Investigation of Evolutionary Computation Techniques for Enhancing Solar Photovoltaic Cell Performance

Abstract

The pursuit of optimized solar photovoltaic (PV) cell parameters is critical for advancing renewable energy technologies amidst global energy security and climate change challenges. This research investigates the efficacy of particle swarm optimization (PSO) and gray wolf optimization (GWO) in fine-tuning PV cell behavior parameters. Leveraging evolutionary computation, the study aims to maximize energy output, minimize costs, and enhance system reliability by optimizing material properties, structural configurations, and operating conditions. Through iterative optimization, PSO and GWO navigate the parameter space with precision, yielding solutions that maximize energy yield and system efficiency.