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.

引用

页码：207 / 213

页数：6

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