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Economics of Electric Vehicle Charging: A Game Theoretic Approach

Tushar, Wayes, Saad, Walid, Poor, H. Vincent, Smith, David B.·2012·IEEE Transactions on Smart Grid

Abstract

In this paper, the problem of grid-to-vehicle energy exchange between a smart grid and plug-in electric vehicle groups (PEVGs) is studied using a noncooperative Stackelberg game. In this game, on the one hand, the smart grid, which acts as a leader, needs to decide on its price so as to optimize its revenue while ensuring the PEVGs' participation. On the other hand, the PEVGs, which act as followers, need to decide on their charging strategies so as to optimize a tradeoff between the benefit from battery charging and the associated cost. Using variational inequalities, it is shown that the proposed game possesses a socially optimal Stackelberg equilibrium in which the grid optimizes its price while the PEVGs choose their equilibrium strategies. A distributed algorithm that enables the PEVGs and the smart grid to reach this equilibrium is proposed and assessed by extensive simulations. Further, the model is extended to a time-varying case that can incorporate and handle slowly varying environments.

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Metadata

Authors
Tushar, W., Saad, W., Poor, H. V., & Smith, D. B.
Venue
IEEE Transactions on Smart Grid
Published
2012-12
Volume
3
Issue
4
Pages
1767-1778
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
OpenAlex
W2065343413
Cited by
410
References
0
FWCI
18.92
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Economics of Electric Vehicle Charging: A Game Theoretic Approach. https://app.cochat.ai/papers/10.1109/tsg.2012.2211901
The paper
Tushar, W., Saad, W., Poor, H. V., & Smith, D. B. (2012). Economics of Electric Vehicle Charging: A Game Theoretic Approach. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2012.2211901

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.