The planning decision is affected by centralized charging stations investors and charging users who play different roles and pursue different benefits. Therefore

we develop a bi-level optimal planning model considering investment cost of investors and user satisfaction. The charging power demand model of electric vehicles（EVs） is established based on the behavior characteristics of different EVs. The objective function of the upper level and the lower level identifies the investment cost and user satisfaction with the investment budget

power grid

charging stations capacity and user demand as constraints. This model reflects the effect of coupled decision among different roles. The Bi-level programming belongs to the strong NP-hard issues. Furthermore

we utilize a quantum genetic algorithm and particle swarm optimization to solve the bi-level model and outputs the planning of charging station. The results show that the planning of charging station considering investment cost and user satisfaction can give overall consideration on each benefit for investors and users

and achieve an effective compromise between investment cost and user satisfaction.