Hybrid Young’s double-slit experiment and differential evolution for enhanced photovoltaic parameter estimation

This article introduces a novel hybrid optimization method, the Young's Double-Slit Experiment-Differential Evolution (YDSE-DE) technique, which is based on the integration of the YDSE approach with the robust capabilities of Differential Evolution algorithms. This integration enables the precise estimation of parameters in photovoltaic (PV) models, enhancing the modeling and simulation of solar energy systems. Employing a comparative assessment strategy, the authors demonstrate that the proposed YDSE-DE algorithm outperforms existing methods such as Ant Lion Optimizer (ALO) and Sooty Tern Optimization Algorithm (STOA) in terms of accuracy and computational efficiency. The effectiveness of this method is confirmed through rigorous testing, which shows improvements in the root mean square error (RMSE) metrics by significant margins across single-diode, double-diode, and three-diode PV models. RMSE values obtained using the YDSE-DE algorithm for the single-diode, double-diode, and three-diode models are documented as 0.0059117, 0.0015218, and 0.0018409, respectively. The present study underscores the considerable potential of the YDSE-DE algorithm in augmenting the precision and effectiveness of photovoltaic system simulation and design. The results develop and enhance the existing methodologies for PV parameter estimation and can be applied to optimize other complex systems requiring reliable and precise simulation. The novelty and scientific contribution of this work lie in the methodical synthesis of physical principles from optics with evolutionary computation, presenting a significant advancement in the field of renewable energy technology.