PROBABILISTIC DISTRIBUTION BASED INTERVAL ARITHMETIC LOAD FLOW ALGORITHM FOR RADIAL DISTRIBUTION SYSTEM

Abstract

Load flow is an essential tool for the study of radial distribution systems (RDS). The inputs to load flow solutions are subject to uncertainties due to load and line parameter variations. Unfortunately, load flow research has to date considered only fixed load and line parameters; inherent uncertainties of the inputs were overlooked. In this article probabilistic distribution-based interval arithmetic approach is proposed to incorporate variations in load parameter. The uncertainties of load variation are represented as Gaussian distribution function. The proposed load flow is tested on standard test systems; the authors find that solutions obtained provide much wider information and all possible solution states are obtained in the closed bounded interval form. It is suggested that proposed power flow could be useful for planning and expansion planning of RDS, where future data always carry a high degree of uncertainties. Composite load model is also incorporated in the algorithm.