Regulating TSO interaction in bid filtering for European balancing markets

Marie Girod, Viktor Terrier, Virginie Dussartre, Jean-Yves Bourmaud, Yannick Perez () and Benjamin Donnot ()
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Marie Girod: LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay, RTE - Réseau de Transport d'Electricité [Paris]
Viktor Terrier: RTE - Réseau de Transport d'Electricité [Paris]
Virginie Dussartre: RTE - Réseau de Transport d'Electricité [Paris]
Jean-Yves Bourmaud: RTE - Réseau de Transport d'Electricité [Paris]
Yannick Perez: LGI - Laboratoire Génie Industriel - CentraleSupélec - Université Paris-Saclay
Benjamin Donnot: RTE - Réseau de Transport d'Electricité [Paris]

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Abstract: Europe is undertaking projects for near real-time common balancing markets to meet the flexibility needs induced by renewable deployment. A new congestion management method, bid filtering, has been authorized by regulation to prevent unsolvable last minute congestion. It is designed to manage internal congestion and is performed by each Transmission System Operator (TSO) separately without knowledge of bids in other zones. Bids from all zones are shared in the same market, which means filtering from one TSO could affect welfare in other zones, depending on its objective and on regulation. This paper evaluates the potential effects of multiple TSOs interacting with different filtering strategies. Three TSO strategies are considered – Benevolent, Local, and Conservative – and different combinations are tested using multi-agent reinforcement learning. Results show that although several TSOs filtering benevolently leads to the highest net Social Welfare, it is unlikely that all TSOs will adopt this strategy considering political and social constraints in EU27 countries. We discuss several regulatory options to create the conditions for a Social Welfare-maximizing filtering and foster coordination between TSOs.

Keywords: Electricity networks; Balancing markets; Congestion management; European integration; Filtering; Multi-agent reinforcement learning (search for similar items in EconPapers)
Date: 2025-10
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Published in Energy Policy, 2025, 205, pp.114713. ⟨10.1016/j.enpol.2025.114713⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05098043

DOI: 10.1016/j.enpol.2025.114713

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