Heat demand in the UK accounts for around 44% of the final energy consumption, and is currently predominantly obtained by burning natural gas and oil, representing about 90% of the fuel share, while renewable energy sources supply only a fraction of it. This outlines the challenge faced in meeting Net-Zero targets in the UK, which we aim to address in project DecarbonISation PAThways for Cooling and Heating (DISPATCH), recently funded by the EPSRC under their Decarbonising Heating & Cooling initiative. The DISPATCH project asserts that the Net-Zero transition could be realised through a bottom-up aggregation and sharing of resources, fully exploring synergies, interoperabilities and integration potential of different energy vectors.
The work and research in this fully funded (UK/Home)* PhD project will analyse and model electrical and thermal energy systems, collaborating closely with other researchers in DISPATCH project from the Schools of Engineering and Informatics at the University of Edinburgh and Herriot-Watt University. The successful candidate will conduct research at and around the interface between the electrical and thermal energy systems. The overarching objective is to develop multi-vector (hybrid) models able to capture, optimise and demonstrate the interactions between the thermal and electrical systems, as well as the end-users. This will involve consideration of currently available, emerging, and future multi-vector energy decarbonisation solutions for realising virtual multi-vector energy plants (VM-VEPs), as an expansion of the familiar concepts of “virtual power plants” and “microgrids”. These closely integrated electrical-thermal energy systems will be coordinated at different aggregation levels, with individual households and buildings as the basic functional blocks. The VM-VEPs will be capable of fully coordinated operation at a local community or network level, and to further interact with aggregators and wider controls at distribution and transmission network levels.
The successful candidate will develop, implement and apply conventional and machine/deep learning methods for the design and optimisation of VM-VEPs. While the main focus will be on developing multi-vector energy system models at different aggregation scales, there is a scope to investigate other areas, such as coordinated system controls, energy exchange and trading schemes, as well as demand-side functionalities, including load and distributed generation/storage forecasting. This will allow development of a wide range of skills in energy system analysis, design and machine/deep learning methods, which will be widely applicable to the candidate’s future career.
* Funds are available to Home students (UK and EU students with settled/pre-settled status who have lived in the UK for 3+ years). Alternatively, self or externally funded International applicants are welcome to apply.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
The potential candidate will have a 2:1 or above (or equivalent) UG (and/or MSc) Degree in related Engineering Disciplines (Electrical, Mechanical, Energy/Power Engineering or similar), or in relevant Physics areas, and possibly Mathematics. Strong background in modelling, analysis and optimisation of Energy Systems, or a closely related area, is highly desirable.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.
Tuition fees + stipend are available for Home/EU students (International students can apply, but the funding only covers the Home/EU fee rate)
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere