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- Publisher Website: 10.1109/TII.2018.2812755
- Scopus: eid_2-s2.0-85043352793
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Article: Online Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems
Title | Online Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems |
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Authors | |
Keywords | Convex relaxation Distributed model predictive control (MPC) Distribution network Electric vehicles (EVs) Optimal charging dispatch |
Issue Date | 2019 |
Publisher | IEEE. 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? |
Abstract | The 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 Identifier | http://hdl.handle.net/10722/259262 |
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 4.420 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zheng, Y | - |
dc.contributor.author | Song, Y | - |
dc.contributor.author | Hill, DJ | - |
dc.contributor.author | Meng, K | - |
dc.date.accessioned | 2018-09-03T04:04:04Z | - |
dc.date.available | 2018-09-03T04:04:04Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | IEEE Transactions on Industrial Informatics, 2019, v. 15 n. 2, p. 638-649 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/259262 | - |
dc.description.abstract | The 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.language | eng | - |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 | - |
dc.relation.ispartof | IEEE Transactions on Industrial Informatics | - |
dc.subject | Convex relaxation | - |
dc.subject | Distributed model predictive control (MPC) | - |
dc.subject | Distribution network | - |
dc.subject | Electric vehicles (EVs) | - |
dc.subject | Optimal charging dispatch | - |
dc.title | Online Distributed MPC-based Optimal Scheduling for EV Charging Stations in Distribution Systems | - |
dc.type | Article | - |
dc.identifier.email | Zheng, Y: zhy9639@hku.hk | - |
dc.identifier.email | Song, Y: songyue@hku.hk | - |
dc.identifier.email | Hill, DJ: dhill@eee.hku.hk | - |
dc.identifier.authority | Song, Y=rp02676 | - |
dc.identifier.authority | Hill, DJ=rp01669 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TII.2018.2812755 | - |
dc.identifier.scopus | eid_2-s2.0-85043352793 | - |
dc.identifier.hkuros | 288615 | - |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 638 | - |
dc.identifier.epage | 649 | - |
dc.identifier.isi | WOS:000458199000003 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1551-3203 | - |