Maximizing Solar PV Efficiency under Partial Shading and Climate Variations: A Novel Hybrid PSO-ROA for Photovoltaic Optimization

Abstract

Photovoltaic systems are highly sensitive to environmental factors like partial shading, temperature, and irradiance. These variations affect power output and efficiency. This study proposes a hybrid optimization approach combining Particle Swarm Optimization (PSO) for global search and Red Kite Optimization Algorithm (ROA) for local refinement. The PSO-ROA method improves parameter estimation accuracy in single-diode PV models. Compared to standalone PSO, it enhances Maximum Power Point Tracking (MPPT) precision, especially under partial shading. It minimizes power losses and ensures stable convergence. Sensitivity analysis shows that performance improves with moderate temperatures (60°C–80°C) and high irradiance (500 W/m²). The hybrid approach achieves near-perfect alignment at the Maximum Power Point (MPP). Although computation time increases, the trade-off results in higher accuracy and robustness. These findings confirm that the PSO-ROA hybrid method is an effective solution for optimizing photovoltaic systems. It ensures reliable performance across varying environmental conditions.