1.上海交通大学电气工程学院,上海市 200240
2.国网江苏省电力公司电力科学研究院,江苏省南京市 211103
陶雯(2001—),女,硕士研究生,主要研究方向:电力系统运行。E-mail:cloud10@sjtu.edu.cn
徐潇源(1989—),男,通信作者,博士,副教授,主要研究方向:电力系统不确定性分析与运行、电力-交通系统协调优化。E-mail:xuxiaoyuan@sjtu.edu.cn
严正(1964—),男,教授,博士生导师,主要研究方向:电力系统优化运行、电力系统稳定分析及智能电网。E-mail:yanz@sjtu.edu.cn
收稿:2025-07-17,
修回:2026-01-12,
网络首发:2026-06-04,
移动端阅览
陶雯,徐潇源,严正等.考虑电动汽车充电站负荷相关性的充电站容量优化分配方法[J/OL].电力系统自动化, http://dx.doi.org/10.7500/AEPS20250717001.
TAO Wen,XU Xiaoyuan,YAN Zheng,et al.Optimal Capacity Allocation Method for Charging Stations Considering Correlation of Electric Vehicle Charging Station Loads[J/OL].Automation of Electric Power Systems, http://dx.doi.org/10.7500/AEPS20250717001.
陶雯,徐潇源,严正等.考虑电动汽车充电站负荷相关性的充电站容量优化分配方法[J/OL].电力系统自动化, http://dx.doi.org/10.7500/AEPS20250717001. DOI:
TAO Wen,XU Xiaoyuan,YAN Zheng,et al.Optimal Capacity Allocation Method for Charging Stations Considering Correlation of Electric Vehicle Charging Station Loads[J/OL].Automation of Electric Power Systems, http://dx.doi.org/10.7500/AEPS20250717001. DOI:
在电动汽车充电站容量优化配置研究中,现有工作存在充电负荷不确定性表征保守和运行阶段充电站容量分配不足的问题。为此,文中提出考虑电动汽车充电站负荷相关性的充电站容量优化分配方法。首先,采用顶点连接算法构建充电负荷多面体不确定集,刻画不同充电站负荷变化的相关性,降低不确定集的保守性。然后,为了根据配电网运行约束分配充电站容量,引入充电站容量削减系数作为决策变量限制容量,建立充电负荷的决策依赖不确定性模型以及充电站容量优化分配的决策依赖鲁棒优化问题。最后,采用改进Benders分解算法与KKT条件求解优化问题,获得各个充电站容量分配方案。仿真结果表明,考虑充电负荷相关性使得充电站容量配置提升了4.29%,配电网运行成本降低了11.6%;同比削减容量策略避免了自由削减策略中不同充电站容量削减比例差超过30%的情况,为充电站运营提供了公平方案;差异受限的自由削减策略可在约束站间削减差异的前提下进一步降低成本,在经济性与公平性之间实现平衡。
In the research on optimal capacity allocation of electric vehicle charging stations
existing studies suffer from conservative characterization of charging load uncertainty and inadequate capacity allocation of charging stations during the operation stage. To address these issues
this paper proposes an optimal capacity allocation method for charging stations considering the correlation of electric vehicle charging station loads. First
the vertex link algorithm is adopted to construct a polyhedral uncertainty set of charging loads
which characterizes the correlation of load variations among different charging stations and reduces the conservativeness of the uncertainty set. Second
to allocate charging station capacity subject to distribution network operation constraints
a capacity curtailment coefficient of charging stations is introduced as a decision variable to limit capacity. A decision dependent uncertainty model of charging loads and a decision dependent robust optimization problem for optimal capacity allocation of charging stations are established. Finally
the improved Benders decomposition algorithm and KKT (Karush-Kuhn-Tucker) conditions are used to solve the optimization problem and obtain the capacity allocation scheme for each charging station. Simulation results demonstrate that considering charging load correlation increases the capacity allocation of charging stations by 4.29% and reduces the operation cost of the distribution network by 11.6%. Compared with the free curtailment strategy
the proportional capacity curtailment strategy avoids the scenario where the difference in capacity reduction ratios among different charging stations exceeds 30%
providing a fair scheme for charging station operation. The free curtailment strategy with bounded differences can further reduce costs while constraining the inter-station curtailment discrepancy
achieving a balance between economy and fairness.
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