Game Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915612431745024 |
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| author | Morri, Francesco Cadre, Hélène Le Gruet, Pierre Brotcorne, Luce |
| author_facet | Morri, Francesco Cadre, Hélène Le Gruet, Pierre Brotcorne, Luce |
| contents | We characterize zonal ancillary market coupling relying on noncooperative game theory. To that purpose, we formulate the ancillary market as a multi-leader single follower bilevel problem, that we subsequently cast as a generalized Nash game with side constraints and nonconvex feasibility sets. We determine conditions for equilibrium existence and show that the game has a generalized potential game structure. To compute market equilibrium, we rely on two exact approaches: an integrated optimization approach and Gauss-Seidel best-response, that we compare against multi-agent deep reinforcement learning. On real data from Germany and Austria, simulations indicate that multi-agent deep reinforcement learning achieves the smallest convergence rate but requires pretraining, while best-response is the slowest. On the economics side, multi-agent deep reinforcement learning results in smaller market costs compared to the exact methods, but at the cost of higher variability in the profit allocation among stakeholders. Further, stronger coupling between zones tends to reduce costs for larger zones. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_03288 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Game Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets Morri, Francesco Cadre, Hélène Le Gruet, Pierre Brotcorne, Luce Multiagent Systems Computer Science and Game Theory General Economics Economics We characterize zonal ancillary market coupling relying on noncooperative game theory. To that purpose, we formulate the ancillary market as a multi-leader single follower bilevel problem, that we subsequently cast as a generalized Nash game with side constraints and nonconvex feasibility sets. We determine conditions for equilibrium existence and show that the game has a generalized potential game structure. To compute market equilibrium, we rely on two exact approaches: an integrated optimization approach and Gauss-Seidel best-response, that we compare against multi-agent deep reinforcement learning. On real data from Germany and Austria, simulations indicate that multi-agent deep reinforcement learning achieves the smallest convergence rate but requires pretraining, while best-response is the slowest. On the economics side, multi-agent deep reinforcement learning results in smaller market costs compared to the exact methods, but at the cost of higher variability in the profit allocation among stakeholders. Further, stronger coupling between zones tends to reduce costs for larger zones. |
| title | Game Theory and Multi-Agent Reinforcement Learning for Zonal Ancillary Markets |
| topic | Multiagent Systems Computer Science and Game Theory General Economics Economics |
| url | https://arxiv.org/abs/2505.03288 |