LAPSE:2019.1366
Published Article
LAPSE:2019.1366
Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids
Weige Zhang, Di Zhang, Biqiang Mu, Le Yi Wang, Yan Bao, Jiuchun Jiang, Hugo Morais
December 10, 2019
A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.
Keywords
battery storage system, decentralized charging strategy, distribution grid, electric vehicle, load variation
Suggested Citation
Zhang W, Zhang D, Mu B, Wang LY, Bao Y, Jiang J, Morais H. Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids. (2019). LAPSE:2019.1366
Author Affiliations
Zhang W: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
Zhang D: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
Mu B: The Key Laboratory of Systems and Control of CAS, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Wang LY: Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA
Bao Y: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
Jiang J: National Active Distribution Network Technology Research Center, Beijing Jiaotong University, Beijing 100044, China
Morais H: Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), ISEP/IPP, 4249 Porto, Portugal [ORCID]
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Journal Name
Energies
Volume
10
Issue
2
Article Number
E147
Year
2017
Publication Date
2017-01-24
Published Version
ISSN
1996-1073
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PII: en10020147, Publication Type: Journal Article
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LAPSE:2019.1366
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doi:10.3390/en10020147
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Dec 10, 2019
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Dec 10, 2019
 
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Calvin Tsay
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