Journal article
Price of anarchy in electric vehicle charging control games: When Nash equilibria achieve social welfare
- Abstract:
- We consider the problem of optimal charging of plug-in electric vehicles (PEVs). We treat this problem as a multi-agent game, where vehicles/agents are heterogeneous since they are subject to possibly different constraints. Under the assumption that electricity price is affine in total demand, we show that, for any finite number of heterogeneous agents, the PEV charging control game admits a unique Nash equilibrium, which is the optimizer of an auxiliary minimization program. We are also able to quantify the asymptotic behaviour of the price of anarchy for this class of games. More precisely, we prove that if the parameters defining the constraints of each vehicle are drawn randomly from a given distribution, then, the value of the game converges almost surely to the optimum of the cooperative problem counterpart as the number of agents tends to infinity. In the case of a discrete probability distribution, we provide a systematic way to abstract agents in homogeneous groups and show that, as the number of agents tends to infinity, the value of the game tends to a deterministic quantity.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Accepted manuscript, pdf, 553.8KB, Terms of use)
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- Publisher copy:
- 10.1016/j.automatica.2018.06.043
Authors
- Publisher:
- Elsevier
- Journal:
- Automatica More from this journal
- Volume:
- 96
- Pages:
- 150-158
- Publication date:
- 2018-07-18
- Acceptance date:
- 2018-06-12
- DOI:
- ISSN:
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0005-1098
- Keywords:
- Pubs id:
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pubs:857047
- UUID:
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uuid:10e2d2ef-66ac-451d-88f5-b8d5c50ad293
- Local pid:
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pubs:857047
- Source identifiers:
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857047
- Deposit date:
-
2018-06-12
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Ltd
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 Elsevier Ltd. This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.automatica.2018.06.043
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