Learning-based probabilistic power flow calculation considering the correlation among multiple wind farms

Abstract

Wind power generation provides a new route for the sustainable development of energy. However, with the large scale integration of wind farms, the volatile wind energy and the correlation among various wind speeds bring a considerable quantity of uncertainty for the power system operation. In this paper, a probabilistic power flow (PPF) analysis model for the power system incorporating the correlation among various wind speeds is proposed. As distinct from existing studies, in this paper, we introduce the relevance vector machine (RVM) into correlation modeling of wind speeds via historical learning samples to construct bivariate joint distribution. Compared with the conventional parameter estimation methods, the proposed method has higher flexibility and computational efficiency. On this basis, the regular vine copula approach is adopted further to build the multivariate joint distribution model of wind speeds. To calculate the PPF of power system with the integration of wind power, we employ the three-point estimation method (3PEM) while the Rosenblatt Transformation technique is proposed to transform the input variables into independent variables. The effectiveness of the proposed calculation framework is examined through simulation studies, and the obtained results illustrate the advantages of the proposed method.