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

Xun Gong, Xiaozhe Wang and Bo Cao

Applied Energy, 2023, vol. 350, issue C, No S0306261923011042

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.

Keywords: Power grid dynamics and control; Data-driven modeling; Koopman operator; Data-driven control; Physics-informed machine learning; System identification and control (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2023.121740

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