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Resilience of Future Power Distribution Networks

   Department of Electronic, Electrical and Systems Engineering

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  Dr Daniel Donaldson  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Project Description

The growing societal dependence on electricity has heightened the need for resilient power system infrastructure. Infrastructure owners face the task of successfully decarbonising the grid while meeting demand for electricity in a safe and affordable fashion. The transition from the current electrical distribution grid to the future is complicated by uncertainty in the future demand, the exposure to natural hazards and other operational threats.

This PhD research will create and assess forecasts of future demand (including electric vehicles and heat pumps) alongside the threat posed by natural hazards (e.g. flooding, wildfires, and/or windstorms) to propose methods to enhance distribution network planning. Statistical and machine learning algorithms will be developed to support optimal decision making in the face of uncertainty.

Project Benefits

  • The studentship will cover home tuition fees (£4,596 for 2022/23) plus an annual tax-free stipend of approximately £16,062 for 3.5 years. (International applicants from outside the UK are welcome to apply, but will need to secure additional funding to cover the difference between international and UK fees.)
  • Opportunity to engage with industry stakeholders and present actionable research findings
  • Conference attendance: Funding is allocated to enable the student to attend and present research at key conferences during their PhD.

Student Profile

Applicants should have (or are about to obtain):

  • An undergraduate degree (2:1 or higher) and preferably a Master's degree in engineering, computer science, statistics, physics, geography, earth and environmental sciences or a related field.
  • Familiarity with at least one programming language (Python, R, etc.)
  • Relevant industry or research experience

Strong knowledge in one or more of the following areas is desirable:

  • Power network modelling and control
  • Familiarity with spatial, time series, and meteorological data.
  • Probabilistic Forecasting
  • Reproducible research (Github, Machine Learning Pipelines, LaTeX/Markdown etc.)

Application Details

To apply, send the following materials to Dr. Daniel Donaldson d.l.donaldson(at) with the subject line 'Prospective PhD Student' by 14 September 2022:

  1. A short statement explaining your motivation for pursuing a PhD, why you would like to undertake this research, and a short proposal on how you would tackle the above challenges (maximum 2 pages)
  2. CV containing academic record and relevant experience
  3. Contact details of two academic referees (and one industry referee if applicable)

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