| 引用本文: | 肖白,高峰.含不同容量充电桩的电动汽车充电站选址定容优化方法[J].电力自动化设备,2022,42(10): |
| XIAO Bai,GAO Feng.Optimization method of electric vehicle charging stations’ site selection and capacity determination considering charging piles with different capacities[J].Electric Power Automation Equipment,2022,42(10): |
|
| 摘要: |
| 针对电动汽车充电站(EVCS)的最佳数量、站址以及容量难以确定的问题，提出了一种在备选站址既定的情况下含不同容量充电桩的EVCS选址定容优化方法。首先，根据电动汽车(EV)的行为特征和待规划充电桩的容量，计算EV产生充电需求时选用不同容量充电桩进行充电的概率，并结合目标年城市预计的车流量采用蒙特卡罗方法对EV充电需求的时空分布、不同容量充电桩的平均充电速率和EV用户选用不同容量充电桩时的平均停车持续时间进行预测；然后，建立EVCS选址定容的双层规划模型，上层模型以EVCS的年化总成本与EV用户的年损失成本之和最小为目标对EVCS进行选址优化，下层模型以EV用户到站的行驶距离最短为目标对每座EVCS的服务范围进行划分，并将各EVCS服务范围内的充电负荷返回给上层模型，结合预测结果进行定容优化；最后，结合模拟退火算法和迪克斯特拉算法对双层规划模型进行求解。算例分析结果验证了所提方法的正确性和有效性。 |
| 关键词: 电动汽车 充电站 选址定容 充电需求预测 双层规划 模拟退火算法 迪克斯特拉算法 |
| DOI：10.16081/j.epae.202207001 |
| 分类号:U469.72;TM715 |
| 基金项目:国家重点研发计划项目(2017YFB0902205)；吉林省产业创新专项基金资助项目(2019C058?7) |
|
| Optimization method of electric vehicle charging stations’ site selection and capacity determination considering charging piles with different capacities |
|
XIAO Bai, GAO Feng
|
|
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
|
| Abstract: |
| Aiming at the problem that the optimal number, site and capacity of EVCSs(Electric Vehicle Charging Stations) are difficult to determine, an optimization method of EVCSs’ site selection and capacity determination considering charging piles with different capacities is proposed under the condition that the alternative sites are fixed. Firstly, according to the behavior characteristics of EVs(Electric Vehicles) and the capacity of charging piles to be planned, the probability of selecting charging piles with different capacities when EVs have charging demand is calculated. The temporal and spatial distribution of EV charging demand, the average charging rate of charging piles with different capacities and the average parking duration when EV users select different capacity of charging piles are forecasted by Monte Carlo method combined with the city’s expected traffic flow in the target year. Then, the two-layer planning model of EVCSs’ site selection and capacity determination is established. The upper-layer model optimizes the EVCSs’ site with the goal of minimizing the sum of EVCSs’ annual total cost and EV users’ annual loss cost. The lower-layer model divides the service range of each EVCS by taking the shortest driving distance of EV users to the stations as the objective, and returns the charging load within the service range of each EVCS to the upper-layer model for capacity determination optimization combined with the forecasting results. Finally, the simulated annealing algorithm and Dijkstra algorithm are combined to solve the two-layer planning model. The correctness and effectiveness of the proposed method are verified by the example analysis results. |
| Key words: electric vehicles charging stations site selection and capacity determination charging demand forecasting two-layer planning simulated annealing algorithm Dijkstra algorithm |
