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Bid filtering for congestion management in European balancing markets – A reinforcement learning approach

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  • Girod, Marie
  • Donnot, Benjamin
  • Dussartre, Virginie
  • Terrier, Viktor
  • Bourmaud, Jean-Yves
  • Perez, Yannick

Abstract

Innovations for near real-time common European balancing markets are underway to meet the flexibility needs induced by the deployment of renewables and new market agents. Never have markets and real-time network operations been run so closely on a continental scale. Our paper investigates a filtering method for integrating congestion management and near real-time markets. Reinforcement Learning is applied to add the cost of physical delivery to bid prices to advantage/disadvantage bids that reduce/create congestion. We assess the impact of this new method on market welfare and congestion management costs and show that it brings significant efficiency gains compared to no filtering or a baseline filtering methodology.

Suggested Citation

  • Girod, Marie & Donnot, Benjamin & Dussartre, Virginie & Terrier, Viktor & Bourmaud, Jean-Yves & Perez, Yannick, 2024. "Bid filtering for congestion management in European balancing markets – A reinforcement learning approach," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002757
    DOI: 10.1016/j.apenergy.2024.122892
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    References listed on IDEAS

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    2. Martin Håberg & Hanna Bood & Gerard Doorman, 2019. "Preventing Internal Congestion in an Integrated European Balancing Activation Optimization," Energies, MDPI, vol. 12(3), pages 1-11, February.
    3. Attar, Mehdi & Repo, Sami & Mutanen, Antti & Rinta-Luoma, Jukka & Väre, Teemu & Kukk, Kalle, 2024. "Market integration and TSO-DSO coordination for viable Market-based congestion management in power systems," Applied Energy, Elsevier, vol. 353(PB).
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