  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.
