文献类型：期刊文献

英文题名：The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

作者：Wang, Jing-min Liu, Yan Yang, Yi-fei Cai, Wei Wang, Dong-xuan Jia, Zhao-wei

第一作者：Wang, Jing-min

通信作者：Liu, Y[1];Liu, Y[2]

机构：[1]North China Elect Power Univ, Dept Econ & Management, 689 Huadian Rd, Baoding 071003, Peoples R China;[2]Guizhou Inst Technol, Sch Elect & Informat Engn, 1 Caiguan Rd, Guiyang 550003, Guizhou, Peoples R China;[3]Hebei Agr Univ, Inst Technol, 1 Bohai Rd, Huanghua 061100, Peoples R China;[4]China Southern Power Grid Co Ltd, Guiyang Power Supply Co, 186 Zhonghua North Rd, Guiyang 550001, Guizhou, Peoples R China

第一机构：North China Elect Power Univ, Dept Econ & Management, 689 Huadian Rd, Baoding 071003, Peoples R China

通信机构：corresponding author), North China Elect Power Univ, Dept Econ & Management, 689 Huadian Rd, Baoding 071003, Peoples R China;corresponding author), Guizhou Inst Technol, Sch Elect & Informat Engn, 1 Caiguan Rd, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院;

年份：2019

卷号：33

期号：8

外文期刊名：INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20191006585250);Scopus(收录号：2-s2.0-85062212163);WOS:【SCI-EXPANDED(收录号:WOS:000472775100013)】；

基金：The authors would like to appreciate the North China Electric Power University for support, help, and advice on this research.

语种：英文

外文关键词：Site selection; electric vehicle charging station (EVCS); robust optimization algorithm

摘要：It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.