基于自适应量子遗传算法的电动出租车充电站规划

电动出租车充电基础设施的科学布局与建设对于电动出租车的推广具有重要的意义。考虑到对于以收益最大为目的的电动出租车驾驶员而言，时间价值具有重要的意义，以充电站建设及运行维护年费用、出租车前往充电站耗时年成本、充电等待时间年成本及配电网网络损耗年费用构建全社会年总成本目标函数，以配电网安全运行为充电站布局规划的约束条件建立模型。在量子遗传算法中引入自适应调整策略，并与Voronoi图法相结合对模型进行求解，基于Voronoi图划分的充电站服务范围，采用排队论的M/G/c对充电站容量进行优化配置，从而实现电动出租车充电站的选址定容。最后，以36节点的路网和33节点的配电网络为例说明了模型和方法的有效性和实用性。

Planning of Electric Taxi Charging Station Based on Adaptive Quantum Genetic Algorithm

The scientific layout and construction of charging infrastructure for electric taxis is of great significance to the promotion of electric taxis. Taking into account the importance of time value to taxi drivers with the purpose of maximizing the benefits， this paper adopts the annual cost of construction and operation maintenance of charging station， the annual time cost of taxi to the charging station， the annual waiting time cost of charging and the annual cost of distribution network loss to establish the total annual cost objective function in the whole society， and takes the safe operation of distribution network as the constraints of charging station layout planning to construct model. We apply adaptive adjustment strategy in the quantum genetic algorithm to solve the model with combining Voronoi diagram method. Then， we divide the service areas of charging station based on Voronoi diagram， and adopt the queuing theory M/G/c model to perform the optimization configuration of charging station， so as to realize the location of electric taxi charging station. Finally， the example analysis of 36-nodes road network and 33-nodes distribution network verifies the effectiveness and practicality of the model.