Coordinated Optimazation Strategy for Electric Vehicles' Charging and Discharging in Different Regions

被引：0
|
作者：
Hou H. [1 ]
Ke X. [1 ]
Wang C. [2 ]
Fan H. [1 ]
Luo J. [1 ]
机构：
[1] Institute of Automation, Wuhan University of Technology, Wuhan
[2] Operation and Maintenance Department, State Grid Hubei Electric Power Company, Wuhan
来源：
Gaodianya Jishu/High Voltage Engineering | 2018年 / 44卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Charge and discharge; Coordination optimization; EV; Genetic algorithm; Multiple target; Region;
D O I：
10.13336/j.1003-6520.hve.20180131040
中图分类号：
学科分类号：
摘要：
The charging and discharging behaviors of electric vehicle (EV), as a kind of moving load, will have profound influences on the power grid. In order to alleviate these influences, we researched the coordinated optimization strategy for EVs' charging and discharging. In a certain region including residential region and industrial-commercial region, we firstly studied the load demand of EVs' charging and discharging under the circumstance of minimum load fluctuation and the minimum economic cost. Then, we explored the relationship between the total charge and discharge power of user and the electricity price. Finally, we guided the intermediary side guides users to coordinate charging and discharging through the rebating policy. The results show that power load variance of the grid side is greatly decreased and the total charging and discharging costs obtained by the intermediary side are increased after the optimization of charging and discharging load through multi-objective genetic algorithm. The intermediary side will save costs to subsidize users, through the 0-1 optimization, reduce battery damage to save user fees, reduce waiting time for the user to bring convenience, while the intermediary side profits for reduced subsidy costs. Through the coordination of the grid side, the intermediary side and the user side can achieve optimization strategy of EVs' charging and discharging behaviors. The optimization strategy makes a complex optimization process to decompose into different modules, which not only satisfies the demands of every part but also makes the process more maneuverable. Finally, an example is given to verify the effectiveness of the proposed strategy. © 2018, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
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页码：648 / 654
页数：6
相关论文
共 27 条
  • [11] Liu X., Cui S., Xie F., Et al., Temporal and spatial distribution forecast for V2G discharge load based on parking demand model, Journal of Electrical Engineering, 10, 8, pp. 22-28, (2015)
  • [12] Han H., The study on the control strategy of V2G participating peak regulation and frequency regulation of the grid, (2011)
  • [13] Tan L., The research charge and discharge control strategy by electric car in V2G mode, (2015)
  • [14] Song X., Xu Z., Zheng D., Optimization strategy for restraining load fluctuation by electric vehicle in V2G mode, Shaanxi Electric Power, 40, 9, pp. 24-27, (2012)
  • [15] Wei D., Zhang C., Sun B., Et al., A time-of-use price based multi-objective optimal dispatching for charging and discharging of electric vehicles, Power System Technology, 38, 11, pp. 2972-2977, (2014)
  • [16] Xiang D., Song Y., Hu Z., Et al., Research on optimal time of use price for electric vehicle participating V2G, Proceedings of the CSEE, 33, 31, pp. 15-25, (2013)
  • [17] Dai S., Lei X., Cheng D., Et al., Study on electric vehicle charging and discharging TOU price, Power System and Clean Energy, 29, 7, pp. 77-82, (2013)
  • [18] Dai X., Yuan Y., Fu Z., Et al., Discharge strategy and economic benefits evaluation of electric vehicles in user side, Proceedings of the CSU-EPSA, 25, 6, pp. 55-61, (2013)
  • [19] Shama A., Shih S., Srinivasan D., A smart scheduling strategy for charging and discharging of electric vehicles, 2015 IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA), pp. 1-6, (2015)
  • [20] Yang X., Zhang Y., Ren S., Et al., Optimal charging and discharging dispatching strategy for electric vehicle based on customer's benefit, 2016 35th Chinese Control Conference (CCC), pp. 8507-8513, (2016)
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