Optimal siting and sizing of charging stations considering dynamic charging demands of users

Due to high investment costs and low efficiency in the planning and construction of electric vehicle （EV） charging stations in cities， an optimal model for siting and sizing of charging stations that takes into consideration the uncertainty of dynamic charging demand of users is proposed. Initially， the characteristics of EV trip are studied based on trip chain theory and origin-destination （OD） matrix. In conjunction with the Dijkstra’s algorithm and the Monte Carlo method， a spatiotemporal prediction model for EV charging load distribution is established. Subsequently， an optimal siting and sizing model for charging stations is constructed， with the aim of minimizing the sum of the annualized cost of charging station operators and the annualized economic loss of EV users. The optimal number， location， and service range of charging stations are determined using the weighted Voronoi diagram and an adaptive simulated annealing particle swarm optimization （ASAPSO）. Finally， an uncertain scenario set that describes the uncertainty of dynamic charging demand of users is introduced into the sizing model， and the charging station capacity is solved using robust optimization. The effectiveness of the model is validated through a case study analyzing the planning of EV charging stations in parts of a city in north China.