Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

作     者：Yongqian Liu Han Wang Shuang Han Jie Yan Li Li Zixin Chen 

作者机构：State Key Laboratory of Alternate Electrical Power System with Renewable Energy SourcesNorthChina Electric Power UniversityBeijing102206P.R.China Chifeng Branch of China Datang CorporationChifeng City024000Inner Mongolia AutonomousRegionP.R.China 

出 版 物：《Global Energy Interconnection》 (全球能源互联网（英文版）)

年 卷 期：2019年第2卷第4期

页      面：318-327页

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0823[工学-交通运输工程] 

基　　金：supported in part by the National Key R&D Program of China (No.2017YFE0109000) the project of China Datang Corporation Ltd 

主　　题：Wind power Detailed volatility Frequency distribution Multiple temporal-spatial scales Typical days Forecasting accuracy 

摘      要：With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.