Variability and Uncertainty in Energy Systems Chris Dent
[email protected] Turing Gateway workshop: Maths and Public
Policy - Cities & Infrastructure 11 March 2015
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Contents Motivations Integration of variable/uncertain
generation Capital planning 10s of billions of investment Efficient
asset renewal Greater scale (Smartgrids) Examples, and areas of
mathematics required Institutional issues Bringing right people
together Technology transfer
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EXAMPLES OF VARIABILITY AND UNCERTAINTY
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Short term forecasting Diagrams from National Grid, INI OfB,
2012 Uncertainty in forecasts Non-stationary Use in reserve setting
Extremes most important Limited data
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Optimal scheduling of generators Diagrams from A. Tuohy et al,
IEEE TPS, 2009 Some conventional generators have large startup
costs, min up/down times, etc Optimise schedule for next 1-2 days
under uncertainty over wind power forecast (and demand and
reliability) Three aspects Write down structure of problem Scenario
tree (need to have simple representation of uncertainty) Solve
optimisation problem (which is large and hard)
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Network capital planning Left diagram from ENSG, 2014 Right
amount of congestion Uncertainty in wind resource, plant location,
demand growth, mechanical reliability, etc etc
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Adequacy of supply Top left from CA study risk of shortfall
Current modelling issues Wind-demand relationship, interconnectors,
costs of shortfalls, capacity market decision making
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Generation investment (e.g. DDM) How to project investment in
generating plant Design of markets, prices in capacity market Need
to imagine being market designer/operator, and make that entitys
assessment of judgments of gencos How to draw conclusions about
real world?
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Interconnection greater scale GB network will look less like an
island Larger scope of modelling required May have lesser quality
of data across wide interconnection
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Efficient asset renewal Diagram source ScottishPower Assessment
of asset base condition Plan renewal programme balancing risk and
capital costs
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Smartgrids greater complexity Large increase in number of
entities interacting with system Centralised control not tractable
New decentralised approaches required
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INSTITUTIONAL ISSUES
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UK skills in mathematics of energy systems e.g. EPSRC call on
Maths underpinning energy research, 2010,
http://gow.epsrc.ac.uk/ViewPanel.aspx?PanelId=5041http://gow.epsrc.ac.uk/ViewPanel.aspx?PanelId=5041
Mathematical foundations for energy networks: buffering, storage
and transmission (Cambridge, Heriot-Watt, Durham): storage,
forecasting, decentralised control Mathematical tools for improving
the understanding of uncertainty in offshore turbine operation and
maintenance (Strathclyde): strategic asset management in absence of
operational experience Locally stationary Energy Time Series
(Bristol/Lancaster): non- stationarity is a natural framework in
many energy applications (e.g. weather systems) Well linked to
industry, to each other, and to some engineering research - but to
mainstream of RCUK Energy Programme? Also workshops at Newton
Institute, with Energy Storage Network 1-2 June @ OU, Lancaster,
Durham Risk Day, PMAPS, etc.
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Institutional issues Many areas of current energy research
require skills from mathematical sciences as much as from the
application communities How to bring right people together for
academic research projects? How to bring together industry with
mathematicians and statisticians who have the skills to work on
their challenges Right team will not always consist of people with
long experience in energy applications Need combination of
methodological and application knowledge Challenges in technology
transfer Greater uncertainty and complexity requires new
mathematical and statistical technologies to be applied in energy
systems These skills are not universal in the industry How to take
into field application useful techniques developed in
universities?
