Description
This paper aims to present a multi-period dynamic power project financing model to
produce pragmatic estimates of benchmark wholesale power prices based on the principles of
normal profit. This, in turn, can guide policymakers as to whether price spikes or bidding above
marginal cost in wholesale electricity markets warrants any investigation at all. One of the
seemingly complex areas associated with energy-only wholesale electricity pools is at what point
market power abuse is present on the supply side. It should not be this way. If a theoretically robust
measure of normal profit exists, identification of potential market power abuse is straightforward.
Such a definition readily exists and can be traced back to the ground-breaking work of financial
economists in the 1960s
Journal of Financial Economic Policy
What is normal profit for power generation?
Paul Simshauser J ude Ariyaratnam
Article information:
To cite this document:
Paul Simshauser J ude Ariyaratnam , (2014),"What is normal profit for power generation?", J ournal of
Financial Economic Policy, Vol. 6 Iss 2 pp. 152 - 178
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What is normal proft
for power generation?
Paul Simshauser
Department of Accounting, Finance & Economics, Griffth University,
Brisbane, Australia and AGL Energy Ltd, Melbourne, Australia
Jude Ariyaratnam
AGL Energy Ltd, Melbourne, Australia
Abstract
Purpose – This paper aims to present a multi-period dynamic power project fnancing model to
produce pragmatic estimates of benchmark wholesale power prices based on the principles of
normal proft. This, in turn, can guide policymakers as to whether price spikes or bidding above
marginal cost in wholesale electricity markets warrants any investigation at all. One of the
seemingly complex areas associated with energy-only wholesale electricity pools is at what point
market power abuse is present on the supply side. It should not be this way. If a theoretically robust
measure of normal proft exists, identifcation of potential market power abuse is straightforward.
Such a defnition readily exists and can be traced back to the ground-breaking work of fnancial
economists in the 1960s.
Design/methodology/approach – Using a multi-period dynamic power project model, the authors
produce pragmatic and theoretically robust measures of normal proft for project fnanced plant and
plant fnanced on balance sheet. These model results are then integrated into a static partial equilibrium
model of a power system. The model results are in turn used to guide policymaking on generator
bidding in energy-only power markets.
Findings – Under conditions of perfect plant availability and divisibility with no transmission
constraints, energy-only markets result in clearing prices which are not economically viable in the long
run. Bidding must, therefore, deviate from strict short-run marginal cost at some stage. To distinguish
between quasi-contributions to substantial sunk costs and market power abuse, a pragmatic and robust
measure of normal proft is required.
Originality/value – This article fnds policymakers can be guided by an ex-post analysis of base
energy prices against pragmatic estimates for the long-run marginal cost of the base plant, and an
ex-ante analysis of call option prices along the forward curve against pragmatic estimates of the
carrying cost of the peaking plant.
Keywords Proft, Project fnance, Electricity prices
Paper type Research paper
1. Introduction
Transient price spikes in gross pool, energy-only, uniform frst-price auction wholesale
markets typically trigger regulatory inquiry. Policy intervention may be warranted if
supranormal profts are being extracted on a sustained basis through generator bidding
behaviour in the presence of demonstrable barriers to entry. But episodes of transient
price spikes in energy-only markets should not represent an “automatic” trigger for
JEL classifcation – D61, L94, L11, Q40
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JFEP
6,2
152
Journal of Financial Economic Policy
Vol. 6 No. 2, 2014
pp. 152-178
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-09-2013-0045
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inquiry, let alone policy intervention, particularly if underlying prices are otherwise
driving merchant generators to a state of fnancial distress.
Economic theory has long demonstrated that energy-only spot electricity markets
can clear demand reliably and can provide suitable investment signals for newcapacity
(Schweppe et al., 1988). But such analyses typically presume unlimited market price
caps, limited political or regulatory interference, and by deduction, a largely equity
capital-funded generation feet able to withstand wildly fuctuating business cycles.
Unfortunately, the real world is not this convenient.
Rarely are generators devoid of scheduled debt repayments and so theories of spot
energy markets suffer from the inadequate treatment of how non-trivial sunk capital
costs are fnanced (Peluchon, 2003; Joskow, 2006; Finon, 2008; Caplan, 2012; Nelson and
Simshauser, 2013). Additionally, wholesale price caps do exist and are enforced – in
some instances excessively so – by regulatory authorities (Besser et al., 2002; Oren, 2003;
de Vries, 2003; Wen et al., 2004; Finon and Pignon, 2008, Joskow 2008a, Simshauser,
2010). Intensely competitive energy-only markets are known to produce inadequate net
revenues to support investment in the optimal least cost-generating portfolio, otherwise
known as the “missing money” problem (Neuhoff et al., 2004; de Vries, 2004; de Vries
et al., 2008; Bushnell, 2005; Roques et al., 2005; Cramton and Stoft, 2006; Joskow, 2008b;
Finon, 2008; Simshauser, 2008). In short, given substantial fxed and sunk costs
associated with power generation, and low marginal production costs, persistent
generator bidding at short-run marginal cost does not result in a stable equilibrium
(Bidwell and Henney, 2004; Simshauser, 2008; Nelson and Simshauser, 2013). Generator
bidding must, therefore, deviate from strict marginal cost at some point. But given an
oligopolistic market environment, at what point might this cross a line and represent a
potential abuse of market power that warrants investigation and possible intervention
by policymakers? If a pragmatic measure of normal proft exists, then contrasting
normal proft with a combination of actual (historic) base-load spot prices and futures
prices for call option contracts can provide suitable guidance.
Identifying a theoretically sound measure of normal proft is of vital importance to
the industry, policymakers and, in turn, the overall effciency and welfare of the
macroeconomy. The allocation of resources within an economy relies quite
fundamentally on such constructs, whether this is through the investment decisions of
frms, or by decisions made by policymakers that infuence or directly impact on
investment outcomes. But normal proft needs to be very carefully defned.
The concept of normal proft is unremarkable in economics but, oddly enough, is very
rarely identifed in practice. This is primarily because proft announcements by frms
(and reported by the media) are dominated by accounting statistics and ratios based on
the traditional measure of net proft after tax (NPAT). Finance theory is clear – NPATis
nothing more than a residual measure. When NPAT is determined by traditional
accounting axioms, it represents the residual funds available for distribution to
stockholders after all other claimants have been satisfed. NPAT does not describe the
“pitch” of profts generated, i.e. normal or otherwise.
To assess the pitch of profts, a suitable benchmark must frst be defned. Economic
theory has long told us that this is measured by the opportunity cost of funds used. By
the 1960s, the economics profession had produced profoundly more refned quantitative
defnitions, viz., the marginal cost of capital or expected returns of rational investors
based on a one-factor, two-parameter model of risk and return. The origins of the capital
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asset pricing model can be traced back to Sharpe (1964) and Lintner (1965). If profts of
the frm meet the marginal cost of capital, a normal proft has been generated.
Supranormal profts are achieved when proft exceeds the cost of capital and,
conversely, economic losses occur when returns fail to meet expected returns.
In our experience, a surprising number of policymakers, regulators and
microeconomics professionals (from academia and industry) misinterpret the
appropriate quantitative value for the marginal cost of capital. The reason for this, we
suspect, is that the theoretical construct of “opportunity cost” must shift from theory to
practice, all the while ensuring that in the process, the result does not violate the basic
axioms and constraints of credit metrics and taxation implications – all of which are
important in defning the true marginal effciency of equity and debt capital. The shift
from theory to practice is thus grounded in fnancial economics, not microeconomics.
For highly capital-intensive industries like power generation, temporal issues
associated with accounting results create further complications. Accounting numbers
use arbitrary reporting periods (e.g. monthly, quarterly and annually) to measure
NPAT. While this is both desirable and necessary for managerial and agency purposes,
discrete accounting period reporting is fundamentally a static analysis, whereas for
investment or policymaking purposes, normal proft should be considered in a dynamic
context. The expected returns of capital-intensive assets occur over multiple decades
and are subject to (nominally) 5-7-year business cycles in power generation, with actual
returns oscillating considerably around the mean outcome. Normal proft, then, must be
considered as a multi-period dynamic inquiry rather than a static analysis based on a
collection of half-hour price spikes during a short episode of hot weather, for example.
In economics literature, from a benchmark perspective, the internal rate of return
(IRR) of investments is the most prominently used theoretical long-run proftability
concept, as explained by Salmi and Virtanen (1997). Finance literature, on the other
hand, has a distinct bias towards net present value analysis, although as Brealey et al.
(2011) observe, IRR comes from respectable ancestry and, for our purposes, can be
expected to provide conforming results under all of the conditions that we envisage in
this article[1].
From a competition perspective, using a normal proft benchmark, analysed over a
business cycle, provides a suitable threshold for regulatory authorities to consider. Normal
proft is also of central importance in broader policymaking – if benchmarks are
overestimated, consumer welfare can be adversely affected and resource allocation to other
industries may be scaled-back to ineffcient levels. Conversely, underestimation of normal
proft can lead to policy settings that beneft consumers in the short run, but the associated
wealth transfers adversely affect industry investment, competition and innovation in the
long run, and will eventually harmconsumers. Neither outcome is desirable.
The purpose of this article is to set out a theoretically robust and pragmatic approach
which defnes “Normal Proft” in power generation which is then transposed into base
electricity and call option prices. This requires that we defne the cost of debt and the cost of
equity capital for the two most prominent business combinations in merchant energy
markets, and to set out a clear quantitative methodology for quantifying the long-run
marginal cost of power generation given prevailing capital and factor market conditions.
While our application to power generation uses Australian data, the framework is equally
applicable to other regions with energy-only market models, such as those in Australia,
Texas, NewZealand, Singapore, Alberta (Canada) and Europe, among others.
JFEP
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This article is structured as follows. Section 2 outlines our dynamic power project
fnancial model which produces generalised long-run marginal cost estimates for
multiple technologies. Section 3 outlines our input assumptions and presents our model
results, while Section 4 applies the results to policymaking. Concluding remarks follow.
2. Generalised long-run marginal cost of generation plant – the PF model
The model we used to produce a theoretically sound proft benchmark and generalised
long-run marginal cost estimates for generating plant is a dynamic, multi-period model
with all outputs expressed in nominal dollars and post-tax discounted cash fows
focusing on and solving for multiple generating technologies, business combinations
and revenue possibilities, and simultaneously solves for convergent price, debt-sizing
and equity returns K
e
.
To begin with, as all outputs are expressed in nominal dollars, costs are increased
annually by a forecast general infation rate estimate (CPI), with prices escalating at a
discount to our assumed CPI. Infation rates for revenue streams ?
j
R
and cost streams ?
j
C
in period (year) j are calculated in the model as follows:
?
j
R
?
?
1 ?
?
CPI ? ?
R
100
??
j
, and ?
j
C
?
?
1 ?
?
CPI ? ?
C
100
??
j
(4)
In this instance, ?
C
is the adjustment factor of 1.0 for costs and ?
R
relates to revenues and
is set at 0.75. The discounted value for ?
R
is intended to refect single-factor learning
rates that characterise generating technologies over time[2].
2.1 PF model for base, semi-base and intermittent power plant
Energy output from each power plant (i) is a key variable in driving revenue streams,
unit fuel costs and variable operations and maintenance costs. Energy output is
calculated by reference to installed capacity k
i
, capacity utilisation rate CF
j
i
and run time
t, which, in the PF model, is 8760 hours for each period j. Auxillary losses Aux
i
arising
fromon-site electrical loads (such as air compressors, lighting, induced and forced draft
fans and so on) need to be deducted:
?
j
i
? CF
j
i
. t . k
i
. (1 ? Aux
i
) (5)
There are two options for convergent electricity prices in the PF model. The frst option
is to exogenously set unique forecast prices for each period j (e.g. froma dynamic partial
equilibrium model of the relevant power system), while the second option, which is our
default option and used throughout this article, is a calculated price in year one which
escalates electricity price P
?
at the rate of ?
j
R
from equation (4). Additionally, ancillary
services revenues have been set at 0.25 per cent of electricity sales[3]. Thus revenue in
each period j is defned as follows:
R
j
E
? (?
j
i
. P
?
. ?
j
R
) ? 1.0025 (6)
To determine the short-run marginal cost of the i
th
plant in the j
th
period, the thermal
effciency for each generation technology ?
i
needs to be defned. The constant term
“3600”[4] is divided by the thermal effciency variable to convert the result fromper cent
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to kJ/kWh, which is then multiplied by the commodity cost of the raw fuel F
i
. Variable
operations and maintenance costs, v
i
, are also added to provide a pre-carbon short-run
marginal cost. To the extent that the plant has non-zero CO
2
emissions and faces an
emissions trading regime, the CO
2
intensity of output for the i
th
plant needs to be
computed along with the relevant CO
2
price, CP
j
. To defne a value for plant carbon
intensity, g
i
, the relevant combustion emissions factor g?
i
and fugitive CO
2
emissions
from the fuel source gˆ
i
are multiplied by the plant heat rate. Marginal running costs in
the j
th
period is then calculated by the product of short-run marginal production costs by
generation output ?
j
i
and escalated at the rate of ?
j
C
:
?
j
i
?
??(
(
3600 / ?
i
)
1000
. F
i
? v
i
)
? (g
i
. CP
j
)
?
. ?
j
i
. ?
j
C
?
g
i
?
(
g?
i
? gˆ
i
)
.
(
3600 / ?
i
)
1000
?
(7)
Fixed operations &and maintenance costs, f
j
i
, of the plant are measured in $/MW/year of
installed capacity FC
i
and are multiplied by plant capacity k
i
and escalated:
f
j
i
? FC
i
. k
i
. ?
j
C
(8)
Earnings before interest tax depreciation and amortisation (EBITDA), a frequently used
accounting-based measure, in the j
th
period can be defned as follows:
EBITDA
j
i
? (R
j
E
? ?
j
i
? f
j
i
) (9)
Unless plant is acquired, capital costs associated with development involve a
multi-period construction program. Capital costs are therefore defned as follows:
X
j
i
? ??
k?1
N
C
k
. (1 ? K
e
)
?k
(10)
Ongoing capital spending for each period j is determined as the infated annual assumed
capital works program:
x
j
i
? c
j
i
. ?
j
C
(11)
Plant capital costs X
j
i
give rise to tax depreciation ( d
j
i
) such that if the current period was
greater than the plant life under taxation law (L), then the value is 0. In addition, x
j
i
also
gives rise to tax depreciation such that:
d
j
i
?
?
X
j
i
L
?
?
?
x
j
i
L ? 1 ? j
?
(12)
From here, taxation payable (?
j
i
) at the corporate taxation rate (?
C
) is applied to
EBITDA
j
i
, less interest on loans defned later in (16), less d
j
i
. To the extent (?
j
i
) results in
a non-positive outcome, tax losses (L
j
i
) are carried forward and are offset against future
periods:
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Max (?
j
i
, 0) ? (EBITDA
j
i
? I
j
i
? d
j
i
? L
j?1
i
) . ?
C
(13)
Our debt fnancing model computes the interest and principal repayments on different
debt facilities depending on the type and tenor of each tranche. For example, we model
project fnancings with two facilities:
(1) Nominally, a 5-year bullet, requiring interest-only payments after which it is
refnanced with two consecutive ten-year amortising facilities. The frst
refnancing is set in a semi-permanent structure with a nominal repayment term
of 20 years, while the second is fully amortised over the ten-year tenor.
(2) The second facility commences with a 12-year tenor as an amortising facility,
again set within a semi-permanent structure with a nominal repayment term of
27 years. This second tranche is refnanced in year 13 and extinguished over a
further 15-year period.
Consequently, all debt is extinguished by year 27. The decision tree for the two tranches
of debt was the same, so for the debt tranche where T ? 1 or 2, the calculation is as
follows:
if j
?
?1, DT
j
i
? DT
j?1
i
? P
j?1
i
?1, DT ? D
0
i
. S
(14)
D
0
i
refers to the total amount of debt used in the project. The split (S) of the debt between
each facility refers to the manner in which the debt is apportioned to each debt tranche.
We assume 35 per cent of the debt was assigned to Tranche 1 and the remainder to
Tranche 2 in a manner consistent with Simshauser (2009). Principal P
j?1
i
refers to the
amount of principal repayment for tranche Tin period j and is calculated as an annuity:
P
j
i
?
?
DT
j
i
?
1 ? (1 ? (R
T
Z
? C
T
Z
))
?n
R
T
Z
? C
T
Z
?
?
z
?
?VI
?PF
?
(15)
In (15), R
T
is the fxed (reference) interest rate and C
T
is the credit spread or margin
relevant to the issued debt tranche. The relevant interest payment in the j
th
period (I
j
i
) is
calculated as the product of the (fxed) interest rate on the loan by the amount of loan
outstanding:
I
j
i
? DT
j
i
? (R
T
Z
? C
T
Z
) (16)
Total debt outstanding D
j
i
, total interest I
j
i
and total principal P
j
i
for the i
th
plant is
calculated as the sum of the above components for the two debt tranches in time j. For
clarity, loan drawings are equal to D
0
i
in year 1 as part of the initial fnancing and are
otherwise 0.
In equation (14), one of the key calculations is the initial derivation of D
0
i
. This is
determined by the product of the gearing level and the acquisition/development cost.
The gearing level, in turn, is formed by applying a cash fow constraint based on credit
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metrics applied by capital markets. In this instance, the variable ? in our PF model
relates specifcally to the business combination and the credible capital structure
achievable. The two relevant combinations are vertically integrated (VI) merchant
utilities and stand-alone project-fnanced (PF) merchant businesses:
if ?
?
?VI, Min
?
FFO
j
i
I
j
i ?
? ?
j
VI ?
Min
?
FFO
j
i
D
j
i ?
? ?
j
VI
?j
?
FFO
j
i
? (EBITDA
j
i
? x
j
i
)
?PF, Min(DSCR
j
i
, LLCR
j
i
) ? ?
j
PF
, ?j
?
DSCR
j
?
(EBITDA
j
i
? x
j
i
? ?
j
i
)
P
j
i
? I
j
i
,
LLCR
j
?
?
J?1
N
?(EBITDA
j
i
? x
j
i
? ?
j
i
) . (1 ? K
d
)
?j
?
D
j
i
(17)
The variables ?
j
VI
and ?
j
VI
are exogenously determined by credit rating agencies. Values
for ?
j
PF
are exogenously determined by project banks and depend on technology (i.e.
thermal vs renewable) and the extent of energy market exposure, that is whether a
power purchase agreement exists or not. For clarity, FFO
j
i
is “Funds From Operations”
while DSCR
j
i
and LLCR
j
i
are the debt service cover ratio (DSCR) and loan life cover ratio,
respectively.
At this point, all of the necessary conditions exist to produce estimates of the long-run
marginal cost of power generation technologies, and a suitable benchmark for what
constitutes normal proft. As noted at the outset of this article, in economics literature
IRR is the widely used theoretical long-run proftability concept. The relevant equation
to solve for the price P
?
which produces a normal proft given K
e
while simultaneously
meeting the binding constraints of ?
j
VI
and ?
j
VI
or ?
j
PF
given the relevant business
combination is as follows:
?X
j
i
?
?
j?1
N
?EBITDA
j
i
? I
j
i
? P
j
i
? ?
j
i
? . (1 ? K
e
)
?( j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?(j )
? D
0
i
(18)
The primary objective is to expand every term which contains P
?
. Expansion of the
EBITDA, interest and tax terms is as follows:
?X
j
i
?
?
j?1
N
?(P
?
. ?
j
i
. ?
j
R
) ? ?
j
i
? f
j
i
? DT
j
i
. (R
T
z
? C
T
z
) ? P
j
i
? ((P
?
. ?
j
i
. ?
j
R
) ? ?
j
i
? f
j
i
? I
j
i
? d
j
i
? L
j?1
i
) . ?
c
? . (1 ? K
e
)
?( j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?( j )
? D
0
i
(19)
We then rearrange the terms such that only the P
?
term is on the left-hand side of the
equation:
Let IRR ? K
e
.
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1
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(
P
T
)
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?( j )
? X
j
i
?
?
j?1
N
??(1 ? ?
c
).?
j
i
? (1 ? ?
c
).f
j
i
? (1 ? ?
c
)
. (DT
j
i
. (R
T
z
? C
T
z
)) ? P
j
i
? ?
c
.d
j
i
? ?
c
L
j?1
i
)
. (1 ? K
e
)
?( j )
? ?
?
j?1
N
x
j
i
.(1 ? K
e
)
?( j )
? D
0
i
(20)
We then solve for P
?
such that:
P
?
?
X
j
i
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
?
?
j?1
N
((1 ? ?
c
) . ?
j
i
? (1 ? ?
c
) . f
j
i
? (1 ? ?
c
) . (DT
j
. (R
T
z
? C
T
z
)) ? P
j
i
? ?
c
. d
j
i
? ?
c
L
j?1
i
) . (1 ? K
e
)
?(j )
)
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?(j )
? D
0
i
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
(21)
2.2 PF model for peaking power plant
Certain adjustments to the PF model are required to accommodate long-run marginal
cost estimates for peaking plant. For base, semi-base and intermittent plant, the normal
estimation procedure is to identify the relevant convergent electricity price P
?
that is
suffcient to produce an IRR equal to K
e
. The value for P
?
is found by solving equation
(21), and one of the most sensitive variables is energy output ?
j
i
, which is in turn driven
by the value for CF
j
i
. In general, values for CF
j
i
are comparatively predictable in that base
load plant typically run at high capacity factors while intermittent plant, over time,
exhibit output factors within a comparatively tight range given long-run average
weather conditions. Peaking plant on the other hand exhibits fuctuating values for CF
j
i
due to the natural volatility inherent in short-run weather patterns and temporal shocks
to the demand – supply balance.
This has two primary implications. First, defning a single electricity price P
?
for
peaking plant is virtually meaningless in the absence of an expected fxed or “sticky”
CF
j
i
. Second, attempting to defne a value for P
?
is likely to render such a project
unbankable due to the uncertainty over values for CF
j
i
in application (i.e. by project bank
credit committees). Consequently, peaking plant requires a distinct revenue model in its
own right. In our opinion, the appropriate way to express generalised long-run marginal
cost estimates for peakingplant is to defne the “carryingcost” of plant capacity, F
?
0
. This
requires only small modifcations to the calculation of revenues in our PF model. The
result produced is a single capacity price P
k
which essentially refects the equilibrium
price of a sold call-option contract at the commonly traded strike price for such
instruments of $300/MWh in Australia’s National Electricity Market (NEM). Equations
(5) and (6) require reconfguring as follows:
159
Normal proft
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6
(
P
T
)
?
j
i
? 0 ?j, and R
j
k
? (k
i
. P
k
. t . ?
j
R
) (22)
Here, the value of energy output ?
j
i
is set to zero, which means marginal running costs ?
j
i
will also be zero. In practice ?
j
i
will have a non-zero value and so P
?
would also be
non-zero, and under most conditions will exceed marginal running costs ?
j
i
. This
differential between P
?
and ?
j
i
, while somewhat contentious to model, will be limited in
each sub-period of j by the call option strike price, and for our purposes we assume it to
be zero. Putting the typically trivial value of this to one side, revenue R
j
k
for the i
th
peaking plant is hence the product of installed capacity k
i
, the price of that capacity P
k
(which is expressed in $/MWh), t being the number of hours in a year as noted earlier in
equation (5), and the revenue escalation factor. The value for R
j
k
, which will also exactly
equals F
?
0
, is a vitally important one as it also defnes the “missing money”[5] in an
energy-only market under conditions of intense competition and perfect plant divisibility
and availability as Section 6 later demonstrates. It also represents a useful benchmark in
helping to establish potential presence of market power abuse and barriers to entry.
3. Model results – generalised long-run marginal cost of power
generation
Our PF model results rely quite crucially on the engineering estimates of capital and
maintenance costs of plant. These key variables along with fuel market costs are set out
in Table I. Note that the parameters have been derived from Worley Parsons (2012),
ACILTasman (2012) and Frontier Economics (2013) who produce these estimates for the
independent market operator (AEMO) and for NewSouth Wales’ independent economic
regulator.
Plant technology types in Table I include black and brown coal, Combined Cycle Gas
Turbines (CCGT), Wind and Open Cycle Gas Turbines (OCGT). It is worth noting that of
the variables listed in Table I, Raw Fuel (excluding brown coal), Carbon Prices and
Capital Costs can be quite volatile and so a regular reviewof estimates is necessary[6]. In
Australia’s NEM (i.e. Queensland, New South Wales, Victoria and South Australia),
plant types are limited by fuel endowments – Victoria for example does not have any
black coal resources, while South Australia has largely exhausted all options for future
coal. In addition to these parameters, we also require fnancial and economic parameters,
which are presented in Table II.
In Figure 1 we present a stylised cost of capital for merchant generation. Debt costs
are largely based on 5-12-year bond pricing for varying credit quality. Note the range for
project fnance runs fromabout 50-67.5 per cent (and by chance the results are currently
equivalent to bond pricing ? 15 basis points). The cost of equity K
e
has been derived
from Simshauser (2013) based on the capital asset pricing model under atypical capital
market conditions. Note that values for K
e
are largely “sticky” at our calculated value for
any credit quality below BBB. We do not contemplate credit ratings beyond A-due to
inherent industry risk, otherwise K
e
varies according to the asset beta in Table II as
Simshauser (2013) explains. This is based on judgement, and a lack of practical evidence
to the contrary. Note that the weighted average cost of capital is notionally minimised at
a BBB credit rating.
Combining the parameters in Tables I and II with equations (3)-(22) in our PF
model produces generalised long-run marginal cost estimates for multiple plant
types in total dollars and on a unit-cost basis – the latter being more useful for any
JFEP
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T
)
Table I.
Engineering and cost
parameters for generating
plant in the NEM
M
o
d
e
l
v
a
r
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%
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.
0
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0
.
0
8
5
.
0
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.
0
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5
.
0
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5
.
0
3
7
.
5
n
/
a
A
u
x
i
l
i
a
r
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l
o
a
d
%
A
u
x
6
.
0
6
.
0
7
.
0
3
.
0
3
.
0
3
.
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3
.
0
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.
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1
.
0
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h
e
r
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a
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f
f
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%
?
3
9
.
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.
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8
.
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0
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0
.
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0
.
0
n
/
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3
5
.
0
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a
w
f
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/
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F
1
.
0
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1
.
4
2
0
.
4
0
5
.
0
0
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.
0
0
8
.
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8
.
5
0
n
/
a
8
.
0
0
V
a
r
i
a
b
l
e
O
&
M
$
/
M
W
h
v
1
.
2
8
1
.
2
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0
.
1
0
4
.
0
8
4
.
0
8
4
.
0
8
4
.
0
8
1
2
.
2
3
1
0
.
1
9
F
i
x
e
d
O
&
M
$
/
M
W
f
5
2
,
7
6
8
5
2
,
7
6
8
3
6
,
7
0
0
1
0
,
1
8
8
1
0
,
1
8
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1
0
,
1
8
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1
0
,
1
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0
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5
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5
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o
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/
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g?
9
0
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9
2
.
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5
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6
5
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.
6
5
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6
–
5
0
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1
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u
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/
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gˆ
8
.
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8
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7
8
.
7
1
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6
1
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.
6
1
8
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6
1
8
.
6
–
5
.
7
C
a
r
b
o
n
p
r
i
c
e
$
/
t
C
P
7
.
5
0
7
.
5
0
7
.
5
0
7
.
5
0
7
.
5
0
7
.
5
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7
.
5
0
7
.
5
0
7
.
5
0
C
a
p
i
t
a
l
c
o
s
t
$
/
k
W
X
2
,
5
0
0
2
,
5
0
0
3
,
0
0
0
1
,
2
0
0
1
,
2
0
0
1
,
2
0
0
1
,
2
0
0
2
,
0
0
0
7
8
3
C
a
p
i
t
a
l
w
o
r
k
s
$
’
0
0
0
x
5
,
0
0
0
5
,
0
0
0
5
,
0
0
0
2
,
0
0
0
2
,
0
0
0
2
,
0
0
0
2
,
0
0
0
1
,
0
0
0
1
,
0
0
0
U
s
e
f
u
l
l
i
f
e
Y
e
a
r
s
L
4
0
4
0
4
0
3
0
3
0
3
0
3
0
2
5
3
0
S
o
u
r
c
e
:
W
o
r
l
e
y
P
a
r
s
o
n
s
(
2
0
1
2
)
,
A
C
I
L
T
a
s
m
a
n
(
2
0
1
2
)
,
F
r
o
n
t
i
e
r
E
c
o
n
o
m
i
c
s
(
2
0
1
3
)
161
Normal proft
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)
Table II.
Financial economic
parameters
Model variable Unit
Infation % CPI 2.5
Risk-free rate % R
f
5.8
Market risk premium (Rf-Rm) % MRP 6.1
Generation asset beta # ?
a
0.73
Equity beta factor VI # ?
VI
1.05
Equity beta factor IPP # ?
IPP
1.58
Tax rate % ?
c
30.0
Effective tax rate % ?
e
21.3
Five-year interest rate swap % R
T
3.6
12-year interest rate swap % R
T
4.5
PF credit spread (5 years) bps C
T
250
PF credit spread (12 years) bps C
T
300
BBB credit spread (5 years) bps C
T
180
BBB credit spread (12 years) bps C
T
225
BBB credit (FFO/I) times ?
VI
4.0/5.0
BBB credit (FFO/D) times ?
VI
0.2
DSCR and LLCR times ?
IPP
1.8-2.2
Source: PWC (2013), Gray (2013a, 2013b), Nelson and Simshauser (2013)
Figure 1.
Stylised cost of capital for
merchant generation[7]
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T
)
subsequent comparative analysis. In Figure 2, we present the model results on a
unit-cost basis under the fnancing conditions historically activated by merchant
power producers using project fnance to either develop or acquire a power project
for each technology i.
At the bottom of each bar in Figure 2 the “per cent” result relates to the gearing level
achievable under energy market equilibriumconditions. So for example, the Black Coal
Queensland (QLD) plant has 65 per cent debt within the capital structure. The bars are
composed of the individual cost elements of the plant, starting with unit fuel costs F
i
,
unit carbon costs (g
i
. CP
j
) through to the unit cost of equity, all of which is expressed
in dollars per megawatt hour ($/MWh). At the top of each bar is the PF model’s
calculated unit price P
?
in which the IRR of the project exactly equals the cost of equity
K
e
while simultaneously meeting all binding credit metrics specifed in equation (17)
using the Table II parameters. To be clear, P
?
is also the generalised long-run marginal
cost or entry cost for each individual technology. Note also that these unit costs also
represent market equilibrium or the “centre of gravity” for futures contract prices over
the long run given an optimal stock and mix of plant. The unit cost of an Open Cycle
Peaking (OCGT) plant is expressed as the “carrying cost of capacity” or F
?
0
for reasons
identifed in Section 2.2.
Our alternate business combination results are presented in Figure 3 and arise from
producing generalised estimates for plant that have been fnanced “on-balance sheet” by
an entity with an investment-grade credit rating. The structural changes to unit costs in
this instance are limited to capital and taxation, and this is primarily due to binding
constraints in equation (17). For ease of comparison, we have included the headline
project fnance results from Figure 2 as the black diamond markers in Figure 3. What
this analysis illustrates is that there is no marked beneft in either structure, with the
Figure 2.
PF model generalised
long-run marginal cost
estimates – project
fnancing
163
Normal proft
D
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6
(
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T
)
exception of wind plant[8]. Note however that wind farms are assumed to be backed by
an investment-grade power purchase agreement, and thus use different credit metrics
(i.e. a debt service cover ratio (DSCR) equal to 1.35?which enables substantially higher
gearing) and a constant value for K
e
(i.e. equivalent to the horizontal section of the K
e
curve in Figure 1).
4. Application to policymaking
Understanding normal proft and the long-run marginal cost of production is clearly
important for investment decision-making. In our experience, frms in the energy
industry use a framework that is consistent with that presented in Sections 2 and 3 and
extend the revenue lines with power system simulation modelling and so it is not
necessary to elaborate further. However, the investment decisions of frms in fxed
power assets in the energy industry are infuenced quite fundamentally by policy
settings – again in our opinion, more so than many other industries, given the essential
service nature of electricity in the economy and the political economy of electricity
tariffs. As such, understanding normal proft is important as it will frequently form a
pivot point in policy formulation and regulatory settings.
4.1 Wholesale market benchmark
Severe price spikes in gross pool, energy-only, uniform frst-price auction wholesale
markets typically forma trigger for regulatory or antitrust inquiry[9]. For policymakers,
intervention may be warranted if supranormal profts are being extracted on a sustained
basis through generator bidding behaviour in the presence of demonstrable barriers to
entry. But episodes of transient price spikes in wholesale markets should not represent
an “automatic” trigger for inquiry, let alone policy intervention given the nature of
Figure 3.
PF model generalised
long-run marginal cost
estimates – balance sheet
fnancing
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T
)
energy-only markets. The reason for this is that there is no stable equilibrium in
energy-only markets, given a market price cap and a reliability constraint due to the
substantial fxed and sunk costs associated with power generation. This can be
demonstrated theoretically and through power system simulation modelling.
Proposition I: In an intensely competitive energy-only market with a uniform frst-price auction
clearing mechanism and perfect plant divisibility and availability, aggregate revenues for the
optimal plant mix and for any number of optimal plant technologies will fall short by at least the
carrying cost of peaking plant capacity, F
?
0
.
Let the i
th
plant have marginal running costs of ?
n
and total fxed and sunk costs
(including the opportunity cost of capital) of F
?
n
. Let price in period t be set on the basis
of a uniform, frst price clearing auction such that the short-run marginal cost of the
marginal plant ?
n
sets price. The intercept between plant n and n ?1 occurs when:
F
?
n
. ??
n
. t ? F
?
n?1
. ??
n?1
. t (23)
Rearranging this becomes:
F
?
n?1
? F
?
n
? (?
n
? ?
n?1
) . t (24)
Under any optimal plant mix for any number of optimal plant technologies, aggregate
revenues will always fall short by at least F
?
0
. We provide a mathematical proof for
Proposition I in Appendix and present a static partial equilibrium analysis in Figure 4
and applied results in Table III using our modelling outcomes from Section 3. The
concept that power system revenues will fall short by fxed capacity costs F
?
0
when
clearing prices are set to marginal cost has long been understood by energy economists
and can be traced back to Electrcitie de France’s then Chief Economist, Marcel Boiteux,
and his ground-breaking article on peak load pricing in electricity wholesale markets
(Boiteux, 1949). The framework developed by Central Electricity Generating Board of
England & Wales’ Chief Economist, Tom Berrie, provides an especially useful static
partial equilibrium model of a power system which demonstrates this – although note
that we have added a third (price duration) chart to Berrie’s (1967) two-chart framework
using our Section 3 results from Figure 2.
Figure 4.1 illustrates the marginal running cost curves of the three generating
technologies for base (brown coal), semi-base (CCGT) and peaking (OCGT) duties. The
y-axis intercept for each technology represents the fxed and sunk costs (F
?
n
), and the
slope of the curves represents marginal running costs ( ?
n
). The x-axis measures plant
capacity factor CF
j
i
fromequation (5). Note intercepts t
1
and t
2
identify the point at which
the most effcient plant switches to the next technology, given the rich blend of fxed and
variable costs in line with equation (24).
Figure 4.2 illustrates a load duration curve. This is a representation of the 17,520
half-hourly electricity load points in a year but ranked in descending order rather than
as a time-series, with the y-axis measuring electricity load (MW) and the x-axis
measuring “time exceeded”[10]. Figure 4.3 is the corresponding price duration curve,
that is the spot price set in each half-hour period in Figure 4.2 under the conditions of a
gross pool, energy-only, uniformfrst-price auction clearing mechanism, with the y-axis
measuring price and the x-axis measuring “time exceeded”.
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Figure 4.
Static partial equilibrium
model (ex-carbon):
Victorian region FY13
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The Figure 4 static partial equilibrium model assumes that frms are free to install
perfectly divisible plant capacity with perfect plant availability (and hence no
requirement for a reserve plant margin) in any combination that satisfes differentiable
equilibrium conditions. The model also assumes a perfect transmission system and
hence no constraints on the economic order of plant dispatch. Table III sets out all
energy production and cost results and notes that the system average spot price of
$55.43/MWh (i.e. the average of results in Figure 4.3) falls short of system average cost
of $66.04/MWh by the amount equivalent to F
?
0
– that is, $10.60/MWh (per Figure 2).
Once the assumptions of perfect plant divisibility and availability are relaxed,
average unit cost can be expected to rise, and average spot price can be expected to fall
as discrete power plant blocks are added (i.e. lumpy plant entry) and requisite excess
capacity (i.e. additional OCGT plant) is purposefully added to ensure power system
reliability constraints are met. Simultaneously, a value of lost load or market price cap is
introduced (i.e. the spot price that applies during shortages) and in Australia’s NEM is
$13,100/MWh. This offsets some, but not all, additional costs and only if the optimal
number of blackouts occurs annually within the reliability constraint as demonstrated
by Simshauser (2008).
4.2 Discussion
The key issue arising from the quantitative plant-specifc results in Section 3 and the
aggregate industry result from Section 4.1 is that even with the perfectly optimal plant
mix and a zero reserve margin, no plant has any prospect of earning normal profts when
conditions refect intense competition. From a policymaking perspective, the question
that follows is does this actually matter? After all, traditional welfare economics
commences withthe presumptionthat economic effciencyis maximisedwhenprices are set
at marginal cost. However, the model of perfect competition also assumes constant or
decreasingreturns to scale andthat clearingprices result innormal profts to effcient frms.
In industries with increasing returns to scale, where fxed and sunk costs are
non-trivial, and where short-run marginal costs are very low (or zero in the case of
renewable energy[11]), clearly a problem exists as demonstrated in Figure 4 and
Table III. Varian (1996) notes that under such conditions, persistent uniform pricing at
industry short-run marginal cost is not economically viable. In a terminal industry, this
may not warrant further consideration. But the electricity industry is not terminal.
While it is true that electricity demand has contracted marginally over the past 3-4 years,
longer-run projections exhibit mild but nonetheless positive growth. Furthermore, the
Table III.
Static partial equilibrium
results, given the perfect
plant and transmission
system availability
Plant type
Capacity
(MW)
Production
(GWh)
Fixed
costs
($m)
Running
costs
($m)
Total
costs
($m)
Average
unit cost
($/MWh) CF
j
i
(%)
Brown Coal 4,853 38,167 1,957 200 2,157 56.52 90
CCGT 1,544 9,067 267 363 630 69.50 67
OCGT 3,191 1,968 290 172 462 234.61 7
Total 9,588 49,202 2,514 735 3,249 66.04 59
Average spot price 55.43
Diff (average unit cost
less average spot price) 10.60
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existing plant is ageing and will one day need replacement, and the new plant will be
requiredto meet renewable policyobjectives. Accordingly, policymakers cannot ignore how
fxedandsunkcostsaretreatedbypolicychanges. It isnot theroleof policymakerstodeliver
profts to power companies. Nor however is it their role to set policies that constrain the
ability of frms to earn normal proft over the business cycle.
How does this translate into practice? Section 4.3 provides a useful applied example,
but in summary, spot price outcomes in an intensely competitive energy-only market
with a reliability constraint cannot be considered a pragmatic benchmark for which to
devise policy settings. Generators persistently bidding at marginal running costs will
result in prices that clear below system average cost as demonstrated in Figure 4 and
Table III. Simshauser (2008) demonstrated that when the assumptions of perfect plant
divisibility and availability are relaxed, there is no stable equilibriumthat will calibrate
supply to demand in a manner consistent with stated reliability standards in the long
run (absent extremely high market price caps). Further, as explained by Bidwell and
Henney (2004, p. 22), even with high wholesale market price caps, a stable fnancial
equilibrium could only be reached if the physical power system is operating near the
edge of collapse. At some point, generator bidding must deviate fromshort-run marginal
cost or blackout-induced market price cap events must increase in frequency.
Benchmarks for policymaking must therefore turn to plant and feet-wide generalised
long-run marginal cost estimates.
4.3 Application to wholesale markets
To demonstrate the importance of these concepts, we consider how benchmarks of
normal proft can be largely ignored by economists when assessing the market power of
incumbent generators in wholesale markets. Mountain (2013) concludes that the
imposition of carbon pricing from2012 improved the proftability of all generators, with
wholesale prices more than doubling and gains extracted after power generators
exercised market power in the Australian market. The analysis compared spot prices
over a nine-month period before and after the carbon policy change – nine months being
the limit of data available at the time. This type of analysis can be reproduced – Table IV
draws on the relevant spot market data from the independent market operator, using
Victorian region data as an example[12].
The time-weighted average spot price for the frst nine months of FY13 in Table IVis
$58.17/MWh and the carbon-adjusted FY13 spot price (column 5) averages
$30.23/MWh. This adjusted price has been derived by stripping the estimated carbon
impact (i.e. CO
2
price ? average carbon intensity) from FY13 spot prices, such that
$58.17 ? ($23/t ? 1.215t) ? $30.23MWh[13]. The fnal column compares the adjusted
FY13 spot price with the FY12 spot price, which results in a difference of $4.41/MWh.
This differential is identifed as a gain to the proftability of generators, as wholesale
price rises exceeded carbon costs[14]. Mountain (2013) concluded that such an outcome
is not consistent with a competitive market, while the Energy Users Association of
Australia added that the regulator should be granted greater scrutiny over generator
bidding[15].
There are many reasons why a regulator might be granted greater scrutiny over
generator bidding – but our Table IV analysis does not comprise one of them, as it
over-simplifes a very complex set of spot market outcomes. It ignores the role and
effects of short-run weather on aggregate demand, generator outages on aggregate
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supply and volumetric losses of individual plants – all of which are known to be
non-trivial. Above all though is the suitability of the initial benchmark.
Using this analysis in Table IV is, in our opinion, inappropriate for the reasons
outlined in Section 4.2. Analysing against a Period-on-Period unit price benchmark that
is demonstrably below system average cost, or worse still, equal to system short-run
marginal cost in an industry with substantial fxed and sunk costs runs a high risk of
producing a Type II error as demonstrated in Figure 4 and Table III. Table III noted that
the perfectly optimal plant mix for the Victorian region would produce aggregate annual
costs (including normal proft) of $3,249 million. The FY12 spot price fromTable IVwas
$25.82 and annual output from Table III was 49,202 GWh meaning that total spot
revenues earned by plant was $1,270 million. As a result, during the “base year” of the
analysis, generators incurred economic losses (compared to spot prices) of at least $1,900
million[16]. Regardless, a nine-month period is too short to drawmeaningful conclusions
on the levels of industry competition and profts of a feet of capital-intensive power
stations with useful lives spanning several decades.
The Table IV analysis lacks a pragmatic and theoretically robust normal proft
benchmark. To remedy this, we have re-run the PF model under conditions of a zero
carbon price for the relevant base plant in Victoria. We also analyse the fnancial
viability of highly effcient newentrants in the PF model whereby the unit price P
?
is set
as an input equivalent to the FY12 value in Table III rather than that derived by
equation (21). Figure 5 illustrates these results.
The horizontal line in Figure 5 represents the FY12 actual spot price of $25.82/MWh.
This is then compared with brown coal and CCGTplant entrants under the two business
combinations, PF and a VI entity using BBB credit-rated debt. The frst two bars show
the brown coal result for PF and VI, while the second two bars show the equivalent
results for a CCGTplant. The fnal two bars illustrate the fnancial out-workings that the
coal plant (i.e. the lowest cost entrant excluding CO
2
) would face if exposed to a
convergent electricity price of $25.82/MWh.
Table IV.
Victorian average spot
prices period-on-period
change (FY12 vs FY13)
Month
FY13 spot
including
CO2
($/MWh)
CO2 price
($/t)
Average carbon
intensity (CO2/MWh)
FY13
adjusted
excluding
CO2
($/MWh)
FY12 spot
pre-CO2
($/MWh)
Spot price
difference
($/MWh)
July 73.46 23.00 1.166 46.65 29.49 17.16
August 55.76 23.00 1.182 28.57 29.95 ?1.38
September 53.48 23.00 1.213 25.58 26.98 ?1.40
October 51.27 23.00 1.225 23.09 23.61 ?0.52
November 77.18 23.00 1.244 48.57 26.58 21.99
December 52.18 23.00 1.258 23.25 22.18 1.07
January 54.27 23.00 1.237 25.81 24.03 1.78
February 53.59 23.00 1.187 26.29 25.85 0.44
March 52.31 23.00 1.221 24.24 23.70 0.54
Average 58.17 23.00 1.215 30.23 25.82 4.41
Source: AEMO
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From inspection of the fnal two bars in Figure 5, it is evident that the Brown Coal PF
plant is completely bankrupt and unable to pay its interest expense, let alone the
principal. There are no equity returns whatsoever. The Brown Coal VI plant, due to its
substantially lower gearing level, is theoretically able to withstand such a price and,
assuming its BBB credit rating is held constant, retains its fnancial stability. In reality,
the credit rating will have been stripped (meaning debt covenants will have been
breached requiring remedy), no taxation will be paid (due to the plant making sizable
taxation losses) and the running cash yield would equate to 1.8 per cent compared to 10.0
per cent when the plant is earning a normal proft.
Our quantitative results in Figure 5 make it clear that quasi-rents (partial
contributions to fxed costs) commence when prices exceed $28-35/MWh. But at any
price ?$53/MWh, brown coal generators are incurring “economic losses”. For a CCGT
plant with gas supplied at $5/GJ, short-run marginal costs are not recovered until prices
exceed $40/MWh, let alone fxed cost recovery. Our generalised cost estimates paint a
radically different picture to that expressed in Table IVin which competition is thought
to be inadequate with generators extracting windfall gains.
How should a policymaker react to such conficting information? Real-world
events that occurred in the period immediately preceding the carbon tax provide
appropriate guidance. The two largest brown coal plants in Victoria, Loy Yang[17]
and Hazelwood[18], were both recipients of emergency recapitalisations in June 2012
– of $1.2 billion and $650 million, respectively. In the absence of these
recapitalisations, in our professional opinion, both plants would have experienced
fnancial distress. Structural adjustment assistance packages were issued to the
brown coal plant by the Australian Government in an attempt to moderate the most
acute effects of the carbon price implementation for reasons explained in the study
by Simshauser and Nelson (2012)[19]. Yet in spite of this, ex-post emergency
Figure 5.
Comparison of generalised
long-run marginal costs
with Table IV FY12 spot
prices
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recapitalisations were still necessary. Loy Yang, for example, incurred an equity
write-off of approximately $400 million or ? 40% of the post-acquisition equity
value of $950 million – and this was after accounting for structural adjustment
assistance. The third largest brown coal plant in Victoria, Yallourn, also incurred
write-downs of $245 million[20].
Our observation is that these outcomes were not a surprise to the industry. Table IV
noted that spot electricity prices in the pre-carbon environment were $25.82/MWh. They
had, in fact, averaged about $27 throughout FY11 and FY12, and as a result, many
plants were in the early stages of fnancial distress. That the incumbent plants lasted
almost two years without defaulting on project debt was due to commodity hedge
contracts signed in prior years under considerably more favourable market
circumstances. However, as these hedge contracts progressively matured and were
replaced with lower-value hedge contracts, emergency recapitalisations became
essential. Using the Table IVFY12 spot price result of $25.82 as an effcient benchmark
and concluding subsequent changes represented evidence of inadequate competition
and windfall gains, while those prices were simultaneously driving generators to a state
of fnancial distress, is not credible.
During FY13, the carbon price could be said to have been transmitted at a
pass-through rate of 116 per cent of the average industry CO
2
intensity[21] – but only
after holding all other variables strictly constant. That is, given a carbon price of $23/t, and
emissions intensity of 1.215t/MWh, one might expect an increase of ($23/t ? 1.215t ?)
$27.94/MWh, whereas FY13 spot prices increased by $32.35/MWh to $58.17/MWh. So
under these conditions, generator losses could be said to be lower than in FY12, provided
their output levels are held constant (although data from the independent market
operator reveal a substantial contraction in brown coal production, from39,856 GWh to
34,112 GWh)[22]. Figure 6 illustrates the comparative impact with the PF model run
using a $23/t carbon price.
In this instance, the normal proft benchmarks change substantially, with brown coal
plant rising to $85-88/MWh, and CCGT plant at $72-74/MWh. In a post-carbon price
analysis, once again, there is no evidence that windfall profts are being extracted. The
fnal two bars in Figure 6 focus on the CCGT plant, as they represent the optimal plant
for base duties given a price on carbon. The CCGT PF plant in Figure 6 is in manifest
breach of its fnancing covenants – unable to repay its full interest cost, let alone
principal repayment. The CCGT VI is, however, able to fnancially withstand the lower
price as was its Brown Coal VI counterpart in Figure 5 – although again the frmwould
be stripped of its investment-grade credit rating and would be in breach of debt
covenants. The running cash yield of the plant is just 0.2 per cent compared to 11.2 per
cent when the plant is producing normal profts.
One potential criticism of the analysis thus far is that the examination of market
power occurs ex-post. However, analysing the price of call option contracts from the
futures market provides ex-ante guidance. In particular, if market power abuse is
present, then we would expect to see sustained increases in the price of $300 call option
contracts across the three-year forward curve at levels above our unit estimate for F
?
0
.
But as with our analysis of base prices, year-ahead futures prices for $300 call options in
Victoria during 2013 were $4.35/MWh – less than half of our unit value for F
?
0
of $10.47/
MWh. Once again, there is little evidence of windfall gains.
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5. Concluding remarks
Policymakers and regulators governing competitive power markets, particularly those
based on a gross pool, energy-only market designs as those that exist in Australia, Texas,
New Zealand, Singapore, Alberta (Canada) and Europe, among others, face an interesting
dilemma. Our Proposition I and associated static partial equilibrium model in Section 4
demonstrate that under conditions of intense competition, perfect plant availability and
divisibility with no transmission constraints, energy-only markets result in clearing prices
which are not economically viable in the long run. When these simplifying assumptions are
relaxed, things get worse, not better. One option, operating a power system on the edge of
collapse as explainedbyBidwell andHenney(2004, p. 22), is not politicallyviable. Thismeans
that biddingmust, bydefnition, deviate fromstrict (short run) marginal cost at some stage. The
judgement of the regulator is todetermine whether deviations represent asustaineddeparture in
the presence of barriers to entry and are a potential abuse of market power, or whether they
merelyrepresent quasi rents anda partial inter-temporal contributiontowards substantial fxed
andsunkcosts. All toooften, regulatoryinquiryisstaticandignorestheessential timedimension.
Policymakers inAustralia andthe regulator inAlberta (respectively) understandthis balance:
Without affording electricity generators, the opportunity to submit dispatch bids or rebids above
marginal cost, new capacity would fail to enter the market and the market would become
vulnerable to periods of inadequate supply. Wholesale market price volatility and the ability of
electricity generators to – fromtime to time – offer electricity into the market at prices above the
marginal cost (sometimes even as high as the market price cap) is entirely consistent with the
design of [Australia ’s] National Electricity Market […] (AEMC, 2013, p. 65).
[…] wholesale price volatility and price polarity (periods of low prices interspersed with shorter
periods of high prices) are an expected outcome in an electricity market such as Alberta’s and
consistent with effective competition. In fact, these price signals promote innovation and
economic effciency […] (MSA, 2012, p. 1).
Figure 6.
Generalised long-run
marginal costs vs
Table IV FY13
carbon-inclusive spot
prices
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An important input to the judgement required of policymakers and regulators was
presented in Section 2 – a theoretically robust measure of normal proft as a suitable
“benchmark” for which to gauge activity over the business cycle. Our PF model focused on
twobusiness combinations andfoundthat there was little difference inthe ultimate long-run
marginal cost estimate, although the differences in the thresholds for bankruptcy were
marked. This analysis was then applied in an ex-post analysis of base energy prices against
pragmatic estimates for the long-run marginal cost of base plant, and an ex-ante analysis of
call optionprices against the carryingcost of peakingplant, usingVictoria as a case inpoint.
Prima facie it may be tempting to argue that our normal proft benchmark is overstated
because it ignores the fact that the actual plant mix is different fromoptimality, or that the
existingplant is aged/depreciatedor holds special resource endowments (suchas long-dated
fuel contracts at below-market rates). On the other hand, existing plant owners may argue
that our benchmark understates the real costs of existing businesses which were developed
or acquired at a higher cost (i.e. when turbine prices were higher in real terms) or fail to
account for the industrial/labour market constraints.
Neither set of arguments holds in the long run. If proft benchmarks are raised to
ineffcient levelsandnewentrantsdonot enter andcompeteawaytheavailablesupranormal
proft over the business cycle, then material barriers to entry may exist and policy
interventionmaybe warrantedwhere market power is beingexercised. Onthe other hand, if
proft benchmarks are set to suboptimal levels, neither incumbents nor entrants will fnd it
proftable in the long run, which has implications for reliability and an inevitable (and typically
unwelcome) price correction of signifcance over time. Ultimately, a pragmatic normal proft
benchmark must be forward-looking, not only with respect to the cost of capital but of what
capital needs to be deployedto meet incremental demandor replace retiringplant.
Notes
1. See Brealey et al. (2011, pp. 109-115) for a discussion of the conditions where IRR calculations
deviate from NPV calculations.
2. See Frontier Economics (2013) at pp. 31-35 for a useful explanation of learning curves as they
relate to power generation technologies.
3. This factor is based on average ancillary services revenues as a percentage of overall energy
market revenues.
4. The derivation of the constant term 3,600 is: 1 Watt ? 1 Joule per second and hence 1 Watt
Hour ?3,600 Joules.
5. The term“missing money” was frst discussed by Cramton and Stoft (2006), and later by Joskow
(2006), and describes a situation whereby market price caps (set low to constrain market power)
adversely affect average clearing prices, thus resulting in inadequate returns to investors in
generation plant. Steed and Laybutt (2011) identify approximately $6 billion in “missing money”
in the NEMfrom2000-2010 even with a market price cap of $10,000/MWh.
6. Reviewing gas cost estimates is particularly important in the Australian case due to the
rapidly changing dynamics occurring in the market for natural gas, viz., the development of
liquefed natural gas (LNG) terminals and the shift in pricing from a domestic “cost plus”
market to international gas pricing. Capital costs can also be quite volatile due to supply
imbalances in plant manufacturing market, and changes in exchange rates due to the
dominance of imported mechanical and electrical equipment.
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7. The cost of capital in Figure 1 is based on a “Vanilla WACC” calculation, that is a post-tax cost
of equity and a pre-tax cost of debt. Post-tax pre-fnancing cash fows are discounted by the
Vanilla WACC. However, in this article, all cash fows are post-tax and post-fnancing, and so
are discounted by the post-tax cost of equity.
8. From this, one would conclude that all wind turbines should be project-fnanced rather than
fnanced on balance sheet. However, our PFmodel has one signifcant limitation in this regard
– it does not model portfolios of plant at the enterprise level. In practice, credit rating agencies
treat half of the power purchase agreement (PPA’s) present value as “synthetic debt” on the
balance sheet of the counterparty who writes the instrument. This, in turn, has a real
opportunity cost to vertically integrated frms – as their level of total corporate debt must be
modifed downwards accordingly, and so the apparent gains fromwriting PPAs over balance
sheet fnancings are partially illusory.
9. For example, the Australian Energy Regulator routinely investigates every price spike
?$5,000, regardless of whether average spot prices are approaching normal proft levels.
10. In this instance, live FY13 half-hourly load data from the Victorian region have been used.
11. While many renewable energy technologies have zero or negligible fuel costs, biomass plant
is a notable exception.
12. Mountain(2013) analyses production-weightedspot prices for all generationplant types. Here, we
focus on the time-weighted spot prices in Victoria and compare these with our estimates of brown
coal plant costs. Mountain’s (2013) FY13 production-weighted price is $58/MWh and FY12 is
$26/MWh – which is consistent with the result in Table IV(results in columns 1 and 5).
13. The average carbon intensity of 1.215t has been derived from AEMO market data. This
analysis assumes the carbon price was passed-through at the industry’s average carbon
intensity for the reasons explained by Nelson et al. (2012). One implication of this is that all
brown coal generators are incurring losses on carbon costs because their CO
2
intensities of
1.28t ?1.55t/MWh are above the average intensity of 1.215t/MWh.
14. See, in particular, Mountain (2013, p. 24).
15. See, in particular, the Energy Users Association of Australia (EUAA) Media Statement
“Power generation prices need closer monitoring” – released on 27 June 2013. Available at:
www.euaa.com.au
16. In reality, hedge contracts struck at higher values in preceding periods would have reduced
these losses materially. On the other hand, the Victorian region is currently oversupplied and,
as such, economic losses would have been greater than implied, given the additional capital
stock deployed.
17. AGL Energy acquired 100% of the 2,200 MW Loy Yang power station and coal mine in June
2012. The implied enterprise value was $3.1 billion or $1,410/kW. AGL Energy raised $1.5
billion fromthe debt and equity capital markets, of which $1.2 billion was used to recapitalise
the project’s existing debt tranches. There is little doubt that when those debt tranches
matured, refnancing would have only been partially successful and additional equity
injections would have been required.
18. The parent entity of Hazelwood power station, GDF Suez, injected $650m of “rescue
capital” into its project debt structure using an intercompany loan during the last week of
June 2012. Hazelwood had been unable to secure bank funding. For further details, see the
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Australian Financial Review at www.afr.com/p/national/rescue_for_power_station_
xk639CP4CyQuJIp9ndewYP
19. See Mountain (2013) for details of the structural adjustment assistance.
20. These write-offs at the three largest brown coal plants occurred because of a legal obligation
that company directors have to ensure that asset values are reported as fair and reasonable
under Sections 295-297 of Australia’s Corporations Act 2001 (Cth). Sections 296 and 297, in
particular, require the application of Australian Accounting Standard AASB136 --
Impairment of Assets. Failure to account for a fair and reasonable carrying value of fxed
assets exposes company directors to penalties under the Corporations Act 2001 (Cth), which
include monetary fnes and jail terms.
21. While the CO
2
average intensity of the Victorian region is 1.215t/MWh, the feet average CO2
intensity of the brown coal feet is closer to 1.37t/MWh, as Simshauser and Doan (2009)
demonstrate. Accordingly, the feet pass-through rate could be thought of as 103% rather
than 116%, again holding all other variables strictly constant.
22. The apparent price premium of $4.41/MWh, when applied to the nine-month FY13 energy
output of 34,112 GWh, results in an apparent “windfall gain” of $150 million. However, the
loss of output fromFY12 to FY13 (of 39,856 GWh less 34,112 GWh) at the so-called break-even
pass-through price of ($58.17 less $4.41/MWh) results in an even greater offsetting loss of $309
million. This result further undermines the Table IV analysis.
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available at: www.eprg.group.cam.ac.uk/category/publications/working-paper-series
Appendix
Mathematical proof of missing money in energy-only markets
Under any optimal plant mix for any number of optimal plant technologies, aggregate revenues
will always fall short by at least F
?
0
.
Proof by mathematical induction:
Let n ? 1
Intercept : F
?
1
? F
?
0
? (?
0
? ?
1
) . t
Profit for 2nd plant ? ?
0
. t
1
? ?
1
. (t
2
? t
1
) ? (?
1
. t
2
? F
?
1
)
Profit ? ?
0
. t
1
? ?
1
. t
2
? ?
1
. t
1
? ?
1
. t
2
? F
?
1
Profit ? ?
0
. t
1
? ?
1
. t
1
? F
?
1
Profit ? (?
0
. ??
1
) . t
1
? F
?
1
177
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Profit ? F
?
1
? F
?
0
? F
?
1
Profit ? ?F
?
0
Let n ? 2
Intercept : F
?
2
? F
?
1
? (?
1
? ?
2
) . t
Profit for 3rd plant ? ?
0
. t
1
? ?
1
. (t
2
? t
1
) ? ?
2
. (t
3
? t
2
) ? (?
2
. t
3
? F
?
2
)
Profit ? ?
0
. t
1
? ?
1
. t
2
? ?
1
. t
1
? ?
2
. t
3
? ?
2
. t
2
? ?
2
. t
3
? F
?
2
Profit ? (?
0
. ??
1
) . t
1
? (?
1
. ??
2
) . t
2
? F
?
2
Profit ? F
?
1
? F
?
0
? F
?
2
? F
?
1
? F
?
2
Profit ? ?F
?
0
Let n ? k
Intercept: F
?
k?1
? F
?
k
? (?
k
? ?
k?1
) . t
k?1
Profit ? ?
i?1
k
(?
i?1
? ?
i
) . t
i
? F
?
k
? ?F
?
0
Let n ? k ? 1
Profit ? ?
i?1
k?1
(?
i?1
? ?
i
) . t
i
? F
?
k?1
Profit ? ?
i?1
k
(?
i?1
? ?
i
) . t
i
? (?
k?1?1
? ?
k?1
) . t
k?1
? F
?
k?1
Profit ? ?F
?
0
? F
?
k
? F
?
k?1
? F
?
k
? F
?
k?1
Profit ? ?F
?
0
Corresponding author
Paul Simshauser can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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This article has been cited by:
1. Tim Nelson. 2015. Australian Climate Change Policy - Where To From Here?. Economic Papers: A journal
of applied economics and policy 34:10.1111/ecpa.2015.34.issue-4, 257-272. [CrossRef]
2. Paul Simshauser, Yuan Tian, Patrick Whish-Wilson. 2015. Vertical integration in energy-only electricity
markets. Economic Analysis and Policy 48, 35-56. [CrossRef]
3. Tim Nelson, Cameron Reid, Judith McNeill. 2015. Energy-only markets and renewable energy targets:
Complementary policy or policy collision?. Economic Analysis and Policy 46, 25-42. [CrossRef]
4. Paul Simshauser. 2014. From First Place to Last: The National Electricity Market's Policy-Induced ‘Energy
Market Death Spiral’. Australian Economic Review 47:10.1111/aere.v47.4, 540-562. [CrossRef]
5. Paul Simshauser. 2014. When Does Electricity Price Cap Regulation Become Distortionary?. Australian
Economic Review 47:10.1111/aere.v47.3, 304-323. [CrossRef]
6. Paul Simshauser. 2014. The cost of capital for power generation in atypical capital market conditions.
Economic Analysis and Policy 44, 184-201. [CrossRef]
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doc_922652107.pdf
This paper aims to present a multi-period dynamic power project financing model to
produce pragmatic estimates of benchmark wholesale power prices based on the principles of
normal profit. This, in turn, can guide policymakers as to whether price spikes or bidding above
marginal cost in wholesale electricity markets warrants any investigation at all. One of the
seemingly complex areas associated with energy-only wholesale electricity pools is at what point
market power abuse is present on the supply side. It should not be this way. If a theoretically robust
measure of normal profit exists, identification of potential market power abuse is straightforward.
Such a definition readily exists and can be traced back to the ground-breaking work of financial
economists in the 1960s
Journal of Financial Economic Policy
What is normal profit for power generation?
Paul Simshauser J ude Ariyaratnam
Article information:
To cite this document:
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What is normal proft
for power generation?
Paul Simshauser
Department of Accounting, Finance & Economics, Griffth University,
Brisbane, Australia and AGL Energy Ltd, Melbourne, Australia
Jude Ariyaratnam
AGL Energy Ltd, Melbourne, Australia
Abstract
Purpose – This paper aims to present a multi-period dynamic power project fnancing model to
produce pragmatic estimates of benchmark wholesale power prices based on the principles of
normal proft. This, in turn, can guide policymakers as to whether price spikes or bidding above
marginal cost in wholesale electricity markets warrants any investigation at all. One of the
seemingly complex areas associated with energy-only wholesale electricity pools is at what point
market power abuse is present on the supply side. It should not be this way. If a theoretically robust
measure of normal proft exists, identifcation of potential market power abuse is straightforward.
Such a defnition readily exists and can be traced back to the ground-breaking work of fnancial
economists in the 1960s.
Design/methodology/approach – Using a multi-period dynamic power project model, the authors
produce pragmatic and theoretically robust measures of normal proft for project fnanced plant and
plant fnanced on balance sheet. These model results are then integrated into a static partial equilibrium
model of a power system. The model results are in turn used to guide policymaking on generator
bidding in energy-only power markets.
Findings – Under conditions of perfect plant availability and divisibility with no transmission
constraints, energy-only markets result in clearing prices which are not economically viable in the long
run. Bidding must, therefore, deviate from strict short-run marginal cost at some stage. To distinguish
between quasi-contributions to substantial sunk costs and market power abuse, a pragmatic and robust
measure of normal proft is required.
Originality/value – This article fnds policymakers can be guided by an ex-post analysis of base
energy prices against pragmatic estimates for the long-run marginal cost of the base plant, and an
ex-ante analysis of call option prices along the forward curve against pragmatic estimates of the
carrying cost of the peaking plant.
Keywords Proft, Project fnance, Electricity prices
Paper type Research paper
1. Introduction
Transient price spikes in gross pool, energy-only, uniform frst-price auction wholesale
markets typically trigger regulatory inquiry. Policy intervention may be warranted if
supranormal profts are being extracted on a sustained basis through generator bidding
behaviour in the presence of demonstrable barriers to entry. But episodes of transient
price spikes in energy-only markets should not represent an “automatic” trigger for
JEL classifcation – D61, L94, L11, Q40
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JFEP
6,2
152
Journal of Financial Economic Policy
Vol. 6 No. 2, 2014
pp. 152-178
©Emerald Group Publishing Limited
1757-6385
DOI 10.1108/JFEP-09-2013-0045
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inquiry, let alone policy intervention, particularly if underlying prices are otherwise
driving merchant generators to a state of fnancial distress.
Economic theory has long demonstrated that energy-only spot electricity markets
can clear demand reliably and can provide suitable investment signals for newcapacity
(Schweppe et al., 1988). But such analyses typically presume unlimited market price
caps, limited political or regulatory interference, and by deduction, a largely equity
capital-funded generation feet able to withstand wildly fuctuating business cycles.
Unfortunately, the real world is not this convenient.
Rarely are generators devoid of scheduled debt repayments and so theories of spot
energy markets suffer from the inadequate treatment of how non-trivial sunk capital
costs are fnanced (Peluchon, 2003; Joskow, 2006; Finon, 2008; Caplan, 2012; Nelson and
Simshauser, 2013). Additionally, wholesale price caps do exist and are enforced – in
some instances excessively so – by regulatory authorities (Besser et al., 2002; Oren, 2003;
de Vries, 2003; Wen et al., 2004; Finon and Pignon, 2008, Joskow 2008a, Simshauser,
2010). Intensely competitive energy-only markets are known to produce inadequate net
revenues to support investment in the optimal least cost-generating portfolio, otherwise
known as the “missing money” problem (Neuhoff et al., 2004; de Vries, 2004; de Vries
et al., 2008; Bushnell, 2005; Roques et al., 2005; Cramton and Stoft, 2006; Joskow, 2008b;
Finon, 2008; Simshauser, 2008). In short, given substantial fxed and sunk costs
associated with power generation, and low marginal production costs, persistent
generator bidding at short-run marginal cost does not result in a stable equilibrium
(Bidwell and Henney, 2004; Simshauser, 2008; Nelson and Simshauser, 2013). Generator
bidding must, therefore, deviate from strict marginal cost at some point. But given an
oligopolistic market environment, at what point might this cross a line and represent a
potential abuse of market power that warrants investigation and possible intervention
by policymakers? If a pragmatic measure of normal proft exists, then contrasting
normal proft with a combination of actual (historic) base-load spot prices and futures
prices for call option contracts can provide suitable guidance.
Identifying a theoretically sound measure of normal proft is of vital importance to
the industry, policymakers and, in turn, the overall effciency and welfare of the
macroeconomy. The allocation of resources within an economy relies quite
fundamentally on such constructs, whether this is through the investment decisions of
frms, or by decisions made by policymakers that infuence or directly impact on
investment outcomes. But normal proft needs to be very carefully defned.
The concept of normal proft is unremarkable in economics but, oddly enough, is very
rarely identifed in practice. This is primarily because proft announcements by frms
(and reported by the media) are dominated by accounting statistics and ratios based on
the traditional measure of net proft after tax (NPAT). Finance theory is clear – NPATis
nothing more than a residual measure. When NPAT is determined by traditional
accounting axioms, it represents the residual funds available for distribution to
stockholders after all other claimants have been satisfed. NPAT does not describe the
“pitch” of profts generated, i.e. normal or otherwise.
To assess the pitch of profts, a suitable benchmark must frst be defned. Economic
theory has long told us that this is measured by the opportunity cost of funds used. By
the 1960s, the economics profession had produced profoundly more refned quantitative
defnitions, viz., the marginal cost of capital or expected returns of rational investors
based on a one-factor, two-parameter model of risk and return. The origins of the capital
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asset pricing model can be traced back to Sharpe (1964) and Lintner (1965). If profts of
the frm meet the marginal cost of capital, a normal proft has been generated.
Supranormal profts are achieved when proft exceeds the cost of capital and,
conversely, economic losses occur when returns fail to meet expected returns.
In our experience, a surprising number of policymakers, regulators and
microeconomics professionals (from academia and industry) misinterpret the
appropriate quantitative value for the marginal cost of capital. The reason for this, we
suspect, is that the theoretical construct of “opportunity cost” must shift from theory to
practice, all the while ensuring that in the process, the result does not violate the basic
axioms and constraints of credit metrics and taxation implications – all of which are
important in defning the true marginal effciency of equity and debt capital. The shift
from theory to practice is thus grounded in fnancial economics, not microeconomics.
For highly capital-intensive industries like power generation, temporal issues
associated with accounting results create further complications. Accounting numbers
use arbitrary reporting periods (e.g. monthly, quarterly and annually) to measure
NPAT. While this is both desirable and necessary for managerial and agency purposes,
discrete accounting period reporting is fundamentally a static analysis, whereas for
investment or policymaking purposes, normal proft should be considered in a dynamic
context. The expected returns of capital-intensive assets occur over multiple decades
and are subject to (nominally) 5-7-year business cycles in power generation, with actual
returns oscillating considerably around the mean outcome. Normal proft, then, must be
considered as a multi-period dynamic inquiry rather than a static analysis based on a
collection of half-hour price spikes during a short episode of hot weather, for example.
In economics literature, from a benchmark perspective, the internal rate of return
(IRR) of investments is the most prominently used theoretical long-run proftability
concept, as explained by Salmi and Virtanen (1997). Finance literature, on the other
hand, has a distinct bias towards net present value analysis, although as Brealey et al.
(2011) observe, IRR comes from respectable ancestry and, for our purposes, can be
expected to provide conforming results under all of the conditions that we envisage in
this article[1].
From a competition perspective, using a normal proft benchmark, analysed over a
business cycle, provides a suitable threshold for regulatory authorities to consider. Normal
proft is also of central importance in broader policymaking – if benchmarks are
overestimated, consumer welfare can be adversely affected and resource allocation to other
industries may be scaled-back to ineffcient levels. Conversely, underestimation of normal
proft can lead to policy settings that beneft consumers in the short run, but the associated
wealth transfers adversely affect industry investment, competition and innovation in the
long run, and will eventually harmconsumers. Neither outcome is desirable.
The purpose of this article is to set out a theoretically robust and pragmatic approach
which defnes “Normal Proft” in power generation which is then transposed into base
electricity and call option prices. This requires that we defne the cost of debt and the cost of
equity capital for the two most prominent business combinations in merchant energy
markets, and to set out a clear quantitative methodology for quantifying the long-run
marginal cost of power generation given prevailing capital and factor market conditions.
While our application to power generation uses Australian data, the framework is equally
applicable to other regions with energy-only market models, such as those in Australia,
Texas, NewZealand, Singapore, Alberta (Canada) and Europe, among others.
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This article is structured as follows. Section 2 outlines our dynamic power project
fnancial model which produces generalised long-run marginal cost estimates for
multiple technologies. Section 3 outlines our input assumptions and presents our model
results, while Section 4 applies the results to policymaking. Concluding remarks follow.
2. Generalised long-run marginal cost of generation plant – the PF model
The model we used to produce a theoretically sound proft benchmark and generalised
long-run marginal cost estimates for generating plant is a dynamic, multi-period model
with all outputs expressed in nominal dollars and post-tax discounted cash fows
focusing on and solving for multiple generating technologies, business combinations
and revenue possibilities, and simultaneously solves for convergent price, debt-sizing
and equity returns K
e
.
To begin with, as all outputs are expressed in nominal dollars, costs are increased
annually by a forecast general infation rate estimate (CPI), with prices escalating at a
discount to our assumed CPI. Infation rates for revenue streams ?
j
R
and cost streams ?
j
C
in period (year) j are calculated in the model as follows:
?
j
R
?
?
1 ?
?
CPI ? ?
R
100
??
j
, and ?
j
C
?
?
1 ?
?
CPI ? ?
C
100
??
j
(4)
In this instance, ?
C
is the adjustment factor of 1.0 for costs and ?
R
relates to revenues and
is set at 0.75. The discounted value for ?
R
is intended to refect single-factor learning
rates that characterise generating technologies over time[2].
2.1 PF model for base, semi-base and intermittent power plant
Energy output from each power plant (i) is a key variable in driving revenue streams,
unit fuel costs and variable operations and maintenance costs. Energy output is
calculated by reference to installed capacity k
i
, capacity utilisation rate CF
j
i
and run time
t, which, in the PF model, is 8760 hours for each period j. Auxillary losses Aux
i
arising
fromon-site electrical loads (such as air compressors, lighting, induced and forced draft
fans and so on) need to be deducted:
?
j
i
? CF
j
i
. t . k
i
. (1 ? Aux
i
) (5)
There are two options for convergent electricity prices in the PF model. The frst option
is to exogenously set unique forecast prices for each period j (e.g. froma dynamic partial
equilibrium model of the relevant power system), while the second option, which is our
default option and used throughout this article, is a calculated price in year one which
escalates electricity price P
?
at the rate of ?
j
R
from equation (4). Additionally, ancillary
services revenues have been set at 0.25 per cent of electricity sales[3]. Thus revenue in
each period j is defned as follows:
R
j
E
? (?
j
i
. P
?
. ?
j
R
) ? 1.0025 (6)
To determine the short-run marginal cost of the i
th
plant in the j
th
period, the thermal
effciency for each generation technology ?
i
needs to be defned. The constant term
“3600”[4] is divided by the thermal effciency variable to convert the result fromper cent
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to kJ/kWh, which is then multiplied by the commodity cost of the raw fuel F
i
. Variable
operations and maintenance costs, v
i
, are also added to provide a pre-carbon short-run
marginal cost. To the extent that the plant has non-zero CO
2
emissions and faces an
emissions trading regime, the CO
2
intensity of output for the i
th
plant needs to be
computed along with the relevant CO
2
price, CP
j
. To defne a value for plant carbon
intensity, g
i
, the relevant combustion emissions factor g?
i
and fugitive CO
2
emissions
from the fuel source gˆ
i
are multiplied by the plant heat rate. Marginal running costs in
the j
th
period is then calculated by the product of short-run marginal production costs by
generation output ?
j
i
and escalated at the rate of ?
j
C
:
?
j
i
?
??(
(
3600 / ?
i
)
1000
. F
i
? v
i
)
? (g
i
. CP
j
)
?
. ?
j
i
. ?
j
C
?
g
i
?
(
g?
i
? gˆ
i
)
.
(
3600 / ?
i
)
1000
?
(7)
Fixed operations &and maintenance costs, f
j
i
, of the plant are measured in $/MW/year of
installed capacity FC
i
and are multiplied by plant capacity k
i
and escalated:
f
j
i
? FC
i
. k
i
. ?
j
C
(8)
Earnings before interest tax depreciation and amortisation (EBITDA), a frequently used
accounting-based measure, in the j
th
period can be defned as follows:
EBITDA
j
i
? (R
j
E
? ?
j
i
? f
j
i
) (9)
Unless plant is acquired, capital costs associated with development involve a
multi-period construction program. Capital costs are therefore defned as follows:
X
j
i
? ??
k?1
N
C
k
. (1 ? K
e
)
?k
(10)
Ongoing capital spending for each period j is determined as the infated annual assumed
capital works program:
x
j
i
? c
j
i
. ?
j
C
(11)
Plant capital costs X
j
i
give rise to tax depreciation ( d
j
i
) such that if the current period was
greater than the plant life under taxation law (L), then the value is 0. In addition, x
j
i
also
gives rise to tax depreciation such that:
d
j
i
?
?
X
j
i
L
?
?
?
x
j
i
L ? 1 ? j
?
(12)
From here, taxation payable (?
j
i
) at the corporate taxation rate (?
C
) is applied to
EBITDA
j
i
, less interest on loans defned later in (16), less d
j
i
. To the extent (?
j
i
) results in
a non-positive outcome, tax losses (L
j
i
) are carried forward and are offset against future
periods:
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Max (?
j
i
, 0) ? (EBITDA
j
i
? I
j
i
? d
j
i
? L
j?1
i
) . ?
C
(13)
Our debt fnancing model computes the interest and principal repayments on different
debt facilities depending on the type and tenor of each tranche. For example, we model
project fnancings with two facilities:
(1) Nominally, a 5-year bullet, requiring interest-only payments after which it is
refnanced with two consecutive ten-year amortising facilities. The frst
refnancing is set in a semi-permanent structure with a nominal repayment term
of 20 years, while the second is fully amortised over the ten-year tenor.
(2) The second facility commences with a 12-year tenor as an amortising facility,
again set within a semi-permanent structure with a nominal repayment term of
27 years. This second tranche is refnanced in year 13 and extinguished over a
further 15-year period.
Consequently, all debt is extinguished by year 27. The decision tree for the two tranches
of debt was the same, so for the debt tranche where T ? 1 or 2, the calculation is as
follows:
if j
?
?1, DT
j
i
? DT
j?1
i
? P
j?1
i
?1, DT ? D
0
i
. S
(14)
D
0
i
refers to the total amount of debt used in the project. The split (S) of the debt between
each facility refers to the manner in which the debt is apportioned to each debt tranche.
We assume 35 per cent of the debt was assigned to Tranche 1 and the remainder to
Tranche 2 in a manner consistent with Simshauser (2009). Principal P
j?1
i
refers to the
amount of principal repayment for tranche Tin period j and is calculated as an annuity:
P
j
i
?
?
DT
j
i
?
1 ? (1 ? (R
T
Z
? C
T
Z
))
?n
R
T
Z
? C
T
Z
?
?
z
?
?VI
?PF
?
(15)
In (15), R
T
is the fxed (reference) interest rate and C
T
is the credit spread or margin
relevant to the issued debt tranche. The relevant interest payment in the j
th
period (I
j
i
) is
calculated as the product of the (fxed) interest rate on the loan by the amount of loan
outstanding:
I
j
i
? DT
j
i
? (R
T
Z
? C
T
Z
) (16)
Total debt outstanding D
j
i
, total interest I
j
i
and total principal P
j
i
for the i
th
plant is
calculated as the sum of the above components for the two debt tranches in time j. For
clarity, loan drawings are equal to D
0
i
in year 1 as part of the initial fnancing and are
otherwise 0.
In equation (14), one of the key calculations is the initial derivation of D
0
i
. This is
determined by the product of the gearing level and the acquisition/development cost.
The gearing level, in turn, is formed by applying a cash fow constraint based on credit
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metrics applied by capital markets. In this instance, the variable ? in our PF model
relates specifcally to the business combination and the credible capital structure
achievable. The two relevant combinations are vertically integrated (VI) merchant
utilities and stand-alone project-fnanced (PF) merchant businesses:
if ?
?
?VI, Min
?
FFO
j
i
I
j
i ?
? ?
j
VI ?
Min
?
FFO
j
i
D
j
i ?
? ?
j
VI
?j
?
FFO
j
i
? (EBITDA
j
i
? x
j
i
)
?PF, Min(DSCR
j
i
, LLCR
j
i
) ? ?
j
PF
, ?j
?
DSCR
j
?
(EBITDA
j
i
? x
j
i
? ?
j
i
)
P
j
i
? I
j
i
,
LLCR
j
?
?
J?1
N
?(EBITDA
j
i
? x
j
i
? ?
j
i
) . (1 ? K
d
)
?j
?
D
j
i
(17)
The variables ?
j
VI
and ?
j
VI
are exogenously determined by credit rating agencies. Values
for ?
j
PF
are exogenously determined by project banks and depend on technology (i.e.
thermal vs renewable) and the extent of energy market exposure, that is whether a
power purchase agreement exists or not. For clarity, FFO
j
i
is “Funds From Operations”
while DSCR
j
i
and LLCR
j
i
are the debt service cover ratio (DSCR) and loan life cover ratio,
respectively.
At this point, all of the necessary conditions exist to produce estimates of the long-run
marginal cost of power generation technologies, and a suitable benchmark for what
constitutes normal proft. As noted at the outset of this article, in economics literature
IRR is the widely used theoretical long-run proftability concept. The relevant equation
to solve for the price P
?
which produces a normal proft given K
e
while simultaneously
meeting the binding constraints of ?
j
VI
and ?
j
VI
or ?
j
PF
given the relevant business
combination is as follows:
?X
j
i
?
?
j?1
N
?EBITDA
j
i
? I
j
i
? P
j
i
? ?
j
i
? . (1 ? K
e
)
?( j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?(j )
? D
0
i
(18)
The primary objective is to expand every term which contains P
?
. Expansion of the
EBITDA, interest and tax terms is as follows:
?X
j
i
?
?
j?1
N
?(P
?
. ?
j
i
. ?
j
R
) ? ?
j
i
? f
j
i
? DT
j
i
. (R
T
z
? C
T
z
) ? P
j
i
? ((P
?
. ?
j
i
. ?
j
R
) ? ?
j
i
? f
j
i
? I
j
i
? d
j
i
? L
j?1
i
) . ?
c
? . (1 ? K
e
)
?( j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?( j )
? D
0
i
(19)
We then rearrange the terms such that only the P
?
term is on the left-hand side of the
equation:
Let IRR ? K
e
.
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j?1
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(1 ? ?
c
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?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?( j )
? X
j
i
?
?
j?1
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??(1 ? ?
c
).?
j
i
? (1 ? ?
c
).f
j
i
? (1 ? ?
c
)
. (DT
j
i
. (R
T
z
? C
T
z
)) ? P
j
i
? ?
c
.d
j
i
? ?
c
L
j?1
i
)
. (1 ? K
e
)
?( j )
? ?
?
j?1
N
x
j
i
.(1 ? K
e
)
?( j )
? D
0
i
(20)
We then solve for P
?
such that:
P
?
?
X
j
i
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
?
?
j?1
N
((1 ? ?
c
) . ?
j
i
? (1 ? ?
c
) . f
j
i
? (1 ? ?
c
) . (DT
j
. (R
T
z
? C
T
z
)) ? P
j
i
? ?
c
. d
j
i
? ?
c
L
j?1
i
) . (1 ? K
e
)
?(j )
)
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
?
?
j?1
N
x
j
i
. (1 ? K
e
)
?(j )
? D
0
i
?
j?1
N
(1 ? ?
c
) . P
?
. ?
j
i
. ?
j
R
. (1 ? K
e
)
?(j )
(21)
2.2 PF model for peaking power plant
Certain adjustments to the PF model are required to accommodate long-run marginal
cost estimates for peaking plant. For base, semi-base and intermittent plant, the normal
estimation procedure is to identify the relevant convergent electricity price P
?
that is
suffcient to produce an IRR equal to K
e
. The value for P
?
is found by solving equation
(21), and one of the most sensitive variables is energy output ?
j
i
, which is in turn driven
by the value for CF
j
i
. In general, values for CF
j
i
are comparatively predictable in that base
load plant typically run at high capacity factors while intermittent plant, over time,
exhibit output factors within a comparatively tight range given long-run average
weather conditions. Peaking plant on the other hand exhibits fuctuating values for CF
j
i
due to the natural volatility inherent in short-run weather patterns and temporal shocks
to the demand – supply balance.
This has two primary implications. First, defning a single electricity price P
?
for
peaking plant is virtually meaningless in the absence of an expected fxed or “sticky”
CF
j
i
. Second, attempting to defne a value for P
?
is likely to render such a project
unbankable due to the uncertainty over values for CF
j
i
in application (i.e. by project bank
credit committees). Consequently, peaking plant requires a distinct revenue model in its
own right. In our opinion, the appropriate way to express generalised long-run marginal
cost estimates for peakingplant is to defne the “carryingcost” of plant capacity, F
?
0
. This
requires only small modifcations to the calculation of revenues in our PF model. The
result produced is a single capacity price P
k
which essentially refects the equilibrium
price of a sold call-option contract at the commonly traded strike price for such
instruments of $300/MWh in Australia’s National Electricity Market (NEM). Equations
(5) and (6) require reconfguring as follows:
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?
j
i
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j
k
? (k
i
. P
k
. t . ?
j
R
) (22)
Here, the value of energy output ?
j
i
is set to zero, which means marginal running costs ?
j
i
will also be zero. In practice ?
j
i
will have a non-zero value and so P
?
would also be
non-zero, and under most conditions will exceed marginal running costs ?
j
i
. This
differential between P
?
and ?
j
i
, while somewhat contentious to model, will be limited in
each sub-period of j by the call option strike price, and for our purposes we assume it to
be zero. Putting the typically trivial value of this to one side, revenue R
j
k
for the i
th
peaking plant is hence the product of installed capacity k
i
, the price of that capacity P
k
(which is expressed in $/MWh), t being the number of hours in a year as noted earlier in
equation (5), and the revenue escalation factor. The value for R
j
k
, which will also exactly
equals F
?
0
, is a vitally important one as it also defnes the “missing money”[5] in an
energy-only market under conditions of intense competition and perfect plant divisibility
and availability as Section 6 later demonstrates. It also represents a useful benchmark in
helping to establish potential presence of market power abuse and barriers to entry.
3. Model results – generalised long-run marginal cost of power
generation
Our PF model results rely quite crucially on the engineering estimates of capital and
maintenance costs of plant. These key variables along with fuel market costs are set out
in Table I. Note that the parameters have been derived from Worley Parsons (2012),
ACILTasman (2012) and Frontier Economics (2013) who produce these estimates for the
independent market operator (AEMO) and for NewSouth Wales’ independent economic
regulator.
Plant technology types in Table I include black and brown coal, Combined Cycle Gas
Turbines (CCGT), Wind and Open Cycle Gas Turbines (OCGT). It is worth noting that of
the variables listed in Table I, Raw Fuel (excluding brown coal), Carbon Prices and
Capital Costs can be quite volatile and so a regular reviewof estimates is necessary[6]. In
Australia’s NEM (i.e. Queensland, New South Wales, Victoria and South Australia),
plant types are limited by fuel endowments – Victoria for example does not have any
black coal resources, while South Australia has largely exhausted all options for future
coal. In addition to these parameters, we also require fnancial and economic parameters,
which are presented in Table II.
In Figure 1 we present a stylised cost of capital for merchant generation. Debt costs
are largely based on 5-12-year bond pricing for varying credit quality. Note the range for
project fnance runs fromabout 50-67.5 per cent (and by chance the results are currently
equivalent to bond pricing ? 15 basis points). The cost of equity K
e
has been derived
from Simshauser (2013) based on the capital asset pricing model under atypical capital
market conditions. Note that values for K
e
are largely “sticky” at our calculated value for
any credit quality below BBB. We do not contemplate credit ratings beyond A-due to
inherent industry risk, otherwise K
e
varies according to the asset beta in Table II as
Simshauser (2013) explains. This is based on judgement, and a lack of practical evidence
to the contrary. Note that the weighted average cost of capital is notionally minimised at
a BBB credit rating.
Combining the parameters in Tables I and II with equations (3)-(22) in our PF
model produces generalised long-run marginal cost estimates for multiple plant
types in total dollars and on a unit-cost basis – the latter being more useful for any
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Table I.
Engineering and cost
parameters for generating
plant in the NEM
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161
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Table II.
Financial economic
parameters
Model variable Unit
Infation % CPI 2.5
Risk-free rate % R
f
5.8
Market risk premium (Rf-Rm) % MRP 6.1
Generation asset beta # ?
a
0.73
Equity beta factor VI # ?
VI
1.05
Equity beta factor IPP # ?
IPP
1.58
Tax rate % ?
c
30.0
Effective tax rate % ?
e
21.3
Five-year interest rate swap % R
T
3.6
12-year interest rate swap % R
T
4.5
PF credit spread (5 years) bps C
T
250
PF credit spread (12 years) bps C
T
300
BBB credit spread (5 years) bps C
T
180
BBB credit spread (12 years) bps C
T
225
BBB credit (FFO/I) times ?
VI
4.0/5.0
BBB credit (FFO/D) times ?
VI
0.2
DSCR and LLCR times ?
IPP
1.8-2.2
Source: PWC (2013), Gray (2013a, 2013b), Nelson and Simshauser (2013)
Figure 1.
Stylised cost of capital for
merchant generation[7]
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subsequent comparative analysis. In Figure 2, we present the model results on a
unit-cost basis under the fnancing conditions historically activated by merchant
power producers using project fnance to either develop or acquire a power project
for each technology i.
At the bottom of each bar in Figure 2 the “per cent” result relates to the gearing level
achievable under energy market equilibriumconditions. So for example, the Black Coal
Queensland (QLD) plant has 65 per cent debt within the capital structure. The bars are
composed of the individual cost elements of the plant, starting with unit fuel costs F
i
,
unit carbon costs (g
i
. CP
j
) through to the unit cost of equity, all of which is expressed
in dollars per megawatt hour ($/MWh). At the top of each bar is the PF model’s
calculated unit price P
?
in which the IRR of the project exactly equals the cost of equity
K
e
while simultaneously meeting all binding credit metrics specifed in equation (17)
using the Table II parameters. To be clear, P
?
is also the generalised long-run marginal
cost or entry cost for each individual technology. Note also that these unit costs also
represent market equilibrium or the “centre of gravity” for futures contract prices over
the long run given an optimal stock and mix of plant. The unit cost of an Open Cycle
Peaking (OCGT) plant is expressed as the “carrying cost of capacity” or F
?
0
for reasons
identifed in Section 2.2.
Our alternate business combination results are presented in Figure 3 and arise from
producing generalised estimates for plant that have been fnanced “on-balance sheet” by
an entity with an investment-grade credit rating. The structural changes to unit costs in
this instance are limited to capital and taxation, and this is primarily due to binding
constraints in equation (17). For ease of comparison, we have included the headline
project fnance results from Figure 2 as the black diamond markers in Figure 3. What
this analysis illustrates is that there is no marked beneft in either structure, with the
Figure 2.
PF model generalised
long-run marginal cost
estimates – project
fnancing
163
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exception of wind plant[8]. Note however that wind farms are assumed to be backed by
an investment-grade power purchase agreement, and thus use different credit metrics
(i.e. a debt service cover ratio (DSCR) equal to 1.35?which enables substantially higher
gearing) and a constant value for K
e
(i.e. equivalent to the horizontal section of the K
e
curve in Figure 1).
4. Application to policymaking
Understanding normal proft and the long-run marginal cost of production is clearly
important for investment decision-making. In our experience, frms in the energy
industry use a framework that is consistent with that presented in Sections 2 and 3 and
extend the revenue lines with power system simulation modelling and so it is not
necessary to elaborate further. However, the investment decisions of frms in fxed
power assets in the energy industry are infuenced quite fundamentally by policy
settings – again in our opinion, more so than many other industries, given the essential
service nature of electricity in the economy and the political economy of electricity
tariffs. As such, understanding normal proft is important as it will frequently form a
pivot point in policy formulation and regulatory settings.
4.1 Wholesale market benchmark
Severe price spikes in gross pool, energy-only, uniform frst-price auction wholesale
markets typically forma trigger for regulatory or antitrust inquiry[9]. For policymakers,
intervention may be warranted if supranormal profts are being extracted on a sustained
basis through generator bidding behaviour in the presence of demonstrable barriers to
entry. But episodes of transient price spikes in wholesale markets should not represent
an “automatic” trigger for inquiry, let alone policy intervention given the nature of
Figure 3.
PF model generalised
long-run marginal cost
estimates – balance sheet
fnancing
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energy-only markets. The reason for this is that there is no stable equilibrium in
energy-only markets, given a market price cap and a reliability constraint due to the
substantial fxed and sunk costs associated with power generation. This can be
demonstrated theoretically and through power system simulation modelling.
Proposition I: In an intensely competitive energy-only market with a uniform frst-price auction
clearing mechanism and perfect plant divisibility and availability, aggregate revenues for the
optimal plant mix and for any number of optimal plant technologies will fall short by at least the
carrying cost of peaking plant capacity, F
?
0
.
Let the i
th
plant have marginal running costs of ?
n
and total fxed and sunk costs
(including the opportunity cost of capital) of F
?
n
. Let price in period t be set on the basis
of a uniform, frst price clearing auction such that the short-run marginal cost of the
marginal plant ?
n
sets price. The intercept between plant n and n ?1 occurs when:
F
?
n
. ??
n
. t ? F
?
n?1
. ??
n?1
. t (23)
Rearranging this becomes:
F
?
n?1
? F
?
n
? (?
n
? ?
n?1
) . t (24)
Under any optimal plant mix for any number of optimal plant technologies, aggregate
revenues will always fall short by at least F
?
0
. We provide a mathematical proof for
Proposition I in Appendix and present a static partial equilibrium analysis in Figure 4
and applied results in Table III using our modelling outcomes from Section 3. The
concept that power system revenues will fall short by fxed capacity costs F
?
0
when
clearing prices are set to marginal cost has long been understood by energy economists
and can be traced back to Electrcitie de France’s then Chief Economist, Marcel Boiteux,
and his ground-breaking article on peak load pricing in electricity wholesale markets
(Boiteux, 1949). The framework developed by Central Electricity Generating Board of
England & Wales’ Chief Economist, Tom Berrie, provides an especially useful static
partial equilibrium model of a power system which demonstrates this – although note
that we have added a third (price duration) chart to Berrie’s (1967) two-chart framework
using our Section 3 results from Figure 2.
Figure 4.1 illustrates the marginal running cost curves of the three generating
technologies for base (brown coal), semi-base (CCGT) and peaking (OCGT) duties. The
y-axis intercept for each technology represents the fxed and sunk costs (F
?
n
), and the
slope of the curves represents marginal running costs ( ?
n
). The x-axis measures plant
capacity factor CF
j
i
fromequation (5). Note intercepts t
1
and t
2
identify the point at which
the most effcient plant switches to the next technology, given the rich blend of fxed and
variable costs in line with equation (24).
Figure 4.2 illustrates a load duration curve. This is a representation of the 17,520
half-hourly electricity load points in a year but ranked in descending order rather than
as a time-series, with the y-axis measuring electricity load (MW) and the x-axis
measuring “time exceeded”[10]. Figure 4.3 is the corresponding price duration curve,
that is the spot price set in each half-hour period in Figure 4.2 under the conditions of a
gross pool, energy-only, uniformfrst-price auction clearing mechanism, with the y-axis
measuring price and the x-axis measuring “time exceeded”.
165
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Figure 4.
Static partial equilibrium
model (ex-carbon):
Victorian region FY13
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The Figure 4 static partial equilibrium model assumes that frms are free to install
perfectly divisible plant capacity with perfect plant availability (and hence no
requirement for a reserve plant margin) in any combination that satisfes differentiable
equilibrium conditions. The model also assumes a perfect transmission system and
hence no constraints on the economic order of plant dispatch. Table III sets out all
energy production and cost results and notes that the system average spot price of
$55.43/MWh (i.e. the average of results in Figure 4.3) falls short of system average cost
of $66.04/MWh by the amount equivalent to F
?
0
– that is, $10.60/MWh (per Figure 2).
Once the assumptions of perfect plant divisibility and availability are relaxed,
average unit cost can be expected to rise, and average spot price can be expected to fall
as discrete power plant blocks are added (i.e. lumpy plant entry) and requisite excess
capacity (i.e. additional OCGT plant) is purposefully added to ensure power system
reliability constraints are met. Simultaneously, a value of lost load or market price cap is
introduced (i.e. the spot price that applies during shortages) and in Australia’s NEM is
$13,100/MWh. This offsets some, but not all, additional costs and only if the optimal
number of blackouts occurs annually within the reliability constraint as demonstrated
by Simshauser (2008).
4.2 Discussion
The key issue arising from the quantitative plant-specifc results in Section 3 and the
aggregate industry result from Section 4.1 is that even with the perfectly optimal plant
mix and a zero reserve margin, no plant has any prospect of earning normal profts when
conditions refect intense competition. From a policymaking perspective, the question
that follows is does this actually matter? After all, traditional welfare economics
commences withthe presumptionthat economic effciencyis maximisedwhenprices are set
at marginal cost. However, the model of perfect competition also assumes constant or
decreasingreturns to scale andthat clearingprices result innormal profts to effcient frms.
In industries with increasing returns to scale, where fxed and sunk costs are
non-trivial, and where short-run marginal costs are very low (or zero in the case of
renewable energy[11]), clearly a problem exists as demonstrated in Figure 4 and
Table III. Varian (1996) notes that under such conditions, persistent uniform pricing at
industry short-run marginal cost is not economically viable. In a terminal industry, this
may not warrant further consideration. But the electricity industry is not terminal.
While it is true that electricity demand has contracted marginally over the past 3-4 years,
longer-run projections exhibit mild but nonetheless positive growth. Furthermore, the
Table III.
Static partial equilibrium
results, given the perfect
plant and transmission
system availability
Plant type
Capacity
(MW)
Production
(GWh)
Fixed
costs
($m)
Running
costs
($m)
Total
costs
($m)
Average
unit cost
($/MWh) CF
j
i
(%)
Brown Coal 4,853 38,167 1,957 200 2,157 56.52 90
CCGT 1,544 9,067 267 363 630 69.50 67
OCGT 3,191 1,968 290 172 462 234.61 7
Total 9,588 49,202 2,514 735 3,249 66.04 59
Average spot price 55.43
Diff (average unit cost
less average spot price) 10.60
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existing plant is ageing and will one day need replacement, and the new plant will be
requiredto meet renewable policyobjectives. Accordingly, policymakers cannot ignore how
fxedandsunkcostsaretreatedbypolicychanges. It isnot theroleof policymakerstodeliver
profts to power companies. Nor however is it their role to set policies that constrain the
ability of frms to earn normal proft over the business cycle.
How does this translate into practice? Section 4.3 provides a useful applied example,
but in summary, spot price outcomes in an intensely competitive energy-only market
with a reliability constraint cannot be considered a pragmatic benchmark for which to
devise policy settings. Generators persistently bidding at marginal running costs will
result in prices that clear below system average cost as demonstrated in Figure 4 and
Table III. Simshauser (2008) demonstrated that when the assumptions of perfect plant
divisibility and availability are relaxed, there is no stable equilibriumthat will calibrate
supply to demand in a manner consistent with stated reliability standards in the long
run (absent extremely high market price caps). Further, as explained by Bidwell and
Henney (2004, p. 22), even with high wholesale market price caps, a stable fnancial
equilibrium could only be reached if the physical power system is operating near the
edge of collapse. At some point, generator bidding must deviate fromshort-run marginal
cost or blackout-induced market price cap events must increase in frequency.
Benchmarks for policymaking must therefore turn to plant and feet-wide generalised
long-run marginal cost estimates.
4.3 Application to wholesale markets
To demonstrate the importance of these concepts, we consider how benchmarks of
normal proft can be largely ignored by economists when assessing the market power of
incumbent generators in wholesale markets. Mountain (2013) concludes that the
imposition of carbon pricing from2012 improved the proftability of all generators, with
wholesale prices more than doubling and gains extracted after power generators
exercised market power in the Australian market. The analysis compared spot prices
over a nine-month period before and after the carbon policy change – nine months being
the limit of data available at the time. This type of analysis can be reproduced – Table IV
draws on the relevant spot market data from the independent market operator, using
Victorian region data as an example[12].
The time-weighted average spot price for the frst nine months of FY13 in Table IVis
$58.17/MWh and the carbon-adjusted FY13 spot price (column 5) averages
$30.23/MWh. This adjusted price has been derived by stripping the estimated carbon
impact (i.e. CO
2
price ? average carbon intensity) from FY13 spot prices, such that
$58.17 ? ($23/t ? 1.215t) ? $30.23MWh[13]. The fnal column compares the adjusted
FY13 spot price with the FY12 spot price, which results in a difference of $4.41/MWh.
This differential is identifed as a gain to the proftability of generators, as wholesale
price rises exceeded carbon costs[14]. Mountain (2013) concluded that such an outcome
is not consistent with a competitive market, while the Energy Users Association of
Australia added that the regulator should be granted greater scrutiny over generator
bidding[15].
There are many reasons why a regulator might be granted greater scrutiny over
generator bidding – but our Table IV analysis does not comprise one of them, as it
over-simplifes a very complex set of spot market outcomes. It ignores the role and
effects of short-run weather on aggregate demand, generator outages on aggregate
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supply and volumetric losses of individual plants – all of which are known to be
non-trivial. Above all though is the suitability of the initial benchmark.
Using this analysis in Table IV is, in our opinion, inappropriate for the reasons
outlined in Section 4.2. Analysing against a Period-on-Period unit price benchmark that
is demonstrably below system average cost, or worse still, equal to system short-run
marginal cost in an industry with substantial fxed and sunk costs runs a high risk of
producing a Type II error as demonstrated in Figure 4 and Table III. Table III noted that
the perfectly optimal plant mix for the Victorian region would produce aggregate annual
costs (including normal proft) of $3,249 million. The FY12 spot price fromTable IVwas
$25.82 and annual output from Table III was 49,202 GWh meaning that total spot
revenues earned by plant was $1,270 million. As a result, during the “base year” of the
analysis, generators incurred economic losses (compared to spot prices) of at least $1,900
million[16]. Regardless, a nine-month period is too short to drawmeaningful conclusions
on the levels of industry competition and profts of a feet of capital-intensive power
stations with useful lives spanning several decades.
The Table IV analysis lacks a pragmatic and theoretically robust normal proft
benchmark. To remedy this, we have re-run the PF model under conditions of a zero
carbon price for the relevant base plant in Victoria. We also analyse the fnancial
viability of highly effcient newentrants in the PF model whereby the unit price P
?
is set
as an input equivalent to the FY12 value in Table III rather than that derived by
equation (21). Figure 5 illustrates these results.
The horizontal line in Figure 5 represents the FY12 actual spot price of $25.82/MWh.
This is then compared with brown coal and CCGTplant entrants under the two business
combinations, PF and a VI entity using BBB credit-rated debt. The frst two bars show
the brown coal result for PF and VI, while the second two bars show the equivalent
results for a CCGTplant. The fnal two bars illustrate the fnancial out-workings that the
coal plant (i.e. the lowest cost entrant excluding CO
2
) would face if exposed to a
convergent electricity price of $25.82/MWh.
Table IV.
Victorian average spot
prices period-on-period
change (FY12 vs FY13)
Month
FY13 spot
including
CO2
($/MWh)
CO2 price
($/t)
Average carbon
intensity (CO2/MWh)
FY13
adjusted
excluding
CO2
($/MWh)
FY12 spot
pre-CO2
($/MWh)
Spot price
difference
($/MWh)
July 73.46 23.00 1.166 46.65 29.49 17.16
August 55.76 23.00 1.182 28.57 29.95 ?1.38
September 53.48 23.00 1.213 25.58 26.98 ?1.40
October 51.27 23.00 1.225 23.09 23.61 ?0.52
November 77.18 23.00 1.244 48.57 26.58 21.99
December 52.18 23.00 1.258 23.25 22.18 1.07
January 54.27 23.00 1.237 25.81 24.03 1.78
February 53.59 23.00 1.187 26.29 25.85 0.44
March 52.31 23.00 1.221 24.24 23.70 0.54
Average 58.17 23.00 1.215 30.23 25.82 4.41
Source: AEMO
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From inspection of the fnal two bars in Figure 5, it is evident that the Brown Coal PF
plant is completely bankrupt and unable to pay its interest expense, let alone the
principal. There are no equity returns whatsoever. The Brown Coal VI plant, due to its
substantially lower gearing level, is theoretically able to withstand such a price and,
assuming its BBB credit rating is held constant, retains its fnancial stability. In reality,
the credit rating will have been stripped (meaning debt covenants will have been
breached requiring remedy), no taxation will be paid (due to the plant making sizable
taxation losses) and the running cash yield would equate to 1.8 per cent compared to 10.0
per cent when the plant is earning a normal proft.
Our quantitative results in Figure 5 make it clear that quasi-rents (partial
contributions to fxed costs) commence when prices exceed $28-35/MWh. But at any
price ?$53/MWh, brown coal generators are incurring “economic losses”. For a CCGT
plant with gas supplied at $5/GJ, short-run marginal costs are not recovered until prices
exceed $40/MWh, let alone fxed cost recovery. Our generalised cost estimates paint a
radically different picture to that expressed in Table IVin which competition is thought
to be inadequate with generators extracting windfall gains.
How should a policymaker react to such conficting information? Real-world
events that occurred in the period immediately preceding the carbon tax provide
appropriate guidance. The two largest brown coal plants in Victoria, Loy Yang[17]
and Hazelwood[18], were both recipients of emergency recapitalisations in June 2012
– of $1.2 billion and $650 million, respectively. In the absence of these
recapitalisations, in our professional opinion, both plants would have experienced
fnancial distress. Structural adjustment assistance packages were issued to the
brown coal plant by the Australian Government in an attempt to moderate the most
acute effects of the carbon price implementation for reasons explained in the study
by Simshauser and Nelson (2012)[19]. Yet in spite of this, ex-post emergency
Figure 5.
Comparison of generalised
long-run marginal costs
with Table IV FY12 spot
prices
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recapitalisations were still necessary. Loy Yang, for example, incurred an equity
write-off of approximately $400 million or ? 40% of the post-acquisition equity
value of $950 million – and this was after accounting for structural adjustment
assistance. The third largest brown coal plant in Victoria, Yallourn, also incurred
write-downs of $245 million[20].
Our observation is that these outcomes were not a surprise to the industry. Table IV
noted that spot electricity prices in the pre-carbon environment were $25.82/MWh. They
had, in fact, averaged about $27 throughout FY11 and FY12, and as a result, many
plants were in the early stages of fnancial distress. That the incumbent plants lasted
almost two years without defaulting on project debt was due to commodity hedge
contracts signed in prior years under considerably more favourable market
circumstances. However, as these hedge contracts progressively matured and were
replaced with lower-value hedge contracts, emergency recapitalisations became
essential. Using the Table IVFY12 spot price result of $25.82 as an effcient benchmark
and concluding subsequent changes represented evidence of inadequate competition
and windfall gains, while those prices were simultaneously driving generators to a state
of fnancial distress, is not credible.
During FY13, the carbon price could be said to have been transmitted at a
pass-through rate of 116 per cent of the average industry CO
2
intensity[21] – but only
after holding all other variables strictly constant. That is, given a carbon price of $23/t, and
emissions intensity of 1.215t/MWh, one might expect an increase of ($23/t ? 1.215t ?)
$27.94/MWh, whereas FY13 spot prices increased by $32.35/MWh to $58.17/MWh. So
under these conditions, generator losses could be said to be lower than in FY12, provided
their output levels are held constant (although data from the independent market
operator reveal a substantial contraction in brown coal production, from39,856 GWh to
34,112 GWh)[22]. Figure 6 illustrates the comparative impact with the PF model run
using a $23/t carbon price.
In this instance, the normal proft benchmarks change substantially, with brown coal
plant rising to $85-88/MWh, and CCGT plant at $72-74/MWh. In a post-carbon price
analysis, once again, there is no evidence that windfall profts are being extracted. The
fnal two bars in Figure 6 focus on the CCGT plant, as they represent the optimal plant
for base duties given a price on carbon. The CCGT PF plant in Figure 6 is in manifest
breach of its fnancing covenants – unable to repay its full interest cost, let alone
principal repayment. The CCGT VI is, however, able to fnancially withstand the lower
price as was its Brown Coal VI counterpart in Figure 5 – although again the frmwould
be stripped of its investment-grade credit rating and would be in breach of debt
covenants. The running cash yield of the plant is just 0.2 per cent compared to 11.2 per
cent when the plant is producing normal profts.
One potential criticism of the analysis thus far is that the examination of market
power occurs ex-post. However, analysing the price of call option contracts from the
futures market provides ex-ante guidance. In particular, if market power abuse is
present, then we would expect to see sustained increases in the price of $300 call option
contracts across the three-year forward curve at levels above our unit estimate for F
?
0
.
But as with our analysis of base prices, year-ahead futures prices for $300 call options in
Victoria during 2013 were $4.35/MWh – less than half of our unit value for F
?
0
of $10.47/
MWh. Once again, there is little evidence of windfall gains.
171
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5. Concluding remarks
Policymakers and regulators governing competitive power markets, particularly those
based on a gross pool, energy-only market designs as those that exist in Australia, Texas,
New Zealand, Singapore, Alberta (Canada) and Europe, among others, face an interesting
dilemma. Our Proposition I and associated static partial equilibrium model in Section 4
demonstrate that under conditions of intense competition, perfect plant availability and
divisibility with no transmission constraints, energy-only markets result in clearing prices
which are not economically viable in the long run. When these simplifying assumptions are
relaxed, things get worse, not better. One option, operating a power system on the edge of
collapse as explainedbyBidwell andHenney(2004, p. 22), is not politicallyviable. Thismeans
that biddingmust, bydefnition, deviate fromstrict (short run) marginal cost at some stage. The
judgement of the regulator is todetermine whether deviations represent asustaineddeparture in
the presence of barriers to entry and are a potential abuse of market power, or whether they
merelyrepresent quasi rents anda partial inter-temporal contributiontowards substantial fxed
andsunkcosts. All toooften, regulatoryinquiryisstaticandignorestheessential timedimension.
Policymakers inAustralia andthe regulator inAlberta (respectively) understandthis balance:
Without affording electricity generators, the opportunity to submit dispatch bids or rebids above
marginal cost, new capacity would fail to enter the market and the market would become
vulnerable to periods of inadequate supply. Wholesale market price volatility and the ability of
electricity generators to – fromtime to time – offer electricity into the market at prices above the
marginal cost (sometimes even as high as the market price cap) is entirely consistent with the
design of [Australia ’s] National Electricity Market […] (AEMC, 2013, p. 65).
[…] wholesale price volatility and price polarity (periods of low prices interspersed with shorter
periods of high prices) are an expected outcome in an electricity market such as Alberta’s and
consistent with effective competition. In fact, these price signals promote innovation and
economic effciency […] (MSA, 2012, p. 1).
Figure 6.
Generalised long-run
marginal costs vs
Table IV FY13
carbon-inclusive spot
prices
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An important input to the judgement required of policymakers and regulators was
presented in Section 2 – a theoretically robust measure of normal proft as a suitable
“benchmark” for which to gauge activity over the business cycle. Our PF model focused on
twobusiness combinations andfoundthat there was little difference inthe ultimate long-run
marginal cost estimate, although the differences in the thresholds for bankruptcy were
marked. This analysis was then applied in an ex-post analysis of base energy prices against
pragmatic estimates for the long-run marginal cost of base plant, and an ex-ante analysis of
call optionprices against the carryingcost of peakingplant, usingVictoria as a case inpoint.
Prima facie it may be tempting to argue that our normal proft benchmark is overstated
because it ignores the fact that the actual plant mix is different fromoptimality, or that the
existingplant is aged/depreciatedor holds special resource endowments (suchas long-dated
fuel contracts at below-market rates). On the other hand, existing plant owners may argue
that our benchmark understates the real costs of existing businesses which were developed
or acquired at a higher cost (i.e. when turbine prices were higher in real terms) or fail to
account for the industrial/labour market constraints.
Neither set of arguments holds in the long run. If proft benchmarks are raised to
ineffcient levelsandnewentrantsdonot enter andcompeteawaytheavailablesupranormal
proft over the business cycle, then material barriers to entry may exist and policy
interventionmaybe warrantedwhere market power is beingexercised. Onthe other hand, if
proft benchmarks are set to suboptimal levels, neither incumbents nor entrants will fnd it
proftable in the long run, which has implications for reliability and an inevitable (and typically
unwelcome) price correction of signifcance over time. Ultimately, a pragmatic normal proft
benchmark must be forward-looking, not only with respect to the cost of capital but of what
capital needs to be deployedto meet incremental demandor replace retiringplant.
Notes
1. See Brealey et al. (2011, pp. 109-115) for a discussion of the conditions where IRR calculations
deviate from NPV calculations.
2. See Frontier Economics (2013) at pp. 31-35 for a useful explanation of learning curves as they
relate to power generation technologies.
3. This factor is based on average ancillary services revenues as a percentage of overall energy
market revenues.
4. The derivation of the constant term 3,600 is: 1 Watt ? 1 Joule per second and hence 1 Watt
Hour ?3,600 Joules.
5. The term“missing money” was frst discussed by Cramton and Stoft (2006), and later by Joskow
(2006), and describes a situation whereby market price caps (set low to constrain market power)
adversely affect average clearing prices, thus resulting in inadequate returns to investors in
generation plant. Steed and Laybutt (2011) identify approximately $6 billion in “missing money”
in the NEMfrom2000-2010 even with a market price cap of $10,000/MWh.
6. Reviewing gas cost estimates is particularly important in the Australian case due to the
rapidly changing dynamics occurring in the market for natural gas, viz., the development of
liquefed natural gas (LNG) terminals and the shift in pricing from a domestic “cost plus”
market to international gas pricing. Capital costs can also be quite volatile due to supply
imbalances in plant manufacturing market, and changes in exchange rates due to the
dominance of imported mechanical and electrical equipment.
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7. The cost of capital in Figure 1 is based on a “Vanilla WACC” calculation, that is a post-tax cost
of equity and a pre-tax cost of debt. Post-tax pre-fnancing cash fows are discounted by the
Vanilla WACC. However, in this article, all cash fows are post-tax and post-fnancing, and so
are discounted by the post-tax cost of equity.
8. From this, one would conclude that all wind turbines should be project-fnanced rather than
fnanced on balance sheet. However, our PFmodel has one signifcant limitation in this regard
– it does not model portfolios of plant at the enterprise level. In practice, credit rating agencies
treat half of the power purchase agreement (PPA’s) present value as “synthetic debt” on the
balance sheet of the counterparty who writes the instrument. This, in turn, has a real
opportunity cost to vertically integrated frms – as their level of total corporate debt must be
modifed downwards accordingly, and so the apparent gains fromwriting PPAs over balance
sheet fnancings are partially illusory.
9. For example, the Australian Energy Regulator routinely investigates every price spike
?$5,000, regardless of whether average spot prices are approaching normal proft levels.
10. In this instance, live FY13 half-hourly load data from the Victorian region have been used.
11. While many renewable energy technologies have zero or negligible fuel costs, biomass plant
is a notable exception.
12. Mountain(2013) analyses production-weightedspot prices for all generationplant types. Here, we
focus on the time-weighted spot prices in Victoria and compare these with our estimates of brown
coal plant costs. Mountain’s (2013) FY13 production-weighted price is $58/MWh and FY12 is
$26/MWh – which is consistent with the result in Table IV(results in columns 1 and 5).
13. The average carbon intensity of 1.215t has been derived from AEMO market data. This
analysis assumes the carbon price was passed-through at the industry’s average carbon
intensity for the reasons explained by Nelson et al. (2012). One implication of this is that all
brown coal generators are incurring losses on carbon costs because their CO
2
intensities of
1.28t ?1.55t/MWh are above the average intensity of 1.215t/MWh.
14. See, in particular, Mountain (2013, p. 24).
15. See, in particular, the Energy Users Association of Australia (EUAA) Media Statement
“Power generation prices need closer monitoring” – released on 27 June 2013. Available at:
www.euaa.com.au
16. In reality, hedge contracts struck at higher values in preceding periods would have reduced
these losses materially. On the other hand, the Victorian region is currently oversupplied and,
as such, economic losses would have been greater than implied, given the additional capital
stock deployed.
17. AGL Energy acquired 100% of the 2,200 MW Loy Yang power station and coal mine in June
2012. The implied enterprise value was $3.1 billion or $1,410/kW. AGL Energy raised $1.5
billion fromthe debt and equity capital markets, of which $1.2 billion was used to recapitalise
the project’s existing debt tranches. There is little doubt that when those debt tranches
matured, refnancing would have only been partially successful and additional equity
injections would have been required.
18. The parent entity of Hazelwood power station, GDF Suez, injected $650m of “rescue
capital” into its project debt structure using an intercompany loan during the last week of
June 2012. Hazelwood had been unable to secure bank funding. For further details, see the
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Australian Financial Review at www.afr.com/p/national/rescue_for_power_station_
xk639CP4CyQuJIp9ndewYP
19. See Mountain (2013) for details of the structural adjustment assistance.
20. These write-offs at the three largest brown coal plants occurred because of a legal obligation
that company directors have to ensure that asset values are reported as fair and reasonable
under Sections 295-297 of Australia’s Corporations Act 2001 (Cth). Sections 296 and 297, in
particular, require the application of Australian Accounting Standard AASB136 --
Impairment of Assets. Failure to account for a fair and reasonable carrying value of fxed
assets exposes company directors to penalties under the Corporations Act 2001 (Cth), which
include monetary fnes and jail terms.
21. While the CO
2
average intensity of the Victorian region is 1.215t/MWh, the feet average CO2
intensity of the brown coal feet is closer to 1.37t/MWh, as Simshauser and Doan (2009)
demonstrate. Accordingly, the feet pass-through rate could be thought of as 103% rather
than 116%, again holding all other variables strictly constant.
22. The apparent price premium of $4.41/MWh, when applied to the nine-month FY13 energy
output of 34,112 GWh, results in an apparent “windfall gain” of $150 million. However, the
loss of output fromFY12 to FY13 (of 39,856 GWh less 34,112 GWh) at the so-called break-even
pass-through price of ($58.17 less $4.41/MWh) results in an even greater offsetting loss of $309
million. This result further undermines the Table IV analysis.
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Appendix
Mathematical proof of missing money in energy-only markets
Under any optimal plant mix for any number of optimal plant technologies, aggregate revenues
will always fall short by at least F
?
0
.
Proof by mathematical induction:
Let n ? 1
Intercept : F
?
1
? F
?
0
? (?
0
? ?
1
) . t
Profit for 2nd plant ? ?
0
. t
1
? ?
1
. (t
2
? t
1
) ? (?
1
. t
2
? F
?
1
)
Profit ? ?
0
. t
1
? ?
1
. t
2
? ?
1
. t
1
? ?
1
. t
2
? F
?
1
Profit ? ?
0
. t
1
? ?
1
. t
1
? F
?
1
Profit ? (?
0
. ??
1
) . t
1
? F
?
1
177
Normal proft
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Profit ? F
?
1
? F
?
0
? F
?
1
Profit ? ?F
?
0
Let n ? 2
Intercept : F
?
2
? F
?
1
? (?
1
? ?
2
) . t
Profit for 3rd plant ? ?
0
. t
1
? ?
1
. (t
2
? t
1
) ? ?
2
. (t
3
? t
2
) ? (?
2
. t
3
? F
?
2
)
Profit ? ?
0
. t
1
? ?
1
. t
2
? ?
1
. t
1
? ?
2
. t
3
? ?
2
. t
2
? ?
2
. t
3
? F
?
2
Profit ? (?
0
. ??
1
) . t
1
? (?
1
. ??
2
) . t
2
? F
?
2
Profit ? F
?
1
? F
?
0
? F
?
2
? F
?
1
? F
?
2
Profit ? ?F
?
0
Let n ? k
Intercept: F
?
k?1
? F
?
k
? (?
k
? ?
k?1
) . t
k?1
Profit ? ?
i?1
k
(?
i?1
? ?
i
) . t
i
? F
?
k
? ?F
?
0
Let n ? k ? 1
Profit ? ?
i?1
k?1
(?
i?1
? ?
i
) . t
i
? F
?
k?1
Profit ? ?
i?1
k
(?
i?1
? ?
i
) . t
i
? (?
k?1?1
? ?
k?1
) . t
k?1
? F
?
k?1
Profit ? ?F
?
0
? F
?
k
? F
?
k?1
? F
?
k
? F
?
k?1
Profit ? ?F
?
0
Corresponding author
Paul Simshauser can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
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doc_922652107.pdf