A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets
Authors: Zunnan Xu, Zhaoxia Jing, Zhanhua Pan
Organizations: School of Electric Power Engineering, South China University of Technology, Guangzhou, 510640, Guangdong, China · Department of Engineering, University of Exeter, Exeter EX4 4PY, U.K.
Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi-segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and then convert it into a feasible bid curve satisfying monotonicity and boundedness through post-processing mappings such as sorting, clipping, or projection. However, such post-processing mappings often fail to satisfy continuous differentiability, injectivity, and invertibility at boundaries or kinks, thereby causing gradient distortion and leading to spurious convergence in simulation results. Meanwhile, most existing studies conduct mechanism analysis and evaluation mainly on the basis of training-curve convergence, without rigorously assessing the distance between the simulation outcomes and Nash equilibrium, which severely undermines the credibility of the results. To address these issues, this paper proposes...
Figures & tables
Figure 1: Conceptual sketches of real-world electricity-market bids and common RL-ABS bid models.
Figure 2: Relationship between Agent Bid Models and Reinforcement Learning Methods.