Optimization of Ordered Charging Strategy for Large Scale Electric Vehicles Based on Quadratic Clustering

被引:3
|
作者
Zhang, Jie [1 ]
Yang, Chunyu [1 ]
Ju, Fei [2 ]
机构
[1] Nanjing Univ Posts & Telecommun, Sch Comp Sci, Nanjing 210023, Jiangsu, Peoples R China
[2] State Grid Corp China, Changzhou Power Supply Co, Changzhou 213017, Jiangsu, Peoples R China
关键词
electric vehicle; hierarchical clustering; genetic algorithm; charging strategy; K-means;
D O I
10.1109/ICISCE.2017.225
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Aiming at the problem of unbalanced distribution of the utilization rate of the charging station, which caused by the disordered charging of a large number of electric vehicles, an orderly charging strategy for electric vehicles is proposed. Firstly, the location of the electric vehicle's charging demand is clustered by using the hierarchical clustering and quadratic division based on K-means to achieve the convergence of electric vehicles with similar properties. Furthermore, this paper makes the utilization rate of the charging station and the traveling time of the electric vehicle as the objective function, and constructs the charging scheduling model based on electric vehicle clustering, which can be solved by the genetic algorithm. The simulation results show that the performance of this method is significantly better than the method only using the general genetic algorithm without clustering and it has high practicality.
引用
收藏
页码:1080 / 1084
页数:5
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