Exploring the Optimal Battery Sizing in Grid-Connected PV Systems: A Comparative Study of PSO and GA in Oax, Mx

Abstract

The optimal sizing of Battery Energy Storage Systems is crucial for maximizing the efficiency and output of grid-connected photovoltaic (PV) systems. This study explores Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), both aimed at minimizing total system costs. The analysis evaluates several performance indicators, including total life cycle cost, optimal battery capacity and power ratings, integrated Levelized Cost of Energy (LCOE), and charge/discharge energy profiles. The results indicate that GA achieves a total cost reduction of approximately 3-4% compared to PSO, alongside a modestly lower integrated LCOE. However, PSO displays enhanced control over discharge depth, yielding a more stable state of charge trajectory.