An electric vehicle charging station siting and sizing method based on a density peaks clustering algorithm

被引：0
|
作者：
Zhang Y. [1 ]
Xu J. [2 ]
Li Q. [1 ]
Zhou J. [2 ]
Wang L. [1 ]
Zhu Z. [2 ]
Li Y. [2 ]
Wang S. [2 ]
机构：
[1] State Grid Henan Economic Research Institute, Zhengzhou
[2] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan
来源：
Li, Yan (liyanhust@hust.edu.cn) | 1600年 / Power System Protection and Control Press卷 / 49期
关键词：
Density peaks clustering; Electric vehicle; Planning of charging station;
D O I：
10.19783/j.cnki.pspc.200565
中图分类号：
学科分类号：
摘要：
An optimization method for the location and capacity of Electric Vehicle (EV) charging stations based on density peaks clustering of traffic flow is proposed to tackle the issue of EV demand. First, planning area traffic flow and parking lot opening index are analyzed, and the spatial distribution of EV charging demand data point collection is constructed. Then, the density peaks clustering method is used to analyze the density of the spatial distribution of charging demand, and the candidate clusters are obtained. Then, considering the cohesion and separation of clusters, the overall average contour coefficient is used to optimize the clustering results, so as to determine the location of the clustering center for EV charging stations. Further, based on the penetration rate of EVs, the number of EVs is predicted to determine the total charging demand of various types of EVs in the planned area. The charging station capacity of the corresponding cluster center is determined according to the charging demand proportion of each cluster. The location and capacity of EV charging stations in a particular urban area are determined, and the demonstration shows the feasibility of the method. © 2021 Power System Protection and Control Press.
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页码：132 / 139
页数：7
相关论文
共 25 条
  • [1] YANG Xi, DENG Jianshen, LI Hongfeng, Et al., Design and research on public service and interactive platform in electric vehicle, Power System Protection and Control, 44, 10, pp. 137-144, (2016)
  • [2] ZHANG Yongming, YAO Zhili, LI Jing, Et al., Electric vehicle charging station planning strategy based on probabilistic power flow calculation of distribution network, Power System Protection and Control, 47, 22, pp. 9-16, (2019)
  • [3] LI Yanqing, LI Ling, YONG Jing, Et al., Layout planning of electrical vehicle charging stations based on genetic algorithm, Electrical Power Systems and Computers, 99, pp. 661-668, (2011)
  • [4] LI Xiaohui, LI Lei, LIU Weidong, Et al., Spatial-temporal distribution prediction of charging load for electric vehicles based on dynamic traffic information, Power System Protection and Control, 48, 1, pp. 117-125, (2020)
  • [5] MEI Jie, GAO Ciwei, Considerations of traffic characteristics in research of grid integration of electric vehicles, Power System Technology, 39, 12, pp. 3549-3555, (2015)
  • [6] ZHAO Shuqiang, LI Zhiwei, DANG Lei, Optimal EV charging station siting and sizing based on urban traffic network information, Electric Power Automation Equipment, 36, 10, pp. 8-15, (2016)
  • [7] REN Yulong, SHI Lefeng, ZHANG Qian, Et al., Optimal distribution and scale of charging stations for electric vehicles, Automation of Electric Power Systems, 35, 14, pp. 53-57, (2011)
  • [8] WANG Hui, WANG Guibin, ZHAO Junhua, Et al., Optimal planning for electric vehicle charging stations considering traffic network flows, Automation of Electric Power Systems, 37, 13, pp. 63-69, (2013)
  • [9] CHENG Shan, WU Siyuan, SUN Weibin, Optimal planning of charging stations for electric vehicles considering voltage stability of distribution system and the quality of service, Power System Protection and Control, 47, 7, pp. 12-21, (2019)
  • [10] MANYIKA J, CHUI M, BROWN B, Et al., Big data: the next frontier for innovation, competition, and productivity, (2011)
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