A modified multi-objective particle swarm optimization (M-MOPSO) for optimal sizing of a solar–wind–battery hybrid renewable energy system

Abstract

This study proposes and utilizes a modified multi-objective particle swarm optimization (M-MOPSO) algorithm for the optimal sizing of a solar-wind-battery hybrid renewable energy system for a rural community in Rivers State, Nigeria. Unlike previous studies that primarily focused on minimizing total economic cost (TEC) and total annual cost (TAC), this research emphasizes minimizing the loss of power supply probability (LPSP) and levelized cost of energy (LCOE). The M-MOPSO algorithm introduces a dynamic inertia weight, a unique repository update mechanism, and a dominance-based personal best update strategy, which collectively enhance its performance. Comparative analysis with PSO, NSGA-II, MOPSO and hybrid GA-PSO demonstrates that M-MOPSO consistently achieves a lower LPSP, although its LCOE remains higher. The M-MOPSO optimal configuration when simulated under various climatic scenarios was able to meet the energy needs of the community irrespective of ambient condition.