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Article Dans Une Revue Applied Energy Année : 2024

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

Résumé

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.
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Dates et versions

hal-04503400 , version 1 (13-03-2024)

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Marie Girod, Benjamin Donnot, Virginie Dussartre, Viktor Terrier, Jean-Yves Bourmaud, et al.. Bid filtering for congestion management in European balancing markets – A reinforcement learning approach. Applied Energy, 2024, 361, pp.122892. ⟨10.1016/j.apenergy.2024.122892⟩. ⟨hal-04503400⟩
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