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On the modelling and analysis of converting existing Canadian residential communities to net-zero energy by Adam D. Wills, B.A.Sc., Mechanical Engineering University of Windsor M.A.Sc., Sustainable Energy Engineering Carleton University A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Science in Mechanical Engineering Department of Mechanical and Aerospace Engineering Carleton University Ottawa, Ontario April, 2018 ©Copyright Adam D. Wills, 2018 The undersigned hereby recommends to the Faculty of Graduate and Postdoctoral Affairs acceptance of the thesis On the modelling and analysis of converting existing Canadian residential communities to net-zero energy submitted by Adam D. Wills, B.A.Sc., Mechanical Engineering University of Windsor M.A.Sc., Sustainable Energy Engineering Carleton University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Applied Science in Mechanical Engineering Professor Ian Beausoleil-Morrison, Thesis Co{supervisor, Department of Mechanical and Aerospace Engineering Professor V. Ismet Ugursal, Thesis Co{supervisor, Department of Mechanical and Aerospace Engineering Professor Ronald E. Miller, Chair, Department of Mechanical and Aerospace Engineering Department of Mechanical and Aerospace Engineering Carleton University May, 2018 ii Abstract Rising energy costs and international pressure has motivated governments and home- owners to reduce the energy consumption and greenhouse gas (GHG) emissions of new and existing dwellings. A popular technical approach to this problem is the adoption of net-zero energy targets, which can achieve substantial energy and GHG emissions reductions. Often focused on new builds, there is growing interest in retrofitting existing buildings to net-zero. Addressing existing stock is essential since they will continue to be a significant portion of the building stock for several decades. Net-zero is not limited to single buildings, and potential benefits of community- scale retrofit projects include greater economic viability and economies of scale. How- ever, challenges faced by such projects include few demonstrations in practice, and the need of detailed analytical models to analyze techno-economic feasibility. An- other challenge is the lack of consensus on formal net-zero definitions. This research was conducted to explore the feasibility and performance of retrofitting Canadian residential communities to net-zero, and the impact of net- zero definition on design. To meet this objective, a new modelling approach was developed which builds upon the detailed and validated Canadian Hybrid Residen- tial End-Use Energy and GHG Emissions Model (CHREM). A new Canadian resi- dential appliance and lighting bottom-up model was developed and integrated into CHREM which realistically captures the behaviour, variability and aggregate electri- cal demands of communities. Building envelope retrofit models, ground-source heat pump (GSHP) space heating, and heat pump hot water models were incorporated into CHREM. A new methodology was developed to estimate the impact of envelope retrofits on airtightness. iii Retrofit community-scale energy systems considered included solar thermal and photovoltaic (PV) systems, district heating and thermal energy storage, and mi- croturbine cogeneration. Detailed models of these systems were developed in the TRNSYS energy simulation tool. An optimization algorithm was used to determine the cost-optimal net-zero solutions for representative residential communities. Com- monly used site and source net-zero energy definitions were considered. The results indicate that deep envelope upgrades and PV and GSHP system retrofits have po- tential to achieve net-zero and significant GHG reductions. The results also indicate that site net-zero likely realizes more GHG reductions compare to source net-zero. iv For M´em´e v Acknowledgments It has been my pleasure to have spent the last few years working and studying at Carleton. I have learned and grown so much, and met so many kind, wonderful, and intelligent people. I am eternally grateful to my supervisors Dr. Ian Beausoleil- Morrison and Dr. V. Ismet Ugursal for taking me on as a student. Their mentorship, encouragement, and support in this endeavour has been incredible. While I am happy to be completing my studies, I will miss working closely along side them. I was also very fortunate to have worked with several amazing and intelligent colleagues over the years. I want to thank my fellow grad students in SBES and 3103A for lending your patient ears when I needed to vent about ESP-r or segmentation faults. Ahmed, Andrea Pietila, b, Jayson Bursill, Courtney Edwards, Curtis Meister, Evan Boucher, Flannel, Geoff Johnson, Hiha, Ifaz, John Kopf, Lumi Dumitrascu, Seb, Skedwards, Suzan, Tiwa Lawal, and Yawen Han, you guys made coming into campus everyday so much better. Through the generosity of so many people I could collect data and access resources that moved this work forward. Id like to thank the following people for sharing their time, knowledge, and resources with me: Alex Ferguson and Julia Purdy of the Housing R&D team at NRCan CanmetENERGY, Sylvain Pitre and Ryan Taylor at Carleton Research Computing Services, Lukas Swan, Dane George, Rasoul Asaee, and Sara Nikoofard at Dalhousie University, Jan Hensen and his research group at TU/e, Ms. Lorraine Gautheir from the Now House Project, and the Mechanical and Aerospace administration and technical staff. Id also like to thank Dr. Cynthia A. Cruickshank and her research group for their advice, friendship and microwave over the years. In particular, thanks to Chris vi Baldwin, David Ouellette, and Patrick Smith. I would also to acknowledge my gratitude to Dr. Liam OBrien and Dr. Burak Gunay for their guidance and support throughout this study. Dr. Iain A. MacDonald and my colleagues at NRC have also been extremely supportive as I wrapped this work up. I am privileged to work with such an amazing group. To my friends in Ottawa and back in Windsor, I want to thank you for cheering me on through this thesis. I feel so blessed to know so many great people. In particular I'd like to thank Simon, Patrice, and the Alpha Ducks for keeping me going. Finally, I would like to thank my family. You have been an endless well of support and love. Mom and Dad, your encouraging words inspired me to stick with it in times I was ready to give up. I could not have done this without you. Spencer, thanks for taking care of everyone back home. vii Table of Contents Abstract iii Acknowledgments vi Table of Contents viii List of Tables xiv List of Figures xx Nomenclature xxiv 1 Introduction 1 1.1 Motivation . 1 1.2 Background on Net-Zero Definitions . 7 1.3 Research Methods . 10 1.3.1 Building Performance Simulation . 10 1.3.2 Energy System Simulation . 12 1.3.3 Optimization Tools . 13 1.4 Thesis Objectives . 15 1.5 Thesis Overview . 16 2 Literature Review 18 2.1 Review of Building Stock Modelling Methodologies . 18 2.1.1 Engineering Bottom-up Approaches . 20 viii 2.1.2 Statistical Bottom-up Approaches . 22 2.1.3 Applicability of Approaches . 25 2.2 Previous Simulation Studies on Residential Stock Retrofits . 28 2.3 Community-scale Retrofit Projects in Practice . 32 2.4 On-site and Renewable Energy Generation Options . 36 2.4.1 Geothermal Energy . 38 2.4.2 Solar Energy . 39 2.4.3 Microturbines . 44 2.5 Existing Net-Zero Communities . 46 2.6 Thesis Tasks . 48 3 Simulation Methodologies of Dwelling-scale Retrofit Options 53 3.1 ESP-r Solution Methodology . 54 3.1.1 Thermal Simulation Domain . 55 3.1.2 Air Flow Simulation Domain . 56 3.2 Summary of Dwelling-scale Retrofits Considered . 58 3.2.1 Retrofit Insulation on Dwelling Envelopes . 59 3.2.2 Glazing Retrofit . 68 3.2.3 Ground Source Heat Pump Retrofit . 72 3.2.4 Heat Pump DHW System Retrofit . 74 3.2.5 Envelope Airtightness . 75 3.2.6 Mechanical Ventilation . 91 3.3 Description of Case Study Communities . 92 3.4 Final Remarks . 96 4 Community-scale Retrofit Systems Considered and Modelling Methodologies 98 4.1 TRNSYS Simulation Methodology . 99 4.2 Community Energy System Overview . 101 4.3 Solar Thermal Loop Simulation Methodology . 106 4.3.1 Solar Thermal Loop Controls . 107 ix 4.3.2 Evacuated Tube Collectors Simulation Methodology and Inputs 110 4.3.3 Solar Thermal Collector Eligibility Criteria . 112 4.4 Solar PV Simulation Methodology and Inputs . 114 4.4.1 Modelling Methodology . 114 4.4.2 Model Parameters . 116 4.5 Solar Collector Roof-mounting Algorithm . 119 4.6 Thermal Energy Storage Simulation Methodology and Inputs . 123 4.6.1 Modelling Methodology . 124 4.6.2 Model Parameters . 126 4.7 Microturbine Simulation Methodology and Inputs . 130 4.7.1 Modelling methodology . 130 4.7.2 Model Parameters . 132 4.7.3 Exhaust Heat Recovery Simulation Methodology and Inputs . 134 4.7.4 Microturbine Operation Strategies . 136 4.8 District Heating Simulation Methodology and Inputs . 140 4.8.1 Temperature Control . 142 4.8.2 Flow Control . 144 4.8.3 Auxiliary Heater . 145 4.9 Solar Roof-Mounting Parameters . 147 4.10 Final Remarks . 149 5 Model Boundary Conditions 150 5.1 Domestic Hot Water Demands . 152 5.2 Appliance and Lighting Demands . 154 5.2.1 Abstract . 155 5.2.2 Introduction . 156 5.2.3 Model summary . 165 5.2.4 Model validation . 175 5.2.5 Impact of baseload implementation . 190 5.2.6 Conclusions . 192 5.2.7 Recommendations and Future Work . 194 x 5.3 Temporal Matching of Boundary Conditions . 196 5.4 Final Remarks . 197 6 Simulation Framework and Performance Metrics 198 6.1 Simulation Framework . 199 6.1.1 The Optimization Problem . 200 6.1.2 The Objective Function Cost Model . 205 6.1.3 Summary of Varied Parameters .

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