A novel maximum power point tracking approach for stand-alone PV/Wind-Battery Hybrid System

This study explores the implementation of a novel Maximum Power Point Track-ing (MPPT) algorithm, referred to as the Tribal Intelligent Evolutionary Optimization (TIEO) algorithm, for concurrent MPPT in both photovoltaic (PV) systems, subject to irra-diance and temperature variations, and wind energy systems, affected by variation in wind speed. The principal objective is to maximize the energy extraction from each renewable source under dynamically changing environmental conditions, thereby enhancing overall system performance and energy efficiency. The TIEO algorithm was subsequently imple-mented and simulated within the MATLAB/Simulink environment for a stand-alone hybrid PV/Wind system incorporating a storage battery. Analysis of the simulation results indi-cates that the TIEO-based MPPT strategy exhibits high effectiveness, strong adaptability to variable operating conditions, and superior tracking accuracy. Consequently, it presents a promising and robust solution for the control and energy management of hybrid renewable energy systems.