Artificial Intelligence and Agent-based Control for Improving Energy Network Resilience to Threats (2026)

About the Project

How can we leverage artificial intelligence to tackle modern serious threats to energy infrastructure that leave millions without power?

This PhD project aims to investigate the use of Artificial Intelligence (AI) tools, including machine learning (ML) and agent-based control, for predicting, managing and improving the resilience of energy networks to disruption.

AI tools will be used to predict the likelihood and impact of cascading failures. Cascading failures can lead to widespread electrical blackouts, typically characterised as High-Impact Low Probability (HILP) events, potentially leaving millions of people without energy, water or communications, risking lives, and costing £ billions. Prior knowledge of the occurrence of such HILP events can enhance the response of infrastructure operators, thus limiting their impact.

You will build on prior research that has been done by the supervisor’s team on leveraging machine learning to predict large-scale blackouts, including the Network Theory Resilience Metric (NTRM) toolkit.

What you will do

• Develop a prototype toolkit, which can be used to assess the resilience of energy networks and link with industrial systems to extract data and advise on the response interventions.
• Work with datasets from energy networks, wherever possible.
• Build advanced simulation models utilising machine learning and agent-based control techniques.
• Collaborate with researchers and industry stakeholders.
• Publish in high-impact journals and conferences.

Skills you will develop

• Energy network and complex systems modelling
• Artificial intelligence methods applied to infrastructure
• Resilience and risk analysis for critical systems
• Experience with real-world datasets