Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/93386
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dc.contributorDepartment of Electrical Engineeringen_US
dc.creatorLu, Xen_US
dc.creatorChan, KWen_US
dc.creatorXia, Sen_US
dc.creatorShahidehpour, Men_US
dc.creatorNg, WHen_US
dc.date.accessioned2022-06-21T08:23:23Z-
dc.date.available2022-06-21T08:23:23Z-
dc.identifier.issn1949-3053en_US
dc.identifier.urihttp://hdl.handle.net/10397/93386-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2020 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.en_US
dc.rightsThe following publication X. Lu, K. W. Chan, S. Xia, M. Shahidehpour and W. H. Ng, "An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators," in IEEE Transactions on Smart Grid, vol. 12, no. 2, pp. 1507-1518, March 2021 is available at https://doi.org/10.1109/TSG.2020.3037053en_US
dc.subjectDistribution companyen_US
dc.subjectDistributionally robust optimizationen_US
dc.subjectElectric vehicle aggregatoren_US
dc.subjectRenewable energyen_US
dc.subjectUncertaintyen_US
dc.titleAn operation model for distribution companies using the flexibility of electric vehicle aggregatorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1507en_US
dc.identifier.epage1518en_US
dc.identifier.volume12en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1109/TSG.2020.3037053en_US
dcterms.abstractAn operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO's deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on smart grid, Mar. 2021, v. 12, no. 2, 51, p. 1507-1518en_US
dcterms.isPartOfIEEE transactions on smart griden_US
dcterms.issued2021-03-
dc.identifier.scopus2-s2.0-85096844918-
dc.identifier.eissn1949-3061en_US
dc.identifier.artn51en_US
dc.description.validate202206 bchyen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberEE-0037-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe Hong Kong Polytechnic University under Research Studentship; National Natural Science Foundation of China; Jiangsu Basic Research Project; Fundamental Research Funds for the Central Universitiesen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS54441264-
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