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传统方法多聚焦于充电站的投资成本和收益,忽略了充电用户选择决策对投资主体规划决策的影响,该文提出一种考虑充电站投资收益和充电用户效用耦合决策的电动汽车充电站双层优化模型。根据城市电动汽车种类及其出行特性,计算规划区域内电动汽车充电功率需求,并以充电站投资收益为上层目标函数,以充电用户满意度为下层目标函数。引入用户选择决策变量耦合关联上下层模型,使用KKT条件实现双单层规划模型解耦。综合粒子群算法的快速搜索能力和变邻域搜索算法的全局搜索优势,采用混合变邻域粒子群算法对解耦模型进行求解。最后,算例仿真结果验证了模型和算法的有效性和可行性。
Traditional methods mostly focus on the investment costs and benefits of charging stations, neglecting the influence of charging user selection decision on investment main planning decision-making. This paper proposes a double-layer charging system for charging stations that takes into account the investment income of charging station and the coupling effect of charging user utility Optimize the model. According to the types of EVs and their travel characteristics, the demand for EV charging power in the planning area is calculated. Taking the investment income of charging stations as the upper objective function and the satisfaction degree of charging users as the lower objective function, The user-selected decision variables are coupled to the upper and lower models, and the dual-layer programming model is decoupled using KKT conditions. Based on the rapid search ability of global particle swarm optimization algorithm and the global search advantage of variable neighborhood search algorithm, the hybrid variable neighborhood particle swarm optimization algorithm is used to solve the decoupled model. Finally, the simulation results show that the model and the algorithm are effective and feasible.