Variability and Uncertainty in Energy
Systems
Chris Dent
[email protected]
Turing Gateway workshop: Maths and Public Policy - Cities & Infrastructure
11 March 2015
Contents
• Motivations
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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
∂
EXAMPLES OF VARIABILITY
AND UNCERTAINTY
Short term forecasting
• Diagrams from National Grid,
INI OfB, 2012
• Uncertainty in forecasts
‒ Non-stationary
• Use in reserve setting
∂
‒ Extremes most important
‒ Limited data
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)
Network capital planning
∂
• Left diagram from ENSG, 2014
• “Right” amount of congestion
‒ Uncertainty in wind resource, plant location, demand
growth, mechanical reliability, etc etc
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
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 entity’s assessment of judgments of gencos
‒ How to draw conclusions about real world?
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
Efficient asset renewal
∂
• Diagram source ScottishPower
‒ Assessment of asset base condition
‒ Plan renewal programme balancing risk and capital costs
Smartgrids – greater complexity
∂
• Large increase in number of entities interacting with system
‒ Centralised control not tractable
‒ New decentralised approaches required
∂
INSTITUTIONAL ISSUES
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=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
∂ maintenance (Strathclyde):
in offshore turbine operation and
strategic asset management in absence of operational experience
‒ Locally stationary Energy Time Series (Bristol/Lancaster): nonstationarity 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.
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?
Any questions?
∂