A C I L
A L L E N
C O N S U L T I N G
REPORT TO
DEPARTMENT OF THE ENVIRONMENT
APRIL 2015
ELECTRICITY
SECTOR
EMISSIONS
MODELLING OF THE
AUSTRALIAN GENERATION
SECTOR
ACIL ALLEN CONSULTING PTY LTD
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AUSTRALIA
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For information on this report please contact:
Owen Kelp
Principal
Telephone
(07) 3009 8711
Email
o.kelp@acilallen.com.au
LEVEL TWELVE, BGC CENTRE
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PERTH WA 6000
AUSTRALIA
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ACILALLEN.COM.AU
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THE PROFESSIONAL ANALYSIS AND ADVICE IN THIS REPORT HAS BEEN PREPARED BY ACIL ALLEN CONSULTING FOR
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PURPOSES SPECIFIED IN IT. THIS REPORT IS SUPPLIED IN GOOD FAITH AND REFLECTS THE KNOWLEDGE,
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ALLEN CONSULTING ACCEPTS NO RESPONSIBILITY WHATSOEVER FOR ANY LOSS OCCASIONED BY ANY PERSON
ACTING OR REFRAINING FROM ACTION AS A RESULT OF RELIANCE ON THE REPORT, OTHER THAN THE ADDRESSEE.
IN CONDUCTING THE ANALYSIS IN THIS REPORT ACIL ALLEN CONSULTING HAS ENDEAVOURED TO USE WHAT IT
CONSIDERS IS THE BEST INFORMATION AVAILABLE AT THE DATE OF PUBLICATION, INCLUDING INFORMATION
SUPPLIED BY THE ADDRESSEE. UNLESS STATED OTHERWISE, ACIL ALLEN CONSULTING DOES NOT WARRANT THE
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CLIENT INVESTMENT TO PERFORM TO THE ADVANTAGE OF THE CLIENT OR TO THE ADVANTAGE OF THE CLIENT TO
THE DEGREE SUGGESTED OR ASSUMED IN ANY ADVICE OR FORECAST GIVEN BY ACIL ALLEN CONSULTING.
© ACIL ALLEN CONSULTING 2015
ACIL ALLEN CONSULTING
The Department of the Environment (the Department) commissioned ACIL Allen Consulting
(ACIL Allen) to undertake projections of greenhouse gas emissions from Australia’s
electricity generation sector over the period 2008-09 to 2034-35. This assignment is
intended to inform emissions projections and support the annual publication of greenhouse
gas emissions projections undertaken by the Department in tracking Australia’s progress
towards its 2020 emissions reduction target.
The electricity sector currently accounts for around one third of Australia’s emissions as
reported under the National Greenhouse Gas Inventory and is the largest single source.
The projections are intended to cover all electricity generation facilities – including those
previously deemed to be part of vertically integrated processes such as mining/minerals
processing and LNG plants. As such, emission values will be higher than those reported
from the 2013 emission projections study as they include some emissions which were
previously covered under ‘Direct Combustion’ activities.
Greenhouse gas Global Warming Potentials (GWPs) for carbon dioxide (CO2), methane
(CH4) and nitrous oxide (N2O) have been taken from the Intergovernmental Panel on
Climate Change Fourth Assessment Report (AR4) in accordance with the current reporting
convention within the National Greenhouse Gas Inventory.
Modelling has been undertaken for two scenarios:
 Baseline scenario: Current electricity demand and supply side measures continue for the
length of the projection (or until the planned end date of a particular program). The
scenario includes modifications to the current Large-scale Renewable Energy Target
(LRET) scheme so that mandated GWh targets in 2020 represent an estimated 20 per
cent of Australian electricity consumption. This was estimated to be 27,000 GWh. All
defined Energy Intensive Trade Exposed (EITE) activities are 100 per cent shielded from
RET impacts. The scenario does not include any changes to the Small-scale Renewable
Energy Scheme (SRES). New conventional coal-fired developments (plants which do
not employ carbon capture and storage) are constrained from entering in this scenario.
 No supply-side measures: Current electricity demand side measures remain in place for
the length of the projection (or until the planned end date of a particular program).
Electricity supply side measures cease from 1 July 2014. Under this scenario, the only
supply-side measures assumed to cease was the RET scheme and the assumption of
restricting conventional coal-fired new entry is relaxed.
Figure ES 1 shows historical and projected emissions for the Australian generation sector
over the period 2008-09 to 2034-35 under the two scenarios modelled. Emissions have
fallen over the last 6 years from 211 Mt CO2-e in 2008-09 to a low of around 180 Mt CO2-e
in 2013-14 (a 15% decline). This has been due to a combination of factors including the
decline in electricity demand, the introduction of renewable generation and carbon pricing. In
addition, generation from hydro power stations was well above normal levels during the
carbon tax period resulting in a temporary lowering of emissions as hydro stations displaced
fossil fuelled stations and drew down water storages.
Under the Baseline scenario, emissions are projected to rise in the near-term, reaching
201 Mt CO2-e by 2019-20. This represents a 7.7% increase over 2012-13 levels (equivalent
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
to average annual growth rate of 1.1% per annum). The projected increase is due to a
number of factors:
 The repeal of the carbon tax effective from 1 July 2014 results in a shift back to coalfired generation and low water storages results in lower than normal hydro output over
coming years as hydro storage levels are restored
 This is further exacerbated by the decrease in gas-fired generation owing to wholesale
gas price increases (which makes gas-fired generation less competitive) and
oversupplied wholesale markets (which results in limited requirement for high-cost
peaking plant operation)
 Projected demand growth under the scenario which results in some mothballed coalfired generators returning to service over time as market conditions improve.
Development of renewable capacity occurs over the period to 2020 in order to meet the
‘Real 20%’ LRET and ongoing installation of rooftop solar PV occurs with support under the
SRES. The introduction of this renewable capacity tends to dampen the growth in emissions
from the generation sector, particularly from 2017-18 to 2020-21 when much of the large
scale capacity development occurs. Through this period, emissions remain relatively flat
despite the growth in electricity demand.
Emissions are projected to continue to increase over the long-term from the sector. Over the
period 2019-20 to 2035-35, projected annual emissions increase by 17.3% to 236 Mt CO2-e
(equivalent to average annual growth of 1.1% per annum). The modelling finds renewable
technologies are generally not commercially competitive in the wholesale market without
additional subsidies (beyond the current RET) and new centralised generation is primarily
gas-fired. Development of decentralised solar PV continues throughout the projection
period.
Figure ES 1 Projected emissions: Baseline and No supply-side scenarios
Source: ACIL Allen
In the ‘No supply-side measures’ scenario, total annual emissions increase from
187 Mt CO2-e in 2012-13 to 214 Mt CO2-e by 2019-20 (a 14.4% increase, equivalent to
annual growth of 1.9% per annum). In aggregate, emissions over this period are
1,583 Mt CO2-e which is around 27.2 Mt CO2-e (1.7%) higher than the Baseline scenario.
This is due to the lower level of renewables developed and increased utilisation of coal-fired
capacity over this period.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
By 2034-35, projected emissions from the generation sector reach 248 Mt CO2-e, a 32.4%
increase over 2012-13 levels. In the ‘No supply-side measures’ scenario, entry of
conventional coal is not restricted and the modelling projects a small amount of new
conventional coal would be developed in the SWIS from around 2020 and in Queensland
from 2025 onwards.
In the absence of the supply-side measures projected installed capacity of renewables is
considerably lower (3,300 MW less wind, 1,800 MW less solar PV by 2025). This is offset by
some increase in fossil fuel capacity, most of which is associated with the earlier return to
service of mothballed plant and the development of some new coal-fired capacity in the
SWIS and Queensland toward the latter part of the projection period.
Aggregate emissions over the projection period (2014-15 to 2034-35) are 201.4 Mt CO2-e
(4.5%) higher in the absence of the supply side measures.
A range of summary figures for emissions under the two scenarios are presented in Table
ES 1 and Table ES 2.
Table ES 1 Summary of emission outcomes by fuel type (Mt CO2-e): Baseline scenario
Black coal
117.9
98.7
117.2
140.9
-16.3%
18.7%
20.3%
855.6
1,892.5
Brown coal
69.0
59.6
61.1
65.4
-13.7%
2.5%
7.1%
506.4
995.2
Peaking Gas
5.5
5.0
1.0
1.6
-9.6%
-78.9%
53.0%
17.0
15.3
Baseload Gas
13.6
17.4
17.9
24.4
28.3%
2.5%
36.4%
134.9
320.0
Cogen
3.3
3.9
1.9
1.4
18.9%
-51.9%
-27.8%
23.1
23.4
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.1%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
201.4
236.2
-11.4%
7.7%
17.3%
1,555.5
3,283.1
Total
Source: ACIL Allen
Table ES 2 Summary of emission outcomes by fuel type (Mt CO2-e): No supply-side measures
Black coal
117.9
98.7
119.5
154.6
-16.3%
21.1%
29.3%
854.9
2,021.0
Brown coal
69.0
59.6
69.7
65.4
-13.7%
16.9%
-6.1%
531.7
1,035.5
Peaking Gas
5.5
5.0
1.5
1.4
-9.6%
-69.0%
-5.7%
18.2
18.0
Baseload Gas
13.6
17.4
19.0
22.3
28.3%
8.8%
17.6%
136.3
322.1
Cogen
3.3
3.9
2.0
1.3
18.9%
-50.1%
-33.4%
23.3
23.7
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-3.9%
9.1%
17.4
35.4
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
214.0
247.6
-11.4%
14.4%
15.7%
1,582.7
3,457.3
Total
Source: ACIL Allen
Figure ES 2 presents projected emission intensity trends (on a ‘sent out’ basis) for the two
scenarios. The Baseline scenario see emissions intensity increase sharply from an
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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estimated 0.77 tonnes CO2-e/MWh sent-out in 2013-14 up to 0.81 tonnes CO2-e/MWh sentout by 2015-16, before falling over the remainder of the period.
In the absence of supply-side measures, the Australian average intensity increases to
0.82 tonnes CO2-e/MWh sent-out by 2019-20 and 0.78 tonnes CO2-e/MWh sent-out by
2034-35. The impact of the modelled supply-side measures is to reduce the emissions
intensity of the generation sector by around 0.04 tonnes CO2-e/MWh.
Figure ES 2 Projected emission intensity Baseline and ‘No supply-side
measures’ scenarios
Source: ACIL Allen
A number of sensitivities were run against the Baseline scenario to test key drivers and
assess the potential range for emission outcomes. Sensitivities included:
 High/Low electricity demand
 High/Low fuel costs
 Uptake of electric vehicles (increased demand)
 Aggregate High/Low sensitivities which included combinations of the above.
Figure ES 3 summarises the emission outcomes for each of these sensitivities. The largest
determinant of emission was found to be the demand assumptions used, whereas fuel price
changes had little effect. The projected bounds for annual emission outcomes in 2019-20
were 3.3% higher under the Aggregate High sensitivity and 3.3% lower under the Aggregate
Low sensitivity. The variation grows over time reaching +8.9% and -9.7% relative to the
Baseline scenario by 2034-35 under the respective sensitivities as detailed in Table ES 3.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Figure ES 3 Emission outcomes from Baseline sensitivities
Source: ACIL Allen
Table ES 3 Summary of emission outcomes from Baseline sensitivities
Mt CO2-e
Change
Change
(Mt)
(%)
Mt CO2-e
Change
Change
(Mt)
(%)
1,555.5
Mt CO2-e
Change
Change
(Mt)
(%)
236.2
Mt CO2-e
Change
Change
(Mt)
(%)
Baseline
201.4
4,838.6
High demand
208.1
6.7
3.3%
1,577.6
22.1
1.4%
252.1
15.9
6.7%
5,063.4
224.8
4.6%
Low demand
194.6
-6.7
-3.3%
1,534.8
-20.7
-1.3%
213.9
-22.3
-9.4%
4,593.6
-245.1
-5.1%
High fuel
201.9
0.5
0.3%
1,557.2
1.7
0.1%
235.8
-0.5
-0.2%
4,846.4
7.8
0.2%
Low fuel
200.8
-0.5
-0.3%
1,556.2
0.7
0.0%
237.0
0.8
0.3%
4,835.4
-3.3
-0.1%
EV uptake
201.8
0.4
0.2%
1,556.7
1.2
0.1%
242.5
6.3
2.7%
4,883.0
44.4
0.9%
Aggregate High
208.0
6.7
3.3%
1,579.4
23.9
1.5%
257.3
21.1
8.9%
5,102.3
263.6
5.4%
Aggregate Low
194.7
-6.7
-3.3%
1,534.5
-20.9
-1.3%
213.4
-22.8
-9.7%
4,595.0
-243.6
-5.0%
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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C o n t e n t s
2.1 Modelling process
5
2.1.1 PowerMark LT
5
2.1.2 LRET implementation in PowerMark LT
7
2.1.3 Retail price model
8
2.1.4 SRES projection model
8
2.1.5 Off-grid and embedded generation
8
2.2 Scenarios
2.2.1 Baseline scenario
2.2.2 No supply-side measures scenario
9
9
10
2.3 Sensitivities
11
2.4 Historical calibration with National Greenhouse Gas Inventory
11
3.1 Baseline scenario
14
3.2 No supply-side measures
20
4.1 High/Low demand
26
4.2 High/Low fuel prices
30
4.3 High uptake of electric vehicles
33
4.4 Aggregate High/Low scenarios
35
Appendix A
Input assumptions
1
List of figures
No table of figures entries found.
Figure 1 Modelling framework used
5
Figure 2 Modelled electricity grids
7
Figure 3 Model calibration against actual National Greenhouse Gas Inventory
values
12
Figure 4 Comparison with National Greenhouse Gas Inventory by year by State (Mt
CO2-e)
13
Figure 5 Australian generation by fuel type: Baseline scenario
14
Figure 6 Australian emissions by fuel type: Baseline scenario
15
Figure 7 Generation by fuel type by grid: Baseline scenario
17
Figure 8 Generation by fuel type by jurisdiction: Baseline scenario
18
Figure 9 Projected emission intensity: Baseline scenario
19
Figure 10 Projected generation capacity: Baseline scenario
19
Figure 11 Australian generation by fuel type: No supply-side measures
20
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Figure 12 Australian generation by fuel type: No supply-side measures change from
Baseline scenario
20
Figure 13 Australian emissions by fuel type: No supply-side measures
21
Figure 14 Impact of supply-side measures on emissions
22
Figure 15 Australian emissions by fuel type: No supply-side measures change from
Baseline scenario
22
Figure 16 Generation by fuel type by grid: No supply-side measures
23
Figure 17 Generation by fuel type by jurisdiction: No supply-side measures
24
Figure 18 Projected emission intensity: No supply-side measures
25
Figure 19 Projected generation capacity: No supply-side measures change from
Baseline scenario
25
Figure 20 Demand sensitivity inputs: Australia
26
Figure 21 Demand sensitivity aggregate emission outcomes: Australia
28
Figure 22 Australian emissions by fuel type: High demand sensitivity change from
Baseline scenario
29
Figure 23 Australian emissions by fuel type: Low demand sensitivity change from
Baseline scenario
30
Figure 24 Fuel cost variations: International fuel price markers
31
Figure 25 Fuel cost sensitivity aggregate emission outcomes: Australia
31
Figure 26 Australian emissions by fuel type: High fuel cost sensitivity change from
Baseline scenario
32
Figure 27 Australian emissions by fuel type: Low fuel cost sensitivity change from
Baseline scenario
33
Figure 28 Incremental demand for EV sensitivity
34
Figure 29 Australian emissions by fuel type: EV sensitivity change from Baseline
scenario
35
Figure 30 Aggregate High/Low sensitivity emission outcomes: Australia
35
Figure 31 Australian emissions by fuel type: Aggregate High sensitivity change from
Baseline scenario
37
Figure 32 Australian emissions by fuel type: Aggregate Low sensitivity change from
Baseline scenario
38
Figure Appendix A1 Comparison of Australian energy demand to 2020
3
Figure Appendix A2 Real wage index used
3
Figure Appendix A3 Calculated netback gas price for Australian gas producers
4
Figure Appendix A4 Assumed new entrant coal costs
4
Figure Appendix A5 Base capital cost comparison with AETA
Figure Appendix A6 Final capital costs for new entrant technologies for selected
years
18
A-3
Figure Appendix A7 National average historic PV installation cost (2011$/kW)
A-10
Figure Appendix A8 National average projected PV installation cost
A-11
Figure Appendix A9 REC/STC prices (nominal $/certificate)
A-13
Figure Appendix A10 Share of installed capacity by system size class
A-18
Figure Appendix A11 Energy impact of electric vehicles by State (medium scenario)
A-25
Figure Appendix A12 Aggregate energy impact of electric vehicles across scenarios
A-26
List of tables
No table of figures entries found.
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Table 1 Real 20% by 2020 annual LRET targets
10
Table 2 Australian generation by fuel type (sent-out TWh): Baseline scenario
15
Table 3 Summary of emission outcomes by fuel type (Mt CO2-e): Baseline scenario
16
Table 4 Australian generation by fuel type (sent-out TWh): No supply-side
measures
21
Table 5 Summary of emission outcomes by fuel type (Mt CO2-e): No supply-side
measures
23
Table 6 Demand sensitivity inputs for selected years and change from baseline:
Australia
26
Table 7 Electricity demand projection for selected years by grid (GWh): High
demand sensitivity
27
Table 8 Electricity demand projection for selected years by grid (GWh): Low
demand sensitivity
27
Table 9 Summary of emission outcomes by fuel type (Mt CO2-e): High demand
sensitivity
28
Table 10 Summary of emission outcomes by fuel type (Mt CO2-e): Low demand
sensitivity
30
Table 11 Fuel cost variations relative to baseline assumptions
31
Table 12 Summary of emission outcomes by fuel type (Mt CO2-e): High fuel cost
sensitivity
32
Table 13 Summary of emission outcomes by fuel type (Mt CO2-e): Low fuel cost
sensitivity
33
Table 14 Summary of emission outcomes by fuel type (Mt CO2-e): EV sensitivity
34
Table 15 Summary of emission outcomes by fuel type (Mt CO2-e): Aggregate High
sensitivity
36
Table 16 Summary of emission outcomes by fuel type (Mt CO2-e): Aggregate Low
sensitivity
38
Table Appendix A1 Breakdown of electricity demand comparison for 2012-13
1
Table Appendix A2 Baseline electricity demand projection for selected years by
jurisdiction (GWh)
2
Table Appendix A3 Baseline electricity demand projection for selected years by grid
(GWh)
2
Table Appendix A4 Existing and committed generators: type, capacity and life
6
Table Appendix A5 Existing and committed generators: efficiency, emissions and
O&M costs
9
Table Appendix A6 Recently mothballed or retired units by NEM region
14
Table Appendix A7 Refurbishment costs for incumbent plant
15
Table Appendix A8 Base capital costs and cost component splits
19
Table Appendix A9 Final capital costs for new entrant technologies for selected
years (Real 2011-12 $/kW installed)
A-1
Table Appendix A10 Average real year-on-year capital cost change for each
decade
A-4
Table Appendix A11 New entrant parameters
A-4
Table Appendix A12 Technology availability and construction profiles
A-5
Table Appendix A13 Technology life and refurbishment costs
A-6
Table Appendix A14 Assumed CO2 transport and storage costs
A-7
Table Appendix A15 Lag between installation and registration of PV installations
with the CER
A-8
Table Appendix A16 PV installation premium/discount by system size
A-11
Table Appendix A17 State/territory variation in system cost
A-11
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Table Appendix A18 Solar Credits multiplier
A-13
Table Appendix A19 Feed-in tariffs by jurisdiction
A-15
Table Appendix A20 Solar zone ratings
A-15
Table Appendix A21 Installed capacity by solar zone
A-16
Table Appendix A22 Export rates
A-16
Table Appendix A23 Uplift factors by customer class
A-20
Table Appendix A24 Hedging factors and capacity credit costs by customer class
A-20
Table Appendix A25 Basis of network cost calculation
A-21
Table Appendix A26 PEC creation and EITE load
A-22
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ACIL ALLEN CONSULTING
Glossary
AEMO
Australian Energy Market Operator, the entity that manages dispatch and planning in the National Electricity Market.
AETA
Australian Energy Technology Assessment, an analysis of future generation costs from various electricity supply
technologies undertaken by BREE in 2012.
ARENA
The Australian Renewable Energy Agency, a statutory authority of the Commonwealth Government to support
renewable energy
Bagasse
A renewable fuel produced from sugar cane waste.
BREE
Bureau of Resources and Energy Economics, a Commonwealth Government research agency.
Capacity factor
A measure of the intensity with which a generator operates, calculated as the generator’s average output divided by its
maximum possible output, and typically expressed as a percentage.
CCGT
Combined-cycle gas turbine, a gas turbine generator where waste heat from the turbine exhaust is captured and used
to drive an auxiliary steam turbine.
CCS
Carbon capture and storage, the capturing of carbon dioxide produced in the process of generating electricity (or some
other industrial process) and storing
CGE
Computable General Equilibrium modelling, a form of modelling that relates the inputs and outputs of different
industries within an economy to determine a ‘general equilibrium’ outcome across all industries when inputs or
assumptions are varied.
CLFR
Concentrated Linear Fresnel Reflector, a form of solar thermal generation technology.
Cogeneration, or ‘cogen’
CO2
CO2CRC
CSIRO
DKIS
Dual axis
EGS
Fixed axis
FOM
A cogeneration plant generates both electricity and steam, with the steam typically being used for industrial process
applications. Cogeneration plants can be based on either a typical steam turbine, with lower pressure steam being
diverted for use as heat rather than for electricity generation, or on a gas turbine, where the gas turbine itself generates
electricity but waste heat is captured to generate steam for use as process heat.
Carbon dioxide, the most common greenhouse gas
The Cooperative Research Centre for Carbon Capture and Storage.
The Commonwealth Scientific Industrial and Research Organisation, an Australian Government scientific research
agency
Darwin-Katherine Interconnected System, the interconnected electricity grid servicing the main population centres of
the northern part of the Northern Territory.
In the context of solar PV generation, this refers to solar PV plates that can change angle to track the sun on two axes,
an axis to track daily east-west movement of the sun across the sky and a second axis to adjust to changes in the sun’s
angle (north-south) with the seasons. See also ‘fixed axis’ and ‘single axis’.
Engineered geothermal system, a form of geothermal generation technology also sometimes known as ‘hot fractured
rocks’.
In the context of solar PV generation, this refers to solar PV plates that are mounted in a fixed position and do not track
the sun. See also ‘single axis’ and ‘dual axis’.
Fixed operating and maintenance costs. These are represented in ACIL Allen’s modelling as a fixed annual payment
required to keep a power station operational.
GALLM
Global and Local Learning Model, CSIRO’s model of generation technology costs.
GGAS
Greenhouse Gas Abatement Scheme, the NSW Government’s former emissions reduction scheme
GWh
Gigawatt-hour, a unit of electricity output or consumption measured over time, which is equivalent to one gigawatt being
produced/consumed continuously for one hour, or one thousand megawatt-hours.
HEGT
High efficiency gas turbine.
HSA
Hot sedimentary aquifer, a form of geothermal generation technology.
IGCC
Integrated gasification combined cycle, a form of generation technology that uses coal as the fuel, and which converts
the coal to a synthetic gas to drive a gas turbine through an integrated process.
IMO
Independent Market Operator, the the entity that manages dispatch and planning in the South-West Interconnected
System.
kW
Kilowatt, a unit of (instantaneous) electricity output or consumption, equal to one one-thousandth of a megawatt.
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LDC
Load duration curve, a representation of the variation in electricity demand over a period of time created by ordering the
electricity demand (or ‘load’) in descending order.
LGC
Large-scale Generation Certificate, the certificate that can be created and traded by renewable generators under the
LRET. Sometimes referred to as a ‘REC’, or Renewable Energy Certificate. LGCs are different from the ‘Small-scale
Technology Certificates’ or STCs created under the SRES.
LP
Linear programming
LRET
Large-scale Renewable Energy Target, the Commonwealth Government’s scheme to promote large-scale renewable
electricity generation. Formerly known as the Mandatory Renewable Energy Target (MRET), and sometimes referred to
simply as the RET.
MLF
Marginal loss factor, the level of transmission losses between a given generator and the point of market settlement
attributed in dispatching bids for electricity supply and therefore in calculating electricity prices.
MW
Megawatt, a unit of (instantaneous) electricity output or consumption, equal to one thousand kilowatts.
MWh
Megawatt-hour, a unit of electricity output or consumption measured over time, which is equivalent to one megawatt
being produced/consumed continuously for one hour.
NEM
National Electricity Market, the interconnected electricity grid covering most of Queensland, New South Wales, Victoria,
Tasmania and South Australia.
NWIS
North-West Interconnected System, the interconnected electricity grid covering the Pilbara region of north-western
Western Australia.
O&M
Operating and maintenance costs – see also FOM and VOM.
OCGT
Open cycle gas turbine, a gas turbine generator where waste heat is vented to the atmosphere rather than captured to
generate electricity or steam, as in a combined-cycle gas turbine (CCGT) or cogeneration plant.
Oxy combustion
A technique used to improve the efficiency of CCS, by firing coal in a primarily oxygen and non-combustible gases
(importantly, in the absence of nitrogen), so as to produce a relatively pure stream of CO2 suitable for capture and
storage.
PC
Pulverised coal. See also ‘pf’
pf
Pulverised fuel, typically coal. See also ‘PC’.
POE
Probability of exceedence, representing the probability that a given forecast will be exceeded in the relevant forecast
period.
PV
Photovoltaic, a form of generation that converts solar radiation to direct current electricity using semi-conductors that
exhibit the photovoltaic effect.
SF
Solar Flagships, the Commonwealth Government’s program to promote large-scale solar generation projects.
Single axis
In the context of solar PV generation, this refers to solar PV plates that can change angle to track the east-west daily
movement of the sun across the sky. See also ‘fixed axis’ and ‘double axis’.
SRES
Small-scale Renewable Energy Scheme, the Commonwealth Government’s scheme to promote small-scale renewable
energy technologies, principally solar PV and solar water heaters. The incentives for these technologies were formerly
combined with those for large-scale renewables through the MRET.
SRMC
Short-Run Marginal Cost, an economic interpretation of the extent to which production costs, in this case electricity
generation costs, vary at the margin when key inputs, particularly the capital equipment comprising the generator,
cannot be varied.
SWIS
South-West Interconnected System, the interconnected electricity grid covering south-western Western Australia. Also
known as the Wholesale Electricity Market, or WEM.
VOM
Variable operating and maintenance costs. These are represented in ACIL Allen’s modelling as costs which vary
linearly with the amount of electricity produced by a given power station (i.e. as a cost in $/MWh).
WACC
Weighted average cost of capital, a benchmark rate of return on capital investments representing an assumed level of
equity and debt financing, and specific rates of return to each of equity and debt.
WCMG
Waste coal mine gas
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1
The Department of the Environment (the Department) commissioned ACIL Allen Consulting
(ACIL Allen) to undertake projections of greenhouse gas emissions from Australia’s
electricity generation sector over the period 2008-09 to 2034-35. This assignment is
intended to inform emissions projections and support the annual publication of greenhouse
gas emissions projections undertaken by the Department in tracking Australia’s progress
towards its 2020 emissions reduction target.
The electricity sector currently accounts for around one third of Australia’s emissions as
reported under the National Greenhouse Gas Inventory and is the largest single source.
Modelling has been undertaken for two scenarios as follows:
 Baseline scenario: Current electricity demand and supply side measures continue for
the length of the projection (or until the planned end date of a particular program). The
scenario includes modifications to the current Large-scale Renewable Energy Target
(LRET) scheme so that mandated GWh targets in 2020 represent an estimated 20 per
cent of Australian electricity consumption. This was estimated to be 27,000 GWh. All
defined Energy Intensive Trade Exposed (EITE) activities are 100 per cent shielded from
RET impacts (both LRET and SRES). The scenario does not include any changes to the
SRES.
 No supply-side measures: Current electricity demand side measures remain in place
for the length of the projection (or until the planned end date of a particular program).
Electricity supply side measures do not continue from 1 July 2014. Under this scenario,
the only supply-side measures assumed to cease was the RET scheme.
In addition to the above policy scenarios, a range of sensitivities were also undertaken to
test the robustness of results and key drivers of particular outcomes. More details on the
scenarios and sensitivities can be found in section 2.2 and 2.3 respectively.
ACIL Allen has utilised its long-term dynamic planning model, PowerMark LT for this
assignment as well as a range of other sub-models. PowerMark LT was used to estimate
development and policy effects in Australia’s major electricity markets namely, the National
Electricity Market (NEM), the Western Australian Wholesale Electricity Market which covers
the South-West Interconnected System (SWIS), the North-West Interconnected System
(NWIS) in the Pilbara region, the Darwin-Katherine Interconnected System (DKIS) and the
grid serving Mount Isa. Other generation facilities outside of these major grids and on-grid
not covered under these market arrangements were projected in separate sub-models.
In terms of generation coverage, ACIL Allen has drawn upon confidential historical data for
years 2011-12 and 2012-13 made available through the National Greenhouse Gas
Inventory and the National Greenhouse and Energy Reporting (NGER) Scheme. These
were used to calibrate input assumptions and settings for individual facilities to align with
historical outcomes as reported by the Department.
The projections are intended to cover all electricity generation facilities – including those
previously deemed to be part of vertically integrated processes such as mining/minerals
processing and LNG plants. As such, emission values will be higher than those reported
from the 2013 emission projections study as they include some emissions which were
previously covered under ‘Direct Combustion’ activities.
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Greenhouse gas Global Warming Potentials (GWPs) for carbon dioxide (CO2), methane
(CH4) and nitrous oxide (N2O) have been taken from the Intergovernmental Panel on
Climate Change Fourth Assessment Report (AR4) in accordance with the current reporting
convention within the National Greenhouse Gas Inventory.
As part of a separate study, pitt&sherry undertook demand projections to be used under the
engagement. This study focused primarily on projected electricity demand on main grids.
ACIL Allen was also tasked with providing the balance of the demand assumptions for those
demand components not covered by the pitt&sherry work, primarily additional large
industrial, mini-grid, off-grid and grid-exempt demand’.
This report represents ACIL Allen’s primary deliverable under this assignment for the
Department. It is accompanied by spreadsheet results from the modelling.
The remainder of this report is structured as follows:
 Section 2 gives an overview of the project, including methodology, the models used, and
a description of the scenarios and sensitivities modelled
 Section 3 highlights the key modelling results for scenarios
 Section 4 outlines the results from the modelled sensitivities
 Appendix A sets out the key modelling assumptions, including those derived from the
demand projections and those adopted within the electricity sector modelling.
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2
2.1
Modelling process
Figure 1 provides an overview of the modelling process used to undertake the projections
for the Department under this project.
Figure 1
Modelling framework used
Market charateristics
- existing generator inputs
- new entrant options and costs
Coverage:
NEM
SWIS
DKIS
NWIS and Mt Isa
SRES model
- projected SWH
- projected SGU (solar PV)
Demand inputs
- market facing sent-out energy
and peak demand
- 100 point sampled load
duration curves
PowerMark LT
Off-grid database
- detailed breakdown of existing
generation ~ 16,000 GWh
- curent renewable composition
approximately 2%
LRET settings
- targets and banking rules
- existing generators
- new entrant generators
- certificate multipliers per MWh
Model outputs
-
Emissions (CO2-e)
Generation/capacity mix
Fuel use
Wholesale electricity prices
LGC prices
LRET market surrenders/shortfalls
New entry tehnology and build timing
Retirements and refurbishments
System resource costs
Retail electricity pricing model
- network costs
- retail costs and margin
- hedging costs (based on load shape)
- retail series for households
- retail series for SMEs/large users
Source: ACIL Allen
2.1.1
PowerMark LT
PowerMark LT simulates the electricity markets across Australia for existing generator
operation, new investment (entry) and retirement decisions (exit). PowerMark LT differs from
our highly detailed, short-term simulation model, PowerMark, but uses similar solving
algorithms to broadly represent the profit-maximising behaviour of energy market
incumbents and potential new entrants, thereby predicting prices, generation patterns and
emissions outcomes.
To aid computation, PowerMark LT uses fewer dispatch periods per model year than
PowerMark (typically 100 for PowerMark LT, compared to 8760, or one per hour, for
PowerMark). Accordingly PowerMark LT solves very quickly and can automatically optimise
generation new entry and dispatch outcomes over long time horizons on an inter-temporal
basis (that is, adjusting outcomes in all periods based on outcomes in all other dispatch
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periods). Use of PowerMark LT implies that the market structures that are in place are
efficient and will result in the least cost outcome over the projection period.
On the supply-side PowerMark LT inputs include:
 Definitions of the markets themselves including price limits; unserved energy and
reserve margin constraints
 Existing interconnector capacities and losses; candidates for interconnector
augmentations including capacity, cost and earliest timing
 A range of assumptions for existing generators including remaining technical life, outage
rates, maximum and minimum capacity factors, thermal efficiency, auxiliaries, fixed and
variable O&M costs, fuel costs, combustion and scope 3 emission intensities, capacity,
refurbishment costs
 Carbon prices and other policy settings
 New entrant technology candidates, includes generator characteristics, availability for
each region, annual and aggregate build limits, capital and operating costs
 A series of generic constraints which can be applied to range of model variables.
The workings of the LRET are fully incorporated into PowerMark LT, with eligible renewable
technologies able to create LGCs as a by-product of their electricity generation. The LRET
scheme’s settings such as the effective penalty price for non-compliance by liable entities
are included and incorporated into the objective function which is to minimise aggregate
resource costs over the period in question.
In terms of geographic scope, PowerMark LT is configured to model all of Australia’s major
electricity grids, namely:
 the National Electricity Market (NEM), covering New South Wales, Australian Capital
Territory, Queensland, Victoria, South Australia and Tasmania
 the South-West Interconnected System (SWIS), also known as the Western Australian
Wholesale Electricity Market (WEM), serving south-western Western Australia including
Perth, Geraldton and Albany
 the North-West Interconnected System (NWIS), the grid serving numerous mines and
towns in the Pilbara region of Western Australia
 the Darwin-Katherine Interconnected System (DKIS), the grid serving the more populous
parts of the Northern Territory
 the grid serving the area around Mt Isa in Queensland.
The geographic extent of these grids is shown in Figure 2.
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Figure 2
Modelled electricity grids
Note: Grid extents are representative only
Source: ACIL Allen
As discussed above, PowerMark LT is designed to optimise outcomes over long model
horizons and can model out to 2050. Under this assignment, the Department required
outcomes to be modelled over the period 2008-09 to 2034-35 (6 years of history; 21 year
projection). ACIL Allen extended the modelling period to 2039-40 (a total of 32 years) to
ensure the outputs do not suffer from any end effects. This extended period also captures
the potential impact of forward-looking investment behaviour toward the end of the results
period leading up to 2034-35.
Generation from non-scheduled and off-grid sources were not be modelled with PowerMark
LT, but estimated using ACIL Allen’s off-grid database. Both components have been
calibrated to achieve alignment with emissions and fuel consumption data within the latest
available NGGI (June 2014 Quarterly) and NGER (2011-12 and 2012-13) datasets.
PowerMark LT was supplemented by some of our other Excel-based models, namely our
SRES projection model, our Retail price model and our off-grid/embedded database. The
following section provides more information on the modelling process and how these
models interact.
2.1.2
LRET implementation in PowerMark LT
The LRET is explicitly incorporated into PowerMark LT, inputs comprise of:
 Certificate demand: mandated targets, voluntary demand through GreenPower and
desalination and other voluntary surrenders
 Supply from existing generators (both scheduled generators and non-scheduled
generators)
 Cumulative banked certificates at the start of the model horizon and certificate financial
holding costs
 Constraints on banking and borrowing, incorporating the three year “make-good”
allowance for liable entity shortfalls
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 New entrants generators creating LGCs are those flagged as eligible technologies within
PowerMark LT’s new entrant list. A certificate multiplier (typically one) can be applied for
each generation technology per MWh of electrical output.
2.1.3
Retail price model
ACIL Allen’s retail price model develops projections of all components of retail electricity
prices for each State and Territory for the purpose of translating wholesale electricity and
LRET/SRES outcomes into average representative prices for residential, SME’s and large
end users in each jurisdiction.
The retail price model takes these wholesale prices and applies projections of the other
components of retail prices to arrive at a retail series applicable for various customer types.
We note that there is a certain amount of circularity between the SRES and retail price
models as changes in retail prices affects the paybacks for behind-the-meter solutions.
The cost components modelled include: network charges; wholesale energy; LRET/SRES;
losses; FiT costs; other green scheme costs; metering charges; retail operating costs, NEM
pool fees, ancillary service costs and retail margin.
2.1.4
SRES projection model
ACIL Allen’s forecasts for uptake of small-scale generation units (SGUs) are based on a
regression model relating historic uptake to historic net financial returns to installing solar
PV systems (the most common form of SGUs). This historic relationship is then applied to
the forecast level of net financial returns to predict future uptake of solar PV.
SGUs comprise renewable generators of less than 100 kW capacity that are eligible to
create STCs under the SRES. These generators can be installed by households and
commercial or industrial premises. Available data on these installations do not distinguish
between installations by different classes of customer and so it is difficult to separately
analyse these different customer types. In practice, residential and commercial/industrial
installations are incorporated within a single regression model (reflecting the undifferentiated
underlying uptake data) and delineated using the simplifying assumption that all installations
of more than 7.5 kW are commercial or industrial installations, and the remainder are
residential. This assumption is consistent with the observation that the vast majority of
historic PV installations have been made by households.
The model uses a quarterly resolution and separately estimates uptake for each state and
territory. Model assumptions relate principally to either historic uptake of solar PV (the
regression model’s ‘dependent variable’) or to the real net financial return to solar PV
installation (the regression model’s key ‘explanatory variable’). These are discussed
separately below. Further, as real financial returns are driven by several distinct factors,
these are discussed separately. These factors are:
 PV system installation costs
 Rebates and subsidies
 Electricity prices
 Payments for exported electricity, generally known as ‘feed-in tariffs’ or ‘buyback rates’
 System output and export assumptions.
2.1.5
Off-grid and embedded generation
ACIL Allen has recently undertaken an extensive analysis of off-grid electricity generation
for the Commonwealth Government, published as a Bureau of Resources and Energy
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Economics (BREE) report in October 2013. This snapshot of off-grid generation was used
as a starting point to help inform the projections of emissions from remote locations.
This was supplemented by detailed analysis of generation and emissions outcomes as
reported from NGERs reports.
2.2
Scenarios
This section outlines the scenario design for the ‘Baseline’ and ‘No supply-measures’
scenarios as defined by the Department.
2.2.1
Baseline scenario
The Baseline scenario includes all current electricity demand and supply side measures
currently in place continue for the length of the projection (or until the planned end date of a
particular program). It includes the effects of a range of specific greenhouse gas abatement
measures, including energy efficiency programs, the Large-scale Renewable Energy Target
(LRET) and the Small-scale Renewable Energy Scheme (SRES).
At the request of the Department, the scenario’s design includes modifications to the LRET
such that the annual GWh targets for compliance years 2016 to 2030 are adjusted such that
the proportion of renewables in Australia’s energy mix in 2020 is 20 per cent.
It also includes the modification of the scheme so that the electricity used in all defined
Energy Intensive Trade Exposed activities are 100 per cent exempt from RET liability (both
LRET and SRES). As this reduces the denominator in the calculation of the RPP and STP
values (the means by which aggregate liabilities are translated to individual customers) nonexempt customers will see larger RET cost imposts as a result of the EITE exemption.
The Department has requested ACIL Allen calculate the revised 2020 and interim targets for
this purpose. The ‘Real 20%’ level has been calculated by ACIL Allen in accordance with
the following formula which is similar in nature to that used during the 2014 RET Review:1
2020 𝐺𝑊ℎ 𝑡𝑎𝑟𝑔𝑒𝑡 = 𝐸2020 ∗ 20% − (𝑅𝐸𝑆𝑅𝐸𝑆 𝑃𝑉 + 𝑅𝐸𝑃𝑟𝑒−𝑅𝐸𝑇 )
Where:
 E2020: Projected electricity generation in calendar year 2020 (in sent-out GWh) across all
Australian grids, including estimates of embedded and off-grid energy.
 RE(SRES PV): Projected electricity generation from SRES-eligible small-scale solar PV. For
consistency with the other components, this generation includes an allowance for
distribution and transmission losses so that energy produced at customer meters is
grossed up to a grid equivalent.
 RE(Pre-RET): Power stations pre-dating the RET can only create LGCs for annual
generation (mainly hydro) above historical baselines set under the RET Regulations.
This component forecasts the ineligible (below-baseline) sent-out generation based on
historical levels and long-term forecasts of hydro resource availability.2
1
See ACIL Allen, RET Review Modelling: Market modelling of various policy options, August 2014, page 38. The 2014 RET
Review formula contained a more detailed breakdown of demand and solar PV components, but had the same overall
intent. The impact of electricity displacement from solar water heater installations is excluded in the calculation.
2
The RET Review settings for the existing baseline energy was set at a higher level of 16,148 GWh which ACIL Allen
acknowledged was at the upper end of a reasonable range. For this exercise a more conservative value of 15,000 GWh
has been adopted.
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This results in a rounded 2020 target of 27,000 GWh, with interim targets (2016 to 2019)
revised as a straight line trajectory as detailed in Table 1. The scenario does not involve and
changes to the currently legislated SRES.
Table 1
Real 20% by 2020 annual LRET targets
2014
16,100
16,100
0
2015
18,000
18,000
0
2016
20,581
19,800
-781
2017
25,181
21,600
-3,581
2018
29,781
23,400
-6,381
2019
34,381
25,200
-9,181
2020
41,000
27,000
-14,000
2021-30
41,000
27,000
-14,000
Note: Values exclude 850 GWh allowance for WCMG
Source: ACIL Allen
The Baseline scenario reflects the repeal of the carbon pricing mechanism from 1 July 2014
but does not include the impact of the Government’s Direct Action Plan.
The scenario includes a restriction on the development of new conventional coal-fired
capacity (i.e. coal-based technologies which do not employ carbon capture and storage).
This aligns with ACIL Allen’s standard assumption when undertaking market outlook studies
and can be justified within a scenario which employs greenhouse gas abatement policies
due to a range of factors including:
 Community views and corporate sustainability policies
 Potential difficulty obtaining generation licences from State and Territory governments
 Long-term risk of explicit carbon pricing being reintroduced
 Difficulty in securing financing on a commercial basis due to these risks.
While the scenario does not allow the development of new coal, it does permit existing
stations to refurbish, through life extension capital projects, where it is economic to do so.
The scenario includes the impact of voluntary abatement provided through GreenPower.
The scenario assumes 1,000 GWh of GreenPower annually throughout the projection
period.
The impacts of other state-based schemes – for example the ACT renewables policy –have
not been incorporated. Whilst the results of the latest wind auction have been made public3,
to-date these projects remain uncommitted and may not proceed. As such the Department
instructed ACIL Allen not to assume this additional generation necessarily occurs.
2.2.2
No supply-side measures scenario
The ‘No supply-side measures’ scenario removes the influence of supply-side policy
interventions designed to reduce greenhouse gas emissions from the electricity generation
sector. Upon review of the measures contained within the Baseline scenario, it was decided
in conjunction with the Department that the only measures removed would be the LRET and
SRES.
3
See http://www.environment.act.gov.au/energy/wind_power
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Consideration was given whether any remaining solar PV feed-in-tariff schemes were above
cost reflective levels thereby providing additional incentives for the deployment of rooftop
solar PV. However upon review, it was decided that those schemes in place have been
scaled back to provide roughly the economic value of energy exported to the grid.
The ‘No supply-side measures’ scenario therefore reduced to a No RET scenario. In
accordance with the scenario design, this was assumed to have occurred from 1 July 2014,
with all renewable deployment which had occurred up until that time assumed to remain in
place.4 Therefore, the results for the period 2008-09 to 2013-14 are unchanged from what
actually occurred in the Baseline scenario and no historical counterfactual modelling was
required.
The No supply-side measures scenario does not have any restrictions on the development
of new conventional coal-fired generation capacity. It is assumed that under this scenario
there is no opposition to development of these technologies and consideration of
greenhouse gas emissions do not form part of developers’ technology evaluations. Existing
stations are also permitted to refurbish to extend lives where it is economic to do so.
2.3
Sensitivities
The project also required ACIL Allen to test the robustness of the modelling results by
running sensitivity analysis on the Baseline scenario across a range of key input
parameters. The sensitivity analysis was used to determine which assumptions have the
greatest impact on projections of Australia’s electricity sector emissions.
The project included four pairs of sensitivity scenarios (high and low) on the baseline
scenario – three individual variable elements and a single combination sensitivity which will
include the modification of a number of input elements simultaneously. These comprised of
combinations of deviations that yield the highest and lowest aggregate emissions.
2.4
Historical calibration with National Greenhouse
Gas Inventory
Before modelling commenced, the Department provided ACIL Allen with a detailed
breakdown of historical National Greenhouse Gas Inventory values from the June 2014
Quarterly report, by generator for the purposes of historical calibration. This revised NGGI
series included the revised definition for fuel consumption by BREE where all fuel consumed
for electricity generation activities is classified as ‘electricity generation’ whereas previously
some of these values would have reported under Direct Combustion.
With the use of the detailed NGERs reports for 2011-12 and 2012-13 ACIL Allen examined
emission outcomes at the facility level and determined emission intensity values. These
were then applied to actual generation outcomes for years 2008-09 to 2013-14 (sourced
from market operator data and other sources) and matched against emissions reported in
the National Greenhouse Gas Inventory for electricity generation.
This process revealed some differences between the two data sets including instances
where some emissions from electricity generation continued to be reported under Direct
Combustion activities rather than electricity generation. This was a result of the method
applied for the National Greenhouse Gas Inventory, which uses reported fuel consumption
by BREE for larger facilities and groups the residual reported fuel consumption into an
4
Renewable projects committed and under construction at that point in time were assumed to be completed as per the
Baseline scenario.
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‘other’ category. By comparison, ACIL Allen’s approach utilised reported generation
volumes based on reported market operator dispatch data.
In order to achieve alignment between the two series, ACIL Allen was asked by the
Department to exclude a number of generation facilities from the historical period and
projections. For consistency these facilities were also excluded from the projections and
emissions associated with them should be accounted for in Direct Combustion within the
Department’s overall projections work. This ensured coverage of emissions across
stationery energy in the Department’s projection match the National Greenhouse Gas
Inventory.
Once these facilities were excluded from the historical series, close alignment between
ACIL Allen and the National Greenhouse Gas Inventory was achieved as shown in Figure 3.
The largest differential of the Australian totals was 0.3 Mt CO2-e in 2013-14 which this
accounting for a 0.17 percent differential.
Figure 4 provides a comparison of the two series by year and by State showing close
alignment even within this more disaggregated view of the data.
Figure 3
Model calibration against actual National Greenhouse Gas
Inventory values
Note: Emissions based on IPCC AR4 GWPs
Source: National Greenhouse Gas Inventory June 2014 Quarterly report, ACIL Allen analysis
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Figure 4
Comparison with National Greenhouse Gas Inventory by year by State (Mt CO2-e)
Note: ET = Extra territories
Source: National Greenhouse Gas Inventory, ACIL Allen analysis
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3
3.1
Baseline scenario
Figure 5 and Table 2 presents the projected results of generation by fuel type under the
Baseline scenario. The period presented includes the historical period of 2008-09 to 201314, with projections through to 2034-35.5
In the Baseline scenario, which includes a modified RET to a real 20% level as discussed in
the previous section, coal-fired generation continues to supply the bulk of Australia’s energy
requirements over the projection period. Whilst the scenario does not introduce any new
coal-fired plant (new conventional coal is restricted from entry in this scenario), incumbent
plant continue to operate through life extension programs.
A key feature of the scenario is the development of new renewables through the RET,
particularly over the period to 2020. In the period to 2020 this new capacity is primarily wind,
with around 4,000 MW of new capacity added to 2020-216, with smaller amounts of utilityscale solar PV in regional grids. On-going development of rooftop solar PV occurs
throughout, encouraged by subsidies under the SRES.
Annual historical aggregate generation volumes have been relatively flat at around 240 TWh
through 2008-09 to 2012-13 (declines in the NEM offset by growth in other regions), with
declines more recently to 234 TWh in 2013-14. Based on the demand projection, aggregate
generation volumes (on a sent-out basis) are expected to increase to 262 TWh by 2019-20
and to 317 TWh by 2034-35.
Figure 5
Australian generation by fuel type: Baseline scenario
Source: ACIL Allen
5
Note that the modelling extended beyond 2034-35 (to 2039-40) to ensure there were no end effects and to capture the impact
of forward-looking investment behaviour.
6
Increase in capacity relative to 2012-13 level. This total includes capacity which is already committed or under construction.
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Table 2
Australian generation by fuel type (sent-out TWh): Baseline
scenario
Black coal
123.7
105.5
124.0
148.6
Brown coal
52.5
44.4
46.1
49.0
Peaking Gas
8.9
8.3
1.9
2.8
Baseload Gas
26.0
34.0
30.1
45.2
Cogen
5.9
7.4
3.8
2.8
Liquid fuel
3.7
4.4
4.0
4.4
Hydro
12.1
18.3
15.7
14.2
Wind
3.9
8.1
22.6
23.2
Solar
0.1
3.6
10.8
23.7
Biothermal
2.5
2.9
3.0
3.0
Geothermal
0.0
0.0
0.0
0.0
239.3
236.7
262.1
317.1
Total
Source: ACIL Allen
Figure 6 shows the projected annual emission outcomes for Australia, broken down by
generation type in million tonnes CO2-e. Coal-fired generation accounts for around 85% of
emissions in 2012-13 and it is the change in coal-fired output over time which is the principal
determinant of the emissions trajectory. Natural gas, being much less emission intensive,
has less influence, currently only accounting for around 14% of aggregate emissions.
In the Baseline scenario aggregate emissions are projected to increase from 187 Mt in
2012-13 to 200 Mt by 2016-17 (a 6.8% increase) driven by the decline in gas-fired
generation and a corresponding increase in coal-fired output. The decline in gas-fired
generation is a result of increasing wholesale gas prices over this period, with gas-fired
peaking output particularly suffering due to the oversupplied wholesale market resulting in
few generation opportunities. Emissions remain relatively flat though to 2019-20 at around
201 Mt despite the demand growth over the period due primarily to the introduction of
renewables under the LRET.
By 2034-35, projected emissions from the generation sector reach 236 Mt, a 26% increase
over 2012-13 levels.
Figure 6
Australian emissions by fuel type: Baseline scenario
Source: ACIL Allen
A range of summary figures for emissions are presented in Table 3. Aggregate emissions
are projected to increase by 7.7% over the period 2012-13 to 2019-20 (equivalent to
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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average annual growth rate of 1.1% per annum). A number of factors are driving this
outcome:
 The repeal of the carbon tax effective from 1 July 2014 results in a shift back to coalfired generation
 This is further exacerbated by the decrease in gas-fired generation owing to wholesale
gas price increases and oversupplied wholesale markets
 Projected demand growth under the scenario which results in some mothballed coalfired generators returning to service
 Limited development of renewables over the period.
Over the period 2019-20 to 2035-35 annual emissions increase by a further 17% (equivalent
to average annual growth of 1.1% per annum).
Table 3
Summary of emission outcomes by fuel type (Mt CO2-e): Baseline scenario
Black coal
117.9
98.7
117.2
140.9
-16.3%
18.7%
20.3%
855.6
1,892.5
Brown coal
69.0
59.6
61.1
65.4
-13.7%
2.5%
7.1%
506.4
995.2
Peaking Gas
5.5
5.0
1.0
1.6
-9.6%
-78.9%
53.0%
17.0
15.3
Baseload Gas
13.6
17.4
17.9
24.4
28.3%
2.5%
36.4%
134.9
320.0
Cogen
3.3
3.9
1.9
1.4
18.9%
-51.9%
-27.8%
23.1
23.4
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.1%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
201.4
236.2
-11.4%
7.7%
17.3%
1,555.5
3,283.1
Total
Source: ACIL Allen
Figure 7 provides generation results by fuel for the NEM, SWIS, a combined NWIS/DKIS
view and for off-grid generation. Key features of the results include:
 A resurgence in coal-fired generation in the NEM (particularly black coal) in the nearterm, offset by steep reductions in gas-fired generation. Coal-fired generation accounted
for 82% of generation in the NEM in 2008-09; this has since fallen to 72% in 2012-13.
The projection sees coal output averaging 75% of total NEM generation through to 203435. Gas-fired output declines significantly over the next few years.
 According to the demand projection used, the SWIS sees significant demand growth
over the period and high wholesale prices attract wind and solar PV developments. Coal
generation increases marginally over the period as underutilised capacity is taken up.
Gas-fired output increases in the longer-term, meeting growth in energy requirements.
 The NWIS and DKIS are dominated by gas-fired generation with large-scale solar PV
developments projected to occur from 2017-18 onwards due to the relatively high
wholesale cost of energy projected in these grids
 Off-grid generation is also dominated by gas-fired generation with liquid fuelled
generation continuing at remote sites including some solar PV penetration. Much of the
growth in gas-fired generation relates to generation units associated with new LNG
plants in Queensland, WA and NT.
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Figure 7
Generation by fuel type by grid: Baseline scenario
Note: Off-grid includes Mt Isa
Source: ACIL Allen
Figure 8 shows a similar view of generation, but broken down by jurisdiction. Key points to
note:
 NSW, Victoria and Queensland are dominated by low cost coal-fired generation with gas
and renewables only accounting for a relatively small proportion of generation output
 South Australia sees a significant shift away from natural gas with renewables
accounting for a large proportion of generation. Coal provides baseload energy, with
natural gas only playing a back-up role to renewables
 Western Australia’s supply mix largely remains constant with growing contributions from
renewables
 Tasmania and NT see some growth in renewables, but overall generation volumes are
largely unchanged over the period.
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Figure 8
Generation by fuel type by jurisdiction: Baseline scenario
Source: ACIL Allen
Figure 9 presents projected emission intensity trends (on a ‘sent out’ basis) for each state,
for the Baseline scenario. Aside from the decline in South Australia, emission intensities are
relatively stable, declining only very slightly over time. The Australian average intensity falls
from 0.79 tonnes CO2-e/MWh sent-out in 2012-13 to 0.77 by 2019-20 and thereafter to 0.75
tonnes CO2-e/MWh sent-out by 2034-35.
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Figure 9
Projected emission intensity: Baseline scenario
Source: ACIL Allen
Figure 10 shows the changes in generation capacity by fuel type over the period. Increases
in generation capacity are almost entirely associated with renewable entry: wind (4,020 MW)
and solar PV (14,850 MW). Large-scale wind developed ceases once sufficient capacity to
meet the ‘real 20% RET’ is achieved whereas solar PV installations (primarily small-scale
rooftop) continues to grow throughout due to the financial attractiveness of this as a
distributed technology.
Figure 10 Projected generation capacity: Baseline scenario
Note: Solar PV capacity includes rooftop PV
Source: ACIL Allen
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3.2
No supply-side measures
Figure 11 and Table 2 presents the projected results of generation by fuel type under the No
supply-side measures scenario.
Outcomes for this scenario are very similar to those under the Baseline scenario, with the
exception that in the absence of the RET, no new wind, nor utility-scale solar PV
developments occur within the projections as these technologies are not commercially
viable without subsidies. Figure 12 shows the change in dispatch outcomes between the
scenarios. The reduction in renewable generation is met by increased fossil-fuelled dispatch
(primarily black and brown coal).
Figure 11 Australian generation by fuel type: No supply-side measures
Source: ACIL Allen
Figure 12 Australian generation by fuel type: No supply-side measures
change from Baseline scenario
Note: Generation results from the scenario minus outcomes under the Baseline scenario
Source: ACIL Allen
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Table 4
Australian generation by fuel type (sent-out TWh): No supply-side
measures
Black coal
123.7
105.5
126.4
166.6
Brown coal
52.5
44.4
52.0
49.0
Peaking Gas
8.9
8.3
2.7
2.6
Baseload Gas
26.0
34.0
32.5
39.9
Cogen
5.9
7.4
3.9
2.7
Liquid fuel
3.7
4.4
4.1
4.4
Hydro
12.1
18.3
16.3
16.3
Wind
3.9
8.1
12.5
12.4
Solar
0.1
3.6
9.1
20.1
Biothermal
2.5
2.9
3.0
3.0
Geothermal
0.0
0.0
0.0
0.0
239.3
236.7
262.4
317.0
Total
Source: ACIL Allen
Figure 13 shows the projected annual emission outcomes for Australia under this scenario,
broken down by generation type in million tonnes CO2-e. In the ‘No supply-side measures’
scenario, total annual emissions increase from 187 Mt in 2012-13 to 214 Mt by 2019-20 (a
14.4% increase). In aggregate, emissions over this period are 1,583 Mt which is around
27.2 Mt (1.7%) higher than the Baseline scenario. This is due to the lower level of
renewables developed and increased utilisation of coal-fired capacity over this period.
The differential between the two scenarios grows over time. In the period 2020-21 to 203435 emissions total 3,457 Mt under this scenario compared with 3,283 Mt under the Baseline
scenario. This represents an increase of around 174 Mt, or 5.3%.
By 2034-35, projected emissions from the generation sector reach 248 Mt, a 32.4%
increase over 2012-13 levels.
Figure 13 Australian emissions by fuel type: No supply-side measures
Source: ACIL Allen
Figure 14 summarises the projected impact of the supply-side measures on emissions
outcomes relative to the Baseline scenario and Figure 18 provides the change in emission
by fuel type.
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Figure 14 Impact of supply-side measures on emissions
Source: ACIL Allen
Figure 15 Australian emissions by fuel type: No supply-side measures
change from Baseline scenario
Note: Emission results from the scenario minus outcomes under the Baseline scenario
Source: ACIL Allen
A range of summary figures for emissions are presented in Table 5 Aggregate emissions
are projected to increase by 14.4% over the period 2012-13 to 2019-20 (equivalent to
annual average growth rate of 1.9% per annum). Over the period 2019-20 to 2034-35
annual emissions increase by 15.7% (equivalent to annual growth of 1% per annum).
Figure 16 provides generation results by fuel by grid and Figure 17 provides results by
jurisdiction. These results are broadly similar to those under the Baseline scenario. The key
difference is the lower level of renewables developed in the absence of the RET. In the No
measures scenario, entry of conventional coal is not restricted and the modelling projects a
small amount of new conventional coal would be developed in the SWIS from around 2020
and in Queensland from 2025 onwards. By the end of the projection a total of around
3,350 MW of new conventional coal is developed in these markets.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Table 5
Summary of emission outcomes by fuel type (Mt CO2-e): No supply-side measures
Black coal
117.9
98.7
119.5
154.6
-16.3%
21.1%
29.3%
854.9
2,021.0
Brown coal
69.0
59.6
69.7
65.4
-13.7%
16.9%
-6.1%
531.7
1,035.5
Peaking Gas
5.5
5.0
1.5
1.4
-9.6%
-69.0%
-5.7%
18.2
18.0
Baseload Gas
13.6
17.4
19.0
22.3
28.3%
8.8%
17.6%
136.3
322.1
Cogen
3.3
3.9
2.0
1.3
18.9%
-50.1%
-33.4%
23.3
23.7
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-3.9%
9.1%
17.4
35.4
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
214.0
247.6
-11.4%
14.4%
15.7%
1,582.7
3,457.3
Total
Source: ACIL Allen
Figure 16 Generation by fuel type by grid: No supply-side measures
Note: Off-grid includes Mt Isa
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Figure 17 Generation by fuel type by jurisdiction: No supply-side measures
Note: Other = Tasmania and the Northern Territory
Source: ACIL Allen
Figure 18 presents projected emission intensity trends (on a ‘sent out’ basis) for the No
supply-side measures scenario relative to the Baseline. The impact of the ‘real 20 per cent’
RET is to reduce the emissions intensity of the generation sector by around 0.04 tonnes
CO2-e/MWh.
In the absence of supply-side measures the Australian average intensity increases from
0.79 tonnes CO2-e/MWh sent-out in 2012-13 to 0.82 by 2019-20 and 0.78 by 2034-35.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Figure 18 Projected emission intensity: No supply-side measures
Source: ACIL Allen
Figure 19 shows the changes in generation capacity by fuel type relative to the Baseline
scenario. In the absence of the supply-side measures projected installed capacity of
renewables is considerably lower (3,300 MW less wind, 2,400 MW less solar PV by 2025).
This is offset by some increase in fossil fuel capacity, most of which is associated with the
earlier return to service of mothballed plant and the development of some new coal-fired
capacity in the SWIS and Queensland toward the latter part of the projection period.
Figure 19 Projected generation capacity: No supply-side measures change
from Baseline scenario
Note: Solar PV capacity includes rooftop PV
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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4
This section provides the results from the sensitivities run against the baseline scenario.
The design of the sensitivities were agreed with the Department prior to modelling with
some input assumptions provided by the Department and some of the variations developed
by ACIL Allen.
4.1
High/Low demand
In long-term electricity market projections, wholesale electricity demand is one of the key
uncertainties. Within the demand projections provided by pitt&sherry, high and low cases
were also developed and these formed the basis for the high and low demand sensitivities.
Figure 20 shows aggregate Australian electricity demand under the high and low demand
sensitivities relative to the baseline assumption. Table 6 also provides figures for selected
years and the percentage deviation from the baseline assumptions. All three cases include
significant growth in electricity demand over the period. Even the low case shows electricity
demand increasing by around 67 TWh from 2014-15 to 2034-35 (a 29% increase).
Figure 20 Demand sensitivity inputs: Australia
Note: TWh sent-out at source of generation (i.e. includes network losses). Includes behind-the-meter
consumption from sources such as rooftop solar PV
Source: ACIL Allen based on pitt&sherry demand projections
Table 6
Demand sensitivity inputs for selected years and change from baseline: Australia
TWh
% change
TWh
% change
TWh
% change
TWh
% change
TWh
% change
High demand
238.1
0.4%
282.1
3.8%
313.5
7.4%
346.0
9.9%
379.1
11.6%
Baseline
237.0
Low demand
235.7
271.9
-0.6%
263.3
292.0
-3.2%
275.5
314.7
-5.7%
289.8
339.7
-7.9%
303.3
-10.7%
Note: TWh sent-out at source of generation (i.e. includes network losses). Includes behind-the-meter consumption from sources such as rooftop
solar PV
Source: ACIL Allen based on pitt&sherry demand projections
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Table 7 and Table 8 summarise demand for Australia by grid under the High and Low
demand sensitivities respectively.
Table 7
Electricity demand projection for selected years by grid (GWh):
High demand sensitivity
NEM
204,697
197,382
191,552
216,962
236,236
257,787
280,887
SWIS
21,088
22,745
23,790
29,050
32,970
37,215
41,969
NWIS
1,551
2,783
2,995
3,681
4,136
4,565
5,003
DKIS
1,437
1,547
1,510
1,675
2,032
2,399
2,890
Mt Isa
1,914
2,198
2,312
2,583
2,941
3,441
3,940
Off grid
8,571
10,014
9,922
17,312
18,950
19,688
19,495
239,258
236,668
232,081
271,263
297,265
325,094
354,184
Total Australia
Note: GWh sent-out at source of generation (i.e. includes network losses). Includes rooftop solar PV;
excludes some generation for calibration purposes
Source: ACIL Allen
Table 8
Electricity demand projection for selected years by grid (GWh):
Low demand sensitivity
NEM
204,697
197,382
190,029
203,059
207,625
215,460
223,808
SWIS
21,088
22,745
22,981
24,857
25,790
27,186
28,678
NWIS
1,551
2,783
2,971
3,445
3,647
3,845
4,033
DKIS
1,437
1,547
1,499
1,567
1,791
2,019
2,328
Mt Isa
1,914
2,198
2,294
2,417
2,592
2,898
3,176
Off grid
8,571
10,014
9,922
17,314
18,951
19,689
19,496
239,258
236,668
229,695
252,658
260,397
271,097
281,519
Total Australia
Note: GWh sent-out at source of generation (i.e. includes network losses). Includes rooftop solar PV;
excludes some generation for calibration purposes
Source: ACIL Allen
Figure 21 shows the resulting emission paths for the Australian generator sector under the
High and Low demand sensitivities. Despite the change in demand being roughly
symmetrical either side of the baseline assumption, emission outcomes do not rise as much
under the High demand assumptions as they fall under the Low demand assumptions. This
is due to the higher demand being met predominantly by development of new gas-fired
capacity which has a lower than average emission intensity. Conversely, under the low
demand assumptions, the reduction in emissions is caused by lower output from coal-fired
power stations.
Emission outcomes in 2019-20 under the High case are 3% higher than the Baseline and
3.6% lower under the Low demand assumptions. The change in cumulative emissions over
the period 2012-13 to 2019-20 exhibit less sensitivity: 1.3% higher under High demand;
-1.4% under Low demand assumptions.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Figure 21 Demand sensitivity aggregate emission outcomes: Australia
Source: ACIL Allen
Table 9 provides summary figures for emissions by fuel type for selected years under the
High demand sensitivity.
Table 9
Summary of emission outcomes by fuel type (Mt CO2-e): High demand sensitivity
Black coal
117.9
98.7
121.4
144.0
-16.3%
23.0%
18.6%
869.8
1,979.0
Brown coal
69.0
59.6
61.6
65.4
-13.7%
3.3%
6.3%
507.5
999.0
Peaking Gas
5.5
5.0
1.3
2.0
-9.6%
-74.3%
53.2%
17.9
21.4
Baseload Gas
13.6
17.4
19.5
36.7
28.3%
12.1%
88.1%
140.6
422.7
Cogen
3.3
3.9
2.0
1.4
18.9%
-49.8%
-28.4%
23.5
26.9
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.0%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
208.1
252.1
-11.4%
11.3%
21.2%
1,577.6
3,485.8
Total
Source: ACIL Allen
As shown in Figure 22, the increased emissions under the High demand sensitivity initially
come from increased output from coal-fired generation as excess capacity is taken up in the
market. Around half to two thirds of the increase in emissions is due to increased coal
output over the period to 2028-29. Relative to the Baseline scenario, emissions increase in
a linear fashion from 2014-15 to 2024-25 before stabilising somewhat thereafter at between
12 Mt and 17 Mt CO2-e per annum.
The additional generation capacity required to meet the higher demands is comprised of
6,150 MW of gas-fired capacity (5,400 MW of baseload/intermediate CCGT; 750 MW of
additional peaking plant) and 850 MW of additional solar PV capacity.
Australia’s emissions intensity is slightly lower under this case (0.71 tonnes CO2-e/MWh
sent-out compared with 0.75 tonnes CO2-e/MWh sent-out by 2034-35 under the Baseline
scenario) due to the increased gas-fired and solar PV generation in the long-term. One of
the reasons for this outcome is the restriction on new coal development assumed under the
scenario. If new conventional coal was allowed to enter, it is likely that conventional coal
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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would be selected as the lowest cost option over gas-fired technologies and a larger impact
on emissions under high demand assumptions could be expected.
Figure 22 Australian emissions by fuel type: High demand sensitivity change
from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
Table 10 and Figure 23 provides the corresponding emission figures by fuel under the Low
demand sensitivity. Under low demand conditions, the market develops very little additional
capacity aside from that required to meet the Real 20% LRET and rooftop solar PV.
The change in emission outcomes shows an increasing reduction in output from coal and
gas-fired generation. By 2034-35 coal-fired generation is around 17 TWh lower and gas
15 TWh lower than under the baseline scenario. By comparison, renewable generation in
2034-35 is slightly lower: wind output (-1.2 TWh) and solar PV output (-0.9 TWh). While the
LRET is set as a fixed target in GWh, slightly earlier wind build under this scenario results in
less overall wind capacity being required. Lower uptake of rooftop solar PV is due to lower
retail prices under the low demand conditions.
Aggregate emissions under the Low demand sensitivity are 6.7 Mt CO2-e lower in 2019-20
and 22.3 Mt CO2-e lower in 2034-35 relative to the baseline assumptions. Cumulative
emissions for the period 2012-13 to 2019-20 are 1.3% lower relative to the baseline.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Table 10
Summary of emission outcomes by fuel type (Mt CO2-e): Low demand sensitivity
Black coal
117.9
98.7
111.6
125.1
-16.3%
13.1%
12.0%
842.0
1,721.7
Brown coal
69.0
59.6
61.0
65.4
-13.7%
2.4%
7.2%
506.4
994.2
Peaking Gas
5.5
5.0
0.9
0.7
-9.6%
-82.7%
-18.6%
17.0
10.2
Baseload Gas
13.6
17.4
16.9
19.0
28.3%
-2.9%
12.0%
128.1
274.0
Cogen
3.3
3.9
1.9
1.3
18.9%
-52.6%
-31.8%
23.1
21.7
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.2%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
194.6
213.9
-11.4%
4.1%
9.9%
1,534.8
3,058.7
Total
Source: ACIL Allen
Figure 23 Australian emissions by fuel type: Low demand sensitivity change
from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
4.2
High/Low fuel prices
The next set of sensitivities tested the impact of higher and lower fossil fuel prices upon
emissions outcomes. Inputs for these sensitivities were developed in consultation with the
Department with the key inputs being percentage deviations from the baseline assumptions
as shown in Table 11.
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Table 11
Fuel cost variations relative to baseline assumptions
High fuel costs
Gas and liquid fuel
8%
8%
15%
23%
30%
35%
35%
35%
35%
Black and brown coal
5%
10%
15%
15%
15%
15%
15%
15%
15%
Low fuel costs
Gas and liquid fuel
-8%
-8%
-15%
-23%
-30%
-35%
-35%
-35%
-35%
Black and brown coal
-5%
-10%
-15%
-20%
-25%
-30%
-30%
-30%
-30%
Source: ACIL Allen and Department of the Environment
These variations were translated through to international traded prices for natural gas and
thermal coal and resulted in the price series shown in Figure 24. As fuel costs to some
power stations are insulated from movements in international prices (for example, vertically
integrated supply or no access to export facilities), domestic mining and gas development
costs were also adjusted such that the percentage change in costs were the same.
Figure 24 Fuel cost variations: International fuel price markers
Source: ACIL Allen
Figure 21 shows that aggregate emission outcomes are relatively insensitive to the
variations in fuel costs examined.
Figure 25 Fuel cost sensitivity aggregate emission outcomes: Australia
Source: ACIL Allen
Table 9 provides the emission outcomes by fuel for selected years under the High fuel costs
sensitivity. Cumulative emissions (2012-13 to 2019-20) are virtually unchanged from the
Baseline scenario, with aggregate emissions only 0.2 Mt CO2-e higher.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Table 12
Summary of emission outcomes by fuel type (Mt CO2-e): High fuel cost sensitivity
Black coal
117.9
98.7
118.1
142.1
-16.3%
19.7%
20.3%
857.3
1,915.6
Brown coal
69.0
59.6
61.1
65.4
-13.7%
2.4%
7.2%
506.8
996.9
Peaking Gas
5.5
5.0
1.0
1.4
-9.6%
-78.8%
38.0%
17.6
14.3
Baseload Gas
13.6
17.4
17.5
22.9
28.3%
0.5%
30.7%
134.2
303.3
Cogen
3.3
3.9
1.8
1.3
18.9%
-53.1%
-26.8%
23.1
22.1
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.1%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
201.9
235.8
-11.4%
8.0%
16.8%
1,557.2
3,289.1
Total
Source: ACIL Allen
The change in emissions is a result of higher coal-fired emissions offset (to some extent) by
lower gas-fired emissions. This is a result of the relative competitiveness of coal versus gas
within the inputs as gas prices tend to move by a larger amount when expressed in $/GJ
terms. The higher fuel prices results in more renewable generation being developed – an
additional 4,400 GWh per annum of solar PV by 2034-35, however the higher prices remain
insufficient to make large-scale renewables competitive without additional subsidies.
Figure 26 Australian emissions by fuel type: High fuel cost sensitivity
change from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
The Low fuel cost sensitivity shows the opposite effect, with higher gas-fired emissions and
lower coal-fired emissions as shown in Table 10 and Figure 23. Under these inputs,
cumulative emissions (2012-13 to 2019-20) are only slightly lower by 0.7 Mt CO2-e relative
to the Baseline scenario. Development of renewables is lower due to lower retail prices
lowering the incentive for rooftop PV installations. Aggregate solar PV output is around
3,100 GWh by 2034-35.
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ACIL ALLEN CONSULTING
Table 13
Summary of emission outcomes by fuel type (Mt CO2-e): Low fuel cost sensitivity
Black coal
117.9
98.7
116.2
139.7
-16.3%
17.7%
20.3%
856.5
1,862.8
Brown coal
69.0
59.6
61.1
65.4
-13.7%
2.4%
7.2%
506.6
994.9
Peaking Gas
5.5
5.0
0.8
1.7
-9.6%
-83.8%
113.2%
15.9
17.1
Baseload Gas
13.6
17.4
18.5
26.1
28.3%
6.5%
40.8%
135.7
341.9
Cogen
3.3
3.9
1.9
1.4
18.9%
-50.9%
-25.2%
23.2
25.6
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.1%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
200.8
237.0
-11.4%
7.4%
18.0%
1,556.2
3,279.2
Total
Source: ACIL Allen
Figure 27 Australian emissions by fuel type: Low fuel cost sensitivity change
from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
4.3
High uptake of electric vehicles
Within the demand projections undertaken by pitt&sherry, the uptake of Electric Vehicles
(EVs) was specifically excluded from the analysis. As part of this project, the Department
asked ACIL Allen to estimate an electricity vehicle uptake scenario and include this
incremental electricity demand to the Baseline scenario as an additional sensitivity.
ACIL Allen estimated the impact of the take up of plug in electric vehicles on energy
consumption across all Australian states through the development of a logistic model which
was used to convert the underlying economic drivers of electric vehicles into an impact on
market share and take-up of the technology. This was done by creating a model which
values each of the attributes that drive the decision to adopt the technology and then to
apply an elasticity or measure of responsiveness of market share to each factor. More
details of the process used is provided in section A.8.
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Figure 28 provides the incremental demand added to the Baseline under the EV sensitivity.
It was assumed that the additional energy would not affect the peak demand projection
(assumes all charging occurs during off-peak periods or is time-shifted so as not to impact
peak periods).
Figure 28 Incremental demand for EV sensitivity
Source: ACIL Allen
Table 14 and Figure 29 provide the emission outcomes under the EV sensitivity. Aggregate
emissions increase by around 44 Mt CO2-e over the period to 2034-35. This equates to a
marginal emissions intensity for the incremental demand of 0.57 tonnes CO2-e/MWh sentout. This figure is well below the average intensity for the system as much of the
incremental generation is met by baseload gas (76%). Only 25% of the incremental demand
is met by coal-fired power as coal-fired generators are operating at close to maximum
output by the time EV demand becomes material.
Table 14
Summary of emission outcomes by fuel type (Mt CO2-e): EV sensitivity
Black coal
117.9
98.7
117.5
143.8
-16.3%
19.0%
22.4%
856.3
1,915.1
Brown coal
69.0
59.6
61.1
65.4
-13.7%
2.4%
7.2%
506.4
995.8
Peaking Gas
5.5
5.0
1.1
1.3
-9.6%
-78.3%
24.6%
17.1
14.7
Baseload Gas
13.6
17.4
17.9
27.9
28.3%
2.9%
55.9%
135.3
339.5
Cogen
3.3
3.9
1.9
1.4
18.9%
-51.7%
-26.3%
23.2
24.3
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.2%
9.1%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
201.8
242.5
-11.4%
7.9%
20.2%
1,556.7
3,326.3
Total
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Figure 29 Australian emissions by fuel type: EV sensitivity change from
Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
4.4
Aggregate High/Low scenarios
The aggregate High/Low scenarios utilise a combination of the previous sensitivities to
create the upper and lower bounds for emissions.
The Aggregate High sensitivity is comprised of: High demand, Low fuel prices and EV
uptake. Conversely, the Aggregate Low sensitivity uses the Low demand and High fuel price
inputs.
Figure 30 shows the emission outcomes under the Aggregate High/Low sensitivities in
comparison with the Baseline scenario. The sensitivities provide an almost uniform spread
around the central case, with emissions in 2019-20 being 6.7 Mt CO2-e (3.3%) higher under
the High case and 6.7 Mt CO2-e (3.3%) lower under the Low case. Cumulative emissions
(2012-13 to 2019-20) are 1.5% higher and 1.3% lower respectively.
Figure 30 Aggregate High/Low sensitivity emission outcomes: Australia
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Table 15 provides summary figures for emissions by fuel type for selected years under the
Aggregate High sensitivity.
Table 15
Summary of emission outcomes by fuel type (Mt CO2-e): Aggregate High sensitivity
Black coal
117.9
98.7
120.7
142.0
-16.3%
22.3%
17.6%
871.6
1,959.8
Brown coal
69.0
59.6
61.6
65.4
-13.7%
3.4%
6.2%
507.7
997.9
Peaking Gas
5.5
5.0
1.1
1.9
-9.6%
-77.6%
70.6%
17.2
30.1
Baseload Gas
13.6
17.4
20.3
43.7
28.3%
16.4%
115.6%
141.1
467.3
Cogen
3.3
3.9
2.0
1.7
18.9%
-48.7%
-14.8%
23.6
30.8
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.0%
9.1%
17.5
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
208.0
257.3
-11.4%
11.2%
23.7%
1,579.4
3,522.8
Total
Source: ACIL Allen
As shown in Figure 31, the increased emissions under the Aggregate High sensitivity come
from increased output from coal-fired generation as excess capacity is taken up in the
market, supplemented by additional gas-fired generation to meet the additional demand
growth.
Relative to the Baseline scenario, emissions increase in a linear fashion from 2014-15 to
2034-35 reaching around 21 Mt CO2-e per annum in the final year
The additional generation capacity required to meet the higher demands is comprised of
6,625 MW of gas-fired capacity (6,720 MW of baseload/intermediate CCGT, offset by a
reduction of 95 MW of peaking plant). The scenario results in less rooftop solar PV being
developed (approximately 2,100 MW less by 2034-35) due to the scenario having generally
lower retail prices for consumers. This is caused by a combination of the low fuel prices
flowing through to wholesale price outcomes7 and the higher demand reducing unit network
charges.
Australia’s emissions intensity is slightly lower under this case (0.70 tonnes CO2-e/MWh
sent-out compared with 0.75 tonnes CO2-e/MWh sent-out by 2034-35 under the Baseline
scenario) due to the increased gas-fired generation in the long-term.
7
Some of the impact of the lower fuel costs on wholesale prices are negated by the higher demand, but the overall impact is
negative.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Figure 31 Australian emissions by fuel type: Aggregate High sensitivity
change from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
Table 16 and Figure 32 provides the corresponding emission figures by fuel under the
Aggregate Low sensitivity. Under low demand conditions, the market develops very little
additional capacity aside from that required to meet the Real 20% LRET and rooftop solar
PV.
The change in emission outcomes shows an increasing reduction in output from coal and
gas-fired generation. By 2034-35 coal-fired generation is around 17 TWh lower and gas
16 TWh lower than under the baseline scenario. By comparison, renewable generation in
2034-35 is slightly lower overall: a reduction in wind output (-1.5 TWh) is partially offset by
higher solar PV output (+0.5 TWh). This is due to the LRET being set as a fixed target in
GWh and uptake of rooftop solar PV being largely unaffected by wholesale demand
conditions.8
Emissions under the Aggregate Low sensitivity are 6.7 Mt CO2-e lower in 2019-20 and
22.8 Mt CO2-e lower in 2034-35 relative to the baseline assumptions. Cumulative emissions
for the period 2012-13 to 2019-20 are 1.3% lower relative to the baseline.
8
The lower wholesale electricity demand increases the network tariff within retail prices (as regulated revenues are spread
across fewer GWh) and therefore improves the attractiveness of rooftop solar PV for residential and commercial
customers.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
37
ACIL ALLEN CONSULTING
Table 16
Summary of emission outcomes by fuel type (Mt CO2-e): Aggregate Low sensitivity
Black coal
117.9
98.7
112.1
124.9
-16.3%
13.6%
11.4%
841.7
1,729.5
Brown coal
69.0
59.6
61.0
65.4
-13.7%
2.4%
7.2%
506.3
995.1
Peaking Gas
5.5
5.0
0.8
0.6
-9.6%
-83.1%
-26.9%
16.8
10.0
Baseload Gas
13.6
17.4
16.5
18.7
28.3%
-5.1%
12.9%
128.4
268.2
Cogen
3.3
3.9
1.8
1.2
18.9%
-53.9%
-30.8%
23.0
20.7
Liquid fuel
1.8
2.3
2.2
2.4
27.5%
-4.2%
9.2%
17.4
35.3
Hydro
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Wind
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
Solar
0.0
0.0
0.0
0.0
0.0%
0.0%
0.0%
0.0
0.0
Biothermal
0.1
0.1
0.1
0.1
11.6%
-0.1%
0.0%
0.9
1.6
Geothermal
0.0
0.0
0.0
0.0
-
-
-
0.0
0.0
211.2
187.0
194.7
213.4
-11.4%
4.1%
9.6%
1,534.5
3,060.5
Total
Source: ACIL Allen
Figure 32 Australian emissions by fuel type: Aggregate Low sensitivity
change from Baseline scenario
Note: Emission results from the sensitivity minus outcomes under the Baseline scenario
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
38
ACIL ALLEN CONSULTING
Appendix A
Input assumptions
A.1
Macro inputs
The following sections outline the key assumptions used in the Baseline scenario. Variations
to the key assumptions were also undertaken through the modelling of sensitivities (see
section 2.3 for more information of these assumptions).
A.1.1
Electricity demand
Under a separate consultancy, pitt&sherry were engaged to provide the Department with
electricity demand forecasts to be used within this project. This study focused primarily on
projected electricity demand on main grids.
The pitt&sherry work provided the following demand components:
 NEM native energy (as defined by AEMO) for each NEM region including network losses
 SWIS energy (in accordance with the definition used by the IMO)
 Energy delivered from the DKIS (as defined by the NT Utilities Commission)
 Energy forecasts covering a number of for mini grids/off-grid users which covered only
residential, business and community consumption.
ACIL Allen provided pitt&sherry with projections of rooftop solar PV for each jurisdiction
under the Baseline scenario assumptions and these were incorporated to arrive a grid-level
energy values.
Peak demand forecasts were also provided by pitt&sherry on a P50 basis for NSW, QLD,
VIC, SA and the SWIS.
ACIL Allen was also tasked with providing the balance of the demand assumptions for those
demand components not covered by the pitt&sherry work, primarily additional large
industrial, mini-grid, off-grid and grid-exempt demand’, which includes approximately
24 TWh of additional demand as shown in Table A1. ACIL Allen’s analysis was verified
through interrogation of NGER data for electricity generation in 2011-12 and 2012-13.
Table A1 Breakdown of electricity demand comparison for 2012-13
NEM AEMO Native Energy
NEM Scheduled and semi-scheduled
189,663
187,511
183,440
NEM Significant non-scheduled
3,105
NEM Small non-scheduled
3,118
NEM Market exempt
4,661
SWIS
22,352
SWIS market
17,706
SWIS Non-market
4,646
18,104
DKIS
1,540
1,585
NWIS
2,761
496
Mt Isa
2,188
370
Off-grid
9,984
1,204
Rooftop solar PV
Total
3,520
3,502
236,668
212,773
Note: GWh sent-out at source of generation (i.e. includes network losses). Includes rooftop solar PV;
excludes some generation for calibration purposes
Source: ACIL Allen analysis
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-1
ACIL ALLEN CONSULTING
Table A2 and Table A3 summarise the Baseline demand projection used in the modelling
broken down by jurisdiction and grid respectively.
Table A2 Baseline electricity demand projection for selected years by
jurisdiction (GWh)
NSW/ACT
71,597
64,900
68,474
70,730
74,114
78,900
84,854
VIC
53,738
51,278
44,248
47,098
50,901
55,374
61,010
QLD
59,780
58,591
57,731
72,089
75,676
80,830
85,956
SA
14,180
13,159
13,632
13,938
14,029
14,324
15,094
TAS
8,683
13,054
10,650
10,574
10,543
10,500
10,484
WA
28,091
32,333
33,628
41,550
45,448
49,041
52,434
NT
3,189
3,353
2,668
5,192
5,619
5,974
6,307
239,258
236,668
231,032
261,170
276,329
294,943
316,141
Total Australia
Note: GWh sent-out at source of generation (i.e. includes network losses). Includes rooftop solar PV;
excludes some generation for calibration purposes
Source: ACIL Allen
Table A3 Baseline electricity demand projection for selected years by grid
(GWh)
NEM
204,697
197,382
190,881
209,101
219,472
233,512
250,538
SWIS
21,088
22,745
23,434
27,109
29,434
32,283
35,493
NWIS
1,551
2,783
2,985
3,546
3,848
4,151
4,488
DKIS
1,437
1,547
1,505
1,613
1,890
2,180
2,592
Mt Isa
1,914
2,198
2,305
2,488
2,735
3,129
3,535
Off grid
8,571
10,014
9,922
17,313
18,951
19,689
19,495
239,258
236,668
231,032
261,170
276,329
294,943
316,141
Total Australia
Note: GWh sent-out at source of generation (i.e. includes network losses). Includes rooftop solar PV;
excludes some generation for calibration purposes
Source: ACIL Allen
Various methods were used to project the balance of demand components, including relying
on industry output projections from the Department for alumina, iron ore, coal and LNG
(developed in consultation with Treasury and the Department of Industry) and various other
projections from BREE.
A key component for off-grid energy is electricity generated associated with liquefaction
facilities of LNG developments. These were undertaken with reference to electricity intensity
indices provided by the Department for each new project.
In aggregate the demand projections under the Baseline scenario aligned reasonably
closely with those used as the Core assumptions from the 2014 RET Review as shown in
Figure A1. The Baseline demand is around 4.7 TWh higher by 2019-20. This gap widens in
the longer-term to be 13.1 TWh higher by 2034-35. Owing to a range of other differences in
input assumptions and scenario definitions, we would not expect outcomes to be the same
as the RET Review process.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-2
ACIL ALLEN CONSULTING
Figure A1 Comparison of Australian energy demand to 2020
Source: ACIL Allen
A.1.2
Foreign exchange rates
The assumed exchange rate series used was provided in confidence by the Department
based on a Treasury series. All foreign equipment is assumed to be priced in US dollars.
A.1.3
Commodity prices and wage indices
ACIL Allen utilise commodity prices and wage indices as modifiers to capital cost
components. These typically include ‘Metals’ and domestic wage price indices. For this
work, the Department provided a wage price index (sourced from the 2014-15 Budget) as
shown in Figure A2. This is applied to domestic labour components. Due to the inability to
provide an update for the metals index, its influence was removed from the capital cost
projections.
Figure A2 Real wage index used
Note: 2009-10 = 100
Source: ACIL Allen based on 2013 Emissions Projections Study
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-3
ACIL ALLEN CONSULTING
A.1.4
Fuel costs
The gas prices used in this work were based on the International Energy Agency’s (IEA)
2014 World Energy Outlook. Within this publication the IEA produces two gas price series:
the New Policies Scenario (baseline scenario) and a Gas Price Convergence Scenario in
which global prices converged on the basis of increased exports from the United States.
The Department has provided equivalent domestic gas price based on a transition to export
netback prices for each of these series as shown in Figure A3. These prices are then
translated throughout the gas network by adjusting for transport differentials. Existing gasfired stations transition from prices under existing contracts to export netback levels over the
next 2-3 years.
Figure A3 Calculated netback gas price for Australian gas producers
Source: ACIL Allen based on data from the Department of the Environment and WEO 2014
Within our coal model, export thermal coal prices are brought back to the mine by deducting
port, rail and washery costs and adjusting for yield (thermal coal for domestic power
generation is generally unwashed coal and hence does not incur washery losses). Forecast
thermal coal price used for this purpose as shown in Figure A4. These prices are converted
back into Australian dollars and adjusted as discussed above for use in the modelling.
Figure A4 Assumed new entrant coal costs
Source: Department of the Environment
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-4
ACIL ALLEN CONSULTING
A.1.5
Carbon pricing
The Clean Energy Future Act 2011 (Commonwealth) implemented a multi-sectoral, nationwide carbon price, with the core effect of imposing a cost on emitting greenhouse gases
from a range of activities including fossil fuel combustion (e.g. in electricity generation) and
fugitive emissions from gas production and coal mining. The scheme was in operation from
2012-13 (nominal $23/tonne CO2-e) through to 2013-14 (nominal $24.15/tonne CO2-e). The
legislation has since been repealed, effective from 1 July 2014. The scenarios do not
incorporate any other explicit carbon pricing policies in the future.
The Government’s Direct Action Plan, including the Emissions Reduction Fund and
safeguard mechanism have not been modelled as policy settings have not been finalised.
A.1.6
Conventional coal entry
ACIL Allen’s standard assumption when undertaking market outlook studies is to restrict
conventional coal (i.e. coal-based technologies which do not employ carbon capture and
storage) from entry. Restricting coal from entering can be justified due to:
 Community views and corporate sustainability policies
 Potential difficulty obtaining generation licenses from State and Territory governments
 Long-term risk of explicit carbon pricing being reintroduced
 Difficulty in securing financing on a commercial basis due to these risks.
Under the Baseline scenario is it assumed that the above considerations make new
conventional coal developments commercial unviable and hence are constrained from entry
within the modelling.
Conventional coal entry will be permitted in scenarios which do not include supply-side
policy measures based purely on the economics of the technology within the market. That
is, the modelling assumes that under a no measures scenario, there is also no risk of
measures being introduced.
A.2
Existing generators
The modelling incorporates a total of around 190 existing generators across the nine regions
modelled as shown in Table A4. For the NEM, these generators represent those that are
scheduled and semi-scheduled (i.e. those that report and participate in AEMO’s central
dispatch functions). These are generally those generators with a nameplate capacity above
30 MW in the NEM and above 1 MW in the SWIS.
Non-scheduled, embedded ‘behind the meter’ and off-grid generation are handled outside of
PowerMark LT. For the most part, non-scheduled and embedded generators (aside from
rooftop solar PV – see section A.6) are held constant at current levels.
For the SWIS, the generators and their capacity corresponds with capacity offered to the
Independent Market Operator (IMO) as part of the wholesale markets net pool functions.
This means that capacity and energy related to own-use consumption (most notably from
cogeneration projects) is not included explicitly and is handled outside the modelling.
For NWIS, DKIS and Mt Isa regions no formal market structure exists and generators
include all major grid connected plants.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-5
ACIL ALLEN CONSULTING
Table A4 Existing and committed generators: type, capacity and life
NSW
QLD
AGL SF PV Broken Hill
Solar PV
Solar
2014
30
2044
53
AGL SF PV Nyngan
Solar PV
Solar
2014
30
2044
106
Bayswater
Subcritical pf
Black coal
1983
53
2036
2,720
Bendeela Pumps
Pump
n/a
1977
150
2127
240
Blowering
Hydro
Hydro
1969
150
2119
80
Boco Rock WF
Wind turbine
Wind
2014
25
2036
113
Colongra
OCGT
Natural gas
2009
30
2039
664
Eraring
Subcritical pf
Black coal
1983
50
2033
2,880
Gullen Range WF
Wind turbine
Wind
2014
25
2036
165.5
Gunning WF
Wind turbine
Wind
2011
25
2036
47
Guthega
Hydro
Hydro
1955
150
2105
60
Hume NSW
Hydro
Hydro
1957
150
2107
29
Hunter Valley GT
OCGT
Liquid fuel
1988
30
2018
51
Liddell
Subcritical pf
Black coal
1972
60
2032
2,100
Mt Piper
Subcritical pf
Black coal
1993
50
2043
1,340
Redbank
Subcritical pf
Black coal
2001
50
2051
150
Shoalhaven Bendeela
Hydro
Hydro
1977
150
2127
240
Smithfield
CCGT
Natural gas
1997
30
2027
176
Tallawarra
CCGT
Natural gas
2009
30
2039
430
Taralga WF
Wind turbine
Wind
2014
25
2036
106.8
Tumut 1
Hydro
Hydro
1959
150
2109
616
Tumut 3
Hydro
Hydro
1973
150
2123
1,500
Tumut 3 Pumps
Pump
n/a
1973
150
2123
400
Uranquinty
OCGT
Natural gas
2009
30
2039
664
Vales Point B
Subcritical pf
Black coal
1978
50
2028
1,320
Wallerawang C
Subcritical pf
Black coal
1978
45
2023
960
Woodlawn WF
Wind turbine
Wind
2011
25
2036
48
Barcaldine
CCGT
Natural gas
1996
30
2026
55
Barron Gorge
Hydro
Hydro
1963
150
2113
60
Braemar 1
OCGT
Natural gas
2006
30
2036
504
Braemar 2
OCGT
Natural gas
2009
30
2039
459
Callide B
Subcritical pf
Black coal
1989
50
2039
700
Callide C
Supercritical pf
Black coal
2001
50
2051
810
Collinsville
Subcritical pf
Black coal
1998
30
2028
190
Condamine
CCGT
Natural gas
2009
30
2039
140
Darling Downs
CCGT
Natural gas
2010
30
2040
630
Gladstone
Subcritical pf
Black coal
1980
50
2030
1,680
Kareeya
Hydro
Hydro
1958
150
2108
81
Kogan Creek
Supercritical pf
Black coal
2007
50
2057
750
Mackay GT
OCGT
Liquid fuel
1975
45
2020
34
Millmerran
Supercritical pf
Black coal
2002
50
2052
851
Mt Stuart
OCGT
Liquid fuel
1998
40
2038
418
Oakey
OCGT
Natural gas
2000
30
2030
282
Roma
OCGT
Natural gas
1999
30
2029
80
Stanwell
Subcritical pf
Black coal
1995
50
2045
1,440
Swanbank E
CCGT
Natural gas
2002
30
2032
385
Tarong
Subcritical pf
Black coal
1985
50
2035
1,400
Tarong North
Supercritical pf
Black coal
2002
50
2052
443
Townsville
CCGT
Natural gas
2005
30
2035
240
Wivenhoe
Hydro
Hydro
1984
150
2134
500
Wivenhoe Pump
Pump
n/a
1984
150
2134
480
Yarwun
Cogeneration
Natural gas
2010
30
2040
168
Bluff WF
Wind turbine
Wind
2011
25
2036
53
Clements Gap WF
Wind turbine
Wind
2008
25
2033
57
Dry Creek
OCGT
Natural gas
1973
45
2018
156
Hallett
OCGT
Natural gas
2002
30
2032
200
Hallett 2 WF
Wind turbine
Wind
2008
25
2033
71
Hallett WF
Wind turbine
Wind
2007
25
2032
95
SA
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-6
ACIL ALLEN CONSULTING
Ladbroke Grove
OCGT
Natural gas
2000
30
2030
80
Lake Bonney 2 WF
Wind turbine
Wind
2008
25
2033
159
Lake Bonney 3 WF
Wind turbine
Wind
2010
25
2035
39
Mintaro
OCGT
Natural gas
1984
30
2014
90
North Brown Hill WF
Wind turbine
Wind
2011
25
2036
132
Northern
Subcritical pf
Brown coal
1985
50
2035
530
Osborne
CCGT
Natural gas
1998
30
2028
180
Pelican Point
CCGT
Natural gas
2000
35
2035
485
Playford B
Subcritical pf
Brown coal
1960
60
2020
231
Port Lincoln
OCGT
Liquid fuel
1999
30
2029
74
Quarantine
OCGT
Natural gas
2002
30
2032
216
Snowtown 2 North WF
Wind turbine
Wind
2014
25
2039
144
Snowtown 2 South WF
Wind turbine
Wind
2014
25
2039
126
Snowtown WF
Wind turbine
Wind
2008
25
2033
99
Snuggery
OCGT
Liquid fuel
1997
30
2027
63
Torrens Island A
Steam turbbine
Natural gas
1967
52
2019
480
Torrens Island B
Steam turbine
Natural gas
1977
50
2027
800
Waterloo WF
Wind turbine
Wind
2011
25
2036
111
Bastyan
Hydro
Hydro
1983
150
2133
80
Bell Bay
Subcritical pf
Natural gas
1971
38
2009
240
Bell Bay Three
OCGT
Natural gas
2006
30
2036
120
Cethana
Hydro
Hydro
1971
150
2121
85
Devils Gate
Hydro
Hydro
1969
150
2119
60
Fisher
Hydro
Hydro
1973
150
2123
43
Gordon
Hydro
Hydro
1978
150
2128
432
John Butters
Hydro
Hydro
1992
150
2142
144
Lake Echo
Hydro
Hydro
1956
150
2106
32
Lemonthyme_Wilmot
Hydro
Hydro
1970
150
2120
82
Liapootah_Wayatinah_Catagunya
Hydro
Hydro
1960
150
2110
170
Mackintosh
Hydro
Hydro
1982
150
2132
80
Meadowbank
Hydro
Hydro
1967
150
2117
40
Musselroe WF
Wind turbine
Wind
2013
25
2038
168
Poatina
Hydro
Hydro
1964
150
2114
300
Reece
Hydro
Hydro
1986
150
2136
231
Tamar Valley
CCGT
Natural gas
2010
30
2040
200
Tamar Valley GT
OCGT
Natural gas
2009
30
2039
58
Tarraleah
Hydro
Hydro
1938
150
2088
90
Trevallyn
Hydro
Hydro
1955
150
2105
80
Tribute
Hydro
Hydro
1994
150
2144
83
Tungatinah
Hydro
Hydro
1953
150
2103
125
Anglesea
Subcritical pf
Brown coal
1969
52
2021
160
Bairnsdale
OCGT
Natural gas
2001
30
2031
92
Dartmouth
Hydro
Hydro
1960
150
2110
158
Eildon
Hydro
Hydro
1957
150
2107
120
Energy Brix
Subcritical pf
Brown coal
1960
58
2018
195
Hazelwood
Subcritical pf
Brown coal
1968
63
2031
1,640
Hume VIC
Hydro
Hydro
1957
150
2107
29
Jeeralang A
OCGT
Natural gas
1979
50
2029
228
Jeeralang B
OCGT
Natural gas
1980
50
2030
255
Laverton North
OCGT
Natural gas
2006
30
2036
312
Loy Yang A
Subcritical pf
Brown coal
1986
50
2036
2,180
Loy Yang B
Subcritical pf
Brown coal
1995
50
2045
1,050
Macarthur WF
Wind turbine
Wind
2013
25
2038
420
McKay
Hydro
Hydro
1980
150
2130
300
Mortlake
OCGT
Natural gas
2011
40
2051
566
Mt Mercer WF
Wind turbine
Wind
2014
25
2039
131
Murray
Hydro
Hydro
1968
150
2118
1,500
Newport
Steam turbine
Natural gas
1980
50
2030
500
Oaklands Hill WF
Wind turbine
Wind
2011
25
2036
63
TAS
VIC
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-7
ACIL ALLEN CONSULTING
Somerton
OCGT
Natural gas
2002
30
2032
160
Valley Power
OCGT
Natural gas
2002
30
2032
300
West Kiewa
Hydro
Hydro
1956
150
2106
62
Yallourn
Subcritical pf
Brown coal
1980
55
2035
1,538
Albany
Wind turbine
Wind
2001
25
2026
22
Alcoa Kwinana Cogen
Cogeneration
Natural gas
1998
30
2028
5
Alcoa Pinjarra Cogen
Cogeneration
Natural gas
1985
35
2020
10
Alcoa Wagerup Cogen
Cogeneration
Natural gas
1990
30
2020
25
Bluewaters
Subcritical pf
Black coal
2009
40
2049
441
BP Cogen
Cogeneration
Natural gas
1996
30
2026
81
Canning/Melville LFG
Reciprocating engine
Landfill gas
2007
15
2022
9
Cockburn
CCGT
Natural gas
2003
30
2033
246
Collgar WF
Wind turbine
Wind
2012
25
2037
206
Collie
Subcritical pf
Black coal
1999
40
2039
333
Emu downs
Wind turbine
Wind
2006
25
2031
80
Geraldton
OCGT
Distillate
1973
40
2013
21
Grasmere
Wind turbine
Wind
2012
25
2037
14
Greenough River
Solar PV
Solar
2012
30
2042
10
Kalgoorlie
OCGT
Distillate
1990
30
2020
63
Kalgoorlie Nickel
OCGT
Natural gas
1996
30
2026
10
Kemerton
OCGT
Natural gas
2005
30
2035
310
Kwinana A
Steam turbine
Natural gas
1970
41
2011
245
Kwinana B
Steam turbine
Natural gas
1974
34
2008
0
Kwinana C
Steam turbine
Natural gas
1976
39
2015
385
Kwinana GT
OCGT
Distillate
1975
40
2015
21
Kwinana HEGT
OCGT
Natural gas
2011
30
2041
201
Muja A&B
Subcritical pf
Black coal
1968
40
2008
240
Muja C
Subcritical pf
Black coal
1981
40
2021
398
Muja D
Subcritical pf
Black coal
1986
40
2026
454
Mumbida
Wind turbine
Wind
2012
25
2037
55
Mungarra
OCGT
Natural gas
1991
30
2021
113
Namarkkon
OCGT
Distillate
2012
30
2042
70
Neerabup Peaker
OCGT
Natural gas
2009
30
2039
330
Newgen Power
CCGT
Natural gas
2007
30
2037
314
Parkeston SCE
OCGT
Natural gas
1996
30
2026
68
Pinjar A B
OCGT
Natural gas
1990
30
2020
228
Pinjar C
OCGT
Natural gas
1992
30
2022
233
Pinjar D
OCGT
Natural gas
1996
30
2026
124
Pinjarra Alinta Cogen
Cogeneration
Natural gas
2007
30
2037
280
Tesla (various sites)
OCGT
Distillate
2012
30
2042
40
Tiwest Cogen
Cogeneration
Natural gas
1999
30
2029
37
Wagerup Alinta Peaker
OCGT
Distillate
2007
30
2037
323
Walkaway
Wind turbine
Wind
2005
25
2030
89
Western Energy Peaker
OCGT
Natural gas
2011
30
2041
106
Worsley
Cogeneration
Black coal
1990
40
2030
0
Worsley SWCJV
Cogeneration
Natural gas
2000
25
2025
116
Burrup Peninsula
OCGT
Natural gas
2006
30
2036
74
Cape Lambert
CCGT
Natural gas
1996
30
2026
105
Cape Preston
CCGT
Natural gas
2009
30
2039
450
Dampier
Steam Turbine
Natural gas
2000
50
2050
120
Dampier C
Steam Turbine
Natural gas
1970
50
2020
120
Karratha
Steam turbine
Natural gas
2005
50
2055
44
Karratha ATCO
OCGT
Natural gas
2010
30
2040
86
Paraburdoo
Reciprocating Engine
Liquid fuel
1985
30
2015
20
Port Hedland
OCGT
Natural gas
1997
30
2027
180
South Hedland
CCGT
Natural gas
2017
30
2047
150
Berrimah
OCGT
Liquid fuel
1979
30
2009
30
Channel Island u1-3
OCGT
Natural gas
1986
30
2016
95
Channel Island u4-6
CCGT
Natural gas
1998
30
2028
95
SWIS
NWIS
DKIS
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-8
ACIL ALLEN CONSULTING
Channel Island u7
OCGT
Natural gas
2006
30
2036
42
Channel Island u8-9
OCGT
Natural gas
2012
30
2042
90
Katherine
OCGT
Natural gas
1987
30
2017
34
LMS Shoal Bay
Reciprocating engine
Landfill gas
2005
15
2020
1
Pine Creek CCGT
CCGT
Natural gas
1989
30
2019
27
Weddell
OCGT
Natural gas
2008
30
2038
128
APA Xstrata OCGT
OCGT
Natural gas
2008
30
2038
30
Diamantina CCGT
CCGT
Natural gas
2014
30
2044
242
Diamantina OCGT
OCGT
Natural gas
2014
30
2044
60
Ernest Henry
Reciprocating Engine
Liquid fuel
1997
30
2027
32
Mica Creek A CCGT
CCGT
Natural gas
2000
30
2030
103
Mica Creek A GT
OCGT
Natural gas
2000
30
2030
132
Mica Creek B
OCGT
Natural gas
2000
30
2030
35
Mica Creek C
CCGT
Natural gas
2000
30
2030
55
Mt Isa Mines Station
Steam turbine
Natural gas
1974
50
2024
38
Phosphate Hill
OCGT
Natural gas
1999
30
2029
42
Mt Isa
Source: ACIL Allen
Table A5 provides the assumed thermal efficiencies, auxiliary use, emissions factors, O&M
costs, outage rates and marginal loss factor (MLF) values for each existing and committed
generator. These values are taken from ACIL Allen’s generator database.
Thermal efficiency and Scope 1 emission factors have been calibrated based on Clean
Energy Regulator publicly released emissions data. This was undertaken on the context of
the assumptions development work for AEMO in mid-2014.9
Table A5 Existing and committed generators: efficiency, emissions and O&M costs
AGL SF PV Broken Hill
0.0%
0
0.000
34,833
0
0.00%
0.00%
1.1026
AGL SF PV Nyngan
0.0%
0
0.000
34,833
0
0.00%
0.00%
1.1026
6.0%
91.8
0.921
46,039
1.11
3.00%
4.00%
0.9653
Bendeela Pumps
0.0%
0
0.000
48,858
8.67
0.00%
0.00%
0.9877
Blowering
0.0%
0
0.000
48,858
4.82
0.00%
4.00%
0.9368
Boco Rock WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
1.0156
Bayswater
35.9%
Colongra
32.0%
1.0%
58.9
0.663
12,214
9.38
1.50%
0.00%
0.9855
Eraring
35.4%
6.0%
90.2
0.917
46,039
1.11
3.00%
4.00%
0.9850
Gullen Range WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9667
Gunning WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9609
Guthega
0.0%
0
0.000
48,858
6.74
0.00%
4.00%
0.8987
Hume NSW
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9232
NSW
Hunter Valley GT
28.0%
1.0%
69.7
0.896
12,214
8.93
2.50%
0.00%
0.9755
Liddell
33.8%
5.0%
90.9
0.968
48,858
1.11
3.00%
8.00%
0.9663
Mt Piper
37.0%
5.0%
93.3
0.908
46,039
1.23
3.00%
4.00%
0.9698
Redbank
29.3%
8.0%
105.0
1.290
46,509
1.11
4.00%
4.00%
0.9744
0.0%
0
0.000
48,858
8.67
0.00%
4.00%
0.9737
Shoalhaven Bendeela
Smithfield
41.0%
5.0%
57.7
0.506
23,489
2.23
2.50%
2.00%
1.0026
Tallawarra
50.0%
3.0%
51.3
0.369
30,249
1.1
3.00%
2.00%
0.9839
Taralga WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9860
Tumut 1
0.0%
0
0.000
48,858
6.74
0.00%
4.00%
0.9363
Tumut 3
0.0%
0
0.000
48,858
10.6
0.00%
4.00%
0.9309
9
See http://www.aemo.com.au/Electricity/Planning/RelatedInformation/~/media/Files/Other/planning/Emissions%202014/20140411_Emissions_report_V2.ashx
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-9
ACIL ALLEN CONSULTING
Tumut 3 Pumps
0.0%
0
0.000
48,858
0
0.00%
0.00%
0.9490
Uranquinty
32.0%
1.0%
52.2
0.588
12,214
9.38
1.50%
0.00%
0.8675
Vales Point B
35.4%
5.0%
87.3
0.887
46,039
1.11
3.00%
8.00%
0.9865
Wallerawang C
33.1%
7.0%
85.5
0.930
48,858
1.23
3.00%
8.00%
0.9729
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9618
1.0%
78.6
1.010
23,489
2.23
2.50%
4.00%
0.9871
0.0%
0
0.000
48,858
10.6
0.00%
4.00%
1.0883
Woodlawn WF
Barcaldine
28.0%
Barron Gorge
Braemar 1
30.0%
1.0%
48.0
0.576
12,214
7.33
1.50%
0.00%
0.9480
Braemar 2
30.0%
1.0%
49.8
0.598
12,214
7.33
1.50%
0.00%
0.9480
Callide B
36.1%
9.0%
94.2
0.939
46,509
1.12
4.00%
4.00%
0.9525
Callide C
36.5%
6.0%
96.6
0.952
46,509
2.54
6.00%
5.00%
0.9525
Collinsville
27.7%
10.0%
89.4
1.162
61,072
1.23
4.00%
2.00%
1.0389
Condamine
48.0%
3.0%
62.5
0.468
30,249
1.1
1.50%
4.00%
0.9519
Darling Downs
46.0%
6.0%
57.1
0.447
30,249
1.1
3.00%
4.00%
0.9480
Gladstone
35.2%
8.0%
90.4
0.924
48,858
1.11
4.00%
4.00%
0.9797
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
1.0870
Kareeya
QLD
Kogan Creek
37.5%
9.0%
94
0.902
45,099
1.17
4.00%
4.00%
0.9493
Mackay GT
28.0%
1.0%
69.7
0.896
12,214
8.4
1.50%
0.00%
1.0305
Millmerran
36.9%
6.0%
90.5
0.883
45,099
2.64
5.00%
8.00%
0.9532
Mt Stuart
30.0%
1.0%
80.5
0.966
12,214
8.4
2.50%
2.00%
1.0041
Oakey
32.6%
1.0%
82.1
0.907
12,214
8.93
2.00%
0.00%
0.9519
Roma
30.0%
1.0%
55.9
0.671
12,214
8.93
3.00%
0.00%
0.9404
Stanwell
36.4%
9.0%
95.7
0.946
46,039
2.99
2.50%
4.00%
0.9693
Swanbank E
47.0%
3.0%
51.9
0.397
30,249
1.1
3.00%
2.00%
0.9934
Tarong
36.2%
8.0%
94.0
0.935
46,509
6.98
3.00%
4.00%
0.9677
Tarong North
39.2%
6.0%
96.5
0.886
45,099
1.33
3.00%
4.00%
0.9678
Townsville
46.0%
3.0%
56.1
0.439
30,249
1.1
3.00%
2.00%
1.0292
Wivenhoe
0.0%
0
0.000
48,858
0
0.00%
4.00%
0.9891
Wivenhoe Pump
0.0%
0
0.000
28,187
0
0.00%
0.00%
0.9930
2.0%
51.3
0.543
23,489
0
3.00%
0.00%
0.9837
Bluff WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9795
Clements Gap WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9658
Yarwun
34.0%
Dry Creek
26.0%
1.0%
58.4
0.808
12,214
8.93
3.00%
0.00%
1.0022
Hallett
24.0%
1.0%
57.2
0.859
12,214
8.93
1.50%
0.00%
0.9869
Hallett 2 WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9843
Hallett WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9869
1.0%
46.4
0.557
12,214
3.34
3.00%
4.00%
0.9884
Lake Bonney 2 WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9665
Lake Bonney 3 WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9665
1.0%
67.4
0.866
12,214
8.93
1.50%
0.00%
0.9879
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9795
Ladbroke Grove
Mintaro
30.0%
28.0%
North Brown Hill WF
SA
Northern
34.9%
10.0%
108.8
1.122
51,676
1.11
5.00%
8.00%
0.9744
Osborne
42.0%
5.0%
61.7
0.529
23,489
4.72
3.00%
2.00%
0.9990
Pelican Point
48.0%
2.0%
54.0
0.405
30,249
1.1
3.00%
4.00%
0.9994
Playford B
21.9%
8.0%
91
1.496
65,770
2.79
10.00%
8.00%
0.9882
Port Lincoln
26.0%
8.0%
67.9
0.940
12,214
8.93
1.50%
0.00%
0.9108
Quarantine
32.0%
5.0%
64.4
0.725
12,214
8.93
2.50%
0.00%
0.9939
Snowtown 2 North WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9861
Snowtown 2 South WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9861
Snowtown WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9136
Snuggery
26.0%
3.0%
67.9
0.940
12,214
8.93
2.00%
0.00%
0.9944
Torrens Island A
27.6%
5.0%
50.0
0.653
36,666
2.05
4.50%
4.00%
1.0004
Torrens Island B
30.0%
5.0%
49.1
0.589
36,666
2.05
4.50%
4.00%
1.0004
Waterloo WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9819
Bastyan
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9396
Bell Bay
29.0%
2.5%
51.3
0.637
36,666
2.05
12.00%
8.00%
0.9999
Bell Bay Three
29.0%
1.0%
51.3
0.637
12,214
7.33
3.00%
0.00%
0.9999
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9593
TAS
Cethana
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-10
ACIL ALLEN CONSULTING
Devils Gate
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9639
Fisher
0.0%
0
0.000
48,858
4.82
0.00%
4.00%
0.9645
Gordon
0.0%
0
0.000
48,858
4.82
0.00%
4.00%
0.9046
John Butters
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9405
Lake Echo
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9028
Lemonthyme_Wilmot
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9683
Liapootah_Wayatinah_Catagunya
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9702
Mackintosh
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9296
Meadowbank
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9644
Musselroe WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.8957
Poatina
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9681
Reece
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9317
Tamar Valley
48.0%
3.0%
56.4
0.423
30,249
1.1
3.00%
2.00%
0.9990
Tamar Valley GT
28.0%
1.0%
51.3
0.660
12,214
8.93
3.00%
2.00%
0.9999
Tarraleah
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9195
Trevallyn
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9745
Tribute
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9339
Tungatinah
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9122
Anglesea
27.2%
8.0%
92.4
1.223
124,962
1.11
3.00%
2.00%
0.9849
Bairnsdale
34.0%
1.0%
50.0
0.530
12,214
2.09
2.50%
0.00%
0.9745
Dartmouth
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
1.0097
Eildon
0.0%
0
0.000
48,858
8.67
0.00%
4.00%
1.0078
Energy Brix
24.0%
15.0%
109.5
1.643
93,957
2.05
2.50%
4.00%
0.9682
Hazelwood
22.0%
10.0%
94.8
1.551
131,539
1.11
3.50%
8.00%
0.9691
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
0.9912
Hume VIC
VIC
Jeeralang A
22.9%
3.0%
53.8
0.845
12,214
8.4
2.50%
0.00%
0.9647
Jeeralang B
22.9%
3.0%
53.8
0.845
12,214
8.4
2.50%
0.00%
0.9647
Laverton North
30.4%
1.0%
64.9
0.768
12,214
7.33
1.50%
2.00%
0.9972
Loy Yang A
27.2%
9.0%
96.3
1.274
122,144
1.11
3.00%
2.00%
0.9704
Loy Yang B
26.6%
8.0%
91.2
1.235
87,738
1.11
4.00%
2.00%
0.9704
Macarthur WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
0.9946
McKay
0.0%
0
0.000
48,858
6.74
0.00%
4.00%
1.0083
1.0%
46.1
0.519
12,214
7.73
2.50%
0.00%
0.9922
Mt Mercer WF
0.0%
0
0.000
32,083
0
0.00%
0.00%
1.0064
Murray
0.0%
0
0.000
48,858
5.78
0.00%
4.00%
1.0178
5.0%
50.9
0.550
37,583
2.09
2.00%
4.00%
0.9953
0.0%
0
0.000
32,083
0
0.00%
0.00%
1.0262
Mortlake
Newport
32.0%
33.3%
Oaklands Hill WF
Somerton
24.0%
1.0%
49.1
0.736
12,214
8.93
1.50%
0.00%
0.9927
Valley Power
24.0%
1.0%
54.8
0.822
12,214
8.93
1.50%
0.00%
0.9704
0.0%
0
0.000
48,858
6.74
0.00%
4.00%
1.0416
10.0%
97.0
1.485
126,842
1.11
4.00%
4.00%
0.9538
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
1.0699
West Kiewa
Yallourn
23.5%
Albany
SWIS
Alcoa Kwinana Cogen
30.0%
1.0%
51.3
0.616
25,000
0
3.80%
5.20%
1.0214
Alcoa Pinjarra Cogen
30.0%
1.0%
51.3
0.616
25,000
0
3.80%
5.20%
0.9951
Alcoa Wagerup Cogen
30.0%
1.0%
51.3
0.616
25,000
0
3.80%
5.20%
0.9868
Bluewaters
36.1%
7.5%
93.1
0.928
52,000
1.58
3.00%
4.90%
0.9992
BP Cogen
33.0%
2.0%
51.3
0.560
23,489
0
5.00%
4.10%
1.0246
Canning/Melville LFG
30.0%
0.0%
0
0.000
50,000
3.68
5.00%
0.00%
1.0296
Cockburn
48.0%
2.4%
51.3
0.385
30,249
4.73
4.20%
10.10%
1.0277
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
1.0146
7.9%
93.1
0.931
52,000
1.58
3.20%
8.50%
0.9956
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
0.9937
0.5%
67.9
0.843
12,214
9.46
5.90%
9.00%
1.0507
Grasmere
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
1.0699
Greenough River
0.1%
0
0.000
50,000
0
0.00%
0.00%
1.0227
Collgar WF
Collie
36.0%
Emu downs
Geraldton
29.0%
Kalgoorlie
33.0%
0.5%
67.9
0.741
12,214
9.46
5.90%
4.10%
1.0782
Kalgoorlie Nickel
33.0%
0.5%
51.3
0.560
12,214
9.46
5.20%
4.70%
1.2253
Kemerton
34.0%
0.5%
51.3
0.543
12,214
9.46
6.00%
7.90%
1.0079
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
A-11
ACIL ALLEN CONSULTING
Kwinana A
32.0%
9.0%
51.3
0.577
40,000
8.41
5.40%
14.80%
1.0201
Kwinana B
32.0%
9.0%
51.3
0.577
40,000
8.41
5.40%
14.80%
1.0201
Kwinana C
33.0%
4.0%
51.3
0.560
40,000
7.35
5.20%
9.90%
1.0201
Kwinana GT
32.0%
0.5%
67.9
0.764
12,214
9.46
5.20%
9.90%
1.0201
Kwinana HEGT
40.0%
0.5%
51.3
0.462
12,214
1.31
5.20%
4.10%
1.0201
Muja A&B
26.4%
8.5%
93.1
1.270
60,000
1.58
4.20%
10.00%
1.0000
Muja C
34.6%
8.0%
93.1
0.969
52,000
1.58
4.20%
9.90%
1.0000
Muja D
35.6%
8.0%
93.1
0.941
52,000
1.58
4.90%
9.90%
1.0000
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
1.0353
Mumbida
Mungarra
29.0%
0.5%
51.3
0.637
12,214
9.46
5.20%
9.90%
1.0353
Namarkkon
30.0%
1.0%
67.9
0.815
12,214
9.46
4.00%
4.00%
1.0405
Neerabup Peaker
32.0%
2.0%
51.3
0.577
12,214
9.46
3.90%
2.20%
1.0379
Newgen Power
48.0%
2.0%
51.3
0.385
30,249
1.1
4.00%
3.30%
1.0224
Parkeston SCE
33.0%
0.5%
51.3
0.560
12,214
9.46
5.20%
4.90%
1.2012
Pinjar A B
29.0%
0.5%
51.3
0.637
12,214
9.46
5.20%
9.90%
1.0312
Pinjar C
29.0%
0.5%
51.3
0.637
12,214
9.46
5.20%
9.90%
1.0312
Pinjar D
29.0%
0.5%
51.3
0.637
12,214
9.46
5.20%
9.90%
1.0312
Pinjarra Alinta Cogen
34.1%
2.4%
51.3
0.542
25,000
0
3.90%
4.10%
1.0184
Tesla (various sites)
28.0%
1.0%
67.9
0.873
12,214
9.46
4.00%
4.00%
1.1229
Tiwest Cogen
32.0%
1.5%
51.3
0.577
25,000
0
5.90%
4.10%
1.0177
Wagerup Alinta Peaker
34.1%
0.5%
67.9
0.717
12,214
9.46
3.90%
4.10%
0.9868
0.0%
0
0.000
42,000
1.05
0.00%
0.00%
0.9560
Walkaway
NWIS
DKIS
Mt Isa
Western Energy Peaker
32.0%
0.5%
51.3
0.577
12,214
9.46
5.20%
4.10%
1.0204
Worsley
28.0%
0.0%
93.1
1.197
25,000
0
4.80%
4.10%
0.9886
Worsley SWCJV
33.0%
2.0%
51.3
0.560
25,000
0
5.00%
4.10%
0.9846
Burrup Peninsula
29.0%
2.0%
51.3
0.637
12,214
9.61
3.00%
8.00%
1.0000
Cape Lambert
45.0%
2.0%
51.3
0.410
30,249
1.1
3.00%
8.00%
1.0000
Cape Preston
50.0%
3.0%
51.3
0.369
30,249
1.1
3.00%
8.00%
1.0000
Dampier
30.0%
5.0%
51.3
0.616
40,000
2.25
3.00%
4.00%
1.0000
Dampier C
30.0%
5.0%
51.3
0.616
40,000
2.25
3.00%
4.00%
1.0000
Karratha
30.0%
5.0%
51.3
0.616
40,000
2.25
3.00%
4.00%
1.0000
Karratha ATCO
40.0%
2.0%
51.3
0.462
12,214
9.61
3.00%
8.00%
1.0000
Paraburdoo
29.0%
2.0%
67.9
0.843
13,000
9.61
3.00%
4.00%
1.0000
Port Hedland
29.0%
2.0%
51.3
0.637
12,214
9.61
3.00%
8.00%
1.0000
South Hedland
46.0%
2.0%
51.3
0.401
40,000
2.25
3.00%
4.00%
1.0000
Berrimah
24.0%
1.0%
67.9
1.019
12,214
9.61
3.00%
8.00%
1.0000
Channel Island u1-3
27.0%
1.0%
51.3
0.684
12,214
9.61
3.00%
8.00%
1.0000
Channel Island u4-6
48.0%
2.0%
51.3
0.385
30,249
1.1
3.00%
8.00%
1.0000
Channel Island u7
37.0%
1.0%
51.3
0.499
12,214
9.61
3.00%
8.00%
1.0000
Channel Island u8-9
37.0%
1.0%
51.3
0.499
12,214
9.61
3.00%
8.00%
1.0000
Katherine
25.0%
1.0%
51.3
0.739
12,214
9.61
3.00%
8.00%
1.0000
LMS Shoal Bay
40.0%
2.0%
0
0.000
80,000
4
3.00%
5.00%
1.0000
Pine Creek CCGT
47.0%
2.0%
51.3
0.393
30,249
1.1
3.00%
8.00%
1.0000
Weddell
35.0%
1.0%
51.3
0.528
12,214
9.61
3.00%
4.00%
1.0000
APA Xstrata OCGT
36.0%
1.0%
51.3
0.513
12,214
9.61
3.00%
8.00%
1.0000
Diamantina CCGT
48.0%
2.0%
51.3
0.385
30,249
1.05
3.00%
4.00%
1.0000
Diamantina OCGT
32.0%
2.0%
51.3
0.577
12,214
9.61
3.00%
5.00%
1.0000
Ernest Henry
29.0%
2.0%
67.9
0.843
13,000
9.61
3.00%
4.00%
1.0000
Mica Creek A CCGT
43.0%
2.0%
51.3
0.429
30,249
1.05
3.00%
8.00%
1.0000
Mica Creek A GT
27.0%
3.0%
51.3
0.684
12,214
9.61
3.00%
8.00%
1.0000
Mica Creek B
27.0%
3.0%
51.3
0.684
12,214
9.61
3.00%
8.00%
1.0000
Mica Creek C
43.0%
2.0%
51.3
0.429
30,249
9.61
3.00%
8.00%
1.0000
Mt Isa Mines Station
25.0%
1.0%
51.3
0.739
40,000
9.61
3.00%
8.00%
1.0000
Phosphate Hill
27.0%
3.0%
51.3
0.684
12,214
1.05
3.00%
8.00%
1.0000
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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A.3
Energy constrained and intermittent generation
Hydro
Within PowerMark LT the annual output of hydro stations can be constrained explicitly to
desired levels.10 Aside from run of river output which occurs independently of wholesale
prices, the model will naturally schedule hydro output during high priced periods in order to
minimise system production costs.
It should be recognised that hydro output does fluctuate considerably year to year and is
also susceptible to drought and flood events as witnessed over the last decade. Whilst the
modelling can account for changes to long-term averages, it is not typically used to predict
fluctuations due to cyclical changes in weather conditions.
Output from the Snowy Mountains Hydro-electric Scheme (Snowy Hydro) has averaged
around 4,000 GWh over the last 10 years. ACIL Allen assumes that over the long-term
output averages 4,700 GWh with a 60/40 split between NSW and Victorian regions, which is
slightly higher than the recent average reflecting prevailing drought conditions for much of
the past decade. Similarly, Tasmanian hydro output has averaged approximately
8,000 GWh over the same period. The modelling assumes 9,100 GWh of output which
corresponds to Hydro Tasmania’s long-term assumption.
Wind
For wind farms, annual output is limited to capacity factors which approximate recent actual
outcomes (if available) or assumed levels based on corresponding nearby operating
facilities. Wind output is profiled according to 30 minute resolution wind traces for a range of
wind regimes across Australia. These wind traces are then mapped back to the sampled
demand profiles in order to ensure wind output correlates properly with demand.
Solar
Solar plants are also limited by annual capacity factor constraints according to the
technologies capability. The only committed large-scale scheduled solar systems within the
modelling are AGL Energy’s 159 MW solar flagship developments in NSW and the 10 MW
Greenough River project in the SWIS.11
ACIL Allen incorporates representative solar PV output profiles for these projects which vary
by time of day and month.
A.4
Mothballing, end of life and refurbishment
A.4.1
Mothballing
Where power station profitability is negatively affected by market events (such as the
introduction of large amount of additional renewable capacity or a temporary decline in
demand) it is natural for supply to respond. For generation portfolios, a typical response is to
withdraw some capacity from the market through mothballing. Incumbent generators can
withdraw from the market in a variety of ways including initially through partial mothballing
10
Simulation models typically use the notion of an opportunity cost for the water which attempts to maximise the net revenue of
the plant but not break the energy constraint.
11
Other smaller existing solar developments are treated as non-scheduled or embedded generation and are therefore handled
outside of the PowerMark LT modelling.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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and eventually full closure and retirement. There are a number of drivers for this supply-side
response by existing generators.
In the old government-owned vertically integrated industry, mothballing and retirement
decisions were taken based on engineering concepts and older capacity was retired to
make way for more efficient new technologies in a cost minimisation planning framework. In
the context of market-based structures, decision making regarding capacity is undertaken by
individual participants in a decentralised manner. The focus of participants is solely on
commercial profitability of their own operations which is likely at times to result in materially
different outcomes to that of a centralised generation planner.
As a general rule, capacity will be withdrawn from the market if the cash returns from
operation (revenues less variable operation and maintenance costs) do not cover the fixed
operating and maintenance costs of the station.
As wholesale electricity prices are somewhat volatile and prices can at times be
suppressed, this negative profitability trigger will only occur if it is apparent that market
conditions are unlikely to improve in the near-to-medium term. It is at this point that a
generator may mothball capacity. In doing so, the aggregate supply is reduced and spot
prices will rise, making any remaining capacity more profitable (all else being equal). The
rise in prices also has a positive effect upon the balance of a generators portfolio and
competing generators.
For a power station with multiple units, mothballing a portion of capacity may improve the
profitability of the remaining station. However the profitability of the remaining units may
need to cover virtually the same fixed O&M burden as some fixed elements would not vary
with the number of units running (for example, mine costs or wages and salaries).
A decision to mothball capacity is taken where there is a reasonable prospect of market
conditions improving in the medium term which would allow the unit to return to service.
A retirement decision is taken where there is little prospect of market conditions improving
such that the generator would earn net revenues above fixed O&M costs. When this occurs,
its economic value is zero and even a sale process would likely yield a zero or negative
result. Permanent retirement of a power station is likely to trigger the obligations of site
rehabilitation depending on the conditions specified with the generating license with the
State Government. These decisions are therefore not taken lightly as site rehabilitation can
run into many millions of dollars and this cost is difficult to estimate prior to closure.12
In recent times there has been a large amount of coal-fired capacity mothballed – primarily
due to the declining demand conditions, but also due to the impost of carbon pricing on the
less profitable generators. Table A6 summarises the plant that have had capacity withdrawn
from the market in recent years.13
Table A6 Recently mothballed or retired units by NEM region
12
It has been suggested that some generators who have mothballed capacity are effectively deferring costs associated with
rehabilitation.
13
It should be noted that after the finalisation of modelling inputs, the announced retirement of Redbank (NSW) and
mothballing of Torren Island A (SA) have occurred, but these changes have not been incorporated into the input
assumptions.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Queensland
Stanwell
Tarong (2 units)
Coal
700
Mothballed Oct 2012, back online late 2014, early 2015. I
unit to return in Oct 2014 to replace Swanbank E
Stanwell
Swanbank E
Gas
385
Currently operating but due to be mothballed Oct 2014 due
to fuel source being on-sold
Stanwell
Swanbank B
Coal
480
Permanently retired, units decommissioned in April 2010,
June 2010, 2011, and May 2012, due to the plant reaching
the end of its operational life
RATCH
Collinsville
Coal
190
Mothballed December 2012, not expected to return to
service
Delta Electricity
Munmorah
Coal
600
Retired July 2012
EnergyAustralia
Wallerawang
Coal
1000
Mothballed whole station March 2014 until viable for return to
service. One unit permanently retired
EnergyBrix
Morwell (2 units)
Coal
95
From July 2012 until viable
EnergyAustralia
Yallourn (1 unit)
Coal
350
From mid-2012 until viable
Alinta Energy
Northern
Coal
540
Mothballed Apr-Sept until 2015
Alinta Energy
Playford
Coal
200
From March 2012 until viable; not expected to return to
service
New South Wales
Victoria
South Australia
Source: ACIL Allen analysis
A.4.2
Refurbishment
All generating plant have a technical design life for which an allowance of ‘stay-in-business’
capital expenditure is provided through annual fixed operating and maintenance costs. The
fixed operating and maintenance cost assumptions however do not provide for abnormal
capital expenditure required for life extension.
Design lives range from 20-30 years for wind and solar, 30 years for gas and 40+ years for
coal. However, as has often been the experience in Australia, most generating plant have
had operational lives extended through refurbishment programmes. Refurbishment requires
a large lump of capital expenditure to refresh/upgrade various components of the power
station. The decision on whether to proceed with a refurbishment is an economic one and is
dependent upon the commercial outlook (present value of expected net revenues against
upfront capital expenditure).
The capital costs for refurbishment will vary greatly across technologies and, often, be site
specific. Therefore some simplifying generic assumptions are required.
Table A7 provides the proposed refurbishment capital costs for plant which reach the end of
their stated technical life. Capital expenditure for the refurbishment is expressed as a
percentage of new entry costs for the same technology and results in the plant being
operational beyond its technical retirement date for a set number of years. The modelling
allows for more than one refurbishment so for example, a subcritical coal plant would incur a
refurbishment cost of 25% of a new coal plant every 15 years after the end of its technical
retirement date.
PowerMark LT evaluates the economics of refurbishment against expected net revenues
able to be earned from continued operation and projects whether the refurbishment would
go ahead or the station be retired. If refurbishment occurs, output from the power station is
reduced in the refurbishment year, reflecting the time units are out of service.
Table A7 Refurbishment costs for incumbent plant
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
CCGT
30
70%
100%
30
Cogeneration
30
70%
100%
30
OCGT
30
85%
100%
30
Solar PV
35
75%
100%
35
Steam turbine
50
25%
30%
15
Subcritical pf
50
25%
30%
15
Supercritical pf
50
25%
30%
15
Wind turbine
25
30%
100%
25
Source: ACIL Allen
A.4.3
Retirement criteria
Existing plant may cease operating if net operating revenues from the market (revenue less
variable O&M) fail to cover their avoidable fixed overheads.14 This profitability metric is
assessed on a standardised per kW basis for each generator. Once this metric turns
negative on a sustained basis (i.e. over several years and there is no prospect of recovery),
the station is retired regardless of its remaining technical asset life. Retirement may be
sculpted over a number of years to avoid large single year shocks to the market and reflects
gradual unit retirement.
A.5
New entrant generators
A range of new entrant generating technologies are made available within the modelling
over the period to 2040. PowerMark LT determines a least cost plant mix for each modelled
region on a dynamic inter-temporal basis.
New capacity is introduced to each region through the use of continuous capacity variables,
that is, generation increments are not set to predetermined sizes and the model allows entry
of any optimal increment.15
A range of cost and generation characteristics are required for each new entrant technology
to solve the model in a way that minimises overall resource costs on a net present value
basis. The key proposed inputs for each of these elements is discussed in the following
sections.
Costs are presented in this section in real 2011-12 dollars to enable comparisons against
the AETA 2012 work. Capital costs will be escalated to today’s dollars prior to modelling
using published CPI.
A.5.1
Capital costs
Capital costs comprise one of the key inputs for long-term electricity sector modelling as
capital is the largest cost component for most generation technologies.
The methodology employed for this study is to commence with a starting capital cost value
(termed the ‘base’ capital cost) and break this down into its component parts: local labour;
local equipment and commodities; and foreign equipment and commodities.
These component parts are then projected forward individually before being recombined into
a final capital cost estimate. This process allows for the influences of learning rates (both
14
For integrated mine mouth brown coal power stations, fixed overheads also include mine overheads as in most cases the
closure of the power station would also result in closure of the mine.
15
The PowerMark LT model is formulated as a linear program. A mixed integer linear program (MILP) formulation is required to
introduce standard increments of new entrant capacity however this increases solution time enormously.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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foreign and local), labour costs, and exchange rates to be properly incorporated into the
final cost estimates.
For the most part, the base capital cost estimates for most technologies were taken from the
2012 Australian Energy Technology Assessment (AETA) published by the Bureau of
Resource and Energy Economics (BREE), with adjustments made in the 2013 update to the
study.16
ACIL Allen has selected a sub-set of 29 of the 40 technologies examined within the AETA
study. Technologies excluded include exotic coal-based technologies that do not employ
carbon capture and storage (IGCC, oxy-fuel and direct injection), solar hybrids, offshore
wind, landfill gas, bagasse and nuclear options.
Table A8 presents the proposed capital costs for each of the technologies considered within
the modelling. The table also includes the headline splits for the cost components taken
from the AETA study.
These capital costs are presented on an ‘overnight’ basis – interest during construction
(IDC) and financing costs are excluded.17 For plants that employ carbon capture, the capital
costs include capture and compression of CO2, but exclude transport and storage costs.
ACIL Allen made some minor modifications to base capital costs for a number of selected
technologies where it has direct recent experience with actual proposed projects in
Australia. Figure A5 shows a comparison of the proposed capital cost figures against those
within the AETA study.
Modifications to the base capital costs were made for the following technologies:
•
Natural gas-fired CCGT (7% higher)
•
Natural gas-fired OCGT (12% higher)
•
Solar PV (20% lower) including corresponding changes to tracking options
•
Onshore wind (9% lower).
16
Note that the 2013 update only updated operating and maintenance costs for a select few technologies and no update to
capital costs.
17
Interest during construction is added within the modelling.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Figure A5 Base capital cost comparison with AETA
Source: ACIL Allen, BREE (AETA 2012)
Hydro-electric generation is not included as a model as a new entrant technology. This
reflects the fact that few commercially viable large-scale hydro-electric sites remain in
Australia for exploitation.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Table A8 Base capital costs and cost component splits
Coal
Natural gas
PC Supercritical – Brown Coal
3,451
3,788
29%
38%
33%
PC Supercritical Black Coal
2,974
3,124
30%
39%
31%
PC Supercritical Black Coal (SWIS Scale)
3,192
3,381
31%
40%
29%
CCGT
1,100
1,127
26%
56%
18%
CCGT SWIS Scale
1,078
1,111
26%
56%
18%
OCGT
800
808
11%
79%
10%
CLFR
4,802
5,220
20%
55%
25%
CLFR with storage
8,550
9,500
25%
55%
20%
Parabolic trough
4,526
4,920
20%
55%
25%
Parabolic trough with storage
8,055
8,950
25%
55%
20%
Central Receiver
5,570
5,900
30%
55%
15%
Central Receiver with storage
7,477
8,308
25%
55%
20%
Solar PV fixed
2,700
2,700
15%
70%
15%
Solar PV single axis tracking
3,180
3,180
15%
70%
15%
Solar PV dual axis tracking
4,730
4,730
15%
70%
15%
On-shore Wind Farm
2,300
2,312
15%
72%
13%
Ocean/Wave
5,900
5,900
30%
40%
30%
Biomass
Other Biomass Waste
4,400
5,000
18%
27%
55%
Geothermal
Geothermal HSA
6,300
7,000
34%
23%
43%
Geothermal EGS
9,646
10,600
37%
17%
46%
PC Supercritical with CCS – Brown Coal
5,902
7,766
29%
35%
36%
PC Supercritical with CCS – Bituminous Coal
4,559
5,434
29%
35%
36%
PC Oxy Combustion Supercritical with CCS
4,274
5,776
33%
35%
32%
CCGT with CCS
2,495
2,772
19%
67%
14%
IGCC with CCS – Bituminous Coal
4,984
7,330
27%
52%
21%
IGCC with CCS – Brown Coal
5,083
8,616
27%
52%
21%
PC Subcritical Brown Coal - Retrofit CCS
2,493
3,945
30%
30%
40%
PC Subcritical Black Coal - Retrofit CCS
1,611
2,244
30%
30%
40%
Existing CCGT with retrofit CCS
1,392
1,547
12%
78%
10%
Solar
Solar PV
Wind
CCS
CCS retrofit
Note: CCS capital costs are inclusive of capture, but exclude transport and storage costs as these are treated separately.
Source: ACIL Allen, BREE (AETA 2012).
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Learning rates
Learning rates are applied to the Base capital costs to reflect cost changes over time
through technology and manufacturing improvements and learning by doing.
Learning rates for each major technology have been taken from CSIRO’s Global and Local
Learning Model (GALLM) as part of the AETA 2012 study. For some technologies
differential learning rates were provided for foreign and local content components and these
have been applied to the respective foreign equipment and local equipment/local labour
components respectively. Learning rates in the GALLM model are endogenous and respond
to the rate of deployment of each technology both locally and internationally.
A complication in this process is the adjustments made by ACIL Allen to the base capital
costs for solar PV and wind technologies from the AETA figures. As these represent a
reduction in the starting base capital cost, it was decided that the learning rates should be
reduced in the early years such that the capital cost for 2020 remained unchanged from the
AETA work. The reported learning rates for these technologies in the period to 2020 will
therefore differ due to the lower starting value.
Other adjustments
Adjustments are made to the some of the capital cost components based on macro
assumptions as reported in section A.1. These include:
 local labour costs are modified through the application of the real labour cost index
 the composite metals price index is used to adjust 25% and 40% of the local and foreign
equipment cost component respectively.
 exchange rates are used to convert the foreign equipment and commodities cost
component (which are projected in US dollars) back into Australian dollars.
A.5.2
Final capital costs
Table A9 presents the final capital costs for each of the technologies after all adjustments
for learning, labour, metals and exchange rates are made. These are also shown graphically
in Figure A6. Table A10 shows the average year-on-year percentage change in capital costs
for each decade of the projection.
Table A9 Final capital costs for new entrant technologies for selected years (Real 2011-12 $/kW installed)
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Coal
Natural gas
Solar
Solar PV
Wind
PC Supercritical – Brown Coal
3,451
3,826
3,797
3,820
3,896
PC Supercritical Black Coal
2,974
2,914
2,904
2,934
3,006
PC Supercritical Black Coal (SWIS Scale)
3,192
3,138
3,129
3,165
3,247
CCGT
1,100
1,285
1,294
1,319
1,372
CCGT SWIS Scale
1,078
1,258
1,268
1,292
1,344
OCGT
800
948
931
919
912
CLFR
4,802
2,849
2,318
2,347
2,413
CLFR with storage
8,550
5,102
4,166
4,241
4,394
Parabolic trough
4,526
2,685
2,185
2,212
2,275
Parabolic trough with storage
8,055
4,807
3,925
3,995
4,139
Central Receiver
5,570
3,344
2,740
2,803
2,925
Central Receiver with storage
7,477
4,462
3,643
3,709
3,842
Solar PV fixed
2,700
1,995
1,580
1,010
961
Solar PV single axis tracking
3,180
2,350
1,861
1,190
1,132
Solar PV dual axis tracking
4,730
3,495
2,768
1,770
1,684
On-shore Wind Farm
2,300
1,967
1,971
1,992
1,973
Ocean/Wave
5,900
6,735
3,227
3,322
3,288
Biomass
Other Biomass Waste
4,400
4,806
4,794
4,646
4,794
Geothermal
Geothermal HSA
6,300
6,962
7,086
7,379
7,809
Geothermal EGS
9,646
10,558
10,807
11,298
12,015
PC Supercritical with CCS – Brown Coal
5,902
6,560
4,828
4,832
4,956
PC Supercritical with CCS – Bituminous Coal
4,559
5,067
3,729
3,733
3,828
PC Oxy Combustion Supercritical with CCS
4,274
4,774
3,527
3,547
3,661
CCGT with CCS
2,495
2,935
2,097
2,063
2,069
IGCC with CCS – Bituminous Coal
4,984
5,747
3,987
3,979
4,073
IGCC with CCS – Brown Coal
5,083
5,861
4,066
4,058
4,154
PC Subcritical Brown Coal - Retrofit CCS
2,493
2,742
2,055
2,059
2,114
PC Subcritical Black Coal - Retrofit CCS
1,611
1,772
1,328
1,331
1,366
Existing CCGT with retrofit CCS
1,392
1,663
1,178
1,150
1,141
CCGT Small Scale
1,886
2,202
2,218
2,261
2,352
CCS
CCS retrofit
Small
Note: CCS capital costs are inclusive of capture, but exclude transport and storage costs which are treated separately.
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Figure A6 Final capital costs for new entrant technologies for selected years
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL ALLEN CONSULTING
Table A10 Average real year-on-year capital cost change for each decade
Coal
Natural gas
Solar
Solar PV
Wind
PC Supercritical – Brown Coal
1.3%
PC Supercritical Black Coal
-0.3%
0.0%
0.1%
0.2%
PC Supercritical Black Coal (SWIS Scale)
-0.2%
0.0%
0.1%
0.3%
CCGT
2.0%
0.1%
0.2%
0.4%
CCGT SWIS Scale
2.0%
0.1%
0.2%
0.4%
OCGT
2.2%
-0.2%
-0.1%
-0.1%
CLFR
-6.3%
-2.0%
0.1%
0.3%
CLFR with storage
-6.2%
-2.0%
0.2%
0.4%
Parabolic trough
-6.3%
-2.0%
0.1%
0.3%
Parabolic trough with storage
-6.2%
-2.0%
0.2%
0.4%
Central Receiver
-6.2%
-2.0%
0.2%
0.4%
Central Receiver with storage
-6.2%
-2.0%
0.2%
0.4%
Solar PV fixed
-3.7%
-2.3%
-4.4%
-0.5%
Solar PV single axis tracking
-3.7%
-2.3%
-4.4%
-0.5%
Solar PV dual axis tracking
-3.7%
-2.3%
-4.4%
-0.5%
On-shore Wind Farm
-1.9%
0.0%
0.1%
-0.1%
-0.1%
0.1%
0.2%
Ocean/Wave
1.7%
-7.1%
0.3%
-0.1%
Biomass
Other Biomass Waste
1.1%
0.0%
-0.3%
0.3%
Geothermal
Geothermal HSA
1.3%
0.2%
0.4%
0.6%
Geothermal EGS
1.1%
0.2%
0.4%
0.6%
PC Supercritical with CCS – Brown Coal
1.3%
-3.0%
0.0%
0.3%
PC Supercritical with CCS – Bituminous Coal
1.3%
-3.0%
0.0%
0.3%
PC Oxy Combustion Supercritical with CCS
1.4%
-3.0%
0.1%
0.3%
CCGT with CCS
2.1%
-3.3%
-0.2%
0.0%
IGCC with CCS – Bituminous Coal
1.8%
-3.6%
0.0%
0.2%
IGCC with CCS – Brown Coal
1.8%
-3.6%
0.0%
0.2%
PC Subcritical Brown Coal - Retrofit CCS
1.2%
-2.8%
0.0%
0.3%
PC Subcritical Black Coal - Retrofit CCS
1.2%
-2.8%
0.0%
0.3%
Existing CCGT with retrofit CCS
2.2%
-3.4%
-0.2%
-0.1%
CCGT Small Scale
2.0%
0.1%
0.2%
0.4%
CCS
CCS retrofit
Small
Source: ACIL Allen
A.5.3
Other new entrant parameters
Table A11 provides other technical parameters and cost assumptions for the new entrant
technologies. For the most part these are aligned with the AETA 2012 and 2013 studies,
with a few modifications.
Table A11 New entrant parameters
Coal
Natural gas
Solar
PC Supercritical – Brown Coal
32.3%
8.9%
85,000
1.30
PC Supercritical Black Coal
41.9%
4.8%
52,000
1.30
PC Supercritical Black Coal (SWIS Scale)
41.4%
5.6%
55,500
8.00
CCGT
49.5%
2.4%
33,000
1.20
CCGT SWIS Scale
49.3%
3.0%
10,000
4.00
OCGT
32.0%
1.0%
14,000
8.00
CLFR
100.0%
8.0%
64,107
15.19
CLFR with storage
100.0%
10.0%
72,381
11.39
Parabolic trough
100.0%
8.0%
59,176
15.19
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Solar PV
Wind
Parabolic trough with storage
100.0%
10.0%
72,381
11.39
Central Receiver
100.0%
5.6%
58,285
7.07
Central Receiver with storage
100.0%
10.0%
71,372
5.65
Solar PV fixed
100.0%
0.0%
25,000
0.00
Solar PV single axis tracking
100.0%
0.0%
30,000
0.00
Solar PV dual axis tracking
100.0%
0.0%
39,000
0.00
On-shore Wind Farm
100.0%
0.5%
32,500
10.00
Ocean/Wave
100.0%
0.0%
190,000
0.00
Biomass
Other Biomass Waste
27.0%
12.0%
125,000
8.00
Geothermal
Geothermal HSA
100.0%
10.0%
200,000
0.00
Geothermal EGS
100.0%
9.0%
170,000
0.00
PC Supercritical with CCS – Brown Coal
20.8%
24.0%
91,500
15.00
PC Supercritical with CCS – Bituminous Coal
31.4%
16.1%
73,200
12.00
PC Oxy Combustion Supercritical with CCS
32.5%
26.0%
62,000
14.00
CCGT with CCS
43.1%
10.0%
17,000
9.00
IGCC with CCS – Bituminous Coal
28.9%
32.0%
98,700
8.00
IGCC with CCS – Brown Coal
25.5%
41.0%
123,400
10.00
PC Subcritical Brown Coal - Retrofit CCS
17.0%
36.8%
37,200
8.40
PC Subcritical Black Coal - Retrofit CCS
26.6%
28.2%
31,000
7.00
Existing CCGT with retrofit CCS
43.0%
10.0%
17,000
9.00
CCGT Small Scale
49.3%
3.0%
10,000
4.00
CCS
CCS retrofit
Small scale
Note: FOM = Fixed operating and maintenance; VOM = Variable operating and maintenance
Source: ACIL Allen AETA (2012, 2013)
Both fixed and variable O&M charges are assumed to escalate at CPI (constant in real
terms).
Table A12 shows the availability and construction profiles for each of the technologies. It is
assumed that CCS based plant would not be available prior to 2030 based on slow
international progress on demonstration plants.
Table A12 Technology availability and construction profiles
Coal
Natural gas
Solar
Solar PV
Wind
PC Supercritical – Brown Coal
2018
4
35%
35%
20%
10%
PC Supercritical Black Coal
2018
4
35%
35%
20%
10%
PC Supercritical Black Coal (SWIS Scale)
2018
4
35%
35%
20%
10%
CCGT
2017
2
60%
40%
0%
0%
CCGT SWIS Scale
2017
2
60%
40%
0%
0%
OCGT
2016
1
100%
0%
0%
0%
CLFR
2018
3
50%
30%
20%
0%
CLFR with storage
2018
3
50%
30%
20%
0%
Parabolic trough
2018
3
50%
30%
20%
0%
Parabolic trough with storage
2018
3
50%
30%
20%
0%
Central Receiver
2018
3
20%
60%
20%
0%
Central Receiver with storage
2018
3
50%
30%
20%
0%
Solar PV fixed
2016
2
70%
30%
0%
0%
Solar PV single axis tracking
2016
2
70%
30%
0%
0%
Solar PV dual axis tracking
2016
2
70%
30%
0%
0%
On-shore Wind Farm
2016
2
80%
20%
0%
0%
Ocean/Wave
2025
2
60%
40%
0%
0%
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Biomass
Other Biomass Waste
2017
2
20%
80%
0%
0%
Geothermal
Geothermal HSA
2020
3
40%
40%
20%
0%
Geothermal EGS
2020
3
40%
45%
15%
0%
PC Supercritical with CCS – Brown Coal
2030
4
35%
35%
20%
10%
PC Supercritical with CCS – Bituminous Coal
2030
4
35%
35%
20%
10%
PC Oxy Combustion Supercritical with CCS
2030
4
35%
35%
20%
10%
CCGT with CCS
2030
2
60%
40%
0%
0%
IGCC with CCS – Bituminous Coal
2030
3
20%
60%
20%
0%
IGCC with CCS – Brown Coal
2030
3
20%
60%
20%
0%
PC Subcritical Brown Coal - Retrofit CCS
2030
3
25%
60%
15%
0%
PC Subcritical Black Coal - Retrofit CCS
2030
3
25%
60%
15%
0%
Existing CCGT with retrofit CCS
2030
3
25%
60%
15%
0%
CCGT Small Scale
2017
2
60%
40%
0%
0%
CCS
CCS retrofit
Small scale
Source: ACIL Allen, AETA (2012, 2013)
Table A13 shows the assumed economic life for each technology taken from AETA. As with
incumbent generation, refurbishments are also applied to new entrants with the
refurbishment capital cost expressed as a percentage of a new facility and resulting in a life
extension expressed as a percentage of the original life. Installations can undergo multiple
refurbishments within the projection horizon.
Table A13 Technology life and refurbishment costs
PC Supercritical – Brown Coal
50
25%
30%
15
PC Supercritical Black Coal
50
25%
30%
15
PC Supercritical Black Coal (SWIS Scale)
50
25%
30%
15
CCGT
30
70%
100%
30
CCGT SWIS Scale
30
70%
100%
30
OCGT
30
85%
100%
30
CLFR
40
75%
100%
40
CLFR with storage
40
75%
100%
40
Parabolic trough
35
75%
100%
35
Parabolic trough with storage
35
75%
100%
35
Central Receiver
35
75%
100%
35
Central Receiver with storage
40
75%
100%
40
Solar PV fixed
35
75%
100%
35
Solar PV single axis tracking
35
75%
100%
35
Solar PV dual axis tracking
35
75%
100%
35
On-shore Wind Farm
25
50%
100%
25
Ocean/Wave
25
75%
100%
25
Biomass
Other Biomass Waste
30
75%
100%
30
Geothermal
Geothermal HSA
40
75%
100%
40
Geothermal EGS
40
75%
100%
40
PC Supercritical with CCS – Brown Coal
50
25%
30%
15
PC Supercritical with CCS – Bituminous Coal
50
25%
30%
15
PC Oxy Combustion Supercritical with CCS
50
25%
30%
15
Coal
Natural gas
Solar
Solar PV
Wind
CCS
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CCS retrofit
CCGT with CCS
45
50%
50%
23
IGCC with CCS – Bituminous Coal
30
50%
50%
15
IGCC with CCS – Brown Coal
30
50%
50%
15
PC Subcritical Brown Coal - Retrofit CCS
30
25%
30%
9
PC Subcritical Black Coal - Retrofit CCS
30
25%
30%
9
Existing CCGT with retrofit CCS
30
50%
50%
15
Source: ACIL Allen, AETA (2012)
A.5.4
Carbon transport and storage costs
For plant that utilise carbon capture, transport and storage costs are applied separately. As
the majority of costs related to transport and storage of CO2 are large upfront fixed costs
(pipeline construction and drilling costs), it is appropriate for these to be levied to new
entrant technologies as a fixed charge rather than through variable charges. This can be
done either through an addition to the capital cost or through an addition charge to the fixed
O&M cost. ACIL Allen adds the cost to fixed O&M values.
Costs for CO2 transport and storage are uncertain and highly dependent upon the scale of
the development for both transmission pipelines and injection infrastructure. A larger CO2
pipeline grid would result in significant economies of scale over a single coal-fired power
station development.
The assumed transport and storage costs are presented in Table A14. These assumptions
have been informed by the AETA 2012 study. Costs are assumed to remain constant in real
terms over the modelling period.
Table A14 Assumed CO2 transport and storage costs
NSW
72
QLD
23
SA
n/a
TAS
n/a
VIC
22
SWIS
14
NWIS
19
DKIS
n/a
Mt Isa
n/a
Source: AETA (2012)
A.6
SRES model assumptions
A.6.1
Model overview
ACIL Allen’s forecasts for uptake of small-scale generation units (SGUs) are based on a
regression model relating historic uptake to historic net financial returns to installing solar
PV systems (the most common form of SGUs). This historic relationship is then applied to
the forecast level of net financial returns to predict future uptake of solar PV.
SGUs comprise renewable generators of less than 100 kW capacity that are eligible to
create STCs under the SRES. These generators can be installed by households and
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commercial or industrial premises. Available data on these installations do not distinguish
between installations by different classes of customer and so it is difficult to separately
analyse these different customer types. In practice, residential and commercial/industrial
installations are incorporated within a single regression model (reflecting the undifferentiated
underlying uptake data) and delineated using the simplifying assumption that all installations
of more than 7.5 kW are commercial or industrial installations, and the remainder are
residential. This assumption is consistent with the observation that the vast majority of
historic PV installations have been made by households. Changes in future PV uptake
trends, particularly increasing rates of commercial installations, are discussed further in
section A.6.8.
The model uses a quarterly resolution and separately estimates uptake for each state and
territory. The regression model is based on observations for 21 quarters, from the start of
2009 to the first quarter of 2014.
Model assumptions relate principally to either historic uptake of solar PV (the regression
model’s ‘dependent variable’) or to the real net financial return to solar PV installation (the
regression model’s key ‘explanatory variable’). These are discussed separately below.
Further, as real financial returns are driven by several distinct factors, these are discussed
separately. These factors are:
 PV system installation costs
 Rebates and subsidies
 Electricity prices
 Payments for exported electricity, generally known as ‘feed-in tariffs’ or ‘buyback rates’
 System output and export assumptions.
A.6.2
Historic uptake
Historic uptake is based on Clean Energy Regulator (CER) data on STC creation by solar
installations. This data was up to date as of the end of April 2014. As STC creation can
occur up to 12 months after installation, the CER data was adjusted for lag to estimate the
likely ‘underlying’ level of installations in the period April 2013 to March 2014 based on raw
STC creation data.
Table A15 shows the lag factors used to adjust installed capacity in each month based on
data on installations or the period April 2012 to March 2013.
Table A15 Lag between installation and registration of PV installations with
the CER
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Months
%
-
0
39%
2.57
1
74%
1.34
2
85%
1.18
3
90%
1.11
4
93%
1.08
5
95%
1.06
6
96%
1.04
7
97%
1.03
8
98%
1.02
9
98%
1.02
10
99%
1.01
11
99%
1.01
12
100%
1
Note: 0 months since installation indicates the installation was registered in the same month as the
installation occurred
Source: ACIL Allen Analysis of CER data
As noted above, this historic data set does not distinguish between residential and nonresidential PV installations, and ACIL Allen separates installations into these two categories
on the basis that all installations under 7.5 kW are residential.
To facilitate this delineation, solar PV uptake data will be broken down into eight size
categories for each state and territory in each quarter of analysis. The eight sizes are:
 Less than 1.5 kW
 1.5 to 2.5 kW
 2.5 to 3.5 kW
 3.5 to 4.5 kW
 4.5 to 5 kW (this category was chosen as some feed-in tariffs were not available for
systems of more than 5 kW of capacity)
 5 to 7.5 kW
 7.5 to 10 kW (this category was chosen as some feed-in tariffs were not available for
systems of more than 10 kW of capacity)
 More than 10 kW.
The first six categories are taken to consist of residential systems and the last two will be
commercial or industrial (‘non-residential’) systems. Shares of installed capacity in each
state and territory for systems in these size categories will be tracked through the historic
data, and the net financial return per kilowatt for each category will be weighted on this
basis to produce a single explanatory variable for each jurisdiction: the ‘weighted real net
financial return per kilowatt’.
A.6.3
PV system costs
Average cost per kW
The cost of installing a PV system has varied over time. ACIL Allen’s estimates of historic
system cost were derived by taking a national average system cost which was scaled to
account for differences in cost due to system size and to account for differences in system
costs between different states and territories. No allowance was made for the cost of
inverter replacement or for ongoing system maintenance costs.
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For the period from October 2012 to May 2014 (inclusive) the national average cost of
installing a PV system was based on SolarChoice’s PV Price Check publication.18
That publication sets out prices for systems of different sizes in each capital city, which were
adjusted for GST and Small-scale Technology Certificate (STC) values to estimate an
underlying system cost. The city level estimates were used to derive a national average
system cost by weighting in proportion to the number of systems installed in each state or
territory.
Before December 2012, PV Price Check was unavailable, so different data sources were
used. The estimated national average cost of installing a PV system between January 2009
and September 2012 (inclusive) was based on:
 from 2009 to mid-2010, AECOM analysis of PV system costs for the NSW Government
(published October 2010),
 from 2010 to November 2011, ACIL Allen (then ACIL Tasman) reviews of internet quotes
for PV systems undertaken as part of analysis for the Clean Energy Regulator (late
2010, mid-2011, late 2011)
 between November 2011 and September 2012 the cost was assumed to move in a
linear fashion between ACIL Allen’s last estimate and Solar Choice estimates.
The national average system cost values are summarised in Figure A7.
Figure A7 National average historic PV installation cost (2011$/kW)
$10,000
$9,000
$/kW installed (2011)
$8,000
$7,000
$6,000
$5,000
$4,000
$3,000
$2,000
$1,000
$0
2009
2010
2011
2012
2013
Note: Cost excludes rebates, subsidies, and GST
Source: AECOM; ACIL Allen; SolarChoice
Small-scale costs were projected by:
 taking the latest data on system cost reported by Solar Choice and adjusted for STC
rebates as a starting point
 assuming a split of total installation cost into labour cost, foreign equipment and local
equipment cost
18
See www.solarchoice.net.au. These are also published from time to time in sources such as Climate Spectator. See for
example, http://www.businessspectator.com.au/article/2013/12/13/solar-energy/solar-pv-price-check-%E2%80%93december
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 growing the separate cost components of roof-top PV installations at the same rate as
for large-scale PV from AETA (including exchange rate adjustments as relevant), noting
that the local labour and components element of small-scale PV will be larger than for
large-scale PV
 summing the three cost elements to produce a total cost series.
Figure A8 shows the average cost of installing a residential and commercial roof-top
solar system projected on this basis and in caparison to large-scale utilise PV systems
on a per kW basis.
Figure A8 National average projected PV installation cost
Note: Cost excludes rebates, subsidies, and GST
Source: ACIL Allen based on Solar Choice, and AETA
Variation by system size and location
Solar Choice’s PV Price Check data were also used to estimate a cost premium or discount
for each state and territory based on averaged variations across the period. Similarly,
smaller and larger systems were given a premium or discount based on observed variation
from the average.
The relative premia/discounts associated with different sized systems are set out in Table
A16.
Table A16 PV installation premium/discount by system size
Premium/discount
12.5%
5.2%
-3.0%
-5.2%
-9.5%
-15%
Note: the 4.5 to 7.5 kW category incorporates the 4.5 to 5 kW and 5 to 7.5 kW categories. Similarly, the
>7.5 kW category incorporates the 7.5 to 10 kW category and the >10 kW category.
Source: ACIL Allen analysis of SolarChoice data
The premia/discounts associated with installations in different states and territories are set
out in Table A17.
Table A17 State/territory variation in system cost
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New South Wales
-6.2%
Victoria
3.0%
Queensland
3.8%
South Australia
2.9%
Western Australia
-6.4%
Tasmania
3.0%
Northern Territory
3.8%
Australian Capital Territory
3.6%
Note: Northern Territory value is set as the Queensland value
Source: Solar Choice
Although these system size and locational premia/discounts were based only on data from
late 2012 to early 2014, they were applied to system costs throughout the historic and
forecast periods.
A.6.4
Rebates and subsidies
Two sources of upfront rebates and subsidies for PV installations were taken into account:
 the former Solar Homes and Communities Program (SHCP), which provided an upfront
cash rebate
 the indirect subsidy provided by the creation of STCs under the SRES, including the
creation of additional STCs through the ‘Solar Credits multiplier’.
Under SHCP, customers who installed PV systems received an upfront rebate of $8,000.
SHCP was in place at the beginning of 2009, and was closed during June 2009. However,
as systems installed in the second half of that based on prior applications for the rebate, it is
analysed as having an effect on some installations in the second half of 2009.
In addition to the upfront payment through SHCP, PV systems were eligible to create
certificates for the renewable electricity they generate during the historic period. The value
of these certificates (initially RECs created under the Renewable Energy Target and then
STCs created under the SRES) provides an upfront subsidy to installation of PV systems.
The value of this subsidy is depended on system size and certificate price. From June 2009
until 31 December 2012, it also depended on the ‘solar credit multiplier’, which was
established under the Solar Credits scheme and allowed eligible customers who installed
PV systems were deemed to create additional RECs/STCs, thereby increasing the amount
of the subsidy. The multiplier was originally 5, meaning that a PV system would create 5
solar credits for every MWh of electricity it was deemed to generate, for the first 1.5 kW of
capacity installed. The multiplier then declined over time.
The SHCP was phased out in favour of Solar Credits during 2009. Customers could benefit
from either the SHCP or the Solar Credits multiplier, but not both. To address the overlap
between these two policies, 50% of PV installations in quarter 3 2009, and 20% in quarter 4
2009 were assumed to receive the SHCP rebate. The remainder were assumed to use the
Solar Credits multiplier to generate extra STCs (then RECs).
The solar multiplier and certificate values factored into the analysis are shown in Table A18.
In effect, a PV system installed in 2009 was assumed to receive part of the SHCP grant and
part of its entitlement through Solar Credits.
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Table A18 Solar Credits multiplier
Solar
Credits
multiplier
SHCP
value
1
3.0
4.2
5
3
2
1
$8,000
$4,000
$1,600
$0
$0
$0
$0
Note: Q3 2009 and Q4 2009 multipliers are ‘implicit’ multipliers based on relative uptake of Solar Credits
and the SHCP rebate. Years and quarters are shown on a calendar year basis.
Source: ACIL Allen; Renewable Energy (Electricity) Regulations 2001
Unlike the SHCP payment, the value of RECs/STCs, and therefore the total rebate derived
from these certificates, varied over time. The assumed values from 2009 to the present are
shown in Figure A9. Beyond 2014, the certificate price and multiplier were assumed to
remain constant (in nominal terms), at $38 per certificate, which is just below the legislated
maximum.
Figure A9 REC/STC prices (nominal $/certificate)
$60
nominal $/certificate
$50
$40
$30
$20
$10
$0
2009
2010
2011
2012
2013
Note: REC prices prior to Q1 2011, STC prices subsequently.
Source: AFMA; ACIL Allen analysis
All systems are assumed to create 15 years of ‘deemed’ RECs/STCs at the time on
installation, and then cease to be eligible for further certificates after 15 years. The eligibility
timeframe for deeming is limited to 2030 such that as we approach 2030, the number of
years ‘deemed’ declines.
A.6.5
Retail electricity prices
Retail electricity prices are important to the financial return on solar PV as every kWh of
solar output that is consumed by the owner of the system avoids the variable component of
the retail electricity price.
For Tasmania, Western Australia, the Northern Territory and the Australian Capital Territory,
historic retail prices were adopted based on published regulated retail prices in the relevant
periods. This approach was adopted because, for the former three jurisdictions, there was
no retail competition in that period, and so no other prices were available. In the case of the
ACT retail competition was in place, but the regulated prices were sufficiently low and
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competitive that ACIL Allen assumes minimal discounts to that level were available and the
published prices are sufficiently reflective of prices paid by consumers.
In the cases of New South Wales, Victoria, Queensland and South Australia, historic retail
prices are estimated based on a ‘cost-stack’ estimate, combining, wholesale, network,
‘green scheme’ and retail components. The variable component of these tariffs are assumed
to be all costs other than the fixed component of network tariffs, and advanced metering
infrastructure (also known as smart meters’) charges in Victoria. Separate cost series will be
developed for residential and non-residential retail prices, and applied to determine financial
returns to systems in the appropriate size categories.
A.6.6
Feed-in tariffs and buyback rates
When solar PV systems produce more power than is required at the premises at which they
are installed, the electricity is exported to the grid and on-sold to other customers. The value
of this exported electricity is another important component of the financial return to PV
installation.
For clarity, this report distinguishes between ‘feed-in tariffs’ and ‘buyback rates’. Within this
categorisation, ‘feed-in tariff’ refers to a premium rate determined by legislation that must be
paid for exported electricity from eligible PV systems.
In general, exported PV output always displaces electricity that would otherwise be
purchased from the wholesale market, and therefore provides some value to the retailer that
on-sells this electricity. Accordingly, retailers that supply power to owners of PV systems are
generally willing to pay some amount for exported PV output that is separate from, and
additional to, any premium feed-in tariff that might be imposed by legislation. The term
‘buyback rate’ refers to these payments by retailers that reflect the value of exported PV
output to the retailer, and which, whilst sometimes regulated, are not intended to offer a
premium rate or purposefully subsidise PV systems.
Within this categorisation, it is necessary to distinguish between three types of feed-in
tariffs:
 A ‘net’ feed-in tariff is the most common form, and pays a premium rate for all exported
PV output
 A ‘gross’ feed-in tariff meters PV output in such a way that all PV output is effectively
exported, earning whatever premium rate is available, and then all of the customer’s
electricity is then imported at the prevailing retail rate
 A ‘one-for-one’ feed-in tariff establishes that the payment for exported PV output must be
equal to the prevailing retail rate.
With this nomenclature established, Table A19 sets out the various feed-in tariffs that have
been or are in operation in Australia.
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Table A19 Feed-in tariffs by jurisdiction
60c gross
Commenced early 2010,
phased out through early
2011. Paid to end 2016
20c gross
Breached cap in Q3
2011. Paid to end 2016
Buyback rate regulated to be
between 6.6 and 11.2 c
Victoria
60c net
October 2009 to September
2011. Paid to end 2024
25c net
Breached cap in mid2012. Paid to end 2016
Regulated minimum buyback
rate of 8c
Queensland
44c net
2008 to mid-2013. Paid to
June 2028
8c net
South Australia
44c net
2008 to September 2011.
Paid to June 2028
16c net
October 2011 to
September 2012. Paid
to June 2016
Regulated minimum buyback
rate of 7.6c
Western
Australia
40c net
Mid 2010 to mid-2011. Paid
for 10 years from
installation
20c net
In place July and
August 2011. Paid for
10 years from
installation
Synergy offers a buyback rate
of 8.8529c; Horizon offers
location-specific rates of 1050c
Tasmania
One-for-one
Still in place
One-for-one feed-in tariff
Northern
Territory
One-for-one
Still in place
One-for-one feed-in tariff
50.05c gross,
then 45.7c gross
from October
2010
April 2009 to mid-2011.
Paid for 20 years from
installation
New South
Wales
Australian Capital
Territory
One-for-one
Feed-in tariff of 8c until 30
June 2014
Mid 2011 to mid-2013
ActewAGL offers a buyback
rate of 7.5c
Note: All rates are in nominal cents per kWh.
Source: ACIL Allen analysis
Buyback rates are generally much lower than feed-in tariffs, typically around 7 to 10 cents
per kilowatt hour, and vary only slightly by jurisdiction.
A.6.7
System output and export rates
System output is estimated based on four solar zones created by the CER for the purpose
of calculating REC and STC creation by solar PV, which have different assumed rates of
solar output per kW of installed capacity. Each postcode is assigned a zone, whereas
multiple solar zones may existing in a given state or territory. Accordingly, the share of
installations in each zone for each state and territory are based on historic installations in
CER data to March 2014.
The solar zone ratings are set out in Table A20, and the historic share of installed capacity
by zone, and implied average output per kW of installed capacity for each state and territory,
are set out in Table A21.
Table A20 Solar zone ratings
Output (MWh per
kW of installed
capacity)
1.622
1.536
1.382
1.185
Source: Clean Energy Regulations 2001, Schedule 5
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Table A21 Installed capacity by solar zone
New South
Wales
0.0%
5.1%
93.2%
1.7%
1.386
Victoria
0.0%
0.0%
8.6%
91.4%
1.202
Queensland
0.0%
1.1%
98.9%
0.0%
1.384
South
Australia
0.0%
1.3%
95.6%
3.1%
1.378
Western
Australia
1.9%
5.5%
89.5%
3.1%
1.389
Tasmania
0.0%
0.0%
0.0%
100.0%
1.185
Australian
Capital
Territory
0.0%
0.0%
100.0%
0.0%
1.382
Northern
Territory
33.2%
65.6%
1.3%
0.0%
1.563
Source: ACIL Allen analysis of CER data
Assumed export rates by state and by system size are set out in Table A22. Export rates
are assumed to be 10% for all systems above 7.5 kW on the basis that most larger systems
will be commercial or industrial systems, and will be sized to minimise exports and thereby
maximise avoided network tariffs.
Table A22 Export rates
New South
Wales
35%
40%
45%
50%
55%
10%
Victoria
35%
40%
45%
50%
55%
10%
Queensland
35%
40%
45%
50%
55%
10%
South
Australia
40%
45%
50%
55%
60%
10%
Western
Australia
40%
45%
50%
55%
60%
10%
Tasmania
30%
35%
40%
40%
45%
10%
Australian
Capital
Territory
35%
40%
45%
50%
55%
10%
Northern
Territory
35%
40%
45%
50%
55%
10%
Source: ACIL Allen assumptions
A.6.8
System size trends
Data on PV uptake illustrates clearly a substantial increase in average system size over
time, in particular as the incentives created by the Solar Credits formula to install smaller
systems has dissipated and been overwhelmed by the attraction of lower system costs.
Further, although CER data does not distinguish between residential and non-residential
systems, ACIL Allen analysis for various distribution businesses indicates recent growth in
the share of larger systems being proposed by non-residential customers. This trend is
widely anticipated within the PV industry, and reflects the financial opportunity from low
installation costs and the opportunity to avoid large variable components of retail prices
across a broad range of retail tariff classes.
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Addressing these trends in analysing historic uptake and paybacks is relatively trivial. As
discussed above, net financial return per kilowatt across different system sizes will be
weighted on the basis of their historic share of installed capacity in each jurisdiction to
produce a single ‘weighted real net financial return per kilowatt’.
However, this issue is slightly more complicated for future installations. Of particular
importance to the forecasting approach is the fact that large/non-residential systems
represent only a small share of historic installations, but are widely expected to grow
strongly from this low base in the future. Further, the low levels of uptake of larger systems
cannot itself be explained by net financial returns per kilowatt, as returns for these systems
appear to have been clearly positive in many jurisdictions for at least a year. In simple
terms, it appears to have taken some time for the PV industry to target its marketing efforts
to this niche, and for non-residential customers to understand the financial opportunity of
installing PV. Given the lagged and relatively recent emergence of this market niche,
econometric analysis of historic uptake and paybacks to larger systems may tend to
underestimate the potential response of commercial installations to improving paybacks. To
address this issue, ACIL Allen has undertaken desktop research to reflect the potential
growth of large, non-residential PV installation rates. The approach used was broadly:
 Estimate the potential for growth in larger (non-residential) systems based on various
industry analyses and compare this growth to forecasts of residential uptake rates
 Increase the share of installed capacity attributed to larger systems over time to reflect
this potential growth, and hold this share constant across different modelling scenarios
 This higher weighting should bring down weighted net financial returns in the future, as
large non-residential systems typically have strong paybacks due to low export rates
(implying a high level of avoided network charges) and lower installation costs
 In turn, the overall level of installations and the level of non-residential shares should
grow over time in a way that reflects changes in their payback
 Importantly, the change in overall uptake between policy scenarios (e.g. in response to
phasing out or closing the SRES) will be determined on an internally consistent basis
across various scenarios.
In the first Quarter of 2014 82% of installed PV capacity across Australia was installed in
smaller residential systems (i.e. systems smaller than 7.5kW). Figure A10 shows the share
of installed capacity across Australia by system size class.
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Figure A10 Share of installed capacity by system size class
Note: 10-kW includes systems of 10-100kW
Source: ACIL Allen Analysis of CER data
ACIL Allen has estimated the growth in the market share of larger commercial systems
based on a logistic growth function. The logistic growth function was fitted to quarterly data
on the share of installed PV capacity that came from systems larger than 7.5kW by
determining the value for the parameter a in the equation below for each state:
𝑆(𝑡) = 𝑒 ln(𝑆(0))∗𝑒
(−𝑎∗𝑡)
Where:
 S(t) is the share of installed capacity from large non-residential systems
 S(0) is the share of installed capacity from large non-residential systems for Q1 2009
 t is the number of quarters since Q1 2009
 a is a constant to fit the curve to data for each of the investigated network regions.
The formula presented above grows the share of installed PV capacity consisting of
non-residential systems to eventually approach 100% without taking into account that only
limited roof-space is available for the installation of commercial systems. In order to account
for the predicted saturation of commercial roof-space with PV installation, the predicted
share of installed capacity was reduced to reflect predicted saturation of commercial roofspace in an iterative process.
A.7
Retail price model
Retail prices are modelled using a ‘cost-stack’ approach accounting for the major
components of retail prices, namely:
 Wholesale energy costs
 Network costs
 RET costs
 Costs associated with other ‘green schemes’
 Costs attributable to losses
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 Metering costs, retail operating costs and retail margins.
Each of these component are discussed further in subsequent sections.
Retail prices are developed for the six jurisdictions in the NEM (NSW, Victoria, Queensland,
South Australia, Tasmania and the ACT), south-west Western Australia and the DarwinKatherine grid in Northern Territory. Retail prices were calculated on a calendar year basis.
The retail series are developed on the basis of a mix of notional customers within four broad
customer classes: residential, commercial, industrial and emissions-intensive trade-exposed
(EITE) industry. The industrial and EITE categories do not overlap, such that the industrial
category is effectively ‘light industry’. Modelled prices for the EITE category are adjusted to
account for partial exemptions from RET costs, as described in section A.7.3.
Three separate retail series are developed:
 An ‘all-inclusive’ retail series that combines fixed, demand, capacity and variable (energy
basis) charges into a single average cost, presented in terms of cents per kilowatt hour.
This series is the core output reported in this report.
 A series representative of the retail charges avoided by own-consumed PV output, which
excludes all fixed, demand and capacity charges, as well as ‘off-peak’ energy charges.
Further, the wholesale component of this series was adjusted to transition over time
away from the wholesale costs associated with supplying the relevant retail load, to the
wholesale market price of solar generation. This adjustment was made on the
assumption that metering advances and increasing solar penetration would promote a
policy and market transition towards accounting for solar output in a way that more
closely reflects its market value. This is discussed in more detail in section A.7.5. This
series was used as an input into the SRES forecasting model.
 A series representative of the ‘buyback rate’ paid to exported PV output, comprising the
wholesale value series described in relation to own-consumed PV output, adjusted for
losses. This series was used as an input into the SRES forecasting model.
A.7.1
Wholesale energy costs
Slightly different estimates for wholesale energy costs were made based on available
information. For the NEM, the following process was followed:
 Historic NEM demand for the years 2008 to 2012 was disaggregated into residential,
commercial, industrial and EITE components by:
 Allocating the net system load profile (NSLP) load to the residential category
 Allocating the demand component identified as ‘industrial’ by AEMO in its 2013
National Electricity Forecasting report to the EITE category, on the basis that this
load is constant over the course of each year
 Separating the remainder into commercial and industrial components on the basis of
an assumed industrial load shape involving demand that is 25% lower in off-peak
times than peak times.
 Calculating ‘uplift’ factors based on this historic decomposition for the non-residential
categories, which are then held constant throughout the projection
 Calculating uplift factors for the residential category that changes over time in response
to modelling outcomes by:
 Regressing historic NSLP load against historic NEM load and time-of-day and
seasonal characteristics for each NEM region and the ACT
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 Using this observed relationship to forecast residential load based on modelled future
NEM region loads
 Adjusting this relationship for the growth in rooftop solar
 Calculating the difference between the average modelled price and the modelled
‘uplifted’ residential price in the Reference case to determine uplift factors for each
NEM region and the ACT in each model year
 Holding these factors constant across all model scenarios and sensitivities.
Uplift factors for each NEM region and the ACT for the non-residential customer classes are
shown in Table A23. For the DKIS, simpler factors were assumed due to a lack of detailed
historic data, also shown in Table A23, and were estimated based on analysis by the AEMC
in 2012 and 2013 of trends in residential retail prices.
Table A23 Uplift factors by customer class
NSW
Time varying
8%
5%
0%
Queensland
South Australia
Time varying
4%
4%
0%
Time varying
15%
7%
0%
Tasmania
Time varying
5%
5%
0%
Victoria
Time varying
10%
5%
0%
ACT
Time varying
8%
5%
0%
DKIS
60%
30%
15%
0%
Source: ACIL Allen analysis of AEMO data and AEMC analysis
Additional hedging factors were assumed for each NEM region to reflect the cost of hedging
against higher price events (e.g. in the event of high demand events or combinations of
outages that cause price spikes). These hedging factors were based on 2011 ACIL Allen
(then ACIL Tasman) analysis for the AEMC on retail price trends, which found that hedging
costs were a relatively stable uplift from load-weighted residential prices. In the SWIS,
constant real capacity credit costs were assumed for each customer class. Assumed
hedging cost factors and capacity credit costs are shown in Table A24.
Table A24 Hedging factors and capacity credit costs by customer class
Hedging factors expressed as a percentage
NSW
13%
13%
10%
10%
Queensland
16%
16%
10%
10%
South Australia
17%
17%
10%
10%
Tasmania
14%
14%
10%
10%
Victoria
15%
15%
10%
10%
ACT
10%
10%
8%
8%
DKIS
10%
10%
8%
8%
Capacity credit costs expressed as real $2014 per MWh
SWIS
$61
$44
$33
$22
Source: ACIL Allen analysis
Carbon costs for the first half of 2014 (before the assumed repeal effective from 1 July
2014) are included within the wholesale energy component.
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A.7.2
Network costs
Network costs can be analysed based on published regulated network charges, and
expected changes in these charges based on forward looking regulatory determinations.
Accordingly, near term trends in network costs can be analysed with a reasonable degree of
confidence.
Published historic prices to 2013-14 were analysed for all regions other than Victoria, with
Victoria’s network prices changing on a calendar year basis and so published prices for
2014 were included in the analysis. Representative tariffs were applied to a range of
notional customers (with assumed average and peak consumption), and then weighted
within customer categories.
Future prices were projected on regulatory determinations where available. Beyond the
regulatory determination period for each region (which ‘roll off’ at varying times), specific
capital and operating expenditure trends for each network were extrapolated and then
gradually harmonised over time to project the core cost components of the return on capital
and operating expenditure using the established ‘building block’ methodology for network
cost calculations. The regulated return on capital (also known as the weighted average cost
of capital, or WACC) was assumed to normalise to 8% for all networks from the end of each
regulatory determination, with this rate reflecting recent Australian Energy Regulator
decisions (e.g. its April 2014 placeholder decision for NSW and ACT distribution networks,
which set a WACC of 8.05%, and its April 2012 decision for the Tasmanian distribution
network, setting a WACC of 8.28%).
Table A25 summarises the historic, regulatory determination based projection and modelled
projection of network costs for each network.
Table A25 Basis of network cost calculation
Ausgrid, Endeavour
Energy, Essential
Energy
To 2013-14
2014-15 to 2018-19*
From 2019-20
NSW transmission
TransGrid
To 2013-14
2014-15 to 2018-19*
From 2019-20
Victoria distribution
CitiPower, Jemena,
Powercor, SP
AusNet, United
Energy
To 2014
2015
From 2016
NSW distribution and
sub-transmission
Victoria transmission
SP AusNet
To 2014
2015 to March 2017
From March 2017
Queensland
distribution
Energex, Ergon
Energy
To 2013-14
2014-15
From 2015-16
Queensland
transmission
Powerlink
To 2013-14
2014-15 to 2016-17
From 2017-18
SA distribution
SA transmission
Tasmania
distribution
Tasmania
transmission
ACT distribution
SAPN
To 2013-14
2014-15
From 2015-16
ElectraNet
To 2013-14
2014-15 to 2017-18
From 2018-19
Aurora
To 2013-14
2014-15 to 2016-17
From 2017-28
Transend
To 2013-14
2014-15 to 2018-19*
From 2019-20
ActewAGL
To 2013-14
2014-15 to 2018-19*
From 2019-20
Western Australia
Western Power
To 2013-14
2014-15 to 2016-17
From 2017-18
Northern Territory
NT Power and Water
To 2013-14
2014-15 to 2018-19
From 2019-20
* ‘Placeholder decision’ by AER
Source: AER, ERAWA, NT Utilities Commission.
Overall cost trends were divided by energy growth trends to give a cost per unit energy
trend (i.e. price) trend.
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The share of network costs recovered from fixed, demand and capacity charges was
assumed to increase over time. This did not affect the all-inclusive retail price series, but
affects the portion of network costs avoided by own consumption of PV output. Fixed,
demand and capacity charges were assumed to comprise 50% of residential and
commercial network charges by 2030, commencing a linear transition from present levels in
the first projection year. Similarly, these charges were assumed to linearly trend to 75% of
industrial and EITE network charges from present levels by 2030.
A.7.3
RET costs
RET costs for non-EITE customers (i.e. customers without access to partial exemptions
from the RET) are calculated as the simple multiplication of the prevailing LGC or STC price
by the ‘renewable power percentage’ (RPP) or ‘small-scale technology percentage’ (STP).
The RPP is calculated directly for each scenario based on exogenous demand targets and
assumed levels of ‘reduced acquisitions’. Reduced acquisitions are equal to ‘relevant
acquisitions’ under the scheme, less the volume of partial exemption certificates (PECs)
issued.
Relevant acquisitions were calibrated to the CER’s estimate of 2014 relevant acquisitions
(approximately 205 TWh), and then grown in line with modelled electricity demand. PECs
were calculated based on analysis of PEC creation from 2010 to 2013, and the implied level
of EITE load underlying this level of PEC creation. EITE load was assumed to reduce by
2,500 GWh in 2015 to reflect the closure of the Point Henry aluminium smelter, and then
grow in line with total demand from that point. The calibration of historic EITE load based on
historic PEC creation is shown in Table A26.
Table A26 PEC creation and EITE load
Actual
Actual
Actual
26,515
32,455
30,437
647
996
955
Total
27,162
33,451
31,392
27,200
Highly
68.4%
77.5%
74.7%
67.6%
Moderately
45.6%
51.6%
49.8%
45.0%
Highly
38,767
41,889
40,729
39,029
Moderately
1,420
1,928
1,917
1,837
Total
40,187
43,827
42,646
40,867
Highly (actual)
PEC creation
(000s)
Moderately
(actual)
Exemption rate
Implied EITE
load (GWh)
CER estimate
Note: Partial exemptions differentiate between activities that are ‘highly’ or ‘moderately’ emissions
intensive. The calculation above addresses these two categories separately.
Source: ACIL Allen analysis of CER data
The STP was taken as the pre-determined STP for 2014. The 2015 STP is adjusted for
over/under achievement of the implied STC creation target in 2014 relative to ACIL Allen’s
modelled rate of STC creation. From 2016 onwards the STP is calculated as the modelled
rate of STC creation divided by reduced acquisitions.
LGC costs for the LRET cost component are taken as the certificate price modelled by ACIL
Allen. The market (as opposed to legislated) STC price paid by customers was assumed to
be $38 (constant nominal) in all scenarios other than where the SRES was repealed.
A.7.4
Green scheme costs, losses and retail costs
These relatively small cost components include:
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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 Various green schemes
 The Energy Savings Scheme in NSW
 The Victorian Energy Efficiency Target
 South Australia’s Residential Energy Efficiency Schemes
 Various feed-in tariff costs
 The cost of losses, comprising the wholesale and green scheme cost components
grossed up for energy losses (which differ by customer class and state/territory)
 Smart metering costs in Victoria
 Retail operating costs (which differ by customer class and state/territory)
 Market fees and ancillary services costs for NEM regions, with these being based on
historic real costs in these categories
 Retail margins, calculated as a constant share of non-network cost components (with
slight variations by region).
A.7.5
Treatment of solar costs
As discussed above, the wholesale component of the retail cost series that is avoided by
own consumption of PV output, and for ‘buyback rates’ for solar exports, were adjusted to
transition over time towards the wholesale market value of solar generation.
This transition was assumed to occur such that:
 Residential customers would avoid the wholesale cost of serving their typical energy
consumption until 2025, after which time this rate would transition to the point where
50% of customers would avoid only the solar dispatch-weighted price by 2030.
 Commercial customers would avoid the wholesale cost of serving their typical energy
consumption until 2025, after which time this rate would transition to the point where
100% of customers would avoid only the solar dispatch-weighted price by 2035.
 Industrial and EITE customers would transition to a rate 100% reflective of the wholesale
value of solar between 2025 and 2030.
Though approximate, this transition implies that as solar penetration increases, and the
correlated solar output depresses wholesale market prices during sunny periods, typical
wholesale costs of supply and the wholesale market value of solar will diverge over time. In
response, we assume that policy-makers and retailers will implement a regulatory and/or
market response that sees rooftop solar systems metered in a way that allows the value of
their output to be more accurately credited, rather than simply credited as avoided a ‘typical’
unit of energy consumption. This transition could be implemented through metering
requirements for new solar installations, or widespread adoption of time-of-use metering for
retail load allowing differentiation between the cost of serving customers with and without
rooftop solar.
The commercial imperative for such a change is substantial, as our modelling suggests that
the value of solar output declines to a value of around 80% of the average market price by
2030 in most NEM regions, and to around 60% by 2040, as solar penetration increases.
A.8
Electric vehicle energy projections
Within the demand projections undertaken by pitt&sherry, the uptake of Electric vehicles
was specifically excluded from the analysis. As part of this project, the Department asked
ACIL Allen to estimate an electricity vehicle uptake scenario and include this incremental
electricity demand to the Baseline scenario as an additional sensitivity.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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ACIL Allen estimated the impact of the take up of plug in electric vehicles on energy
consumption across all Australian states by:
1. Obtaining ABS population projections for each state
2. Projecting the number of passenger and light commercial vehicles in each state by
applying historical vehicle ownership rates to the projected population
3. Using a LOGIT model specification to calculate the share of new vehicle sales that are
captured by plug in electric vehicles
4. Making some assumptions concerning driving patterns and vehicle charging patterns to
estimate the total impact of electric vehicles on energy consumption.
Definition of vehicles
There are four specific vehicle types used within the analysis. These are:
 ICE (Internal Combustion Engine)
 HEV (Hybrid Electric Vehicle)
 PHEV (Plug in Hybrid Electric Vehicle)
 EV (Electric Vehicle).
While HEVs are classified as electric vehicles, we do not consider these as part of the
projections as they do not connect to the electricity grid.
The two categories of vehicles which are relevant to this study are PHEVs and EVs. Both
are charged by connecting to the electricity grid. While PHEVs also run on conventional
fossil fuels, EVs are completely powered by electricity.
Separate projections are produced for passenger and light commercial vehicles. Moreover,
the passenger vehicle categories are further disaggregated into Small, Medium and Large
vehicle categories.
Economics of electric vehicles
The key economic factors that are likely to play a significant role in the potential take up of
electric vehicles are:
 Vehicle prices
 Petrol and electricity prices
 Vehicle fuel efficiency
 Running costs
 Range
 Charging convenience
A logistic modelling framework is used to convert the underlying economic drivers of electric
vehicles into an impact on market share and take-up of the technology. This is done by
creating a model which values each of the attributes that drive the decision to adopt the
technology and then to apply an elasticity or measure of responsiveness of market share to
each factor.
Input assumptions
Three separate scenarios are presented, a low, medium and high scenario.
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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Under the low scenario, population growth corresponds to series C of the ABS publication
3222 Population Projections, Australia, 2012 to 2101. Series B is used in the medium
scenario while Series A corresponds to the high growth scenario.
The other factor that varies under the three scenarios is the relative rate of decline in plug-in
electric vehicle costs. Under the medium scenario, the real cost of plug in electric vehicles
is projected to decline at a rate of 4% per annum. Under the high scenario the real rate of
price decline is 6% per annum, while it is 2% under the low scenario.
Under all three scenarios, the real price of conventional ICE vehicles is projected to remain
unchanged.
A.8.1
Impact on energy consumption
To determine the energy consumption of the stock of electric vehicles we assume the
average distance travelled per day is 40 km. This is equivalent to 14,610 kilometres per
annum.
The average driving distance is then used in conjunction with the actual and projected fuel
efficiency of plug in electric vehicles. The energy consumption of each electric vehicle is
determined in this way. The total energy consumption of the entire stock of electric vehicles
is then calculated by multiplying this measure by the number of projected electric vehicles.
The projected energy impact of electric vehicles across Australia is shown in Figure A11.
Energy consumption of PHEV/EVs is projected to reach 12,310 GWh by 2035.
Figure A11 Energy impact of electric vehicles by State (medium scenario)
Source: ACIL Allen
Figure A12 compares the aggregate energy impact across the three uptake scenarios.
Under the ‘high uptake’ scenario, energy demand is 67% higher in 2040; whereas the ‘low
uptake’ scenario projects energy consumption to be around 70% lower in 2040 when
compared with the medium case. This highlights the level of uncertainty within the
projections.
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ACIL ALLEN CONSULTING
Figure A12 Aggregate energy impact of electric vehicles across scenarios
Source: ACIL Allen
ELECTRICITY SECTOR EMISSIONS MODELLING OF THE AUSTRALIAN GENERATION SECTOR
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