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通过强化学习评估电力市场清算机制:价格、报酬和竞争动态

Evaluation of Electricity Market Clearing Mechanisms via Reinforcement Learning: Prices, Remuneration and Competitive Dynamics

Polytechnic University of Bari(巴里理工大学);Regulatory Authority for Energy, Networks and Environment (ARERA)(能源、网络和监管机构);University of Pisa(比萨大学)

Andrea Altamura, Fabrizio Lacalandra, Antonio Frangioni

AI总结:

本文通过强化学习评估SPaC机制,发现其在降低价格波动和维持公平激励方面优于PaC和PaB,为市场设计改革提供支持工具。

摘要:

当前欧洲电力市场使用的Pay-as-Clear(PaC)机制在天然气成本超过一定水平时,能够为可再生能源源产生显著的次边际利润。这使消费者面临与天然气成本相关的高价格波动。本报告分析了最近提出的分段Pay-as-Clear(SPaC)机制作为市场替代方案,通过基于强化学习(Q-Learning)的模拟,评估其系统成本效益,以建模运营商的战略行为。三种市场模型被比较,包括两种经典机制PaC和PaB以及SPaC,在两种场景下进行分析:一种基于2030年NECP目标的简化模型,另一种基于十家运营商的组合,该组合来自GME 2024年的公开报价。结果表明,SPaC市场清算机制相比PaC减少了内在边际利润和价格波动,同时保持了所有运营商公平的参与激励,并在寡头垄断情境下比PaB更具鲁棒性。所开发的框架可以作为监管机构和政策制定者评估市场设计改革提案的支持工具。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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