NSP can rely on the framework to produce a capex forecast that meets the NER criteria. While not generally examined in detail, we may also assess the governance framework as it applies to opex decisions. COSBOA had limited faith in the ability of methodology reviews, and governance and policy reviews to determine efficient expenditure by NSPs. COSBOA considered that such techniques should not be relied upon to set allowed expenditures and should be only be used if necessary (e.g., they can add something to the AER’s decisions). We acknowledge that methodology and governance reviews aren’t assessment techniques that produce an alternative efficient allowance themselves. However, these techniques do provide information regarding whether the process used by the NSP to develop the forecast was sound. These assessment techniques, in combination with other techniques, provide a set of information of assistance to the AER in forming a view about an NSP’s forecast expenditure. Predictive modelling Predictive modelling uses statistical analysis to determine the expected efficient costs over the regulatory control period associated with the demand for electricity services for different categories of works. We acknowledge that modelling will generally be a simplification of reality and will have inherent limitations as a result. We will consider any limitations in the modelling when using the results of modelling in our assessment. The MEU was strongly supportive of the use of predictive models as they would provide a good indication of what level of expenditure is appropriate (such as repex and augex), recognising the inherent risks of using such models as definitive tools for setting allowances. In contrast, COSBOA considered that the limitations associated with such tools may lead them to being of narrower value. The NSW DNSPs expressed concerns about the deterministic use of the repex model. More detail on predictive modelling and submissions on its use are considered in Attachment A which covers the modelling of replacement and augmentation capex. Trend analysis We will compare a NSP’s forecast expenditure and work volumes to its historical levels. In doing so, we will expect NSPs to explain where its forecast expenditures and volumes are materially different to recent
also declines through the regulatory control period when it is expected a single base year will be used to forecast opex. The EBSS also addresses this and provides a continuous incentive throughout the regulatory control period. In assessing forecast opex, we must have regard to whether the opex forecast is consistent with any opex incentive scheme. Of importance here is our assessment of base opex. We may make base year adjustments for two reasons: a NSP's recurrent expenditure is inefficient compared to its peers a NSP's base year expenditure is not reflective of efficient recurrent expenditure due to a one-off factor in the base year. We consider below the impacts of these adjustments on the incentive a NSP has to improve opex efficiency. A number of NSPs also raised concerns that including a productivity forecast in the opex forecast could undermine the operation of the EBSS. We also consider this below. Efficiency of recurrent expenditure and the EBSS As stated, we will use a single year revealed cost forecasting approach to assess opex forecasts. However, if the base year opex reflects inefficient costs, then continuing with a revealed cost approach may not result in a prudent, efficient forecast of costs consistent with the opex criteria. This situation may arise because a NSP does not respond to incentives or appropriate incentives were not in place for efficient expenditure decisions. Where a NSP does not respond to incentives, the sharing of rewards or penalties would not be in the long term interests of consumers. Where a NSP responds to incentives, it will make efficient expenditure decisions regardless of the forecast. These efficient (historic) expenditures can then be used as the basis for opex forecasts. Where there is an EBSS in place, there is a continuous incentive across a regulatory period for a NSP to pursue efficiency gains. Hence, when there is an EBSS in place and a NSP appropriately responds to incentives, revealed costs provide a good indication that forecasts will be efficient. However, we will not assume a NSP is appropriately responding to incentives simply because an EBSS is in place. We must test the NSP's efficiency before relying on its revealed costs. We will likely use a number of techniques to assess the efficiency of a NSP's base year, including economic benchmarking techniques.
those forecast, the EBSS allows NSPs to retain less than 30 per cent of total efficiency gains. They imply this is inconsistent with the CESS which allows NSPs to retain 30 per cent of efficiency gains (losses). However, the CESS also rewards NSPs for efficiency gains (losses) relative to those included in the capex forecast. In considering whether incentives are balanced, the critical consideration is whether the marginal sharing ratios are similar, not the absolute sharing ratios. Both the EBSS and CESS provide a marginal sharing ratio of approximately 30:70 (NSP:customers). When marginal sharing ratios are balanced, a NSP will have no incentive to substitute a dollar capex with a dollar of opex (or vice versa). But if the opex marginal sharing ratio was, for example, higher than the capex marginal sharing ratio, then the NSP would have an incentive to substitute opex with capex, even if it was more costly. This incentive would remain even if the opex absolute sharing ratio was lower than that for capex. Timeframes Table 6.1 outlines our proposed timeframes and steps for determining whether we will consider making adjustments to base year expenditures under the revealed cost approach. Table 6.1 Timeframes for base year review Publication Decision F&A paper and Annual benchmarking report We will provide an initial view on whether we consider a NSP's historical costs are likely to reflect efficient costs. Issues paper In the issues paper, we will publish our first pass assessment using data from the NSP's proposal and base year. This assessment will provide our preliminary view of the proposed opex forecast based upon analysis that can be undertaken within the issues paper timeframes. We intend to run our benchmarking models as part of this process, including economic benchmarking (incorporating an econometric model of opex) and category analysis benchmarking. Draft determination We will set out the full base year assessment in the draft determination. Final determination We will consider our position in the draft in light of submissions. In advance of the reviews commencing in 2014, we cannot, at the F&A stage, provide an opinion on whether a NSP's costs reflect efficient costs based on the category analysis and economic benchmarking
Nov 2015 Publish 2015 benchmarking report Jan 2016 Issue draft RIO for 2016 benchmarking report RIN responses due for Aurora Apr 2016 Issue final RIO Jul 2016 RIO responses due Aug 2016 Issue final RINs for ElectraNet and Murraylink Nov 2016 Publish 2016 benchmarking report
Model validation and testing NSPs should validate and test the models they use to produce demand forecasts. Validation and testing includes assessing the statistical significance of explanatory variables, how well the model explains the data, the in-sample forecasting performance of the model against actual data, and out of sample forecast performance. Use of the most recent input information NSPs should use the most recent input information to derive their demand forecast. We may use more up to date input information as it becomes available during the determination process. Spatial (bottom up) forecasts validated by independent system level (top down) forecasts NSPs should prepare their spatial forecasts and system level forecasts independently of each other. Using system level data, a NSP is better able to identify and forecast the impact of macroeconomic, demographic and weather trends. On the other hand, spatial forecasts capture the underlying characteristics of individual areas in the network, including prospects for future load growth. They also have a more direct relationship with expenditure. Generally, spatial forecasts should be constrained to system level forecasts. The reconciliation should consider the relationships between the variables that affect system level and spatial forecasts respectively. For example, NSPs should reconcile the economic growth assumptions in their spatial forecasts (which are more likely to use judgement) with those in their system forecasts (for which data are more readily available). Demand forecasts at different levels of aggregation should be consistent with each other. Inconsistency at the different levels of aggregation affects the overall reasonableness of the forecasts. Accuracy at the total level may, for example, mask errors at lower levels (for example, at zone substations) that cancel each other out. Adjusting for temporary transfers Before determining historical trends, NSPs must correct actual maximum demands at the spatial level to system normal conditions by adjusting for the impact of temporary and permanent network transfers arising from peak load sharing and maintenance.
works over time. We chose classifications and defined connection types, meter works, fee/quoted services and public lighting works that we can use to compare customer-initiated works across NSPs. We also designed a template for the DNSPs to report descriptor metrics that will explain scale, scope and locational differences across DNSPs, and allow us to account for the unique circumstances of each DNSP. Accounting for key cost drivers Consistent with NSP's submissions to date, and comments made in workshops, we chose the following factors, as the most significant elements which determine the cost of customer-initiated works, as well as for asset works captured in repex and augex data requirements: whether a connection is a replacement or new connection whether a connection is an underground or overhead connection the voltage of the connection the location of the connection maximum demand relative to asset capacity. Additionally, DNSPs considered the costs of connection works for distribution networks are also influenced by: the location of connection works (e.g. CBD, rural or urban locations) the scale of a new development (that is, the size and density of subdivisions / newly constructed buildings) the reliability demands of the customer whether a connection is simple or complex. We have taken account of these cost drivers for connections and others drivers for metering, public lighting and fee/quoted services by including the following items within our descriptor metrics of customer-initiated works: Volume of CBD, urban and rural locations Cost per subdivision lot of large and intermediate subdivision connections Volume and cost of metering activities for meter types 1–7