On the Optimization Strategy of EV Charging Station Localization and Charging Piles Density

被引:7
作者
Li, Wenzao [1 ,2 ]
Yang, Lingling [1 ]
Wen, Zhan [1 ]
Chen, Jiali [1 ]
Wu, Xi [3 ]
机构
[1] Chengdu Univ Informat Technol, Coll Commun Engn, Chengdu 610225, Peoples R China
[2] Univ Elect Sci & Technol China, Network & Data Secur Key Lab Sichuan Prov, Chengdu 610054, Peoples R China
[3] Chengdu Univ Informat Technol, Sch Comp Sci, Chengdu 610225, Peoples R China
关键词
ALLOCATION;
D O I
10.1155/2021/6675841
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The penetration rate of electronic vehicles (EVs) has been increasing rapidly in recent years, and the deployment of EV infrastructure has become an increasingly important topic in some solutions of the Internet of Things (IoT). A reasonable balance needs to he struck between the user experience and the deployment cost of charging stations and the number of charging piles. The deployment of EV's charging station is a challenging problem due to the uneven distribution and mobility of EV. Fortunately, EVs move with a certain regularity in the urban environment. It makes the deployment strategy design of EV charging stations feasible. Therefore, we proposed a deployment strategy of EV charging station based on particle swarm optimization algorithm to determine the charging station localization and number of charging piles. This strategy is designed based on the nonuniform distribution of EV in a city scene map, at the same time, the distribution of EV at different times, which makes the strategy more reasonable. Extensive simulation results further demonstrated that the proposed strategy can significantly outperform the K-means algorithm in the urban environment.
引用
收藏
页数:13
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