Comparative Analysis of MPC, RL and P&O for MPPT of PV Systems Under Partial Shading Conditions

This paper provides a comparison between three maximum power point tracking (MPPT) methods in photovoltaic (PV) systems: Model Predictive Control (MPC), Reinforcement Learning (RL), and traditional perturb and observe (P&O) methods. The goal of the paper is to compare and analyze how well the techniques perform, their adaptability, and effectiveness in varying operating conditions. MPC uses a model-predictive framework to optimize the control actions over a finite horizon, while reinforcement learning uses an adaptive learning system to maximize power extraction without explicit modeling of the system. The focus of this work is on the distinguishing features of the three types of MPPT, their response time, compute intensity, and adaptability. MPC quickly maximizes and adapts to the MPP while respecting system constraints, making it consist with previously defined dynamics. RL relies on continuous learning and adaption to changes in irradiance and temperature and provides a flexible response to changing environments that have a great deal of uncertainty. RL typically requires a great deal of training and tuning to optimize the algorithm. This paper summarizes the tradeoffs between predictive control and learning algorithms and helps guide researchers and practitioners on the most effective approaches of MPPT for PV systems, resulting in efficient and sustainable PV energy management practices.