MPPT command enhancement based on an ameliorated grey wolf optimization algorithm for a standalone PV system

Photovoltaic (PV) energy is a widely adopted renewable energy source renowned for its abundance, non-polluting attributes, and minimal maintenance requirements. Despite these benefits, it remains one of the least efficient methods for converting sunlight to electricity. Moreover, PV cell efficiency substantially declines when they operate away from their maximum power point (MPP), which shifts based on varying environmental factors. Numerous strategies have been employed to track the MPP effectively. This research aims to enhance PV systems, especially those embedded in electric vehicles and satellites, by developing and refining a maximum power point tracking (MPPT) algorithm using the grey wolf optimization (GWO) method. This approach is designed to minimize oscillations around the global maximum power point tracker (GMPP) and reduce tracking time. The proposed technique has been corroborated through MATLAB/Simulink simulations. Results demonstrate that the advanced MPPT method significantly improves GMPP tracking by notably decreasing tracking time and diminishing power oscillations, thereby increasing the energy harnessed from mobile PV systems. This study markedly contributes to the enhancement of photovoltaic system efficiency and its more effective integration into portable devices.