Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24826
Title: A Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibility
Authors: Wang, H
Jia, Y
Shi, M
Xie, P
Lai, CS
Li, K
Keywords: EV charging flexibility;incentive program;optimal incentive price selection;adaptive ADMM
Issue Date: 16-Aug-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Wang, H., et al. (2022) 'A Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibility', IEEE Transactions on Smart Grid, 14 (1), pp. 476 - 488. doi: 10.1109/TSG.2022.3197422.
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.
URI: https://bura.brunel.ac.uk/handle/2438/24826
DOI: https://doi.org/10.1109/TSG.2022.3197422
ISSN: 1949-3053
Other Identifiers: ORCID iDs: Han Wang https://orcid.org/0000-0002-8200-9134; Youwei Jia https://orcid.org/0000-0003-3071-5552; Mengge Shi https://orcid.org/0000-0002-5520-1198; Peng Xie https://orcid.org/0000-0002-4850-4772; Chun Sing Lai https://orcid.org/0000-0002-4169-4438; Kang Li https://orcid.org/0000-0001-6657-0522.
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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