Agent-based decentralized optimal charging strategy for plug-in electric vehicles

Latifi, M., Rastegarnia, A., Khalili, A. and Sanei, S. ORCID: 0000-0002-3437-2801, 2018. Agent-based decentralized optimal charging strategy for plug-in electric vehicles. IEEE Transactions on Industrial Electronics. ISSN 0278-0046

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Abstract

This paper presents a game theoretic decentralized electric vehicle charging schedule for minimizing the customers' payments, maximizing the grid efficiency, and providing maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers' privacy, we take advantages of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real time dynamic pricing model, and supports the vehicle-to-grid (V2G) and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short term uncertainties.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Industrial Electronics
Creators:	Latifi, M., Rastegarnia, A., Khalili, A. and Sanei, S.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	13 July 2018
ISSN:	0278-0046
Identifiers:	Number Type 10.1109/tie.2018.2853609 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	17 Jul 2018 10:46
Last Modified:	20 Jul 2018 10:39
URI:	https://irep.ntu.ac.uk/id/eprint/34090

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