Vortex search and Chu-Beasley genetic algorithms for optimal location and sizing of distributed generators in distribution networks: A novel hybrid approach

Abstract

In this study, we analyzed the optimal location and sizing of distributed generators (DGs) in radial distributed networks using a hybrid master-slave metaheuristic technique. The master stage corresponds to the selection of suitable points for the locations of the DGs, whereas the slave stage is the optimal dimensioning problem. The Chu-Beasley genetic algorithm (CBGA) is employed to solve the master stage, and the optimal power flow (OPF) method via the vortex search algorithm (VSA) is employed to solve the slave stage. The OPF solution from the VSA technique uses a successive approximation power flow to determine the voltage profiles and power losses by guaranteeing the energy balance in all the nodes of the network. The conventional and widely used 33- and 69-node test feeders are used to validate the hybrid CBGA-VSA for analyzing the optimal location and sizing of the DGs in the distribution networks using MATLAB software. The numerical results demonstrate the efficiency of the proposed optimization method in terms of power loss reduction as compared with the results available in the literature. An additional 24-h dimensioning analysis is included for demonstrating the efficiency and applicability of the proposed methodology for daily operations with renewable generation.