Hybrid Grey Wolf Optimizer for Efficient Maximum Power Point Tracking to Improve Photovoltaic Efficiency

Abstract

Today, the demand for Renewable Energy (RE) sources has increased a lot; out of all Renewable Energy Sources (RES), Solar Energy (SE) has emerged as a better solution due to its sustainability and abundance. However, energy sources from the sun directly depend on the efficiency of the photovoltaic (PV) systems employed, whose efficiency depends on the variability of solar irradiance and temperature. So harvesting the maximum output from PV panels requires optimized Maximum Power Point Tracking (MPPT) systems. The traditional MPPT systems that involved Perturb and Observe (P&O) and Incremental Conductance (IncCond) are the most widely used models. However, those models have limited efficiency due to rapidly changing environmental conditions and their tendency to oscillate around the Maximum PowerPoint (MPP). This paper proposes a Hybrid Heuristic Model (HHM) called the Hybrid Grey Wolf Optimizer (HGWO) Algorithm, which employs the Genetic Algorithm (GA) model for optimizing the Grey Wolf Optimizer (GWO) algorithm for effectively utilizing MPPT in PV systems. The simulation decreases fluctuation, boosting how the system responds to shifts in the surrounding atmosphere. The framework evolved through several experiments, and its ability to perform was assessed concerning the results of different models for the factors that were considered seriously throughout several solar radiation and temperature scenarios. During all of the tests, the recommended HGWO model scored more effectively than the other models. This succeeded by accurately following the MPP and boosting the power supply.