specify economic benchmarking techniques in our Guidelines. This will allow us to refine our techniques over time. In determinations, we will use economic benchmarking models based on their intended use, and the availability and quality of data. Some models could be used to cross-check the results of other techniques. At this stage, it is likely we will apply multilateral total factor productivity (MTFP), data envelopment analysis (DEA) and an econometric technique to forecast opex. We anticipate including economic benchmarking in annual benchmarking reports. We are likely to use economic benchmarking to (among other things): measure the rate of change in, and overall, efficiency of NSPs. This will provide an indication of the efficiency of historical expenditures and the appropriateness of their use in forecasts. develop a top down total cost forecast of total expenditure. develop a top down forecast of opex taking into account: the efficiency of historical opex the expected rate of change for opex. Economic benchmarking will also indicate the drivers of efficiency change which will assist us in targeting our expenditure reviews. We consider economic benchmarking (including submissions) in more detail in Attachment A. Category level benchmarking Category level benchmarking allows us to compare expenditure across NSPs for categories at various levels of expenditure. It can inform us, for example, of whether a NSP's: base expenditure can be used for trend analysis forecast unit costs are likely to be efficient forecast work volumes are likely to be efficient forecast expenditure is likely to be efficient.
Table A.1 Preferred DNSP specification Quantity Value Price Outputs Customers (no.) Revenue*Cost share Value/Customers System capacity (kVA*kms) Revenue*Cost share Value/kVA*kms Interruptions (customer minutes) –1*Customer minutes*VCR per customer minute –1*VCR per customer minute Inputs Nominal opex/Weighted average price index Opex (for network services group adjusted to remove accounting items not reflecting input use that year) Weighted average of ABS EGWWS WPI and five ABS producer price indexes Overhead lines (MVA–kms) Annual user cost (return of and on overhead capital) Overhead annual user cost/MVA–kms Underground cables (MVA–kms) Annual user cost (return of and on underground capital) Underground annual user cost/MVA–kms Transformers and other (MVA) Annual user cost (return of and on transformers and other capital) Transformers and other annual user cost/ MVA Abbreviations: VCR – value of customer reliability, ABS – Australian Bureau of Statistics, EGWWS – electricity, gas, water and waste services, WPI – wage price index, MVA – megavolt amperes, kVA – kilovolt amperes. Source: Economic Insights and AER analysis. Table A.2 Preferred TNSP specification Quantity Value Price Outputs System capacity (kVA*kms) Revenue*Cost share Value/kVA*kms Entry and exit points (no.) Revenue*Cost share Value/No. Loss of supply events (no.) –1*loss of supply events*average customers affected*VCR per customer interruption –1*average customers affected*VCR per customer interruption Aggregate unplanned outage duration (customer mins) –1*customer mins*VCR per customer minute –1*VCR per customer minute
We consider energy delivered satisfies the criteria that the output reflects services to customers. However, peak demand or system capacity has more influence on the expenditure objectives than energy delivered. Consequently, energy delivered may also not significantly affect a DNSP’s costs, relative to peak demand and system capacity. To limit the number of variables in our preferred output specification (which will also alleviate our degrees of freedom concerns), we consider the preferred model specifications should include only the most significant variables. Energy delivered does not satisfy this requirement. It could be included in sensitivity analysis (given its common use in other economic benchmarking studies). Revenue We consider revenue data may be limited in providing output weights. Similarly, stakeholders did not consider a billed outputs specification to be appropriate. They also argued there was no link between revenue and functional outputs. However, it could still be used in sensitivity analysis and to examine the link between revenue and billed outputs (such as customers and throughput).This is because customers are included in our preferred model specification for DNSPs and energy delivered is included in our alternative model specifications for both DNSPs and TNSPs. We consider revenue data are not an onerous data requirement. We already receive aggregated annual revenue and disaggregating revenue into customer types and chargeable quantity is consistent with the disaggregation required for customer and energy delivered data. System security, electricity quality and safety We consider network security, electricity quality and safety should not be included in our economic benchmarking analysis until a robust measure is available for these variables. Some stakeholders, such as CitiPower, Powercor and SA Power Networks, considered quality of supply should be included in economic benchmarking but noted the data limitations. They also noted quality of supply is expected to increase in Victoria with the introduction of advanced metering infrastructure meters. We consider reliability, not system security, better reflects the quality of services provided to customers. System security is also difficult to quantify for economic benchmarking. We agree with Economic Insight’s consideration that electricity quality and safety are important, but that they should not be included as an output variable. Safety and quality standards are a basic requirement of DNSP operation and are unlikely to be substantially different across DNSPs. There is also no single overall summary measure for quality and safety.
one of: physical proxy for capital (which includes the MVA–kms of overhead and underground lines) RAB straight line depreciation proxy depreciated RAB proxy. Table A.9 outlines our preferred NSP input specification and alternative inputs. Table A.9 NSP input specifications Quantity Value Price Preferred input specification Nominal opex/Weighted average price index Opex (for prescribed services adjusted to remove accounting items not reflecting input use that year) Weighted average of ABS EGWWS WPI and five ABS producer price indexes Overhead lines (MVA–kms) Annual user cost (return of and on overhead capital) Overhead annual user cost/MVA–kms Underground cables (MVA–kms) Annual user cost (return of and on underground capital) Underground annual user cost/MVA–kms Transformers and other (MVA) Annual user cost (return of and on transformers and other capital) Transformers & other annual user cost/MVA Alternative opex inputs Nominal opex/Weighted average price index Opex (for network services group adjusted to remove accounting items not reflecting input use that year) Weighted average of ABS EGWW AWOTE and five ABS producer price indexes Alternative capital inputs Nominal RAB straight–line depreciation/ ABS EGWWS CGPI AUC (Return of and on capital) AUC/Constant price RAB depreciation Nominal depreciated RAB/ABS EGWWS CGPI Revenue minus opex (Revenue minus opex)/Constant price depreciated RAB Abbreviations: VCR – value of customer reliability, ABS – Australian Bureau of Statistics, EGWWS – electricity, gas, water and waste services, WPI – wage price index, MVA – megavolt amperes, kVA – kilovolt amperes.
TNSP environmental variables Our shortlist of environmental variables is based on the shortlist recommended by Economic Insights. We have also included one additional terrain factor that accounts for difficult terrain. Table A.11 TNSP environmental variables shortlist Variable Definition Source Weather factors Extreme heat days Number of extreme heat days (average temperature above 25°C) BoM Extreme cold days Number of extreme cold days (average temperature below 12°C) BoM Extreme wind days Number of days with peak wind gusts over 90 km/hour BoM Average wind speed Average recorded wind speeds for a representative sample of weather stations BoM Terrain factors Bushfire risk Number of days with over 50 per cent of service area subject to severe or higher bushfire danger rating BoM and FAs Rural proportion Percentage of route-line length classified as short rural or long rural RIN Vegetation growth and encroachment Percentage of route-line length requiring active vegetation management by the TNSP TNSPs Standard vehicle access Proportion of network that does not have standard vehicle access TNSPs Altitude Percentage of circuit line length above 600 metres TNSPs Network characteristics Line length Route length of lines IDR Variability of dispatch Proportion of energy dispatch from non-thermal generators TNSPs Concentrated load distance Greatest distance from node having at least 30 per cent of generation capacity to node. TNSPs
Our demand forecast assessment will include reviewing the technical elements of a NSP's forecasting approaches. We will assess, for example, weather correction approaches and regression techniques for rigor and supporting evidence. We will also carry out data validation and testing of the trends that underpin the demand forecasts. This work may include checking customer number growth in different regions of the network, or cross checking data from the NSP with other data sources. We will assess whether the NSP considered a variety of sources for its inputs, and whether the input selection process potentially introduces bias to the demand forecast. We will likely review a sample of the NSPs' spatial demand forecasts given a full review of all spatial forecasts is infeasible. In distribution determinations, for example, we will likely review in detail the demand forecasts and forecasting approaches for a sample of zone substations. Preliminary analysis, including discussions with the NSP and other stakeholders, will likely inform the appropriate approach for selecting zone substations for closer review. We may target our sample to include those zone substations associated with a relatively high capex forecast over the next regulatory control period. Alternatively, we may choose a random sample or a mix of targeted and random sampling. The extent to which we extrapolate our findings on the sample to all other zone substations will depend on the issues that arise and the nature of the NSP's forecast. Our detailed review will assess whether issues appear common in the forecast, or are confined to one part of the network. We may find, for example, an unreasonable demand forecast for only one zone substation in the sample. If so, we would not extrapolate adjustments for that zone substation to other zone substations. If we find a more general issue (such as reconciliation between the top down and bottom up forecasts), then we will likely infer findings from the detailed review to the rest of the spatial forecasts. Demand forecast assessment principles We will assess whether a NSP's approach exhibits the following principles of best practice demand forecasting: accuracy and unbiasedness transparency and repeatability incorporation of key drivers weather normalisation model validation and testing
customer type voltage connection type meter type and location of public lighting works. We will collect cost data for categories of customer initiated works with a view to grouping a variety of works with similar costs, as outlined in Table B.8. We also expect the DNSPs to recast historical data into the categories presented in Table B.9 to Table B.12. Table B.8 Classification of connections capital work categories Customer type Connection type Residential installations Simple type connection (LV) Complex type connection (LV) Complex type connection (HV) Commercial and industrial installations Simple type connection (LV) Complex type connection (LV) Complex type connection (HV) Subdivision (residential/commercial and industrial) Complex type connection (HV) Embedded generation installations Complex type connection (LV) Complex type connection (HV) Notes: HV and LV are abbreviations for high voltage and low voltage, respectively. Table B.9 Metering capital work categories Meter type Cost category Meter model xx Meter purchase cost Meter testing Meter investigation Special meter reading
We consider applying trend assessment to specific vegetation management activities is useful, given their predictable and recurrent nature. Trending forward actual data on specific activities will provide us with a better understanding of the reasonableness of NSPs' forecasts. Benchmarking costs at the activity level will indicate the relative efficiency of the NSP in conducting vegetation management works. This will be useful in addition to trend assessment because it will indicate the NSPs' historical efficiencies, and it will allow us to adjust a NSP's revenue allowance accordingly. We intend to benchmark a number of activities on a per kilometre of line basis. We consider this is an effective comparative measure because a per unit comparison—specifically, a per kilometre measure—will be simple to calculate. Such benchmarks are expected to form a solid basis for comparing like activities and various cost differences between NSPs, and hence will help us understand NSPs' individual operating environments. The geographical disaggregation (urban/rural for DNSPs; forest, grassland, etc. for TNSPs) is expected to reflect discrete sections of NSPs' networks where costs are affected by different geographical characteristics. The urban/rural disaggregation for DNSPs is already a commonly used measure for a variety of activities. Disaggregation by forest, grassland, and other measures is expected to be the most reasonable disaggregation that can be applied for transmission networks. If they do so already, NSPs should also classify expenditure and quantitative measures according to 'zones'. Classification by zone is intended to broadly reflect material differences in the type and growth rates of vegetation as well as legal obligations that do not affect the network uniformly. This is expected to be the case only for NSPs operating over larger geographic areas. Tree trimming data requirements Data on the number of trees trimmed will be examined with the overall cost of tree trimming by zone to determine the cost per tree cut. Tree trimming is a significant part of vegetation management expenditure; therefore we consider it important to examine the unit cost of performing this activity. We also consider ground clearance costs and easement clearance costs are material and should be examined on a unit cost basis. We considered recording data on spans cleared and separating works on hazard trees. Ofgem records tree trimming/removal work by the number of spans affected, irrespective of the number of trees per span. We consider this would not be an ideal measure in Australia given the wide discrepancy in span length across
Category analysis questions––Expenditure categorisation Question 45 Do you agree with this list of expenditure drivers? Are there any others that should be added? Energy Networks Association Ergon Energy SP AusNet Aurora Energy Grid Australia Major Energy Users Inc. ENA: The ENA agrees with the list of expenditure drivers identified in the issues paper, however noted a number of issues including but not limited to: the relative contribution of, and the interrelationships between, drivers that network assets can become obsolete before their condition deteriorates that safety and environment cost drivers associated with legislative changes would generally be captured in the changes in obligations category there are expenditure drivers resulting from state based regulation which can differ by state. (Attachment B p. 1) Ergon: The list is incomplete. Further industry consultation is required to develop suitable expenditure drivers, and to further understand how each driver should interact with others. (pp. 13–14) SP AusNet: Agrees with the list of expenditure drivers, noting that they relate to DNSPs. (p. 23) Aurora: Considers the list appears sufficient. (p. 17) Grid Australia: Although relevant to transmission, the terminology used in transmission is slightly different. (p. 28) MEU: These should be reduced into exogenous and endogenous categories and further reduced into sub elements of CBD, urban, etc. (p. 32) Question 46 To what extent do you think the expenditure drivers are correlated with Energy Networks Association Ergon Energy ENA: The expenditure drivers identified are not necessarily correlated, and that if they were the extent of correlation would be open to subjectivity resulting in errors due to inconsistency. …where there is some correlation between expenditure drivers, the extent that those correlations appear would already be captured in the examination of historical expenditure. (Attachment B p. 2)
analysed data as possible. Consumers would favour the AER consider using benchmarks as a forecast, that is, in considering whether proposals for expenditure are acceptable. We intend to apply benchmarking in conjunction with other expenditure assessment techniques to review forecasts. We will not preclude using benchmarking to determine expenditure allowances. If, on the balance of evidence (accounting for submissions), we consider benchmarking provides the most appropriate forecast, then we will use it to set expenditure allowances. At this stage, it is too early to form a view on the appropriate weight to apply to assessment techniques. AER should recognise weakness in the current regime assuming symmetrical risk. Where they can include it into RAB if overspend, and if they underspend it doesn't get clawed back as it is under the allowance. This issue is addressed in the design of the capital expenditure incentive arrangements. This is considered in the explanatory statement to the Capital Expenditure Sharing Scheme. Where expenditure decisions are being made it would be helpful to know why it is being spent and when it is being spent. As an example, is expenditure occurring in the last year in particular? We will report NSPs’ actual expenditure in our annual performance and benchmarking reports. Consumers would like up-to-date capex. For example, if expectations of demand were wrong, then consumers would like to know what capex is being spent. We will report NSPs’ actual expenditure and actual demand in our annual performance and benchmarking reports.