Dear all,
We have at two PhD and one research Masters opportunities in power systems analysis at Durham University. These involve use of statistical and optimisation modelling, so would be very well suited to candidates from these backgrounds.
I have included brief details of the projects below. For further information on postgraduate research in Engineering at Durham, please see
http://www.dur.ac.uk/ecs/ecs_research/research_degrees/
For Mathematical Sciences, please see
http://www.dur.ac.uk/mathematical.sciences/postgrads/
The full range of energy research at Durham may be explored through the Durham Energy Institute's pages:
http://www.dur.ac.uk/dei/
If you are interested in these positions, please contact me in the first instance on [log in to unmask] If you know of anyone who might be interested, please forward this message.
Best regards,
Chris Dent
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Dr. Chris Dent,
Lecturer in Energy Systems Modelling,
School of Engineering and Computing Sciences,
Durham University,
South Road,
Durham DH1 3LE.
U.K.
Tel: +44 (0) 191 33 42451
Personal webpage: http://www.dur.ac.uk/ecs/profiles/?id=7876/
Durham Energy Institute: http://www.dur.ac.uk/dei/
Postgraduate Tutor at University College: http://www.dur.ac.uk/castle.mcr/
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MSc project: Statistical methods for planning of future electricity distribution networks
Electricity distribution networks are in a period of great change. The capacity of generation embedded within local networks is continually increasing (both renewable such as solar and wind, and combined heat and power). Also, over the next 10-20 years there will be a greatly increased ability for demand to be incentivised to reduce or time-shift when this is of benefit to the system. This requires a major change in the methodologies for network planning, as to date low voltage networks have been planned assuming unresponsive demand and no generation. Moreover, these decisions as to required network capacity must be made under uncertainty not only over demand growth, but also over the rate at which new demand-side technologies will be developed and rolled out. This project will develop statistical methods to support these network planning decisions. We will have access to a large customer survey (>10,000 network users) carried out for this project, plus possibly additional data from other publicly funded research on electricity demand. The key will be development of systematic methods to aggregate the various uncertainties (imperfect survey data, variability between customers and areas, variability of peak demand between years, future demand patterns etc). This will be supported by access to expert knowledge both within Durham and other members of the project team.
Supervisors: Dr Chris Dent (Engineering and Computing Sciences), Dr David Wooff (Mathematical Sciences).
Funding: Stipend £20000 for 15-month duration. Home and EU tuition fees paid.
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PhD project: Uncertainty Audit and Statistical Emulation for Investment Decisions
This is a collaborative project between the department of mathematical sciences and the department of engineering. We are looking for a student with a strong background in statistics and an interest in developing new methodology in an important and challenging practical application area.
Power network components typically have very long design lives of 40 or more years, on top of 5-10 years for planning and construction. At the time investment decisions are made, there is therefore great uncertainty over many properties of the future system in which the asset will be operated, e.g. installed generation capacities and locations, market prices, and behaviour of interconnectors to other systems.
System simulations, which balance the cost of network reinforcements against the cost of finite network capacity restricting the generation schedule, are used to support these decisions. This project will use statistical emulators to understand how the input data drives the results of these simulations, and hence derive systematic approaches to making investment decisions under uncertainties in that data. Statistical emulation of the power system simulator is necessary because the full simulator is too computationally intensive to be evaluated at all relevant parts of the parameter space.
This work is part of the "Autonomic power systems" project. As such, there will be particular concentration of planning in the face of uncertainty over what generation and demand technologies will be developed over the life of the network assets - with the rapid development in smartgrid technologies involving distributed control and participation of demand in electricity markets, the operation of power systems will look very different in 20-30 years' time. There will also be opportunities to collaborate with industrial partners of the project, including National Grid.
Supervisor: Professor M. Goldstein (Mathematical Sciences) and Dr. Chris Dent (Engineering and Computing Sciences)
Funding: EPSRC, Fees/stipend paid for home/EU students.
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PhD project: Methodologies for Massively Decentralised Trading.
The necessary coordination of trades to maintain network security is currently achieved by a central System Operator responsible for a given service area. In contrast, the concept of an autonomic power system is based around millions of market players trading in a decentralised manner across many overlapping areas with decentralised arbitraging between individual conflicting objectives. The aim of the project will be investigate the pros and cons of the new paradigm of massively decentralised trading.
Supervisor: Professor J.W. Bialek (Engineering and Computing Sciences)
Funding: EPSRC, Fees/stipend paid for home/EU students.
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