Electric Vehicle Charging Station Planning Based on Multiple-Population Hybrid Genetic Algorithm

被引：10
|
作者：
He, Jun [1 ]
Zhou, Buxiang [2 ]
Feng, Chao [2 ]
Jiao, Hengxin [3 ]
Liu, Jinhua [4 ]
机构：
[1] Chengdu Sichuan Elect Power Corp, Elect Power Bur, Chengdu, Peoples R China
[2] Sichuan Univ, Sch Elect Engn & Informat, Chengdu, Peoples R China
[3] Huainan Power Supply Co, Anhui Elect Power Corp, Automat Inst, Huainan, Peoples R China
[4] Ertan Hydropower Dev Co LTD, Chengdu, Peoples R China
来源：
2012 INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING AND COMMUNICATION TECHNOLOGY (ICCECT 2012) | 2012年
关键词：
Geographic Information System; multiple population; Hybrid Genetic Algorithm; electric vehicle charging stations; locating and sizing;
D O I：
10.1109/ICCECT.2012.45
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Establishing electric vehicle charging station's minimum comprehensive cost model which considers charging station' construction and operation cost and the cost of charging people. According to the characteristics of the electric vehicle charging station planning, this article puts forward a new kind of Multiple-Population Hybrid Genetic Algorithm (MPHGA). The algorithm combines the Standard Genetic Algorithm (SGA) with Alternative Location and Allocation Algorithm (ALA). According to the multi-objective of the charging station planning, use the concept of multigroup to do collaborative evolution search. Based on the Geographic Information System (GIS), the geographic information' influence on the location of the charging station will be considered. The model and method are proved that they have great correctness and effectiveness by a charging station planning example of a city.
引用
收藏
页码：403 / 406
页数：4
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