Probabilistic Load Flow Calculation Based on Rank Correlation Coefficient of Input Random Variables

With numerous new energy resources integrated into the power system,the influences brought about by random variables have to be properly considered in the operation of power systems.Probabilistic load flow is one of the effective tools. In this paper,the method for probabilistic load flow considering the dependence among variables is studied.The Spearman rank correlation coefficient is used to model the dependence among variables,and the inherent relation between Latin hypercube sampling and rank correlation coefficient is analyzed.Latin hypercube sampling combined with genetic algorithm is proposed to solve probabilistic load flow.Simulation results show that the method has a better performance than others in describing the dependence between wind speeds,and is not influenced by different marginal distributions.Moreover,it can handle positive and non-positive rank correlation coefficient matrices.