Mitigating Cloud Movement Effects on Solar Panel Performance through Different Re-configuration Techniques

The integration of Renewable Energy Sources (RES) into the grid for reliable and stable operation is witnessing significant growth. Solar photovoltaic (PV) systems are key contributors to enhancing the grid's RES capacity. The Solar PV installed capacity improved by 30 % in 2023. Challenges arise in partially shaded conditions, where some parts of the solar PV array may be obstructed, leading to suboptimal performance of the entire array. Achieving optimal performance in the connected solar PV arrays is essential, typically accomplished by configuring arrays in parallel and/or series to operate at the Maximum Power Point (MPP). This paper explores the application of various metaheuristic methods, including Particle Swarm Optimization, Bald Eagle Search, Manta Ray Foraging Optimization, and Gorilla Troop Optimizer. The study considers complete cloud movement across a 9X9 solar PV array. The movement of the clouds is categorized into 9 different stages of partial shading. The switching of the solar panels is employed to maximize output power under shaded conditions. The results are categorized into dynamic and static reconfiguration. MATLAB is employed for the implementation of these metaheuristic techniques.