Probabilistic Load Flow Calculation Based on Cumulant Method Considering Correlation Between Input Variables

Probabilistic load flow(PLF) calculation is an important tool for system steady state performance analysis.Traditional PLF based on cumulant method(PLF-CM) requires that the input variables should be independent,so it can’t be directly applied to the circumstance in which the input variables are correlated.Therefore,a novel PLF-CM considering the correlation between input variables based on Cholesky decomposition was proposed.In order to solve the problem that the cumulants of some input variables are hard to be obtained by conventional numerical method,a method based on Monte Carlo sampling was proposed,which calculated the cumulants of input variable by its sample.The modified IEEE 14-bus system was used in the simulation.The simulation results verified the effectiveness,accuracy and practicability of the propose method.The impacts of wind speed correlation(WSC) on power system operation characteristic were investigated by the proposed method and the results show that the system operation is affected by WSC.