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iEnergy 2023, 2(3): 200-221 https://doi.org/10.23919/IEN.2023.0023
Review | Open Access | Issue | Published: 30 September 2023

Data-driven modeling of power system dynamics: Challenges, state of the art, and future work

Show Author's Information Heqing Huang1,  Yuzhang Lin1( ),  Yifan Zhou2,  Yue Zhao2,  Peng Zhang2,3,  Lingling Fan4
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01852, USA
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Interdisciplinary Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA
Department of Electrical and Computer Engineering, University of South Florida, Tampa, FL 33620, USA
Keywords:
machine learning, Data-driven modeling, parameter identification, system identification, model construction, power system dynamics
Cite this article:
Huang H, Lin Y, Zhou Y, et al. Data-driven modeling of power system dynamics: Challenges, state of the art, and future work. iEnergy, 2023, 2(3): 200-221. https://doi.org/10.23919/IEN.2023.0023
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Abstract Full text About this article

With the continual deployment of power-electronics-interfaced renewable energy resources, increasing privacy concerns due to deregulation of electricity markets, and the diversification of demand-side activities, traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges. Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge, higher capability of handling large-scale systems, and better adaptability to variations of system operating conditions. This paper discusses about the motivations and the generalized process of data-driven modeling, and provides a comprehensive overview of various state-of-the-art techniques and applications. It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.


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About this article

Data-driven modeling of power system dynamics: Challenges, state of the art, and future work

Show Author's information Heqing Huang1, Yuzhang Lin1( ), Yifan Zhou2, Yue Zhao2, Peng Zhang2,3, Lingling Fan4
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01852, USA
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794, USA
Interdisciplinary Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA
Department of Electrical and Computer Engineering, University of South Florida, Tampa, FL 33620, USA

Abstract

With the continual deployment of power-electronics-interfaced renewable energy resources, increasing privacy concerns due to deregulation of electricity markets, and the diversification of demand-side activities, traditional knowledge-based power system dynamic modeling methods are faced with unprecedented challenges. Data-driven modeling has been increasingly studied in recent years because of its lesser need for prior knowledge, higher capability of handling large-scale systems, and better adaptability to variations of system operating conditions. This paper discusses about the motivations and the generalized process of data-driven modeling, and provides a comprehensive overview of various state-of-the-art techniques and applications. It also comparatively presents the advantages and disadvantages of these methods and provides insight into outstanding challenges and possible research directions for the future.

Keywords: machine learning, Data-driven modeling, parameter identification, system identification, model construction, power system dynamics

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Received: 12 July 2023
Revised: 31 August 2023
Accepted: 04 September 2023
Published: 30 September 2023
Issue date: September 2023

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© The author(s) 2023.

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Acknowledgements

This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38456.

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