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A new point estimate method for probabilistic load flow with correlated variables including wind farms

In this paper, a novel probabilistic load flow (PLF) method that combines Nataf transformation with Zhao's point estimate method (PEM) is proposed. The new method can deal with correlated non-normal input random variables (RVs) in PLF evaluation with improved accuracy and less computation time than existing methods. The proposed method is applied to a modified IEEE 118-bus test system with wind farms. A comparison with Monte Carlo Simulation (MCS) using correlated variables is presented. In the end, the sensitivity analysis considering different correlation coefficients of wind speed is shown.