1. Augmentation capex regulatory tool - AER augex tool tutorial Presentation Nuttall Consulting

2. Purpose overview of the form and use of the AER’s augex tool Not
Detailed reference material on the underlying spreadsheets
Defence of the tool’s regulatory role and suitability
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3. Summary Background Augex model data requirements
Overview of workbook – augex modelling tool
Overview of augmentation algorithm
Discussion of issues raised
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4. Background
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5. Background – capex category
Capex driver
Capex activity
Replacement
Additional assets
Demand driven: customer connection
replacement and development of assets to facilitate the connection of customers
Augex tool
Demand driven: augmentation
replacement and development of network assets to increase the capacity of the network
Repex tool
Non-demand driven
replacement of aged assets with modern equivalents
installation of new assets
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6. 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
But not intended to determine specific network constraints and solutions
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7. Overview Similar in form to repex tool
Asset state
network utilisation and capacity
growth in peak demand
Planning parameters
utilisation thresholds
augmentation costs
Outputs
forecast capacity
forecast costs
forecast utilisation
Similar in form to repex tool
Similar to tools used by ESV and NSPs
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8. Overview Tool is spreadsheet based - uses VBA functions
Does not rely upon proprietary – or “black box” – algorithms
Uses probability model – to apply utilisation threshold
relatively simple to independently verify
Asset utilisation = peak loading / normal rating
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9. Anticipated application
Augex tool assessment
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
inform other elements of the review
for example, targeting matters for more detailed review, set expenditure allowance
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10. Data requirements
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11. Network representation - segments
Multiple network segments used to improve accuracy
allows for differences between networks
reduce impact of aggregation e.g. differences in planning parameters
Individual segments represent typical planning components
sub-transmission lines – load type and/or arrangements
sub-transmission/zone substations – load type and/or transformer number
distribution feeder – load type
distribution substations – load type
15 – 30 separate segments
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12. Network representation - grouping
Each network segment must be assigned to a group
allows aggregation for analysis and reporting
Example distribution groups
Sub-transmission lines
CBD distribution feeders
CBD distribution substations
Sub-transmission substations
Urban distribution feeders
Urban distribution substations
Zone substations
Short rural distribution feeders
Short rural distribution substations
Long rural distribution feeders
Long rural distribution substations
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13. Segment input data For each network segment Asset state data
Utilisation profile – array of the capacity (MVA) of assets at utilisations (0% to 151% - in 1% increments)
Utilisation annual growth rate – %pa average over study period
Planning parameters
Utilisation threshold – mean and standard deviation (assumes a normal distribution)
Capacity factor – capacity added = capacity factor x capacity requiring augmentation
Augmentation unit cost - (k$ per MVA of capacity)
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14. Data – asset status Obtain: loading, rating, type and growth data
Form of data required:
loadings, ratings, types, should reflect actual data
individual components: sub-transmission lines and substations, zone substations, and distribution feeders
grouping of data for distribution substations (and LV feeders?)
latest loading and rating data is used to develop utilisation profile input
other actual data is used to assess changes (calibration)
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15. Data – asset status General comments
Actual maximum demand should exclude abnormal operating conditions
Maximum demand estimate acceptable if not directly measured
Distribution feeders – trunk model
maximum demand at feeder exit
rating of main trunk section
Distribution substations
expect segments to be based upon load type or categories of substation capacity or substations type
Demand growth rates – most appropriate that are available for category
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16. Data – capex and capacity
Obtain: historical and future capex and capacity
Form of data required:
Capex should be exclusive of corporate overheads
Unmodelled capex – separately identify augmentation capex that is not considered to be related to utilisation and peak demand growth
Assuming there will be an “augmentation” capex category in the regulatory reporting requirements – overall capex should be reconcilable to this category
The type of capacity should reflect rating types defined for asset status
Capex ideally should be “as commissioned”
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17. Supporting information
Explanations of data sources, calculations, assumptions for tool data inputs
rationale for segments if defined by NSP
basis of asset status data - maximum demand, ratings, growth rates
derivation of planning parameters
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18. Possible other supporting information
Other responses to aid understanding of data
Inclusions and exclusions in capex
actual development costs, direct project analysis/planning costs, other costs allocated to the project
Capex-capacity allocations rules
it is expected that internal systems may not align to AER groups
Explanation of unmodelled capex
Project/program descriptions, drivers, outcome on capacity
Comparability with other DNSPs
Differences that may impact augmentation requirements – utilisation levels, project types and costs
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19. Overview of workbook
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20. workbook structure Input sheets Output sheets Chart sheets
Model initialisation data sheet – “Tables”
segment data input sheet – “Asset data”
Output sheets
Asset category summary sheet – “Utilisation profile summary”
Augmentation forecast sheet – “aug forecast”
Chart sheets
Utilisation profile – “utilisation profile chart”
Augmentation forecast – “Forecast Ch1” and “Forecast Ch2”
Internal calculation sheets
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21. Overview of demo model
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22. See handbook for more detailed reference material
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23. Augmentation algorithm
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24. Forecasting – for each segment
probabilistic model to account for variations in the threshold
(similar to asset lives in repex model)
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25. VBA function Array function
=aug_calc(utilisation profile index, capacity profile, growth, capacity factor, utilisation threshold, SD, method, years)
Inputs
utilisation profile index – array of utilisation (%) increments
capacity profile – array of capacity (MVA) matched to utilisation profile index
growth – average per annum compound growth in peak demand (%)
capacity factor – as discussed
utilisation threshold – mean utilisation at augmentation point (%)
SD – standard deviation of utilisation at augmentation point (%)
method – augmentation algorithm method (only 1 at this time)
Years – number of years for forecast
Outputs
Array of forecast capacity added by year
Array of forecast average utilisation by year
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26. VBA function – worked example
Assume a network segment is defined in the model
Where – utilisation threshold
Mean utilisation threshold = 65%
SD of threshold = 5%
Capacity factor – 0.5 (i.e. add 1 MVA for every 2 MVA requiring augmentation)
Augmentation cost = $1,000 per MVA added
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27. VBA function – worked example
VBA function steps through each element of the utilisation profile to prepare a forecast for the existing capacity at that utilisation level
That is, the capacity that has survived to that utilisation level
For each element of capacity in the utilisation profile
Step 1 – calculate for each forecast year the amount of capacity requiring augmentation (the probabilistic calculation)
Step 2 – calculate the amount of capacity to be added (using the capacity factor)
Post-function calculation - expenditure forecasts calculated outside of VBA function as - capacity added x augmentation cost
For example, assume we still have 100 MVA that is utilised at 60%
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28. Step 1 – probability calculation
Proportion of capacity requiring augmentation (CRA) at utilisation, u, given the capacity has survived to be utilised at 60%
= 𝑢𝑛𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑓𝑜𝑟 𝑎𝑢𝑔𝑚𝑒𝑛𝑡𝑖𝑛𝑔 𝑎𝑡 𝑢𝑡𝑖𝑙𝑖𝑠𝑎𝑡𝑖𝑜𝑛, 𝑢 𝑢𝑛𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛𝑎𝑙 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑓𝑜𝑟 𝑎𝑢𝑔𝑚𝑒𝑛𝑡𝑖𝑛𝑔 𝑤ℎ𝑒𝑛 𝑢𝑡𝑖𝑙𝑖𝑠𝑒𝑑 𝑎𝑏𝑜𝑣𝑒 60%
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29. Step 1 – transform u to year
using peak demand growth for that segment – assume here it is 2% pa.
transform the x-axis from utilisation to forecast year
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30. Step 2 – capacity added For each forecast year
Using forecast capacity requiring augmentation (CRA)
Capacity added = CRA x capacity factor
For example
in year 1, CAR is 7.7 MVA (utilised at 61.2%)
Capacity added = 7.7 MVA x 0.5 = 3.9 MVA
Utilisation of augmented capacity = demand associated with CAR / (CAR + capacity added) = (7.7 x 61.2%) / ( ) = 41% utilisation
Return to step 1 to calculate any later augmentation associated with this augmented capacity.
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31. Common issues
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32. Common issues raised Anomaly for high growth rates
More detail in manual
HV trunk feeder model
Use of N-1 rating rather than N for utilisation
Sensitivity to utilisation threshold
Including LV segments
Clarifications on modelling certain circumstances
Cross feeder ties
Non-trunk HV feeder augmentations
Projects with long lead times
Meshed networks
Preparing $/MVA for different projects
Weather correcting utilisation
Augmenting one segment to address constraints on other segments
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33. Questions?
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