Comparative analysis of Grey Wolf Optimization algorithm and Ant Colony Optimization algorithm for PV panel under extreme conditions

Abstract

Under ideal circumstances, every energy generating systems created by mankind gives near perfect result. However, we hardly ever get the chance to work under such impeccable conditions. The same will always be true for any solar system. With varying temperature and solar irradiation, we may mistake the local peak power as the true potential of our systems. This may lead us to add additional panels to meet our demand, thus increasing the overall price of the system. To offset this predicament, we use various MPPT optimization. algorithm. Two of these optimization techniques are grey wolf and ant colony optimization. Both techniques have remarkable performance, yet we must still pick one MPPT as efficiency is paramount. Even if we do manage to select one, considering the escalating impact of global climate change leading to heightened temperature extremes and an alarming increase in rainfall in certain regions, the need for a robust comparative analysis of these optimization techniques becomes more pronounced. This research conducts a comprehensive comparative analysis of the grey wolf and ant colony optimization techniques under diverse climate conditions. The simulation, conducted in Simulink, meticulously presents each technique independently, culminating in a detailed evaluation and comparison of their performance under the dynamic scenarios posed by varying temperatures and near-total shading conditions, yielding nigh on kindred results.

Keywords: Grey wolf algorithm, ant colony algorithm, maximum power point tracking, PV system