Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

作     者：Cheng Yan Yi Tang Jianfeng Dai Chenggen Wang Shengjun Wu 

作者机构：School of Electrical EngineeringSoutheast UniversityNanjingChina College of AutomationNanjing University of Posts and TelecommunicationsNanjingChina State Grid Jiangsu Electric Power Co.Ltd.Electric Power Research InstituteNanjingChina 

出 版 物：《Protection and Control of Modern Power Systems》 (现代电力系统保护与控制（英文）)

年 卷 期：2021年第6卷第1期

页      面：276-288页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0807[工学-动力工程及工程热物理] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Science and Technology Project of State Grid Corporation of China(State Grid Jiangsu Electric Power Research Institute Power Coordinated Control Technology Research Service for Energy Storage and New Energy Power Stations in the Black Start Process Contract Number:SGJSDK00XTJS2000357) 

主　　题：Inertial response Primary frequency control Error distribution Mixed skew generalized error distribution Uncertainty modeling 

摘      要：Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.