Intelligent Optimisation and Control for Future Power Systems at Queen Mary University of London

About the Position

Future power systems will integrate large numbers of diverse resources, ranging from uncertain renewable generation to flexible, controllable, and data-preserving assets such as data centres, electric vehicles, energy storage, and heat pumps. These resources will be owned and operated by different parties (e.g. consumers, aggregators, and system operators), connected at different voltage levels, and coordinated through a combination of electricity markets (e.g. flexibility), network services (e.g. ancillary services) and local control systems (e.g. active distribution networks).

Optimisation-driven energy management and control are essential to ensure safe and reliable power systems operations while controlling energy resources in the most economical way to improve energy affordability, integrate renewable generation and respond to changing system conditions. However, existing methods, including many machine learning (ML)-enabled methods investigated in smart grid, remain limited in addressing pressing challenges such as maintaining system resilience under unseen and adverse events, reconciling conflicting optimisation objectives, and handling incomplete or privacy-sensitive data. This PhD project aims to address the emerging challenges posed by increasingly decentralised and data-rich power systems, developing novel optimisation and ML-based frameworks that can coordinate heterogeneous resources while maintaining system security, resilience, and economic performance.

Research Objectives

The successful candidate will investigate how distributed energy resources, flexible demand, and emerging cyber-physical infrastructures can be coordinated while respecting physical, computational, and privacy constraints. Research directions may include, but are not limited to:

• Network-constrained optimisation of distributed energy resources. Develop optimisation and control methods for coordinating distributed energy resources while respecting network constraints and operational requirements. This includes supporting the secure and economical operation of increasingly complex and active distribution networks.
• Privacy-preserving energy flexibility management. Investigate approaches for coordinating energy flexibility without requiring access to sensitive operational or consumer data.
• Machine learning-based grid control under adverse conditions. Explore learning-enabled control and decision-making methods for smart grid that can maintain reliable performance under challenging operating conditions.

Your research direction will be shaped by the synergy between your interests and background, which you will refine into a detailed proposal during the first months of the PhD. We welcome candidates with backgrounds in Electrical Power Engineering or Electrical and Electronic Engineering; candidates from Computer Science or Artificial Intelligence who have a strong interest in power systems research are also welcome to apply. Strong programming skills (e.g. Python) and knowledge of machine learning are required. Demonstrated interest in research (e.g. publications) will be an advantage.

Supervisor

Dr Da Huo (he/his) – d.huo@qmul.ac.uk

Funding

The PhD student will receive tuition fees at the home rate and a London stipend at QMUL stipend rates (currently in 2026/27 of £22,618 per year, to be confirmed for subsequent years) annually during the PhD period, which can span for 3 years. Non-home/non-UK students may apply, but if accepted, they will be responsible for the substantial difference in tuition fees, as no additional funding or fee waiver is available to cover this gap.

Contact

• Email: d.huo@qmul.ac.uk

Questions about this position, eligibility or the application process should be sent to the contacts above or raised through the channel given on the official announcement page.

How to Apply

Applicants should submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/

The application should include the following:

• CV (max 2 pages)
• Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident
• Research proposal (max 500 words)
• 2 References
• Certificate of English Language (for students whose first language is not English)
• Other Certificates

About Queen Mary University of London

Queen Mary University of London is a public research university and a member of the Russell Group. It offers undergraduate, master's and doctoral study across medicine, dentistry, science, engineering, humanities and social sciences. Based in London, its academic community connects research and teaching with healthcare, industry and broader societal questions.

Official website: https://www.qmul.ac.uk/

Application dates

Milestone	Date
Application Deadline	30 November 2026
Interviews	December 2026
Start Date	January, April, or September 2027

Official Link

https://www.qmul.ac.uk/eecs/phd/phd-studentships/intelligent-optimisation-and-control-for-future-power-systems/

Important Notes

• Funded PhD Project (Students Worldwide).
• Home tuition fees covered and London stipend of £22,618 per year (2026/27 rate).
• Non-home/non-UK students may apply but must cover the substantial difference in tuition fees.
• Strong programming skills (e.g. Python) and knowledge of machine learning are required.
• Demonstrated interest in research (e.g. publications) will be an advantage.

Application guides

• How to Email a PhD Supervisor: 7 Templates and Practical Tips
• How to Read a PhD Job Advert: Funding, Eligibility and Application Steps
• UK Student Visa Application: CAS, Documents and Financial Evidence