| 张江,徐林.基于出行数据的电动汽车充电站规划[J].井冈山大学自然版,2025,(1):91-99 |
| 基于出行数据的电动汽车充电站规划 |
| ELECTRIC VEHICLE CHARGING STATION PLANNING BASED ON TRAVEL DATA |
| 投稿时间：2024-08-21 修订日期：2024-09-13 |
| DOI：10.3969/j.issn.1674-8085.2025.01.012 |
| 中文关键词: 出行数据 充电站规划 排队论 粒子群算法 |
| 英文关键词: travel data charging station planning queuing theory particle swarm optimization |
| 基金项目:国家自然科学基金项目(51777058) |
|
| 摘要点击次数: 118 |
| 全文下载次数: 284 |
| 中文摘要: |
| 目前大规模的充电行为给配电网安全运行带来了挑战，与此同时,充电需求在时空分布上的随机性，也使得充电站规划成为一个棘手的难题。针对该难题，提出了一种基于出行数据的电动汽车充电站规划方法。首先，对出行数据预处理得到出行矩阵等信息；其次，基于单位里程耗电模型和充电模型建立电动汽车充电负荷预测模型；然后，运用蒙特卡洛方法随机模拟充电需求概率分布信息，并以此建立充电站规划模型；最后，利用M/M/c排队论和Voronoi图优化充电站数量，利用改进粒子群算法确定充电站最佳位置。为了验证方法的可行性，以某市部分区域的出行数据进行了仿真分析。仿真结果表明，所提出的方法切实有效。 |
| 英文摘要: |
| Large-scale charging behavior brings challenges to the safe operation of the distribution network. At the same time, the randomness of charging demand in spatial and temporal distribution makes the charging station planning a difficult problem. To address this challenge, this paper proposes an electric vehicle charging station planning method based on the travel data. First, the travel data is preprocessed to obtain the travel matrix and other information; second, the electric vehicle charging load prediction model is established based on the unit mileage power consumption model and the charging model; then, the Monte Carlo method is used to stochastically simulate the probability distribution information of charging demand and to establish the charging station planning model; lastly, the charging station planning model is established by using the M/M/c queuing theory and Voronoi diagram are used to optimize the number of charging stations, and an improved particle swarm algorithm is used to determine the optimal location of charging stations. For the feasibility of the method, a simulation analysis is carried out with the travel data of some areas in a city. The simulation results show that the proposed method is effective. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
