Designing Human-Centred Digital Twins for Reliable and Resilient Energy Networks

Primary supervisor

Ee Hui Lim

Future electricity networks are becoming increasingly complex due to the rapid growth of distributed energy resources (DERs), batteries, renewable generation and intelligent infrastructure. Although modern networks collect large volumes of operational, asset and maintenance data, this information is often distributed across multiple systems, making it difficult for engineers and operators to understand the true reliability and resilience of the network or identify emerging risks before failures occur.

Digital twins provide an opportunity to integrate these heterogeneous data sources into a unified representation of the network. However, most existing research focuses on improving simulation or optimisation, with comparatively little attention given to how reliability information should be presented to support human decision-making.

This project investigates how human-centred digital twins can help engineers, asset managers and network operators better understand, trust and act upon reliability and resilience information. Students may explore information visualisation, interactive dashboards, explainable AI, graph analytics, user-centred design or decision-support systems that communicate complex network behaviour in intuitive and actionable ways.

Applications may include maintenance prioritisation, redundancy analysis, reserve assessment, fault diagnosis, resilience planning and supporting the integration of distributed energy resources.

The project is suitable for students interested in applying computing and human-centred design to critical infrastructure and the future of sustainable energy systems.

Aim/outline

This project aims to investigate how human-centred digital twins can improve decision-making for electricity network reliability and resilience.

Possible research topics include:

  • Designing digital twin interfaces that improve understanding of network reliability.
  • Interactive visualisation of asset health, redundancy and resilience.
  • Human-AI collaboration for maintenance planning and operational decision support.
  • Explainable AI for reliability and risk assessment.
  • Graph-based representations of electricity networks and critical infrastructure.
  • User-centred evaluation of digital twin interfaces with engineers and infrastructure practitioners.
  • Digital twins supporting distributed energy resource (DER) integration and resilience planning.

Students may undertake interface design, software development, user studies, data analytics or AI-assisted decision support depending on their interests.

URLs/references

Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.

Rasheed, A., San, O., & Kvamsdal, T. (2020). Digital twin: Values, challenges and enablers from a modelling perspective. IEEE Access, 8, 21980–22012.

Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37(1), 32–64.

Lim, E. H., & Bodi, F. (2012). Managing the complexity of a telecommunication power systems equipment replacement program. Proceedings of the IEEE International Telecommunications Energy Conference (INTELEC).

Bodi, F., & Lim, E. H. (2012). Criteria for emerging telecom and data center powering architectures. Proceedings of the IEEE International Telecommunications Energy Conference (INTELEC).

Bodi, F., & Lim, E. H. (2011). 380/400 V DC powering option. Proceedings of the IEEE International Telecommunications Energy Conference (INTELEC).

Required knowledge

Students should have completed units in one or more of the following areas:

  • Human-Computer Interaction
  • Software Engineering
  • Data Science
  • Information Visualisation
  • Artificial Intelligence
  • Interactive Systems
  • Programming (Python, Java or JavaScript)

Students with an interest in sustainability, infrastructure, smart cities or critical infrastructure are encouraged to apply.