电动汽车充放电优化管理分析

doi:10.16180/j.cnki.issn1007-7820.2019.06.013

Abstract

Abstract:

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.

Key words: electric vehicle, data fitting, multi-objective optimization, Monte-Carlo algorithm, optimization management, charge and discharge model

CLC Number:

TN91

SHAN Zhifei,SUN Yuxuan,ZHANG Xiang,CHEN Qiang,ZHENG Zhiyi. Research on Optimization Management of Electric Vehicle Charging and Discharging[J].Electronic Science and Technology, 2019, 32(6): 64-69.

Figures/Tables 9

Figure 1.

Figure 2.

Figure 3.

Table 1

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Table 2

References 15

[1]	石飞飞, 卢曦, 刘斌 . 电动汽车动力性及经济性能仿真研究[J]. 电子科技, 2016,29(2):25-29,33.
	Shi Feifei, Lu Xi, Liu Bin . Simulation research on dynamic and economic performance of electric vehicles[J]. Electronic Science and Technology, 2016,29(2):25-29,33.
[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. doi: 10.1109/TSG.2011.2172643
[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. doi: 10.1109/TSG.2011.2162009
[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. doi: 10.1109/TSG.2011.2159278
[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. doi: 10.1109/TPWRS.2010.2057456
[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. doi: 10.3390/en5062053
[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. doi: 10.1109/TSG.2011.2163174

电动汽车数量/万辆	年用电量/kWh
1	5.12×10⁷
8 000	4.10×10¹¹

Research on Optimization Management of Electric Vehicle Charging and Discharging

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 15

Related Articles 15

Metrics

Comments

Recommended 7

[1]	JIANG Chengcheng,ZHU Jian'an,ZHU Chengming,WANG Ke,ZHANG Wenjun. Research on EV Modeling in V2G Mode [J]. Electronic Science and Technology, 2022, 35(2): 74-78.
[2]	LU Huan. Research on Improving the Efficiency of Parallel Charger Based on Constraint Optimizations [J]. Electronic Science and Technology, 2020, 33(8): 53-58.
[3]	JIANG Qin,ZHANG Xuanxiong. Parameter Identification and State of Charge Estimation of Lithium Ion Battery Model for Electric Vehicles [J]. Electronic Science and Technology, 2020, 33(2): 32-36.
[4]	SHI Xun,YI Yingping,WANG Xiaoli. Research on Flux Weakening Control Technology of Interior Permanent Magnet Synchronous Motor [J]. Electronic Science and Technology, 2020, 33(2): 14-19.
[5]	ZHANG Jinwei,LI Guoping. Economic Operation of Microgrid Considering Operational Risk and Customer Satisfaction [J]. Electronic Science and Technology, 2019, 32(3): 26-31.
[6]	YU Hui,WANG Yujia,CHEN Qiang,XIAO Shanli. Multi-Objective Particle Swarm Optimization Based on Multi-population Dynamic Cooperation [J]. Electronic Science and Technology, 2019, 32(10): 28-33.
[7]	XIAO Shan-Li, WANG Yu-Jia, XU Hui. Multi-Objective Particle Swarm Optimization Based on Dimensional Learning for Solving the Disassembly Line Balancing Problem [J]. , 2018, 31(3): 5-.
[8]	WANG Wen-Tao, XU Zhong-Hai. Optimization Design of Electro-Permanent Magnetic Chuck Based on Response Surface Method [J]. , 2018, 31(1): 67-.
[9]	SUN Jinglun,ZHOU Ping,SUN Yuedong. Parameters Matching and Simulation for Power Train of Pure Electric Vehicle [J]. , 2016, 29(1): 51-.
[10]	MA Wei,ZHANG Honghao,DONG Pengju. Research on Electric Vehicle Battery Monitoring and Early Warning Systems Based on LabVIEW [J]. , 2015, 28(9): 115-.
[11]	TAO Yaohuan,Wang Shaojie,ZHANG Xianshuai,WANG Wenhua. Design of Energy Management System for Electric Vehicle Charging Station Based on Multi Agent [J]. , 2015, 28(12): 59-.
[12]	CHEN Xueqiang,JIN Peng,LIN Peng. Design of a Simulation Experiment for Electric Vehicle Power Battery's Working Condition [J]. , 2014, 27(10): 142-.
[13]	ZHOU Yawei,NIE Jianhua. Efficiency Optimization of the EV-motor Control System [J]. , 2013, 26(5): 37-.
[14]	SONG Tong, ZHUANG Yi, GUO Yun. A Multi-objective Optimization Algorithm Based on Differential Evolution with the Bidirectional-search Mechanism [J]. , 2012, 25(5): 119-.
[15]	SHANG Bao-Wei, LIANG Tao-Nian, LI Xuan. ABS Control System of Hybrid Electric Vehicle Based on the SAE J1939 Protocol [J]. , 2011, 24(11): 90-.