Enhancing Global Maximum Power Point Tracking in Partically Shaded Photovoltaic Systems Using Agent Colony Optimization

Photovoltaic (PV) systems under partial shading conditions face multi-peak power-voltage characteristics, rendering conventional maximum power point tracking (MPPT) algorithms ineffective in locating the global maximum power point (GMPP). This study addresses the critical challenge of optimizing energy harvest in partially shaded PV systems, which is vital for improving renewable energy efficiency and grid reliability. The research proposes an Agent Colony Optimization (ACO)-based MPPT algorithm to enhance tracking accuracy and stability under dynamic environmental conditions. The methodology integrates Python-based validation of the ACO algorithm with MATLAB/Simulink simulations, utilizing partial shading patterns and parameter optimization (e.g., agent count, movement intervals). Results demonstrate that the ACO algorithm achieves 99.7% accuracy with an RMSE below 0.03%, outperforming traditional methods like Perturb & Observe and Incremental Conductance. The algorithm's adaptability to irradiation and temperature variations makes it a robust solution for real-world PV applications, significantly boosting energy yield and system reliability.