| 引用本文: | 张新松,朱晨旭,李大祥,等.考虑截获交通流量与充电行驶距离的电动汽车充电网络规划[J].电力系统保护与控制,2024,52(17):40-50.[点击复制] |
| ZHANG Xinsong,ZHU Chenxu,LI Daxiang,et al.Electric vehicle charging network planning considering captured traffic flows and charging driving distance[J].Power System Protection and Control,2024,52(17):40-50[点击复制] |
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| 摘要: |
| 为优化电动汽车充电网络布局，提高充电服务能力与效率，提出了同时考虑截获交通流量与充电行驶距离的充电网络规划模型。电动汽车动力电池初始荷电状态的不确定性导致充电网络截获交通流量具有随机特性，采用蒙特卡洛模拟方法对其概率特性进行了分析。为提升充电网络在任何情况下的充电服务能力，所提模型以充电网络截获交通流量最小值最大为优化目标之一。为提升充电服务效率，模型另一个优化目标为平均充电行驶距离最短。此外，模型考虑了充电行驶距离机会约束及充电站建设数目约束，采用非支配遗传算法对所提模型进行求解，获得Pareto最优解集。最后，以25节点交通网络为例进行了仿真实验，验证了所提方法的有效性。并基于仿真结果，分析了机会约束置信度与充电站数目对规划结果的影响。 |
| 关键词: 电动汽车 截获交通流量 充电行驶距离 充电网络规划 非支配遗传算法 |
| DOI：10.19783/j.cnki.pspc.231537 |
| 投稿时间：2023-12-04修订日期：2024-05-03 |
| 基金项目:国家自然科学基金项目资助(52377104)；江苏省高校基础科学(自然科学)研究项目资助(22KJA470006) |
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| Electric vehicle charging network planning considering captured traffic flows and charging driving distance |
| ZHANG Xinsong,ZHU Chenxu,LI Daxiang,LUO Laiwu |
| (School of Electrical Engineering, Nantong University, Nantong 226001, China) |
| Abstract: |
| To optimize the layout of an electric vehicle charging network, and thus improving charging service capacity and efficiency, this paper develops a charging network planning model that considers both captured traffic flows and charging driving distance. The uncertainty of the initial state of charge of the electric vehicle power battery leads to stochastic characteristics of the captured traffic flows of the charging network. The Monte Carlo simulation method is used to analyze its probability characteristics. To improve the charging service capability in any situation, the model takes the maximum value of minimum traffic flows captured by the charging network as one of the optimization objectives. To upgrade charging efficiency, another optimization goal of the model is to minimize the average charging driving distance. In addition, the model considers the opportunity constraints of charging driving distance and the number of charging station construction constraints, and uses a non-dominated sorting genetic algorithm II (NSGA-II) to analyze the proposed model to obtain the Pareto optimal solution set. Finally, a simulation experiment is conducted on a 25-node transportation network to verify the effectiveness of the proposed method. Based on the results, the impact of chance constrained confidence and the number of charging stations on the solutions is analyzed. |
| Key words: electric vehicle captured traffic flows charging driving distance charging network planning nondominated sorting genetic algorithm II (NSGA-II) |
