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<p>Decarbonized energy-based systems can be solved by green hydrogen, which can be used to support the growing demands in the world. The purpose of this paper is to optimize photovoltaic (PV)-integrated proton exchange membrane (PEM) electrolyzer systems through proper estimation of the photovoltaic (PV) parameters by using the bio-inspired Starfish Optimization Algorithm (SFOA). Both the Single-Diode and Double-Diode Models had been optimized at constant test conditions, and the changeability of the environment (irradiance and temperature) had been added when performing system-level simulations. The results demonstrate that the SFOA is never inferior compared to the conventional optimization techniques, namely Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). Specifically, SFOA obtained a reduction of 20–35% with the optimized Double-Diode Model, pointing to the RMSE of 0.000745. These improvements in accuracy were translated into significant improvements in operation. There had been a 36 per cent reduction in series resistance, 22 per cent in photocurrent and 30 per cent in battery efficiency. In addition, the optimized PV-PEM system was found to have a greater degree of robustness in the environment and demand variations, which highlights its future applications in mass production at the level of hydrogen fueling stations and the industrial level. Overall, this work transforms SFOA into a valuable and applicable optimization model that can contribute to the emergence of effective and cost-efficient green hydrogen systems to a considerable extent by enhancing the precision of PV parameters.</p>

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Optimization of Photovoltaic-Integrated Proton Exchange Membranes Electrolyzer Systems for Sustainable Green Hydrogen Production using Starfish Algorithm

  • 期刊论文

  • 发表日期：

    2026年1月30日

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  • Yara A. Sultan,
  • Asmaa H. Rabie,
  • Abdelfattah A. Eladl,
  • Amal Moharam

摘要

Decarbonized energy-based systems can be solved by green hydrogen, which can be used to support the growing demands in the world. The purpose of this paper is to optimize photovoltaic (PV)-integrated proton exchange membrane (PEM) electrolyzer systems through proper estimation of the photovoltaic (PV) parameters by using the bio-inspired Starfish Optimization Algorithm (SFOA). Both the Single-Diode and Double-Diode Models had been optimized at constant test conditions, and the changeability of the environment (irradiance and temperature) had been added when performing system-level simulations. The results demonstrate that the SFOA is never inferior compared to the conventional optimization techniques, namely Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). Specifically, SFOA obtained a reduction of 20–35% with the optimized Double-Diode Model, pointing to the RMSE of 0.000745. These improvements in accuracy were translated into significant improvements in operation. There had been a 36 per cent reduction in series resistance, 22 per cent in photocurrent and 30 per cent in battery efficiency. In addition, the optimized PV-PEM system was found to have a greater degree of robustness in the environment and demand variations, which highlights its future applications in mass production at the level of hydrogen fueling stations and the industrial level. Overall, this work transforms SFOA into a valuable and applicable optimization model that can contribute to the emergence of effective and cost-efficient green hydrogen systems to a considerable extent by enhancing the precision of PV parameters.