考虑到分布式电源(distributed generator,DG)并入配电网的未来前景,提出了电动汽车(electric vehicle,EV)充电站的模糊服务半径新概念,建立了一种计及环境代价、交通流量、电能质量和建设成本等综合因素的EV充电站选址定容的新模型,在满足多目标约束条件下求取年均利润最优.提出了一种改进的云自适应粒子群算法(ICAPSO),使其更适用于大数据EV充电站优化模型的寻优迭代求解.将多个DG并入IEEE123节点配电网拓扑,用MATLAB仿真进行了算法对比,验证了所提优化模型和算法在多DG并网情景下的可行性和有效性,且算法具有较好的全局寻优能力和防早熟收敛特性.“,”Considering the prospective of multiple distributed generators (DG) interconnection in the distribution network,a new concept of the fuzzy service radius of electric vehicle (EV) charging station was presented.A new optimal location and parameter setting model of electric vehicle charging station was constructed,considering the factors of DG,service radius,traffic flow,power quality and construction cost,in order to calculate the optimal objective function of annual profits under the condition of multi-objective constraint.An improved cloud adaptive particle swarm algorithm (ICAPSO) was proposed,to make it more suitable for the large-scale optimization model.MATLAB simulation based on IEEE123 node distribution network with multiple DG interconnection,the results verified the feasibility and effectiveness of the proposed optimization model and algorithm.The comparison of convergence procedure shows that the proposed algorithm has better ability of global optimization and preventing premature convergence.