PROVIDING INCENTIVES FOR SERVICE QUALITY – INCENTIVE … · PROVIDING INCENTIVES FOR SERVICE...

PB ASSOCIATES

DRAFT REPORT

PROVIDING INCENTIVES FOR SERVICE QUALITY – INCENTIVE RATES FOR S FACTORS

Prepared for

Independent Pricing and Regulatory Tribunal of NSW

Parsons Brinckerhoff Associates 13/01/2004 Page 1 of 83

Providing Incentives for Service Quality Incentive Rates For S Factors

TABLE OF CONTENTS

SECTIONS

1. EXECUTIVE SUMMARY..................................................................................................2

2. OVERVIEW.......................................................................................................................4

2.1 INTRODUCTION ...........................................................................................................................................4 2.2 SCOPE OF WORKS........................................................................................................................................6 2.3 REVIEW METHOD AND APPROACH ....................................................................................................6 2.4 WORK PLAN....................................................................................................................................................8

3. COSTS OF RELIABILITY................................................................................................11

3.1 CONCEPTUAL FRAMEWORK AND ASSESSMENT APPROACH.................................................11 3.2 RESCALING OF THE VICTORIAN VALUES ......................................................................................19 3.3 DIRECT ASSESSMENT OF THE NSW DISTRIBUTORS RELIABILITY COSTS ........................23 3.4 SUMMARY AND CONCLUSIONS ...........................................................................................................36

4. CUSTOMER RELIABILITY VALUE.............................................................................. 41

4.1 BACKGROUND TO CUSTOMER VALUE ............................................................................................41 4.2 CUSTOMER VALUE AND COSTS OF RELIABILITY........................................................................41 4.3 LINKED AND UNLINKED PRICING INCENTIVES........................................................................43 4.4 VALUE OF LOST LOAD (VOLL) .............................................................................................................44

5. PROPOSED S-FACTORS FOR NSW DNSP’S................................................................. 49

5.1 CONTEXT FOR PROPOSED S-FACTOR RATES................................................................................49 5.2 PROPOSED RATES......................................................................................................................................50

6. CRITICAL ISSUES .......................................................................................................... 54

6.1 DATA INTEGRITY ......................................................................................................................................54 6.2 LONG TERM NATURE OF RELAIBILITY IMPROVEMENTS AND EXPENDITURES..........55 6.3 VARIATIONS IN THE VALUE AND COSTS OF CUSTOMER RELIABILITY............................56 6.4 RESPONSES BY DNSP ‘S............................................................................................................................57

APPENDICES:

Appendix A: Discussion of the Victorian Essential Services Commision S-Factor Methodology

Appendix B: Data for DNSP Marginal Costs of Reliability

Appendix C: Glossary



1. EXECUTIVE SUMMARY

PB Associates was appointed by the Tribunal to provide estimations of incentive rates for electricity distribution reliability. The rates are to be considered by the Tribunal for possible inclusion into price regulations for NSW DNSP’s.

This draft report was prepared based on information available at this time to assist the Tribunal in its consideration of the draft pricing determination. As such the report provides the following comments and proposals;

• The value of electricity distribution to customers is a functions of service levels and price. It is therefore relevant to consider service levels in making judgements for appropriate distribution pricing levels.

• Pricing incentives, such as the S-Factor(s) considered in this report, are intended to offer appropriate incentives for guiding distribution system design, construction and management to deliver optimal customer value outcomes.

• Reliability is a key customer deliverable of electricity distribution systems and warrants specific consideration in determining pricing incentives.

• Incentives for reliability should be set at levels that reflect the range of customer values, and marginal reliability costs. The ultimate levels of reliability and price should seek to equate these values.

• In attempting to emulate competitive market forces, any margin between costs and customer value should be removed from the reliability incentive, to the extent that this enables delivery of optimal distribution investment and management. By setting rates at lower levels, it is possible to provide incentives for distributors to invest in suboptimal network design that is ultimately made redundant by higher average cost solutions in the future. The step change functions between the average marginal costs of reliability initiatives is crucial to identifying the appropriate levels for incentives for each DNSP.

• Data used for this report was not entirely adequate for a definitive estimation of marginal reliability costs for NSW DNSP’s. However, the rates proposed reflect the information that was available and provides a basis for further discussion and refinement.



PB Associates proposes the following reliability incentive rates for each of the NSW DNSPs; Table 1 - Reliability Incentive Rates

Marginal Cost Rates (Normalised SAIDI)

Alternative Proposed Rates (Normalised SAIDI)

S-Factor ($/MWh)

%Revenues/ SAIDI Minute

S-Factor ($/MWh)


Energy Australia 4,000 0.0297 15,000 0.1113

Integral Energy 6,000 0.0355 15,000 0.0887 Country Energy 8,000 0.0313 15,000 0.0588

Australian Inland Energy

6,000 0.0277 15,000 0.0693

• The rates proposed have been compared with customer value studies, which indicate that the average value of unserved energy (reliability) is between $30,000 and $35,000 per MWh.

• The customer values for reliability derived from the various Australian and overseas studies indicate substantial variations between values depending on many factors. These include customer type, end-use application, location, levels to which customers have become accustomed and/or are dependent, and also reflect a lack of differentiation between reliability and other network service attributes such as safety, customer service, environmental impact and aesthetic amenity.

• The rates proposed within this report have been developed within the context of an application similar to that adopted by the ESC in Victoria and need to be read in line with that methodology. This includes aspects of data integrity, exclusions, asset roll-forward and the pricing formula.

• The rates proposed by PB Associates indicate that there are only modest levels of revenues exposed to risk or reward resulting from the S-Factor incentive itself. It is likely that DNSPs will place more emphasis when making network investments on the incentives offered through the normal building block methodology and in particular, the acceptability of investments for inclusion in the asset base at future regulatory determinations.



2. OVERVIEW

2.1 INTRODUCTION

The value offered to customers by electricity distribution is a function of the levels of services they receive relative to the costs they incur. It is therefore essential to assess the levels of services in determining appropriate distribution charges.

Equating current charges to existing service levels, however, is only one part of the exercise. It is potentially far more significant to determine incentive levels that guide future design and management of distribution businesses to an optimal mix of cost minimisation and service delivery.

In this draft report PB Associates has sought to analyse the nature of the relationship between customer value and service delivery costs. In particular, given the short timeframe for this draft report, PB Associates has focussed primarily on reliability as the key service attribute provided by electricity distributors to customers. Reliability is defined in terms of minutes of lost supply per customer per annum, which has been converted to volumes of unserved energy to enable comparisons for pricing purposes.

The Tribunal has undertaken considerable review of service and price relationships for the purpose of establishing a financial incentive that links DNSP revenues to service outcomes to some extent, potentially through an S-factor. Any such S-factor would form an extra component of the price control, which would allow prices to be adjusted downwards if DNSPs fail to meet S factor targets, and upwards if DNSPs exceed S-factor targets.

The Tribunal released an Issues Paper on service level incentives in May 2003, which sought comments from stakeholders regarding the manner in which such an incentive might work. Responses from DNSPs indicated their preference was for any S-factor to be introduced initially as a “paper trial”, with no monetary incentives/penalties, primarily due to concerns about the accuracy of the service quality data available at this stage.

PB Associates has prepared its draft report based on two key elements

1. Different customers will ascribe very different values to distribution reliability and the value they ascribe is likely to vary depending on the end use applications. This means that the values are also likely to change depending on the time of day, time of year and the levels of reliability to which customers (and suppliers of electrical equipment) have become accustomed.

2. The underlying costs of providing network reliability depends heavily on planning, constructions and maintenance standards, the technical characteristics and loads of individual feeders and substations, and the levels of redundancy (extra capacity or alternative supply routes) available throughout the network.

In competitive markets, customers can be segmented to allow suppliers to provide various combinations of product/service quality and price. The commercial performance of companies in such markets is determined by their



ability to meet customer expectations at competitive prices. In electricity distribution, which is segregated as a regulated monopoly, it is not always possible to vary the mix of price and service quality to meet the various expectations of individual customers.

Electricity distribution networks have generally been constructed around intuitive judgements of the appropriate balance between reliability (or network robustness) and cost. In more densely populated areas and concentrated commercial or industrial load centres, the networks will generally exhibit higher levels of interconnectivity and standby capacity than in rural and remote regions. However the nature of electricity distribution means that it provides relatively generic service characteristics within given geographic areas. Customers taking supply from shared zone and distribution substations and from shared feeders will obviously receive similar levels of service in relation to reliability and supply quality. Therefore, judgement needs to be exercised as to the optimal combination that satisfies the majority (or value weighted majority) of customers. Differentiating service levels at a more localised level through the provision of alternate supplies, system automation, undergrounding, etc, also introduce issues of equity unless they are accompanied by geographic pricing, which further complicates any comparison of service and price at aggregated levels.

There are also perhaps many other attributes of electricity distribution that might be considered in the evaluation of service levels, such as safety, aesthetic amenity, and environmental impact. Customers may even consider aspects of customer service such as emergency response and handling of customer inquiries as being services that offer additional value and that should be considered in assessing distribution charges and performance incentives. Conversely, customers are currently paying for distribution services and justifiably expect an appropriate level of service. To the extent that this is not achieved, what compensation should be passed back to customers?

In addressing the issue of appropriate pricing incentives for electricity distribution service there are primarily four questions to answer:

• What do customers want?

• What is it worth to them?

• What can be done?

• What does it costs?

In terms of regulating DNSPs, these questions translate also to how to provide appropriate incentives and penalties that will recognise changes in the value of services delivered to customers. These are very complex questions, but must be addressed in order to develop appropriate regulatory incentives. Otherwise more prescriptive regulatory approaches need to be applied to mandate service levels relating to pricing decisions.

For the development of a commercial incentive for inclusion in the proposed pricing formula, the anticipated behaviour of both distributors and customers must also be considered. These incentives will provide long term investment signals for distributors in particular, and should result in some fundamental improvements in planning, construction and maintenance of the networks.



However, the long term nature of these investments means that incentives which over or understate the cost and value may lead to inappropriate trends in services and prices. Having made this statement, it must also be recognised that the lack of a quantified relationship between price and service can have a greater detrimental impact by extending the uncertainty of investment recognition under regulatory determinations.

This report therefore addresses the incentive regulation question by analysing the underlying costs of providing additional reliability (and savings from reducing reliability levels) and the value derived by customers as a result of changes to the mix of reliability and price. By understanding these two parameters it is possible to introduce effective commercial incentives for reliability that can appropriately balance future network investments to the benefit of all stakeholders.

2.2 SCOPE OF WORKS

PB Associates was appointed by the Tribunal to evaluate appropriate “S-Factor” coefficients for NSW electricity distributors that could potentially form part of the price regulation formula. IPART’s stated intention for this assignment was to establish financial incentives for electricity distribution service levels.

Specifically, IPART required investigations into the appropriate incentive rates that would apply to “S-Factors” for electricity distributor service level regulatory formulas.

PB Associates were asked to identify incentive rate estimates for IPART’s 2004 Electricity Network Pricing draft determination and submit this information to the Tribunal in the form of a short report, including:

• details of the approach taken and information used;

• the reasons for the approach taken;

• an indication of any limitations to the estimates – for example, it might be appropriate to provide a range around a central estimate.

Separate estimates were required for each of the DNSPs, reflecting their operational differences. Incentive rates were required to be estimated for each DNSP’s network as a whole, as well as by feeder type, although it was recognised that information on feeder types was not generally available for NSW distributors. It was also recognised that data relating to reliability was still being refined by NSW DNSP’s and may vary over time as data capture systems become more accurate.

2.3 REVIEW METHOD AND APPROACH

Based on the extremely tight timeframe allowed for the preparation of the Draft Report, PB Associates adopted an approach that enabled preparation of a draft report by 1 December for inclusion in IPART’s draft determination. To achieve this has meant that the initial formulation of incentives and levels was based on existing information and without extensive consultation. Therefore, the PB Associates team has drawn on previous experience with the ESC S-



Factor preparation, monitoring and review1, as well as our work with NSW distributors in assessing the data quality of SAIDI, SAIFI and CAIDI information for IPART.

The preparation of the draft report involved a tight timeframe and exploration of very complex technical and economic issues. PB Associates therefore applied the following program for identifying the key issues, reviewing available data and preparing the methodology and initial levels for the S-Factor coefficients.

Set Up and Draft Report

Submit Draft Report

Review Data and References • Review the information provided by NSW distributors

relating to reliability levels. • Review ESC S-Factor mechanism • Review VOLL and other customer value research data • Determine appropriate methodology for NSW S-Factor

Kick-off Meeting

Award of assignment to PB Associates

Initiate consultancy and verify arrangements with IPART

Work with IPART to obtain available

NSW data

Model average reliability incentive range • Determine average values of reliability by DB • Determine average costs for specific reliability

improvement projects by DB

Consider implications of proposed S-Factors • Historical reliability trends • Projected reliability levels based on capex and opex proposals • Compare reliability levels against peers • Consider data quality and associated issues • Consider likely DB behaviours relating to incentive proposals

1 Other PB projects drawn on include reliability and incentive projects for OTTER, ESCOSA, IPART and Ofgem.



2.4 WORK PLAN

2.4.1 Review of Existing Data and References

IPART noted in its Invitation to tender, that there was insufficient time for preparation of the draft report to allow for additional data requests from distributors. The initial review stage of this project therefore involved analysing the data already provided and determining:

• Data integrity

• Consistency between distributors and across periods

• Assumptions incorporated in the data

• Trends

• Data exclusions and anomalies

The data made available by the Tribunal was critical to the consideration of appropriate incentive levels and therefore the data was scrutinised to assess the levels of consistency and integrity. This information was also compared against other interstate and overseas distributors to establish relative service levels. An appreciation of the different approaches to formulation of the data was therefore established.

This initial review stage also required analysis of other service level incentive regulation mechanisms, in particular that adopted by the ESC in Victoria. PB Associates’ examined both the establishment of the ESC S-Factors and the subsequent reviews of specific exclusions from S-Factor calculations.

PB Associates also explored research undertaken over the past decade into the values ascribed by customers to various characteristics of electricity supply. In particular, the National Electricity Market has sought to understand the Value of Lost Load (VOLL) so that appropriate mechanisms could be incorporated into transmission and generation planning, standby and dispatch to ensure that the competitive energy market delivers the preferred balance of price and service reliability.

There are clearly many synergies between those market arrangements and the Tribunal’s intention to provide appropriate incentives to distributors for managing supply reliability. An integral step in this project therefore includes a thorough analysis of VOLL calculations and consideration of the application of such figures for distribution service incentives. Information on alternative estimations for the value of distribution services was also considered where relevant to this review, including the results of initial “Willingness to Pay” reviews undertaken by a number of distributors.

2.4.2 Modelling Average Reliability Incentives

In developing appropriate incentives for inclusion in the pricing regulation formula it is important that the value of the incentive rate is not less than the marginal costs of providing the desired improvement and not greater than the value derived by customers for the improvement they receive. By setting the



value of the incentive within this range it is possible to offer incentives that provide benefits to customers and distributors.

PB Associates modelled various coefficient values derived from our estimations of the cost and value range to determine the likely financial impact on distributors and customers. This modelling helped to determine the average costs of specific reliability improvement projects relative to estimated customer values and estimate the revenue implications of the proposed rates.

2.4.3 Implications of Proposed S-Factors

The incorporation of incentive S-Factors into the price regulation formula can have significant revenue implications for distributors unless rates are set at modest levels and/or constraints are placed on the revenue impact. These incentives need to accurately reflect the benefits derived by customers and should not encourage unexpected inefficient outcomes, either in terms of network asset management or customer utilisation. As the Tribunal noted in its Invitation to Tender, any incentive offered to distributors to derive additional income from improved service levels, must clearly provide for reductions in revenues should service levels fall. It is important that both outcomes are consistent with the preferences of customers, and that additional or reduced charges to customers are balanced by the increase or decrease in value they derive. Although our analysis did not enable a detailed exploration of the variability of customer value, it is likely that customer preferences for service levels against price are not linear, and that the distributors’ cost curves for provision of varying service levels are non linear. For this reason this review explored many options for restricting variations in revenues and service levels in the short term, and mechanisms for updating incentive levels over time.

PB Associates also evaluated the likely outcomes of the proposed S-Factors and explored a range of issues that arise. Issues such as data integrity/accuracy/consistency, types of reliability projects likely to be implemented by distributors, localisation of improvement projects, which customers are likely to benefit first and last, timing of incentives, long term nature of investments (by both distributors and customers), reliability levels implicit in proposed capital and operating cost projections, implications of safety and licence requirements, and relative service levels of NSW distributors compared with interstate and overseas peers.

Following the analysis set out in the methodology described above, PB Associates’ draft report seeks to cover the following requirements set out in the Tribunal’s Invitation to Tender:

• The links between price and service for electricity distribution

• The methodology for including incentives for service levels into the regulated price formula

• Timing for implementation of such an incentive mechanism, including the pros and cons of adopting a “paper trial” initially for an evaluation period

• The suggested levels for such incentives and the ranges within which they should apply



• The key issues that are likely to arise with the implementation of such service level incentives

• Comparisons of reliability levels across distribution businesses

• Comparison of the S-Factors applying in Victoria with those proposed in this review

• References to other service level incentive schemes applying to regulated distribution businesses



3. COSTS OF RELIABILITY

3.1 CONCEPTUAL FRAMEWORK AND ASSESSMENT APPROACH

3.1.1 Reliability Improvement, and Construction and Planning Standards

The reliability improvement opportunities, presented by any electricity distribution network will depend, first and foremost, on the design of the network, as determined by the construction and planning standards to which it was built. The condition of the network and the environment in which it operates are relevant, as these are the primary causes of, or triggers for, asset breakdown. However the vulnerability of the network to damage, and the translation of actual asset breakdown into customer outage is determined entirely by network design and the operational arrangements in place to respond to such breakdown. The most basic network designs comprise simple radial overhead networks, with un-insulated outdoor substations, no redundancy or interconnection and, the minimum network segregation, consistent with plant ratings, feeder loads and voltage regulation. At the other end of the spectrum, the most sophisticated networks comprise substantial underground construction, enclosed substations, a high degree of network segmentation, interconnection, sufficient redundancy to support all credible contingencies, and automated switching to instantaneously isolate faults and re-supply load by exploiting the interconnections and redundancy.

Whilst the most basic designs clearly offer the greatest opportunities for reliability improvement, such networks are generally found in areas of lower customer density and/or sensitivity, where the costs and benefits of introducing more sophisticated designs have been judged to preclude their introduction. In Australia many of the remote rural networks fit this description. The more sophisticated networks, on the other hand, tend to be found in areas of high population density and customer sensitivity, with the most sophisticated being reserved for CBDs. For these networks reliability improvement opportunities tend to be fewer, but the opportunities that are available are generally a long way from having been fully exploited. The networks of the NSW Distributors, generally comprise a mix of deigns ranging from relatively basic to relatively sophisticated, reflecting the customer densities and sensitivities of the areas they serve. Energy Australia and Integral Energy each have a mix of network designs, sitting towards the more sophisticated end of the spectrum whilst Country Energy and Australian Inland Energy each have a mix which sits towards the more basic end. The reliability improvement opportunities that are available to each Distributor are therefore quite different, reflecting the substantial differences in their current network designs. Reliability improvement opportunities can nevertheless be broadly categorised as follows:



a. Speed of Operational Response – This strategy accepts that the design

of the network is what it is and seeks deploy operational resources and support them with information systems and physical infrastructure to improve the speed with which outages are dealt with. For networks with redundancy and interconnections, the quantum and management of switching resources are key. For networks without redundancy, repair and reconstruction resources are key. Operational response will not however be a prime focus of this report.

b. Vulnerability Reduction – This strategy involves either managing the

network condition and environment to reduce the frequency and intensity of breakdowns, or changing the network construction design to eliminate or reduce the incidence of breakdowns caused by environmental impacts. Whilst managing network condition (maintenance and replacement) and environment (tree trimming and easement clearing) are legitimate strategies, this report takes the position that, expenditure on these activities beyond that which is required to fulfil duty of care responsibilities, will in general have only marginal impact on reliability outcomes and, that apart from some quite limited and specific maintenance programmes (such as high voltage bond replacements), do not justify further consideration. Tree trimming, particularly in urban areas, is appropriately viewed as a constant, set at a level required to fulfil duty of care, in accordance with established standards. Any trimming beyond the requirements of the standard is neither practically achievable, given Local Government opposition to tree trimming, nor productive in terms of the marginal reliability outcomes achievable. Easement clearing is only really a productive strategy in situations where clearing has been previously neglected. Changing network construction designs, on the other hand does offer scope for very considerable reliability improvement, and this is so regardless of whether the network is well endowed with redundancy and interconnection capacity or not. Segmentation (i.e. fewer customers per feeder, or feeder segment) is the simplest of these strategies, which whilst it does not alter the actual incidence of network damage, does reduce the number of customers impacted by each damage incident. The insulation of overhead conductors, using CCT for high voltage, or ABC for low voltage, is a very effective technique for reducing the actual incidence of damage to the network, and of course the ultimate strategy for reducing damage incidence is undergrounding.

c. Increased Redundancy and Interconnection – This strategy accepts

that some level of network damage or asset failure is inevitable and seeks to ensure that, for all or most credible contingencies there is enough redundant or ‘reserve’ capacity in the network to ‘pick up’ the load lost, by switching to alternative supply paths. The critical variable in this strategy is the quantum of redundancy provided. Switching to ‘pick up’ the lost load may be: manual, remote controlled or, automatic. Distributors generally adopt deterministic planning standards stipulating the level of redundancy and the mode of switching to be provided in a range of different circumstances. These standards vary from ‘no redundancy’ (N-0), typical for customer connection assets and in areas of low customer density, to limited N-2



redundancy, with automatic switching, in aspects of the supply (at the transmission level) to Sydney’s CBD. (N-2 redundancy provides sufficient reserve capacity or redundant system elements to compensate for the coincident failure of two interdependent elements.) For most systems, the N-1 level of redundancy is the maximum that is normally provided, and often only at the higher system levels, or only partially at lower system levels. Generally, automatic switching is reserved for the higher system levels, although there is a trend to introduce more automatic switching at the high voltage distribution level. (The circumstances and conditions that justify the use of the N-2 standard are rare, and are generally confined to CBDs in situations where, in highly interconnected systems, the number of interdependent system elements is high (N>10), and the likelihood of coincident failure becomes ‘non trivial’.)

Outage probabilities, as between one redundancy level and the next higher

level drop by orders of magnitude in a step change between one level and the next. Which is why, when it comes to redundancy planning, distributors generally plan to simple deterministic criteria, rather than reliability criteria. There is little advantage in describing a precise level of reliability, set with or without regard to customer value, when the choice, as it almost always is, is between zero or one level of redundancy. There is however choice, often occasioned by a period of growth, as to whether to provide sufficient redundancy, to match full peak load or, something less.

In applying their ‘deterministic’ planning standards, distributors generally

allow some level of risk exposure to lower levels of redundancy over limited periods of the load cycle (corresponding to maximum load). In practice this means that, for short durations, they accept that the available reserve capacity will be less than the potential capacity loss in the event of equipment breakdown. The actual quantum of load (MWh) left without ‘redundancy’ cover in these circumstances will depend on the Load Duration Curve for the particular assets involved. In general, because of the steepness of the Load Duration Curve, in the proximity of peak demand (typically 30 times higher than average), the actual duration of the risk, for any particular level of ‘uncovered demand’ (sometimes referred to as ‘demand at risk’) is quite small. Even substantial levels of ‘demand at risk’ will be exposed for relatively short periods of time. (Typically 10% of ‘demand at risk’ will be exposed for only 0.2% of time, and even 25%, for only 5% of time). The actual ‘Energy at risk, when the probability of failure is taken into account is much smaller again.

Ideally, the level of demand at risk, tolerated for any asset, would be set so

as to match the customer value of the ‘energy at risk’ to the costs saved by tolerating the ‘demand at risk’. However, in practice the balance between benefits and costs is quite unstable. At a point, typically somewhere between 10% and 25% of demand at risk the gradient of the load duration curve changes from being very steep to being comparatively quite flat and the ‘energy at risk’ escalates rapidly. Thus design parameters set in terms of maximum ‘demand at risk’, at the asset level, are a far more responsible and practical approach, than design parameters set in terms of reliability targets or ‘energy at risk’ valuations.

d. Automated Redundancy Access - This strategy is, of course, only

available for networks which are provided with substantial levels of redundancy, and seeks to exploit the available redundancy in the fastest possibly way through automatic switching. As discussed previously,



automated switching has traditionally been reserved for the higher system levels, although originally many of the early underground high voltage systems were built with substantial automatic switching. Overhead high voltage networks, on the other hand, have only recently started to have automatic switching elements inserted into them, as the technology capable of dealing with all the switching, loading and protection issues has developed. The advantage of automatic switching at high voltage is that it eliminates the time taken to manually patrol and switch. Manual patrol and switching, can typically take up to two hours, in urban networks, depending on circumstances. It can also instantaneously exploit the available segmentation to reduce the number of customers impacted by each damage or failure incident.

3.1.2 Specific Reliability Improvement Initiatives

A comprehensive work examining the costs and reliability outcomes of a range of reliability improvement initiatives was provided in a paper, submitted to IPART, in connection with its 1999 Pricing Review, by Energy Australia. This paper examines the cost and reliability outcomes of some thirteen different reliability improvement initiatives, as applied to the Energy Australia network, as it existed, and relative to its performance, at that time. None of these initiatives involved altering redundancy levels within the network, but encompass: four segmentation with automation scenarios, (two with basic and two with advanced automation) and; nine scenarios which involve the use of CCT, ABC or undergrounding. Clearly many of the scenarios are not mutually exclusive. Despite the age of this paper it does provides a useful and informative platform from which to assess the costs, and in particular the marginal costs, associated with these various initiatives. The first of the two tables below takes the material presented by Energy Australia, updates it for five years of asset inflation, and presents the average cost of each initiative in terms of $ per minute of SAIDI and $ per MWh of USE saved. As reported in the paper, the SAIDI measure predominantly used included all outages other than ‘TransGrid’, which by implication is taken to mean, transmission and generation failure, and load shedding at the direction of the System Operator. It thus appears to be the same as IPART’s ‘Distribution Outages’ (Planned and unplanned) defined in its request to the Distributors, dated 29th September 2003, and therefore on the same basis as the Victorian measure. In most cases, however, the calculations have also been undertaken on the basis of “normalised” outage figures, which also exclude significant outage event. The second table synthesises the thirteen reliability improvement scenarios into twelve mutually exclusive programmes and presents them in ascending order of the average marginal cost of each programme (incremental cost, per unit of incremental benefit, over the previous scenario). The average marginal costs are expressed as $ per MWh of ‘USE’ (un-served energy) saved. The figures presented are for the maximum SAIDI improvement, identified in the paper and the conversion to USE was made using the forecast average per customer consumption of Energy Australia for the next Regulatory Period of 19.3 MWh per annum. A WACC of 7% and a thirty year project life was assumed.



Table 2 - Energy Australia’s 1998 Reliability Improvement Initiatives and their Costs

Table 3- The Twelve Mutually Independent Scenarios – Costs & Marginal Costs

Cost Cost max. Mutually Independent Scenarios 1998 2003 SAIDI Average

Cost Marg Cost

$m $m Improvement $/MWh $/MWh 1 Urban Reclosers only 43 50.21 23.2 3,061 3,061 2 Urban & Rural Reclosers 89 103.93 29.23 5,028 12,597 3Rural Reclosers & Urban OH Automation 299 349.16 49.22 10,031 17,347 4 Rural Reclosers, Urban OH Automation 408 476.45 51.62 13,051 74,995 and LV ABC (underbuilt) - 5 Rural Reclosers, Urban OH Automation 1012 1,181.78 61.02 27,386 106,103 and LV ABC (underbuilt and on own poles) - 6 Rural Reclosers, bare OH HV UGing& LV ABC 1862 2,174.39 72.27 42,544 124,762 7 Rural Reclosers, all OH HV Uging & all LV ABC 1891 2,208.25 72.6 43,010 145,111 8 add Urban HV UG Automation 2860 3,339.82 83.23 56,742 150,525 9 add LV bare wire undergrounding instead of ABC 5006 5,845.86 88.52 93,383 669,872 10 addundegrounding of existing ABC 5143 6,005.84 88.77 95,668 904,895 11 add Rural OH CCT 5435 6,346.83 89.23 100,579 1,048,196 12 add 33kV undergrounding. 6401 7,474.90 89.98 117,468 2,126,833

These costs, both average and marginal are also presented in the following graph, showing costs as a function of reliability improvement.

Cost Cost max. Reliability Improvement Scenario 1998 2003 SAIDI $/MWh $m $m Improvement

1. 11kV Reclosers Urban 43 50.21 23.2 3,061 2. 11kV Reclosers Rural 46 53.72 6.03 12,597 3. 11kV Automation Urban Overhead 253 295.45 43.19 9,673

4. 11kV Automation Urban Underground 969 1,131.57 10.63 150,525 5. 33kV Undergrounding 966 1,128.07 0.75 2,126,833 6. 11kV bare wire const. Undergrounding 1,103 1,288.05 54.44 33,456 7. 11kV CCT const. Undergrounding 29 33.87 0.33 145,111

8. 11kV bare wire const. conversion to CCT Urban 231 269.75 35.09 10,870 9. 11kV bare wire const. conversion to CCT Rural 338 394.71 6.49 85,998 10. LV bare wire Undergrounding 2,859 3,338.65 17.09 276,242 11. LV ABC const. Undergrounding 137 159.98 0.25 904,895 12. LV bare wire conversion to ABC own poles 604 705.33 9.4 106,103 13. LV bare wire conversion to ABC underbuilt 109 127.29 2.4 74,995



Figure 1 – Marginal and Average Reliability Costs for Energy Australia

The bottom part of this Graph, below $160,000 per MWh, is shown below.

Figure 2– Marginal and Average Reliability Costs below $160,000 per MWh for Energy Australia

Marginal and Average Cost of reliability

UG Automation11kV UrbanUG

Urban+ Rural

Reclosers

LV ABC ub

11kv OH Urban

automation

LV ABC op

Avergage Cost

Marginal Cost

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0.00 20.00 40.00 60.00 80.00 100.00

SAIDI improvement

Mar

gin

al c

ost

per

MW

h s

aved

Marginal and average Cost of reliability

+ Rural CCT

LV UG

+ 33kV UG

Average Cost

Marginal Cost

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

0.00 20.00 40.00 60.00 80.00 100.00

SAIDI improvement

Mar

gin

al c

ost

per

MW

h s

aved



Whilst this cost analysis is not directly applicable to other networks, and really requires further updating in regard to its current applicability to Energy Australia’s network, a number of quite strong conclusions can be drawn.

Firstly, the non-linear nature of reliability improvement costs, as between one improvement programme and the next, is strongly apparent. The improvement opportunities afforded by the lower cost programmes have limited outcomes and as increasingly higher reliability improvement objectives are pursued so we must move to increasingly more costly improvement programmes. (Many of which inevitably replace the lower cost programmes with more expensive ones). This phenomenon of diminishing returns is also a feature of the individual programmes themselves, as is discussed in the original Energy Australia paper. Secondly, it is apparent that within the bounds of even the most liberal assessments of the value of USE, the majority of more costly vulnerability reduction strategies, such as undergrounding, and ABC, are more than the customer value delivered, at least in the context of their average system wide application. There is little doubt that this conclusion is still currently valid for Energy Australia’s network and that it is transferable to the other NSW networks. It should be noted that the reason CCT in Urban networks (Initiative 8 of the original Energy Australia initiatives – the first table) does not feature in the mutually exclusive programmes synthesised by this review ( the second table), is that the Energy Australia’s analysis showed it be marginally more expensive and offering less benefit than automation. It thus has no place in the logical sequence of increasingly more costly programmes. In reality however it can be thought of as an alternative to automation, the costs of which, as assessed in the context of average global implementation on Energy Australia’s system are slightly higher than the expected costs of automation. It is probable that CCT is justifiable in some circumstances, as an alternative to automation in networks with low redundancy levels. It should also be noted that the relative costs of CCT and automation are particularly sensitive to load or customer density, with high load densities favouring CCT. Moreover, whilst CCT is a well established technology with costs that are well known and understood, some of the more sophisticated automation options are still in their infancy and their costs are somewhat less certain. Given the small magnitude of the margin between the costs of automation and CCT it is quite possible that the cost relationship may shift, in favour of CCT for a much wider range of circumstances. If applied selectively and sparingly, in particularly vulnerable parts of a network of particularly high load density, it is also possible that some small component of the globally more expensive options may be appropriate.

Thirdly, the graph of the marginal costs demonstrates a very sharp transition between what is potentially viable and what is clearly not. That is to say irrespective of whether the value of USE is $20 000 or $60 000 per MWh none of the strategies other than reclosers, automation and/or CCT are even remotely likely to be viable, on a global basis.



3.1.3 Variation of Redundancy Levels

Whilst the relationship between network redundancy levels and the value of customer load at risk, as a result of failure to maintain redundancy levels at levels consistent with deterministic standards, is conceptually well understood by the industry, the relationship is not one that has been well quantified. Such quantification, as has been undertaken, tends to have been undertaken at the relevance of asset failure probabilities and asset level load duration curves to the assessment. Whilst there is nothing in any of the Distributors submissions to this individual asset or asset class level, which is hardly surprising given the Pricing Review, to indicate whether any of the Distributors have a complete global model of the aggregate relationship for the entire network, it is clear that Energy Australia and Integral Energy both adopt risk measures and set risk targets, in relation to this factor. Integral Energy measures and sets targets in terms of Zone Substation ‘Demand at Risk’. Energy Australia measures and sets targets in terms of the number of Zone Substation loaded in excess of 117% of Firm Rating. Energy Australia, as indicated in the content of their submission, also employs a methodology for assessing the value of ‘Customer load at Risk’, as a result of this loading beyond firm rating, but has provided no details of the methodology or the relationship between loading and ‘Energy at Risk’. All distributors have formally documented deterministic ‘Planning Standards’. There is scope for developing a reasonably sophisticated model taking account of, typical load duration curves, the statistical distribution of individual asset redundancy deficiency about the mean, and the statistical variation in failure rates, within and across asset classes. Such sophistication may however require a level of data, which simply is not available from all of the NSW Distributors, in which case a simpler model may have to suffice. It would also demonstrate that the slope of the USE vs. Redundancy Deficiency curve changes rapidly, at a critical point, from being almost flat to almost vertical, and that as a corollary the cost savings per MWh of USE due to the Deficiency switch from being a moderately high value to being very low and declining and that consequently, irrespective of the customer value of USE (over a wide range) the decision about the optimum redundancy level is the same.

3.1.4 Assessment Approach and Expectations

In attempting to assess the marginal cost of reliability improvement (and cost saving from reliability deterioration) three different approaches have been pursued.

Firstly, an attempt has been made to rescale the Victorian values, as determined

by the ESC for the Victorian Distributors, to match the circumstances and network status of the NSW Distributors. The outcome and limitations of this work are reported in Section 4.2.

Secondly, an attempt has been made to apply the methodology, or at least the

principles adopted by the methodology, used by the ESC in Victoria to assess the marginal cost of reliability departures from target reliability levels, relying on the limited data included in the NSW Distributor’s submissions regarding their proposed expenditures and, their expected reliability and risk outcomes, (using both ‘base case’ and, alternative scenario data, where provided).The outcomes and limitations of this approach are reported in Section 3.3.



Finally the submissions of the Distributors were reviewed in an effort to find

any material that would shed light on or provide information that might constructively inform the assessment. This work is also reported in Section 4.3.

The level and relevance of the data that was provided by the NSW Distributors,

through their submissions, and from other publicly accessible sources, was not sufficient to enable confident assessments to be made, or indeed for the ESC methodology to be faithfully applied. The assessments that have been made are only as good as the data on which they are based, and are offered at this stage, only as indicative levels of the marginal costs of reliability, which the Distributors may take account of, when responding to this report. It is also expected that the Distributors will be given the opportunity to clarify data already provided, and provide pertinent additional data, so that more accurate and reliable assessments can be made ahead of any formal S Factor implementation.

3.2 RESCALING OF THE VICTORIAN VALUES

Ideally, in order to rescale the Victorian figures to match the conditions and network status of the NSW Distributors, comparative data on the asset mix, customer densities and redundancy levels, and even the environmental circumstances of each network, would be used in an attempt to develop a normalising methodology, that could then be used in reverse to establish comparable NSW figures. Unfortunately, time restrictions and limitations in the ready availability of publicly accessible data, allowed only a very shallow assessment to be made, based on customer and load density as measured by customers and MWhs per kilometre of mains.

Using data for all five Victorian Distributors, no statistically significant

correlations between any of the measures could be found. However by removing CitiPower from the data set two very high correlations (albeit based on only four data points) were found. The best of these, and the relative position of CitiPower, are shown on the two Graphs below.

The independent or driver variable in this relationship is MWh per km.



Figure 3– Marginal Costs of Reliability for Victorian DNSP’s

Marginal Reliability Costs vs MWh/km

TXU

United Energy

AGL

PowercorCitipower

y = 0.9722x + 6927.8R

2 = 0.0325

0

2,000

4,000

6,000

8,000

10,000

12,000

0 500 1000 1500

MWh per km

Ad

j.Mar

g R

EL

Co

st $

/MW

h

Figure 4– Marginal Costs of Reliability for Victorian DNSP’s (Excluding CitiPower)

Marginal Reliability Costs vs MWh/km

AGL

United Energy

TXU

Powercor

y = -10.423x + 10452R2 = 0.982

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

0 100 200 300 400 500 600 700

MWh per km

Ad

j. M

arg

RE

L C

ost

$/M

wh

The extreme position of CitiPower is, it is suggested, attributable to the

radically different asset mix of CitiPower, with its predominantly underground supplied CBD, influencing the mix. This point is of course relevant in any attempt to translate the relationship established for the four non-CBD Victorian Distributors to NSW. It also highlights the importance of asset mix, as identified in the previous section.



If the Victorian non CBD relationship is applied to Energy Australia, there is

obviously scope for substantial error, not just because of its CBD inclusion, but also because of its substantial underground 11kV network. Ideally in dealing with Energy Australia, its CBD data would be disaggregated from the rest and so too would its 11kV underground km and load. Unfortunately, the data to do this was not available.

The impact of including Energy Australia’s CBD and underground supplied

loads (even with the underground km’s included), will somewhat inflate the load density measure and thereby somewhat understate the translated value of marginal reliability cost. A further factor that may cause the translation to be somewhat on the low side, for all distributors is the different voltages of the two States high voltage systems. NSW at 11kV has inherently greater segmentation of its networks than Victoria at 22kV, which other factors aside, would normally indicate substantially better reliability for the same customer density and more costly reliability improvement opportunities. (Authors note: a comparison of NSW and Victorian reliability figures, taking customer density into account, indicates that there is little difference, suggesting that other compensating factors apply.)

Despite these qualifications, the regression formula, obtained for the four

Victorian non CBD Distributors, can be applied to the five NSW Distributors to yield the following results: Energy Australia $ 6,726 per MWh Integral Energy $ 7,260 per MWh Country Energy $10,032 per MWh Australian Inland $10,125 per MWh

These figures of course represent the costs per MWh of USE from all causes, other than transmission and generation failure and System Operator directed load shedding. If SAIDI (and MWh of USE) is to be measured on any other basis then these figures would need to be rescaled to suit, as is discussed later.

As discussed previously, in a more rigorous translation all values could be expected to be somewhat higher and the Energy Australia value considerably higher.

Obviously this methodology could be improved, with the availability of more

disaggregated data, particularly regarding the asset mix and customer populations of the different network classifications. Disaggregation of customer, reliability and asset data for all nine Distributors, promises to yield a very sound translation formula. In an attempt to shed some light on the CBD and underground vs. overhead asset mix differences, a second set of correlations for the Victorian Distributors was investigated. These are shown below: The independent variable in this relationship is customers per km. [This driver can be expected to dilute the impact of the higher loads per customer, typical of CBD and underground high voltage networks.]



Figure 5 – Marginal Costs of Reliability for Victorian DNSP’s (Customers per km)

Adj. Marginal Reliability Costs vs Cust/km

AGL

United Energy

TXUPowercor

Citipower

y = -7.1418x + 7731.6R

2 = 0.0044

0

2,000

4,000

6,000

8,000

10,000

12,000

0 20 40 60 80

Customers per km

Ad

j.Mar

g R

EL

Co

st $

/MW

h

Figure 6– Marginal Costs of Reliability for Victorian DNSP’s Excluding CitiPower (Customers per km)

Adj. Marginal Reliability Costs vs Cust/km

AGL

United Energy

TXU

Powercor

y = -131.12x + 10243

R2 = 0.917

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

0 10 20 30 40 50 60

Customers per km

Ad

j.Mar

g R

EL

Co

st $

/MW

h

Once again, although not as strong as for the previous relationship, a strong relationship, for the non CBD Victorian Distributors is apparent. And again CitiPower appears to be extremely different from the rest.



If the regression formula, for this relationship, obtained for the four Victorian, non CBD Distributors, is applied to the five NSW Distributors the results shown in the table below are obtained. These results are shown alongside the previous results.

Normaliser MWh per km Customers per km Energy Australia $ 6726 per MWh $ 7823 per MWh Integral Energy $ 7260 per MWh $ 8042 per MWh Country Energy $10032 per MWh $ 9864 per MWh Australian Inland $10125 per MWh $10053 per MWh Whilst the second set of results is considered to be a better translation of the Victorian figures to NSW (because it dilutes the corrupting influence of the CBD and underground supplied loads) it still, nevertheless understates the Energy Australia translation. Figures of $8,000 for Energy Australia and Integral Energy and, $10,000 for Country Energy and Australian Inland Energy, will nevertheless be taken as the best estimate of the Victorian translation. As discussed previously these figures of course represent the costs per MWh of USE from all causes, other than transmission and generation failure and System Operator directed load shedding. If SAIDI (and MWh of USE) were to be measured on the ‘normalized’ basis, (as defined in IPART’s letter to the Distributors of the 29th September 2003), then they need to be rescaled to reflect the fact that for every measured MWh of USE there is a further quantum of MWH (typically associated with severe storms), to be accounted for. Since reliability improvement initiatives are generally just as effective in ameliorating storm related outages, they should legitimately be included. Adjusted figures presented on a ‘Normalized’ SAIDI basis, are shown below. Normaliser MWh per km Customers per km Energy Australia NA NA Integral Energy $23159 per MWh $25654 per MWh Country Energy $12540 per MWh $12330 per MWh Australian Inland $17820 per MWh $17693 per MWh Rounded figures of $25000 for Integral Energy, $13000 for Country Energy and, $18000 for Australian Inland Energy are derived. It is not possible to determine a figure for Energy Australia as it has provided Reliability figures on a single basis (assumed to be modified to reflect “normalised” outage data) only.

3.3 DIRECT ASSESSMENT OF THE NSW DISTRIBUTORS RELIABILITY COSTS

This Section reviews the information, pertinent to reliability expenditure and outcomes, contained in the NSW Distributors’ submissions and attempts to use the available information to assess the marginal costs of reliability for each Distributor. It seeks to apply the same methodology for assessing the marginal cost of reliability as was adopted by the ESC in Victoria in its 2001 to 2005 Pricing Review. However because two of the NSW Distributors propose substantial “Demand at Risk’ reduction (or redundancy enhancement)



programmes, as well as specific reliability improvement programmes, this Section also reviews these programmes and seeks to examine their cost effectiveness in terms of customer value. The Victorian approach relied on detailed information that was provided by the Victorian Distributors for alternative, ‘base case’ and ‘enhanced case’ expenditure and outcomes scenarios. The information provided, covered Reliability Outcome Targets for each of the five years of the Determination and, specific Reliability Improvement Expenditures over the same period. The Reliability Outcome Targets were expressed in terms that captured all outages, other than those due to transmission or generation failure or load shedding at the direction of the System Operator. This information was used to assess an average marginal cost of reliability improvement for each distributor. (Essentially - the differential expenditure between the two scenarios, divided by the differential outcome.) Distributor Reliability Targets were set by ‘network type’, based on three classifications, CBD, Urban and, Rural. Customer numbers, within each classification, were also available for each distributor. The assessment did not attempt to distinguish between costs in the three network classifications, but did take into account the different average consumptions of each. IPART, through its Pricing Enquiry submission process sought similar information from the NSW Distributors and specifically invited them to put forward alternative outcome and expenditure scenarios for assessment and consideration. The response from the NSW distributors has been patchy. It is true to say that none of the distributors has provided the complete information, in the formats offered by IPART. Energy Australia and Integral Energy have both provided considerable information, however, including selective information about alternative ambitious improvement scenarios, and in Energy Australia’s case an alternative constrained expenditure scenario as well (although Energy Australia has only provided reliability figures on a single basis, assumed to be modified or “normalised”). This information, though not being in precisely the formats sought, is sufficient, if taken at face value, to make a tentative assessment. The assessments made, however (except for Energy Australia) do not follow the strict differential scenarios approach of the Victorian ESC but rely on whole programme data, with the obvious limitations of that approach, discussed in the Section 4.3. There are also concerns, that some key outcome targets may have been conservatively (even overly conservatively) estimated, while others may have been optimistically estimated and, that some of the expenditure classifications are insufficiently disaggregated. Moreover there has been no independent prudency assessment of any of the alternative scenarios. Country Energy has provided insufficient information about proposed Reliability improvement expenditure, and has not offered proposals for alternative scenarios. Country Energy, through its submission, and confirmed in subsequent discussion, sees reliability improvement as a natural outcome of its refurbishment and ‘black spots’ programmes, rather than as a separately and individually costed programme in its own right. They have nevertheless, proposed Reliability Objectives for each of the five years of the determination, in the three network classifications of Urban, Rural Short and Rural Long. These Objectives show an apparently declining trend in reliability, which Country Energy attributes to planned improvements in measurement accuracy. They offer no opinion as the direction or quantum of the underlying actual reliability outcomes.



Australian Inland Energy, in its submission, identifies a number of specific reliability improvement expenditures in its capital expenditure programme and, provides annual reliability improvement targets, but offers no alternative scenario proposals. The submissions and marginal reliability costs assessments of Energy Australia, Integral Energy and, Australian Inland Energy, are discussed in greater detail below. Energy Australia and Integral Energy’s assessments share a number of common issues and limitations. Whilst both have provided specific reliability improvement capital expenditure expenditures, it is apparent that this expenditure classification may include expenditures which, whilst they may broadly fit the “reliability” description, do not contribute directly or exclusively to reliability improvement. For example it is understood that some expenditures relating to quality of supply get picked up in this description. Also, a major component of this classification is the so called “black spot” expenditures, which, whilst they are clearly properly classified and serve a valuable customer outcome, do not contribute proportionally to improvement in any of the reliability outcome measures likely to be used in S Factor adjustment. Secondly, both Distributors propose to implement, in their base cases and in their alternative improvement scenarios, substantial programmes to increase redundancy at the zone substation level. Lack of definitive data to enable the separation of the contribution to reliability improvement arising from this initiative, from that arising from the specific reliability enhancement programmes is an issue. Moreover whilst both Distributors have provided full programme data on the annual capital expenditure spend for the growth classification, the Distributors have not separated out the expenditure on improving redundancy levels from the general growth classification. Whilst both Distributors were given the opportunity to clarify these outstanding data issues the time available was not really sufficient for a thorough and complete resolution of the issues. Integral Energy did however provide additional information pertinent to the analysis and clarifying some of these issues.

3.3.1 Energy Australia

Energy Australia, within its submission has provided period end reliability targets (on a single basis, assumed to be modified or “normailised”), from which an assessment can be made. These appear in its table showing ‘Projected Customer Outcomes by Scenario’ (page 87 – Section F). It appears however that these targets are expressed on a ‘modified’ basis and so will understate the true customer benefit involved. The same table also presents values of ‘Energy at Risk’, apparently associated with Energy Australia’s redundancy level improvement initiatives and other initiatives of their enhanced case. These appear to have been reported separately from the SAIDI improvements, although the text on page 33 (Section B) of the submission could be interpreted otherwise but, if it were so interpreted, would gives rise to discrepancies in the table. It is also not clear, from their submission whether ‘Energy at Risk’ means, literally, the energy that would be lost from assets lacking redundancy cover, in the event of failure, or the probable energy loss from failures, taking into account the probability of failure, during the periods when the assets lack full redundancy cover. For the purposes of this report the latter will be assumed.



In the analysis that follows, ‘Energy at Risk’ figures have, where appropriate, been added to the SAIDI improvements at the ‘overall’ rate of the ‘customer value of energy not supplied’, of $23,830 per MWh reported in Attachment18 of the Energy Australia submission. Elsewhere in the analysis the relationship between SAIDI and MWh at risk is taken to be 58 MWh per one minute of SAIDI, as determined by a simple averaging the total energy transported (average for the Regulatory Period) of 30.44 million MWh over the 525,600 minutes in a year. (Which effectively values SAIDI at $m1.38 per minute (compared to the $m1. per minute suggested by Energy Australia on page 33 of their Submission), which is equivalent to a capital expenditure of $m17.1 per minute - at a WACC of 7%, assuming a 30 year life.) The table in Appendix A sets out the data used in the subsequent analysis. This data has been directly extracted from Energy Australia’s submission. Costs and Benefits of the Redundancy Improvement Initiative As mentioned previously Energy Australia has not provided sufficient detail in its ‘Growth Capex’ expenditure break up to enable a ready assessment of the quantum of Capex required for its redundancy enhancement programme. In order to shed some light on the magnitude of this expenditure the relationship between the ‘Growth Capex’ of the three different growth scenarios and the growth rates of these scenarios was examined. The Chart below shows the relationship between the ‘Growth’ expenditure classification of capital expenditure and the energy transported’ growth rates of the three scenarios (low medium and high growth) reported by Energy Australia in its financial spreadsheet submission to IPART.

Figure 7 – Energy Australia Capital Expenditure and Growth Comparisons

Growth Capex vs Growth

High

Medium

Low

y = 332.12x + 311.83R2 = 0.9719

0

200

400

600

800

1000

1200

-0.5 0 0.5 1 1.5 2 2.5GWh Growth %

Gro

wth

Cap

ex $

(000

)



Expressed algebraically this relationship is: GROWTH CAPEX ($m) = 312 + 332 (ENERGY GROWTH in %) It should be noted that the growth rates calculated from the financial submission are slightly lower than the forecast growth rates reported in the body of the submission. However, regardless of which energy growth rates are used, and regardless of whether aggregate summer or aggregate winter demand growth rates are used (as reported in the text of the submission), the residual expenditure, apparently unexplained by growth, remains at around $m 300. Not too much emphasis should be placed on the precise value of this residual however, since it has been determined in relation to three data points only, and could be subject to a wide range of error. It is nevertheless quite apparent that there is a substantial residual and for the purposes of what follows that residual will be taken to be $m300. There are apparently two reasons for this residual. The first and the one pertinent to the reliability improvement issue, is the expenditure on the proposed programme to improve redundancy levels. The second is the expenditure which is driven by the spatial differentials in demand, which in times of normal growth see demands in some parts of the network growing much faster than in other parts. In periods of no or very low growth demand in some parts of the network may actually decline while demand in other parts grows. Thus even when there is no aggregate growth there is still some level of growth expenditure required to provide capacity to match the changes in the spatial distribution of demand. There also appears to be a third minor contribution to this expenditure which relates to differences in the time periods for which the data and targets of Energy Australia’s ‘Projected Customer Outcomes by Scenario’ are presented. If the ‘Current Performance’ as reported in this table is the performance in the 2002/2003 year, (i.e. the period current when the submission was made), then the number of substations at risk will have grown somewhat by the time the new regulatory period commences, (depending on the adequacy of current expenditure levels). In order to assess the quantum of these various components, the relationship between Energy Australia’s 2009 “Do Nothing” Forecast Utilisation and the 2002 Historical Utilisation of its Zone Substations, reported in Attachment 6 of Energy Australia’s Submission, was evaluated. This relationship, as determined by simple regression analysis is as follows: 2009 Do Nothing Utilisation (%) = 1.26 times the 2002 Utilisation (%). The average and standard deviations being:

• 88.9 and 24.4 for 2002, and • 111.8 and 36.6 for 2009.

The regression coefficient being r = 0.84 and there being virtually no residual.



On the basis of this analysis, and taking into account the different time periods involved, it can be demonstrated, on the basis that the substantial increase in standard deviation is attributable to spatial differences in demand growth, that: Of the 56 Substations which would breach the 117% Firm Rating criterion, under a do nothing scenario, 15 are attributable to the spatial demand growth differential, (with a possible further 2 attributable to growth in the year between ‘Current Performance’ and the beginning of the Regulatory Period). Thus on a pro-rata basis the investment required to reduce the number of substations at risk by 10, as proposed in the base case, is $m130, which when annualised is equivalent to around $m10 per annum or twice the reported ‘Energy at Risk’ savings to customers. This analysis of course fails to take account of the fact that the ‘Growth’ category of expenditure also includes growth driven expenditure in the network, upstream and downstream of the Zone Substations, and consequently that in addition to the investment in the 15 Substations at risk, attributable to spatial differences in demand growth, there is also substantial investment in the rest of the network that is also driven by these spatial differences. Without a careful analysis of Energy Australia’s ‘Growth Expenditure’ Capex programme, and/or additional direct information from Energy Australia, it is not possible to definitively quantify this expenditure. However if it were of a similar magnitude to the expenditure on the estimated 15 substations then, on a similar pro rata basis, the annualised costs of reducing the number of Zone Substations at risk by 10 would be comparable to the customer ‘Energy at Risk’ saving reported. Whilst there are clearly many uncertainties in the above, which only Energy Australia can resolve, it does appear, quite strongly that, for growth capital expenditure, Energy Australia is operating at or around a marginal cost of reliability that matches customer value. This observation is confirmed by a simple analysis of Energy Australia’s constrained case scenario, which if taken at face value reports $m6 of increased customer ‘Energy at Risk’ arising from a $m60 reduction in capital expenditure on substations at risk. Costs and Benefits of the Specific Reliability Improvement Programmes If the reported ‘Specific Reliability Capex’ expenditure and targeted SAIDI improvement for the base case, are taken at face value, then the capital cost of the specific reliability enhancement initiatives can be calculated to be $m101 per minute, which translates, at a WACC of 7% and an assumed 30 year life, to approximately $143 000 per MWh. This assessment is subject to the issues and limitations mentioned previously. The expenditure classification, in particular, appears to include substantial ‘black spot’ expenditure, for reducing, by more than half, the number of ‘excess interruptions’. However even allowing for this and the possibility that there could be other expenditures included in the classification, which don’t contribute directly to SAIDI improvement, the figure is so high as to suggest, either that the SAIDI improvement target has been conservatively assessed or that a large part of the expenditure is required simply to maintain reliability at current performance. If this is the case then this classification of expenditure is not particularly helpful in assessing marginal reliability costs.



By reference to the “Constrained Case’ Scenario, however two further figures can be obtained. These are calculated by annualising the $m20 reduction in Reliability Expenditure identified for the ‘Constrained case on page 33 (Section B) of the submission and dividing by either the 6 minute (348MWh) deterioration in SAIDI reported in the Table on page 87 (Section F) or by dividing by the 8 minute (456 MWh) deterioration reported in the text (page 33). The result is either $4400 per MWh or $3,300 per MWh. To the extent that the $m20 reduction appears to include substantial ‘black spot’ expenditure reduction (excess interruptions increase be 58%) these figures are almost certainly too low. A figure of $4,000 per MWh will nevertheless be taken. In a manner, this calculation is more closely aligned to the Victorian methodology than any of the calculations (for any of the Distributors) that the available data would allow. This is contrary to expectations in that it was obtained by comparing the ‘base case’ with a ‘reduced outcomes’, rather than an ‘enhanced outcomes’ scenario. The enhanced case, though scant in detail, can similarly be used to assess marginal reliability costs. Whilst the $m250 of additional expenditure involved is proposed to cover a complete suite of reliability improvement initiatives, including further modest reductions in the number of Substations at Risk, no detail of the break up of the proposed expenditure is provided. Moreover, whilst some $m45 of additional savings of customer energy at risk and a SAIDI reduction of 10.1 minutes are identified (presumed to be mutually exclusive), no detail of which initiatives contribute to which outcomes is provided. It is quite clear, however that only a very small portion of the additional ‘Energy at Risk’, saving can be attributed to the further reduction by 8 of the number of Substations at risk, and that the bulk of the reductions must be due to some or more of the other initiatives. Consequently only an overall average cost of the very substantial reliability improvements proposed can be made. Again, if the figures are taken at face value, and if the ‘Energy at Risk’ and SAIDI improvements are taken to be mutually exclusive (and therefore sum to be equivalent to 42.7 minutes of SAIDI, or 2464MWh) then, the overall average cost can be calculated to be of the order of $8136 per MWh. Given that the suite of Reliability improvement measures proposed include some relatively expensive components, such as selective undergrounding and 11kV reinforcement, and the reduction of a further 8 ‘substations at risk’, this figure does seem somewhat low when assessed in comparison with Energy Australia’s 1998 submission to IPART on the costs of reliability. As set out in Section 4.1.2 above, that assessment suggests that a reliability improvement of around 49 minutes could be achieved for a total cost of $m350, at an average cost of $10,000 per MWh and a marginal cost of $17,300 per MWh. This disparity would appear to be even greater, when it is considered that the 1998 work was on ‘Distribution Network’ SAIDI basis, whereas the above analysis was on what has been assumed to be a “normalised” SAIDI basis. However, given the time that has elapsed since the 1988 submission, and on the basis that the undergrounding and reinforcement components would be applied sparingly and very selectively, the figures are broadly consistent. It could be expected therefore that marginal costs of at least $15000 per MWh, could be involved, and possibly substantially more, if there is a need for 11kv reinforcement. The $8000 figure calculated from the submission data will nevertheless be taken, and will be taken to represent the costs measured on both a ‘Distribution Network SAIDI’ basis and a “Normalised’ basis.



3.3.2 Integral Energy

Integral Energy provided full details of its reliability targets over the regulatory period, in response to IPART’s request of the 29th September 2003, and provided further details of the reliability break up between its distribution and sub-transmission system, in response to a further request made during this review. Integral Energy also provided a full set of financial spreadsheet data, for the three growth scenarios requested, for both a base case set of customer outcomes and for an enhanced set of customer outcomes. Included in those customer outcomes are proposals to reduce customer demand at risk, by 100 MVA for the base case and by 150 MVA for the enhanced outcomes (described as the low risk scenario) case. Integral did not attempt to put a value on the reductions in ‘Demand at Risk’, or to translate these reductions into SAIDI equivalents. Integral were able to provide additional information regarding the breakdown of both their ‘Growth Capex’ spend and their ‘Specific Reliability Improvement Capex’ spend. This additional information has enabled a more confident assessment of the marginal costs of reliability improvement to be made than would otherwise have been the case. In what follows, the relationship between SAIDI and MWh of Unserved Energy is taken to be 29 MWh per one minute of SAIDI, as determined by a simple averaging the total energy transported of 15.70 million MWh over the 525,600 minutes in a year. The table in Appendix B sets out the data used in the subsequent analysis. This data has been directly extracted from Integral Energy’s submission. Costs and Benefits of the Redundancy Improvement Initiative Whilst Integral Energy did, during the course of the review, provide a break up of its ‘Growth Capex’ showing the planned expenditure on “Demand at Risk’ reduction, the relationship between the ‘Growth Capex’ of the three different growth scenarios and the growth rates of these scenarios was nevertheless, for the sake of completeness, examined. The Charts below show the relationship between the ‘Growth’ expenditure classification of capital expenditure and the energy transported’ growth rates of the three scenarios (low medium and high growth) and for both the base and the enhanced outcomes case as reported by Integral Energy in its financial spreadsheet submission to IPART.



Figure 8 – Integral Energy Capital Expenditure and Growth Comparisons (Base Case)

Growth Capex vs Growth (base case)

High

Medium

Low

y = 147.58x + 303.46R2 = 0.9613

0

100

200

300

400

500

600

700

800

0 0.5 1 1.5 2 2.5 3GWh Growth %

Gro

wth

Cap

ex $

(000

)

Figure 9– Integral Energy Capital Expenditure and Growth Comparisons (Enhance Case)

Growth Capex vs Growth(enhanced case)

Low

MediumHigh

y = 147.93x + 389.47R2 = 0.8309

0

100

200

300

400

500

600

700

800

900

0 0.5 1 1.5 2 2.5 3GWh Growth %

Gro

wth

Cap

ex $

(000

)



Expressed algebraically the relationship is: Base Case GROWTH CAPEX ($m) = 303 + 148 (ENERGY GROWTH in %) Enhanced case GROWTH CAPEX ($m) = 389 + 148 (ENERGY GROWTH in %) It should be noted that the growth rates calculated from the financial submission are slightly higher than the forecast growth rates reported in the body of the submission However, regardless of which energy growth rates are used, and regardless of whether aggregate summer or aggregate winter demand growth rates are used (as reported in the text of the submission), the residual expenditure, unexplained by growth, remains at around $m300 and, $m390. It should also be noted that the differential between the residues, unexplained by growth of $m90 is $m16 less than the $m106 additional ‘Growth Expenditure’ of the Enhanced outcomes case, reported in the text of the submission. Equally significant differences occur between the figures of the financial spreadsheet and the text, for the growth capital expenditure of both scenarios. For the purposes of this assessment the figures of the financial spreadsheet have been used. Not too much emphasis should be placed on the precise value of these residuals however, since they have been determined in relation to three data points only, and could be subject to a wide range of error. It is nevertheless quite apparent that there are substantial residuals and for the purposes of what follows those residuals will be taken to be $m300 and $m390. One notable observation about these graphs is that their approximately equal gradient, of $m148 per 1% of growth, is 45% of the gradient of $m332 per 1% of growth, of the Energy Australia graph. Given that Integral Energy’s ‘energy transported’ is 48% that of Energy Australia’s, this would seem to indicate that there is considerable alignment between the underlying unit capital construction costs of these two distributors. The fact that the unexplained residual for the two distributors is approximately equal, is partly explained by the fact that Integral’s expected growth rates are one third higher than Energy Australia’s, and therefore that they have a relatively much bigger spatial demand growth differential than Energy Australia to deal with. It also suggests the effort of stabilizing their distribution feeder risk profile may, as a consequence of the spatial demand differentials, be contributing a proportionally higher expenditure requirement, than Energy Australia. Clearly some part of the residual is required for the planned reduction in ‘Demand at Risk’. Integral Energy, as indicated above, has provided a break up of its ‘Growth Capex ‘spend. This break up indicates that 10% of the total ‘Growth Capex’ spend is for reduction of the demand at risk for the base case scenario. This break up of total ‘Growth Related Capex is much more closely aligned with the capital expenditure figures reported in the Tables in the body of the Submission, (pages 104 and 107), and for this reason the figures from the body of the submission will be used for assessing the marginal costs of the Demand at Risk initiatives.



By using Integrals figure of $m53.3 for the 100MVA reduction in ‘Demand at Risk’ of the base case scenario and their figure of $m106 additional expenditure for the further reduction of another 50MVA of the enhanced outcomes case, unit capital costs of $m0.533 and, $m2.12 per MVA are obtained. The quadrupling of these unit costs as between the base case and the enhanced case is not surprising as, as previously discussed in Section 4.1 these costs are quite non linear and subject to serious diminishing returns as the demand (and even more so energy) at risk diminishes. The conversion of demand at risk to energy at risk or its SAIDI equivalent is not possible with the data provided, and Integral Energy have been unable to provide an assessment in the time available. However it can be assessed that at an average customer value of $20,000 per MWh the Demand at Risk reduction of the base case would need to result in a long term average SAIDI improvement of 7.4 minutes and the Demand at Risk reduction of the enhanced case would need to result in a further reduction of 14.7 minutes. Given that the SAIDI contribution from this level of the network has averaged 40 minutes over the past five years (with an annual standard deviation of 27), the first outcome is quite plausible and the second, at least not logically impossible. Costs and Benefits of the Specific Reliability Improvement Programmes Integral Energy’s Submission identified some $m106 of capital expenditure in the ‘Reliability’ category. However during the course of the review Integral were able to provide a detailed breakdown of this expenditure, which revealed that almost 40% of this expenditure was for power quality monitoring and improvement, the actual value of the reliability improvement expenditure being $m65. On this basis, and relying on the stated reliability improvement target of 27 minutes of normalised SAIDI, the average cost of SAIDI improvement, based on a 7% WACC and 30 year investment life is $180,000 per minute or $6,200 per MWh of Unserved Energy saved. If expressed in terms of the expected reductions in ‘Distribution Network – planned and unplanned’ SAIDI, this cost becomes $1,900 per MWh of Unserved Energy. Integral Energy also advised, as reported in their submission, that a key strategy for them in reducing SAIDI will be to provide (and to require Accredited Service Providers, connecting to their network, to provide) network support (temporary connections or mobile generation) in order to substantially reduce planned interruptions. If the operating costs associated with this strategy were to be added into the calculation the above assessments would be somewhat higher. In the absence of any knowledge of these costs a figure of $2,000 per MWh, on a ‘Distribution Network’ basis and a figure of $6000 on a ‘Normalized’ basis will be taken. Integral Energy’s enhanced case scenario identifies a further $m10.2 to be spent on reliability enhancement projects, but does not identify any reliability improvement benefit that can be associated with this expenditure. Integral Energy were given the opportunity to assess the benefit, during this review, but were unable to do so in the time available.



3.3.3 Country Energy

Information available for Country Energy did not permit a detailed review of the relationship between their capital works program and identifiable reliability improvements that could be measured through the average SAIDI, CAIDI and SAIFI indexes. The timeframe for this review did not provide scope for extensive consultation and further data capture to facilitate more detailed interrogation of Country Energy’s reliability improvement program. For this reason, the results for Australian Inland Energy have been taken to broadly reflect the characteristics of Country Energy’s network and have been applied as indicative rates for this draft report.

3.3.4 Australian Inland Energy

Australian Inland Energy provided full details of its reliability targets over the regulatory period, in response to IPART’s request of the 29th September 2003. It also provided a sufficiently detail break up of its proposed capital expenditure on system improvement programmes, for an assessment of its reliability improvement expenditures to be made. There were, however a number of large, non specific regional expenditures, amounting to about 40% of the total for which no assessment could be made. Consequently expenditure assessment may be somewhat low. In what follows the relationship between SAIDI and MWh of Unserved Energy is taken to be 0.85 MWh per one minute of SAIDI, as determined by a simple averaging the total energy transported of 447,000 MWh over the 525,600 minutes in a year. The table below sets out the data used in the subsequent analysis. This data has been directly extracted from Australian Inland Energy’s submission.



Table 4 – Australian Inland Energy Reliability Expenditure and Growth Comparisons

A The Average Energy Transported over The Regulatory Period 447,000 MWh Total Reliability Improvement 25 min

Total Reliability Improvement Capex $m 1.544

Capital Cost per Minute $ 61,600

Annualized cost per minute $ 4,965

Annualized cost per MWh $ 5,841

It is noted however, that the reliability improvements proposed, follow on an unusually large $m 1.074 expenditure on reliability improvement (mostly SCADA upgrade) programme in 2003/4. The impact of this programme, though undertaken before the commencement of the new Regulatory Period, will undoubtedly contribute to the ongoing improvement throughout the new Regulatory. For this reason the cost of reliability has also been calculated on a six year basis. (2003/04 to 2008/09 inclusive). The reliability improvement from 2002/3 t0 2003/4 is projected by Australian Inland Energy to be 18 minutes. On this basis: Total Reliability Improvement 43 min

Total Reliability Improvement Capex $m 2.618

Capital Cost per Minute $60,884

Annualized cost per minute $ 4 907


2003/4 2004/5 2005/6 2006/7 2007/8 2008/9 Total

MWh Transported (Medium Growth)

427,000 433,000 440,000 447,000 454,000 461,000

SAIDI (min) – [Distribution Network]

SAIDI (min) – [Normalised ]

292

175

275

158

275

158

267

150

267

150

267

150

Reliability Improvement Expenditure $(000)

654 260 210 210 210 1,544



Annualized cost per MWh $ 5,773 Whilst the above cost has been derived on what appears to be quite solid information, it does appear to reflect the costs associated with one particular and possibly ‘one–off’ opportunity to upgrade SCADA systems. Whether this figure is representative of what it would cost to further enhance reliability, or whether such costs would be substantially higher, as they may well be, is unknown. A cost of $6,000 per MWh has nevertheless been taken, and since all reliability improvement targets are the same, regardless of the basis on which SAIDI is measured this figure applies for both the ‘Distribution Network’ basis of SAIDI and the “Normalized’ basis of SAIDI.

3.4 SUMMARY AND CONCLUSIONS

Before drawing together the implications of the outcomes of the foregoing approaches to reviewing the costs of reliability improvements of the NSW Distributors, it is important to note that the data on which much of the work relied was, unverified, was subject, in parts, to inconsistencies and, was incomplete. Any conclusions drawn from the analysis, involving the quantification of values are therefore tentative. Needless to say, with additional information from and, the cooperation of the NSW Distributors, and ideally also, with additional information from Victoria, a much more rigorous analysis would be possible and much stronger conclusions, both quantitative and qualitative could be reached.

3.4.1 Management of Redundancy Levels

Despite serious deficiencies in the data supporting this analysis, it does appear that Energy Australia and Integral Energy, in their proposals to reduce ‘Demand at Risk’ at the Zone Substation and Sub/Transmission level are both planning to incur costs per MWh of ‘Un Served Energy’ (USE) saved that are comparable to current system wide estimates of the customer value of USE, and that in their Enhanced Scenarios, may be exceeding such system wide estimates. As discussed in the next Section, the nature of outages at this level of the system is, however, such that the use of USE values, several times greater than average can be justified. Whilst time did not permit detailed modelling of the relationship between the value of USE and redundancy levels, it is likely that, such modelling would demonstrate that the inherently unstable nature of the equilibrium between the two, is such that responsible planning, at this level of the system is best conducted by continuing to use deterministic standards supported by simple ‘maximum demand at risk criteria’ set to ensure the stability of the risk involved.

3.4.2 S Factor Incentive Marginal Costs

Rescaling of the marginal costs of reliability (specific reliability enhancement projects) used by the ESC of Victoria in their 2001 to 2005 Pricing Determination, though subject to considerable data limitations, indicates, with reasonable confidence that the Victorian figures translate to something in excess of $10,000 per MWh for Country Energy and Australian Inland Energy,



on a ‘Distribution Network’ basis for the measurement of SAIDI. The translation for Integral Energy and Energy Australia, in particular, are much less confident, because of asset mix differences, but a minimum translation of $8,000 per MWh is indicated. The Energy Australia figure if properly translated is probably significantly higher. It will be a simple matter, with disaggregated data to improve the accuracy and confidence of these translations.

Calculations based on the limited data that could be extracted from the NSW Distributors submissions however indicated, in general somewhat lower values. For Integral Energy and Australian Inland Energy, for which the best data was available, the figures were $1,900 and $6,000, on a ‘Distribution Network’ basis for the measurement of SAIDI. A figure of around $4,000 was calculated for Energy Australia, with a figure of around $8,000 for their ‘Enhanced Case Scenario’. The doubts surrounding the data and the details of both Integral Energy and Energy Australia’s programmes were, however, such as to suggest that all of these figures should have been higher. It should also be noted that the Energy Australia figure appears to be on a ‘modified SAIDI’ basis, which is taken for the purposes of this report to represent the ‘normalised’ outage definition.

The data from the 1998 Energy Australia paper on the costs and benefits of a full range of reliability enhancement technology initiatives (‘distribution planned and unplanned SAIDI’ basis), indicated that, for tha t Distributor at least, the minimum cost of even the most basic reliability improvement technology (remote reclosers), if rolled out on a significant scale would be $3,000 per MWh, on a ‘Distribution Network’ basis for the measurement of SAIDI and that any widespread implementation of 11kv automation or CCT would involve average costs in excess of $10,000 per MWh, and marginal costs (relative to what can be done with simple reclosers) of $17,000 per MWh. Rescaling of these figures to the other NSW Distributors, using techniques similar to the Victorian rescaling should be possible with additional information. The rescaling is likely to show similar costs and marginal costs for Integral Energy, but higher costs for Country Energy and Australian Inland Energy, such as to justify only highly selective or partial roll-outs. (townships only).

Irrespective of the accuracy of the above estimates of the marginal costs of reliability, what this review has established is that the choice of a marginal reliability cost for incorporation in an S Factors depends very much on the underlying purpose for which the S Factor is being introduced. It also appears to have established that, for Energy Australia and Integral Energy, at least, there is a considerable disparity between the marginal costs of each Distributors programmes to manage reliability through the provision of adequate levels of redundancy, and their specific reliability improvement programmes, suggesting that they should each be putting more expenditure into reliability improvement programmes.

If the purpose is merely to adjust the Distributors incomes to reflect minor variations around their current modest reliability enhancement aspirations, then relatively low values reflecting the current costs of their base case programmes are appropriate. These values, based on the above calculations, with the data concerns noted, are tabulated below.

If the purpose is to establish S Factors that reflect the underlying costs of reliability improvement in Victoria, and perhaps to provide incentives for the NSW Distributors to perform on a par with Victoria, then the values translated



from Victoria, ideally after they have been further refined, are appropriate. These values based on the available data are tabulated below.

If the purpose is to provide incentives for the NSW Distributors to undertake significant reliability enhancement, consistent with customer value, then the values indicated from the analysis of the Energy Australia 1998 submission would be required. As mentioned above the values indicated would require some translation for application to the other distributors in the State, and for Country Energy and Australian Inland Energy selective or partial roll outs only may be justified. The values required for this purpose would approach current estimates of the Customer value of USE, and for an optimally economically efficient roll out would ultimately be set at the customer value.

Table 5 – Summary of Marginal Costs for Unserved Energy on a ‘Distribution Network’ basis for the measurement of SAIDI

Objective Energy

Australia Integral Energy

Country Energy


Revenue Adjustment for Programmes Proposed $4,000 (a) $2,000 $6,000 $6,000

Victorian Parity $8,000 $8,000 $10,000 $10,000

Serious Reliability Enhancement $8,000 (a)

Parity with Redundancy Programmes $20,000 $20,000

(a) This figure relies on Energy Australia’s SAIDI target differentials, which it is assumed have been reported on a ‘modified basis’, which is equivalent to the “normalised” definition. All others are on a “Distribution Network - planned and unplanned’ basis.

Table 6 – Summary of Marginal Costs for Unserved Energy on a ‘Normalised’ basis for the measurement of SAIDI

Objective Energy

Australia Integral Energy

Country Energy


Revenue Adjustment for Programmes Proposed $4,000 $6,000 $8,000 $6,000

Victorian Parity N A $25,000 $13,000 $18,000

Serious Reliability Enhancement $8,000

Parity with Redundancy Programmes NA NA



This review also appears to have established that, for Energy Australia and Integral Energy, at least, there is a considerable disparity between the marginal costs of each Distributors’ programmes to shore up reliability through the provision of adequate levels of redundancy, and their specific reliability improvement programmes, suggesting that they should each be putting more expenditure into specific reliability improvement programmes, such as to ultimately equalise the marginal cost of reliability delivered by both programmes..

3.4.3 Implementation

In view of this last conclusion and because of the doubts about data, and the precise nature of some of the programmes proposed, there are at least two items that need to be addressed before appropriate marginal costs can be settled and S Factor implementation commenced.

The first is to refine the analysis undertaken, using verifiable and fulsome data from the NSW Distributors. The outcome of such refinement being an analysis of the marginal costs by network type, where the network type classifications would reflect, not so much customer density, as the Victorian classifications appear to do but rather the customer mix. (Such classification is already undertaken by some Distributors, usually at a feeder level, for plant rating purposes). The advantage of such classification is not only that the disaggregation will allow a much more accurate assessment of cost, but that it will allow a much more accurate and confident estimation of the customer benefits as measured by value of USE.

The second is to provide the NSW Distributors with an opportunity to review their specific reliability improvement programmes, and to review what it is they are trying to achieve by these programmes, in the light of what is ultimately established about the cost effectiveness of these programmes as measured by cost of USE saved.

Timing for the implementation of the S Factor incentive would need to take into consideration that the Distributors have not developed their programmes in the context of a reliability incentive scheme and would need to be given the opportunity to set new programmes, for commencement coincident with the S Factor implementation. The initial S Factor coefficients would then need to be set having regard to the new programmes and the end objective of ensuring that over time the coefficients move towards values consistent with customer value.

In setting the initial values, the value would need to be set high enough, so as to ensure that technologies that would be made redundant by higher coefficient values were not adopted, but sufficiently below the anticipated customer value so as to avoid creating opportunities for Distributors to appropriate substantial consumer surplus. An initial value, expressed on a whole of system basis, on a ‘Distribution Network’ basis for the measurement of SAIDI, of $10,000 per MWh and rising to $20,000 per MWh as and when reliability improvements materialise, should reasonably achieve both of these goals. In rescaling these figures to a “normalised” SAIDI measure, the full upper limit jumps considerably and is likely to be well in excess of the average customer value for reliability. It would therefore be appropriate to cap the upper



limit at the average customer value of $30,000 and propose an interim step of $15,000 per MWh of USE.

3.4.4 Other Enhanced Outcomes Expenditures

The ‘Enhanced Outcomes’ scenarios of both Energy Australia and Integral Energy included substantial proposals for expenditures that would clearly not be funded by the implementation of the S Factors, envisaged by this report. S Factors which deliver a revenue stream linked to reliability outcomes will not fund the enhanced asset replacement/refurbishment programme (as the link between these programmes and reliability outcomes is extremely weak). And, the magnitude of the S Factor, envisaged in the recommendations of this report, will not be sufficient to fund the further reductions in ‘Demand at Risk’ (Substations at Risk) proposed by both Distributors.

The Tribunal will therefore need to give separate consideration to whether it will provide for the funding of these other ‘enhanced outcomes’ or ‘reduced risk’ proposals, in its determination.



4. CUSTOMER RELIABILITY VALUE

4.1 BACKGROUND TO CUSTOMER VALUE

In general, competitive markets are categorised by large numbers of suppliers and relatively homogeneous products. Prices are established through the balancing of customer demand (the utility or perceived value customers derive from the product) and supplier costs, (including profit expectations).

Electricity distribution is generally regarded as a natural monopoly and in NSW electricity distribution prices are regulated to ensure appropriate service delivery and scrutiny of expenditures. In many respects regulation of electricity distribution within Australia attempts to emulate the disciplines of a competitive market whilst avoiding the need for network duplication. Prices or revenues for DNSP’s are usually determined through the summation of underlying distribution costs. The services have generally been defined to include attributes such as reliability of supply, network availability, supply quality, safety, aesthetics, and customer service.

The Tribunal, however, faces the challenge of determining the preferences of customers for electricity distribution services and make judgements regarding the equilibrium between price and service for each service attribute. In most cases this judgement has needed to be exercised without an accurate assessment of the price elasticity of demand. Establishing the demand equations is clearly very complex and will inevitably be based on a range of assumptions but would greatly assist in determining appropriate levels of expenditures and target levels of services for distributors.

Electricity distribution systems are essentially transport networks for the supply of energy from generators to customers. As such there are predominantly two key measures of service delivery - supply reliability (a measure of the length of time electricity connection to customers is lost) and supply quality (how well the system accommodates customer load requirements, particularly in terms of maintaining appropriate voltage levels). As discussed in the introduction, this report is focussed on incentives for reliability of supply. Supply quality is currently managed through prescribed standards, which need to be tightly controlled to protect customer equipment and maintain safety.

4.2 CUSTOMER VALUE AND COSTS OF RELIABILITY

In line with the identification of reliability as a key service characteristic of electricity distribution, and the need for regulators to appropriately recognise consumer preferences for reliability in setting distribution prices, a number of studies have been undertaken since the introduction of a contestable National Electricity Market in Australia to estimate the value of reliability. One goal of such research is to establish the equation for supply reliability. The customer value curve represents the maximum amount that a customer would be willing to pay for a prescribed level of service – in this case reliability. Conversely, in a competitive market, suppliers would be assessing the underlying costs of providing the desired level of reliability and be seeking to increase the levels of service up to the point where the costs of supply equal the value derived by customers. In the context of this report, such a measure would provide the maximum amount for any reliability incentive, i.e. the cost of providing



additional reliability could be weighed against the additional value derived by customers to determine the appropriate reliability projects to be undertaken.

For distributors to receive the appropriate investment incentives the rate for reliability improvement should reflect the customer value received. This ensures that distributors seek to design and construct their networks to deliver reliability levels up to the point where the costs and value of marginal reliability are aligned. Conversely, the penalty for reducing reliability levels needs to reflect the decrease in utility derived by customers.

Most research to date has not explored the function of customer value against reliability. Studies have generally tried to identify a value for individual customers suggesting the relationship is relatively inelastic. If this is true then setting the reliability incentive at the customer value level will provide the most appropriate reliability investment signals. However, if the value equation is more elastic and potentially non-linear then it becomes more important to identify the reliability level at which the value assessment is made. For example, if current reliability levels are well below the intersection of value and cost, then a measure of customer value for reliability may overstate the equilibrium point as shown in the following diagram.

Figure 10 – Representative Reliability Cost/Value Curve

[Note: The above diagram is provided as a demonstration only and does not purport to represent the true relationship between costs, customer value and reliability]

Based on the relationships shown in the above diagram, if the customer value equation is somewhat elastic, then the value derived when reliability levels were at point A would deliver an incentive for distributors to supply at point B, which would exceed customer preferences.

In practice the customer value of reliability is multidimensional and is likely to vary based on many factors such as time of day, customer end use, season, levels of customer dependence on electricity, load duration, etc. To properly model customer reliability value would therefore require many levels of customer and network segmentation.

Reliability Cost/Value Curve

0

20

40

60

80

100

0 2 4 6 8 10

Reliability

Cos

t/Val

ue

A B

Cost Customer Value



4.3 LINKED AND UNLINKED PRICING INCENTIVES

As discussed above, the appropriate incentive for reliability should be the customer value derived to ensure that distributors invest in reliability up to the point where value and cost are equal. However, in a competitive market suppliers would not be permitted to extract the full amount of customer value over the costs of supply (consumer surplus). Competition should ensure that prices reflect efficient costs.

In NSW IPART has addressed this issue by determining allowable network prices based on assessments of efficient costs. Prices are therefore linked to historical and projected expenditure levels that reflect existing network design and operation standards (and therefore existing service levels). Regulatory risks associated with acceptance of capital investments into future allowable pricing decisions potentially provide incentives for distributors to be risk averse. This may cause DNSP’s to avoid projects that relate to improvements in service levels, as these have not been easily incorporated into past regulatory reviews.

The separation of pricing determinations from service attributes such as reliability and supply quality has been a considerable issue for the Tribunal and has been discussed in a number of IPART papers, including Regulation of Electricity Network Service Providers – Incentives and Principles for Regulation2, in which the Tribunal notes that,

“There is a difficult balance to be struck between: (a) the regulator’s responsibility to ensure that inflated estimates of capital expenditure are not rolled forward into the regulatory asset balance, lifting the revenue cap, and (b) the disincentive effects of a potentially intrusive approach that relies heavily on regulatory judgement.”

The linked approach historically adopted by the Tribunal, which relies on building up revenue requirements based on approved expenditures means that DNSP’s are not directly financial accountable for the delivery of services. As the Tribunal notes in the above extract, the relationship is one of regulatory judgement and difficult for all parties to predict.

The adoption of an “S-Factor” as an additional component of the cost linked price formula potentially introduces conflicting principles. The S-Factor enables a relationship between allowable revenues and reliability outcomes. However, the vast majority of revenues are linked to agreed capital and operating costs which will inevitably include impacts on underlying reliability levels. Therefore the inclusion of a reliability incentive requires the dissection of base case expenditures to identify expected reliability implications so that there is no duplication or exclusion of revenue impacts through the combination of the unlinked S-Factor and linked building block price levels. In Victoria this has been achieved through the identification of specific reliability targets relating to specific agreed reliability improvement projects. This is discussed further in appendix A.

The relative weightings of the linked and unlinked components will ultimately determine the incentives offered to DNSP’s for network investment. A low S-factor is likely to lead distributors to rely on the inclusion of capital investments in the regulated asset base and therefore respond to the general incentives of the building block methodology, i.e. “prudent investment”. A higher S-Factor weighting could lead to greater emphasis on the short-term opportunities

2 Discussion Paper DP-32 January 1999



offered by the incentive to gain revenues specifically from targeted reliability improvement projects.

4.4 VALUE OF LOST LOAD (VOLL)

There have been a number of studies undertaken within Australia to gain an understanding of the value that customers ascribe to lost electricity supply. Most of these studies were aimed at assisting in the regulation of energy markets to facility competition without exposing the market to catastrophic financial impacts. These VOLL figures are used to establish a ceiling for energy prices in the event of insufficient generation or transmission capacity.

Calculations of VOLL for the Australian energy market have generally been based on underlying costs of providing peaking generation capacity that is required for only small periods during the year. When translated to energy rates, the cost of these facilities is very high and these figures are then used to cap pool prices in times of supply constraints and also to offer incentives to market participants for demand and supply side initiatives.

The application of VOLL figures for the contestable energy market, however, can be quite different from those applying for distribution and transmission services. As is noted by VENCorp in its paper - Assessment of the Value of Customer Reliability (VCR), 20023.

“With respect to the issue of the “VoLL” wholesale market price cap as an indicator of the value of customer reliability that should be applied in transmission investment decision analysis, VENCorp recognises that risk management and other considerations may well, in practice, lead to the adoption of a value of VoLL for the wholesale market that is below the consumers’ true value of reliability. Any supply reliability issues arising as a result of the attenuation of the market price cap can be managed in the wholesale market through the occasional deployment of “reliability safety net” arrangements i.e. Reserve Trader Mechanism. These safety-net arrangements are designed to ensure the maintenance of a generation system reliability standard that is determined “outside of the market” by the NECA Reliability Panel.”

An alternative approach to measuring customer value is provided by “Willingness to Pay” studies undertaken in some states and by various distribution businesses within Australia. These studies seek to confront customers with hypothetical tradeoffs between combinations of various service and price packages - in particular, tradeoffs between reliability (lost supply) and network prices.

Results from these studies will inevitably provide a wide range of values depending on individual customer circumstances, particularly their end-use (i.e. industrial, commercial or residential applications). As indicated in section 4.2, the results may also vary depending on customers’ experiences with reliability. This may be due to the levels of dependence customers place on electricity supply as well as the relative elasticity of the demand curve. Depending on the location, it may be possible to attribute values to specific parts of the network that reflect these underlying differences. However, in many cases the interconnected and shared nature of the network obfuscates these differences meaning that averaging of values is required.

3 Assessment of the Value of Customer Reliability (VCR) – (http://www.vencorp.com.au/docs/elecplng/VCR%20Final%20Report.PDF)



Of the customer value studies identified during this review, most present findings in terms of customer satisfaction or acceptance levels and do not seek to derive specific values for unserved energy or availability of network services (reliability). For example, the UK study by BMR4 showed customers were willing to pay identified amounts for the combinations of specific features such as undergrounding and network refurbishment that would have impacts on reliability but would also have additional perceived benefits such as safety and aesthetic amenity. A UK study by MORI5 showed that 95 per cent of domestic customers, and 93 per cent of businesses were satisfied with their level of reliability, however residential customers would be willing to pay additional charges for prescribed network service improvements such as quicker supply restorations, undergrounding additional lines and improvements in telephone answering services.

Similarly, KBA provided studies for a number of Australian distributors, in particular one for Powercor in Victoria, which identified that customers would be willing to pay additional fees for changes to networks such as additional undergrounding of mains and the installation of ABC in place of bare conductors.

Importantly, these studies indicate that customers view electricity distribution not only from a reliability perspective but also in terms of physical characteristics such as aesthetic amenity, safety and environmental impact. This feature is of importance to the development of regulatory incentives as it demonstrates that any single measure, such as reliability, will not capture the full range of customer services. This issue is countered by the need to address the more important service criteria first and that additional complexity of any incentive schemes must be balanced against the materiality of the potential additional value received by customers and the impact on DNSP’s revenues.

The merit of these studies for the specific purpose of applying incentive rates for reliability to NSW DNSP’s is highly questionable as it is evident that customer values are highly variable based on many criteria. Overseas studies in particular, should be viewed as guides to sensitivities rather than setting indicative rates.

In terms of specific quantifications of the value to customers of reliability, the key studies that are often cited are the Monash6 and VENCorp7 reviews. These studies looked specifically at reliability and quantified the customer values by customer classes. A summary of these values is provided in the following table:

4 Market Research Ltd. (1999), "Customer Research to Support Norweb’s Year 2000 Price Review: A Report

prepared for NORWEB Distribution." 5 MORI. (1999), "Quality of Supply: Attitudes of Business and Domestic Electricity Customers". A research study

conducted for OFFER (Office of Electricity Regulator). 6 Monash VoLL Study - 1997 7 Assessment of the Value of Customer Reliability (VCR) – VENCorp, prepared by Charles River Associates (Asia

Pacific) Pty Ltd, December 2002.



Table 7 –Value of Customer Reliability (Unserved Energy) - $/MWh

Residential Commercial Agricultural Industrial Total

Monash 1997 740 75,860 96,190 11,190 28,890

VCR - VENCorp 2002

11,880 56,670 55,490 18,540 29,600

(Note: Figures shown in the above table have not been indexed)

These results show the dramatic variations between reliability values ascribed by different customer classes and also the significant variations between the studies for each customer class. The nature of the survey techniques adopted for these studies means that they are susceptible to the form of the survey and the demographics of individual customers surveyed. For customer value results to be considered applicable for specific distribution businesses they should be based on surveys for each NSW DNSP and for considerable rigour to be applied in the survey process to avoid distortions. In the VENCorp study, CRA noted that improvements could be achieved in future studies through:

• Use a trade-off method for estimating residential customer VCR.

• Increase the sample sizes for some sectors.

• Conduct Future VCR Studies NEM Wide

• Consider Wide-Area Impacts

It should be noted that these studies were not undertaken for determining customer values for distribution reliability. The VENCorp study, in particular, was aimed at enabling VENCorp to establish appropriate transmission-planning criteria. Whilst there are obvious synergies between distribution and transmission an optimal survey of reliability values for each sector is likely to have subtle differences in approach.

Nevertheless, the total values for reliability of between $30,000 and $35,000 per MWh (indexed to 2003 dollars) provides an indication of the likely upper limit that might be expected for distribution systems. Within these figures, however, there are clearly substantial variations that will reflect the types of customers by geographic location and the levels of reliability to which these customers have become accustomed.

In its submission to the ACCC regarding appropriate VOLL figures for the NEM, the NECA Reliability Panel proposed a value of $20,000. This figure is based on a defined level of agreed “unserved energy” of .002% for the NEM. Whether the design standard was determined based on the VOLL figure or vice-versa is uncertain, however the $20,000 remains an important element in the operations and economics of the NEM. The ACCC subsequently adopted a value of $10,000 as the VOLL for the NEM. In doing so the ACCC made the following comments:



“Following consideration of the issues, the Commission considers that the proposed VoLL Code changes may involve significant public detriment, primarily due to:

• the additional risk which a higher VoLL introduces to the market, which is not easily accommodated by market participants;

• concerns over how generator market power may manifest itself with a higher level of VoLL; and

• the likelihood of higher prices across the NEM as a consequence of the proposed increase in VoLL.”8

The ACCC recognised that there were potential economic benefits of the higher VOLL rate, but was concerned that this would introduce unacceptable market risks.

The variations in reliability values between customer classes create a considerable challenge for regulating reliability levels. Clearly it is not appropriate for a single rate to apply across large distribution areas. Likewise, as discussed in section 3 there is considerable interconnection of customers within urban and CBD areas, which means that values need to be averaged based on customer mix. However, the reliability incentive formula needs to be consistent with distribution pricing arrangements, which in NSW are highly averaged, across regional areas. Geographic based reliability incentives that reflect regional variations in customer mix and service levels are unlikely to be translated to regional prices and therefore the level of detail in determining the incentive is somewhat academic.

Despite these comments, any reliability incentive is likely to encourage DNSP’s to implement network changes that optimise their net financial position. If the incentives are applied as general averages, but the majority of reliability improvements are available for customers who ascribe lower reliability values (i.e. residential customers), then we are unlikely to see an optimal improvement in the total value of reliability across the network resulting from the incentive. This assumes that the average incentive rate does not equate to the average customer value for those customers impacted by a specific network investment. This situation is more likely to occur at the lower voltage levels and on radial feeders, both of which, as discussed in section 3 are expected to be lower priorities for DNSP’s and therefore should not substantially diminish the application of average reliability values. In Victoria, this concern was addressed through the separation of assets into categories of CBD, Urban and Rural, which are individually assessed.

Another feature of the value of reliability is that the nature of individual outages can have significantly different customer values. For example, longer duration outages tend to have higher values per MWh of unserved energy as customers are forced to make more expensive alternative arrangements. This result is illustrated in the Canadian study of system reliability worth9.

8 Determination - Applications for Authorisation-VoLL, Capacity Mechanisms and Price Floor, 20 December 2000 9 South Australian Independent Industry Regulator Information Paper No. 1 ELECTRICITY TARIFFS AND

SECURITY OF SUPPLY June 2000 - Tollefson, G., Billinton, R., Wacker, G., Chan, E. and Aweya, J. (1994), "A Canadian Customer Survey to Assess Power System Reliability Worth", IEEE Transactions on Power Systems, Vol. 9, No. 1, pp. 443-450.



Table 8 - Interruption Costs for Commercial and Business Customers in Canada ($ Canadian 1991)

(Note: The above table is provided as a broad reference to indicate the potential for customer value to vary substantially depending on outage duration. It is noted that these studies do not necessarily reflect the values ascribed by Australian (NSW) customers and may not have been conducted in a manner that is consistent with applying the results to NSW distribution reliability values. Also note that the 20 second duration probably should read 20 minutes.)

Whilst the values presented in the above table for $/MWh do not align with the VOLL figures presented earlier, the considerable variations is value depending on the outage duration are significant. In terms of establishing incentives for distribution reliability, greater priority needs to be assigned to preventing longer duration outages. This is discussed in section 3 and emphasises the need to ensure that systems are designed in ways that recognise the high customer value that is implied for preventing long duration supply interruptions. Whilst a reliability incentive may only effectively operate within a small band around existing reliability levels, general system design needs to address the very high values for major interruptions.

This issue also highlights the difficulty in setting incentives that operate as both financial opportunities and penalties. High frequency long duration interruptions might generally be regarded to be at the lower end (far left) of the supply/demand curve shown in Table 8. At this level the penalties for poor reliability need to reflect the lost value caused by significant supply interruptions. However, it may also be the case that the average costs of improving network supply security are lower and therefore the financial incentives offered to distributors that would reflect an efficient market outcome would be lower than customer value.

This relativity between value and efficient cost demonstrates the difficulty in assigning an average value for reliability that would apply across a full spectrum of reliability outcomes. In a fully unlinked approach the cost and value curves would need to be quantified and where the costs of reliability are lower than value (i.e. left of the equilibrium) the penalties for declining reliability might relate to customer value, whilst the rewards for improved reliability might be more reasonably be based on cost. This does not provide a symmetrical relationship and is heavily reliant on the ability to manage regulatory risk and data anomalies. It should also be noted that although Table 8 shows simple cost and value equations for reliability, there are in fact multiple equations and multiple dimensions for both cost and value.

Interruption CommercialCustomers Industrial CustomersDuration $/interruption $/kWh $/kW $/interruption $/kWh $kW

2 second 140.71 0.001214 0.2684 1,048.36 0.009129 0.90331 minute 170.87 0.00114 1.8847 1,192.85 0.009488 2.158520 seconds 400.45 0.002714 5.5764 1,721.04 0.010028 3.08871 hour 1,182.61 0.008088 15.065 3,323.21 0.012627 6.52642 hours 2,087.45 0.014191 31.6023 4,808.70 0.014752 11.57954 hours 4,325.91 0.032114 75.904 8,496.43 0.022505 23.80838 hours 7,806.86 0.055109 121.9695 14,820.69 0.042049 44.05971 day 17,138.70 0.114068 146.9024 24,707.79 0.056046 70.1317



5. PROPOSED S-FACTORS FOR NSW DNSP’S

5.1 CONTEXT FOR PROPOSED S-FACTOR RATES

Although there are many factors that could reasonably be incorporated into incentives for NSW DNSP’s through the pricing formula, the dominance of supply reliability as a critical customer outcome warrants its identification and inclusion.

The difficulties associated with recognising the link between reliability and either optimal costs or customer values make the inclusion of a single reliability S Factor coefficient very challenging. The most concerning issue is whether such an incentive will lead to optimal investment and management of networks by DNSP’s. As stated throughout this report, there are numerous reliability improvement techniques available to distributors and numerous customer value outcomes that will emerge as a result.

Nevertheless, it is likely that the adoption of a clear commercial relationship between DNSP revenues and reliability levels will strengthen the current regulatory framework for NSW and reduce regulatory uncertainty for all stakeholders.

The rates proposed in this report have been developed in line with the approach adopted by the Victorian Essential Services Commission, which incorporates an S Factor for each distributor into the regulatory pricing formula. The rates should be read in this context including aspects of asset roll-forward, data capture, minimum standards and exclusions.

The intention of the formula is to provide long-term signals to distributors for reliability changes that can be sustained for the benefit of customers.

Given that data capture systems for reliability are still being enhance by NSW DNSP’s it is appropriate that the implementation of an S-Factor for reliability be timed to coincide with agreed programs for such enhancements. Capital expenditure programs would nee to be framed in line with the operation of an S-Factor incentive to ensure consistency and compatibility with base capital expenditures.



5.2 PROPOSED RATES

Based on the proposal to adopt the Victorian S Factor methodology, the following rates are suggested as indicative levels for each NSW DNSP.

Table 9 – Proposed S-Factor Rates for NSW DNSP’s

S-Factor ($/MWh)

Planned & Unplanned

S-Factor ($/MWh)

Normalised

Energy Australia 4,000 4,000

Integral Energy 2,000 6,000

Country Energy 6,000 8,000

Australian Inland Energy 6,000 6,000

The rates in the above table reflect the revenue compensation calculation that is the basis for the Victorian S-Factor rates. They apply to the increase or decrease in USE above or below target levels for a particular period.

These rates reflect only the specified reliability improvement projects available to each DNSP. They do not capture the relativity between the base capital expenditure programs and the underlying levels of reliability. Care needs to be taken, therefore, in applying these rates for penalties. Such rates should reflect customer value lost. Any reductions in reliability would be difficult to assign to either the base capital works programs incorporated into the building block revenue determinations, or the reliability improvement projects identified. Therefore, any penalties for not achieving reliability target levels would need to be addressed in conjunction with the general capital expenditure programs of distributors under the normal building block methodology.

If the ESC Victorian approach is adopted, the penalty would be set at the marginal cost levels shown in Table 9 and would therefore provide a symmetrical incentive scheme. This does not effectively acknowledge the difference between value and cost at levels below the cost/value equilibrium. A penalty rate that is established at levels lower than those incorporated into some base capital expenditure projects may provide signals for DNSP’s to reduce expenditures and allow reliability levels to weaken. (i.e. the capital expenditure savings are greater than the penalty imposed). However, it might be assumed that DNSP’s would not be motivated to allow reliability levels to decline as this would impact on rolled forward asset values (which is likely to remain the fundamental commercial incentive) and could also incur customer dissatisfaction.

The underlying costs of reliability improvement projects are shown in section 3 to range from $2,000/MWh to $143,000/MWh (planned and unplanned outages) and VOLL figures may be in the range of $11,000/MWh to $60,000/MWh with an average of between $30,000/MWh and $35,000/MWh.



These rates suggest that figures in line with the proposed levels will only offer opportunities for DNSP’s to undertake modest improvement projects.

Based on the marginal cost rates, the revenues for each DNSP can vary by between 0.0118% and 0.0297% due to a one minute change in reliability (SAIDI) would yield the following additional revenues.

Table 10 – Revenue Sensitivities Resulting from a One Minute Change in Reliability (Planned and Unplanned SAIDI) – Marginal Cost Rates

SAIDI MWh/ Minute

Unserved Energy (MWh)

S-Factor ($/MWh)

$M/ SAIDI Minute


Energy Australia 102 58 5,916 4,000 0.2320 0.0297 Integral Energy 366 29 10,614 2,000 0.0580 0.0118 Country Energy 376 20 7,520 6,000 0.1200 0.0235


275 0.85 234 6,000 0.0051 0.0277

Table 11 – Revenue Sensitivities Resulting from a One Minute Change in Reliability (Normalised SAIDI) – Marginal Cost Rates

SAIDI MWh/ Minute


S-Factor ($/MWh)

$M/ SAIDI Minute


Energy Australia 102 58 5,916 4,000 0.2320 0.0297 Integral Energy 114 29 3,306 6,000 0.1740 0.0355 Country Energy 301 20 6,020 8,000 0.1600 0.0313 Australian Inland Energy

158 0.85 134 6,000 0.0051 0.0277

The tables shows that the impact of changes in reliability on overall DNSP revenues based on the proposed S-Factor rates are very modest. It would require substantial improvements in reliability levels for any material benefits to flow to DNSP’s. Conversely, reductions in reliability would have little effect on revenues but could be attributed to significant savings in capital expenditure and therefore may need to be separately reviewed during the period. (Note, however, the earlier comment that the potential short-term incentives to allow reliability to worsen are heavily countered by other factors. This is consistent with the Victorian experience, which has shown that distributors have generally tried to achieve improvements in reliability in response to the incentives offered).

In light of the very low marginal rates indicated in Table 10, it would be appropriate for the Tribunal to adopt rates that reflect the distributors’ marginal costs of providing reliability through their base capital programs (i.e. redundancy enhancement programs). This would be expected to provide increases in the S-Factor rates that approach the $10,000 per MWh “planned



and unplanned” or $15,000 per MWh “normalised” as shown in section 3. These rates are more likely to deliver the required incentives for reliability improvements but require considerably better data before finalising.

Table 12 – Revenue Sensitivities Resulting from a One Minute Change in Reliability (Planned and Unplanned SAIDI) – Marginal Costs for Redundancy Enhancement

SAIDI MWh/ Minute


S-Factor ($/MWh)

$M/ SAIDI Minute



275 0.85 234 10,000 0.0085 0.0462

Table 13 – Revenue Sensitivities Resulting from a One Minute Change in Reliability (Normalised SAIDI) – Marginal Costs for Redundancy Enhancement (Interim Stage)

SAIDI MWh/ Minute


S-Factor ($/MWh)

$M/ SAIDI Minute



158 0.85 134 15,000 0.0128 0.0693

Table 14 – Revenue Sensitivities Resulting from a One Minute Change in Reliability (Normalised SAIDI) – Marginal Costs for Redundancy Enhancement (Final Stage – Capped at VOLL)

SAIDI MWh/ Minute


S-Factor ($/MWh)

$M/ SAIDI Minute



158 0.85 134 30,000 0.0255 0.1386

In setting the initial rates, the value would need to be set high enough, to ensure that technologies that would be made redundant by higher coefficient values were not adopted, but sufficiently below the anticipated customer value



so as to avoid creating opportunities for Distributors to appropriate substantial consumer surplus. An initial value expressed on a whole of system basis, of $15,000 per MWh and rising to $30,000 per MWh (estimated VOLL) as and when reliability improvements materialise, should reasonably achieve both of these goals.



6. CRITICAL ISSUES

The adoption of reliability incentives is likely to have a significant impact on NSW DNSP’s. The experiences in Victoria indicate that even relatively modest incentives can result in significant new investments, changes to underlying design and construction standards, new employee performance measures, changes in maintenance practices and even greater focus on outage response management. The setting of the incentives and the operation of the pricing formula, therefore need to be fully considered before implementation.

This section sets out the critical issues identified by PB Associates and those experienced in Victoria and offers suggestions to manage these issues effectively without losing the potential benefits of appropriate reliability incentives for NSW DNSP’s.

6.1 DATA INTEGRITY

The establishment of a reliability incentive is a complex matter that essentially deals with a large number of factors that are difficult to accurately quantify. The collection and analysis of accurate and reliable data is a critical component to successfully establishing a meaningful and effective incentive mechanism.

A general process for establishing a value for a reliability incentive is as follows;

• Determination of historical reliability improvements

• Determination of the volume of unserved energy

• Review of the associated costs of these improvements

• Determination of the associated cost of unserved energy

• Compare costs to estimated customer value.

The first issue to be considered in reviewing the above process is the accuracy and consistency of the input data. It is preferable that the input data has been consistently collected to a common standard and definition.

PB Associates notes significant variability in the current reliability reporting process, systems and application amongst the NSW DNSPs. Of itself, this is not a major concern when establishing an incentive regime, as the mechanism acts on each business independently.

The primary concern in relation to reliability data accuracy is the consistency of the data over time. Unreliable historical data will impact the value setting process, whilst future variability will result in undeserved incentives or penalties on the target businesses.

It is foreseeable10 that a material improvement in the reliability data capture and reporting mechanisms of the DNSPs will result in a sustained change in

10 Reference: PB Associates report to IPART - Review of NSW Distribution Network Service Provider’s

Measurement and Reporting of Network Reliability. URL: http://www.ipart.nsw.gov.au/pdf/Rp24.pdf.



reported reliability. PB Associates considers that a material change in the capture and reporting processes will require review and possibly a reset of the incentive targets.

PB Associates considers that the systems and processes that have been identified for implementation over the next 2 years by the NSW DNSPs have the capacity to significantly improve the accuracy and consistency of the reliability data as well as the breakdown across feeder categories.

6.2 LONG TERM NATURE OF RELAIBILITY IMPROVEMENTS AND EXPENDITURES

A feature of electricity distribution assets is that they generally have long operational lives. Key assets such as poles, wires and substations have prescribed depreciable lives of up to 40 years which transcend a number of regulatory determinations. In providing a financial incentive for DNSP’s to invest in such long life assets requires careful consideration of the impact those assets will have on the network and the risks associated with recovery of those costs over time.

DNSP’s operate under well-defined design and construction standards that have been developed over time. The addition of a final incentive relating to reliability is likely to alter the focus of these standards, perhaps only slightly, in favour of reliability outcomes. These changes may only be subtle but will have long term impacts on the underlying costs of network construction and maintenance practices. Over time these changes will come to form part of the base capital and operating costs of DNSP’s and therefore the base reliability outcomes. This will make it necessary to manage the separate identification of base and S-Factor reliability components to prevent duplication of cost recovery.

The life of assets over numerous regulatory periods also means that there is regulatory risk associated with reliability investments. The implementation of the S Factor needs to minimise the uncertainty of returns that relate to bonafide on-going reliability improvements. This is perhaps best achieved by accruing these investments into the regulatory asset base in future reviews based on prescribed threshold criteria. In practice the relatively small S-Factor incentives are likely to be swamped by the more substantive effect of the inclusion of investments into the rolled forward capital base.

An additional issue relating to the possible long-term nature of some reliability investments is that reliability improvements may not occur immediately. In many cases, the investment in reliability will be based on probabilistic estimations of improvements, such as reduced exposure to storm impact or higher levels of redundancy in case of equipment failure. On a highly diversified network gradual investments in these areas will have an average incremental impact on overall measures of reliability. However, in many cases the “lumpiness” of the investments will only average out over time, in terms of both cost and reliability. The operation of an S Factor needs to recognise that optimal investments for reliability may not simply be short-term projects, but will often include longer-term strategies. Whether this is best addressed through the base target reliability projections or through the S-Factor incentive will be somewhat a measure for regulatory judgement, but again highlights one of the



6.3 VARIATIONS IN THE VALUE AND COSTS OF CUSTOMER RELIABILITY

The two key VOLL studies reference in section 4.4 demonstrate the wide variations between customer values for reliability (i.e. unserved energy). This range has considerable impact on the formulation of reliability incentives that may be offered through the regulated pricing formula for distributors. The maximum levels of any incentives need to reflect the underlying customer mix for each distributor. However, this alone does not ensure that the value is suitable for individual reliability investments. The interconnected nature of distribution systems in urban and CBD areas will mean that the average loads by customer type can be used to generate weighted average customer value figures for reliability. For more radial networks, the specific customer profiles need to be determined if customer reliability values are to be applied for incentive purposes. This is required so that reliability improvement projects can properly be assessed against the customer value potential for those customers affected.

This issue highlights one of the difficulties in applying cost based measures of reliability in the absence of some appreciation of the underlying customer value. An optimal reliability incentive will encourage improvements to occur firstly in areas where the customer value most exceeds the costs. A more general reliability incentive would encourage DNSP’s to invest in areas where there is likely to be the greatest impact on reliability, regardless of the underlying customer value derived.

The data available for this draft report was not sufficient to enable a detailed analysis of areas where there may be highest reliability value margins. However, at a more aggregated level the following table provides some indication of the impact on customer reliability values of the various NSW DNSP’s customer load mix.

Table 15 - NSW DNSP Weighted Average VOLL Based on VENCorp Study ($2003/MWh)

Residential Commercial Industrial Weighted Average

VENCorp Sector VOLL's 12,199 58,194 19,039 30,386 Australian Inland 26.11% 35.63% 38.26%

3,185 20,734 7,284 31,203

Country Energy 46.38% 42.07% 11.55%

5,658 24,482 2,199 32,339

Energy Australia 37.66% 46.15% 16.19%

4,595 26,855 3,082 34,532

Integral Energy 34.57% 28.52% 36.91%

4,217 16,597 7,028 27,842



The figures provided in the above table can only be viewed as indicative estimations of VOLL based on the VENCorp study. A comparison of the above figures for Energy Australia against figures provided in their submission to the Tribunal, which were based on the 1997 Monash study results, shows considerable variations.

Table 16 – VOLL Comparison - EA Submission and VENCorp Study

Customer Class EA

Submission VENCorp

extrapolation Variance

$/MWh $/MWh $/MWh

Overall $23,830 $30,400 $6,570

Domestic $5,700 $12,200 $6,500

Commercial $60,770 $58,190 ($2,580)

Agricultural $66,100 $56,980 ($9,120)

Industrial $23,770 $19,040 ($4,730)

The comparison shows that the VENCorp overall result is considerably higher than the figure derived by EA from the Monash 1997 study. This is not consistent with the findings of PB Associates and requires further analysis.

Nevertheless, the table does show that a range of VOLL figures is likely to apply for NSW distributors depending on the load mix of customer classes.

In addition to the variations in values between customers there is also a high probability that reliability values will vary as reliability levels change. A non-linear relationship between value and reliability could mean that customers in different geographic areas that currently receive different levels of reliability may ascribe different values to changes in reliability, regardless of end use or customer class.

As with the effects of variations in customer values of reliability relating to customer class and end use, non-linear reliability relationships suggest that some disaggregation of data in terms of both reliability costs and values would be needed to enable optimal network solutions to be developed.

6.4 RESPONSES BY DNSP ‘S

As mentioned throughout this report, DNSP’s already have a number of commercial incentives that guide their management. In particular, the use of a linked “Building Block” approach to price regulation by IPART means that DNSP’s can achieve improved performance through cost minimisation between regulatory determinations. This performance is clearly subject to other operating disciplines relating to good organisational governance, technical standards, safety and specific distribution licence requirements.



The inclusion of an S Factor for reliability introduces incentives for possible alternative behaviours from DNSP’s to achieve optimal financial outcomes. For this reason the formulation of a financial reliability incentive must recognise the incentives created. Some of the key issues that need to be thoroughly considered are:

• An aggregate incentive versus geographically or customer segmented rates – a general aggregated reliability incentive is likely to encourage DNSP’s to explore opportunities to improve reliability by the maximum amount, regardless of the number or type of customers impacted. On a fully interconnected network this potential for inefficient outcomes is reduced, however on more radial networks the specific customers and their reliability value ascriptions could be material.

• Black spot versus general improvements – it is generally the case that improvements in “black spot” areas of reliability where current levels are below prescribed minimum standards require expenditures in excess of the value derived by those customers. As with many network issues, there are substantial cross subsidies between customers in terms of value and price. Expenditures for improving in black spot areas cannot be regarded in the same way as a reliability incentive as such an incentive would be intended to encourage distributors to improve the overall mix of value and cost, i.e. improve reliability in areas where costs are less than the value derived by customers. Black spot expenditures would need to form part of the overall capital expenditure program and recovered through the general price formula components. Reliability levels relating to these customers would be best regulated through specific minimum standards such as numbers and duration of outages with prescribed penalties for non-achievement.

• Data management – a critical issue for the Tribunal is how to ensure that appropriate data is captured and reported. As with any financial incentive, there will be a desire for DNSP’s to optimise their financial outcome, which means presenting data in the most favourable manner permitted. Ensuring clear data definitions and rigorous data capture are essential aspects of the process. The Tribunal needs to specify precise data requirements and a timeframe for DNSP’s to comply.

• Demand management – one aspect that is not well defined within the regulatory process is the inclusion of demand management initiatives. Some uncertainty exists for DNSP’s regarding the treatment of capital expenditures relating to supply solutions that do not involve network augmentation. This same issue arises for reliability improvements, which may be able to be achieved through non-network solutions. Such initiatives need to be supported by any reliability incentive program. If reliability incentives are calculated based on network augmentation solutions and subsequently assessed on that basis this may not provide for alternative improvement opportunities. Incentives that are available to DNSP’s regardless of the final solution should be considered in the incentive program.

• Short term versus long term reliability programs – the nature and operation of the Tribunal’s reliability incentive in the pricing formula can influence the types of projects undertaken by DNSP’s. Longer-term projects that span multiple pricing determinations will be subject to the regulatory risk offered through the formula. If there remains uncertainty regarding acceptance of reliability capital expenditures then DNSP’s may choose to undertake



shorter term projects that can deliver reliability improvements more quickly. This may not be in the best interests of customers and this balance needs to be achieved through the incentive program.



APPENDIX A Victorian S-Factor



Victorian S-factor analysis

This section describes the Victorian electrical distribution incentive scheme that was established under the Electricity Distribution Price Determination of 2001 to 2005. The areas reviewed include the scheme’s establishment, the outcomes of the scheme and the identified problems that have been experienced.

Background

The Office of the Regulator General (now the Essential Services Commission) introduced financial incentives to encourage Victoria’s distributors to meet and/or exceed target levels of reliability over the 2001 to 2005 price control period. The scheme consisted of two primary components;

• A factor (S) included in the price control formula; result CPI – X + S. If a distributor provides an overall average level of reliability above targeted levels, distribution tariffs will increase across subsequent years. Should average reliability be below target levels, distribution tariffs will fall. The incentive has been set at the estimated marginal cost of reliability for each distributor.

• Guaranteed payments to customers using less than 160 MWh/yr for low reliability. Customers are to receive a credit of $80 if they experience more than a specified number of interruptions in a calendar year. This figure is set at 9 interruptions per annum for urban customers and 15 for rural customers. All customers receive the $80 credit if they experience a single interruption of greater than 12 hours duration.

Scheme Design

In the Victorian scheme it was decided to adopt only reliability of supply indicators as reliability was considered the primary concern of customers. The indicators utilised were;

• Unplanned interruption frequency

• Duration of unplanned outages

• Minutes of supply for planned outages

The setting of the targets required that the indicators be robust and with sufficient history to enable the setting of realistic targets. The Victorian distributors had 3 years of reported data at the high voltage feeder level and 5 years of solid data at the distributor level.

The ESC would have liked to have included momentary outages within the S factor scheme, however the available information was deemed not to be sufficiently robust.

The Victorian targets were set by the distributors with much discussion concerning the sustainability of the targets with the ESC. The process was cognizant of the need for the data to be accurate and reliable to ensure that any bias in reporting was translated to both targets and actuals. The Victorian distributors databases provide connectivity for each customer to the



distribution substation level. This provides for a high degree of accuracy in determining the number of customers interrupted by an outage of most network elements.

Targets were then set for the distribution feeders across the 3 categories of CBD, Urban and Rural to minimise the potential for the scheme to encourage improvement in one category to the detriment of another.

Each indicator was assigned a weighting. The intent of the weighting was to reflect a customers’ perspective of the relative important of each factor. Interruption frequency was weighted at 100%, duration at 75% and planned minutes off supply at 25%.

The Victorian scheme recognised the need to balance the incentive; i.e. not to over-state or under-state the incentive. As part of achieving a balance, the revenue at risk was set at the incremental cost of carrying out a reliability improvement project ($/MWh). The information leading to this cost was drawn from the distributor’s submissions to the Electricity Distribution Price Review 2001 to 2005. The reliability improvement projects were also analysed for the ESC to test the prudency of the proposed expenditure.

It should be noted that a linear cost of reliability improvement was assumed for the purposes of simplicity. It is generally recognised that the cost of reliability improvement is not linear, but it was considered that acting within a narrow band of reliability would minimise any assumption errors.

An adjustment factor was applied to ensure that the net present value of the revenue allowed in the price determination for reliability improvements would be returned to customers in full by the service incentive scheme if there were no overall improvement in reliability throughout the regulatory period.

A cap on revenue charges was not set due to the natural annual variability of reliability. Concerns that a cap would leave the scheme without effect when the cap was reached or exceeded were also considered. The use of dead bands was also considered, but not adopted, primarily due to the added complexity and perceived minimal value of such a system.

The reward (or penalty) associated with the incentive scheme is retained for a period of 5 years – in line with the efficiency carry-over mechanism. This was deemed necessary as the S factor acts to change revenue in the year of application and in all subsequent years. After 5 years the increase revenues are returned to consumers through lower network prices and reduced revenues due to penalties are cancelled.

One important item of note is that the calculation of the incentive includes the gap between targets and actual performance in the previous year (t-1) less the gap in the year before that (t-2). By acting on the cumulative difference between actual and target performance, the scheme only rewards (or penalises) long-term systemic changes in performance. Natural variations about a trendline result in revenue fluctuations that average to zero (ignoring the time value of money).

Asymmetric risk was an issue of major importance to the Distributors. It was argued that performance can suffer considerably due to weather conditions whilst random improvements were likely to be of a lesser quantum. It was



decided by the ESC that asymmetric incentives could lead to a distortion of the scheme in that it would not satisfy both of the following conditions;

1) Ensuring that a failure to meet targets would result in the revenue allowed in the price determination for improvements to be returned to customers, and

2) That the cost of reliability improvements above that allowed for in the initial targets would be fully funded through the incentive scheme.

To mitigate the potential impact of the asymmetric risk, the ESC determined to apply a set of exclusion criteria that excluded certain events. Under the exclusion provisions, distributors may apply to the ESC to exclude certain events from the S factor calculation or the obligation to make guaranteed payments for low reliability.

ESC Exclusion Criteria;

• Supply interruptions made at the request of the distribution customer affected;

• Load shedding due to a generation shortfall;

• Load interruptions caused by a failure of the shared transmission network;

• Load interruptions caused by a failure of transmission connection assets, to the extent that the interruptions were not due to inadequate planning of transmission connections; and

• ·Widespread interruptions due to rare events, which were not reasonably able to be foreseen and, to the extent that the distribution business was not reasonably able to mitigate their impact.

Victorian S-Factors and Incentive Rates

The following table is an extract from ESC’s price controls embodied in the Electricity Distribution Price Determination 2001 – 2005. The table describes the reliability performance targets upon which the incentive payments are applied.



Table A17 - ESC Reliability Performance Incentive Targets

ESC RELIABILITY PERFORMANCE TARGETS 2001 2002 2003 2004 2005

CBD CITIPOWER PTY Planned minutes off supply 5.9 5.9 5.9 5.9 5.9 Planned minutes off supply 0.25 0.25 0.25 0.25 0.25 Unplanned interruption duration 63 63 63 63 63

URBAN AGL ELECTRICITY LIMITED

Planned minutes off supply 6 6 6 6 6 Planned minutes off supply 1.42 1.36 1.32 1.3 1.27 Unplanned interruption duration 60 59 59 58 58 CITIPOWER PTY Planned minutes off supply 9.9 9.9 9.9 9.9 9.9 Planned minutes off supply 0.95 0.92 0.89 0.85 0.8 Unplanned interruption duration 59 55 51 48 44 POWERCOR AUSTRALIA LTD Planned minutes off supply 20 19 18 17 16 Planned minutes off supply 1.7 1.68 1.67 1.66 1.63 Unplanned interruption duration 68 66 64 62 60 TXU ELECTRICITY LIMITED Planned minutes off supply 9 9 9 9 9 Planned minutes off supply 1.94 1.9 1.86 1.82 1.78 Unplanned interruption duration 60 60 60 60 60 UNITED ENERGY LTD Planned minutes off supply 15 14 13 13 13 Planned minutes off supply 1.46 1.34 1.26 1.17 1.06 Unplanned interruption duration 60 59 58 57 56

RURAL AGL ELECTRICITY LIMITED

Planned minutes off supply 14 14 14 14 14 Planned minutes off supply 2.38 2.38 2.25 2.25 2.25 Unplanned interruption duration 51 51 50 50 50 POWERCOR AUSTRALIA LTD Planned minutes off supply 60 58 56 53 51 Planned minutes off supply 3.15 3.01 2.89 2.76 2.64 Unplanned interruption duration 86 85 84 82 81 TXU ELECTRICITY LIMITED Planned minutes off supply 39 39 39 39 39 Planned minutes off supply 3.91 3.74 3.56 3.39 3.22 Unplanned interruption duration 69 69 69 69 69 UNITED ENERGY LTD Planned minutes off supply 24 22 21 21 21 Planned minutes off supply 2.82 2.56 2.4 2.24 2.03 Unplanned interruption duration 50 50 49 48 47

The following table is an extract from ESC’s price controls embodied in the Electricity Distribution Price Determination 2001 – 2005. The table describes



the incentive rates for the reliability incentive mechanism. The rates are applied to the gap between actual and target performance.

Table A18 - Reliability Performance Incentive Rates

INCENTIVE RATES (sr,n) AGL ELECTRICITY LIMITED Urban Rural Unplanned interruption frequency %/0.01 interruptions 0.0236 0.0013 Unplanned interruption duration %/minute 0.0364 0.004 Planned minutes off supply %/minute 0.0099 0.0006 CITIPOWER PTY CBD Urban Unplanned interruption frequency %/0.01 interruptions 0.0268 0.0318 Unplanned interruption duration %/minute 0.0068 0.0333 Planned minutes off supply %/minute 0.0105 0.0135 POWERCOR AUSTRALIA LIMITED Urban Rural Unplanned interruption frequency %/0.01 interruptions 0.035 0.0233 Unplanned interruption duration %/minute 0.0572 0.0555 Planned minutes off supply %/minute 0.013 0.0068 TXU ELECTRICITY LIMITED Urban Rural Unplanned interruption frequency %/0.01 interruptions 0.0152 0.0232 Unplanned interruption duration %/minute 0.0321 0.0859 Planned minutes off supply %/minute 0.0064 0.0084 UNITED ENERGY LTD Urban Rural Unplanned interruption frequency %/0.01 interruptions 0.0287 0.0018 Unplanned interruption duration %/minute 0.0458 0.0067 Planned minutes off supply %/minute 0.0121 0.0009

Victorian S-Factor Outcomes

The S factor scheme has been operating for 2 years with the 2002 results yet to be analysed.

• The 2001 S factors (expressed as a percentage of revenues) were; -0.02%, 0.76%, 1.29%, 2.30% and 1.95% in a year that was considered to have had reasonably benign weather.

• The large S factors have resulted in greater than expected volatility in customer prices, with one distributor exceeding the rebalancing constraint of 2% due to the S factor alone.

• In 2001 and 2002 four distributors achieved a bonus with the 5th suffering a small penalty.

• PB Associates has been advised that some distributors are linking managers’ performance bonuses to the outcomes of the scheme.

• The ESC has been presented with some evidence that the scheme is encouraging distributors to reduce the risk of penalties by addressing the volatility of reliability performance, particularly those external events that can be impacted by design changes such a fully insulated substation structures to reduce exposure to animals and wind-blown debris, and improved response times to fault repairs.



• The ESC (and its consultants) have expressed difficulty with the wording of the final exclusion provision; particularly the use of the words “rare” and “mitigate”. A strict definition of a “rare” event has not been possible whilst “mitigate” has been interpreted to related to potential action both before and after the event in question.

• It has been recognised that the average reliability figures that were utilised for the creation of the reliability targets were inclusive of some events that would now be considered exclusion events.

Future Considerations for Victorian S-Factor Scheme

• The ESC is presently considering some form of smoothing to limit the potential volatility that can occur due to the incentive outcomes.

• The potential still exists to incorporate an incentive for momentary outages into the S factor scheme.

• The final exclusion provision will likely be redesigned to incorporate a “reasonableness” provision and remove the use of the word “rare”. Alternatively, a 3 minute SAIDI cap is also being considered.

• The ESC may consider revising the assumption of a linear cost of reliability improvement.

Victorian Exclusion Scheme

This information contained draws heavily on the previous workings and reports that PB Associates has made to the ESC in relation to outage exclusions.

In the Electricity Distribution Price Determination (EDPD) of September 2000, the Office of the Regulator General (now the ESC) introduced an incentive mechanism whereby distributors were rewarded for high levels of supply reliability and penalised if the reliability of supply did not meet agreed targets.

This incentive mechanism had two separate components:

1. A reliability incentive scheme that adds an “S-factor” to the traditional CPI-X revenue formula. The S factor is positive, thereby increasing allowed revenues, if the reliability of supply exceeds agreed targets and negative if the reliability does not meet the targets. This scheme is described in clauses 2.3.8 – 2.3.11 of the Price Controls embodied in the Office’s Electricity Distribution Price Determination, which came into effect on 1 January 2001.

2. A guaranteed payment scheme whereby a distributor makes payments to any customer when the reliability of the electricity supply to that customer does not meet a guaranteed minimum level. This scheme is described in clause 6 of the Electricity Distribution Code (Code).

In introducing this scheme, the ESC noted in the EDPD that it “accepts the general point that distributors should not bear risks arising from certain events over which they have no control or are unable to mitigate the impact thereof”1. The ESC further states that its preferred approach was to “exclude a certain



event from the operation of the incentive mechanism where it can be clearly demonstrated that it:

a) was outside the control of the distributor and the distributor was unable to mitigate the impact of the event; and

b) had a material impact on the distributor’s reliability performance”.

This lead to the design of dual schemes that provides for certain categories of supply interruption not be counted when calculating the level of supply reliability for the purposes of determining the position of a distributor in relation to either scheme. However, if a distributor wishes certain supply interruptions to be excluded from the scheme, it is required to make formal application to the ESC stating the grounds on which it believes the interruptions should be excluded.

The criteria for exemptions in relation to the Service Incentive scheme are contained in clause 2.3.11 of the ESC’s Document “Electricity Distribution Price Determination 2001- 2005 – Volume II Price Controls”. This states:

On approval by the Office, the impact of the following events will be excluded:

(a) supply interruptions made at the request of the distribution customer affected;

(b) load shedding due to a shortfall in generation;

(c) supply interruptions caused by a failure of the shared transmission network;

(d) supply interruptions caused by a failure of transmission connection assets, to the extent that the interruptions were not due to inadequate planning of transmission connections; and

(e) widespread supply interruptions due to rare events, which were not reasonably able to be foreseen and, to the extent that the distribution business was not reasonably able to mitigate their impact.

Criteria for exemptions from the supply restoration and low reliability payment scheme are contained in clause 6.3 of the Electricity Distribution Code. This states:

“….on application from a distributor the Office will excuse the distributor from making a supply restoration payment or a low reliability payment if the Office is satisfied that the obligation to make the payment arises from an interruption which relates to:

1. load shedding due to a shortfall in generation;

2. a failure of the shared transmission network;

3. a failure of a transmission connection, but only to the extent that the interruption is not due to inadequate planning of transmission connections; and



4. widespread supply interruptions due to rare events, which are not reasonably able to be foreseen, but only to the extent that the distributor is not able to mitigate the impact of such interruptions on customers.”

Clause 2.3.11 (e) of the Price Controls and clause 6.3 (d) the Code both specify three tests that must be applied to any application for an exemption:

1. there must have been widespread supply interruptions;

2. these supply interruptions must be due to rare events; and

3. the rare events must not reasonably be able to be foreseen by the distributor.

These tests follow naturally one from the next and should therefore be applied in a logical sequence. It is clear from the wording that any application must pass all three tests before it qualifies for an exemption. The other important issue is the question of mitigation. However mitigation does not determine whether an application qualifies for an exemption but, rather, allows the Office to grant only partial relief where a distributor’s management of the situation resulted in the consequences being more severe than they need have been.

Test 1 – Wide Spread Interruptions

The Australian Concise Oxford Dictionary defines widespread as “widely distributed or disseminated”.

PB Associates has interpreted this definition such that an application for exemption should only be granted if the interruptions referred to in the application were either large in number or covered a large area. In this context we would suggest that:

1. The application should be considered in relation to the number of customers interrupted and the area covered by the interruptions and not in relation to the quantity of electricity not supplied. An application covering a small number of large industrial customers would fail even though it could result in a significantly greater loss of revenue to the distributor than a successful application affecting a large number of small residential customers.

2. PB Associates has recommended a subjective view on what constitutes a large number or what is a large area. It is not unreasonable to make this interpretation in relation to the circumstances of the application. For example it can be expected that a successful application covering interruptions in a rural area would involve fewer customer interruptions over a much wider area than would be the case in an urban situation.

3. It is not necessary for all the interruptions embodied in an application to occur at the same time or for the areas where the interruptions occur to be contiguous. For example, it would be legitimate for a distributor to amalgamate a series of separate interruptions due to a rash of bush fires or extensive and prolonged storm activity into a single application.

Test 2 – Rare Event



The next step is to determine whether the disruptions were caused by “rare events”. In applying this test the key word is “rare”. The Australian Concise Oxford Dictionary defines “rare” as seldom occurring, uncommon, unusual, or few and far between.

In applying this test it is necessary to consider the frequency with which the events can be expected to occur. In doing this, it should take account of all the circumstances surrounding the events that gave rise to the application.

By way of example, a single bush fire of average intensity in a designated bush fire area is unlikely to be considered on its own to be a rare event since such bush fires are frequent and predictable. However a series of bush fires comparable to the Ash Wednesday fires, taken together would undoubtedly be considered rare. In other circumstances location could have a bearing. In a New Zealand context, for example, a low intensity earthquake in the Wellington region is unlikely to constitute a rare event since Wellington lies on a known fault line. However an earthquake in Victoria of similar intensity could be considered rare since earthquakes in this area are much less common.

Test 3 - Not Reasonably able to be Foreseen

This is a difficult test to apply since all events, no matter how improbable, can be foreseen by a person with sufficient imagination. Hence they key to the application of this test is the interpretation of the word “reasonably”. The Australian Concise Oxford Dictionary defines “reasonably” as having sound judgment, not greatly less or more than might be expected.

PB Associates proposed that the ESC interpret the word “reasonably” in the context of what distributors are required to do in order to comply with the terms of their Distribution License. Of particular relevance is the license requirement to comply w ith the Electricity Distribution Code. Clause 3.1 of the Code, requires distributors to use good asset management practices in the development and operation of their distribution network assets. In applying this interpretation the Office would be saying, in effect, that an event is reasonably foreseeable if the possibility of the event occurring would have been predicted, and allowed for, by a distributor applying good asset management practices in accordance with clause 3.1.

More specifically, the Code requires that a distributor must use best endeavours to:

1. “develop and implement plans for the acquisition, creation, maintenance, operation refurbishment, repair and disposal of its distribution system assets …..to minimise the risks associated with the failure or reduced performance of assets; and

2. “develop, test or simulate and implement contingency plans to deal with events which have a low probability of occurring, but are realistic and would have a substantial impact on customers”.

On this basis the test as to whether or not the events could have been reasonably foreseen becomes one of whether or not the distributor could have avoided the widespread outages by prudent asset management. Prudent management requires that it be in accordance with good distribution engineering practice and also that it be economically sensible. In this context, the Office should apply three further tests:



1. Should the distributor have foreseen the events that gave rise to the widespread outages.

2. If it is determined that these events should have been foreseen the next issue is whether they could have been avoided by the implementation of prudent asset management plans.

3. If it is determined that the events could have been avoided by prudent asset management plans then the Office will need to consider whether such plans were in place and whether they were properly implemented.

It could be argued that this third test is unnecessary on the basis that, if it was possible to avoid the interruptions by the implementation of prudent asset management plans, the distributor should have been able to prevent the interruptions from occurring. It is PB Associates’ position that this approach is too severe since it is possible for well designed and implemented plans to fail on account of circumstances outside the distributor’s control. In such circumstances it may be appropriate that a distributor is not penalised.

Mitigation

Finally the Victorian regulations require that relief only be granted to the extent that the distribution business was not reasonably able to mitigate the impact of the events that gave rise to the widespread interruptions. With respect to mitigation there are some differences in the wording of clause 2.3.11 (e) of the Price Controls and clause 6.3.4 (d) of the Code.

Specifically:

• Clause 2.3.11 (e) of the Price Controls states: “widespread supply interruptions due to rare events, which were not reasonably able to be foreseen and, to the extent that the distribution business was not reasonably able to mitigate their impact”.

• Clause 6.3.4 (d) of the Code states: “widespread supply interruptions due to rare events, which are not reasonably able to be foreseen, but only to the extent that the distributor is not able to mitigate the impact of such interruptions on customers”.

From a practical perspective the only significant difference in the wording is that the Code does not explicitly require the mitigation to be “reasonable”. However PB Associates considers the requirement of reasonableness to be implicit in the Code. If this is the case, there is no significant difference in the meaning of the two clauses from the perspective of analysing application for exemption from the service incentives.

The Australian Concise Oxford Dictionary defines mitigate as making milder, or less intense or severe, or moderating. In the context of the Victorian regulations, mitigation deals with whether the management of the event after it happened was appropriate.

The impact of events that pass the regulatory tests have on customers will largely be determined by how well the distributor manages the situation after it occurs. It is possible that supply interruptions turn out to be more severe than



they might have been because a distributor makes an error that should have been avoided or, alternatively because it did not do something that it should have done.

In determining the question of mitigation PB Associates would review the distributor’s management of the event(s) to determine whether it/they were appropriate. If it is determined that the situation was made worse because of the actions of the distributor, PB Associates would recommend to not allow the claim only to the extent of the interruptions that would have occurred had the distributor managed the situation in a prudent and competent way.

It should not be the purpose of this test to determine whether, in hindsight, the distributor minimised the consequences in every way possible. Rather the test should determine whether, based on the information available at the time, the actions taken and the decisions made were appropriate and in accordance with good operational practice.

Victorian Exclusions and Outcomes

The following is a summary of exclusion application made by the Victorian DNSPs and the decisions made by the ESC. The data is drawn from the ESC exclusion decisions published on the ESC website.

CitiPower application – 2 January 2001

On 31 December, one of two feeders supplying zone substation JA from West Melbourne Terminal Station was removed from service by CitiPower due to low oil pressure. The CBD network was reconfigured to supply JA from a second feeder.

On the morning of 2 January, an alarm indicated that one of the feeders supplying JA (WMTS-VM3) was loaded beyond its continuous rating. CitiPower removed load from that feeder, leaving JA supplied from one feeder (WMTS-VM2). WMTS-VM3 was available as a back-up.

Following a rapid unanticipated increase in load, the WMTS-VM2 feeder was interrupted twice, following operation of the protection system for that feeder. After the first interruption, CitiPower physically patrolled the feeder and was unable to locate a fault, and therefore returned the feeder to service. After the second interruption, the WMTS-VM2 feeder was left out of service for safety reasons.

CitiPower then temporarily supplied JA solely from the WMTS-VM3 feeder while it proceeded to further reconfigure the network to supply JA from Richmond Terminal Station.

The WMTS-VM3 feeder is not normally used to supply JA and was only able to carry the full load connected to JA if one cable circuit (VM-W) was loaded well beyond its physical rating. This cable – which had been damaged some years before by a ‘dig-in’ – failed.

This left the western part of the CBD supplied only from one feeder from the West Melbourne Terminal Station, necessitating load shedding from the other zone substations supplying the CBD. Supply was progressively restored after the load on JA had been transferred to the Richmond Terminal Station.



The events of 2 January resulted in a loss of supply to 12,200 customers for an average of 32 minutes each. The impact on average minutes off supply for the CBD was 11.5 minutes.

The Office has concluded that the interruptions of 2 January were widespread and were caused by events that appear to have been (in combination) rare.

However, the interruptions would have been prevented had the protection setting on cable VM2-JA matched the physical rating of the cable. Because it did not, WMTS-VM2 tripped twice, interrupting supply to zone substation JA and contributing to the failure of cable VM-W.

It is normal practice for the protection setting to match the physical rating of the cable circuit. CitiPower has not adequately explained why it did not in this case.

The events that caused the interruptions of 2 January therefore do not justify the granting of an exclusion from the S factor. The Office therefore proposed not to approve CitiPower’s application for the interruptions in the CBD on 2 January to be excluded from the S factor.

TXU application – 12 February 2001

The interruptions that were the subject of TXU’s application were the result of an accident involving the overturning of a LPG tanker on the Princes Highway. The Country Fire Authority asked TXU to interrupt supply within a five kilometre radius of the incident.

The Office has concluded that the interruptions were widespread and resulted from an event that was rare and not reasonably foreseeable, and the impact of which was not able to be mitigated.

The Office proposed to approve TXU’s application for an exclusion from the obligation to make guaranteed payments to the 117 customers who experienced interruptions of more than 12 hours on the 12th and 13th of February 2001; and approve TXU’s application for the interruptions of the 4,389 customers affected to be excluded when determining the eligibility of the affected customers for payments for receiving interruptions in excess of the relevant frequency threshold.

Powercor Application – 24 May 2001

On 24 May 2001, Powercor Australia applied to have interruptions caused by a fault on 10 April 2001 at SPI PowerNet’s Brooklyn terminal station excluded from the calculation of the S factor. The grounds for the application are that the interruptions were caused by the failure of transmission connection assets, and were not due to the inadequate planning of transmission connections.

The Commission approved this application.

TXU and United Energy Application – 10 September 2001

On 10 September 2001, TXU and United Energy jointly applied to have interruptions caused by a fault on the No 1 bus at Ringwood terminal station on 30 July 2001 excluded from the calculation of the S factor. The grounds for the application were that the interruptions were caused by the failure of



transmission connection assets, and were not due to the inadequate planning of transmission connections.

The Commission approved this joint application.

CitiPower Application – 16 November 2001

On 16 November 2001, CitiPower applied to have interruptions caused by a loss of supply on 23 October 2001 to four feeders supplied from West Doncaster zone substation, which is owned and operated by United Energy, excluded from the calculation of the S factor and, if required, from the obligation to make low reliability payments. The application was made on the grounds that the interruptions were material, outside the control of CitiPower and could not be mitigated.

The Commission did not approve this application on the ground that the application did not meet the requirements for exclusion, in particular that assets at United Energy’s West Doncaster zone substation were not transmission connection assets.

AGL and CitiPower Application s– 30 November & 6 December 2001

On 30 November 2001, AGL Electricity applied to have interruptions caused by a loss of supply on 9 November 2001 to West Melbourne terminal station excluded from the calculation of the S factor and, if necessary, from the obligation to make low reliability payments. On 6 December 2001, CitiPower made a similar application in relation to the same event. Both applications were made on the grounds that the interruptions were caused by the failure of transmission connection assets, and were not due to the inadequate planning of transmission connections.

The Commission approved the applications by AGLE and CitiPower.

Powercor Application – 6 March 2002

On 6 March 2002, Powercor applied to have an interruption to the 22kV Feeder BAN8 that occurred on 6 February 2002 excluded from the calculation of the S factor and low reliability payments. The application was made on the basis that the interruption was caused by a failure of the shared transmission network.

The Commission did not approve this application, as it did not consider that the interruption was caused by a failure of the shared transmission network.

CitiPower Application – 25 March 2002

On 25 March 2002, CitiPower applied to have an interruption to customers supplied from AGL Electricity’s zone substation Fairfield on 14 February 2002 excluded from the calculation of the S factor and low reliability payments. The application was made on the basis that the event was material, outside the control of CitiPower and the distribution business was not reasonably able to mitigate its impact.

The Commission did not approve this application because the application did not address the criteria for exclusion contained in the relevant regulations.



Powercor Application – 4 September 2002

On 4 September 2002, Powercor applied to have an interruption on 11 August 2002 to customers supplied from Cohuna Substation of Powercor excluded from the calculation of the S factor and low reliability payments. The basis for the application is that the event was caused by a failure of the transmission connection assets.

The Commission did not approve Powercor’s application because it did not consider the interruption to be caused by a failure of transmission connection assets.

Powercor Application – 5 September 2002

On 5 September 2002, Powercor applied to have an interruption of supply to its Stanhope Zone Substation and the connected customers on 30 August 2002 excluded from the calculation of the S factor and low reliability payments. The application was made on the basis that the interruption was caused by a failure of the shared transmission network.

The Commission did not approve this application because it did not consider the interruption to be caused by a failure of the shared transmission network.

Powercor Application – 10 December 2002

On 10 December 2002, Powercor applied to have an interruption to the 22kV Feeder CMN002 that occurred on 12 November 2002 excluded from the calculation of the S factor and low reliability payments. The application was made on the basis that the interruption was caused by a failure of the shared transmission network.

The Commission did not approve this application because it did not consider the interruption to be caused by a failure of the shared transmission network.

Powercor Application – 8 January 2003

On 8 January 2003, Powercor Australia applied to have interruptions to its customers supplied from the 22kV Feeders SHN011, SHN024, STN011 and STN024 that occurred on 26 November 2002 excluded from the calculation of the S factor and low reliability payments. The application was made on the basis that the supply interruptions were widespread, caused by an event that was rare and could not be foreseen or reasonably mitigated.

The Commission did not approve the application from Powercor on the basis that the supply interruptions are not widespread in the context of the financial incentive exclusion requirements.

Powercor Application – 4 February 2003

On 4 February 2003, Powercor Australia applied to have interruptions to customers supplied from the Red Cliffs Terminal Station (RCTS) and local zone substations as a result of a fault on a transformer cable at RCTS, which occurred on 19 December 2002, excluded from the calculation of the S factor and low reliability payments. The basis for the application is that the event was caused by a failure of transmission connection assets.



The Commission did not approve the application from Powercor on the basis that the interruptions were the result of planning decisions made by Powercor, which cannot be considered as adequate.

TXU Application – 7 March 2003

On 7 March 2003, TXU applied to have the supply interruptions caused by the bushfires that occurred in January of 2003 excluded from the calculation of the S factor and low reliability payments. The application was made on the grounds that the supply interruptions were caused by a rare event and TXU could not be reasonably expected to foresee or mitigate the impact of such event.


Powercor Application – 7 July 2003

On 7 July 2003, Powercor applied to have the supply interruptions caused by an incident that occurred at Horsham Terminal Station (HOTS) on 27 May 2003 and resulting in outages of Powercor ’s Horsham, Stawell and Nhill Zone Substations excluded from the calculation of the S factor and low reliability payments. The application was made on the grounds that the supply interruptions were caused by a failure of the shared transmission network.


TXU and United Energy Application – 21 and 28 July 2003

TXU and United Energy applied to the Commission on 21 and 28 July 2003 respectively to have the supply interruptions caused by the de-energisation of 22kV buses No.2 and No.3 of Ringwood Terminal Station that occurred on 24 June 2003 excluded from the calculation of the S factor and low reliability payments. These applications were made on the grounds that the supply interruptions were caused by a failure of transmission connection assets.

The Commission approved these applications.

Powercor Application – 25 August 2003

On 25 August 2003, Powercor applied to have the supply interruptions caused by an incident that occurred on the 220kV transmission network on 26 July 2003 resulting in the loss of supply to its Horsham, Stawell, Nhill and Charlton Zone Substations, excluded from the calculation of the S factor and low reliability payments. The application was made on the grounds that the supply interruptions were caused by a failure of the shared transmission network at Horsham Terminal Station (HOTS) and the failure of transmission connection assets at Bendigo Terminal Station (BETS).

The Commission approved the supply interruptions due to the loss of supply to Horsham, Stawell and Nhill Zone Substations.

The Commission did not approve the supply interruptions due to the loss of supply to Charlton Zone Substations because there is insufficient evidence that the supply interruptions were caused by a failure of the transmission connection assets.

United Energy and CitiPower Application – 11 and 14 October 2003



On 11 and 14 of October, United Energy and CitiPower respectively applied to have interruptions caused by storm activities on 2-3 September 2002 excluded from the calculation of the S factor and low reliability payments. The applications were made on the basis that the interruptions were widespread, caused by an event that was rare and could not be foreseen or reasonably mitigated.

The Commission did not approve the applications from United Energy and CitiPower on the basis that the evidence provided does not support that the event was rare in the context of the financial incentive exclusion requirements



APPENDIX B Data for DNSP Marginal Costs of Reliability

NB. SAIDI is assumed to be the “modified” figure

ENERGY AUSTRALIA2002/3 2003/4 2004/5 2005/6 2006/7 2007/8 2008/9 Average for Total %

Reg Period GrowthLOW GROWTH

Reliability Capex $m 9.9 12.42 25.69 25.07 26.03 27.16 27.15 26.22 131.1Growth Capex $m 111.5 158.13 127.06 127.06 139.5 113.38 115.9 124.574 622.87Total Capex $m 237.35 263.12 332.18 337.25 361.9 341.66 337.5 342.096 1710.5

Energy Transported TWh 28.64 28.63 28.89 29.11 29.38 29.62 29.9 29.38 146.9 0.87%Customer Numbers (000) 1478 1489 1500 1512 1524 1532 1542 1522 0.70%

MEDIUM GROWTH


Energy Transported TWh 28.64 29.05 29.5 29.96 30.37 30.89 31.43 30.43 152.15 1.59%Customer Numbers (000) 1478 1496 1510 1527 1540 1555 1571 1540.6 7703 0.98%

HIGH GROWTH


Energy Transported TWh 28.64 29.71 30.42 31.05 31.67 32.42 33.2 31.752 158.76 2.25%Customer Numbers (000) 1478 1511 1533 1550 1569 1586 1606 1568.8 1.23%

PERFORMANCE METRICS(all medium growth)

BASE CASE

SAIDI (min) 102.00 101

Substations at Risk 23.00 10Energy at Risk $m 100.00 95

CONSTRAINED CASE

SAIDI (min) 102.00 107


ENHANCED CASE

SAIDI (min) 102.00 90.9




INTEGRAL ENERGY2002/3 2003/4 2004/5 2005/6 2006/7 2007/8 2008/9 Average for Total %

Reg Period GrowthLOW GROWTH



MEDIUM GROWTH



HIGH GROWTH


Energy Transported TWh 14.33 14.91 15.4 15.84 16.25 16.58 17 16.214 81.07 2.66%Customer Numbers (000) 785 806 826 848 871 893 916 870.8 2.59%

PERFORMANCE METRICS(all medium growth)

BASE CASE

SAIDI (min) Raw 374 354 338 318 302SAIDI (min) Modified 125.00 119 114 108 103 97 92

Damand ar Risk (MVA) 250 150

ENHANCED CASE

SAIDI (min) Network 366 346 329 310 295SAIDI (min) Normalised 125.00 119 114 108 103 97 92

Demand at Risk (MVA) 250 100



INTEGRAL ENERGY2002/3 2003/4 2004/5 2005/6 2006/7 2007/8 2008/9 Average for Total %

ENHANCED CASE Reg Period Growth

LOW GROWTH



MEDIUM GROWTH

Reliability Capex $m 7.05 24.7 25.92 27.66 27.45 27.39 17.5 25.184 125.92Growth Capex $m 82.59 147.42 153.53 155.19 153 146.84 134.5 148.602 743.01Total Capex $m 120.14 295.9 331.17 313.92 333.8 329.91 305.7 322.894 1614.5


HIGH GROWTH


Energy Transported TWh 14.33 14.91 15.4 15.84 16.25 16.58 17 16.214 81.07 2.66%Customer Numbers (000) 785 806 826 848 871 893 916 870.8 2.59%



APPENDIX C Glossary



Term Definition

ACCC Australian Competition and Consumer Commission. The Federal body charged with monitoring competition throughout Australia.

AMP Asset Management Plan

CAIDI Customer Average Interruption Duration Index - The average length of supply interruptions (in minutes) per customer

CAPEX Capital Expenditure

CBD Central Business District

Distributor A licensed corporate entity authorised to deliver Electricity to retailers. Also referred to as a Distribution Network Service Provider (DNSP).

DNSP Distribution Network Service Provider (also referred to as the Distributor)

ESC Essential Services Commission - Victorian Energy Regulator

GWh Gigawatt Hours - 1000 MWh

IPART Independent Pricing and Regulatory Commission of NSW

IT Information Technology

kWh Kilowatt Hour - 1000 watt hours - base units of energy consumed in one hour

Metering A system for processing meter reads. In most cases, participants implement two metering systems (one for manual meters, one for smart meters). The Metering system is normally responsible for “profiling” as well and keeping historical information on meter reads for regulatory and marketing purposes.

MWh Megawatt Hours - 1000 kWh

ND Non-Demand. Usually referring to capital expenditure that results from the replacement of assets due to deteriorating performance, age, condition or risk profiles.

NEC National Electricity Code.

NEM The National Electricity Market, operated by NEMMCO on behalf of the NSW and Victoria jurisdictions; amongst others.

OFGEM UK Energy Regulatory body

OPEX Operating and Maintenance Expenditure.

Retailer A licensed corporate entity authorised to sell Electricity to end-users. (i.e. the public)



Term Definition

RNPP Reliability and Network Planning Panel

SAIDI System Average Interruption Duration Index - The average length of supply interruptions (in minutes) per annum

SAIFI Supply Average Interruption Frequency Index – A measure of the number of interruptions experience by the average customer

UMS Unmetered Supply.

USE Unserved Energy - The volume of energy not delivered due to supply interruptions

VCR Value of Customer Reliability - Customer value ascribed to a unit of USE specifically relating to network connection

VOLL Value of Lost Load - Customer value ascribed to a unit of USE

VUSE Value of Unserved Energy - USE times the customer value or marginal cost rate

WIP Work In Progress

PROVIDING INCENTIVES FOR SERVICE QUALITY – INCENTIVE … · PROVIDING INCENTIVES FOR SERVICE...

Documents

Transcript of PROVIDING INCENTIVES FOR SERVICE QUALITY – INCENTIVE … · PROVIDING INCENTIVES FOR SERVICE...