Table of Contents
Acknowledgements v
1 Introduction 1
1.1 The changing energy landscape . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 The growth of renewables and its drivers . . . . . . . . . . . 1
1.1.2 The advent of electric vehicles . . . . . . . . . . . . . . . . . 5
1.1.3 The potential synergy between electric vehicles and renewable
energy sources . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.4 Changing roles in future power systems . . . . . . . . . . . . 6
1.2 This thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Problem description . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.3 Thesis outline, structure, research methods and scope . . . . 7
2 Electric vehicles in future power systems 11
2.1 Power systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Technical aspects . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.2 Load and generation profiles . . . . . . . . . . . . . . . . . . 14
2.1.3 Non-technical aspects: organizational, economical, and regulatory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2 Electric vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.1 Actor analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Driving data . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2.3 EV battery model . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.4 Uncontrolled charging . . . . . . . . . . . . . . . . . . . . . . 31
2.2.5 Electric vehicle charging as optimization problem . . . . . . . 32
3 Literature review 39
3.1 Trends in literature on the role of EVs in smart grids . . . . . . . . . 39
3.2 Discussion of some important papers per sub-field . . . . . . . . . . 42
3.3 Relative positioning of this thesis regarding the literature . . . . . . 47
4 Network impacts and cost savings of controlled EV charging 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2 Research method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
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4.2.1 Distribution networks . . . . . . . . . . . . . . . . . . . . . . 52
4.2.2 New load profiles . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.3 Power flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2.4 Energy loss estimation . . . . . . . . . . . . . . . . . . . . . . 58
4.2.5 Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.1 MV/LV Transformers . . . . . . . . . . . . . . . . . . . . . . 62
4.3.2 MV cables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3.3 HV/MV substations . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.4 Economic figures . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5 Impacts of controlled EV charging on cross-border electricity flows 71
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Model formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2.1 EV charging model . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2.2 EV data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2.3 Charging scenarios . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.4 Typical EV fleet . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.5 One node unit commitment model . . . . . . . . . . . . . . . 74
5.2.6 Multi node unit commitment model with flexible EV load . . 75
5.3 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.3.1 Two node conceptual system . . . . . . . . . . . . . . . . . . 77
5.3.2 Generator parameters . . . . . . . . . . . . . . . . . . . . . . 77
5.3.3 Wind and solar time series . . . . . . . . . . . . . . . . . . . 78
5.3.4 Other simulation details . . . . . . . . . . . . . . . . . . . . . 78
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.4.1 Dispatch profiles . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.4.2 Demand function for transmission . . . . . . . . . . . . . . . 82
5.4.3 Further analysis . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6 Renewable energy sources and responsive demand. Do we need
congestion management in the distribution grid? 91
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Problem analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.2.1 The need for congestion management due to the weakening
corellation between wholesale electricity prices and demand . 93
6.2.2 Minimum cost EV charging formulation . . . . . . . . . . . . 94
6.2.3 Simulation of the current situation (flat grid tariff) . . . . . . 96
6.3 Congestion management mechanism design . . . . . . . . . . . . . . 97
6.3.1 Dynamic network tariff . . . . . . . . . . . . . . . . . . . . . 98
6.3.2 Advance capacity allocation . . . . . . . . . . . . . . . . . . . 99
6.3.3 Distribution grid capacity market . . . . . . . . . . . . . . . . 99
6.3.4 Proxies for optimal tariff . . . . . . . . . . . . . . . . . . . . . 100
6.3.5 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
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6.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.4.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.4.2 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . 103
6.4.3 Comparison of results to the literature . . . . . . . . . . . . . 105
6.4.4 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.4.5 IT infrastructure requirements . . . . . . . . . . . . . . . . . 107
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7 A refined view on electric vehicle charging 111
7.1 Equivalence of centralized and decentralized demand scheduling . . . 112
7.1.1 Theoretical analysis of EV dispatch . . . . . . . . . . . . . . 113
7.1.2 Simulations comparing centralized and decentralized EV dispatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.2 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7.2.1 Inter-temporal generation constraints . . . . . . . . . . . . . 122
7.2.2 Influence of the forecast horizon . . . . . . . . . . . . . . . . 124
7.2.3 Influence of charging availability . . . . . . . . . . . . . . . . 126
7.3 System level networks impacts of minimum cost charging . . . . . . 127
7.4 Other settings and applications for demand response . . . . . . . . . 128
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8 Conclusions and recommendations 133
8.1 Conclusions and answers to research questions . . . . . . . . . . . . . 134
8.2 Contours of a new paradigm for a clean and intelligent power system. 137
8.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.3.1 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
8.3.2 Considerations for policy makers . . . . . . . . . . . . . . . . 141
Appendix A: The potential of EVs in an isolated power system 143
Appendix B: EV impacts in residential low voltage grids 149
Appendix C: Carbon emmissions due to EV charging 151
Appendix D: Synthetic driver profiles 159
Appendix E: Cold storage as another resource for demand response 163
Bibliography 169
Nomenclature 176
Summary 181
Samenvatting 187
List of publications 193
iv Contents
Curriculum vitae 195
NGInfra PhD Thesis Series on Infrastructures 197
Abstract
Electric vehicles (EVs) have the potential to play a crucial role in clean and intelligent power systems. The key to this potential lies in the flexibility that EVs provide by the ability to shift their electricity demand in time. This flexibility can be used to facilitate the integration of renewable energy sources by adjusting EV demand to the variable production of wind or solar energy. On the other hand, the same flexibility can be employed to reduce peaks in network load that could result from a massive adoption of EVs. This PhD thesis aims to improve the understanding of the value of flexible EV demand in the context of multi-actor power systems with a high share of renewable energy sources. We first explore flexible EV demand from a distribution network point of view, and then in the light of renewable energy integration. Moreover, we also bring these perspectives together and investigate mechanisms to align the different objectives related to the distribution networks and renewable energy integration. This thesis thus demonstrates the value of demand response in the sustainable power systems of the future.