面向多目标优化的充电站选址定容方法

Multi-Objective Optimization-Based Charging Station Site Selection and Capacity Determination Method

为了给电动汽车(EV,electric vehicle)出行提供充足的能源支持,对充电站进行合理部署至关重要,但在规划时不同主体间存在难以耦合的利益冲突,为此兼顾投建经济性与用户充电便利性,提出一种基于遗传算法的自适应粒子群算法(APSOGA)用于选址定容。首先,通过分析EV日行驶规律,预测EV日充电负荷分布情况,以确定充电站数量范围;其次,构建兼顾EV和充电站承包商利益的充电站选址定容规划模型;最后,将遗传算法的交叉和变异操作引入粒子更新环节,设计了APSOGA算法,以寻优求解生成最佳的选址定容方案。实验结果表明,与经典的充电站选址定容方法相比,所提方法综合成本降低了7.19%~14.37%,充电站利用率提升了14.28%~29.03%,能够为用户提供高质量充电服务。

To provide sufficient energy support for electric vehicle(EV)travel,it is crucial to deploy charging stations reasonably.However,there are difficult-to-reconcile interest conflicts between different stakeholders in planning.In order to balance investment economy and user charging convenience,a method for site selection and capacity determination based on the Adaptive Particle Swarm Optimization Genetic Algorithm(APSOGA)was proposed.Firstly,by analyzing the daily driving patterns of EVs,the daily charging load distribution was predicted to determine the range for the number of charging stations.Secondly,a charging station planning model that took into account the interests of EVs and charging station contractors was constructed.Finally,crossover and mutation operations from the genetic algorithm were integrated into the particle update process,and the APSOGA algorithm was designed to optimize the site selection and capacity determination.Experimental results show that compared with classical charging station planning methods,the proposed method reduces comprehensive costs by 7.19%~14.37%and increases charging station utilization rate by 14.28%~29.03%,which can provide users with high-quality charging services.