// Research needs in statistical
modelling for energy system
planning
Chris Dent
Amy Wilson / Meng Xu / Antony Lawson
/ Edward Williams
Stan Zachary / Matthias Troffaes /
Michael Goldstein
ATI Energy Workshop
29 January 2016
Mathematics and energy
systems
 Variability and
uncertainty
(renewable
generation)
 Complexity
(smartgrids, demand
side participation)
 Doing the same things
cheaper and more
robustly (asset
management)
What I do
Maths/OR background, worked in
Engineering since 2007
Mainly work in planning
All papers since 2010 in applied
probability and statistics
Why planning – skills and
opportunities
Risk of absolute supply shortages
The GB Capacity Assessment Study
• ‘Will the lights stay on’, ‘Will the wind be there when we
really need it’
‒ Or ‘What is the risk of generating capacity shortfalls in a future
season’
• Originally report by Ofgem, technical modelling
designed by NG
∂
‒ Project team included me and Stan Zachary
• What did mathematical sciences bring
‒ Model specification, clarification of assumptions
‒ Probability theory work to understand drivers of model
outputs
‒ Applied statistical work, particularly wrt sparse data
Current work:
‘traditional’ statistics
‘Will the wind be blowing
when we really need it?’
(or what’s prob dist)
Wind-demand link:
temperature, time of
day/week/year
Conditional
independence
Report for Grid on
non-sequential model
(Wilson, Zachary)
Current work: UQ
Don’t always have
traditional data
Expert judgment
Computer models
Embedded ops models
Limited # runs
Capital planning
Economic projection
EMR
Wilson/Lawson/Xu
Sources of uncertainty (MG)
•
•
•
•
•
•
•
•
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Parametric uncertainty (each model requires a, typically high dimensional,
parametric specification)
Condition uncertainty (uncertainty as to boundary conditions, initial conditions, and
forcing functions)
Functional uncertainty (model evaluations take a long time, so the function
is unknown almost everywhere)
Stochastic uncertainty (either the model is stochastic, or it should be)
Solution uncertainty (as the system equations can only be solved to some
necessary level of approximation) ∂
Structural uncertainty (the model only approximates the physical system)
Measurement uncertainty (as the model is calibrated against system data all of
which is measured with error)
Multi-model uncertainty (usually we have not one but many models related
to the physical system)
Decision uncertainty (to use the model to influence real world outcomes, we
need to relate things in the world that we can influence to inputs to the
simulator and through outputs to actual impacts. These links are uncertain.)
Managing Uncertainty in Complex
Models (www.mucm.ac.uk)
∂
Need for statistics / data
science
Lots of statistical problems
Planning, operation, futurology, policy
Until recently little involvement of
statisticians
Or math sci in general
Except in short term forecasting
Need to integrate methodology and
application communities
Research
Funding (RCUK and industry)