Performance optimization of PV panels operating under varying environmental conditions using a genetic algorithm and power law model

This paper introduces a novel photovoltaic (PV) modeling approach that combines the power law model (PLM), the single diode model (SDM), and a genetic algorithm (GA). The proposed method leverages the simplicity of the PLM and the utility of the SDM to establish two expressions for the PLM parameters. These expressions are formulated as functions of three physical parameters: the ideality factor, series resistance, and shunt resistance. Furthermore, a GA is employed to optimize these physical parameters. The primary objective of this proposed approach is to enhance the optimization of the current–voltage characteristics and the maximal power of PV modules using the PLM model across various environmental conditions, including temperature and irradiation. To assess the effectiveness of the proposed approach, experimental validation is performed using various types of PV modules. The experiments are carried out at the National Renewable Energy Laboratory (NREL) in Florida, specifically at the Cocoa site. The results obtained under different conditions are then compared with those generated by traditional methods. The comparative analysis reveals that the proposed approach exhibits improved agreement with the experimental data, further highlighting its efficacy in accurately optimizing the behavior and performance of PV modules under real operating conditions.