Probabilistic load flow algorithms considering correlation between input random variables: A review

Abstract

Plenty of uncertainty and correlation factors exist in power systems. These factors have important influence on power system operation. The probabilistic load flow (PLF) algorithm considering correlation between input random variables is an efficacious tool to handle these factors. Three commonly used modeling techniques of correlated input random variables are analyzed, including Nataf transformation, polynomial normal transformation and Copula theory. The procedure, feature, advantage and disadvantage of different PLF algorithms, such as Monte Carlo simulation method, cumulant method and point estimate method, are reviewed considering correlation between input random variables.