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Optimal configuration framework of hybrid renewable energy technologies-based hydrogen energy storage system assessment using enhanced artificial rabbit algorithm

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  • Güven, Aykut Fatih
  • Rizk-Allah, Rizk M.

Abstract

This study introduces the enhanced artificial rabbit optimization (EARO) algorithm, which was developed specifically to optimize hybrid renewable energy systems (HRES) for efficient electricity and hydrogen production. The EARO algorithm was designed to meet specific load demands and economic metrics to promote sustainable energy solutions. By integrating wind turbines, photovoltaic panels, fuel cells, biomass generators, and inverters into a comprehensive HRES, the EARO algorithm effectively manages and allocates power, thus ensuring both cost reduction and reliability.

Suggested Citation

  • Güven, Aykut Fatih & Rizk-Allah, Rizk M., 2025. "Optimal configuration framework of hybrid renewable energy technologies-based hydrogen energy storage system assessment using enhanced artificial rabbit algorithm," Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:energy:v:326:y:2025:i:c:s0360544225010503
    DOI: 10.1016/j.energy.2025.135408
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    Cited by:

    1. Güven, Aykut Fatih, 2025. "A novel hybrid Salp Swarm Kepler optimization for optimal sizing and energy management of renewable microgrids with EV integration," Energy, Elsevier, vol. 334(C).
    2. Rabbi, Mohammad Fazle, 2025. "Cross-framework hybrid artificial intelligence for high-penetration renewable energy integration: Multi-regional forecasting and adaptive control," Applied Energy, Elsevier, vol. 401(PC).

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