基于权重自适应调整的混沌量子粒子群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 ACQPSO algorithm

针对城市电动汽车充电站的定容和选址的问题,从实际情况出发,建立将土地价格、建设成本、运行成本、交通流量、服务距离、服务能力考虑在内的数学模型,该模型以年均综合费用最小为目标,以充电能力,充电距离为约束条件。采用权重自适应调整的混沌量子粒子群算法对北方的某市某区进行规划,该算法在迭代过程中会根据粒子不同的适应值,对惯性权重做出相应的调整,从而调整对粒子的搜索能力。利用混沌算子的遍历性,使得该算法具有很好的收敛速度和精度。利用该算法对所建立的数学模型进行求解,经过进一步的筛选,确定了该地区充电站的建址坐标、容量和费用。

In mathematical flow, service view of the problem of the capacity and location of the electric vehicle charging station, we establish the model from the actual situation, considering of the land price, construction cost, operation cost, traffic range and service ability, and the target of the model is to minimize annual comprehensive cost, and the constraints of the model are charging ability and distance. In this paper, based on the chaotic quantum particle swarm optimization algorithm of the adaptive weight adjustment, we plan the city district in the north. In the iteration process, based on the different adaptive values of particles, the algorithm will adjust inertia weight correspondingly to adjust the search ability of particle. The ergodicity of chaotic operator is used to make the algorithm have good conver- gence speed and accuracy. We adopt this algorithm to solve this established mathematical model in this paper. Thor- ough further screening, the coordinates, capacity and the cost of charging stations in the region are determined eventu- ally.