Dimension Reduction Method for Probabilistic Power Flow Computation Considering Wind Farms

With the large-scale integration of renewable energy sources into the power system, a new source of uncertainty is added to the operation planning problem. In this paper, the rank correlation coefficient is introduced to characterize the dependency among random variables in power flow equations, and Nataf transformation is used to map the probabilistic power flow (PPF) problem to the independent standard normal space. Dimension reduction model is introduced to approximation the function relationship between PPF inputs and outputs. Gauss-Hermite quadrature is used to obtain the statistical moments of the univariate function, whereby the statistical moments of outputs of power flow equations are obtained. Testing on an IEEE-118 system, the dimension reduction method is compared with Hong's point estimate method, it is found the dimension reduction method can improve the accuracy without extra computational burden.