Modelling of Wind Power Uncertainty Considering Heteroskedasticity Effect and Its Application in Power System Dispatching

被引：0
|
作者：
Li L. [1 ,2 ,3 ]
Miao S. [1 ,2 ,3 ]
Tu Q. [1 ,2 ,3 ]
Li Y. [1 ,2 ,3 ]
Li C. [1 ,2 ,3 ]
Duan S. [1 ,2 ,3 ]
机构：
[1] School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[2] State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan
[3] Key Laboratory of Electric Power Security and High Efficiency of Hubei Province (Huazhong University of Science and Technology), Wuhan
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2020年 / 44卷 / 08期
基金：
国家重点研发计划; 中国国家自然科学基金;
关键词：
Autoregressive integrated moving average (ARIMA); Dynamic Copula; Forecasting error; Generalized autoregressive conditional heteroskedasticity (GARCH); Unit commitment;
D O I：
10.7500/AEPS20190430016
中图分类号：
学科分类号：
摘要：
As the penetration rate of wind power in the power system continues to increase, its uncertainty poses a great challenge to the safe and economic operation of the power system. In order to obtain accurate wind power uncertainty model and help operators to achieve safe and economic operation of the system, this paper proposes a conditional probability distribution modelling method of wind power forecasting error considering the heteroskedasticity effect. Firstly, the dependence of wind power forecasting error and various factors is analyzed. Based on the results and the dynamic Copula theory, a dynamic dependence model of wind power forecast error is established. Then, based on the time-domain features displayed by the edge distribution, combined with the autoregressive integrated moving average (ARIMA) model and the generalized autoregressive conditional heteroskedasticity (GARCH) model, this paper develops a time-varying edge distribution model with the consideration of heteroskedasticity effect. Finally, the two models are combined to give the forecasting error distribution of wind power conditions at different fluctuation levels, and the verification is performed in the uncertain unit combination model, which proves the validity of the model. © 2020 Automation of Electric Power Systems Press.
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页码：36 / 47
页数：11
相关论文
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