HV and LV Representative Networks: A Realistic Large-Scale Assessment of LCT Impacts and Smart Grid Solutions in the UK

Institution: University of Manchester

Dept/School/Faculty: EPSRC Centre for Doctoral Training in Power Networks

PhD Supervisor: Dr LN Ochoa

Application Deadline: Applications accepted all year round

Funding Availability: Funded PhD Project (European/UK Students Only)

Student background required: 
BSc degree in Electrical engineering with emphasis on Power Systems. Ideally, with an MSc (or MEng) degree in Power Systems-related areas or equivalent industrial experience. 

Benefit to / Impact on Industry: 
This project will, for the first time in the UK, be able to produce truly representative high and low voltage distribution networks, in particular for ENWL, so that the most cost-effective Smart Grid schemes can be identified for different scenarios of low carbon technologies (LTCs: photovoltaics, electric vehicles, etc.).

What novelty will the student base their PhD on? 
The production of representative networks based on the analysis of real large samples is still an extremely new area (only a handful of studies exist), this work will also be innovative as it will consider sophisticated clustering techniques. Based on these representative networks, this PhD project will determine for the first time, the true extent of the impacts from LCTs as well as the most adequate portfolio of Smart Grid schemes that can be deployed according to the type of network and LCT penetration. 

Project overview: 
The objective of this project is to develop representative networks based on a thorough clustering analysis of a large population of real low voltage (LV) and high voltage (HV) distribution networks to ensure their representativeness and validity. These representative networks will be used to truly characterise the effects of the adoption of LCTs and to support the development of a realistic suite of mitigation measures to best address the emerging challenges of low carbon economies and the changing demands of customers. 

Outline Proposed Project Plan: 
Year 1: Taught courses and preparatory study 
Year 2: Year 2 will focus on the extraction of key information from ENWL’s HV and LV networks to characterise the networks for the corresponding clustering analysis. This will also be used to produce OpenDSS-based models for power flow time-series analyses. By the end of year 2, key clustering approaches should be identified and tested resulting in the initial production of the representative HV and LV networks. 
Year 3: The (initial) representative HV and LV networks will be validated by analysing the whole population of networks; further clustering analyses might be required to produce the final representative networks. The latter will then be used to assess the impacts from LCTs and determine the benefits from a range of Smart Grid schemes. 
Year 4: Refine year 3 results in terms of potential LCT scenarios and the ‘life-span’ of Smart Grid schemes as opposed to traditional reinforcements. Write up thesis. 

Funding Notes:

This project is funded by EPSRC, the University of Manchester and our Industry partners. Funding is available to UK candidates. EU candidates are also eligible if they have been studying or working continuously in the UK for three or more years (prior to the start date of the programme). The successful candidates will have their fees paid in full and will receive an enhanced maintenance stipend.

See here for information on how to apply and entry requirements: www.power-networks-cdt.manchester.ac.uk/study/projects-apply