微型电脑应用

针对电动汽车充电站的规划问题，提出一种基于多目标优化的双层规划方法。为了实现充电需求满足率、成本和负荷方差的综合优化，通过算法协同优化充电站的选址和充电策略。研究建立电动汽车充电需求模型，分析用户满意度，并采用动态预测模型来模拟充电行为。构建充电站选址规划模型，采用改进粒子群算法求解上层规划问题，通过建立充电桩的状态变量求解下层规划最佳的充电选项。仿真结果表明，所提出的方法在反转世代距离(IGD)和超体积(HV)指标上均优于现有主流算法，验证了其有效性和实用性。所提出的方法为充电站规划提供了科学、合理的决策支持，为电动汽车充电基础设施的可持续发展贡献了理论和实践指导。

A bi-level planning method based on multi-objective optimization is proposed for the planning problem of electric vehicle charging stations. This method aims at comprehensive optimization of charging demand satisfaction rate, cost, and load variance, and collaboratively optimization of the site and charging strategy of charging stations through algorithms. The study first establishes an electric vehicle charging demand model, analyzes user satisfaction, and uses a dynamic prediction model to simulate charging behavior. Subsequently, a charging station site planning model is constructed, and an improved particle swarm optimization algorithm is used to solve the upper level planning problem. The optimal charging option for the lower level planning is determined by establishing the state variables of the charging piles. The simulation results show that the proposed method outperforms existing mainstream algorithms in both inverted generational distance(IGD) and hypervolume(HV) metrics, verifying its effectiveness and practicality. The proposed method provides scientific and reasonable decision support for the planning of charging stations, and contributes theoretical and practical guidance for the sustainable development of electric vehicle charging infrastructure.