Intelligent Twin for Fleet-Coordinated Renewable Power
Keywords:
Cognitive energy twin, asset prognostics, autonomous fleet coordination, resilience in renewables. These concepts are introduced first, and their boundaries and stakeholders are specified subsequently.Abstract
Insufficient energy awareness threatens both the long-term sustainability of payer healthcare infrastructures and the operational efficiency of population-level health services. A formal, evidence-based framework for energy-aware demand intelligence in healthcare would allow continuous assessment of system energy consumption, climate impact, and the behavioral intentions of stakeholders—laying the groundwork for energy-efficient operational methods. By embedding energy consumption and climate impact into dynamic demand intelligence for population-level operational planning, data-driven computing and scheduling decisions can be validated against energy profiles.
This approach would allow payer infrastructures to move toward sustainability: actual energy consumption trends would converge toward the most energy-efficient configurations, energy scaling would correlate more closely with demand, data traffic would become more predictable, out-of-band energy peaks would be minimized, and long-term forecasts would better track the seasonal patterns of energy-intensive computing resources. Population-scale operations also stand to benefit from energy-aware computing design through three complementary capabilities: load management optimization, demand-shaping during extreme weather events, and improved performance across service throughput, wait times, and energy consumption. Bidirectional, energy-aware intelligence paired with synchronous decision-making would support timely adaptation of operational policies, processes, and systems.
Recent advances in deep learning, edge computing, and Industry 4.0 technologies are driving the digital transformation of operational sectors—shifting the focus from supervisory control of physical assets toward direct cognitive management of data and models. These developments enable the coordinated integration of data and computing needs across diverse industrial assets, giving rise to the concept of a Cognitive Industrial Energy Twin. Such a twin would support asset prognostics for managing remaining useful life, autonomous fleet coordination, and resilient operations incorporating renewable power generation and consumption.
A Cognitive Industrial Energy Twin represents the operational behavior of interconnected industrial assets through sensor data, data quality assessment, and a layered hierarchy of data-driven, physics-informed, and hybrid models. Its cognitive capability emerges from self-learning on telemetry data, predictive modeling of asset health, and algorithmic decision-making designed to minimize the effects of dynamic environments and human intervention on operations. This twin offers a new paradigm for fleet operations management, underpinned by the growing adoption of in-situ monitoring, autonomous decision-making, edge computing, and artificial intelligence.
References
1. Park, H.-A., Byeon, G., Son, W., Jo, H.-C., Kim, J., & Kim, S. (2020). Digital twin for operation of microgrid: Optimal scheduling in virtual space of digital twin. Energies, 13(20), 5504.
2. Saad, A., Faddel, S., & Mohammed, O. (2020). IoT-based digital twin for energy cyber-physical systems: Design and implementation. Energies, 13(18), 4762.
3. Hao, Y., Dong, L., Liang, J., Liao, X., Wang, L., & Shi, L. (2020). Power forecasting-based coordination dispatch of PV power generation and electric vehicles charging in microgrid. Renewable Energy, 155, 1191–1210.
4. Sasidhar Reddy Mondeddula. (2022). AI-Driven Fraud Detection Using Ensemble Machine Learning for Secure Digital Banking Transactions. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 597–605. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8515
5. Nikam, V., & Kalkhambkar, V. (2021). A review on control strategies for microgrids with distributed energy resources, energy storage systems, and electric vehicles. International Transactions on Electrical Energy Systems, 31(1), e12607.
6. Muhtadi, A., Pandit, D., Nguyen, N., & Mitra, J. (2021). Distributed energy resources based microgrid: Review of architecture, control, and reliability. IEEE Transactions on Industry Applications, 57(3), 2223–2235.
7. De Kooning, J. D. M., Stockman, K., De Maeyer, J., Jarquin-Laguna, A., & Vandevelde, L. (2021). Digital twins for wind energy conversion systems: A literature review of potential modelling techniques focused on model fidelity and computational load. Processes, 9(12), 2224.
8. Mashetty, S., & Mukesh, A. (2022). Application of Machine Learning in Loan Default Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 123-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P114
9. You, M., Wang, Q., Sun, H., Castro, I., & Jiang, J. (2022). Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, 305, 117899.
10. Adianto, Y., Baguley, C., Madawala, U., Hariyanto, N., Suwarno, S., & Kurniawan, T. (2022). The coordinated operation of vertically structured power systems for electric vehicle charge scheduling. Energies, 15(1), 27.
11. Bazmohammadi, N., Madary, A., Vasquez, J. C., Mohammadi, H. B., Khan, B., Wu, Y., & Guerrero, J. M. (2022). Microgrid digital twins: Concepts, applications, and future trends. IEEE Access, 10, 2284–2302.
12. Ghofrani, M., & Majidi, M. (2021). A comprehensive optimization framework for EV-renewable DG coordination. Electric Power Systems Research, 194, 107086.
13. Sreenivasulu Naidu Yalakaturi. (2022). Rule-Based Data Quality Monitoring for Multi-System Healthcare Information. International Journal of Medical Toxicology and Legal Medicine, 25(3 and 4), 23–31. Retrieved from https://ijmtlm.org/index.php/journal/article/view/1504
14. Twaisan, K., & Barışçı, N. (2022). Integrated distributed energy resources (DER) and microgrids: Modeling and optimization of DERs. Electronics, 11(18), 2816.
15. Jia, S., Kang, X., Cui, J., Tian, B., Zhang, J., & Xiao, S. (2022). Multi-layer coordinated optimization of integrated energy system with electric vehicles based on feedback correction. Frontiers in Energy Research, 10.
16. Ospina-Bohórquez, A., López-Rebollo, J., Muñoz-Sánchez, P., & González-Aguilera, D. (2022). A digital twin for monitoring the construction of a wind farm. Engineering Proceedings, 17(1), 3.
17. Chen, T., Chu, K. F., Lam, A. Y. S., Hill, D. J., & Li, V. O. K. (2020). Electric autonomous vehicle charging and parking coordination for vehicle-to-grid voltage regulation with renewable energy. In 2020 IEEE Power & Energy Society General Meeting (pp. 2826–2830).
18. Gadi, A. (2020). The Role of Digital Twins in Automotive R&D for Rapid Prototyping and System Integration. International Journal on Recent and Innovation Trends in Computing and Communication.
19. Bayani, R., Manshadi, S. D., Liu, G., et al. (2022). Autonomous charging of electric vehicle fleets to enhance renewable generation dispatchability. CSEE Journal of Power and Energy Systems, 8(3), 669–681.
20. Sharma, P., Mathur, H. D., Mishra, P., & Bansal, R. C. (2022). A critical and comparative review of energy management strategies for microgrids. Applied Energy, 327, 120028.
21. Mannini, R., Eynard, J., & Grieu, S. (2022). A survey of recent advances in the smart management of microgrids and networked microgrids. Energies, 15(19), 7009.
22. Tightiz, L., & Yoo, J. (2022). A review on a data-driven microgrid management system integrating an active distribution network: Challenges, issues, and new trends. Energies, 15(22), 8739.
23. Kamyabi, L., Lie, T. T., & Madanian, S. (2022). Applications of digital twins in power systems: A perspective. Transactions on Energy Systems and Engineering Applications, 3(2), 1–9.
24. Sørensen, J. V., Ma, Z., & Jørgensen, B. N. (2022). Potentials of game engines for wind power digital twin development: An investigation of the Unreal Engine. Energy Informatics, 5, Article 39.
25. Moghadam, F. K., & Nejad, A. R. (2022). Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mechanical Systems and Signal Processing, 162, 108087.
26. Rajesh Reddy Ellenki. (2022). Predictive Demand Forecasting Framework for Adaptive Retail Inventory Planning . International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 412–418. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8543
27. Mo, Y., & Qin, Z. (2022). Day-ahead flexibility enhancement via joint optimization for new energy vehicle fleets and electric vehicle charging/hydrogen refuelling stations. IET Renewable Power Generation.
28. Tazikeh Lemeski, A., Ebrahimi, R., & Zakariazadeh, A. (2022). Optimal decentralized coordinated operation of electric vehicle aggregators enabling vehicle to grid option using distributed algorithm. Journal of Energy Storage, 54, 105213.
29. Adhikari, R., et al. (2022). Applications, operational architectures and development of virtual power plants as a strategy to facilitate the integration of distributed energy resources. Energies, 15(3), 775.
30. Moghadam, H. M., Foroozan, H., Gheisarnejad, M., & Khooban, M.-H. (2021). A survey on new trends of digital twin technology for power systems. Journal of Intelligent & Fuzzy Systems, 41(2).
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 European Advanced Journal for Science & Engineering (EAJSE)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Articles published in the European Advanced Journal for Science & Engineering (EAJSE) are made freely available online immediately upon publication under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited. Authors retain copyright of their work. By submitting to EAJSE, authors grant the journal the right of first publication. For details, visit: https://creativecommons.org/licenses/by/4.0/
