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，电力市场的重新设计作为宏观层面的必要举措，旨在适应高比例可再生能源并网，并实现电力系统运行的安全性、经济性与环境友好性。然而，现有市场设计方法存在三大缺陷：缺乏对电能现货市场（ESM）、辅助服务市场（ASM）与金融市场（FM）的协调机制（即“联合市场”），以及缺乏可靠的基于仿真的验证。为解决上述问题，本两部分系列论文提出了一种基于强化学习（RL）仿真的联合市场设计范式理论及具体方法。在第一部分中，我们提出了这一新型市场设计理念的理论与框架。首先，将设计联合市场时的争议性市场设计选项归纳为目标研究问题；其次，建立马尔可夫博弈模型以描述联合市场中的报价博弈过程，并将待确定的市场设计选项融入其中；第三，构建了部署多类型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，基于仿真结果对市场设计进行验证。