A Copula-Based Uncertainty Modeling of Wind Power Generation for Probabilistic Power Flow Study

In this paper, a probabilistic power flow (PPF) modeling of the uncertainty of wind power generation is proposed. The developed PPF is based on the theories of point estimate method (PEM) and regular vine (R-vine) copula. For a large number of wind power resources, the computational burden related to construction of an R-vine copula increases significantly. Therefore, an algorithm based on Kullback-Leibler (KL) distance is introduced to tackle the computational burden to form an R-vine. The proposed PPF is tested on two different testbeds: IEEE 57-bus and Illinois 200-bus systems. The obtained results show that the proposed method is effective and accurate when compared to PPF results obtained by either a Monte Carlo simulation (MCS) or a multivariate Gaussian copula.