AI-Enabled Digital Twins for Smart Grids: Real-Time Monitoring, Predictive Control, Cybersecurity and Future Research Directions
Jennifer Ikeoluwa Ade-moomiyo1, Micheal Abimbola Oladosu*2, Moses Adondua Abah3, Ugonna Stanley Nwani4, Ifechukwu Victor Muoghalu5, Anyan Adebayo Anyan6
1Jennifer Ikeoluwa Ade-moomiyo, Department of Electrical and Information Engineering, Landmark University, Faculty of Engineering, Omu-Aran, Kwara State, Nigeria.
2Micheal Abimbola Oladosu, Department of Chemical Science, Anchor University, Ayobo, Lagos, Nigeria.
3Moses Adondua Abah, Department of Biochemistry, Federal University of Wukari, Faculty of Pure and Applied Sciences, Wukari, Taraba, Nigeria.
4Ugonna Stanley Nwani, Department of Computer Engineering, Obafemi Awolowo University, Faculty of Computing, Ile-Ife, Osun State, Nigeria.
5Ifechukwu Victor Muoghalu, Department of Mechanical Engineering, University of Coimbra, Portugal, Faculty of Engineering, Paço das Escolas, Coimbra, Portugal.
6Anyan Adebayo Anyan, Department of Social Science, College of Management and Human Potential, Walden University, Minneapolis, United States of America (USA).
Manuscript received on 05 August 2026 | First Revised Manuscript received on 14 August 2026 | Second Revised Manuscript received on 06 September 2026 | Manuscript Accepted on 15 September 2026 | Manuscript published on 30 September 2026 | PP: 13-19 | Volume-15 Issue-10, September 2026 | Retrieval Number: 100.1/ijitee.K130915111026 | DOI: 10.35940/ijitee.K1309.15111026
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital twin (DT) technology combined with artificial intelligence (AI) is becoming a key enabler for resilient and self-optimising electrical power networks that are increasingly digitalised. A digital twin is a continuously updated virtual representation of a physical grid asset or network that uses real-time sensor, phasor measurement unit (PMU), and Internet of Things (IoT) data to closely mirror its physical counterpart. Digital twins become predictive, prescriptive, and self-healing decision-support systems when machine learning (ML) and deep learning (DL) algorithms are integrated into this virtual-physical feedback loop. This review aims to bring together the recent literature (2020-2025) on the field of AIpowered digital twins under four thematic areas: (i) architectural foundations and enabling technologies, (ii) real-time monitoring and state estimation, (iii) predictive control and maintenance, and (iv) cybersecurity monitoring and threat mitigation. Tables summarise enabling technologies, predictive-control case studies, and case studies of cyber-threat countermeasures reported in the literature. The review also pinpoints open research challenges such as interoperability standards, federated and privacypreserving learning, explainability of AI-driven decisions on the grid, and quantum-assisted twin computation. It suggests a research roadmap for further work. The synthesis signals that while AI-powered digital twins can tangibly reduce unplanned downtime, improve forecasting precision, and boost cyberphysical attack resilience, standardisation, data governance, and trustworthy AI remain to be addressed to enable widespread adoption.
Keywords: Digital twin, Artificial Intelligence, Smart Grid, Real-time Monitoring, Predictive Control, Cyber Security, Machine Learning, Power Systems.
Scope of the Article: Computer Science and Engineering