STUFF WE’VE BEEN DOING

Presentation to EPOC WW 2013

Overview

•  Reunion’s involvement in Transpower’s Pole 3 HVDC Commissioning

•  Motivation for alternative predictive tools •  Alliance with Milliman Inc. •  Nutty stuff – Bayesian Networks, Machine Learning etc

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Who are we?

•  Principals are 4 former colleagues –  James Moulder –  Stephen Batstone –  Neil Williams –  David Reeve

•  Small team of analysts and consultants •  Shared focus between advisory and

product development

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Regulatory

Risk Management

Engineering

Renewables

Trading

Investment

Asset Management

COMMISSIONING POLE 3

Pole 3

•  Transpower’s $672M (approved) new converter equipment at each end of HVDC

•  Effectively replaces Pole 1, which failed in late 2007 leaving the remaining Pole 2 at ~700MW capacity

•  Once commissioning completed, ~1200MW capacity

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Haywards  

Benmore  

Pole 3

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~160 Commissioning Tests

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Easy ones

Test 65.003 “Transfer between 200MW and 400MW Northwards for an hour, on Pole 3 only”

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Haywards  

Benmore  

400MW  

200MW  

Hard ones

Test 65.015 “Transfer between 300MW and 400MW Northwards for an hour, then ramp back to 0MW for half an hour, the decrease further to 200MW South and hold for 15 minutes”

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Haywards  

Benmore  

Constraints

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Constraints

•  A semi-specified order, with some hard dates •  Shift constraints •  But some time-of-day and day-of-week flexibility •  ~ 3 months

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RPC TESTING

OFF VOLTAGE

LOW POWER MED POWER HIGH POWER

FEB   MAR   APR   MAY  

Mandatory  outage  10  days    Must  be  finished  by  8th  March  

Using the Market

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Scheduling

•  Ideally, schedule each test to coincide with what the market wanted to do anyway (“natural flow”)

•  But even with complete flexibility this wouldn’t give you the full range of conditions you needed (hydrology), let alone in the right order

•  In situations when natural flow did not suit the test: –  Delay the test, but domino effect, or –  Contract with generators to create the right flow, at a cost

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!600$

!400$

!200$

0$

200$

400$

600$

800$

MW#Flow#(+

ve#=#North)#

Dec#5th#and#6th#2012#Trial#Op@mal#Test#Plan#

Actual$HVDC$Flow$2007$

Actual$HVDC$Flow$2008$

Actual$HVDC$Flow$2009$

Ideal$Test$Flow$

Test$Tolerance$Low$

Test$Tolerance$High$

Optimisation

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-­‐750

-­‐500

-­‐250

0

250

500

750

1000

1250

9/11/12 16/11/12 23/11/12 30/11/12 7/12/12 14/12/12 21/12/12 28/12/12 4/01/13 11/01/13 18/01/13 25/01/13 1/02/13

MW  Flow

Op0mal  test  Plan  for  2010  hydrology  Would  have  to  force  HVDC  flow  south  by  contracNng  to:  •  Increase  NI  generaNon  •  Decrease  SI  generaNon  

Scheduling

•  Needed a dynamic scheduling optimisation which –  Had the objective of minimising the cost of deviating the market from its

natural intention –  Explicitly considered evolving hydrological scenarios and consequential

option nature of decision

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Mathema0cal  Op0misa0on  of  Scheduling  Hydro  Experiments  

•  Engaged Stochastic Optimisation Ltd to build MOSHE –  Stochastic Dynamic Program –  Markov Chain for Hydrology

MOSHE

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MOSHE  

HYDROLOGY  

TEST  REQUISITES  

COST  FUNCTION  

OpNmal  test  sequence,  given  storage,  sequence  completed,  and  

period  

2003 2004 2005 2006 2007 2008 2009 20101+Sep 23 14 24 25 43 27 17 92+Sep 17 11 19 46 42 27 16 123+Sep 15 9 31 42 15 16 12 124+Sep 16 28 41 17 12 13 15 375+Sep 28 38 18 18 7 9 31 236+Sep 44 14 15 22 8 29 37 147+Sep 42 14 14 17 10 33 16 128+Sep 14 14 13 17 26 16 29 129+Sep 12 19 14 33 27 15 17 1110+Sep 12 23 35 39 12 14 18 1311+Sep 10 42 38 16 15 16 20 2212+Sep 25 43 13 16 17 16 38 2913+Sep 44 18 12 14 13 41 45 1014+Sep 45 15 11 20 16 48 18 1215+Sep 30 16 13 23 40 39 23 1016+Sep 29 13 14 37 48 43 14 1117+Sep 33 16 27 37 20 36 15 1218+Sep 33 31 25 13 14 17 15 1319+Sep 36 36 11 13 14 28 34 2420+Sep 48 16 7 13 14 47 35 1221+Sep 47 22 9 14 18 48 13 1222+Sep 28 14 11 14 36 30 12 1023+Sep 27 13 11 28 26 33 11 1024+Sep 32 16 33 38 14 31 10 1425+Sep 34 43 39 14 15 29 13 3326+Sep 37 43 20 13 12 22 32 4227+Sep 48 19 15 11 13 26 19 1628+Sep 28 13 15 10 28 44 16 1529+Sep 17 16 12 14 40 16 16 1330+Sep 22 17 17 30 46 22 17 131+Oct 22 16 43 28 19 16 16 122+Oct 32 40 39 15 16 24 19 283+Oct 26 40 15 14 15 38 34 224+Oct 41 17 13 15 16 48 27 125+Oct 41 17 13 16 36 41 8 116+Oct 28 21 11 27 48 23 15 137+Oct 37 19 15 45 46 17 18 128+Oct 37 25 33 45 17 15 14 129+Oct 18 46 46 16 16 15 13 1610+Oct 25 46 16 16 16 20 31 1611+Oct 43 30 15 17 16 46 39 1212+Oct 44 29 16 25 18 46 16 1313+Oct 18 39 17 31 44 16 12 1214+Oct 26 36 37 48 37 16 17 1215+Oct 35 37 48 45 16 15 25 1416+Oct 37 38 47 13 18 14 18 2517+Oct 42 46 37 11 16 16 29 2018+Oct 48 19 16 12 14 25 22 1419+Oct 48 34 16 16 29 34 14 1220+Oct 31 37 14 14 47 16 14 1221+Oct 35 37 24 25 48 15 17 1222+Oct 30 40 44 28 46 13 16 1223+Oct 21 48 48 27 30 12 32 3624+Oct 19 48 46 15 17 13 48 3725+Oct 48 43 16 14 19 25 48 2326+Oct 48 37 16 14 16 34 33 1327+Oct 43 31 15 16 27 42 29 1228+Oct 18 21 17 27 43 11 29 1229+Oct 23 20 35 33 16 16 21 1130+Oct 18 46 42 16 16 14 37 1331+Oct 38 48 16 16 15 16 48 14

Simulations

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2012  

2011  

2010  

RPC TESTING

OFF VOLTAGE POLE 3 SYSTEM TESTS

MANAGING TESTS

A day (or night) in the life of…

•  Agreeing test requirements with engineers… •  Predictions of HVDC natural flow for test… •  Predictions of generator and load behaviour… •  Negotiating contracts with generators and load… •  Deal capture, confirmations, approvals… •  Monitoring tests in real time and executing real-time

options (wind, load control)… •  Reporting…

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Prediction

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Tues?  

The result

•  Trading room with 3-5 people •  110 days •  > 180 tests successful •  540 contracts negotiated

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THE NATURE OF PREDICTION

Rethinking Uncertainty Modelling

OUTCOMES  ?  

DRIVERS  (Uncertain)  

THE  SYSTEM  (Complex  and  Uncertain)  

Pure  StaNsNcal  Models  (Variability/Risk)  

Fundamental  Models  (Why)  

How  much  uncertainty  do  I  push  into  my  dispatch  model?  How  many  system  variables  do  I  push  into  my  regression?  How  much  of  the  system  structure  do  I  reflect?  

(Tipping  2010)  

Alternatives

•  Alternative approaches exist in the broad domain of complex adaptive systems – focus is on using the system structure to explain

•  Reunion and Milliman Inc. joined forces to explore opportunities in the global energy industry

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InformaNon  Theory  

Bayesian  networks  

Psychology  

Graph  theory  

Complex  systems  

Systems  dynamics  

Behavioural  science  

CladisNcs   CogniNve  mapping  

Systems Approach

Traditional Risk Management Frameworks Statistical models, assuming constant drivers Registers assuming single characteristics Scenarios “imagined” Emerging risks by spotting events

Frameworks based on Complexity Science Models based on system drivers Descriptions of risk profile taken holistically Scenarios derived from risk profile Emerging risks spotted early from system

Some Tools

Tools help bring rigour to elicitation and analysis of expert insight… …and offer modelling methods which leverage expert knowledge, but which can adapt and evolve

Cognitive mapping

Information theory metrics

Cladistics

Predictive analytics

Bayesian Networks

Minimally complex structure

Storage + Inflows

Probability  distribu5on  of  contract  payoff  

Rela5onships,  Structure  

Expert  Input  Uncertainty  

Supply  =  Demand  Constraints  

FTR Model Analysis

§  Utilise Bayes’ theorem to explore outcomes bottom-up and top-down

§  Bottom-up scenario testing –  Test impact of low inflows and increase

in demand

§  Top-down reverse stress tests –  What do I have to believe about the

system to expect > $20 differential?

Dry  scenario  

Base  

Price  Differen5al  

An alternative - Machine Learning?

•  Predictive data analytics: Super-charged pattern/association recognition: –  Supervised vs unsupervised learning: Doesn’t necessarily require

the modeller to have a view on what variables are relevant –  Consequently black-box (vs predictive influence models e.g.,

Bayesian) •  ‘Art’ is in

–  Fit for purpose - nature of prediction and system underpinning it –  Design of algorithm and ability to “learn” from signals –  Data extraction key: Huge capability for absorbing structured and

unstructured data. •  Exploring a range of applications (eg, asset failure)

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