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Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm

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  • Fathy, Ahmed
  • Yousri, Dalia
  • Alanazi, Turki
  • Rezk, Hegazy

Abstract

In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.

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  • Fathy, Ahmed & Yousri, Dalia & Alanazi, Turki & Rezk, Hegazy, 2021. "Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm," Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:energy:v:225:y:2021:i:c:s036054422100565x
    DOI: 10.1016/j.energy.2021.120316
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    References listed on IDEAS

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    Cited by:

    1. Büyük, Mehmet & İnci, Mustafa, 2023. "Improved drift-free P&O MPPT method to enhance energy harvesting capability for dynamic operating conditions of fuel cells," Energy, Elsevier, vol. 267(C).
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    3. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    4. Zhimin Guo & Zhiyuan Ye & Pengcheng Ni & Can Cao & Xiaozhao Wei & Jian Zhao & Xing He, 2023. "Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System," Energies, MDPI, vol. 16(6), pages 1-21, March.
    5. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    6. Masmoudi, Abdelkarim & Abdelkafi, Achraf & Krichen, Lotfi & Saidi, Abdelaziz Salah, 2022. "An experimental approach for improving stability in DC bus voltage of a stand-alone photovoltaic generator," Energy, Elsevier, vol. 257(C).
    7. Zhaowen Liang & Kai Liu & Jinjin Huang & Enfei Zhou & Chao Wang & Hui Wang & Qiong Huang & Zhenpo Wang, 2022. "Powertrain Design and Energy Management Strategy Optimization for a Fuel Cell Electric Intercity Coach in an Extremely Cold Mountain Area," Sustainability, MDPI, vol. 14(18), pages 1-16, September.

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