多场景下EV充电站与DG多目标规划  

Multi-objective Planning for EV Charging Station and DG Charging Station in Multiple Scenarios

作　　者：陈贤达 江修波[1] CHEN Xian-da;JIANG Xiu-bo(College of Electrical Engineering and Automation,Fuzhou University,Fujian 350108,China)

机构地区：[1]福州大学电气工程与自动化学院,福建福州350108

出　　处：《电气开关》2020年第3期54-59,108,共7页Electric Switchgear

摘　　要：一直依赖化石能源不是长久之计,可再生分布式电源(Distributed Generation,DG)及电动汽车(Electric Vehicle,EV)作为两大新能源技术,受到各国的重视。但是大规模的EV与DG接入配电网会造成许多影响,在进行配电网规划时需要加以考虑,文中提出了一种多场景下EV充电站与DG的联合规划模型,利用K-means算法对一年内的DG出力与常规负荷数据进行聚类,然后利用蒙特卡洛方法预测电动汽车的充电负荷,最后构建以网损最小、负荷波动最小为目标函数的多目标规划模型,以改进的人工蜂群-粒子群算法进行求解,结合IEEE-33节点配电网进行仿真分析,验证了模型的有效性与可行性。Thas not been a long-term solution to rely on fossil energy.Renewable Distributed Power(DG)and Electric Vehicle(EV)are two new energy technologies that have received attention from all countries.However,large-scale EV and DG access to the distribution network will have many impacts.It needs to be considered when planning the distribution network.This paper proposes a joint planning model of EV charging station and DG in multiple scenarios,using K-means.The algorithm clusters DG output and conventional load data in one year,then uses Monte Carlo method to predict the charging load of electric vehicle,and finally constructs multi-objective function with minimum network loss and minimum load fluctuation to improve artificial bee colony-The particle swarm optimization algorithm is used to solve the simulation and the simulation analysis of the IEEE-33 node distribution network is carried out to verify the validity and feasibility of the model.

关 键 词：EV充电站 分布式电源 人工蜂群 多目标 

分 类 号：TM91[电气工程—电力电子与电力传动]