Mathematical Modeling for Solar Cell Optimization: Evaluating Sustainability With Different Diode Configurations

It is widely acknowledged within the scientific community that solar cells serve as an environmentally friendly and sustainable source of renewable energy, making them highly versatile in their applications within both the industrial and residential sectors. Consequently, existing algorithms for solar cell design primarily focus on meeting the energy demands of domestic settings, necessitating the development of precise mechanisms that can cater to the needs of both domestic and industrial environments. In light of this, the author of this study proposes an enhanced version of the crow optimization algorithm by incorporating opposition-based learning, thereby ensuring improved exploration of the search space. The primary objective of this algorithm is to estimate the parameters of solar cells, which can be achieved by employing three distinct diode models: the single diode model, the double diode model, and the three-diode model. It is of utmost importance to accurately estimate the internal parameters in these models in order to optimize the performance of solar cells, particularly in industrial applications. An algorithm that incorporates the three-diode model is better suited for industrial use due to its superior ability to accurately represent the behavior of solar cells. In order to validate the efficiency of the proposed approach, RMSE and computational time is been recorded of each algorithm using similar approaches and datasets from solar cells and photovoltaic modules. Furthermore, the algorithm’s exploration capability across complex optimization problems was assessed by subjecting it to various benchmark optimization functions. Based on the outcomes of these experiments and comparisons, it can be confidently affirmed that the proposed algorithm is not only effective but also efficient in effectively addressing the challenges associated with this specific problem domain.