Solving for 'x' - the New South Wales Gas Supply Cliff
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Solving for ‘x’ – the New South Wales Gas Supply Cliff
Paul Simshauser and Tim Nelson
Level 6, 144 Edward Street
Brisbane, QLD 4001
March 2014
Abstract
On Australia’s east coast over the period 2013-2016, we forecast that aggregate demand
for natural gas will increase three-fold, from 700 PJ to 2,100 PJ per annum, while our
forecast of system coincident peak demand increases 2.4 times, from 2,790 TJ to 6,690 TJ
per day. This extraordinary growth is being driven by the development of three Liquefied
Natural Gas plants at Gladstone, Queensland. Almost simultaneously, a non-trivial
quantity of existing domestic gas contracts currently supplying NSW will mature. Much
of that gas has been recontracted to LNG producers in Queensland – thus creating a gas
supply cliff in NSW. Compounding matters, recent policy developments have placed
binding constraints over the development of new gas supplies in NSW. In this article, we
present our dynamic partial equilibrium model of the interconnected gas system and
produce forecasts with daily resolution. We find that absent additional supply-side
development, unserved load events will remain more than a theoretical possibility due to
inter-temporal spatial constraints…
Keywords: Gas Markets, Energy Policy, Energy Security.
JEL Codes: L95, L98, Q41 and Q48.
1. Introduction
Australia’s east coast gas market, which spans Queensland, New South Wales (NSW), the
Australian Capital Territory (ACT), Victoria, South Australia and Tasmania, is undergoing a
large demand-side shock. Over the past 10 years, the demand for natural gas has averaged year-
on-year growth of 2.1%. In 2013, aggregate demand was about 700 peta joules per annum (PJ/a)
with a coincident peak winter load of 2,690 tera joules per day (TJ/d). By 2016, just three years
later, we forecast gas demand to rise to 2,100 PJ/a with a system-wide coincident peak winter
load of 6,690 TJ/d. This represents a three-fold increase in aggregate demand and a 2.4 times
increase in peak load – driven by the development of three Liquefied Natural Gas (LNG)
facilities in Gladstone comprising 6 trains, each with theoretical ex-field loads of between 250-
290 PJ/a. We are unaware of any mature, large-scale national energy markets experiencing a
three-fold increase in aggregate demand in such a short period of time. The first of the LNG trains
will be commissioned in Q4 2014, with the balance of the six trains commissioned in rapid
succession thereafter.
The history of LNG developments can be traced back to discoveries of large reserves of Coal
Seam Gas (CSG) in Queensland.1 Queensland has been highly successful in developing its gas
industry with proved plus probable (i.e. 2P2) reserves escalating dramatically from 3,400 PJ in
Paul Simshauser is the Chief Economist at AGL Energy Ltd and Professor of Economics at Griffith University. Tim Nelson is Head
of Economics & Sustainability at AGL Energy Ltd. The authors are indebted to Tony Stone for his work on our GPEM Model. We
are also grateful for the insightful comments and criticisms on an earlier draft of this article provided by Paul Taliangis (Core Energy),
Paul Hyslop (ACIL Allen), Dr Graeme Bethune (EnergyQuest), Mick McCormack (APA Group), Michael Fraser (AGL Energy), Paul
Ashby (AGL Energy) and AGL Energy’s Applied Economic and Policy Research Council (See Section 9). However, all views, errors
and omissions are entirely the responsibility of the authors. Our declaration is contained in Section 9.
1
The CSG ‘resources’ were known to exist at least as far back as the 1980s. Advances in drilling technology enabled the resource to
be booked as economically recoverable reserves during the 2000s.
2
1P or proved reserves are those thought to be reasonably certain (i.e. 90% confidence limit) of being recovered (i.e. production wells
have been drilled). 2P or proved plus probable reserves have a 50% confidence limit (i.e. commercial gas flow from a pilot well has
been demonstrated as a stabilised flow over several months for a CSG well). 3P or proved plus probable plus possible reserves have a
10% confidence limit of being recovered.
Page 1AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
2005 to 41,200 PJ by 2013. During this discovery and appraisal phase, it was evidently clear to
resource owners that the east coast gas market was not sufficiently large enough to enable the
monetisation of reserves in suitable timeframes and at the scale necessary to maximise profit, and
so developing an export market for natural gas in the form of LNG was a logical strategic
solution. Not only would it result in the rapid expansion of aggregate demand, but would also
have the benefit of linking domestic gas prices, historically ca$33 per gigajoule (/GJ), to the north
Asian export market price of ca$6-9/GJ equivalent ex-field ‘netback price’ over the medium term
(Simshauser et al, 2011). The practical evidence is that this strategy has worked. Forward gas
prices have risen beyond the top-end of this range, and aggregate demand is now trending
towards 2,100 PJ/a. For Queensland, LNG developments are forecast to produce $850 million
per annum in state royalties and taxes once the industry is fully operational, along with 18,000
jobs and add $3 billion per annum to Gross State Product.4
While the gas supply industry has experienced rapid growth in Queensland, NSW, which also has
natural gas resources, placed a moratorium on CSG development over an 18 month period to
September 2012. Then, after releasing a comprehensive regulatory framework for natural gas
development, the NSW Government declared non-scientifically based ‘2 km exclusion zones’
during 2013 in response to community concerns. The energy industry grossly underestimated
community sentiment because, in our opinion, the scientific, economic and engineering evidence
points towards an industry that can develop natural gas safely and economically given an
appropriate regulatory framework.5 A dispassionate assessment could only conclude that the
industry’s initial response to community concern was entirely inadequate. By the end of 2013,
industry engagement with the community had improved dramatically. But the NSW policy was
in advanced stages of being reset and ultimately the arbitrary exclusion zones were announced in
February 2013 and implemented in January 2014.
There is, however, an irony with the restrictive policy in NSW by comparison to the
accommodative policy in Queensland. NSW is the region most vulnerable to a large demand
shock because it is almost completely reliant on interstate gas supplies to satisfy local demand.
Under conditions of a supply crisis, state Governments constitutionally hold emergency powers
and are capable of redirecting contracted gas to stabilise the energy market or maintain system
pressures, and is initiated following exhaustion of Part 20 of the National Gas Rules and
curtailment by gas network operators.6 However, as the South Australian Energy Minister
recently stated:
…if there is some sort of state-wide emergency and we need to keep power going, we can
direct gas to the power stations… But unless there has been an explosion or a pipeline
issue, I would not allow any commercial arrangement [in South Australia] to be broken
to supply gas to Sydney; I would not let NSW break a single commercial contract… I
would say fix your own regulatory problems… (Martin, 2013, p.4).
In our view, the South Australian position is likely to be commonly held in Queensland and
Victoria. To be clear, east coast Australia has vast gas resources and substantial gas reserves.
However, an inter-temporal mismatch between supply and demand is predictable because gas
consumption patterns display diurnal cycles with marked seasonal variation (i.e. a winter bias),
3
All financial data are expressed in Australian Dollars unless otherwise specified.
4
See Queensland Government at http://www.industry.qld.gov.au/lng/key-points.html .
5
See for example the NSW Chief Scientist at http://www.chiefscientist.nsw.gov.au/__data/assets/pdf_file/0016/31246/130730_1046_CSE-CSG-July-report.pdf
6
Part 20 of the National Gas Rules refers to the calling of Contingency Gas by the Australia Energy Market Operator, as the financial
market operator. This is the last on-market step before the distribution network system operator initiates involuntary
curtailment. Jurisdictional Energy Ministers or their delegate can direct the use of on-shore gas inside their jurisdiction, and this can
extend to directions which stop interstate exports. This latter aspect is crucial to NSW's supply situation. The use of such emergency
powers are of course intended to deal with short term supply disruption events (e.g. Longford, Varanus Island, extreme weather
events). Systemic unserved load events, in a practical sense, should be resolved through higher prices and consequent demand-side
adjustment.
Page 2AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
and gas pipelines face capacity constraints on technical and economic grounds. As a result, a
granular analysis of the east coast gas system is required in order to examine the true extent of
any potential mismatch, and more importantly, what government policy and industry
development options exist to help remedy the NSW gas supply cliff.
In this article we present a dynamic partial equilibrium model of the east coast gas system with
daily resolution over the five-year period 2014-2018. Our model solves for differential
equilibrium conditions given binding constraints associated with maximum theoretical field
production, shipper nominations, pipeline capacity and storage facility limits. Our principal focus
is not to present forecast gas prices by node, but rather, to examine the issue of energy security.
We focus specifically on infrastructure augmentation and field production given our aggregate
load forecast – thereby enabling us to identify any unserved loads.7 Our analytical procedure is
therefore consistent with what Joskow (1976) has loosely described as the ‘British Approach’ to
energy system modelling.8 This article is structured as follows. In Section 2, we present
aggregate gas market demand data while Section 3 outlines gas reserves and production data. In
Section 4 we outline the nature of the problem facing NSW. Our gas market model is explained
in Section 5 along with our detailed demand-side, supply-side and infrastructure forecast
assumptions. Section 6 presents the results of our quantitative analysis. Policy recommendations
and concluding remarks follow.
2. The aggregate demand for natural gas
The production and consumption of natural gas on the east coast of Australia can be traced back
to 1969-1970 with the development of Victoria’s Bass Strait fields, the Roma fields in
Queensland and the Cooper Basin in South Australia. Figure 1 presents annual consumption for
the east coast states. Growth in aggregate demand over the past decade has averaged 2.1% year-
on-year.
Figure 1: East Coast aggregate demand for natural gas – 1969-20139
Source: AGL Energy, Core Energy, EnergyQuest, esaa.
7
Or as Paul Hyslop (ACIL Allen) suggested to the authors – ‘unmet demand’.
8
As Joskow (1976) observed, the British approach tended to focus on optimising the supply-side for a given load curve. The
American approach tended to use a homogeneous technology and focus on periodic and shifting loads. The French approach focused
on optimising the supply-side given stochastic demand and hence represented a combination of the British and American approach
(and pre-dated both). See for example Turvey (1969), Steiner (1957) and Boiteux (1949, 1956) respectively.
9
Note that Townsville demand, which is isolated from the existing east coast interconnected grid, has been excluded from this
analysis.
Page 3AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
The aggregate demand for natural gas on the east coast is set to treble due to export loads
associated with LNG plants in Queensland, which is illustrated in Figure 2.
Figure 2: Aggregate demand 2000-2018f
Source: AGL Energy, Core Energy, Frontier Economics, ACIL Allen, EnergyQuest, esaa,
One trend worth noting is that while we project total demand to rise rapidly to 2100 PJ/a in 2016
(driven by LNG exports), our forecast of ‘underlying’ domestic gas demand incorporates a
material contraction. Figure 2 notes that the domestic market falls from 704.2 PJ/a in 2014 to just
559.6 PJ/a in 2017 – that is, a 20.4% contraction in domestic gas load. Our analysis tends to
indicate that domestic consumption will peak during the 2012-2014 period, driven primarily by
the rise of gas used in power generation – a trend that we forecast to reverse as the market value
of gas increases relative to electricity. In Table 1, we provide domestic gas consumption data by
consumer segment and by region for our weather corrected base year of 2012.
Table 1: East Coast annual gas consumption (TJ/a) by region and consumer segment for 2012
Residential & Industrial & Power
REGION Total
SME Commercial Generation
ACT 7,629.1 3,246.5 - 10,875.6
NSW 37,719.6 82,207.9 31,746.4 151,673.9
VIC 116,106.2 84,669.6 17,938.1 218,713.9
QLD* 5,218.8 78,334.4 114,585.5 198,138.6
SA 10,888.5 28,338.8 64,620.0 103,847.3
TAS 529.7 5,640.5 12,551.8 18,722.0
TOTAL 178,091.9 282,437.7 241,441.8 701,971.5
QLD* (ex Townsville)
Mt Isa - 15,670.1 20,480.5 36,150.5
Brisbane 5,218.8 28,989.6 14,084.2 48,292.7
Gladstone - 33,674.6 14,972.6 48,647.3
Regional Qld - - 65,048.2 65,048.2
TOTAL 5,218.8 78,334.4 114,585.5 198,138.6
Source: AGL Energy, AEMO, Core Energy.
Page 4AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
3. Aggregate supply of natural gas
Aggregate 2P reserves on the east coast of Australia have risen sharply from 2008, driven
primarily by natural gas exploration and development in Queensland. The build-up in reserves
from 2005 through to the time of writing in 2014 is illustrated in Figure 3.
Figure 3: East Coast 2P Reserves 2005-2013
Source: EnergyQuest
A key issue facing the market for natural gas is how these 2P reserves are notionally allocated,
that is, with an ‘export-market bias’ or a ‘domestic market bias’. As Figure 4 highlights, ca80%
of existing 2P reserves are notionally allocated for export to North Asian markets in the form of
LNG cargoes as they are owned by market participants with financial obligations (or corporate
strategic targets) to supply LNG. While price rather than reserve ownership will be the ultimate
determinant of how gas flows within the east-coast interconnected system, Figure 4 tends to
indicate that ceteris paribus, the default directional flow for a majority of gas will be to LNG
loads.
Page 5AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 4: Notional allocation of 2P Reserves as at 2013
Source: EnergyQuest, AGL Energy Ltd.
In Figure 5, we present regional gas production along with regional gas consumption for our base
year of 2012. This is important because it highlights the reliance that NSW currently has on
interstate transfers to satisfy indigenous demand.
Figure 5: Regional Gas Production vs. Regional Gas Consumption - 2012
Source: Core Energy, EnergyQuest, AGL Energy Ltd.
Note that in 2012, local gas production represented only 4% of final NSW gas demand. The
majority of this supply comes from the Camden CSG field, in Western Sydney
4. Solving for ‘x’ – the NSW gas supply cliff
In this article, our primary interest relates to how the market for natural gas will dynamically
adjust during the period in which LNG facilities commence their production run-up and
simultaneously various long-dated gas supply contracts in NSW mature. Historic gas supply
Page 6AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
agreements vary considerably in tenor, price and quantity. At the time of writing, our database
indicates that there are about 70 ‘industrial strength’ gas supply agreements.10 These supply
agreements can be further broken down into ‘reseller agreements’, ‘power station agreements’
and ‘industrial supply agreements’. Reseller contracts range from 2-20 years in tenor with an
average term-to-maturity of 12.1 years. Pricing for this otherwise homogeneous commodity on a
unity-load factor basis spans the range of ca$3/GJ for contracts written in the mid-2000s all the
way through to contemporary oil price-linked contracts with values as high as ca$10/GJ. Annual
contract quantities vary considerably, from as low as 1 PJ/a to more than 100 PJ/a with an
average contract quantity of 18.4 PJ/a. However, while the average contract could be thought of
as 12.1 years in tenor with a contract quantity of 18.4 PJ/a and a market value of ca$6-9/GJ, the
maturity profile of existing contracts is highly congested. The average term-to-maturity for
reseller contracts as at 2013 was just 4.7 years on a volume-weighted basis. Our database of gas
supply agreements to power stations displays pricing trends reflective of their earlier
commencement, with substantially longer tenors and an average term-to-maturity of 11.7 years.
This is not entirely surprising given that one of the constraints associated with the development of
project financed gas-fired power stations is a secure, long-dated fuel supply contract (Nelson and
Simshauser, 2013). Finally, our analysis of industrial supply agreements indicates remaining
terms-to-maturity of 4.8 years. Figure 6 presents a time series which focuses on gas supply
agreements relevant to the NSW region as at the end of 2013.11 Note the sharp run-down in
contracted supply during 2016-2018:
Figure 6: The NSW gas supply cliff: 1990-2018f
This ‘cliff-edge’ in contracted gas supply to NSW is not a unique episode. An equivalent run-
down in contracts occurred during the early-2000s and was resolved without fanfare through a
series of renegotiations between gas producers, resellers and industrial consumers. However, the
combination of rapid LNG load growth in Queensland and more recent (and unexpected) supply-
side development constraints in NSW has created uncertainty as to how the situation in Figure 6
will be resolved this time around. Figure 7 combines our data from Figures 1, 2 and 6, and is the
sole reason why the NSW gas supply cliff warrants policy and quantitative analysis at all.
10
We use the term industrial strength to reflect substantive gas supply agreements with power stations, resellers and large industrial
customers who have transacted directly with gas producers.
11
Note that supply agreements would technically be higher than consumption but flexibility mechanisms typically ensure supply
agreements equal consumption.
Page 7AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 7: Aggregate gas demand vs. aggregate gas supply – 1969-2018f
Solving for ‘x’ – that is, the notional contracted gas supply gap in Figure 7 – does not relate to
reserves. The problem that we identify is seasonal and spatial. That is, the market needs to
satisfy inter-temporal gas demand while remaining within the physical, technical and economic
envelope of the interconnected gas system. The issue facing policymakers is that even under
ideal operating conditions, there is insufficient gas production, pipeline and storage capacity
during the height of winter to meet system-wide peak gas demand, particularly during working
weekdays when diurnal patterns reach their maximum. Figure 8 illustrates the seasonal and
diurnal variation in aggregate gas demand by contrasting daily winter consumption (i.e. Sunday 1
July 2012 to Tuesday 31 July 2012) with summer consumption (i.e. Sunday 1 January 2012 to
Tuesday 31 January 2012) and annual average consumption by day. Note that the peak winter
load is 80% higher than summer load.
Figure 8: Winter vs. summer gas load
Page 8AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
High pressure natural gas pipelines are vitally important to the proper functioning of the
interconnected gas system. They connect locationally distributed gas demand with locationally
distributed gas supply sources, and are necessarily a scarce resource because of the substantial
upfront investment commitment and high ongoing fixed capital and operating costs. In the
absence of quantitative analysis, one might be tempted to conclude that the NSW gas supply cliff
can be readily resolved by rapidly expanding existing gas pipeline capacity and storage
infrastructure between NSW and Victoria, for example. This is technically possible, and as our
modelling later reveals, all economic options will be absolutely crucial. But as with all network
infrastructure assets there is an economic saturation point – major augmentation of pipeline
capacity as the sole augmentation option would be financially unviable in the long run given
demand uncertainty, and because at the margins, other lower-cost supply options exist. Indeed,
the financial cost of solving for ‘x’ solely through pipeline augmentation would almost certainly
do more damage than good to the proper functioning of the gas market, and would result in
sizable welfare losses.12 Economically efficient marginal pipeline compression and looping has
been identified and we expect those expansions to be underwritten by gas shippers, and
committed to by profit maximising pipeliners. We present the beneficial effects of these
economic infrastructure expansion projects later in Section 6. In this respect, the market is
responding as expected – at least where policy constraints do not exist.
From a practical perspective, to the extent that any residual (and transient) mismatch exists
between supply and demand, price-sensitive gas-intensive loads may be forced to exit. However,
it is worth reviewing what happens when unplanned system shortages occur, and specifically,
which customer segments are exposed. There are 1.3 million residential and business
connections to the reticulated gas network in NSW (esaa, 2013). Of these, 1.1 million are defined
as household and commercial users, with the balance being industrial users – and as IPART
(2012) note, there are about 450 large industrial users. If system security is threatened due to
insufficient supply being available to meet the aggregate demand of all users, even after dispatch
of any contingency gas, then the gas network operator commences curtailment. The activation of
curtailment procedures is authorised under the network operator’s approved access arrangement,
which has the status of subordinate energy legislation in that jurisdiction. Such procedures are
intended for short-term imbalances. Under more severe conditions, the NSW Government will
invariably be required to direct available supply (i.e. local gas production and any interstate gas
imports) to some users and not others – meaning that under The Energy and Utilities
Administration Act 1987, and specifically s24-26, the NSW Minister for Energy has the power to
override the legally binding gas supply agreements of resellers and the largest industrial
consumers. Emergency services and hospitals are likely to be prioritised over households, who
are in turn likely to be prioritised over industrial consumers. In short, the largest industrial users
are progressively shed from the system until physical equilibrium between forecast demand and
supply is restored, regardless of the existence of legally binding gas supply agreements.13
Quantifying the economic impacts of an inherent imbalance in the market for natural gas is a
difficult exercise and requires Computable General Equilibrium Modelling (and to be sure, is well
beyond the scope of this article).14 However, demonstrating that short run adverse economic
consequences are possible is relatively straight forward. In his classic article on marginal cost
pricing, Hotelling (1938) noted long ago that the economic way to handle scarcity situations is to
charge a sufficiently high price to limit demand. And so gas-intensive industrial users in the
NSW region who have supply contracts maturing over the 2016-2017 period will face one of
three general options during an episode of scarcity; (1) pay a substantially higher price for natural
12
Material pipeline expansions from Victoria to meet Sydney’s winter gas load would represent substantially under-utilised assets and
face perennial stranding risk from further demand destruction or expansion in indigenous supply. Such uncertainty would make
pipeline augmentation (to meet peak loads) unbankable for the private sector, and too costly for a state or Federal Government to fund.
13
The NSW Government last used its emergency powers in 2007 when gas shortfalls, caused by unexpected weather-driven co-
incidental peaks in different jurisdictions, resulted in around 35% of large industrial consumers being curtailed for two days.
14
For an example of such CGE Modelling, see ACIL Allen (2013).
Page 9AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
gas supply; (2) cease trading; or (3) reduce production. Under these general conditions, it is
difficult to imagine employment levels associated with NSW manufacturers being completely
unaffected in the short run.
At this point we would caution readers not to draw alarmist conclusions about the likely state of
the broader economy. While ABS data reveals that the NSW manufacturing sector has ca185,000
employees, not all manufacturers are gas-intensive, and manufacturers that are gas-intensive (and
therefore at risk) will invariably represent a small share of total national employment. However,
Burgan and Spoehr (2013) and Brain (2013) analysed the macroeconomic effects and
employment cycles associated with the closure of manufacturing plants in South Australia. Key
findings from their study is that while the broader macro economy may barely register the
change, “recessionary conditions” can appear in the immediately affected local community with
pronounced flow-on effects around the plant closure location. As Burgan and Spoehr (2013) and
Brain (2013) explain in the manufacturing case, during the first quarter after plant closure, job
losses stood at around 6,500 but over the four years following swelled to almost 12,000 with
much of this occurring in three suburbs.15
Figure 9 shows the locational relationship between manufacturing-related employment and
industrial gas consumers in NSW. The line series, measured on the LHS y-axis, presents
manufacturing jobs as a percentage of total employment using Australian Bureau of Statistics
NSW electoral district data, assembled in descending order. The bar series, measured on the RHS
y-axis, presents AGL data on the number of large gas consuming sites within that district. So for
example in Smithfield, 17.3% of the workforce is estimated to be employed in manufacturing,
and AGL consumer account data indicates there are at least 12 large gas consuming sites. At the
other extreme, Vaucluse has just 3% of its workforce estimated to be involved in manufacturing
employment with no large gas consuming sites.
Figure 9: Proportion of manufacturing jobs and gas consuming sites by location in NSW
Source: ABS and AGL data
15
One peer reviewer correctly pointed out that the South Australian manufacturing case involved a single industry with highly specific
skills, and the economic analysis did not utilise CGE Modelling. In the case of a gas market disruption event in NSW (i.e. either
demand destruction or energy shortages), employment impacts in NSW are more likely to be dispersed and transient in nature.
Page 10AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Our observation is that the overwhelming majority of public analysis and debate relating to east-
coast energy security has thus far relied on, at best, annualised modelling results (while in a
surprising number of instances, such debate contains no quantitative analysis whatsoever).
Annualised data gives a broad indication of the overarching pricing pressures that customers on
the east-coast are facing. However, annualised analysis of the gas market, given the substantial
seasonal variation in aggregate demand, is simply unable to identify binding inter-nodal pipeline
constraints and intra-zonal storage limitations, and in turn, the frequency and intensity of any
unserved load events. This is important given that there is at least the potential for regulatory
intervention to override gas supply contracts under such conditions. In order to understand the
extent of inter-temporal mismatches between aggregate gas demand and supply and the binding
production, pipeline and storage constraints facing gas producers, shippers and consumers, a
dynamic partial equilibrium model of the interconnected gas system with daily resolution is
necessary – which we present in Section 5.
5. Dynamic partial equilibrium model of the interconnected gas market: ‘GPEM Model’
Our gas model (GPEM Model) is a template interconnected gas system model that can be
modified to mimic local market conditions. The GPEM Model assumes gas can be shipped from
any supplier to any consumer subject to pipeline constraints, along with gas shipper nomination
constraints. A series of regions exist with each node comprising the three domestic consumer
segments identified in Table 1, along with the export LNG terminal segment at Gladstone. Nodes
are interconnected via a series of 19 high pressure gas pipelines. Our GPEM Model ultimately
seeks to maximise welfare in the market for natural gas, and this objective is implemented
formally by maximising the sum of consumer and producer surplus after satisfying differentiable
equilibrium conditions.
5.1 Definition of Regions and Suppliers
In the GPEM Model, Ɲ is the ordered set of regions or ‘nodes’ in our interconnected gas system
and |Ɲ| is the number of nodes in the set. Let ƞi be node i where
i ∈{1..|Ɲ|} ^ ƞi ∈ Ɲ. (1)
Let Qi be the maximum demand for all consumer segments at node ƞi expressed in TJ/d.
Let Ψi be the set of gas suppliers at node ƞi. Let Ṗψi be the maximum productive capacity of gas
supplier ψ at node ƞi. Let ρψi be the quantity of gas supplied at node ƞi by supplier ψ where
ψ ∈{1..|Ψi|} (2)
Let ci be the quantity of gas delivered to node ƞi expressed in Tera Joules per day (TJ/d).
5.2 Definition of Pipelines and Transport Pathways
In the GPEM Model, T is the ordered set of pipeline segments in the system and |T| is the number
of segments in the set. Let tj be node j where
j ∈{1..|T|} ^ tj ∈ T (3)
Let Ʊj and j be the two nodes that are directly connected to pipeline segment tj where
∈ ∈ | (4)
And let fj be gas flow on pipeline segment tj from Ʊj to j expressed in TJ/d.
Page 11AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Let R be the ordered set of all paths. Let Rk be path k between two nodes ƞx and ƞy. Let rkj be
node j in path Rk where
j ∈ {1..| Rk |} ^ rkj ∈ Rk (5)
Let Tr be the ordered set of pipeline segments in path Rk. Let tkj be pipeline segment j in path
Rk where
j ∈ {1..(|Rk|-1)}. (6)
Let fcj be the maximum allowed flow along pipeline segment tj. Let fmj be the minimum allowed
flow along pipeline tj. Let frr be the flow of gas along path Rk. Let ᵽk be the cost of shipping 1
unit of gas (i.e. 1 TJ of gas) along path k, subject to:
∀k,x,y rkx rky | x y (7)
and
∃tj | [Ʊj = rki ∧ j = rk(i+1)] ∨ [ j = rki ∧ Ʊj = rk(i+1)] (8)
The purpose of equation (7) is to ensure that each node appears only once in a path, while the
purpose of equation (8) is to ensure that all nodes are connected via pipelines.
5.3 Model Calculations
The flow on any given pipeline is the sum of flows attributed to all paths (that is, forward flows
less reverse flows), as follows:
∑ | ∈ ∃ ∑ | ∈ ∃
(9)
The clearing vector of quantities demanded or supplied (including from storage facilities) in node
i = 1…n, is given by the sum of flows in all paths starting at that node, less flows in paths ending
at that node if applicable:
∑ | ∑ | | | (10)
Net positive quantities at a node are considered to be net supply ρψi and negative quantities imply
net demand ci:
{ (11)
5.4 Demand Functions
Let Ci(q) be the valuation that consumer segments at node ƞi are willing to pay for quantity q TJ
of gas. We explicitly assume that demand in each period i to be independent of other demand
periods. Let Pψi(q) be the prices that supplier ψ expects to receive for supplying q TJ of gas at
node ƞi.
5.5 Objective Function:
Optimal welfare will be reached by maximising the sum of producer and consumer surplus, given
by the integrals of demand curves less gas production and pipeline costs. The objective function
is therefore expressed as:
Page 12AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
∑| |
∫ ∑| |
∑ ∫ ∑ . (12)
Subject to:
fmi fi fci ^ Ṗψi
5.6 The Netback Price of Natural Gas
One of the crucial calculations within our GPEM Model is the netback price of gas as this
provides guidance to the marginal valuation Ci(q) of LNG producers at the Wallumbilla node ƞw.
The netback price of gas is important because it identifies the price for which an LNG exporter is
financially indifferent as to whether at the margins they produce LNG and load it onto a ship, or
sell their intended feedstock back to the broader gas market (i.e. to another LNG producer or to a
domestic market participant).
LNG exports from Australia to North Asia are generally referenced against the most popularly
traded Asian energy index derivative, the Japanese Crude Cocktail or JCC16, which is an average
of the roughly top 20 crude oils by volume in that country.17 Provided the clearing price of oil
(expressed in dollars per barrel or US$/bbl) remains within a non-extreme trading range, then a
simple ‘rule-of-thumb’ netback price of gas (in the form of LNGfob cargo at Gladstone in US$/GJ)
can be thought of as roughly 13.7% of the oil price.18
For our purposes, we are interested in a more granular analysis referenced to our Wallumbilla
node. In practice, LNG export sales contracts are often subject to an ‘S-Curve’ which has the
effect of muting extreme oil price movements to buyers and sellers by placing partial caps and
floors on the traded LNG price. Additionally, as we are interested in the marginal price of east
coast Australian gas supply, then the LNG supplier with the highest contract value is the relevant
focal point for netback calculations. Our view is that this is the GLNG project, which is thought
to have a netback price of ca14% of the JCC. Our netback model of gas prices ex-field, which
we take to be ex-Wallumbilla, is expressed as follows:
⁄
[ ( )]⁄ ( )| .[ . ].
(∑ { . }. ) (∑ { . . } { . }. )
[ ⁄(1 )] [ ⁄(1 )] (13)
( . . ) ( . )
The ex-field netback price of gas ( is calculated by taking the LNG price Delivered Ex-
Ship ( into North Asia. We make use of the historical relationship between the price of
Brent Crude and JCC in producing our estimates via historically derived intercept
and slope ( coefficients. The ‘S-Curve’ which is then applied to our oil price estimate uses
conventional LNG intercept terms ( and slope ( , with the details of how this is applied set
out in equation (14).
̂ ̂ . .1
.[ . ]. | [ . ]{ ̂ . .11 (14)
̂ . .11
Returning to equation (13), our value for is then converted from US$/MMBtu to US$/GJ
by dividing this result with the appropriate conversion constant . Boil-off losses (b) are then
deducted. To transform the loss-adjusted LNG export price (expressed in USD) to an AUD
16
Japanese Crude Cocktail is in fact a nickname for the JCC. The formal name is ‘Japan Customs-cleared Crude’.
17
JCC reference prices can be obtained from the Petroleum Association of Japan at http://www.paj.gr.jp/english/statis.html
18
It is worth noting that 13.7% x JCC (expressed in US$/GJ) is the equivalent of 14.5% x JCC (expressed in US$/MMBtu) – the latter
being conventional units for LNG pricing.
Page 13AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
LNGfob price at Gladstone, boil-off costs , shipping costs and trading costs are
deducted, with the resulting outcome converted into Australian dollars at our assumed
exchange rate.
In order to arrive at a suitable estimate of the ex-field netback price of gas ), the cost of
Liquefaction ( ) and inter-nodal pipeline tariffs ( ) need to be deducted. Cost and quantity
streams associated with LNG terminals are discounted at a nominal pre-tax hurdle rate. The ‘all-
in’ cost of liquefaction at the jth plant is derived by discounting the cost streams associated with
the initial LNG plant capital and ongoing capital works , along with fixed and variable
O&M costs . Cost streams are the subject to projected inflation rates, ( . Plant output
, which is initially expressed in Mt pa for producing the various cost streams, is subject to the
constant term which in this instance will convert LNG cargo from Mt/a to GJ/a and is then
escalated at the relevant revenue inflator and discounted by the cost of capital. Dividing the
present value of the cost stream by the present value of the quantity stream produces the long run
marginal cost of an LNG terminal, ( ).
Inter-nodal pipeline tariffs ( ) are derived by discounting the cost streams associated with the
initial pipeline capital costs and ongoing capital works , along with fixed operating costs
, all of which are subject to projected inflation rates, ( . Annual flow rates are also
escalated at projected inflation rates, ( . Dividing the present value of the cost streams by the
present value of the quantity streams then produces our long run average cost of transportation,
( ).
An issue of considerable importance during the ramp-up phase of LNG productive capacity in
terms of defining the relevant value for is whether short run or long run costs are the
defining variable in relation to netback calculations, and specifically, our value for . Over the
long run, it seems clear enough that the appropriate value to ascribe in equation (13) is indeed the
full value for . However, this is not in our view the relevant value in the short run. Utilising a
short run value for the jth LNG plant, would have the effect of increasing, quite materially, the
ex-field netback price of gas .
Our logic here, which all peer reviewers were in strong agreement with, is as follows. Assume
the jth LNG plant has productive capacity of Ṗ but in the event only has shipper nominations equal
to ṗ such that Ṗ > ṗ during the initial commissioning year(s). In this instance, is it the long run
average cost of liquefaction or their short run marginal cost of LNG production from the jth plant
that will maximise profit under conditions of scarcity? It seems clear to us that it is the latter, and
so in the short run, particularly during the period of our analysis19, the appropriate value for
will indeed be elevated through the use of a short run marginal cost for .
5.7 Input Assumptions
Regional nodes ƞi in the GPEM Model are best viewed in the context of an East Coast map,
which we present in Figure 10. The major pipeline routes are also included along with the
pipelines under active construction at the time of writing.
19
One reviewer noted that in the long run, during any supply-side disruption event (such as major flooding), short run dynamics would
once again prevail.
Page 14AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 10: GPEM Model Nodes and Pipelines
Major gas pipelines (tj) and their associated maximum flow rates (fci) and tariffs (ᵽk) are
presented in Table 2.
Table 2: Pipelines and Pipeline Capacity
Gas Pipeline Pipeline name Length From Node To Node Max Flow Tariff
(km) (TJ/d) ($/GJ)
(t j ) (ƞ i ) (ƞ i ) (fc i ) (ᵽ k )
CBR Canberra to Dalton 58 Dalton Canberra 77 $0.04
CGP Carpentaria Gas Pipeline 840 Ballera Mt Isa 119 $1.44
EGP Eastern Gas Pipeline 797 Longford Sydney 294 $0.96
LMP Longford to Melbourne Pipeline 174 Longford Melbourne 1030 $0.24
MAP Moomba to Adelaide Pipeline 1185 Moomba Adelaide 253 $0.50
MSP Moomba to Sydney Pipeline 1300 Moomba Sydney 439 $0.82
NVI NSW - Victoria Interconnect 88 Culcairn Young 71 $0.15
NVI_1 NSW - Victoria Interconnect 320 Melbourne Culcairn 92 $0.32
QGP Queensland Gas Pipeline 627 Wallumbilla Gladstone 145 $0.96
RBP Roma to Brisbane Pipeline 438 Wallumbilla Brisbane 240 $0.51
SEAGas South East Australia Gas Pipeline 680 Pt Campbell Adelaide 314 $0.58
SWP South West Pipeline 150 Pt Campbell Melbourne 353 $0.27
QSN QSN Link Pipeline Ballera Moomba 527 $0.40
937
SWQP South West Queensland Pipeline Wallumbilla Ballera 404 $0.85
TGP_1 Tasmanian Gas Pipeline Longford Bell Bay 129 $2.00
734
TGP_2 Tasmanian Gas Pipeline Bell Bay Hobart 129 $2.00
APLNG APLNG Pipeline 530 Surat Gladstone 1560 $0.55
QCLNG QCLNG Pipeline 540 Surat Gladstone 1510 $0.55
GLNG GLNG Pipeline 435 Surat Gladstone 1429 $0.55
Sources: ACIL Allen, AEMO, AGL Energy Ltd, APA, Frontier Economics.
Given the number of pipeline routes Rk between the set of nodes Ɲ, there are 159 plausible supply
combinations and associated constraint equations along with 570 variables in solving for each
Page 15AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
period (i.e. day). Our forecast for aggregate daily gas load through to 2018 uses the weather-
corrected 2012 consumption data (by regional node and by consumer segment) as outlined in
Table 1. These data are projected forward at differential growth rates for each segment in each
node – but as noted earlier in Section 2, overall we forecast domestic gas demand to contract by
more than 20%. In the Residential & SME market segment, our forecast growth rates are broadly
consistent with those of the Independent Market Operator (AEMO, 2013) with Queensland, South
Australia, NSW, Victoria and Tasmania at 2.4%, 0.7%, 2.3%, 1.1% and 2.7% per annum,
respectively. Our Commercial & Industrial market segment growth rates for Queensland, South
Australia, NSW, Victoria and Tasmania are 1.7%, 0.4%, 1.3%, 0.2% and 1.3% per annum,
respectively.20 However, to be sure, while our growth rates are similar, our industrial and mass
market load is 27 PJ/a lower than AEMO (2013) by 2018.
Our forecast contractions in the demand for natural gas from the power station fleet21 can only be
described as very substantial. We assume 2014 gas-fired electricity generation largely mimics
2013, but in 2015 we envisage a 41% reduction, followed by a 12% fall in 2016, and a further
20% contraction in 2017 due to substitution effects (to coal-fired power generation).22 To put this
into perspective, during 2012 gas consumed by power stations represented 30.6% of total
domestic demand during the winter peak season with total annual demand of 241.5 PJ/a. By
2018, we forecast that gas used in power stations will contract to just 70.8 PJ/a, representing only
9.8% of domestic demand during the winter peak, and less than 3% of total system demand.23
Daily gas consumption by each LNG terminal is modelled discretely given their importance to
overall system demand. The initial ramping phase of the first LNG train is assumed to commence
in 2014 with the sixth and final LNG train commencing from early-2016. We assume each
facility follows a roughly 120-day commissioning schedule to full load.24 Initially, on-site gas
turbine plants are commissioned over a 30 day period, followed by an additional 30 day period of
ancillary plant commissioning. First liquefaction therefore occurs after two months of
commissioning activities with the plant operating at a full and stable load two months later. We
apply a 1.5% forced outage rate to each LNG facility which is randomly distributed throughout
the year via a Monte Carlo simulation. Our forecast aggregate gas load curve from 2012-2018 by
consumer segment with daily resolution is set out Figure 11.
20
These growth rates are also broadly consistent with AEMO (2013). A number of industrial consumers may well seek to convert gas
boilers to coal over the long run in response to rising gas prices. However, our view is that as this would require associated
environmental permitting due to metropolitan air-shed constraints, it is unlikely to occur en-masse within our study timeframes.
Implicit in our assumptions is an own price elasticity of demand for natural gas in the Commercial & Industrial market segment of -
0.20, a result broadly consistent with Hill and Cao (2012).
21
Our gas-fired generation has been exogenously derived from a dynamic partial equilibrium model of the National Electricity Market
for each discrete gas-fired power station on the interconnected gas system.
22
In February 2014 Stanwell announced that it would withdraw their 385MW Swanbank CCGT plant from the energy market from
October 2014 and re-sell their fuel to the gas market. See http://www.stanwell.com/latest-news.aspx. We understand from discussions with the
LNG producers that other gas-fired generators such as Origin Energy’s 630MW Darling Downs CCGT and Alinta’s 450MW Breamar
OCGT have also struck contracts to on-sell their fuel. However, unlike Stanwell’s decision to mothball their plant, Darling Downs
CCGT and Braemar OCGT plant will remain available for emergency duties in the electricity market for a certain (and limited)
number of days each year – that is, they will have the option to re-call their fuel during high electricity demand/price event days.
23
The reduction in gas-fired power generation (assuming an average heat rate of 8,000kJ/kWh) equates to ca21,350 GWh/a or the
equivalent of 4,850 MW of gas-fired plant running at a utilisation rate of 50%.
24
We assume the first two LNG trains are commissioned in Q4 2014 and therefore take longer than 120 days to achieve full load. One
reviewer noted that following LNG terminal construction, 180 days may be more appropriate to account for commissioning activities.
Page 16AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 11: Aggregate gas load forecast for the east coast 2012-2018f
Our forecast aggregate supply curve for 2018 and set of gas suppliers (Ψi), is presented in Figure
12. The y-axis is based on our maximum theoretical daily quantities capable of being produced
by each supplier (Ṗψi). After incorporating storage facilities (at Iona and Newcastle), maximum
theoretical daily output in 2018 is 6,865 TJ/d on an unconstrained basis, noting that gas pipeline
and gas storage constraints will place a lower ‘practical limit’ on quantities produced.25 Note that
Figure 12 also includes more than 300 TJ/d of productive capacity from fields in NSW that are
subject to policy uncertainty and (non-scientific) exclusion zones.
Figure 12: Aggregate gas supply for the east coast for 2018f
25
There is also the 150 TJ/d Dandenong storage facility in Victoria and the MSP also has storage capacity of 150 TJ/d. We have not
incorporated these facilities in our GPEM Model. Dandenong is of course on the southern constraint to NSW, and the MSP will face
the same issues as our NGSF – i.e. restocking economically (see Section 6.3 for details).
Page 17AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
In terms of our calculation for the netback price of gas ( ) Table 3 presents our key input
assumptions. Note that our LNG plant discount rate is 12% in line with EnergyQuest (2010),
Core Energy (2010) and Simshauser et al.(2011) while our gas pipeline discount rate is 11%.
When these inputs are combined with equations (13) and (14), they produce a long run netback
price of $7.95/GJ and a short run netback price of $11.20/GJ.
Table 3: LNG netback input assumptions
Variable Unit Value
Energy Conversion MMBtu/GJ 1.055056
Boil-off Losses (%) b 1.90
Boil-off Costs US$/GJ Бj 0.25
Shipping Costs US$/GJ ςj 0.80
Trading Costs US$/GJ Ṫj 0.50
AUD Exchange Rate A$/US$ x∆ 0.85
Brent Intercept US$/bbl a 2.77
Brent Slope US$/bbl Ƅ 0.97
Brent Oil Price US$/bbl Bi 95.00
S Curve Intercept US$/MMBtu α 0.50-3.00
S Curve Slope US$/MMBtu β .11-.140
LNG Cost of Capital (%) PV 12.00
LNG Capex A$M/t 1,300
LNG Capital Works (%) 0.25
LNG Fixed O&M A$Mt pa 27.17
LNG Variable O&M A$t pa 5.00
LNG Production Mt pa 7.36
Energy Conversion Mtpa/GJpa 55.43
Pipeline Capex $/km/inch Kj 55.00
Pipeline Diameter inches zj 42
Pipeline Length km dj 540
Pipeline Opex $/km oj 69,000
Pipeline Cost of Capital (%) PV 11.00
Inflation Rate (%) 2.50
Sources: Simshauser, Nelson and Doan (2011), Core Energy,
AGL Energy, Bloomberg, ACIL Allen.
6. GPEM Model Results
We have used our GPEM Model and input assumptions from Section 5 to simulate gas market
conditions on the east coast of Australia over the five-year period 2014-2018. In our base case
scenario, all demand-side and supply-side investments in Queensland are expected to proceed as
envisaged (i.e. LNG terminal loads and natural gas production, respectively).
6.1 Base case scenario
In our base case scenario, we assume that all existing supply contracts and associated shipper
nominations relevant to NSW remain in place as outlined in Figures 6-7. However, we
specifically assume no new investment in infrastructure or field development occurs within NSW
which enables us to establish a suitable business as usual baseline while meeting the objective
function set out in equation (12). Our aggregate system-wide results for served and unserved load
(TJ/d) are presented in Figure 13.
LNG fleet unserved load, which are evident from inspection of Figure 13, occurs from April 2016
and continues to occur most days of the year. These shortages coincide with the commissioning
of the sixth and final LNG train.26
26
The second terminal at GLNG is scheduled to ramp-up to full load over a 2-3 year window. See slide 21 at
http://www.santos.com/library/Santos%202013%20Full-year%20results%20presentation.pdf (viewed Feb 2014).
Page 18AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 13: GPEM base case model results for the five year period 2014-2018
The dynamics of the east coast grid change quite fundamentally once the LNG fleet commences
operation – the directional flow of gas along the QSN Pipeline27 eventually reverses with
increasing intensity as aggregate demand in Queensland begins to outstrip localised supply. In
other words, the Queensland region switches from an exporting region to an importing region.
Based on our aggregate supply assumptions, total LNG fleet demand is unable to be met as
Figure 14 clearly illustrates. Shortfalls occur for more than 330 days per annum (i.e. 90% of the
year) and by as much as 250 TJ/d with a median shortage of 130 TJ/d. However, in the context of
a 2,100 PJ/a system this represents a shortfall of just 2.0%.
Figure 14: GPEM base case model results LNG Fleet - 2014-2018
We noted earlier that our long run marginal value for Ci(q) associated with LNG terminals at the
Wallumbilla hub is ca$7.95/GJ but that under scarcity conditions – which based on Figure 14
27
The QSN pipeline connects the Moomba Production Node with the Ballera Interconnection Node. See Figure 10 for details.
Page 19AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
evidently exist – our forecast of the appropriate value for Ci(q) for the LNG fleet is our short run
netback estimate of ca$11.20/GJ at the Wallumbilla hub. Taking these two statistics with the
results from Figure 14 helps to explain why our east coast gas contract database has recently
registered short run contracts struck at values of $10/GJ for the period 2014-2016.
While not evident in our aggregate supply function presented in Figure 12, we assume that in
Queensland, theoretical productive capacity progressively increases to just over 4,100 TJ/d by
2016 and that Cooper Basin productive capacity increases from the current level of ca275 TJ/d
over the next two years to ca420 TJ/d. To the extent that field production undershoots these
parameters, the supply shortages outlined in Figure 14 would be amplified.
In terms of the domestic market, given our aggregate demand assumptions set out in Figure 11,
unserved load events begin to appear in the most ‘energy security-vulnerable’ region (i.e. NSW)
from May of 2016 as Figure 15 illustrates. In the analysis presented in Section 4 and Figure 6 in
particular, we highlighted that a material quantity of gas supply contracts to NSW expire during
2016-2018. Furthermore, Figure 5 demonstrated that NSW supplies less than 4% of indigenous
demand and that development constraints currently exist. These factors, along with binding
constraints along the Victoria-NSW pipelines, are key drivers of the unserved winter peak load.
To be clear, no energy shortages occur in the South Australian, Victorian, Tasmanian, or
Queensland domestic markets so our focus hereafter will remain on the NSW region. The LNG
fleet on the other hand experiences persistent shortages in a manner largely consistent with Figure
14 in each subsequent scenario we present.
Figure 15: GPEM base case model results NSW and ACT - 2014-2018
In our base case scenario, the extent of shortages in NSW is very material. In all, there are 118
days of energy shortages or ‘event days’ per annum with varying degrees of intensity. To be sure,
these results are mild by comparison to the Independent Market Operators equivalent case
(AEMO, 2013).28 The frequency distribution of the intensity of event days is illustrated in Figure
16 and displays a median shortage of 102 TJ/d.29 The maximum shortage is 256 TJ/d and under
these conditions, 39.7% of the NSW market would technically be “unserved”.
28
See in particular Figure 12 in AEMO (2013) which assumes no augmentation or enhanced NSW supply for the future year 2018 –
and has 193 days of unserved load. AEMO (2013) did not disclose their forecast NSW imbalance for the 2016-2017 calendar years.
29
As a number of our peer reviewers noted, a 102 TJ/d shortage represents less than 1.5% of total east coast gas system load.
However, this is nonetheless a significant volume of unserved load in the NSW region.
Page 20AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Figure 16: Intensity of ‘event days’ in the base case scenario in 2016
A key issue for the energy industry, policymakers and society more generally is the welfare
impact of transient (but systemic) unserved load events in NSW. From a strictly theoretical
perspective, what would a median unserved load event of 102 TJ/d mean for the NSW economy
in terms of demand rationing? In Figure 17, we have ranked AGL’s largest 100 gas consumers in
NSW by annual consumption in descending order (x-axis). On the LHS y-axis, we have plotted
the maximum daily demand of each customer as an index, and the RHS y-axis illustrates the
Cumulative Maximum Daily Quantity Curve. Based on this analysis, a median event day of 102
TJ/d would require the 48 largest industrial consumers in NSW to be shed from the system
(including 56 days during the winter season in 2016) in line with how curtailment occurs in
practice. As an aside, under a 60th percentile outage of 123 TJ/d, all 100 consumers in Figure 17
(incorporating businesses as diverse as food manufacturers to International Hotels to large
suburban entertainment venues) would need to be curtailed due to the diminishing maximum
daily demand of Commercial & Industrial consumers.
Figure 17: Maximum Daily Quantity of Large Industrial & Commercial Customers in NSW
Page 21AGL Applied Economic and Policy Research Working Paper No.40 – Solving for ‘x’
Under this scenario (which to be sure is merely our base or business as usual scenario), the NSW
gas network would be operating near the edge of collapse for almost a third of the year. This is
an unacceptable outcome and so our modelling efforts must turn to the economic expansion of
pipeline capacity, storage infrastructure and field development options.
6.2 Pipeline expansion scenario
The first expansion to infrastructure we consider assumes that gas resellers underwrite additional
looping and/or compression along the Eastern Gas Pipeline (EGP) and the Culcairn
Interconnect.30 We assume that the EGP is expanded by 60 TJ/d to 354 TJ/d and the Culcairn
Interconnect is increased by 50TJ/d to 120 TJ/d prior to the crucial 2016 winter season. These are
vitally important augmentations of scarce pipeline resources, and are likely to represent ‘the last
of the low hanging fruit’.
In the base case, these two pipelines between Victoria and NSW formed binding constraints – the
combined maximum flow of gas from Victoria to NSW along the EGP and the Culcairn
Interconnect was limited to 366 TJ/d and was reached on 188 days during 2016 (i.e. more than
50% of the year). The constraints were binding in all but three days of the peak winter season –
and the only reason the pipelines were not fully loaded on those three winter days was due to a
major forced outage of an LNG train in Gladstone (producing a surplus market). Pipeline
capacity expansion will therefore be highly beneficial to gas shippers trying to clear the market in
NSW, and to (Victorian) producer and (NSW) consumer welfare. Figure 18 presents the results
of the pipeline expansion scenario.
Figure 18: GPEM Pipeline Expansion - NSW 2016-2018
The extent of unserved load is, in relative terms, reduced dramatically in NSW from 118 to 55
‘event days’ with the median deficit falling from 102 TJ/d to 50TJ/d. Supply shortages to the
LNG fleet continues to persist in line with Figure 14. The frequency distribution of the intensity
of event days in NSW in our pipeline expansion scenario is presented in Figure 19, along with
markers from our base case scenario for comparative purposes.
30
The EGP connects the Longford Production Node in Victoria to the Sydney Demand Node. The Culcairn NSW-Vic Interconnect
(NVI) connects the Moomba to Sydney (MSP) pipeline in NSW to the Victorian Transmission System. See Figure 10 for details.
Page 22You can also read
