Recommended DNSP output and input specification

Our recommended DNSP output and input specification is presented in table 8. Table 8: Recommended DNSP specification

Quantity Value Price

Outputs Customers (No)

System capacity (kVA*kms) Throughput (GWh)

Interruptions (Customer mins)

Revenue * Cost share Revenue * Cost share Revenue * Cost share

–1 * Customer mins * VCR per customer minute Value / Customers Value / kVA*kms Value / GWh –1 * VCR per customer minute1 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 WPI and five ABS producer price indexes

O/H lines (MVA–kms) U/G cables (MVA–kms) Transformers & other (MVA)

AUC (Return of & on O/H capital) AUC (Return of & on U/G capital) AUC (Return of & on

Transformers & other capital)

O/H AUC / MVA–kms U/G AUC / MVA–kms Transformers & other AUC / MVA

1 _{VCR per customer minute will vary by DNSP depending on the DNSP’s energy deliveries.}

Abbreviations: EGWW – Electricity, gas, water and waste sector; WPI – Wage price index; O/H – overhead; U/G – underground; AUC – annual user cost of capital

The first output included is customer numbers representing relatively fixed services the DNSP supplies. These are activities the DNSP has to undertake regardless of the level of energy delivered and include connection related infrastructure (eg having more residential customers may require more local distribution transformers and low voltage mains), customer calls, etc. Going back to the road analogy discussed in section 3, the DNSP will need to provide and maintain local access roads for its customers, regardless of the amount of traffic on those roads.

In line with previous energy network economic benchmarking studies we propose to measure the quantity of this output by the number of customers, or connections to be more specific. The value of the output would be revenue multiplied by its cost share derived from a combination of econometric cost function analysis, evidence from previous studies and information provided by DNSPs on their allocation of costs across the various outputs. Previous studies have generally found the customer numbers output to receive around half the weight in multiple output specifications (see, for example, Lawrence 2003 and Economic Insights 2012a). The price of this output would be its value divided by the number of connections.

The second output we recommend for inclusion is system capacity as approximated by the product of circuit line length and the total capacity of distribution level transformers. Going back to the road analogy discussed in section 3, this output captures the quantity of ‘road network’ that the DNSP has to provide to cater for users’ peak demands and energy consumption. We prefer this measure to the alternative that has been suggested of peak demand. Using peak demand would require some form of smoothing which makes the results dependent on the form and degree of smoothing undertaken. Given the long–lived nature of DNSP assets and the need to allow a margin above recent observed peak demands to allow for infrequent extreme weather conditions, the system capacity variable better reflects the underlying functional output. It is also able to draw on robust data held and maintained by all DNSPs.

We believe the advantages of using system capacity to measure this function far outweigh any possible disadvantage from it not distinguishing between DNSPs that have provided adequate capacity and those that may have provided excess capacity. We note that comparisons of network utilisation levels across DNSPs would provide more information on whether excess capacity was a relevant consideration but any such comparison would need to allow for differing operating environment conditions across DNSPs and whether greater extremes of weather conditions, for example, required more capacity to be in place compared to DNSPs with the same recent peak but facing more temperate conditions.

The value of the system capacity output would be revenue multiplied by its cost share. Previous studies have generally found the system capacity output to receive around 30 per cent of the weight in multiple output specifications (see, for example, Lawrence 2003 and Economic Insights 2012a). The price of this output would be its value divided by the product of circuit length and distribution level transformer capacity.

The third recommended output is throughput or energy deliveries. While throughput has a small direct impact on DNSP costs, it reflects the main output of value to customers and maintains consistency with earlier economic benchmarking studies, nearly all of which have

included throughput as an output. The value of the throughput output would be revenue multiplied by its cost share, which we would expect to be relatively small given that costs are not likely to be greatly influenced by small variations in throughput. Previous studies have generally found the throughput output to receive around 20 per cent of the weight in multiple output specifications (see, for example, Lawrence 2003 and Economic Insights 2012a). The price of this output would be its value divided by energy deliveries.

The fourth output is the duration of customer interruptions which captures the DNSP’s reliability performance. This is an important dimension of DNSP performance for customers. As discussed in section 3, treating interruptions as an undesirable output allows it to be readily incorporated with a negative price and, hence, a negative value. We recommend adopting the distribution STPIS valuation of consumer reliability (VCR). As the STPIS VCR is presented as an amount per MWh consumed, this first has to be converted to an amount per customer minute depending on the individual DNSP’s annual energy deliveries. The price is then the negative of the DNSP’s VCR per minute and the value of customer interruptions is the product of this negative price and the total minutes of customer interruptions. This method is easy to implement and will produce relatively robust results. An earlier Australian study using this approach found an average weight for the reliability output of around (minus) 8 per cent of revenue (Lawrence 2000).

Turning to the input side of the recommended specification, opex would be measured by the narrow coverage discussed in section 5 of taking expenditure consistent with the AER’s network services group component of standard control services. If necessary, adjustments would be made to remove accounting items that did not reflect current year input use. The recommended price of opex is a weighted average price index consisting of the ABS Wages price index and five Producer price indexes covering business, computing, secretarial, legal and accounting, and public relations services and using the weights set out in section 5.6. The quantity of opex inputs is derived by deflating the value of opex by its price index.

There has been much debate over the merits of using the WPI or AWOTE as the appropriate labour price index. The WPI will be the more theoretically appropriate price index if labour price changes only reflect changes in skill levels and quality. However, in times of vigorous competition for labour between different sectors of the economy, labour price changes may reflect efforts to retain employees rather than any underlying changes in skill levels. In that situation AWOTE may be the better measure. While each measure has advantages and disadvantages, the important thing is to ensure that the same index is used in productivity and efficiency calculations and in the labour price component of rate of change roll forward calculations to ensure consistency. There would then be little difference in the net regulatory effect of using either the WPI or AWOTE. We have opted in favour of the WPI because it has some theoretical advantages and may be the preferable index to use in the longer term once labour markets return to more normal conditions.

The recommended capital input specification uses physical measures to proxy the quantities of three capital input components – overhead lines, underground cables, and transformers and other capital. The input quantities of overhead lines and underground cables are proxied by their respective MVA–kilometres. This measure allows the aggregation of lines and cables of differing voltages and capacities into a robust aggregate measure. The input quantity of

transformers and other capital is proxied by the NSP’s total transformer capacity (at all transformation levels) in MVA. The other capital component is usually quite small for NSPs and, since much of this residual component is related to substations, it is included with transformer capital inputs.

The value of capital inputs or annual user cost is taken to be the return on capital and return of capital for each of the three components, calculated in a way which approximates the corresponding building blocks calculations. The input price for each of the three capital components is then derived by dividing their annual user cost by their respective physical quantity proxy.

This approach has the advantage of reflecting the one hoss shay physical depreciation characteristics of the individual component assets while using the most robust source of NSP data available (that from DNSP physical asset registers) and accurately capturing actual asset lives. This approach is also broadly similar in principle to the productive capital stock used by leading statistical agencies to proxy the quantity of aggregate structures inputs in productivity measurement.

We believe this approach to measuring capital inputs is more robust and likely to be more accurate than approaches which rely on regulatory depreciation and depreciated asset data. By using an exogenous user cost of capital covering the return on and return of capital it ensures consistency with other building blocks calculations.

Operating environment factors

The number of operating environment factors that can be allowed for in the recommended specification will depend on the number of observations available (as this process will likely have to either rely on econometric adjustments to efficiency scores or else rely on inclusion of more items directly into DEA or SFA methods). Some aspects of both customer density and energy density are already captured in the recommended specification with throughput, customer numbers and some aspects of length included as outputs (although length is less directly included). The priority for including a density operating environment factor therefore lies with the demand density variable listed in table 5.

Given that reliability is being included as an output it will be important to include at least one of the weather operating environment factors proposed in table 5. We propose that extreme temperature days be included as the priority weather variable most likely to affect DNSP performance. Extreme wind days should also be considered for inclusion.

Of the terrain factors included in table 5, we propose priority be given to including the vegetation encroachment indicator as this will have a particular impact on opex and economic benchmarking is more likely to have a role in assessing opex expenditure forecasts in the first instance. Vegetation growth may also be a good indicator of other challenging climatic conditions facing the DNSP.