Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/24826
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dc.contributor.authorWang, H-
dc.contributor.authorJia, Y-
dc.contributor.authorShi, M-
dc.contributor.authorXie, P-
dc.contributor.authorLai, CS-
dc.contributor.authorLi, K-
dc.date.accessioned2022-07-08T13:43:57Z-
dc.date.available2022-07-08T13:43:57Z-
dc.date.issued2022-08-16-
dc.identifierORCID 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.-
dc.identifier.citationWang, 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.en_US
dc.identifier.issn1949-3053-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/24826-
dc.description.abstractWith 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.en_US
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 72071100); Shenzhen Basic Research Program (Grant Number: JCYJ20210324104410030); Young Elite Scientist Sponsorship Program by CSEE (Grant Number: CSEE-YESS-2020027).-
dc.format.extent476 - 488-
dc.format.mediumPrint-Electronic-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.rightsCopyright © 2022 Institute of Electrical and Electronics Engineers (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 by sending a request to pubs-permissions@ieee.org. See https://www.ieee.org/publications/rights/index.html for more information.-
dc.rights.urihttps://www.ieee.org/publications/rights/index.html-
dc.subjectEV charging flexibilityen_US
dc.subjectincentive programen_US
dc.subjectoptimal incentive price selectionen_US
dc.subjectadaptive ADMMen_US
dc.titleA Hybrid Incentive Program for Managing Electric Vehicle Charging Flexibilityen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TSG.2022.3197422-
dc.relation.isPartOfIEEE Transactions on Smart Grid-
pubs.issue1-
pubs.publication-statusPublished-
pubs.volume14-
dc.identifier.eissn1949-3061-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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