引用本文: | 徐杨杨,张新松,陆胜男,郭云翔.多重随机特性下的电动汽车充电网络机会约束规划[J].电力系统保护与控制,2021,49(6):30-39.[点击复制] |
XU Yangyang,ZHANG Xinsong,LU Shengnan,GUO Yunxiang.Chance constrained optimization of an electric vehicle charging network with multiple stochastic characteristics[J].Power System Protection and Control,2021,49(6):30-39[点击复制] |
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摘要: |
针对分布式光伏接入背景下的电动汽车(Electric Vehicle, EV)充电网络规划问题,建立了同时考虑分布式光伏出力与EV充电负荷随机特性的机会约束规划模型。首先,采用场景概率法分析配电系统规划典型日内的概率潮流特性。在此基础上,给出EV充电网络机会约束规划模型中的节点电压偏移机会约束与线路潮流越限机会约束。在充电站建设总数和最低建设容量给定的情况下,模型通过优化充电站的建设位置和建设容量,尽可能降低配电系统的网损。采用遗传算法对EV充电网络机会约束规划模型进行求解。此外,为提高遗传算法的寻优性能,面向EV充电网络机会约束规划模型的物理特征,对遗传算法中的变异操作算子和交叉操作算子进行了改进。基于IEEE 33 节点配电系统的仿真实验验证了所提方法的有效性。 |
关键词: 电动汽车 充电网络规划 随机特性 遗传算法 机会约束 |
DOI:DOI: 10.19783/j.cnki.pspc.200571 |
投稿时间:2020-05-22修订日期:2020-11-25 |
基金项目:国家自然科学基金项目资助(51877112);江苏省高等学校自然科学研究重大项目资助(18KJA470003);南通市基础科学研究项目资助(JC2019127) |
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Chance constrained optimization of an electric vehicle charging network with multiple stochastic characteristics |
XU Yangyang,ZHANG Xinsong,LU Shengnan,GUO Yunxiang |
(School of Electrical Engineering, Nantong University, Nantong 226019, China) |
Abstract: |
To realize a plan for Electric Vehicle (EV) charging networks with a background of distributed photovoltaics, a chance constrained optimization is developed, in which stochastic characters from EV charging loads and distributed photovoltaic output are considered simultaneously. First, a scenario-based probability method is used to investigate the probabilistic power flow of distribution systems during a typical planning day. On this basis, the chance constraints on the voltage deviations and power flow over limits in the optimization are established. Given the total number of EV charging stations and the minimum construction capacity, the optimization minimizes the network loss of the distribution system by optimizing the construction location and capacity of the EV charging stations. A Genetic Algorithm (GA) is used to establish the optimization. In addition, to enhance the performance of the GA, the mutation operator and the crossover operator in the GA are customized according to the physical characteristics of the chance constrained optimization. A simulation based on IEEE 33-bus distribution systems validates the formulation and method proposed in this paper.
This work is supported by the National Natural Science Foundation of China (No. 51877112), the Natural Science Key Research Program of Jiangsu Colleges (No. 18KJA470003) and the Basic Sciences Research Project of Nantong City (No. JC2019127). |
Key words: electric vehicle charging network planning stochastic characteristics genetic algorithm chance constraints |