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.

翻译：面对电力行业脱碳的迫切需求，作为宏观层面的应对策略，电力市场的重新设计势在必行，以容纳高比例可再生能源发电，并实现电力系统运行安全、经济高效与环境友好。然而，现有市场设计方法存在能源现货市场、辅助服务市场与金融市场（即“联合市场”）之间缺乏协调，以及缺乏可靠模拟验证的问题。为应对这些不足，本两篇系列论文开发了基于强化学习模拟的联合市场设计范示理论及具体方法。在第一部分中，提出这一新型市场设计哲学的理论与框架。首先，将联合市场设计中存在争议的市场设计选项总结为针对性研究问题。其次，构建马尔可夫博弈模型描述联合市场中的投标博弈，并纳入待确定的市场设计选项。第三，开发部署多种强化学习算法模拟市场模型的框架。最后，提出若干市场运行性能指标，根据模拟结果验证市场设计方案。