Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

• Published in: Frontiers in energy research 2024-01, Vol.11
• Main Authors: Linjuan, Zhang, Han, Fu, Zhiheng, Zhou, Shangbing, Wang, Jinbin, Zhang
• Format: Article
• Language: English
• Subjects: adaptive simulated annealing particle swarm algorithm; capacity determination; charging stations; robust optimization; site selection; weighted voronoi diagram
• ISSN: 2296-598X; 2296-598X
• DOI: 10.3389/fenrg.2023.1295043

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.