Enhancing Distributed Energy Markets in Smart Grids Through Game Theory and Reinforcement Learning

The rapid growth of distributed energy resources (DERs) in smart grids has necessitated innovative strategies to manage and optimize energy markets. This paper introduces an architectural framework that leverages game theory and reinforcement learning (RL) as foundational methodologies to enhance the efficiency and robustness of distributed energy markets. Through simulations and case studies, we demonstrate how these approaches can facilitate improved decision-making among market participants, leading to better energy distribution and consumption. This exploratory approach is intended to lay the groundwork for more complex implementations that account for physical and regulatory constraints. Our preliminary results indicate a 25% reduction in energy costs and a 30% improvement in energy distribution efficiency compared to traditional methods.