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Optimal Online Adaptive Electric Vehicle Charging

Guo, Linqi and Erliksson, Karl F. and Low, Steven H. (2017) Optimal Online Adaptive Electric Vehicle Charging. In: 2017 IEEE Power & Energy Society General Meeting. IEEE , Piscataway, NJ, pp. 1-5. ISBN 978-1-5386-2213-1.

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We propose an online linear program (OLP) based algorithm for scheduling electric vehicle (EV) charging. To determine the charging rates in each control period, OLP solves a linear program based only on EVs currently in the charging facility, assuming no future EV arrivals. We prove that OLP achieves the offline optimal where all future EV arrivals are assumed to be known in advance, provided the cost coefficients are uniformly monotone. For general cost functions, we prove that the competitive ratio is upper bounded by the variability in the cost coefficients. We demonstrate the performance of OLP using real charging data from Google and Caltech's Adaptive Charging Network.

Item Type:Book Section
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Low, Steven H.0000-0001-6476-3048
Additional Information:© 2017 IEEE. We thank Yorie Nakahira and Fengyu Zhou of caltech for helpful discussions, and George Lee and Ted Lee of PowerFlex for providing charging data. This work has been supported by NSF grants through PFI:AIR-TI award 1602119. EPCN 1619352, CNS 1545096 CCF 1637598 and ARPA-E grant through award DE-AR0000699.
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Advanced Research Projects Agency-Energy (ARPA-E)DE-AR0000699
Record Number:CaltechAUTHORS:20180209-075953281
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Official Citation:L. Guo, K. F. Erliksson and S. H. Low, "Optimal online adaptive electric vehicle charging," 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 2017, pp. 1-5. doi: 10.1109/PESGM.2017.8274166
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84754
Deposited By: Ruth Sustaita
Deposited On:09 Feb 2018 17:53
Last Modified:03 Oct 2019 19:22

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