Intelligent Distribution System Operation for Low-Carbon Power Systems

About the Project

Electricity networks around the world are undergoing a rapid transformation as renewable generation, electric vehicles, heat pumps, and battery storage are deployed at scale. These technologies are essential for achieving climate targets, but they also place unprecedented stress on local electricity distribution networks, which were not originally designed to manage large numbers of flexible and decentralised energy resources.

This PhD project will develop new AI-driven methods for operating smart distribution networks so that they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation in real time, allowing homes, businesses, and communities to actively support the electricity system while benefiting from lower costs and lower carbon emissions.

The student will design and test optimisation and learning-based control algorithms that can make decisions under uncertainty, using realistic network models and large-scale simulations. These methods will be evaluated on representative UK distribution networks to understand how different technologies and user behaviours interact and how flexibility can be shared across neighbourhoods in a transparent and equitable way.

This project sits at the intersection of power systems, artificial intelligence, and sustainability. It offers training in modern energy system modelling, machine learning, optimisation, and data analysis, with applications that are directly relevant to network operators, policymakers, and technology providers working toward a low-carbon energy future.

We warmly welcome applications from candidates of all backgrounds and identities. The project is designed to be accessible to students from engineering, physical sciences, mathematics, or data science who are motivated to apply their skills to real-world energy and climate challenges.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Funding

This 3.5-year PhD studentship is open to Home (UK) applicants only. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift), and full tuition fees will be paid. We expect the stipend to increase each year. The expected start date is September/October 2026.

We recommend that you apply early as the advert will be removed once the position has been filled.

Before you apply

We strongly recommend that you contact the supervisor for this project before you apply. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

How to apply

Apply online through our website: https://uom.link/pgr-apply-2425

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.