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Article: Online Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems

TitleOnline Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems
Authors
KeywordsConvex relaxation
Distributed model predictive control (MPC)
Distribution network
Electric vehicles (EVs)
Optimal charging dispatch
Issue Date2019
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424
Citation
IEEE Transactions on Industrial Informatics, 2019, v. 15 n. 2, p. 638-649 How to Cite?
AbstractThe increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.
Persistent Identifierhttp://hdl.handle.net/10722/259262
ISSN
2019 Impact Factor: 9.112
2015 SCImago Journal Rankings: 2.973
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZheng, Y-
dc.contributor.authorSong, Y-
dc.contributor.authorHill, DJ-
dc.contributor.authorMeng, K-
dc.date.accessioned2018-09-03T04:04:04Z-
dc.date.available2018-09-03T04:04:04Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Industrial Informatics, 2019, v. 15 n. 2, p. 638-649-
dc.identifier.issn1551-3203-
dc.identifier.urihttp://hdl.handle.net/10722/259262-
dc.description.abstractThe increasing popularity of electric vehicles (EVs) has made electric transportation a popular research topic. The demand for EV charging resources has significantly reshaped the net demand profile of power distribution systems. This paper proposes an online optimal charging strategy for multiple EV charging stations in distribution systems with power flow and bus voltage constraints satisfied. First, we formulate the online optimal charging problem as an optimal power flow problem that minimizes the total system energy cost based on short-term predictive models and operates in a time-receding manner with the latest system information. Then, the problem is convexified by a modified convex relaxation technique based on the bus injection model, so that the globally optimal solution can be obtained with high efficiency. Moreover, a distributed model predictive control based scheme is designed to solve the optimization problem per concerns regarding data privacy, individual economic interests, and EV uncertainties. The obtained optimal schedules are dispatched to the EVs parked at each charging station according to a fuzzy rule, which guarantees full charging at the departure time for each vehicle. The effectiveness of the proposed method is demonstrated via simulations on a modified IEEE 15-bus distribution system with charging stations located in both residential and commercial areas.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424-
dc.relation.ispartofIEEE Transactions on Industrial Informatics-
dc.subjectConvex relaxation-
dc.subjectDistributed model predictive control (MPC)-
dc.subjectDistribution network-
dc.subjectElectric vehicles (EVs)-
dc.subjectOptimal charging dispatch-
dc.titleOnline Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems-
dc.typeArticle-
dc.identifier.emailZheng, Y: zhy9639@hku.hk-
dc.identifier.emailSong, Y: songyue@hku.hk-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.authoritySong, Y=rp02676-
dc.identifier.authorityHill, DJ=rp01669-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TII.2018.2812755-
dc.identifier.scopuseid_2-s2.0-85043352793-
dc.identifier.hkuros288615-
dc.identifier.volume15-
dc.identifier.issue2-
dc.identifier.spage638-
dc.identifier.epage649-
dc.identifier.isiWOS:000458199000003-
dc.publisher.placeUnited States-

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