Towards Renewable Energy in Iraq: Accurate Photovoltaic Parameter Estimation Using Metaheuristic Algorithms

الملخص

Nowadays there is a global transition to renewable energy because of the urgent need to combat climate change and enhance energy sustainability. For Iraq, which has been facing frequent power shortages and the negative consequences of climate change, such as water shortages and desertification, the investment in renewable energy is no longer optional. Iraq has a high potential for using solar energy due to its high solar radiation levels and long days of sunlight. Solar photovoltaic (PV) systems offer a clean and reliable alternative to diesel generators, but their effectiveness relies largely on exact approximation of nonlinear internal parameters. In this work, three recent metaheuristic algorithms (MAs): FATA (an efficient optimization approach based on geophysics), Moss Growth Optimization (MGO), and Polar Lights Optimizer (PLO), are applied for parameter estimation of single-diode, double-diode, and modified PV models. Results showed that the FATA consistently outperformed MGO and PLO, obtaining the lowest average fitness over 30 independent runs, which means improved accuracy in PV parameter extraction. The achieved results demonstrate how MAs can be a significant tool for enhancing the performance of PV systems.