Reinforcement Learning (RL) in Energy Systems: A Review of Adaptive Optimization, Current Challenges, and Future Directions

Document Type : Review Paper

Authors

1 Associate Professor, School of Energy Engineering and Sustainable Resources, Head of the Institute of Soft Technologies, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran

2 PhD Student in Energy Systems Engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran

Abstract

With the increasing global demand for energy and the increasing complexity of energy systems, especially in the field of renewable resources and smart grids, the need for intelligent methods to optimize energy production, distribution, and consumption is increasingly felt. Reinforcement learning (RL), as an advanced branch of artificial intelligence, has provided new solutions for energy system management with the ability to learn optimal policies through dynamic interaction with the environment and adapt to uncertainties. This paper reviews the basic concepts of reinforcement learning, such as Markov decision processes and related algorithms, the advantages and disadvantages of this method, its practical applications in smart grid management, energy storage optimization, and electric vehicle management. RL is also compared with other optimization methods, such as supervised machine learning, evolutionary algorithms, and traditional mathematical models, and its future directions, including integration with new technologies such as the Internet of Things and blockchain, are reviewed. A special focus is placed on the potential of RL in solving Iran's endemic challenges, such as frequent blackouts and inefficient distribution networks, to propose solutions for energy sustainability at the national level.

Keywords

Main Subjects

References

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Journal of sustainable Energy Systems
Volume 5, Issue 3
July 2026
Pages 527-547

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