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

Ameni Boumaiza () and Kenza Maher
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Ameni Boumaiza: Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar
Kenza Maher: Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University (HBKU), Doha P.O. Box 34110, Qatar

Energies, 2024, vol. 17, issue 22, 1-17

Abstract: 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.

Keywords: demand-side management; artificial intelligence; electricity efficiency; smart grids; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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