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

• Published in: 2012 International Conference on Control Engineering and Communication Technology pp. 403 - 406
• Main Authors: Jun He, Buxiang Zhou, Chao Feng, Hengxin Jiao, Jinhua Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2012
• Subjects: Batteries; electric vehicle charging stations; Genetic algorithms; Geographic Information System; Geographic information systems; Hybrid Genetic Algorithm; locating and sizing; multiple population; Optimization; Planning; Power systems; Sociology
• ISBN: 9781467344999; 1467344990
• DOI: 10.1109/ICCECT.2012.45

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.