Comparative Study of Hybrid/Enhanced Nature-Inspired Optimization Algorithms for Solar Photovoltaic Model

Photovoltaic (PV) cell modeling and parameter estimation have gained significant attention in recent years due to the increasing demand for renewable energy sources; however, accurate modeling of PV cells is essential for optimizing their performance. Photovoltaic (PV) cell modeling is a process of creating mathematical and computational models to simulate the behavior and performance of photovoltaic cells, also known as solar cells. These models aim to predict the electrical characteristics of the cells under different operating conditions, such as varying irradiance levels, temperature, and electrical loads. Nature-inspired algorithms have emerged as a promising solution for the optimization of complex problems such as parameter estimation of PV cell models. Accurate parameter estimation is crucial for developing and validating PV cell models as it enables researchers to make predictions about the performance of the cell under different operating conditions, such as changes in irradiance, temperature, and spectral distribution. This, in turn, allows for the optimization of PV cell designs and the development of more efficient and cost-effective solar energy technologies. In this paper, we present a comparative study of some popular nature-inspired algorithms, hybridized/enhanced for more efficient parameter estimation of PV cell models.