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i ii

Acknowledgements

First, I want to thank my two supervisor, Marc Petit and Yannick Perez, who have been a great team during these three years. Their guidance, questioning and support has been really valuable for my research and for myself. I really value their complemen- tary approaches to research, as well as the large autonomy they provide and the many opportunities to share my work and learn about others'. I would like to thank the two reviewers who took the time and eort the understand and question my thesis, Vincent Debusschere and Hossein Farahmand. Their comments were very valuable and helped improve this thesis. I would like to thank as well the rest of the jury members, Demba Diallo, Gianfranco Chicco, Willet Kempton and Frank Geerts. In particular Willet for its insightful and high-picture comments and Frank for his hard (but good!) questions. I want to thank the people in Stellantis, in particular Paul Codani who started as my industrial supervisor and specially Damien-Pierre Sain ou who continued the task till the end and whose comments were always on the spot. I want to thank the many friends that I encountered at the GeePs and LGI labs, with who we discussed our work and shared good moments: Ferreol, Jean, Simon, Elena, Bassem, Quentin, Icaro, Bogdan, MarcO, Tanguy, Mokrane and many others I might forget. I want to specially thank Loc who proved to be an amazing and friend climbing partner, and Abaranana who was a great support, specially during the writing phase. Finalmente, quiero agradecer a mi familia por todo el cari~no y apoyo durante estos tres a~nos a pesar de la distancia. Las vacaciones en Chile eran una recarga de energia para todo el a~no (y de mermeladas y otras cosas ricas). Felipe GONZALEZ VENEGAS has beneted from the support of the Chair Armand Peugeot: Hybrid technologies and Economy of Electromobility, led by CentraleSupelec and ESSEC Business School, and sponsored by Stellantis, and the nancial support of the ANRT, France, for the CIFRE contract n°2018/0031. iii

ACKNOWLEDGEMENTS

iv

Contents

Acknowledgements

iii

Lists of Acronyms

xiii

Nomenclature

xv

Publications

xvii

1 Introduction: towards future smart grids

1

1 Towards a low-carbon future

1

2 Smart grids and the need for

exibility 3

3 Thesis objectives

5

2 Active integration of EVs into distribution systems

7

1 Methodology

7

2 Technical aspects

8

3 Economic aspects

17

4 Regulatory aspects

25

5 End-user aspects

27

6 Discussion

29

7 Partial conclusions

34

3 Plug-in behavior of EV users: modeling, insights from a large-scale trial

and impacts for grid integration studies 35

1 Introduction

35

2 Literature review

36

3 EV simulation model

41

4 Insights from a large-scale EV trial and model calibration

44

5 Impact of non-systematic plug-in behavior on EV grid integration studies

48

6 Partial conclusions

59

4 Assessing EV integration in distribution grids: a data-driven approach.

61

1 Relevant works on EV integration into distribution grids

62

2 A data driven methodology to build realistic case studies

64

3 EV charging impact at the primary substation level

69

4 EV and PV integration in realistic MV grids

77

5 Partial conclusions

85
v

CONTENTS

5 Participation of electric vehicle

eets in local exibility tenders: Ana- lyzing barriers to entry and workable solutions 87

1 Introduction

87

2 Looking for decentralized

exibility markets 88

3 Methodology and case study

97

4 Results

103

5 Partial conclusions

113

6 Final conclusions

115

A Detailed models for EV charging

125

1 Cost-optimization charging

125

2 Decentralized valley lling

125

B Grid reconstruction from GIS data

127

1 Datasets

127

2 Grid reconstruction methodology

129

C Computational times

139
D Complementary results fromLa Boriettecase study141

1 Spatial distribution of EVs and PV installations

141

2 Maximum line loading

143

E Resume en francais.

145
vi

List of Figures

1.1 Global anthropogenic green-house gas emissions since 1850.

1

1.2 Global green-house gas emissions by economic sector

2

1.3 Life-cycle GHG emissions for a 24 kWh battery electric vehicle

3

1.4 Example of uncontrolled charging, smart charging, and vehicle-to-grid

ex- ibility to reduce peak load in a system. 5

2.1 Analytical framework for literature review

8

2.2 Main

exibility services to be provided by EVs 12

2.3 Charger eciency during charge and discharge cycles at dierent SOC levels.

14

2.4 Main communication links for

exibility services 16

2.5 Illustration of variable capacity contract

18

2.6 Screenshot of the Piclo

ex exibility platform 21

3.1 Flowchart of the developed agent-based EV simulation model

41

3.2 Plug-in probability curve according toSoCat arrival for three plug-in

preferences (). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3 Illustration of implemented EV charging strategies

43

3.4 Distribution of battery sizes in the Electric Nation trial

44

3.5 Charging behavior indicators of EV users in the Electric Nation trial

46

3.6 Average weekly charging frequency from EV model simulations varying

theparameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.7 Charging behavior indicators for heterogeneoussimulation. . . . . . . 47

3.8 Arrival and departure probability distributions

49

3.9 Load curves for uncontrolled charging, 20 EVs [kW/EV]

50

3.10 Load curves for uncontrolled charging, 1000 EVs [kW/EV]

50

3.11 Peak load for varying

eet sizes [kW/EV], uncontrolled charging 51

3.12 Peak load for varying

eet sizes [kW/EV], price-responsive charging 53

3.13 Average charging and

exible times of charging sessions 55

3.14 Average share of connected EVs along the day

56

3.15 Average

exible power [kW/EV] during high-availability hours 56

3.16 Example of the accessible storage of a charging session of a single EV.

57

3.17 Average accessible storage [kWh/EV] from an aggregator point of view,

for dierent battery sizes and charger power levels. 58

4.1 Flowchart of implemented methodology to analyze EV and PV integration

into MV grids 64

4.2 Average daily commuting distances by commune of residence [km]

66
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