Comparative Analysis of Hybrid GAPSO Optimization Technique With GA and PSO Methods for Cost Optimization of an Off-Grid Hybrid Energy System

Abstract

In this study, a new methodology, hybrid GAPSO (HGAPSO), has been developed to design and achieve cost optimization of an off-grid hybrid energy system (HES). Since standard particle swarm optimization (PSO) algorithm suffers from premature convergence due to low diversity, and genetic algorithm (GA) suffers from a low convergence speed, in this study modification strategies have been used in GAs and PSO algorithms to achieve the properties of higher capacity of global convergence and the faster efficiency of searching. This improved algorithm HGAPSO described and implemented in a MATLAB environment has been compared with GAs and PSO algorithms in finding the optimum minimum annual cost of a real off-grid energy system (a group of villages in India). The optimization process resulted in HES, utilizing photovoltaic (PV) arrays, batteries, a diesel generator, and other renewable sources, which, in turn, may prove to be a feasible and sustainable power supply alternative for a remote unelectrified rural area. The superiority of HGAPSO algorithm over GAs and PSO algorithms for the problem at hand is shown in terms of convergence generations and computation time.