摘要
传统分时电价(time-of-usetariff,TOUT)和实时电价(real-timeprice,RTP)需求响应机制均会在负荷低谷时段产生新的负荷高峰,针对这一问题,考虑电网侧的调峰需求以及不同用户对充电电量、充电成本的不同需求和参与意愿,提出一种分时电价动态优化方法。所提方法根据每辆电动汽车(electricvehicle,EV)接入电网时的负荷信息动态更新该EV的峰谷电价,弥补了TOUT和RTP充电方式的缺点。基于所提分时电价动态优化方法,通过建立充电电量最多和充电成本最小多目标函数,采用粒子群算法对每辆EV的充(放)电行为进行两阶段优化,并通过引入虚拟荷电状态对优化后的充(放)电行为进行修正,由每位用户自主响应实现EV的有序充(放)电。为验证所提方法的有效性,基于2017年全美家用车辆调查结果(NHTS2017),采用蒙特卡洛法模拟某居民区1000辆EV的充电需求,并对不同充电策略、不同优化权重、不同参与度和不同V2G(vehicle to grid)响应度下的充电需求进行了仿真分析,结果表明,相较于其他充电策略,所提优化策略可以明显降低用户的充电成本和负荷曲线的峰谷差。
Both the traditional time-of-use tariff(TOUT)and the real-time price(RTP) demand response mechanism will generate new load peaks during the low load period. To solve this problem, considering the demand for peak load regulating on the grid side and the different needs and willingness of different users for charging capacity and charging costs, a dynamic optimization method for TOUT was proposed. The proposed method dynamically updateed the peak-to-valley price of each electric vehicle(EV) based on the load information when the EV was connected to the grid, which made up for the shortcomings of the TOUT and RTP charging methods. Based on the proposed dynamic o p t i mi z a t i o n me t h o d o f TO U T, by e s t a b l i s h i n g a multi-objective function with the most charging capacity and the least charging cost, the particle swarm optimization(PSO) was used to optimize the charging(discharging)behavior of each EV in two stages. And by introducing a virtual state of charge(SOC) to modify the optimized charging(discharging) behavior, each user autonomously responded to realize the coordinated charging(discharging)of the EV. To verify the effectiveness of the proposed method, based on the results of the 2017 National Household Vehicle Survey(NHTS2017), the Monte Carlo(MC) method was used to simulate the charging demand of 1,000 EVs in a residential area. And the charging demand under different charging strategies, different optimization weights, different participation levels and different V2G(vehicle to grid)responsiveness was simulated and analyzed. The results show that compared with other charging strategies, the proposed optimization strategy can significantly reduce the user’s charging cost and the peak-to-valley difference of the load curve.
作者
张良
孙成龙
蔡国伟
黄南天
吕玲
ZHANG Liang;SUN Chenglong;CAI Guowei;HUANG Nantian;LYU Ling(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education(Northeast Electric Power University),Jilin 132012,Jilin Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2022年第5期1837-1851,共15页
Proceedings of the CSEE
基金
吉林省科技厅国际科技合作项目(20210402080GH)。
关键词
电动汽车
有序充放电
优化策略
粒子群算法
electric vehicle
coordinated charging and discharging
optimization strategy
PSO algorithm
