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Purpose
 overview of the form and use of the AER’s repex
tool
 Not
 Detailed reference material on the underlying spreadsheets
 Defence of the tool’s regulatory role and suitability
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Summary
 Background
 Repex model data requirements
 Overview of workbook – repex modelling tool
 Overview of replacement algorithm
 Discussion of issues raised
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Background – capex category
Network capex driver
Demand driven
Non-demand driven
Asset activity
Replacement of assets with increased
capacity (higher service level)
Development of new
network
Replacement of assets with
modern equivalent (similar
service level)
Installation of new assets
Non-demand-driven replacement of an asset with its
modern-equivalent, where the timing of the need can be
directly or implicitly linked to the age of the asset
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Background - key aims
 Regulatory tool NOT planning/management tool
 Should account for main driver at aggregate level

but not concerned with excessive detail
 Allow intra- and inter-company comparisons
 Targeting of matters for detailed review
 Development of benchmarks
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Form of model
 Similar, in principle, to tools used by other regulators and
NSPs
 Ofgem in the UK
 ESV, OTTER, ESCOSA – the “PB model”
 Numerous NEM DNSPs – the “PB model” and internal
Inputs
Outputs
• asset state
• asset ages and quantities
• planning parameters
• asset lives and replacement
cost
• forecast replacement volumes
• forecast replacement capex
• forecast average ages
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Form of model
 Tool is spreadsheet based - uses VBA functions
 Does not rely upon proprietary – or “black box” –
algorithms
 Uses standard probability theory – covered in numerous
text books and papers
 Relatively simple to independently verify
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Role – past application of tool
Repex tool assessment
1 - Base-case
2 - Calibration
3- Comparison
• Prepare individual DNSP
models based upon
DNSP data
• Derive planning
parameters from actual
historical information of
DNSP
• Prepare individual DNSP
calibration models
• Derive benchmarks
parameters based upon
set of DNSPs’ calibrated
planning parameters
• Prepare individual DNSP
benchmark models
inform other elements of the review
for example, targeting matters for more
detailed review, set expenditure allowance
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Format of network model
 Physical representation of network – volumes of assets
 Multiple asset categories used to improve accuracy
 allows for differences between networks
 reduce impact of aggregation
 For example, for poles we may have separate categories
 Different voltage levels carried by poles
 Different pole construction materials
 Different locations.
Historically 30 – 100 separate asset categories defined
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Data – asset grouping
 Each asset category must be assigned to an asset group
 allows aggregation for analysis and reporting
AER previously defined 13 asset groups for distribution
Poles
Distribution transformers
Zone other
Pole top structures
Distribution switchgear
SCADA and protection
Conductors
Distribution other
Other
Underground cables
Zone transformers
Services
Zone switchgear
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Data – asset category data
 For each asset category
1. Asset group ID
2. Asset replacement unit cost (mean unit cost)
3. Asset replacement life parameters
Mean life
b)
Standard deviation
(assumes a normal distribution)
a)
4. Replacement method
5. Age profile – array of the volume of assets at ages (0 to
90 years old)
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Supporting data
 Previous RINs have included information requests to
support DNSP data and aid in the AER’s analysis
 For each asset category defined by the DNSP




descriptions of the asset category
historical asset replacement levels and expenditure
explanations of the DNSP’s determination of asset life
parameters, including appropriate distributions
explanations of the DNSP’s determination of the unit costs,
including variability and relationship to historical costs
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workbook structure
 Input sheets
 Model initialisation data sheet – “Tables”
 Asset category data input sheet – “Asset data”
 Output sheets
 Asset category summary sheet – “Age profile summary”
 Replacement forecast sheet – “RRR hist-forc”
 Chart sheets
 Age profile – “age profile”
 Replacement forecast – “Forecast Ch1” and “Forecast Ch2”
 Internal calculation sheets
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Overview of demo model
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See handbook for more
detailed reference material
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Forecasting algorithm
 To account for variations in lives, a probabilistic asset
replacement life is used
Planning parameters
asset life
replacement unit cost
(probability distribution)
Asset state
volume of
assets survived to
age - a
Probabilistic
model
Volume
replaced
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X
Capex
probabilistic algorithm
 Use survivor / hazard curve principles to predict
replacement quantities in a forecast year
 Given
the unconditional probability distribution for the replacement life
of the asset
 existing volume of assets at a certain age – i.e. the volume of
assets that have survived to that age
 The unconditional probability distribution is then transformed into a
conditional distribution appropriate for the assets, given they have
survived to that age
 The condition probability distribution is then used to determine the
proportion of these asset replaced in future years

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VBA function
 Array function
 =repcalc(age profile, method, life, SD, years, recursive, initial year)
 Inputs
 Age profile – array of age profile (replacement cost by installation date)
 Method – replacement method
 Life – mean replacement life
 SD – standard deviation of life
 Years – number of years for forecast
 Recursive – if TRUE, allow replaced assets to be replaced
 Initial year - if TRUE, 1st year of forecast is year after last year of age profile
 Outputs
 Array of forecast replacement expenditure by year
 Array of forecast average age by year
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VBA function – worked example
 Pre-function calculations – to form age profile for VBA function
 Asset data sheet
 Use input volume age profile
 Multiply by replacement cost to form replacement cost age profile
 Age profile (Inst) sheet
 Transformer to replacement cost age profile by installation date
 Now assume an asset category defined in the model
 We are using the probabilistic replacement approach, where


Mean replacement life = 50 years
SD of replacement life = 10 years
 Replacement cost = $1,000 per unit replaced
 And we have array replacement cost age profile by installation date
 1st year of forecast is 2014
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VBA function – worked example
 VBA function steps through each element of the age profile to prepare a
forecast for assets installed at that date
 That is, assets that have survived to current date
 For example, assume we still have 100 assets that were installed in 1960
 That is, 100 asset that have survived to be 53 year old
 Or a replacement value of $100,000 that has survived to be 53 year old
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Probability distributions
unconditional replacements
value replaced ($k)
12.0
replaced, given survived to 53
10.0
8.0
6.0
4.0
2.0
0.0
20
30
40
50
60
70
age
Proportion replaced in year, y, given the assets have survived to be 53
=
𝑢𝑛𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑓𝑜𝑟 𝑟𝑒𝑝𝑙𝑎𝑐𝑖𝑛𝑔 𝑎𝑡 𝑎𝑔𝑒 53 + 𝑦
𝑢𝑛𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑟𝑒𝑝𝑙𝑎𝑐𝑒𝑚𝑒𝑛𝑡𝑠 𝑤ℎ𝑒𝑛 𝑜𝑙𝑑𝑒𝑟 𝑡ℎ𝑎𝑛 53
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80
Aggregating
replaced, given survived to 53
value replaced ($k)
12.0
10.0
8.0
6.0
4.0
2.0
0.0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
forecast year
Forecast is summation of this calculation for each element of the age profile
Algorithm also tracks and outputs the average age of the forecast age profile
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Common issues raised
 age is not a proxy for condition/risks as assumed by the model
 model does not allow for different operating environments
 use of “normal” probability distribution rather than “Weilbull”
 use of square root of mean as the standard deviation
 “inferred historical lives” often above “industry benchmark”
lives
 use of “estimated” volumes and costs for inferring historical
lives
 “goodness of fit” and “fit for purpose” of model forecasts
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