Enhancements to the Cumulant Method for probabilistic load flow studies

This paper introduces two enhancements to the Cumulant Method (CM) for probabilistic load flow studies. The first one is to handle the correlation between input random variables. This enhancement models the correlated input random variables as a function of several independent ones by Cholesky decomposition and modifies CM equations. The second one is to adopt Monte Carlo sampling techniques to calculate the cumulants of input random variable with complex distribution function. The accuracy and efficiency of the proposed approaches are verified against Monte Carlo simulation method on modified IEEE 14-bus system. The impacts of wind speed correlation on power system operation are investigated by the proposed approaches.