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Published May 2013 | public
Journal Article

Optimal Decentralized Protocol for Electric Vehicle Charging

Abstract

We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guide their updates. The algorithm converges to optimal charging profiles (that are as "flat" as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation.

Additional Information

© 2012 IEEE. Manuscript received November 12, 2011; revised February 29, 2012 and May 28, 2012; accepted July 13, 2012. Date of publication September 27, 2012; date of current version April 18, 2013. This work was supported by Bell Labs of Alcatel-Lucent, NSF NetSE grant CNS 0911041, ARPA-E grant DE-AR0000226, Southern California Edison, National Science Council of Taiwan, R.O.C, grant NSC 101-3113-P-008-001, Resnick Institute, Okawa Foundation, Boeing Corporation, Cisco, and AFOSR award number FA9550-12-1-0302. Paper no. TPWRS-01087-2011. The authors would like to thank K. Mani Chandy and S. Adlakha for inspiring discussions.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023