AI Empowered Solar Energy: Reinforcement Learning and Comparative Analysis for Grid-Connected Photovoltaic Systems Optimization

This study addresses the optimization of grid-connected photovoltaic (PV) systems, particularly focusing on overcoming challenges posed by shading conditions. Employing machine learning (ML) technology, specifically Reinforcement Learning (RL), this research conducts a comparative analysis with traditional optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for Maximum Power Point Tracking (MPPT). Simulation results, executed using Simulink/Matlab, highlight RL's superior performance in terms of convergence speed and effectiveness compared to PSO and GA, without the need for prior system knowledge. This study contributes valuable insights into the application of ML-based algorithms in enhancing PV system efficiency, paving the way for advancements in renewable energy technologies.