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Published in: Dynamic Games and Applications 4/2020

04-10-2019

Power Markets with Information-Aware Self-scheduling Electric Vehicles

Authors: F. Selin Yanikara, Panagiotis Andrianesis, Michael Caramanis

Published in: Dynamic Games and Applications | Issue 4/2020

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Abstract

We consider multi-period (24-h day-ahead) multi-commodity (energy and regulation reserves) decentralized electricity transmission and distribution (T&D) market designs. Whereas conventional centralized generators with uniform price quantity offers are scheduled by a transmission system operator, low-voltage network-connected distributed energy resources (DERs) with complex preferences and requirements, such as electric vehicles (EVs), are allowed to self-schedule adapting to spatiotemporal marginal cost-based prices. We model the salient characteristics of interconnected T&D networks, and we consider self-scheduling DER responses under alternative distribution network information-aware or information-unaware market designs. Moreover, we consider a single (EV load aggregator) network information-aware scheduler market design. Our contribution is the characterization and comparative analysis—analytic as well as numerical—of equilibria, using game-theoretical approaches to prove existence and uniqueness, and the investigation of the role of information on self-scheduling and EV aggregator coordinated EV scheduling. Finally, we derive conclusions on the impact to social welfare and distributional equity of information-aware/information-unaware self-scheduling as well as single EV aggregator scheduling and implications that are relevant to market design and policy considerations.

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Appendix
Available only for authorised users
Footnotes
1
In reality, DLMPs are a function of other marginal costs, for instance transformer loss of life [27], that are not modeled here for simplicity, but have qualitatively similar impacts to those of marginal network losses.
 
2
Index n is interchangeably used for both the distribution feeder location and the line connecting this location to the transmission bus.
 
3
The reader is forewarned that in the DLMP directed decentralized market designs, distribution feeder information-aware EVs will have access to the exact functional form of DLMPs and price anticipation will be possible impacting the associated Nash equilibrium.
 
4
Moreover, due to privacy concerns, EV owners might not opt to communicate their preferences directly to the TSO.
 
5
Note, however, that if multiple EV groups that are internally homogeneous, as for example fleets of electrified UPS, FEDEX, Amazon delivery, etc., are scheduled by competing load aggregators, the Nash equilibrium notion becomes relevant.
 
6
If (30) does not hold, an EV might decide to provide no reserve or strictly smaller reserve than \(q_{j,t}^P\). For instance, for a 2-h case where \(\{\lambda _{n,1}^P,\lambda _{n,1}^R\}=\{20,15\}\), \(\{\lambda _{n,2}^P,\lambda _{n,2}^R\}=\{50,10\}, \overline{q}_j=3, \underline{s}_j=3\), the optimal solution is \(q_{j,1}^{P}=3, q_{j,1}^{R}=q_{j,2}^{P}=q_{j,2}^{R}=0\).
 
7
This will be the case when fast chargers, with say a 240kW capacity, are widely available.
 
8
For simplicity, we omit location index n from loss factor \(\gamma _n\) and inelastic demand \(d_{n,t}^f\).
 
9
Assuming that inelastic demand levels in a single feeder are not large enough to affect this inequality.
 
10
Under a large number of EVs, we also assume the generation capacity of generator i is adjusted as \(\overline{Q}_i=\overline{Q}_i+J\underline{s}\), so that wholesale prices are not significantly affected by the increased demand from EV charging.
 
11
Replace \(d_t^f=0\) to see this clearly.
 
12
Or equivalently a low-loss transmission network with no line congestion, a choice that does not compromise the qualitative generality of our results.
 
13
A large portion of the social cost is associated with fixed/inelastic demand.
 
Literature
1.
go back to reference Andrianesis P, Caramanis M (2019) Distribution locational marginal costs—Part I: Novel AC OPF including transformer degradation. arXiv:​1906.​01570 Andrianesis P, Caramanis M (2019) Distribution locational marginal costs—Part I: Novel AC OPF including transformer degradation. arXiv:​1906.​01570
4.
go back to reference Basar T, Olsder GJ (1999) Dynamic noncooperative game theory. SIAM, Philadelphia MATH Basar T, Olsder GJ (1999) Dynamic noncooperative game theory. SIAM, Philadelphia MATH
5.
go back to reference Bertsekas DP (1999) Nonlinear programming, Ch. 4 and 5, 2nd edn Bertsekas DP (1999) Nonlinear programming, Ch. 4 and 5, 2nd edn
15.
go back to reference International Energy Agency (2018) Global EV Outlook 2018, p 9 International Energy Agency (2018) Global EV Outlook 2018, p 9
27.
go back to reference Ntakou E (2017) Distribution power markets: detailed modeling and tractable algorithms. Dissertation, Boston University Ntakou E (2017) Distribution power markets: detailed modeling and tractable algorithms. Dissertation, Boston University
Metadata
Title
Power Markets with Information-Aware Self-scheduling Electric Vehicles
Authors
F. Selin Yanikara
Panagiotis Andrianesis
Michael Caramanis
Publication date
04-10-2019
Publisher
Springer US
Published in
Dynamic Games and Applications / Issue 4/2020
Print ISSN: 2153-0785
Electronic ISSN: 2153-0793
DOI
https://doi.org/10.1007/s13235-019-00331-1

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