Optimizing Solar Photovoltaic Cell Parameters Using Evolutionary Computation Techniques

Abstract

The optimization of solar photovoltaic (PV) cell parameters through evolutionary computation techniques represents a pivotal avenue for advancing renewable energy technologies. As the world grapples with the dual challenges of energy security and climate change mitigation, the imperative to maximize the efficiency and output of solar energy systems has become increasingly paramount. This study embarks on a comprehensive exploration of genetic algorithms (GAs), particle swarm optimization (PSO), gray wolf optimization (GWO), and the newly incorporated Cuckoo Search Optimization (CSO) as potent tools for fine-tuning the intricate parameters governing PV cell behavior. By leveraging these techniques, the research aims to maximize energy yield, minimize costs, and enhance system reliability. Rigorous experimentation and comparative analysis are employed to discern the strengths and limitations of each optimization method. The outcomes of this study offer invaluable insights into optimal design strategies and operational practices for solar PV systems, fostering a scalable and sustainable transition towards a low-carbon future.