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：这篇由两部分组成的论文开发了基于强化学习（RL）仿真的联合电力市场设计的范式理论与详细方法。在第二部分中，通过阐述设计电力现货市场（ESM）以及辅助服务市场（ASM）中的备用容量产品（RC）和金融市场（FM）中的虚拟投标产品（VB）的详细方法，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市场设计方案选项。随后，建立了马尔可夫博弈模型，展示了如何在模型构建中纳入市场设计方案选项和不确定风险。详细阐述了多智能体策略近端优化（MAPPO）算法，作为第一部分开发的广义市场仿真方法的实际实现。最后，案例研究展示了如何基于实施MAPPO算法生成的仿真结果，利用第一部分提出的部分市场运行性能指标来挑选最佳市场设计方案，并讨论了不同市场设计方案对市场参与者投标策略偏好的影响。