基于量子粒子群优化算法的城市电动汽车充电站优化布局

Optimal Planning of Charging Station for Electric Vehicle Based on Quantum PSO Algorithm

针对如何确定电动汽车充电站位置及规模的问题,建立考虑地理信息、建设成本和运行成本的综合优化目标函数。此目标函数以土地成本、配电变压器的投资等建设成本及包含供电损耗的运行费用为基础,以交通流量为约束条件,比较科学、全面地反映电动汽车充电站选址、定容问题的实质。电动汽车充电站选址定容问题是非凸、非线性、组合优化问题。在确定目标函数的基础上,提出用量子粒子群优化算法对此问题进行求解。此算法采用了量子理论中的叠加态特性和概率表达特性,潜在地增加了种群的多样性和全局寻优能力及寻优效率。运用此算法和构建的优化模型,对某区域的电动汽车充电站进行规划,通过对比分析,表明该方法可行、有效。

A comprehensive objective function was presented to address the problem of locating and sizing of electric vehicle charging stations in this paper. The constructed objective function includes construction costs, equipment costs and running costs including power supply loss etc. This problem is a classical non-convex, non-linear, and combinatorial optimization one. A quantum particle swarm optimization （QPSO） algorithm was proposed to solve this problem. The characteristics of superposition state and probability expression in the quantum theory was adopted in the algorithm to potentially increase the diversity of the population, global optimization searching capability and optimizing efficiency. The proposed algorithm and optimization model were tested by a given example to verify its feasibility and effectiveness from the calculation results and reasonable analysis.