Intelligent Energy Management in Microgrids Using Multi-Agent Reinforcement Learning

  • Authors

    • Anatoly Kitov Colonel & Computer Scientist, Soviet Academy of Sciences, Russia Author
    • Mikhail Kartsev Chief Designer, Research Institute of Computer Systems, Russia Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2024PII3P6Y

    Published 11-03-2024

  • Intelligent Microgrids, Multi-Agent Reinforcement Learning, Energy Management System, Distributed Energy Resources, Smart Grid, Renewable Energy Integration, Battery Energy Storage, Demand Response, Artificial Intelligence, Deep Reinforcement Learning

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    Intelligent Energy Management in Microgrids Using Multi-Agent Reinforcement Learning. (2024). International Journal of Modern Research in Science & Engineering, 7(2), 01-17. https://doi.org/10.67228/30716357/IJMRSE-2024PII3P6Y

  • Abstract

    The increasing integration of renewable energy sources and distributed energy resources has made smart microgrid energy management more complex due to renewable intermittency and dynamic operating conditions. This paper proposes a Multi-Agent Reinforcement Learning (MARL)-based energy management framework in which intelligent agents coordinate renewable generation, energy storage, controllable loads, and grid interaction. Through distributed learning and real-time decision-making, the framework optimizes energy scheduling, improves system reliability, reduces operational cost and carbon emissions, enhances renewable energy utilization, and provides a scalable, autonomous, and resilient solution for next-generation smart microgrids.

  • References

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