计及电动汽车时空分布状态的充电站选址定容优化方法

An Optimization Method for Location and Capacity Determination of Charging Stations Considering Spatial and Temporal Distribution of Electric Vehicles

电动汽车(electricvehicle,EV)充电站的布局直接影响经营者投资建设成本,也关乎用户出行的便捷性与经济性。为了平衡充电站经营者与用户之间的利益,同时为站内充电桩的数量和容量配置提供合理依据,该文针对EV充电站规划问题展开研究。首先,以充电站成本和用户经济损失(包括时间损失和路途电量损耗)最小为目标提出EV充电站选址定容数学模型。然后,基于EV保有量历史数据,使用BP神经网络预测目标规划区未来EV数量的时间分布,并利用目标区域的实测交通数据确定EV的空间分布。最后,提出基于区域访问量的动态概率变异方法改进粒子群算法,并使用该算法对EV充电站选址定容模型进行求解,得到目前居民收入水平下的充电站规划优化方案。以北京市海淀区为例进行案例分析,验证该文提出的优化模型具有经济性好、用户满意度高的优点。同时,改进的粒子群算法在计算过程中体现出了寻优速度快,适应性强的特点。

The layout of electric vehicle(EV) charging stations directly affects the investment and construction cost of operators, and it also relates to the convenience and economy of users’ travel. In order to balance the benefits of charging station operators and users, and provide reasonable solutions for configuring charging piles in a station, this paper studied the planning method of EV charging stations. Firstly, the mathematical model of EV charging station location selection and capacity configuration was proposed to minimize the cost of charging station and the economic loss of users(including time loss and power loss). Then, based on the historical data of EV population, BP neural network was applied to predict the future time distribution of EV in the target planning area. In addition, the actual traffic data of the target area was used to determine the spatial distribution of EV. Finally, the dynamic probability mutation method based on regional visits was proposed to improve the particle swarm optimization algorithm. The algorithm was used to solve the EV charging station location selection and capacity configuration model, and the charging station planning optimization scheme under the current residents’ income level was obtained. Taking Haidian District of Beijing as an example, the case analysis showed that the optimization model proposed in this paper had the advantages on economy and user satisfaction. Meanwhile, the improved particle swarm optimization algorithm had fast optimization speed and strong adaptability.