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Article: Optimal Scheduling With Vehicle-to-Grid Regulation Service

TitleOptimal Scheduling With Vehicle-to-Grid Regulation Service
Authors
Issue Date2014
PublisherIEEE.
Citation
IEEE Internet of Things Journal, 2014, v. 1, p. 556-569 How to Cite?
AbstractIn a vehicle-to-grid (V2G) system, aggregators coordinate the charging/discharging schedules of electric vehicle (EV) batteries so that they can collectively form a massive energy storage system to provide ancillary services, such as frequency regulation, to the power grid. In this paper, the optimal charging/discharging scheduling between one aggregator and its coordinated EVs for the provision of the regulation service is studied. We propose a scheduling method that assures adequate charging of EVs and the quality of the regulation service at the same time. First, the scheduling problem is formulated as a convex optimization problem relying on accurate forecasts of the regulation demand. By exploiting the zero-energy nature of the regulation service, the forecast-based scheduling in turn degenerates to an online scheduling problem to cope with the high uncertainty in the forecasts. Decentralized algorithms based on the gradient projection method are designed to solve the optimization problems, enabling each EV to solve its local problem and to obtain its own schedule. Our simulation study of 1000 EVs shows that the proposed online scheduling can perform nearly as well as the forecast-based scheduling, and it is able to smooth out the real-time power fluctuations of the grid, demonstrating the potential of V2G in providing the regulation service.
Persistent Identifierhttp://hdl.handle.net/10722/217036

 

DC FieldValueLanguage
dc.contributor.authorLin, J-
dc.contributor.authorLeung, KC-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T05:46:38Z-
dc.date.available2015-09-18T05:46:38Z-
dc.date.issued2014-
dc.identifier.citationIEEE Internet of Things Journal, 2014, v. 1, p. 556-569-
dc.identifier.urihttp://hdl.handle.net/10722/217036-
dc.description.abstractIn a vehicle-to-grid (V2G) system, aggregators coordinate the charging/discharging schedules of electric vehicle (EV) batteries so that they can collectively form a massive energy storage system to provide ancillary services, such as frequency regulation, to the power grid. In this paper, the optimal charging/discharging scheduling between one aggregator and its coordinated EVs for the provision of the regulation service is studied. We propose a scheduling method that assures adequate charging of EVs and the quality of the regulation service at the same time. First, the scheduling problem is formulated as a convex optimization problem relying on accurate forecasts of the regulation demand. By exploiting the zero-energy nature of the regulation service, the forecast-based scheduling in turn degenerates to an online scheduling problem to cope with the high uncertainty in the forecasts. Decentralized algorithms based on the gradient projection method are designed to solve the optimization problems, enabling each EV to solve its local problem and to obtain its own schedule. Our simulation study of 1000 EVs shows that the proposed online scheduling can perform nearly as well as the forecast-based scheduling, and it is able to smooth out the real-time power fluctuations of the grid, demonstrating the potential of V2G in providing the regulation service.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE Internet of Things Journal-
dc.rightsIEEE Internet of Things Journal. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2015 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.-
dc.titleOptimal Scheduling With Vehicle-to-Grid Regulation Service-
dc.typeArticle-
dc.identifier.emailLeung, KC: kcleung@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLeung, KC=rp00147-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/JIOT.2014.2361911-
dc.identifier.hkuros254283-
dc.identifier.volume1-
dc.identifier.spage556-
dc.identifier.epage569-

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