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

发布时间：2018-04-12 10:09

本文选题：电动汽车 + 权重自适应调整的混沌量子粒子群算法　；参考：《电测与仪表》2017年13期

【摘要】：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。
[Abstract]:In view of the problem of fixed capacity and location of charging station of city electric vehicle, a mathematical model considering land price, construction cost, operation cost, traffic flow, service distance and service capacity is established from the actual situation.The model takes the minimum annual comprehensive cost as the goal and the charging capacity and the charging distance as the constraint conditions.The chaotic quantum particle swarm optimization (QPSO) algorithm with adaptive weight adjustment is used to plan a certain area of a city in the north. In the iterative process, the weight of inertia is adjusted according to the different fitness values of particles.This adjusts the ability to search for particles.By using the ergodicity of chaotic operator, the algorithm has good convergence speed and precision.The algorithm is used to solve the mathematical model, and through further screening, the location coordinates, capacity and cost of the charging station in this area are determined.
【作者单位】：东北电力大学电气工程学院;
【分类号】：TP18;U491.8

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