Engineering Local Electricity Markets for Residential Communities Zur Erlangung des akademischen Grades (Dr. rer. pol.) Doktor der Wirtschaftswissenschaften von der KIT-Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation von Dipl.-Wi.-Ing. Esther Marie Mengelkamp Tag der mündlichen Prüfung: 23. Mai 2019 Referent: Prof. Dr. Christof Weinhardt Korreferent: apl. Prof. Dr. Ute Karl Karlsruhe, 2019 Acknowledgments I would like to take the opportunity to thank many people, without whom this work would never have been possible. First of all, I offer my full gratitude to my supervisor Prof. Dr. Christof Weinhardt for his exceptional support, constant encouragement, and enthusiasm. His visionary thinking inspired and guided me in the last three years. I thank him for providing me with the environment and opportunity to complete this thesis. I would also like to thank apl. Prof. Dr. Ute Karl for taking on the role of my co-advisor, her encouragement and constructive criticism, and enabling me to conduct stimulating research with her team at EIFER. I also thank Prof. Dr. Ali Sunyaev and Prof. Dr. Thomas Lützkendorf for their ideas, challenging discussions and, finally, for serving on my thesis committee. At the Institute of Information Systems and Marketing I had the great pleasure of being part of an extraordinary, talented, lively and competent research team. I want to thank all of my for- mer and current colleagues for their support, discussions and encouragement throughout the last years. A special thanks goes to Alex and Johannes for serving as my postdocs, and the en- tire energy group for challenging discussions, fruitful projects, and many inspiring moments. A special thanks goes to Bent for many shared experience in and outside of the office. I also want to thank Enrique, Samrat, Jan, and Bastian at EIFER for many joint projects. Thanks also to David for always being available for constructive criticism and the best coffee I have ever tasted, Benedikt for introducing me to new ideas, Greta for her priceless graphical un- derstanding, and all of my other colleagues and students, especially Caro, David, Juan, and Thomas for their continuous support. Thanks also to the entire LAMP project team and my NYU colleagues, especially Yury and Robert. Finally, I want to thank my family and friends for making my live worth living and encour- aging me in all my endeavors. Most of all, I thank Konstantin for his never-ending patience, endless support, and unconditional love. Karlsruhe, May 2019 Esther Marie Mengelkamp Abstract In line with the progressing decentralization of electricity generation, local electricity mar- kets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (re- newable) electricity between residential agents (consumers, prosumers, and producers) within their community. Based on a structured literature review, a market engineering framework for LEMs is de- veloped. The work focuses on two of the framework’s eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 re- spondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys’ results. Theoretical solutions of the LEM bi-level optimization problem with complete infor- mation and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. The (micro) market structure is investigated with regards to LEM business models, informa- tion systems and real-world application projects. Potential business models and their char- acteristics are combined in a taxonomy based on the results of 14 expert interviews. Then, the Smart Grid Architecture Model is utilized to derive the organizational, informational, and technical requirements for centralized and distributed information systems in LEMs. After providing an overview on current LEM implementations projects in Germany, the Landau Microgrid Project is used as an example to test the derived requirements. In conclusion, the work recommends current LEM projects to focus on overall discount elec- tricity trading. Premium priced local electricity should be offered to subgroups of households with individual higher valuations for local generation. Automated self-learning algorithms are needed to mitigate the trading effort for residential LEM agents in order to ensure participa- tion. The utilization of regulatory niches is suggested until specific regulations for LEMs are established. Further, the development of specific business models for LEMs should become a prospective (research) focus. Contents List of Figures i List of Tables iv List of Abbreviations v List of Symbols viii I Basics2 1 Introduction 3 1.1 Motivation . 4 1.2 Research Questions . 6 1.3 Structure . 8 2 Fundamentals of Power Systems 10 2.1 The European and German Regulation . 11 2.2 European and German Energy Goals . 14 2.3 The Electricity Value Chain . 15 2.3.1 Generation System . 16 2.3.2 Transmission & Distribution System . 18 2.3.3 Consumption Structure . 21 2.3.4 The German Electricity Market . 25 3 Fundamentals of Local Electricity Markets 30 3.1 Introduction to Local Electricity Markets . 31 3.2 Literature Review on Local Electricity Markets . 36 3.2.1 Literature Review Research Methodology . 36 3.2.2 Literature Analysis and Trends . 38 3.3 Market Engineering . 46 Contents 3.4 Summary . 50 II Agent Behavior 52 4 Participation Factors and Design Attributes 53 4.1 Participation Factors for Local Electricity Markets . 54 4.1.1 Structural Equation Modeling . 56 4.1.2 Methodology and Survey Execution . 57 4.1.3 Evaluation and Results . 61 4.1.4 Discussion . 64 4.2 Design Attributes and Household Preferences . 66 4.2.1 Conjoint Methodology . 66 4.2.2 Developing the Conjoint Analysis . 68 4.2.3 Execution of Survey and Collection of Data . 73 4.2.4 Evaluation and Results . 75 4.2.5 Discussion . 84 4.3 Summary . 86 5 Agent Learning Behavior 87 5.1 Customer System as a Regulatory Niche for LEMs . 88 5.2 Multi-Agent Simulation . 91 5.2.1 Multiple-Leaders-Single-Follower Optimization Problem . 92 5.2.2 Reward-based Generation Learning . 97 5.2.3 Reward-based Demand Learning . 107 5.2.4 Discussion . 111 5.3 Summary . 113 III Implementation 116 6 Application of Local Electricity Markets 117 6.1 Business Models . 119 6.1.1 Methodology of Expert Interviews and Taxonomy Development . 120 6.1.2 Execution and Evaluation . 126 6.1.3 Results and Interpretation . 135 6.1.4 Discussion . 140 6.2 Information Systems . 141 Contents 6.2.1 SGAM Methodology . 142 6.2.2 SGAM Application for LEMs . 144 6.2.3 IS Requirements for LEMs . 149 6.2.4 Distributed Ledger Technologies for LEMs . 152 6.2.5 Discussion . 156 6.3 Real-World Application . 157 6.3.1 Current LEM Implementations in Germany . 158 6.3.2 Case Study: The Landau Microgrid Project . 162 6.3.3 Discussion . 170 6.4 Summary . 171 IV Finale 174 7 Conclusion 175 7.1 Summary and Implications . 176 7.2 Outlook . 178 Bibliography 182 Appendix 223 A Keywords for Literature Review 223 B Structural Equation Modeling 225 B.1 Survey Items . 225 B.2 Correlation Matrix, Effect Sizes and Q2 Measures . 225 C Screenshots of Conjoint Survey 229 D Significance in Simulation Studies 234 E Expert Interviews 236 E.1 Interview Guide . 236 E.2 Interview Protocols . 237 E.3 Definitions of Dimensions and Characteristics . 345 F Overview of LEM Implementation Projects 355 Contents F.1 Market Watch of LEM Projects . 355 F.2 Description of German LEM Projects . 356 F.2.1 Landau Microgrid Project . 356 F.2.2 Pebbles Project . 357 F.2.3 Virtual Power Plant Project . 357 F.2.4 SoLAR Project . 359 F.2.5 RegHee . 360 F.3 LAMP Smartphone Application . 361 Eidesstaatliche Versicherung 362 i List of Figures 1.1 Structure of the dissertation . 9 2.1 Energy trilemma . 13 2.2 German electricity value chain . 15 2.3 German electricity generation . 16 2.4 Merit order dispatch curve and effect . 18 2.5 Development of yearly redispatch costs in Germany . 20 2.6 Sources of the 2017 German energy consumption . 21 2.7 Composition of the 2017 electricity price . 22 2.8 Development of the German electricity prices from 2006-2018 . 23 2.9 Structure of the German electricity market . 26 3.1 Concept of a LEM in Germany . 32 3.2 Development of LEM publications . 37 3.3 Schematic overview of the MEF-LEMs . 47 4.1 Path diagram of the SEM of LEM participation factors . 60 4.2 Process of the conjoint survey . 69 4.3 Preference questions . 75 4.4 Experience questions . 76 4.5 Relative importances of the attributes. 80 4.6 Willingness-to-participate in LEMs . 82 5.1 Bi-level LEM optimization problem . 95 5.2 LEM case study . 100 5.3 Accumulated profit . 103 5.4 Mean total costs and autarchy . 104 5.5 Generation dispatch with generation learning . 106 5.6 Demand learning total costs . ..