Digital Twin Technologies for Smart Energy Systems: Real-time Monitoring, State Estimation and Control

SCOPE AND DETAILS

Modern power systems and integrated energy infrastructures are evolving into highly coupled cyber-physical systems characterized by high variability (large-scale renewables), distributed resources (DERs, microgrids) and pervasive sensing/communication layers. Traditional state estimation and control paradigms face challenges of nonlinearity, model mismatch, limited observability and adversarial events. Digital twin (DT) technologies – dynamic virtual replicas of physical assets synchronized with live data – have emerged as a cornerstone of this digital evolution. They enable operators to accurately model, monitor, predict, and optimize system behavior. In particular, DTs provide continuous situational awareness and improved state estimation in grids with high renewable penetration. Accurate state estimation is the backbone of power system operation, and data-driven DT frameworks (leveraging IoT sensors and AI) promise robust, adaptive estimation under uncertainty. By fusing real-time measurements with advanced analytics, DTs facilitate predictive maintenance and enable closed-loop control strategies that improve grid resilience and efficiency.
This special issue seeks to advance the theoretical foundations and practical applications of Digital Twin technologies for smart energy systems, specifically for the real-time perception, precise state estimation, and intelligent control of smart energy systems. We aim to address core challenges such as the fusion of physics-based models with data-driven algorithms, the management of uncertainties in both models and measurements, and the deployment of DT-enabled control strategies that enhance grid resilience and efficiency. Of particular interest are contributions that demonstrate how DTs can revolutionize the management of energy storage systems.

 

TOPICS

1. DT architectures and frameworks for power and energy systems
2. Edge/fog computing and 5G/6G communication for deploying DTs in smart grids
3. IoT and sensor integration in real-time data acquisition and transmission of DT models
4. AI/ML techniques for DT-based state estimation and predictive modeling
5. Real-time monitoring, visualization and diagnostics of grid status using DTs
6. Advanced DT-enabled state estimation and parameter identification methods
7. DT-driven control strategies (predictive, optimal, decentralized) for grid optimization
8. DT applications in renewable energy integration, microgrids and DER management
9. DTs for energy storage systems and battery management
10. Cybersecurity, privacy and resilience issues in energy system DTs
11. Standards, data models and interoperability for energy DTs
12. Case studies of DT solutions in smart energy domains

 

Important Dates

Manuscript Submission Deadline:  30.10.2026
Authors Notification: 31.12.2026
Revised Papers Due: 26.02.2027
Final notification: 31.03.2027

 

Main Guest Editor

Shunli Wang, Inner Mongolia University of Technology, China, shunlw.imut@gmail.com

 

Guest Editors

Mohan Lal Kolhe, University of Agder, Norway, mohan.l.kolhe@uia.no

Tek Tjing Lie, Auckland University of Technology (AUT), New Zealand, tek.lie@aut.ac.nz