LAPSE:2019.0162
Published Article
LAPSE:2019.0162
A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles
Jun Yang, Jiejun Chen, Lei Chen, Feng Wang, Peiyuan Xie, Cilin Zeng
January 31, 2019
With the popularization of electric vehicles (EVs), the out-of-order charging behaviors of large numbers of EVs will bring new challenges to the safe and economic operation of power systems. This paper studies an optimal charging strategy for EVs. For that a typical urban zone is divided into four regions, a regional time-of-use (RTOU) electricity price model is proposed to guide EVs when and where to charge considering spatial and temporal characteristics. In light of the elastic coefficient, the user response to the RTOU electricity price is analyzed, and also a bilayer optimization charging strategy including regional-layer and node-layer models is suggested to schedule the EVs. On the one hand, the regional layer model is designed to coordinate the EVs located in different time and space. On the other hand, the node layer model is built to schedule the EVs to charge in certain nodes. According to the simulations of an IEEE 33-bus distribution network, the performance of the proposed optimal charging strategy is verified. The results demonstrate that the proposed bilayer optimization strategy can effectively decrease the charging cost of users, mitigate the peak-valley load difference and the network loss. Besides, the RTOU electricity price shows better performance than the time-of-use (TOU) electricity price.
Keywords
electric vehicles, node layer model, optimization scheduling, regional layer model, RTOU electricity price model, user responsivity
Suggested Citation
Yang J, Chen J, Chen L, Wang F, Xie P, Zeng C. A Regional Time-of-Use Electricity Price Based Optimal Charging Strategy for Electrical Vehicles. (2019). LAPSE:2019.0162
Author Affiliations
Yang J: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Chen J: School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Chen L: School of Electrical Engineering, Wuhan University, Wuhan 430072, China [ORCID]
Wang F: Computer School of Wuhan University, Wuhan 430072, China
Xie P: State Grid Hunan Power Supply Company, Changsha 410007, China
Zeng C: State Grid Hunan Power Supply Company, Changsha 410007, China
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Journal Name
Energies
Volume
9
Issue
9
Article Number
E670
Year
2016
Publication Date
2016-08-24
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9090670, Publication Type: Journal Article
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LAPSE:2019.0162
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doi:10.3390/en9090670
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Jan 31, 2019
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Jan 31, 2019
 
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Calvin Tsay
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