Skip to main navigation menu Skip to main content Skip to site footer

Publications

Vol. 4 No. 2 (2026): From Theory to Action: Implementing Sustainability Principles

Digital Twin Technology for Renewable Energy Systems: Applications, Challenges, and Future Prospects

Submitted
May 24, 2026
Published
2026-07-12

Abstract

Renewable energy systems are increasingly deployed to support decarbonization and energy security, yet their operation remains challenged by intermittency, equipment degradation, grid instability, storage limitations, and the need for real-time decision-making. Digital twin technology offers a promising pathway for addressing these challenges by linking physical renewable energy assets with virtual models for monitoring, prediction, optimization, and control. This review examines the applications, enabling technologies, benefits, limitations, and future prospects of digital twin technology in renewable energy systems. A structured narrative review approach was adopted, in which 60 papers were initially considered and 44 studies were retained after screening for relevance to the scope of the study. The review shows that digital twins can support solar photovoltaic systems, wind turbines, battery storage units, hybrid microgrids, and smart grids through real-time monitoring, fault detection, power forecasting, predictive maintenance, battery health estimation, energy management, and grid optimization. However, practical deployment remains constrained by poor data quality, high implementation cost, cybersecurity risks, interoperability issues, model-validation challenges, and limited field testing. The review concludes that digital twin technology has strong potential to transform renewable energy systems from passively monitored assets into intelligent, adaptive, and actively managed energy systems. Future research should focus on affordable, scalable, secure, and field-validated digital twin frameworks, especially for developing countries where renewable energy adoption is growing but digital infrastructure remains limited.

References

  1. Acharya, S., Khan, A. A., & Päivärinta, T. (2024). Interoperability levels and challenges of digital twins in cyber–physical systems. Journal of Industrial Information Integration, 42, 100714.
  2. Aghazadeh Ardebili, A., Zappatore, M., Ramadan, A. I. H. A., Longo, A., & Ficarella, A. (2024). Digital Twins of smart energy systems: a systematic literature review on enablers, design, management and computational challenges. Energy Informatics, 7(1), 94.
  3. Al‐Shetwi, A. Q., Atawi, I. E., El‐Hameed, M. A., & Abuelrub, A. (2025). Digital Twin Technology for Renewable Energy, Smart Grids, Energy Storage and Vehicle‐to‐Grid Integration: Advancements, applications, key players, challenges and future perspectives in modernising sustainable grids. IET Smart Grid, 8(1), e70026.
  4. Bendaouia, A., Wang, F., Ouifak, H., & Li, J. (2026). Digital twin for battery energy storage systems. Renewable and Sustainable Energy Reviews, 226, 116347.
  5. Dimitrova-Angelova, D., González-González, J. F., Carmona-Fernández, D., & Jaramillo-Morán, M. A. (2025). Photovoltaic Digital Twins: Mathematical Modeling vs. Neural Networks for Energy Management in Smart Buildings. Applied Sciences, 15(16), 8883.
  6. Elnosh, A., Calais, M., & Parlevliet, D. (2026). A systematic literature review of digital twin research for photovoltaic systems: Trends, challenges, and opportunities. Renewable and Sustainable Energy Reviews, 226, 116326.
  7. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE access, 8, 108952-108971.
  8. Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems: New findings and approaches (pp. 85-113). Cham: Springer International Publishing.
  9. Hong, Y. Y., & Pula, R. A. (2024). Diagnosis of photovoltaic faults using digital twin and PSO-optimized shifted window transformer. Applied Soft Computing, 150, 111092.
  10. Jiang, C., & Chen, N. Z. (2025). G-Twin: Graph neural network-based digital twin for real-time and high-fidelity structural health monitoring for offshore wind turbines. Marine Structures, 103, 103813.
  11. Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP journal of manufacturing science and technology, 29, 36-52.
  12. Kabir, M. R., Halder, D., & Ray, S. (2024). Digital twins for IoT-driven energy systems: A survey. IEEE Access, 12, 177123-177143.
  13. Kandemir, E., Hasan, A., Kvamsdal, T., & Abdel-Afou Alaliyat, S. (2024). Predictive digital twin for wind energy systems: a literature review. Energy Informatics, 7(1), 68.
  14. Kazeem, O. U., Godwin, I. O., Oyubu, O. A., IIori, E. G., Peretomode, E., & Ewere, O. E. (2026). Design and Implementation of a Voice-Activated Security Door System Using Matlab. NIPES-Journal of Science and Technology Research, 8(1), 315-323.
  15. Khazaeli Moghadam, F., Keprate, A., & Gao, Z. (2024). Online monitoring of wind power plants using digital twin models. Frontiers in Energy Research, 12, 1531689.
  16. Kim, M., Ghobadi, F., Tayerani Charmchi, A. S., Lee, M., & Lee, J. (2025). Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges. Sustainability, 18(1), 43.
  17. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. Ifac-Papers Online, 51(11), 1016-1022.
  18. Leon-Medina, J. X., Tibaduiza, D. A., Parés, N., & Pozo, F. (2025). Digital twin technology in wind turbine components: A review. Intelligent Systems with Applications, 26, 200535.
  19. Malik, M. T., Obaid, H. M., & Rasool, H. F. (2026). Integrating digital twin and sensor technologies for future-ready smart grids. Discover Electronics, 3(1), 42.
  20. Mbasso, W. F., Harrison, A., Dagal, I., Jangir, P., Khishe, M., Kotb, H., ... & Kumar, R. (2025). Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions. Energy Strategy Reviews, 61, 101814.
  21. Mchirgui, N., Quadar, N., Kraiem, H., & Lakhssassi, A. (2024). The applications and challenges of digital twin technology in smart grids: A comprehensive review. Applied Sciences, 14(23), 10933.
  22. Nasri, I., Mansouri, M., Refaat, S. S., Dhibi, K., & Skorek, A. W. (2025). Digital Twin-Based Kernel RF for Fault Detection and Diagnosis in Photovoltaic Systems. IEEE Access, 13, 211533-211542.
  23. Nneoma Ugwu, C., Chukwudi Ogenyi, F., Nnenna Ugwu, J., Okechukwu Ugwu, P. C., & Ben Okon, M. (2026). Integration of AI-driven digital twins for real-time optimization of renewable energy grids. Frontiers in Energy Research, 14, 1748233.
  24. Obuseh, E., EBISINE, E. E., OKIEKE, U. J., ALOAMAKA, C. A., & ANAMONYE, G. U. (2025). Sustainability of Decarbonization: A Systematic Review of Policy Integration and Regional Realities. Nigerian Journal of Sustainability Research, 3(2), 77-95.
  25. Obuseh, E., Eyenubo, J., Alele, J., Okpare, A., & Oghogho, I. (2025). A systematic review of barriers to renewable energy integration and adoption. Journal of Asian Energy Studies, 9, 26-45.
  26. Obuseh, E., Otuagoma, S., Efenedo, G. I., ANAMONYE, G., Okieke, U. J., & EYENUBO, J. (2026). Sustainability of Solar Photovoltaic Systems: A Life Cycle Assessment Review. Nigerian Journal of Sustainability Research, 4(1), 387-410.
  27. Oghenegare, A. J., Nkemdilim, E. J., Ewere, O. E., Donald, I., & Ekene, M. C. (2024). Renewable energy in Nigeria: a comprehensive examination of the viability of solar energy in Nigeria. Int J Innov Res Technol, 11(5), 2433-2438.
  28. Olatunji, O. O., Adedeji, P. A., Madushele, N., & Jen, T. C. (2021, May). Overview of digital twin technology in wind turbine fault diagnosis and condition monitoring. In 2021 IEEE 12th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT) (pp. 201-207). IEEE.
  29. Pastor-Sanchez, A., Garcia-Espinosa, J., Di Capua, D., Servan-Camas, B., & Berdugo-Parada, I. (2025). Real-time digital twin for structural health monitoring of floating offshore wind turbines. Journal of Marine Science and Engineering, 13(10), 1953.
  30. Patrick, D., & Amusan, A. (2026). Digital Twin Model of a Solar Panel for Intelligent Monitoring Using Artificial Neural Network. ABUAD Journal of Engineering Research and Development (AJERD), 9(1), 97-112.
  31. Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE access, 6, 3585-3593.
  32. Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modeling perspective. IEEE access, 8, 21980-22012.
  33. Semeraro, C., Aljaghoub, H., Al-Ali, H. K. M. H., Abdelkareem, M. A., & Olabi, A. G. (2025). Harnessing the future: Exploring digital twin applications and implications in renewable energy. Energy Nexus, 18, 100415.
  34. Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021). Digital twin paradigm: A systematic literature review. Computers in industry, 130, 103469.
  35. Shi, Q., & Zhou, J. (2026). Digital Twin-Driven Dynamic Reactive Power and Voltage Optimization for Large Grid-Connected PV Stations. Electronics, 15(4), 821.
  36. Sifat, M. M. H., Haque, M. E., Islam, S., Ronanki, D., & Kumar, B. K. (2026). Digital twin based microgrid control and energy management: Trends, challenges, and future directions. Computers and Electrical Engineering, 134, 111100.
  37. Tabassum, S., Babu, A. R. V., Dheer, D. K., & Narasipuram, R. P. (2026). A Digital Twin Framework Driven Energy Management Strategy for Hybrid DC Microgrids with Integrated xEV Charging Applications. Sustainable Computing: Informatics and Systems, 101364.
  38. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9), 3563-3576.
  39. Testasecca, T., Bellesini, F., Arnone, D., & Beccali, M. (2025). A Digital Twin for Real-Time and Predictive Optimization of Electric Vehicle Charging in Microgrids Integrating Renewable Energy Sources. Energies, 18(21), 5605.
  40. Thwe, M. M., Ştefanov, A., Rajkumar, V. S., & Palensky, P. (2025). Digital twins for power systems: Review of current practices, requirements, enabling technologies, data federation and challenges. IEEE Access.
  41. Wang, Y., Xiao, J., Soo, Y. Y., Chen, Y., & Chen, Z. (2025). Digital twins for battery health prognosis: A comprehensive review of recent advances and challenges. ETransportation, 100489.
  42. Zhang, Y., Sun, R., & Shang, J. (2025). Prescribed Performance Bounded-H∞ Control for Flexible-Joint Manipulators Without Initial Condition Restriction. Sensors, 25(7), 2195.
  43. Zhao, K., Liu, Y., Zhou, Y., Ming, W., & Wu, J. (2025). Digital twin-supported battery state estimation based on TCN-LSTM neural networks and transfer learning. CSEE Journal of Power and Energy Systems, 11(2), 567-579.
  44. Zhao, X. (2024). A novel digital-twin approach based on transformer for photovoltaic power prediction. Scientific Reports, 14(1), 26661.

Downloads

Download data is not yet available.

Similar Articles

1-10 of 24

You may also start an advanced similarity search for this article.