Replacement capex regulatory tool- AER REPEX tool tutorial

PresentationNuttall Consulting

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Purposeoverview of the form and use of the AER’s

repex tool

NotDetailed reference material on the underlying spreadsheets Defence of the tool’s regulatory role and suitability

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SummaryBackgroundRepex model data requirementsOverview of workbook – repex modelling toolOverview of replacement algorithmDiscussion of issues raised

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Background

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Background – capex category

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Network capex driver Asset activity

Demand driven Replacement of assets with increased capacity (higher service level)

Development of new network

Non-demand drivenReplacement 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

Background - key aimsRegulatory tool NOT planning/management tool

Should account for main driver at aggregate level but not concerned with excessive detail

Allow intra- and inter-company comparisonsTargeting of matters for detailed reviewDevelopment of benchmarks

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Form of model

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Similar, in principle, to tools used by other regulators and NSPsOfgem in the UKESV, OTTER, ESCOSA – the “PB model”Numerous NEM DNSPs – the “PB model” and internal

Inputs• asset state

• asset ages and quantities• planning parameters

• asset lives and replacement cost

Outputs• forecast replacement

volumes• forecast replacement capex• forecast average ages

Form of model

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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

Role – past application of tool

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1 - Base-case• Prepare individual DNSP

models based upon DNSP data

2 - Calibration• Derive planning

parameters from actual historical information of DNSP

• Prepare individual DNSP calibration models

3- Comparison• Derive benchmarks

parameters based upon set of DNSPs’ calibrated planning parameters

• Prepare individual DNSP benchmark models

Repex tool assessment

inform other elements of the reviewfor example, targeting matters for more

detailed review, set expenditure allowance

data requirements

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Format of network model Physical representation of network – volumes of assetsMultiple asset categories used to improve accuracy

allows for differences between networksreduce impact of aggregation

For example, for poles we may have separate categoriesDifferent voltage levels carried by polesDifferent pole construction materialsDifferent locations.

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Historically 30 – 100 separate asset categories defined

Data – asset groupingEach asset category must be assigned to an asset group

allows aggregation for analysis and reporting

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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

Data – asset category dataFor each asset category

1. Asset group ID2. Asset replacement unit cost (mean unit cost)3. Asset replacement life parameters

a) Mean lifeb) Standard deviation(assumes a normal distribution)

4. Replacement method5. Age profile – array of the volume of assets at ages (0 to

90 years old)

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Supporting dataPrevious RINs have included information requests to

support DNSP data and aid in the AER’s analysisFor 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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Overview of workbook

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workbook structureInput sheets

Model initialisation data sheet – “Tables”Asset category data input sheet – “Asset data”

Output sheetsAsset category summary sheet – “Age profile summary”Replacement forecast sheet – “RRR hist-forc”

Chart sheetsAge profile – “age profile”Replacement forecast – “Forecast Ch1” and “Forecast Ch2”

Internal calculation sheetsNuttall Consulting

Overview of demo model

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See handbook for more detailed reference material

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Replacement algorithm

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Forecasting algorithmTo account for variations in lives, a probabilistic asset

replacement life is used

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Probabilistic model XAsset state

volume of assets survived to age - a

Volumereplaced Capex

Planning parameters asset life replacement unit cost(probability distribution)

probabilistic algorithm

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Use survivor / hazard curve principles to predict replacement quantities in a forecast yearGiven

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

VBA function

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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

VBA function – worked example

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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

VBA function – worked example

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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

Probability distributions

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0.0

2.0

4.0

6.0

8.0

10.0

12.0

20 30 40 50 60 70 80

valu

e re

plac

ed ($

k)

age

unconditional replacements replaced, given survived to 53

Proportion replaced in year, y, given the assets have survived to be 53

Aggregating

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0.0

2.0

4.0

6.0

8.0

10.0

12.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

valu

e re

plac

ed ($

k)

forecast year

replaced, given survived to 53

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

Common issues

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Common issues raised

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age is not a proxy for condition/risks as assumed by the modelmodel does not allow for different operating environmentsuse of “normal” probability distribution rather than “Weilbull”use of square root of mean as the standard deviation“inferred historical lives” often above “industry benchmark”

livesuse of “estimated” volumes and costs for inferring historical

lives“goodness of fit” and “fit for purpose” of model forecasts

Questions?

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