Adaptive dynamic planning method for electric vehicle charging stations considering active interaction with EV growth

Against the backdrop of rapid development in China's electric vehicle (EV) industry, the adequacy of charging infrastructure has emerged as a critical constraint. In this context, the exploration of economical and adaptable charging station (CS) planning methods is paramount. Therefore, this paper proposes an adaptive phased planning method for electric vehicle charging stations that considers transformation and expansion, based on system dynamics (SD) theory. The method explicitly accounts for the active interaction between EV ownership and planning results. Firstly, to reflect the interests of CSs, the power grid, and users, the division of phases as well as the transformation and expansion of CSs are incorporated as decision variables in the planning model. Building on this framework, an adaptive dynamic planning method is developed to minimize the combined cost of investment and queuing. Then, a predictive model for EV ownership is formulated based on SD theory to capture the active interaction between EV ownership and planning results. Finally, the immune genetic algorithm is used to solve the above programming model. The simulation results demonstrate that the proposed method can effectively address the challenges arising from the growth in EV ownership and enhance the adaptability and economy of CS planning schemes.