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Day-ahead trading and power control for hybrid wind-hydrogen plants with multi-agent reinforcement learning

Author

Listed:
  • Ally, Stijn
  • Verstraeten, Timothy
  • Nowé, Ann
  • Helsen, Jan

Abstract

Offshore wind farms and hybrid wind-hydrogen plants derive revenue from multiple revenue sources, each subject to uncertainties and trade-offs. As a consequence, maximizing their overall profitability is challenging. Since electricity is typically traded ahead of its actual generation, weather forecasts play a crucial role in the power trading strategy. Additionally, the trading and control strategies of other market participants influence the balance of the public grid, affecting the revenue that can be generated by grid balancing. Moreover, the operational status of the electrolyzer may impact both the immediate and near-term hydrogen production potential.

Suggested Citation

  • Ally, Stijn & Verstraeten, Timothy & Nowé, Ann & Helsen, Jan, 2025. "Day-ahead trading and power control for hybrid wind-hydrogen plants with multi-agent reinforcement learning," Applied Energy, Elsevier, vol. 401(PA).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pa:s0306261925013182
    DOI: 10.1016/j.apenergy.2025.126588
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    References listed on IDEAS

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    1. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
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