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

Marie Girod, Benjamin Donnot, Virginie Dussartre, Viktor Terrier, Jean-Yves Bourmaud and Yannick Perez ()

Applied Energy, 2024, vol. 361, issue C, No S0306261924002757

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.

Keywords: Balancing markets; Congestion management; Filtering; European integration; Reinforcement learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.122892

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