Wang, H, Shi, M, Xie, P et al. (3 more authors) (2022) Electric Vehicle Charging Scheduling Strategy for Supporting Load Flattening under Uncertain Electric Vehicle Departures. Journal of Modern Power Systems and Clean Energy. ISSN 2196-5625
Abstract
The scheduled electric vehicle (EV) charging flexibility has great potential in supporting the operation of power systems, yet achieving such benefits is challenged by the uncertain and user-dependent nature of EV charging behavior. Existing research primarily focuses on modeling the uncertain EV arrival and battery status yet rarely discusses the uncertainty in EV departure. In this paper, we investigate the EV charging scheduling strategy to support load flattening at the distribution level of the utility grid under uncertain EV departures. A holistic methodology is proposed to formulate the unexpected trip uncertainty and mitigate its negative impacts. To ensure computational efficiency when large EV fleets are involved, a distributed solution framework is developed based on the alternating direction method of multipliers (ADMM) algorithm. The numerical results reveal that unexpected trips can severely damage user convenience in terms of EV energy content. It is further confirmed that by applying the proposed methodology, the resultant critical and sub-critical user convenience losses due to scheduled charging are reduced significantly by 83.5% and 70.5%, respectively, whereas the load flattening performance has merely been sacrificed by 17%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Electric vehicle (EV) , EV fleets , uncertain departure , user convenience , distributed solution |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Oct 2022 10:44 |
Last Modified: | 19 May 2023 14:38 |
Status: | Published online |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.35833/MPCE.2022.000220 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:191886 |