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The Concept of a Digital Twin for the Wały Śląskie Hydroelectric Power Plant: A Case Study in Poland

Author

Listed:
  • Artur Machalski

    (Department of Energy Conversion Engineering, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Przemysław Szulc

    (Department of Energy Conversion Engineering, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Dominik Błoński

    (Department of Energy Conversion Engineering, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Aneta Nycz

    (Department of Energy Conversion Engineering, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Magdalena Nemś

    (Department of Thermodynamics and Renewable Energy Sources, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Janusz Skrzypacz

    (Department of Energy Conversion Engineering, Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Przemysław Janik

    (TAURON Ekoenergia sp. z o.o, Obrońców Pokoju 2B, 58-500 Jelenia Góra, Poland)

  • Zuzanna Satława

    (TAURON Ekoenergia sp. z o.o, Obrońców Pokoju 2B, 58-500 Jelenia Góra, Poland)

Abstract

This paper introduces a conceptual framework for the development and implementation of a digital twin specifically designed for the Wały Śląskie Hydroelectric Power Plant. The primary focus is on defining the digital twin concept, addressing critical challenges encountered during its conceptualization, such as incomplete plant documentation, limited real-time data availability, and complexities in system integration. The digital twin architecture developed to address these issues features modular components, including data acquisition, data processing, visualization, and integration layers, emphasizing scalability, adaptability, and secure interoperability. Computational Fluid Dynamics (CFD) simulations and AI-based modeling techniques are integrated into the proposed framework as essential tools, providing critical data inputs for efficiency modeling and operational analysis. The proposed digital twin concept ensures accurate and reliable digital representation, facilitating informed decision-making and efficient operational management within hydropower plants.

Suggested Citation

  • Artur Machalski & Przemysław Szulc & Dominik Błoński & Aneta Nycz & Magdalena Nemś & Janusz Skrzypacz & Przemysław Janik & Zuzanna Satława, 2025. "The Concept of a Digital Twin for the Wały Śląskie Hydroelectric Power Plant: A Case Study in Poland," Energies, MDPI, vol. 18(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2021-:d:1634868
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    References listed on IDEAS

    as
    1. Razeen Hashmi & Huai Liu & Ali Yavari, 2024. "Digital Twins for Enhancing Efficiency and Assuring Safety in Renewable Energy Systems: A Systematic Literature Review," Energies, MDPI, vol. 17(11), pages 1-34, May.
    2. Hong Wang & Shiqi (Shawn) Ou & Ole Gunnar Dahlhaug & Pål-Tore Storli & Hans Ivar Skjelbred & Ingrid Vilberg, 2023. "Adaptively Learned Modeling for a Digital Twin of Hydropower Turbines with Application to a Pilot Testing System," Mathematics, MDPI, vol. 11(18), pages 1-20, September.
    3. Martin Wynn & Jose Irizar, 2023. "Digital Twin Applications in Manufacturing Industry: A Case Study from a German Multi-National," Future Internet, MDPI, vol. 15(9), pages 1-16, August.
    4. Vagnoni, Elena & Gezer, Dogan & Anagnostopoulos, Ioannis & Cavazzini, Giovanna & Doujak, Eduard & Hočevar, Marko & Rudolf, Pavel, 2024. "The new role of sustainable hydropower in flexible energy systems and its technical evolution through innovation and digitalization," Renewable Energy, Elsevier, vol. 230(C).
    5. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    6. Carlotta Tubeuf & Felix Birkelbach & Anton Maly & René Hofmann, 2023. "Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform," Energies, MDPI, vol. 16(4), pages 1-10, February.
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