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

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.). This is key for the industry as it allows defining adequate and timely planning and investment strategies for the different types of networks.

What novelty will the student base their PhD on?
Despite the fact that 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 in the world), this work will also be innovative as it will consider sophisticated clustering techniques. More importantly, based on these representative networks, this PhD project will determine for the first time, the true extent of the impacts from low carbon technologies (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, for the first time in the UK, 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 low carbon technologies (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. As both design and operation have a direct effect on end customers, there is a case for appropriate simulation and testing of new designs and changed operating practices before using such new approaches for real. Therefore, impact analysis of LCT as well as the application of Smart Grid solutions will be carried out on these representative networks to characterise their hosting capabilities and the extent to which certain solutions bring benefits.

Outline Proposed Project Plan:
Year 1: Taught courses and preparatory study
Year 2: Based on the literature review produced in Year 1 and the initial Geographical Information System (GIS) data processing, year 2 will focus on the extraction of key information from ENWL’s HV and LV networks so as to characterise the networks for the corresponding clustering analysis. This data processing 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 low carbon technologies (LCTs) and determine the benefits from a range of Smart Grid schemes.
Year 4: This year will be used to refine the results from year 3 in terms of potential LCT scenarios and the ‘life-span’ of Smart Grid schemes as opposed to traditional reinforcements. This year also considers the writing of the PhD thesis.