The construction main bodies of centralized charging stations are getting more and more diversified along with the deepening of reform of electric power systems. In order to balance the benefits between independent developers of centralized charging stations and distribution companies

a multi-objective model adopting bi-level programming is developed for centralized charging stations

which considers the peak load shaving effects of centralized charging stations as a controllable load where wind farms are already accessible. The upper multi-objective functions are yearly reduced values of network losses and the benefits of centralized charging developers

while the lower objective function is the subsidiary benefits of centralized charging developers. The strategy of solution by combining the non-inferior sorting genetic algorithm and adaptive mutation particle swarm optimization algorithm is used to optimize the location

capacity and scheduling of the centralized charging station. The results have verified the rationality of the model and the feasibility of the algorithm. This work is supported by National Natural Science Foundation of China(No. 51377162).