Forecasting Scenario Generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation

作     者：Qingyu Tu Shihong Miao Fuxing Yao Yaowang Li Haoran Yin Ji Han Di Zhang Weichen Yang Qingyu Tu;Shihong Miao;Fuxing Yao;Yaowang Li;Haoran Yin;Ji Han;Di Zhang;Weichen Yang

作者机构：State Key Laboratory of Advanced Electromagnetic Engineering and TechnologyHubei Electric Power Security High Efficiency Key LaboratorySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina Department of Electrical EngineeringTsinghua UniversityBeijingChina 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2021年第9卷第4期

页      面：837-848页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：This work was supported by the National Key Research and Development Program of China(No.2017YFB0902600) 

主　　题：Scenario generation wind farm regular vine Copula spatial-temporal correlation time-series characteristics 

摘      要：Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.