Abstract
Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods.