Evaluation of Electricity Market Clearing Mechanisms via Reinforcement Learning: Prices, Remuneration and Competitive Dynamics
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
The Pay-as-Clear (PaC) mechanism currently used in the European electricity market can generate significant submarginal profits for renewable sources when the clearing price is determined by the marginal offers of gas-fired generation units and the cost of natural gas exceeds certain levels. This exposes consumers to high price volatility related to the cost of natural gas. This report analyzes the recently proposed Segmented Pay-as-Clear (SPaC) mechanism as a market alternative, evaluating its system cost-effectiveness through simulations based on Reinforcement Learning (Q-Learning) to model the strategic behavior of operators. Three market models are compared, the two classic Pay-as-Clear (PaC) and Pay-as-Bid (PaB) along with SPaC, under two scenarios: a simplified one based on the 2030 NECP objectives and one built on the portfolios of ten operators obtained from the GME's 2024 public offers. The results show that the SPaC market clearing mechanism reduces intramarginal profits and price volatility compared to PaC, while maintaining fair participation incentives for all operators, and is more robust than PaB to the exercise of market power in oligopolistic contexts. The developed framework can serve as a support tool for regulators and policymakers in the evaluation of proposals for market design reforms.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 16 references that are in the map. First posted on arXiv 1 Feb 2026.
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