Correlation modeling of multiple wind farms based on piecewise cloud representation and regular vine copulas. (December 2020)

• Record Type: Journal Article
• Authors: Qu, Kai; Si, Gangquan; Yang, Zeyu; Huang, Yuehui; Li, Pai
• Is Part Of: Energy reports. Volume 6(2020)Supplement 9
• Page Start: 289
• Page End: 297
• Publication Date: 2020-12
• Subjects: Multiple wind farms -- Correlation -- Piecewise cloud representation -- Vine copulas; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2020.11.239
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: The accuracy of correlation modeling for multiple wind farms will directly affect the assessment results of absorption capacity in electric utilities. Due to the rapid increase in installed capacity of wind power in recent years, there are often multiple wind farms with complex correlations in a region, and traditional correlation models are inaccurate and highly computational. In order to enhance the accuracy of correlation modeling for multiple wind farms, this paper combines the piecewise cloud representation and regular vine (R-vine) copulas for classification modeling purposes. The piecewise cloud representation is used to divide the multiple wind farms' data into different categories, and the correlation models of different categories are established based on the R-vine copulas. A case of the SCADA system record data of 6 wind farms in Northwest China has been adopted to evaluate the effectiveness compared with its competitors. Case studies have demonstrated that the proposed method not only has good performances on modeling the correlation better than the traditional method but also has strong robustness, especially in the case that the correlation of different wind farms is inconsistent. Most importantly, this model is able to extract the correlations of different features in multiple wind farms' data and then targeted modeling.