Optimizing Green Hydrogen Production Using MPPT Algorithms in a PV System
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
University of Msila
Abstract
Green hydrogen production via photovoltaic (PV) systems represents a promising pathway toward
sustainable energy. Maximizing the efficiency of such systems relies heavily on effective integration and
control strategies, particularly under variable environmental conditions. This study presents an in-depth
investigation into the optimization of a PV-powered alkaline electrolyzer using advanced Maximum Power
Point Tracking (MPPT) algorithms. The system—comprising a PV array, boost converter, and various
MPPT controllers (Perturb and Observe, Cuckoo Search, and FDB-TLABC)—was modeled and simulated
in MATLAB/Simulink. Hydrogen (nH₂) and oxygen (nO₂) production rates were employed as key
performance indicators of the electrolyzer, directly influenced by the stability and accuracy of power point
tracking. Under standard irradiance, conventional MPPT methods (P&O, Incremental Conductance)
demonstrated consistent operation. However, under partial shading conditions, intelligent optimization
techniques such as Cuckoo Search and FDB-TLABC significantly outperformed traditional algorithms by
effectively mitigating power losses and avoiding local maxima. These findings underscore the crucial role
of algorithm selection in ensuring stable and efficient hydrogen production, particularly in scenarios
characterized by intermittent solar irradiance. This work contributes a validated framework for the
Abstract
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development of resilient and high-performance PV-electrolyzer systems, highlighting the interplay between
solar energy optimization and electrochemical efficiency.
Description
Keywords
green hydrogen, PV-electrolyzer system, MPPT algorithms, partial shading, MATLAB/Simulink, alkaline electrolysis, Cuckoo Search, FDB-TLABC.