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Award details

Improving Network Visibility to Support Active Management of Electricity Distribution Network. EMPS College Home fees Studentship, PhD in Engineering. Ref: 4324

About the award

Supervisors

Lead Supervisor:

Dr. Shanika Matharage, College of Engineering, Mathematics and Physical Sciences, Streatham Campus, University of Exeter

Co-supervisor: 

Prof. Zhongdong Wang, College of Engineering, Mathematics and Physical Sciences, Streatham Campus, University of Exeter

Location:

Department of Engineering, Streatham Campus, Devon, University of Exeter

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences  is inviting applications for a fully-funded PhD studentship to commence in January 2022 or as soon as possible thereafter. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study. 

This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Project Description:

Electricity has played a leading role in achieving the UK's net-zero carbon targets. As a result, the electricity network is undergoing a revolution from the traditional unidirectional power flow network with centralised generation into a network of interconnected microgrids with renewables based embedded generation. This has reduced grid inertia and increased fluctuations in electricity supply. Furthermore, decarbonisation has resulted in new demands such as the electric vehicles and heat pumps, which altogether will also increase demand fluctuations.

Most of these changes are impacting the distribution network and create challenges with managing the network.  Some examples include the embedded generation masking load growth; power quality, protection and network stability issues related to less visible photovoltaic (PV) systems; difficulties in predicting load demand from new technologies such as electric vehicles and heat pumps. Therefore, the distribution network operators will have to move away from a fit-and-forget culture, becoming distribution system operators who need to have the capability to implement active network management strategies with real time monitoring and control to maintain the network security and resilience.

Distribution networks are much complex than the transmission network in terms of the number of nodes in the network and customers. However, the availability of data from the distribution network is rather limited, creating challenges for moving towards active management of the network.

Some network visibility strategies include application of distributed monitoring; categorising types of networks; and utilising data from sources such as smart meters and EV charging data. Nevertheless, there is still a lack of definition on key information/measurement required from distribution network visibility point of view, and there is an urgent need to optimise the way of utilising such information to increase network resilience and reduce the cost of electricity from a whole system approach. This PhD research will study the challenges of active management of distribution networks and propose solutions from a whole system approach with minimal measurement points and the optimal use of available data. Machine learning algorithms will be developed to improve network visibility and to support active network management with quantifiable benefits for both network operator and customer. 

Entry requirements

This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. 

Candidates with a degree in the areas related to Computer Science, Electrical Engineering and Data Science are encouraged to apply for this position.

If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project. 

Alternative tests may be acceptable (see http://www.exeter.ac.uk/postgraduate/apply/english/).

How to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.

• CV
• Letter of application (outlining your academic interests, prior project work experience and reasons for
  wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an
   interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email
   the reference direct to pgrenquiries@exeter.ac.uk quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.  Please see the entry requirements information above.

The closing date for applications is midnight on 24th January 2022.  Interviews will be held online on the week commencing 7th February 2022.

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk

Project-specific queries should be directed to the main supervisor at shanika.matharage@exeter.ac.uk

Summary

Application deadline:24th January 2022
Value:Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study.
Duration of award:per year
Contact: PGR Admissions Office pgrenquiries@exeter.ac.uk