Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 62, Issue 10, Pages 5289-5295Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2016.2636740
Keywords
Charging control; demand response; online learning; regret minimization
Funding
- NSF [1239224, 1312390]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1312390] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1239224] Funding Source: National Science Foundation
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We propose an algorithm for distributed charging control of electric vehicles (EVs) using online learning and online convex optimization. Many distributed charging control algorithms in the literature implicitly assume fast two-way communication between the distribution company and EV customers. This assumption is impractical at present and also raises security and privacy concerns. Our algorithm does not use this assumption; however, at the expense of slower convergence to the optimal solution and by relaxing the sense of optimality. The proposed algorithm requires one-way communication, which is implemented through the distribution company publishing the pricing profiles for the previous days. We provide convergence results for the algorithm and illustrate the results through numerical examples.
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