{"title":"Optimal location of electric vehicle charging stations using genetic algorithm","authors":"Shuangshuang Chen, Yue Shi, Xingyu Chen, F. Qi","doi":"10.1109/APNOMS.2015.7275344","DOIUrl":null,"url":null,"abstract":"In this paper, we investigate the optimal location of electric vehicle (EV) charging stations. As one of the crucial infrastructures of EV, electric charging stations must be widely deployed to meet the growing needs of EV. In this study, we propose a locating method of charging station when considering economics, capacity, coverage and convenience. In order to solve the locating problem, an optimization model for charging stations location is established first, which minimizes the investment cost and transportation cost, meanwhile, the constraints of capacity, coverage and convenience should be satisfied simultaneously. Then, an improved genetic algorithm (GA) is proposed to solve the optimization problem. The simulation results indicate that the proposed locating method is effective and practical.","PeriodicalId":269263,"journal":{"name":"2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APNOMS.2015.7275344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 22
Abstract
In this paper, we investigate the optimal location of electric vehicle (EV) charging stations. As one of the crucial infrastructures of EV, electric charging stations must be widely deployed to meet the growing needs of EV. In this study, we propose a locating method of charging station when considering economics, capacity, coverage and convenience. In order to solve the locating problem, an optimization model for charging stations location is established first, which minimizes the investment cost and transportation cost, meanwhile, the constraints of capacity, coverage and convenience should be satisfied simultaneously. Then, an improved genetic algorithm (GA) is proposed to solve the optimization problem. The simulation results indicate that the proposed locating method is effective and practical.