About the Project
The transition to a net-zero energy system is transforming electricity networks. Rapid growth in electric vehicles, heat pumps, rooftop solar panels, batteries and flexible demand is creating new opportunities, but also significant operational challenges for network operators. A key challenge is that many of these resources are connected behind the meter, meaning their characteristics, operating patterns and even their existence may not be fully visible to network operators.
This PhD project will investigate how future low-voltage electricity networks can be operated and planned when critical information is incomplete, uncertain or only partially observable. Rather than assuming perfect knowledge of customer behaviour, network assets and distributed energy resources, the research will develop innovative approaches for decision-making under real-world conditions where information is often missing or uncertain.
The student will explore how artificial intelligence, machine learning, optimisation and data analytics can be combined to improve network visibility and support the coordination of distributed energy resources, electric vehicle charging, heat electrification and flexible demand. The project will investigate fundamental questions such as: How can network operators infer what is happening in parts of the network they cannot directly observe? How can flexibility services be coordinated when key information is uncertain? How can future electricity, heat and transport systems interact effectively to support a resilient net-zero transition?
The research will also consider broader societal impacts, including how different flexibility approaches affect diverse customer groups and how future energy systems can be designed to support an equitable and inclusive transition to net zero.
The successful candidate will join the internationally recognised Power Systems Group at the University of Manchester and become part of a vibrant research environment working on future energy systems, network resilience, digitalisation and net-zero infrastructure. The student will receive training in power system modelling, machine learning, optimisation, uncertainty analysis, programming and data analytics, while gaining experience working with industry stakeholders including electricity network operators.
This project offers an exciting opportunity to contribute to one of the most important challenges facing the energy sector: enabling secure, resilient and sustainable operation of increasingly complex electricity networks in a world where perfect information is rarely available. The outcomes will help shape the next generation of intelligent network management tools needed to deliver a cost-effective and socially responsible transition to net zero.
This project is expected to start in September 2027.
Before you apply: We strongly recommend that you contact the supervisors for this project before you apply.
How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships. If you don’t already have an applicant account, please follow the instructions here.
When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications. Please note late or incomplete applications will not be considered.
Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status.
We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder.
Eligibility: The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree (or international equivalent) in Electrical and Electronic Engineering, Power Systems, Energy Engineering, Control Engineering, Computer Science, Applied Mathematics or Physics OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Electrical and Electronic Engineering, Power Systems, Energy Engineering, Control Engineering, Computer Science, Applied Mathematics or Physics. Applicants with an interest in energy systems, optimisation, machine learning, data analytics, and future net-zero infrastructures are encouraged to apply.
This project will remain open until filled.
If your application is submitted by 1st November 2026, you can expect a decision by 18th December 2026.
If your application is submitted by 15th January 2027, you can expect a decision by 30th March 2027.
Self or externally funded students can also be considered for this project.
FSE_SoE
Funding Notes
Funding for this project covers tuition fees, UKRI minimum annual stipend (currently £21,805/annum) and up to a £2.5k/annum research training support grant for the full duration of the 3.5-year programme.