 𝑫𝑷𝑷 =𝐥𝐧 (𝟏𝟏−𝑰𝑪×𝒓𝑬)𝐥𝐧 (𝟏 + 𝒓)⁄ ;        Equation 5 where E is the annual saving from replacing conventional energy supply and IC is the initial investment cost.  The equation for calculating profitability index (PI) is given below.  𝑷𝑰 =  𝑷𝑽 𝒐𝒇 𝒄𝒂𝒔𝒉 𝒊𝒏𝒇𝒍𝒐𝒘𝒔𝑷𝑽 𝒐𝒇 𝒄𝒂𝒔𝒉 𝒐𝒖𝒕𝒇𝒍𝒐𝒘𝒔        Equation 6 The annual saving in energy cost was obtained as a function of the replaced electricity and natural gas supplies through renewable energy. It was assumed that 100% of the energy provided renewable electricity sources contributed to the replacement of grid electricity supply, while the energy provided by renewable heat sources would be split in replacing natural gas and electricity respectively.  4.2.3 Decision making under uncertainty Fuzzy based techniques are useful in accommodating uncertain or imprecise information, and have been commonly used in energy system related problems [211]. Crisp numbers represent data points which have an absolutely certain fixed value [208]. In contrast, a fuzzy set defines a membership function “μ” for an uncertain “x” parameter. The values of x within the membership function range from 0 to 1.  𝝁?̃?(𝒙)  ∈ [𝟎, 𝟏]          Equation 7 Triangular and trapezoidal fuzzy numbers are commonly used in representing linguistic information. A triangular fuzzy number can be used to represent a variable which has a most likely point value. Trapezoidal fuzzy numbers are useful in representing variables whose value falls within a likely range, as is the case with items such as cost data [208]. Fuzzy extension principle can be used in doing the algebraic computations for fuzzy numbers [245]. The fuzzy arithmetic operations for generalised trapezoidal fuzzy numbers are given below.  If two trapezoidal fuzzy numbers A and B are represented as A = (a1, a2, a3, a4) and B = (b1, b2, b3, b4); 67  Addition:   A+B = (a1+b1, a2+b2, a3+b3, a4+b4) Subtraction:  A-B = (a1-b4, a2-b3, a3-b2, a4-b1) Multiplication: A(.)B = (a1b1, a2b2, a3b3, a4b4) Division:  A÷B = (a1/b4, a2/b3, a3/b2, a4/b1) Scalar multiplication: kA = (ka1, ka2, ka3, ka4); if k>0    kA = (ka4, ka3, ka2, ka1); if k<0 The minimum and maximum boundary values for the fuzzy numbers were established assuming a 10% tolerance for data variability in the decision making. However, this boundary can be adjusted based on the uncertainty tolerance levels of the decision makers in a given context. To develop the LCC model, the initial installation cost, O&M cost, and other cost factors were assumed to be subject to epistemic uncertainties due to limitations in available data. The use of fuzzy numbers can account for the uncertainty associated with the cost factors and other data in the MADM problem [208]. Trapezoidal fuzzy numbers were selected to represent the model variables and the assessment indicators as many of the values are in the form of ranges [208]. In cases where point values are available, a triangular fuzzy number (a, b, c) can be conveniently converted to a trapezoidal number as (a, b, b, c) [246]. The plant capacity factor6 was also assumed to be a trapezoidal fuzzy number, within the possible capacity factor range identified based on literature. The median plant design life was obtained from literature. This factor was treated as a scenario uncertainty, as the plant lifetime is a design parameter decided upon by the planners. Similarly, the LCI data was converted to fuzzy triangular numbers. In calculating the scores for LCOE, payback period, and PI, as well as in aggregating the LCI data into categories, fuzzy arithmetic operations were performed.  Energy planning is a problem which heavily relies on the preferences and priorities of the decision makers’ and other stakeholders. Since these priorities can vary based on the decision context and local requirements, it is not feasible to set universally applicable weights to the indicators. Weighting schemes which represent different decision scenarios are a technique used in previous                                                  6 Capacity factor is the ratio of actual energy output to the maximum possible energy output under the rated capacity over the same period of time.  68  studies to account for this variability in preferences [247][248]. Three decision scenarios were defined for the present study as described below in Table 4-2, each with a weight scheme representing the relative importance of different criteria categories to the community.  ▪ Neutral: All criteria are weighted equally, with no preference.  ▪ Pro-environment: Environmental and human health criteria are of higher importance to the community thus being given higher weightage. Environmental and human health criteria carry 80% of the weight. ▪ Pro-economic: Economic performance of the energy system is of higher importance, and thus given higher weightage. Economic criteria carry 80% of the weight. Table 4-2: Weighting schemes for different decision scenarios Criteria  Weighting  Scenarios Neutral Pro-environment Pro-economic Economic LCOE 0.125 0.05 0.3  Payback period 0.125 0.05 0.2  Profitability index 0.125 0.05 0.2 Environmental Climate change 0.125 0.2 0.05  Damage to ecosystem 0.125 0.2 0.05  Resource depletion 0.125 0.2 0.05 Social Job creation 0.125 0.05 0.1  Human health 0.125 0.2 0.05 4.2.4 Fuzzy TOPSIS for technology ranking Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was selected as the MADM method for this study, as it is a relatively simple and straightforward process which can represent the rationale in individual human choices and can be used in a spreadsheet form [196][249]. In this method, the best and worst scenarios can be presented at the same time, allowing the best alternative to be identified under each criterion [250]. These factors are important in the current study, where decision makers’ choices play a key role, and it is necessary to develop user-friendly tools whose computational logic can be easily explained. The weights were taken as crisp values in the decision making as they were defined according to weighting schemes under decision makers’ priorities, hence not being subject to data uncertainty. The goal of TOPSIS is to evaluate the alternatives based on the distance to the ideal solutions, where the best alternative will have the shortest distance from the positive ideal solution (PIS), and the longest distance from the negative ideal solution (NIS) [246].  69  LCOE, payback period, environmental impacts, and the human health impacts were considered to be cost criteria, while profitability index and job creation were taken as benefit criteria in the decision matrix. The normalising for cost and benefit attributes are different, as cost attributes need to be minimised and benefit attributes should be maximised. In the fuzzy decision matrix for the MADM problem, the trapezoidal fuzzy number for ith RET alternative under jth assessment criterion 𝑥𝑖𝑗 was defined as 𝒙𝒊𝒋 = (𝒂𝒊𝒋, 𝒃𝒊𝒋, 𝒄𝒊𝒋,𝒅𝒊𝒋). The normalised fuzzy value of 𝑟𝑖𝑗 is defined in equations 3 and 4, using linear scale transformation [251].  𝐫𝐢𝐣 = (𝐱𝐢𝐣𝐝∗𝐣) ; for benefit criteria; where 𝐝∗𝐣 = 𝐦𝐚𝐱𝒊(𝐝𝐢𝐣)    Equation 8 i.e. 𝒓𝒊𝒋 = (𝒂𝒊𝒋𝒅∗𝒋,𝒃𝒊𝒋𝒅∗𝒋,𝒄𝒊𝒋𝒅∗𝒋,𝒅𝒊𝒋𝒅∗𝒋) 𝐫𝐢𝐣 = (𝐚−𝐣𝐱𝐢𝐣) ; for cost criteria; where 𝐚−𝐣 = 𝐦𝐢𝐧𝒊(𝐚𝐢𝐣)    Equation 9 i.e. 𝒓𝒊𝒋 = (𝒂−𝒋𝒅𝒊𝒋,𝒂−𝒋𝒄𝒊𝒋,𝒂−𝒋𝒃𝒊𝒋,𝒂−𝒋𝒂𝒊𝒋) The weighted normalised decision matrix is derived using the following equation [251].  𝐯𝐢𝐣 =  𝐫𝐢𝐣(. )𝐰𝐣          Equation 10 Fuzzy positive ideal solution: A* = (v*1, v*2, … v*n);        v*j is the maximum vij under each attribute.  Fuzzy negative ideal solution: A- = (v -1, v -2, … v -n);       v -j is the minimum vij under each attribute. Under each attribute (criteria), the vij with the largest d value v*j, and one with the smallest a value is v-j. The distance from FPIS and FNIS are given by the following equations [251].  𝒅∗𝒊 =  ∑ 𝒅(𝒗𝒊𝒋,𝒏𝒋=𝟏 𝒗∗𝒋)         Equation 11 𝒅−𝒊 =  ∑ 𝒅(𝒗𝒊𝒋,𝒏𝒋=𝟏 𝒗−𝒋)         Equation 12 The Euclidean distance between two trapezoidal fuzzy numbers is given by the following equation, which can be used to calculated the distance measurement of each alternative from A* and A- [251].  𝒅(𝒙𝟏, 𝒙𝟐) =  √𝟏𝟔[(𝒂𝟏 − 𝒂𝟐)𝟐 +  𝟐(𝒃𝟏 − 𝒃𝟐)𝟐 + 𝟐(𝒄𝟏 − 𝒄𝟐)𝟐 +  (𝒅𝟏 − 𝒅𝟐)𝟐]   Equation 13 70  Finally, the closeness coefficients (CCi) are determined, for ranking the alternatives [251].  𝑪𝑪𝒊 =  𝒅−𝒊𝒅∗𝒊+𝒅−𝒊          Equation 14 The RET selection was done based on the relative ranks assigned through the MADM process.  4.2.5 Case-specific methods and analysis Different RET alternatives which can be used in developing decentralised energy systems were identified through a comprehensive literature review. The identified RET which were considered in developing the selection model for the case study are listed in Table 4-3. Out of these, major hydro plants were omitted from the selection as these were considered to be infeasible at regional level distributed energy systems due to the relative scale of the projects.  4.2.5.1 Technical feasibility assessment  IEA has classified RET into three categories as 1st, 2nd, and 3rd generation technologies, according to the maturity [106]. Thus, the 3rd generation technologies are still under development, and have not achieved widespread market penetration. These technologies were considered to be unsuitable for community level applications in their present status due to the lack of technological reliability.            71  Table 4-3: Renewable energy technologies  RE sources Technology Energy output Maturity level Solar Solar PV - Building level Electricity Medium a, f  Solar PV - Centralised plant Electricity Medium a, f  CSP - Parabolic trough Electricity Low a, b  CSP – Linear Fresnel Electricity Low a, b  CSP - Power tower Electricity Low a  Solar thermal- Flat plate collector Heat Medium a  Solar thermal- Tube collector Heat Medium a Wind Wind - Onshore Electricity Medium a, f  Wind - Offshore Electricity Low a, b Biomass Biomass boiler (heat) Heat High a  Direct combustion  Electricity/Heat High a  CHP Electricity/Heat Medium a  Gasification Electricity Medium a Waste Mass combustion Electricity Medium a, c  CHP Electricity/Heat Medium a  Biogas - Anaerobic digestion Heat/Electricity Medium a, c  Landfill gas Heat/Electricity High a, c Hydro Large hydro Electricity High a  Small hydro Electricity High a Geothermal Heat pumps Heat High a, b  Flashing Electricity High a, b  Binary Electricity Low a, d  EGS (Hot dry rock) Electricity Low a, d Marine Wave energy converters Electricity Low a, b, e  Tidal stream Electricity Low a, b, e  Tidal barrage Electricity Low a, b,  e  Ocean thermal energy Electricity Low a, b, e a Maturity classified based on data published by the International Energy Agency (IEA) on 1st, 2nd, and 3rd generation RET [106]. b Technologies classified as RD&D, early-commercial, and commercial by the IEA [115], c Based on data published by IRENA for biomass [163] d Data obtained from" Renewable Energies and CO2: Cost Analysis, Environmental Impacts and Technological Trends” [252] e Based on published data on marine energy technologies [253]. Wave and OTEC technologies are yet in the technology development stage while tidal technologies as just beginning to enter the market push stage.  f Based on data published by the IEA for wind and solar in 2016 [254] A case study is used to demonstrate the developed energy technology selection method. In this, the data relevant to British Columbia, Canada, are used as inputs to the selection framework. The selected municipality is located North to Vancouver, BC, and has potential for solar, wind, and geothermal energy resources [255]. The proximity to the ocean also opens up the possibility of marine energy for the community. RE resource potentials were established based on British Columbia Green Electricity Resource Maps [138]. The following data provided in Table 4-4 about the theoretical feasibility of RE is summarised from the resource data provided in the above maps, and other published material on resource potential and site selection for RE facilities. 72  Table 4-4: RE resource benchmark levels for energy generation Resource Potential and resource quality Wind Electricity generation can take place from predicted wind speed 3ms-1 and above (~65 m above ground level) Low: below 6.5 ms-1  Medium: between 6.5 – 8 ms-1  High: above 8 ms-1 [138] Solar Average solar radiation received by a south facing PV panel inclined at a latitude equivalent angle Low: below 3 kWh/m2/day Moderate: 3 – 3.5 kWh/m2/day Medium: 3.5 – 4 kWh/m2/day High: above 4 kWh/m2/day [138] Small hydro Potential site characteristics Low: low precipitation and winter temperatures; low to moderate relief (of the river basin) Medium: medium precipitation and moderate winter temperatures; moderate to high relief High: high precipitation, high relief, and moderate winter temperatures Proximity to power transmission or distribution line is a requirement, with the general boundary of 15 km distance [138]. Geothermal Based on gradient heat and temperatures at the potential zone Low: no significant potential Medium: Gradient heat with temperatures up to 200 °C High: hot fluids present usually above 200 °C [138] Tidal current Potential sites: presence of sites with current speeds above 2 ms-1 [138] Good sites: Sites with current speeds above 2.4 ms-1 and estimated energy potential of over 10 MW Wave energy Minimum wave energy density threshold for site selection 7 kW/m [137] Low: less than 10 kW/m Moderate: 10-20 kW/m High: Above 20 kW/m  Shore areas with grid proximity of a general boundary up to 15 km distance power transmission or distribution line [138]. Tidal range Site selection based on tidal range between high and low tide. Low: Less than 0.8 m  Medium: 0.8 – 2 m High: Above 2 m [139] Ocean thermal energy To be viable for ocean thermal energy conversion, the depth should be above 1000 m, and the temperature difference between 20m and 1000 m depths should be above 15°C. Cold currents should not be present [256]. Low: Less than 18 °C (temperature difference) Medium: between 18 to 22 °C High: Above 22 °C Based on the rules defined in Table 4-1, the technical feasibility of the RET were established for the screening. The RET selected from the screening step were considered in the TBL assessment.  4.2.5.2 Data collection The cost and design parameters for different RET are listed in Table 4-5. An installed plant capacity of 1kW was assumed for each technology for the assessment. (The costs given in USD were converted to CAD at a conversion rate of 1.3. The discount rate for the LCC was taken as 3% [56].) 73  Table 4-5: Renewable energy plant characteristics RE sources Technology Service life Capacity factor Initial investment ($) Annual O&M costs ($) Jobs created  Years % (USD/kW) (USD/kW-year) (jobs/MW) Solar Solar PV - Building level  20 a,b 12-20 b 3700-6800 b 19-110 b 20.2 k  Solar PV - Centralised plant 15-21 b 2700-5200 b 14-69 b  CSP - Parabolic trough 25 a 25-28 e 6066 h 66 h 7.6-36.5 k  CSP – Linear Fresnel 22-24 e 3745 h  55 h  CSP - Power tower 55 e 4169 h 55 h  Solar thermal- Flat plate collector 10-15 b 4.1-13 b 1421 m 28-50 m 5.9 l  Solar thermal- Tube collector 1677 m  Wind Wind - Onshore 20 a,b 20-40 b 1200-2100 b 46 i 12.2 k  Wind - Offshore 20 b 35-45 b 3200-5000 b 76.1i 18.3 k Biomass Biomass boiler (heat) 20-30 c 13-29 b 310-1200 b 13-43 b 13.2 k  Direct combustion  20 e 85 e,f 1880-4260 e 84 b  CHP 25 a 63-74 b 3550-6820 e 54-86 b  Gasification 20-25 d 80 f 2140-5700 e 65-71 b Waste Mass combustion 25 a 85 e,f 5000-6000 e 90-200 f 16 k  Gasifier CHP 25 a 85 e 5570-6820 e 15-130 b  Biogas - Anaerobic digestion 20 a 68-91 b 2574-6104 e 37-140 b  Landfill gas 15 a 60-90 f 1540-2470 f 90-200 f 6 l Hydro Large-scale power plants 35 a 17-59 g 1400-3680 f 25-75 b 7.8 k  Small hydro 34-50 g 1590-4150 f 25-75 b 20.3-22.9 k Geothermal Heat pumps 20 b 25-30 b 900-3,800 b 0.028-0.0327 7.2-11.1 k  Flashing 25-30 b 60-95 b,f 1800-3600 b 150-190 b  Binary 85-95 f 2100-5200 b 150-190 b  Hot dry rock (EGS) 95 g 6000-20000 j   130-390 j Marine Wave energy converters 20 b 25-40 b 6,200-16,100 b 180 b 10-20 [257]  Tidal stream 20 b 26-40 b 5,400-13,400 b 140 b  Tidal barrage 40 b  22.5-28.5 b 4,500-5,000 b 100 b  OTEC 20 b 90-95 e 4,200-12,300 b 37-400 [142] a Based on data published by the International Energy Agency (IEA) in 2016 [115] b Based on a report compiled by Intergovernmental Panel on Climate Change (IPCC) [165]. Assumptions made based on values for similar technologies where exact data was unavailable.  c Based on data published by the National Renewable Energy Laboratory of the U.S. Department of Energy [146] d Assumption made based on data published by the International Renewable Energy Agency (IRENA) for biomass technologies [163] e Based on data published by IRENA for CSP [160], biomass [163] and OTEC [258]  f Based on the data published by the World Energy Council in 2013 [131] g Based on data published by the National Renewable Energy Laboratory of the U.S. Department of Energy [259]. The interquartile ranges are taken as most likely capacity factor range for each technology. h Based on the data obtained for CSP from the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory [260].  i Based on the data published by the U.S. Energy Information Administration of the U.S. Department of Energy, under assumptions for Annual Energy Outlook report [161] j Based on the data published by the International Energy Agency [134] k Based on a report published by the International Renewable Energy Agency (IRENA) on jobs in renewable energy [166]. Manufacturing, construction, installation, and O&M jobs are considered for the technologies. A range of values are provided where data specific to North America is not available. l Published data in the book “Electric capitalism: recolonising Africa on the power grid” [261] m Published data from the book " Renewable Energies and CO2” [252]                                                  7 O&M cost expressed in USD2005/kWhth. The costs have been converted to 2015 values based on an inflation rate of 1.25 for the U.S. Dollar.  74  The energy price was assumed to be subject to variations, thereby leading to aleatory uncertainty. The fuzzy numbers for electricity price [208] and natural gas price8 [67][56] are given below.  𝐸𝑙𝑒𝑐𝑡𝑟𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 (𝐶𝐴𝐷 𝑘𝑊ℎ⁄ ) = (0.042, 0.0797, 0.1075, 0.1872) 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝑔𝑎𝑠 𝑝𝑟𝑖𝑐𝑒 (𝐶𝐴𝐷 𝐺𝐽⁄ ) = (5.4702, 6.0800, 6.0800, 6.6880) 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝑔𝑎𝑠 − 𝑏𝑎𝑠𝑖𝑐 𝑎𝑛𝑛𝑢𝑎𝑙 𝑐ℎ𝑎𝑟𝑔𝑒 (𝐶𝐴𝐷 𝐺𝐽. 𝑦𝑟⁄ ) = (1.5213, 1.6903, 1.6903, 1.8693) The renewable heat generated was assumed to be split 55:45 in replacing natural gas and grid electricity respectively. (Statistics Canada reports that 55% of British Columbia’s heating energy needs are residential level is supplied through natural gas [67].) The current prices of grid electricity and natural gas were then used to calculate the cost savings. The life cycle impact assessment (LCIA) was carried out with the SimaPro software, using the Ecoinvent 3 database. Canadian data was used for the assessment in most cases. Where Canadian data is not available, US or North American data has been substituted as a reasonable approximation. Landfill gas and AD energy conversion factors were assumed as 0.122 MWh/ton and 0.792 MWh/ton respectively based on literature [262]. This information was used in synthesizing the LCI data from the available processes in the Ecoinvent database to compensate for data limitations. The life cycle impacts for marine technologies [263] and gasification [264] were identified through published literature, due to their unavailability in the database. The life cycle impacts for the technologies considered in the case study are provided in Appendix A   The LCI data were then processed and aggregated as per the methodology described in section 4.2.2 to identify the environmental and human health performance of the RET. In aggregating the impact indicators under the four life cycle impact categories, equal weights were assigned to the indicators, with the exception of the resource depletion category. Water depletion was considered to be of higher importance under that category due to British Columbia’s water problem, and was assigned 50% weighting.                                                  8 Average annual natural gas use per household in BC 84 GJ [67] Natural gas variable cost = $ 6.08 per GJ [56] Basic daily charge = $ 0.389 per household [56] 75   Results In the initial screening for technology and resource level feasibility, 11 technologies were filtered out, based on the rule based method defined in Table 4-1. The technologies in their R&D or early commercial stages, such as CSP and marine energy, were deemed to be too risky and beyond the expertise level of a community in long-term operations. Large hydro plants were not considered in the assessment as they are beyond the capacity of community level distributed energy systems. The results of the technology screening and the assigned ratings are listed in Table 4-6.  Table 4-6: Results of resource screening Source Technology Maturity Resource quality Supply reliability Rating Solar Solar PV - Building level Medium High Non-dispatchable Fair  Solar PV - Centralised plant Medium High Non-dispatchable Fair  Solar thermal- Flat plate collector Medium High Non-dispatchable Fair  Solar thermal- Tube collector Medium High Non-dispatchable Fair Wind Wind - Onshore High High Non-dispatchable Very Good Biomass Biomass boiler (heat) High Medium Dispatchable Very Good  Direct combustion  High Medium Dispatchable Very Good  CHP Medium Medium Dispatchable Fair  Gasification Medium Medium Dispatchable Fair Waste Mass combustion Medium High Dispatchable Good  Biogas - Anaerobic digestion Medium High Dispatchable Good  Landfill gas High High Dispatchable Very Good Hydro Small hydro High High Dispatchable Very Good Geothermal Heat pumps High High Dispatchable Very Good  Flashing High High Dispatchable Very Good Technology rankings were obtained for the options which passed the initial screening, using the method detailed in section 4.2.4. The RET rankings under the three different decision scenarios are given below in Table 4-7. The electricity and heating sources were ranked separately due to their differing purposes in energy supply.       76  Table 4-7: Technology rankings under different decision scenarios Alternatives Ranking Source Technology Neutral Pro-Environment Pro-Economic Renewable Electricity Solar Solar PV - Building level 8 6 11  Solar PV - Centralised plant 5 5 10 Wind Wind - Onshore 9 2 7 Biomass Direct combustion 2 3 1  CHP 11 10 9  Gasification 4 8 5 Waste Mass combustion 10 11 8  Biogas - Anaerobic digestion 3 4 4  Landfill gas 6 9 3 Hydro Small hydro 1 1 2 Geothermal Flashing 7 7 6 Renewable Heat Solar Solar thermal- Flat plate collector 4 3 3  Solar thermal- Tube collector 3 2 4 Biomass  Biomass boiler (heat) 1 1 1 Geothermal Heat pumps 2 4 2 An overall comparison of the TBL performance of the different RET can be obtained through the closeness coefficients in fuzzy TOPSIS ranking depicted in Figure 4-4. The resources with low or zero emissions during operational phase perform better than those with operational emissions such as waste-to-energy technologies.  Figure 4-4: Closeness coefficients for RET 77  The performance assessment ranking changes with different decision scenarios. This variation occurs since the weighting factors for environmental, economic, and social criteria are applied based on the decision makers’ priorities. To further explore the variability in results, the technical rating obtained in Table 4-6 was integrated to the selection in pro-environment and pro-economic scenarios, with a 80:20 split between weighting assigned to TBL and technical assessments respectively. As three rating levels were assigned in the technical assessment as “very good”, “good”, and “fair”, a 3-point linguistic scale was used to represent triangular fuzzy numbers for the technology ratings [265].  Very good (0.5, 1, 1) Good  (0, 0.5, 1) Fair  (0, 0, 0.5) Figure 4-5 depicts the variations in the ranks with the integration of technical rating criteria under pro-environment and pro-economic scenarios for the renewable electricity technologies.   Figure 4-5: Change in ranking with technology rating for renewable electricity technologies 78  It can be noted that for several technologies, the assigned rank changes under the alternative decision scenario with technical rating, as the RET with higher resource quality and less technology risk perform better in the selection. The ranks of RET with steady supply such as geothermal plants and municipal solid waste (MSW) incineration have improved significantly. The ranks allocated to non-dispatchable RET such as solar PV worsened. The ranking of wind energy improved due to the higher resource quality in the selected area.   Discussion In the ranking results for the pro-environment scenario, the technologies with higher life cycle impacts such as emissions were ranked lower. Interestingly, when life cycle impacts due to manufacturing, facility construction, and end-of-life are considered, some of the so-called “zero-emissions” technologies such as solar PV do not get a high pro-environment ranking. Small hydro performs the best under the pro-environment scenario and is ranked second under the pro-economic scenario due to the low life cycle environmental impacts, longer plant life, and high plant capacity factor. Under the heating sources, geothermal heat pumps perform the worst under the pro-environment scenario. Among the electricity sources, direct combustion of biomass in conventional boilers performs best as it is a cost-effective resource with high capacity factors, low levelised cost, and a steady supply. Wind power also performs well under a pro-environment decision scenario with and without technical rating, due to the low life cycle impacts and low levelised cost. Taking a life cycle perspective of the environmental and economic impacts is important so that the real costs of energy use are considered in selecting the most suitable energy options.  Another interesting finding is that solar PV, which is gaining in popularity across Canada, is one of the best technology options under a pro-economic scenario. While solar PV has gained significant market penetration in the recent times [165], the levelised cost is still high, especially with building level applications. In the present study, the levelised cost of building level PV ranged from approximately 160-860 CAD per MWh, which is in line with the 180-860 USD/MWh range reported in literature [165]. However, on a pro-environment perspective, solar PV performs much better, due to the lack of emissions in the operational stage. Both wind and solar technologies are still improving in terms of market maturity and cost reduction, and are likely to become more 79  competitive in the future under a pro-economic scenario.  The efficiencies of PV panels show an increasing trend with time [266], and costs have been projected to reduce with increasing market maturity [134]. The prices of wind and solar technologies are decreasing as the technologies improve, and next generation technologies are emerging [267][254]. The market shares of wind and solar are increasing with time with the increase of decentralized energy systems and innovative technologies for grid integration [254].  Taking a purely, economic perspective, it can be noted that the biomass incineration is lucrative, due to the simple and low-cost technology, assuming that a continuous biomass supply is readily available in close proximity to the community site. British Columbia has a considerable supply of biomass which can be utilised in energy generation [268]. Another option is the dedicated growth of energy crops for biomass energy [269]. However, the competing uses for biomass and land, especially when it comes to agriculture and food crops, should be considered in planning for such a choice.  Waste-to-energy (WtE) technologies involving MSW perform badly under environmental and human health related criteria (with the exception of anaerobic digestion which involves only organic waste). This is due to the very high emissions and other life cycle environmental impacts of energy generation from waste. However, they make a good business case, and perform fairly well under a pro-economic scenario. The avoided impacts of waste management such as groundwater pollution due to leachate and odors should be considered in assessing WtE technologies [270]. Therefore, if a community still wishes to go ahead with WtE in managing their waste and supplying their energy, a human health risk assessment is essential. However, WtE has another goal in community planning in addition to energy generation, which is waste management. Therefore, while WtE is classified as a RET, it is a special case that requires additional attention on quantifying the social risks and benefits. An analysis conducted on selecting the best WtE options based on their human health impacts on the basis of energy generation and waste processing capacity is presented under Appendix B   Furthermore, geothermal plants too have mid-level performance under both pro-environment and pro-economic scenarios. Even though they are considered to be low emission  technologies, geothermal plant construction creates significant environmental impacts when exploiting subterranean resources [271], and the levelised cost of energy is also high.  80  The median plant design life was used in the economic assessment, and design life was not considered as a separate assessment criterion in the MADM problem. Similarly, factors such as efficiencies, capacity factors, and installation and operational costs were not considered as MADM criteria, as they were embedded in the selected criteria of LCOE, payback period, and PI. This was to avoid effect from criteria from being duplicated in the by repeated inclusion in the assessment. The weighting factors were set to reflect decision makers’ priorities in each scenario. However, the ranking results can change further if the weights are changed.  In the life cycle cost model for the case study demonstration, the discount rate considered the effect of inflation on the nominal interest rates. The discount factor can be further refined to reflect the perceived risks of an investment by using a risk-adjusted interest rate. As the goal of the ranking model was to compare RET that had reached sufficient market maturity, it was assumed that all the filtered technologies in the case study face similar risks or fall within a close range. Thus, for comparison, the inflation-adjusted real interest rate was deemed to be sufficient, as all RET are being comparing on a common basis. However, the developed decision frameworks can be adjusted to include risk-adjusted interest rates based on decision maker preferences and knowledge on the perceived risks of different energy technologies. This approach can be especially useful for comparing emerging technologies with mature technologies, as emerging technologies are associated with a higher risk level.  In general, looking at the closeness coefficient comparison in Figure 4-4, it is evident that renewable electricity technologies perform better than heat technologies. This is partially due to the higher economic and environmental costs per unit of energy provided associated with the heating sources. It also needs to be noted that in several Canadian provinces such as Quebec, electricity is used to supply the major portion of heating needs of residential, commercial, and industrial sectors [67]. This may have an effect on the cost savings associated with renewable heat sources, and thus change their economic performance.  The present study compiles established impact assessment methods and evaluation parameters, standard mathematical techniques, and proven equations in the developed decision making model. The life cycle impact and cost data are from reputable sources and databases, and can be replicated in future studies. The above methods have been combined in a novel way to address the practical decision making needs related to pre-project planning stage of community level energy systems, 81  to overcome the limitations of the previous studies, and to extend renewable energy technology selection from an academic exercise to a practical engineering solution for community level decision makers. For this, taking a project management perspective to RE-based net-zero energy transformation is necessary. The practical implication of this method is geared towards providing decision makers with flexible tools which assist in making their choices. It does not necessarily follow that a community planner involved with energy system design needs to limit his selection to the top ranked technologies. Instead, the technical rating and TBL performance ranking together can provide an overall assessment of a given technology under a particular decision context, and the most suitable choice on be made based on human thinking involved in decision making. This flexibility of choice is important in any urban planning issue, particularly as this assessment is expected to aid in the pre-project planning stage of renewable energy projects. Other criteria which may be included in the assessment to further improve the decision making are the proximity of the RE resources to the community site location and the technical risk and required level of expertise associated with each technology.   The social aspects related to decision making in energy system was introduced under the human health and job creation aspects. Life cycle sustainability assessment considers social criteria in addition to environmental and economic considerations [39]. There is an increasing trend towards considering social criteria in planning at various levels, as an important part of human development will be otherwise neglected in decision making.  The above study encompasses the three phases of search, analysis and selection. During the search stage, different renewable energy technologies, and energy system planning objectives are identified based on state-of-the-art literature. This is followed by detailed analysis to identify the performance scores under the assessment criteria for the planning objectives. Finally, a multi-stage selection process was taken to separately assess the technical viability of a technology and its TBL performance. In this assessment, fuzzy logic was used to address the limitations in data and other uncertainties, in order to improve the quality of energy system related decision making. In previous studies, there is limited focus on uncertainties and variations, and most decision making frameworks follow a deterministic assessment approach. In addition, a few studies of similar nature have used Monte Carlo simulation to address the aspect of uncertainty. However, while Monte Carlo simulation has the capacity to deal with aleatory uncertainties, it has limitations in 82  addressing epistemic uncertainties [272]. Furthermore, it is more time consuming and requires a greater computational effort compared to fuzzy logic [273]. This study took a step further in integrating non-deterministic and variable data into energy decision model with the use of fuzzy logic. Fuzzy triangular and trapezoidal numbers can accommodate performance scores that are in the form of a range of possible values, and can also accommodate linguistic and qualitative variables. However, considering the uncertainty distributions as triangular or trapezoidal also leads to some limitations in fuzzy-based computing, as these assumptions tend to generalise the actual reality of variations and uncertainties to a simpler form.  Taking a multi stage approach to RET selection ensures that only viable technologies are being selected. Assessing technical performance in tandem with TBL performance, which is the approach taken in most previous studies, creates the risk that a technology with low technical feasibility may be selected based on high scores in other criteria categories. Decision making should avoid such theoretical pitfalls, and provide practical solutions to engineering problems.  While some of the technology options have been screened out during the technology assessment stage due to the risks associated with immature technologies and low resource quality, these outcomes may change with time if conditions change. For example, as marine energy technologies achieve higher maturity and become commercially viable, they may prove to be more suitable options for the community than the existing technologies. By providing flexible decision support tools to community planners and developers, it enables the community to reach for continuous developments in their energy system based on future technological advancements.  While this study covers the technology selection stage of community energy planning, further work needs to be done on defining energy system. The sizing and the exact mix of RE sources and technologies need to be determined through further assessment in the next steps of community level energy system planning. The technologies selected through the present decision making phase will be inputs to the next stage, in determining the above-mentioned optimal size and mix of the energy system. During the system planning stage, the selected energy technologies need to be further assessed to investigate aspects such as interactions with the grid, system reliability, and the ability to match the load.  83   Summary Net-zero energy systems can deliver environmental and economic benefits, while easing the present concerns on climate change and other impacts of energy use. At community-level, this initiative can lead to enhanced energy security and energy independence. “Energy security has been defined by the International Energy Agency (IEA) as “the uninterrupted availability of energy sources at an affordable price” [274]. The implementation of net-zero energy system concept ensures that the energy supply of a community is based on the locally available resources, thereby limiting the reliance on external energy resources and the impact of energy price fluctuations on communities. In communities such as those in Canada’s North, this can reduce energy poverty, and ensure that the existence of communities is not solely dependent on externally supplied fossil fuel resources.” The goal of planning renewable-based energy systems at community level is to deliver solutions which best suit the local conditions and needs, and can make use of the resources available in that particular locality. Technical, economic, environmental, and social aspects need to be considered in selecting the optimal energy options for communities. This work demonstrates a strategy in selecting the energy options for a community energy system with a holistic approach, under different decision priorities. The results obtained in an energy selection problem, or any urban planning decision for that matter, are highly dependent on the available data. The accuracy of the results is subject to the validity and accuracy of the input data. While the use of fuzzy logic partially accounts for the data uncertainty, the final results are still relative to the information inputs to the decision system. The cost factors had to be identified from multiple sources due to the lack of a comprehensive compilation of up-to-date information on RET from a single reputable source. In addition, some of the life cycle impact data had to be obtained from literature due to the limitations in the life cycle impact database.  The choice of MADM method also affects the results of the final ranking. Fuzzy TOPSIS has been used in this study. However, such decision making methods are affected by problems such as rank reversal when alternatives are added or removed [250]. Mathematical techniques including changing the MADM method or algorithm can provide solutions for such issues [250]. However, 84  as the focus of this study was to demonstrate a decision making process for a community level energy system, exploring this aspect was considered to be out of its scope.  In developing a hybrid energy system at community level, the optimal energy mix and plant capacities needs to be determined. This information is necessary in order to assess the viability of developing a net-zero energy system to supply the local energy demand in an economically and environmentally sustainable manner. Since energy planning is a multi-faceted problem as previously mentioned, a multi-objective optimisation approach needs to be taken in defining the optimal energy system. More importantly, the characteristics of RET including costs and efficiencies change significantly over time. The decisions which are made regarding renewable energy systems need to change to accommodate these time-based variations. The impact of system variability with time on the energy system decision needs to be further explored through a system dynamics perspective in making the optimal energy choices and assessing the future viability of an energy investment.  85  Chapter 5: Optimal renewable energy supply choices for net-zero communities in Canada Parts of this chapter has been published in the Journal of Cleaner Production (Elsevier), as an article titled “Renewable energy integration into community energy systems: A case study of new urban development” [275].   Background Multi-objective optimisation (MOO) is used in defining the optimal choices for net-zero energy systems, with respect to sizing and energy mix. Following the selection and ranking of renewable energy technologies for the community through a multi-attribute decision making approach, the optimal energy mix for the community can be defined. Renewable integration for net-zero energy developments needs to be conducted at both building level and community level. Optimisation techniques use objective functions to define particular criteria of the system which are to be minimised or maximised [203]. 5.1.1 Renewable based energy systems for buildings As buildings are accountable for 32% of the world’s energy use and 19% of the energy-related GHG emissions, as well as around one-third of black carbon emissions, focusing on building-level clean energy initiatives is critical, during both new construction and renovation projects [276]. The net-zero energy (NZE) concept has become popular in the recent times, with the growing interest in clean energy initiatives. Under this concept, the energy needs of a community or a building are reduced through energy saving and conservation measures, and the remainder is met using RE [27]. On-site renewable energy generation is the preferred option, rather than purchasing off-site RE credits [27]. Building-level RE generation can be used to replace the conventional energy supply in net-zero energy buildings (NZEB). However, the possibility of reaching NZE status at building level is constrained by several factors, including limited resource availability and lack of funding [168][277]. Effective decision making is required in assessing how much the building energy consumption can be reduced, and what fraction of the building’s energy demand can be supplied via RE, while maintaining economic, environmental, and social sustainability.  Canadian residential sector accounts for 17% of the energy use and 14% of the GHG emissions in the country [275]. At present, the energy demand of Canadian residential and commercial building 86  sectors are primarily supplied through grid electricity and natural gas. In the residential sector, the shares of electricity and natural gas are 41.3% and 48.5% respectively [278]. For commercial and institutional sector, electricity and natural gas shares are 49.3% and 47.3% respectively [279]. In addition to the above sources, wood, coal, propane, and heating oil are used for residential energy applications. The main end use shares of the residential sector in British Columbia (BC), Canada are listed in Table 5-1 based on 2015 data. In the attempt to develop net-zero buildings, the above energy end uses can be catered with renewable energy sources, such as solar, wind, biomass, and geothermal energy.  Table 5-1: Energy end uses of the residential sector in BC Residence type Single family detached Single family attached Apartments (multi-unit) Space heating 57.53% 42.90% 34.13% Water heating 24.75% 35.89% 40.41% Space cooling 0.89% 1.06% 0.64% Lighting 4.77% 4.41% 3.26% Appliances 12.06% 15.75% 21.56% Moving towards NZEB has been mandated by the BC Energy Step Code [280]. However, there are multiple challenges present in using RE technologies (RET) at building level. While the use of renewable sources in energy systems is expected to reduce the associated emissions of energy generation, it is necessary to assess the overall life cycle costs and impacts of replacing conventional energy sources with renewables [33]. The need for specialized infrastructure and the high capital and operational costs to accommodate RET are only a part of the problem. RE resources such as solar and wind are intermittent in nature, and therefore, the energy supply from these is subject to fluctuations [281]. With all of the above constraints and issues, planning RE-based energy supply for NZE buildings becomes a problem of multiple dimensions. This problem becomes more complicated due to the involvement of multiple stakeholders. Moreover, the parties who incur the costs and those who reap the benefits may not necessarily be the same groups. While studies have been conducted on developing optimised energy systems at both building and community level, these studies have limitations in terms of practical applicability. The practical engineering implications of integrating clean energy in buildings have not been considered in most studies, especially in combination with a life cycle thinking based decision making perspective. At present, there is a lack of decision making frameworks which consider the life cycle economic 87  and environmental impacts of energy use at building level, to be used during the pre-project planning stage of community planning for optimal energy system design. In addition, the previous studies and existing energy planning tools neglect to consider the effect of uncertainties. Therefore, the energy mix solutions proposed by them are of a deterministic nature. Renewable energy system feasibility assessment models which can be used during building and community project planning stage can be useful in guiding decision makers towards more effective energy choices, and will improve the penetration of RE at community level.  5.1.2 Renewable energy system planning for communities Moving ahead from the building level, centralised facilities can be installed at community level for energy generation at a larger scale, with the goal of sharing the generated energy among the community.  Both building level and community level RE installations together will drive the transformation towards net-zero status for communities. As discussed in the literature review, many methods can be used for energy planning at community level. Currently, identifying the best energy systems at community level is a challenge, and is mostly done through an ad-hoc decision process without considering all the possibilities. Scenario-based planning is the most widely used method in defining the most suitable energy systems at community level [53][282].  To initially tackle the problem of community energy system planning, a preliminary scenario-based analysis was conducted on renewable integration for a proposed neighbourhood community in Okanagan, British Columbia, Canada [56]. The total population of the community is 6500. Once the community is fully developed, there will be 2125 single-family attached (SFA) units, 40 single-family detached (SFD) units, and 725 senior congregate care (SCC) units. The following table details the energy consumption data for the proposed residences in the community based on BC averages [67], and the average floor area for different types of housing. (The square footage information for dwellings were obtained through consultation with FortisBC.) Table 5-2: Details of the proposed housing units in the community Dwelling type Annual energy consumption per household (GJ) Number of units Household area  (sq. ft.) Single-family attached 76 40 2259 Single-family detached 125 2115 1988 Apartments 46 725 1094 88  Some results from the above study are presented below to compare the findings of scenario-based analysis with the optimisation model proposed in the current study. The four scenarios developed for the case study analysis are detailed in Table 5-3. Each scenario has a different energy mix for supplying the electricity and heating needs of the community. In the first scenario, where a business-as-usual approach is taken, BC grid electricity and natural gas are used to supply the electricity and heating needs respectively. FortisBC data indicates that using natural gas for heating purposes can lead to significant cost savings [283]. Therefore, space and water heating was assumed to be supplied 100% through natural gas, while appliances, lighting and space cooling are powered 100% through grid electricity9. In the next three scenarios, the RE share in the mix was gradually increased through solar PV and biomass electricity, and geothermal heating for the residences.  Table 5-3: Energy system scenarios developed for the community Scenario Energy supply Electricity Heating Scenario 1 – Business-as-usual Renewable -  -  Non-Renewable BC grid electricity Natural gas Scenario 2 – Solar PV Renewable 1. Centralised 2 MW solar PV plant 2. Rooftop solar PV systems at household level System capacities SFA – 2 kW SFD – 4 kW SCC - None -  Non-Renewable Remaining electricity needs supplied through BC grid Remaining heating energy needs supplied through natural gas                                                  9 As a newly planned and developed community, it was assumed that all residences would be supplied with a similar energy system. 89  Scenario Energy supply Electricity Heating Scenario 3 – Solar PV & geothermal heating     Renewable 1. Centralised 2 MW solar PV plant 2. Rooftop solar PV systems at household level System capacities SFA – 2 kW SFD – 4 kW       SCC - None 1. Geothermal heating at household level through an Earth Energy System (EES) designed to provide 60% of the household heating load* Non-Renewable Remaining electricity needs supplied through BC grid Remaining heating energy needs supplied through natural gas Scenario 4 – Solar PV & geothermal heating with waste-to-energy Renewable 1. Centralised 2 MW solar PV plant 2. Rooftop solar PV systems at household level System capacities SFA – 2 kW SFD – 4 kW       SCC – None 3. Centralised 300 kW waste-to-energy (WtE) incineration plant generating electricity using municipal solid waste (MSW) 1. Geothermal heating at household level through an Earth Energy System (EES) designed to provide 60% of the household heating load Non-Renewable Remaining electricity needs supplied through BC grid Remaining heating energy needs supplied through natural gas * Natural Resources Canada recommends EES systems to be sized to provide 60%-70% of the demand load (for both water and space heating) for maximum cost effectiveness [88].  The following results were revealed in the analysis, under the above developed scenarios. Approximately two thirds of the community’s energy demand would be supplied through renewable sources in scenario 4, as displayed under Figure 5-1.  90   Figure 5-1: Share of fuel sources by scenario The annual energy cost of the community was estimated considering annualised LCC of the renewable energy systems installed at distributed and centralised levels, and the energy bills incurred for the use of natural gas and grid electricity. (Details cost factors on renewable technologies and conventional utilities are provided in Appendix B  .) As expected, the annualised energy cost of the community increases with a higher share of renewables in the energy mix. However, the emissions increase slightly from scenario 3 to 4, as described previously, possibly due to the inclusion of biomass based electricity. The annualised energy cost was compared with the total community level emissions to study the overall effect of each scenario, and to compare the actual costs incurred with the emissions. This comparison is presented in Figure 5-2.   Figure 5-2: Community level cost and emissions forecast 1 2 3 4Conventional 100.00% 86.79% 37.59% 33.29%Renewable 0.00% 13.21% 62.41% 66.71%0%10%20%30%40%50%60%70%80%90%100%ShareScenarios02468101201234567891 2 3 4Annual emissions (103 ×TCO2e/a)Annualised community level cost (Millions of CA$/a)ScenarioCommunity level total annual cost Emissions91  Table 5-4 provides details on community level annual expenditure on alternative energy scenarios, and the emissions reduction achieved by the investment made under each scenario. The annual capital cost was calculated based on the annualised LCC of investment for RE facilities. The highest emissions reduction per dollar invested is obtained through scenario 3. While an additional investment is required for the MSW-to-energy system, it does not result in a corresponding reduction in emissions.  Table 5-4: Community level investment and emissions reduction Scenario Emissions reduction (kgCO2e/a) Reduction per dollar spent (kgCO2e/CA$) 1 0 0.00 2 73901 0.04 3 6538848 1.04 4 6455365 0.75 The additional investment requirements for a house of each dwelling type are given in Table 5-5. The additional investment required per square foot and at a community increases through scenarios 1 to 4.  Table 5-5: Additional investment requirement Scenario Additional investment per HH (CA$/HH) Additional investment for the community (CA$) Additional investment per square foot of floor area (CA$/sq.ft.) SFA SFD SCC SFA SFD SCC Overall community 1 0 0 0 0 0.00 0.00 0.00 0.00 2 12181 22593 1128 27,606,501 6.13 10.00 1.03 5.40 3 37181 47593 26128 99,856,501 18.70 21.07 23.88 19.55 4 38962 49616 27107 104,430,707 19.60 21.96 24.78 20.44 This information can be used to estimate the additional development costs for the community, and thereby the additional financial burden on home buyers due to the implementation of green energy technologies in the community energy system. The additional investment requirement calculated for a square foot of each dwelling type can be used to estimate the cost variations for housing of similar type with different floor area. For a developer, the per square foot investment requirement can also be used to assess the funding requirement with variations in size and composition of the community. The extra costs will be incurred by the developer at the community development stage. The additional costs may be passed onto home buyers as premiums when the developed residences are being sold. Table 5-6 details the percentage increase in home prices due to the additional 92  investment made on alternative energy options. This information can be compared with green housing premiums to evaluate the acceptability of the interventions to the stakeholders based on a financial perspective.  Table 5-6: Percentage increase in housing prices due to RE interventions Dwelling type Price** (CA$/HH) Percentage increase in home prices Scenario 1 Scenario 2 Scenario 3 Scenario 4 SFA 389,030 0.00% 3.13% 9.56% 10.02% SFD 572,260 0.00% 3.95% 8.32% 8.67% SCC 292,050 0.00% 0.39% 8.95% 9.28% ** The home prices in Table 9 were obtained based on the data published for Central Okanagan by Okanagan Mainline Real Estate Board in 2016 [284]. The saving in recurring expenditure on energy has been tabulated under each scenario for a 25-year lifespan in Table 5-7. This is the economic benefit to the homeowners in terms of energy cost savings during their occupancy. However, the lifetime reduction in recurring costs does not match with the additional investment requirements, and thereby the increase in home prices. Therefore, in a purely financial sense, the additional investment may not payoff for the homeowners if the development cost is incurred by them.  Table 5-7: Lifetime reduction in recurring costs Scenario Reduction in recurring costs (CA$/HH) SFA SFD SCC Scenario 1 0 0 0 Scenario 2 4242 7553 594 Scenario 3 5734 11597 522 Scenario 4 7492 12657 286 The study results lead to the conclusion that scenario 3 is the most suitable option for the community. While scenario 4 promises a slight increase in RE fraction and emissions reduction, the incremental cost to gain these benefits is very high, thereby making it less attractive compared to scenario 3. The findings of this preliminary study made it evident that renewable integration at community level leads to significant reductions in emissions, and that it can also lead to a reduction in the operational energy costs for the occupants. Yet, the exact benefit cost and emissions benefit which can be achieved depends of the energy source and technology that is selected in the planning.  93  Further, while there is an increase in the home prices with the added investment on RE integration, there is a positive return with the operational energy savings. Studies indicate that homeowners are prepared to pay higher premiums for “green-rated” houses, and a green certification label adds 9% on average to the selling price of housing [285].  It can be seen in Table 5-6 that the increase in home prices after adding the RE interventions in each scenario falls within or close to this range.  While scenario-based planning can be used to identify the best energy system out of a limited number of choices as seen above, it has several limitations as discussed under Chapter 3. One such limitation is that it does not present a rational and evidence-based method for considering all available technological options and selecting the most suitable RET. While the above limitation in scenario-based planning can be rectified by using the technology prioritisation model proposed in Chapter 4, scenario-based method still cannot consider all possible variations in system planning, and thus leads to many uncertainties. The “in-between” scenarios are neglected, and it is not possible to identify the exact sizing and capacity that will lead to the best results. Therefore, there is limited potential in identifying the truly optimal solutions, as can be seen in the above planning exercise. Energy system planning needs to be conducted in a more systematic manner identify the truly optimal solutions. As discussed under Chapter 3, energy system optimisation is a problem that tackles sizing and deciding the ideal “mix” of energy sources in the system [202][203][204]. The objective of this chapter is to introduce a model to address the limitations of scenario-based energy planning, which can identify the optimal mix renewable energy while also accounting for uncertainties. The study aims to investigate the feasibility of net-zero building energy systems under different decision priorities, considering triple bottom line (TBL) impacts of energy use. The life cycle economic and environmental impacts of energy use are considered for both conventional and renewable energy resources, and the optimal energy mix for buildings are identified based on this and the resource availability. A fuzzy logic-based approach is taken to integrate the data uncertainties and potential variations in the current conditions. An end goal of this study is to investigate the feasibility of developing net-zero energy buildings and communities. The findings will inform and guide community developers and other stakeholders with an interest in residential buildings, on the most suitable clean energy options for their building project during the pre-project planning stage. The decision support framework developed in this study can be used to plan the building 94  energy supply based on the decision makers’ priorities. Moreover, the findings can inform policy makers in developing building codes and energy codes targeted towards achieving net-zero buildings and communities.  Methods and Procedure This this study, a framework was developed to identify the optimal energy mix and fraction of energy which can be supplied through RE in residential buildings and in small-scale residential communities. Therefore, the methodology is presented in two main sections for the building energy system development, and following that, the community level energy facility development. 5.2.1 Renewable integration in building energy systems Building energy use was considered under the end use categories identified in Table 5-1. Different renewable energy technologies (RET) which have the potential to be used at building level were identified, and the life cycle environmental impacts and costs pertaining to these RET were evaluated. Energy supply mix variations were defined as combinations, and the overall impacts of each supply combination were assessed. The following sections detail how the modelling was conducted for building energy demand and RE technologies, and how the optimisation algorithm was developed to identify the most suitable energy mix to meet the demand. The overall study methodology is depicted in Figure 5-3. Initially, data was collected on RE technologies and their energy generation potential. A case study location was selected to apply and demonstrate the developed framework. The energy technologies ranked in the previous study phase were further analysed in order to select the most suitable options for the selected given community. Building energy databases were analysed to identify the energy end uses, the end use intensities by residential floor space, and monthly energy use variations across different seasons. Similarly, seasonal variations of RE generation potential were also assessed. The above data was used as inputs to the optimisation model, which was developed through a combinatorial approach. The life cycle economic, environmental, and human health impacts were evaluated for each of the selected RE technologies, in order to assess the performance of different energy system combinations. A fuzzy ranking method was used to sort the energy system combinations based on their overall performance, in order to identify the best energy supply mix for the community. Detailed descriptions on the methods used ae presented in the following sections under the Methodology.  95   Figure 5-3: Overall methodology for building energy system optimisation The uncertainties and variable data regarding energy use, supply, and cost factors were represented in the form of triangular fuzzy numbers (TFN). For a triangular fuzzy number, x, the membership function is represented by Equation 15.  𝝁?̃?(𝒙) = {𝟎𝒙 − 𝒂 𝒃 − 𝒂⁄𝒄 − 𝒙 𝒄 − 𝒃⁄      ; 𝒙 < 𝒂, 𝒙 > 𝒄; 𝒂 ≤ 𝒙 < 𝒃; 𝒃 ≤ 𝒙 < 𝒄 Equation 15 96  Here, a, b, and c are real numbers, and a < b < c. The notations a and c represent the lower and upper bounds respectively. The membership for the most likely value b, is denoted by 𝝁?̃?(𝒃) = 1. 5.2.1.1 Building energy use Both energy demand and availability of renewable energy supply in Canada change with seasonal variations. This variation needs to be accounted for when identifying the best renewable energy systems at building or community level. While the developed model is applicable for any type of residential building, Multi-unit Residential Building (MURB) in BC were selected as the focus of analysis. A MURB is defined as multiple housing units contained within one building or several buildings, and the share of MURBs in the residential housing stock has grown with the increase in urban population and the need for urban densification [286]. In Canada’s largest metropolitan areas, more than 50% of the newly planned residential construction is expected to be in the form of MURBs [286]. Due to the above reasons, it was identified that any planned clean energy initiatives for the building sector need to especially focus on MURBs.  The energy use in any residential building can be modelled considering the five end uses defined previously. Out of them, the space and water heating energy can be provided with either heat or electricity sources, while lighting and appliance energy can only be supplied with electricity sources. While space cooling is generally done via electricity supply, geothermal systems can also be used as an alternative space cooling mechanism [287]. In hybrid building energy systems, multiple energy sources (both conventional and renewable) are used to supply the energy demand of the building [46][288]. In Canada, significant energy use variations can be observed during different times of the year. As space heating accounts for the largest fraction of the total energy consumption in a residential building, the lowest energy consumption occurs in summer months. In order to identify the behaviour of end use energy demand on a monthly basis, the assumption of a non-variable baseline energy load is assumed [289]. A study conducted for BC, Canada notes that space heating is either turned off or dormant during mid-summer (July and August). The non-variable component of the energy includes non-direct heating (e.g. domestic hot water), appliances, and lighting [289]. (This may be subject to minor variations due to space cooling. However, as space cooling accounts for less than 1% of the energy demand in MURBs, this was assumed to be negligible [278]. By removing the baseline loads from the total energy demand, it is possible to analyse the monthly heating loads.  97  Out of these monthly energy loads, The baseline energy demand (Emb) in a building was assumed to be the lowest monthly energy demand, which occurs in July or August [289]. (Monthly energy demand is denoted by Emj.) 𝐸𝑚𝑏 = 𝑚𝑖𝑛(𝐸𝑚𝑗) ;  𝑓𝑜𝑟 1 ≤ 𝑗 ≤ 12 Equation 16 The monthly energy intensity (EIm) of a building, a quantity which represents the energy use per unit area, was estimated using the gross floor area (Af). The energy intensity provides a measure of comparison amongst buildings of different sizes. The residential energy intensity values are also presented under Appendix D   𝐸𝐼𝑚 =  𝐸𝑚𝑗𝐴𝑓 ;  𝑓𝑜𝑟 1 ≤ 𝑗 ≤ 12  Equation 17 The minimum monthly energy intensity is referred to as the baseline energy intensity (EIb) in each building. For the analysed building cluster, a triangular fuzzy number (TFN) was defined for the monthly baseline energy intensity using the 10th percentile, the median, and the 90th percentile of the EI value range respectively as the lower bound, most likely value, and the upper bound of a triangular fuzzy number in the form of (a, b, c).  EImi represents the range of energy intensity values from all buildings in the cluster.  𝐸𝐼𝑏 = [𝑎𝑏𝑐] =  {𝑃10(𝐸𝐼𝑚𝑖)𝑃50(𝐸𝐼𝑚𝑖)𝑃90(𝐸𝐼𝑚𝑖)  } Equation 18 An urban developer using the proposed model to plan community energy systems can predict the energy performance of a proposed building cluster using the following method based on the average regional building data. Based on the median values in the building cluster information, the representative building can be defined considering the parameters of median number of storeys, median gross floor area, and median ground floor area.  To define an alternative energy supply model for a residential building, it is necessary to separate the heat and electric end uses, as the potential supply sources can vary based on the type of end 98  use. The baseline load was accordingly split into the fractions of water heating and other electric load (appliances, lighting, and space cooling). An assumption was made that all appliances, lights, and cooling systems are powered by electricity. The average energy end use ratios for the apartment buildings (MURB) mentioned under Table 5-1 were used in defining the split between water heating and other electric loads. Accordingly, out of the baseline load, 61.35% is consumed for water heating, and the remaining 38.65% is for other electric loads.  The monthly space-heating load (SHImj) was evaluated by subtracting the baseline load of each month, and following the same procedure using the 10th percentile, the median, and the 90th percentile of the value to define the fuzzy number.  𝐸𝐼𝑆𝐻𝑚𝑗 =  𝐸𝐼𝑚 −  𝐸𝐼𝑏 Equation 19 𝑆𝐻𝐼𝑚𝑗 = [𝑎𝑏𝑐] =  {𝑃10(𝐸𝐼𝑚 −  𝐸𝐼𝑏)𝑃50(𝐸𝐼𝑚 − 𝐸𝐼𝑏)𝑃90(𝐸𝐼𝑚 − 𝐸𝐼𝑏)  } Equation 20 𝑆𝐻𝐵 =  𝑆𝐻𝐼𝑚𝑗 × 𝐴𝐵 Equation 21 𝐵𝐿𝐸𝐵 =  𝐸𝐼𝑏 ×  𝐴𝐵 Equation 22 𝑊𝐻𝐵 =  𝐹𝑊𝐻  ×  𝐵𝐿𝐸𝐵  Equation 23 𝑂𝐸𝐵 =  𝐵𝐿𝐸𝐵 − 𝑊𝐻𝐵  Equation 24 𝐴𝐵 = 𝐵𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑔𝑟𝑜𝑠𝑠 𝑓𝑙𝑜𝑜𝑟 𝑎𝑟𝑒𝑎 (𝑚2)  𝑆𝐻𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑠𝑝𝑎𝑐𝑒 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝐵𝐿𝐸𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝐹𝑊𝐻 = 𝐹𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑓𝑜𝑟 𝑤𝑎𝑡𝑒𝑟 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑙𝑜𝑎𝑑 𝑊𝐻𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑤𝑎𝑡𝑒𝑟 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝑂𝐸𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑜𝑡ℎ𝑒𝑟 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 99  The above defined energy loads and building characteristics were used for developing the energy model for an average MURB, to explore the feasibility of integrating building-level RE systems and achieving net-zero status for MURB.  5.2.1.2 Renewable energy resources and technologies The first step in developing a renewable based hybrid energy system for a MURB is to select the most suitable technologies. Solar technologies are widely used at building level. Wind resource availability for buildings is limited due to structural, noise, and wind pattern considerations. Therefore wind power generators are not usually installed on buildings, especially as the resource availability is highly site specific [168]. Similarly, the biomass option was eliminated due to the challenges in a residential context related to ensuring a reliable and constant supply, and the need for additional transportation of supplies. In addition, building level geothermal systems can be used to supply the heat demand in residences [117].  A) Solar energy The solar energy potential of a residential building is limited by the rooftop and/or façade area available in the building [290]. In the current study, only rooftop area was considered for modelling solar energy potential. The available roof area is utilised by solar PV panels, solar thermal collectors, or both.  𝐴𝑠𝑜𝑙𝑎𝑟 =  𝐴𝑃𝑉 +  𝐴𝑆𝑇 Equation 25 𝐴𝑠𝑜𝑙𝑎𝑟 = 𝑆𝑜𝑙𝑎𝑟 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑟𝑜𝑜𝑓 𝑎𝑟𝑒𝑎 (𝑚2) ;   𝐴𝑃𝑉 = 𝑃𝑉 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑟𝑜𝑜𝑓 𝑎𝑟𝑒𝑎 (𝑚2) ; 𝐴𝑆𝑇 = 𝑆𝑜𝑙𝑎𝑟 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑟𝑜𝑜𝑓 𝑎𝑟𝑒𝑎 (𝑚2)  A simple rule of thumb has been defined by the International Energy Agency on the fraction of rooftop area that is utilisable for solar installations, in terms of sufficient annual solar insolation and architectural suitability [290][291]. The rule can be summed up as the solar utilisation factors for roofs and facades are 0.4 and 0.15 respectively, i.e. for every 1 m2  of building ground floor area (AGF), there is 0.4 m2 of solar suitable rooftop area [290]. This rule was used in defining the 100  constraints for the building solar energy model. The area required for installing 1kW of PV capacity10 is 6 m2.  𝐴𝐺𝐹 =  𝐴𝐵𝑁𝐵⁄  Equation 26 𝐴𝑠𝑜𝑙𝑎𝑟,𝑚𝑎𝑥 = 𝑈𝐹 × 𝐴𝐺𝐹  Equation 27 𝐴𝑆𝑇,𝑎𝑣𝑎𝑖𝑙 =  𝐴𝑠𝑜𝑙𝑎𝑟,𝑚𝑎𝑥 − 𝐴𝑃𝑉 Equation 28 𝐴𝑃𝑉 =  𝐴𝑃𝑉,𝑢𝑛𝑖𝑡  ×  𝐶𝑃𝑉 Equation 29 𝐴𝑆𝑇 =  𝐴𝑆𝑇,𝑢𝑛𝑖𝑡 ×  𝑛𝑆𝑇 Equation 30 𝑁𝐵 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑡𝑜𝑟𝑒𝑦𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 𝑈𝐹 = 𝑅𝑜𝑜𝑓 𝑢𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 𝐴𝑠𝑜𝑙𝑎𝑟,𝑚𝑎𝑥 = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑟𝑜𝑜𝑓𝑡𝑜𝑝 𝑎𝑟𝑒𝑎 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑓𝑜𝑟 𝑓𝑜𝑟 𝑠𝑜𝑙𝑎𝑟 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑎𝑡𝑖𝑜𝑛𝑠 (𝑚2)  𝐴𝑃𝑉,𝑢𝑛𝑖𝑡 = 𝐴𝑟𝑒𝑎 𝑓𝑜𝑟 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑖𝑛𝑔 1 𝑘𝑊 𝑜𝑓 𝑃𝑉 (𝑚2)  𝐴𝑆𝑇,𝑢𝑛𝑖𝑡 = 𝐴𝑟𝑒𝑎 𝑓𝑜𝑟 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑖𝑛𝑔 1 𝑠𝑜𝑙𝑎𝑟 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 (𝑚2)  𝐴𝑆𝑇,𝑎𝑣𝑎𝑖𝑙 = 𝑇𝑜𝑡𝑎𝑙 𝑎𝑟𝑒𝑎 𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑓𝑜𝑟 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑖𝑛𝑔 𝑠𝑜𝑙𝑎𝑟 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟𝑠 (𝑚2)  𝐶𝑃𝑉 = 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑠𝑜𝑙𝑎𝑟 𝑃𝑉 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑘𝑊) 𝑛𝑆𝑇 = 𝑁𝑢𝑚𝑏𝑒 𝑜𝑓 𝑖𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑠𝑜𝑙𝑎𝑟 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟𝑠 The performance ratio (PR) of a system is a dimensionless parameter that represents the overall effect of system losses (due to inverter losses, wiring, mismatches, other power losses, temperature, reflection, soiling or snow, system down time, and component failures) on the rated                                                  10 Based on the data published by Canadian Solar Inc. on their 275 W PV module, the dimensions are 1650×992 mm, leading to 5.952 m2 [300]. 101  output [292]. PR does not depend on module efficiency (i.e. efficiency variation based on panel type) as it is defined based on the nominal (rated) power of the PV system. The following equation defines the energy generation of a PV system. 𝐸𝑃𝑉 = 𝐻 × 𝑁 × 𝑃𝑅 × 𝐶𝑃𝑉 Equation 31 𝐸𝑃𝑉 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 (𝑘𝑊ℎ)  𝐻 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑚𝑒𝑎𝑛 𝑑𝑎𝑖𝑙𝑦 𝑖𝑛𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑙𝑎𝑛𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑃𝑉 𝑎𝑟𝑟𝑎𝑦 (𝑘𝑊ℎ 𝑚2⁄ ) 𝑁 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑛𝑡ℎ The performance ratio of a PV system can vary based on a number of factors. Natural resources Canada has identified the most likely value as 0.75, and that the PR of most PV systems fall within a 15% range of this value [293]. This aligns with the findings of the National Renewable Energy Laboratory of U.S. Department of Energy, which states that PR usually varies between 0.6 to 0.8 [292]. Based on the above, a TFN was defined for the performance ratio as follows.  𝑃𝑅 = (0.6375, 0.75, 0.8625) The electricity produced from PV system is primarily directed towards fulfilling the other electric loads in the house, excluding space and water heating. The equation for number of daylight hours is; 𝑁 =  215cos−1(− tan 𝜙 tan 𝛿) Equation 32 Where 𝜙 is the latitude, and 𝛿 is the declination angle; 𝛿 = 23.45 × sin(360 [284 + 𝑁365]) The equation for the thermal energy output in ST collectors is given below [290]. Since solar thermal water heating cannot be carried out on demand, and the heat energy generation can only be carried out during a limited time period during the day, the efficiency of heat energy storage has to be considered.  An assumption was made that the heat retention in the hot water storage is 50% efficient, and only half the energy produced by the collectors is effectively transferred to meet the water heating load [294]. The ISO-efficiency equation is reported based on gross collector area 102  and not the net aperture area of the collector as per ASHRAE standards [295]. Therefore, the thermal energy output is calculated based on the gross area, but the thermal energy output does not change due to the above simplification [295].  𝐸𝑆𝑇 =  50% 𝑜𝑓 𝐸𝑆𝑇,𝑡ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 =  0.5 × 𝐴 𝑆𝑇,𝐺 × 𝜂 × 𝐻 Equation 33 𝜂 × 𝐴 𝑆𝑇,𝐺 =  𝜂𝐴𝑃  × 𝐴 𝑆𝑇,𝐴 Equation 34 𝜂𝐴𝑃 = 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑖𝑛 𝑡𝑒𝑟𝑚𝑠 𝑜𝑓 𝑛𝑒𝑡 𝑎𝑝𝑒𝑟𝑡𝑢𝑟𝑒 𝑎𝑟𝑒𝑎 𝜂 = 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑖𝑛 𝑡𝑒𝑟𝑚𝑠 𝑜𝑓 𝑔𝑟𝑜𝑠𝑠 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑟𝑒𝑎 𝐴 𝑆𝑇,𝐺 = 𝐺𝑟𝑜𝑠𝑠 𝑐𝑜𝑙𝑙𝑒𝑐𝑡𝑜𝑟 𝑎𝑟𝑒𝑎 𝐴 𝑆𝑇,𝐴 = 𝑁𝑒𝑡 𝑎𝑝𝑒𝑟𝑡𝑢𝑟𝑒 𝑎𝑟𝑒𝑎 In the developed optimisation model, the energy supplied from the solar thermal collectors was prioritised for catering the water heating demand in the residence.  B) Geothermal energy Ground source heat pumps (GSHP) are effective for heating purposes in cold climates [296]. These are more efficient than commonly used air source heat pumps [297], and therefore, were selected as a suitable residential thermal energy source for the selected building location [117]. For urban or suburban areas which do not have access to a water body or a well, a vertical closed loop geothermal heat system is more suitable due to space restrictions [117]. Therefore, this type of system was selected for the case study.    For one ton of heat pump capacity (approximately equivalent to 3.5 kW or 12000 Btu/h), the length of piping required is approximately 80 to 110 m [117]. When selecting the suitable heat pump capacity for the MURB, it was assumed to be constrained by the maximum design power for space heating. This maximum design power occurs in the month with the highest heating load, which is January as per Table 5-11. The equations for heat energy generation from GSHP and design power for space heating can be calculated from the equation given below. In cold climates, it is recommended to size the pump to suit the heating load [298]. A flat space heat load curve is assumed in this analysis to reduce the computational complexity and optimising time.  𝐸𝐺𝑆𝐻𝑃 =  𝐶𝐺𝑆𝐻𝑃 × 𝑇𝑑 × 𝑁 103  Equation 35 𝑃𝑆𝐻,𝑚𝑎𝑥 =  𝑆𝐻𝐵,𝑚𝑎𝑥𝑇𝑑 × 𝑁=  𝐶𝐺𝑆𝐻𝑃,𝑚𝑎𝑥  Equation 36 𝐸𝐺𝑆𝐻𝑃 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝐺𝑆𝐻𝑃 (𝑘𝑊ℎ)  𝐶𝐺𝑆𝐻𝑃 = 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝐺𝑆𝐻𝑃 (𝑘𝑊) 𝑇𝑑 = 𝐻𝑜𝑢𝑟𝑠 𝑜𝑝𝑒𝑟𝑎𝑡𝑒𝑑 𝑝𝑒𝑟 𝑑𝑎𝑦 𝑁 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑛𝑡ℎ 𝑃𝑆𝐻,𝑚𝑎𝑥 = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑑𝑒𝑠𝑖𝑔𝑛 𝑝𝑜𝑤𝑒𝑟 𝑓𝑜𝑟 𝑠𝑝𝑎𝑐𝑒 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 (𝑘𝑊)  The energy supplied from the GSHP system is prioritised for meeting the space heating load in the MURB. Space cooling can also be provided from the same system, based on the cooling COP and the cooling capacity of the GSHP system. However, as the space cooling load of MURB is only 0.64% of the total energy demand, the cooling energy benefit from GSHP is minimal compared to the heating supply potential.  5.2.1.3 Identifying the optimal building energy mix  As the goal of the study is to identify the best energy supply mix and assess the feasibility of net-zero energy buildings, it was necessary to evaluate the performance of the building energy system with a mixed supply from the renewable and conventional energy sources. A discrete combinatorial optimisation approach was adopted in evaluating the building energy system and identifying the optimal energy mix. Combinatorial optimisation method is most suited to practical problems where activities and resources (such as machines and number of trips) are indivisible [299]. The reason for adopting the above approach in the current study is to provide a practically feasible engineering solution to the energy technology selection and system planning problem during the pre-project planning stage of residential development. Assuming a continuous range of values for the energy technology system capacities is not a sensible approach in tackling practical engineering problems, as energy installations sizing is done with discrete and indivisible capacity sizes based on standard system components and equipment available in the market.  Solar PV installed capacity was incremented by steps of 0.275 kW, to reflect the standard capacity of a commercial solar panel available in the market [300]. For a commercially available Solar 104  thermal collector, capacity incrementing was done based on the number of collectors. The installed capacity of GHSP was incremented by 1 ton (3.5 kW) steps [117]. Based on the information in RETScreen product database, a 3.37 kW commercially available ground source heat pump (heating COP of 3.1) has a cooling COP of 4.25, and a cooling capacity of 4.78 kW, and this capacity was approximated in calculating the heating load supply11. The remainder of the space heating load (which cannot be catered by the GSHP) is supplied from grid electricity. While water heating energy is primarily supplied from solar thermal collectors, any remaining water heating energy requirement is supplied from electricity. Grid electricity is also used in fulfilling the remaining electricity demand, if the PV electricity supply is insufficient to meet the full electric load of the MURB.                                              With the above incrementing process, different energy mix combinations are defined for the building energy supply system. The developed optimisation framework simulates all the possible combinations of alternative capacities for the identified energy supply technologies, with the goal of supplying the total energy demand of the building. The performance of each combination is simulated based on a number of objective functions representing economic and environmental impacts. This type of combinatorial optimisation approach reduces the computational time compared to a continuous optimisation process. The use of a fuzzy-logic based approach in the optimisation model and the resulting decision framework helps to integrate the effect of uncertain and variable data inputs into the decision making [208]. In this model, fuzzy numbers are used to represent data which do not have deterministic of crisp values or where the possible values vary within a range, and similar approaches have been used in previous studies to solve engineering problems with uncertain inputs [208][244].  5.2.1.4 Performance objectives and weighting for building energy model Performance objectives were selected to represent the energy (EN), economic (EC), and environmental (EV) aspects related to building energy systems. The economic performance objectives were to minimise the total energy system cost for the entire building life cycle, and to maximise the operational energy cost savings. As the ultimate target of renewable integration at                                                  11 Addison DWPG017 model 105  building level is to develop net-zero energy buildings, maximising the RE fraction in building energy supply was selected to be the energy performance objective. As RE integration in decentralised energy systems leads not only to reduced emissions but also to improved energy independence and energy security for communities [57], it is necessary to maximise the RE fraction in the supply.  Reduction of operational emissions is commonly used as an objective in energy system optimisations. However, it is necessary evaluate all possible environmental impacts (which occur in addition to emissions) with a life cycle perspective, in order to holistically assess the energy system’s performance. Therefore, the environmental performance objective was to reduce the life cycle impacts of the energy system. Life cycle impacts were evaluated under the impact categories of climate change, damage to ecosystems, resource depletion, and human health risk [57]. The solar available roof area acts as a constraint to the solar energy installations as detailed in section 5.2.1.2. As the goal of this study is to develop a framework for defining the best renewable based energy mix for buildings, it is important to consider the local conditions and user priorities in the decision making. To integrate this aspect, the maximum levelised cost of energy (LCOE) and the minimum fraction of RE in the energy supply were defined as flexible constraints which can be changes based on user preferences.  Levelised cost of energy is defined as the ratio of lifetime costs to the lifetime energy generation, is an important measure in establishing grid parity (whether a unit of RE can be provided at a comparable price to that of a conventional centralised energy supply unit, i.e. unit of grid electricity) [57][172]. As the energy generated at building level should be affordable, a constraint was defined to represent the closeness to grid parity. Under this, the LCOE of the developed energy system model should not exceed 1.2 times the price of a grid electricity unit.  𝑳𝑪𝑶𝑬 =  ∑𝑰𝒕 +  𝑴𝒕 + 𝑭𝒕(𝟏 + 𝒓)𝒕𝒏𝒕=𝟏∑𝑬𝒕 (𝟏 + 𝒓)𝒕𝒏𝒕=𝟏⁄  Equation 37 It  = Investment expenditure for year t;  Mt  = O&M expenditure for year t; Ft  = Fuel expenditure for year t; 106  Et  = Electricity generated in year t; r  = Discount rate; n  = Facility lifetime (service life) Another aspect in integrating user priorities and requirements to a decision making model is through weighting. Weights represent the relative importance of each performance objective. In the developed decision model for the optimal energy mix, a standard fuzzy weighting scheme was used to represent the user-defined importance of the performance objectives, based on previous literature [301].  Table 5-8: Fuzzy weighting scheme of relative importance Linguistic Term Very Low (VL) Low (L) Medium (M) High (H) Very High (VH) Membership Function (0,0,0.3) (0,0.3,0.5) (0.2,0.5,0.8) (0.5,0.7,1) (0.7,1,1) As energy system planning is a multi-stakeholder problem, the performance objectives need to be weighed with reference to the interests of all parties in the given context. As an example, while a MURB involves property developers, building management, and occupants as key stakeholders, house owners are the primary stakeholders in single-family detached housing. Therefore, the weights defined for the performance objectives were case-specific.  5.2.1.5 Impact and outcome assessment for performance objectives The life cycle environmental and economic impacts of the different energy technologies (i.e. solar PV, solar thermal collectors, GSHP) were evaluated in order to quantify the total impacts of the developed hybrid energy system for the building [57]. A summary of the life cycle impact assessment method is provided in Figure 5-4. 107   Figure 5-4: Life cycle impact assessment approach The life cycle environmental impacts for different RET were quantified using a life cycle assessment (LCA) approach. The system boundary defined for the RET evaluation extended from the raw material extraction to the end of life of the RE generation systems. A functional unit of 1 MWh of produced energy was used on conducting the LCA, which was done under the ReCiPe midpoint assessment method. Midpoint assessment method was selected to conduct the LCA as it provides information on emissions and other environmental impacts in a quantitative form, and are more accurate than endpoint results [241]. Thus, midpoint indicators are subject to less uncertainties in comparison to endpoint indicators that are derived through much higher environmental mechanism modelling [242]. The study was conducted using the SimaPro software, and the Ecoinvent database. Fuzzy logic is a method that is recommended for processing uncertainties in LCA [302]. In the current study, a 10% tolerance for data variability was assumed in defining the upper and lower boundary values when converting the obtained life cycle impact 108  assessment (LCIA) data into fuzzy numbers [57]. The impact data from all categories were aggregated to four main environmental impact indicators, as depicted in Figure 5-4. Three indicators were defined based on the general midpoint to endpoint aggregation process in ReCiPe, namely damage to ecosystems, human health impacts, and resource depletion [243]. The other indicator was global warming potential, which represents the carbon emissions related climate impact of the energy system. The following equation represents the division by sum approach adopted in normalising the impacts for aggregation [303].  𝑁𝑖𝑗 =  𝐼𝑀𝑖𝑗∑ 𝐼𝑀𝑖𝑗𝑗 Equation 38 Where;  IMij = Impact value of the ith impact category for the jth RE technology   Nij = Normalised value for the indicator   j = number of compared items (RE technologies)  Climate change effect of energy supply is a critical aspect which needs to be considered in energy system design, especially in light of the Canadian climate action goals [15][16]. Therefore, global warming potential was assigned a weight of 50% when aggregating the impact indicators to evaluate the ultimate performance under life cycle environmental impact objective, and the other categories were assigned equal weights [57].  Equation 39 was used in calculating the total energy system costs over the building lifetime, considering all energy supply sources in the building energy system. The next two equations represent the present value of a one time and a recurring cashflow respectively [208].  Each cost variable was defined in the form of a TFN, based on literature-based cost data. The building lifetime and discount rate were assumed to be crisp numbers, where building life was 50 years, and the discount rate was 3% (rate of inflation, f = 2%; interest rate, i = 5%; discount rate, r = i – f = 3%), as these are aleatory rather than epistemic uncertainties and thus cannot be reduced by fuzzy based modelling [56][209]. 𝑇𝐸𝐶 =  ∑ 𝐼𝐶𝑡+  ∑ 𝐹𝐶 +𝑡∑ 𝐴𝑂𝑀 +  ∑ 𝑅𝐶 +𝑡∑ 𝐷𝐶 − ∑ 𝑅𝑉𝑡𝑡𝑡 Equation 39 109  𝑃𝑉 =  𝐹𝑉(1 + 𝑟)𝑡 Equation 40 𝑃𝑉𝐴 =  𝐴[(1 + 𝑟)𝑡 − 1]𝑟(1 + 𝑟)𝑡 Equation 41 𝑇𝐸𝐶 =  𝑇𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑦𝑠𝑡𝑒𝑚 𝑐𝑜𝑠𝑡 𝐼𝐶 = 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 𝐹𝐶 = 𝐹𝑢𝑒𝑙 𝑎𝑛𝑑 𝑐𝑜𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑢𝑝𝑝𝑙𝑦 𝑐𝑜𝑠𝑡𝑠 𝑅𝐶 = 𝑅𝑒ℎ𝑎𝑏𝑖𝑙𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑜𝑟⁄ 𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 𝐴𝑂𝑀 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑎𝑛𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡 𝐷𝐶 = 𝐷𝑖𝑠𝑝𝑜𝑠𝑎𝑙 𝑐𝑜𝑠𝑡 𝑅𝑉 = 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 𝑃𝑉 = 𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎 𝑓𝑢𝑡𝑢𝑟𝑒 𝑜𝑛𝑒 𝑡𝑖𝑚𝑒 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 (𝐹𝑉)𝑜𝑐𝑐𝑢𝑟𝑖𝑛𝑔 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡 𝑃𝑉𝐴 = 𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎 𝑓𝑢𝑡𝑢𝑟𝑒 𝑎𝑛𝑛𝑢𝑎𝑙𝑙𝑦 𝑟𝑒𝑐𝑢𝑟𝑟𝑖𝑛𝑔 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 (𝐴)𝑜𝑣𝑒𝑟 𝑡 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠 𝑟 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 The life cycle costing carried out for the energy technologies is presented under Appendix F  The total annual operational energy cost savings were calculated in comparison to the utility payments for a conventional energy supply, i.e. the cost of using 100% grid electricity to cater the entire building energy demand. This is a reasonable assumption as electric heating is recommended over natural gas heating for new constructions in BC [304]. The price of grid electricity (CA$/kWh) was defined as (0.042, 0.0936, 0.1872), and price of natural gas(CA$/GJ) was defined as (5.4702, 6.0800, 6.6880) [208][57]. The following equation was defined to calculate the total operational energy cost savings. (Conventional supply costs are calculated for the leftover building energy load which cannot be supplied with RE under a given energy supply combination.) 𝐴𝑂𝑆 = (∑[𝑆𝐻𝐵 + 𝑂𝐸𝐵]𝑡× 𝐺𝐸𝑃 + ∑[𝑊𝐻𝐵]𝑡× 𝑁𝐺𝑃) − (∑ 𝐶𝑆𝐶 + ∑ 𝑅𝐹𝐶𝑡𝑡) Equation 42 𝑆𝐻𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑠𝑝𝑎𝑐𝑒 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝑊𝐻𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑤𝑎𝑡𝑒𝑟 ℎ𝑒𝑎𝑡𝑖𝑛𝑔 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝑂𝐸𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑜𝑡ℎ𝑒𝑟 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 110  𝐵𝐿𝐸𝐵 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑙𝑜𝑎𝑑 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑢𝑖𝑙𝑑𝑖𝑛𝑔 (𝑘𝑊ℎ) 𝐴𝑂𝑆 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 𝐺𝐸𝑃 = 𝐺𝑟𝑖𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 𝑁𝐺𝑃 = 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝑔𝑎𝑠 𝑝𝑟𝑖𝑐𝑒 𝐶𝑆𝐶 = 𝐶𝑜𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑎𝑙 𝑠𝑢𝑝𝑝𝑙𝑦 𝑐𝑜𝑠𝑡𝑠 𝑅𝐹𝐶 = 𝑅𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑓𝑢𝑒𝑙 𝑐𝑜𝑠𝑡𝑠 Fraction of RE in the building energy supply (FRE) is calculated as follows on an annual basis. 𝐹𝑅𝐸 =  ∑ (𝐸𝑃𝑉 + 𝐸𝑆𝑇 + 𝐸𝐺𝑆𝐻𝑃)12𝑗=1∑ (𝑆𝐻𝐵 + 𝐵𝐿𝐸𝐵)12𝑗=1 Equation 43 The following table details the system life and cost factors associated with solar PV, ST collectors, and GSHP. The cost values were obtained from the distributed renewable energy generation costs published by National Renewable Energy Laboratory [159]. The residual values and salvage costs were assumed to be zero at the end of useful life. The upper and lower limits fuzzy numbers were defined based on the standard deviation of the cost values in the above database.  Table 5-9: Renewable energy technology characteristics Technology Cost factor Unit L M H Std. Dev Solar PV Installed cost $/kW 3008 3897 4786 ±889 Fixed O&M cost $/kW-yr 1 21 41 ±20 Fuel and/or water cost  $/kWh 0 0 0 - Lifetime Year 22 33 44 ±11 Solar thermal Installed cost $/ft2 71 162 253 ±91 Fixed O&M cost $/ft2-yr 0.355 1.215 2.53 N/A12 Fuel and/or water cost  $/ton 0 0 0 - Lifetime Year 17 31 45 ±14 GSHP Installed cost $/ton 3133 7765 12397 ±4632 Fixed O&M cost $/ton-yr 15 109 203 ±94 Fuel and/or water cost  $/ton 5 397 789 ±392 Lifetime Year 13 38 63 ±25                                                  12 O& M cost is 0.5 to 1.0 % of the initial installed cost [159] 111  When developing the cost model for the total energy system cost throughout lifetime of the building, the system replacements were considered based on the life expectancy of each technology.  5.2.1.6 Fuzzy-based building energy supply optimisation approach Algebraic computations for triangular fuzzy numbers are conducted through fuzzy arithmetic principles, which are represented in the equations given below [57]. A and B TFNs where A = (a1, a2, a3) and B = (b1, b2, b3), assuming all a1, a2, a3 and b1, b2, b3 >0; Addition:   A+B = (a1+b1, a2+b2, a3+b3) Subtraction:  A-B = (a1-b3, a2-b2, a3-b1) Multiplication: A(.)B = (a1b1, a2b2, a3b3) Division:  A÷B = (a1/b3, a2/b2, a3/b1) Scalar multiplication: kA = (ka1, ka2, ka3); if k>0    kA = (ka3, ka2, ka1); if k<0 For the optimisation, the performance scores under the objectives in Table 5-20 are evaluated for all energy supply combinations (solutions). Then, the aggregate performance score for a particular solution is evaluated in the following manner. The performance scores under individual objectives such as TEC and OS are normalised, and then the normalised values are multiplied by the fuzzy weights defined in Table 5-8, based on the relative importance levels assigned in Table 5-20. A normalised fuzzy number (under the “divide by sum” approach) is defined by the following equations [305]. If the performance score for the ith option under the jth performance criterion is represented by a fuzzy number 𝑋𝑖 , then the normalised fuzzy decision matrix 𝑃 = [𝑝𝑖𝑗] is represented as follows [306]. 𝐼𝑓 𝑋𝑖 𝑖𝑠 𝑎 𝑓𝑢𝑧𝑧𝑦 𝑛𝑢𝑚𝑏𝑒𝑟 𝑑𝑒𝑛𝑜𝑡𝑒𝑑 𝑏𝑦 (𝑥𝑖𝑗1, 𝑥𝑖𝑗2, 𝑥𝑖𝑗3),𝑎𝑛𝑑 𝑥𝑚𝑎𝑥 = 𝑚𝑎𝑥(𝑥𝑖𝑗3) 𝑎𝑛𝑑 𝑥𝑚𝑖𝑛 = 𝑚𝑖𝑛(𝑥𝑖𝑗1)  𝒑𝒊𝒋 = {(𝒙𝒊𝒋𝟏 − 𝒙𝒎𝒊𝒏𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏,𝒙𝒊𝒋𝟐 − 𝒙𝒎𝒊𝒏𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏,𝒙𝒊𝒋𝟑 − 𝒙𝒎𝒊𝒏𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏) ; 𝒇𝒐𝒓 𝒃𝒆𝒏𝒆𝒇𝒊𝒕 𝒄𝒓𝒊𝒕𝒆𝒓𝒊𝒂(𝒙𝒎𝒂𝒙−𝒙𝒊𝒋𝟏𝑵𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏,𝒙𝒎𝒂𝒙 − 𝒙𝒊𝒋𝟐𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏,𝒙𝒎𝒂𝒙 − 𝒙𝒊𝒋𝟑𝒙𝒎𝒂𝒙 − 𝒙𝒎𝒊𝒏) ; 𝒇𝒐𝒓 𝒄𝒐𝒔𝒕 𝒄𝒓𝒊𝒕𝒆𝒓𝒊𝒂 112  Equation 44 If the weight assigned to the jth performance objective is 𝑤𝑗 , The weighted normalised value of the ith solution under the jth performance objective is represented by;  𝑣𝑖𝑗 =  𝑟𝑖𝑗(. )𝑤𝑗 Equation 45 The overall performance score (𝒑𝒊) of a particular solution (energy supply combination) considering m number of performance objectives can be represented by the following. Here, 𝒑𝒊 is a TFN, and a higher 𝒑𝒊 denotes a better performance in the solution.  𝑝𝑖 = ∑ 𝑤𝑖𝑗 ∗ 𝑟𝑖𝑗𝑚𝑗=1 Equation 46 The constraints defined in section 5.2.1.4 (i.e. grid parity, roof area, minimum RE fraction) are used to filter out the non-viable solutions out of all the defined energy system combinations.  The likely values for the LCOE and RE generation is used in setting the filtering criteria based on grid parity and RE fraction constraints. The likely value was established based on mean of maximum (MoM) method, where the defuzzified value is represented by (a+b)/2 [208]. For a TFN, the mean of maximum corresponds to the likely value based on the above equation.  In order to identify the optimal solution out of the remaining viable combinations, the TFNs for 𝒑𝒊 need to be ranked. One method which has been widely used in literature for ranking fuzzy numbers under similar contexts is the maximising set and minimising set method [301][307]. This method can be used to select the most suitable solution out of the energy system combinations using the fuzzy appropriateness index (𝐹𝑖; 𝑖 = 1,2, … . . 𝑛), which represents the overall performance score. The TFN for 𝐹𝑖 is 𝐹𝑖 = [𝑎𝑖, 𝑏𝑖, 𝑐𝑖]. The following equations define the maximising set (M) and minimising set (G) for the range of alternative combinations [301]. (Here, 𝑓𝑀(𝑥) is the membership of 𝑥 to 𝑀, and 𝑓𝐺(𝑥) is the membership of 𝑥 to 𝐺.) 𝑀 = {(𝑥, 𝑓𝑀(𝑥))|𝑥 ∈ 𝑅} 𝑓𝑀(𝑥) = {𝑥 − 𝑥𝑚𝑖𝑛𝑥𝑚𝑎𝑥 −  𝑥𝑚𝑖𝑛       ;  𝑥𝑚𝑖𝑛 ≤ 𝑥 ≤  𝑥𝑚𝑎𝑥  0               ;  𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒  113  Equation 47 𝐺 = {(𝑥, 𝑓𝐺(𝑥))|𝑥 ∈ 𝑅} 𝑓𝐺(𝑥) = {𝑥 −  𝑥𝑚𝑎𝑥𝑥𝑚𝑖𝑛 − 𝑥𝑚𝑎𝑥   ;  𝑥𝑚𝑖𝑛 ≤ 𝑥 ≤  𝑥𝑚𝑎𝑥 0             ;  𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒  Equation 48 where 𝑥𝑚𝑖𝑛 = 𝑖𝑛𝑓𝑆;  𝑥𝑚𝑎𝑥 = 𝑠𝑢𝑝𝑆;  𝑆 = ⋃ 𝐹𝑖;   𝐹𝑖 = {𝑥|𝑓𝐹𝑖(𝑥) > 0}𝑚𝑖=1 ;  𝑖 = 1,2, … , 𝑚 The right utility value 𝑈𝑀(𝐹𝑖), left utility value 𝑈𝐺(𝐹𝑖), and total utility value 𝑈𝑇(𝐹𝑖) for a given (i) combination are defined as follows.  𝑈𝑀(𝐹𝑖) = sup (𝑓𝐹𝑖(𝑥)⋂𝑓𝑀(𝑥)) , 𝑖 = 1,2, … , 𝑚 Equation 49 𝑈𝐺(𝐹𝑖) = sup (𝑓𝐹𝑖(𝑥)⋂𝑓𝐺(𝑥)) , 𝑖 = 1,2, … , 𝑚 Equation 50 𝑈𝑇(𝐹𝑖) =𝑈𝑀(𝐹𝑖) + 1 − 𝑈𝐺(𝐹𝑖)2 Equation 51 The highest performing (optimal) energy system combination (solution) under the given conditions and constraints is the one with the highest utility value, and all viable combinations will be ranked according to their respective 𝑈𝑇(𝐹𝑖). 5.2.1.7 Energy system performance assessment After identifying the best energy system combinations, their performance scores under different objectives were defuzzified to analyse the system characteristics further, using the following equation representing the center of gravity (COG) defuzzication method [308].  𝐼𝑓 𝑋𝑖 𝑖𝑠 𝑎 𝑓𝑢𝑧𝑧𝑦 𝑛𝑢𝑚𝑏𝑒𝑟 𝑑𝑒𝑛𝑜𝑡𝑒𝑑 𝑏𝑦 (𝑥𝑖𝑗1, 𝑥𝑖𝑗2, 𝑥𝑖𝑗3) 𝐶𝑂𝐺𝑋𝑖 =  (𝑥𝑖𝑗1 +  𝑥𝑖𝑗2 +  𝑥𝑖𝑗3)3 Equation 52 114  The performance of the selected optimal solution will be evaluated based on its potential to reduce emissions, total life cycle cost, and payback time of the system investment. The emissions reduction potential is calculated based on the grid emissions factor, which is 10.67 tCO2e/GWh in British Columbia [309]. The following equation is used in calculating the discounted payback period (DPP) of the energy system.  𝐷𝑃𝑃 =ln (11 −𝐼𝐶 × 𝑟𝐴𝑂𝑆)ln (1 + 𝑟)⁄ Equation 53 The economic performance of the system was further analysed using the fuzzy DSW algorithm to aggregate the cost and revenue factors. DSW algorithm applies fuzzy extension principle in a simplified manner for calculations involving fuzzy variables and functions [208]. Under this technique, α-cut intervals are used in standard interval analysis to calculate complex output membership functions made up of multiple variables (which are represented as fuzzy numbers). The following steps are used in the calculation. a. An “α” value within the range 0 ≤ α ≤ 1 is selected. b. The intervals that correspond to the selected α-cut level (in the input membership functions) are found.  c. The output membership function’s interval for LCC is determined using standard binary interval operations at the selected α-cut level. d. The above steps are repeated for different α-cut levels between (0,1) membership In the results, the top-ranked energy system combinations were analysed to assess their economic and environmental performance compared to a conventional energy system.  5.2.2 Community level renewable energy integration In this phase of the study, the community energy model was further extended from the building level assessment in the previous phase, by developing an optimisation framework for the community level (centralised) energy generation system.  Similar to the previous phases in the study, uncertainties in the model parameters were represented using fuzzy logic, and triangular fuzzy numbers (TFN) were defined for the uncertain parameters.  115  𝝁?̃?(𝒙) = {𝟎𝒙 − 𝒂 𝒃 − 𝒂⁄𝒄 − 𝒙 𝒄 − 𝒃⁄      ; 𝒙 < 𝒂, 𝒙 > 𝒄; 𝒂 ≤ 𝒙 < 𝒃; 𝒃 ≤ 𝒙 < 𝒄 Equation 54 A pro-environment scenario was adopted in filtering the available technologies, based on the results of the previous phase in energy technology ranking (discussed under Chapter 4). The top-ranking five technologies were small hydro, onshore wind generation, direct combustion of biomass, anaerobic digestion of MSW, and centralised solar PV plant. Out of these options, the small hydro option was abandoned, as it was unsuitable for the location and the capacity of the community. Anaerobic digestion (AD) option was similarly eliminated as the feasibility of adopting the technology was limited while complying with the health regulations and bylaws (due to the additional requirement for odour and emissions control), and the organic waste mass was insufficient for a significant energy generation potential through AD. Instead, mass combustion of MSW to produce electricity was selected as an additional option, with the goal of supporting waste management for the community.  5.2.2.1 Renewable energy generation in centralised RE facilities The solar energy generation assessment equations used in section 6.2.2.1 were used in assessing the solar energy potential for the community energy model as well.  𝐴𝑃𝑉 =  𝐴𝑃𝑉,𝑢𝑛𝑖𝑡  ×  𝐶𝑃𝑉 Equation 55 𝐸𝑃𝑉 = 𝐻 × 𝑁 × 𝑃𝑅 × 𝐶𝑃𝑉 Equation 56 𝐸𝑃𝑉 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑃𝑉 𝑠𝑦𝑠𝑡𝑒𝑚 (𝑘𝑊ℎ)  𝐻 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑚𝑒𝑎𝑛 𝑑𝑎𝑖𝑙𝑦 𝑖𝑛𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 𝑡ℎ𝑒 𝑝𝑙𝑎𝑛𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑃𝑉 𝑎𝑟𝑟𝑎𝑦 (𝑘𝑊ℎ 𝑚2⁄ ) 𝑁 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑛𝑡ℎ 𝐶𝑃𝑉 = 𝐼𝑛𝑠𝑡𝑎𝑙𝑙𝑒𝑑 𝑠𝑜𝑙𝑎𝑟 𝑃𝑉 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑘𝑊) The same performance ratio for PV was used in the energy generation assessment, based on the findings of the National Renewable Energy Laboratory of U.S. Department of Energy.  𝑃𝑅 = (0.6375, 0.75, 0.8625) 116  For the community optimisation model, the installed capacity of the PV plant was incremented by 100 kW steps, based on the sizing of existing commercial plants. The wind power generation model was developed using the technique of statistical distributions for modelling approximate energy generation throughout the year. A statistical Rayleigh distribution can be used to estimate the probability of the wind speed falling within a specified range, provided that the average wind speed of a site is known [129]. The following cumulative density function (CDF) is used to calculate the probability of wind speed being equal or lower than a given speed value, U, for a known Uavg.  𝑝(𝑤𝑖𝑛𝑑 𝑠𝑝𝑒𝑒𝑑 ≤ 𝑈) = 1 − 𝑒[(−𝜋4)(𝑈𝑈𝑎𝑣𝑔)2] Equation 57 The power generation for one square metre of rotor cross sectional area can be derived using the following equation.  𝑃 =  12× 𝜌 × 𝑈3 × 𝐴 Equation 58 𝑃 = 𝑃𝑜𝑤𝑒𝑟 𝑜𝑓 𝑡ℎ𝑒 𝑤𝑖𝑛𝑑 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 𝜌 = 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑤𝑖𝑛𝑑 𝑎𝑡 𝑠𝑖𝑡𝑒 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝐴 = 𝑅𝑜𝑡𝑜𝑟 𝑠𝑤𝑒𝑝𝑡 𝑎𝑟𝑒𝑎 A bin method can be used to estimate the energy potential of a selected site location. Each wind speed bin has minimum (UL) and maximum values (UH), and an average bin speed (Ux). The annual energy generation for bin “x” is calculated using the probability of wind speed falling in a particular bin, which is the difference between the probabilities at the maximum value and the minimum value. Wind turbines function between cut-in and cut out speeds. Below the cut-in speed, the power from the wind is insufficient to overcome the rotor resistance and turn the turbine blades, and above the cut-out speed, a brake is applied to stop the device in order to prevent damage to the turbine [129]. Accordingly, the following method is used to calculate the total theoretical energy generation [310].  𝑃𝑥,𝑡ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 =  {0         ; 𝑈𝑥 < 𝑈𝑐𝑢𝑡−𝑖𝑛, 𝑈𝑥 > 𝑈𝑐𝑢𝑡−𝑜𝑢𝑡 0.5𝜋𝜌𝑅2𝐶𝑝𝑈3    ;  𝑈𝑐𝑢𝑡−𝑖𝑛 ≤ 𝑈𝑥 ≤ 𝑈𝑐𝑢𝑡−𝑜𝑢𝑡     Equation 59 117  𝑅 = 𝑅𝑜𝑡𝑜𝑟 𝑟𝑎𝑑𝑖𝑢𝑠 𝐶𝑝 = 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 The power curve of a wind turbine indicates how much electrical energy the turbine can generate at various wind speeds. The power curve for each turbine is provided by the manufacturer based on field tests conducted under varying conditions, and this observed power differs from the theoretical power  [310]. In developing the model, the power curve of a turbine was simplified as a linear function between the cut-in wind speed and the rated wind speed, between which the output power varies between 0 and the rated power of the turbine, until the cut-out speed is reached. The power curve was defined as a function of wind speed, from 0 to 25 ms-1 for incremental steps of 1 ms-1, and bins were defined for the same speed range [311]. The power at a specific speed (𝑈𝑥) is calculated as follows.  𝑃𝑥 =  {𝑃𝑟𝑎𝑡𝑒𝑑(𝑈𝑥 − 𝑈𝑐𝑢𝑡 𝑖𝑛)(𝑈𝑟𝑎𝑡𝑒𝑑 − 𝑈𝑐𝑢𝑡 𝑖𝑛) ; 𝑓𝑜𝑟 𝑈𝑐𝑢𝑡 𝑖𝑛  ≤ 𝑈𝑥 < 𝑈𝑟𝑎𝑡𝑒𝑑 𝑃𝑟𝑎𝑡𝑒𝑑 ; 𝑓𝑜𝑟 𝑈𝑥 > 𝑈𝑟𝑎𝑡𝑒𝑑 Equation 60 The following method was used in estimating the annual energy output (AEO) of the turbine. The probability of the wind speed falling in bin “x” is denoted as p(x). For each month, the average wind speed for the selected site in assessing the monthly energy generation, and this value is aggregated over 12 months to calculate AEO. A 10% variability was assumed for this energy generation in defining the fuzzy number.  𝑝(𝑥) = 𝑝(𝑈𝐻,𝑥) − 𝑝(𝑈𝑙,𝑥) Equation 61 𝐴𝐸𝑂 =  ∑ ∑ 24 × 𝑑𝑗 × 𝑝(𝑥) ×25𝑥=0𝑃𝑥12𝑗=1 Equation 62 𝑗 = 𝑚𝑜𝑛𝑡ℎ 𝑜𝑓 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟 𝑑𝑗 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 𝑖𝑛 𝑚𝑜𝑛𝑡ℎ To develop the wind energy component of the proposed optimisation model, commercially available wind turbine configurations were analysed from a comprehensive database [312]. The 118  potential turbine models which can be used at the proposed site are selected using the following algorithm, using the most likely values. The wind turbine capacities were defined with the maximum monthly energy demand as an upper bound to the monthly energy output of the turbines.  𝐸𝑚,𝑚𝑎𝑥 = 𝑚𝑎𝑥(𝐸𝑚𝑗) ;  𝑓𝑜𝑟 1 ≤ 𝑗 ≤ 12 𝐸𝑚,𝑚𝑎𝑥 ≤ 𝑛 × ∑ 24 × 𝑑𝑗,𝑚 × 𝑝(𝑥) ×25𝑥=0𝑃𝑥,𝑖 Equation 63 𝐸𝑚,𝑚𝑎𝑥 = 𝑀𝑎𝑥𝑚𝑢𝑚 𝑚𝑜𝑛𝑡ℎ𝑙𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑚𝑎𝑛𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑒 𝑦𝑒𝑎𝑟 𝑑𝑗,𝑚 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑎𝑦𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑜𝑛𝑡ℎ 𝑤𝑖𝑡ℎ 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑚𝑎𝑛𝑑 𝑛 =  𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑢𝑟𝑏𝑖𝑛𝑒𝑠 𝑃𝑥,𝑖 = 𝑅𝑎𝑡𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑡ℎ𝑒 𝑖𝑡ℎ 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 𝑚𝑜𝑑𝑒𝑙 The maximum number of turbines is constrained by the available land area, and the total land area required for wind installation depends on the number of turbines installed. In developing the optimisation model, the installed capacity of wind turbines are incremented on a turbine basis, to reflect the practical reality of community energy system development.  For the initial filtering based on resource availability, it was decided that wind speeds must remain above the cut-in speed at least 50% of the time for wind power to be feasible at a given location. Inability to meet this constraint means that energy generation is zero for over half the time in a given time duration.  In developing the biomass electricity generation component of the optimisation model, the following method was used based on the regional averages for such facilities. Based on a BC Hydro report, it is estimated that 0.72 ODT of wood fibre is sufficient to generate 1 MWh of electricity.  The maximum annual energy production capacity of a biomass electricity plant was defined as follows.  𝐸𝑃𝐵𝑖𝑜,𝑚𝑎𝑥(𝑘𝑊ℎ) =  𝑚𝑏𝑖𝑜,𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 × (1 − 𝑚𝑤) ×10000.72 Equation 64 In developing the energy system optimisation model, capacity increment steps for biomass plants was set as 100 kW. The saleable energy (ESbio) produced for a pre-defined plant capacity is 119  represented by the following equation [313]. Here, plant capacity factor (CF) is used to account for operational inefficiencies. 𝐸𝑆𝐵𝑖𝑜(𝑘𝑊ℎ) =  𝐶𝑝,𝑏𝑖𝑜 × 𝐶𝐹 × 24 × 365 Equation 65  The maximum energy generation potential of waste-to-energy (WtE) facilities was defined using the following equation, under the assumption that the entire waste mass of the community is directed to WtE under maximum capacity conditions.  𝑚𝑀𝑆𝑊,𝑝 × 𝐸𝑃𝑡𝑀𝑆𝑊 × 𝑃𝑐𝑜𝑚 Equation 66 𝑃𝑐𝑜𝑚 = 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 𝐸𝑃𝑡𝑀𝑆𝑊 = 𝑃𝑒𝑟 𝑡𝑜𝑛 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 𝑜𝑓 𝑀𝑆𝑊 𝑚𝑀𝑆𝑊,𝑝 = 𝑃𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝑀𝑆𝑊 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛  5.2.2.2 Technology performance of centralised facilities The cost factors and facility lifetimes associated with the selected energy technologies are listed in Table 5-10 (all values in USD) [314] [315] [159].  Table 5-10: Renewable energy technology characteristics Technology Cost factor Unit L M H Std. Dev Solar PV (large-scale) Installed cost $/kW 1719 2493 3267 ±774 Fixed O&M cost $/kW-yr 4 19 34 ±15 Fuel and/or water cost  $/kWh 0 0 0 - Lifetime Year 22 33 44 ±11 Wind Installed cost $/kW 2375 3751 5127 ±1376 Fixed O&M cost $/kW-yr 21 31 41 ±10 Fuel and/or water cost  $/kWh 0 0 0 - Lifetime Year 16 16 16 ±0 Biomass combustion Installed cost $/kW 3030 5792 8554 ±2762 Fixed O&M cost $/kW-yr 69 98 127 ±29 Acquisition & transport  $/ton 4 20 36 - Lifetime Year 20 28 36 ±8 Waste-to-energy  Installed cost $/annual ton 298 596 887 ±50% O&M cost $/ton 35 50 65 ±30% Lifetime Year 25 25 25 0 120  The land use requirement for solar PV and wind facilities are 8.1 and 60 acres per MW respectively [316]. It was assumed that WtE and biomass plants have similar land requirements for facility installation, at approximately 0.75 m2/ design ton [317].  5.2.2.3 Optimal community energy system A combinatorial optimisation approach was used to analyse the best energy system mix and renewable component sizing, similar to the previous phase in building energy system design. By the reasoning applied in section 6.2, using a continuous range of values is impractical in energy system planning and similar engineering problems. Therefore, a discrete combinatorial approach was decided to be most suitable for a problem where resources (facilities) are indivisible [299]. The constraints for the optimisation model were land area availability, levelised cost of energy (LCOE), maximum allowable system cost, and minimum RE fraction of a , and these constraints were defined as flexible inputs in the decision model. Similar to the previous phase, a grid parity factor of. 1.2 was set so that the LCOE of the new RE system was not above 20% higher than the cost of grid electricity.  𝑳𝑪𝑶𝑬 =  ∑𝑰𝒕 +  𝑴𝒕 + 𝑭𝒕(𝟏 + 𝒓)𝒕𝒏𝒕=𝟏∑𝑬𝒕 (𝟏 + 𝒓)𝒕𝒏𝒕=𝟏⁄  Equation 67 It  = Investment expenditure for year t;  Mt  = O&M expenditure for year t; Ft  = Fuel expenditure for year t; Et  = Electricity generated in year t; r  = Discount rate; n  = Facility lifetime (service life) The land area availability and maximum allowable energy system cost constraints were set based on the community to which the model is applied. Based on decision maker preference, this constraint can vary for other applications. Similarly, the total funding allocation for RE system development was set to be 10% of the community development cost. In the background under section 6.1.2, it has been established that homeowners are prepared to pay higher premiums for 121  “green-rated” houses, and the average increase in selling price for such a residence is 10% of normal housing prices. In Table 5-6, it can be seen that the average increase in housing prices after RE integration falls closely within this range in a scenario-based analysis. Therefore, 10% was assumed to be a reasonable added cost to the housing price. Similar to the previous phase in the building energy model, the minimum allowable RE fraction in the energy system was set at 40%.  The following equations were used in economic impact assessment for the community energy model, similar to the previous phase in building energy system modelling.  𝑇𝐸𝐶 =  ∑ 𝐼𝐶𝑡+  ∑ 𝐹𝐶 +𝑡∑ 𝐴𝑂𝑀 +  ∑ 𝑅𝐶 +𝑡∑ 𝐷𝐶 − ∑ 𝑅𝑉𝑡𝑡𝑡 Equation 68 𝑃𝑉 =  𝐹𝑉(1 + 𝑟)𝑡 Equation 69 𝑃𝑉𝐴 =  𝐴[(1 + 𝑟)𝑡 − 1]𝑟(1 + 𝑟)𝑡 Equation 70 𝑇𝐸𝐶 =  𝑇𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑦𝑠𝑡𝑒𝑚 𝑐𝑜𝑠𝑡 𝐼𝐶 = 𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 𝐹𝐶 = 𝐹𝑢𝑒𝑙 𝑎𝑛𝑑 𝑐𝑜𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑠𝑢𝑝𝑝𝑙𝑦 𝑐𝑜𝑠𝑡𝑠 𝑅𝐶 = 𝑅𝑒ℎ𝑎𝑏𝑖𝑙𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑜𝑟⁄ 𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡 𝑐𝑜𝑠𝑡 𝐴𝑂𝑀 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠 𝑎𝑛𝑑 𝑚𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒 𝑐𝑜𝑠𝑡 𝐷𝐶 = 𝐷𝑖𝑠𝑝𝑜𝑠𝑎𝑙 𝑐𝑜𝑠𝑡 𝑅𝑉 = 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝑣𝑎𝑙𝑢𝑒 𝑃𝑉 = 𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎 𝑓𝑢𝑡𝑢𝑟𝑒 𝑜𝑛𝑒 𝑡𝑖𝑚𝑒 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 (𝐹𝑉)𝑜𝑐𝑐𝑢𝑟𝑖𝑛𝑔 𝑎𝑡 𝑡𝑖𝑚𝑒 𝑡 𝑃𝑉𝐴 = 𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑎 𝑓𝑢𝑡𝑢𝑟𝑒 𝑎𝑛𝑛𝑢𝑎𝑙𝑙𝑦 𝑟𝑒𝑐𝑢𝑟𝑟𝑖𝑛𝑔 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 (𝐴)𝑜𝑣𝑒𝑟 𝑡 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑦𝑒𝑎𝑟𝑠 𝑟 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 (3%) 𝐴𝑂𝑆 = (∑ 𝐸𝐷𝑚𝑡× 𝐺𝐸𝑃) − (∑ 𝐶𝑆𝐶 + ∑ 𝑅𝐹𝐶𝑡𝑡) Equation 71 𝐸𝐷𝑚 = 𝑀𝑜𝑛𝑡ℎ𝑙𝑦 𝑐𝑜𝑚𝑚𝑢𝑛𝑖𝑡𝑦 𝑒𝑛𝑒𝑟𝑔𝑦 𝑑𝑒𝑚𝑎𝑛𝑑 (𝑘𝑊ℎ) 𝐴𝑂𝑆 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑠𝑡 𝑠𝑎𝑣𝑖𝑛𝑔𝑠 122  𝐺𝐸𝑃 = 𝐺𝑟𝑖𝑑 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑝𝑟𝑖𝑐𝑒 𝑁𝐺𝑃 = 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝑔𝑎𝑠 𝑝𝑟𝑖𝑐𝑒 𝐶𝑆𝐶 = 𝐶𝑜𝑛𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑎𝑙 𝑠𝑢𝑝𝑝𝑙𝑦 𝑐𝑜𝑠𝑡𝑠 𝑅𝐹𝐶 = 𝑅𝑒𝑛𝑒𝑤𝑎𝑏𝑙𝑒 𝑓𝑢𝑒𝑙 𝑐𝑜𝑠𝑡𝑠 Different energy system combinations were defined for the centralised community energy system, based on the capacity increment steps proposed for each technology in section 6.3.1. In each solution, the community energy demand fraction which cannot be supplied with RE under the current constraints are provided through BC grid electricity. All possible energy mixes are simulated, with the goal of supplying the entire energy demand of the community. The same fuzzy based optimisation approach with fuzzy numbers ranking was used to find the optimal energy system combination at community level. Energy system performance assessment was conducted using the same aggregation, defuzzification, and DSW methods described in detail in section 6.2.4.  5.2.3 Case-specific methods and analysis As the developed framework is demonstrated for the average residential sector conditions in British Columbia (BC), Canada, the RE data was also compiled for a medium-scale municipality located in the Okanagan region of BC. To demonstrate the proposed building energy optimisation model, data was extracted from a report published on the energy use in low-rise buildings to indicate the average MURB energy demand in BC [318]. The above mentioned building energy database is presented in Appendix D  The location coordinates of the case study community were defined as 49.7711° N (latitude), 119.7275° W (longitude) to represent the District of Peachland, a municipality in the Central Okanagan Regional district in BC. (The proposed community selected for the scenario-based assessment in section 5.1.2 is located here.) The energy intensity variation for the total building energy use and baseline load of the selected building cluster is represented in Figure 5-5. Here, the energy use above the baseline load is attributed to space heating.  123   Figure 5-5: Energy use intensity The fuzzy number defined for the MURB energy intensity by floor areas as per Equation 18 is given below.  𝐸𝐼 (𝑘𝑊ℎ 𝑚2⁄ ) = (5.93, 8.70, 12.11) For the MURB sector in BC, Canada, the representative MURB was defined as a 4-storeyed building with a gross floor area of 7009 m2 and a ground floor area of 1752 m2. The alternative energy supply model was developed for this building. Table 5-11 presents the fuzzy numbers for the monthly heating load intensities of the case study building.  Table 5-11: Monthly space heating load intensity (kWh/m2)  Heating load (SHmj)  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec a 6.32 4.84 4.94 3.16 1.71 0.42 0.01 0.00 0.85 2.73 5.08 6.11 b 12.74 7.33 7.45 5.45 3.53 1.22 0.09 0.00 2.13 4.91 7.93 9.78 c 23.23 17.75 18.29 12.77 7.88 2.71 0.32 0.03 4.67 11.38 18.91 22.40 Table 5-12 presents the relevant mean daily insolation values for the selected location under for different panel orientations, based on the photovoltaic and solar resource maps published by Natural Resources Canada [319]. For the purpose of the case study, the array was assumed to be south facing with a tilt equalling latitude.  124  Table 5-12: Monthly mean daily insolation for the selected location Month Mean daily global insolation (kWh/m2) South-facing vertical (tilt=90°) South-facing tilt=latitude South-facing tilt=lat+15° South-facing tilt=lat-15° Two-axis sun-tracking Horizontal (tilt=0°) January 1.8 1.8 1.89 1.62 2.11 1.03 February 2.74 2.97 3.02 2.76 3.54 1.92 March 3.63 4.5 4.37 4.39 5.76 3.34 April 3.47 5.11 4.68 5.29 7.23 4.79 May 3.15 5.28 4.62 5.69 8.06 5.66 June 3 5.44 4.63 5.98 8.97 6.17 July 3.21 5.75 4.94 6.28 9.49 6.48 August 3.57 5.67 5.07 5.96 8.57 5.51 September 4.06 5.38 5.12 5.33 7.33 4.01 October 3.5 4.01 4.01 3.77 5.02 2.42 November 2.17 2.23 2.32 2.01 2.65 1.15 December 1.57 1.53 1.62 1.36 1.79 0.81 Annual 2.99 4.14 3.86 4.21 5.89 3.62 Liquid glazed type solar thermal collector is the most commonly used in Canada [320].  Therefore, this type of collector was selected for the case study. As the energy generation potential of solar thermal collectors are dependent on the collector type, a glazed flat plate collector13 was selected from the ratings published by the Solar Rating & Certification Corporation of the International Code Council (ICC) [321]. The performance characteristics of the collector are provided in Table 5-13 [322]. (It was assumed that the panel orientation for PV applies similarly to solar thermal for architectural purposes.) Climate category D was determined to be most applicable (space and water heating in a cool climate) to the selected building location.  Table 5-13: Solar thermal collector characteristics Kilowatt-hours (thermal) per panel per day) Climate   High Radiation (6.3 kWh/m2.day) Medium Radiation (4.7 kWh/m2.day) Low Radiation (3.1 kWh/m2.day) Category (Ti –Ta) A (-5 ºC) 11.8 8.9 6.0 B (5 ºC) 11.2 8.3 5.3 C (20 ºC) 10.0 7.1 4.2 D (50 ºC) 7.6 4.8 2.1 E (80 ºC) 4.8 2.3 0.3 Collector gross area (AST, G) = 2.503 m2                                                  13 OG-100 ICC-SRCCTM Certified Solar Collector #2012027A (Brand name: GOBI HT) 125  Collector aperture area (AST, A) = 2.313 m2 Iso-efficiency equation for the collector (Based on gross area and (𝑃 =  𝑇 𝑖– 𝑇𝑎) 𝜂 = 0.763 − 2.40630 (𝑃𝐺) − 0.01420 (𝑃2𝐺) G = solar irradiance on the collector (W/m2); This is calculated based on the irradiance data provided in Table 5-12.  The solar energy generation potential for PV and ST in the selected location per unit installation is presented in Appendix E   The case study for the community level optimisation model too was conducted for the above-mentioned proposed neighbourhood community located in Okanagan, BC, Canada. The population and housing data presented in section 5.1.2 and Table 5-2 were used as data inputs for this case study. The expected energy demand of the SFD, SFA, and SCC residences was determined based on the average residential energy consumption data published for BC [278]. (SCC residences were assumed to be represented by apartment buildings, as per the community development plan.) The average energy intensities for years 2010 to 2015 are listen in Table 5-14. Based on this data, a fuzzy number for the total community energy demand (TED) within a year was estimated as follows.  Table 5-14: Annual residential energy intensities for BC Housing Annual energy intensity (GJ/m2) 2010 2011 2012 2013 2014 2015 SFD 0.59 0.65 0.62 0.60 0.56 0.53 SFA 0.55 0.61 0.58 0.56 0.52 0.50 SCC 0.50 0.55 0.52 0.50 0.46 0.44 𝑇𝐸𝐷 (𝑘𝑊ℎ/𝑎) = (63941343, 72253412, 79197177) A building database compiled on building energy use in BC, Canada, was used in estimating the average monthly fraction of the total annual energy demand [318]. The monthly energy use fractions were calculated based on the median monthly energy intensities (per square metre) of the buildings in the database. The results are presented below in Table 5-15.    126  Table 5-15: Monthly energy demand for the community Month Fraction Energy demand (kWh) – Emj a b c Jan 11.70% 7482691 8455406 9267995 Feb 9.99% 6388687 7219186 7912971 Mar 10.20% 6523754 7371811 8080264 Apr 8.43% 5391010 6091815 6677257 May 7.16% 4578784 5174004 5671241 Jun 5.87% 3751836 4239556 4646990 Jul 5.31% 3392688 3833721 4202153 Aug 5.26% 3362927 3800092 4165292 Sep 6.19% 3958136 4472674 4902511 Oct 8.11% 5185383 5859458 6422570 Nov 10.31% 6591288 7448124 8163911 Dec 11.47% 7334159 8287564 9084023 Annual 100.00% 63941343 72253412 79197177 Table 5-16 details the monthly solar PV electricity generation potential for a unit installation in a centralised PV facility.  Table 5-16: Monthly solar energy generation potential Month Mean global daily insolation (H) Number of days (N) Solar PV energy generation (kWh/kW) (kWh/m2/day)  a b c Jan 1.8 31 35.57 41.85 48.13 Feb 2.97 28 53.01 62.37 71.73 Mar 4.5 31 88.93 104.63 120.32 Apr 5.11 30 97.73 114.98 132.22 May 5.28 31 104.35 122.76 141.17 Jun 5.44 30 104.04 122.40 140.76 Jul 5.75 31 113.63 133.69 153.74 Aug 5.67 31 112.05 131.83 151.60 Sep 5.38 30 102.89 121.05 139.21 Oct 4.01 31 79.25 93.23 107.22 Nov 2.23 30 42.65 50.18 57.70 Dec 1.53 31 30.24 35.57 40.91 Annual 4.14 365 964.35 1134.53 1304.70 Considering the power requirement of the community and maximum land area utilisation, a wind turbine model with a rated capacity of 500 kW from a brand with high global market share was selected for the case study14 [323][312]. For this turbine the specifications are given below.                                                   14 Vestas V39 model 127  Cut-in wind speed = 4 ms-1 Rated speed = 15 ms- Cut-out speed = 25 ms-1  Rotor diameter = 39 m  Hub height = 40.5 / 53 m Table 5-17 lists the wind speed distribution frequency for the selected site location. Based on this table, it can be seen that the wind speed exceeds the cut-in speed at only 15% of the time, which is considerably below the minimum threshold level of 50%. Therefore, it was determined that wind was not a feasible energy option based on resource availability for the optimisation model. This finding is also corroborated by a resource assessment study conducted for this region [315].  Table 5-17: Wind speed distribution and frequency for the proposed site Bin Min speed Max speed Avg speed Bin probability Frequency (hours) 1 0 0 0 0.00% 0.00 2 0 1 0.5 10.97% 960.87 3 1 2 1.5 26.20% 2295.20 4 2 3 2.5 27.68% 2425.11 5 3 4 3.5 19.56% 1713.67 6 4 5 4.5 10.11% 885.35 7 5 6 5.5 3.95% 346.13 8 6 7 6.5 1.19% 104.15 9 7 8 7.5 0.28% 24.35 10 8 9 8.5 0.05% 4.45 11 9 10 9.5 0.01% 0.64 12 10 11 10.5 0.00% 0.07 13 11 12 11.5 0.00% 0.01 14 12 13 12.5 0.00% 0.00 15 13 14 13.5 0.00% 0.00 16 14 15 14.5 0.00% 0.00 17 15 16 15.5 0.00% 0.00 18 16 17 16.5 0.00% 0.00 19 17 18 17.5 0.00% 0.00 20 18 19 18.5 0.00% 0.00 21 19 20 19.5 0.00% 0.00 22 20 21 20.5 0.00% 0.00 23 21 22 21.5 0.00% 0.00 24 22 23 22.5 0.00% 0.00 25 23 24 23.5 0.00% 0.00 Total 100% 8760.00 The range of values for annual per capita MSW generation was defined based on the data published for the Central Okanagan Regional District, where the proposed community is located [324]. It 128  was assumed that MSW generation is uniform throughout all months of the year. For the per ton energy generation potential (EPt) of MSW, a fuzzy number was defined, based on literature [314].  𝑚𝑀𝑆𝑊,𝑝(𝑡𝑜𝑛 𝑝𝑒𝑟 𝑎𝑛𝑛𝑢𝑚⁄ ) = (0.609, 0.650, 0.707) 𝐸𝑃𝑡𝑀𝑆𝑊(𝑘𝑊ℎ/𝑡𝑜𝑛) = (750, 800, 850) In developing the biomass electricity generation component of the optimisation model, the following method was used based on the regional averages for such facilities. The plant capacity factor (CF) was defined as 91% [313]. The plant was assumed to operate 24 hours per day, and 365 days per year.  𝐶𝐹 (%) = (83, 91, 96) In terms of wood based residue, the main categories can be identified in the following table. Table 5-18: Biomass types and their characteristics Fuel type Average Delivery Cost [313] $/ODT Dry shavings  10 Sawdust 10 Roadside wood waste 50 Hog fuel 10 Standing timber 60 The biomass resource available in the District of Peachland was identified based on a renewable energy opportunity study carried out for the municipality, which rates the biomass energy feasibility as high [315]. The resource availability is listed below for different biomass sources. The moisture content (mw) of the wood-based biomass was approximated as 23% by weight based on literature [325].  Table 5-19: Annual biomass resource availability at selected site Source of biomass (type) Availability (tons) Cost of acquisition and delivery ($/ton) a) Yard and agricultural residue 500 - 800 50 (approximated from Table 5-18) b) White wood 800 0 - 30 c) Green wood 1500 - 2000 0 - 35 d) Chipped wood, preserved wood, timber waste 4500 - 5000 0 - 35 e) Wood fibres Upon requirement 70 - 100 Total biomass availability 7300 – 8600 (Can buy from fibre suppliers upon necessity) 129  The mix of supply from a, b, c, and d sources in Table 5-19 was considered in defining an average fuzzified value for the cost of supply acquisition and delivery (CS) for the biomass fuel supply from locally available sources.  𝐶𝑆 ($/𝑡𝑜𝑛) = (4.09, 19.91, 35.72) Any additional supply was assumed to come from the wood fibre purchased from local producers at (70, 85, 100) $/ton as per Table 5-19. For the case study, the maximum plant capacity (likely value) that can be supplied with the locally available resources is 1.0 MW. If the designed plant has a capacity above that, additional wood fibre has to be purchased. For the case study of the community energy system, it was defined that at least 40% of the energy supply needed to be from RE sources. The total land area allocated for developing RE facilities was assumed to be 125% of the total housing development land area, considering the needs and condition of the selected community. In the present case study, the linguistic weight terms assigned to each performance objectives are as follows. The fuzzy weighting scheme defined in Table 5-8 was used for the weighting. The rationale behind the importance weighting given in the case study for different performance objectives are described in Table 5-20 and Table 5-21. This logic can be used by decision makers who apply the proposed framework when defining their own performance priorities.  Table 5-20: Weights assigned to performance objectives for building energy system Performance objective Importance Rationale EC1 Minimising the total life cycle energy system cost Very high The total project costs represent the overall energy system related expenditure incurred by different stakeholders throughout the lifetime of the building. In a MURB, the total cost is a significant factor in deciding whether the property developers and building management can afford the implementation and upkeep of the energy system. A higher total system cost means that developers have to incur a higher cost upfront, the management has to face high facility maintenance during the operations, and residents are burdened with increased housing prices. As this is a measure of the economic burden and financial feasibility of the developed energy system, its importance was rated as VH.  EC2 Maximising the operational energy cost savings High The operational cost saving is most relevant to the occupants of a MURB, and it decides the affordability of the energy supply for them. (In MURB context, the occupants will be responsible for the energy bills, but not the major replacement activities of energy systems.) Therefore, the importance of this performance objective was rated as H.  130  Performance objective Importance Rationale EN1 Maximising the RE fraction in building energy supply Very low In most locations in BC, there is constant grid electricity and natural gas supply, and the grid mix is dominated by hydropower [56][326]. This means the emissions factor of the conventional supply is lower. Therefore, maximising the RE fraction in the energy supply holds a lesser importance compared to cost, emissions, and affordability. However, in remote/Northern locations in Canada which lack grid connectivity, increasing the RE fraction to achieve net-zero status is very important, in order to ensure energy independence and energy security and reduce energy poverty in communities [57]. EV1 Minimising life cycle environmental impacts Medium A key goal of RE integration is to mitigate the negative environmental impacts associated with conventional energy use [24]. While emissions and other related impacts are not as high in a hydropower dominated location such as BC, other locations in Canada (e.g. Alberta) with high grid emissions factors may find this to be a key goal in building energy planning [56]. For the present case study, the importance of this objective was rated as M.   Table 5-21: Weights assigned to performance objectives for community energy system Performance objective Importance Rationale EC1 Minimising the total life cycle energy system cost Very high The total project costs represent the economic burden of the energy system to various stakeholders through the system life. In community development, the total cost is a significant factor in deciding whether the energy system is financially and economically feasible for the community developers and residents. A higher total system cost translates to a higher upfront investment cost for the developers, and therefore, a higher housing price for the residents of the community. Due to the above reasons, its importance was rated as VH.  EC2 Maximising the operational energy cost savings High The operational cost saving is important to the residents, as it determines how the RE integration will benefit them in the long run. Higher energy cost savings means the energy supply is more affordable to the residents, and ultimately leads to reduced economic burdens. Therefore, its relative importance was rated as H.  EN1 Maximising the RE fraction in energy supply High Increasing the RE fraction to achieve net-zero status is very important in order to ensure energy independence and energy security and reduce energy poverty in communities [57]. This is especially true in remote/Northern locations in Canada which lack grid connectivity. As the ultimate goal of RE based energy planning is to develop net-zero communities which are energy independent and energy secure, maximising the RE fraction in the total community energy supply was rated as H.  EV1 Minimising life cycle environmental impacts Medium Use of RE can reduce the environmental impacts associated with conventional energy supplies [24]. In BC, the grid emissions factor is also low due to the dominance of hydropower in the grid mix, and therefore, environmental impact mitigation is a lower priority for the present case study [56]. Therefore, the importance of this objective was rated as M.   131   Results This section details the results for both optimisation models, at building level and community level, as they are interlinked. The building level optimisation results were used in the community energy model to estimate the leftover demand that needs to be catered with centralised facilities and supplemented by the grid.  5.3.1 Building-level optimal RE integration The results of the life cycle impact assessment, it was identified that all three renewable energy options carry lower life cycle impacts per MWh of energy generation in comparison to grid electricity. While the BC grid carries lower life cycle impacts in energy production due to the domination in hydropower, voltage transformations and transmission results in significant losses, leading to a considerable increase in life cycle impacts per MWh of grid electricity.  Table 5-22: Life cycle impacts of energy generation Impact category Unit Per MWh of generated energy  PV ST GSHP GE Climate change kg CO2 eq 7.82E+01 1.11E+01 2.44E+02 2.74E+02 Ozone depletion kg CFC-11 eq 1.67E-05 2.79E-06 1.58E-04 4.80E-05 Terrestrial acidification kg SO2 eq 4.88E-01 1.35E-01 1.10E+00 1.19E+00 Freshwater eutrophication kg P eq 7.18E-02 2.39E-02 1.18E-01 2.11E-01 Marine eutrophication kg N eq 3.57E-02 7.36E-03 4.78E-02 8.30E-02 Human toxicity kg 1,4-DB eq 1.18E+02 4.93E+01 9.19E+01 1.65E+02 Photochemical oxidant formation kg NMVOC 3.15E-01 5.13E-02 6.10E-01 9.45E-01 Particulate matter formation kg PM10 eq 2.51E-01 5.67E-02 7.96E-01 9.33E-01 Terrestrial ecotoxicity kg 1,4-DB eq 1.42E-01 4.95E-03 1.25E-02 2.85E-02 Freshwater ecotoxicity kg 1,4-DB eq 1.17E+01 9.13E-01 2.79E+00 6.19E+00 Marine ecotoxicity kg 1,4-DB eq 1.07E+01 9.59E-01 2.59E+00 5.59E+00 Ionising radiation kBq U235 eq 8.76E+00 1.05E+00 2.32E+01 3.56E+01 Agricultural land occupation m2a 6.14E+00 1.31E+00 9.22E+00 5.96E+02 Urban land occupation m2a 8.87E-01 2.19E-01 1.38E+00 3.93E+00 Natural land transformation m2 1.27E-02 5.31E-03 2.90E-02 5.27E-02 Water depletion m3 2.58E+00 2.30E-01 1.53E+00 6.81E+01 Metal depletion kg Fe eq 2.29E+01 1.58E+01 6.22E+00 9.64E+00 Fossil depletion kg oil eq 2.04E+01 2.77E+00 5.70E+01 7.94E+01 The present value of the lifetime costs of each RE technology for a unit installation is given below in Table 5-23.  132  Table 5-23: Present value of RE system costs RE technology Unit of installed capacity Present value of lifetime cost ($) Low Likely High Solar PV Per kW 3944 5769 7593 Solar thermal Per panel 2807 6769 11141 Ground source heat pumps Per ton 4742 27020 49297 Based on the algorithm presented under the methodology, the possible energy system combinations were simulated. After applying the constraints, 94,034 viable options were identified. The top-ranked combinations that were identified are provided below. The RE fraction was established by defuzzifying the fuzzy output using the COG method.  Table 5-24: Best energy system combinations Solution (Ranked) Solar PV Solar thermal GSHP 𝑼𝑻(𝑭𝒊) RE fraction (defuzzified) kW # of collectors ton Combination 1 116.6 0 17 0.4865 44.15% Combination 2 116.6 0 18 0.4864 45.43% Combination 3 116.05 0 17 0.4863 44.10% Combination 4 116.05 0 18 0.4862 45.37% Combination 5 115.5 0 17 0.4862 44.04% Combination 6 116.6 0 19 0.4861 46.70% Combination 7 115.5 0 18 0.4860 45.31% Combination 8 114.95 0 17 0.4859 43.98% Combination 9 116.05 0 19 0.4858 46.64% Combination 10 116.6 0 20 0.4857 47.97% Combination 11 114.95 0 18 0.4857 45.25% Combination 12 114.4 0 17 0.4856 43.92% Combination 13 115.5 0 19 0.4856 46.58% Combination 14 116.05 0 20 0.4856 47.91% Combination 15 114.4 0 18 0.4855 45.19% Combination 16 113.85 0 17 0.4855 43.86% Combination 17 114.95 0 19 0.4855 46.52% Combination 18 115.5 0 20 0.4854 47.85% Combination 19 113.85 0 18 0.4854 45.13% Combination 20 113.3 0 17 0.4853 43.80% The maximum installation potential under the available rooftop area is 116.6 kW for solar PV, and 288 collectors for solar thermal. The By analysing the top-ranked energy system combinations, it can be observed that while solar PV installations are maximised in the top solutions, solar thermal installations are zero, thereby indicating that solutions with solar thermal energy are not optimal under the given circumstances. The maximum potential for GSHP under the current peak heating lead is 28 tons. In the top solutions, GSHP cap