Multi-Agent Reinforcement Learning Bidding Strategy for Virtual Power Plants Based on Regularization Mechanism

Abstract

This paper presents a multi-agent reinforcement learning (MARL) bidding strategy for virtual power plants (VPPs) incorporating a regularization mechanism. The approach coordinates distributed energy resources through multi-agent interactions while the regularization mechanism ensures stability and convergence of the bidding process, addressing key challenges in electricity market participation under uncertainty.