IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i8p2021-d1634868.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/8/2021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/8/2021/
    Download Restriction: no
    ---><---

    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. 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).
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ama Ranawaka & Damminda Alahakoon & Yuan Sun & Kushan Hewapathirana, 2024. "Leveraging the Synergy of Digital Twins and Artificial Intelligence for Sustainable Power Grids: A Scoping Review," Energies, MDPI, vol. 17(21), pages 1-52, October.
    2. Mitra Pooyandeh & Insoo Sohn, 2023. "Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach," Mathematics, MDPI, vol. 11(23), pages 1-37, December.
    3. Sifat, Md. Mhamud Hussen & Choudhury, Safwat Mukarrama & Das, Sajal K. & Pota, Hemanshu & Yang, Fuwen, 2025. "Novel abstractions and experimental validation for digital twin microgrid design: Lab scale studies and large scale proposals," Applied Energy, Elsevier, vol. 377(PC).
    4. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    6. Eduardo Gómez-Luna & John E. Candelo-Becerra & Juan C. Vasquez, 2023. "A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks," Energies, MDPI, vol. 16(14), pages 1-23, July.
    7. Luo, Xiaolin & Huang, Li & Sui, Xin, 2025. "Capacity optimization of existing reservoir hydropower expansion and its impact on power system flexibility," Applied Energy, Elsevier, vol. 377(PD).
    8. Namita Kumari & Ankush Sharma & Binh Tran & Naveen Chilamkurti & Damminda Alahakoon, 2023. "A Comprehensive Review of Digital Twin Technology for Grid-Connected Microgrid Systems: State of the Art, Potential and Challenges Faced," Energies, MDPI, vol. 16(14), pages 1-19, July.
    9. Artur Pawelec & Agnieszka Pawlak & Aleksandra Pyk & Paweł Grzegorz Kossakowski, 2024. "Research on the Possibilities of Expanding the Photovoltaic Installation in the Microgrid Structure of Kielce University of Technology Using Digital Twin Technology," Sustainability, MDPI, vol. 16(21), pages 1-14, October.
    10. Juan R. Lopez & Jose de Jesus Camacho & Pedro Ponce & Brian MacCleery & Arturo Molina, 2022. "A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network," Energies, MDPI, vol. 15(19), pages 1-25, October.
    11. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    12. Simon Eyselein & Alexander Tismer & Rohit Raj & Tobias Rentschler & Stefan Riedelbauch, 2025. "AI-Based Clustering of Numerical Flow Fields for Accelerating the Optimization of an Axial Turbine," Energies, MDPI, vol. 18(3), pages 1-24, January.
    13. Sri Nikhil Gupta Gourisetti & Sraddhanjoli Bhadra & David Jonathan Sebastian-Cardenas & Md Touhiduzzaman & Osman Ahmed, 2023. "A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications," Energies, MDPI, vol. 16(13), pages 1-58, June.
    14. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. Zeli Ye & Wentao Huang & Jinfeng Huang & Jun He & Chengxi Li & Yan Feng, 2023. "Optimal Scheduling of Integrated Community Energy Systems Based on Twin Data Considering Equipment Efficiency Correction Models," Energies, MDPI, vol. 16(3), pages 1-22, January.
    16. Zhen Huang & Xuechun Xiao & Yuan Gao & Yonghong Xia & Tomislav Dragičević & Pat Wheeler, 2023. "Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications," Energies, MDPI, vol. 16(17), pages 1-26, August.
    17. Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.
    18. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    19. Bianca Goia & Tudor Cioara & Ionut Anghel, 2022. "Virtual Power Plant Optimization in Smart Grids: A Narrative Review," Future Internet, MDPI, vol. 14(5), pages 1-22, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:8:p:2021-:d:1634868. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.