ABSTRACT With the increasing integration of electric vehicles (EVs) into urban energy systems, the strong coupling among the stochastic nature of EV charging behaviours, the dynamic operation of power grids, and the variability of transportation networks poses significant challenges to urban infrastructure planning. To address this issue, this paper proposes a charging station planning method incorporating dynamic traffic load forecasting. First, a charging demand prediction model is developed by integrating the urban traffic network structure with EV travel behaviour characteristics. Then, an optimisation model for charging station siting and capacity planning is formulated with the objective of minimising the total integrated cost, while considering the coupling constraints between the transportation network and the distribution system. The model is solved using an improved particle swarm optimisation (IPSO) algorithm. Finally, case studies based on the IEEE 33‐bus distribution system and a 25‐node transportation network are conducted. Simulation results demonstrate that the proposed method can effectively accommodate the dynamic charging demands of EVs, achieve rapid convergence, and reduce the overall cost of coordinated operation between charging stations and the distribution system by more than 10% compared with traditional methods, thereby enhancing overall operational efficiency.
Ji et al. (Mon,) studied this question.