Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93386
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.creator | Lu, X | en_US |
dc.creator | Chan, KW | en_US |
dc.creator | Xia, S | en_US |
dc.creator | Shahidehpour, M | en_US |
dc.creator | Ng, WH | en_US |
dc.date.accessioned | 2022-06-21T08:23:23Z | - |
dc.date.available | 2022-06-21T08:23:23Z | - |
dc.identifier.issn | 1949-3053 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93386 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_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.rights | The 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.2020.3037053 | en_US |
dc.subject | Distribution company | en_US |
dc.subject | Distributionally robust optimization | en_US |
dc.subject | Electric vehicle aggregator | en_US |
dc.subject | Renewable energy | en_US |
dc.subject | Uncertainty | en_US |
dc.title | An operation model for distribution companies using the flexibility of electric vehicle aggregators | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1507 | en_US |
dc.identifier.epage | 1518 | en_US |
dc.identifier.volume | 12 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.doi | 10.1109/TSG.2020.3037053 | en_US |
dcterms.abstract | An 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on smart grid, Mar. 2021, v. 12, no. 2, 51, p. 1507-1518 | en_US |
dcterms.isPartOf | IEEE transactions on smart grid | en_US |
dcterms.issued | 2021-03 | - |
dc.identifier.scopus | 2-s2.0-85096844918 | - |
dc.identifier.eissn | 1949-3061 | en_US |
dc.identifier.artn | 51 | en_US |
dc.description.validate | 202206 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EE-0037 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University under Research Studentship; National Natural Science Foundation of China; Jiangsu Basic Research Project; Fundamental Research Funds for the Central Universities | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54441264 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Lu_Operation_Model_Distribution.pdf | Pre-Published version | 1.09 MB | Adobe PDF | View/Open |
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