基于实时电价的含电动汽车微电网两阶段优化调度  

Two-stage Optimization Scheduling of Microgrid with Electric Vehicles Based on Real-time Electricity Price

作　　者：徐嘉启 郭红霞[1] 邹桂林 XU Jia-qi;GUO Hong-xia;ZOU Gui-lin(School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China)

机构地区：[1]华南理工大学电力学院,广州510640

出　　处：《科学技术与工程》2023年第13期5571-5578,共8页Science Technology and Engineering

基　　金：国家自然科学基金重点项目(51937005)。

摘　　要：为解决大量电动汽车(electric vehicle,EV)无序充电对微电网负荷曲线产生新的峰值或峰上加峰等现象,提出了电动汽车入网技术(vehicle to grid,V2G)下两阶段优化方法,第一阶段考虑实时电价的前提下以满足用户充电需求为目标建立电动汽车有序充放电模型,第二阶段以微电网综合成本最低和微电网出力波动最小为目标,确定电动汽车有序充放电功率。为了解决多目标优化的问题,采用改进型多目标粒子群算法(improve multi-targeting particle swarm optimization,IMPSO)。为验证所提算法的有效性,用蒙特卡洛法模拟微电网内电动汽车的充电需求后采用所提算法优化,结果证明所提算法在降低微电网经济成本和出力波动的同时,降低了用户成本。To solve a large number of electric cars,disordered charge to generate new peak or peak and peak load curve,two phase optimization method were adopted,the first stage under the premise of considering the real-time electricity price charge in order to meet user needs as the goal to establish orderly electric vehicle charging and discharging model,the second phase of micro power grid economic benefit was the highest and the grid with target of minimizing the output fluctuation to determine the orderly charging and discharging power of electric vehicles.In order to solve the problem of multi-objective optimization,an improved multi-objective particle swarm optimization(IMPSO)algorithm was adopted.In order to verify the effectiveness of the proposed method,the Monte Carlo method was used to simulate the charging demand of electric vehicles in a microgrid,and the results of different scheduling methods,different optimization weights and different participation were compared.The results show that the proposed scheduling method reduces the economic cost and output fluctuation of the microgrid while reducing the user cost.

关 键 词：两阶段 实时电价 微电网 多目标粒子群 

分 类 号：TM73[电气工程—电力系统及自动化]