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On data-driven modeling and control in modern power grids stability: Survey and perspective

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  • Gong, Xun
  • Wang, Xiaozhe
  • Cao, Bo

Abstract

Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind turbines, energy storages, flexible loads, electric vehicles (EVs), etc. The grid control is confronted with low inertia, uncertainty and nonlinearity that challenge the operation security, efficacy and efficiency. The ongoing digitization of power grids provides opportunities to address the challenges with data-driven and control. This paper provides a comprehensive review of emerging data-driven dynamical modeling and control methods and their various applications in power grid. Future trends are also discussed based on advances in data-driven control.

Suggested Citation

  • Gong, Xun & Wang, Xiaozhe & Cao, Bo, 2023. "On data-driven modeling and control in modern power grids stability: Survey and perspective," Applied Energy, Elsevier, vol. 350(C).
  • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011042
    DOI: 10.1016/j.apenergy.2023.121740
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    1. Abdollah Younesi & Hossein Shayeghi & Pierluigi Siano, 2020. "Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids," Energies, MDPI, vol. 13(5), pages 1-22, March.
    2. Kou, Peng & Liang, Deliang & Wang, Chen & Wu, Zihao & Gao, Lin, 2020. "Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks," Applied Energy, Elsevier, vol. 264(C).
    3. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    4. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    5. Gong, Xun & Wang, Xiaozhe, 2023. "A novel Koopman-inspired method for the secondary control of microgrids with grid-forming and grid-following sources," Applied Energy, Elsevier, vol. 333(C).
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