如何利用强化学习促进未来电力市场设计？第一部分：一种范式理论
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory
AI总结：
本文提出一种基于强化学习仿真的联合电力市场设计范式理论，通过马尔可夫博弈建模竞价过程，并利用多种RL算法仿真验证，以协调现货、辅助服务和金融市场，提升系统运行安全、经济与环境效益。
摘要：
面对电力部门脱碳的迫切需求，电力市场的重新设计作为一种宏观层面的方法，对于容纳高比例可再生能源发电、实现电力系统运行安全、经济效率和环境友好性而言是必要的。然而，现有的市场设计方法存在能源现货市场（ESM）、辅助服务市场（ASM）和金融市场（FM）之间缺乏协调（即“联合市场”）以及缺乏可靠的基于仿真的验证的问题。为了解决这些缺陷，本两部分的论文开发了一种范式理论以及基于强化学习（RL）仿真的联合市场设计的详细方法。在第一部分中，提出了这种新颖市场设计哲学的理论和框架。首先，设计联合市场时存在争议的市场设计方案被总结为目标研究问题。其次，建立了马尔可夫博弈模型来描述联合市场中的竞价博弈，其中纳入了待确定的市场设计方案。第三，开发了一个部署多种类型强化学习算法来仿真市场模型的框架。最后，提出了若干市场运行性能指标，以基于仿真结果验证市场设计。
英文摘要：
In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results.
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。
