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Digital twin in the power generation industry

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Abstract

The energy industry is undergoing unprecedented changes as it pursues global trends towards decarbonization, decentralization, and digitalization. Rapid development and deployment of big data and artificial intelligence technology over the past few decades have transformed the power generation industry in turning into a smarter industry that can monitor and adjust the status of key assets in real time. Digital twins, which are currently in the spotlight, are a technology that reproduce real physical assets using physical and data-driven models to simulate or predict the state of a system. There is a wide range of assets suitable for digital twins in power generation. They aim to identify changes in the system to detect anomalies or make maintenance decisions. The need for digital twins for energy transformation continues to grow. This paper provides a review of digital twin technology specific to the power generation industry. Among the power generation systems, digital twin studies for the combined cycle gas turbine, wind turbine, solar, and nuclear power plant were classified according to the lifecycle, complexity, and type of digital twin model, and the specific features and limitations of each application were analyzed. The goal is to provide readers with a curated summary of use cases which they may find useful in applying to their own work. The paper also explores the challenges and potential future research directions for increasing efficiency, availability, reliability, and solving environmental problems.

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Acknowledgements

The authors wish to acknowledge Danny Fregosi of EPRI for his contributions to the PV section of this paper.

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Authors and Affiliations

  1. KEPCO Research Institute, Daejeon, 34056, Republic of Korea

    Woosung Choi

  2. Turbine Logic, Atlanta, GA, USA

    Katie Hudachek, Steven Koskey & Christopher Perullo

  3. Electric Research Institute, Charlotte, NC, USA

    David Noble

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  1. Woosung Choi
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Correspondence to Woosung Choi.

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Choi, W., Hudachek, K., Koskey, S. et al. Digital twin in the power generation industry. JMST Adv. 6, 103–119 (2024). https://doi.org/10.1007/s42791-024-00065-1

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