PhD project: Electricity Transmission Investment Planning
Under
Uncertainty Using Statistical Emulators
Durham University -School of Engineering and Computing Sciences
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, fees plus
limited stipend paid for overseas students.
Start date: by January 2013
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 this position, please contact Chris Dent in
the first instance on chris.dent@durham.ac.uk.
Uncertainty Using Statistical Emulators
Durham University -School of Engineering and Computing Sciences
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, fees plus
limited stipend paid for overseas students.
Start date: by January 2013
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 this position, please contact Chris Dent in
the first instance on chris.dent@durham.ac.uk.
