Wang, H, Jia, Y, Shi, M et al. (3 more authors) (2023) A Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibility. IEEE Transactions on Smart Grid, 14 (1). pp. 476-488. ISSN 1949-3053
Abstract
With the mass roll-out of electric vehicles (EVs) and rapid progress in battery technology, utilizing EV charging flexibility has become a promising solution for supporting economic and secured power system operations. This work proposes a novel hybrid incentive program, which encourages EV owners to sell their charging flexibility to a charging station (CS) and achieve a win-win situation for both EV owners and the CS. Unlike existing approaches, the proposed hybrid incentive program is simultaneously featured with simplicity, consistency, and controllability. To determine the incentive payment parameters, an optimal incentive price selection model is developed. In the solution methodology, we first linearize the original problem, then develop an adaptive ADMM algorithm to efficiently solve the formulated problem. Case studies confirm the superiority of the proposed hybrid incentive program over the state-of-the-arts, achieving 22.51% of EV owners’ cost reduction, 31.18% of energy market bill reduction, and 64.13% of potential charging flexibility utilization.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | EV charging flexibility, incentive program, optimal incentive price selection, adaptive ADMM |
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 Jul 2022 13:47 |
Last Modified: | 17 May 2023 01:21 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TSG.2022.3197422 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188927 |