Comparative Analysis of Different Probabilistic Load Flow Methods: A Case Study on Ondokuz Mayis University Campus, Turkey

Abstract

Nowadays, the deterministic load flow (DLF) methods are not adequate to meet the expectations of power system operators because of the uncertainties and variations with the consideration of renewable energy sources in power systems. Since the DLF methods use specific values instead of the stochastic values, they cannot give reliable results under the uncertainty. Therefore, a probabilistic load flow (PLF) has been included in literature as a new title to fulfill the lack of DLF methods. In this study, a comparative analysis of the Monte Carlo simulation with Latin Hypercube sampling (LHS) and the Unscented Transform (UT) methods are presented based on the results obtained from the classical Monte Carlo (CMC) simulation method. Ondokuz Mayis University (OMU) campus in Turkey is selected as a test system to implement the proposed methods and to see the results. The results show that the UT approximate method is faster and more reliable than the LHS based MC simulation method.