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Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning

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  • Federico Rossi

    (Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
    These authors contributed equally to this work.)

  • Giancarlo Storti Gajani

    (Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
    These authors contributed equally to this work.)

  • Samuele Grillo

    (Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
    These authors contributed equally to this work.)

  • Giambattista Gruosso

    (Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Piazza Leonardo da Vinci, 32, I-20133 Milano, Italy
    These authors contributed equally to this work.)

Abstract

The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids.

Suggested Citation

  • Federico Rossi & Giancarlo Storti Gajani & Samuele Grillo & Giambattista Gruosso, 2025. "Future Smart Grids Control and Optimization: A Reinforcement Learning Tool for Optimal Operation Planning," Energies, MDPI, vol. 18(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:10:p:2513-:d:1654844
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    References listed on IDEAS

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    1. Benedetto-Giuseppe Risi & Francesco Riganti-Fulginei & Antonino Laudani, 2022. "Modern Techniques for the Optimal Power Flow Problem: State of the Art," Energies, MDPI, vol. 15(17), pages 1-20, September.
    2. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
    3. Mahmoud Kiasari & Mahdi Ghaffari & Hamed H. Aly, 2024. "A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems," Energies, MDPI, vol. 17(16), pages 1-38, August.
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