风光发电与电动汽车充放电协同优化调度  

Collaborative Optimization Scheduling of Wind and Photovoltaic Generation and Electric Vehicle Charge and Discharge

作　　者：杨秀茹 郭兴众[1] 王昊 YANG Xiuru;GUO Xingzhong;WANG Hao(Advanced Equipment Advanced Perception and Intelligent Control Ministry of Education Key Laboratory,Anhui Polytechnic University,Wuhu 241000,China)

机构地区：[1]安徽工程大学高端装备先进感知与智能控制教育部重点实验室,安徽芜湖241000

出　　处：《四川轻化工大学学报（自然科学版）》2021年第5期55-61,共7页Journal of Sichuan University of Science & Engineering(Natural Science Edition)

基　　金：安徽省自然科学基金面上项目(2008085MF208)。

摘　　要：针对大规模电动汽车无序接入电网给电力系统带来的负荷压力问题,提出一种含不确定性电源的电动汽车有序充放电控制策略。首先,采用电动汽车与电网(V2G,Vehicle-to-grid)互动模式解决电网中光伏发电和风力发电的不稳定性与易波动性。其次,在分时电价策略的引导下,以降低电网等效日负荷波动与减少电动汽车充放电成本为目标函数,建立一个考虑风光发电与电动汽车协调优化的多目标调度模型。最后,采用基于帕累托(Pareto)最优解集的多目标粒子群算法(MOPSO,multi objective particle swarm optimization)对该模型进行求解。通过算例对比分析表明,该模型不仅能平抑负荷波动,而且能降低用户成本,使电网与用户互利共赢。Aiming at the problem of the load pressure caused by large-scale electric vehicle disorderly access to power grid,an orderly charge-discharge control strategy of electric vehicle with uncertain power supply is proposed.Firstly,in order to overcome the unstable and volatile characteristics of photovoltaic power generation and wind power generation in the power grid,the interaction mode between electric vehicle and power grid(V2G,Vehicle-to-grid)is adopted.Secondly,under the guidance of the time-sharing price strategy,a multi-objective scheduling model considering the coordinated optimization of wind and photovoltaic generation and electric vehicle is established,using an objective function composed of the reducing equivalent daily load fluctuation and the charge and discharge cost of electric vehicles.Finally,a multi-objective particle swarm optimization(MOPSO,multi-objective particle swarm optimization)based on the Pareto optimal solution set is used to solve the model.The comparative analysis shows that the model can not only suppress the load fluctuation,but also reduce the cost for users,making power grid and users achieve mutual benefit and win-win.

关 键 词：风光发电 电动汽车 有序充放电 多目标粒子群算法 帕累托最优解 

分 类 号：TM73[电气工程—电力系统及自动化]