Supporting Document:
Long Run Marginal Cost
Considerations in Developing
Network Tariffs
March 2015
Contributors to this supporting document (Long Run Marginal Costs) include Frontier Economics, Energia Consulting,
Harry Colebourn Pty Ltd and Smart Grid Partners, with support from Ergon Energy.
……………………………………………………………………………………
Ergon Energy is seeking further customer and stakeholder input as we progress our
future network tariff strategy.
There has been a major shift in the way Ergon Energy’s customers use the electricity
network over recent years. In response, to help ensure we can continue to meet our
customers needs into the future for the best possible price, we are changing the way
we charge for the use of the network. The changes will also make network charges
more equitable.
Our proposed changes aim to help our customers make informed decisions,
especially when making investments relating to their use of electricity. To do this we
are restructuring charges so that they better reflect the impact of a customer’s
electricity use on the electricity network.
Our reform journey has already started. Following consultation with stakeholders, we
introduced a number of new tariffs and made structural changes to some tariffs in
July 2014. We are now focused on further changes for 2015-16 and beyond.
This paper builds on the Consultation Papers available online, with the detail around
the issue of Long Run Marginal Cost – an important element of future network tariff
design.
……………………………………………………………………………………
Purpose of this supporting document
……………………………………………………………………………………
The purpose of this supporting document is to:
 provide the detail of Ergon Energy’s considerations in determining our Long Run Marginal
Cost (LRMC) and where and how it is applied in the tariffs for each user group

support the stakeholder overview provided in the Consultation Paper Aligning Network
Charges to the Cost of Peak Demand (Long Run Marginal Cost).
This paper builds on the consultation process undertaken to date. Earlier consultation papers,
the associated documents are available at www.ergon.com.au/futurenetworktariffs
Contributors to this supporting document (Long Run Marginal Costs) include Frontier Economics, Energia Consulting,
Harry Colebourn Pty Ltd and Smart Grid Partners, with support from Ergon Energy.
Contents
……………………………………………………………………………………
1. Context and history .......................................................................................................................2 2. Economics of pricing .....................................................................................................................3 2.1 How should marginal cost be defined? ................................................................................3 2.2 How should residual costs be recouped? ............................................................................5 3. Determination of LRMC .................................................................................................................6 3.1 Conceptual approaches to LRMC ........................................................................................6 3.2 Nature of costs for inclusion.................................................................................................8 3.3 Ergon Energy's current approach ........................................................................................9 4. Application of LRMC to structuring tariffs ....................................................................................13 4.1 Guidance from the NER .....................................................................................................13 4.2 Theoretical ideal signal ......................................................................................................14 4.3 Practical broad options ......................................................................................................14 5. Relevant considerations for designing cost-reflective tariffs .......................................................16 5.1 Potential LRMC-signalling options .....................................................................................16 5.2 Framework for choosing between options .........................................................................18 5.3 Base case assumptions and outcome ...............................................................................18 5.4 Extending the basic model .................................................................................................19 5.5 Choosing a tariff structure ..................................................................................................25 5.6 Locational LRMCs and the ‘dynamic layer’ ........................................................................27 6. Options for the residual charge ...................................................................................................28 6.1 Conceptual considerations.................................................................................................28 7. Abbreviations ..............................................................................................................................29 Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
1
1.
Context and history
The National Electricity Rules (NER) applying to Distribution Network Service Providers (DNSPs) in
the National Electricity Market (NEM) oblige Ergon Energy to set tariffs for each customer tariff class
based on the Long Run Marginal Cost (LRMC) of providing the relevant service to that class. The
method of calculating and applying LRMC must have regard to a number of considerations including
the:1

costs and benefits of each approach to calculating and applying a particular tariff formulation

additional costs likely to be associated with meeting demand from the customers assigned to the
tariff at times of greatest utilisation of the relevant part of the distribution network

geographic location of customers assigned to the tariff and the extent to which costs might vary
between different locations in the distribution network.
To the extent that tariffs based on LRMC do not recover the total efficient costs of serving the
customers assigned to the tariff, or do not enable us to recover our regulated revenue, we are
permitted to apply other tariff components or approaches to meet those requirements. However, any
additional tariff components or other approaches to setting tariffs must influence customers’
behaviour as little as possible relative to the behaviour arising under ‘pure’ LRMC tariffs.
The NER also required tariff classes to be established in such a way as to group retail customers
together on an economically efficient basis, subject to the need to avoid unnecessary transaction
costs.
The NER has always emphasised the importance of reflecting LRMC in setting tariffs for distribution
network customers. However, recent changes to the NER have increased this emphasis and have
given the Australian Energy Regulator (AER) greater powers to scrutinise DNSPs’ tariff-setting
methodologies. The recent changes to the NER follow the Australian Energy Market Commission’s
(AEMC) 2012 Power of Choice Review2, which was intended to increase the economic integrity of
signals faced by end-use electricity customers and to increase the scope for them to respond to
these enhanced signals. The motivation for enhancing tariff signals arose from the strong growth in
electricity peak demand experienced over the 2000s and the large increase in network investment
required to serve that much higher level of peak demand. Policy-makers took the view that if retail
customers faced tariffs that better reflected the costs of meeting demand, they would have incentives
to change their behaviour in ways that could reduce overall system costs.
The issue faced by Ergon Energy and many other DNSPs in the NEM is that policy-makers, and the
new NERs, are requiring more cost-reflective network tariffs despite:

technical limitations on the existing stock of meters, which limit the scope to provide effective
signals to customers about the costs of each customer’s electricity usage decisions

formal or informal jurisdictional limitations on the extent to which tariffs to customers can be
reformed over time or across geographic areas or customer classes

slowing growth in network augmentation expenditure in response to flattening peak demand
across most of the network, which potentially lessens the urgency of tariff reform.
Nevertheless, Ergon Energy believes the benefits of more efficient LRMC-based tariff structures are
likely to be substantial in the long run. Accordingly, this report has been developed to clearly explain
what tariff reforms are likely to be necessary and how tariff redesign should be undertaken.
1
2
NER clause 6.18.5.
http://www.aemc.gov.au/Major-Pages/Power-of-choice
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
2
2.
Economics of pricing
The reason policy-makers have increased the NER’s emphasis on setting tariffs to reflect LRMC is
grounded in economic theory. Economics suggests that society’s resources will be allocated most
efficiently when prices reflect the marginal cost of supplying the good or service in question. ‘Marginal
cost’ refers to the incremental or avoidable cost of providing one more or one less unit of the relevant
good or service. Put differently, it is the change in the total costs of providing a good or service when
satisfying an additional unit of demand.
The reason why prices reflecting marginal cost should maximise efficiency is that at such prices,
customers will only increase their consumption if the value they place on consuming more of the
good or service at least equals the incremental value of the resources used up to provide that good
or service. So long as customers value an additional kWh of electricity as much as the value of the
resources required to provide it, it is efficient for customers to consume more electricity. Conversely,
if customers place a lower value on an additional kWh of electricity than the costs of providing it,
society would be better off if the resources required to provide that kWh were allocated in a different
way.
In many contexts, the application of marginal cost pricing is fairly straightforward and provides a
comprehensive solution to how prices should be set to promote efficiency. However, the ‘natural
monopoly’ characteristics of distribution network infrastructure can make the setting of optimal
network prices a more complex exercise. These characteristics include:

economies of scale in the provision of distribution networks – such that it is typically cheaper (on
a per customer or per kVA capacity basis) for a network to:
o
supply more customers than fewer customers and to
o
expand capacity by a larger amount than by a smaller amount.

‘lumpiness’ of network infrastructure – network assets tend to be only available at particular
capacities and voltages and cannot be scaled up in small increments.

‘sunk’ costs – investment in network assets cannot usually be reversed if the assets are made
redundant or their full capacity is no longer required, meaning that there is very low opportunity
cost in utilising the existing network. In other words, once an investment is made, there is little
additional cost if more energy flows through the network, up until the point of requiring further
investment. Alternatively there is little scope to reduce costs if less energy flows through the asset
once the investment is made. The form of regulation applying to distribution networks in the NEM
also means that sunk costs must be recouped from customers.
These characteristics give rise to two key dilemmas for network businesses in setting efficient
network tariffs:

how should marginal cost be defined?

how should regulated network revenue not recovered through marginal cost tariffs be recouped?
2.1
How should marginal cost be defined?
The definition of marginal cost can vary depending on the extent to which different inputs are
regarded as fixed when assessing how total cost changes in response to an increase in demand.
Although there is not necessarily a relationship between the proportions of a business’s inputs that
are fixed and particular lengths of time:
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
3

Short Run Marginal Cost (SRMC) refers to marginal cost when at least one input is fixed – this
corresponds to a timeframe of minutes, hours, days, weeks or months, depending on the industry
in question.

LRMC refers to marginal cost when all inputs can be changed – this often corresponds to a
timeframe of months or years (sometimes decades), again depending on the industry.
For electricity distribution networks, due to economies of scale and the ‘lumpiness’ of distribution
network investment, the conservative nature of most distribution reliability standards and the fact that
capital expenditures on the existing network are largely sunk, the SRMC of a network service will
typically be limited to incremental distribution losses3 and other variable operating costs. Conversely,
the LRMC of network service will typically be much higher than the SRMC because LRMC
incorporates the longer run investment cost implications of higher demand.
How do you choose?
The choice between using SRMC or LRMC to set network tariffs effectively involves making a tradeoff between promoting economic efficiency in the short run versus in the long run.
To encourage maximum utilisation of existing distribution network assets in the very short term,
network tariffs should reflect the SRMC of providing network services; that is, the additional costs
incurred to serve an increment of demand holding fixed the capital invested in the existing network.
Taking a given half-hour in isolation, tariffs set to reflect SRMC would provide the most efficient
signal to customers as to whether they should consume more or less grid-delivered electricity in that
half-hour. SRMC-based pricing is the principle behind the operation of the NEM wholesale spot
market.
However, setting tariffs to reflect SRMC has a number of drawbacks. First, it would require locational
marginal pricing (ie. nodal pricing) at the distribution level so that the effect of network losses and any
‘congestion’ on the network could be signalled in real-time. This would require:

the development of detailed ‘constraint equations’ – as is the case for the transmission network –
to derive individual nodal prices at each point on the distribution network

substantial investment in ‘smart grid’-type infrastructure to enable the monitoring of real-time
flows and conditions on individual lines – say, at least down to the zone substation (ZS) level – in
order to set localised prices.
Second, given that congestion on the distribution network is virtually non-existent (outside outages)
due to conservative network planning/security standards, SRMC-based tariffs would provide very
little information to customers about the future cost consequences of increased demand for network
services. This would defeat the very purpose of more cost-reflective network pricing. Relatedly,
SRMC-based tariffs would recover very little of the total costs of providing distribution network
services, necessitating very substantial other charges.
Setting tariffs to reflect LRMC can provide more useful long-run signals to customers about the
implications of their increased demand for network services at peak utilisation times. As customers
and prospective customers make decisions to invest in particular types of facilities and/or locate in
particular geographic areas, LRMC-based tariffs should encourage them to take into account the cost
consequences of their decisions. Further, by incorporating network capital costs, LRMC-based tariffs
should recover a much larger proportion of total network costs than SRMC-based tariffs.
3
Note that network losses are factored into retail market settlements. Both distribution and transmission loss factors are,
however, annual averages. Thus changes in the short run cost of transmission losses are factored into market
settlements residuals and distribution loss variations are managed through true-up provisions.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
4
The key drawback of LRMC-based pricing is that, by its very nature, it requires the DNSP to derive
the long run investment implications of customers’ short-term network usage decisions. This exercise
is inherently assumptions-driven and as such may appear to produce arbitrary or inappropriate tariffs
in particular cases. Nevertheless, even an imperfect signal to customers regarding the long-run cost
consequences of their decisions is likely to encourage more efficient decisions than no signal. For
these reasons, most networks and policy-makers express a preference for tariff-setting based on
LRMC. LRMC pricing has also been used to set regulated prices in other utility sectors such as water
and telecommunications networks. It is also the form of pricing established for distribution networks
in the NER.
Sections 3, 4 and 5 discuss approaches to determining LRMC-based tariffs in more detail.
2.2
How should residual costs be recouped?
Setting tariffs to reflect marginal cost – whether SRMC or LRMC – will typically not by itself allow a
distribution network to recover all of its historical capital costs. This follows from the economies of
scale inherent in network provision and the lumpiness of network investment. Together, these
characteristics can cause marginal cost to fall below the average cost of providing the network. To
the extent that the magnitude of a DNSP’s historical capital expenditures drives the determination of
its allowed regulated revenues, this means that setting tariffs equal to marginal cost may not enable a
distribution network to recover its regulated revenues.
As noted above, the NER permits DNSPs to modify LRMC-based tariffs to recover their total
regulated revenues. However, any additional tariff components or other approaches to setting tariffs
must be designed to influence customers’ behaviour as little as possible relative to the behaviour that
would arise under ‘pure’ LRMC-based tariffs. This requirement seeks to preserve, as far as possible,
the behavioural signals provided by LRMC-based tariffs. Accordingly, residual cost recovery tariffs
that achieve this objective are described by economists as ‘least-distortionary’ tariffs, where the
‘distortion’ in question is deviations from the behaviour resulting from LRMC-based tariffs.
This raises the question of how tariffs should be structured so as to have the smallest possible
unintended impact on customers’ consumption decisions. The conventional economic thinking
around least-distortionary pricing suggests that the best way, theoretically, to set the second-part
tariff is to recover outstanding network costs from customers in proportion to their overall willingness
to pay for the provision of the distribution network rather than their willingness to pay for an additional
unit of network services. If customers are:
(i) charged a price below their overall willingness to pay for the distribution network, and
(ii) on a basis not related to their usage of the network; then by definition they should not stop using
the network.
Willingness to pay and scope for bypass
Recovering outstanding sunk costs on the basis of willingness to pay means that it is necessary to
examine what alternatives customers have to paying for (and receiving) network access. This
involves considering options for physical or economic bypass.
Bypass broadly refers to avoiding use of the network:

Physical bypass – refers to building a private network to one or a group of generators to avoid
using and paying for the regulated network in question.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
5

Economic bypass – refers to avoiding use of the network in question, either by not
investing/locating in/connecting to the service provider’s network or by disconnecting from its
network. This may involve developing some form of distributed generation, possibly accompanied
by energy storage facilities.
Generally distribution network customers cannot realistically engage in physical bypass of the
network. The scope for complete economic bypass is also limited at present, as residential and most
commercial customers are heavily dependent on some form of external access to reliable supplies of
electricity. However, thousands of customers are presently engaging in a form of partial economic
bypass through the installation of solar photovoltaic (PV) systems. Metering is most commonly
configured to record the net energy consumption and PV systems enable customers to consume less
grid-supplied energy, reducing the extent to which they pay volumetric network charges. As the bulk
of network charges to Standard Asset Customers (SAC), Large and Small, is recovered directly or
indirectly on the basis of electricity consumption, the result has been that customers with solar PV
are contributing significantly less to the recovery of sunk network costs than customers without PV
units, even though PV customers would likely place a similar value on network access as non-PV
customers. This has provided an artificially strong (and inefficient) incentive for customers to install
solar PV units, because in doing so they can avoid paying the same amount for network access as
other customers. In other words, the present structure of residual cost-recovery tariffs for
accumulation metered customers is substantially distorting customer behaviour.
In summary, economic efficiency is likely to be enhanced if the residual costs of the network not
recovered through marginal cost-based tariffs are recovered from tariffs that reflect the overall value
customers place on network access rather than the amount of electricity customers consume.
Section 6 discusses approaches for setting tariffs to recover residual costs.
3.
Determination of LRMC
Conceptually speaking, there are a number of ways to interpret and calculate LRMC. The two key
broad approaches highlighted by the AEMC are the Turvey ‘perturbation’ approach and the Average
Incremental Cost (AIC) approach. These are briefly described below.
Having defined LRMC, the next step is to work out which network costs ought to be included in the
calculation of LRMC. Ergon Energy’s approach to this question is also outlined below.
3.1
Conceptual approaches to LRMC
Turvey
The Turvey approach to estimating LRMC is also known as the ‘perturbation’ or marginal incremental
cost approach. This approach, developed by the late Professor Ralph Turvey, is based on deriving
the present value cost of additional capacity required to serve a permanent increase in forecast
demand at a particular location. This approach takes as given the existing network and planned
network investment, and considers how the present value of future costs expected to be incurred
would change if forecast demand increased incrementally and permanently.
The Turvey approach requires the preparation of a base case forecast of future demand and
investment and then an assessment of the investment timing and cost implications of permanently
increasing (or ‘perturbating’) forecast demand relative to the base case. The LRMC is calculated as
the present value of the costs of bringing forward or expanding the investment required under the
base case.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
6
In NERA’s consultant report on network pricing for the AEMC4 they defined LRMC using a
perturbation approach as:
LRMC (perturbation) =
PV (revised optimal capex plus opex – optimal capex plus opex)
PV (revised demand – initial demand)
NERA commented: (p.15)
The principal feature of the perturbation approach is that it directly estimates the change in
demand as a consequence of small changes in demand, which most closely resembles the
theoretical ‘marginal cost’. Where capital expenditure is necessarily lumpy, this approach takes
into account current conditions and so will result in lower estimates of the LRMC where current
capacity is sufficient to satisfy incremental changes in demand. Equivalently, it produces higher
estimates of the LRMC where small changes in demand lead to bringing forward near term
investments. This most closely resembles the price signals that promote more efficient use of
network infrastructure.
The AEMC’s approach to determining prices for firm transmission access under its Optional Firm
Access (OFA) proposal are based on a Turvey-style approach to determining LRMC.
Average Incremental Cost
The AIC approach to estimating LRMC takes the present value of incremental costs expected to be
incurred over a future period of time and divides this by the present value of the additional demand
expected to be served over the same period.
In NERA’s network pricing report for the AEMC, they defined LRMC using an AIC approach as:
LRMC (Ave Incremental Cost) =
PV (new network capacity + marginal operating costs)
PV (additional demand served)
NERA commented: (p.15):
By definition the average incremental approach uses an ‘average’ cost to approximate the
marginal cost change. Such averaging will be reasonable where capital expenditure is relatively
smooth due to incremental changes in demand. It follows that the AIC will not be a good
approximation of the marginal costs where capital expenditure is lumpy.
A third alternative – Long Run Incremental Approach
The Long Run Incremental (LRIC) approach calculates the annualised cost of the next proposed
investment measured relative to an increment in demand. An example of this approach is the
Common Distribution Charging Methodology (CDCM), which has formed the basis for distribution
tariffs in the United Kingdom for many years5.
4
5
http://www.aemc.gov.au/getattachment/e03c20c9-273d-4ea7-84e5-3045141b487b/NERA-EconomicConsulting-–-Network-pricing-report.aspx
Energy Networks Association (UK), CDCM model user manual Model Version: 102, 28 February 2013.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
7
This model is based upon the creation of a hypothetical network for the supply of a demand of
500 MW, using the spatial characteristics and standardised equipment typical for the distributor.
Choosing between approaches
As noted above, the Turvey approach is often viewed as providing a more theoretically ‘pure’
estimate of LRMC than the AIC approach because the Turvey approach focuses on the specific cost
implications of an increment of demand. Conversely, the AIC approach does not seek to make a
direct link between a particular increment of demand and the resulting change in cost.
Any estimate of LRMC requires a large number of assumptions to be made, and many of these will
necessarily be speculative. However, using the Turvey approach to calculating LRMC is typically a
more subjective and arbitrary exercise than calculating LRMC using the AIC approach. This is
because the Turvey approach requires the price-setter to take a view on the path of future investment
with and without a particular increment of demand. Also, as Turvey LRMC is usually determined in
discrete time rather than continuous time (meaning that only changes in investment between years
and not within years are taken into account), the value of Turvey LRMC is likely to be very sensitive
to starting conditions, the timing of planned investment, the lumpiness of investment and the sizeincrement of demand.
In response, decision-makers often develop or utilise measures that are designed to act as proxies
for LRMC. The third alternative – Long Run Incremental method is an example of this. For example,
the British transmission network operator, National Grid, applies the Investment Cost Related Pricing
(ICRP) methodology. In the NEM context, Transmission Network Service Providers apply the CostReflective Network Pricing (CRNP) methodology or variants based on CRNP. Both ICRP and CRNP
use load-flow modelling to estimate a deemed long-run cost of serving customers at different
locations. In 2010, Ofgem in the United Kingdom introduced a Common Distribution Charging
Methodology (CDCM) for low voltage (LV) customers. The approach is based on estimating the
incremental costs of a hypothetical 500MW increment in capacity. This is also known as the ‘500MW
model’. The approach is based on estimating the incremental capital and operating costs of a
hypothetical 500MW increment in capacity.
3.2
Nature of costs for inclusion
The conceptual approaches to LRMC do not address the issue of which costs ought to be included in
the calculation of LRMC and whether different costs should be included in determining LRMCs for
different customers.
The categories of costs that could potentially be included in the determination of LRMC can be
considered across a number of dimensions:

type of cost – the nature of costs that could be included in an LRMC calculation could span the
following range: augmentation capital expenditure, replacement capital expenditure, operating
and maintenance expenditure (which may be asset-related or non-asset-related)

network level – the nature of costs that could be included in an LRMC calculation could span the
following range: subtransmission bus, subtransmission lines, ZS, high voltage (HV) bus, HV
lines, distribution substation, LV bus and LV lines

geography – network zone or locational characteristics/customer density (eg.
urban/suburban/rural/remote).
The guiding principle for whether a cost should be included in the calculation of LRMC is causation
or, alternatively, avoidability. If an incremental increase or decrease in a customer’s demand could
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
8
affect the size or timing of a cost, then the cost should be included in the calculation of LRMC
applicable to that customer.
This principle suggests that ideally, different LRMCs – incorporating different costs – should be
derived for customers connecting at different levels of the network and in different parts of the grid.
This is consistent with the NER requirements. For example, a separate LRMC should be derived for
SAC-Small customers connecting to the LV network in Townsville to a Connection Asset Customers
(CAC) customer connecting to the HV network near Roma.
The key countervailing consideration is that deriving more voltage- or locationally-specific LRMCs to
reflect the conditions facing different customers at different locations imposes higher transactions and
implementation costs. The calculation approach and its associated assumptions will limit the
granularity which may be achieved.
3.3
Ergon Energy's current approach
Ergon Energy has to date used the Benchmark Cost of Supply (BCS) as a proxy for a broad-based
network-wide LRMC. Our November 2013 Tariff Implementation Report noted that BCS was
originally developed to assess the appropriateness of undertaking non-network alternative initiatives.
The report noted our decision to use this as a rough estimate of LRMC across the network as a
whole. This decision was made in the context of:
 The need for some measure of LRMC to establish more cost reflective tariffs, which Ergon Energy
was keen to implement over the shortest period of time;
 Ergon Energy not having an alternative measure of LRMC;
 Consultation occurring through the Australian Energy Market Commission regarding the specifics
of LRMC and how it should be calculated, which influenced our decision to defer a more
sophisticated calculation of LRMC till after consultation on the Rule change was completed;
 Ergon Energy being in the process of formulating its demand and capital expenditure forecasts for
the Regulatory Proposal to the AER – this information would be available for use in LRMC
calculation in October 2014.
In other words, the use of BCS to establish LRMC was an initial step to ensure the pathway to tariff
reform could commence and was always intended to be reviewed in the context of new information
on expenditure and demand as well as statutory requirements.
The BCS is a measure of the network-average cost (in $/kVA/year) of providing additional network
capacity to meet additional peak demand at the high voltage level of the network. The BCS is derived
using data from across the network and is not based on augmentation costs at any individual location
or customer tariff class. The estimate of Ergon Energy’s BCS used for prices in 2014-15 was
$162/kVA/year, which represents a network average value based on the capital works program at the
time of compilation.
The BCS captures the network augmentation costs arising between (but not including):
 the subtransmission bus at bulk supply points (BSPs), and
 LV distribution substations (DS).
This means it includes the cost of subtransmission lines, ZS, HV buses and HV lines/feeders.
However, the BCS does not include the cost of bulk supply point busses on the upstream side or
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
9
distribution substations and the LV network on the downstream side. The BCS also does not (as yet)
incorporate asset replacement expenditures.
The BCS is calculated using a Forward Looking Incremental Cost (FLIC) approach. In applying the
FLIC approach, Ergon Energy first calculates the Average Capital Cost of Capacity (ACCC).
This is the:

sum of forecast capital expenditure associated with the installation of additional network capacity
for the next five years
divided by the

amount of additional capacity expected to be installed over that time.
The annualised BCS is then derived as follows:
ACCC * (WACC (%) + Annual depreciation (%) + Annual Opex (%))
Between the two broad approaches for estimating LRMC outlined above, the FLIC methodology for
calculating the BCS more closely resembles an AIC approach than a Turvey-style marginal
incremental cost approach. However, there are a number of important differences between BCS and
AIC, including the:





type of expenditures included:
o
AIC ideally includes all augmentation capital expenditure, replacement capital expenditure and
incremental operating costs
o
BCS presently only includes augmentation or ‘growth’ capital expenditure.
level of network expenditure:
o
AIC ideally includes all expenditure that may change as a result of a change in demand –
which, in the case of LV customers, would include DSs
o
BCS does not include DSs or expenditures on LV assets.
intra-period timing of expenditure:
o
AIC uses a positive real discount rate to discount expenditures expected to be incurred in
different years over the assessment period
o
BCS sums real capex over the five-year assessment period and does not apply a real discount
rate.
intra-period timing of new capacity:
o
AIC uses a positive real discount rate to discount the quantity of new capacity provided and
used to serve demand in different years over the assessment period
o
BCS sums the quantity of new network capacity expected to be developed over the five year
assessment period and does not apply a real discount rate.
lumpiness of capacity:
o
AIC reflects the ‘lumpiness’ of new capacity relative to additional demand served. This means
that AIC will be high if a large ‘lumpy’ investment cannot be avoided even when demand growth
is relatively slow and gradual – the denominator in the AIC calculation is the PV of additional
demand served
o
BCS ignores the lumpiness of new capacity because the denominator in the BCS calculation is
the expected quantity of new capacity provided by planned investment rather than the expected
increment of demand served by that new investment. This means the BCS will not be affected if
the growth in demand is different to the growth in the level of network capacity.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
10

utilisation of capacity:
o
The BCS calculation makes assumptions concerning the proportion of ZS capacity that is able
to be utilised and assumes that HV feeders will be fully utilised (which is not the case).
o
The full rating of equipment that is added to the network cannot usually be utilised, because of
the need to provide adequate security of supply to meet reliability targets (eg. ‘n-1’) and also to
meet supply quality requirements (such as acceptable supply voltage).
The BCS model contains assumptions on average utilisation of the capacity of zone transformer
additions. In the case of the high voltage feeders, it effectively assumes that the full rated capacity of
the feeder is added. HV feeder costs comprise 27% of the BCS value.
It should be noted that BCS costs have not been adjusted for the time value of money and were
compiled approximately five years ago. The current value adjusted for 2010-15 CPI escalation of
10% would be $178/kVA p.a. If adjusted for an average high voltage utilisation of 50%, this estimate
would increase to $226/kVA. Moreover, a review of zone transformer utilisation using current
average utilisation rates is expected to result in a further increase in this estimate.
As distribution transformer and low voltage network costs are not considered, the BCS estimate is
only applicable at the high voltage level.
Updating Ergon Energy’s estimate of LRMC
The current Rules provisions place greater emphasis on the use of LRMC in tariff formulation. The
issues noted above with BCS, plus the need for a more robust and up-to-date estimate of LRMC has
driven Ergon to investigate alternative estimation processes.
The three generally accepted alternatives for the calculation of LRMC for a network, as outlined in
the AEMC’s consultation documentation for the Distribution Pricing change to the Rules6,7, are the:

perturbation or “Turvey” approach, which involves estimating the incremental expenditure
associated with a permanent increment in demand

Average Incremental Cost (AIC) approach. This is estimated from consideration of the demand
related component of forecast expenditures, and the associated demand growth, and

Long Run Incremental Cost (LRIC) approach. In this case the estimate uses the annualised cost
of an investment required to service a hypothetical increment in demand.
The Commission considered “that there is merit in providing flexibility to use either the AIC or
Perturbation methodologies, or other accepted methodologies, depending on how strong the LRMC
price signals need to be in order to send signals to consumers about the cost or benefit of
undertaking or deferring additional network expenditure”8.
Of these alternatives, the:

perturbation approach is the most resource-intensive, in effect requiring the re-estimation of the
capital and operating cost programs for the DNSP

AIC approach has been widely adopted by other DNSPs in Australia and is relatively
straightforward. However it is reliant upon long-term forecasts of demand related expenditure
and demand and has some other limitations discussed below; and
6
7
8
AEMC, Rule Determination - National Electricity Amendment (Distribution Network Pricing Arrangements) Rule 2014, 27
November 2014.
NERA, Economic Concepts for Pricing Electricity Network Services - A Report for the Australian Energy Market
Commission, 21 July 2014.
AEMC, Op. Cit., p.118.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
11

LRIC approach has been in place in the United Kingdome for many years as the basis for setting
distribution tariffs.
Ergon Energy has thus estimated the network LRMC using the AIC approach and is also
investigating the LRIC approach as a means of confirming the value chosen as the basis for tariff
setting.
Outcome of Ergon Energy’s revised LRMC estimates
The approach used to estimate the forecast components for, and the mechanism of, the AIC
calculation are described elsewhere9. The outcomes are repeated below for the expenditure
coincident with the demand increase, and with the expenditure lagged by three years (as capital
works often have a significant lead time from their committal to meet forecast demand growth).
System Level
Subtransmission
High Voltage
Low Voltage
AIC $/kVA p.a.
Coincident
3 year lag
$32
$27
$320
$257
$471
$378
The AIC values above for high voltage are significantly higher than the current BCS estimate.
It should be noted that there is some uncertainty associated with the AIC outcome, associated with
the:

‘lumpiness’ of capital expenditure on a few relatively large projects, particularly at times of low
demand growth

extent to which network and corporate overhead costs are included in the calculation

limited span of the regulatory forecasts employed, and

limitations in estimating the incremental demand growth at functional levels within the distribution
network.
Implementation of LRMC in 2015-16
As a consequence of these uncertainties and the significant increase in LRMC over the current
estimate, the values in the above table have been adjusted, in an interim arrangement for 2015-16,
which will:

transition existing prices towards an increased LRMC value

permit consultation on prices that are unlikely to be affected by the adjustment of the AIC,
following the AER’s determination and review of inputs to the calculation

permit more detailed consideration of the demand forecasts used for the calculation, and

permit the development of a LRIC model of Ergon Energy’s network, to confirm the LRMC values.
The preliminary indications from this modelling are that this approach is likely to support the AIC
outcomes.
The following table shows the current BCS value, the CPI-adjusted BCS value, and the LRMC values
proposed for Ergon Energy’s 2015-16 tariffs.
9
Harry Colebourn Pty Ltd, Report to Ergon Energy Estimating the Average Incremental Cost of Ergon Energy’s
Distribution Network - First draft, 5 February 2015.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
12
System Level
Subtransmission
High voltage
Low voltage
BCS $/kVA p.a.
Current
CPI Adj
$162
$178
-
AIC $/kVA p.a.
Average
50% Avg
$29
$15
$289
$145
$425
$213
Refining the estimates of LRMC
During 2015-16, Ergon Energy will refine its estimates of LRMC and may make further adjustments
to trend to a higher value. The proposed sequence of events is as follows:

continue to develop the LRIC model of Ergon Energy’s network, to confirm the LRMC values.
This model will be the subject of a future report detailing the findings

review the AIC model following the AER’s determination and modification of inputs to the
calculation

undertake more detailed consideration of the demand forecasts used for the calculation, and

develop a recommendation on LRMC values based on robust analysis and, if necessary, a
transition path to incorporate the final values into network prices during the 2015-20 regulatory
control period.
4.
Application of LRMC to structuring tariffs
4.1
Guidance from the NER
In structuring tariffs to reflect LRMC (howsoever derived), it is important for tariffs to be based on the
variable(s) with the greatest ability to influence future costs. The NER indicates that tariffs should
reflect the additional costs associated with meeting demand from the relevant class of customers at
times of peak utilisation on the relevant part of the network. The NER also notes that the costs of
meeting demand may vary at different points on the network.
This offers us some guidance for setting LRMC-based tariffs:


tariffs should signal the costs of serving additional demand at peak network utilisation times –
meaning that charges should ideally only be based on a customer’s individual peak demand or
usage to the extent that either a customer’s individual peak demand:
o
is an important driver of shared network costs, and/or
o
coincides with peak utilisation of the relevant part of the network.
tariffs may vary by location and connection voltage – as these variables are relevant to the
customer’s impact on future costs – but ideally should not vary by customer ‘type’ (eg.
commercial, residential, industrial), because customers of all types impose similar cost
consequences when they consume electricity at a particular time in a particular location.
However, where, because of limitations on their structure, tariffs are averaged and applied to
types of customers having different consumption profiles, different tariff rates may be needed to
reflect their average cost consequence.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
13
4.2
Theoretical ideal signal
LRMC under the NER refers to the present value of future costs imposed by a customer’s decision to
consume more electricity during the time of greatest utilisation of the network and for which
investment is most likely to be contemplated. This means that ideally, customers should face an
LRMC-based tariff on their kW demand at the time of greatest utilisation of the part of the network
that serves their incremental demand. If this time were known with certainty in advance – say, it was
t* – we could set a tariff for all customers of $LRMC on their individual demand at t*. As customers
would know the level of the LRMC-based tariff and would be able to predict the timing of t*,
customers would face appropriate signals to curb their demand at the time when the network that
serves them was most stretched.
For example, if the LRMC of serving additional demand at peak utilisation periods was $236/kW/year
(equivalent to the proposed AIC-based value of $213/kVA/year), customers would receive a bill
based on their demand (in kW) at the peak utilisation time (t*) multiplied by $236.
Customers would then have incentives to:

reduce their demand at t* if they expected to receive less than $236/kW benefit from incremental
consumption at t*

increase their demand at t* if they expected to receive more than $236/kW benefit from
incremental consumption at t*.
4.3
Practical broad options
As the timing of peak demand on different segments of the network is not known with certainty in
advance, we have two broad options for structuring LRMC-based tariffs:

impose an LRMC-based tariff in an ex post manner, charging customers on the basis of whatever
their demand happens to be at the time of greatest utilisation of the network in the relevant
month/season/year (Ex post charging).

impose an LRMC-based tariff in a way that reflects the expected probability of the timing of the
greatest utilisation of the relevant part of the network (Ex ante probabilistic charging).
These options are described below.
Ex post charging
Under ex post charging, customers would be charged the LRMC rate (in $/kW) on whatever their
demand happens to be at the time of greatest utilisation of the relevant part of the network that
serves them. In this way, customers would pay a charge equal to the LRMC they impose on the
network. A retail electricity contract incorporating wholesale pool price pass-through would represent
a form of ex post charging, because customers would be obliged to pay for their electricity
consumption at prices (ie. trading interval spot prices) that were only known with certainty after the
relevant trading interval.
The key economic benefit of ex post charging is that provides customers with incentives to discover
and utilise relevant information right up until real-time in order to minimise the costs they impose on
the network. Given that the timing of peak network utilisation is uncertain, ex post charging would
provide customers with incentives to use whatever information they could cost-effectively obtain to try
to predict when the peak might be. For example, customers would have incentives to follow weather
forecasts and plan to avoid unnecessary electricity consumption on particularly hot or cold days
(depending on whether the network load in their area is summer or winter-peaking).
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
14
Ex post charging at the end-use customer level is, however, extremely rare for two main reasons.
First, the extent of customer response to ex post charging is likely to be limited. Ergon Energy could
provide customers with some information to guide their predictions of peak network utilisation, such
the timing of network peaks in previous periods and the key factors that influence electricity demand
(eg. temperature, day of the week, timing of holiday periods). Some customers may respond sensibly
to this information and curb their electricity consumption at the relevant times. However, most of
Ergon Energy’s residential and small business customers are unlikely to be particularly well-informed
about or responsive to likely peaks in network utilisation. This suggests that ex post pricing will not
produce economically efficient outcomes.
Second, ex post charging may be regarded as inequitable because customers would not know the
price they would have to pay on their network usage at the time of their usage. Customers would only
know how much they were charged after the timing of peak network utilisation was established.
Ex ante probabilistic charging
Most existing network tariff levels and timings are set in advance of when they apply, providing
customers with certainty over how much they will be charged if they utilise the network at different
times and in different ways. For example, Ergon Energy’s published approved tariffs for 2014-15
provide customers with all the information they need to estimate their total network charges from their
intended electricity usage decisions. A customer on any given tariff can predict with a high degree of
precision what its bill is likely to be if it consumes particular amounts of electricity at particular times.
The drawback with ex ante tariff structures is that because the precise timing of peak network
utilisation each season or year is highly uncertain, it is not possible to structure tariffs in a way that
will precisely reflect the LRMC of utilising the network at different times throughout a regulatory
control period. Tariffs can only be structured on an expected probability-weighted basis, requiring
Ergon Energy to make an assessment – months or even years ahead of real-time – of when
episodes of peak network utilisation are most likely to occur. This is the rationale behind DNSPs
setting different tariff rates for designated ‘peak’, ‘shoulder’ and ‘off-peak’ periods. However, not only
is the appropriateness of such designated periods very rough initially, it is also likely to diminish over
time as network conditions change. For example, the increased take-up of solar PV by households
and businesses over time may defer the daily timing of peak utilisation on a network.
This drawback is exacerbated by the NER requirements around Tariff Structure Statements (TSS),
which require the timing of tariff structure periods to be fixed during the course of a regulatory control
period unless the TSS is varied.
Nevertheless, it may be possible to design ex ante tariff structures in ways that capture at least some
of the benefits of ex post charging in terms of utilising more timely information about network
utilisation peaks. For example, Critical Peak Pricing (CPP) is a form of ex ante charging that seeks to
provide signals to customers reflecting close-to-real-time information about likely peaks in network
utilisation. CPP typically involves DNSPs notifying customers 12-24 hours in advance of real-time of
the application of much higher tariffs than would normally apply at those times. CPP tariffs may apply
on up to a certain maximum number of occasions each season or year. For example, a CPP tariff
could allow the DNSP to inform customers by SMS or email of the calling of a ‘critical peak day’ by
6pm on the previous day. The calling of a critical peak day would mean that electricity consumption
during specified hours (say, 2pm to 8pm) would be charged at six times the peak period rate (eg.
$1/kWh instead of 16c/kWh). DNSPs could establish a framework that enabled them to call up a
limited number of critical peak days each tariff year. Under CPP, the DNSP can utilise very timely
information about weather forecasts, load and network outage conditions to send a much more
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
15
targeted signal to customers about likely network utilisation peaks than would be provided
otherwise10.
Similarly, to the extent that the timings of individual customers’ maximum demands are correlated to
the timing of network peak utilisation, charges based on individual customers’ maximum demands
may provide better signals than charges based on average demand over a pre-determined peak
period. For example, assume that a typical residential customer has an individual peak demand of
3kW on a typical summer day but 5kW on the system peak day. By charging the customer on the
basis of its individual maximum demand, the customer is likely to face a strong deterrent to
increasing its peak demand on the system peak day. This could help attenuate the level of the
system peak. On the other hand, the timing of a customer’s individual maximum demand may not
coincide with the timing of the system peak demand, meaning that an individual maximum demand
tariff could inappropriately:
(i)
penalise individual customer demand peaks, and
(ii)
fail to deter customer demand outside individual customer demand peaks.
Despite its limitations, as ex ante charging is by far the most common approach to setting network
tariff structures, the remainder of this document will refer only to ex ante tariff structure options.
5.
Relevant considerations for designing cost-reflective tariffs
This section discusses the key relevant considerations in designing ex post tariff structures that are
likely to signal LRMC most effectively and practicably.
5.1
Potential LRMC-signalling options
As neither Ergon Energy nor our customers have perfect foresight about the timing of network
utilisation peaks, the theoretical ideal tariff described in section 4.2 is not achievable. This means that
a choice needs to be made between a large number of potential ‘second-best’ ex ante tariff options.
The options – all based on an assumed LRMC of $236/kW/year – are as follows:
1. Maximum demand tariffs:
a) Annual maximum demand – customer pays $236/kW x annual maximum demand (in kW)
during designated peak periods (eg. 10am to 8pm working weekdays) over an entire tariff
year
b) Seasonal maximum demand – customer pays $236/kW x seasonal maximum demand (in
kW) during designated peak periods (eg. 10am to 8pm working summer weekdays) over
an entire summer period (December to February inclusive)
c) Seasonal monthly maximum demand – customer pays $78.67/kW x summer monthly
maximum demand (in kW) during designated peak periods in each summer month
(December, January and February)
10
In the November 2013 Tariff Implementation Report, Frontier noted that Ergon had investigated and
consulted on CPP as an option for tariffs in regional QLD. There was limited support for its introduction. In
addition, Frontier noted that in the context of managing Ergon Energy’s dispersed and non-coincident
distribution system peaks, the value of introducing CPP tariff structures is unlikely to exceed the costs for the
foreseeable future, especially given the available alternatives.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
16
2. Top ‘n’ maximum demand tariffs: As for (1) except that the customer pays a charge based on
a simple average of its highest ‘n’ demands over the relevant period. For example, if n=4 and
the customer’s:
a) highest half-hourly demand during the designated peak period is 5kW
b) second-highest half-hourly demand during the designated peak period is 4.5kW
c) third-highest half-hourly demand during the designated peak period is 4kW
d) fourth-highest half-hourly demand during the designated peak period is 3.5kW.
then the customer pays the LRMC rate x 4.25kW (being [5+4.5+4+3.5]/4). If the relevant
period is a summer month and the LRMC rate is $78.67/kW/year then the customer would
pay $334 for the month. If the relevant period is an entire summer or year and the LRMC rate
is $236/kW/year then the customer would pay $1003 for the year.
3. Scaled maximum demand tariffs: As for (1) except that the tariff (in $/kW) is scaled down to
reflect the lack of coincidence between individual customers’ maximum demands and the
timing of greatest network utilisation. For example, if the sum of individual customers’
maximum demands during the designated peak period (300MW) is three times the level of
peak network loading (100MW), the tariff rates in (1) are scaled down by two-thirds. This
scaling is greater the longer the designated peak periods. For example, if the designated peak
period is all year, then the sum of individual customers’ maximum demands will be larger than
if the peak period were only 20 hours a year. Scaling ensures that:
a) the DNSP recovers its forecast avoidable costs through the LRMC element of its tariff
b) if customers increase or decrease their individual demand profile in response to the tariff,
condition (a) continues to hold – the amount the DNSP needs to recover from residual
cost charges does not change.
4. Scaled top ‘n’ maximum demands tariffs: As for (2) except that the tariff (in $/kW) is scaled
down to reflect the lack of complete coincidence between individual customers’ maximum
demands and the timing of greatest network utilisation.
5. Average demand tariffs:
a) Time-of-use tariffs – customer pays a time-varying tariff on its average demand during
designated periods (eg. $236/[no. peak hours] x average peak period demand (in kW)).
Shoulder period tariff rates may be appropriate in some instances – see below
b) Critical peak pricing tariffs – customer pays a time-varying tariff that may be dramatically
higher than normal during a finite number of multi-hour periods notified 12-24 hours in
advance of real-time11.
6. Scaled average demand tariffs: As for (5) except that the tariff (in $/kW) is scaled up to reflect
the presence of some coincidence between individual customers’ average designated period
demands and the timing of greatest network utilisation. This scaling is greater the longer the
designated peak periods. For example, the sum of customers’ average peak period demands
(80MW) may be less than the level of peak network loading (100MW). In this case, the ($/kW)
tariff rate would be multiplied by 1.25 (being 100/80).
11
As noted earlier, other considerations around the introduction of CPP for pricing in Ergon Energy’s network
area were identified in the Frontier Economics Tariff Implementation Report, November 2013.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
17
7. Revenue reconciled demand or energy tariffs. The forecast coincident demand of the tariff
(applied to a number of customers) is used to determine the aggregate contribution of the
customers on that tariff to the LRMC.
Tariff contribution = LRMC rate x forecast coincident demand
The tariff contribution is then converted into a demand or peak energy rate to apply during the
peak periods when the network demand is expected to occur:
Demand (or peak energy) rate = tariff contribution/tariff volumes (kW or peak kWh)
This form of tariff setting is expected to deliver the appropriate contribution towards the
network LRMC.
5.2
Framework for choosing between options
The best way to choose between the multitude of tariff structure options outlined above is to start
from a restrictive and somewhat artificial set of assumptions about customer demand and network
usage and try to understand which option would provide the most efficient signals under those
assumptions. We can then see whether the preferred option changes as we individually relax those
restrictive assumptions to allow consideration of more realistic conditions. The final step is to
consider the best tariff when all assumptions have been relaxed.
As conditions become more realistic, it is likely that no single option will be clearly preferable and it
may be necessary to compromise on certain ancillary properties or benefits offered by some tariffs
but not others.
5.3
Base case assumptions and outcome
The initial set of assumptions is as follows:

Network costs (ie. the LRMC) is driven entirely (100%) by ZS annual peak demand: This
means that an individual customer’s maximum demand per se does not impose any costs on
Ergon Energy – it is only relevant in so far as it affects ZS annual peak demand.

The ex ante probability of ZS annual peak demand occurring is uniform across
all seasonal peak hours and remains equal right up to the commencement of real-time: For
example, assume there are 65 summer weekdays per annum and 10 peak hours per day (ie.
10am to 8pm) = 650 hours per annum. Before it is experienced, each summer peak hour is
assumed to have the same 0.15% chance of being the peak hour for the year and this probability
does not change even as the peak hour approaches.

The timings of customers’ maximum demands are independent of each other and of the
timing of ZS peak demand: In other words, customers’ maximum demands are highly diverse
and tell us nothing about the timing of other customers’ maximum demands or the timing of the
ZS annual peak demand.
To reiterate, these assumptions are intended to unrealistic, but they allow us to identify a clearly
preferable option. In this case, it is an average seasonal demand tariff of $0.36/kW/peak hour (or
equivalently, $0.36/peak kWh). This is derived from $236/kW/650 peak hours = $0.36/average
kW/peak hour.
The reason this option is preferable is that, by assumption, the probability of experiencing the annual
ZS peak demand – and hence the highest level of network utilisation for the year, which drives the
need for network augmentation – is the same, ex ante, across all summer peak hours and this
probability does not change as each peak hour approaches.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
18
This means that the benefit of incrementally reducing demand (or alternatively, the cost of
incrementally higher demand) is the same on an ex ante basis across all peak hours. Therefore,
customers should face a tariff structure that provides the same price signal across all 650 peak
hours. Further, because an individual customer’s maximum demand is assumed to not be a driver of
costs, there is no need to impose a charge on individual customers’ maximum demands.
5.4
Extending the basic model
The next step in the design of the LRMC signalling tariff is to gradually relax the base case
assumptions and test whether and how this may alter the structure of the tariff best equipped to
signal future costs.
LRMC not entirely driven by ZS annual peak demand
Relaxing the first of the restrictive base case assumptions allows us to consider the implications of
network costs not being driven entirely by the annual peak demand at the relevant customer’s ZS. All
of other assumptions continue to hold.
It is likely that some distribution system costs (eg. LV network) will be driven by demand peaks
geographically or electrically ‘closer’ to the customer than the ZS. If this is the case, it suggests that
individual customer maximum demands impose some costs additional to or separate from the costs
they impose through their contribution to peak demand at the ZS. This suggests that some weight
should be placed on the level of an individual customer’s maximum demand in determining the best
tariff.
For example, if:

An LV-inclusive AIC LRMC is $236/kW/year, and
o
20% of the costs incorporated in the AIC (ie. $47/kW/year) are imposed by the impact of an
individual customer’s maximum demand on the LV network regardless of the timing of the
maximum demand at the relevant ZS, and
o
80% of the costs incorporated in the AIC (ie. $189/kW/year) are imposed by the impact of the
customer’s demand on coincident peak demand at the ZS, which is the primary driver for
augmentation of the LV, HV and subtransmission networks,
... then the customer should face a tariff incorporating:

$47/max kW/year to reflect the fact that an increase in the customer’s maximum demand leads to
the need for Ergon Energy to upgrade the LV network near the customer, and

$0.29/average kW/peak hour to reflect the fact that an increase in the customer’s demand at any
time during the peak period contributes to the probability-weighted need for Ergon Energy to
upgrade the HV and ST networks and those part of the LV network more remote from the
customer.
Probability of ZS annual peak demand not uniform across peak times
In reality, the probability of attaining the annual peak demand at a ZS is unlikely to be equal across
all designated ‘peak’ hours and remain equal as real-time approaches. It is more likely that the
probability of attaining the annual ZS peak will not be uniform across all peak hours when tariffs are
set; and even if the probability is uniform at the time tariffs are set, it certainly will not be equal as
real-time approaches. This has implications for the design of the preferred tariff.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
19
Probability of reaching peak annual demand is not uniform when tariffs are set
Assume that the designated peak summer period is 10am to 8pm on weekdays from 1 December to
28 February. Based on historical experience, it may be possible for Ergon Energy to infer that the
probability of reaching the relevant ZS peak will be higher between 4pm to 8pm over the thirty
weekdays from 12 January to 20 February inclusive than during other ‘peak’ hours. If this is the case,
it would make sense for Ergon Energy to refine peak periods accordingly before setting tariffs, with
the aim of ensuring that the designated peak period truly reflects hours for which there is a similar
probability of experiencing the ZS annual peak demand. The refined peak hours could then be called
‘super-peak’ hours, or alternatively, the refined hours could be called ‘peak’ hours and the remaining
formerly-peak hours could be referred to as ‘shoulder’ hours. If this refining is done, the peak tariff
rate will rise to reflect the concentrating of the LRMC signal across a shorter period of time. For
example, if the peak period is narrowed to four hours a weekday for six weeks a year, the peak rate
will rise to $1.97/average kW/peak hour (being $236/kW/[4x5x6]), or equivalently, $1.97/peak kWh.
Shoulder period charging
The tariff rate for shoulder periods should be based on the probability of attaining the ZS annual peak
demand during a shoulder hour, which by definition should be lower than the probability of attaining
ZS annual peak demand during a peak hour. For example, assume that LRMC is $236/kW/year and
the probability of reaching the ZS annual peak demand is 80% during the peak 120 hours (4pm to
8pm, 12 January to 20 February) and 20% during the 530 shoulder hours (being non-peak summer
weekday hours from 10am to 8pm). This would mean that the:

peak tariff rate would be $1.57/average kW/peak hour (ie. [0.8x236/kW]/120)

shoulder tariff rate would be $0.09/average kW/shoulder hour (ie. [0.2x236/kW]/530).
Probability of reaching peak annual demand not equal as real-time approaches
Even if it is not possible to utilise historical data to narrow down the designated peak period well in
advance, it is likely that the ex ante probability of attaining peak demand will change as real-time
approaches and more information about future demand and network conditions becomes available.
The fact that new information regarding the likely timing of the ZS peak will become available over
time can be used to design a more appropriate tariff.
For example, after having received a weather forecast for a severe heatwave in the first week of
February across all of Ergon Energy’s network area (or across the east or west zone), it is
theoretically possible for Ergon Energy to infer that the probability of reaching the annual ZS peak
demand during the week of the expected heatwave is much higher than during a normal summer
week. Ergon Energy could utilise this information through the application of a CPP tariff and by
calling a critical peak day on one or more of the weekdays during the heatwave. If a CPP tariff is
developed that can be applied for up to six hours on a dozen days a year (72 hours in total), and if
we are 90% confident that the annual system peak will occur during the critical peak hours we call,
then the critical peak rate will rise to $2.95/average kW/critical peak hour (being $236x0.9/kW/72), or
equivalently $2.95/critical peak kWh.
There are a number of reasons why such an alternative has practical issues when attempting to
apply to Ergon Energy’s customer base. As an alternative to a critical peak tariff Ergon Energy may
also consider the use of tariff incentives and direct control incentives for customers to reduce
demand (ie. carrots, rather than sticks). Ergon Energy has developed the concept of a “dynamic
layer” in its December 2014 consultation paper and will further develop the idea of dynamic layer
pricing in conjunction with broad based tariff reform.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
20
Customers’ maximum demands not independent
Even if the probability of attaining the ZS annual peak demand is not uniform as real-time
approaches, it may not be appropriate for Ergon Energy to adopt a CPP tariff structure. CPP has a
number of drawbacks, such as the difficulty of explaining the tariff to retailers and customers, and the
difficulty of informing retailers and customers of the calling of a critical peak day. Further, a DNSP
may call the ‘wrong’ days as critical peak days and in so doing exhaust all of its opportunities to call a
critical peak day just when it matters, such as towards the end of a prolonged heat wave. Such
tariffs, if widespread, could materially impact the variability of revenue. This is undesirable with the
revenue cap form of regulatory control as it would cause year-to-year price variations and in the case
of a price cap would introduce business risk for the distributor.
Another way to make use of newly available information about demand conditions in the lead-up to
real-time is to design tariff structures that leverage any reliable correlation between different
customers’ maximum demands. This involves relaxing the final base case assumption that
customers’ maximum demands are independent of one another. In reality, while customers’
maximum demands are not 100% (perfectly) correlated with one another, they are also not
completely independent. For example, if we assume two household customers, A and B, it is likely
that A’s and B’s demands will be both higher at 5pm on a hot summer day than at 5pm on a mild
summer day. If the timings of individual customers’ maximum demands are correlated in such a way,
this suggests that there may also be a correlation between the timing of an individual customer’s
maximum demand and the timing of peak utilisation of the network. Under these conditions, a tariff
based on customers’ individual maximum demands could help deter network usage at times that are
relatively likely to be the ZS or system peak demand.
Analysis by external consultants Energeia found that the typical coincidence between SAC-Small
customers’ maximum demands and system annual peak demand was approximately 0.33 and the
coincidence with ZS annual peak demand was approximately 0.37.
This fairly low apparent degree of coincidence could be interpreted in three ways:
 Demand diversity within customer tariff classes – eg it may be that SAC-Small customers’
demands are very peaky but they peak in fairly divergent ways from one another. To test this
hypothesis, one could compare the above Energeia SAC-Small coincidence factors with the
coincidence between SAC-Small customers’ maximum demands and peak demand for the entire
SAC-Small class. If the typical coincidence between SAC-Small customers’ maximum demands
and peak demand across the SAC-Small class is not much greater than the typical coincidence
between SAC-Small customers’ maximum demands and the system or ZS peak demand, then
this is likely to be the correct interpretation.

Intra-day demand diversity between customer tariff classes – eg. it may be that SAC-Small
customers typically peak at similar times to one another, but at different times on the same day to
other customer tariff classes (eg. SAC-Large, CAC). To test this hypothesis, it would be useful
knowing the correlation between the daily peak demand of the SAC-Small customer class and the
daily peak demand of other customer tariff classes. If the typical day-coincidence between the
peak demands of the SAC-Small customer class and other customer classes is high, then this is
likely to be the correct interpretation.

Inter-day demand diversity between customer tariff classes – eg. it may be that SAC-Small
customers typically peak at similar times to one another, but on different days to other customer
tariff classes (eg. SAC-Large, CAC). If the typical day-coincidence between the peak demands of
the SAC-Small customer class and other customer classes is low, then this is likely to be the
correct interpretation.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
21
The implications of these alternatives are discussed below.
Demand diversity within customer tariff classes
If customers within the same tariff class tend to reach their individual maximum demands at different
times to one another – ie. the timings of their maximum demands are not correlated – this suggests
that an individual, say, SAC-Small customer’s maximum demand is unlikely to provide any useful
information about the timing of the system or ZS annual peak demand. If this is the case, there
seems to be little point in basing the LRMC charge on individual customers’ maximum demands.
Intra-day diversity in maximum demands with other customer tariff classes
If customers within the same tariff class tend to reach their individual maximum demands at similar
times to one another but at different times during the same day as customers in other tariff classes,
this suggests that customers’ individual maximum demands do provide useful information about the
day-timing (if not the precise hourly timing) of the ZS or system peak demand. Therefore, charges
based on customers’ individual maximum demands may provide better signals than charges based
on average demand over a pre-determined peak period.
Average demand on top “n” demand days
An even better option than an individual maximum tariff may be to apply a seasonal monthly top ‘n’
structure across the three summer months in which the customer is charged on its average demand
during a designated multi-hour peak period on each of the days on which it attains its top ‘n’
maximum demands. If, say, n=4 and there are 10 peak hours on a peak day, the customer would be
charged $78.67 x average kW over the 40 peak hours contained within those four days for the
relevant summer month. This could be described as a customer self-selected or ‘DIY’ CPP tariff
because the customer effectively determines when its own critical peak day is called based on its
individual maximum demands. Whether the required level of participation by small customers could
be generated and sustained for such a tariff is a moot point.
Inter-day diversity in maximum demands with other customer tariff classes
If customers within the same tariff class tend to reach their individual maximum demands at similar
times to one another but on different days to customers in other tariff classes, this suggests that
individual maximum demands do not provide particularly useful information about the timing of the ZS
or system peak demand. Accordingly, if this is the case, there seems to be little point in basing the
LRMC charge on customers’ individual maximum demands or adopting something like the average
demand on top “n” demand days tariff described above.
The appropriateness of scaling LRMC-based tariffs
The opposite of customer maximum demand correlation is demand diversity. If all customers’
maximum demands were perfectly correlated, that would imply zero demand diversity. It would also
mean that the sum of all customers’ maximum demands would equal the ZS (and system) peak
demand. This would imply a scaling factor of 1 (ie. no scaling) under tariff options 3 and 4.
If tariffs are based on individual customers’ maximum demands, then the less correlated are
customers’ maximum demands, the higher will be the scaling factor applied under options 3 and 4.
The application of scaling raising two questions:

first, is scaling likely to achieve its primary objective of ensuring the DNSP recovers its forecast
avoidable costs through the LRMC component of its tariffs; and
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
22

second, even if scaling achieves its primary objective, does scaling provides the right economic
signals to customers.
Is scaling likely to achieve its objective?
As noted above, the objective of scaling is to ensure the recovery of forecast avoidable costs through
the LRMC element of the tariff and that if customers increase or decrease their individual maximum
demand in response to the tariff, the amount needed to be recovered from residual cost charges
would not change. The need for scaling to achieve this objective arises due to the imperfect
coincidence between individual customers’ maximum demands and the timing of peak loading on the
network.
The scaling factor for an individual maximum demand tariff is derived by dividing the relevant network
annual peak demand (system peak or the sum of ZS peaks) by the sum of customers’ individual
maximum demands. Energeia estimated the scaling factor for SAC-Small customers as 0.33 when
considering system peak demand and 0.37 when considering ZS peak demands. The assumption
behind scaling is that, on average, individual customers’ demands during a network peak will be
approximately equal to their individual maximum demands multiplied by the relevant scaling factor. A
further important assumption behind scaling is that to the extent customers respond to an individual
maximum tariff by reducing their maximum demands, they will reduce their coincident system or ZS
demands in the same proportion as they reduce their maximum demands.
Take the following example – assume that:

SAC-Small customers originally face a flat anytime energy tariff and subsequently face an
individual maximum demand tariff.

due to limited coincidence between SAC-Small customers’ maximum demands and the system
peak demand, the scaling factor for SAC-Small customers is 0.33. Therefore, if the LRMC
estimate is $236/kW/year, the scaled maximum demand tariff to SAC-Small customers will be
$78.67/kW/year.

the typical SAC-Small customer has an annual maximum demand of 5kW and a system peak
coincident demand of 1.67kW (ie. 0.33 of its maximum demand of 5kW).

facing a maximum demand tariff of $78.67/kW/year, SAC-Small customers will reduce their:
o
individual maximum demands by 20% to 4kW, and
o
system coincident demands by 20% to 1.34kW.
If this occurred, the DNSP’s:

future avoidable costs would fall by $78.67 in present value terms (being 0.33kW x $236) and

revenues from LRMC charges would also fall by $78.67 (being $78.67/kWx1kW).
This illustrates the symmetry between the DNSP’s costs and LRMC-related revenues that scaling
seeks to achieve.
However, it is unclear whether the (~3:1) relationship between a customer’s maximum demand and
its coincident demand at the system or ZS peak will remain stable if the customer shifts from an
anytime energy tariff to an individual maximum demand tariff. A rational customer would have
incentives to reduce only its maximum demand rather than its coincident ZS or system peak demand.
In other words, it would have incentives to flatten its load profile. For example, the typical SAC-Small
customer could reduce its maximum demand by 20% from 5kW to 4kW but not change its coincident
system peak demand of 1.7kW.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
23
If this occurred, the DNSP’s future avoidable costs would not change, while its revenues would fall by
$78.67. Of course, any disconnect between revenues and costs would be even greater in the
absence of scaling. For example, without scaling, the DNSP’s revenues could fall by $236 while its
costs may not fall at all. Accordingly, the potential lack of stability between a customer’s maximum
demand and its coincident system or ZS peak demand is a problem relating to maximum demand
tariffs more generally.
Does scaling improve the economic efficiency of tariff signals?
The achievement of symmetry between a DNSP’s avoidable costs and its LRMC-related revenues is
not determinative of whether price signals are economically efficient. Note that in a conventional
competitive market (see
Figure ) in which price equals marginal cost, suppliers will tend to earn revenues (areas A+B) much
higher than their avoidable costs (area B).
Figure 2: Competitive market prices, revenues and avoidable costs
$/MWh
Equilibrium
Supply =
marginal
cost
Price
A
B
Demand
= WTP
Quantity
MWh
Indeed, in most markets this is necessary for producers to recover their sunk costs – most producers
do not have the ability to charge multi-part tariffs, with one part to recover marginal/avoidable costs
and another part to recover sunk costs. Setting a price equal to avoidable cost – LRMC in the present
case – can therefore be efficient even if it results in over-recovery of avoidable costs.
A better reason for scaling network tariffs based on individual maximum demands is that if scaling
does not occur, customers face the LRMC rate on their individual maximum demands even though
these generally do not coincide with the system or ZS peak demand.
This means that if scaling is not undertaken, SAC-Small customers will face:

the correct LRMC signal to the extent that their individual maximum demand coincides with the
system or ZS peak, and

an excessively strong LRMC signal to the extent that their individual maximum demand does not
coincide with the system or ZS peak.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
24
Conversely, adopting scaling of maximum demand tariffs would mean that SAC-Small customers will
face:

an excessively weak LRMC signal to the extent their individual maximum demand coincides with
the system or ZS peak, and

an excessively strong LRMC signal to the extent their individual maximum demand does not
coincide with the system or ZS peak.
This means that the decision of whether or not to apply scaling of individual maximum demand tariffs
involves trading-off the costs of:

over-signalling LRMC to most customers (without scaling) against

under-signalling LRMC to a few customers and over-signalling LRMC to most customers (with
scaling).
Therefore, determining the most efficient (or least inefficient) approach is an empirical matter.
5.5
Choosing a tariff structure
The process of choosing the best available tariff structure involves:

assessing the extent to which and manner in which real-world conditions diverge from the base
case assumptions described above and

assessing the likely empirical consequences of making various compromises or trade-offs
between different tariff options – taking account of the risks of under- or over-signalling LRMC to
different customer classes under each tariff structure.
Stylised example 1: SAC-Small residential customer class
Assume the following stylised facts:

on average, SAC-Small residential customers’ maximum demands coincide one-third with system
annual peak (ie. scaling factor of 0.33)

on average, SAC-Small residential customers’ maximum demands coincide:
o
Two-thirds (0.67) with the SAC-Small residential customer class annual peak – meaning that
the typical SAC-Small residential customer is consuming two-thirds of its annual maximum
demand at the time of the class annual peak demand.
o
One-quarter (0.25) with the combined annual peak of the SAC-Small business, SAC-Large and
CAC customer classes – meaning that the typical SAC-Small residential customer is
consuming one-quarter of its annual maximum demand at the time of the combined annual
peak demand for the commercial customer classes.
This means that between-class demand diversity between SAC-Small residential customers
and the commercial classes is greater than the within-class diversity amongst SAC-Small
residential customers.

The correlation between the daily peak of the SAC-Small residential customer class and the daily
peak of the commercial customer classes is 0.9. This means that the residential and commercial
customer classes tend to attain high levels of demand on similar days.
Under these conditions, it may be worth departing from the simple average peak demand tariff that
would be optimal under the base case assumptions. If CPP was not practicable, some sort of top ‘n’
periods tariff could be applied.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
25
Some of the key questions to be addressed would then be:

Should the tariff be based on customers’ individual maximum demand or their average demand
across a designated peak period?
Given the relatively low coincidence between SAC-Small residential customers’ maximum
demands and the system peak demand and the strong correlation between the daily peaks of the
residential and business customer classes, it may be worth basing the tariff to each SAC-Small
customer on its average demand during a calibrated system peak period (say, 12 noon to 8pm).
This would fit the description of a self-called or ‘DIY’ CPP of the type outlined above. The purpose
of this tariff would be to deter SAC-Small residential customers from consuming electricity
throughout periods likely to incorporate the system peak, even if such periods extend well beyond
those incorporating the demand peak for the SAC-Small residential class itself. However, such a
tariff may, like conventional CPP, be impracticable to explain and apply in practice, and a top ‘n’
maximum demand tariff may on balance be preferable.

If a top ‘n’ tariff is applied to SAC-Small customers’ individual maximum demands are customers
likely to reduce their maximum and system peak coincident demands in the same proportions or
are they likely to respond by flattening the shape of their load profiles?
Given the relatively low coincidence between typical SAC-Small residential customers’ maximum
demands and the timing of the annual system peak, rational customers should be able to safely
maintain their demand at coincident peak times and reduce their charges by focusing on reducing
their individual maximum demands. If this occurred, the outcome would be inefficient overconsumption by residential customers at system peak times. However, if SAC-Small residential
customers responded by reducing their entire summer load profile – say, by installing more
energy-efficient appliances – then the outcome could be more efficient.

Should scaling be applied?
As noted above, whether scaling should be applied depends on the respective risks and costs of
over- and under-signalling the cost of consumption at the time of the system peak demand. Due
to the low coincidence between SAC-Small residential customers’ maximum demands and the
system peak demand in this example, it may be that risk and cost of over-penalising residential
customers’ maximum demands by not scaling is greater than the risk and cost of underpenalising a few customers’ maximum demands by scaling. If so, scaling should be applied.
Stylised example 2: ‘party’ house vs ‘air-con’ house
A stylised example Ergon Energy sometimes uses to test the appropriateness of alternative tariff
structures is the comparison between two hypothetical SAC-Small residential customers known as
the ‘party house’ and the ‘air-con house’:
The party house represents a customer who throws a monthly party but is otherwise often absent –
this customer has a monthly maximum demand of 8kW but its next seven-highest monthly demands
are only 4kW.
The air-con house represents a customer who is typically home and runs air-conditioning daily – this
customer has a monthly maximum demand of 8kW and its next seven-highest monthly demands are
also 8kW.
Assuming:
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
26

most of Ergon Energy’s costs of serving both customers arise from the need to meet demand at
the relevant ZS (ie. most costs are not driven by LV augmentation ‘close’ to the customer), and

the ex ante probability of the ZS peak arising is uniform across the designated 650-hour peak
period and remains uniform up until real-time,
… then a peak average demand tariff of 25c/peak kWh would appear to make most sense.
However, in the more realistic case that customers’ demands are correlated and hence that an
individual customer’s maximum demand provides information about the timing of the ZS peak
demand, it may be more appropriate to apply a top ‘n’ maximum demands tariff. If n=8 and the LRMC
rate was $78.67/monthly average maximum demand kW/year, this would produce the following
monthly charges (assuming no scaling) for the:

party house: $354/month – based on [(8kW+(7x4kW))/8x$78.67]

air-con house: $629/month – based on [(8x8kW)/8x$78.67].
The application of scaling would reduce these charges on a proportionate basis. A ‘DIY’ critical peak
tariff similar to the one described above could also be applied in this situation. As the probability that
the party house will impact at the time of peak demand for the zone substation is less, this outcome
seems reasonable.
5.6
Locational LRMCs and the ‘dynamic layer’
Determining averaged or ‘generic’ LRMCs to apply across broad geographic networks (eg. East,
West, Mt Isa), different voltage levels and lengthy time periods implies a degree of abstraction from
the actual underlying LRMC applicable at any particular location and time to a particular customer.
Such generic LRMCs can only approximate the ‘true’ LRMC.
However, deriving individual LRMCs to enable tariffs to be set for every distribution connection point,
for every potential size and type of customer and at different points in time would be extremely timeconsuming and impracticable. Any such LRMCs developed would only be as robust as the analysis
used to derive them – unlike market-determined prices (such as wholesale spot prices), network
tariffs (whether based on LRMC or some other metric) do not reflect demand and supply from profitseeking agents, but are the outworkings of models that rely on a host of assumptions.
An alternative to determining a multitude of LRMCs is to overlay a ‘dynamic layer’ of bespoke
demand management or other non-network incentives/mechanisms on top of generic LRMCs to
encourage efficient non-network options in specific locations at specific times where generic LRMCs
are likely to be particularly inaccurate. Such bespoke options can be developed by using our
knowledge of specific sections of the network and its loading conditions and particular customer
characteristics.
For example, the LRMC for Ergon Energy’s East Zone may be $213/kVA/year, but a major network
bottleneck may be developing in, say, Rockhampton: the next 50MVA of demand may require a
major network upgrade in Rockhampton that would have a levelised cost equivalent to
$500/kVA/year. Instead of formulating a local LRMC to reflect the high cost of serving the next
increment of demand and using this figure to adjust the local tariffs in an attempt to promote efficient
decision-making by current and prospective customers, it may be better to request offers from
customers and third parties to provide demand-side management or distributed generation solutions.
So long as the cost of these options was less than the cost of a network augmentation, it would be
worthwhile to contract for these options and recover the costs through network charges, by-passing
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
27
the need to formulate a specific localised LRMC that would in most cases provide little additional
benefit.
The use of a dynamic layer approach lacks the visibility of a pure tariff-based approach to promoting
efficient decisions. However, it is likely to be less assumptions-driven, quicker and less costly in
achieving the same desired (efficient) outcomes.
Ergon Energy is likely to face a trade-off between relying on:

more locationally- and temporally-refined LRMCs to set tariffs to promote efficiency, as compared
to

relatively generic LRMCs with a dynamic layer of non-tariff procurement or contracting of nonnetwork options.
Wherever on this spectrum we ultimately progress with we will need to be able to explain and justify
our position to the AEMC and AER; both of whom are likely to favour the use of more refined LRMCs
to the greatest extent possible.
6.
Options for the residual charge
6.1
Conceptual considerations
Regulated revenue not recovered through the first-part signalling charge should be recovered in a
manner that has as little influence as possible on patterns of electricity demand.
A number of choices are available to recover residual costs – these include:

fixed charges ($/day)

off peak or anytime energy charge (c/kWh)

top ‘n’ off-peak demand or capacity charge ($/kW capacity).
Higher fixed charges are unlikely to lead to network by-pass, at least for the foreseeable future.
However, they are likely to have negative distributional effects for lower-consuming customers within
each tariff class.
Off-peak energy tariffs in aggregate rise with the off-peak consumption of the customer. This should
have favourable distributional and efficiency effects to the extent that off-peak energy consumption is
correlated with customer size and willingness to pay. However, such charges will also tend to
inefficiently deter off-peak network usage (eg. irrigation customers), because the opportunity cost of
such use will generally be very low. If the rate is set low, the negative effect on usage should be
similarly small. As off-peak periods are often overnight, installation of solar PV would not be likely to
enable customers to avoid the charge. If weekends are off-peak periods, then the charge may
encourage inefficient installation of solar PV to enable PV customers to avoid some of the charge.
Off peak energy tariffs have the effect of penalising high load factor customers. This can be a
particular issue with customers that have energy intensive industrial processes.
Off-peak capacity tariffs also rise with the maximum off-peak demand of the customer. This should
again have favourable distributional and efficiency effects. However, like off-peak anytime energy
rates, such charges will tend to inefficiently deter off-peak network usage, especially where off-peak
usage is occasional or sporadic (eg. irrigation customers). This type of charge is much harder to
avoid by installing solar PV. But it may be avoidable by a combination of PV and storage, or just
storage.
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
28
Off-peak charges can have similar distortionary effects as inappropriate peak time periods. The harm
caused by these charges depends on both the:

price elasticity of off-peak demand or consumption – the more price-elastic is off-peak demand,
the larger the distortionary impact from a given off-peak tariff

elasticity of substitution between off-peak and peak demand or consumption – the more willing
customers are to shift off-peak consumption to peak periods, the larger the distortionary impact
from a given off-peak tariff.
If the price elasticity of demand during both off-peak and peak periods is relatively low but the offpeak to peak elasticity of substitution is relatively high, then one option may be to impose an off-peak
demand charge and augment the peak demand charge (ie. set it above LRMC) in order to maintain
the differential between peak and off-peak charges for use of the network. Under these assumptions,
the effect of the higher charge on peak usage should be relatively low and it should help to deter
load-switching from off-peak to peak periods.
7.
Abbreviations
AEMC
Australian Energy Market Commission
kW
kilowatt
AER
Australian Energy Regulator
kWh
kilowatt hour
ACCC
Average Capital Cost of Capacity
kVA
kilovolt ampere
AIC
Average Incremental Cost
LRIC
Long Run Incremental Cost
BCS
Benchmark Cost of Supply
LRMC
Long Run Marginal Cost
BSPs
Bulk supply points
MW
megawatt
CAC
Connection Asset Customers
OFA
Optional Firm Access
CDCM
Common Distribution Charging Methodology
NEM
National Electricity Market
CPP
Critical Peak Pricing
NER
National Electricity Rules
CRNP
Cost-Reflective Network Pricing
PV
Photovoltaic
DCOS
Distribution Cost of Supply
SAC-Large
Standard Asset Customers – Large
DNSPs Distribution Network Service Providers
SAC-Small
Standard Asset Customers – Small
DUOS
Distribution Use of System
SRMC
Short Run Marginal Cost
DS
Distribution substations
ToU
Time-of-Use
FLIC
Forward Looking Incremental Cost
TSS
Tariff Structure Statement
HV
High Voltage
ZS
Zone substation
ICC
Individually Calculated Customers
WACC Weighted Average Cost of Capital
ICRP
Investment Cost Related Pricing
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
29
Supporting Document: Long Run Marginal Costs Considerations in Developing Network Tariffs
2