This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service market (ASM) and a virtual bidding (VB) product in the financial market (FM). Following the theory proposed in Part 1, firstly, market design options in the joint market are specified. Then, the Markov game model is developed, in which we show how to incorporate market design options and uncertain risks in model formulation. A multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a practical implementation of the generalized market simulation method developed in Part 1. Finally, the case study demonstrates how to pick the best market design options by using some of the market operation performance indicators proposed in Part 1, based on the simulation results generated by implementing the MAPPO algorithm. The impacts of different market design options on market participants' bidding strategy preference are also discussed.

翻译：本文分为上下两篇，提出了基于强化学习仿真的联合电力市场设计的范式理论及详细方法。在第二部分中，我们通过详细阐述电力现货市场（ESM）的设计方法，以及辅助服务市场（ASM）中的备用容量产品（RC）和金融市场（FM）中的虚拟竞价产品（VB），进一步论证了这一理论。遵循第一部分提出的理论，首先明确了联合市场中的设计选项。随后，建立了马尔可夫博弈模型，展示了如何将市场设计选项和不确定性风险纳入模型构建。作为第一部分所提出的通用市场仿真方法的具体实现，我们详细阐述了多智能体近端策略优化（MAPPO）算法。最后，通过案例研究，基于MAPPO算法的仿真结果，运用第一部分提出的部分市场运行绩效指标，展示了如何选择最优市场设计选项，并讨论了不同市场设计选项对市场参与者报价策略偏好的影响。