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Smoothed Least-Laxity-First Algorithm for EV Charging

Chen, Niangjun and Kurniawan, Christian and Nakahira, Yorie and Chen, Lijun and Low, Steven H. (2021) Smoothed Least-Laxity-First Algorithm for EV Charging. . (Unpublished)

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Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs' energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper ItemConference Paper
Chen, Niangjun0000-0002-2289-9737
Nakahira, Yorie0000-0003-3324-4602
Chen, Lijun0000-0001-6694-4299
Low, Steven H.0000-0001-6476-3048
Additional Information:Attribution 4.0 International (CC BY 4.0).
Subject Keywords:power generation scheduling, scheduling, road vehicle power systems, resource management, battery chargers
Record Number:CaltechAUTHORS:20210510-080403337
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109019
Deposited By: Tony Diaz
Deposited On:10 May 2021 19:55
Last Modified:10 May 2021 19:55

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