Siting and sizing of electric vehicle fast‐charging station based on quasi‐dynamic traffic flow. Issue 19 (5th March 2021)

• Record Type: Journal Article
• Authors: Qian, Zhang; Yi, Zhu; Zhong, Wang; Yue, Hu; Yaojia, Su
• Is Part Of: IET renewable power generation. Volume 14:Issue 19(2020)
• Page Start: 4204
• Page End: 4214
• Publication Date: 2021-03-05
• Subjects: particle swarm optimisation -- electric vehicles -- electric vehicle charging -- iterative methods -- computational geometry -- road traffic; particle swarm optimisation algorithm -- iterative calculations -- Voronoi diagram -- sizing model -- optimal siting -- charging infrastructure -- EV charging demand -- shortest path method -- spatio‐temporal distribution -- electric vehicle fast‐charging stations -- quasidynamic traffic flow; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2020.0439
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : In engineering practice, the Voronoi diagram is often used to plan electric vehicle (EV) fast‐charging stations but it does not consider the spatio‐temporal distribution of EV trip. Research on the spatio‐temporal distribution of EVs mainly uses the shortest path method to reduce the amount of iterative calculations. The influence of the external environment and traffic congestion on the travel of EVs has not been considered, which results in the inaccuracy of the EV charging demand. To solve the above problems, this study proposes a method for predicting the spatio‐temporal distribution of EVs based on quasi‐dynamic traffic flow. This method takes into account the external environment and the impact of traffic congestions on EV trips and balances the problem between simulation accuracy and calculation efficiency. Based on this, the particle swarm optimisation algorithm is used to optimise the travel cost of the EVs and the cost of the construction and operation of the charging infrastructure. An optimal siting and sizing model for the fast‐charging station based on quasi‐dynamic traffic flow is established. Simulation results verify the effectiveness of the model.