Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Perera, Piyaruwan Harindra Kaluthantirige
Doctor of Philosophy - PhD
University of British Columbia
Decarbonizing the road transportation sector has gained immense attention with the greenhouse gas targets stipulated by the Canadian government in 2007. Transport electrification using low-emission electricity has been identified as one of the key methods for achieving climate action targets and reducing the extensive fossil fuel demands. Substituting conventional light-duty vehicles with electric vehicles (EVs) is considered more scalable in the Canadian context. However, the limited vehicle ranges, limited recharging infrastructure availability, and extensive switching costs are key challenges that limit the widespread adoption of EVs. Conventional EV recharging infrastructure planning and investment strategies are based on ad-hoc decisions. Those practices have overlooked lifecycle impacts and costs of electric transport systems, including multi-period recharging demands and investment paybacks, strategies for sustainable infrastructure deployment, and acquiring anticipated recharging demands. The primary goal of this study is to develop a planning and management framework for EV recharging infrastructure. A lifecycle thinking approach was used to identify the best desirable low-emission fuel technology and strategies to deploy recharging infrastructure for Canadian provinces. In most provinces, EVs showed higher cradle-to-gate emissions and lower cradle-to-grave emissions compared to conventional vehicles. Moreover, the EV cost of ownership is considered as one of the key barriers that limit the widespread adoption of EVs. Hence, an incentive planning framework was developed to identify the most appropriate incentive-scheme for Canadian regions to strategically sustain the anticipated recharging demands. Accordingly, vehicle purchasing rebates, sales tax waivers, government subsidize recharging, and carbon-tax policies were found as viable incentives and tax options. A project delivery method selection framework was developed to encourage investors by incorporating partnering and collaborative approaches to deploy infrastructure for multiple periods. Public-private-partnerships for early adoption and integrated project management for business-as-usual was identified as the most desirable project delivery methods for recharging infrastructure deployment process. Municipalities and government institutions can use developed frameworks to identify locations for recharging facilities, evaluate different incentive options, and compare the overall impacts of transport electrification at the planning stages. Moreover, the outcome of this research advocates communities reducing their transport-based carbon footprint and growing the low-emission travel culture for a sustainable future.
RECHARGING INFRASTRUCTURE PLANNING FOR ELECTRIC VEHICLES: A LIFECYCLE THINKING APPROACH by Kaluthantirige Piyaruwan Harindra Perera M.B.A., Postgraduate Institute of Management, University of Sri Jayewardenepura, 2015 M.Sc. Eng., University of Moratuwa, 2011 B.Sc. Eng. (Hons), University of Moratuwa, 2009 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2020 © Piyaruwan Kaluthantirige, 2020ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Recharging Infrastructure Planning for Electric Vehicles: A Lifecycle Thinking Approach submitted by Kaluthantirige Piyaruwan Harindra Perera in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Dr. Kasun N. Hewage, School of Engineering Supervisor Dr. Rehan Sadiq, School of Engineering Co-supervisor Dr. Shahria M. Alam, School of Engineering Supervisory Committee Member Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Nathan Pelletier, Faculty of Management University Examiner Dr. Mohamed H. Issa, University of Manitoba External Examiner iii Abstract Decarbonizing the road transportation sector has gained immense attention with the greenhouse gas targets stipulated by the Canadian government in 2007. Transport electrification using low-emission electricity has been identified as one of the key methods for achieving climate action targets and reducing the extensive fossil fuel demands. Substituting conventional light-duty vehicles with electric vehicles (EVs) is considered more scalable in the Canadian context. However, the limited vehicle ranges, limited recharging infrastructure availability, and extensive switching costs are key challenges that limit the widespread adoption of EVs. Conventional EV recharging infrastructure planning and investment strategies are based on ad-hoc decisions. Those practices have overlooked lifecycle impacts and costs of electric transport systems, including multi-period recharging demands and investment paybacks, strategies for sustainable infrastructure deployment, and acquiring anticipated recharging demands. The primary goal of this study is to develop a planning and management framework for EV recharging infrastructure. A lifecycle thinking approach was used to identify the best desirable low-emission fuel technology and strategies to deploy recharging infrastructure for Canadian provinces. In most provinces, EVs showed higher cradle-to-gate emissions and lower cradle-to-grave emissions compared to conventional vehicles. Moreover, the EV cost of ownership is considered as one of the key barriers that limit the widespread adoption of EVs. Hence, an incentive planning framework was developed to identify the most appropriate incentive-scheme for Canadian regions to strategically sustain the anticipated recharging demands. Accordingly, vehicle purchasing rebates, sales tax waivers, government subsidize recharging, and carbon-tax policies were found as viable incentives and tax options. A project delivery method selection framework was developed to encourage investors by incorporating partnering and collaborative approaches to deploy infrastructure for multiple periods. Public-private-partnerships for early adoption and integrated project management for business-as-usual was identified as the most desirable project delivery methods for recharging infrastructure deployment process. Municipalities and government institutions can use developed frameworks to identify locations for recharging facilities, evaluate different incentive options, and compare the overall impacts of transport electrification at the planning stages. Moreover, the outcome of this research advocates communities reducing their transport-based carbon footprint and growing the low-emission travel culture for a sustainable future.iv Lay Summary Electric vehicle (EV) demands are limited in Canada due to the lack of recharging infrastructure availability, premium costs, range limitations of EVs, and low investment potential due to uncertain demands. Recharging infrastructure can be placed strategically to promote EVs while improving infrastructure pay-backs and sustaining anticipated demands throughout the infrastructure life span. This study presents a comprehensive approach in planning and managing EV recharging infrastructure for urban centers. The tools proposed in this study integrate the life cycle thinking approach with low-emission fuel selection, incentive planning, and recharging infrastructure development for multi-period EV demands. Outcomes of this research will assist urban planners, municipalities, and investors in locating potential recharging facilities and developing infrastructure capacity improvement plans to propose incentives and tax policies for low-emission fuel-based vehicles. Researchers can extend the proposed methodology to plan for similar infrastructure needs in the future. v Preface I, Piyaruwan Perera, intellectualized and developed the entire thesis under the supervision of Dr. Kasun Hewage and Dr. Rehan Sadiq. Five journal articles, two conference proceedings, and one poster presentation, which are currently published, accepted, or under review, have been prepared directly or indirectly from the research presented in this thesis. The first journal paper was focused on the selection of alternative low-emission fuels to decarbonize the existing transportation in Canada, which also comprised a section of the literature review. The incentive planning for household interventions for domestic and transport activities, and prioritizing those activities to be incentivized, were determined in the second article. Dr. Sharia Alam, who acts as a committee member, assisted for the second paper by providing his recommendations and suggestions for improvements. A cluster-based electric vehicle recharging infrastructure capacity planning and location-allocation approach was introduced in the third journal article. In addition to that, the fourth journal article, which is on the selection and ranking of project delivery methods for small-scale distributed infrastructure, is in preparation. This article will support sustainable infrastructure planning, construction, operations, and management in the long-term electric vehicle recharging infrastructure deployment process. The final journal paper and conference articles are indirectly related to this thesis. The references for the completed and in-progress papers are provided below. Journal Articles (Published): 1. Perera, P., Hewage, K., Sadiq, R., (2017) Are we ready for alternative fuel transportation systems in Canada: A Regional Vignette, Journal of Cleaner Production, doi: 10.1016/j.jclepro.2017.08.078. 2. Perera, P., Hewage, K., Alam, M. S., Merida, W., Sadiq, R. (2018) Scenario-based Economic and Environmental Analysis of Clean Energy Incentives for Households in Canada: Multi-Criteria Decision Making Approach, Journal of Cleaner Production, doi: 10.1016/j.jclepro.2018.07.014. vi 3. Perera, P., Hewage, K., Sadiq, R., (2020) Electric Vehicle Recharging Infrastructure Planning and Management in Urban Communities: Journal of Cleaner Production, doi: 10.1016/j.jclepro.2019.119559. Journal Articles (Under Preparation) 4. Perera, P., Hewage, K., Sadiq, R., Project Delivery Method Selection for Electric Vehicle Refuelling Infrastructure Deployment: A Fuzzy MADM based Approach: Expected to submit Journal of Cleaner Production in June 2020. 5. Perera, P., Amin., S., Amaiya, K., Hewage, K., Sadiq, R., Mobile Energy Hub Panning for Complex Urban Networks: A Robust Optimization Approach: Expected to submit Journal of Cleaner Production in June 2020 Conference Proceedings 6. Perera, P., Rana, A., Hewage, K., Alam, M. S., Sadiq, R. (2019) Solar Photovoltaic Electricity for Single-Family-Detached-Households: Lifecycle Thinking-based Assessment, 8th CSCECRC International Construction Specialty Conference, Montreal, Canada. Poster Presentations: 7. Perera, P., Hewage, K., Sadiq, R., (2018) Lifecycle Thinking Approach for Recharging Infrastructure Planning of Electric Vehicles, The 4th Annual Engineering Graduate Symposium, Kelowna, Canada. vii Table of Contents ABSTRACT .............................................................................................................................................................. III LAY SUMMARY ..................................................................................................................................................... IV PREFACE ................................................................................................................................................................... V TABLE OF CONTENTS ........................................................................................................................................ VII LIST OF TABLES ...................................................................................................................................................... X LIST OF FIGURES .................................................................................................................................................. XI LIST OF ABBREVIATIONS ................................................................................................................................. XII ACKNOWLEDGMENTS ..................................................................................................................................... XIV DEDICATION ....................................................................................................................................................... XVI CHAPTER 1 INTRODUCTION ............................................................................................................................... 1 1.1 THE CHALLENGE .............................................................................................................................................. 1 1.2 RESEARCH GAP ................................................................................................................................................ 3 1.3 RESEARCH MOTIVATION .................................................................................................................................. 6 1.4 RESEARCH OBJECTIVES ................................................................................................................................... 7 1.5 THESIS ORGANIZATION .................................................................................................................................... 7 CHAPTER 2 RESEARCH METHODOLOGY ..................................................................................................... 11 CHAPTER 3 LITERATURE REVIEW ................................................................................................................. 15 3.1 TRANSPORTATION AND ENERGY USE ............................................................................................................. 15 3.2 ROAD TRANSPORTATION DECARBONIZING .................................................................................................... 17 3.3 TRANSPORTATION ELECTRIFICATION ............................................................................................................. 22 3.4 RECHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES ............................................................................ 23 3.4.1 Incentives and Policies to Encourage EVs and EV-RI Investments ..................................................... 24 3.4.2 Consumer Recharging Behaviours ...................................................................................................... 26 3.4.3 Consumer Concerns about Energy Depletion and GHG Emissions .................................................... 26 3.4.4 Consumer Cost Perception .................................................................................................................. 27 3.4.5 Range Preference of Vehicle Consumers ............................................................................................. 28 3.5 PLANNING ELECTRIC VEHICLE RECHARGING INFRASTRUCTURE ................................................................... 28 3.5.1 EV Demand Modeling and Sustaining Approaches ............................................................................. 29 3.5.2 Facility Location Selection .................................................................................................................. 31 3.5.3 EV-RI Construction, Maintenance and Disposal Process ................................................................... 33 3.5.4 Project Delivery Methods .................................................................................................................... 34 3.6 ENVIRONMENTAL AND ECONOMIC ASSESSMENTS ......................................................................................... 36 3.6.1 Life Cycle Assessment (LCA) ............................................................................................................... 36 3.6.2 Life Cycle Cost (LCC).......................................................................................................................... 39 viii 3.6.3 Eco-efficiency Assessment ................................................................................................................... 40 3.7 MULTI-CRITERIA DECISION MAKING ............................................................................................................. 40 3.8 DECISION-MAKING UNDER UNCERTAINTY ..................................................................................................... 41 3.8.1 Scenario-based Assessment ................................................................................................................. 41 3.8.2 Fuzzy Logic and Fuzzy Sets ................................................................................................................. 42 3.8.3 Fuzzy Multi-Attribute Decision Making ............................................................................................... 44 CHAPTER 4 SELECTION OF DESIRABLE ALTERNATIVE FUEL TRANSPORTATION SYSTEMS .... 45 4.1 BACKGROUND ................................................................................................................................................ 45 4.2 METHODOLOGY TO SELECT DESIRABLE TRANSPORT FUEL OPTIONS ............................................................. 46 4.3 RESULTS AND DISCUSSION ............................................................................................................................. 59 4.3.1 Life Cycle Inventory for Different Fuel Options and Different Mixes of the Source Energy ............... 59 4.3.2 Compare Vehicle Options using Cradle-to-Gate Emissions ................................................................ 63 4.3.3 Compare Vehicles using Cradle-to-Grave Emissions .......................................................................... 65 4.3.4 Life Cycle Costs of Different Fuel Options .......................................................................................... 68 4.3.5 Eco-efficiency-based Alternative Fuel Option Selection ..................................................................... 69 4.4 SUMMARY ...................................................................................................................................................... 70 CHAPTER 5 ELECTRIC VEHICLE RECHARGING INFRASTRUCTURE PLANNING AND MANAGEMENT FOR URBAN CENTERS ........................................................................................................... 72 5.1 BACKGROUND ................................................................................................................................................ 72 5.2 METHODOLOGY FOR EV-RI CAPACITY PLANNING AND LOCATION-ALLOCATION FRAMEWORK ................... 73 5.3 CASE STUDY-BASED MODEL DEMONSTRATION ............................................................................................. 85 5.3.1 Data Migration and Development of the Optimization Model ............................................................ 86 5.3.2 ArcGIS-based Distance Matrix ............................................................................................................ 87 5.3.3 Optimal Capacity Planning and Location Allocation Model for EV-RIs ............................................ 89 5.3.4 Case Study: Results and Discussion .................................................................................................... 90 5.3.5 Case Study: Model Validation ............................................................................................................. 93 5.4 SUMMARY ...................................................................................................................................................... 98 CHAPTER 6 STRATEGIC INCENTIVE AND TAX PLANNING APPROACH FOR SUSTAINED RECHARGING INFRASTRUCTURE ................................................................................................................. 100 6.1 BACKGROUND .............................................................................................................................................. 100 6.2 METHODOLOGY FOR HOUSEHOLD INCENTIVE AND TAX PLANNING TOOL ................................................... 102 6.3 HIPT DEMONSTRATION USING A CASE STUDY ............................................................................................ 112 6.3.1 Household Data Collection for the Demonstration ........................................................................... 112 6.3.2 Incentive and Tax Policies for Electric Vehicles in Canada .............................................................. 113 6.3.3 Local Building Upgrades and Retrofit Options ................................................................................. 114 6.4 RESULTS AND DISCUSSION ........................................................................................................................... 117 ix 6.4.1 Regional Retrofit or Upgrade Selection for Single Detached Houses ............................................... 117 6.4.2 Best Incentive and Tax Policies for Electric Vehicles........................................................................ 119 6.4.3 Decision Making for Government Incentive Investment .................................................................... 120 6.4.4 Consumer-centric Decision-making .................................................................................................. 121 6.5 SUMMARY .................................................................................................................................................... 124 CHAPTER 7 PROJECT DELIVERY METHOD SELECTION FOR ELECTRIC VEHICLE REFUELLING INFRASTRUCTURE DEPLOYMENT ................................................................................................................ 126 7.1 BACKGROUND .............................................................................................................................................. 126 7.2 METHODOLOGY FOR PDM SELECTION FRAMEWORK................................................................................... 127 7.3 PDM SELECTION MODEL DEMONSTRATION ................................................................................................ 136 7.3.1 EV-RI Deployment Stages for Multiple Periods ................................................................................ 136 7.3.2 Stakeholder Data Collection for the Case Demonstration................................................................. 138 7.3.3 PDM Decision Matrix for Ranking .................................................................................................... 141 7.3.4 Multi-period and Multi-stakeholder-based Attribute Weights ........................................................... 141 7.3.5 Ranking Alternative PDMs and PDM Selection ................................................................................ 142 7.4 SUMMARY ............................................................................................................................................... 145 CHAPTER 8 CONCLUSIONS AND RECOMMENDATIONS ......................................................................... 147 8.1 SUMMARY AND CONCLUSIONS ..................................................................................................................... 147 8.2 ORIGINALITY AND CONTRIBUTIONS ............................................................................................................. 151 8.3 LIMITATIONS OF THE STUDY ........................................................................................................................ 152 8.4 FUTURE RESEARCH ...................................................................................................................................... 153 REFERENCES ........................................................................................................................................................ 155 APPENDICES .......................................................................................................................................................... 171 x List of Tables Table 3-1 Available Alternative Fuel Options for Transportation ............................................................................... 17 Table 3-2 Comparison of Electric and Hydrogen based transportation with conventional Gasoline transportation ... 19 Table 3-3 Potential Incentive for Electric Vehicles ..................................................................................................... 25 Table 3-4 Maturity Stages of the EV Market and Consumer Behaviours [50] ............................................................ 30 Table 3-5 Existing Solutions for Optimal Infrastructure Location-Allocation ............................................................ 32 Table 3-6 Literature-based Typical Project Delivery Methods .................................................................................... 35 Table 4-1 Compare Alternative Fuel Technologies for Road Transportation ............................................................. 49 Table 4-2 Life Cycle Cost Calculation for the System (LCC_S)................................................................................. 56 Table 4-3 Vehicle Life-Cycle Inventory ...................................................................................................................... 60 Table 4-4 Electricity (Generation to Recharging) Life-Cycle Inventory ..................................................................... 61 Table 4-5 Hydrogen and Gasoline Well to Pump (WtP) Life-Cycle Inventory ........................................................... 62 Table 5-1 Indicators for Preliminary Site Selection .................................................................................................... 77 Table 5-2 Potential EV-RI Locations for Kelowna, BC .............................................................................................. 86 Table 5-3 Simple Payback Calculations ...................................................................................................................... 91 Table 5-4 Scenarios Developed for Model Validation ................................................................................................ 95 Table 6-1 Incentive and Tax Policies for Low-emission Fuel Vehicles .................................................................... 113 Table 6-2 Energy Retrofits for Single Detached Households .................................................................................... 114 Table 6-3 Provincial Incentive Schemes for Residential Buildings ........................................................................... 116 Table 6-4 Regional Retrofit Selection for SFDHs ..................................................................................................... 118 Table 6-5 Province-based Incentive and Tax Policies ............................................................................................... 120 Table 7-1 PDM Selection Factors .............................................................................................................................. 129 Table 7-2 Decision Matrix for the Proposed PDM Selection Approach.................................................................... 131 Table 7-3 Triangular-fuzzy Numbers used to Convert Attributes of Different PDMs .............................................. 131 Table 7-4 Triangular-fuzzy Numbers that Represent Stakeholder Judgment ............................................................ 132 Table 7-5 Triangular-fuzzy Numbers to Incorporate Stakeholder Expertise for Decision-making ........................... 134 Table 7-6 Focus Group Interview-based Data Collection and Database Development ............................................. 138 Table 7-7 Stakeholder Judgment Matrix ................................................................................................................... 139 Table 7-8 Linguistic Terms to Indicate LoI towards the Considered Project Stage .................................................. 139 Table 7-9 Stakeholder Expertise in EV-RI Deployment and Conventional Infrastructure Projects .......................... 140 Table 7-10 Linguistics Terms to Indicate LoE of the Stakeholders on the Subjected Criteria [156] ........................ 140 Table 7-11 Linguistic Terms to Convert the Decision Matrix into Fuzzy Numbers [250] ........................................ 141 Table 7-12 Final Weights of the Expert Data Collected as Inputs for the PDM Selection Tool ............................... 142 xi List of Figures Figure 1-1 Integration of Objectives, Information Flow, and Thesis Organization ..................................................... 10 Figure 2-1 Research Methodology .............................................................................................................................. 11 Figure 3-1 Energy use for transportation in Canada by Mode [75] [77] ...................................................................... 16 Figure 3-2 LCA Framework as Shown in ISO14041 .................................................................................................. 37 Figure 3-3 Conventional Vehicle Life Cycle and Fuel Life Cycle [8]......................................................................... 38 Figure 3-4 A Trapezoidal Fuzzy Number A = (a,b,c) .................................................................................................. 42 Figure 4-1 Methodology Framework to Select the Low-emission Fuel Technology .................................................. 47 Figure 4-2 Boundary of the Life Cycle Assessment .................................................................................................... 50 Figure 4-3 Cradle-to-gate Emission of Alternative Fuel-based Vehicle Options ........................................................ 64 Figure 4-4 Mid-point Indicators for Alternative Fuel Options .................................................................................... 66 Figure 4-5 Environmental Scores of Alternative Fuel Options (Most Likely and Conventional) ............................... 67 Figure 4-6 Provincial-based LCC for Electric, Gasoline, and Hydrogen Light-duty Vehicles.................................... 68 Figure 4-7 Eco-efficiency-based Comparison for Alternative Fuel Options ............................................................... 69 Figure 5-1 EV-RI Planning and Management Framework for Complex Urban Network ........................................... 74 Figure 5-2 Development of Distance Matrix ............................................................................................................... 79 Figure 5-3 Model Developed to Formulate Distance Matrix Using ArcGIS Model-Builder ...................................... 88 Figure 5-4 Multi-Period Improvement Plan for Public Recharging Infrastructure Network for Kelowna, BC ........... 92 Figure 5-5 EV-RI Network Demand vs. Payback Period ............................................................................................ 93 Figure 5-6 Results of Software Generated Dual Fitness Functions for Multiple Iterations ......................................... 94 Figure 5-7 Results of the Scenarios Developed Using Conventional EV-RI Planning Approach ............................... 96 Figure 6-1 Proposed Research Framework for Household Incentive Planning Tool (HIPT) .................................... 103 Figure 6-2 Building Level GHG Emissions and LCC vs. Retrofitting Investment for BC, Canada .......................... 117 Figure 6-3 Difference of LCC of EV with LCC of ICEV vs. Potential Incentives for BC, Canada .......................... 119 Figure 6-4 Compare the Eco-efficiencies of Electrified Transportation and Building Retrofitting ........................... 121 Figure 6-5 Household GHG Emissions and Consumer Annual Cost Comparison .................................................... 122 Figure 6-6 Province-wise Household Eco-efficiency Index (HEEI) ......................................................................... 123 Figure 7-1 PDM Selection Methodology for EV-RIs ................................................................................................ 128 Figure 7-2 Maturity Stage-wise EV-RI Deployment ................................................................................................. 137 Figure 7-3 F-TOPSIS-based PDM Rankings for Multi-period EV-RI Deployment Projects .................................... 143 Figure 8-1 Proposed Strategic Map ........................................................................................................................... 150 xii List of Abbreviations AHP Analytic hierarchy process AFV Alternative fuel vehicle ALW Acidification of land and water BC British Columbia CAPEX Capital Expenditure CC Closeness Coefficient CECT CANMET Energy Technology Center CF Cost Factors CFL Compact fluorescent lamp CM Construction Management at Risk CoC Carbon off-set cost CoCf Carbon off-set cost factor DB Design-Build DBB Design-Bid-Build DBOT Design-Build-Operate-Transfer DC-FC Direct Current Fast Charging DNR Depletion of non-renewable energy resources EF Environmental Factors EN Eutrophication EoL End-of-life EV Electric vehicle EV-RI Electric Vehicle Recharging Infrastructure FF Fossil Fuels F-MADM Fuzzy Multi-Attribute Decision Making F-TOPSIS Fuzzy Technique for Order of Preference by Similarity to Ideal Solution GGRT Greenhouse Gas Reduction Targets Act GHG Greenhouse gas GIS Geographic Information System GREET Greenhouse gases, regulated emissions, and energy use in transportation GWP Global warming potential H2M Houses with retrofits (Home of Tomorrow) H2T Houses without retrofits (Home of Today) HEEI Household Eco-efficiency Index HEPS Household environmental performance score HES Household eco-score HFC Hydrogen Fuel Cell HFCV Hydrogen fuel cell Vehicle HHC Household Cost HHE Household Emissions HIPT Household Incentive and Tax Planning Tool ICE Internal combustion engine ICEV Internal combustion engine vehicle JSON JavaScript Object Notation LB Lower Bound LCA Life cycle assessment LCC Life cycle cost LD-EV Light-duty Electric vehicle LD-HFCV Light-duty hydrogen fuel cell vehicle LDV Light-duty vehicle LED Light-emitting diode bulb xiii LoE Level of Expertise LoI Level of Impact LP Linear Programming LS Likert Scale LT Linguistic Term MAGDM Multi-attribute group decision-making MCDM Multi-criteria decision making ML Most Likely MSRP Manufacturerâs suggested retail price NG Natural Gas NIS Negative Ideal Solution NOGEPA Netherlands Oil and Gas Exploration and Production Association OCP Official Community Plan OD Origin Destination OPEX Operational Expenditure PBP Profit-based priced PDM Project Delivery Method PEV Plug-in electric vehicle PHEV Plug-in hybrid electric vehicles PIS Positive Ideal Solution POF Photochemical ozone formation PPP Private-Public Partnership PM Project Manager PV Photovoltaic RB Building Intervention RI Recharging Infrastructure RT Transport Intervention SBP Service-based priced SF Social Factors SFDH Single-family detached houses SOD Stratospheric ozone depletion ST Vehicle sales tax TAZ Traffic Analysis Zones TF Technical Factors TOPSIS Technique for Order of Preference by Similarity to Ideal Solution UB Upper Bound USA United State of America W2P Well to Pump cycle W2W Well to Wheel cycle xiv Acknowledgments First and foremost, I would like to convey my special thanks to Dr. Kasun Hewage for his timely guidance, continuous support, patience, and faith in me throughout my graduate studies. His inspiration, motivation, kindness, and energy have made me strong, resilient, and diligent. I have gained a lot from my supervisor towards my academic and professional growth. I will forever be in debt to him for his support throughout my graduate studies. My heartfelt appreciation goes to my co-supervisor, Dr. Rehan Sadiq, for his mentorship, guidance, and support during the course of my studies at UBC. He has kept following my progress, providing advice and moral support amidst his busy schedule and responsibilities. I would like to express my thanks to Dr. Shahria Alam, who guided me as a committee member and as the principal investigator of the Wilden Living Lab project. The knowledge and the expertise gained from him has helped me to mature my research ideas with exposure to real-world applications. I would also like to thank my other committee members, Dr. Abbas Milani, and a former committee member, Dr. Ahmed O. Idris. The advice and encouragement I received from them has helped me to shape my initial research idea and enhance the quality of this thesis. Furthermore, the valuable knowledge and techniques I learned from Dr.Ahmed O. Idris, Dr.Abbas Milani, Dr.Zheng Liu, Dr. Jannik Eikenaar, and Mr. Bill Berry helped me to effectively improve my research. In addition to that, I would like to acknowledge the School of Engineering and the administrative staff of UBC-Okanagan for their continuous support during the past few years. Part of my research funding was from the Natural Science and Engineering Research Council of Canada (NSERC) through the Wilden Living Lab project and the other part was from the Fortis-UBC Energy Chair Project, which was funded by Mitacs, Canada. The input of the project partners was vital for the successful completion of my research. Hence, I would like to thank Mrs. Karin-Eger Blenk (Wilden), Mr. Russ Foster (Wilden), Mr. Martin Blenk (Wilden), Mr. Scott Tyerman (Authentech Homes), Mr. Rafael Villarreal (City of Kelowna), Mrs. Carol Suhan (FortisBC), and xv Michael Leyland (FortisBC). Moreover, financial support from NSERC, Mitacs, Wilden, and FortisBC were beneficial in proceeding with my studies. The support I received from the project life cycle management laboratory and the research team has been enormous. I would like to express my special thanks to Dr. Rajeev Ruparathna for the moral support and the guidance delivered for my studies as well as my stay in Canada. I really appreciate Rajeev and his wife, Kaushiâs support, especially at difficult times of this journey. Moreover, my thanks go to Dr. Gyan Kumar Chippi-Shrestha, Mr. Fasihur Rahman, Dr. Ezzeddin Bakhtavar, Dr. Hirushie Karunathilake, Mr. Isuru Gamalath, Mr. Tharaka Wanniarachchi, Ms. Ravihari Kotagodahetti, Dr. Amin Shotorbani, Ms. Anber Rana, and all other current and past research team members who have supported me in various situations. My gratitude goes to my loving parents, Mr. K.S Perera and Mrs. Nandawathie Menika, who have undergone many hardships, especially during the early part of my life to raise me up and make me into the person who I am. I consider my self fortunate to be their son. Moreover, I would like to convey my gratitude to my extended family, Mrs. Piumi Perera, Mr. Thranga Dissanayaka, Dr. Dananjaya Eleperuma, Mrs. Ranjani Eleperuma, Dr. Dharshana Eleperuma, and Dr. Esha Eleperuma. Last but not least, I would like to appreciate Mrs. Manisha Eleperuma, my wife, for her love, caring, and moral support within the last few years. Her patience, commitment, and understanding helped a lot to balance both studies and the family. xvi Dedication Lovingly dedicated to, MY PARENTS, MANISHA, ERAN & YASIRU1 Chapter 1 Introduction 1.1 The Challenge Climate change and resource depletion have gained extensive public attention as a critical global issue. Road transportation using Fossil Fuels (FF) is one of the largest contributors (19%) to the national Greenhouse Gas (GHG) inventory of Canada [1]. FF is the main transportation fuel source, accounting for 82.5% of transportation emissions inventory [1]. The larger environmental impacts of FF consumption include global warming, stratospheric ozone depletion, acidification of land and water, eutrophication, tropospheric ozone formation, and depletion of non-renewable energy resources [2][3]. The increasing demand for FF in transportation has been a topic of major discussions due to the aforementioned adverse environmental impacts and the possibility of running out in the near future. Thus, enhancing vehicle efficiencies, improving and promoting active transportation while reducing automobile dependency, and shifting to lower-carbon or non-carbon power trains are considered preliminary solutions to reduce global FF consumption [4][5]. Out of the above strategies, alternative fuel-based vehicles can be considered a disruptive technology that needs extensive research and development to ensure wider adaptation and long term sustainability. Low-emission renewable energy technologies and systems have been identified as an effective strategy for decarbonizing road transportation [6][5][6]. The traditional line of thinking has had tunnel vision when evaluating alternative transportation fuel sources where operational emission was the predominant consideration [7][8][9]. Electric and hybrid vehicles are at the forefront of public attention as a sustainable mode of transportation. The above vehicles gained public trust due to their technological and commercial viability and the possibility of operating from renewable, low-emission energy sources [4]. Despite its significant future potential, Electric Vehicle (EV) adoption is still in prenatal stages in Canada. Reasons for slow EV adoption in Canada include higher switching costs of EVs [10][11], limited on-board electricity storage (Vehicle range) [11][12], and inadequate availability/access to recharging infrastructure [10][13][11]. Systematic planning and optimal deployment of recharging infrastructure networks can be used to minimize the aforementioned challenges and support the widespread adoption of electric transportation [14]. 2 Electric vehicle recharging infrastructure (EV-RI) is a critical element in electric-based transportation [15]. EV-RIs transfer electricity from the smart grid (energy cycle) to vehicles (mobility cycle) [15]. Currently, around 500 public direct-current fast-charging stations and nearly 4,000 public slow-charging facilities have been established in Canada [16]. The existing EV-RI capacities do not satisfy the zero-emission vehicle mandates established by federal and provincial governments [17]. Moreover, there is no strategic approach developed to deploy an optimal network to service current and future EV demands. Thus, federal, provincial, and municipal level decision-makers, practitioners, and investors need to take more stringent approaches to collectively plan and manage more EV-RIs to achieve potential recharging demands. EVs can be recharged at home using domestic recharging units. Fast charging has gained immense attention from EV consumers due to limited access to domestic and office charging and longer recharging periods. However, the recharging demands met by domestic and office recharging facilities have not been incorporated into existing recharging demand estimations. Furthermore, as a logical sequence for EV-RI deployment projects and supported procedures, tools have not been developed and distributed to Canadian municipalities. Thus, an ad-hoc EV-RI placement and capacity planning approach was adopted conventionally to develop existing EV-RI facilities [18]. The conventional EV-RI planning and managing approach reduces the aforementioned challenges pertaining to EV consumers, to a degree [18]. However, these systems might create investor-related challenges, such as lack of return on investments and higher infrastructure payback periods, due to the lack of a systematic EV-RI placement and capacity improvement approach [19]. Because of these challenges, timely investments in EV-RIs can not be expected as required, where there will be limited public EV-RI availability and access. Hence, inadequate infrastructure availability and access may discourage potential vehicle users from switching to EVs by escalating demand uncertainties for future EV-RIs. Ultimately, the government mandates for zero-emission vehicles, and GHG reduction targets will not be met in the long run. Therefore, there is a need for a systematic EV-RI planning and management approach to enhance the efficiencies of EV-based transport systems and to ensure benefits to all stakeholders. A systematic EV-RI planning and managing approach consists of determining EV recharging demands for public EV-RIs, developing strategies to sustain the anticipated demands, identifying 3 an optimal need of EV-RIs, and deploying and maintaining EV-RIs for multiple periods [20]. Although there are zero-emission vehicle mandates available for practitioners to develop an optimal EV-RI network, a method should be in place to estimate public fast-charging demands to determine optimal EV-RI capacities. Then there should be a scientific EV-RI planning tool to determine the stochastic and temporal deployment of EV-RIs for the long run. In that case, the optimized infrastructure placement and capacity enhancement will reduce the range anxiety of potential consumers [21][22][23]. However, there are several other tasks to be completed by the infrastructure investors, government, and policymakers to maximize the benefits of EV-RIs to all relevant stakeholders. These are planning and managing incentives; and potential financial aids, risk, and uncertainty sharing that can be used to motivate investors and consumers in order to achieve anticipated EV demands and EV recharging investments in the long run [24]. From a government perspective, the government and policymakers can develop an incentive scheme to enhance infrastructure investment and to reduce the upfront costs of alternative fuel vehicles. However, the aforementioned incentive schemes should focus on their long-term GHG targets and other community development strategies. In addition, project delivery methods can be used to share the risks and costs in EV-RI projects to make them more feasible for local investors at EV early adoption stages. 1.2 Research Gap A comprehensive review revealed the following key knowledge gaps on electric vehicle recharging infrastructure planning and management. Lifecycle thinking is lacking in recharging infrastructure planning: The literature for recharging infrastructure planning and development revealed that the majority of low-emission fuels were selected, and corresponding planning was done, by considering emissions during operational phase of the vehicles [25]. Furthermore, the recharging infrastructure placement was conducted using the upfront costs of infrastructure facilities without considering the costs related to other stakeholders at the different phases of the transportation system (e.g. access costs, location-based costs, etc.) [26][27][28][29][30]. Although the literature reveals that decision making related to infrastructure facilities needs to be assisted by the life cycle thinking approach, which quantifies long-term economic and environmental impacts for society [31], the integration 4 of life cycle emissions and costs of both mobility and energy cycles into transport-electrification related decision-making was overlooked in past studies. Lack of research knowledge in multi-period EV-RI network planning and management: The published literature contains several studies focusing on the location-allocation of EV-RI in the past few years [32][33][34]. These studies have focused on minimizing the investments and access cost and/or maximizing the vehicle flow coverage [35][27][30][28][36][37]. The vehicle range, the maximum EV-RI facility capacity, and the local government policies were obtained as key constraints when selecting the most desirable locations for potential EV-RIs [35][27][30][28][36][37]. However, long-range EV-RI capacity prediction, network planning, and recharging management approach should consider the dynamic variations of future EV recharging demands for healthy decision-making. By doing that, potential investors can optimize their cash flow while facilitating the required EV-RI demand consistently [19]. The dynamic nature of recharging demands was overlooked in the existing studies while making investment decisions, which results in extensive investments and longer paybacks than is optimal. Household incentive planning has overlooked environmental and economic impacts of EV-based transportation: The literature reveals that favourable tax and incentive policies can be used to enhance consumer attraction for clean energy-based interventions [24]. Planning incentives for these interventions are not straightforward, requiring the selection of the most desirable intervention and prioritizing their environmental and economic impacts on local communities [38]. Generally, the energy consumption and GHG emissions of a community depend on the factors associated with the buildings and their envelope characteristics, household transport mode, and consumer behaviours [39]. A comprehensive literature review shows that existing studies have only considered either building retrofits or transport interventions separately to reduce potential GHG emissions [34]. Numerous studies have been conducted to identify potential building energy retrofits to reduce energy consumption and emissions of residential buildings [40][41][42][43][44] [45][46][47]. In contrast, energy-efficient transport interventions were identified to reduce transport-based GHG emissions [48][10]. Having said that, assessing individual activities and developing retrofits, incentives, and best practices for those particular activities might be expensive, which may not achieve the provincial GHG targets within the given timeline [49]. Thus, 5 a holistic analysis of household activities, including the integrated behaviour of domestic and transport activities, may enhance the opportunities to select the most desirable interventions or retrofits to be incentivized. Scientific project delivery selection approaches are lacking for multi-period distributed-infrastructure projects: EV consumers can be categorized into five key categories innovators, early adopters, early majority, late majority, and laggards - based on their user behaviours and market characteristics [50]. EV demands are dynamic in the above market maturity levels with a high degree of uncertainty [51][52]. The literature reveals that demand uncertainty would lead to significant structural and cost variations in the deployment of infrastructure networks [53]. A project delivery method (PDM) can be used to transfer project risks and costs to other parties while minimizing the impacts of the uncertainties [54]. Typically, the PDM is decided based on the experience and influences of the project manager and other relevant stakeholders [55]. The uncertainty of those decisions and the expert experiences of the knowledge area are not considered in current PDM decision-making practices [56][57][58][59]. Thus, conventional PDM selection practices are not desirable for the initial phases of recharging infrastructure deployment projects due to lack of industry experience and possible project risks and costs. Moreover, there is no evidence on enhanced PDM selection practices specifically designed for EV-RI development projects in the existing literature. By considering the gaps mentioned above in the existing body of knowledge, the following research questions arose in this study. i) What are the best energy sources and technologies for road passenger transportation when considering regional factors and life cycle environmental and economic impacts? ii) How can electric vehicle recharging infrastructure placement and expansions be conducted for the growing public recharging demands, considering multiple stakeholder perspectives? iii) What are the incentives for household energy interventions to encourage local communities while achieving the regional GHG targets faster? iv) What are the project delivery methods for small-scale, long-term infrastructure development projects to handle demand and technological uncertainties? 6 1.3 Research Motivation The motivation for this research originated with the zero-emission vehicle mandate developed and launched by Transport Canada. According to this mandate, 10% of new light-duty vehicle sales are to be zero-emission vehicles by 2025, 30% by 2030, and 100% by 2040 [60]. This zero-emission vehicle mandate was launched to support the Greenhouse Gas Reduction Act (GGRTA), which aims to reduce total GHG emissions in Canada by 30% in 2030 and 80% in 2050 from the 2005 levels [61]. The reduction of GHG emissions will benefit communities, neighbourhoods, and municipalities by reducing human health impacts, global warming, and climate change potentials. The proposed zero-emission vehicles program will supply incentives for new zero-emission vehicle purchases, including battery-electric, plug-in hybrid, and hydrogen fuel cell vehicles [60]. Electric and hydrogen vehicle demands are expected to increase with time, and therefore investments in low-emission electric vehicle recharging infrastructure were given significant importance in provincial clean transport deployment agendas [62]. Accordingly, most Canadian provinces allocate funding (e.g. British Columbia has allocated CAD 40 Million in 2017) to support future low-carbon and zero-carbon refueling/ recharging infrastructure projects [63][64]. However, existing studies indicate that there is a lack of knowledge on an efficient EV planning and management approach for urban centers in the Canadian context. Hence, EV technologies have not yet achieved the expected consumer attraction in Canada due to insufficient recharging infrastructure accessibility, insufficient government regularities, and limited commercial viability [65][66][67]. At present, EV-RI planning and decision-making are generally done on an ad-hoc basis in a reactive manner. Hence, an integrated approach that combines technical, economic, and environmental aspects to develop an optimized EV-RI network is emphasized by many previous studies, including Natural Resources Canada (NRCan) [68]. To plan EV-RI infrastructure effectively and proactively, the developers, planners, practitioners, investors, and policymakers need to be given scientific tools, forms, and checklists to support their EV-RI planning and decision management. These tools should address all required information and EV-RI deployment needs, considering an effective EV-RI network to meet multi-period EV demands. The work conducted within this study attempts to fulfill the above industrial need for the Canadian context. 7 1.4 Research Objectives The goal of this research is to develop a planning and management framework for public fast-recharging infrastructure for light-duty electric vehicles in Canada using life cycle thinking approach. The specific objectives of this study are as follows: 1. Determine economic and environmental impacts of alternative low-emission fuel options for transportation 2. Quantify current and future EV demands and potential public recharging demands in urban centers. 3. Develop a temporal and spatial model for EV-RI placement and capacity improvement. 4. Evaluate different incentive options considering regional environmental and economic impacts. 5. Assess project delivery methods for construction, maintenance, expansion, and operation of EV-RIs considering multi-period EV demands. The outcomes of the research are expected to bring a scientific planning and management approach for EV-RI facilities for light-duty electric vehicles by considering multi-period recharging demands. The inefficient ad-hoc planning approach can be substituted with the proposed planning and management approach to enhance the efficiencies and stakeholder engagement of the entire deployment process. Moreover, the developed tools and proposed project delivery methods can be used as a comprehensive package for recharging infrastructure planning, consistent with the selection of most appropriate low-emission alternative energy source for transportation, location-allocation and capacity improvement of electric vehicle recharging infrastructure, household incentive planning for energy interventions, and pre-project planning of recharging infrastructure projects. The outcomes and deliverables of this research will help policymakers such as municipalities, infrastructure and urban planners, developers, and infrastructure investors to come up with scientific decisions to decarbonize the transport sector in Canada. 1.5 Thesis Organization This thesis consists of eight chapters that focus on the logical sequence of infrastructure planning and deployment. 8 The first chapter provides an overall introduction to the background and pressures, research gaps, motivation, objectives and deliverables, research concepts, and the overall electric vehicle public recharging infrastructure planning framework proposed in the study. The second chapter provides an insight into the key research phases, and the methods followed in achieving the goal of each phase. Each phase of this methodology is further detailed in the content chapters, 4 to 7. The third chapter provides a comprehensive literature review on the state-of-the-art electric vehicle recharging infrastructure, current limitations and deployment state, and the need for electric vehicle recharging infrastructure planning for the Canadian context. The gaps identified in this chapter were used to develop the research objectives and methodologies. The literature-based database developed in this section was used in the content chapters, 4 to 7. The fourth chapter presents the life cycle assessment and life cycle cost assessment of each energy system, including both energy and mobility cycle. The database developed in Chapter 3 was used to conduct the life cycle assessment. The developed life cycle data were stored in a separate database to be used for Chapters 5, 6, and 7. This chapter covers all the life cycle economic and environmental impacts of electric vehicles and other alternative fuel options that are required in the first objective. The core methodology of recharging infrastructure planning is covered in Chapter 5, which aligns with the expectations of the second and third objectives. Multi-period recharging demand was predicted as required by the second objective and those recharging demands, and the life cycle cost data obtained from Chapter 4 were used in developing the EV-RI location-allocation and capacity improvement planning tool. Furthermore, several other factors and assumptions were obtained from the literature-based database that was developed in Chapter 3. The material in Chapter 6 support the content in Chapter 5. The incentive planning method discussed in Chapter 6 sustains the anticipated recharging demands in the previous chapter. This method follows a life cycle thinking-based approach, where the potion of life cycle data was 9 obtained from the life cycle database developed in the third chapter. This chapter is related to the forth objective of the study. Chapter 7 proposes a stakeholder judgment-based PDM selection tool as required in the final objective. The data relevant to market maturity levels, PDM selection factors, and PDM characteristics were obtained from the database developed in the third chapter. The different project characteristics were obtained from the fifth chapter based on the case study data. Finally, Chapter 8 consists of the findings derived from the complete study. Moreover, the recommendations for EV-RI planning and management in the Canadian context, the originality of the study, and future research potential is also explained in this chapter. Figure 1-1 shows the interconnections between the research objectives and thesis chapters. A detailed description of all the chapters is given below. 10 Figure 1-1 Integration of Objectives, Information Flow, and Thesis Organization 11 Chapter 2 Research Methodology The focus of the research is to develop a recharging infrastructure planning and management framework considering dynamic electric vehicle demands. The objectives, as mentioned in section 1.4, were achieved in several research phases. The details of the proposed framework and the demonstration steps are explained in the body of this thesis. Figure 2-1 depicts the connection between the various research phases. Figure 2-1 Research Methodology 12 Phase 1 â Literature review, content analysis, and data collection This phase involved a comprehensive literature review on EVs and recharging infrastructure technologies. Moreover, the life cycle emissions and cost data for EVs and EV-RIs and local traffic data for case studies, were also collected to demonstrate the proposed model. EVs and infrastructure technologies: A comprehensive content analysis was conducted to identify the existing state-of-the-art technologies available for EVs and EV-RIs. The characteristics of EV-RI and consumer perception values were also identified in this study. Articles published in reputed journals within the last 15 years were considered for this review. Moreover, the expert data related to the EV-RI projects and their characteristics were collected through several brainstorming sessions, which were conducted with local developers, contractors, utilities, and municipalities. Life cycle emissions and life cycle costs: The Greenhouse gases, Regulated emissions, and Energy use in Transportation Model (GREET) database developed by Argonne National Laboratory was used to identify the potential emissions of the fuel and vehicle life cycle in both conventional and alternative fuel-based road transportation. Moreover, the recently published literature was used to extend the life cycle assessment to recharging infrastructure to obtain the total life cycle costs of the EV-based transport culture. Traffic and transport data collection: Regional-based traffic and transport data and relevant socio-demographic data were collected from the databases of relevant public and private institutions. This included trip origin-destination matrix, mode-split model, and traffic assignment rules from the year 2020 to 2050. The transport planning managers of relevant municipalities (City of Kelowna) were contacted to access the municipal databases, and research directors of relevant utility companies (FortisBC) were contacted to access the databases developed by the utility companies to identify the EV recharging and EV-RI connection-related data. The detailed findings of the literature review and data collection are presented in Chapter 3. 13 Phase 2 - Low-emission fuel selection and prioritization A preliminary selection method was proposed to choose low-emission alternative fuel sources for the Canadian context. At the initial stages of this study, a rule-based alternative fuel selection method was introduced to filter commercially viable low-emission fuel options. The selected options were analyzed using an eco-efficiency index to identify the most desirable low-emission fuel options for different regions in Canada. Factors such as life cycle economic and environmental aspects of both transport energy and mobility cycles and the technical know-how of the alternative fuel options, were assessed and compared with conventional fossil fuel-based transportation considering regional variations of grid mixes. The detailed methodology of the low-emission fuel selection and prioritization framework is provided in Chapter 4, section 4.2. Phase 3 â Recharging infrastructure capacity and location planning This phase involved defining a lifecycle thinking-based multi-period infrastructure-planning framework to develop sustainable public EV-RIs for complex urban road networks. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. The recharging access distances, life cycle cost of the entire EV-RI network, and EV coverage were considered for the multi-objective optimization model. The above methodology is detailed in Chapter 5, section 5.2. Phase 4 â Household incentive planning for low-emission transportation and domestic activities This phase involved achieving local governments GHG targets faster by selecting the most desirable interventions for Canadian households through incentives, rewards, and tax concessions. Globally available incentive policies for low-emission vehicles and locally available retrofit options for single-family detached houses were identified during this phase. The life cycle thinking approach was used to assess economic parameters, such as capital investment and annualized consumer cost, and environmental parameters such as greenhouse gas emissions for the identified intervention options. Multi-attribute decision-making approaches were used to rank different interventions, and a scenario-based approach was used to select the most desirable interventions 14 for different regions in Canada. The detailed methodology for household incentive planning for low-emission transportation and domestic activities is given in Chapter 6, section 6.2. Phase 5 â Selection of project delivery method for electric vehicle recharging infrastructure projects A stakeholder judgment-based assessment was proposed to select the most desirable project delivery method for the small-scale distributed infrastructure planning process. A fuzzy multi-attribute decision-making technique was used to aggregate expert opinions and obtain attribute weights and rank project delivery methods. Furthermore, different project delivery methods for different project phases were prioritized using the market maturity levels shown in the Rogers diffusion model [69]. This methodology is detailed in Chapter 7, section 7.2. Phase 6 â Developing decision support tools and case study demonstration The research findings of the phases mentioned above were used to develop a decision support tool for the planning and management of electric vehicle recharging infrastructure network projects for small-scale urban centers. The deliverables are in the form of an Excel-based decision support tool (DST). ArcGIS, GREET, HOT2000, and IBM ILOG CIPLEX software were used to simulate specific stages of the planning process to obtain intermediate inputs to the Excel-based decision support tool. The decision-making tool was demonstrated via a case study of a medium-scale community in the Okanagan region of BC and the results were validated when necessary. The implementation of the proposed methods in an urban center is described as a strategic map in Chapter 8, section 8.1. 15 Chapter 3 Literature Review Parts of this chapter have been published in the Journal of Cleaner Production, as articles titled âAre we ready for alternative fuel transportation systems in Canada: A regional vignette,â âScenario-based economic and environmental analysis of clean energy incentives for households in Canada: Multi-criteria decision making approach,â and âElectric vehicle recharging infrastructure planning and management in urban communitiesâ; and in conference proceedings of the CSCE Construction Specificity Conference 2019 as âSolar photovoltaic electricity for single-family detached households: Life cycle thinking-based assessmentâ [70][38][71][72]. 3.1 Transportation and Energy Use Transportation plays a vital role in the formation of society. Typically, transportation consists of two sectors known as âmobilityâ and âaccessibility.â Mobility refers to the movement of people and goods, whereas accessibility refers to the ability to reach goods and services in a desired destination [73]. Enhancing mobility to maximize accessibility is the key objective of an effective transportation system [74]. The demand side of transportation is serviced by different transportation modes that can be categorized as active and passive transportation modes [64]. Active transportation modes are walking, cycling, and public transport methods, where the per capita energy consumption is zero or very low [64]. However, transport by heavy-duty vehicles, light-duty vehicles, and motorcycles is known as passive transportation, where the transport energy use per capita is significantly higher than in active transportation [64]. Figure 3-1 shows the different active transportation methods in Canada [75]. Transportation as a whole is one of the most significant contributors to national GHG inventory, accounting for 38% [76]. According to Figure 3-1, road transportation consumes the largest amount of national energy (79%), which accounts for 82.5% of national transportation emissions [1]. Although road transportation consists of different modes of transportation, light-duty vehicle-based transportation is the key energy consumer (59%) and also has comparatively high per capita energy use [77]. The majority of light-duty vehicles are Internal Combustion Engine (ICE) vehicles that use gasoline or diesel as their primary source of energy [78]. 16 Figure 3-1 Emissions and Energy Use for Transportation in Canada by Mode [75] [77][79] The demand growth of fossil fuels (FF) such as gasoline and diesel has been a topic of major discussion in the past few years, mainly due to their adverse environmental consequences and the possibility of running out in the near future. Accordingly, the extensive consumption of FF results in global warming, stratospheric ozone depletion, acidification of land and water, eutrophication, tropospheric ozone formation, and depletion of non-renewable energy resources [2][3]. The use of conventional fuel began with the exploration and production of crude oil, which is refined into fuels, stored, and distributed to supply chain networks of retail stations [80]. The crude oil refining process was developed in 1850 and became popular after the development of Internal Combustion Engine Vehicles (ICEV) [81]. Although gasoline and diesel are used as energy sources for conventional road transportation, gasoline is the primary fossil-based fuel used in Canada [78]. Gasoline is mainly used for private light-duty conventional vehicles such as cars, sport utility vehicles, light-duty trucks, etc. [78]. Propane, hydrogen fuel cell, biodiesel, electricity, and natural gas can be identified as key alternative fuel sources for road transportation [82]. However, these fuel technologies have their own unique emissions and cost behaviours, and their popularity depends on the fulfillment of 79%3%10%4% 4%86%2%4%4%4%Road Marine Air Rail Other59%41%Light-duty Heavy-dutyEnergy Use Emissions Road Transport Contribution 17 consumer perceived values [83] [84]. Table 3-1 shows the existing alternative energy solutions for transportation needs. Table 3-1 Available Alternative Fuel Options for Transportation Fuel type Source Emission Vehicle type and availability Cost of fuel Availability of fuel and RIs Propane By product of crude oil refinery process (Non-renewable source) [85] Similar to conventional fuel [85] Conventional vehicles can be converted [85] Similar to conventional fuel [85] Available and sufficient [85] Hydrogen fuel cell Renewable energy-based Hydrogen is viable [86] Depending on the production methods and sources [86] Prototype vehicles are available in the market. [87] Currently, the cost is high [88] Mass production is not available. The downstream network needs to be developed fully [86] Biodiesel Produce from plant oil, animal fats and recycled cooking oil (Renewable source) [85] Lower than fossil fuel [85] Conventional vehicles can be converted [85] Higher than fossil fuel [85] Mass production is not available. The downstream network needs to be developed fully [85] Battery electric Renewable Electricity is available [89] Emissions are depending on the primary source [89] Already available in the market [90] Cost comparatively low [91] The electricity grid is already developed. Need RIs [91] Natural gas Naturally available for extraction (Non-renewable source) [85] Similar to conventional fuel [85] Conventional vehicles can be converted [85] Similar to conventional fuel [85] Fuel available. But not sufficient RIs [85] The ability to produce low-emission energy for transportation is considered the key requirement to reduce regional GHG emissions. As shown in Table 3-1, alternative transportation systems, which use low-emission electricity and hydrogen fuel cells as their primary source of energy, are scalable emerging technologies for decarbonizing the transportation sector [80]. 3.2 Road Transportation Decarbonizing Decarbonizing road transportation has gained an immense focus in order to reduce potential GHG emissions in the country. Reducing the consumption of FF per kilometer, improving active transportation while reducing automobile dependency, and shifting to lower-carbon or non-carbon power trains are core solutions to reduce fossil fuel for Canadian transportation [4]. 18 Magnusson et al. emphasized that GHG emissions can be decreased by reducing the consumption of fuel per kilometers (fuel-efficient vehicles), reducing car use (improving active transportation and reducing automobile dependency), and shifting to lower-carbon or non-carbon fuels or power trains such as Hydrogen Fuel Cell Vehicles (HFCVs) and EVs [4]. Hence, vehicle fuel efficiency improvements in conventional vehicles were considered a prevalent marketing tool by vehicle manufacturers and marketers in the recent past. However, despite the enhancement of fuel efficiencies and the affordability of fossil fuels, there has been no decrease in energy consumption as a whole or in potential GHG emissions, due to high population growth and an increase of vehicle dependency [92]. Thus, vehicle fuel efficiency improvements have not been affected by transport energy savings and emission reductions as significantly as expected by decision-makers. Therefore, past and current researchers and vehicle manufacturers have focused more on alternative fuel-based vehicles with low- and zero-carbon emissions [80]. Hydrogen fuel cell (HFC) and low-emission electricity can be identified as the most desirable environmentally-friendly fuel sources for road transportation [82]. These fuel technologies have their unique values, and the popularity of these fuels depends on the fulfillment of consumer perceived values [83] [84]. Table 3-2 shows a comparison of the aforementioned alternative energy solutions considering their primary energy source, emissions, vehicle type and availability, fuel cost and supply chain network, and availability. 19 Table 3-2 Comparison of Electric and Hydrogen based transportation with conventional Gasoline transportation Fuel type Gasoline Hydrogen Electric Vehicle Types Conventional Internal Combustion Engine Vehicle (ICEV) [81] Hydrogen Fuel Cell vehicle (HFCV) [87] Plug-in hybrid electric vehicle (PHEV) & Plug-in electric vehicle (PEV) [89] Fuel production method Crude oil refining process [80] Alkaline water electrolysis using electricity, Central steam methane reformation, Central coal gasification, Biomass gasification and Thermochemical water splitting with nuclear CuâCl cycle are used to produce Hydrogen fuel [93], [94]. Hydropower, natural gas-fuelled thermal plants, biomass power plants, wind power plants, diesel electricity, solar photovoltaic (PV) energy, tidal power arrays and geothermal power plants are used to generate electricity [95] Fuel Transportation Transport through dedicated pipe-lines over long-distance and distribution using rail, ship or road tankers [81] Gaseous hydrogen can be distributed using dedicated pipe-lines over long distance and the liquefied hydrogen can be transported by rail, ship or road tankers [93]. Transport using a smart grid or on-site production and storage using a stationary battery pack [96] Fuel retailing and re-fuelling infrastructure Well established re-fuelling network, which includes short-term, and long-term storage facilities, transportation trough road tankers, ships and pipelines, Oil separation using refineries and retailing using fuel dispensers [97]. Gaseous hydrogen can be distributed using dedicated pipe-lines over long distance and the liquefied hydrogen can be transported by rail, ship or road tankers [93]. Dedicated fuel dispensers are using to re-fuelling the HFCVs. The infrastructure network needs to be developed from scratch, which needs extensive investments. Grid electricity, Solar or wind electricity retail through, Conventional options, 1. Domestic: Level 01 charging [89] [98] 2. Office/commercial: Level 02 charging [89] [98] 3. Public: Level 03 fast-charging [89] [98] Advanced options: 1. Battery swapping method [99] 2. In-motion/ inductive re-charging [90], [98], [100]. Fuel-cycle emissions (W2P cycle) Depending on the crude oil extraction, transportation, and the time of the year [101] [102] [103]. The average emission level has been identified as 0.295 kgCO2e per liter of gasoline [104]. Depending on the source characteristics and the emissions of production, supply, and storage of the produced hydrogen [86]. Appendix A1 shows the emissions for different production methods. The emissions due to electricity production emissions change with energy source [105]. The common energy sources and the respective emission rates are provided in Appendix A2. 20 Fuel type Gasoline Hydrogen Electric Vehicle manufacturing and emissions The manufacturing emissions are assumed as proportionate to the weight of the vehicle [106]. Based manufacturing emission for 1532kg weighted vehicle is 2013 kgCO2e [106] The manufacturing emissions are assumed as proportionate to the weight of the vehicle [106]. Based manufacturing emission for 1532kg weighted vehicle is 2013 kgCO2e [106] The manufacturing emissions are assumed as proportionate to the weight of the vehicle [106]. Based on manufacturing emission for 1532kg weighted EV is 2244 kgCO2e including the battery emissions [106] The average manufacturing emissions of the EV battery (Li-ion) has been estimated as 70 kg CO2e per kWh [107]. Vehicle operation mechanism and emissions The energy for the conventional vehicle is generated using gasoline as the fuel for the Internal Combustion Engine [81]. The operational emission of the vehicles are depending on vehicle fuel consumption [108]. It generates electricity inside the HFCV through an electrochemical reaction of Hydrogen and Oxygen and operates as an EV [87]. However, HFCV has on-board fuel storage, driving range and re-fuelling system as similar to the ICEV [87]. The vehicle operation emission can be considered as negligible [108] The grid electricity or other electricity source is used to re-charge the on-board battery of the EV. The battery electricity used to operate the electric motor of the vehicle [87]. The vehicle operation emission can be considered as negligible [108]. Vehicle maintenance and re-cycling emissions End-of-life emissions and emissions from maintenance and repairs for automobiles are not significant [35]. End-of-life emissions and emissions from maintenance and repairs for automobiles are not significant [35]. End-of-life emissions and emissions from maintenance and repairs for automobiles are not significant [35]. The end-of-life emissions of the EV battery is proportionate to the total mileage traveled during the vehicleâs lifetime [107]. Approximate re-fuelling time and range1 3-5min time for re-fuelling and it has approximately 500millage per one re-fuelling cycle 3-5min time for re-fuelling and it has approximately 500millage per one re-fuelling cycle [87]. Conventional options: 15min to 8hrs [109] Advance options: 0min to 2min [110] Range varies from 110km (Mitsubishi iMiEV) [111] to 435km (Tesla Model S) [112]. Re-fuelling/ recharging cost Depending on the proximity to the source and the time of the year [101] [102]. The provincial-level average prices of Due to the cost of the infrastructure and small-scale production, the average retail price of Hydrogen is CAD13/kg H2 [88]. The provincial-level average domestic rates of grid electricity are shown in Appendix A3. 1 Range is known as the estimated driving distance (kilometers) on fully charged battery or full tank of liquid fuel (hydrogen or gasoline) [108] 21 Fuel type Gasoline Hydrogen Electric Gasoline for June 2017 are shown in Appendix A4 [113]. Advantages in mass-market adoption Very convenient to store vehicle on-board energy Already experienced by the market and proven as reliable and affordable [97] Availability of specialized and extensive logistics infrastructure network [97] Very convenient to store vehicle on-board energy [114] Vehicle cost can be reduced than EV in mass production Decentralized and centralized production is possible [115] The infrastructure investment is relatively smaller than hydrogen due to the use of existing smart grid [116] Home-based re-charging stations are available which may convenient for users [116] Renewable small scale energy production can be used to fuel transportation [116] The disadvantage in mass-market adoption High GHG emission [92] High carcinogenic and non-carcinogenic chemical emissions [92] High investments in hydrogen infrastructure and supply chain development lack of technical know-how [117] Cost of vehicle-related issues [11], [12] Range anxiety2 [12], [23] 2 The vehicle range can be varied with on-board energy storage capacity [140], consumption rate and the availability of infrastructure [12], [140]. For widespread adoption of AFVs, the range can be considered as a physiological barrier [12]. Limitations in available locations of alternative fuel infrastructure and the lower range of vehicle cause a psychological effect on consumers which is known as ârange anxietyâ [12], [23]. Range anxiety makes potential consumers to decide against the use of AFVs [21]â[23]. 22 Hydrogen is the least capable technology to take advantage of the existing or low-cost fuel infrastructure investment where the hydrogen fuel infrastructure investment is significantly higher than the electricity fuel infrastructure cost [116]. Therefore, the electricity-based transport system is preferred for this study3. Alternative fuel-based transportation can be compared and evaluated with the viability of alternative fuel vehicles by explaining the life cycle based emission and cost of each vehicle and fuel cycle [118]. 3.3 Transportation Electrification Transport electrification consists of energy and mobility sectors [119]. The energy sector consists of energy generation to energy storage and distribution system (e.g. smart grid for electricity), whereas the mobility sector consists of using alternative fuel-based vehicles (e.g. EVs and HFCVs) for transportation [119]. Energy Sector: Regional electricity emissions and costs vary with the grid electricity mix and the primary energy source. The electricity grid energy mix, costs, and overall emissions for Canada are shown in Appendix A3. Moreover, the recharging of EVs from the existing electricity grid may result in higher electricity demand [89]. The current electricity transmission system is optimized for the current electricity usage, and additional electricity consumption may destabilize the system [96]. This may cause high overloads in the on-line transformers, feeder congestion, and undue circuit failures [120][121]. Therefore, it is essential to consider the âfeeder capacityâ (electricity supply or excess electricity availability) when allocating recharging infrastructure for EVs. Mobility Sector: The âtime required for recharging,â âaccess to the recharging infrastructure,â and âgovernment policiesâ are the critical concerns in an EV recharging infrastructure network. Additionally, having âhome charging accessâ has been identified as the most significant predictor for future PEV interest [10]. People living in apartments or condos do not have access to their own recharging stations [122], so a properly arranged public charging (Level 3) network will be 3 A regional based analysis was done using literature-based data to evaluate the different energy options for British Columbia (BC) transportation needs. Accordingly, Electricity-based transportation was identified as best clean energy technology for BC. "Are we ready for alternative fuel transportation systems in Canada: A Regional Vignetteâ Submitted to Journal of Cleaner Production (Elsevier) in February 2017. (Menu script: JCLEPRO-D-17-00819) 23 beneficial for a widespread increase in EVs. The geographical, economic, and environmental constraints are considered the core concerns for the spatial planning of EV-RIs [15]. Land-use patterns such as potential developments, traffic generation, and distribution, etc. can be integrated into the transport infrastructure plan by linking national political and social conditions, such as government policies and consumer behaviours [15]. Generally, the energy sector and mobility sector are connected via an optimal network of electric vehicle recharging infrastructure. 3.4 Recharging Infrastructure for Electric Vehicles âLevel 1 (Domestic),â âLevel 2 (Domestic and Work),â and âLevel 3 (Commercial or Public)â are the conventional plug-in recharging infrastructure (RI) types currently available for day-to-day use in electric vehicle recharging [89]. In addition, extensive research is being conducted on enhancing re-charging speed while reducing system losses. Current research is focusing on: 1) Innovative ways to enhance battery storage capacities; 2) Innovative ways to implement in-motion charging mechanisms, which will reduce initial vehicle cost and allow travel with higher mileage [123]; and 3) Innovative ways to establish net-zero energy systems using renewable distributed energy systems. Currently, the following recharging infrastructures and techniques are found in the literature: 1) Level 1 chargers are developed for household use using domestic electricity (110-120V) [89][98]. This is the cheapest recharging infrastructure investment [98], with a cost of around CAD 150 - 500 for basic installation [98]. The charging time is comparatively high (11-36 hours) [98] and the cost of re-charging is comparatively low [87]. 2) Level 2 charging is done using 220-240V charging points [98][10]. This is an additional installation for potential PEV consumers, at a cost of CAD 1,600 to CAD 12,000 depending on labour charges [87][89]. The charging time is 2-6 hours, depending on the vehicle battery capacity [98]. 3) A Level 3 type charging station uses 208â600V electricity and offers fast, direct-current recharging (DC-FC). It has high voltage output and charging is done within 0.4â1 hour based on 24 the EV battery capacity [89][98]. This facility would cost around CAD 35,000 to CAD 46,000 [87]. Level 3 type is used for public recharging facilities for consumers who prefer high-speed charging [98]. 4) The battery-swapping infrastructure is another innovative and proven EV recharging concept. In this system, the EV battery is owned by the company that operates the battery swapping facility [13][124][99], so the battery and the electric vehicle are considered two separate cost and ownership entities [13]. Battery swapping infrastructure-based systems will potentially reduce EV battery replacement cost (due to the battery being owned by the company) [99] and vehicle capital cost (upfront vehicle cost without battery) [13][99], and increase the vehicleâs driving range [99]. 5) Additionally, current research studies are focusing on EV in-motion charging by wireless power transmission technology using the inductive power transfer concept [98][100][90]. In such a system, EV designers can use low capacity batteries, which reduces the battery purchase cost and thereby reduces the purchasing cost of the EV [90]. Moreover, in-motion charging can be used to reduce the constraints related to energy storage and existing charging issues [98][90], and mitigate the charging time obstacles and range related issues [125] by providing a significant amount of energy on the roadway. However, high capital investment is required to modify existing roadways for wireless power transfer technology [110]. The use of the above recharging facilities and potential investments vary with several factors related to consumers, investors, and developers. Consumer behavioural factors, consumer cost perception, consumer concerns about energy depletion and GHG emission, range expectancy, and current and potential incentives and taxes are critical factors in deciding to switch to EVs. Those factors are further discussed below, 3.4.1 Incentives and Policies to Encourage EVs and EV-RI Investments Several countries provide incentives to encourage the use of cleaner vehicles to reduce potential GHG emissions. The government contribution in the top four countries, Norway, the Netherlands, China, and the USA, is discussed in Table 3-3 to identify favourable potential government incentives for Canada. 25 Table 3-3 Potential Incentive for Electric Vehicles Region/ Country Capital incentives Operational incentives Disposal incentives Ref. Norway Exempt purchasing tax and 25% value-added tax Reduce 50% of tax if an EV is used as a company vehicle Exempt from annual vehicle tax Exempt road tolls Free parking at Municipal parking facilities Access to transit lanes Exempt from car ferry charges None [126] [127] Netherlands Reduce purchase tax Purchase rewards to commercial EVs Conventional vehicle scrap reward for potential EV buyers Exempt monthly road tax 4% taxable income additions for EV drivers Environment investment allowance up to 36% On-the-road-electric mobility Free EV parking facility Subsidiary for EV home recharging None [128] [127] Shanghai, China Government subsidies for EV purchases Exempt from license plate lottery system Exempt from road tolls US$14 billion for EV recharging infrastructure development Exempt from sales tax [127] California, USA Federal and local government income tax credit for EV users Rebates to switch from conventional vehicle to EVs Reduce register and license plate tax for new purchases Access to HOV lanes PEV only parking space Motor vehicle inspection exemption and emissions testing waiver Toll reduction and exemptions EV infrastructure tax exemption EV battery tax exemption Exemption from state fuel taxes Free recharging at state-owned charging facilities Sales tax reduction [129] Although these policies have significant positive outcomes (see Table 3-3) that contribute to the development of clean energy-based transportation, there can be adverse impacts on the government. As an example, income from toll fees, parking fees, and ferry fees will not be sufficient for future infrastructure investments and regular maintenance of the existing infrastructure [128]. However, Transport Canada is currently working on EV-favourable policies for Canadian transportation, and the province of BC has allocated CAD 40Mn to improve its EV infrastructure and related policies [62][63]. Although government and government-related institutions provide incentives for both energy-efficient buildings and vehicles, household transportation and residential buildings are considered separate entities in planning incentive schemes. Hence, incentives for households have been introduced on an ad-hoc basis, ignoring the integrating effect of energy consumption for household transportation and domestic activities. Therefore, it is vital to introduce a comprehensive model to 26 integrate residential economic activities and their resulting emissions with the resulting emissions of vehicle-based economic activities. A comprehensive literature review revealed that there is a lack of knowledge of incentive planning for overall household energy reduction [130]. Evaluation and selection of the optimal incentive out of the feasible options is a formidable task that needs a scientific platform. Even though the literature is saturated with multi-criteria based retrofit selection tools for buildings [44][45][46], there are no comprehensive frameworks to select overall household energy interventions that suit the expectations of multiple stakeholders. A proper energy intervention selection framework would be used to identify an integrated set of government policies and practices that guide consumers to a low-emission based economy [70]. National, provincial, and municipal governments and non-government institutions are more concerned with local emission trends related to residential buildings and transport, as they need to reduce their GHG emissions and achieve their GHG targets quickly. 3.4.2 Consumer Recharging Behaviours Per the Canadian survey on transport electrification, about 63% of EV users rely on overnight charging at home, and having home-based charging access is identified as the most significant predictor for EV interests in the future [10]. However, consumers who recharge using public recharging facilities tend to refuel in pre-defined areas according to their mental maps and are usually refueling at the beginning or end of their journey [131][132]. In this case, the refueling station is known as the âbrick & motorâ location [84] and the business is driven by the destination or the location [84]. In simple terms, the re-fueling facility needs the physical presence of the consumer/vehicle in order to fuel the vehicle [84]. The main disadvantage of plug-in re-charging facilities is the safety concerns in a wet and hostile environment, longer access to the recharging units, and extensive time needed for recharging [90][109][123]. 3.4.3 Consumer Concerns about Energy Depletion and GHG Emissions Although Canadian transportation sectors contribute more to the national GHG inventory, the literature reveals that the consumer perception of energy on emissions in the Canadian residential sector is significantly higher than the transport sector. Canadian households contribute to GHG emissions directly and indirectly. Direct emissions are comprised of emissions from residential 27 energy use and emissions from motor vehicle use, while indirect emissions are GHGs emitted when producing goods and services for household use [133]. In 2015, the residential sector consumed about 17% of domestic energy and contributed to 14% of the national GHG inventory [134]. Household energy consumption and GHG emissions depend on factors associated with building type, envelope characteristics, household transport mode, and consumer behaviours [39]. Some research is focusing on studies related to building energy retrofits, and other research is focusing on interventions for transportation-based GHG reduction. According to the literature, numerous studies have been conducted to identify potential building energy retrofits to reduce energy consumption and emissions of residential buildings [40][41][42][43]. Gustafsson et al. (1991), Flourentzou et al. (2002), and Asadi et al. (2012) developed models to optimize building energy consumption, thermal comfort, and retrofit costs [44][45][46]. The use of a life cycle assessment-based approach to identify optimal retrofits for net-zero emission buildings has been conducted by Ruparathna et al. (2017) [47]. In contrast, energy-efficient transport interventions were identified to reduce transport-based GHG emissions [48][10]. However, there is a lack of knowledge on the combined research approach, which considers both building energy retrofits and low-emission transport interventions considering household consumer behaviours, local utility emissions, costs, etc. 3.4.4 Consumer Cost Perception Per the transport survey done by Canada and Norway, the cost of fuel factors heavily in current EV usersâ decision to buy an electric vehicle [24][10]. In the Norwegian market, 41% of consumers buy EVs to save money [24], whereas the Canadian survey showed that the Canadian EV market is also sensitive to vehicle operating cost, which is mainly the price of fuel [10]. However, the study done by Turrentine et al. suggested that, practically, automobile owners do not have basic costing attributes to make calculated decisions on better fuel economy, and most of them do not track their fuel cost over a significant period [135]. The fuel cost for EV depends on local electricity prices [136]. Accordingly, the fuel cost of EVs can be considered CAD 3 per 100km to CAD 4 per 100km in BC, depending on the vehicle type for domestic charging facility. The cost of public recharging, however, varies with costing policy; some charge CAD 0 to 10 per hour [91] while others charge it as a parking cost [91]. 28 Moreover, the life cycle cost of the battery is the main component of the EV life cycle cost [137]. This includes the purchasing cost of the battery, battery lifetime, battery cycle time, battery replacement cost, etc. [137]. High costs of batteries will increase the switching costs and user costs of EVs as well as increasing the insurance and registration costs of the vehicle [137]. 3.4.5 Range Preference of Vehicle Consumers âRangeâ can be described as the mileage expected by the consumer in a single refueling cycle [12]. EVs have been designed with an inadequate battery level, which makes it impossible to travel longer distances in one charging cycle due to the high cost of high capacity batteries [21][22][80]. Tran et al. (2013) suggested that increasing recharging facilities may offset the consumer expectation for a long-range vehicle [138][139]. Limitations in the available locations of EV-RI and the lower range of vehicles cause a psychological effect on consumers, which is known as ârange anxietyâ [12][23]. Range anxiety makes potential consumers decide against the use of EVs [21][22][23], so the limited range of affordable EVs and limited EV-RI facilities are considered critical constraints for the widespread adoption of EVs [12]. This barrier can be mitigated by scientifically improving charging opportunities to enhance them at minimal cost. Having said that, there are only a limited number of EV recharging facilities in Canada compared to traditional (gasoline) refueling facilities due to the lack of EV demand [23][140][141]. Therefore, an efficient EV-RI network (EV-RI system) is vital for widespread adoption of an EV-based transportation system in Canada. 3.5 Planning Electric Vehicle Recharging Infrastructure Infrastructure system planning plays a vital role in transport system planning [142]. The approach used for this can vary with infrastructure use and characteristics such as type, capacity, location, etc., source characteristics, economic factors, consumer demands, government policies, and procedures [143]. Generally, macroscopic modeling is used for system planning. According to Pollalis (2016), sustainable infrastructure system planning can be explained using a four-level approach [20]: 29 1. Determine the demand and consumption required to service by the proposed infrastructure system. 2. Develop a strategic approach to employ resources to achieve the required demands. 3. Identify facilities and operations that realize the strategic plans developed. 4. Locate facilities and operations to cover the spatial needs of the system. Accordingly, the travel demand assessment and facility location-allocation are critical elements in infrastructure system planning. 3.5.1 EV Demand Modeling and Sustaining Approaches Travel demand forecasting and sustaining the current and potential demands are vital to ensure the continuity of EV-based transportation systems. In these cases, travel demand forecasting models are used to evaluate the impacts of land use, transport facilities, infrastructure, and demographic changes in the existing transport system [144]. Firstly, existing transportation systems need to be decoded to estimate transport demands in the future. The transport system decoding model includes four-steps: trip generation, trip distribution, mode split modeling, and trip assignment [20]. Secondly, behavioural models can be used to investigate the impacts of incentives and taxes that can be used to sustain EV demands through variations. Thus, EV-RI locations can be strategically assigned, considering anticipated EV demands. 3.5.1.1 Estimate and forecast EV demands Estimated and forecasted trips can be estimated using trip generation models that can be developed initially. The purpose of trip generation is to understand the need for transportation. Trip (ððððð) can be described as a one-way movement from origin to destination, which can be classified as home-based (THB) and non-home-based (TNHB) trips [145]. Trip generation models are used to describe trip lengths, distribution in time, the number of trips generated, trip classification, and land-use pattern (trip generation and attraction) [20]. Generally, the trip pattern changes with socio-demographic and land-use factors [145]. The theory of adoption and diffusion (Rogers diffusion model) [146], explains the market growth of an innovative product, which can be applied to EVs as well [34]. Accordingly, EV consumers can be categorized as innovators, early adopters, 30 early majority, late majority, and laggards [50]. Table 3-4 shows the typical EV technology adopter categories and their behaviours. Table 3-4 Maturity Stages of the EV Market and Consumer Behaviours [50] EV consumer type Description Innovators Technologically savvy, high-risk appetite, high-income category Early adoptors Younger in age, high income, highly educated, Socially forward, high opinion leadership Early majority Slow adoption, average social status, imitators, average opinion leadership Late majority Average income, low social status, high imitators, low-average opinion leadership Laggards No-opinion leadership, No risk appetite, âtraditionalâ behaviours, generally âbusiness-as-usualâ type According to the expectation of the above consumer categories, trip behaviour can be changed with the dynamic EV market. Therefore, the trip classification needs to be defined according to the expectations of the actual users [50]. Linear regression modeling and cross-classification modeling are conventional techniques used for trip generation simulations [145]. However, these models have extensive data requirements, which are based on travel surveys of the considered traffic zones. Moreover, trip distribution modeling can be used to match the origins and destinations (O-D) of the trip generated and develop a âmost-likelyâ trip matrix considering multiple zones. A gravity model can be used to define the trip matrix (O-D matrix) for a given network, which is given as Equation (1). ððð = ð¼ ððð·ðððð Equation 1 Where, α â proportionality factor, ððð â The generalized function of time, distance, and cost [144] Generally, forecasting of continuing patterns and/or relationships are easy to model, while pattern changes are complicated to model [147]. 31 3.5.1.2 Extended EV demands using EV favourable cost and incentive policies Developing the mode-choice model will support examination of social, economic, and environmental impacts to society from the proposed modes [20]. Multinomial Logit Model is the conventional method to achieve mode-choices of trip makers. Axen et al. (2015) used the Multinomial Logit Model to develop an EV market share assessment model for BC [34]. A survey approach was used to obtain the data required to train the developed model, which included a survey sample of 538 respondents in BC [34]. The Norwegian and Californian policies were considered the best global regulatory due to the highest EV per capita globally. Hence, Norwegian and Californian EV-favourable policies4 were considered strong demand-focused policies [34]. Literature reveals that the availability of multiple charging point access and the cost of electricity are the key supply-based factors, which could affect the selection of EVs by a potential vehicle buyer. 3.5.2 Facility Location Selection The facility location selection can be âMacroscopic (macro-level)â and âMicroscopic (micro / individual-level)â. The macro-level can be defined as regional or community level RI network planning, whereas the micro-level is individual level facility planning. The UBC TIPS lab shows a scenario-based approach to identify individual sites for EV-RI locations [50]. Visibility, convenience, cultural branding, reliability, affordability, operating cost, initial cost, competitiveness, gas vehicle displacement, and energy use were used as multiple attributes to rank the alternative locations for each scenario [50]. However, the macro-level planning approach has not been considered in this study. As a macro level, long-range planning mechanism, RI locations need to be placed strategically by considering the locations of neighbouring facilities that cater to the dynamic demands of EVs in the future. An optimization approach can be used with a multi-period demand assessment. A content analysis was conducted for Automated Teller Machines (ATMs), gasoline stations, 4 Refer to Table 3-3 for Norwegian and California EV favourable policies. 32 convenience stores, and EV recharging station location-allocation research-based publications. Multi-objective genetic programming and particle sworn optimization were identified as the standard mechanisms used to simulate the aforementioned models [148][149]. Table 3-5 shows the published literature on transportation-based location optimization. Table 3-5 Existing Solutions for Optimal Infrastructure Location-Allocation Author/s Model developed Objective function Constraints and parameters used Approach Methods Used Ref No. Maximize flow captured Minimize cost Range Waiting/access time Battery charging efficiency/time Grid constraints By-law constraints Single station based Corridor based Network-based Kuby and Lim (2005) The flow-refueling location problem for alternative-fuel vehicles â â â MIP [35] Upchurch, Kuby, and Lim, (2009) Capacitated flow refueling location model (CFRLM) based on flow capturing location model (FCLM) â â â â MIP [26] Lim and Kuby (2010) Heuristic algorithms for siting alternative refueling location using FCLM â â â MIP [27] Kim and Kuby (2012) The deviation-flow refueling location model (DFRLM) â â â MILP [150] Capar and Kuby (2012) An efficient flow refueling location model for alternative-fuel stations â â â MBIP [151] Chen, Kockelman, Khan, (2013) EV charging station location problem: parking based assignment method to Seattle â â â â MIP [28] Shao-yun, Liang, Hong, and Long (2012) The planning of EV charging stations in the urban area (location and sizing) â â â â â Queuing theory [37] Mehar and Senouci (2013) An optimization location scheme for electric charging stations â â â GA [152] Yan (2014) Optimal layout and scale of charging stations for EVs â â â â Heuristic algorithm [36] Huang et al. (2010) A GIS-based framework for bus network optimization â â â GA and GIS [29] Wang and Lin (2013) Locating capacited multiple types of recharging stations â â â â â â MIP [30] 33 Grid constraints such as substation capacity, charging power, node voltage aptitudes, feeder mix, and access point capacity are the key constraints related to the electricity grid [152]. The constraints stated on government policies, by-laws, and guidelines need to be assessed in order to ensure a standardized EV recharging infrastructure network [26]. Moreover, either âcost minimizationâ [152][37] or âflow captured maximizationâ [35][26] were selected as an objective for the above methods. According to Table 3-5, mixed integer programming (MIP) and genetic algorithms (GA) are the most common techniques used to solve this problem. However, Wang and Lin (2013) developed a method using mixed-integer programming by considering both âcost minimizationâ and âflow captured maximizationâ to locate slow-recharging stations [30]. Moreover, Li et al. (2009) studied the teller machine location optimization problem where the âreturn on investments maximizingâ was considered an objective function to maximize the returns on investment [149]. Hence, they expect to ensure the maximum returns from those machines for a sustainable service. Geographic information systems (GIS) can be used as a spatial analytical tool in this study [29] [149]. The main purpose of the GIS platform is to convert the continuous plan into a coordinate grid system [153]. According to the study done by Liu et al. (2006), mixed integer programming and genetic algorithms can be coded using C, C++, and PYTHON. These scripts can be registered into a dynamic link library [153]. Those coded functions can be linked to the macro Visual Basic script in ArcGIS that can be called upon to proceed with the optimization [153]. 3.5.3 EV-RI Construction, Maintenance and Disposal Process Infrastructure systems are subjected to construction, maintenance, and rehabilitation [154]. Effective management of infrastructure leads to maximized benefits to all stakeholders [154]. Generally, risk factors such as political risk, construction risk, legal risk, economic risk, operation risk, market risk, project selection risk, relationship risk, project finance risk, and inherent risks are significant in infrastructure development and maintenance projects [155]. The project time, costs, and quality performances of a construction project will be affected by the risk involved [156]. 34 According to 3.4.1, EV demands are increasing with government regulations, which leads to an increase in recharging demands. The capacity of EV-RIs needs to be increased with time to ensure maximum access for potential EV consumers. However, these EV demands have significant uncertainty on RI development scope and financials. Hence, the financial risk (due to EV demand uncertainty) and scope changes (due to capacity required) are critical factors for the implementation of EV-RI facilities. 3.5.4 Project Delivery Methods A project delivery method (PDM) is a method of procurement in which the project risk and performance can be transferred to other parties [54]. This can be used to establish a framework for the design, procurement, and construction of infrastructure projects. The selection of an appropriate PDM is one of the core decision strategies that affect the successful completion of a project [157]. This will drive project cost, quality, and schedule [158]. Generally, PDMs can be categorized as traditional, collaborative, integrative, and partnership strategies [159]. Sample PDMs in these four categories, which are commonly used in the construction industry are listed in Table 3-6. The selection of an appropriate PDM is vital to ensure successful project completion [157]. Conventionally, the choice of the PDM is made as part of the procurement strategy. In most cases, this decision is made by the owner with the help of the project manager at the project pre-planning stage [160]. In general, a PDM evaluation matrix is prepared by the project manager, and the owner selects the PDM method based on the details given in the matrix [161]. The factors considered in these matrices vary with the project and the expertise of the project managers. Mafakheri et al. (2007) have shown that the project delivery method is based on the project cost, schedule, quality, complexity, value engineering, experience, risk, scope changes, uniqueness, external approvals, culture, financial guarantee, and project size [162]. A study, done by Vanhouche (2012) identified that project complexity (technical know-how), project risk, and project are critical factors in PDM selection [163]. Moreover, the Government of Idaho proposed that complexity and innovation, delivery schedule, level of design, risk, agency factors, market factors, and third party coordination are critical factors in selecting an appropriate PDM for a dynamic project [161]. However, there is very little scientific knowledge of PDM selection using MCDM. 35 Table 3-6 Literature-based Typical Project Delivery Methods PDMs / Category Description Advantages Disadvantages Ref Design-Bid-Build (DBB) - Traditional In DBB, design, and construction are considered as separate entities. Construction starts once the design is completed. The bidding will be followed afterward. Familiar PDM Defined roles and responsibilities Presents the lowest possible cost to the building High potential for change orders and conflicts No cost-sharing No âfast-trackingâ available [164] [165] Design-Build (DB) - Collaborative The private party will be responsible for designing and building the total project. Single contract for design and build Can proceed with fast track projects No change orders needed Cost-sharing is possible The owner has limited involvement, The process will not bring the best design and best builder together Quality checks will be done by the contractor [164] [165] Design-Build-Operate-Transfer (DBOT) - Partnership Public property owner enters into the contract with a private party for the design, construction, finance, and operation for a pre-authorized period. Ownership will be transferred afterward. An external source of capital Time-saving (speed-up with the private sector experiences) Cost-saving (By using the private sector contracting mechanisms) Risk transferring possible Lack of familiarity in similar applications Specialized projects Limited public owner control over the project until the transferring is done. [166] Build-Own-Operate-Transfer (BOOT) - Partnership Public property owner enters in to contract with a private party for the design, construction, finance, and operation for a pre-authorized period of time. The ownership will be transferred to the public owner at the end of the period. (used for large scale projects). An external source of capital, Time-saving (speed-up with the private sector experiences) Cost-saving (By using the private sector contracting mechanisms), Risk transferring (finance, quality, schedule, operation, and maintenance), Possibility to control over the design Lack of familiarity in similar applications Specialized projects Change orders might be possible [164] Public-private partnership (PPP) - Partnership A corporate venture between private and public partners to achieve a common public goal by using their expertise through proper resource allocation, risk-sharing, and rewarding. An external source of capital Time-saving (speed-up with the private sector experiences) Cost-saving (By using the private sector contracting mechanisms) Risk-sharing is possible The conflict between public sector welfare-based objectives and private sector profit-based objectives [167] [168] [169] Construction Management at-risk - Collaborative An owner selects the construction manager (CM) based on his qualifications and fees. Then the CM has to represent the owner and proceed with the construction. Ensure high quality at minimal cost, owner transfers the risk to CM, Cost-sharing is possible CM will be selected based on qualifications and fees. Perceptional price competition is limited. The owner has limited control over general contractors [165] Integrated Project Delivery (IPD) Collaborative A collaborative alliance of project resources (people, systems, equipment etc.,) to acquire optimal project performance. A trust-based method focuses on the overall project goal. Mutual respect and trust, Scope and goal definition, risk, cost-sharing are possible, the project schedule can be dynamic and project quality control is better than other PDMs. Control over the design and involvement after awarding are limited. [170] 36 An AHP-based tool was developed to evaluate the above parameters and select the most suitable project delivery method from prior-selected PDMs [162]. However, in some developments such as the deployment of EV-RIs, demand uncertainty would lead to significant structural and cost variations in the optimal refueling infrastructure network [53][71]. Thus, cost and demand uncertainties need to be captured for selection of the most desirable PDMs for EV-RI deployment projects. Mostafavi, A. et al. (2010) describe a fuzzy TOPSIS-based approach to incorporate project uncertainties to select project delivery methods for construction projects [157]. EV-RIs can be considered smaller-scale infrastructure developments than traditional transportation infrastructures such as roads, bridges, etc. Local developers, investors, and other decision-makers do not have much knowledge of the deployment at the early stage of EV-RI deployment projects. Hence, the use of multi-stakeholder synergies will be vital for successful implementation of EV-RI networks in urban communities. In that case, selecting PDMs considering the rogers model to evaluate EV recharging market factors and incorporating multiple stakeholder opinions would enhance project performances [71]. There is a lack of knowledge on an integrated approach to select PDM strategies based on the owner, project manager and other relevant stakeholders' opinions for novel and long-term projects similar to EV-RI deployment. 3.6 Environmental and Economic Assessments The life cycle environmental and economic assessment can be conducted using a life cycle assessment (LCA) and life cycle cost assessment (LCC) [171]. Accordingly, the LCC and LCA are explained as follows. 3.6.1 Life Cycle Assessment (LCA) The life cycle assessment (LCA) is an important process development technique in terms of environmental sustainability [172][173]. According to the United State Environmental Protection Agency, LCA can be defined as âA methodology for estimating the environmental burdens of processes and products (goods and services) during their lifecycle from the cradle to the graveâ (USEPA 1995), and according to the Society of Environmental Toxicology and Chemistry, LCA is defined as âA process to evaluate the environmental burdens associated with products, processes, or activities by identifying and quantifying energy and material used and water released 37 to the environment; to assess the impact of this energy, and material uses a release to the environment, and to identify and evaluate opportunities to affect environmental improvementsâ (SETAC 1993). ISO 14041 shows a comprehensive framework of LCA with the respective phases to be conducted. According to Figure 3-2, the life cycle assessment consists of a goal, scope, inventory analysis, impact assessment, and interpretation. The goal of the LCA consists of the objective, the target of the audience, and the reason for the study [174]. LCA focuses on the environmental impacts of the process/product over its total life cycle. LCA enables decision-makers to improve the environmental performance of a particular product in a strategic planning process [118]. The scope of the LCA study consists of the functions of the product system, the functional unit, the product system boundaries, allocation procedures, types of impact and methodology of impact assessment, subsequent interpretation to be used, data requirements, assumptions, limitations, initial data quality requirements, type of critical review if any, and type and format of the report required for the study [174]. According to ISO 14041, the life cycle inventory analysis and results interpretation include the following steps. 1) Inventory classification that is required to assign each inventory item to different impact categories; 2) Life cycle impact identification by converting inventory items into impacts using conversion factors, which is called characterization; 3) Normalizing potential impacts into a comparable format; 4) Grouping impacts and assigning weights to emphasize the importance of each impact category; and 5) Evaluating the life cycle impact assessment results. Life Cycle Assessment Framework Goal and Scope Definition Inventory Analysis Definition Impact Assessment Interpretation Figure 3-2 LCA Framework as Shown in ISO14041 38 The inventory analysis needs to be conducted after the scope definition in order to identify the overall life cycle inventory. According to GREET, the vehicle life cycle can be divided into two key cycles. Vehicle Life Cycle: The vehicle life cycle study is comprised of vehicle manufacturing, vehicle operations, and vehicle end-of-life [175][176]. Vehicle life cycle (LCA_V) emissions can be explained as Equation (2) as follows: LCA_V = E + Erecurring + Eend-of-life Equation 2 Where, E â Vehicle manufacturing emissions (kgCO2e), Erecurring â fuel-based operational emissions and maintenance emissions (kgCO2e), Eend-of-life â End-of-life emissions (kgCO2e) Fuel life cycle: Fuel life cycle can be categorized as âWell to Pump cycle (W2P)â and âWell to Wheel cycle (W2W)â [177]. The feedstock (including feedstock recovery, transportation, and storage) and fuel (including fuel production, transportation, storage, and distribution) are cumulatively known as the W2P cycle [177]. The extended cycle, which includes the operational phase emissions of the vehicle, is known as the W2W cycle [177]. Both of these processes generate emissions and consume energy. According to Argonne National Laboratory (2016), the relationship between the vehicle cycle and the fuel cycle can be shown in Figure 3-3. Raw Material ExtractionVehicle ManufacturingVehicle OperationVehicle Maintenance/RepairVehicle DisposalVehicle Life CycleFuel Life CycleFuel Production / Electricity generationTransportation / TransmissionRetaillingRaw MaterialEnergyEnvironmental ImpactsEconomic Impacts Figure 3-3 Conventional Vehicle Life Cycle and Fuel Life Cycle [8] 39 3.6.2 Life Cycle Cost (LCC) Life cycle cost (LCC) can be defined as the external and internal costs associated with a project or a process over its total lifetime [178]. The LCC can be assessed for the life cycles of fuel and vehicle to obtain the complete life cycle of vehicle transportation. As described in section 2.1, the vehicle purchase/manufacturing cost, operational cost, maintenance cost, repair cost, recycle cost, fuel extraction, storage, transportation, and retailing cost are major parts of the vehicle life cycle. The LCC for a product can be expressed as Equation (3) [179]. LCC = C + PV recurring â PV residual-value Equation 3 Where, PV recurring = Present value of the all recurring costs, C = Initial investment / purchase cost, PV residual-value = Present value of the end-of-life value NPV and annualized cost can be calculated by using Equation (4) and Equation (5) [180], ððð(ð, ð) = âð ð¡(1+ð)ð¡ðð¡=0 Equation 4 ð¸ðð¢ðð£ððððð¡ ð´ððð¢ðð ð¶ðð ð¡ = ððð . (ð1â1(1+ð)ð¡) Equation 5 Where: NPV -Net Present Value, Rt - net cash flow, i - discount rate, N - study period, t - time of the cash flow Vehicle fuel cost depends on energy production costs, supply chain cost, and retailer overheads. The energy production costs vary based on the energy source and the production method. Moreover, alternative fuel infrastructure cost varies with respect to the refueling infrastructure capacity [87], refueling/recharging method [181], safety requirement [181], service radius [181], terrain condition [181], and environment conditions [181]. Reducing the cost of alternative fuel sources is the core factor in motivating users to shift from traditional transportation to alternative energy transportation [24][10][182]. Therefore, the alternative fuel infrastructure costs need to be kept to the minimum, in order to provide low-cost fuel [177][181]. 40 3.6.3 Eco-efficiency Assessment Eco-efficiency is a concept that is used to measure the combined effect of environmental and economic costs and benefits of a particular product or product system through its entire life cycle [183]. Huppes and Ishikawa (2007) described a concept called âeco-efficiency ratioâ to compare different alternatives and to identify trade-offs in order to select the best alternative [184]. Using general terms, the units of value generation per unit of environmental impact is known as the eco-efficiency of the particular product [183][185]. Equation (6) can be used to calculate the eco-efficiency ratio. Eco â efficiency Ratio = Value generation ($)Units of enviornmental impacts (kg CO2eq) Equation 6 3.7 Multi-criteria Decision Making Multi-criteria decision making (MCDM) is prevalent in engineering applications, where the decisions are related to complex problems with conflicting objectives and multiple stakeholders [186]. MCDM problems are described using a decision matrix to indicate multiple attributes and alternatives [187]. These methods can be categorized into non-compensatory and compensatory methods based on the decision-making approach. Non-compensatory methods: These methods can not be used for decisions where there are no trade-offs between multiple attributes [187]. In this case, each attribute stands on its own, and the comparison should be made based on the individual attribute [187]. Dominance, maximin, maximax, conjunctive-constraint, and disjunctive-constrain are some of the common non-compensatory methods used for engineering applications [187]. Compensatory methods: These methods are used to incorporate trade-offs between multiple attributes during the decision-making process [187]. The following compensatory methods are frequently used for engineering applications. 1) Scoring methods â Here, a score is used to express the decision-makersâ preference for each alternative [187]. Analytical Hierarchy Process (AHP) and Simple Additive Weighting method are popular scoring methods used in the literature. 41 2) Compromising methods â These methods choose the best desirable alternative that has the lowest distance to the ideal solution [187]. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is a prevalent compromising method for engineering applications. 3) Concordance methods â These methods establish concordance measures and generate a preference ranking to obtain the best desirable solution [187]. In this case, alternatives with highly ranked attributes should get higher ranks. The linear programming method is a prevalent approach that uses concordance-based decision-making. 4) Evidential Reasoning Approach â This is a novel MCDM approach in which each attribute of an alternative is denoted by a distributed assessment using a belief structure [187]. The above-mentioned MCDM methods can be used to obtain ideal solutions, non-dominated solutions, satisfying solutions, and preferred solutions based on the nature of the problem, nature of the desired solution, and the method used. Moreover, these methods can be further improved by integrating uncertainty. 3.8 Decision-making under Uncertainty Uncertainty can be described as the vagueness of available data and lack of data availability. Several methods can be used for decision making by incorporating the data and project uncertainties, where the Scenario-based and fuzzy-based approaches are prominent in the existing studies [188]. 3.8.1 Scenario-based Assessment Scenario-based assessment is prevalent in the long-range infrastructure planning method under uncertainties [189]. Decision-makers are using scenario-based assessment for long-range infrastructure expansion projects such as water treatment plants [189], renewable energy storage plants, district energy systems [190], electricity production plants, and other commodity production plants [191]. Literature reveals that time-sensitive infrastructure demands can be assessed using well-developed scenarios [189]. Hence, decision-makers such as facility managers, developers, and public and private authorities can decide the appropriate time to invest in infrastructure expansion to increase capacity for potential demands [189]. 42 Scenarios can be described as plausible futures [192]. Scenario planning defines evidence-based, hypothetical alternatives for how planning might proceed [192]. This method can be used to establish model changes in the product life cycle or process life cycle, such as replacing the conventional product at the âover-maturityâ stage by a new product category [192]. The scenario-based assessment consists of the following steps: - Identify scenarios (e.g. best case, worst case, most likely case, etcâ¦) [193] - Define variables that affect future outcome, based on the selected scenarios [193] - Create scenarios by assigning the qualitative and quantitative reasonable value to the defined variable [193]. 3.8.2 Fuzzy Logic and Fuzzy Sets According to Zadeh (2006), classical logic theories have limitations in automated decision making, including imprecision, uncertainty, incompleteness of information, conflicting information, partiality of truth, and partiality of possibility information [194][195]. Therefore, the fuzzy approach can be used to associate uncertainty in the strategic decision-making process [196]. In that case, the elements that the transition is not well-defined are known as fuzzy sets [196]. Because, the boundaries of fuzzy sets can be vague and ambiguous [196], these sets are measured with a function that attempts to describe vagueness and ambiguity [196]. Equation (7) defines a basic function of a fuzzy set. Consider a classical set A of the universe U, a fuzzy set A is defined by a set of ordered pairs, a binary relation, A = {(x , µA (x)) | x ϵ [0,1] } Equation 7 Where, 0< µA (x)ï£ 1 is a membership function of x and A. A trapezoidal fuzzy number was also introduced as Figure 3-4 [197]. µA (x)= (x-a)/(b-a), aï£xï£b (c-x)/(c-b), bï£xï£c 0, Otherwise, a b c x µA (x) 1 0 Figure 3-4 A Trapezoidal Fuzzy Number A = (a,b,c) 43 Let fuzzy number A1 = (a1,b1.c1) and A2 = (a2,b2,c2) and A1, A2 > 0, the arithmetic operations of fuzzy numbers [198] are shown as follows. Fuzzy addition is shown as Equation (8): A1ï A2 = (a1+a2, b1+b2, c1+c2) Equation 8 Fuzzy subtraction is shown as Equation (9): A1¶ A2 = (a1- c2, b1-b2, c1-a2) Equation 9 Fuzzy multiplication is shown as Equation (10): A1ï A2 = (a1.a2, b1.b2, c1.c2) Equation 10 Fuzzy division is shown as Equation (11): A1⨸A2 = (a1/c2, b1/b2, c1/a2) Equation 11 Moreover, the distance between two triangular fuzzy numbers d(A1,A2) is shown in Equation (12) [199]. ð(ð´1 , ð´2) = â[(ð1 â ð2)2 + (ð1 â ð2)2 + (ð1 â ð2)2] 3â Equation 12 The centroid method, weighted average method, mean max method, and lambda-cut method can be used as defuzzification methods; the centroid method is widely used in engineering studies due to its simplicity and ability to solve different fuzzy sets [200]. Crisp number (Z*) calculated using the centroid method can be expressed as Equation (13). ðâ = â« µ ð´(ð¥) . ð ðð¥ â« µ ð´(ð¥) ðð¥â Equation 13 The above fuzzy operations were used to solve the complex fuzzy-based tool developed for F-MADM based decision-making. 44 3.8.3 Fuzzy Multi-Attribute Decision Making The F-MADM approach can be selected based on the attributes available and the required results of the study. Generally, Fuzzy Analytic Hierarchy Process (F-AHP), Fuzzy Elimination Et Choix Traduisant la Realité (F-ELECTRE), Fuzzy Rule-based selection, and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) can be selected as typical F-MADM methods, which are used to select the best desirable alternatives under data uncertainty [200]. 45 Chapter 4 Selection of Desirable Alternative Fuel Transportation Systems Sections of this chapter have been published in the Journal of Cleaner Production, as articles titled âAre we ready for alternative fuel transportation systems in Canada: A regional vignetteâ and âScenario-based economic and environmental analysis of clean energy incentives for households in Canada: Multi-criteria decision making approachâ [70][38]. 4.1 Background Decarbonizing the transportation sector using low-emission alternative energy sources has gained immense attention from city planners, developers, and policymakers to steadily achieve GHG emission reduction targets considering long-range urban planning [5] [6]. The emission level of the transport energy cycle changes with the primary energy source and the energy production method. According to the available literature, propane, hydrogen fuel cell, biodiesel, electricity, and natural gas are being used to decarbonize road transportation depending on their availability, accessibility, technical know-how, and favourable economic impacts [82]. However, as explained in recent studies, alternative transportation systems, that use low-emission electricity and hydrogen fuel cell as their primary source of energy are considered scalable emerging technologies for decarbonizing the transportation sector [80]. These fuel technologies have their own unique emissions, cost behaviours, and regional availability and appropriateness. For electricity, some provinces use low-emission primary energy sources such as hydro, wind, solar photovoltaic (PV), etc. to generate low-emission electricity, while other provinces use high-emission fuels such as coal, fossil fuels, natural gas, etc. to generate electricity. For hydrogen fuel cells, some hydrogen production methods emit high emissions based on the method and primary energy sources used to produce hydrogen fuel cells for vehicles. Moreover, commercial production of hydrogen fuel cells requires major investments and better paybacks for those investments based on investor perspectives [59]. Therefore, the selection of the most desirable low-emission fuel technology for transpiration is not straightforward and requires scientific analysis of the environmental and techno-economic factors considering the stochastic (regional) variations of the primary sources of energy. 46 A literature review showed a significant number of studies and methodologies available to evaluate and compare the life cycle emissions of alternative fuel vehicles [7][8]. The greenhouse gases, regulated emissions, and energy use in transportation (GREET) model developed by Argonne national laboratory is one of the popular models available in the recent body of the knowledge [8][9]. However, the aforementioned models only consider the life cycle emissions of alternative and conventional vehicles for a given location. Hence, there is a knowledge gap in terms of decision support perspective, where there is a need for life cycle cost and technical viability assessment to evaluate the commercial/investment readiness of the proposed low-emission road transportation method and mode. Accordingly, a systematic investigation needs to be conducted to consider all the regional economic and environmental factors throughout the entire life of the conventional and potential passive road transportation options. This chapter focuses on selecting the best desirable alternative fuel-based low-emission transportation option to replace conventional light-duty vehicles. Factors such as the economic and environmental aspects of both transport energy and mobility cycles, and the technical know-how of the alternative fuel options were assessed considering the life cycle thinking aspect and the regional variations of grid mixes. The proposed framework consists of potential GHG emissions considering the primary energy source and the energy production method used in different regions in Canada. In addition to that, the economic factors (e.g. vehicle purchasing cost and operating cost) [10], [11], vehicle range limitations [11], [12], limited fuel production, storage, and re-fuelling infrastructure availability [10], [11], [13] were also considered as limitations of the use of alternative fuel-based vehicle options. This work can drive policy decision making related to provincial alternative transport infrastructure planning in Canada. 4.2 Methodology to Select Desirable Transport Fuel Options This section discusses the methodology used to select the best desirable low-emission fuel technology for different regions in Canada. A life cycle thinking-based scientific framework is proposed and developed to assess the regional economic and environmental viability of the use of EVs and HFCVs compared to conventional internal combustion engine vehicles (ICEVs). 47 Figure 4-1 Methodology Framework to Select the Low-emission Fuel Technology48 The research methodology diagram is shown in Figure 4-1. This methodology consists of five key phases: 1) Data collection and database preparation; 2) Preliminary selection of low-emission alternative fuel options; 3) Life cycle assessment; 4) Life cycle cost assessment; and 5) Eco-efficiency based fuel option selection for different regions in Canada. Phase 1: Data Collection and Database Preparation A comprehensive literature review was conducted to collect the necessary data and develop the required database. Accordingly, the study used âGoogle Scholarâ and âCompendex Engineering Villageâ to search peer-reviewed articles relevant to this subject area. Hence, the study used âAlternative fuel vehicles,â âElectrified Transportation,â âRange Anxiety,â and âEmission Factorsâ as its keywords. Moreover, both electronic and printed materials were analyzed using the document analysis procedure [201], which includes an analysis of historical data, newspaper articles, advertisements, web-based documents, survey reports, and organizational and institutional reports. The life cycle inventory data were collected from the databases provided by the Argonne National Laboratory (2015), Athena Impact estimator, and SimaPro databases. Phase 2: Preliminary Selection of Low-emission Alternative Fuel Options Literature-based data was used to filter and select the scalable alternative fuel-based road transportation technologies for the Canadian Context. A preliminary model was developed considering techno-economic, environmental, and social factors. The factor categories and considered factors are given in Table 4-1. The linguistic terms (LT) and the respective Likert scale (LS) are shown in Table 4-1. The rule-based method was used to filter the desirable low-emission fuel technologies to replace the conventional transportation system in Canada. A four-point Likert scale was used to rate the factor levels according to the defined rules. The following rules were defined in order to filter the potential low-emission alternative fuel technologies for road transportation. Rule 1: All technical factors should be positive. The technologies that do not have commercial production of vehicles, commercial production of fuels, or available non-renewal energy options cannot be considered in this work. (TF1 > 0 and TF2 > 0 and TF3 > 0) 49 Rule 2: Potential emission reduction should be positive. There is no net benefit in incurring higher costs to substitute conventional fuel with a similar emission alternative fuel source. (EF1 > 0). Table 4-1 Compare Alternative Fuel Technologies for Road Transportation Fuel type Propane HFCV Biodiesel EV Carbon Neutral Natural Gas Natural Gas Indicators LT LS LT LS LT LS LT LS LT LS LT LS Technical factors (TF) Availability of RE (TF1) None 0 High 3 High 3 High 3 High 3 Low 1 Commercial production (TF2) High 3 Low 1 None 0 High 3 Low 1 High 3 Commercial production of vehicles (TF3) Conventional 3 Low 1 Conventional 3 Moderate 2 None 0 None 0 Environmental factor (EF) Potential Emission Reduction (EF1) None / Low 0 High 3 Moderate 2 High 3 High 3 None / Low 0 Costs Factors (CF) Incremental cost of Vehicle (CF1) Low 1 High 3 Low 1 High 3 High 3 Low 1 Incremental cost of fuel (CF2) Low 1 High 3 Moderate 2 Low 1 High 3 Low 1 Availability of retail network (CF3) Moderate 2 Low 1 None 0 Moderate 2 None 0 Moderate 2 Social Factor (SF) Public Awareness (SF1) Low 1 Low 1 Low 1 High 3 Low 1 Low 1 Filtered technologies were then used to conduct further analysis to suggest the best desirable fuel technology based on the regional energy mix. Phase 3: Life Cycle Assessment According to ISO 14044, Life Cycle Assessment (LCA) studies the environment-related potential impacts of a product or a service throughout its life from raw material acquisition through production, use, and disposal [174]. The goal of the LCA study was to find the life cycle emissions of an electric vehicle-based transportation system considering both the energy and mobility sectors. The potential audience was identified as policymakers working on urban transportation-related tasks. Energy, raw materials, and water were considered key inputs to the system, and the 50 environmental impacts and by-products were considered key outputs from the system. According to the scope definition in ISO 14040, the boundary of this work is shown in Figure 4-2 below. The study boundary consists of the life cycle assessment of energy and mobility sectors of electric vehicles, hydrogen fuel-cell vehicles, and conventional gasoline vehicles. The life cycle stages considered in this study are shown in Figure 4-2. Energy (E), raw materials (RM), and water (W) were considered the system inflows, and emissions (CO2eq) and wastes (SW) were considered the system outflows. Different functional units were considered for different life cycle stages based on their functionality. For example: for the mobility cycle, vehicle use was measured as mileage km travel, and the fuel consumption of Liter/100km and the vehicle construction and demolition emissions were calculated as the vehicle as one single unit. Figure 4-2 Boundary of the Life Cycle Assessment 51 The duration of the life cycle and the average mileage operated per year were considered to estimate the total operational energy consumption of the vehicle within the considered life cycle. For the energy cycle, the life cycle assessment was conducted to find the total emission of unit fuel from well-to-pump. Ultimately, however, all those units were converted to kgCO2eq/vehicle, considering the emissions factors for the impact category to find the total life cycle impacts of the vehicle. In addition to that, the following assumptions were made for the life cycle assessment of EV, HFCV, and conventional ICEV: 1. According to previous studies, the vehicle life span is considered 12-15 years [202]. However, in this study, the vehicle use period/ life span is assumed as 5 years due to the lack of data on maintenance after 5 years. 2. Due to the various vehicles available in the market, three specific vehicles were considered to obtain life cycle cost data for the analysis. Those representative vehicles and the reason for the selection are: 1) A mid-size passenger car (Nissan Leaf S 5DR) [203] (Electric), selected based on its high market penetration as an economical EV; 2) A mid-size sports utility vehicle (Hyundai Tucson FCEV) [204] (Hydrogen), selected because it is a popular, commercially available vehicle in HFCV market segment; 3) A spark-ignition mid-size passenger car (Toyota Yaris 4DR) [205], selected as the ICEV for this study due to high market penetration as an economical vehicle and its similar engine power torque (Hp) to aforementioned alternative vehicles. 3. The annual mileage of a vehicle in Canada is assumed as 18,000km per year [206]. In addition to the mobility cycle, the energy cycle for each energy alternative considered in this analysis can be explained as follows: Gasoline for transportation: Octane 85 gasoline was considered to find the life cycle inventory for conventional ICEV refueling. The functional unit for this fuel was taken as litre fuel consumption per 100 kilometers (l/100km). The database developed by Argonne National Laboratory was used to collect and prepare the life cycle inventory database for gasoline fuel-based light-duty vehicles. Hydrogen fuel-cell technology for transportation: Hydrogen fuel cell (HFC) production using Natural Gas (NG) gasification was considered the commercially available method to produce 52 mass-scale hydrogen fuel cells for road transportation [207]. Hence, the functional unit of hydrogen was considered as litre hydrogen fuel cells consumed per 100 kilometers (l/100km). Furthermore, the LCA impacts of the common low-emission hydrogen production methods, which are alkaline water electrolysis using low-emission electricity, thermochemical water splitting with nuclear Cu-Cl cycle, and central biomass gasification, etc., were also considered in this analysis. The database developed by the Argonne National Laboratory (2015) and other North American databases were used to obtain data and develop the LCA databases for the analysis. Electricity for transportation: As shown in Appendix A2, electricity generation emissions vary with the primary energy source. The electricity mix of Canada changes by province due to the availability of resources, raw materials, and technology. Hence, the life cycle emissions of the use of different fuels vary with the location. The LCA inventory of grid electricity from electricity generation to the consumer recharging point via the smart grid was calculated using GREET software developed by Argonne National Laboratory. Different energy mixes of the electricity generation process were defined in the aforementioned software to find the mid-point indicators for the life cycle inventory. The system inputs were: 1) Fossil Fuel (GJ/Unit vehicle or kJ/100litre fuel); 2) Coal Fuel (GJ/Unit vehicle or J/100litre fuel); 3) Natural Gas Fuel (GJ/Unit vehicle or kJ/100litre fuel); 4) Petroleum Fuel (GJ/Unit vehicle or kJ/100litre fuel); 5) Renewable (GJ/Unit vehicle or kJ/100litre fuel); 6) Biomass (GJ/Unit vehicle or kJ/100litre fuel); 5) Nuclear (J/Unit vehicle or kJ/100litre fuel); 6) Non-Fossil Fuel (GJ/Unit vehicle or kJ/100litre fuel); and 7) Total Water (m3/Unit vehicle or m3/100litre fuel). The system outputs were: 1) Volatile organic compound (VOC) (kg/Unit vehicle or mg/100litre fuel); 2) Carbon Monoxide (CO) (kg/Unit vehicle or mg/100litre fuel); 3) Nitrogen Oxides emissions (NOx) (kg/Unit vehicle or mg/100litre fuel); 4) Particles between 2.5 and 10 microns (micrometers) in diameter (PM10) (kg/Unit vehicle or mg/100litre fuel); 5) Fine particulate matter less than 2.5 micrometers in diameter (PM2.5) (kg/Unit vehicle or mg/100litre fuel); 6) Sulphur oxides emissions (SOx) (kg/Unit vehicle or mg/100litre fuel); 7) Methane (CH4) (kg/Unit vehicle or mg/100litre fuel); 8) Carbon Dioxide (CO2) (kg/Unit vehicle or mg/100litre fuel); 9) Nitrous 53 Oxide (N2O) (g/Unit vehicle or mg/100litre fuel); 10) Bio Carbon (BioC) (g/Unit vehicle or mg/100litre fuel); and 11) Particle Oxidation Catalysts (POC) (g/Unit vehicle or mg/100litre fuel). Equation (14) was used to find operational emissions based on the emissions of unit fuel consumption. ð¸(ðððð¢ððððð) = ð¾ Ã ð´ð Ã ð¿ð ðð¹ð¶â Equation 14 Where, E(recurring) â Vehicle operation (fuel) emissions (kgCO2e); ð¾ â Emission factor (emissions per unit fuel); AM â Annual mileage (km/year); LT â life span (year); VFC â Fuel consumption (km/per unit fuel) The vehicle (from cradle-to-grave) with fuel (from well-to-pump) was considered for the complete life cycle of the mobility and energy cycles, used to describe the total emissions related to road transportation [177]. Hence, the life cycle of the overall system was described as Equation (15). The possible light-duty vehicle options were developed accordingly, using different fuel-production methods, regional energy-mixes, and commercial availability of mass fuel sources. The options considered in this work including hydrogen fuel cell vehicles with different hydrogen fuel production methods, EVs with different grid-mixes within Canada, and conventional gasoline fuel-based vehicles (ICEVs). LCA_S = LCA_V + LCA_Fw2p Equation 15 Where, LCA_S â Life cycle emissions of the system (vehicle life cycle & fuel life cycle) (kgCO2e); LCA_V â Life cycle emissions of the vehicle (kgCO2e)*; LCA_Fw2p â Life cycle emissions of fuel from well to wheel (kgCO2e)** The impacts of the inventory were then organized into impact categories. Literature-based inventory-impact conversion factors obtained from the study conducted by Asdrubali, F., et al. (2015), were used to find LCA impacts of each impact category [208]. The mid-point indicators were considered in this chapter to show the relationship to emission flows and used for further comparison. Hauschild, M., H., and Huijbregts, M., A., J., (2015) showed a comparison of mid-point indicators and end-point indicators where the mid-point indicators are highly suitable to show elementary flows and lower modeling uncertainty [209]. The following mid-point impact 54 categories were considered in this analysis and will be most affected by local communities in the long-run [210] [211]. There are: 1) Global warming potential (GWP); 2) Photochemical ozone formation (POF); 3) Acidification of land and water (ALW); 4) Eutrophication (EN); 5) Stratospheric ozone depletion (SOD); and 6) Depletion of non-renewable energy resources (DNR). Phase 4: Life Cycle Cost Assessment According to the available literature, life cycle cost (LCC) can be explained as the complete costs of a vehicle in its entire life span. Typically, the vehicle life cycle cost varies with the vehicle type, use behaviours, local fuel prices, other vehicle costs, and vehicle end-of-life cost. Generally, the vehicle shows negative end-of-life cost (positive income), which reduces the overall life cycle cost. The LCC for the vehicle can be expressed as Equation (16) [179]. LCCVehicle = Vcapex + Vopex + VEoL Equation 16 Where, LCCvehicle = Life cycle cost of the vehicle (CAD/vehicle); Vcapex = Vehicle capital expenditure / purchasing price (CAD/vehicle); Vopex = Present value of the all operational expenditure (CAD/vehicle); VEoL = Vehicle end-of-life costs (CAD/vehicle) The data provided in Table 4-2 was used to calculate the vehicle capital expenditure (CAPEX), operational expenditure (OPEX), and vehicle end-of-life costs (EoL). The operational cost was calculated using Equation (17). Vopex = VOC + VM&RC + EVBC Equation 17 Where, Vopex = Present value of the all operational expenditure (CAD/vehicle); Voc = Present value of the operating costs (CAD/vehicle.lifecycle); VM&RC = Present value of the maintenance and repair cost (CAD/vehicle.lifecycle); EVBC = Cost of electric vehicle battery replacement (EVBC) â (CAD/battery.life cycle) The vehicle operating costs and electric vehicle battery replacement costs were calculated as Equation (18) and Equation (19), respectively. 55 VOC = Fc . AM VFCâ Equation 18 Where, Voc = Vehicle operating cost (CAD/vehicle.life cycle); Fc = Fuel or electric cost of the considered region (CAD/litre or CAD/kwh); AM = Average annual millage travel (km/year.vehicle); VFC = Vehicle fuel / electricity consumption (Litres/100km or km/kWh) EVBC = CF. EVCB. AM EVBRC. Rngâ Equation 19 Where, CF= Cost factor for EV battery â (CAD 300 /kWh [212]); EVCB = Capacity of battery â 30kWh (see Annexure D); EVBRC = No. of re-charging cycles in battery life cycle â Li-ion battery with 3,000 cycles; Rng = Vehicle range per one recharging cycle (km/cycle); AM = Average annual millage travel (km/year.vehicle) The Net Present Value (NPV) was calculated using Equation (20) [180] in order to compare the costs of different vehicles. NPV(i, N) = â Rt (1 + i)tâNt=0 Equation 20 Where, NPV = Net Present Value (CAD), Rt = Net cash flow (CAD); i = Discount rate (%); N = Study period (Years); t = Time of the cash flow The cost data of the considered vehicles are given in Table 4-2. The full collection of the data is attached as appendices, as stated in Table 4-2. The vehicle purchase price (in British Columbia, Canada) was considered as the product purchase cost or the initial investment. Operational expenses such as fuel expenses and maintenance and repair expenses were considered as recurring costs. Additionally, end-of-life cost after the life of the vehicle was considered positive, which is generally a gain to the consumer. 56 Table 4-2 Life Cycle Cost Calculation for the System (LCC_S) Gasoline Hydrogen Electricity Vehicle purchase price (C) Vehicle capital expenditure / purchasing price (Vcapex) CAD 21,151/vehicle CAD 66,940/vehicle5 CAD 26,688/Vehicle Vehicle operational and maintenance cost (PVrecurring) Fuel or electric cost (Fc) Refer Appendix A4 Provincial data not available. The average price is CAD 5 per kg Hydrogen [213] Refer Appendix A3 and retailor margin of CAD 0.02915/ kWh. Fuel consumption rate (Vfc) 12.32 km per Liter of Gasoline 77 km per kg of Hydrogen 34 kWh per 100km Maintenance and repair cost (VM&RC) Refer Appendix B1 Refer Appendix B1 Refer Appendix B1 Cost of Battery Replacement (EVBC) Negligible Negligible CAD 406 / (year.vehicle) Vehicle residual value (PVresidual-value) End-of-life cost CAD 8,006 CAD 20,082 CAD 6,345 The following assumptions were made to perform necessary LCC calculations. 1. The insurance costs of the vehicles depend on the valuation of the vehicle, driver, policies, and vehicle use [214]. Therefore, insurance costs have not been considered for the LCC of vehicles. 2. The regional hydrogen production method was assumed based on the availability of green electricity and natural gas in a particular region. 3. The vehicle end-of-life cost was assumed as 30% of the vehicle purchasing cost. 4. Assumed 10 year payback period for EV infrastructure facility and zeroed residual value after ten years lifetime. 5. EV re-charging facility operates with an 80% efficiency rate for 16 hours per day and 365 days per year. The public re-charging station capacity was assumed as 50kW [215]. 5 Includes fuel cost, maintenance and repair cost for first 03 years. Please see Appendix B1. 57 6. The consumer price index for Canada is 2% [113], and the discount rate for Canada is taken as 3.5% (Innovation, Science and Economic Development Canada, 2014) for annualizing the cost factors. 7. The retailerâs profit mark-up was assumed as 12%. 8. Average cost of level III facility is CAD 35,000 to 45,000 per facility [87]. 9. CAD 2,000 per year without including the cost of energy [217]. According to the aforementioned methodology, the overall life cycle cost of each fuel option was calculated based on the provincial data obtained in Canada. Phase 5: Select the most desirable alternative fuel option for road transportation This section explains the methodology used to select the best desirable fuel option for road transportation considering regional economic and environmental parameters of the available fuel options. As the first stage, the alternative fuel options were defined using different combinations considering the regional fuel availability. Those were coded as follows to enhance the clarity of the explanation. OP1_EV EVs operated using the provincial grid electricity (Central) OP2_EV EVs operated using renewable energy such as Solar(PV), wind, etc. (Distributed) OP3_HFCV HFCVs operated using hydrogen fuel cells (HFCs) produced by the provincial grid electricity (Central) OP4_HFCV HFCVs operated using HFCs produced by water electrolysis using renewable energy such as Solar(PV), wind, etc. (Distributed) OP5_HFCV HFCVs operated using HFCs produced by central coal gasification (Central) OP6_HFCV HFCVs operated using HFCs produced by biomass gasification (Distributed) OP7_HFCV HFCVs operated using HFCs produced by thermochemical water splitting with nuclear CuâCl cycle (Central) OP8_HFCV HFCVs operated using Canadian natural gas-based HFCs Production (Central) BC_ICEV The conventional use of ICEVs using gasoline as the fuel source 58 The mid-point impacts identified from the LCA to obtain the damage impacts to the environment were calculated for the options mentioned above (including the conventional fuel-based vehicles). Mid-point indicators were compared and conclusions were given considering environmental perspective. However, the comparison showed different options for different indicators. Therefore, the environmental score was defined to obtain a single value for each considered option to compare and select the most desirable vehicle option. The single life cycle impact index obtained using Netherlands Oil and Gas Exploration and Production Association (NOGEPA) suggested weights can be defined as the âEnvironmental Score (EnvS)â for each alternative fuel option. NOGEPA weights were selected from the literature to indicate the relative importance of each impact indicator considering the industrial (oil and gas) applicability and appropriateness of those weights for the eco-efficiency-based studies [218]. The NOGEPA weights for GWP, SOD, ALW, EN, POF, and DNR were considered as 32, 5, 6, 13, 8, and 8, respectively [218]. The indicator values were normalized using Equation (21) prior to multiplying with the NOGEPA weights. Xi,normalized = (xi,j â xi,min) (xi,max â xi,min)â Equation 21 Where, X I,normalized â Normalized value for the alternative I and indicator j; Xi,j â Value given in alternative I and indicator j; Xi,min â Value given in alternative I and minimum value of indicator j; Xi,max â Value given in alternative I and maximum value of indicator j The normalized mid-point impacts were then multiplied with NOGEPA weights and aggregated to obtain a single impact per each alternative fuel option [219]. LCC for the aforementioned fuel options were also assessed and normalized in order to obtain the âEconomic Score (EconS)â of the different alternative fuel options. Equation (21) was used to normalize the LCC values, and the normalized values were aggregated to find the economic score. EnvS and EconS were compared individually and discussed the appropriateness of each fuel option in different provinces. A single indicator-based eco-efficiency analysis was conducted to evaluate and obtain the most desirable alternative fuel type for a given region, considering the economic and environmental perspective. Equation (22) was used to calculate the eco-efficiency of the selected fuel option. 59 Eco â efficiency score = EnvS EconSâ Equation 22 Where, EnvS â Environmental score; EconS â Economic score Accordingly, the most desirable fuel options were determined for each province in Canada. 4.3 Results and Discussion The Canadian federal government and provincial governments are more concerned with reduction of transport-based GHG emissions, which will eventually lead to achieving provincial greenhouse gas reduction targets. This section discusses the results of the aforementioned study. Accordingly, the alternative fuel options for Canada were assessed using the rule-based method, and EVs and HFCVs were chosen based on their appropriateness. Afterward, the EVs and HFCVs were further analyzed with a detailed LCC and LCA. Different regional energy mixes and technologies were considered in this analysis. The results obtained from the LCC, LCA and enhanced LCC calculations are described in this section. Light-duty vehicles operated using electricity, hydrogen fuel cells, and gasoline were compared with each other. 4.3.1 Life Cycle Inventory for Different Fuel Options and Different Mixes of the Source Energy The life cycle inventory results for hydrogen fuel cell vehicles, electric vehicles, and conventional vehicles are shown in Table 4-3. In this analysis, the fuel cycle was not considered and the vehicle cradle-to-grave was considered without the operational emissions. Table 4-3 shows the resource required to manufacture vehicles, and resultant emissions are significantly higher than the vehicle maintenance and end-of-life phase. Conventional vehicles show the lowest manufacturing emissions in almost all categories, whereas EVs show the highest manufacturing emissions in most emission categories. However, operational emissions need to be integrated into the above life cycle values to identify the most desirable vehicle categories. Hence, cradle-to-grave emissions of hydrogen fuel cells and electricity are shown in Table 4-4 and Table 4-5, respectively. 60 Table 4-3 Vehicle Life-Cycle Inventory Car: EV - Electricity (Type 1 Li-Ion/NMC111 Conventional Material) Car: ICE - Gasoline (Spark Ignition-Conventional Material) Car: FCV - Gaseous H2 (Type 1 Ni-MH Conventional Material) Weights of the considered vehicle kg 1,293 201 NA 1,392 NA NA 1,578 NA NA Name Units Manufacturing Maintenance - 5 years (Battery & Fluids / Except Fuel) End-of-life EV vehicle Life cycle Inventory Manufacturing Maintenance - 5 years ( Fluids / Except Fuel) End-of-life ICEV vehicle life cycle inventory Manufacturing Maintenance - 5 years ( Fluids / Except Fuel) End-of-life HFCV vehicle life cycle inventory Input Inventory (Raw materials, Energy, & Water) Fossil Fuel GJ 50.4 15.0 13.3 78.7 37.7 4.1 9.3 51.2 79.5 2.4 9.3 91.3 Coal Fuel GJ 23.3 4.0 4.1 31.4 17.8 0.2 2.9 20.9 25.2 0.6 2.9 28.7 Natural Gas Fuel GJ 23.3 8.8 9.0 41.1 15.7 1.1 6.3 23.1 44.6 1.4 6.3 52.3 Petroleum Fuel GJ 6.3 2.0 0.2 8.5 4.2 2.8 0.1 7.2 9.2 0.5 0.1 9.7 Renewable GJ 3.4 1.6 0.8 5.7 2.2 0.0 0.5 2.7 4.5 0.1 0.5 5.1 Biomass GJ 0.1 0.1 0.0 0.2 0.1 0.0 0.0 0.1 0.2 0.0 0.0 0.2 Nuclear GJ 1.9 0.5 0.9 3.4 1.4 0.0 0.7 2.1 3.0 0.1 0.7 3.7 Non Fossil Fuel GJ 5.3 2.1 1.7 9.1 3.6 0.0 1.2 4.8 7.5 0.2 1.2 8.8 Total Water m^3 26.3 10.4 3.1 39.8 18.3 0.3 2.2 20.8 28.9 0.8 2.2 31.9 Output Inventory (Emissions & Waste) VOC kg 3.2 13.8 1.7 18.6 2.5 9.7 1.2 13.4 2.9 9.6 1.2 13.7 CO kg 17.9 68.9 0.5 87.3 13.8 0.1 0.3 14.3 15.7 0.2 0.3 16.2 NOx kg 4.2 1.4 0.8 6.5 3.0 0.4 0.6 4.0 6.0 0.2 0.6 6.8 PM10 kg 1.8 0.7 0.1 2.7 1.4 0.1 0.1 1.6 2.0 0.1 0.1 2.2 PM2.5 kg 0.8 0.2 0.1 1.2 0.7 0.0 0.1 0.8 0.9 0.0 0.1 1.0 SOx kg 24.0 12.6 1.1 37.6 13.9 1.4 0.8 16.1 20.8 3.7 0.8 25.2 CH4 kg 9.1 2.4 2.2 13.7 6.6 0.5 1.6 8.7 13.8 0.5 1.6 15.9 CO2 kg 3,589.0 968.0 902.1 5,459.1 2,654.9 221.0 640.0 3,515.9 5,371.8 127.8 640.0 6,139.5 N2O g 79.9 21.1 20.0 121.0 55.8 5.3 14.1 75.2 127.4 4.0 14.1 145.5 BioC g 29.2 16.5 5.9 51.7 19.1 2.9 4.2 26.2 42.9 3.1 4.2 50.2 POC g 46.5 20.1 13.8 80.4 33.3 3.2 9.8 46.2 79.9 2.4 9.8 92.0 61 Table 4-4 Electricity (Generation to Recharging) Life-Cycle Inventory Input Inventory (Raw materials | Energy | Water) Province-wise grid-electricity generation (Unit impact per MJ electricity) Unit British Columbia Manitoba Quebec Newfoundland and Labrador Yukon Northwest Territories Ontario New Brunswick Saskatchewan Nova Scotia Alberta Prince Edward Island Nunavut Water m^3 0.004251 0.494 0.0044 0.0044 0.0043 0.0021 0.0015 0.0012 0.00093 0.000751 0.000035 0.0000107 0.001954 Non fossil fuels KJ 1,192 1,000 1,035 950.61 920.92 418.25 988.17 804.42 180.99 439.09 171.93 1,025 53.68 Renewable KJ 1,192 1,000 1,035 950.27 920.41 413.7 396.57 442.54 180.45 437.53 171.15 1,025 24.05 Biomass KJ 271.5 0 45.25 0.001 0.016 145.83 45.3 181.06 0.017 226.3 90.53 45.25 0.949 Fossil Fuels KJ 61.61 0 1.14 154.01 241.86 1906 125.74 949.36 2105 2075 2312 34.22 12,000 Petroleum Fuels KJ 36.35 0 1 121.14 181.76 1665 2929.06 254.7 21.43 389.39 114.09 31.27 11,000 Natural Gas Fuels KJ 24.79 0 0.012 31.44 57.94 221.89 115.76 242.11 803.63 345.35 944.46 2.59 8875 Coal Fuels J 460.07 0 13.61 1,427.03 2,154.97 19340 7,055.08 452,550 1,280,000 1,340,000 1,253,000 363 125.8 Nuclear J 107.99 0 3.19 335.76 506.92 4,552.8 591,600 361,880 0.544 1,564.47 788.22 85.5 29.63 Output Inventory (Emissions | Waste) Province-wise grid-electricity generation (Unit impact per MJ electricity) Unit British Columbia Manitoba Quebec Newfoundland and Labrador Yukon Northwest Territories Ontario New Brunswick Saskatchewan Nova Scotia Alberta Prince Edward Island Nunavut Total CO2 g 0.51 0 0.058 0.012 18.25 150 7.37 20.83 170 54.66 180 2.69 950 VOC mg 0.11 0 0.41 1.02 1.63 12.04 1.88 0.77 16.57 1.29 18.85 0.62 73.52 CO mg 1.27 0 13.18 3.62 5.89 40.78 19.62 3.92 54.96 6.57 90.51 13.86 240 NOX mg 0.67 0 2.9 48.47 73.27 660 11.11 10.42 120 24.37 170 14.73 4260 PM10 mg 0.48 0 5.78 2.06 3.11 27.99 6.14 1.59 19.82 3.85 32.73 6.29 180 PM2.5 mg 0.16 0 1.7 1.5 2.27 20.29 1.98 1.03 8.59 2.61 13.16 2.06 130 SOX mg 0.36 0 1.81 33.1 50.68 460 4.94 41.7 310 120 330 10.17 3010 CH4 mg 0.25 0 1.47 15.44 24.99 180 22.7 1.41 240 1.69 260 4.41 1060 N2O mg 0.02 0 0.18 0.11 0.17 1.3 0.29 0.3 3.15 0.8 3.74 6.21 8.05 BioC mg 0.02 0 0.24 0.12 0.18 1.57 0.3 0.069 0.56 0.15 1.16 0.27 10.09 POC mg 0.05 0 0.55 0.14 0.21 1.7 0.66 0.16 1.43 0.32 2.71 0.58 10.68 H2 mg 0 0 0 0 0 0 0 0 0 0 0 0 0 62 Table 4-5 Hydrogen and Gasoline Well to Pump (WtP) Life-Cycle Inventory Input Inventory (Raw materials | Energy | Water) Unit Raw materials, Energy, and Water consumption for 1 Liter of Hydrogen Fuel Cell WtP WtP energy consumption for 1 Liter of Gasoline Natural Gas Gasification Nuclear-based Thermo-Chemical Cracking Coal Gasification Biomass Gasification Alkaline Water Electrolysis Water m^3 0.003436 0.007899 0.003792 0.001731 0.000434 0.030034 Non fossil fuels KJ 1,471 12,000 21.29 28,000 15,000 21,000 Renewable KJ 664 26.35 9.608 28,000 15,000 21,000 Biomass KJ 25.83 1.024 0.378 28,000 0.004 21,000 Fossil Fuels KJ 19,000 374.83 27 1,983 45.69 17,000 Petroleum Fuels KJ 167.44 71.29 415.34 699.18 40.12 6,651 Natural Gas Fuels KJ 15,000 162.67 85.78 980.77 5.028 8,448 Coal Fuels J 3,551 140.87 26,000 302.62 0.5 1,421,000 Nuclear J 806 12,000 11,680 68.79 0.12 167,590 Output Inventory (Emissions | Waste) Unit Environmental Impacts and Wastes of 1 Liter Hydrogen Fuel Cell WtP WtP impact for 1 Liter of Gasoline Natural Gas Gasification Nuclear-based Thermo-Chemical Cracking Coal Gasification Biomass Gasification Alkaline Water Electrolysis Total CO2 g 1210 27.77 2,350 140 3.33 840 VOC mg 0.18 10.68 210 150 1.32 1,120 CO mg 0.55 42.51 130 280 5.18 780 NOX mg 0.85 73.01 570 480 21.77 1,790 PM10 mg 76.23 4.23 250 62.5 0.73 290 PM2.5 mg 41.86 2.66 44.13 43.42 0.63 110 SOX mg 1020 36.91 280 1,770 0.56 1,510 CH4 mg 3660 63.91 3660 320 4.59 2,330 N2O mg 25.03 0.45 14.29 100 0.047 640 BioC mg 3.89 0.51 2.56 680 0.074 12.48 POC mg 7.5 0.82 7.14 13.84 0.34 21.6 H2 mg 130 680 680 10.17 560 0 63 Table 4-4 shows the topological variations of cradle-to-grave resource consumption and resultant emissions for grid electricity due to different energy mixes in different provinces. Manitoba, British Columbia, Quebec, and Prince Edward Island show lower consumption of non-renewable energy and result in lower emissions compared with Saskatchewan, Alberta, Nunavut, and the Northwest Territories. Table 4-5 shows the fuel life cycle for hydrogen fuel cells. Accordingly, alkaline water electrolysis using renewable energy sources has the lowest resource consumption and emissions, followed by the gasification of biomass, natural gas, coal, and nuclear-based thermo-chemical cracking. The carbon-capturing techniques for HFC production were not considered in this analysis due to a lack of available data. According to the life cycle inventory information, the following comparisons were conducted and the results of the same are given as follows: 1. Vehicle cradle-to-gate environmental impacts - To identify the alternative vehicle category that emits the highest emissions in its production process. 2. Vehicle cradle-to-grave environmental impacts â To identify the alternative vehicle categories that emit the highest emissions in their entire life cycle. This includes fuel-based emissions from well-to-wheel. 4.3.2 Compare Vehicle Options using Cradle-to-Gate Emissions Cradle-to-gate emissions of the selected alternative fuel-based vehicles were compared in this section to identify the production emissions of the considered vehicle options. The following vehicle manufacturing emissions, including vehicle battery-based emissions, were considered in the analysis: VOC, CO, NOx, PM10, PM2.5, SOx, CH4, CO2, N2O, BC or BioC, and POC. The conversion factors below were used to convert the emissions mentioned above into mid-point indicators [209] [220]. ï· Depletion of non-renewable resources is a cumulative total of fossil fuels, 6.12 times of petroleum fuels, 7.8 times of natural gas fuels, and 0.25 times of coal fuels. ï· Global warming potential is a cumulative total of total CO2 emissions, 28 times of CH4, and 265 times of N2O. 64 ï· Photochemical ozone creation is a cumulative total of 0.416 times of VOC, 0.036 of CO, and 0.007 times of CH4. ï· Eutrophication is a cumulative total of 0.04 times of NOx and 0.09 times of N2O. ï· Acidification and land and water is a cumulative total of 40.04 times of NOx and 50.79 times of SOx. The mid-point indicator values were considered to compare both EVs and HFCVs based on their cradle-to-gate emissions, which are shown in Figure 4-3. Figure 4-3 Cradle-to-gate Emission of Alternative Fuel-based Vehicle Options As indicated, EVs and HFCVs contribute to the environment differently. On one hand, resource consumption is higher in HFCVs than EVs, and therefore more resources are needed to manufacture an HFCV than an EV. HFCV manufacturing enhances global warming and eutrophication compared to similar EVs. On the other hand, EVs emit a higher amount of photo-oxidants and acids to the environment compared with HFCV as a result of the emissions created by the EV battery. The NOGEPA weights were used to develop a single environmental score for each HFCV and EV cradle-to-gate emissions to compare both vehicles. The environmental scores were calculated as 56.22 for the HFCV and 48.78 for the EV. Accordingly, the cradle-to-gate emissions of EVs are considerably higher than similar ICEVs, which is slightly lower than a similar HFCVs. However, these emissions can be lowered with technological advancements such as low 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Acdification land and waterEutrophicationGlobal Warming PotentialPhotochemical Ozone CreationDepletion of non-renewable energy resourcesEV cradle-to-gate emissions HFCV cradle-to-gate emissions65 emission production methods, high recycling and reuse of materials, carbon-capturing technologies, etc. in the future. 4.3.3 Compare Vehicles using Cradle-to-Grave Emissions The vehicle cradle-to-gate emissions, operational emissions including the wheel to pump emissions of the fuel, and vehicle end-of-life emissions were considered in this analysis. The cradle-to-grave LCA mid-point indicators for EVs and HFCVs were compared to each other to observe the environmental appropriateness of each fuel option, to replace conventional fuels in the long run. The comparison is shown in Figure 4-4 for different fuel options. According to the analysis, the OP4_HFCV, OP2_EV, and OP7_HFCV showed lower emissions in all provinces. However, OP4_HFCV and OP2_EV need nearly zero-emission electricity to achieve the given emission targets, which might not be an ideal option for most of those provinces due to the lack of renewable resources. OP7_HFCV requires HFCVs to operate using HFCs produced by thermochemical water splitting with nuclear CuâCl cycle, which is a significantly higher cost option. The above transport options need green electricity, while some provinces have minimal renewable options due to lack of resources, investments, and technology know-how. The OP1_EV, OP3_HFCV, OP5_HFCV, and OP6_HFCV showed comparatively better environmental performances except for the Northwest Territories, Saskatchewan, Nova Scotia, Alberta, and Nunavut. Moreover, ICEV-based conventional transportation showed a negative impact on the environment in all provinces except Nunavut. To analyze this further, an environmental index for each fuel option was calculated, as discussed in the methodology. Accordingly, the OP2_EV, OP4_HFCV, and OP7_HFCV were considered optimistic options, though they are beyond the current scope of the work. OP1_EV, OP3_HFCV, OP6_HFCV, OP5_HFCV, and OP8_HFCV were considered as most likely fuel options that are viable to achieve within the foreseeable future. Conventional fuel was considered the pessimistic fuel option, which can be continued if other options fail in the deployment process. 66 Figure 4-4 Mid-point Indicators for Alternative Fuel Options OP1_EV OP2_EV OP3_HFCV OP4_HFCV OP5_HFCV OP6_HFCV OP7_HFCV OP8_HFCV BC_ICEVBRITISH COLUMBIAALW 0.28 0.28 0.00 0.00 0.28 0.84 0.03 0.40 1.00EN 0.07 0.00 0.00 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.00 0.00 0.01 0.00 1.00 0.07 0.02 0.54 0.78POF 0.27 0.27 0.00 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.02 0.00 0.03 0.00 0.27 0.10 0.02 1.00 0.90MANITOBAALW 0.28 0.28 0.00 0.00 0.28 0.84 0.03 0.40 1.00EN 0.00 0.01 0.01 0.01 0.18 0.21 0.03 0.02 1.00GLW 0.00 0.02 0.02 0.02 1.00 0.09 0.03 0.55 0.79POF 0.27 0.27 0.00 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.00 0.00 0.00 0.00 0.27 0.10 0.02 1.00 0.90QUEBECALW 0.29 0.28 0.01 0.00 0.28 0.84 0.03 0.40 1.00EN 0.00 0.01 0.01 0.01 0.17 0.20 0.02 0.02 1.00GLW 0.00 0.02 0.02 0.02 1.00 0.09 0.03 0.55 0.79POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.00 0.00 0.00 0.00 0.27 0.10 0.02 1.00 0.90NEWFOUNDERLAND & LABRADORALW 0.47 0.28 0.23 0.00 0.28 0.84 0.03 0.40 1.00EN 0.08 0.00 0.12 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.00 0.02 0.02 0.02 1.00 0.09 0.03 0.54 0.79POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.06 0.00 0.07 0.00 0.27 0.10 0.02 1.00 0.90YUKONALW 0.57 0.28 0.36 0.00 0.28 0.84 0.03 0.40 1.00EN 0.13 0.00 0.18 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.03 0.00 0.07 0.00 1.00 0.07 0.01 0.54 0.78POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.09 0.00 0.11 0.00 0.27 0.10 0.02 1.00 0.90NORTHWEST TERRITORIESALW 0.89 0.09 1.00 0.00 0.09 0.26 0.01 0.12 0.31EN 0.79 0.00 1.00 0.00 0.11 0.12 0.01 0.01 0.63GLW 0.40 0.00 0.53 0.00 1.00 0.07 0.01 0.54 0.78POF 0.32 0.27 0.06 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.67 0.00 0.84 0.00 0.27 0.10 0.02 1.00 0.90ONTARIOALW 0.32 0.28 0.05 0.00 0.28 0.84 0.03 0.40 1.00EN 0.01 0.00 0.03 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.00 0.00 0.03 0.00 1.00 0.07 0.01 0.54 0.78POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.05 0.00 0.07 0.00 0.27 0.10 0.02 1.00 0.90NEWBRUNSWICKALW 0.41 0.28 0.16 0.00 0.28 0.84 0.03 0.40 1.00EN 0.01 0.00 0.03 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.04 0.00 0.07 0.00 1.00 0.07 0.01 0.54 0.78POF 0.28 0.27 0.00 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.22 0.00 0.28 0.00 0.27 0.10 0.02 1.00 0.90SASKATCHEWANALW 1.00 0.21 0.99 0.00 0.21 0.62 0.02 0.29 0.74EN 0.23 0.00 0.30 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.46 0.00 0.60 0.00 1.00 0.07 0.01 0.54 0.78POF 0.34 0.27 0.09 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.43 0.00 0.54 0.00 0.27 0.10 0.02 1.00 0.90NOVA SCOTIAALW 0.65 0.28 0.46 0.00 0.28 0.84 0.03 0.40 1.00EN 0.04 0.00 0.06 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.13 0.00 0.19 0.00 1.00 0.07 0.01 0.54 0.78POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.36 0.00 0.46 0.00 0.27 0.10 0.02 1.00 0.90ALBERTAALW 0.98 0.18 1.00 0.00 0.18 0.55 0.02 0.26 0.65EN 0.33 0.00 0.43 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.49 0.00 0.64 0.00 1.00 0.07 0.01 0.54 0.78POF 0.36 0.27 0.11 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.52 0.00 0.65 0.00 0.27 0.10 0.02 1.00 0.90PRINCE EDWARD ISLANDALW 0.34 0.28 0.07 0.00 0.28 0.84 0.03 0.40 1.00EN 0.04 0.00 0.07 0.00 0.17 0.20 0.02 0.01 1.00GLW 0.00 0.01 0.02 0.01 1.00 0.08 0.02 0.54 0.78POF 0.28 0.27 0.01 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.01 0.00 0.02 0.00 0.27 0.10 0.02 1.00 0.90NUNAVUTALW 0.81 0.01 1.00 0.00 0.01 0.04 0.00 0.02 0.05EN 0.80 0.00 1.00 0.00 0.02 0.02 0.00 0.00 0.10GLW 0.79 0.00 1.00 0.00 0.30 0.02 0.00 0.16 0.23POF 0.58 0.27 0.38 0.00 0.11 0.07 0.01 0.02 1.00DNR 0.80 0.00 1.00 0.00 0.03 0.01 0.00 0.11 0.1067 These normalized indicators were multiplied by NOGEPA weights and aggregated to find the environmental score of different fuel options. Figure 4-5 shows the environmental score of different fuel options for different provinces. Accordingly, OP1_EV, OP3_HFCV, and OP6_HFCV were shown as better fuel options for British Columbia, Manitoba, Quebec, Newfoundland and Labrador, Yukon, Ontario, New Brunswick, Nova Scotia, and Prince Edward Island. Only OP6_HFCV was shown as a better option for Saskatchewan, Northwest Territories, Alberta, and Nunavut. Figure 4-5 Environmental Scores of Alternative Fuel Options (Most Likely and Conventional) Accordingly, EV operation emissions are highly dependent on the provincial grid-electricity mix and the emissions of the HFCVs highly depend on the primary energy source that is used to produce hydrogen fuel cells. The provinces with low-emission electricity such as Manitoba, British Columbia, Quebec, and Prince Edward Island, show high potential for EVs. Moreover, the use of HFCVs powered by hydrogen fuel-cells produced using alkaline water electrolysis from low-emission electricity shows the lowest impact compared to other alternative vehicle options. However, the mass production and distribution of hydrogen fuel cell energy needs to be developed throughout the country in a cost-effective manner. Therefore, the LCC of the aforementioned fuel options needs to be considered in decision making on a sustainable alternative fuel option for the 68 future. Hence, the LCC-based cost parameter would benefit from selecting the final alternative fuel option for Canadian provinces. 4.3.4 Life Cycle Costs of Different Fuel Options Generally, the switching cost of the vehicle, infrastructure, and operational expenditure are high when substituting conventional products with innovative products. For alternative fuel vehicles, the daily operating cost consists of vehicle re-fueling cost and maintenance cost. Based on the literature, the hydrogen fuel cost is two times higher than the gasoline cost and approximately four times higher than the domestic electricity cost. Hence, conventional gasoline vehicle users are more likely to switch over to EVs than HFCVs by only considering the economic perspective. However, the cost of hydrogen fuel depends on the method of production, distribution and other supply chain costs. Therefore, the actual hydrogen cost and production capacity can vary from one province to another. The calculated LCC for electric, hydrogen fuel cell, and gasoline fuel-based light-duty vehicles are shown in Figure 4-6. The detail calculations of LCC of EVs and ICEVs are shown in Appendix C1 and Appendix C2, respectively. Figure 4-6 Provincial-based LCC for Electric, Gasoline, and Hydrogen Light-duty Vehicles LCC for HFCV was assumed as fixed for all fuel options since there are not many details in the literature regarding mass commercial production and retailing of Hydrogen in different regions in Canada. The cost of mass hydrogen production using natural gas reforming is approximately CAD 5per kg of HFCs, whereas other methods show higher costs due to the non-availability of mass 0100002000030000400005000060000 LCC-ICEV LCC_EV LCC-HFCV69 production capacities [213]. Hence, the well-to-pump costs of all hydrogen fuel cell production methods were assumed as similar to the hydrogen fuel cell production using natural gas gasification. Accordingly, Figure 4-6 shows that the LCC of HFCV is the highest of all the three alternative fuel light-duty vehicles. However, as a percentage, the gap between ICEV and EV life cycle costs is less than 10% in Quebec, British Columbia, Manitoba, Yukon, and Newfoundland and Labrador, where low-cost green electricity is available. According to the above analysis, the alternative fuel selection criteria is not straight-forward due to the environmental concerns of the government and the cost sensitivity of Canadian consumers. Therefore, a scientific-based decision-making approach needs to be incorporated to select the most desirable fuel option for each province, considering environmental, economic, and regional variations. 4.3.5 Eco-efficiency-based Alternative Fuel Option Selection As discussed in the literature section, the eco-efficiency approach was used to select the most desirable alternative fuel option for light-duty transportation in Canadian provinces. The environmental and economic scores developed in the above sections were used to compare EVs and HFCVs with conventional ICEVs. Figure 4-7 Eco-efficiency-based Comparison for Alternative Fuel Options 70 The eco-efficiency index-based comparison of different fuel alternatives is shown in Figure 4-7. Figure 4-7 indicates that light-duty electric vehicles are highly feasible in Quebec, British Columbia, Manitoba, Yukon, and Newfoundland and Labrador in terms of their life cycle GHG emissions and EV life cycle costs. Accordingly, reducing upfront costs and enhancing the recharging infrastructure network may improve EV penetration in the aforementioned regions. Provinces such as Ontario, New Brunswick, Nova Scotia, Prince Edward Island, Saskatchewan, and Alberta show relatively good potential for electric vehicle-based transportation. The use of EVs will not reduce the emissions of Nunavut and the Northwest Territories due to their high impact grid electricity, so conventional transportation methods or a renewable energy-based EV or HFCV-based transport system can be deployed to achieve provincial GHG targets in those provinces. However, the cost reduction of HFCVs and hydrogen production using alkaline water electrolysis using green electricity such as solar (PV), mini-hydro, wind-based electricity, etc. Biomass gasification and nuclear-based thermo-chemical cracking may enhance the potential for HFCVs in Canada due to their low environmental and economic impacts in the long run. 4.4 Summary The government is committed to reduce GHG emissions by promoting alternative fuel-based transportation methods in Canada. Hence, several initiatives are being deployed by the federal and provincial governments to promote alternative fuel-based vehicles. However, there is a lack of knowledge on the overall life cycle costs and environmental impacts of proposed alternative fuel vehicles due to different provincial costs, taxes, and primary energy sources. This chapter analyzed the commercial viability of the potential alternative fuel systems for Canada. As the initial phase of this work, a rule-based alternative fuel selection method was introduced to filter commercially viable low-emission fuel options. EVs and HFCVs were identified as desirable fuel options considering the fundamental environmental, economic, and social parameters. Then, a detailed life cycle cost and impact assessment were conducted to find the provincial suitability of EVs and HFCVs as substitutes for conventional fuels. The provincial fuel and electricity costs, fuel well-to-pump emissions, grid electricity mix and their emissions, impacts of different hydrogen fuel cell production methods, and different primary energy sources were considered in 71 the aforementioned analysis. The conclusions of this chapter are: 1) Low cradle-to-grave emissions can be expected from EVs compared to conventional ICEVs except for the province of Nunavut; 2) EVs have 71% more cradle-to-gate emissions than conventional ICEVs due to the environmental impacts of EV batteries; 3) HFCVs are possible only if low cost and low-emission mass hydrogen fuel cells can be produced; 4) The provinces with a high-emission grid can focus on centralized electricity production methods with carbon-capturing options, or decentralized low-emission electricity production using renewable electricity options such as solar (PV); 5) Conventional vehicles can be substituted by EVs if EV purchasing and operating costs are reduced by 6% to 22% (varied with provincial electricity prices) through favourable government policies. The methodology and the database developed in this study can be used to formulate the steps similarly for other regions and find the most desirable alternative low-emission fuel source for road transportation. 72 . Chapter 5 Electric Vehicle Recharging Infrastructure Planning and Management for Urban Centers Sections of this chapter have been published in the Journal of Cleaner Production, as an article titled âElectric vehicle recharging infrastructure planning and management for urban communitiesâ [71]. 5.1 Background Transport electrification is identified as a commercially viable solution to reduce conventional fossil fuel-based transportation in regions with a low-emission electricity grid [70][38]. Therefore, public and private investors, developers, and policymakers are focusing on electricity-based transport infrastructure investments to fulfill future electric vehicle (EV) growth. According to the literature, higher switching costs [10][11], limited vehicle range (on-board electricity storage issues) [11][12], and limited re-charging infrastructure availability/access [10][13][11] are key barriers for the growth of electrical vehicle-based transportation systems in Canada. These barriers can be resolved using a well-planned electric vehicle-recharging infrastructure (EV-RI) network that is capable of catering to total EV recharging demands while minimizing the life cycle costs of the EV transportation system. Hence, scientific knowledge in planning and management of an optimal EV-RI network is a key solution to enhance the sustainability of transportation electrification in Canada. An optimal EV-RI network will maximize recharging infrastructure availability and access, and offset the aforementioned barriers by deploying an eco-friendly transportation system. The published literature contains several studies focusing on the location-allocation of EV-RI in the past few years [32][33][34]. These studies have focused on minimizing the investments and access cost and/or maximizing the vehicle flow coverage [35][27][30][28][36][37]. Vehicle range, maximum EV-RI facility capacity, and local government policies were obtained as key constraints when selecting the most desirable locations for potential EV-RIs. However, the dynamic nature of recharging demands was overlooked in the above studies while making investment decisions. As a result, the most desirable facility location and capacity were estimated without considering different stakeholder costs (e.g. EV-RI access cost to vehicle owners, cost of facility overheads, 73 . etc.), location-based cost variations (e.g. land lease cost, grid connection cost, etc.), and dynamic variations in EVs recharging demands. This conventional approach has increased the switching costs of electricity-based transportation for both infrastructure investors and consumers and shown longer payback periods. Therefore, the resultant infrastructure plan and the payback suggested by the existing methods were far from the most desirable EV-RI locations and capacities. Long-range EV-RI capacity prediction, network planning, and a recharging management approach considering the dynamic variations of future EV recharging demands are vital in healthy decision making where investors can optimize their cash flow while facilitating the required EV-RI demand consistently [19]. This chapter proposes a comprehensive planning and management framework for a sustainable EV-RI network in an urban context by considering multi-period EV public net recharging demands, life cycle costs, and total elapsed times. This methodology consists of five key phases: 1) Life cycle cost assessment of direct-current fast-charging (DC-FC) (level III) electric vehicle recharging infrastructure; 2) Determine shortest path matrix between the centroids of all the traffic analysis zones (TAZ) and potential refueling locations; 3) Obtain factorial demands for DC-FC based EV recharging; 4) Develop a location-allocation model using multi-objective optimization; and 5) Integrate dynamic variation of EV demands and develop a comprehensive planning and management framework. A case study was conducted to demonstrate the proposed methodology by exploring the EV-RI locations and capacities for Kelowna, British Columbia, Canada, by considering location-based factors. The developed framework can help decision-makers in municipalities, EV-RI investors, and builders to predict multi-period net public recharging demands and plan EV-RI facilities accordingly. 5.2 Methodology for EV-RI Capacity Planning and Location-Allocation Framework This section discusses the methodology used to select the best desirable location and capacities for electric vehicle recharging facilities in different periods. This work started with a literature review and database development on EV-RI deployment-related information. A methodology was proposed to plan and manage an EV-RI network for urban networks considering multi-period EV demands. However, the scope of this work was limited to the deployment of public fast-recharging facilities for the use of light-duty passenger vehicles in small, medium, and large scale urban 74 . communities with complex route networks. The proposed research framework is illustrated in Figure 5-1. Figure 5-1 EV-RI Planning and Management Framework for Complex Urban Network The phases shown in Figure 5-1 are further explained below. Phase 1: Data Collection and Database Development Literature-based data is essential to identify characteristics and life cycle stages associated with state-of-the-art recharging facilities. Published high-impact peer-reviewed journal articles, 75 . conference proceedings, institutional reports, and online articles were referenced to obtain EV-RI life cycle costs, local utility rates, and other recharging infrastructure-based information. In addition to the data obtained from the literature review, municipality household survey data, location-based multi-period vehicle demands for conventional vehicles, conventional vehicle and electric vehicle growth factors, neighbourhood-based dwelling mix, and other necessary demographic data were collected to develop an application to the proposed model using a case study for a medium-scale municipality. The data collected from the above methods were arranged in a systematic database for future reference. Phase 2: Preliminary Site Selection Potential EV-RI locations were pre-screened, and selected vacant lands were considered for further evaluations of best desirable locations for future EV-RI facilities by considering the practical aspects of infrastructure planning. However, it is possible to find the best desirable locations from a given municipality without having pre-screened potential locations for EV-RI facilities. In that case, geo-processing tools can be used to create raster maps in the municipality and consider each raster cell as a potential location for an EV-RI facility. In that case, the analysis may increase the simulation time and cost due to an extensive number of iterations. Therefore, it is sensible to use pre-screened locations in the optimization process. The Site Inspection Form (SIF_2019) was developed to collect existing parking infrastructure data, which needs to be collected by multiple site visits to the potential parking infrastructure. A sample SIF_2019 is attached as Appendix D1. The data collected from the aforementioned SIF_2019 was used to develop indicator categories, indicators, and indicator levels, which were used to develop the preliminary site selection framework. The pre-feasibility planning framework developed by the University of British Columbia Transportation Infrastructure and Public Space (UBC-TIPS) Laboratory in 2012 [50] was used to identify the indicators and indicator categories for the proposed preliminary site selection framework. The relevant indicators and indicator levels are shown in Table 5-1. Accordingly, the data obtained from SIF_2019 was transferred into Table 5-1 using linguistics terms (LT) and converted to numerical values using the given Likert scale (LS) (á¿¶pqr) given in the same table for each indicator level. The weights of the indicator category and indicators (WPQ) of the matrix were weighted using expert opinion based on the decision-makers' perspective and the 76 . conditions of the communities. Decision-makers such as public sector planners, city, utility providers, and other interested stakeholders can be used to generate the aforementioned weights using focus group meetings with the selected subject experts. The normalized weight (á½¦pqr) of each indicator level was obtained from Equation (23). á½¦ððð = á¿¶ððð â á¿¶ðððð¹ð=ðâ Equation 23 Where, p = Indicator categories (âð = 1,2, â¦ , ð); q = Indicators (âð = 1,2, â¦ , ð); r = Indicator levels (âð = 1,2, â¦ , x, â¦ , ð ); á¿¶pqr = Nominal weight of rth indicator level at pth indicator category level and qth indicator, á½¦pqr = Relative importance (normalized weight) of rth indicator level at pth indicator category level and qth indicator The weighted sum model was used to obtain the Priority Index (PI) of a particular site, which was then used to rank multiple sites and select suitable parking infrastructure for the optimization-based EV-RI capacity planning module. Accordingly, the PI of a particular parking infrastructure was obtained from Equation (24). PI(m) = â â â á½¦pqr . WpqRr=1Qq=1Pp=1 Equation 24 Where, m = Number of potential EV-RI sites in the study area (âð = 1,2, â¦ , ð); p = Indicator categories (âð = 1,2, â¦ , ð); q = Indicators (âð = 1,2, â¦ , ð); r = Indicator levels (âð =1,2, â¦ , x, â¦ , ð ); Wpq = Weightage of the pth indicator category and qth indicator; á½¦pqr = Relative importance (Normalized weight) of rth indicator level at pth indicator category level and qth indicator, PI(m) = Priority Index of mth parking infrastructure. 77 . Table 5-1 Indicators for Preliminary Site Selection Indicator Category (P) Indicators (Q) Indicator Level 1 (r=1) Indicator Level 2 (r=2) Indicator Level 3 (r=3) Indicator Level 4 (r=4) Existing Condition of the parking facility Availability of EV recharging infrastructure LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 The average size of the parking infrastructure LT High Average Low Not Available LS 3 2 1 0 The average size of the parking stall LT High Average Low Not Available LS 3 2 1 0 Need for re-sizing parking stalls for mobile-hub LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Land-Use requirements Access to the nearest highway LT High Average Low Not Available LS 3 2 1 0 Access to the nearest interchange LT High Average Low Not Available LS 3 2 1 0 Access to traffic from multiple directions LT High Average Low Not Available LS 3 2 1 0 No. of lanes of the access road LT 1 2 3 More (4 or 5) LS 4 3 2 1 Posted speed of the access road (Max) - V LT V<49km/h 50km/h<V<69km/h 70km/h<V<100km/h 100km/h<V LS 4 3 2 1 Availability of disability access LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 Utility requirements Step-down transformers LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 Step-up transformers LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 Electricity availability LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 Proximity to the nearest Utility Service Panel LT Not possible to install High Average Available in the site LS 0 1 2 3 Availability of lighting LT Not possible to install Expensive to Install Cheap to Install Already Available LS 0 1 2 3 78 . Indicator Category (P) Indicators (Q) Indicator Level 1 (r=1) Indicator Level 2 (r=2) Indicator Level 3 (r=3) Indicator Level 4 (r=4) Comply with regulatory requirements Satisfy with Canadian electricity codes LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Satisfy with Canadian occupation health and safety (COHS) regulations LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Satisfy with Workers compensation act (WCA) LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Satisfy with local by-laws LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Satisfy with local building codes LT Satisfied Partly Satisfied Not Satisfied Not Applicable LS 3 2 1 0 Access to renewable sources Accessibility to the district energy system LT High Average Low Not Available LS 3 2 1 0 Accessibility to renewable energy sources LT High Average Low Not Available LS 3 2 1 0 Safety-related requirement Standing water/flood issues LT High Average Low Not Available LS 3 2 1 0 LT â Linguistic Terms LS â Likert Scale 79 . Phase 3: Development of the Distance Matrix The municipality under consideration can be segregated geographically into multiple Traffic Assessment Zones (TAZ) in order to determine micro-level trip distribution throughout the municipality. Accordingly, the TAZ map of the considered municipality was collected from the respective municipality. Two types of trips were identified from the municipal trip database: 1) Trips generated from one TAZ to a different TAZ were defined as inter-zonal trips; and 2) Trips generated from a specific TAZ to the same TAZ were defined as intra-zonal trips. For a given municipality, the trip distance matrix consists of shortest distances of inter-zonal trips, intra-zonal trips, and recharging trips from the selected origin to the destination through an available recharging facility. Figure 5-2 shows the development of the distance matrix. Figure 5-2 Development of Distance Matrix In this work, centroids of TAZs are considered trip generation and trip attraction points. ArcGIS geo-processing tools were used to obtain the centroid of each TAZ. These centroids were used as the trip generator and trip attraction of each TAZ. The inter-zonal, intra-zonal, and shortest distance via recharging infrastructure can be further explained as follows: 1) The distance of inter-zonal trips: There are several methods that can be used to find the shortest distance between two points. Dijkstraâs algorithm is one of the common methods used to solve the single-source shortest path problem [221]. Dijkstraâs algorithm can be explained using triangular inequality shown in Equation (25). 80 . d(Oi,Dj) â¤ d(Oi,Y) + d(Y ,Dj) Equation 25 Where, ð(ðð,ð·ð) - The shortest path distance between Oi and Dj (km), ð(ðð,ð) â The distance between the origin Oi and the variable point of Y (km), ð(ð ,ð·ð) - The distance between the variable point Y to the destination Dj (km) 2) The distance of intra-zonal trips: According to Parker et.al. (1989), a TAZ can be assumed as a nearly circular shape and the population is distributed uniformly through out the area of the TAZ [222]. Therefore, the intra-zonal distances of a TAZ can be calculated using Equation (26) [222]. dii = 0.846 . â Ai Ïâ Equation 26 Where, dii â Intra-zonal distance of ith TAZ (km), Ai â TAZ ith area (sq. km) 3) Shortest distance from the trip origin to destination via an available EV-RI location: Feasible locations for EV-RI facilities (Rm) were obtained using the previously discussed preliminary site selection framework. The distance from the origin to the selected future EV-RI location (Oi~Rm) and the distance from a future EV-RI location to the trip destination (Rm~Dj) were calculated for all possible route combinations. However, some assumptions were made to ease the simulation. There are: 1) A particular vehicle recharges on its way to the destination or while it is returning to the origin (assumed no additional trips were generated for recharging purposes); 2) The vehicle follows the shortest path throughout the trip. Accordingly, the recharging trip requires a detour from the original route, which increases the burden on EV consumers. The origin, EV-RI point, and destination were arranged in ascending order to find the best desirable location for potential EV-RI where the lowest distance can be achieved. Dijkstraâs algorithm was applied to obtain the shortest path. The path from origin to the EV-RI and EV-RI to destination were determined and the extra mileage was used as the input parameter to the optimization model. ð(ð,ð) = ð(ðð,ð) + ð(ð,ð·ð) Equation 27 Where, ð(ð,ð) = Shortest distance of nth trip through mth recharging facility (Oiâ Rm~Dj) (km), ð(ðð,ð) = Shortest distance from origin ith to mth recharging facility (Oi~Rm) (km), 81 . ð(ð,ð·ð) = Shortest distance from mth recharging facility to destination jth (Rm~Dj) (km), n = Route which has the origin of Oi and the destination of Dj , m = Potential EV-RI The literature revealed that the Geographic Information System (GIS) can be used as a spatial analytical tool to conduct a network-based analysis in complex urban systems [29][149]. In the proposed tool, the network analyst extension of ArcGIS was used to find the aforementioned distance matrices of a given route network. The model builder module of ArcGIS was used to develop an iteration-based simulation to program Dijkstraâs algorithm to obtain the all-to-all distance matrix (shown in Figure 5-2) using the pre-screened potential locations for future EV-RIs. Python programming script was used to record the results of the above iterations and a JavaScript Object Notation (JSON) file was developed as a result of the simulation. The JSON file was used as an input file for the optimization model. Phase 4: Module to Determine Multi-Period Public Fast-Charging Demands This module was developed using the data obtained from the location-based databases. Municipal household travel surveys data were used to obtained origins, destinations, and the amount and type of municipal trips. The following assumptions were made to find the public fast-charging (DC-FC) demands of the selected municipality: 1. Factored vehicle growth (gvt) rates and multi-period EV demands (Revt) for the region were obtained from the literature and institutional reports. This was used to obtain the multi-period recharging demands within the considered TAZ. 2. Range (FREV) can be described as the mileage expected by the consumer in a single refueling/recharging cycle [12][140]. However, according to Frank A, et al., (2013), EV users are comfortable utilizing approximately 75%-80% of their FREV before they recharge their EVs [47]. Therefore, EV recharging frequency was calculated by considering the number of days that a typical EV consumer needed to utilize 80% of EV range. 3. The number of O-D trips was obtained from the trip survey data and assumed that the travel pattern of EVs is similar to the general travel patterns of the municipality. Trip distances were assumed as the shortest path from trip origin to the destination. 4. The availability of home-based recharging facilities causes a reduction in potential demand for public recharging facilities [10]. Moreover, the availability of off-street dedicated parking space with access to grid electricity also improves the viability of domestic recharging. However, due to a variety of parking configurations and a lack of local parking 82 . information, this tool assumed the owners who are staying in apartments, townhouses, and other multifamily residences do not own home recharging facilities and do not have the potential to share a neighborâs recharging facilities [223]. Accordingly, the âhome-based recharging ratio (RHC)â was defined based on the availability of multi-family residences as a percentage of total residences of the considered TAZ. 5. The overall contribution of the privately-owned and operated recharging facilities (e.g. offices, hotels, shops, etc.) was obtained as a percentage of total recharging demand, which was assumed as âother recharging impactsâ (Ro). The EV recharging demands for the tth year from Oi to Dj (Route n) was calculated using Equation (28) below. TEV(Oi,Dj)t = 1 0.8 . FREVâ (T(Oi,Dj)o . d(Oi,Dj). gvt . REVt . RHC. Ro) Equation 28 Where, ð(ðð,ð·ð)-The shortest distance between Oi and Dj (km), ð(ðð,ð·ð)ð - Traffic demand from origin to destination at the base year (vehicles/day), ðð£ð¡ â The vehicle growth rate from base year to tth year in the considered TAZ (%), ð ð¸ðð¡ - Estimated electric vehicle registered as a percentage of the total vehicle registered as at tth year (%), ð ð»ð¶- Home-based recharging ratio (as described in phase 3), ð ð- Other recharging impacts (E.g., impact on work-based recharging, public slow recharging etc.), FREV â EV range or millage run per a single recharging cycle (km/recharge), ðð¸ð(ðð,ð·ð)ð¡ - EV traffic demand from origin to destination (nth route) at tth year (vehicles/day) Phase 5: Life Cycle Cost Assessment The life cycle cost (LCC) of electric vehicle recharging infrastructure can be defined as the total cost of ownership associated with the recharging facility over its total lifetime, which consists of DC-FC units, installation cost, cost of civil works, operational cost, maintenance cost, repair cost, recycle cost, and end-of-life cost. The investment cost of a recharging facility was kept to a minimum by assuming that all EV-RIs are connected to the same grid and do not have an alternative in-site electricity generation method. The LCC for the kth EV-RI at time of t (ð¿ð¶ð¶ðð¡) can be expressed as Equation (29). 83 . ð¿ð¶ð¶ðð¡ = â ð(ð,ð)ð¡ðð=1 (ð½. ð¶ + ð½. ð¿ð¶ð + ð½. ð¸ð¼ + ð½. ð¶ð + ð¸ð¶ + ðð¹ + ð½. ðð¹ + ð½. ð ð¹) + ðð¶ Equation 29 Where NUkt = Number of DC-FC units proposed in the kth EV-RI at the time of t, which is also equal to β. â X(k,n)tNn=1 , β = Average daily recharging capacity of a typical DC-FC port (charger/vehicles.day), C = Unit investment of DC-FC unit (CAD/unit), LCk = Present worth of total land rent/cost/lease per unit DC-FC unit in the kth EV-RI (CAD/unit) (According to EV-RI design guideline provided by UBC-TIPS Lab in 2015 [224], the accessible parking space is approximately 60m2 per DC-FC unit), EI = Unit cost of the electrical installation of DC-FC (CAD/unit), CW = Cost of civil work per DC-FC unit (e.g. paving, curbs etc.) (CAD/unit), MF = Present worth of total management fee (paid annually) per unit (CAD/unit), RF = End-of-life cost per unit (CAD/unit), X(k,n)t = Total number of vehicles used nth route and get served from kth facility at time t, EC = Present worth of electricity units consumer by an average vehicle (CAD/vehicle), NF = Present worth of network fee per vehicle recharge (CAD/vehicle), MC = Present worth of total miscellaneous fee incurred by the EV-RI (CAD/facility) Phase 6: Multi-Objective Optimization-Based EV-RI Capacity Planning Multiple objectives were considered to obtain the optimal recharging facility locations and capacities considering multi-stakeholder perspectives. EV consumers and EV-RI investors were considered the key stakeholders in the EV-RI network development process. Therefore, the consumer and investor perspectives were optimized using the following methodology. EV consumer perspective: Due to the ad-hoc placement and lack of available recharging facilities, consumers should drive an additional mileage (detour distance) to reach recharging facilities, which acts as a barrier to switching from conventional transportation. Therefore, this additional distance needs to be minimized in order to increase usersâ satisfaction with electrified transportation. Accordingly, Equation (30) was used to minimize the aforementioned detour distance for all EV trips in the municipality. In addition, the range anxiety of potential EV consumers can be lowered by providing sufficient recharging infrastructure access and availability that may encourage potential vehicle users to switch from conventional vehicles to EVs [24][10][182]. Therefore, the proposed recharging network service coverage should be maximized to meet the overall public recharging demands within 84 . the considered municipality. However, there is a capacity constraint in each EV-RI facility, hence the EV recharging demand serviced by the particular facility should be less than or equal to the maximum capacity of the specific recharging facility. Both the demand and capacity constraints were defined as follows to develop this module in order to obtain the optimal EV-RI network. Z1 = Min(â â (X(m,n)tNn=1Mm=1 . d(m,n))) Equation 30 Subject to, EV-RI capacity constraint, â ð(ð,ð)ð¡ðð=1 â¤ β. ðððð¡ EV-RI demand constraint, â ð(ð,ð)ð¡ðð=1 = ðð¸ð(ð,ð)ð¡ ð(ð,ð)ð¡ â¥ 0 âð, ð, ð¡ Where, m = Number of EV-RIs in the study area (âð = 1,2, â¦ , ð), n = Total number of routes (âð = 1,2, â¦ , ð), t = Considered time periods (âð¡ = 1,2, â¦ , ð), ð(ð,ð)ð¡ = No. of EVs that are traveling from nth trip (OiâDj) served from mth EV-RI at time t, ð(ð,ð) = Detoured distance of nth trip (OiâDj) through mth EV-RI facility, ðððð¡ = Number of DC-FC units proposed in the mth EV-RI at the time of t, β = Average daily recharging capacity of a typical DC-FC port (charger/vehicles.day), ðð¸ð(ð,ð)ð¡ = EV traffic demand from origin to destination (nth route) at tth year (vehicles/day) EV-RI investor perspective: Financial benefits are essential to encourage more investments in the early stages of the industry. Having lower investments and higher returns may encourage investors to invest more [177][181]. Aligning EV-RI investments with accurate EV-RI DC-FC demands will provide required recharging availability and optimal investments. In addition, lowering investments will result in lower recharging rates, which may encourage EV consumers to use more public recharging facilities [24][10][182]. Although the unit costs of DC-FC stations (e.g. investment cost, civil work, installation, etc.) do not vary based on location, Equation (6) shows that the land acquisition costs, recharging facility size, and shape do vary by location as well as the location-based demands. However, EV-RI demand is not uniformly distributed throughout the day, resulting in underutilized DC-FC units in the facility. Therefore, the number of units in the facility will be estimated based on DC-FC port to EV ratio (β), which depends on regional travel behaviours, location type, and regional charging 85 . behaviours [223]. Equation (31) was used to incorporate the aforementioned aspect into the optimization module. ð2 = ððð (ð½. â â ð¿ð¶ð. ð(ð,ð)ð¡ðð=1ðð=1 ) Equation 31 Subject to, ð(ð,ð)ð¡ â¥ 0, âð, ð, ð¡ Where, m = Number of EV-RIs in the study area (âm = 1,2,â¦.,M), n = Total number of routes (ân = 1, 2,â¦..N), t = Considered time periods (âð¡ = 1,2, â¦ , ð), ð(ð,ð)ð¡ = No. of EVs that are traveling from nth trip (OiâDj) served from mth EV-RI at time t, ð(ð,ð) = Detoured distance of nth trip (OiâDj) through mth EV-RI facility, ðððð¡ = Number of DC-FC units proposed in the mth EV-RI at the time of t, β = Average daily recharging capacity of a typical DC-FC port (charger/vehicles.day), ðð¸ð(ð,ð)ð¡ = EV traffic demand from origin to destination (nth route) at tth year (vehicles/day) The Linear Programming (LP) method was used in this tool to obtain the most desirable EV-RI locations and capacities for a given period. The linear objectives were enable the use of LP model that can handle comparatively big databases by using user-friendly tools. Moreover, LP models are flexible in terms of adding new constraints into the optimization algorithm. Accordingly, the proposed methodology was used to obtain the multi-period EV-RI network for a medium-scale municipality in British Columbia, Canada. Each period (t) was analyzed separately and combined to obtain the complete EV-RI development plan for the selected municipality. 5.3 Case Study-based Model Demonstration The Census Metropolitan Area of Kelowna, which is located at Southern interior British Columbia, was the primary focus are in this case study. Kelowna has a population of 194,885, a total of 81,380 households as of 2016, and it is distributed into 181 Traffic Analysis Zones (TAZ) [225]. In 2015, Kelowna was declared the fastest growing population in Canada, with a population growth rate of 3.1% [225]. The City of Kelowna was consulted to obtain the data required to conduct the case study. The data obtained from the municipality consist of household survey data, multi-period conventional vehicle demands, vehicle growth factors, neighbourhood-based dwelling mix, and other necessary demographic data. The Official 86 . Community Plan (OCP) was also referenced to identify the short-term objectives of the local government and the budget allocated for the development of green transportation. 5.3.1 Data Migration and Development of the Optimization Model The data obtained from the aforementioned sources were migrated to developed databases and formulated optimization module based on the given methodology. Accordingly, the following factors and assumptions were made to carry out the analysis. 1. The unit cost of land ð¿ð¶ðrequired for the facility was assumed based on real estate prices in Kelowna (see Table 5-2). The average land requirement per DC-FC recharging unit was assumed as 60m2 [50]. Table 5-2 Potential EV-RI Locations for Kelowna, BC Location ID (m) Neighborhood Longitude Latitude TAZ No. Land cost Factor (LCm) (CAD/ m2) Max. Capacity (NUm) 1 LAKESHORE/PANDOSY -119.491 49.86083 107 2,500 10 2 UNIVERSITY PLAZZA -119.389 49.92223 154 2,900 10 3 MCCURDY CORNER -119.405 49.90209 63 3,200 20 4 COOPER CENTRE -119.445 49.88261 52 3,500 10 5 BANKS-SPRINGFIELD -119.425 49.88871 55 3,500 10 6 CAPRI CENTRE MALL -119.475 49.88117 34 3,200 16 7 ORCHARD PLAZA -119.469 49.88195 35 3,600 10 8 SPALL PLAZA -119.458 49.8821 28 3,300 16 9 GLENMORE -119.443 49.91531 160 2,500 20 10 RICHTER -119.49 49.88477 24 3,800 10 11 CLEMENT -119.476 49.89292 15 2,600 10 12 SPRINGFIELD -119.456 49.87627 50 2,600 20 13 BANKS -119.428 49.8836 58 3,600 10 14 RUTLAND -119.398 49.89007 77 2,000 20 2. The traffic demands data relevant to the 2014 household travel survey was obtained from the municipality. Accordingly, the forecasted traffic growth from the year 2014 to year tth (ðð£ð¡ ) was assumed as 43% in 2020- Period 1 (gv1), 45% in 2030-Period 2 (gv2), 50% in 2040-Period 3 (gv3) and 55% in 2050-Period 4 (gv4). 3. According to the studies conducted in BC, Canada, light-duty electric vehicle stock was estimated as 4% in 2020 (Rev1), 24% in 2030 (Rev2), 71% in 2040 (Rev3) and 98% in 2050 (Rev4) by assuming that strong government policies force the adoption of EVs [226]. 4. R0 and RHC were assumed as 1, considering there is a negligible number of home and workplace recharging facilities due to the lack of available databases. 87 . 5. DC-FC port to EV ratio (β) was assumed as 1/10. Accordingly, it was assumed that the average recharging rate is 10 vehicles per day and mostly done during peak hours. The actual average EV recharging data were obtained from several existing EV-RI facilities and the average was calculated accordingly. Geographic maps (shape files) were collected from the City of Kelowna. This data consists of the city boundary, TAZ boundaries, road network, and other relevant geographic data. Those were imported to ArcGIS and centroids of TAZs were mapped. 5.3.2 ArcGIS-based Distance Matrix The model-builder module of ArcGIS software (ArcMap 10.4.1) was used to formulate a network analysis-based model. According to the aforementioned methodology, the distance matrix was calculated and recorded as JSON files. The overall model developed from the model builder is shown in Figure 5-3. A custom-made python script was used to record the distance matrix and create the JSON file. As per the case study data, 180 trip origins (Oi) were considered with the same 180 trip destinations (Dj). The model hierarchy 2 and 3 were used to find the shortest distance of all the OD pairs (ð(ðð,ð·ð)ðð ð(ð,ð)) and formulate those as base OD matrix (180 x 180). The elements of the OD matrix were considered as routes (n=32,400). The pre-screened locations (Rm) were selected for potential recharging infrastructure (m = 14), shown in Table 5-2. All the models given in Figure 5-3 were used to find the shortest routes from all origins to all destinations through all Rm locations (ð(ð,ð)). This was formulated as a new matrix that contained 453,600 elements (32,400 x 14), which were recorded as a JSON file. This model was simulated for approximately 72 hours using an Intel Core I7-7700 16GB desktop computer. The resultant JSON file was used as an input to the optimization model. 88 . Figure 5-3 Model Developed to Formulate Distance Matrix Using ArcGIS Model-Builder 89 . 5.3.3 Optimal Capacity Planning and Location Allocation Model for EV-RIs The objective functions were combined and derived as a single fitness function f(x(i,j)), given below. Three weight schemes can be obtained using multi-stakeholder perspectives and obtain the fitness function. These are: 1) Buyerâs perspective: more weight to consumer elapsed distance/time; 2) Investorâs perspective: more weight to investor cost; and 3) Balance perspective: equal weights to buyers and investors. However, due to the non-availability of exact data on investorâs and buyerâs perspective, equal weights were considered to each objective while creating the fitness function. Therefore, w1 and w2 were introduced as 0.5 and 0.5 to develop the f(x(m,n)), and linear scaling transformation-based normalization was used to convert input parameters to align with the fitness function. The fitness function for the case study problem is shown in Equation (32). The source code developed for this problem is given in Appendix D2. f(x(m,n)) = ( w1 Z1 + w2Z2) Equation 32 Subject to, â ð(ð,ð)ð¡32,795ð=1 â¤ β. ðððð¡ â ð(ð,ð)ð¡14ð=1 = ðð¸ð(ð,ð)ð¡ ð(ð,ð)ð¡ â¥ 0 âð, ð, ð¡ Where, Using Equation (29) , ð1 = ððð(â â (ð(ð,ð)ð¡32,975ð=114ð=1 . ð(ð,ð))) and using Equation (8), ð2 = ððð (10. â â ð¿ð¶ð. ð(ð,ð)ð¡32,975ð=114ð=1 ), ð¤1 and ð¤2 = Weights of each objective function in order to obtain the fitness function, m = Number of EV-RIs in the study area (âð = 1,2, â¦ ,14), n = Total number of routes (âð = 1,2, â¦ ,32975), t = Period 1-2020 to period 4 - 2050 (âð¡ =1,2,3,4), ð(ð,ð)ð¡ = No. of EVs, which are traveling from nth trip (OiâDj) served from mth EV-RI at time t, ð(ð,ð) = Detoured distance of nth trip (OiâDj) through mth EV-RI facility (As per the results obtained from the ArcGIS model), ðððð¡ = Number of DC-FC units proposed in the mth EV-RI at the time of t (see Appendix D3), ðð¸ð(ð,ð)ð¡ = EV traffic demand from origin to destination (nth route) at tth year (vehicles/day) Linear programming (LP) is a mathematical method for determining a way to achieve the best outcome within the defined constraints. A single-index transportation problem [227] was 90 . formulated based on the linear programming method to solve the above EV-RI location-allocation and capacity planning problem. The model was created to minimize f(x(m,n)) in order to find the optimal location and capacities of EV-RI infrastructures in a given time t. The dual simplex method of the IBM ILOG CPLEX Studio IDE 12.8.0 was used to run the simulation and obtain the optimization results. This simulation was consumed approximately 30 minutes to complete the overall calculations using an Intel Pentium (R) 1.90GHz 8GB laptop computer. Then the developed model was validated using the software-generated curves for different capacities and infrastructure locations. 5.3.4 Case Study: Results and Discussion Based on the assumptions made in section 5.3.1, the optimal EV-RI network proposed for Kelowna, BC will provide access to 14,400 consumers in 2020 (t1), 93,600 consumers in 2030 (t2), 306,000 consumers in 2040 (t3), and 468,000 consumers in 2050 (t4). The multi-period DC-FC recharging network development plan is shown in Figure 5-4 and the resultant capacities with the most desirable locations for multi-period EV demands are shown in Appendix C. According to the life cycle cost calculations, the investment (cradle-to-gate) cost for the proposed EV-RI network is CAD 392,400 in 2020, CAD 2,158,200 in 2030, CAD 5,784,900 in 2040, and CAD 4,414,500 in 2050. The operational cost was calculated assuming a tier-2 domestic electricity rate of CAD 0.15/kWh [228] and 2% interest rates for the land lease [70]. Assuming 80% recharging of 30kWh battery with 340Wh/km electricity consumption, the recharging capacity per vehicle was estimated as 15.2kWh/recharge [70][229]. Therefore, considering 10 vehicles per day for each DC-FC station, the annual operational electricity cost per DC-FC station was estimated as CAD 8,208/year. The land lease costs were calculated as CAD 35,226/year in 2020, CAD 226,035/year in 2030, CAD 741,586/year in 2040, and CAD 1,039,175/year in 2050, considering a 20year mortgage term. The total annual operating cost was estimated as CAD 8,633/year by considering the annual networking cost of CAD 225/year and the annual DC-FC maintenance cost of CAD 200/year. The overhead costs and end-of-life cost of EV-RI stations were considered negligible. The total revenue of the DC-FC unit was calculated as CAD 30,636/year by assuming CAD 8.51 (CAD 7.06 recharging fee and CAD 0.91 transaction fee) per typical charging event for a consumer [230]. The simple payback periods of the multi-period EV-RI investments are shown 91 . in Table 5-3. Here, the DC-FC cost reduction rate due to economy of scale and technology advancement was assumed as the same as the discount rate. Table 5-3 Simple Payback Calculations Period 1:2020 Period 2:2030 Period 3:2040 Period 4:2050 Total DC-FC units 4 26 85 130 Capital cost CAD 392,400 CAD 2,158,200 CAD 5,787,900 CAD 4,414,500 Variable (land)- Annual CAD 35,226 CAD 226,035 CAD 741,586 CAD 1,039,175 Electricity cost- Annual CAD 32,832 CAD 213,408 CAD 697,680 CAD 1,067,040 Revenue - Annual CAD 122,544 CAD 796,536 CAD 2,604,060 CAD 3,982,680 Simple payback (years) 7.20 6.04 4.97 2.35 Multi-period payback periods were compared with the EV trip growth in the municipality as shown in Figure 5-5. According to Figure 5-5, the payback of the proposed EV-RI network will be higher in the early stages of the EV deployment process, due to the low number of consumers and the capital costs needed to initiate these EV-RI facilities. This payback will be gradually reduced with the maturity of the EV market, which may enhance the investment opportunities and benefits for the infrastructure investors. 92 . Figure 5-4 Multi-Period Improvement Plan for Public Recharging Infrastructure Network for Kelowna, BC 93 . Figure 5-5 EV-RI Network Demand vs. Payback Period Accordingly, financial incentives and government corporations are needed in the early stages of the EV-RI deployment process. Reducing the initial investments through interest-free loans, delayed mortgage payment mechanisms, government tax benefits, reward programs, and public-private partnerships for the development of EV fast-charging facilities may reduce the payback period obtained above. The low paybacks may enhance potential EV-RI investments, which will be a better solution to cover the existing and potential recharging infrastructure gap in the community. 5.3.5 Case Study: Model Validation The framework validation is vital to determine the effectiveness and reliability of the proposed method compared to the conventional EV-RI planning approach. There is a lack of information in location planning and expansion scheduling for EV-RI placement in complex networks. According to municipal expert interviews, municipal planning of the aforementioned EV-RIs is conducted using expert recommendations, municipal by-laws, and general infrastructure guidelines. However, there are no dedicated frameworks or tools to compare and improve those recommendations using a scientific background. In that case, model validation can be done using two approaches. There are: 1) Validate based on software-generated alternative capacities and locations (random-bases); and 2) Validate based on expert-based alternative capacities and locations (conventional-based planning). Both validation results are explained below. EV-RI demands per year Simple pay-back period (years) 0500001000001500002000002500003000003500004000004500005000000.001.002.003.004.005.006.007.008.00Period 1:2020 Period 2:2030 Period 3:2040 Period 4:2050Simple payback Average demand of the period94 . Validated based on the software-generated locations and capacities The simulation was conducted as dual optimization, where the minimization function was initially converted to a maximization function. The solution to the dual problem provides a lower bound to the solution of the primal (minimization) problem [231]. Hence, the software used an iterative approach to find the maximized value of the given optimization problem. Accordingly, the software itself generated random alternatives during the simulation and obtained the maximized value for the dual function. Those curves related to the years 2040 and 2050 can be used to prove the validity of the obtained answers (very few iterations were simulated to solve period 1 & 2 due to the simplicity of the optimization model). Figure 5-6 shows the multiple iterations and the corresponding dual f(x(m,n)) values based on the data obtained from the CIPLEX model. Accordingly, the final iterations show the maximum value that is optimal for this problem. Hence, the software-based value can be identified as the optimal capacities and the locations for the EV-RI infrastructure planning problem. Validated based on locations and capacities generated using the conventional approach In this case, the expert opinion was considered to develop multiple scenarios and the results of the aforementioned case study were considered as the base case scenario. Accordingly, six possible scenarios were developed using expert opinion-based placements and capacities, which are shown 76200764007660076800770007720077400776000 2000 4000 6000 8000 10000 120001160001170001180001190001200001210001220001230000 5000 10000 15000 20000Period 3 - 2040 Period 4 - 2050 dual f(x(m,n)) dual f(x(m,n)) No. of iterations No. of iterations Figure 5-6 Results of Software Generated Dual Fitness Functions for Multiple Iterations 95 . in Table 5-4. The results obtained from the proposed framework were compared with expert-developed scenarios to validate the effectiveness and reliability of the proposed method. The proposed model was used to generate the life cycle cost, payback, and catered demand results for developed scenarios. Figure 5-7 shows the results of the aforementioned scenarios in terms of payback period, unutilized capacity, and required capacity. Those results were compared with the optimistic or the base-case scenario to find best scenario considering the multi-stakeholder perspective. Table 5-4 Scenarios Developed for Model Validation Scenario No. Description Additional recharging capacities as a percentage of estimated total EV net public fast charging demands for year 2050 2020 2030 2040 2050 1 Pessimistic Scenario (Full demands in 2020) 100% 0% 0% 0% 2 Fast-Track Scenario 50% 50% 0% 0% 3 Linear Growth Scenario 25% 25% 25% 25% 4 Early-Majority Focused Scenario 0% 50% 50% 0% 5 Late-Majority Focused Scenario 0% 25% 75% 0% 6 Laggards Focused Scenario 0% 0% 100% 0% Scenario 1 can be considered a pessimistic scenario. This scenario was developed to cater to the total EV market penetration calculated for the year 2050. The full infrastructure requirement will be developed and operated from 2020 without considering the actual variation of EV public fast charging demands. Figure 5-7 shows that the upfront cost is much higher, resulting in a higher payback of 35 years. Infrastructure utilization is significantly lower than all other scenarios. In this scenario, investments are very high and investors will not get the anticipated payback. Financial assistance from the government will be very high if policymakers want to achieve the required payback for this scenario. However, EV consumers will get access to multiple facilities where consumer satisfaction will be significantly higher than in other scenarios. In Scenario2, 50% of recharging infrastructure development was proposed in 2020 and the remaining 50% was proposed to be completed by the year 2030. The dynamic market variation was not considered and the planning was done based on the estimated EV penetration for the year 2050. 96 . As shown in Figure 5-7, the upfront cost is significantly higher (though lower than scenario 1), resulting in a higher payback of 32 years. Infrastructure utilization is comparatively low and investment is high, resulting in high payback periods. Financial assistance from the government will be comparatively high if policymakers want to achieve the required payback for this scenario. However, EV consumers will get access to multiple facilities where consumer satisfaction will be high. A linear fast-recharging infrastructure development method was proposed in scenario 3. Accordingly, 25% of the full EV infrastructure requirement will be developed in 2020, 25% in Figure 5-7 Results of the Scenarios Developed Using Conventional EV-RI Planning Approach 97 . 2030, 25% in 2040, and 25% in 2050. Linear EV growth was considered and the planning was done based on the estimated EV penetration for the year 2050, which required lower investments than scenarios 1 and 2. However, there will be unutilized recharging capacity based on Figure 5-7, which increases the payback period to 27 years. The upfront costs and the payback periods are higher than the optimal scenario. Scenarios 4, 5, and 6 were planned for late-adopters without considering potential early-adopters. Scenario 4 focused on the early majority, which proposed 50% EV fast-recharging capacity in the year 2030 and 50% capacity in the year 2040. Scenario 5 focused on the late-majority, which proposed 25% EV fast recharging capacity in the year 2030 and 75% capacity in the year 2040. Scenario 6 focused only on the laggards, which proposed 100% capacity development in the year 2040. All these scenarios (4, 5, and 6) have ignored the early-adopters and carry unutilized capacity after their development. Accordingly, scenarios 4, 5, and 6 will not satisfy consumers in the early stages of EV adoption and showed longer payback periods compared to the base case. The case study has shown that the proposed planning framework is better than the conventional approach used by EV recharging infrastructure planners and developers. Furthermore, the proposed approach can be fulfilled using an appropriate project delivery method and an appropriate contracting strategy based on the maturity level of the EV market. The selection of an appropriate project delivery method is one of the core decision strategies that affects successful completion of a project [157]. Public-private partnerships, design-built-finance-operate-transfer, build-own-operate-transfer, and built-own-lease-transfer are some of the viable project delivery methods that can be used to ensure sustainable deployment of the EV-RI network for the urban context. Further to that, this study can be extended with a detailed investment framework that shows the impacts of different factors used in this study. That framework can be used to develop policies and best practices to make infrastructure investments more effective. The methodology helps EV-RI planners to determine dynamic changes in capacities, costs, payback periods, and locations of EV-RI facilities based on estimated EV consumer demands. Site selection data and municipality data (geographic maps, route network, etc.) were the key inputs that change with the selected municipality. Therefore, the application of this methodology for different municipalities is reasonably simple and can be used with minimal expertise. In addition, 98 . the proposed number of DC-FC units depends on the pre-defined average productive recharging capacity of a typical DC-FC port per day (β), which may vary with local trip behaviours. The proposed framework can be simulated with the pre-defined β and derive the best desirable plan for a multi-stage EV-RI deployment process. An additional advantage of the proposed framework is that time-based EV recharging rate policies can be introduced to enhance the utilization of DC-FC stations, and increase β. Those policies can be simulated as a scenario approach to obtain the most desirable time-based recharging pricing schemes for public EV-RI facilities. In that case, the utilization of existing EV-RI facilities will increase and EV-RI investments will decrease in the long run. Moreover, a minimum payback period will also be shown for EV-RI infrastructure investors. 5.4 Summary Recharging infrastructure (RI) deployment plays a vital role in improving public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires a massive RI network. Though consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this work, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of 1) A preliminary site selection framework; 2) A distance matrix to model the shortest distance between TAZs; 3) A temporal model to find dynamic EV-RI demands; and 4) A stochastic model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was used to demonstrate the proposed framework. Furthermore, the results obtained from the aforementioned model were validated using randomly generated alternative combinations and conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviours, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework could be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning 99 . approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long run. The proposed approach will reduce upfront costs, maximize infrastructure utilization throughout the infrastructure life cycle, and result in minimal payback periods. The multi-period recharging demands of each facility, traffic impacts to the existing route network, and potential electricity demands from the grid can be assessed using the proposed framework. Moreover, this framework can be used to evaluate different incentives, rewards, and loan facilities in order to obtain payback periods for long-term decision-making. Furthermore, the scientific knowledge generated from this study can be generalized for other alternative fuel-based infrastructure development projects in the future.100 . Chapter 6 Strategic Incentive and Tax Planning Approach for Sustained Recharging Infrastructure Sections of this chapter have been published in the Elsevier journal of Cleaner Production, as articles titled âScenario-based economic and environmental analysis of clean energy incentives for households in Canada: Multi-criteria decision making approachâ and âAre we ready for alternative fuel transportation systems in Canada: A regional vignetteâ [70] [232]. 6.1 Background Fossil fuel-based low-occupancy trips can be categorized into home-based, work-based, and non-work-based, based on trip purpose [233]. These trips, which are related to light-duty vehicles, can be analyzed at the household level using the data obtained from a travel survey. Electrifying the trips, as mentioned earlier, using EVs would be expensive for vehicle users and infrastructure investors. On the one hand, potential vehicle users need to invest in expensive low-emission vehicles even though the recharging infrastructure and vehicle ranges are not on par with conventional vehicles. On the other hand, potential investors need to invest in recharging infrastructure even though they have uncertain recharging demands and paybacks at the early stages of EV deployment. The above mentioned additional costs and limitations can discourage consumers from adopting some GHG reduction interventions [130]. As a solution for this, the government can absorb the aforementioned additional expenditure by providing incentives, tax benefits, and rewards for potential interventions to encourage consumers to move towards energy-efficient buildings and low-emission transport options [24]. Major portions of the national and local incentives are primarily sourced by the government and government institutions using the tax money paid by the citizens of the country. Carbon offset taxes can be imposed on high-emission energy sources to discourage consumers and to invest in those incentives for low-emission interventions [31]. Therefore, maximizing the favourable impacts of potential incentives is vital to achieve national and local targets faster. However, incentive and tax planning are not straight-forward. This requires a logical and comprehensive framework that aligns the national priorities. In this case, the municipal level incentives for clean energy-based interventions can be divided into two sectors considering their application: 101 . 1) Incentives to improve the energy efficiency of buildings via energy upgrading in new households and energy retrofitting in existing households, using energy-efficient systems and appliances. Fundamentally, consumers are affected by the additional costs for energy and emission-related interventions of households, and resultant savings or costs. An additional cost for retrofit installation, operations, and maintenance for buildings can be expected with innovative and proven interventions for building-level applications. 2) Incentives to improve the emission levels of road transportation via incentivizing alternative low-emission transport interventions such as low-emission vehicles, active transportations, and high-occupancy vehicles. An additional cost for low-emission vehicle purchases, operation, and maintenance can also be expected for vehicle level applications [48] [10]. In addition, carbon offset taxes can be considered an additional source of income that contributes to municipal clean energy funds. These funds can be used to provide the aforementioned incentives and infrastructure investments for clean energy-based interventions. The carbon offset tax can be explained as an additional amount imposed on energy, based on estimated GHG emissions [234]. Decision-making to obtain the optimal incentive and tax schemes using key factors such as multiple stakeholders, regional climate profile, emissions, and cost profiles of different energy mixes, regional practices, and consumer behavioural patterns have been overlooked in previous studies [39]. Stakeholders such as homeowners, government institutes, utility suppliers, etc., need to know the net economic and environmental impacts of their clean energy investments to ensure business continuity. Therefore, multi-stakeholder analysis of household GHG reduction interventions (including residential building retrofits and clean energy transport) and benefits of different clean energy opportunities need to be assessed to achieve greenhouse gas reduction targets at the lowest possible cost. A comprehensive literature review shows that existing studies have only considered either building retrofits or transport interventions to reduce potential GHG emissions [34]. This chapter presents a comprehensive analysis of both building retrofits and transport interventions for Canadian households in order to develop incentive and tax schemes to achieve municipal GHG targets. A decision support tool was developed to identify the most desirable 102 . intervention to reduce GHG emissions from household transport and domestic activities considering regional economic and environmental factors. Accordingly, this chapter discussed the methodology and demonstration of the development of the Household Incentive and Tax Planning Tool (HIPT), which will consider overall energy use and GHG emissions of a selected household for both domestic and transportation activities. The scenario-based analysis allows decision-makers such as public and private authorities to decide the appropriate sector to incentivize in order to minimize energy demands and GHG emissions in a timely way and for a given location [189]. 6.2 Methodology for Household Incentive and Tax Planning Tool A scenario-based approach was used to assess plausible futures. However, the methodology started with data collection and database development of regional characteristic-based EV policies and building retrofit incentives. The key regional characteristics considered in this tool are regional meteorological data, energy cost and accessibility, local energy tariffs, and energy mix [235]. Provinces with a population greater than one million were considered in this analysis, and EVs were assumed as the only scalable alternative fuel technology available in Canada. Moreover, the scope of this study was limited to single-family residential buildings and light-duty vehicles, considering data availability. The proposed research framework for HIPT is given in Figure 6-1.103 . Figure 6-1 Proposed Research Framework for Household Incentive Planning Tool (HIPT) 104 . Phase 1: Data Collection and Database Development To identify the parameter values for this work, the recent literature was examined with a focus on the provincial analysis of residential buildings and transport-based emissions. The database developed in Chapter 4 and the output life cycle data were used to obtain the EV and ICEV related data for this study. The provincial incentives and tax policies for clean energy vehicles were collected from federal and provincial reports. However, municipal level incentives were ignored due to the complexity of the data collection. Moreover, data related to household retrofits, government and utility incentives, transport interventions, clean energy-related incentives, travel behaviour-related data, and other regional characteristics were collected from peer-reviewed journal articles, institutional reports, and online data sources. Local builders were contacted, and data related to local building materials, construction types, costs, and commercially available retrofit types were collected from their databases. Moreover, the RS-Means database was used to collect the up-to-date cost of materials within the province. Data relevant to the case study were also collected from local databases provided by the developers, utilities, and municipalities. Phase 2: Incentive/Tax Policies for Low-emission Transportation Options The purpose of this phase is to identify the optimal incentive scheme to increase low-emission vehicle demands based on regional characteristics. The potential incentives and tax policies were identified initially using the literature and expert opinions. Those were indicated from RT1 to RT(n) considering n number of incentives and tax policies. Moreover, the intermediate levels of each incentive and tax policies were also identified with their potential financial impacts to the consumer and society. Those were inserted as inputs to the HIPT. Impact of Different Tax/Incentive Policies for Vehicle Life Cycle Cost The vehicle LCC consists of vehicle purchase/manufacturing cost, operational cost, maintenance cost, repair cost, recycle cost, fuel extraction cost, storage cost, transportation cost, and retailing cost, which are the key cost components of a vehicleâs life cycle. Equation (33) was derived using Equation (16), which was used to calculate the annualized LCC of alternative and conventional fuel vehicles. Accordingly, HIPT is an Excel-based tool that was used to assess the annualized LCCs of light-duty vehicles. In this tool, the vehicle lifespan, annual mileage, vehicle 105 . characteristics, fuel tariffs, incentive/tax/rewards, and interest rates were obtained from the existing literature and can be changed based on regional characteristics. HHCVannualized = VCAPEXannualized+VOPEXannualized + + VEOLannualized Equation 33 where, ðð¶ð´ðð¸ððððð¢ðððð§ðð â Annualized vehicle capital costs (CAD/vehicle.year); ðððð¸ððððð¢ðððð§ðð â Annualized vehicle operating costs (CAD/vehicle.year); VEOL - Vehicle end-of-life cost (CAD/vehicle.year); HHCVAnnualized - Household annualized life cycle cost of the vehicle (CAD/vehicle.year) According to Equation (17), the formula for VOPEX can be shown as Equation (34). VOPEXannualized = VOCannualized + VM&RCannualized + VACannualized + EVBCannualized Equation 34 Where, ðððð¸ððððð¢ðððð§ðð - Annualized value of all operational expenditure (CAD/vehicle.year); ððð¶ðððð¢ðððð§ðð âAnnualized value of the operating costs (CAD/vehicle.year); ðð&ð ð¶ðððð¢ðððð§ðð - Annualized value of the maintenance and repair cost (CAD/vehicle.year); ðð´ð¶ðððð¢ðððð§ðð â Annualized other costs such as parking, road tolls, insurance costs, and vehicle licensing costs, etc. (CAD/vehicle.year); ð¸ððµð¶ðððð¢ðððð§ðð - Annualized cost of electric vehicle battery change (CAD/battery.year). Vehicle operating costs and EV battery costs were calculated using equations given in Chapter 4. The vehicle capital costs (VCAPEX) were calculated as Equation (35) below. VCAPEX = (MSRP - DMSRP) (ST% * RST%) Equation 35 Where, VCAPEX â Vehicle final consumer capital cost; the manufacturerâs suggested retail price (MSRP) (CAD/vehicle); ST â Provincial vehicle sales tax percentage from the selling price (%) , DMSRP - Purchase rebate from MSRP (CAD/vehicle); and RST% - Tax rebate from sales tax (%). The identified incentives and tax policies were then combined and the combined effect on the countryâs economy was found for all possible combinations. Therefore, a customized Python script was coded and integrated to HIPT to propagate the individual incentives and identify the combined 106 . effects of different incentives and tax policy combinations. The HIPT was further extended to obtain the overall LCC impact of different EV incentive combinations for different regions. Select the Best Incentive and Tax Policies for Low-emission Fuel Vehicles The selection of the most desirable incentives and tax policies is not straight-forward. There are relevant factors that need to be considered in order to find the most viable incentive scheme for clean energy vehicles. On the one hand, consumers would like to get more incentives for EVs to reduce their costs; on the other hand, the government wants to reduce incentives and improve the required taxes to balance their budgets. Accordingly, the HIPT was further extended to select the most desirable incentive combination with the lowest positive economic impacts to the region. A comprehensive survey conducted by the National Renewable Energy Laboratory, USA, shows that the majority (71%) of existing vehicle owners would not pay more or would like to pay less for new automobile technologies [236]. Considering the consumer perspective, therefore, the LCC of low-emission vehicles should be less or equal to the LCCs of conventional vehicles. Equation (36) shows the above constraint for the incentive selection problem. ð»ð»ð¶ð¸ðð´ððð¢ðððð§ðð < = ð»ð»ð¶ð¼ð¶ð¸ðð´ððð¢ðððð§ðð Equation 36 Subjected to, ð»ð»ð¶ð¸ðð´ððð¢ðððð§ðð& ð»ð»ð¶ð¼ð¶ð¸ðð´ððð¢ðððð§ðð ð ð»ð»ð¶ðð´ððð¢ðððð§ðð ð»ð»ð¶ð¸ðð´ððð¢ðððð§ðð; ð»ð»ð¶ð¼ð¶ð¸ðð´ððð¢ðððð§ðð â¥ 0 Where, ð»ð»ð¶ð¸ðð´ððð¢ðððð§ðð- Annualized household life cycle cost for electric vehicles (CAD/year); ð¿ð¶ð¶ð¼ð¶ð¸ðð´ððð¢ðððð§ðð â Annualized household life cycle cost for conventional gasoline vehicles (CAD/year). Considering the government's perspective, the total economic impacts from additional incentives and tax policies need to be minimized. Moreover, the value generated from those incentives needs to be maximized in order to enhance the efficiency of capital deployment. Therefore, the objective function is ððð ( ðð ðð´ððð¢ðððð§ðð), where ðð ðð´ððð¢ðððð§ðð is the annualized component of the total 107 . vehicle taxes and incentives calculated from the list identified in 3.4.1. The rule-based method or linear graphical method can be used to find the most desirable solution to minimize government expenditure on incentives and keep the LCCs of low-emission vehicles lower than conventional vehicles. Phase 3: Retrofit Selection for Single Family Detached Houses Phase 3 of the HIPT development is discussed in this section, which discusses identifying the most desirable building upgrade/ retrofit options to reduce building level GHG emissions and energy consumption in a given region. Identify locally available upgrade/ retrofit options Innovative and proven locally available energy efficiency upgrades /retrofit options were identified using expert opinion and databases developed by local suppliers and developers. Those upgrades were categorized into five key categories based on their energy consumption behaviours. They are: 1) Building heating, ventilation, air conditioning (HVAC) system; 2) Domestic hot water system; 3) Insulation of the building envelope; 4) In-situ renewable energy generation; and 5) Building appliances and lighting. Those retrofits were labeled as RB1 to RB5. Moreover, a base case house, known as the conventional house, was defined using the minimum building standards of the municipality. Conventional house and the selected upgrade/ retrofit options were evaluated by a panel of developers, builders, energy experts, and research academics to proceed with necessary simulations. Energy simulation, costs, and GHG emissions of the proposed upgrades Energy simulation tools such as HOT2000, Design Builder, etc. can be used to simulate building energy. In the base case, the conventional building was simulated using the selected energy simulation software. Then the building was modified with the above-identified upgrade/retrofit option and simulated. The outputs of the aforementioned energy simulations were transferred to HIPT for the selected upgrade/retrofit options. In the meantime, the capital costs of those upgrade/retrofit options and GHG emission factors were also inserted into the HIPT. Then the developed python script was used to propagate those energy values, costs, and emissions to 108 . identify the energy efficiencies and capital cost impacts of all possible combinations. The analysis was simplified by assuming zero interactions between different retrofit options while combining those into a single building [47]. Provincial electricity and natural gas tariff data were updated to the HIPT using the utility tariff data (cost per unit energy and tier levels) obtained from the respective utility providers. Similar to phase 2, HIPT was used to calculate the capital upgrade/retrofit investments and annualized life cycle costs of each retrofit option based on regional characteristics. Equation (37) [47] was used to calculate the annual GHG emissions from the energy consumption data obtained from the aforementioned HOT2000 building energy models. Literature-based regional emission factors were inserted to HIPT to calculate the GHG potential. HHEH.jAnnualized = ECj x GHGfk Equation 37 Where "ð»ð»ð¸ð».ðð´ððð¢ðððð§ðð" is the âAnnual household GHG emissions of the house (H) with retrofit jâ, âECjâ is the âAnnual energy consumption of house with retrofit jâ and "ðºð»ðºðð" is the âGHG emission factor for the fuel type kâ. Equation (5) [70] [180] was used to calculate the annualized cost of the capital investment for SFDHs with or without retrofit options. Equation (38) shows the annualized life cycle cost (LCC) for SFDHs with the retrofit option âjâ. HHCH.JAnnualized = EACjcapitalâincentives+ OCjannualized + MCjannualized+EoLjannualized Equation 38 Where, âHHC H.J Annualizedâ is âAnnual household life cycle cost for a house (H) with retrofit jâ, âEACfcapital-incentivesâ is âEquivalent Annual cost of the capital cost with incentives for a house with retrofit jâ, âOCfannualizedâ is âAnnualized operational cost of a house with retrofit jâ, âMCfannualizedâ is âAnnualized maintenance cost of a house with retrofit jâ and âEoLfannualizedâ is âEnd-of-life cost of a house with retrofit jâ. However, the maintenance cost and end-of-life cost of houses were assumed as negligible during these calculations due to the lesser impacts of both maintenance cost and end-of-life cost compared to operational and purchasing cost. House lifetime was assumed as 109 . 25 years since the general mortgage period of home purchases is between 25 and 30 years in Canada. Ranking Upgrade/Retrofit Options The purpose of retrofit planning is to minimize potential GHG emissions and energy costs and enhance the value generated by potential retrofit investments [47]. Therefore, the retrofit ranking was developed to identify the most desirable retrofit option to achieve minimal GHG emissions, investment, and annual consumer cost. Multi-criteria decision making (MCDM) methods such as weighted sum, Analytic Hierarchy Process (AHP), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) can be used to rank potential retrofit options[237][238]. The TOPSIS method is simple and can be extended easily with fresh alternatives by swapping the final ranking when new alternatives are included in the model [239]. Therefore, the TOPSIS method was used to extend the HIPT to rank upgrade/retrofit options. According to Opricovic et al. (2004), the following steps were conducted to calculate the shortest distance to the positive ideal solution and rank all the retrofits [238]: - Calculated the normalized decision matrix using vector normalization - Calculated weighted normalized decision matrix - Determined the positive ideal and negative ideal solutions - Calculated the separation measures, using the n-dimensional Euclidean distance - Calculated the relative closeness to the ideal solution - Ranked the preference order based on relative closeness Phase 4: Scenario Development and Assessment Scenario-based assessment is prevalent in long-range infrastructure planning in response to uncertainties [189]. Scenario planning method defines evidence-based future imaginations for how planning might proceed [192]. The literature reveals that this method can be used to establish model changes in replacing the conventional product (at the âover-maturityâ stage) with a new product category [192]. Therefore, a scenario-based assessment was considered to identify the most desirable household type for different regions in the country. The scenario-based assessment consists of the following steps: 110 . - Identify scenarios (e.g.. best case, worst case, most likely case, etcâ¦) [193] - Define variables that influence future outcomes, based on the selected scenarios [193] - Create scenarios by assigning the qualitative and quantitative reasonable value to the defined variable [193]. Scenarios were developed using multiple combinations of light-duty vehicle options and residential buildings combined to be operated as a household. Two building types were defined based on the retrofits identified in phase 2: conventional or base-house, which is âHouse without retrofitsâ; and upgraded house, which is âHouse with retrofitsâ. Moreover, conventional (ICE) and electric (EV) light-duty vehicles were used to define different transport opportunities for households. Scenario Development to Prioritize Sectors for Investment Scenario development consists of identifying scenarios, and defining variables that affect future outcomes, and assigning quantitative and qualitative values to the defined variables [193]. In this analysis, four scenarios were developed as follows. Scenario 1: This scenario is titled âElectric Mobility.â The modern electricity-based transport technology, EV, was considered with the conventional residential building (House without retrofits) to find the overall environmental and economic impact of alternative transportation within the household cluster. Electricity and natural gas are considered the key secondary energy sources for residential buildings, and electricity is considered the unique energy source for transportation. Scenario 2: This scenario is titled âBusiness as Usual.â This is the reference case for most provinces in Canada. Conventional transport technology (ICE vehicles) was considered with the conventional residential building (House without retrofits) to find the environmental and economic performance of the conventional household cluster. Scenario 3: This scenario is titled âModerate Future.â The modern electricity-based transport technology (EV) was considered with upgraded residential buildings (House with retrofits) to find 111 . the overall environmental and economic performance of the energy-efficient interventions within the household cluster. Scenario 4: This scenario is titled âBeyond Conventional.â Conventional transport technology (ICE) was considered with the upgraded residential building (House with retrofits) to find the overall environmental and economic impact of residential building operational performance in the household cluster. Residential costs and GHG emissions calculated in Phase 2 and the transport costs and GHG emissions calculated in Phase 3 were used to assign parameter values for all aforementioned scenarios. Accordingly, the tool developed in Phase 2 and 3 was further extended with an eco-efficiency methodology to compare different scenarios. Scenario-based Decision Making Using Household Eco-efficiency Index (HEEI) Considering the concept of eco-efficiency (see Section 3.6.3), the Household Eco-efficiency Index (HEEI) was defined as the âunits of household environmental impact reduction per dollar invested on interventions.â Accordingly, Equation (39) shows the HEEI calculation for the ith scenario (HEEISi). HEEISi = HEPSSi HESSiâ Equation 39 The household value generated was defined as the âHousehold eco-score (HES)â and was calculated by normalizing the annualized household cost of the particular scenario (Si) from the reference scenario (SR). HES for ith scenario (HESSi) can be calculated using Equation (40). HESSi = HHCAnnualizedSi HHCAnnualizedSRâ or GCCSi GCCSRâ Equation 40 Where, HESSi - Household eco-score for the ith scenario (Si ϵ S1, S2, S3, and S4); HHC = HHCH + HHCV; HHCSiAnnualized - Annualized household cost for the ith scenario (CAD/year); GCCSi - Government total cost contribution for the ith scenario (CAD/house); HHCSRAnnualized â Annualized household cost for the reference scenario (In this section reference scenario is considered as âBusiness As Usualâ (Scenario 2)) (CAD/year); GCCSR â Government total cost contribution for 112 . the reference scenario ( âBusiness As Usualâ (Scenario 2) was considered as the reference case) (CAD/house) The household environmental performance score (HEPS) can be calculated by normalizing the annualized household emissions of the particular scenario (Si) from the reference scenario (SR). HEPS for ith scenario (HEPSSi) can be calculated using Equation (41). HEPSSi = HHEAnnualizedSi HHEAnnualizedSRâ Equation 41 Where, HEPSSi - Household environmental performance score for the ith scenario (Si ϵ S1,S2, S3, and S4); HHE = HHEH + HHEV where resident emissions were obtained from Equation (33) and vehicle emissions were obtained from the literature; HHESiAnnualized - Annualized household emissions for the ith scenario (kgCO2e/year.house); HHESRAnnualized â Annualized household emissions for the reference scenario (âBusiness As Usualâ (Scenario 2) was considered as the reference case) (kgCO2e/year.house) The HEEIs for each scenario were compared and ranked for different provinces to identify the most desirable scenario for each province. 6.3 HIPT Demonstration Using a Case Study The case study was conducted to demonstrate the methodology mentioned above and develop the HIPT for Canadian provinces using Single Family Detached Households (SFDH). Provincial data were collected and the data obtained for Chapter 3 were used when necessary. 6.3.1 Household Data Collection for the Demonstration Building- and vehicle-related data were obtained as the household data. Accordingly, data related to a typical local household, a typical conventional vehicle, and similar horse-power electric vehicles were selected as follows. Residential Building: A mid-size SFDH built in the Southern Interior of BC was considered for building retrofitting and energy simulation purposes. As-built drawings and building specifications 113 . are shown in Appendix E1. The floor area and building details, envelope characteristics, building mechanical systems, and appliances are shown in Appendix E2. Alternative and conventional fuel automobiles: This study focuses on conventional and low-emission fuel-based vehicles to identify changes in residentsâ transportation-related emissions. Two types of vehicles were selected based on their fuel types. EV and ICE data were assumed as similar to the vehicles considered in Chapter 4. The vehicle characteristics used in this study are shown in Appendix B2 and the provincial gasoline and electricity tariffs are indicated in Appendix A4 and Appendix A3. 6.3.2 Incentive and Tax Policies for Electric Vehicles in Canada The USA (California), Norway, China (Shanghai), and the Netherlands have higher EV market share in the particular region [240]. Therefore, the EV-favourable financial incentives and tax policies adopted in the USA (California), Norway, China (Shanghai), and the Netherlands were identified in order to identify the viability of the Canadian region. Accordingly, Table 6-1 shows potential incentive options and tax policies that were considered in this analysis. Table 6-1 Incentive and Tax Policies for Low-emission Fuel Vehicles Incentive / Tax Policy Parameter No. Amount Government rebates for zero-emission/ low-emission vehicles from the dealerâs price Price reduction (CAD/vehicle) RT1-A 6,000 RT1-B 3,000 RT1-C 1,000 RT1-D 0 Waiver for sales taxes (12%) Waiver as a % of sales tax (%) RT2-A 100% RT2-B 50% RT2-C 25% RT2-D 0% Carbon taxes for fuels based on the carbon emission Carbon tax based on fuel emission levelized (costCAD/TonCO2e) RT3-A 32.5 RT3-B 52.5 RT3-C 73.75 Parking fee waiver for low-emission vehicles (CAD $100/month) Parking fee waiver (%) RT4-A 100% RT4-B 0% Waivers on re-charging/re-fueling at public charging facilities Re-charging fee waiver RT5-A 100% (FREE) RT5-B Service-based priced (SBP) RT5-C Profit-based priced (PBP) Five key incentive/tax categories, shown in Table 6-1, were identified and labeled as RT1 to RT5. Different incentive levels were defined within each category and sub-labeled using A to D. For 114 . example, âWaiver for Sales Taxâ was identified as a key incentive for electric vehicles and labeled as RT2. The tax waiver can be a âFull waiverâ (100%), labeled as RT2-A; or a âhalf waiverâ (50%), labeled as RT2-B, and so on. Accordingly, 1,215 incentive schemes (combinations) were identified for an EV-based transport system using the individual incentive options given in Table 6-1. The linear programming graphical method was used to identify the most desirable incentive policy for each province. 6.3.3 Local Building Upgrades and Retrofit Options The primary upgrades /retrofit options were identified from Okanagan builders and developers for local single-detached households. Table 6-2 shows the aforementioned upgrade/retrofit options that represent the above major components. Table 6-2 Energy Retrofits for Single Detached Households Retrofit No. Category Retrofitting component Retrofit RB1 Energy-efficient building envelope (enhance insulation and enhance U-value of windows) (Additional Investment: CAD16,932) Foundation ICF blocks, 6â RCA reinforced, R-14 insulation, 2â Styrofoam Exterior wall (Section 1) 3/8â EPS Styrofoam, 2âÃ6â wood studs @ 24â OC, 3/8â OSB sheeting, R-20 insulation, ½â drywall Ground floor Engineered I joist 11 7/8â @ 19.2â OC, ¾â plywood, R-14 insulation Ceiling R-20 batt, R-50 blown-in ceiling, ½â drywall Windows Vinyl triple glazed windows c/w 366 lowE Air Tightness: 0.2 L/s.m2 RB2 Appliances (Additional Investment: CAD2,535) Heat recovery system ECM variable-speed blower Stove and oven Energy Star rated double ovens (Avg: 450kWh/year, Max: 615kWh) Hot water system Energy star rated heat recovery electric hot water tank 2EF (Hot water temperature: 1310F) RB3 Effective lighting (Ex: LED and CFL) (Additional Investment: CAD1,000) Lighting (interior) More than 75% LED or CFL RB4 Fuel switching to greener HVAC systems to reduce potential emissions (e.g., geo exchange-based HVAC with multi-zoning) (Additional Investment: CAD8,370) Space heating system Ground heat source pump 4COP & Energy Star rated electric fireplace 100% SS HVAC controller SMART HVAC controller with multi-zoning Space cooling system Ground heat source pump 5.6COP RB5 Alternative in-situ green energy production (CAD9,385) Solar (PV) 140 ft2 solar array, 150 Azimuth, 14.2% module efficiency and 90% inverter efficiency 115 . The specifications of the conventional household are shown in Appendix E3. Initially, the energy model of the base case building was modeled using HOT2000 V11.3b90 developed by CANMET Energy Technology Center (CECT). The model was further modified with multiple simulations considering all possible retrofit combinations (32 retrofit combinations), and the resultant energy consumptions were obtained and recorded for all provinces. The provincial weather profiles were obtained from CECT weather data libraries. The results generated from the energy simulations were transferred to HIPT with the specification of each combination. The utility tariff data used in this study are indicated in Appendix A5. Moreover, the current incentive programs for SFDH upgrades available in Canadian provinces are shown in Table 6-3. Provincial electricity GHG emission factors were obtained from Perera et al. (2017) [70]. Natural gas cradle-to-grave emission factors were obtained as 49.87 kgCO2e/GJ Natural Gas [241]. Moreover, the HIPT was further extended with the TOPSIS method, which was used to rank all 32 building upgrade /retrofit options for all seven provinces. 116 . Table 6-3 Provincial Incentive Schemes for Residential Buildings Source: On-line databases of provincial utility providers and government institutions (Accessed in November 2017) Quebec (QC) Ontario (ON) British Columbia (BC) Alberta (AB) Saskatchewan (SK) Manitoba (MN) Nova Scotia (NS) Base house H2M (House with retrofits) H2D H2M H2D H2M H2D H2M H2D H2M H2D H2M H2D H2M H2D H2M 8â reinforced concrete, R-22 insulation ICF blocks, 6â RCA reinforced, R-14 insulation, 2âstyroform 0 0 625 1250 0 0 132 132 0 0 0 0 0 5000 2âÃ6â wood studs @ 24â OC, 3/8â OSB sheeting, R-20 insulation, ½â drywall 3/8â EPS Styrofoam, 2âÃ6â wood studs @ 24â OC, 3/8â OSB sheeting, R-20 insulation, ½â drywall 0 0 1875 1875 0 0 1298 1298 0 0 Engineered I joist 11 7/8â @ 19.2â OC, ¾â plywood, R-11 insulation Engineered I joist 11 7/8â @ 19.2â OC, ¾â plywood, R-14 insulation 0 0 190 190 0 0 0 0 0 0 R-22 Batt, R-40 blown in celling, ½â drywall R-20 batt, R-50 blown in celling, ½â drywall 0 0 500 750 0 0 788 788 0 0 Vinyl double-glazed windows c/w 180 lowE Air Tightness: 0.2 L/s.m2 Vinyl triple-glazed windows c/w 366 lowE Air Tightness: 0.2 L/s.m2 0 0 0 0 0 0 0 500 0 0 Single stage PSC blower ECM variable speed blower 0 0 375 375 0 0 0 0 0 0 150 150 0 0 Standard Refrigerator Standard Refrigerator 0 0 0 0 100 100 100 100 0 0 0 0 0 0 Standard energy star cloth dryer Standard energy star cloth dryer 0 0 0 0 100 100 100 100 0 0 0 0 0 0 Standard Energy Star rated Cooker and oven (565kWh/year) Energy Star rated Double ovens (Avg: 450Wh/year, Max: 615kWh) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Energy star rated natural gas hot water tank 1EF (Hot water temperature: 1310F) (60Gal) Energy star rated heat recovery electric hot water tank 2EF (Hot water temperature: 1310F) 0 0 375 375 1000 1000 945 0 0 0 0 0 0 0 25%-75% LED or CFL More than 75% LED or CFL 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Energy star rated duel fuel (Natural Gas & electric) EF - 92.1%, 56000 BTU/hr, switching temperature 350F & Natural gas fireplace 2kW, 6824.28 BTU/hr 30% SS Ground heat source pump 4COP & Energy Star rated electric fireplace 100% SS 0 2800 625 4375 2000 0 0 0 0 0 0 0 0 0 SMART HVAC controller SMART HVAC controller with multi-zoning 25 225 0 0 0 0 100 100 0 0 0 0 0 0 Energy star rated central split system, (electric), 14SEER Ground heat source pump 5.6COP 0 0 250 250 0 0 0 0 0 0 0 0 0 0 Not available 140 ft2 Solar array, 150 Azimuth, 14.2% module efficiency & 90% inverter efficiency 0 0 0 0 0 0 0 2607 0 0 0 5000 0 0 117 . 6.4 Results and Discussion The results obtained from the transport incentive and building retrofit assessment are discussed in this section. 6.4.1 Regional Retrofit or Upgrade Selection for Single Detached Houses All the retrofit options and their combinations (32 combinations) were assessed and ranked to obtain the best retrofit option based on regional characteristics. As an example, the results obtained for BC, Canada are shown in Figure 6-2. Figure 6-2 Building Level GHG Emissions and LCC vs. Retrofitting Investment for BC, Canada According to Figure 6-2, the annual GHG emissions and building life cycle costs to the consumer are indicated against the additional retrofitting investment per residential building. The trend shows that annual GHG emissions of residential buildings decrease with increasing retrofit investments. However, the annualized consumer cost also increases with additional retrofitting investments due to resultant mortgage payments of premium upgrade costs. Therefore, in this section, the environment and economic parameters were given equal weights. Accordingly, capital investment and annualized consumer cost were each assigned a weight of 0.25 and GHG emissions Annual GHG emissions (kgCO2eq/year) 118 . were assigned a weight of 0.5. Based on the TOPSIS results, the distance to the ideal solution was calculated. Accordingly, the best retrofit option for each province is shown in Table 6-4. Table 6-4 Regional Retrofit Selection for SFDHs Most desirable retrofit combination based on regional characteristics Quebec (RQC) Ontario (RON) Alberta (RAB) British Columbia (RBC) Saskatchewan (RSK) Manitoba (RMN) Nova Scotia (RNS) RB2+RB4 RB3 RB3 RB4 RB3 RB2+RB4 RB3 Parameter values for the house with retrofits RQK RON RAB RBC RSK RMN RNS HHEH 58.11 1,626.63 1,2476.25 152.48 11,133.80 87.02 12,436.25 HHCH 26,716.63 28,401.52 26,126.33 27,297.00 28,598.36 26,264.29 29,802.53 GCCH 3,025.00 9,440.00 5,623.98 3,200.00 500.00 5,150.00 5,000.00 Parameter values for house without (w/o) retrofits W/O-RQK W/O-RON W/O-RAB W/O- RBC W/O-RSK W/O-RMN W/O-RNS HHEH 168.13 1,657.96 1,2695.78 199.43 11,358.06 234.23 12,603.43 HHCH 27,255.49 28,375.56 26,073.52 27,319.01 28,575.40 27,013.01 29,770.26 GCCH 25.00 4,815.00 3,461.74 1,200.00 0.00 150.00 0.00 According to Table 6-4, RB2 (high energy-efficient appliances) and RB4 (fuel switching to greener HVAC systems) are affordable and the most effective retrofitting methods for the provinces with low-emission electricity grid. Energy-efficient equipment is capable of reducing electricity consumption in the house, and greener HVAC systems (e.g. ground source heat pump) can reduce the use of natural gas consumption for space heating and cooling. Traditional homes in provinces with high-emission electricity grids (fossil and coal power source electricity) can reduce their electricity usage by promoting energy-efficient lighting systems. Heat-source pump-based HVAC systems are not desirable for provinces with high-emission electricity grids due to the extensive electricity consumption of the heat pump. Hence, energy-efficient natural gas HVAC systems can be more productive and affordable. Additional wall and roof insulation and multiple-pane windows (RB1) will enhance energy efficiency in all provinces. However, according to the analysis, the capital retrofitting investment for additional insulation would be significant. 119 . 6.4.2 Best Incentive and Tax Policies for Electric Vehicles The life cycle cost (LCC) of the EV was assessed and arranged in ascending order to select the best incentive scheme based on regional characteristics. Vehicle lifespan was assumed as five years [70]. Figure 6-3 shows the LCC gap between EV and ICE vehicles in BC for different incentive schemes (LCC gap between EV and ICE vehicles for different incentives in all other provinces are shown in Annex E). The majority of those schemes show more significant LCC reductions to the consumer. However, those LCC savings will directly or indirectly affect provincial and federal governments as a loss of income or as an additional expenditure. Therefore, the selection criteria can be changed with the decision makerâs opinions and expert judgment by incorporating a premium cost amount based on the potential buyersâ willingness to pay for electric vehicles. However, in this work, it was assumed that potential buyers would pay no premium for electric vehicles. Hence, the resultant incentive schemes were selected where the LCC of potential EVs should be equal to or less than the LCC of ICEs. Figure 6-3 Difference of LCC of EV with LCC of ICEV vs. Potential Incentives for BC, Canada According to Figure 6-3, multiple results (points) that match the selection criteria were indicated from the analysis. However, the combinations of individual retrofit types were selected to find the provincial economic and environmental impacts of improving low-emission vehicle demands. RT1, RT2, RT4, and RT5 reduce consumer cost and RT3 reduces the gap of the costs between conventional and low-emission vehicles. Moreover, RT3 increases the operating cost of both conventional and low-emission vehicles, which discourages private automobile usage in the long 120 . run. Table 6-5 shows the incentive and tax policies selected for each province based on regional characteristics. Table 6-5 Province-based Incentive and Tax Policies Quebec Ontario Alberta British Columbia Saskatchewan Manitoba Nova Scotia Desirable policy combination RT1D+RT2C+RT3C+RT4B+RT5B RT1C+RT2B+RT3B+RT4B+RT5B RT1D+RT2B+RT3B+RT4B+RT5B RT1D+RT2C+ +RT4B+RT5B RT1B+RT2B+RT3B+RT4B+RT5B RT1C+RT2D +RT4B+RT5B RT1A+RT2D+RT3C+RT4B+RT5C Incentive category Scheme RT1 ($/vehicle) 0 1,000 0 0 3,000 1,000 6,000 RT2 25% 50% 50% 25% 50% 0% 0% RT3 ($/TonCO2e) 73.75 52.50 52.50 0 52.50 0 73.75 RT4 50% 50% 50% 50% 50% 50% 50% RT5 SBP SBP SBP SBP SBP SBP PBP EV with Incentives EVQC EVON EVAB EVBC EVSK EVMN EVNS HHEV 826.95 1,396.72 5,827.60 859.38 5,399.20 830.01 5,093.20 HHCV 11,466.90 11,447.56 11,423.05 11,672.16 11,447.50 11,510.96 11,409.96 GCCV 1,527.75 3,282.98 860.29 1,535.07 4,346.27 1,854.46 4,951.61 ICE w/o incentives ICEQK ICEON ICEAB ICEBC ICESK ICEMN ICENS HHEV 2,980.64 2,980.64 2,980.64 2,980.64 2,980.64 2,980.64 2,980.64 HHCV 11,473.57 11,448.62 11,440.58 11,674.81 11,476.20 11,530.58 11,410.25 GCCV -158.93 -112.87 -112.87 0.00 -112.87 0.00 -158.93 6.4.3 Decision Making for Government Incentive Investment The results shown in Table 6-4 and Table 6-5 are used to identify the eco-efficiency of transport and residential building incentive programs in each province. Accordingly, this work identified the amount of GHG emission reduction per dollar spent by the government for building and transport sector incentives. Equation 5 was used to obtain the eco-efficiency ratio for these cases. The eco-efficiency ratios obtained for each province are shown in Figure 6-4. Accordingly, the incentives given for building retrofits in Nova Scotia, Alberta, and Saskatchewan show positive GHG reductions, whereas the incentives given for EVs result in negative GHG reductions. Accordingly, in these provinces, electrified transportation may increase life cycle emissions in the 121 . long run. Therefore, the provinces with high-emission electricity grids can focus on incentive schemes for building retrofits. Figure 6-4 Compare the Eco-efficiencies of Electrified Transportation and Building Retrofitting Figure 6-4 shows lower emission reductions for building retrofits compared to the incentives given for electrified transportation. Therefore, provinces with low-emission electricity grids, such as British Columbia, Manitoba, and Quebec, can focus on incentives and tax rewards for EVs and EV infrastructure development projects. Proper incentive schemes for EVs will result in these provinces achieving their GHG targets faster. 6.4.4 Consumer-centric Decision-making The provincial-based annual household emissions for different scenarios (HHESiAnnualized) are indicated in Figure 6-5. Accordingly, provinces that have low-emission electricity, like British Columbia, Manitoba and Quebec, show significantly lower emissions in both scenario 1 and scenario 3. Accordingly, electricity-operated vehicles and residential buildings have high potential in these provinces due to the low-emission hydroelectric grid. Nova Scotia, Saskatchewan, and Alberta show comparatively high emissions in all scenarios. However, scenario 4 shows slightly lower emissions than other scenarios. Therefore, the provinces that have high emission electricity can focus on retrofitting residential buildings instead of electric mobility. Conventional fuel vehicles with greater fuel efficiencies might reduce GHG emissions in the short run. Nevertheless, -15.0-10.0-5.00.05.010.0QuebecOntarioAlbertaBritish ColumbiaSaskatchewanManitobaNova ScotiaElectrified transportation eco-efficiency ration (kgCO2 reduction /$ invested)Residential building retrofitting(kgCO2 reduction / $ invested)122 . active transportation systems such as walking, cycling, and low-emission public transit systems would be the best options in the long run. Figure 6-5 Household GHG Emissions and Consumer Annual Cost Comparison In addition to GHG emissions, consumer operational cost is also essential in describing the practicality of these greener interventions. Therefore, the annualized household cost for different scenarios (HHCSiAnnualized) was also analyzed and shown in Figure 6-5. Consumer costs for the four scenarios in all provinces do not show much difference, because the transport incentives have made the cost of EVs approximately similar to the cost of conventional vehicles. However, there is a minor cost difference among scenarios, which is mostly based on the operational cost of residential buildings due to climate actions of different provinces. However, the eco-efficiency method was used to analyze these scenarios further. The GHG emission reduction requirements to achieve local and provincial targets and the consumer cost premiums for reduced GHG levels were considered in the HEEI analysis. The results obtained for HHC and HHE are provided in Appendix E4 for different scenarios. The provincial comparison for HEEI is shown in Figure 6-6. A lower HEEI represents an increase in GHG reduction for each dollar spent. According to Figure 6-6, Manitoba, Ontario, Quebec, and British Columbia show a similar pattern in all scenarios, where scenario 3 and scenario 1 have significantly lower HEEIs than the other scenarios. Scenario 3 shows the lowest HEEI, which contains H2M with EVs. The premium price pays for the H2M and EVs has reduced GHG 123 . Figure 6-6 Province-wise Household Eco-efficiency Index (HEEI) emissions drastically. Accordingly, the government can promote both H2M and EVs by giving more tax rewards to users. Additionally, improving availability and accessibility to infrastructure for electric mobility will reduce range anxiety6, which leads to increases in electric mobility in these provinces. Therefore, local/provincial authorities with low-emission electricity grid can quickly achieve their long-term and short-term GHG reduction targets due to increasing EV demands. Other provinces, like Nova Scotia, Saskatchewan, and Alberta, do not show significant differences in HEEI in all scenarios 6For widespread adoption of AFVs, the range can be considered as a physiological barrier [12]. Limitations in available locations of alternative fuel infrastructure and the lower range of vehicle cause a psychological effect on consumers which is known as ârange anxietyâ [12]. Range anxiety makes potential consumers to decide against the use of AFVs. 124 . and thus can focus on low-emission based retrofits for residences, such as LED lighting, energy-efficient appliances, and insulation for building envelope. The provincial governments of the aforementioned provinces need to focus their incentives more on residential building retrofit improvements to achieve their provincial GHG targets. The outlined analysis demonstrates that provinces with low-emission electricity, like British Columbia, Manitoba, and Quebec, need to invest in electrified light-duty vehicles to reduce GHG emissions. The provincial government of these provinces can provide incentives and invest in charging-infrastructure facilities to enhance EV penetration. As the second priority, the above provinces can develop an incentive scheme for alternative-fuel HVAC systems to reduce GHG emissions further, which reduces the potential fossil fuel (natural gas) consumption for space heating and water heating. Hence, the low-emission and low-cost alternative fuels or fuel hybrids such as electricity/geo-exchange, etc. were used to replace fossil fuel-based appliances. As the third priority, the aforementioned provinces can award rebates for energy-efficient appliances and LED lighting. Finally, incentives can be granted for additional building envelope insulation upgrades or retrofits. Alberta, Nova Scotia, and Saskatchewan have high-emission electricity grids. Therefore, the use of electricity should be minimized in these provinces, and alternative fuels such as natural gas and hydrogen can be used for household activities. Hydrogen-based vehicles can be a better option for the above provinces. However, hydrogen vehicles were not considered in this analysis due to the lack of available data. Moreover, provincial incentives can be developed to increase the use of energy-efficient appliances, LED lighting, and energy-efficient natural gas space heating and water heating systems. 6.5 Summary Premium investment costs and higher payback periods are the fundamental limitations of electric vehicle transportation and energy-efficient building upgrades. Various incentive policies are used to share the total impacts of investment cost among multiple stakeholders and encourage consumers to switch to greener initiatives. Accordingly, local, provincial, and federal governments are providing incentives for different sectors without assessing the overall household impacts 125 . scientifically. Direct and indirect incentives that are provided for energy retrofits of residential buildings and greener transportation can be considered as the domestic sector incentivizes to reduce potential GHG emissions. Prioritizing interventions within the household cluster and providing the right amount of incentives to the most desirable interventions is vital to maximizing the effectiveness and efficiency of governmentsâ contribution, which will ensure maximum consumer engagement to achieve government GHG targets faster. This work focused on the emissions and cost of residential buildings and the transport activities within the household cluster. A comprehensive literature review was conducted, and the life cycle costs and impact data for EVs were collected from prior phases. Subsequently, a decision support tool was prepared for all combinations of retrofits and incentive schemes. The MCDM-based framework was used with a scenario-based decision-making approach. The potential economic and environmental impacts of low-emission intervention investments were also discussed for the considered scenarios. Government-centric and consumer-centric investment priorities for each province were identified. The results of this chapter indicate that the âModerate futureâ incentive scenario, which consists of electrified residential buildings with EVs are more desirable for provinces like Manitoba, British Columbia, Ontario, and Quebec. These interventions would reduce building and transport GHG emissions in the long run. The proposed incentive schemes for EVs can be comprised of purchasing rebates and sales tax waivers for EVs, EV parking fee waivers, and government-subsidized recharging costs. It was recommended to implement carbon tax policies to discourage fossil fuel vehicles and reduce passive transportation in the future. The âBeyond Conventionalâ incentive scenario, consisting of houses with retrofits with conventional fossil fuel vehicles is more desirable for provinces like Saskatchewan, Alberta, and Nova Scotia. Hence, their incentive policies can be focused on incentives for residential building retrofits to achieve their GHG targets faster. Incentive schemes can be developed on energy-efficient natural gas-based HVAC systems and energy-efficient appliances. Moreover, the additional income from carbon taxes can be used as incentives for infrastructure investments and to develop recharging infrastructure for EVs. Hence, the overall economic impacts to the government due to electrified transportation can be reduced, which may lead to achieving provincial GHG targets faster than usual. 126 Chapter 7 Project Delivery Method Selection for Electric Vehicle Refuelling Infrastructure Deployment A version of this chapter is under preparation for a peer-reviewed journal paper, as an article titled âProject delivery method selection for electric vehicle refuelling infrastructure deployment: A fuzzy-based approachâ that will be submitted to the Journal of Cleaner Production in July 2020. 7.1 Background The life cycle of the electric vehicle recharging infrastructure project can be described using key project phases: construction, operation, maintenance, and end-of-life [175][176]. The construction phase has a greater investment, whereas the operational phase generates revenue [242]. The success of EV-RI projects depends on acquiring the anticipated demands; identifying potential risks; deploying risk sharing; allocation, and mitigation strategies; and maintaining the anticipated project performance [157]. According to the Rogers diffusion model, EV recharging demands and associated economic risks are dynamic in nature and vary with the maturity level of the EV market. Project Delivery Methods (PDMs) are used to share project risks among project stakeholders and ensure minimal risk to concerned parties [54]. In addition to the risk, properly selected PDMs allows projects to obtain third-party assistance in relation to technical know-how, resources, design and management standards, and other project-related benefits while ensuring possible benefits to all stakeholders [162][243]. Lower paybacks, unnecessary responsibility allocation, and adverse opportunity costs are possible disadvantages of selecting less suitable PDMs [243]. Moreover, PDMs can be used to create professional relationships among project stakeholders such as project owners, designers, builders, and managers to pursue and complete a project successfully while utilizing team synergies [244][245]. The current knowledge on existing projects shows that project owners or project managers are the key decision-makers in selecting the PDM method [243][245]. Concerning innovative and novel projects, project owners, and project managers might not have the necessary experience and expertise to select the most suitable PDM, so key stakeholder perception also enhances the effectiveness of the above decisions [243]. Hence, PDM selection for public EV-RI development 127 projects requires project stakeholder opinion to overcome possible deployment challenges such as defining needs, acquiring funding, and dealing with project-related risks, design, and construction issues [246]. A comprehensive literature review revealed that multiple researchers published articles on PDM selection for construction projects [162][53]. However, most of those researchers overlooked PDM selection complications that arise with novel and innovative projects and the involvement of key stakeholders [55][247]. By integrating stakeholder opinion on PDM selection, the synergies that are formed by project stakeholders can be used to overcome project limitations such as lack of technical know-how and experience with innovative and novel projects at their initial stages. Moreover, the financial risks of those projects due to demand uncertainties and other potential technical uncertainties can be managed collectively, and the risk can be minimized for project decision-makers. A compensatory MCDM approach will strengthen the collective decision making and provide a scientific platform. This chapter presents a framework to select the stakeholder expertise-based PDM method for innovative and novel projects. A fuzzy multi-attribute decision-making approach was introduced to rank alternative PDM methods to identify the most desirable PDM method for long-range EV-RI deployment projects in urban centers. The developed methodology was applied to the case study in Chapter 5 to identify possible PDMs for multi-period EV-RI planning projects for Kelowna, BC. 7.2 Methodology for PDM Selection Framework The methodology of this work initiated with a comprehensive literature review and expert interviews on current PDM selection strategies for construction projects. Figure 7-1 shows the method adopted to develop the PDM selection framework considering the stochastic and temporal variation of the estimated EV demands. 128 Figure 7-1 PDM Selection Methodology for EV-RIs The phases of the PDM selection framework are further explained below. Phase 1: Identification of PDM selection attributes The database related to project delivery methods (PDMs), factors, and different characteristics of mostly available PDMs were collected from recently published literature. Recent peer-reviewed journal articles, conference proceedings, and institutional reports were used to collect the background data related to infrastructure management and PDMs for infrastructure development 129 projects. PDMs for infrastructure projects, PDM selection factors, infrastructure risk management, and infrastructure cost-sharing were used as keywords for the literature review. Table 7-1 shows prominent attributes that help determine the most desirable PDM for small-scale and medium-scale infrastructure development projects such as EV-RI facilities. These attributes were selected using existing literature and validated by subject experts. Table 7-1 PDM Selection Factors Section Attributes Selection Attribute No. Description Ref. Project characteristics Scope definition SA 1 Maximize the importance of having a pre-defined scope baseline for the project (Not an agile type of a project) [162] Project schedule SA 2 Maximize the importance of having very accurate and tight project schedules and baselines. [162] [161] Contract Strategy SA 3 Minimize the cost uncertainties due to not having cost baseline and contract strategy (High-cost uncertainties in measure and pay, cost-plus, etc.) [162] Complexity SA 4 Maximize the external expertise for complex projects for identifying the technical-know-how requirement [162] [163] [161] Owners pre-requests Constructability studies SA 5 Maximize the importance of the constructability studies before the project initialization [162] Value engineering studies SA 6 Maximize the importance of the involving value engineering processers during the project to enhance the project efficiency [162] Contract packaging SA 7 Maximize the importance of creating work packages and award contracts accordingly. [162] Contract awarding criteria SA 8 Maximize the importance of having a systematic contract awarding criteria, which will consider cost, quality, and previous experiences for the selections. [162] Cost Sharing SA 9 Maximize the importance of cost-sharing or additional funding due to the expensiveness and lower pay-back of the project [162] [161] Owners preferences Responsibility SA 10 Minimize the ownersâ responsibilities to obtain minimal risks and responsibilities [161] Design control SA 11 Maximize the importance of having design control to the owner at the initiation and during the project. [163] [161] Involvement after awarding SA 12 Minimizing the ownersâ involvement during the project and keep minimal disturbance to the owner [163] Risk Change orders SA 13 Minimize the possibilities of initiating a high amount of change orders during the project. [162] Risk-sharing SA 14 Maximize the importance of the risk-sharing or risk allocating ability from the owner to a third party in a high-risk project. [162] Quality Quality control and quality assurance SA 15 Maximize the importance of having a specific avenue to conduct third party quality control and quality assurance. [162] 130 These factors were used to develop the decision matrix for alternative PDMs. Phase 2: Determine the importance of PDMs and develop a decision matrix The literature revealed that there are multiple PDMs for different infrastructure development projects. The key PDMs can be categorized as Traditional; Partnership; or Collaborative [159]. Literature-based details on the selected PDMs are shown in Table 3-6. Six PDMs were considered to develop the decision matrix: 1) Design-Bid-Build (DBB) (Traditional method); 2) Design-Build (DB) (Collaborative method); 3) Construction Management at Risk (CM) (Collaborative method); 4) Design-Built-Operate and Transfer (DBOT) (Partnership method); 5) Public-private partnership (PPP) (Partnership method); and 6) Integrated Project Delivery (IPD) (Collaborative method). The studies conducted by Oyetunji A. A. and Anderson S. D (2006) and Mostafavi A. and Karamouz M. (2010) were used to identify the relative importance of each factor for the above considered PDMs [157]. The following linguistics terms were used to indicate the relative importance of the attributes for each PDM: ï· âNot Applicableâ was used when the attribute is not applicable to be considered for the EV maturity level ï· âEqually Importantâ was used when the attribute is applicable but have no considerable impact on the selection of PDM. ï· âModerately Importantâ was used when the attribute is applicable and has considerable impact on the selection of a PDM. ï· âStrong Importantâ was used when the attribute is applicable and has a significant impact on the selection of a PDM. ï· âVery Strong Importantâ was used when the attribute is applicable and has a very strong impact on the selection of a PDM. ï· âExtremely Importantâ was used when the attribute is applicable and has an extremely high impact on the selection of a PDM. Table 7-2 shows the relevant attribute values based on the PDM. 131 Table 7-2 Decision Matrix for the Proposed PDM Selection Approach Attribute No. Decision Type Linguistic Terms MAX MIN DBB DB CM DBOT PPP IPD SA 1 1 0 Equal Important Extremely strong Equal Important Strong important Extremely strong Extremely strong SA 2 1 0 Equal Important Equal Important V. Strong important Equal Important Strong important Equal Important SA 3 0 1 Equal Important Equal Important Extremely strong Equal Important Equal Important Strong important SA 4 1 0 Extremely strong Equal Important V. Strong important Strong important V. Strong important Extremely strong SA 5 1 0 Not Applicable Extremely strong Not Applicable Extremely strong Moderate Important V. Strong important SA 6 1 0 Not Applicable Extremely strong Not Applicable Extremely strong Moderate Important Extremely strong SA 7 1 0 Not Applicable Extremely strong Not Applicable Extremely strong Moderate Important V. Strong important SA 8 1 0 Equal Important Extremely strong Equal Important V. Strong important Strong important V. Strong important SA 9 1 0 Not Applicable Moderate Important Not Applicable Extremely strong Extremely strong Moderate Important SA 10 0 1 V. Strong important Extremely strong Equal Important Moderate Important Extremely strong Moderate Important SA 11 1 0 Extremely strong Equal Important Strong important Equal Important Strong important Moderate Important SA 12 0 1 Equal Important Extremely strong Equal Important V. Strong important Strong important Moderate Important SA 13 0 1 Extremely strong Equal Important Strong important Equal Important Strong important Moderate Important SA 14 1 0 Equal Important Moderate Important Strong important Extremely strong Extremely strong Moderate Important SA 15 1 0 Extremely strong Moderate Important V. Strong important Equal Important V. Strong important Extremely strong The attributes shown in Table 7-2 were converted to triangular-fuzzy numbers using the scale shown in Table 7-3. Table 7-3 Triangular-fuzzy Numbers used to Convert Attributes of Different PDMs Linguistic Terms Triangular fuzzy numbers Not Applicable (ð1, ð1, ð1) Equal Important (ð1, ð2, ð3) Moderately Important (ð2, ð3, ð4) Strong Important (ð3, ð4, ð5) Very Strong Important (ð4, ð5, ð6) Extreme Important (ð5, ð6, ð6) 132 The attributes need weights to indicate their relative impacts in order to choose the best desirable PDM. Phase 3: Determine the relative importance/impacts of attributes for a specific project Traditionally, the weights of various factors/attributes are decided by project manager, owner/s, and sponsor/s considering characteristics of the project [248]. However, in this work, a multiple stakeholder opinion-based approach was considered to allocate weights for attributes due to the lack of know-how and potential demand, risk, and cost uncertainties. Thus, this phase consists of three sub-phases that are given below. 1) Stakeholder-based weights to indicate the Level of Impacts (LoI) of different attributes This sub-phase consists of the allocation of weights to indicate the relative impact of each attribute to select the best desirable PDM. Appendix F1 shows the checklist that was used to collect and record stakeholder judgment of LoI for each attribute. This data can be collected for a single stage or for multiple stages of the project considering pre-defined project phases (e.g. projects that have different stages per the Rogers Diffusion Theory can have multiple forms filled by each stakeholder to obtain the data related to the particular stage). These judgments contain significant uncertainties due to the multiple perspectives of the experts. Linguistic terms were adopted to convert non-numerical data into numerical content to proceed with the analysis [157][249]. Linguistics terms were then converted to triangular-fuzzy numbers, considering the vagueness of the aforementioned expert judgments. aEI = (ð1, ð1, ð2) was considered the equal impact value, and higher impact levels were denoted using triangular-fuzzy numbers that are shown in Table 7-4. Table 7-4 Triangular-fuzzy Numbers that Represent Stakeholder Judgment Linguistic Terms Triangular fuzzy numbers Not Applicable (NA) (ð1, ð1, ð1) Equal Impact (EI) (ð1, ð1, ð2) Moderate Impact (MoI) (ð2, ð3, ð4) Strong Impact (SI) (ð3, ð4, ð5) Very Strong Impact (VsI) (ð4, ð5, ð6) Extreme Impact (ExI) (ð5, ð6, ð6) 133 Accordingly, each stakeholder was denoted by a weight based on the importance of their decision to select the PDM. The weights of the ith attribute, which was denoted by the jth stakeholder, was shown as lower bound (LB) value of âpijâ, most likely (ML) value of âqijâ, and upper bound (UB) value of ârijâ [249]. Accordingly, the triangular fuzzy membership value of the ith criteria denoted by jth stakeholder was (pij, qij, rij). âXijâ was considered as the matrix of the weights of the ith criteria denoted by the jth stakeholder and is expressed in Equation (42). Equation 42 Generally, these decisions are based on personal opinion, skills, experiences, and other available resources of the selected decision-makers [248]. Areas of expertise of expertsâ are not uniform. Some experts have more knowledge of some attributes where other experts might have a lack of knowledge. Thus, the PDM selection approach needs to be able to integrate different expertise in the decision-making process. 2) Evaluate stakeholder expertise and incorporate Level of Expertise (LoE) weights The LoI of the above attributes was defined by decision-makers or subject experts based on their experiences and expertise, which are typically a qualitative (non-numerical) judgment [248]. Thus, linguistics terms were used to express the Level of Expertise (LoE) of the decision-makers to adjust the relative importance of the considered attributes. CNE = (ð1, ð1, ð1) was considered no expertise and CLE = (ð1, ð1, ð2) was considered low expertise. Accordingly, higher expertise levels were denoted using fuzzy-triangular numbers, shown in Table 7-5 [157]. Xij = (p11,q11,r11) (p12,q12,r12) â¦ (p1j,q1j,r1j) â¦ (p1n,q1n,r1n) (p21,q21,r21) (p22,q22,r22) â¦ (p2j,q2j,r2j) â¦ (p2n,q2n,r2n) ... ... ... ... (ai1,bi1,ci1) (ai2,bi2,ci2) â¦ (aij,bij,cij) â¦ (ain,bin,cin) ... ... ... ... (am1,bm1,cm1) (am2,bm2,cm2) â¦ (amj,bmj,cmj) â¦ (amn,bmn,cmn) 134 Table 7-5 Triangular-fuzzy Numbers to Incorporate Stakeholder Expertise for Decision-making Description Linguistic Term Triangular fuzzy numbers Not an expert No Expertise (ð1, ð1, ð1) Had some experience Low Expertise (ð1, ð1, ð2) An expert, but currently not practicing Moderate Expertise (ð1, ð2, ð3) An expert and working as a professional High Expertise (ð2, ð3, ð3) 3) Multi-attribute group decision-making approach (Compensatory method) Generally, multi-stakeholder problems are solved using mathematical approaches that are used to optimize the decisions of rational decision-makers. The Simple Additive Weighting method can be used to score combined judgments [187][186]. This is a compromising method that permits trade-offs between stakeholders [187]. Accordingly, the jth stakeholderâs expertise to decide the ith criteria was given the weight of âWijâ considering other stakeholders. âWâijâ represents the fuzzy weights given based on the relative importance of the jth stakeholder to decide ith criteria. The weighted sum of each criterion was calculated, and the final weights were created to proceed with further analysis [249]. Accordingly, the equations for the final attribute weights can be shown as Equation (43). Equation 43 Basic fuzzy arithmetic operations consisting of fuzzy addition, multiplication, and division (see Section 3.8.2) were used to solve Equation (42). The fuzzy weights associated with the multiple stakeholder perspectives were used to proceed with PDM selection for the considered EV-RI deployment. ð«ðâ² = ð¾ðâ² = â (ð¾ððâ¨ð¿ðððð=ð) â ð¾ðððð=ðâ ð¾ðâ² = â (ð¾ððâ¨ð¿ðððð=ð) â ð¾ðððð=ðâ ... ð¾ðâ² = â (ð¾ððâ¨ð¿ðððð=ð) â ð¾ðððð=ðâ â¦ ð¾ðâ² = â (ð¾ððâ¨ð¿ðððð=ð) â ð¾ðððð=ðâ 135 Phase 4: Select the most desirable PDM using F-TOPSIS The weights obtained from phase 3 (from different sub-phases) were considered for this phase. Each alternative method was simulated individually. Fuzzy weights given to the kth alternative were normalized to obtain the fuzzy decision matrix, which is Ãik = [ãik]. The positive ideal criteria is shown in Equation (44) and the negative ideal criteria is shown in Equation (45). To the positive ideal; Ãðð = (ððð ððââ , ððð ððââ , ððð ððââ ) ððð ððâ = maxð{ððð} Equation 44 To the negative ideal; Ãðð = (ððâ ðððâ , ððâ ðððâ , ððâ ðððâ ) ððð ððâ = minð{ððð} Equation 45 The ð·ðâ² weights were used to calculate the weighted normalized fuzzy decision matrix, which is Ãâik = [ãâik]. Equation (46) shows the calculation of Ãâik . Ãððâ² = Ãðð ï ð·ðâ² Equation 46 Then the fuzzy positive ideal solution (PIS) (most desirable alternative) and the fuzzy negative ideal solution (NIS) (worst case alternative) were obtained [197][249]. To the positive ideal; ð´â = (Ã1â²â , Ã2â²â, â¦ â¦ . , Ãð¾â²â), ð¤âððð Ãðâ²â = ððð¥ð{Ãðððð¿â² } Equation 47 To the negative ideal; ð´â = (Ã1â²â , Ã2â²â, â¦ â¦ . , Ãð¾â²â), ð¤âððð Ãðâ²â = ðððð{Ãððð¿ðµâ² } Equation 48 Accordingly, Equation (47) and Equation (48) were used to calculate fuzzy PIS and fuzzy NIS. Equation (12) was used to calculate the distances between the PIS that denotes as ð(Ãððâ² , Ãðâ²â), and 136 NIS, which denotes as ð(Ãððâ² , Ãðâ²â). Accordingly, the above-calculated distances, the cumulative distances (ððâ) and (ððâ) were calculated using Equation (49) and Equation (50). ððâ = â ð(ð¾ð=1 Ãððâ² , Ãðâ²â) Equation 49 ððâ = â ð(ð¾ð=1 Ãððâ² , Ãðâ²â) Equation 50 The above distances were used to calculate the closeness coefficient (CCi) for each PDM as shown in Equation (51). ð¶ð¶ð = ððâ (ððââ + ððâ) Equation 51 The closeness coefficient was used to rank the considered PDMs based on project attribute values. The PDM method with the highest closeness coefficient (CC) was chosen as the most desirable and recommended project delivery method to proceed with the project. Other PDMs were ranked according to the CC obtained from the above analysis. Then the applicability of the selected PDMs was discussed with favourable and adverse impacts considering the sustainability of the transport electrification in a given urban center. 7.3 PDM Selection Model Demonstration The data obtained from Chapter 5 was used to demonstrate the proposed PDM selection model. Accordingly, multi-period EV-RI deployment data for the Census Metropolitan Area of Kelowna were taken as model inputs, which were organized into two product maturity stages. 7.3.1 EV-RI Deployment Stages for Multiple Periods The theory of adoption and diffusion (Rogers model) [146] was used to explain the market growth of EVs in the long run. Accordingly, five key product maturity stages can be considered in EV-RI deployment to the business-as-usual stage: 1) Innovators; 2) Early adopters; 3) Early majority; 4) Late majority; and 5) Laggards [50]. The innovation stage can be considered as the very early stage of electric vehicle testing in which there is a lack of commercialized product at the local market [250]. Hence, this work was started by early adopters and continue up to laggards. As mentioned 137 by Perera et al. (2020), the different EV-RIs were segregated by the different deployment stages, shown in Figure 7-2. Figure 7-2 Maturity Stage-wise EV-RI Deployment However, the aforementioned stages were combined and considered as two stages, described below, to reduce the complexity of the data collection and expert consultation. 1. The initial deployment stage: Early adopters and early majority stages were combined in this stage. The infrastructure developed and invested in early stages of the product maturity can be considered in this stage of the project, which consists of higher financial risk due to limited public charging demands and longer payback, inadequate technical know-how due to lack of local DC-FC recharging infrastructure construction experience, and a high potential for significant design changes during the construction and operating periods. Constructability analysis can be done at the pre-planning stage of the project. However, it might be too early to conduct work packaging and value engineering, since the project processers, durations, and costs have higher uncertainties due to lack of experience. The possibility of having potential design changes, change orders, and baseline changes are considerably greater at the early stages of product maturity. 2. The way forward to the business-as-usual stage: Late majority and laggards were combined in this stage. These investments have comparatively lower risk and lower paybacks than the previous stage. Furthermore, the technical know-how is higher, where the potential demands and risks can be calculated more accurately using historical data obtained from the earlier investments. Infrastructure developers can use the previous project cost, time, and schedule data to ensure proper costing, scheduling, and operating of the infrastructure. 138 Potential change orders will be limited during the project life cycle, and the owners' control after awarding the contract will be minimized. With time, the deployment process turns in to a usual development task, where there are minimal financial and technical risks. The project implementation, operation, and management process will be in-placed, and the project can be done as work packages. Value chain optimization can be done to minimize costs and obtain a competitive advantage by enhancing the value of the infrastructure facilities. The payback periods should be significantly lower compared to the previous stage. 7.3.2 Stakeholder Data Collection for the Case Demonstration Potential project stakeholders and subject experts were consulted to acquire necessary data to demonstrate the developed methodology for a medium-scale community in BC. The ethics approval obtained for the MITACS Fortis Energy Chair Project was used to conduct the expert consultation process. In this case study, the experts were categorized as: 1) Utility providers and existing investors, and 2) Project manager and developers. Their expertise was collected using focus group interviews. The experts who were interviewed are shown in Table 7-6. The collective opinion of each expert category was recorded using several focus-group interviews that were conducted with the aforementioned stakeholders/experts. Table 7-6 Focus Group Interview-based Data Collection and Database Development Stakeholder Category Knowledge Area Number of Experts consulted 1 Investors and Owners: This category includes current owners, investors, utility providers, EV-RI operators, and EV-RI facility managers 4 2 Project Managers and builders: This category includes building and infrastructure developers, Marketing managers, technology managers, PMs, and other construction-related decision-makers. 4 The different weights obtained from the aforementioned experts are shown in Table 7-7 for both the first and second stages of the EV-RI deployment process. 139 Table 7-7 Stakeholder Judgment Matrix Attribute No. The initial deployment stage The way forward to the business-as-usual stage Stakeholder Category 1 Stakeholder Category 2 Stakeholder Category 1 Stakeholder Category 2 SA 1 Extreme Impact Moderate Impact Very Strong Impact Extreme Impacts SA 2 Equal Impacts Equal Impacts Very Strong Impact Extreme Impacts SA 3 Very Strong Impact Equal Impacts Extreme Impacts Very Strong Impact SA 4 Strong Impacts Strong Impacts Moderate Impact Not Applicable SA 5 Strong Impacts Very Strong Impact Equal Impacts Equal Impacts SA 6 Equal Impacts Not Applicable Strong Impact Very Strong Impact SA 7 Not Applicable Very Strong Impact Extreme Impacts Very Strong Impact SA 8 Equal Impact Moderate Impact Extreme Impacts Very Strong Impact SA 9 Extreme Impact Extreme Impact Strong Impact Moderate Impact SA 10 Strong Impact Not Applicable Equal Impacts Moderate Impact SA 11 Very Strong Impact Strong Impact Equal Impacts Equal Impacts SA 12 Moderate Impact Strong Impact Equal Impacts Equal Impacts SA 13 Strong Impact Extreme Impact Equal Impacts Equal Impacts SA 14 Very Strong Impact Very Strong Impact Equal Impacts Moderate Impact SA 15 Very Strong Impact Not Applicable Extreme Impacts Extreme Impacts The triangular-fuzzy numbers indicated in Table 7-8 were used to convert the above experts' judgments into a fuzzy-judgment matrix. Table 7-8 Linguistic Terms to Indicate LoI towards the Considered Project Stage Fuzzy Number Linguistic Terms (LoI) Triangular fuzzy number 0 Not Applicable (0, 0, 0) 1 Equal Impact (0, 0.2, 0.4) 2 Moderate Impact (0.2, 0.4, 0.6) 3 Strong Impact (0.4, 0.6, 0.8) 4 Very Strong Impact (0.6, 0.8, 1) 5 Extreme Impact (0.8, 1, 1) According to Table 7-7, the stakeholder LoI for different attributes shows different judgments based on their expertise. Considering those variations in the decision-making process is vital for innovative infrastructure deployment projects where existing and local knowledge is limited. However, it is important to evaluate the experience and expertise of each stakeholder on each attribute to understand the rationale for the aforementioned judgment. Thus, the experts' or stakeholders' experiences and knowledge were also evaluated in order to improve the closeness of the final decision into the preference made by the subject experts. Accordingly, the knowledge level of the expertise chosen for the case study is shown in Table 7-9 below. The decisions for PDM selection attributes for regular infrastructure development projects were considered for the 140 business-as-usual stage due to the lack of expertise on current mass scale EV-RI infrastructure projects in the concerned community. Table 7-9 Stakeholder Expertise in EV-RI Deployment and Conventional Infrastructure Projects Attribute No. Knowledge related to initial phases of EV-RI planning and management Knowledge related to conventional infrastructure planning and management Stakeholder Category 1 Stakeholder Category 2 Stakeholder Category 1 Stakeholder Category 2 SA 1 Low Expertise High Expertise Low Expertise High Expertise SA 2 Low Expertise High Expertise Low Expertise High Expertise SA 3 Low Expertise High Expertise Low Expertise High Expertise SA 4 High Expertise Low Expertise High Expertise Low Expertise SA 5 High Expertise Low Expertise High Expertise Low Expertise SA 6 Moderate Expertise Moderate Expertise Moderate Expertise High Expertise SA 7 No Expertise No Expertise No Expertise High Expertise SA 8 No Expertise Moderate Expertise Moderate Expertise Moderate Expertise SA 9 High Expertise Moderate Expertise High Expertise Moderate Expertise SA 10 High Expertise Moderate Expertise High Expertise Moderate Expertise SA 11 High Expertise Low Expertise No Expertise High Expertise SA 12 High Expertise Low Expertise No Expertise High Expertise SA 13 Moderate Expertise High Expertise No Expertise High Expertise SA 14 High Expertise Moderate Expertise High Expertise Moderate Expertise SA 15 Moderate Expertise No Expertise No Expertise High Expertise Table 7-9 shows that the experience and expertise of all stakeholders are not uniform. Although the individual stakeholder expertise did not fulfill the full attribute list to finalize the best desirable PDM for EV-RI projects, the combined expertise has covered the majority of those attributes. Hence, combining multi-stakeholder judgments using compensatory methods will enhance the applicability and reliability of the final PDM decision compared to the conventional individual decision-making concepts. The triangular-fuzzy numbers that are indicated in Table 7-10 were used to convert the linguistic terms used in Table 7-9. Table 7-10 Linguistics Terms to Indicate LoE of the Stakeholders on the Subjected Criteria [157] Fuzzy Number Linguistic Terms (Expertise) Triangular fuzzy number 0 No expertise (0, 0, 0) 1 Low expertise (0, 0, 0.5) 2 Moderate expertise (0, 0.5, 1) 3 High expertise (0.5, 1, 1) The LoE and LoI matrices are shown in Appendix F2 and Appendix F3, respectively. 141 7.3.3 PDM Decision Matrix for Ranking Table 7-2 can be converted into a fuzzy matrix using triangular-fuzzy numbers. The triangular-fuzzy numbers shown in Table 7-11 were used for the conversion. Table 7-11 Linguistic Terms to Convert the Decision Matrix into Fuzzy Numbers [251] Fuzzy Number Linguistic Terms (LoI) Triangular fuzzy number 0 Not Applicable (0, 0, 0) 1 Equal Important (0, 0.2, 0.4) 2 Moderate Important (0.2, 0.4, 0.6) 3 Strong Important (0.4, 0.6, 0.8) 4 Very Strong Important (0.6, 0.8, 1) 5 Extremely Strong (0.8, 1, 1) The fuzzy decision matrix for alternative PDMs is shown in Appendix F4. 7.3.4 Multi-period and Multi-stakeholder-based Attribute Weights As described in the methodology section 7.2, the compensatory weights were calculated using Equation (43) for both stage one and two of EV-RI deployment projects. Those weights are given in Appendix F5. The individual and compensatory weights for each attribute in stages one and two were defuzzified to crisp numbers using the Center of Gravity method to show their relative importance in the PDM selection process. Accordingly, Table 7-12 shows the impact of each attribute that was used to select the best desirable PDM for EV-RI projects in different product maturity stages. 142 Table 7-12 Final Weights of the Expert Data Collected as Inputs for the PDM Selection Tool 7.3.5 Ranking Alternative PDMs and PDM Selection The F-TOPSIS method discussed in the methodology was used to select the best desirable PDM method for different EV-RI deployment stages. Normalized attribute matrices, distances from the negative and positive ideal solutions, and closeness coefficients for different PDM methods were calculated. The final closeness coefficient of alternative PDM methods and rankings of those for different life cycle stages are shown in Figure 7-3. 143 Figure 7-3 F-TOPSIS-based PDM Rankings for Multi-period EV-RI Deployment Projects Figure 7-3 shows the rankings obtained using different stakeholder opinions and different characteristics of EV-RI deployment stages. Considering the initial EV-RI deployment stage, the resultant PDMs obtained from both individual stakeholder opinions and their compensatory opinion resulted in a partnering PDM approach. The analysis shows that the PPP method was proposed by stakeholder category 1: the potential and current owners and investors based on their expertise. In addition, the DBOT method was proposed by stakeholder category 2: PMs, builders, and developers based on their knowledge. As discussed in section 7.3.4, lack of expertise is present in different stakeholder categories due to a lack of project-related experiences. Thus, the compensatory method makes more sense to fulfill the StakeholderCategory 1StakeholderCategory 2CompensatoryWeightStakeholderCategory 1StakeholderCategory 2CompensatoryWeightThe initial deployment stageThe way forward to the business-as-usualstageDBB 0.322 0.186 0.334 0.212 0.165 0.203DB 0.464 0.553 0.429 0.625 0.680 0.637CM 0.455 0.358 0.488 0.398 0.359 0.395DBOT 0.629 0.796 0.648 0.636 0.658 0.643PPP 0.801 0.730 0.787 0.702 0.690 0.708IPD 0.682 0.653 0.650 0.731 0.757 0.7550.0000.1000.2000.3000.4000.5000.6000.7000.8000.900Closeness Coefficient (CC)EV-RI Initial Stages EV-RI Mature Stages 3 1 2 1 2 3 3 1 2 3 2 1 3 2 1 3 2 1 144 required expertise from multiple stakeholders and proceed with group-based decision-making. The PPP method has resulted from the compensatory approach for the initial stages of EV-RI deployment. Accordingly, the PPP can be considered the best desirable PDM for the EV-RI initial development stages. Based on the EV-RI location-allocation and capacity enhancement plan for Kelowna, BC, the potential fast public recharging facilities proposed for Glenmore, Springfield, Rutland, Pandosy, Clement, and University Plaza can be developed using the PPP method. Thus, the aforementioned EV-RI deployments may reduce the advance financial impacts to private investors or municipalities, where larger-scale government or private investments will take place. As a result of partial or full funding by third-party institution, EV-RI facilities will be built and transferred to an individual small-scale institution to overlook operations. Hence, the financial and operational risk to small-scale investors will be minimal. This approach will also solve the concern of lack of technical know-how with the potential technical support from the partnering institution. The analysis done for the second stage of the EV-RI deployment project, which is on the way forward to the business-as-usual stage, showed that the IPD method was the most desirable PDM. Generally, facilities developed in this stage will have shorter payback, relatively higher demands, and considerably lower financial risks. Moreover, demand trends can be forecasted using historical data, which helps to predict EV-RI cash flow more accurately. Therefore, the scope, schedule, and cost baselines can be defined more precisely compared with stage 1. Local experts can work on the project with experience gained from the previous stages, and these projects can be awarded as packages based on the diversion of technical know-how. Per the results obtained from the EV-RI location-allocation and capacity enhancement plan, fast public EV-RIs proposed for McCurdy corner, Cooper center, Banks, Capri center, Spall plaza, and Richter can be developed using the IPD method. Thus, the construction speed and value generated by the project will be increased, and the project costs and paybacks will be minimized. Accordingly, EV-RI investors, developers, and municipalities can work together and use the aforementioned PDM selection approach to decide the most suitable PDM method for potential EV-RI projects. The use of compensatory methods with multiple stakeholder judgments to adjust the final decision based on their expertise may reduce potential impacts to all stakeholders and sustain the overall EV-RI deployment process. 145 7.4 Summary Generally, project delivery methods (PDM) are used to establish a framework to design, procure, construct, and manage infrastructure or an asset, which affects the successful completion of the project. The selection of the most desirable PDM is not straightforward as it depends on several project related factors and market maturity. Therefore, the best suitable PDM can change with the decision-maker, their expertise, the nature of the project, and market factors. Hence, a scientific framework to select the most desirable PDM for innovative projects is vital. The proposed PDM selection framework discusses an essential section of the electric vehicle recharging infrastructure (EV-RI) planning and management framework considering different stages of the electric vehicle product maturity cycle (multi-period) and the vagueness (uncertainty) of the project stakeholders and subject experts. The existing body of knowledge, expertsâ judgment, and existing databases were used to develop the decision-matrix for alternative PDM methods. The PDMs related to conventional, partnering, and collaborative approaches were selected for the decision matrix. Then the methodology was developed to acquire multi-stakeholder knowledge on different attributes and convert those to a fuzzy-judgment matrix to incorporate vagueness of those judgments. Moreover, the different expertise of different stakeholder categories was also evaluated and incorporated with the judgment matrix using compensatory MCDM methods. An F-TOPSIS selection framework was used to select the most desirable PDM method for different project stages of the EV-RI projects. Furthermore, a case study for Kelowna, BC, was conducted to demonstrate the developed PDM selection procedure, and the data obtained from the EV-RI location-allocation model was used as the key data input for the proposed framework. The procedure developed to select the most desirable PDM can minimize the adverse economic and social impacts of innovative long-term infrastructure development projects. The project stakeholder can integrate their expertise and perceptions on potential risks, costs, and benefits to enhance the EV-RI deployment for more extended periods. Thus, the potential financial risks to the investors due to uncertain demands and extensive investments can be minimized using local expertise. As a conclusion of the results obtained for the case study, a partnering approach in the form of a private-public partnerships (PPP) method can be proposed for the initial stages of EV-146 RI deployment, and a collaborative approach in the form of Integrated Project Delivery (IPD) can be proposed for the later stages of EV-RI deployment projects. Hybrid strategies can also be used based on the characteristics of the projects, which need to be further analyzed with in-detail project specifications. The proposed PDM selection procedure and the extended decision matrix would help urban designers, planners, investors, practitioners, and contractors to select the appropriate PDM for small-scale, long-range infrastructure development projects.147 Chapter 8 Conclusions and Recommendations Decarbonizing the transport sector using electric vehicles (EVs) has gained immense attention from policymakers and practitioners. However, EVs have not being attractive to vehicle users and infrastructure investors due to lack of proper recharging network, limited vehicle range, higher switching costs, and lack of potential infrastructure investments. Although there is a vast potential for EVs as alternative low-emission fuel vehicles for Canadian road transportation, the EV market is still in its early adoption stages due to the limitations mentioned above. This research proposes to fill a critical gap in multi-period planning and managing recharging infrastructure for urban centers. This study has developed decision support tools to locate and expand recharging infrastructure networks considering multi-period public recharging demands, plan incentives and tax policies to sustain anticipated EV demands, and to encourage investors to invest in public recharging infrastructure network by using the best desirable project delivery methods. The outcome of this study delivers a scientific framework that supports long-term public recharging facility planning, operating, and managing the process. Government and private institutions in Canada that are responsible for the development, planning, and managing of EV-RIs can use the proposed framework to improve electrified transportation by evaluating and identifying the most desirable long-term policies and practices. Local urban planners, developers, and infrastructure investors can also utilize developed decision support tools and methodologies to deploy low-emission transportation solutions, incorporating the expertise of local professionals. Furthermore, the proposed framework can be generalized with a few modifications to incorporate future low-emission fuel-based transportation such as hydrogen fuel cells, etc. Ultimately, this framework will assist in reducing fossil fuel use for road transportation, achieving national GHG emission targets more rapidly, and reducing local contribution to the depletion of non-renewable energy sources. 8.1 Summary and Conclusions A summary of the specific sections of the study and the main conclusions are presented below. Chapter 4 proposed a life cycle thinking approach to compare and evaluate alternative energy options by considering multiple factors. Literature-based data was used to identify the technical, 148 environmental, economic, and social characteristics of different fuel options that can be used as an energy source for road transportation. A framework was developed as a two-step model to choose the best desirable fuel option to reduce transport-based GHG emissions in Canada. The first step was used to filter desirable fuel technologies using a rule-based method, and the second step was used to select the best desirable low-emission renewable fuel option using the life cycle thinking-based assessment. This approach would assist decision-makers such as government institutions and policymakers in making high-level decisions on long-term transportation fuel options. The database developed in this analysis can be used by researchers and practitioners to evaluated different fuel options using local characteristics. The implementation of this framework was demonstrated for Canadian provinces, and a comprehensive life cycle impact database was developed for different fuel-options that can be used for transportation purposes in Canada. In addition, the following conclusions were made from the case study. Those are, 1) The cradle-to-gate impacts of EVs are 71% higher than similar ICEVs due to the adverse impacts of EV batteries; 2) Although EVs do not emit adverse substances to the environment in their operating stage, the FF-based mass electricity generation process may have some adverse impacts on the environment; 3) EVs are more desirable for provinces with low-emission grid electricity and EVs may not be environmentally feasible for 90%-100% FF-based grids; and 4) HFCVs are commercially viable only if low-cost and low-emission mass HFC can be produced locally using distributed production plants. Chapter 5 presented a spatial-temporal planning framework to determine the multi-period capacities and locations of recharging infrastructure for electric vehicles in a given city center. Three different modules were introduced and integrated in order to extend the existing body of knowledge to achieve both temporal and spatial planning approaches. Those are, 1) A literature-based multi-period public recharging demand assessment module; 2) A distance-matrix for the selected municipality; and 3) A multi-period optimal recharging infrastructure location-allocation and capacity planning module. A case study of a typical medium-scale municipality in Kelowna, BC, Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The results of the case study showed that the proposed framework estimated multi-period public recharging demands, minimized lifecycle costs, maximized service coverage and infrastructure utilization, and resulted in lower paybacks for 149 infrastructure investments compared to conventional planning approaches. The conventional ad-hoc planning approach required 1%-117% additional investment that recovered in additional 10 to 30 years. This framework can be used to compare different investment strategies and municipal recharging tariff policies, which are required in the early stages of the RI deployment process to encourage investors, and existing and potential EV users. Furthermore, government institutions, utility providers, and private institutions can use this framework to appraise public recharging demands, permitting, and developing RIs in the long run. Chapter 6 proposed a systematic and holistic approach for incentive planning and developing tax policies to reduce municipal GHG emissions. This approach evaluates both building and transport interventions together and prioritize the most desirable sectors and interventions to be incentivized. A multi-criteria decision-making approach was used by considering multiple investment scenarios. The developed method was demonstrated for a single-family detached household located in the Okanagan region of BC. The case study results indicated that incentives for EVs can be developed by provinces with low-emission grid electricity, and sales tax waivers can be imposed to reduce upfront costs. Furthermore, EV parking fee waivers and government-subsidized recharging costs can be used to reduce operating costs. Carbon taxes can also be imposed on vehicle emissions and building systems to discourage the use of high-emission vehicles and encourage switching to low-emission vehicle options. Furthermore, provinces with a high-emission grid can focus on possible incentives for residential building interventions. Carbon taxes will support municipal clean energy incentive budgets. Accordingly, this approach will strategically determine the most desirable clean-energy interventions for new and existing households. The developed tool can be used by municipalities and policymakers to develop favourable policies to achieve municipal, provincial, and national level GHG targets faster by promoting key interventions. Chapter 7 presented a stakeholder judgment-based method to select the most desirable project delivery method for infrastructure deployment projects. A fuzzy-based compensatory MCDM approach was used to incorporate uncertainties associated with stakeholder judgment and stakeholder expertise. The developed methodology was applied to the case study given in the Chapter 5, which was used to identify possible PDMs for multi-period EV-RI planning projects for Kelowna, BC. The results of the case study showed that partnering approaches such as private-150 public-partnerships (PPP) are best for EV-RI development projects at low EV penetration. Collaborative PDM approaches such as Integrated Project Management (IPD) are best for EV-RI projects with high EV penetration. The decision-matrix developed in this work can be further extended with hybrid PDMs to select hybrid-PDM methods for specific projects. Further to that, the method will deliver a local expert judgment-based selection approach for small-scale infrastructure development projects considering the presence of high risks and low technical know-how. Hence, the proposed PDM selection procedure can be implemented in a wide variety of innovative projects at the municipal level, where there is limited expertise available. The reproducibility of the results from this work was assessed by deploying the above-proposed methodologies to implement by-directional recharging facilities. Accordingly, these models were re-stimulated by a different researcher, and the same results were acquired to ensure the reproducibility of the work. Figure 8-1 depicts the strategic map where the methods developed in this study and proposed tools are fit into municipal EV-RI development projects. This study can be used to strategically identify an environmentally friendly and economically affordable alternative fuel system for a region, and the necessary incentives can be planned scientifically to achieve national GHG reduction targets. Figure 8-1 Proposed Strategic Map 151 The methods mentioned to select the PDM strategy and to develop an optimal temporal and spatial recharging/refueling facility network can be used to enhance the sustainability of alternative fuel availability and access throughout the country. This strategic map can be used by the federal, provincial, and municipal governments to scientifically plan and manage EV-RI networks for a growing EV population and achieve their GHG reduction targets faster. 8.2 Originality and Contributions This research study brought the following unique contributions to the existing body of knowledge that will assist with clean energy-based transport culture. Life Cycle Thinking-based Multi-period EV Recharging Infrastructure Planning: Conventional recharging infrastructure planning approaches have ignored the life cycle thinking approach and multi-period planning strategies in their decision-making process. The proposed research framework integrates life cycle costs and emissions of both energy and mobility cycles into multi-period planning and management of electric vehicle recharging infrastructure for urban centers. Moreover, this study was conducted beyond the traditional focuses by incorporating multi-period user behaviours for different EV maturity stages by enhancing the applicability of the proposed methodology for long-range infrastructure planning. The life cycle results obtained here can be used by the federal and provincial government level policymakers to develop policies to promote low-emission transport options. A strategic sequence with a comprehensive planning approach to develop EV-RI: Conventional EV-RI planning is based on ad-hoc decisions of decision-makers that increase the infrastructure investments and results in longer paybacks. This study presents a strategic sequence in the EV-RI planning framework for urban centers. The efficiencies of conventional planning approaches were expanded with scientific decision-making approaches and state-of-the-art software by proposing interactive forms, tools, and checklists. The comprehensive planning approach proposed in this study can be used to plan and manage recharging infrastructure deployment projects more effectively and cost-efficiently than conventional practices. An incentive planning framework to motivate clean energy use for households: There is a lack of information on a unified incentive planning approach that considers both transport and building 152 sectors to achieve government GHG targets rapidly. This study proposes an integrated incentive planning approach by considering both building and transport electrification. This approach will accelerate GHG emission reduction, enhance energy-efficiencies, and promote renewable low-emission energy sources by incentivizing the most desirable upgrade, regardless of individual transport or building-related upgrade assessments. The tools developed in this research can be used by policymakers such as local municipalities, and provincial and federal government institutions to design and develop the most desirable incentive schemes and tax policies to achieve faster GHG reduction and to achieve national GHG goals. A multi-stakeholder opinion-based planning and management framework: Transport electrification is novel for most Canadian municipalities. And there is a lack of expertise. In this study, multi-stakeholder perspectives were used to develop a scientific plan for EV-RIs within municipalities. The consumer perspective was considered by maximizing EV-RI service coverage, minimizing EV-RI access distance, and establish possible incentive options to reduce EV switching costs. The investor perspective was considered in lowering construction costs and paybacks and selecting the most appropriate PDMs. The developer and urban planner perspectives were considered by providing a platform to find the most desirable and cost-effective location from available sites within the municipality. Ultimately, the government perspective was considered by incentivizing the most appropriate interventions to maximize local GHG reduction. Accordingly, the proposed EV-RI planning framework will help all key stakeholders make decisions scientifically for planning and management of the EV-RI network. 8.3 Limitations of the Study Several limitations were identified in this study. Reasonable adjustments were made to mitigate the impacts of those limitations and generalize the results of this study. Data limitations: EV-RI experts and EV related databases for the Canadian context are minimal and challenging because EV deployment is in the early stages. Data obtained from local utility providers, survey databases of BC higher institutions, and literature-based data were used in this study, with limited representation of the entire country. However, the objective of this research is to provide and deploy the proposed methodology in a selected city center, so the methodology was 153 provided with reasonable assumptions that can be easily modified with data from any other municipality for their decision making. Focused only on a specific transportation mode: This research only focused on light-duty vehicles in developing the aforementioned decision-making tools. Although light-duty vehicles contribute the highest amount of GHG emissions in the country, they do not represent all possible vehicle options in Canada. Other vehicle options such as heavy-duty vehicles, and public transit, can also be considered for conversion to electricity and incorporated into the decision-making tools mentioned in this study. Focus only on current technologies: The developed decision support frameworks only consider EV-based mobility with current vehicle ranges and battery level. However, the rate of technology change is significant and a more cost-effective vehicle with better ranges and lower recharging requirements may be coming. Furthermore, recharging durations might decrease with innovative recharging technologies and facilities. There may also be other low-emission transport system options that offer lower costs and more significant savings. In that case, different attributes, cost of tariffs, emission factors, and recharging methods (stationary or mobile) will need to be inserted into the model. For example, this model requires a significant modification to deal with battery swapping and dynamic recharging options. 8.4 Future Research The following research areas were identified as possible extensions of this research. Incorporate technological uncertainty: EV recharging and battery technologies change rapidly with the extensive attention of researchers and scientists [98][100][90]. In such a system, EV designers can come-up with advanced EV technologies that are less expensive and solve current range anxiety issues. Therefore, expanding this study by integrating technological uncertainty would benefit all stakeholders for the long-range deployment of EVs. Integrate and compare with active transport options: This research is aimed to improve the energy efficiency of the passive transport option in order to achieve GHG emission reductions and reduce urban sprawl. The literature revealed that the fuel savings from reducing automobile 154 transportation is higher than the fuel-saving from increasing vehicle fuel efficiency [252]. 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[Accessed: 25-Jul-2016]. 171 Appendices Appendix A â Provincial Energy-Related Data Appendix A1 â Emissions for different Hydrogen Production Methods Hydrogen Production Method GHG Emission (CO2 kg/kg H2) Alkaline water electrolysis using Green Electricity 0.13 [253] Alkaline water electrolysis using Wind Electricity 0.97 [254] Alkaline water electrolysis using PV Electricity 2.41 [254] Central steam methane reforming 11.89 [254] Central coal gasification 11.3 [254] Biomass gasification 4.86 [253] Thermochemical water splitting with nuclear CuâCl cycle 12.3 [254] Appendix A2 â Alternative Energy-based Emissions Energy Source GHG Emission (tones CO2e/GWh) [105] Coal 888 Oil 733 Natural Gas 499 Solar PV electric 85 Nuclear electric 29 Hydroelectric 26 Biomass 45 Wind 26 Appendix A3 â Provincial Electricity Emissions and Tariffs Province Domestic Average Electricity Rate (CAD $ for 100kWh) including tax Electricity Production Emissions (gCO2/kWh) Major Electricity production Source Nunavut 30.15 [255] 330 [256] Oil [257] Quebec 8.12 [258] 2.9 [259] Hydro [260] Northwest Territories 28.25 [261] 330 [259] Oil [262] Ontario 15.20 [263] 96 [259] Nuclear & Hydro [262] British Columbia 10.39 [263] 8.2 [259] Hydro [262] Alberta 12.94 to 14.08 [263] 820 [259] Coal & Gas [262] Saskatchewan 16.05 [263] 750 [259] Coal & Gas [262] Manitoba 9.12 [263] 3.4 [259] Hydro [262] Yukon 11.02 [264] 40 [259] Hydro [265] Newfoundland and Labrador 12.94 [263] 20 [259] Hydro [262] New Brunswick 13.63 [263] 420 [259] Hydro, Oil & Coal [262] 172 Nova Scotia 16.83 [263] 700 [259] Coal [262] Prince Edward Island 17.34 [263] 22 [259] Wind [262] Appendix A4 â Other Utility Tariffs for Households Natural Gas price Gasoline Province Base ($/month) level 1 level2 level3 ($/100 liter) Quebec (Montreal) 16.57 $0.2674/ m3 (~900m3) $0.1829 /m3 (900m3~2170 m3) $0.15995/m3 (2179 m3~) 107.00 Ontario (Ottawa) 20.00 $ 0.2504/m3 106.10 British Columbia (Mainland) 12.06 $ 7.16 / Gj 121.60 Alberta (Edmonton) 30.16 $ 1.83/GJ 105.60 Saskatchewan (Saskatoon) 23.20 $ 0.2311/m3 107.80 Manitoba (Winnipeg) 14.00 $ 0.2235/m3 112.70 Nova Scotia (Halifax) 21.87 $ 18.075/GJ 103.10 Source: On-line databases of provincial utility providers (Accessed in November 2017) Appendix B â Characteristics of Representing Vehicles Appendix B1 â Maintenance and Repair Cost Data for Vehicles Considered in the Study Based on cars.com Cars.com, 2016, the maintenance cost as at 2016 from year 1 to 5 years are as below, Year 00 (CAD$)6 Year 01 (CAD$)7 Year 02 (CAD$)8 Year 03 (CAD$)9 Year 04 (CAD$)10 Year 05 (CAD$)11 Yaris 4DR LE 2016 (1500 cc) 108 247 2,061 975 880 4,272 Hyundai Tucson FCEV 4 DR5 0 0 0 0 802 4,789 Nissan Leaf S 5DR 213 357 2,412 1,008 802 4,789 5 Hyundai maintenance cost is assumed as similar to the Nissan leaf due to the similar power train options. But the capital cost covered the cost of maintenance and cost of fuel for the next 03 years, and the last 02 years are assumed to be similar to the Nissan Leaf. 6,7,8,9,10,11Source: [266] Appendix B2 â Vehicle Specifications Nissan Leaf S 5DR1 Hyundai Tucson FCEV2 Toyota Yaris 4DR LE3 Unit Year of Manufacturing 2016 2016 2016 Range 133 500 500 Km/ per re-fueling cycle Consumption 34 kWh/100km 77 km/kg 12.32 km/l Size of Battery Pack 30 - - kWh Cost of Vehicle 26,688 66,9404 21,151 CAD $ Engine power torque 107 134 106 hp Gross Weight 1481 1882 1515 kg 1 Source [203] 2 Source [204] 3 Source [205] 4 This includes the fuel cost and general maintenance cost for the first 03 years of the vehicle. 173 Appendix C â Intermediate Results of the Life Cycle Assessment Appendix C1: The life cycle impacts of the vehicle except for the operational phase Electric vehicle ICEV HFCV - Weight kg 1293 201 NA 1392 NA NA 1578 NA NA Name Units Con. RM EoL T_LCIA_EV Con. RM EoL T_LCIA_ICEV Con. RM EoL T_LCIA_HFCV Inputs Total Energy GJ 26.1 16.9 15.0 58.1 41.3 4.2 10.5 56.0 87.3 2.6 10.5 100.4 Water m^3 26.3 10.4 3.1 39.8 18.3 0.3 2.2 20.8 28.9 0.8 2.2 31.9 Non fossil fuels GJ 5.3 2.1 1.7 9.1 3.6 0.0 1.2 4.8 7.5 0.2 1.2 8.8 Renewable GJ 3.4 1.6 0.8 5.7 2.2 0.0 0.5 2.7 4.5 0.1 0.5 5.1 Biomass GJ 0.1 0.1 0.0 0.2 0.1 0.0 0.0 0.1 0.2 0.0 0.0 0.2 Fossil Fuels GJ 50.4 15.0 13.3 78.7 37.7 4.1 9.3 51.2 79.5 2.4 9.3 91.3 Petroleum Fuels GJ 6.3 2.0 0.2 8.5 4.2 2.8 0.1 7.2 9.2 0.5 0.1 9.7 Natural Gas Fuels GJ 23.3 8.8 9.0 41.1 15.7 1.1 6.3 23.1 44.6 1.4 6.3 52.3 Coal Fuels GJ 23.3 4.0 4.1 31.4 17.8 0.2 2.9 20.9 25.2 0.6 2.9 28.7 Nuclear GJ 1.9 0.5 0.9 3.4 1.4 0.0 0.7 2.1 3.0 0.1 0.7 3.7 Outputs CO2 kg 3589.0 968.0 902.1 5459.1 2654.9 221.0 640.0 3515.9 5371.8 127.8 640.0 6139.5 VOC kg 3.2 13.8 1.7 18.6 2.5 9.7 1.2 13.4 2.9 9.6 1.2 13.7 CO kg 17.9 68.9 0.5 87.3 13.8 0.1 0.3 14.3 15.7 0.2 0.3 16.2 NOx kg 4.2 1.4 0.8 6.5 3.0 0.4 0.6 4.0 6.0 0.2 0.6 6.8 PM10 kg 1.8 0.7 0.1 2.7 1.4 0.1 0.1 1.6 2.0 0.1 0.1 2.2 PM2.5 kg 0.8 0.2 0.1 1.2 0.7 0.0 0.1 0.8 0.9 0.0 0.1 1.0 SOx kg 24.0 12.6 1.1 37.6 13.9 1.4 0.8 16.1 20.8 3.7 0.8 25.2 CH4 kg 9.1 2.4 2.2 13.7 6.6 0.5 1.6 8.7 13.8 0.5 1.6 15.9 N2O g 79.9 21.1 20.0 121.0 55.8 5.3 14.1 75.2 127.4 4.0 14.1 145.5 BC g 29.2 16.5 5.9 51.7 19.1 2.9 4.2 26.2 42.9 3.1 4.2 50.2 POC g 46.5 20.1 13.8 80.4 33.3 3.2 9.8 46.2 79.9 2.4 9.8 92.0 H2 9 0 0 0 0 0 0 0 0 0 0 0 0 Con. â Construction phase RM â Regular maintenance EoL â End of life T_LCIA_V â Total life cycle impacts of the vehicle (Except the operation phase) 174 Appendix C2: Vehicle Operational Impacts for the Considered Life Cycle of the Vehicle Appendix C3 Total life cycle inventory of the different fuel options Input Inventory Province-wise energy and resources consumption for EVs (Complete Life Cycle Inventory) Raw materials and Energy for HFCVs ICEVs Unit BC MN QC NF YK NWT ON NB SK NS AB PEI NU Natural Gas Nuclear Coal Biomass Electrolysis Water m^3 5.E+02 5.E+04 5.E+02 5.E+02 5.E+02 3.E+02 2.E+02 2.E+02 1.E+02 1.E+02 4.E+01 4.E+01 3.E+02 9.E+01 2.E+02 9.E+01 6.E+01 4.E+01 5.E+02 Non fossil fuels GJ 1.E+05 1.E+05 1.E+05 1.E+05 1.E+05 5.E+04 1.E+05 9.E+04 2.E+04 5.E+04 2.E+04 1.E+05 6.E+03 2.E+04 2.E+05 4.E+02 5.E+05 2.E+05 3.E+05 Renewable GJ 1.E+05 1.E+05 1.E+05 1.E+05 1.E+05 5.E+04 4.E+04 5.E+04 2.E+04 5.E+04 2.E+04 1.E+05 3.E+03 1.E+04 4.E+02 2.E+02 5.E+05 2.E+05 3.E+05 Biomass GJ 3.E+04 2.E-01 5.E+03 3.E-01 2.E+00 2.E+04 5.E+03 2.E+04 2.E+00 2.E+04 1.E+04 5.E+03 1.E+02 4.E+02 2.E+01 6.E+00 5.E+05 3.E-01 3.E+05 Fossil Fuels GJ 7.E+03 8.E+01 2.E+02 2.E+04 3.E+04 2.E+05 1.E+04 1.E+05 2.E+05 2.E+05 3.E+05 4.E+03 1.E+06 3.E+05 6.E+03 5.E+02 3.E+04 8.E+02 3.E+05 Unit BC MN QC NF YK NWT ON NB SK NS AB PEI NUNatural Gas Nuclear Coal Biomass ElectrolysisWater m^3 468 54419 485 485 474 231 165 132 102 83 4 1 215 57 130 63 29 7 496Non fossil fuels GJ 131311 110160 114016 104719 101449 46074 108857 88615 19938 48370 18940 112914 5913 24294 198187 352 462436 247734 346827Renewable GJ 131311 110160 114016 104682 101392 45573 43686 48750 19878 48198 18854 112914 2649 10966 435 159 462436 247734 346827Biomass GJ 29908 0 4985 0 2 16065 4990 19946 2 24929 9973 4985 105 427 17 6 462436 0 346827Fossil Fuels GJ 6787 0 126 16966 26643 209965 13852 104581 231887 228582 254690 3770 1321920 313796 6191 446 32750 755 280765Petroleum Fuels GJ 4004 0 110 13345 20023 183416 322665 28058 2361 42895 12568 3445 1211760 2765 1177 6860 11547 663 109845Natural Gas Fuels GJ 2731 0 1 3463 6383 24443 12752 26671 88528 38044 104042 285 977670 247734 2687 1417 16198 83 139524Coal Fuels GJ 51 0 1 157 237 2130 777 49853 141005 147614 138030 40 14 59 2 429 5 0 23469Nuclear GJ 12 0 0 37 56 502 65171 39865 0 172 87 9 3 13 198 193 1 0 2768Impacts of ICEV Total CO2 kg 56 0 6 1 2010 16524 812 2295 18727 6021 19829 296 104652 19984 459 38812 2312 55 30559VOC kg 0.0 0.0 0.0 0.1 0.2 1.3 0.2 0.1 1.8 0.1 2.1 0.1 8.1 0 0 3 2 0 26CO kg 0.1 0.0 1.5 0.4 0.6 4.5 2.2 0.4 6.1 0.7 10.0 1.5 26.4 0 1 2 5 0 166NOX kg 0.1 0.0 0.3 5.3 8.1 72.7 1.2 1.1 13.2 2.7 18.7 1.6 469.3 0 1 9 8 0 36PM10 kg 0.1 0.0 0.6 0.2 0.3 3.1 0.7 0.2 2.2 0.4 3.6 0.7 19.8 1 0 4 1 0 5PM2.5 kg 0.0 0.0 0.2 0.2 0.3 2.2 0.2 0.1 0.9 0.3 1.4 0.2 14.3 1 0 1 1 0 2SOX kg 0.0 0.0 0.2 3.6 5.6 50.7 0.5 4.6 34.1 13.2 36.4 1.1 331.6 17 1 5 29 0 25CH4 kg 0.0 0.0 0.2 1.7 2.8 19.8 2.5 0.2 26.4 0.2 28.6 0.5 116.8 60 1 60 5 0 39N2O g 2047 0 20 12 19 143 32 33 347 88 412 684 887 413 7 236 1652 1 10993BC g 2329 0 26 13 20 173 33 8 62 17 128 30 1112 64 8 42 11231 1 81POC g 5478 0 61 15 23 187 73 18 158 35 299 64 1177 124 14 118 229 6 492H2 g 0 0 0 0 0 0 0 0 0 0 0 0 0 2147 11231 11231 168 9249 0Input for ICEV operationImpacts of HFCV vehicle operationProvince-wise raw material group and environmental impacts based on grid electricity mix (Total operational impacts)Province-wise raw material group and environmental impacts based on grid electricity mix (Total operational impacts)Raw materials for HFCV vehicle operation175 Petroleum Fuels GJ 4.E+03 8.E+00 1.E+02 1.E+04 2.E+04 2.E+05 3.E+05 3.E+04 2.E+03 4.E+04 1.E+04 3.E+03 1.E+06 3.E+03 1.E+03 7.E+03 1.E+04 7.E+02 1.E+05 Natural Gas Fuels GJ 3.E+03 4.E+01 4.E+01 4.E+03 6.E+03 2.E+04 1.E+04 3.E+04 9.E+04 4.E+04 1.E+05 3.E+02 1.E+06 2.E+05 3.E+03 1.E+03 2.E+04 1.E+02 1.E+05 Coal Fuels GJ 8.E+01 3.E+01 3.E+01 2.E+02 3.E+02 2.E+03 8.E+02 5.E+04 1.E+05 1.E+05 1.E+05 7.E+01 5.E+01 9.E+01 3.E+01 5.E+02 3.E+01 3.E+01 2.E+04 Nuclear GJ 2.E+01 3.E+00 4.E+00 4.E+01 6.E+01 5.E+02 7.E+04 4.E+04 3.E+00 2.E+02 9.E+01 1.E+01 7.E+00 2.E+01 2.E+02 2.E+02 5.E+00 4.E+00 3.E+03 Output Inventory Province-wise environmental impacts of EVs (Complete Life Cycle Inventory) Method-wise impacts of HFCVs ICEVs Total CO2 kg 6.E+03 5.E+03 5.E+03 5.E+03 7.E+03 2.E+04 6.E+03 8.E+03 2.E+04 1.E+04 3.E+04 6.E+03 1.E+05 3.E+04 7.E+03 4.E+04 8.E+03 6.E+03 3.E+04 VOC kg 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 3.E+01 1.E+01 1.E+01 2.E+01 2.E+01 1.E+01 4.E+01 CO kg 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 9.E+01 1.E+02 9.E+01 1.E+02 2.E+01 2.E+01 2.E+01 2.E+01 2.E+01 2.E+02 NOX kg 7.E+00 6.E+00 7.E+00 1.E+01 1.E+01 8.E+01 8.E+00 8.E+00 2.E+01 9.E+00 3.E+01 8.E+00 5.E+02 7.E+00 8.E+00 2.E+01 1.E+01 7.E+00 4.E+01 PM10 kg 3.E+00 3.E+00 3.E+00 3.E+00 3.E+00 6.E+00 3.E+00 3.E+00 5.E+00 3.E+00 6.E+00 3.E+00 2.E+01 3.E+00 2.E+00 6.E+00 3.E+00 2.E+00 7.E+00 PM2.5 kg 1.E+00 1.E+00 1.E+00 1.E+00 1.E+00 3.E+00 1.E+00 1.E+00 2.E+00 1.E+00 3.E+00 1.E+00 2.E+01 2.E+00 1.E+00 2.E+00 2.E+00 1.E+00 3.E+00 SOX kg 4.E+01 4.E+01 4.E+01 4.E+01 4.E+01 9.E+01 4.E+01 4.E+01 7.E+01 5.E+01 7.E+01 4.E+01 4.E+02 4.E+01 3.E+01 3.E+01 5.E+01 3.E+01 4.E+01 CH4 kg 1.E+01 1.E+01 1.E+01 2.E+01 2.E+01 3.E+01 2.E+01 1.E+01 4.E+01 1.E+01 4.E+01 1.E+01 1.E+02 8.E+01 2.E+01 8.E+01 2.E+01 2.E+01 5.E+01 N2O g 2.E+03 1.E+02 1.E+02 1.E+02 1.E+02 3.E+02 2.E+02 2.E+02 5.E+02 2.E+02 5.E+02 8.E+02 1.E+03 6.E+02 2.E+02 4.E+02 2.E+03 1.E+02 1.E+04 BC g 2.E+03 5.E+01 8.E+01 6.E+01 7.E+01 2.E+02 8.E+01 6.E+01 1.E+02 7.E+01 2.E+02 8.E+01 1.E+03 1.E+02 6.E+01 9.E+01 1.E+04 5.E+01 5.E+01 POC g 6.E+03 8.E+01 1.E+02 1.E+02 1.E+02 3.E+02 2.E+02 1.E+02 2.E+02 1.E+02 4.E+02 1.E+02 1.E+03 2.E+02 1.E+02 2.E+02 3.E+02 1.E+02 5.E+02 H2 g 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 0.E+00 2.E+03 1.E+04 1.E+04 2.E+02 9.E+03 0.E+00 Appendix C4: Life Cycle Cost Assessment of Electric Vehicles Province Average Electricity Domestic rate (CAD $ for 100kWh) including tax Infrastructure cost (Annualized) (CAD $ for 100kWh) Total electricity retail price with 12% margin (CAD$ for 100kWh) Electricity cost for 5yr operation (CAD $) LCC for EV for 5 year life span (CAD $) Nunavut 30.15 2.92 37.03 11332.07 36835.74 Quebec 8.12 2.92 12.36 3781.95 29285.62 Northwest Territories 28.25 2.92 34.90 10680.90 36184.58 Ontario 15.2 2.92 20.29 6208.40 31712.08 British Columbia 10.39 2.92 14.90 4559.92 30063.60 Alberta 13.51 2.92 18.40 5629.21 31132.88 Saskatchewan 16.05 2.92 21.24 6499.72 32003.39 Manitoba 9.12 2.92 13.48 4124.67 29628.34 Yukon 11.02 2.92 15.61 4775.83 30279.51 Newfoundland and Labrador 12.94 2.92 17.76 5433.86 30937.53 New Brunswick 13.63 2.92 18.53 5670.33 31174.01 Nova Scotia 16.83 2.92 22.11 6767.04 32270.71 Prince Edward Island 17.34 2.92 22.69 6941.83 32445.50 176 Appendix C5: Life Cycle Cost Assessment of Hydrogen Fuel-based Vehicles Province Gasoline end-user Price (CAD $ for 100 liters) Gasoline price for 5 years (CAD $) LCC for ICE vehicle for 5 year life span (CAD $) Nunavut 135 9862.01 28938.71 Quebec 107 7816.56 26893.26 Northwest Territories 122.9 8978.08 28054.78 Ontario 106.1 7750.81 26827.51 British Columbia 121.6 8883.12 27959.82 Alberta 105.6 7714.29 26790.99 Saskatchewan 107.8 7875.00 26951.70 Manitoba 112.7 8232.95 27309.65 Yukon 121.6 8883.12 27959.82 Newfoundland and Labrador 126.2 9219.16 28295.86 New Brunswick 104.9 7663.15 26739.85 Nova Scotia 103.1 7531.66 26608.36 Prince Edward Island 104.7 7648.54 26725.24 177 Appendix D â EV-RI Location Allocation Data Appendix D1 â Site Inspection Form for Site-specific Data Gathering 178 Appendix D2 â Multi-objective Problem Solving using IBM ILOG CPLEX Source Code /********************************************* * OPL 12.8.0.0 Model * Author: piyaru1 * Creation Date: Feb 11, 2019 at 12:46:51 PM *********************************************/ // parameters int n = ...; //No of demand points int m = ...; //No of re-charging stations range route = 1..n; range EVRI = 1..m; float distance[route,EVRI]=...; float demand_cap[route]=...; float supply_cap[EVRI]=...; float Cap_cost[EVRI]=...; float Var_cost[EVRI]=...; //variables dvar float+ x[route][EVRI]; // Decision variable minimize ((sum (j in EVRI, i in route)x[i][j]*Var_cost[j]/10)+ sum (i in route, j in EVRI) distance[i][j]*x[i][j]); // fitness function subject to { forall(i in route) available_demand_cap: sum(j in EVRI) x[i][j] == demand_cap[i]; // Demand constraint forall(j in EVRI) available_supply_cap: sum(i in route) x[i][j] <= supply_cap[j]; // Supply constraint } /********************************************* * OPL 12.8.0.0 Data * Author: piyaru1 * Creation Date: Feb 11, 2019 at 12:46:51 PM *********************************************/ n=32975; // Number of routes m=14; // Number of potential EV-RI facilities SheetConnection my_sheet ("Worksheet2.xlsx"); SheetConnection my_sheet1 ("DEF.xlsx"); distance from SheetRead(my_sheet,"'Sheet1'!B2:O32976"); // Output from the ArcGIS model demand_cap from SheetRead(my_sheet,"'Sheet1'!P2:P32976"); // Array changes with the time period supply_cap from SheetRead(my_sheet,"'Sheet1'!S2:S15"); // Location-based capacity inputs Cap_cost from SheetRead(my_sheet,"'Sheet1'!T2:T15"); // Location-based cost inputs Var_cost from SheetRead(my_sheet,"'Sheet1'!U2:U15"); // Location-based cost inputs x to SheetWrite(my_sheet1,"'Sheet1'!B2:O32976"); 179 Appendix D3 â Model inputs for the case study Paving and land preparation cost (CAD/sq.meter) Cp $135.00 land required per each EV-RI - Fast charging (sq.m/unit) LI 60 EV-RI unit costs Paving cost per each EV-RI (CAD/fast charging unit) CI $8,100.00 Unit cost of Electric vehicle infrastructure - Fast Charging (CAD/unit) Ï1 $40,000 Installation and Labour cost (CAD/unit) $50,000 Total DC-FC related costs $98,100 Annual networking fee (CAD/unit.year) $225 Annual maintenance (CAS/unit.year) $200 Total operating cost per year per unit (CAD/unit.year) (Except the cost of electricity) $425 Current electricity cost ($/kWh) ê0 0.15 kW power consumption of the vehicle I (kWh/100 km) λi 34 Useful power supply from EV-RI- Fast Charging(kW/unit) Pu1 50 Average recharging sessions per year per unit 3600 Vehicle types (TIPSLab-UBC) Battery operated vehicles (Electricity only) 160 km maximum fast charging 80% Battery capacity 30 kWh Consumption 340 Wh/km Therefore, Recharge capacity per time 15.2 kWh/recharge Land mortgage term 20 years Inflation/ discount rate 3.50% 180 Appendix D4 : Total investment cost and payback calculation based on the optimization results Location ID Neighbourhood Multi-period EV-RI capacities in terms of DC-FC units (NUmt ) and potential locations (Optimization Results) Multi-period EV-RI capacities improvement in terms of DC-FC units Langitude Latitude TAZ No. Land cost Factor (LCm) (CAD/sq.m) Max. Capacity (NUm) Maximum vehicle capacity New DC-FC investment Land acquisition costs 2020 2030 2040 2050 2020 2030 2040 2050 (2020) (2030) (2040) (2050) (2020) (2030) (2040) (2050) 1 PANDOSY 1 6 10 10 1 5 4 0 -119.491 49.86083 107 2500 10 36000 $98,100 $490,500 $392,400 $0 $9,174 $55,041 $82,562 $27,521 2 UNIVERSITY PLAZZA 0 1 5 10 0 1 4 5 -119.389 49.92223 154 2900 10 36000 $0 $98,100 $392,400 $490,500 $0 $10,641 $53,206 $95,771 3 MCCURDY CORNER 0 0 1 9 0 0 1 8 -119.405 49.90209 63 3200 20 72000 $0 $0 $98,100 $784,800 $0 $0 $11,742 $105,679 4 COOPER CENTRE 0 0 0 2 0 0 0 2 -119.445 49.88261 52 3500 10 36000 $0 $0 $0 $196,200 $0 $0 $0 $25,686 5 BANKS-SPRINGFIELD 0 0 0 1 0 0 0 1 -119.425 49.88871 55 3500 10 36000 $0 $0 $0 $98,100 $0 $0 $0 $12,843 6 CAPRI CENTRE MALL 0 0 5 16 0 0 5 11 -119.475 49.88117 34 3200 16 57600 $0 $0 $490,500 $1,079,100 $0 $0 $58,710 $187,873 7 ORCHARD PLAZA 0 0 0 0 0 0 0 0 -119.469 49.88195 35 3600 10 36000 $0 $0 $0 $0 $0 $0 $0 $0 8 SPALL PLAZA 0 0 0 7 0 0 0 7 -119.458 49.8821 28 3300 16 57600 $0 $0 $0 $686,700 $0 $0 $0 $84,763 9 GLENMORE 1 3 14 20 1 2 11 6 -119.443 49.91531 160 2500 20 72000 $98,100 $196,200 $1,079,100 $588,600 $9,174 $27,521 $119,256 $146,776 10 RICHTER 0 0 0 4 0 0 0 4 -119.49 49.88477 24 3800 10 36000 $0 $0 $0 $392,400 $0 $0 $0 $55,775 11 CLEMENT 0 3 10 10 0 3 7 0 -119.476 49.89292 15 2600 10 36000 $0 $294,300 $686,700 $0 $0 $28,621 $95,404 $66,783 12 SPRINGFIELD 1 4 20 20 1 3 16 0 -119.456 49.87627 50 2600 20 72000 $98,100 $294,300 $1,569,600 $0 $9,540 $38,162 $181,269 $143,107 13 BANKS 0 0 0 1 0 0 0 1 -119.428 49.8836 58 3600 10 36000 $0 $0 $0 $98,100 $0 $0 $0 $13,210 14 RUTLAND 1 9 20 20 1 8 11 0 -119.398 49.89007 77 2000 20 72000 $98,100 $784,800 $1,079,100 $0 $7,339 $66,049 $139,437 $73,388 Network Investment Cost (CAD) $392,400 $2,158,200 $5,787,900 $4,414,500 $35,226 $226,035 $741,586 $1,039,175 181 Appendix E â Household Related Data Appendix E1 â Residential Building As-Is Drawing (Source: AuthenTech Homes Ltd.) Appendix E2 â Specifications of Base-Case Residential Building Building parameters (Reference case) Year of manufacturing 2017 Total floor area 2,690 ft2 Orientation Northwest Thermal zone settings Single Thermostat Main floor set points: Daytime â 69.80F & night time â 64.40F Cooling set point: 770F Night time set back duration: 8 hrs Occupancy 2 â Adults & 1 â Child (50% occupancy time) Foundation 8â reinforced concrete, R-22 insulation Basement slab 4â concrete Exterior wall (Section 1) 2âÃ6â wood studs @ 24â OC, 3/8â OSB sheeting, R-20 insulation, ½â drywall Interior wall 2âÃ4â wood studs, ½â drywall Ground floor Engineered I joist 11 7/8â @ 19.2â OC, ¾â plywood, R-11 insulation Ceiling R-22 Batt, R-40 blown in insulation, ½â drywall Roof Engineered trusses (wood), 1/2â OSB sheeting, Asphalt Windows Vinyl double glazed windows c/w 180 low-E Air Tightness: 0.2 L/s.m2 Space heating system Energy star rated dual fuel (Natural Gas & Electric) EF - 92.1%, 56000 BTU/hr, switching temperature 350F & Natural gas fireplace 2kW, 6824.28 BTU/hr 30% SS Space cooling system Energy star rated central split system, (electric), 14SEER 182 Washer & Dryer Standard (916kWh/year) Heat recovery system Single stage PSC blower Refrigerator Standard (639kWh/year) Stove & Oven Standard Energy Star rated cooker and oven (565kWh/year) Hot water system Energy star rated natural gas hot water tank 1EF (Hot water temperature: 1310F) (60Gal) Other electricity use 12kWh/day Lighting 25%-75% LED or CFL Solar(PV) electricity generator Not available House construction cost (CAD)7 414,032.99 Appendix E3 Results of EV Incentive Scheme Ranking 7 Purchase cost excluding the land value and the profit of the builders, contractors and developers. 183 Appendix E4 - Building Retrofit Options Quebec**** Ontario*** British Columbia* Alberta** Saskatchewan Manitoba Nova Scotia## Building retrofit incentive scemes Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Annualized consumer cost savings Annualized GHG reduction : H2D (kgCO2e/year) Null 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 R1 973.33 4.61 698.08 35.63 933.74 6.42 838.38 409.70 950.49 275.17 982.83 7.03 593.33 397.80 R2 188.69 13.34 98.77 -127.20 150.43 3.71 170.96 -1414.50 286.63 -1249.05 134.31 15.13 125.23 -1053.52 R3 36.17 0.43 25.96 31.33 40.59 1.65 52.80 219.53 22.95 224.26 48.57 0.50 32.26 167.19 R4 -727.55 96.69 -72.54 -299.04 99.34 46.95 -956.63 -4562.11 -416.33 -4579.52 -883.03 132.07 -172.85 -2767.49 R5 352.22 8.13 232.88 256.17 341.79 18.45 319.16 2255.10 160.50 2219.65 120.10 10.16 191.59 1739.49 R1+R2 1162.02 17.95 796.86 -91.57 1084.17 10.13 1009.35 -1004.81 1237.12 -973.89 1117.14 22.16 718.56 -655.72 R1+R3 1009.51 5.04 724.05 66.95 974.34 8.07 891.19 629.23 973.45 499.42 1031.41 7.54 625.60 564.98 R1+R4 245.79 101.29 625.55 -263.42 1033.08 53.37 -118.24 -4152.41 534.16 -4304.36 99.80 139.11 420.48 -2369.69 R1+R5 1325.55 12.74 930.96 291.80 1275.53 24.87 1157.54 2664.80 1110.99 2494.82 1102.93 17.19 784.92 2137.29 R2+R3 224.86 13.77 124.74 -95.87 191.02 5.36 223.77 -1194.97 309.58 -1024.80 182.88 15.63 157.49 -886.33 R2+R4 -538.86 110.02 26.24 -426.24 249.77 50.66 -785.66 -5976.62 -129.70 -5828.58 -748.72 147.20 -47.62 -3821.01 R2+R5 540.90 21.47 331.65 128.98 492.22 22.16 490.12 840.60 447.12 970.60 254.40 25.29 316.81 685.97 R3+R4 -691.37 97.12 -46.57 -267.71 139.94 48.60 -903.82 -4342.58 -393.37 -4355.27 -834.45 132.58 -140.58 -2600.31 184 R3+R5 388.39 8.57 258.84 287.50 382.38 20.10 371.96 2474.64 183.45 2443.91 168.67 10.66 223.85 1906.67 R4+R5 -375.33 104.82 160.34 -42.87 441.13 65.40 -637.47 -2307.01 -255.83 -2359.87 -762.93 142.23 18.74 -1028.00 R1+R2+R3 1198.20 18.38 822.82 -60.25 1124.77 11.78 1062.15 -785.27 1260.08 -749.63 1165.71 22.67 750.83 -488.53 R1+R2+R4 434.47 114.63 724.32 -390.61 1183.51 57.08 52.72 -5566.92 820.79 -5553.41 234.11 154.24 545.71 -3423.21 R1+R2+R5 1514.24 26.08 1029.73 164.60 1425.96 28.58 1328.51 1250.30 1397.62 1245.76 1237.24 32.32 910.15 1083.77 R1+R3+R4 281.96 101.73 651.51 -232.09 1073.68 55.02 -65.44 -3932.88 557.12 -4080.10 148.38 139.61 452.75 -2202.51 R1+R3+R5 1361.72 13.17 956.92 323.13 1316.12 26.52 1210.35 2884.33 1133.94 2719.07 1151.50 17.69 817.18 2304.47 R1+R4+R5 598.00 109.43 858.42 -7.24 1374.87 71.82 200.92 -1897.31 694.66 -2084.71 219.90 149.27 612.07 -630.20 R2+R3+R4 -502.68 110.46 52.20 -394.91 290.37 52.31 -732.86 -5757.09 -106.75 -5604.32 -700.15 147.71 -15.36 -3653.82 R2+R3+R5 577.08 21.90 357.61 160.30 532.81 23.81 542.93 1060.13 470.08 1194.85 302.98 25.79 349.08 853.16 R2+R4+R5 -186.64 118.15 259.11 -170.07 591.56 69.11 -466.50 -3721.51 30.80 -3608.93 -628.63 157.36 143.97 -2081.52 R3+R4+R5 -339.16 105.25 186.30 -11.54 481.72 67.05 -584.66 -2087.48 -232.88 -2135.62 -714.36 142.74 51.00 -860.82 R1+R2+R3+R4 470.65 115.06 750.28 -359.29 1224.11 58.73 105.52 -5347.39 843.75 -5329.16 282.69 154.74 577.98 -3256.02 R1+R2+R3+R5 1550.41 26.51 1055.70 195.93 1466.55 30.23 1381.31 1469.83 1420.57 1470.02 1285.81 32.82 942.41 1250.95 R1+R2+R4+R5 786.69 122.76 957.20 -134.44 1525.30 75.53 371.88 -3311.81 981.29 -3333.76 354.21 164.40 737.30 -1683.72 R1+R3+R4+R5 634.18 109.86 884.39 24.08 1415.46 73.47 253.72 -1677.78 717.61 -1860.45 268.47 149.77 644.33 -463.02 R2+R3+R4+R5 -150.47 118.59 285.08 -138.74 632.15 70.76 -413.70 -3501.98 53.75 -3384.67 -580.05 157.86 176.23 -1914.34 R1+R2+R3+R4+R5 (H2M) 822.86 123.20 983.16 -103.11 1565.89 77.18 424.68 -3092.28 1004.24 -3109.50 402.78 164.90 769.56 -1516.54 185 Appendix F âPDM Selection Process Appendix F1 â Data Collection Form to Collect the Stakeholder Perception of Different Attributes Section Attributes Description Very Important Better to have it Not that important Not Applicable Project characteristics Scope definition Having an exact goal about the EV-RI development Project schedule Having an exact schedule for the development Contract Strategy Having an exact payment method for the EV-RI development Complexity The complexity of the development process and evaluate the technical know-how of the development Owners pre-requests Constructability studies Do a feasibility study beforehand Value engineering studies Do value engineering to optimize the construction procedure Contract packaging Do the modular type of construction by having multiple subcontractors to distribute work Contract awarding criteria Evaluate contract awarding criteria before starting the procurement process Cost Sharing Share the cost of the recharging infrastructure with a third party (having a separate funding partner) Owners preferences Responsibility Share the responsibility of the recharging infrastructure with a third party (having a construction manager for the project) Design control Control the design by the owner or having it contracted to a third party Involvement after awarding Involve with decisions during the construction phase of the project Risk Change orders Change the initial design during the construction Risk-sharing Share the risk of the recharging infrastructure with a third party (having separate insurance to cover the risk) Quality Quality control and quality assurance Check the quality of the final product 186 Appendix F2 â Judgment Matrix for LoE Attribute No. The initial deployment stage The way forward to the business-as-usual stage Stakeholder Category 1 Stakeholder Category 2 Stakeholder Category 1 Stakeholder Category 2 SA 1 (0, 0.3, 1) (0, 1, 1) (0, 0.3, 1) (0, 1, 1) SA 2 (0, 0.3, 1) (0, 1, 1) (0, 0.3, 1) (0, 1, 1) SA 3 (0, 0.3, 1) (0, 1, 1) (0, 0.3, 1) (0, 1, 1) SA 4 (0, 1, 1) (0, 0.3, 1) (0, 1, 1) (0, 0.3, 1) SA 5 (0, 1, 1) (0, 0.3, 1) (0, 1, 1) (0, 0.3, 1) SA 6 (0, 0.6, 1) (0, 0.6, 1) (0, 0.65, 1) (0, 1, 1) SA 7 (0, 0, 1) (0, 0, 1) (0, 0, 1) (0, 1, 1) SA 8 (0, 0, 1) (0, 0.6, 1) (0, 0.65, 1) (0, 0.65, 1) SA 9 (0, 1, 1) (0, 0.6, 1) (0, 1, 1) (0, 0.65, 1) SA 10 (0, 1, 1) (0, 0.6, 1) (0, 1, 1) (0, 0.65, 1) SA 11 (0, 1, 1) (0, 0.3, 1) (0, 0, 1) (0, 1, 1) SA 12 (0, 1, 1) (0, 0.3, 1) (0, 0, 1) (0, 1, 1) SA 13 (0, 0.6, 1) (0, 1, 1) (0, 0, 1) (0, 1, 1) SA 14 (0, 1, 1) (0, 0.6, 1) (0, 1, 1) (0, 0.65, 1) SA 15 (0, 0.6, 1) (0, 0, 1) (0, 0, 1) (0, 1, 1) Appendix F3 â Judgement Matrix for LoI Attribute No. The initial deployment stage The way forward to the business-as-usual stage Stakeholder Category 1 Stakeholder Category 2 Stakeholder Category 1 Stakeholder Category 2 SA 1 (0, 1, 1) (0, 0.4, 1) (0, 0.8, 1) (0, 1, 1) SA 2 (0, 0.2, 1) (0, 0.2, 1) (0, 0.8, 1) (0, 1, 1) SA 3 (0, 0.8, 1) (0, 0.2, 1) (0, 1, 1) (0, 0.8, 1) SA 4 (0, 0.6, 1) (0, 0.6, 1) (0, 0.4, 1) (0, 0, 0) SA 5 (0, 0.6, 1) (0, 0.8, 1) (0, 0.2, 1) (0, 0.2, 1) SA 6 (0, 0.2, 1) (0, 0, 0) (0, 0.6, 1) (0, 0.8, 1) SA 7 (0, 0, 0) (0, 0.8, 1) (0, 1, 1) (0, 0.8, 1) SA 8 (0, 0.2, 1) (0, 0.4, 1) (0, 1, 1) (0, 0.8, 1) SA 9 (0, 1, 1) (0, 1, 1) (0, 0.6, 1) (0, 0.4, 1) SA 10 (0, 0.6, 1) (0, 0, 0) (0, 0.2, 1) (0, 0.4, 1) SA 11 (0, 0.8, 1) (0, 0.6, 1) (0, 0.2, 1) (0, 0.2, 1) SA 12 (0, 0.4, 1) (0, 0.6, 1) (0, 0.2, 1) (0, 0.2, 1) SA 13 (0, 0.6, 1) (0, 1, 1) (0, 0.2, 1) (0, 0.2, 1) SA 14 (0, 0.8, 1) (0, 0.8, 1) (0, 0.2, 1) (0, 0.4, 1) SA 15 (0, 0.6, 1) (0, 0, 1) (0, 1, 1) (0, 1, 1) 187 Appendix F4 â Decision Matrix Attribute No. Decision Type Linguistic Terms MAX MIN DBB DB CM DBOT PPP IPD SA 1 1 0 (0,0.2,0.4) (0.8,1,1) (0,0.2,0.4) (0.4,0.6,0.8) (0.8,1,1) (0.8,1,1) SA 2 1 0 (0,0.2,0.4) (0,0.2,0.4) (0.6,0.8,1) (0,0.2,0.4) (0.4,0.6,0.8) (0,0.2,0.4) SA 3 0 1 (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) SA 4 1 0 (0.8,1,1) (0,0.2,0.4) (0.6,0.8,1) (0.4,0.6,0.8) (0.6,0.8,1) (0.8,1,1) SA 5 1 0 (0,0,0) (0.8,1,1) (0,0,0) (0.8,1,1) (0.2,0.4,0.6) (0.6,0.8,1) SA 6 1 0 (0,0,0) (0.8,1,1) (0,0,0) (0.8,1,1) (0.2,0.4,0.6) (0.8,1,1) SA 7 1 0 (0,0,0) (0.8,1,1) (0,0,0) (0.8,1,1) (0.2,0.4,0.6) (0.6,0.8,1) SA 8 1 0 (0,0.2,0.4) (0.8,1,1) (0,0.2,0.4) (0.6,0.8,1) (0.4,0.6,0.8) (0.6,0.8,1) SA 9 1 0 (0,0,0) (0.2,0.4,0.6) (0,0,0) (0.8,1,1) (0.8,1,1) (0.2,0.4,0.6) SA 10 0 1 (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) SA 11 1 0 (0.8,1,1) (0,0.2,0.4) (0.4,0.6,0.8) (0,0.2,0.4) (0.4,0.6,0.8) (0.2,0.4,0.6) SA 12 0 1 (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) SA 13 0 1 (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) (0,0,0) SA 14 1 0 (0,0.2,0.4) (0,0.2,0.4) (0.4,0.6,0.8) (0.8,1,1) (0.8,1,1) (0.2,0.4,0.6) SA 15 1 0 (0.8,1,1) (0.2,0.4,0.6) (0.6,0.8,1) (0,0.2,0.4) (0.6,0.8,1) (0.8,1,1) Appendix F5 â Data Collection for Case Demonstration Background of the project: The use of low-carbon and non-carbon based alternative fuels for transportation has gained immense global attention, which would have long-term impacts on the reduction of anthropogenic Greenhouse Gas (GHG) emissions. According to Transport Canada (2016), an innovative, green, and integrated transport system that supports a cleaner environment plays a key role in its vision statement. Accordingly, electric vehicle recharging infrastructure (EV-RI) development is being considered as their third objective to ensure energy security for light-duty electric vehicles (LD-EVs) in 2030 [1]. Therefore, the province of BC allocated CAD 40 Million in 2017 to support future low-carbon and zero-carbon refueling/ recharging infrastructure projects [2]. Hence, the government of Canada, Government of British Columbia (BC), and municipalities are working towards the fully-fledged EV-RI planning and management framework to increase the growth rate of LD-EVs in BC to achieve Greenhouse Gasses Reduction Target Act (GGRTA) [3]. BC has a 93% renewable electricity grid that enabled travelers to travel with nearly emission-free travel using LD-EVs. However, alternative transportation technologies such as electric vehicles and hydrogen fuel cell vehicles have not achieved the expected consumer attraction in BC due to insufficient recharging/ refueling infrastructure accessibility, insufficient government regularities, and lack commercial viability [4][5][6][7]. This will limit the potential public and private investments on alternative fuel refueling/recharging infrastructure due to a lack of economic, environmental, and social feasibility. 188 The motivation of this study is to develop a macroscopic planning and management framework for an effective EV-RI network by considering customers, private infrastructure investors, and government perspectives. This will minimize range anxiety by developing an efficient EV-RI network and maximize returns on EV-RI investments. Government and private institutions in BC at municipal, provincial, and federal levels who are responsible for the design, operation, maintenance, rehabilitation, and development of EV recharging infrastructure would directly benefit from the outcomes of this research study. Overall objective: The goal of this research is to develop a planning and management framework for LD-EV recharging infrastructure for road transport electrification in the Southern interior of BC. Data Requirement: The City of Kelowna (CoK) is identified as one of the highest developing municipalities in Canada [14]. The annual population growth of the region is 2.7% [14]. Therefore, it is vital to be focused on the dynamic changes in future transport demands and resultant carbon footprint to support regional GHG targets. Hence, in this study, the CoK will be considered as a medium-scale municipality in Southern interior BC. The data obtained from the municipality and expert opinion will be used to develop and validate the model. A brief description of the requested data and their expected uses are shown in Table 1. 189 Appendix F6 â Data Collection for EV-RI behaviours, policies, etc. Demand-side management is used to modify consumer demand through various means, to level and pattern electricity use optimally. The energy hub concept can be used to shift peaks in demand to periods where more energy resources are available. The interest in electric vehicles (EV) is growing with the search for cleaner energy alternatives, and these EVs can be used as a mobile energy hub to manage the peak loads in grid electricity demand. To operationalize this concept, it is necessary to adapt the vehicle-to-grid charging infrastructure and integrate the EVs to the grid. Furthermore, to establish the business case for this initiative, an EV demand prediction and a feasibility assessment of mobile energy hubs are necessary for British Columbia. The proposed research will address the above needs and will focus on developing new business models for the 190 electricity sector in the province and across Canada. The study will develop a load balancing strategy for the central grid using the EV fleet as a mobile energy hub, and tariff schemes and incentives will be investigated to identify the optimal business model for mobile energy bus-based demand-side management. The following data inputs are required to conduct the proposed research, and the collected data will be solely used for research purposes only. 3.1. EVs, Batteries, and EV Demands in BC A: Discussion points 1. EV growth with the time (Ex: EV sales as a percentage of conventional vehicles and EV registered in the province/country as a percentage of conventional vehicles) 2. Existing federal/provincial/municipal rewards, incentives, policies, and procedures related to promoting EVs. 3. EV recharging patterns and EV user behaviours (Consumer behavioral/survey data) 4. EV battery lifetime, replacement costs, capacities, and battery degradation curves B: Additional information 1. Potential rewards, incentives, policies, and procedures related to promoting EVs. 2. EV user behavior changes with the time 3. Potential technological changes related to the EV battery lifetime, replacement costs, capacities, and battery degradation curves 3.2. V2G Technologies A: Discussion points 1. Existing V2G technologies (types, costs, transfer rates, risks, limitations) 2. Primary utility rates for different levels of V2G unidirectional electricity transfers B: Additional information 1. Potential V2G technologies (types, costs, transfer rates, risks, limitations) 2. Potential rewards, incentives, and financial assistance for V2G infrastructure development 3. Potential costs associated with V2G transfer (e.g. the overhead costs, network fee, and management fee) 3.3. G2V Technologies A: Discussion points 1. Existing G2V technologies (types, costs, transfer rates, risks, limitations) 191 2. A database on public recharging facilities in the province/country (details related to location, capacities, costs, and average use) 3. A database on shared or private recharging facilities in the province/country (Ex. Apartments, office buildings, shopping malls, universities and other institutions etc.) 4. A database on domestic recharging facilities in the province/country 5. Existing rewards, incentives, and financial assistance for recharging infrastructure development 6. Primary utility rates for different levels of G2V unidirectional electricity transfers B: Additional information 1. Potential G2V technologies in Canada (types, costs, transfer rates, risks, limitations for wireless static charging and dynamic charging facilities) 2. Potential rewards, incentives, and financial assistance for recharging infrastructure development 3. Potential costs associated with G2V transfer (e.g. the overhead costs, network fee, and management fee) 4. Factors considering to identify the best-desirable location of the facility 3.4. Mobile Energy Hub Planning for BC A: Discussion points 1. Viability of V2G and G2V technologies use as a smart bidirectional recharging system (SBRS) 2. Existing information on smart bidirectional recharging systems (SBRS) (types, costs, technical applicability to the grid, limitations etc.) 3. The ability of net-metering in SBRS technologies and the tariff calculation 4. The possibility of supplying electricity for the electricity grid (to smoothen the peak load) using mobile energy hubs. Acceptable technologies, benefits, costs, risks, and limitations. B: Additional information 1. Impacts to batteries due to SBRS technologies and the additional costs/benefits to the consumers and utility providers 2. Potential limitations, risks, advantages, and disadvantages of SBRS. (Details of any prototype or conceptual designs developed for Canadian context) 3. The possibility of converting the existing parking infrastructure to mobile energy hubs. The political, environmental, social, and technological impacts.
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Recharging infrastructure planning for electric vehicles : a lifecycle thinking approach
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