Turing Fellow at the Alan Turing Institute Thanks to Amy Wilson Stan Zachary Michael Goldstein and too many others to name Statistical modelling for planning and policy applications It matters ID: 815534
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Slide1
Dr. Chris DentChancellor’s Fellow and Reader in Industrial MathematicsTuring Fellow at the Alan Turing InstituteThanks to Amy Wilson, Stan Zachary, Michael Goldstein – and too many others to name
Statistical modelling for planning and policy applications
Slide2It matters!Variability/uncertainty of renewablesUncertainty in planning backgroundLinking models to the worldI am not a statistician!BA Maths, PhD Physics, MSc OR, CEng, FORS, SMIEEEWhat I learned about modelling from PhysicsNot much studied at INI – or anywhere!Stats work mostly short term forecastingPlanning work mostly optimisation not uncertainty specificationWorking out what models tell us about world is unfashionableStatistical thinking in resource adequacy: Zachary, Wilson, Dent?, Tindemans?, Gorinevsky, Kujala, Ekisheva, Astrape?
Why am I talking about this?
Slide3Resource adequacyInference from dataLarge computer modelsCapital planningPolicy – what if background very complex?Where next? Scoping work:Centre for Digital Built Britain network on planning under uncertaintyTuring Institute project on use of models in organisationsEarlier energy policy reportContents
Slide4Inference from dataResource adequacy
Slide5Risk of absolute supply shortages
Slide6Evolution of margin through peak season
available (conventional, VG) – interconnection, storage?
demand,
time resolution
ranges over future season under study
Risk indices
Expected duration of shortfall in a future season (19-20, or 23-24)
Expected energy (volume of demand) not supplied
Probability distribution of duration of shortfall
Probability distribution of energy not supplied
Monetary quantification of cost of shortfalls
This is basically applied stats
Much open space for research
GB CA study probability model
Slide7Lots of numbers… but how much information
Lots of numbers… but how much information?
Slide8Source: Wilson/ZacharyHow much (relevant) data?
Slide9Limited relevant data
Few data points at times of high demand/low wind
7 years of data used here
Approaches based on using distribution of
Hindcast
– use historic empirical distribution (EVT variants)
General independence?
Use longer meteorological time series?
Slide10Source: National GridInterest in risk level conditional on severity of winterHigh level decision makers and analystsMeaningful to make point estimate of ‘long-run’ LOLE?
How to interpret list of examples of historic years?
Mapping historic years’ demands to common future scenario
Relevance of frequentist interpretation of probability?
Limited relevant years
Slide11Lots of numbers… but how much information
Calculating the right thing?
Slide12Expected value indices used in most industrial studiesExcept Belgium! See National Grid reporthttp://sites.ieee.org/pes-rrpasc/files/2016/08/Daniel-Burke-National-Grid-GB-Security-of-Supply-International-Study-on-Standards-and-Implementation.pdfBut they are not everything…‘How bad can things credibly get?’No information on variability about meanGenerally expected money does not represent real decision makers Time series modelling required!
Example
Fix LOLE
Vary amount of windVariability of EU
Indices and decision analysis
Slide13Lots of numbers… but how much information
Validate models!!!
Slide14Distributions should be the same
Source: Kevin Carden /
Astrape
Slide15Lots of numbers… but how much information
Extremes matter!
Slide161 system, time-collapsed1 system, time series2 systemsExamples of EVT applications
Slide17Capital planningPolicy – what if background very complex?Large computer models
Slide18Sources of uncertainty (MG)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.)
Uncertainty about what is meant by
uncertainty
and
probability
Slide19Toy example by Amy Wilson – model
Emulator quantifies uncertainty in value of function for all
Interested in e.g. how
propagates to uncertainty in
Uncertainty in
combines with that in
for given
Avoid dependence on choice of evaluations
Framework to consider full range of uncertainties
Statistical emulators
Slide20Great interest in contribution of interconnection to GB(Other systems will have their equivalent issues)Two linked issuesUncertainty in margin in Europe greater than link capacity
ENTSO-e models complex and high dimensional
How to model this?
And multivariate time series with focus on extremes!
Interconnectors and
SoS
Slide21DEFRA – the Horrendogram
Slide22Lots of numbers… but how much information
Capital planning optimisation
Slide23Antony Lawson Transmission capacity planning
Meng Xu
Calibration of generation investment projection model
How to do optimization?
Examples from power systems
Slide24Lots of numbers… but how much information
Right for the right reason?
Slide25Distribution of event sizes
https://doi.org/10.1109/PES.2008.4596715
Slide26Background – getting away from specific methodology for UQ etcThere should be a clear statement of what the study is claiming to say about the real world…… along with a logical argument to back this up
What proportion of studies satisfy (1) and (2)?
Key requirements of an applied modelling study
Slide27Scoping activity
Slide28Attendees from government, industry, academiaReport available (email me!)Key outputs – clear demand for R+DManagement of uncertainty in energy systems modellingCommunication of modelling results (inc uncertainty) beyond technical
modellers
The role of modelling within policy processes, approaches to quality assurance
Not a shopping list for specific technical work
Alan Turing Institute project ‘Managing uncertainty in government modelling’
Energy policy scoping workshop
Slide29All the usual things – which do matterUncertainty, communication, organisational mattersMore intriguing outcomesEnabling work on state of knowledge/practiceMore nuanced understanding of failure/success
Communication is a two way process
Resource allocated to analysis for strategic planning
Scenarios, de-risking contracts, engineering standards
Data availability – enabling activity
Design funding calls to enable interdisciplinarity
Newton Gateway: “Evidence based decisions”
Similar issues, e.g. V complex backgrounds
Part of RC research/project agenda
CDBB, Turing and Newton
Slide30Wilson/Zachary on wind/demand: http://sites.ieee.org/pes-rrpasc/working-groups/wg-on-lole-best-practices/Zachary on decision analysis, Wilson/Goldstein on UQhttp://icms.org.uk/workshops/energytutorialdayLawson/Dent/Goldstein on transmission planning (multistage to follow)http://dx.doi.org/10.1016/j.segan.2016.05.003Xu/Wilson/Dent on generation projection model calibrationhttp://dx.doi.org/10.1016/j.segan.2015.10.007Sheehy et al on distribution of outcome metricshttps://doi.org/10.1109/PMAPS.2016.7764199Dent on “What is a blackout?”https://www.dur.ac.uk/dei/resources/briefings/blackouts/HM Treasury Aqua Bookhttps://www.gov.uk/government/publications/the-aqua-book-guidance-on-producing-quality-analysis-for-governmentEnergy policy reporthttps://www.research.ed.ac.uk/portal/en/publications/modelling-in-public-policy(354d2e01-4cbe-48d9-983c-0e4f193dbef2).html
Cambridge Energy Efficient Cities Initiative
https://www.eeci.cam.ac.uk/
References