Enhanced MPPT efficiency in photovoltaic systems with a new artificial protozoa optimizer method

Renewable energy systems have become indispensable in the pursuit of sustainable development, with photovoltaic (PV) technologies emerging as one of the most accessible and scalable solutions. In this context, the present work introduces a new metaheuristic optimization algorithm, known as the Artificial Protozoa Optimizer (APO), which is tailored to improve maximum power point tracking (MPPT) in PV systems.Compared to established techniques such as Perturb and Observe (P&O), Particle Swarm Optimization (PSO), and Fuzzy Logic Control (FLC), APO has shown notable improvements in terms of efficiency, reaching values as high as 99% in simulation tests.The algorithm is specifically designed to minimize power fluctuations and energy losses, which translates into enhanced system stability and energy yield.Through extensive simulations, APO has demonstrated its capacity to overcome common MPPT challenges, such as slow convergence, sensitivity to rapid environmental variations, and oscillations near the optimal point.The simulated setup includes a buck converter interfaced with a battery, enabling an effective energy storage mechanism. All scenarios were tested using MATLAB/Simulink, under both uniform and non-uniform irradiation profiles to assess the algorithm’s robustness in dynamic conditions.