Charging load forecasting method based on instantaneous charging probability for electric vehicles

被引：0
作者：
Wang H. [1 ]
Zhang Y. [1 ]
Mao H. [1 ]
机构：
[1] School of Electric Power, South China University of Technology, Guangzhou
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2019年 / 39卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Charging load forecasting; Electric vehicles; Models; Monte Carlo method; Probability model; Time;
D O I：
10.16081/j.issn.1006-6047.2019.03.033
中图分类号：
学科分类号：
摘要：
The charging load forecasting of EVs(Electric Vehicles) plays an important role in the promotion of EVs. In order to overcome the shortcomings of subjective setting of some parameters and lack of matching between the forecasting model and the random driving behaviors of EV users, the EVs are particularly classified, the probability model of influencing factor for charging load forecasting is established, and the charging load forecasting method based on instantaneous charging probability is proposed by using the probability statistics and Monte Carlo simulation method. The charging duration is derived by the daily mileage obtained by scientific analysis instead of the subjectively given initial SOC(State Of Charge) and the charging load is determined by using the more random instantaneous charging probability instead of the calculated charging period. Taking a city as the example, the daily charging load curve of related EVs is forecasted and compared with the results of common load forecasting methods, which verifies that the proposed load forecasting method can scientifically forecast the EV users' charging load and provide a reliable basis for power management strategy for power grid and users. © 2019, Electric Power Automation Equipment Press. All right reserved.
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收藏
页码：207 / 213
页数：6
相关论文
共 17 条
  • [1] Li L., Zhang Y., Chem Z., Et al., Merger between smart grid and energynet: mode and development prospects, Automation of Electric Power Systems, 40, 11, pp. 1-9, (2016)
  • [2] Islam M.S., Mithulananthan N., Hung D.Q., A day-ahead forecasting model for probabilistic EV charging loads at business premises, IEEE Transactions on Sustainable Energy, 9, 2, pp. 741-753, (2018)
  • [3] Nguyen H., Hansen C.K., Short-term electricity load forecasting with time series analysis, 2017 IEEE International Conference on Prognostics and Health Management, pp. 214-221, (2017)
  • [4] Gao C., Zhang L., A survey of influence of electrics vehicle charging on power grid, Power System Technology, 35, 2, pp. 127-131, (2011)
  • [5] Xu Z., Hu Z., Song Y., Et al., Coordinated charging of plug-in electric vehicles in charging stations, Automation of Electric Power Systems, 36, 11, pp. 38-43, (2012)
  • [6] Liu W., Xu X., Zhou X., Daily load forecasting based on SVM for electric bus charging station, Electric Power Automation Equipment, 34, 11, pp. 41-47, (2014)
  • [7] Zhu H., Yang X., Chen Y., Overview of the charging load forecasting methods of plug-in electric vehicles, Electric Power Information and Communication Technology, 14, 5, pp. 44-47, (2016)
  • [8] Liu Q., Qi Z., Electric vehicles load forecasting model based on Monte Carlo simulation, Electric Power Science and Engineering, 30, 10, pp. 14-19, (2014)
  • [9] Luo Z., Hu Z., Song Y., Et al., Study on plug-in electric vehicles charging load calculating, Automation of Electric Power Systems, 35, 14, pp. 36-42, (2011)
  • [10] Shao S.N., Pipattanasomporn M., Rahman S., Challenges of PHEV penetration to the residential distribution network, 2009 IEEE Power & Energy Society General Meeting, pp. 1-8, (2009)
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