An improved transformation based probabilistic load flow analysis using appropriate reference variable. (September 2020)

• Record Type: Journal Article
• Authors: Rostami, Amir; Mohammadi, Mohammad; Rastegar, Mohammad
• Is Part Of: International journal of electrical power & energy systems. Volume 120(2020)
• Publication Date: 2020-09
• Subjects: Probabilistic load flow -- Random variable -- Correlation -- Appropriate reference variable; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106052
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Overcomes the (PEM) limitations in estimating high statistical moments of PLF outputs. Transforms non-linear dependence input random variables to independent reference variables. The proposed method leads accurate results in comparison with MCS, at higher speed. The average RE of proposed method is smaller than those obtained by NPEM and RPEM. The simulation times are almost the same by applying proposed method and NPEM. 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.