Uncertainty Based Performance Evaluation of Wind-PV Integrated Distribution Network using MOGWO

Abstract

Major problem of distribution network is to allocate Distributed Generation (DG) optimally to enhance the performance of the system. In this research, two types of DG sources are ideally positioned in a 69-bus radial distribution network (RDN) under uncertainty to minimize network real power losses (RPL), maximize voltage control (VC), and improve voltage stability index (VSI). The uncertainty in power availability from photo voltaic (PV) and wind turbine (WT) DG sources along with load demand, have been simulated using the 2m point estimate approach (PEM). From the results obtained it is observed probabilistic approach provides more realistic results considering the uncertainties present in the RDN. In this paper comparative assessment of Grey Wolf Optimization (GWO) with Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional TLBO (QOTLBO) techniques have been performed. Results prove the efficacy of GWO algorithm over other existing techniques.