| 引用本文: | 刘丽军,吴桐,陈贤达,郑文迪,徐启峰.基于时空特性以及需求响应的DG和EV充电站多目标优化配置[J].电力自动化设备,2021,41(11): |
| LIU Lijun,WU Tong,CHEN Xianda,ZHENG Wendi,XU Qifeng.Multi-objective optimal allocation of DG and EV charging station based on space-time characteristics and demand response[J].Electric Power Automation Equipment,2021,41(11): |
|
| 摘要: |
| 针对可再生分布式电源(DG)及电动汽车(EV)大规模接入给配电网带来的用电量增长以及电压波动问题，提出一种基于时空特性以及需求响应的DG和EV充电站多目标协调优化配置方法。通过提取城市路网的拓扑结构，监测路网流量，基于交通规划软件TransCAD进行起讫点(OD)矩阵反推，构建出行概率矩阵以描述用户的出行特性；基于蒙特卡洛方法模拟EV的时空分布特性，考虑EV、DG与常规负荷的时序特性，并基于改进K-means算法构建风-光-负荷的典型运行场景；兼顾电网侧与用户侧，以综合效益、系统负荷波动以及充电耗时成本为目标，构建DG和EV充电站的多目标联合配置模型，并采用改进粒子群优化算法进行求解。结合IEEE 33节点配电网与某城区主干道路网模型进行仿真分析，结果验证了所建模型的有效性与可行性。 |
| 关键词: 分布式电源 电动汽车 充电站 时空分布 需求响应 起讫点矩阵 多目标优化配置 |
| DOI：10.16081/j.epae.202109003 |
| 分类号:U469.72;TM761 |
| 基金项目:福建省自然科学基金资助项目(2017J01480) |
|
| Multi-objective optimal allocation of DG and EV charging station based on space-time characteristics and demand response |
|
LIU Lijun1,2, WU Tong1, CHEN Xianda3, ZHENG Wendi1,2, XU Qifeng1
|
|
1.College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China;2.Fujian Key Laboratory of New Energy Generation and Power Conversion, Fuzhou 350108, China;3.Fuzhou Power Supply Company of State Grid Fujian Electric Power Co.,Ltd.,Fuzhou 350009, China
|
| Abstract: |
| Aiming at the power consumption increase and voltage fluctuation problems caused by the large-scale access of renewable DG(Distributed Generation) and EV(Electric Vehicle) to the distribution network, a multi-objective coordinated and optimal allocation method of DG and EV charging station is proposed based on space-time characteristics and demand response. By extracting the topology structure of urban road network and monitoring the traffic flow of road network, the OD(Origin-Destination) matrix is backward deduced based on the traffic planning software TransCAD, and the trip probability matrix is constructed to describe users’ trip characteristics. Based on the Monte Carlo method, the time-space distribution characteristics of EV are simulated, the timing characteristics of EV, DG and conventional load are considered, and typical wind-photovoltaic-load operation scenarios are constructed based on the improved K-means algorithm. Taking both the grid side and the user side into account, a multi-objective joint allocation model of DG and EV charging station is built with the goals of comprehensive benefit, system load fluctuation and charging time cost, which is solved by using the improved particle swarm optimization algorithm. The simulation analysis is carried out by combining the IEEE 33-bus distribution network and an urban main road network model, and the results verify the validity and feasibility of the proposed model. |
| Key words: distributed power generation electric vehicles charging station time-space distribution demand response OD matrix multi-objective optimal allocation |
