2023/05/04 by Ziqing Zhu, Zhu, Ziqing, Siqi Bu +7
Energy · Engineering · #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Electric Power System Optimization #Energy Efficiency and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2305.06921
openalex publication_date 2023/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.