Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93408
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 | Zhang, X | en_US |
dc.creator | Wang, G | en_US |
dc.creator | Li, F | en_US |
dc.date.accessioned | 2022-06-21T08:23:32Z | - |
dc.date.available | 2022-06-21T08:23:32Z | - |
dc.identifier.issn | 0885-8950 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93408 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2019 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, X. Zhang, G. Wang and F. Li, "A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of EVAs," in IEEE Transactions on Power Systems, vol. 35, no. 3, pp. 2212-2221, May 2020 is available at https://doi.org/10.1109/TPWRS.2019.2951108 | en_US |
dc.subject | Day-ahead planning | en_US |
dc.subject | Distribution system | en_US |
dc.subject | Distributionally robust optimization | en_US |
dc.subject | Electric vehicle aggregator | en_US |
dc.subject | Temporal flexibility | en_US |
dc.subject | Uncertainty mitigation | en_US |
dc.title | A model to mitigate forecast uncertainties in distribution systems using the temporal flexibility of EVAs | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Title on author’s file: A Model to Mitigate Forecast Uncertainties in Distribution Systems Using the Temporal Flexibility of Electric Vehicle Aggregators | en_US |
dc.identifier.spage | 2212 | en_US |
dc.identifier.epage | 2221 | en_US |
dc.identifier.volume | 35 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1109/TPWRS.2019.2951108 | en_US |
dcterms.abstract | Electric vehicles (EVs) provide new options for energy balancing of power systems. One possible way to use EVs in energy balancing is to let each distribution system mitigate its forecast uncertainties through the flexibility of EVs. In consideration of the difficulties to directly govern a large number of EVs, it is more reasonable for distribution systems to dispatch electric vehicle aggregators (EVAs). Without influencing driving activities of EVs in the next day, a model is established for distribution systems to make use of EVAs, whose contributions are delaying uncertainties through their temporal flexibility and thus creating opportunities for uncertainties from different hours to offset each other. In the established model, a scheme of uncertainty transferring is proposed to relieve interruption to EVAs and distributionally robust optimization is adopted to evaluate the operation plans' average performance with temporal and spatial uncertainty correlations considered. Comprehensive case studies are carried out based on charging demands of EVAs simulated from real traffic data to verify the effectiveness of the proposed model. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on power systems, May 2020, v. 35, no. 3, 8890715, p. 2212-2221 | en_US |
dcterms.isPartOf | IEEE transactions on power systems | en_US |
dcterms.issued | 2020-05 | - |
dc.identifier.scopus | 2-s2.0-85080936851 | - |
dc.identifier.eissn | 1558-0679 | en_US |
dc.identifier.artn | 8890715 | en_US |
dc.description.validate | 202206 bchy | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EE-0123 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The Hong Kong Polytechnic University; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 26684985 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Lu_Model_Mitigate_Forecast.pdf | Pre-Published version | 1.33 MB | Adobe PDF | View/Open |
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