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Semeraro, C., Aljaghoub, H., Housni Ibrahim Mdallal, A., Abdelkareem, M. A., & Olabi, A. G. Efficiency and Enhanced Performance: Exploring Digital Twin Implementation in Power Plants. Renewable and Sustainable Energy Technology. 2025, 1(1), 5. doi: https://doi.org/10.53941/rset.2025.100005

The utilization of digital twin technology in large-scale power plants has recently attracted considerable attention. This is because a digital twin can be an opportunity to improve multiple aspects of power plant operations, such as performance monitoring, predictive maintenance, and fault diagnosis. As a result, this study aims to present a comprehensive survey of the existing literature on applying digital twins in large-scale power plants. These applications include thermal, nuclear, and hydropower plants. Furthermore, this paper explores the distinct architecture of a large-scale power plant digital twin. This comprehensive survey paves the way for accurately identifying gaps and constraints restraining the use of digital twins for large-scale power plants. There is a clear indication of a research gap when examining the challenges and practical considerations in implementing digital twins in large-scale power plants.

References

  1. Dassisti, M.; Giovannini, A.; Merla, P.; et al. An approach to support Industry 4.0 adoption in SMEs using a core-metamodel. Annu. Rev. Control 2019, 47, 266–274. https://doi.org/10.1016/J.ARCONTROL.2018.11.001.
  2. Grieves, M.; Vickers, J. Grieves Origins of the Digital Twin Concept. Fla. Inst. Technol. 2016, 8, 3–20. https://doi.org/10.13140/RG.2.2.26367.61609.
  3. Glaessgen, E.; Stargel, D. The digital twin paradigm for future NASA and US Air Force vehicles. In Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, Honolulu, HI, USA, 23–26 April 2012; p. 1818. doi: 10.2514/6.2012-1818
  4. Chen, Y. Integrated and intelligent manufacturing: Perspectives and enablers. Engineering 2017, 3, 588–595. doi: 10.1016/J.ENG.2017.04.009
  5. Liu, Z.; Meyendorf, N.; Mrad, N. The role of data fusion in predictive maintenance using digital twin. In AIP Conference Proceedings; AIP Publishing LLC: Melville, NY, USA, 2018; p. 20023. doi: 10.1063/1.5031520
  6. Semeraro, C.; Lezoche, M.; Panetto, H.; et al. Digital twin paradigm: A systematic literature review. Comput. Ind. 2021, 130, 103469. doi: 10.1016/j.compind.2021.103469
  7. Alobaid, F.; Mertens, N.; Starkloff, R.; et al. Progress in dynamic simulation of thermal power plants. Prog. Energy Combust. Sci. 2017, 59, 79–162. https://doi.org/10.1016/j.pecs.2016.11.001.
  8. Masuyama, F. History of power plants and progress in heat resistant steels. ISIJ Int. 2001, 41, 612–625. doi: 10.2355/isijinternational.41.612
  9. Adibhatla, S.; Kaushik, S.C. Energy and exergy analysis of a super critical thermal power plant at various load conditions under constant and pure sliding pressure operation. Appl. Therm. Eng. 2014, 73, 51–65. https://doi.org/10.1016/J.APPLTHERMALENG.2014.07.030.
  10. Alami, A.H.; Olabi, A.G.; Mdallal, A.; et al. Concentrating solar power (CSP) technologies: Status and analysis. Int. J. Thermofluids 2023, 18, 100340. https://doi.org/10.1016/J.IJFT.2023.100340.
  11. Tritt, T.M.; Böttner, H.; Chen, L. Thermoelectrics: Direct Solar Thermal Energy Conversion. MRS Bull. 2008, 33, 366–368. https://doi.org/10.1557/MRS2008.73.
  12. Olesen, J.F.; Shaker, H.R. Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges. Sensors 2020, 20, 2425. https://doi.org/10.3390/S20082425.
  13. Kuria, M. Operational Challenges Facing Performance of Thermal Power Plants in Kenya. 2013. Available online: http://erepository.uonbi.ac.ke/handle/11295/58576 (accessed on 9 April 2023).
  14. Porton, M.; Latham, H.; Vizvary, Z.; et al. Balance of plant challenges for a near-term EU demonstration power plant. In Proceedings of the 2013 IEEE 25th Symposium on Fusion Engineering, SOFE, San Francisco, CA, USA, 10–14 June 2013. https://doi.org/10.1109/SOFE.2013.6635331.
  15. Sayed, E.T.; Olabi, A.G.; Alami, A.H.; et al. Renewable Energy and Energy Storage Systems. Energies 2023, 16, 1415. https://doi.org/10.3390/EN16031415.
  16. Mahmoud, M.; Dutton, K.; Denman, M. Dynamical modelling and simulation of a cascaded reserevoirs hydropower plant. Electr. Power Syst. Res. 2004, 70, 129–139. https://doi.org/10.1016/J.EPSR.2003.12.001.
  17. Cuartas, L.A.; Cunha, A.P.M.D.A.; Alves, J.A.; et al. Recent Hydrological Droughts in Brazil and Their Impact on Hydropower Generation. Water 2022, 14, 601. https://doi.org/10.3390/W14040601/S1.
  18. Losier, L.-M.; Fernandes, R.; Tabarro, P.; et al. The Importance of Digital Twins for Resilient Infrastructure A Bentley White Paper. 201. Available online: www.bentley.com (accessed on 25 April 2023).
  19. Ghenai, C.; Husein, L.A.; Al Nahlawi, M.; et al. Recent trends of digital twin technologies in the energy sector: A comprehensive review. Sustain. Energy Technol. Assess. 2022, 54, 102837. https://doi.org/10.1016/J.SETA.2022.102837.
  20. Semeraro, C.; Aljaghoub, H.; Abdelkareem, M.A.; et al. Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining. Energy 2023, 273, 127086. https://doi.org/10.1016/J.ENERGY.2023.127086.
  21. Semeraro, C.; Olabi, A.G.; Aljaghoub, H.; et al. Digital twin application in energy storage: Trends and challenges. J. Energy Storage 2023, 58, 106347. https://doi.org/10.1016/J.EST.2022.106347.
  22. Sleiti, A.K.; Kapat, J.S.; Vesely, L. Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Rep. 2022, 8, 3704–3726. https://doi.org/10.1016/J.EGYR.2022.02.305.
  23. Sun, J.; Zhang, B.; Cheng, S.; et al. Investigation of single pressure point off-line correction in matrix-solved steam pipe network model for digital twins application. Ann. Nucl. Energy 2022, 179, 109426. https://doi.org/10.1016/J.ANUCENE.2022.109426.
  24. Ji, H.; Li, J.; Zhang, S.; et al. Research on Water Resources Intelligent Management of Thermal Power Plant Based on Digital Twins. In Proceedings of the 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA, Chengdu, China, 24–26 April 2021; pp. 557–562. https://doi.org/10.1109/ICCCBDA51879.2021.9442503.
  25. Tao, F.; Zhang, H.; Liu, A.; et al. Nee Digital twin in industry: State-of-the-art. IEEE Trans. Ind. Inform. 2018, 15, 2405–2415. doi: 10.1109/TII.2018.2873186
  26. Liu, M.; Fang, S.; Dong, H.; et al. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021, 58, 346–361. doi: 10.1016/j.jmsy.2020.06.017
  27. Schmitt, J.; Horstkötter, I.; Bäker, B. State-of-health estimation by virtual experiments using recurrent decoder–encoder based lithium-ion digital battery twins trained on unstructured battery data. J. Energy Storage 2023, 58, 106335. https://doi.org/10.1016/j.est.2022.106335.
  28. Yi, Y.; Xia, C.; Feng, C.; et al. Digital twin-long short-term memory (LSTM) neural network based real-time temperature prediction and degradation model analysis for lithium-ion battery. J. Energy Storage 2023, 64, 107203. https://doi.org/10.1016/j.est.2023.107203.
  29. Yuan, Z.; Pan, Y.; Wang, H.; et al. Fault data generation of lithium ion batteries based on digital twin: A case for internal short circuit. J. Energy Storage 2023, 64, 107113. https://doi.org/10.1016/j.est.2023.107113.
  30. Li, W.; Rentemeister, M.; Badeda, J.; et al. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. J. Energy Storage 2020, 30, 101557. doi: 10.1016/j.est.2020.101557
  31. Tang, H.; Wu, Y.; Cai, Y.; et al. Design of power lithium battery management system based on digital twin. J. Energy Storage 2022, 47, 103679. https://doi.org/10.1016/j.est.2021.103679.
  32. Moghadam, H.M.; Foroozan, H.; Gheisarnejad, M.; et al. A survey on new trends of digital twin technology for power systems. J. Intell. Fuzzy Syst. 2021, 41, 3873–3893. https://doi.org/10.3233/JIFS-201885.
  33. Yu, J.; Liu, P.; Li, Z. Hybrid modelling and digital twin development of a steam turbine control stage for online performance monitoring. Renew. Sustain. Energy Rev. 2020, 133, 110077. https://doi.org/10.1016/J.RSER.2020.110077.
  34. Pei, F.Q.; Tong, Y.F.; Yuan, M.H.; et al. The digital twin of the quality monitoring and control in the series solar cell production line. J. Manuf. Syst. 2021, 59, 127–137. https://doi.org/10.1016/J.JMSY.2021.02.001.
  35. Al-Ali, A.R.; Gupta, R.; Batool, T.Z.; et al. Digital Twin Conceptual Model within the Context of Internet of Things. Future Internet 2020, 12, 163. https://doi.org/10.3390/FI12100163.
  36. Li, M.; Lu, Q.; Bai, S.; et al. Digital twin-driven virtual sensor approach for safe construction operations of trailing suction hopper dredger. Autom. Constr. 2021, 132, 103961. https://doi.org/10.1016/J.AUTCON.2021.103961.
  37. Warke, V.; Kumar, S.; Bongale, A.; et al. Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis. Sustainability 2021, 13, 10139. https://doi.org/10.3390/SU131810139.
  38. Werner, A.; Zimmermann, N.; Lentes, J. Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin. Procedia Manuf. 2019, 39, 1743–1751. https://doi.org/10.1016/J.PROMFG.2020.01.265.
  39. Falekas, G.; Karlis, A. Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects. Energies 2021, 14, 5933. https://doi.org/10.3390/EN14185933.
  40. Leng, J.; Wang, D.; Shen, W.; et al. Digital twins-based smart manufacturing system design in Industry 4.0: A review. J. Manuf. Syst. 2021, 60, 119–137. https://doi.org/10.1016/J.JMSY.2021.05.011.
  41. Qi, Q.; Tao, F.; Hu, T.; et al. Enabling technologies and tools for digital twin. J. Manuf. Syst. 2021, 58, 3–21. https://doi.org/10.1016/J.JMSY.2019.10.001.
  42. Semeraro, C.; Lezoche, M.; Panetto, H.; et al. Data-driven invariant modelling patterns for digital twin design. J. Ind. Inf. Integr. 2023, 31, 100424. https://doi.org/10.1016/J.JII.2022.100424.
  43. Semeraro, C.; Aljaghoub, H.; Abdelkareem, M.A.; et al. Guidelines for designing a digital twin for Li-ion battery: A reference methodology. Energy 2023, 284, 128699. https://doi.org/10.1016/J.ENERGY.2023.128699.
  44. Yu, J.; Petersen, N.; Liu, P.; et al. Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development. Energy 2022, 260, 125088. https://doi.org/10.1016/J.ENERGY.2022.125088.
  45. Spinti, J.P.; Smith, P.J.; Smith, S.T. Atikokan Digital Twin: Machine learning in a biomass energy system. Appl. Energy 2022, 310, 118436. https://doi.org/10.1016/J.APENERGY.2021.118436.
  46. Li, J.; Liu, T.; Zhu, G.; et al. Uncertainty quantification and aerodynamic robust optimization of turbomachinery based on graph learning methods. Energy 2023, 273, 127289. https://doi.org/10.1016/J.ENERGY.2023.127289.
  47. Mukherjee, T.; Gupta, A.; Deodhar, A.; et al. Real-time coal classification in thermal power plants. Control Eng. Pract. 2023, 130, 105377. https://doi.org/10.1016/J.CONENGPRAC.2022.105377.
  48. Park, J.; Lee, W.; Huh, K.Y. Model order reduction by radial basis function network for sparse reconstruction of an industrial natural gas boiler. Case Stud. Therm. Eng. 2022, 37, 102288. https://doi.org/10.1016/J.CSITE.2022.102288.
  49. Lee, W.; Jang, K.; Han, W.; et al. Model order reduction by proper orthogonal decomposition for a 500 MWe tangentially fired pulverized coal boiler. Case Stud. Therm. Eng. 2021, 28, 101414. https://doi.org/10.1016/J.CSITE.2021.101414.
  50. Hu, M.; He, Y.; Lin, X.; et al. Digital twin model of gas turbine and its application in warning of performance fault. Chin. J. Aeronaut. 2023, 36, 449–470. https://doi.org/10.1016/J.CJA.2022.07.021.
  51. Cruz-Manzo, S.; Panov, V.; Bingham, C. GAS turbine sensor fault diagnostic system in a real-time executable digital-twin. J. Glob. Power Propuls. Soc. 2023, 7, 85–94. https://doi.org/10.33737/JGPPS/159781.
  52. Zhang, H.; Liu, X.; Ma, D.; et al. Digital twin for directional solidification of a single-crystal turbine blade. Acta Mater. 2023, 244, 118579. https://doi.org/10.1016/J.ACTAMAT.2022.118579.
  53. Wang, J.; Zhang, Z.; Liu, Z.; et al. Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis. Reliab. Eng. Syst. Saf. 2023, 234, 109152. https://doi.org/10.1016/J.RESS.2023.109152.
  54. Ağbulut, Ü. Turkey’s electricity generation problem and nuclear energy policy. Energy Sources Part. A: Recovery Util. Environ. Eff. 2019, 41, 2281–2298. https://doi.org/10.1080/15567036.2019.1587107.
  55. Adams, S.; Odonkor, S. Status, opportunities, and challenges of nuclear power development in Sub-Saharan Africa: The case of Ghana. Progress. Nucl. Energy 2021, 138, 103816. https://doi.org/10.1016/J.PNUCENE.2021.103816.
  56. Song, H.; Song, M.; Liu, X. Online autonomous calibration of digital twins using machine learning with application to nuclear power plants. Appl. Energy 2022, 326, 119995. https://doi.org/10.1016/J.APENERGY.2022.119995.
  57. Gong, H.; Zhu, T.; Chen, Z.; et al. Parameter identification and state estimation for nuclear reactor operation digital twin. Ann. Nucl. Energy 2023, 180, 109497. https://doi.org/10.1016/J.ANUCENE.2022.109497.
  58. Gong, H.; Cheng, S.; Chen, Z.; et al. An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics. Ann. Nucl. Energy 2022, 179, 109431. https://doi.org/10.1016/J.ANUCENE.2022.109431.
  59. Yang, J.; Sui, X.; Huang, Y.; et al. Assessment of reactor flow field prediction based on deep learning and model reduction. Ann. Nucl. Energy 2022, 179, 109367. https://doi.org/10.1016/J.ANUCENE.2022.109367.
  60. Nguyen, T.N.; Ponciroli, R.; Bruck, P.; et al. A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring. Ann. Nucl. Energy 2022, 170, 109002. https://doi.org/10.1016/J.ANUCENE.2022.109002.
  61. Alnaqbi, S.; Alasad, S.; Aljaghoub, H.; et al. Applicability of Hydropower Generation and Pumped Hydro Energy Storage in the Middle East and North Africa. Energies 2022, 15, 2412. https://doi.org/10.3390/en15072412.
  62. Wang, Z.; Jia, W.; Wang, K.; et al. Digital twins supported equipment maintenance model in intelligent water conservancy. Comput. Electr. Eng. 2022, 101, 108033. https://doi.org/10.1016/J.COMPELECENG.2022.108033.
  63. Voronin, S.; Davlatov, A.; Kosimov, B. Development directions of power supply for rural areas of Tajikistan. In Proceedings of the 2019 International Ural Conference on Electrical Power Engineering, UralCon 2019, Chelyabinsk, Russia, 1–3 October 2019; pp. 157–161. https://doi.org/10.1109/URALCON.2019.8877688.
  64. Dreyer, M.; Nicolet, C.; Gaspoz, A.; et al. Monitoring 4.0 of penstocks: Digital twin for fatigue assessment. IOP Conf. Ser. Earth Environ. Sci. 2021, 774, 012009. https://doi.org/10.1088/1755-1315/774/1/012009.
  65. Xu, B.; Wang, J.; Wang, X.; et al. A case study of digital-twin-modelling analysis on power-plant-performance optimizations. Clean. Energy 2019, 3, 227–234. https://doi.org/10.1093/ce/zkz025.
  66. Popp, T.; Heberle, F.; Weiß, A.P.; et al. Thermodynamic evaluation of an orc test rig-from comprehensive experimental results to a simulation model. In Proceedings of the 6th International Seminar on ORC Power Systems, Munich, Germany, 11–13 October 2021. Available online: https://www.researchgate.net/profile/Tobias-Popp/publication/357242413_THERMODYNAMIC_EVALUATION_OF_AN_ORC_TEST_RIG_-_FROM_COMPREHENSIVE_EXPERIMENTAL_RESULTS_TO_A_SIMULATION_MODEL/links/61c3040f52bd3c7e0583c739/THERMODYNAMIC-EVALUATION-OF-AN-ORC-TEST-RIG-FROM-COMPREHENSIVE-EXPERIMENTAL-RESULTS-TO-A-SIMULATION-MODEL.pdf (accessed on 20 June 2025).
  67. Zeng, G.; Wang, J.; Zhang, L.; et al. Multi-Domain Modeling and Analysis of Marine Steam Power System Based on Digital Twin. J. Mar. Sci. Eng. 2023, 11, 429. https://doi.org/10.3390/JMSE11020429.
  68. Sandhu, H.K.; Bodda, S.S.; Gupta, A. A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities. Energies 2023, 16, 2628. https://doi.org/10.3390/EN16062628.
  69. Tang, W.; Chen, X.; Qian, T.; et al. Technologies and Applications of Digital Twin for Developing Smart Energy Systems. Chin. J. Eng. Sci. 2020, 22, 74. https://doi.org/10.15302/J-SSCAE-2020.04.010.
  70. Wang, Y.; Su, Z.; Guo, S.; et al. A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects. IEEE Internet Things J. 2023, 10, 14965–14987. https://doi.org/10.1109/JIOT.2023.3263909.
  71. Mozo, A.; Karamchandani, A.; Gómez-Canaval, S.; et al. B5GEMINI: AI-Driven Network Digital Twin. Sensors 2022, 22, 4106. https://doi.org/10.3390/S22114106.
  72. Khan, L.U.; Saad, W.; Niyato, D.; et al. Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions. IEEE Commun. Mag. 2022, 60, 74–80. https://doi.org/10.1109/MCOM.001.21143.
  73. Aydemir, H.; Zengin, U.; Durak, U.; et al. The digital twin paradigm for aircraft–review and outlook. AIAA Scitech 2020, 1, 1–12. https://doi.org/10.2514/6.2020-0553.
  74. López, C.E.B. Real-time event-based platform for the development of digital twin applications. Int. J. Adv. Manuf. Technol. 2021, 116, 835–845. https://doi.org/10.1007/S00170-021-07490-9/METRICS.
  75. Faleiro, R.; Pan, L.; Pokhrel, S.R.; et al. Digital Twin for Cybersecurity: Towards Enhancing Cyber Resilience. Lect. Notes Inst. Comput. Sci. Soc. Inform. Telecommun. Eng. LNICST 2022, 413, 57–76. https://doi.org/10.1007/978-3-030-93479-8_4/COVER.
  76. Eckhart, M.; Ekelhart, A. Digital Twins for Cyber-Physical Systems Security: State of the Art and Outlook. Secur. Qual. Cyber Phys. Syst. Eng. 2019, 383–412. https://doi.org/10.1007/978-3-030-25312-7_14/COVER.
  77. Romero, D.; Bernus, P.; Noran, O.; et al. The operator 4.0: Human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In Advances in Production Management Systems. Initiatives for a Sustainable World; IFIP Advances in Information and Communication Technology; Nääs, I., Vendrametto, O., Reis, J.M., et al., Eds.; Springer International Publishing: Cham, Switzerland, 2016; Volume 488, pp. 677–686. https://doi.org/10.1007/978-3-319-51133-7_80.
  78. Gobio-Thomas, L.B.; Darwish, M.; Stojceska, V. Review on the economic impacts of solar thermal power plants. Therm. Sci. Eng. Prog. 2023, 46, 102224. https://doi.org/10.1016/j.tsep.2023.102224.