电动汽车充放电优化管理分析
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摘要
随着电动汽车普及保有量逐年上升,电动汽车无序充放电将会对电力系统产生极大的冲击。文中首先对影响充电负荷的各个因素进行分析,建立数据特征函数,并通过最小二乘法拟合得出电动汽车的函数模型。然后,根据数学模型特征,对多个电动汽车的负荷进行规划,建立多目标优化函数,并使用蒙特卡洛算法构造概率过程从已知概率分布中抽样建立对电动汽车充电负荷曲线。通过仿真验证,证明提出的模型能通过少量样本精确的模拟大规模电动汽车接入状况。文中对电动汽车充放电优化问题进行的改良,能有效减少设备投资、降低无序充电的峰谷差,维护系统稳定运行。
With the increasing popularity of electric vehicles, the disordered charging and discharging of electric vehicles will have a great impact on the power system. Firstly, the factors affecting the charging load were analyzed, and the data feature function was established. The function model of electric vehicle had been fitted by least squares method. Secondly, according to the characteristics of the mathematical model, the load of multiple electric vehicles was planned. Then, the multi-objective optimization function was established by the Monte-Carlo algorithm used for constructing the probability process to sample the charging load curve of the electric vehicle from the known probability distribution. Finally, the simulation proved that the proposed model could accurately simulate the access situation of large-scale electric vehicles through a small number of samples. The improvement of the charging and discharging optimization problem of electric vehicles effectively reduced equipment investment, the peak-to-valley difference of disordered charging, and maintained stable operation of the system.
With the increasing popularity of electric vehicles, the disordered charging and discharging of electric vehicles will have a great impact on the power system. Firstly, the factors affecting the charging load were analyzed, and the data feature function was established. The function model of electric vehicle had been fitted by least squares method. Secondly, according to the characteristics of the mathematical model, the load of multiple electric vehicles was planned. Then, the multi-objective optimization function was established by the Monte-Carlo algorithm used for constructing the probability process to sample the charging load curve of the electric vehicle from the known probability distribution. Finally, the simulation proved that the proposed model could accurately simulate the access situation of large-scale electric vehicles through a small number of samples. The improvement of the charging and discharging optimization problem of electric vehicles effectively reduced equipment investment, the peak-to-valley difference of disordered charging, and maintained stable operation of the system.
引文
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[15] Wu D,Aliprantis D C,Ying L.Load scheduling and dispatch for aggregators of plug-in electric vehicles[J].IEEE Transactions on Smart Grid,2012,3(1):368-376.
[2] Li G,Zhang X P.Modeling of plug-in hybrid electric vehicle charging demand in probabilistic power flow calculations[J].IEEE Transactions on Smart Grid,2012,3(1):492-499.
[3] Ashtari A,Bibeau E,Shahidinejad S,et al.PEV charging profile prediction and analysis based on vehicle usage data[J].IEEE Transactions on Smart Grid,2012,3(1):341-350.
[4] 杨少兵,吴命利,姜久春,等.电动汽车充电站负荷建模方法[J].电网技术,2013,37(5):1190-1195.Yang Shaobing,Wu Mingli,Jiang Jiuchun,et al.An approach for load modeling of electric vehicle charging station[J].Power System Technology,2013,37(5):1190-1195.
[5] Bae S,Kwasinski A.Spatial and temporal model of electric vehicle charging demand[J].IEEE Transactions on Smart Grid,2012,3(1):394-403.
[6] 蔡世雷,孙刘杰.基于ZigBee与Web的移动支付充电桩设计[J].电子科技,2017,30(2):126-129.Cai Shilei,Sun Liujie.Design of mobile payment charging pile based on ZigBee and Web[J].Electronic Science and Technology,2017,30(2):126-129.
[7] 刘鹏,刘瑞叶,白雪峰,等.基于扩散理论的电动汽车充电负荷模型[J].电力自动化设备,2012,32(9):30-34.Liu Peng,Liu Ruiye,Bai Xuefeng,et al.Charging load model based on diffusion theory for electricvehicles[J].Electric Power Automation Equipment,2012,32(9):30-34.
[8] 周念成,熊希聪,王强钢.多种类型电动汽车接入配电网的充电负荷概率模拟[J].电力自动化设备,2014,3(2):1-7.Zhou Niancheng,Xiong Xicong,Wang Qianggang.Simulation of charging load probability for connection of different electric vehicles to distribution network[J].Electric Power Automation Equipment,2014,34(2):1-7.
[9] Qian K,Zhou C,Allan M,et al.Modeling of load demand due to EV battery charging in distribution systems[J].IEEE Transactions on Power Systems,2011,26(2):802-810.
[10] Guo Qinglai,Wang Yao,Sun Hongbin,et al.Factor analysis of the aggregate electric vehicle load based on data mining[J].Energies,2012,5(6):2053-2070.
[11] 陶耀寰,王少杰,张先帅,等.基于多代理的电动汽车充电站能量管理系统[J].电子科技,2015,28(12):59-61.Tao Yaohuan,Wang Shaojie,Zhang Xianshuai,et al.Multi agent based energy management system for electric vehicle charging station[J].Electronic Science and Technology,2015,28(12):59-61.
[12] Transport Nsw.Summary report of 2008 household travel survey[R].Sydney:The Transport Data Centre of Transport NSW,2010.
[13] 罗卓伟,胡泽春,宋永华,等.电动汽车充电负荷计算方法 [J].电力系统自动化,2011,35(14):36-42.Luo Zhuowei,Hu Zechun,Song Yonghua,et al.Study on plug-in electric vehicles charging load cal culating[J].Automation of Electric Power Systems,2011,35(14):36-42.
[14] 田立亭,史双龙,贾卓.电动汽车充电功率需求的统计学建模方法[J].电网技术,2010,34(11):126-130.Tian Liting,Shi Shuanglong,Jia Zhuo.A statistical model for charging power demand of electric vehicles [J].Power System Technology,2010,34(11):126-130
[15] Wu D,Aliprantis D C,Ying L.Load scheduling and dispatch for aggregators of plug-in electric vehicles[J].IEEE Transactions on Smart Grid,2012,3(1):368-376.