An improved transformation based probabilistic load flow analysis using appropriate reference variable

Abstract

Power systems are faced with a huge number of uncertainties, which should be addressed in the operational analyses. In this paper, an improved transformation method is used to consider the uncertainties of random variables in probabilistic load flow (PLF) problems. This method transforms non-normal input random variables with non-linear dependence to appropriate independent reference variables. The proposed transformation overcomes the point estimation method (PEM) limitations in estimating high statistical moments of PLF outputs. The proposed PLF method is examined on IEEE 14-bus and 118-bus test systems, consisting of uncertain loads, wind farms, and PV power plant generation. The results of Monte Carlo Simulation (MCS) are used as a benchmark. The results are compared with Nataf transformation, Rotational transformation, and particle swarm optimization clustering methods to prove the accuracy and efficiency of the proposed method.