Optimizing the electricity demand of electric vehicles

300 Pages·2015·7.3 MB·English

ETH Library
Optimizing the electricity demand
of electric vehicles
Creating value through flexibility
Doctoral Thesis
Author(s):
González Vayá, Marina
Publication date:
2015
Permanent link:
https://doi.org/10.3929/ethz-a-010546604
Rights / license:
In Copyright - Non-Commercial Use Permitted
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DISS. ETH NO. 22785
Optimizing the electricity
demand of electric vehicles:
creating value through
flexibility
A thesis submitted to attain the degree of
DOCTOR OF SCIENCES of ETH ZURICH
(Dr. sc. ETH Zurich)
presented by
´ ´
MARINA GONZALEZ VAYA
Dipl.-Ing. TU Mu¨nchen, Diplˆome d’Ing´enieur Sup´elec
born on 10.10.1985
citizen of
Spain
accepted on the recommendation of
Prof. Dr. G¨oran Andersson, examiner
Prof. Dr. Ian Hiskens, co-examiner
2015
ETH Zurich
EEH - Power Systems Laboratory
Physikstrasse 3
8092 Zurich, Switzerland
(cid:13)c Marina Gonz´alez Vay´a, 2015
[email protected]
For a copy visit: http://www.eeh.ee.ethz.ch
Printed in Switzerland by Druckzentrum ETH, Zurich, 2015
Preface
This thesis was written during my time as a researcher at the Power
Systems Laboratory of the ETH Zurich between 2010 and 2015.
I would like to thank my thesis supervisor Prof. Dr. G¨oran Andersson
for his guidance. I really appreciated the trust and freedom given to
conduct this work.
ThanksalsotoProf.Dr.IanHiskens,the co-examinerofthis thesis,for
his feedback and fruitful discussions.
It was a pleasure to share my time at the Power Systems Laboratory
withwonderfulcolleagues,whohavesupportedandinspiredmeinmany
different ways.
During my time at the Power Systems Laboratory I had the chance
to supervise many Semester and Master thesis. I’d like to thank the
students whose projects I supervised for their hard work and excellent
results, that have certainly contributed to this thesis.
I would also like to thank the colleagues I worked with in the research
projects THELMA and PlanGridEV for the successful cooperation.
Finally I would like to thank my family and my partner for all their
support in the past years.
iii

Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Kurzfassung . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . xv
List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . xxi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . xxvii
1 Introduction 1
1.1 Backgroundand motivation . . . . . . . . . . . . . . . . 2
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Outline of the thesis . . . . . . . . . . . . . . . . . . . . 9
1.4 List of publications . . . . . . . . . . . . . . . . . . . . . 10
2 Modeling PEVs’ demand flexibility 15
2.1 Driving patterns . . . . . . . . . . . . . . . . . . . . . . 16
2.1.1 Driving pattern generation . . . . . . . . . . . . 17
2.1.2 Fromdeterministictoprobabilisticdrivingpatterns 19
2.2 From driving patterns to an individual PEV flexible de-
mand model . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1 Literature survey . . . . . . . . . . . . . . . . . . 23
2.2.2 Linear PEV flexible demand model . . . . . . . . 23
2.3 Fromdrivingpatterns to anaggregatedPEVflexible de-
mand model . . . . . . . . . . . . . . . . . . . . . . . . . 25
v
vi Contents
2.3.1 Literature survey and contributions . . . . . . . 26
2.3.2 Deriving individual power and energy trajectories 30
2.3.3 Virtual battery model . . . . . . . . . . . . . . . 33
2.3.4 Accountingfordrivingpatternuncertaintyinthe
virtual battery model . . . . . . . . . . . . . . . 35
2.4 Concluding remarks . . . . . . . . . . . . . . . . . . . . 41
3 Charging cost minimization 43
3.1 Literature survey and contributions. . . . . . . . . . . . 45
3.1.1 Centralized control, unidirectional communication 46
3.1.2 Centralized control, bidirectional communication 47
3.1.3 Decentralized control, unidirectional communica-
tion . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.1.4 Decentralized control, bidirectional communication 49
3.1.5 Comparison of the different approaches . . . . . 51
3.1.6 Contributions of the proposed charging schedul-
ing models . . . . . . . . . . . . . . . . . . . . . 52
3.2 Social welfare maximizing centralized scheduling of PEVs 53
3.3 Strategic centralized scheduling of PEVs . . . . . . . . . 55
3.3.1 Bilevel model with perfect information . . . . . . 57
3.3.2 Bilevel model under market bid uncertainty . . . 58
3.3.3 MILP formulation . . . . . . . . . . . . . . . . . 60
3.4 SocialwelfaremaximizingdecentralizedschedulingofPEVs 63
3.4.1 Optimal charging problem formulation . . . . . . 65
3.4.2 Solving the optimal charging problem with ADMM 67
3.4.3 Driving pattern sampling . . . . . . . . . . . . . 71
3.5 Strategic decentralized scheduling of PEVs . . . . . . . 71
3.5.1 General framework . . . . . . . . . . . . . . . . . 73
3.5.2 PEV demand bids . . . . . . . . . . . . . . . . . 73
3.5.3 Learning algorithm . . . . . . . . . . . . . . . . . 76
3.5.4 Bid aggregation. . . . . . . . . . . . . . . . . . . 77
3.5.5 Market clearing . . . . . . . . . . . . . . . . . . . 79
Contents vii
3.5.6 Driving pattern uncertainty . . . . . . . . . . . . 80
3.5.7 Perfect market information benchmark . . . . . . 80
3.6 Price-based control . . . . . . . . . . . . . . . . . . . . . 80
3.6.1 DefiningaTOUtariffunderperfectfleetinforma-
tion . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.6.2 DefiningaTOUtariffunderpartialfleetinformation 85
3.6.3 Constraints on the TOU tariff. . . . . . . . . . . 86
3.6.4 Possible extensions . . . . . . . . . . . . . . . . . 86
3.7 Hierarchical control - Combined centralized and decen-
tralized framework . . . . . . . . . . . . . . . . . . . . . 87
3.8 Preliminary considerations on a simplified model . . . . 88
3.8.1 Social welfare maximizing scheduling . . . . . . 89
3.8.2 Strategic centralized scheduling . . . . . . . . . . 92
3.8.3 Strategic decentralized scheduling . . . . . . . . 95
3.9 Case study . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.9.1 Case study setup . . . . . . . . . . . . . . . . . . 98
3.9.2 Additional benchmarks . . . . . . . . . . . . . . 108
3.9.3 Method comparison . . . . . . . . . . . . . . . . 108
3.9.4 Considering several market scenarios in the cen-
tralized strategic approach. . . . . . . . . . . . . 124
3.9.5 Hierarchicalcontrol . . . . . . . . . . . . . . . . 127
3.10 Concluding remarks . . . . . . . . . . . . . . . . . . . . 130
4 Ancillary service provision 133
4.1 Literature survey and contributions . . . . . . . . . . . . 136
4.1.1 Secondary frequency control . . . . . . . . . . . . 136
4.1.2 Renewable energy balancing . . . . . . . . . . . . 138
4.1.3 Contributions of the proposed charging and re-
serve scheduling models . . . . . . . . . . . . . . 139
4.2 Centralized reserve scheduling . . . . . . . . . . . . . . 140
4.2.1 Virtual battery constraints . . . . . . . . . . . . 140
4.2.2 Bilevel model with co-optimization of regulation
and day-ahead markets . . . . . . . . . . . . . . 153
viii Contents
4.2.3 Wind forecast error balancing . . . . . . . . . . . 155
4.3 Decentralized reserve scheduling . . . . . . . . . . . . . 156
4.3.1 Optimal charging and reserve scheduling formu-
lation . . . . . . . . . . . . . . . . . . . . . . . . 157
4.3.2 Solvingtheoptimalchargingandreserveschedul-
ing problem with ADMM . . . . . . . . . . . . . 158
4.4 Generating scenarios of service requests . . . . . . . . . 161
4.5 Case study . . . . . . . . . . . . . . . . . . . . . . . . . 162
4.5.1 Case study setup . . . . . . . . . . . . . . . . . . 162
4.5.2 Centralized scheduling of regulation reserves . . 166
4.5.3 Centralized scheduling of wind balancing reserves 181
4.5.4 Decentralized scheduling of regulation reserves . 198
4.6 Concluding remarks . . . . . . . . . . . . . . . . . . . . 203
5 Case study for Switzerland 205
5.1 Model inputs . . . . . . . . . . . . . . . . . . . . . . . . 206
5.1.1 Power system . . . . . . . . . . . . . . . . . . . . 207
5.1.2 PEV fleet . . . . . . . . . . . . . . . . . . . . . . 211
5.2 Charging approaches . . . . . . . . . . . . . . . . . . . . 215
5.2.1 Uncontrolled charging . . . . . . . . . . . . . . . 215
5.2.2 Indirectly controlled charging . . . . . . . . . . . 215
5.2.3 Directly controlled charging . . . . . . . . . . . . 216
5.3 Regulation provision . . . . . . . . . . . . . . . . . . . . 216
5.3.1 Establishing the regulation capacity potential . . 216
5.3.2 Computing individual responses . . . . . . . . . 217
5.4 Battery degradation . . . . . . . . . . . . . . . . . . . . 218
5.5 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
5.5.1 PEV patterns . . . . . . . . . . . . . . . . . . . . 219
5.5.2 PEV charging patterns. . . . . . . . . . . . . . . 222
5.5.3 Supply and demand mixes . . . . . . . . . . . . . 225
5.5.4 Asset loading and requirements for network ex-
pansion . . . . . . . . . . . . . . . . . . . . . . . 233
Contents ix
5.5.5 Regulation capacity potential . . . . . . . . . . . 235
5.5.6 Battery degradation . . . . . . . . . . . . . . . . 235
5.6 Concluding remarks . . . . . . . . . . . . . . . . . . . . 236
6 Conclusion and outlook 239
6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 240
6.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 241
6.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
Bibliography 245
Appendices 265
A Priced-based control MILP problem 267
Curriculum Vitae 271