
GenCost 2022-23
Final report
Paul Graham, Jenny Hayward, James Foster and Lisa Havas
July 2023



Contact
Paul Graham
+61 2 4960 6061
paul.graham@csiro.au
Citation
Graham, P., Hayward, J., Foster J. and Havas, L. 2023, GenCost 2022-23: Final report, CSIRO, Australia.
Copyright
© Commonwealth Scientific and Industrial Research Organisation 2023. To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO.
Important disclaimer
CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.
CSIRO is committed to providing web accessible content wherever possible. If you are having difficulties with accessing this document please contact www.csiro.au/en/contact.
Contents
Consultation and acknowledgments	vii
Executive summary	viii
1	Introduction	11
1.1	Scope of the GenCost project and reporting	11
1.2	CSIRO and AEMO roles	11
1.3	Incremental improvement and focus areas	12
1.4	The GenCost mailing list	12
1.5	Technology selection principles	12
1.6	Overview of feedback received	12
2	Current technology costs	13
2.1	Current cost definition	13
2.2	Capital cost source	13
2.3	Current generation technology capital costs	14
2.4	Current storage technology capital costs	16
3	Scenario narratives and data assumptions	19
3.1	Scenario narratives	19
4	Projection results	30
4.1	Incorporating short term inflationary pressures	30
4.2	Global generation mix	32
4.3	Changes in capital cost projections	34
4.4	Hydrogen electrolysers	49
5	Levelised cost of electricity analysis	51
5.1	LCOE estimates	52
5.2	Storage requirements underpinning variable renewable costs	58
Appendix A	Global and local learning model	61
Appendix B	Data tables	64
Appendix C	Technology inclusion principles	76
Appendix D	Responses to feedback	79
Shortened forms	90
References		93

Figures
Figure 21 Comparison of current capital cost estimates with previous work	13
Figure 22 Annual increase in capital costs by technology	14
Figure 23 Capital costs of storage technologies in $/kWh (total cost basis)	16
Figure 24 Capital costs of storage technologies in $/kW (total cost basis)	17
Figure 31 Projected EV sales share under the Current policies scenario	24
Figure 32 Projected EV adoption curve (vehicle sales share) under the Global NZE by 2050 scenario	24
Figure 33 Projected EV sales share under the Global NZE post 2050 scenario	25
Figure 41 Projected global electricity generation mix in 2030 and 2050 by scenario	31
Figure 42 Global hydrogen production by technology and scenario, Mt	32
Figure 43 Projected capital costs for black coal supercritical by scenario compared to 2021-22 projections	33
Figure 44 Projected capital costs for black coal with CCS by scenario compared to 2021-22 projections	34
Figure 45 Projected capital costs for gas combined cycle by scenario compared to 2021-22 projections	35
Figure 46 Projected capital costs for gas with CCS by scenario compared to 2021-22 projections	36
Figure 47 Projected capital costs for gas open cycle (small) by scenario compared to 2021-22 projections	37
Figure 48 Projected capital costs for nuclear SMR by scenario compared to 2021-22 projections	38
Figure 49 Projected capital costs for solar thermal with 15 hours storage compared to 2021-22 projections which were for 12 hours storage	39
Figure 410 Projected capital costs for large scale solar PV by scenario compared to 2021-22 projections	40
Figure 411 Projected capital costs for rooftop solar PV by scenario compared to 2021-22 projections	41
Figure 412 Projected capital costs for onshore wind by scenario compared to 2021-22 projections	42
Figure 413 Projected capital costs for fixed and floating offshore wind by scenario compared to 2021-22 projections	43
Figure 414 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant)	44
Figure 415 Projected capital costs for pumped hydro energy storage (12 hours) by scenario	45
Figure 416 Projected technology capital costs under the Current policies scenario compared to 2021-22 projections	46
Figure 417 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2021-22 projections	47
Figure 418 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2021-22 projections	48
Figure 419 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2021-22	49
Figure 51 Range of generation and storage capacity deployed in 2030 across the 9 weather year counterfactuals in NEM plus Western Australia and NEM only	52
Figure 52 Levelised costs of achieving 60%, 70%, 80% and 90% annual variable renewable energy shares in NEM and WA in 2030	53
Figure 53 Calculated LCOE by technology and category for 2022	55
Figure 54 Calculated LCOE by technology and category for 2030	56
Figure 55 Calculated LCOE by technology and category for 2040	56
Figure 56 Calculated LCOE by technology and category for 2050	57
Figure 57 2030 NEM maximum demand, demand at lowest renewable generation and generation capacity under 90% variable renewable generation share	59

Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation	61
Apx Figure D.1 Historical maximum, minimum and average capacity factors for existing NEM solar PV (left) and onshore wind (right) generation	80
Apx Figure D.2 Historical high, low and weighted average capacity factors for existing global solar PV (left) and onshore wind (right) generation.	81
Apx Figure D.3 Historical maximum, minimum and average capacity factors for existing NEM brown coal (left) and black coal (right) generation.	82
Apx Figure D.4 Changes in storage, peaking plant and transmission deployment with changes in VRE share, Progressive change scenario	86
Apx Figure D.5 Changes in storage, peaking plant and transmission deployment with changes in VRE share, Step change scenario	87
Tables
Table 31 Summary of scenarios and their key assumptions	19
Table 32 Assumed technology learning rates that vary by scenario	20
Table 33 Assumed technology learning rates that are the same under all scenarios	22
Table 34 Hydrogen demand assumptions by scenario in 2050	26
Table 35 Maximum renewable generation shares in the year 2050 under the Current policies scenario, except for offshore wind which is in GW of installed capacity.	27

Apx Table A.1 Cost breakdown of offshore wind	62
Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario	64
Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario	65
Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario	66
Apx Table B.4 One and two hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW)	67
Apx Table B.5 Four and eight hour battery cost data by storage duration, component and total costs  (multiply by duration to convert to $/kW)	68
Apx Table B.6 Pumped hydro storage cost data by duration, all scenarios, total cost basis	69
Apx Table B.7 Storage current cost data by source, total cost basis	70
Apx Table B.8 Data assumptions for LCOE calculations	71
Apx Table B.9 Electricity generation technology LCOE projections data, 2022-23 $/MWh	73
Apx Table B.10 Hydrogen electrolyser cost projections by scenario and technology, $/kW	74
Apx Table C.1 Examples of considering global or domestic signficance	76
Apx Table C.1 Rate of new entrant investment required to increase VRE share reliably and efficiently	87

Consultation and acknowledgments
A draft of this report was provided as one of several documents supporting AEMO’s December 2022 to February 2023 consultation on its Draft 2023 Inputs, Assumptions and Scenarios Report (IASR). For details, go to AEMO | Current and closed consultations. Feedback received has been used to improve content. Parts of this report were also shared with stakeholders in an October 2022 webinar for initial feedback. The authors are grateful for all feedback received.
Executive summary
Technological change in electricity generation is a global effort that is strongly linked to global climate change policy ambitions. Following COP27 in Sharm el-Sheikh, world leaders reaffirmed their support for limiting global average temperature rise to 1.5 degrees Celsius. At a domestic level, the commonwealth government, together with all Australian states and territories aspire to or have legislated net zero emissions (NZE) by 2050 targets.
Globally, renewables (led by wind and solar) are the fastest growing energy source and the role of electricity is expected to increase materially over the next 30 years with electricity technologies presenting some of the lowest cost abatement opportunities.
Common to global scenarios presented here and by other leading international bodies is the implication that in order to limit emissions, the energy system must evolve and become more diverse. Chiefly: renewable energy is increasingly important, fossil fuels will remain in use (although increasingly challenged), and societies will redefine mobility. Also, Australia’s efforts are characterised both by the value chains (and associated emissions) of our energy exports and our own consumption of energy.
GenCost update
GenCost is a collaboration between CSIRO and AEMO to deliver an annual process of updating the costs of electricity generation, energy storage and hydrogen production with a strong emphasis on stakeholder engagement. GenCost represents Australia’s most comprehensive electricity generation cost projection report. It uses the best available information each cycle to provide an objective annual benchmark on cost projections and updates forecasts accordingly to help to guide decision making, given electricity costs change significantly each year. This is the fifth update following the inaugural report in 2018.
Technology costs are one piece of the puzzle. They are an important input to electricity sector analysis which is why we have made consultation an important part of the process of updating data and projections.
The report encompasses updated current capital cost estimates commissioned by AEMO and delivered by Aurecon. Based on these updated current capital costs, the report provides projections of future changes in costs consistent with updated global electricity scenarios which incorporate different levels of achievement of global climate policy ambition. Levelised costs of electricity (LCOEs) are also included and provide a summary of the relative competitiveness of generation technologies.
Global inflationary pressures
The COVID-19 pandemic has resulted in global supply chain constraints which have impacted the prices of raw materials needed in technology manufacturing and in freight costs. As a result, the capital costs of all technologies which are currently being considered for construction have increased and Aurecon has provided data on the new cost levels. For technologies which are not currently being deployed, we assume their costs would also have increased had contracts for construction been entered into. We estimate the likely cost increase for these technologies based on increases in the costs of inputs they would require in their construction and installation. The data sourced for these calculations include price indices for consumer goods and services, imported equipment, domestic equipment and labour. We combine this information with data on their local content, imported content and installation share of costs.
The data indicates that compared to 2021-22 data, technology costs have increased 20% on average but with significant diversity. Cost increases are as low as 9% for solar PV and up to 35% for wind. The difference in cost increases mostly reflects differences in material inputs and exposure to freight costs. Some variation may also represent the extent to which cost increases had already flowed through to the previous year’s estimate as we consider the beginning of this inflationary cycle to have started in 2020. Whilst prices had not risen in 2020, it appears cost reductions had started to slow from that time for some technologies.

ES Figure 0-1 Increase in current costs of selected technologies relative to GenCost 2021-22
The inflationary cycle is assumed to be at its peak in 2022-23 and to take until 2027 to return to normal costs under current global climate policy commitments or to 2030 under if stronger global climate policy commitments are made which would require faster global technology deployment. After this adjustment period, our standard projection methodologies are resumed.
Levelised cost of electricity
Levelised cost of electricity (LCOE) data is an electricity generation technology comparison metric. It is the total unit costs a generator must recover to meet all its costs including a return on investment. Each input to the LCOE calculation has a high and low assumption to create an LCOE range for each technology (ES Figure 0-2). The technology capacity factor is one input to the LCOE calculation and describes the percentage of year that the technology is generating to full capacity. As part of this update the capacity factors for both renewables and flexible technologies have been widened to recognise historical trends.
The LCOE is estimated on a common basis for all technologies. However, an additional process is undertaken to calculate the integration costs of variable renewables. The required amount of additional investment depends on the amount or share of variable renewable energy (VRE) generated. We calculated the additional costs of variable renewable generation for annual VRE shares from 60% to 90%1 for the National Electricity Market (NEM) and Western Australia. We found that the additional costs to support a combination of solar PV and wind generation in 2030 is estimated at between $25 to $34/MWh depending on the VRE share. The key costs for supporting reliable supply of electricity under high shares of variable renewable electricity are additional transmission, storage and peaking gas capacity.
All estimates are based on a maximum of costs across nine weather years over which the costs were estimated. When added to variable renewable generation costs and compared to other technology options, these estimates indicate that onshore wind and solar PV remain the lowest cost new-build technologies.

ES Figure 0-2 Calculated LCOE by technology and category for 2030

1 
1 Introduction
Current and projected electricity generation and storage technology costs are a necessary and highly impactful input into electricity market modelling studies. Modelling studies are conducted by the Australian Energy Market Operator (AEMO) for planning and forecasting purposes. They are also widely used by electricity market actors to support the case for investment in new projects or to manage future electricity costs. Governments and regulators require modelling studies to assess alternative policies and regulations. There are substantial coordination benefits if all parties are using similar cost data sets for these activities or at least have a common reference point for differences.
The report provides an overview of updates to current costs in Section 2. This section draws significantly on updates to current costs provided in Aurecon (2023) and further information can be found in their report. The global scenario narratives and data assumptions for the projection modelling are outlined in Section 3. Capital cost projection results are reported in Section 4 and LCOE results in Section 5. CSIRO’s cost projection methodology is discussed in Appendix A. Appendix B provides data tables for those projections which can also be downloaded from CSIRO’s Data Access Portal2.
1.1 Scope of the GenCost project and reporting
The GenCost project is a joint initiative of the CSIRO and AEMO to provide an annual process for updating electricity generation and storage cost data for Australia. The project is committed to a high degree of stakeholder engagement as a means of supporting the quality and relevancy of outputs. Each year a consultation draft is released in December for feedback before the final report is completed towards the end of the financial year.
The project is flexible about including new technologies of interest or, in some cases, not updating information about some technologies where there is no reason to expect any change, or if their applicability is limited. GenCost does not seek to describe the set of electricity generation and storage technologies included in detail.
1.2 CSIRO and AEMO roles
AEMO and CSIRO jointly fund the GenCost project by combining their own resources. AEMO commissioned Aurecon to provide an update of current electricity generation and storage cost and performance characteristics (Aurecon, 2023). This report focusses on capital costs, but the Aurecon report provides a wider variety of data such as operating and maintenance costs and energy efficiency. Some of these other data types are used in levelised cost of electricity calculations in Section 5.
Earlier drafts of Aurecon’s capital cost data were initially shared with stakeholders during a webinar in October 2022. Project management, workshops, capital cost projections (presented in Section 4) and development of this report are primarily the responsibility of CSIRO.
1.3 Incremental improvement and focus areas
There are many assumptions, scope and methodological considerations underlying electricity generation and storage technology cost data. In any given year, we are readily able to change assumptions in response to stakeholder input. However, the scope and methods may take more time to change, and input of this nature may only be addressed incrementally over several years, depending on the priority.
In this report, our main priority has been to understand how inflationary pressures are impacting the short to medium-term outlook for technology costs. We have implemented a change to our methodology up to the year 2030 to capture this significant driver.
1.4 The GenCost mailing list
The GenCost project would not be possible without the input of stakeholders. No single person or organisation is able to follow the evolution of all technologies in detail. We rely on the collective deep expertise of the energy community to review our work before publication to improve its quality. To that end the project maintains a mailing list to share draft outputs with interested parties. The mailing list is open to all. To join, use the contact details on the back of this report to request your inclusion. Some draft GenCost outputs are also circulated via AEMO’s Forecasting Reference Group mailing list which is also open to join via their website.
1.5 Technology selection principles
A set of technology selection principles has been included in Appendix C. Feedback on these principles is always welcome.
1.6 Overview of feedback received
GenCost receives unsolicited feedback throughout the year and also specifically as one of several documents supporting AEMO’s December 2022 consultation on its most recent Inputs, Assumptions and Scenarios Report (IASR). For details, go to AEMO | Current and closed consultations. This report does not detail how all individual feedback was addressed. However, the overlapping feedback has been grouped into themes in Appendix D. We are grateful for all feedback.
2 Current technology costs
2.1 Current cost definition
Our definition of current capital costs are current contracting costs or costs that have been demonstrated to have been incurred for projects completed in the current financial year (or within a reasonable period before). We do not include in our definition of current costs, costs that represent quotes for potential projects or project announcements.
While all data is useful in its own context, our approach reflects the objective that the data must be suitable for input into electricity models. The way most electricity models work is that investment costs are incurred either before (depending on construction time assumptions) or in the same year as a project is available to be counted as a new addition to installed capacity3. Hence, current costs and costs in any given year must reflect the costs of projects completed or contracted in that year. Quotes received now for projects without a contracted delivery date are only relevant for future years. This point is particularly relevant for technologies with fast reducing costs (e.g., batteries). In these cases, lower cost quotes will become known well in advance of those costs being reflected in recently completed deployments – such quotes should not be compared with current costs in this report but with future projections.
For technologies that are not frequently being constructed, our approach is to look overseas at the most recent projects constructed. This introduces several issues in terms of different construction standards and engineering labour costs which have been addressed by Aurecon (2023). Aurecon (2023) also provide more detail on specific definitions of the scope of cost categories included. Aurecon cost estimates are provided for Australia in Australian dollars. CSIRO makes adjustments to the data when used in global modelling to take account of regional differences in costs.
2.2 Capital cost source
AEMO commissioned Aurecon (2023) to provide an update of current cost and performance data for existing and selected new electricity generation, storage and hydrogen production technologies. We have used data supplied by Aurecon (2023) which is consistent with either the beginning of financial year 2022-23 or middle of 2022. Aurecon provides several measures of project capacity (e.g., rated, seasonal). We use the capacity at 25oC to determine $/kW costs. Aurecon state that the uncertainty range of their data is +/- 30%.
Technologies not included in Aurecon (2023) are typically those which are not being deployed in Australia but are otherwise of interest for modelling or policy purposes. For these other technologies we have applied an inflationary factor to last year’s estimate based on a bundle of consumer price indices applied to knowledge of the relative mix of imported and local content for each technology.
Pumped hydro has also not been updated by Aurecon (2023), but we have revised this data to be mostly consistent with AEMO’s June 2022 ISP Input and Assumptions Workbook. CSIRO has modified AEMO’s data for the years 2022 to 2027 to include the same inflationary effects as other mature technologies. Nuclear SMR current costs are not reported since there is no prospect of a plant being deployed in Australia before 2030. However, some improved data on nuclear SMR may be available in future reports4 and projected capital costs for SMR have been included from 2030 onward.
2.3 Current generation technology capital costs
Figure 21 provides a comparison of current (2022-23) cost estimates (drawing primarily on the Aurecon (2023) update) for electricity generation technologies with those from previous years: GenCost 2018 to GenCost 2021-22 (which are a combination of Aurecon (2021, 2022), GHD and CSIRO data), Hayward and Graham (2017) (also CSIRO) and CO2CRC (2015) which we refer to as APGT (short for Australian Power Generation Technology report).

Figure 21 Comparison of current capital cost estimates with previous work

All costs are expressed in real 2022-23 Australian dollars and represent overnight costs. Rooftop solar PV costs are before subsidies from the Small-scale Renewable Energy Scheme.
Whilst there had been some steady declines over the years for technologies such as solar PV and wind, for 2022-23, there has been an increase in capital costs impacting all technologies. The source of this increase is a combination of global supply chain constraints following the COVID-19 pandemic which has increased freight and raw material costs. The cost increase has not been uniform as shown in Figure 22 which is the percentage increase in capital costs by technology relative to 2021-22.
The increase in capital costs for gas technologies, onshore wind and solar PV are from observation by Aurecon (2023) of projects reaching financial close. The exception is Australian gas with CCS projects. Cost increases for this technology category are inferred from the gas technologies without CCS.
The increase in capital costs for black coal technologies has been calculated by CSIRO based on a bundle of consumer price indices applied to knowledge of the relative mix of imported and local content for each technology.
Overall, the differences in cost increases reflect different levels of exposure to increases in cost inputs. However, there is higher uncertainty about the impact of current inflationary pressure for those technologies not currently being deployed. Some variation may also represent the extent to which cost increases had already flowed through to the previous year’s estimate as we consider the beginning of this inflationary cycle to have started in 2020. Whilst prices had not risen in 2020, it appears cost reductions had started to slow from that time for some technologies.

Figure 22 Annual increase in capital costs by technology

2.4 Current storage technology capital costs
Updated and previous capital costs are provided on a total cost basis for various durations5 of battery and pumped hydro energy storage (PHES) in $/kW and $/kWh. Aurecon (2023) has also made available a cost for adiabatic compressed air energy storage (A-CAES) at 12 hours duration and a new capital cost estimate for concentrating solar thermal (CST) at 15 hours duration from Fichtner Engineering (2023) which became available to the GenCost project in April 20236.
Total cost basis means that the costs are calculated by taking the total project costs divided by the capacity in kW or kWh7. As the storage duration of a project increases then more batteries or larger reservoirs need to be included in the project, but the power components of the storage technology remain constant. As a result, $/kWh costs tend to fall with increasing storage duration (Figure 23). The downward trend flattens somewhat with batteries since its power component, mostly inverters, is relatively small but adding more batteries is costly. However, the hydroelectric turbine in a PHES project is a large capital expense while adding more reservoir is less costly. As a result, PHES costs fall steeply with more storage duration. Note that these $/kWh costs are not for energy delivered but rather a capacity of storage. GenCost does not present levelised costs of storage. However, these are available from the CSIRO (2023) Renewable Energy Storage Roadmap. While A-CAES and CST appear relatively higher capital cost at present, they are mainly competing with pumped hydro for longer duration storage applications. PHES is not expected to improve in costs and may be more distant to some locations.
Storage capital costs in $/kW increase as storage duration increases because additional storage duration adds costs without adding any additional power capacity to the project (Figure 24). Additional storage duration is most costly for batteries. These trends are one of the reasons why batteries tend to be more competitive in low storage duration applications, while PHES is more competitive in high duration applications. A combination of durations may be required depending on the behaviour of other generation in the system, particularly the scale of variable renewable generation and peaking plant (see Section 5 and Appendix D).

Figure 23 Capital costs of storage technologies in $/kWh (total cost basis)
Round trip efficiency, project design life and the potential for co-location all play a role in competitiveness of alternative storage technologies. Depth of discharge in batteries can be an important factor. However, all Aurecon battery costs are presented on a usable capacity basis such that depth of discharge is 100%8. Aurecon (2023) also includes estimates of battery costs when they are integrated within an existing power plant and can share some of the power conversion technology. This results in a 6% lower battery cost for a 1-hour duration battery, scaling down to a 1% cost reduction for 8 hours duration. PHES is more difficult to co-locate.
Like the generation technologies the current capital costs of the storage technologies have both increased. Battery costs (battery and balance of plant in total) have increased by 13% for one hour duration batteries and up to 28% for eight hour duration batteries. The higher increase for longer duration batteries indicates that the cost of the battery pack has been the greater source of cost increases compared to the inverter and other balance of plant components.
PHES current cost estimates have increased by 17% and the rate of increase is not assumed to vary significantly by duration. These increases are based on a bundled consumer price increase approach and are therefore less certain. Ordinarily, besides inflationary pressures, PHES have a wider range of uncertainty owing to the greater influence of site-specific issues. Batteries are more modular and as such costs are relatively independent of the site. As an indicator of the influence of site costs, we have included the cost of Tasmania pumped hydro for 24 and 48 hours duration. AEMO’s IASR provides other state cost adjustment factors for PHES.
A-CAES is not yet integrated into our projection methodology and so its future costs are not presented here. While some components are mature, its deployment is not widespread relative to other options. CST is also relatively less common than other technologies but projections are available in Section 4.

Figure 24 Capital costs of storage technologies in $/kW (total cost basis)

3 Scenario narratives and data assumptions
The scenarios were redesigned for GenCost 2021-22. For 2022-23 the scenario narratives have not changed but there have been some minor updates to data assumptions.
3.1 Scenario narratives
The global climate policy ambitions for the Current policies, Global NZE post 2050 and Global NZE by 2050 scenarios have been adopted from the International Energy Agency’s 2022 World Energy Outlook (IEA, 2022) scenario matching to the Stated Policies scenario, Announced Pledges Scenario respectively and Net Zero Emissions by 2050. Various elements, such as the degree of vehicle electrification and hydrogen production, are also consistent with the IEA scenarios.
3.1.1 Current policies
The Current policies scenario applies a 2.5 degrees of global warming consistent climate policy (using a combination of carbon prices and other climate policies9). This represents mid- 2022 climate and renewable energy policy commitments with no extension beyond targets existing at that time. This implies that the 2030 Paris Nationally Determined Contributions (NDCs) are met but that the planned ramping up of ambition to prevent a greater than 2 degrees increase in temperature is limited to only those countries that had committed to further action. This scenario has the strongest constraints applied with respect to global variable renewable energy resources and the slowest technology learning rates. Subsequently, electricity sector greenhouse gas abatement costs are higher. This is consistent with a lack of any further progress on emissions abatement beyond recent commitments. Demand growth is moderate with moderate electrification of transport and limited hydrogen production and utilisation.
3.1.2 Global NZE post 2050
The Global NZE post 2050 has moderate renewable energy constraints and middle of the range learning rates. It has a carbon price and other policies consistent with a 1.7 degrees of warming climate change ambition which provides the investment signal necessary to deploy these technologies. The scenario covers all announced climate-related commitments, even those that are not backed by policy, including net zero emissions by 2050 targets, NDCs and energy access commitments. Hydrogen trade (based on a combination of gas with CCS and electrolysis) and transport and industry electrification are higher than in Current policies.
3.1.3 Global NZE by 2050
Under the Global NZE by 2050 scenario there is strong climate policy consistent with maintaining temperature increases of 1.5 degrees of warming and achieving net zero emissions by 2050 worldwide. The achievement of these abatement outcomes is supported by the strongest technology learning rates and the least constrained (physically and socially) access to variable renewable energy resources. Balancing variable renewable electricity is less technically challenging. Reflecting the low emission intensity of the predominantly renewable electricity supply, there is an emphasis on high electrification across sectors such as transport, hydrogen-based industries and buildings leading to the highest electricity demand across the scenarios.
Table 31 Summary of scenarios and their key assumptions
Key driversGlobal NZE by 2050 Global NZE post 2050Current policiesIEA WEO scenario alignmentNet zero emission by 2050Announced pledges scenarioStated policies scenarioCO2 pricing / climate policyConsistent with 1.5 degrees worldConsistent with 1.7 degrees worldConsistent with 2.5 degrees worldRenewable energy targets and forced builds / accelerated retirementHigh reflecting confidence in renewable energyRenewable energy policies extended as neededCurrent renewable energy policiesDemand / ElectrificationHighMedium-highMediumLearning rates1StrongerNormal maturity pathWeakerRenewable resource & other renewable constraints2Less constrainedExisting constraint assumptionsMore constrained than existing assumptions DecentralisationLess constrained rooftop solar photovoltaics (PV)2Existing rooftop solar PV constraints2More constrained rooftop solar PV constraints21 The learning rate is the potential change in costs for each doubling of cumulative deployment, not the rate of change in costs over time. See the next section for assumed learning rates.
2 Existing large-scale and rooftop solar PV renewable generation constraints are as shown in Table 37.
3.1.4 Technologies and learning rates
The technical approach to applying learning rates is explained in Appendix A and involves a specific mathematical formula. The projection approach uses two global and local learning models (GALLM) which contain applications of the learning formula. One model is of the electricity sector (GALLME) and the other of the transport sector (GALLMT). GALLME projects the future cost and installed capacity of 31 different electricity generation and energy storage technologies and now four hydrogen production technologies. Where appropriate, these have been split into their components of which there are 21 (noting that in total 52 items are modelled). Components have been shared between technologies; for example, there are two carbon capture and storage (CCS) components – CCS technology and CCS construction – which are shared among all CCS plant and hydrogen technologies. 
Key technologies are listed in Table 32 and Table 33 showing the relationship between generation technologies and their components and the assumed learning rates under the central scenario. Learning is either on a global (G) basis, local (L) to the region, or no learning (-). Up to two learning rates are assigned with LR1 representing the initial learning rate during the early phases of deployment and LR2, a lower learning rate, that occurs during the more mature phase of technology deployment.
Table 32 Assumed technology learning rates that vary by scenario
TechnologyScenarioComponentLR 1 (%)LR 2 (%)ReferencesPhotovoltaicsCurrent policiesG3513(IEA 2021, IRENA, 2021, Fraunhofer ISE, 2015)L-17PhotovoltaicsGlobal NZE by 2050G3523L-17PhotovoltaicsGlobal NZE post 2050G3523L-17ElectrolysisCurrent policiesG105(Schmidt et al., 2017)L105ElectrolysisGlobal NZE by 2050G189L189ElectrolysisGlobal NZE post 2050G105L105OceanCurrent policiesG105(IEA, 2021)Global NZE by 2050G2010Global NZE post 2050G147Fixed offshore windCurrent policiesG105(Samadi, 2018; Zwaan, et al. 2021;Voormolen et al. 2016; IEA, 2021)G--Fixed offshore windGlobal NZE by 2050G2010G--Fixed offshore windGlobal NZE post 2050G157.5G--Floating offshore windCurrent policiesG105G105Floating offshore windGlobal NZE by 2050G2010G2010Floating offshore windGlobal NZE post 2050G157.5G157.5Pumped hydroCurrent policiesG--(Grübler et al., 1999; McDonald and Schrattenholzer, 2001)L-5Pumped hydroGlobal NZE by 2050G--L-20Pumped hydroGlobal NZE post 2050G--L20Utility scale energy storage – Li-ionCurrent policiesG-7.5L-7.5Utility scale energy storage – Li-ionGlobal NZE post 2050G-10L-10Utility scale energy storage – Li-ionGlobal NZE by 2050G-15L-15Solar photovoltaics is listed as one technology with global and local components however there are two separate PV plant technologies in GALLME. Rooftop PV includes solar photovoltaic modules and the local learning component is the balance of plant (BOP). Large scale PV also include modules and BOP. However, a discount of 25% is given to the BOP to take into account economies of scale in building a large scale versus rooftop PV plant. Inverters are not given a learning rate instead they are given a constant cost reduction, which is based on historical data.
The potential for local learning means that technology costs are different in different regions in the same time period. This has been of particular note for technology costs in China which can be substantially lower than other regions. GALLME uses inputs from Aurecon (2023) to ensure costs represent Australian project costs. For technologies not commonly deployed in Australia, these costs can be higher than other regions. However, the inclusion of local learning assumptions in GALLME means that they can quickly catch up to other regions if deployment occurs. However, they will not always fall to levels seen in China due to differences in production standards for some technologies. That is, to meet Australian standards, the technology product from China would increase in costs and align more with other regions. Regional labour construction and engineering costs also remain a source of differentiation.
To provide a range of capital cost projections for all technologies, we have varied learning rates for technologies where there is more uncertainty in their learning rate. We focus on variable renewable energy and storage given that these technologies tend to be lower cost and crowd out opportunities for competing low emission technologies. Table 32 shows the learning rates by scenario for solar PV, electrolysis, ocean energy (wave and tidal), offshore wind, batteries and pumped hydro. The remainder of learning rate assumptions, which do not vary by scenario are shown in Table 33.
Table 33 Assumed technology learning rates that are the same under all scenarios
TechnologyComponentLR 1 (%)LR 2 (%)ReferencesCoal, pf---Coal, IGCCG-2(IEA, 2008; Neij, 2008)Coal/Gas/Biomass with CCSG105(EPRI 2010; Rubin et al., 2007)L2010As above + (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001)Gas peaking plant---Gas combined cycle---NuclearG-3(IEA, 2008)Nuclear SMRG2010(Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001)Diesel/oil-based generation---Reciprocating engines---Hydro---BiomassG-5(IEA, 2008; Neij, 2008)Concentrating solar thermal (CST)G14.67(Hayward & Graham, 2013)Onshore windG-4.3(IEA, 2021; Hayward & Graham, 2013)L-11.3As aboveCHP---Conventional geothermalG-8(Hayward & Graham, 2013)L2020(Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001)Fuel cellsG-20(Neij, 2008; Schoots, Kramer, & van der Zwaan, 2010)Steam methane reforming with CCSG105(EPRI, 2010; Rubin et al., 2007)L2010As above + (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001)In addition to the offshore wind learning rate, we have included an exogenous increase in the capacity factor up to the year 2050 of 6% in lower resource regions, and 7% in higher resource regions, up to a maximum of 55%, in capacity factor. This assumption extrapolates past global trends (see Appendix D). As discussed in Appendix D, Australia has had a flat onshore wind capacity factor trend and so these global assumptions do not apply to Australia. The capacity factor for floating offshore wind is assumed to be 5.6% higher than that of fixed offshore wind, based on an average of values (Wiser et al., 2021). Capacity factors for offshore wind are assumed to improve in Australia in line with the rest of the world.
3.1.5 Electricity demand and electrification
Various elements of underlying electricity demand are sourced from the World Energy Outlook (IEA, 2021; IEA 2022). Demand data is provided for the Announced Pledges scenario, which is used in our Global NZE post 2050 scenario. The demand data from the Stated Policies (STEPS) scenario is used in our Current policies scenario. Global NZE by 2050 demand is sourced from the Net Zero Emissions by 2050 scenario. We also allow for some divergence from IEA demand data in all scenarios to accommodate differences in our modelling approaches and internal selection of the contribution of electrolysis to hydrogen production.
Global vehicle electrification
Global adoption of electric vehicles (EVs) by scenario is projected using an adoption curve calibrated to a different shape to correspond to the matching IEA World Energy Outlook scenario sales shares to ensure consistency in electricity demand. The rate of adoption is highest in the Global NZE by 2050 scenario, medium in the Global NZE post 2050 scenario and low in the Current policies scenario consistent with climate policy ambitions. The shape of the adoption curve varies by vehicle type and by region, where countries that have significant EV uptake already, such as China, Western Europe, India, Japan, North America and rest of OECD Pacific, are leaders and the remaining regions are followers. Cars and light commercial vehicles (LCV) have faster rates of adoption, followed by medium commercial vehicles (MCV) and buses. The EV adoption curves for the Current policies, Global NZE by 2050 and Global NZE post 2050 scenarios are shown in Figure 31, Figure 32 and Figure 33 respectively. The adoption rate is applied to new vehicle sales shares.

Figure 31 Projected EV sales share under the Current policies scenario

Figure 32 Projected EV adoption curve (vehicle sales share) under the Global NZE by 2050 scenario


Figure 33 Projected EV sales share under the Global NZE post 2050 scenario
3.1.6 Hydrogen 
In GenCost projections prior to 2022-23, hydrogen demand was imposed together with the type of production process used to supply hydrogen. In our current model, GALLME determines which process to use – steam methane reforming with or without CCS or electrolysers. This choice of deployment also allows the model to determine changes in capital cost of CCS and in electrolysers.
The model does not distinguish between alkaline (AE) or PEM electrolysers. That is, we have a single electrolyser technology. The approach reflects the fact that GALLME is not temporally detailed enough to determine preferences between the two technologies which are mainly around their minimum operating load and ramp rate. There is currently a greater installed capacity of AE which has been commercially available since the 1950s, whereas PEM is a more recent technology.
The IEA have included demand for electricity from electrolysis in their scenarios. Since GALLM is endogenously determining which technologies are deployed to meet hydrogen demand, we have subtracted the IEA’s demand for electricity from electrolysis from their overall electricity demand. The assumed hydrogen demand assumptions for the year 2050 are shown in Table 34 and include existing demand, the majority of which is currently met by steam methane reforming. The reason for including existing demand is that in order to achieve emissions reductions the existing demand for hydrogen will also need to be replaced with low emissions sources of hydrogen production.
Table 34 Hydrogen demand assumptions by scenario in 2050
ScenarioTotal hydrogen demand (Mt)Current policies118Global NZE post 2050243Global NZE by 20504753.1.7 Government climate policies
Carbon trading markets exist in major greenhouse gas emitting regions overseas at present and are a favoured approach to global climate policy modelling because they do not introduce any technological bias. We directly impose the IEA carbon prices. The IEA also includes a broad range of additional policies such as renewable energy targets and planned closure of fossil-based generation. The GALLME modelling includes these non-carbon price policies as well but cannot completely match the IEA implementation because of model structural differences. The IEA have greater regional and country granularity and are better able to include individual country emissions reduction policies. Some policies are difficult to recreate in GALLME due to its regional aggregation. Where we cannot match the policy implementation directly, we align our implementation of non-carbon price policies so that we match the emission outcomes in the relevant IEA scenario.
We align our scenarios with the IEA and the IEA does not include more recent announcements or changes of government policy since the IEA report was complete. As such, the country policy commitments included are not completely up to date.
3.1.8 Resource constraints
The availability of suitable sites for renewable energy farms, available rooftop space for rooftop PV and sites for storage of CO2 generated from using CCS have been included in GALLME as a constraint on the amount of electricity that can be generated from these technologies (Table 37) (see Government of India, 2016, Edmonds, et al., 2013 and Hayward & Graham, 2017 for more information on sources). With the exception of rooftop PV these constraints are removed in the Global NZE by 2050. Floating offshore wind has some technical limitations in regions but these limitations are greater than electricity demand.
3.1.9 Other data assumptions
GALLME international black coal and gas prices are based on (IEA, 2022) with prices for the Stated Policies scenario applied in all cases. The IEA tends to reduce its fossil fuel price assumptions in scenarios with stronger climate policy action. Whilst we agree that stronger climate policy action will lead to lower demand for fossil fuels, we do not think it follows that fossil prices must fall10. This is one of the very few areas where we do not align with all IEA scenario assumptions. Brown coal is not globally traded and has a flat price of 0.6 $/GJ.
Table 35 Maximum renewable generation shares in the year 2050 under the Current policies scenario, except for offshore wind which is in GW of installed capacity. 
RegionRooftop PV
%Large scale PV
%CSP
%Onshore wind
%Fixed offshore wind
GWAFR21NANANANAAUS35NANANANACHI14NANANA1073EUE21NANANANAEUW2122322NAFSU25NANANANAIND721184302JPN161121110LAM25NANANANAMEA21NANANANANAM30NANANANAPAO1118815.5SEA143328NANA means the resource is greater than projected electricity demand. The regions are Africa (AFR), Australia (AUS), China (CHI), Eastern Europe (EUE), Former Soviet Union (FSU), India (IND), Japan (JPN), Latin America (LAM), Middle East (MEA), North America (NAM), OECD Pacific (PAO), Rest of Asia (SEA), and Western Europe (EUW)
Power plant technology operating and maintenance (O&M) costs, plant efficiencies and fossil fuel emission factors were obtained from (Aurecon, 2023) (Aurecon, 2022) (Aurecon, 2021) (IEA, 2016b) (IEA, 2015), capacity factors from (IRENA, 2022) (IRENA, 2021) (IEA, 2015) (CO2CRC, 2015) and historical technology installed capacities from (IEA , 2008) (Gas Turbine World, 2009) (Gas Turbine World, 2010) (Gas Turbine World, 2011) (Gas Turbine World, 2012) (Gas Turbine World, 2013) (UN, 2015a) (UN, 2015b) (US Energy Information Administration, 2017a) (US Energy Information Administration, 2017b) (GWEC) (IEA, 2016a) (World Nuclear Association, 2017) (Schmidt, Hawkes, Gambhir, & Staffell, 2017) (Cavanagh, et al., 2015).

4 Projection results
4.1 Incorporating short term inflationary pressures
Over the last two years the cost of a range of technologies including electricity generation, storage and hydrogen technologies has increased rapidly driven by two key factors: increased freight and raw materials costs. The most recent period where similar large electricity generation technology cost increases occurred was 2006 to 2009 with wind turbines and solar PV modules being most impacted. The cost drivers at that period of time were policies favouring renewable energy in Europe, which led to a large increase in demand for wind and solar. This coincided with a lack of supply due to insufficient manufacturing facilities of equipment and polysilicon in the case of PV and profiteering by wind turbine manufacturers (Hayward and Graham, 2011). Once supply caught up with demand the costs returned to those projected by learning-by-doing and economies of scale.
CSIRO has explored a number of resources to understand cost increases already embedded in technology costs and project how this current increase in costs will resolve. We normally use our model GALLM to project all costs from the current year onwards. While GALLM takes into account price bubbles caused by excessive demand for a technology (as happened in 2006-2009) the drivers of the current situation are different and thus we have decided to take a different approach, at least for projecting costs over the next five to seven years. It is not appropriate to project long term future costs directly from the top of a price bubble, otherwise all future costs will contain the current temporary market conditions.
Some feedback received did suggest that some believe the price bubble is not a price bubble but rather a permanent feature that will strengthen. To sustain real price increases, supply needs to be either constrained by a cartel or resource scarcity or technology demand needs to grow faster than supply (which implies strong non-linear demand growth since, once established, a given supply capacity can meet linear growth at the rate of that existing capacity11).
Historical experience and the projections available for global clean energy technology deployment do not provide confidence that the required market circumstances for sustained real price increases will prevail for the entire projection period (see Appendix D for more discussion). However, it is considered that the period to 2030 will likely experience extra strong technology deployment, particularly for the Global NZE by 2050 and Global NZE post 2050 scenarios. This is partly because of the low global clean technology base (from which non-linear growth is more feasible) but also because governments and industry often use the turning of a decade as a target date for achieving energy targets. The Current policies scenario requires less growth in technology deployment and as such, for that scenario only, 2027 remains the date at which costs resume their pre-pandemic modelled pathway.
The exception to the resumption of a modelled cost path after 2027 or 2030 is that the projection has been adjusted to recognise that land may be a source of ongoing input scarcity. Land costs generally make up 2% to 9% of generation, storage and electrolyser capital costs. The projections take the land share of capital costs provided in Aurecon (2023) and inflate that proportion of costs by the real land cost index that is published in Mott MacDonald (2023)12. This common land cost index provides some consistency between the treatment of land costs between transmission, generation and storage assets in AEMO’s modelling. The inclusion of a specific land cost inflator is a new feature of GenCost projections, not included in previous reports.
All projections start from a current cost and the first and primary source of 2022 costs is Aurecon (2023). Aurecon (2023) provides an update on the current costs of contracting the deployment of most of the technologies included in GenCost. The exceptions are some mature technologies such as coal and less commonly deployed and emerging technologies such as wave energy. Aurecon (2023) costs for 2022 were not used directly for offshore wind, hydrogen and natural gas reciprocating engines. These are cases where inflationary effects had not been included or they were lower than the 2021 costs which is contrary to recent cost increases seen in all other technologies.
The 2022 costs for technologies not included in Aurecon (2023) and both types of reciprocating engines and offshore wind have been calculated by multiplying the previous year’s costs by a “basket of costs” factor to take into account the 2022 cost increases. To project the 2023 and 2024 costs the “basket of costs” calculation was extended out to those years and was applied to all technologies except for more commonly deployed renewable technologies where we were able to source more detailed information. 
The approach used to determine this “basket of costs” factor is similar to that already taken for mature technologies (Appendix A 1.3) where we apply historical values of the CPI, imported equipment, domestic equipment and labour indices. However, for this approach we used projections of the CPI, continued the current trend in the labour index out to 2024 and estimated the domestic13 and imported equipment indices based on materials and logistics cost projections (Mukherjee, 2022). These “basket of costs” factors have been calculated and applied to each technology on an annual basis, based on the share of local and imported equipment and installation costs.
For onshore wind, solar PV, batteries and electrolysers we were able to source projected costs to the year 2024 from S&P Global (Mukherjee 2022; Berg 2022; Jin & Zocco, 2022; Huntington 2022; Nikoleishvili & Klaesig, 2022; Davies & Nikoleishvili, 2022). However, these costs were not specifically designed for Australia but rather for the technologies on a global level. We thus used the Aurecon (2023) costs as a starting point and applied the trend in costs from S&P Global to the Aurecon (2023) 2022 cost. 
For all technologies, an interpolation is carried out between the 2024 projected cost and either the 2027 or 2030 modelled cost, depending on the scenario.
While we have used the trends in price indices of selected goods to inform our analysis of short-term changes in technology costs, all projections remain in real terms. That is, all projected price changes after 2022 are in addition to the general level of inflation.
4.2 Global generation mix
The rate of technology deployment is the key driver for the rate of reduction in technology costs for all non-mature technologies. However, the generation mix is determined by technology costs. Recognising this, the projection modelling approach simultaneously determines the global generation mix and the capital costs. The projected generation mix consistent with the capital cost projections described in the next section is shown in Figure 41.


Figure 41 Projected global electricity generation mix in 2030 and 2050 by scenario
The technology categories displayed are more aggregated than in the model to improve clarity. Solar includes solar thermal and solar photovoltaics.
Current policies has the lowest electrification because it is a slower decarbonisation pathway than the other scenarios considered. However, it has the least energy efficiency and industry transformation14. For this reason, while it has the lowest demand by 2050 it is only slightly below Global NZE post 2050 in 2030. Both Global NZE scenarios have high vehicle electrification and high electrification of other industries including hydrogen. However, they also have high energy efficiency and industry transformation which partially offsets these sources of new electricity demand growth in 2030. Figure 42 shows the contribution of each hydrogen technology to hydrogen production in each scenario.
Current policies has the lowest non-hydro renewable share at 41% of generation by 2050. Coal, gas, nuclear and gas with CCS are the main substitutes for lower renewables. Gas with CCS is preferred to coal with CCS given the lower capital cost and lower emission intensity. In absolute capacity terms, nuclear increases the higher the climate policy ambition of the scenario, but is around 8% in all scenarios by 2050.
The Global NZE by 2050 scenario is close to but not completely zero emissions by 2050. 99% of generation from fossil sources is with CCS accounting for 13% of generation by 2050. Offshore wind features strongly in this scenario at 22% of generation by 2050. Renewables other than hydro, biomass, wind and solar are 5% of generation in 2050. The greater deployment of renewables and CCS leads to lower renewable and CCS costs.

Figure 42 Global hydrogen production by technology and scenario, Mt

4.3 Changes in capital cost projections
This section discusses the changes in cost projections to 2050 compared to the 2021-22 projections. For mature technologies, where the current costs have not changed and the assumed improvement rate after 2027 or 2030 (depending on the scenario) is very similar, their projection pathways often overlap. In this final report, there is less overlap because, in addition to the assumed mature rate of technological improvement, a land cost inflator is also applied. The land cost inflator results in a 56% increase in land costs by 2050. The assumed annual rate of cost reduction for mature technologies post-2027 or 2030 (depending on the scenario) is 0.35% (the same as the 2021-22 report). The method for calculating the reduction rate for mature technologies is outlined in Appendix A. Data tables for the full range of technology projections are provided in Appendix B and can be downloaded from CSIRO’s Data Access Portal15.
4.3.1 Black coal supercritical
The cost of black coal supercritical plant in 2022 has been assumed to increase and then return to its previous level by 2027 in Current policies and by 2030 in the Global NZE scenarios, reflecting our approach for incorporating current inflationary pressures for mature technologies outlined at the beginning of this section. The assumed rate of improvement in costs for mature technologies over time is the same as in 2021-22, however the inclusion of a land cost escalator means that the 2022-23 projections are higher.

Figure 43 Projected capital costs for black coal supercritical by scenario compared to 2021-22 projections

4.3.2 Coal with CCS
The current cost of black coal with CCS from 2022 to 2027 in Current policies or 2022 to 2030 in the Global NZE scenarios has been updated in a similar manner as mature technologies, but with differences to take account of its unique set of inputs. Thereafter, the capital cost of the mature parts of the plant improve at the mature technology cost improvement rate. For the CCS components, the cost reductions are a function of global deployment of gas and coal with CCS, steam methane reforming with CCS and other industry applications of CCS. Cost reductions up to 2027 or 2030 are not technology related but rather represent the weakening of current inflationary pressures.
Current policies has no uptake of steam methane reforming with CCS in hydrogen production. Consequently, cost reduction from the late 2030s are mainly driven by the deployment of CCS in other industries. While black coal with CCS benefits from co-learning from deployment of CCS in other industries, there is only a negligible amount of generation from black coal with CCS throughout the projection period.

Figure 44 Projected capital costs for black coal with CCS by scenario compared to 2021-22 projections
Global NZE by 2050 and Global NZE post 2050 take up CCS in hydrogen production and both gas and coal electricity generation (although gas generation with CCS is significantly more preferred). Given the scale of generation and hydrogen production required in those scenarios, together with assumed high other industry use of CCS, the total deployment of CCS technologies across all applications is high. The total CSS deployment in electricity generation and hydrogen production is higher in Global NZE by 2050. However, CCS deployment in other industries is higher in Global NZE post 2050. Subsequently, those scenarios experience a similar amount of learning and cost reduction by 2050 but with differences in the timing of reductions. The assumed land cost increases has also been applied and may partially account for slightly higher long term costs than the 2021-22 projections
4.3.3 Gas combined cycle
Aurecon (2023) have included an increase in gas combined cycle costs for 2022 and CSIRO has imposed an assumed return to previous costs levels by 2027 in Current policies and 2030 in the Global NZE scenarios. After the return to normal period, because gas combined cycle is classed as a mature technology for projection purposes, its change in capital cost is governed by our assumed cost improvement rate for mature technologies together with a land cost increase for all scenarios. Consequently, the rate of improvement is constant across the Current policies, Global NZE by 2050 and Global NZE post 2050 scenarios.

Figure 45 Projected capital costs for gas combined cycle by scenario compared to 2021-22 projections
4.3.4 Gas with CCS
The current cost for gas with CCS has been revised upwards for the 2022-23 projections based on Aurecon (2023). The relativities between the scenarios reflect the differences in global deployment in electricity generation, hydrogen production and other industry uses of CCS. Global NZE by 2050 and Global NZE post 2050 have the highest total deployment of all CCS technologies. Subsequently gas with CCS is lowest by 2050 in those scenarios. Conversely, CCS is highest cost in Current policies where CCS deployment is lowest. CCS deployment for electricity generation purposes has occurred around five years earlier than in the 2021-22 projections under Current policies and deployment is a little deeper overall in the Global NZE scenarios.
The IEA CCS database16 indicates there are around 30 planned electricity related projects which are yet to make a financial investment decision, two under construction and one completed. The advanced projects are for smaller volumes and/or low capture rates. Given the current state of the pipeline of projects, the earliest date for commercial, high capture rate, electricity CCS projects has been set at 2035. This has forced a delay in deployments and cost reductions until that date relative to the 2021-22 projections.

Figure 46 Projected capital costs for gas with CCS by scenario compared to 2021-22 projections
4.3.5 Gas open cycle (small and large)
Figure 47 shows the 2021-22 and updated 2022-23 cost projections for small and large open cycle gas turbines. Aurecon (2023) provides the details for the unit sizes and total plant capacity that defines the small and large sizes. Current costs are higher for both sizes based on the updated data but are assumed to converge towards their previous projected levels by 2027 or 2030. They do not return back to 2021-22 levels because the 2022-23 projections include increasing land costs over time. Aside from the land costs, open cycle gas is classed as a mature technology for projection purposes and as a result its change in capital costs is governed by our assumed cost improvement rate for mature technologies. Consequently, the rate of improvement is constant across the scenarios.

Figure 47 Projected capital costs for gas open cycle (small) by scenario compared to 2021-22 projections
4.3.6 Nuclear SMR
Global commercial deployment of SMR is limited to a small number of projects and the Australian industry does not expect any deployment here before 2030. In this context, we do not report a current cost for Australian nuclear SMR. Instead, the projection begins from 2030.
The scenarios present a divergent set of possibilities for nuclear SMR. In the consultation draft of this report nuclear SMR was deployed globally in all scenarios. However, we have added a new technology to our model for this final report- floating offshore wind - and this has resulted in nuclear SMR deployment not growing in the Current policies scenario. Current policies has evolved towards being a higher abatement scenario since the 2021-22 report, with some countries increasing the ambition in their stated policies, which provides more incentive to grow nuclear SMR deployment particularly in regions with limited locations to deploy renewable generation. However, floating offshore wind provides an alternative source of renewable generation that does not require traditional locations for renewables. With nuclear SMR costs not improving due to deployment, their costs increase due to assumed increasing land costs (which impact all technologies). Assuming the world makes progress on climate policies, Current policies will converge towards the other scenarios over time and so this higher cost result in Current policies for 2022-23 may only be temporary.
In the Global NZE scenarios, the scale of abatement and growth in demand means that existing commercial technologies are not sufficient to achieve the electricity sector emissions reduction. As a result, deployment of nuclear SMR proceeds and significant cost reduction are delivered through the learning rate assumptions which may be partly driven by modular manufacturing processes. Modular plants reduce the number of unique inputs that need to be manufactured. There is some variation in the timing and depth of reductions in the early 2030s. Capital costs are around $7500/kW in the Global NZE scenarios which is slightly lower than the low range of the 2021-22 projections (after adjusting for inflation).

Figure 48 Projected capital costs for nuclear SMR by scenario compared to 2021-22 projections
4.3.7 Solar thermal
The starting cost for solar thermal has been updated from Fichtner Engineering (2023) which also includes an adjustment for current inflationary pressures. Also, additional input from the solar thermal industry has indicated that the future preferred application of solar thermal has changed. Plans in Australia and elsewhere are not for standalone projects but rather for solar thermal to be integrated with other renewables such as solar PV and wind. In this case the role for solar thermal switches to provision of evening and nighttime generation. This role changes the configuration of the plant such as the ratio of the solar field to the power block. Therefore while we include the 2021-22 projections in Figure 49, they are not a like for like comparison.
We apply the same adjustments for inflationary pressure and land costs to solar thermal as other technologies. The projections diverge according to their scenario with the greatest cost reductions projected to be stronger the greater the global climate policy ambition.


Figure 49 Projected capital costs for solar thermal with 15 hours storage compared to 2021-22 projections which were for 12 hours storage
4.3.8 Large scale solar PV
Large-scale solar PV costs have been revised upwards for 2022-23 based on Aurecon (2023) and reflecting current global inflationary pressures. Under the Current policies scenario, costs return to their normal cost pathway by 2027 and as a result this scenario has the lowest costs before 2030. In the Global NZE scenarios, inflationary pressures remain higher for longer due to faster technology deployment to meet stronger climate policies, but after 2030 these two scenarios become the lowest cost with Global NZE by 2050 being the lowest cost overall from the 2040s.
By 2050 the three scenarios project a capital cost range of $500/kW to just under $700/kW. The final minimum cost level for solar PV is one of the most difficult to predict because, unlike other technologies, and notwithstanding current extreme inflationary pressures, the historical learning rate for solar PV has not slowed. The modular nature of solar PV appears to be the main point of difference in explaining this characteristic.


Figure 410 Projected capital costs for large scale solar PV by scenario compared to 2021-22 projections

4.3.9 Rooftop solar PV
The current costs for rooftop solar PV systems are higher and this increase has been aligned to that being experienced by large-scale solar PV. The price aligns to a 7kW system but it should be noted that rooftop solar PV is sold across a broad range of prices17. This data is best interpreted as a mean and may not align with the lowest cost systems available.
Rooftop solar PV benefits from co-learning with the components in common with large scale PV generation and is also impacted by the same drivers for variable renewable generation deployment across scenarios. As a result, we can observe similar trends in the rate of capital cost reduction in each scenario as for large-scale solar PV.


Figure 411 Projected capital costs for rooftop solar PV by scenario compared to 2021-22 projections

4.3.10 Onshore wind
The updated Aurecon (2023) data indicates that onshore wind has experienced one of the highest increases in costs in 2022 (around a 35% increase). Our assumption is that this cost does not reflect normal conditions and that wind will return to its normal cost path by 2027 in Current policies and by 2030 in the Global NZE scenarios. Like other technologies, wind costs will be reduced with greater global climate policy ambition and subsequent deployment and are also subject to land cost increases. Cost reductions are stronger with stronger global climate policy ambition resulting a range of around $1600/kW to $1900/kW by 2050.

Figure 412 Projected capital costs for onshore wind by scenario compared to 2021-22 projections
4.3.11 Fixed and floating offshore wind
CSIRO decided that fixed and floating offshore wind should be represented separately in its projections. Our general approach is not to include similar technologies because of model size limits and because the model will usually choose only one of two similar technologies to deploy, therefore adding no new insights. However, while the two offshore technologies have a lot of common technology, floating wind is less constrained in terms of the locations in which it can be deployed. As the global effort to reduce greenhouse gas emissions looks increasingly to electricity as an energy source, many countries will be seeking to use technologies that have fewer siting conflicts. Fixed offshore wind is the least cost offshore technology but its maximum deployment is limited by access to seas of a maximum depth of around 50 metres18 and any navigation or marine conservation issues within those zones. Floating offshore wind can be deployed at much greater depths increasing its potential global deployment and providing a unique reason to select the technology.
Figure 413 presents projections for both fixed and floating compared to 2021-22 (which only included fixed offshore wind). The current costs for both types of offshore wind are provided in Aurecon (2023), however we make some additional adjustments for inflationary pressure as discussed in Section 4.1. From 2023 we’ve allowed the offshore wind costs to resume cost reduction consistent with a stronger climate ambition as a result fixed offshore wind costs start to reconnect with the previous 2021-22 trajectory in 2027 for Current policies and in 2030 for the Global NZE scenarios. In some scenarios they are slightly higher reflecting some competition from floating offshore wind.
In Current policies, floating offshore wind deployment is low. As such cost improve to 2027 reflecting an assumed reduction in inflationary pressures. However, costs reduction after that point are low. Cost reductions are deeper in the Global NZE scenarios where the demand of low emission electricity is higher and climate policy ambitions are stronger. Just before 2050 the cost of floating offshore wind falls below that of fixed offshore wind. This result could be plausible if we consider that in this scenario and time period most readily accessible fixed offshore wind sites adjacent to the highest demand countries may already be claimed shifting the focus of global manufacturing to supplying floating offshore wind technology.

Figure 413 Projected capital costs for fixed and floating offshore wind by scenario compared to 2021-22 projections
4.3.12 Battery storage
The projections for batteries include a 20% increase in total costs and an underlying 37% increase in the battery component (excluding balance of plant). Consequently, batteries are one of the highest impacted technologies under the current global inflationary pressures. However, batteries have been able to sustain high rates of cost reduction over time and is it assumed that they are able to converge back to their underlying cost pathway by 2027 in Current policies and by 2030 in the Global NZE scenarios.
The projections use different learning rates by scenario in order to reflect the uncertainty as to whether they will be able to continue to achieve historical cost reduction rates. Historical cost reductions have mainly been achieved through deployment in industries other than electricity such as in consumer electronics and electric vehicles. However, small- and large-scale stationary electricity system applications are growing globally. Under the three global scenarios, batteries have a large future role to play supporting variable renewables alongside other storage and flexible generation options and in growing electric vehicle deployment. The projected future change in total cost of battery projects is shown in Figure 414 (battery and balance of plant).

Figure 414 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant)
Battery deployment is strongest in the Global NZE by 2050 scenario reflecting stronger deployment of variable renewables, which increases electricity sector storage requirements, and stronger uptake of electric vehicles to support achieving net zero emissions by 2050. Together with an assumed high learning rate this leads to the fastest cost reduction. The remaining scenarios have more moderate cost reductions reflecting slower uptake of electric vehicles and stationary storage and assumed lower learning rates. A breakdown of battery pack and balance of plant costs for various storage durations are provided in Appendix B.
Aurecon (2023) has included current costs for small-scale batteries, designed to be installed in homes. They are estimated at $16000 for a 5kW/10kWh system or $1600/kWh, including installation. This is more than twice the cost of large-scale battery projects.
4.3.13 Pumped hydro energy storage
Pumped hydro energy storage is assumed to be a mostly mature technology with only a small proportion of site piping/drilling costs having the potential to improve with deployment19. Given the strong deployment of variable renewables in all scenarios and subsequent need for storage, this component of learning is maximised in all scenarios so that their cost trajectory is identical over time. The source of data is the 2020-21 and 2021-22 is the AEMO ISP input and assumptions workbooks – December 2020 and June 2022 respectively. These have been adjusted for ordinary inflation (being older estimates), for the current global inflationary pressures and for a continuing increase in land costs. Appendix B includes the costs of pumped hydro energy storage at different durations. We also assume that the costs for Tasmania 24 and 48 hour pumped hydro storage are 62% and 46%, respectively, of mainland costs. This approach is consistent with the AEMO ISP and reflects greater confidence in Tasmanian project cost estimates. The AEMO data also includes some other state differences that are not included in the national figures presented here.
Unlike the other technologies all three scenarios assume costs return to normal by 2030 (rather than in 2027 for Current policies). This reflects the longer lead time for PHES projects which means it is unlikely the level of global climate ambition will result in different cost trajectories before 2030. Site variability is more likely a greater source of variation in costs.

Figure 415 Projected capital costs for pumped hydro energy storage (12 hours) by scenario

4.3.14 Other technologies
There are several technologies that are not commonly deployed in Australia but may be important from a global energy resources perspective or as emerging technologies. These additional technologies are included in the projections for completeness and discussed below. They are each influenced by revisions to current costs. the increase in current costs and the downward trend to either 2027 or 2030 have been included using the same methodology for mature technologies. Updated projections include land costs which were not included in 2021-22 projections. The scale of increase in 2022 fuel cell costs was sourced from Aurecon (2023).
Current policies
Biomass with CCS is not deployed in the Current policies scenario because the climate policy ambition is not strong enough to incentivise deployment. Cost reductions after 2027 reflect co-learning from other CCS technologies which are deployed in electricity generation and in other sectors. Fuel cell cost improvements are mainly a function of deployment and co-learning in the vehicle sector rather than in electricity generation. Neither wave nor tidal/ocean current are deployed to any significant level mainly reflecting the lack of climate policy ambition needed to drive investment in these relatively higher cost renewable generation technologies.

Figure 416 Projected technology capital costs under the Current policies scenario compared to 2021-22 projections
Global NZE by 2050
Biomass with CCS is adopted in the Global NZE by 2050 scenario but can only achieve learning in the CCS component of the plant. Cost reductions reflect learning from its own deployment and co-learning from deployment of CCS in other electricity generation, hydrogen and other industry sectors. Biomass with CCS is an important technology in some global climate abatement scenarios if the electricity sector is required to produce negative abatement for other sectors. However, we are not able to model that scenario with GALLME. GALLME only models the electricity sector and from that perspective alone, biomass with CCS is a relatively high-cost technology.
Fuel cells and wave energy are deployed although the early reduction in fuel cells reflects their use in the transport sector. Tidal/ocean current generation is not deployed to any significant level.

Figure 417 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2021-22 projections

Global NZE post 2050
Biomass with CCS is deployed at about 85% the level of Global NZE by 2050. However, the cost reductions achieved are similar to that scenario because the majority of cost reductions reflect co-learning from deployment of other types of CCS generation or use of CCS in other applications. Both scenarios have significant deployment of gas with CCS generation and steam methane reforming with CCS which brings down the cost of all CCS technologies sooner compared to Current policies. Similar to Global NZE by 2050, wave and fuel cell generation are preferred to tidal/current generation. 

Figure 418 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2021-22 projections
4.4 Hydrogen electrolysers
Hydrogen electrolyser costs have increased in 2022 and the increase is sourced from Aurecon (2023). Alkaline electrolysers are lower cost than proton-exchange membrane (PEM) electrolysers at present. However, PEM electrolysers have a wider operating range which gives them a potential advantage in matching their production to low-cost variable renewable energy generation. As the costs of both technologies fall, capital costs become less significant in total costs of hydrogen production. This development could make it attractive to sacrifice some electrolyser capacity utilisation for lower energy costs (by reducing the need to deploy storage in order to keep up a minimum supply of generation). Under these circumstances, the more flexible PEM electrolysers could be preferred if their costs are low enough.
GALLME does not directly model the competition between PEM and alkaline technologies since it does not have the temporal resolution to evaluate the trade-off between capital utilisation and lower cost electricity. We model a single electrolyser technology, with current cost based on alkaline electrolyser costs and we assume PEM costs converge to alkaline costs by 2040.
The current costs applied at the starting point of the projection are for 10MW electrolysers. This scale is far smaller than we would expect to see deployed over the long term where multi-gigawatt renewable zones are being considered to supply hydrogen production hubs. No other technology in this report is presented at trial scale. We therefore adjust the scale over time in the projection to recognise electrolysers moving out of the trial stage and into full scale production. We assume full scale is 100MW and after that size they are deployed in 100MW modular units. Applying typical engineering cost scaling factors this movement to full scale accounts for around an 80% reduction in costs. Electrolysers costs would otherwise remain similar to 2022 levels in 2023 and the subsequent cost reduction rate thereafter significantly slower without this scale effect.
Electrolyser deployment is being supported by a substantial number of hydrogen supply and end-use trials globally and in Australia. Experience with other emerging technologies indicates that this type of globally coincident technology deployment activity can lead to a scale-up in manufacturing which supports cost reductions through economies of scale. Very low costs of electrolysers, at the bottom end of the projections here, have been reported in China. However, differences in engineering standards and operating and maintenance costs mean these are not able to be immediately replicated in other regions. They do indicate, however, a potentially achievable level of costs for other regions over the longer term.
Deployment of electrolysers and subsequent cost reductions are projected to be greatest in the Global NZE by 2050 scenario. Consistent with their lower global climate policy ambition, hydrogen electrolyser production is 57% lower by 2050 in Global NZE post 2050 and 79% lower in Current policies.

Figure 419 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2021-22

5 Levelised cost of electricity analysis
Levelised cost of electricity (LCOE) data is an electricity generation technology comparison metric. It is the total unit costs a generator must recover to meet all its costs including a return on investment. Modelling studies such as AEMO’s Integrated System Plan do not require or use LCOE data20. LCOE is a simple screening tool for quickly determining the relative competitiveness of electricity generation technologies. It is not a substitute for detailed project cashflow analysis or electricity system modelling which both provide more realistic representations of electricity generation project operational costs and performance. Furthermore, in the GenCost 2018 report and a supplementary report on methods for calculating the additional costs of renewables (Graham, 2018), we described several issues and concerns in calculating and interpreting levelised cost of electricity. These include:
* LCOE does not take account of the additional costs associated with each technology and in particular the significant integration costs of variable renewable electricity generation technologies
* LCOE applies the same discount rate across all technologies even though fossil fuel technologies face a greater risk of being impacted by the introduction of current or new state or commonwealth climate change policies.
* LCOE does not recognise that electricity generation technologies have different roles in the system. Some technologies are operated less frequently, increasing their costs, but are valued for their ability to quickly make their capacity available at peak times.
In Graham (2018), after reviewing several alternatives from the global literature, we proposed a new method for addressing the first dot point – inclusion of integration costs unique to variable renewables. That new method was implemented in the 2020-21 GenCost report and we update results from that method in the present report. For an overview of the method see GenCost 2020-21 Section 5.1.
To address the issues not associated with additional cost of renewables, we:
* Separate and group together peaking technologies, flexible technologies and variable technologies
* Include additional LCOE data on fossil fuel technologies which includes an additional risk premium of 5% based on Jacobs (2017).
5.1 LCOE estimates
5.1.1 Calculating additional costs of variable renewables
We calculate the integration costs of renewables for 203021, imposing a required variable renewable energy (VRE) share above the business as usual and running the model to determine the optimal additional investment to support the VRE share. In practice, although wave, tidal/current and offshore wind are available as variable renewable technologies, onshore wind and large-scale solar PV are the only variable renewables deployed in the modelling due to their cost competitiveness22.
The VRE share does not include rooftop solar. The impact of rooftop solar is accounted for, however, in the demand load shape as is the impact of other customer energy resources. A portion of customer-owned battery resources are available to support the wholesale generation sector if designated as virtual power plans (VPPs) consistent with the approach taken in the AEMO ISP.
The standard LCOE formula requires an assumption of a capacity factor. Our approach in this report is to provide a high and low assumption for the capacity factor (which we report in Appendix B) in order to create a range23. Stakeholders have previously indicated they prefer a range rather than a single estimate of LCOE. However, it is important to note that these capacity factors are not used at all in the modelling of renewable integration costs. When modelling renewable integration costs, we use the variable renewable energy production traces published by AEMO for its Integrated System Plan. We incorporate the uncertainty in variable renewable production by modelling nine different weather years, 2011 to 2019, and the results represent the highest cost outcome from these alternate weather years.
The model covers the NEM, the South West Interconnected System (SWIS) in Western Australia (WA) and the remainder of WA. Northern Territory (NT) is not yet included in the model.
In our counterfactual or business as usual (BAU) against which integration costs are calculated, we implement all existing state renewable energy targets resulting in a 64% renewable share and 56% variable renewable share in Australia ex-NT. The share fluctuates a few percent depending on the nine weather years. The counterfactual VRE share reflects the impact of existing state renewable targets, planned state retirements of coal capacity in the case of WA and an already existing high VRE share in South Australia.
New South Wales, Queensland, Victoria and the SWIS are the main states that are impacted by the higher VRE shares imposed because Tasmania and South Australia are already dominated by renewables such that the BAU already includes all necessary investment to support very high VRE shares. The NEM is an interconnected system, so we are also interested in how those states support each other and the overall costs for the NEM. The VRE share is applied in each state at the same time, but individual states can exceed the share if it is economic to do so.
The BAU includes similar retirements of existing coal plants to previous AEMO ISP modelling. As we implement higher variable renewable energy shares, we must further forcibly retire coal plant as meeting the variable renewable share and the minimum load requirements on coal plant would otherwise eventually become infeasible24. Snowy 2.0 and battery of the nation pumped hydro projects are assumed to be constructed before 2030 in the BAU as well as various transmission expansion projects already flagged by the ISP process to be necessary before 2030. New South Wales (NSW) gas peaking plants at Kurri Kurri and Illawarra are assumed to have been constructed. The NSW target for an additional 2 GW of at least 8 hours duration storage is also assumed to be met by 2030.
Annual variable renewable energy shares (VREs) are explored in the range 60% to 90%. Below 60% is not of interest because the BAU already exceeds 50%. Above 90% VRE share is also not of interest because it would mean forcibly retiring other non-variable renewables such as hydro and biomass which would not be optimal for the system.

Figure 51 Range of generation and storage capacity deployed in 2030 across the 9 weather year counterfactuals in NEM plus Western Australia and NEM only
In the nine weather year counterfactuals, the model does not choose to build any new fossil fuel-based generation capacity (Figure 51). However, it also chooses a similar level of pumped hydro storage. The main investment response to the different weather is to vary wind capacity by up to 3.3GW, solar PV capacity by 2.7GW and large-scale batteries (VPP capacity is fixed) by 1.9GW. The capacities shown have been compared with the AEMO ISP 2030 capacity projections. The NEM coal retirements to 2030 are aligned with Step Change (June 2022 release) but the overall demand and renewable generation is lower. Wind capacity is more clearly preferred over solar PV by 2030. This preference is stronger in the ISP25. The NEM and WA total variable renewable shares are 56% and 57% on average across the weather years. The announced closure of the Muja and Collie coal generators by 2029 and 2027 respectively has increased the BAU variable renewable share in WA.
The costs of VRE share scenarios were compared against the same counterfactual weather year to determine the additional integration costs of achieving higher VRE shares. We use the maximum cost across all weather years as the resulting integration cost on the basis that the maximum cost represents a system that has been planned to be reliable across the worst outcomes from weather variation.
The results, shown in Figure 52, include storage, transmission and synchronous condenser costs. Synchronous condensers are one of several technologies that can be used replace lost inertia from mainly fossil fuel-based generation when it retires to make way for the higher VRE shares.
As expected, the results indicate that additional costs increase with higher VRE shares. Relative to the 2021-22 analysis, the NEM has a more even expenditure on storage and other transmission. Other transmission represents expenditure to strengthen the links between existing transmission zones (rather than connecting new renewable energy zones). Storage and transmission are somewhat in competition because they both help to manage variable renewable generation. Storage can shift variable renewable generation to a different time. Transmission supports access to a greater diversity of variable renewable generation by accessing resources in other regions which can help smooth supply. As transmission costs are updated, they have tended to increase in cost and this has likely led to a reduced reliance on transmission to balance supply in the modelling.
 
Figure 52 Levelised costs of achieving 60%, 70%, 80% and 90% annual variable renewable energy shares in NEM and WA in 2030
REZ expansion costs appear to be required at similar levels for each additional 10% increase in VRE share and in each state. Other transmission costs have a rising trend in the NEM. The highest other transmission expenditure is in New South Wales and Victoria reflecting their central positions in the NEM and access to pumped hydro storage.
The SWIS and other WA systems are unable to use other transmission expenditure to significantly diversify renewable generation sources to reduce storage needs. WA therefore has relatively higher expenditure on storage offset by lower transmission costs. Queensland and Victoria have the next greatest storage needs reflecting less developed storage in the BAU compared to New South Wales. Queensland has recently announced some major pumped hydro projects to be completed by 203526, which is after the analysis period here.
Additional expenditure on synchronous condenser capacity is required in most states and increases moderately with VRE share. Higher VRE share leads to the retirement of fossil fuel-based capacity that otherwise supplies most of system inertia.
Higher or lower costs in different states or regions are averaged out at the aggregate level for the NEM and WA. The cost of REZ transmission expansions adds around $6/MWh to $8/MWh, as the VRE share increases from 60% to 90%. Synchronous condensers costs are low at between $1.7/MWh to $2.3/MWh increasing moderately with VRE share. Other transmission adds $5 to $6/MWh with costs accelerating with VRE share. Storage adds $9 to $14/MWh.
5.1.2 Variable renewables with and without integration costs
The results for the additional costs for increasing variable renewable shares are used to update and extend our LCOE estimates. We expand the results for 2030 to include a combined wind and solar PV category for different VRE shares. Integration costs to support renewables are estimated at $25 to $34/MWh depending on the VRE share (Figure 54).
Onshore wind and solar PV without transmission or storage costs are the lowest cost generation technology by a significant margin. Offshore wind is higher cost but competitive with other alternative low emission generation technologies and its higher capacity factor could result in lower integration costs. Integration costs have only been calculated for onshore wind in this report given it remains the lowest cost form of wind generation.
The additional integration costs associated with increasing variable renewable generation from onshore wind and solar PV are presented for 2030. The analysis confirms that when integration costs are included variable renewables remain the lowest cost new-build technology. The next lowest cost flexible technology in 2030 is gas generation but only if it could be financed at a rate that does not include climate policy risk. Of the low emissions flexible technologies, gas with carbon capture and storage is the next most competitive.
5.1.3 Peaking technologies
The peaking technology category includes two sizes for gas turbines, a gas reciprocating engine and a hydrogen reciprocating engine. Fuel comprises the majority of costs, but the lower capital costs of the larger gas turbine make it the most competitive. Reciprocating engines have higher efficiency and consequently, for applications with relatively higher capacity factors and where a smaller unit size is required, they can be the lower cost choice.
Hydrogen reciprocating engines are higher cost at present. However, their costs are expected to fall over time. Providing the hydrogen is made from low emission sources, this technology is a low emission option for provide peaking services.
5.1.4 Flexible technologies
Nuclear SMR, black coal, brown coal and gas-based generation technologies fall into the category of technologies that are designed to deliver energy for the majority (around 50% to 90%) of the year. They are the next most competitive generation technologies after variable renewables (with or without integration costs). The large reduction in fossil fuel generation costs between 2022 and the remaining years is not as a result of technological improvement. It represents a reduction in fuel prices from their current historical highs.
Of the fossil fuel technologies, it difficult to say which is more competitive as it depends very much on the price outcome achieved in contracts for long term fuel supply and the investor’s perception of climate policy risk.

Figure 53 Calculated LCOE by technology and category for 2022
New fossil fuel generation faces the risk of higher financing costs over time because all states and the commonwealth have either legislated or have aspirational net zero emission by 2050 targets. We address these risks in the cost estimations by including a separate estimate which assumes a 5% risk premium on borrowing costs27. Natural gas-based generation is less impacted by the risk premium because of its lower emission fuel, higher thermal efficiency (in combined cycle configuration only) and lower capital cost.
We do not include a risk premium for low emission flexible technologies. Gas with CCS and small modular reactor (SMR) nuclear are the next most competitive. Achieving the lower end of the nuclear SMR range requires that SMR is deployed globally in large enough capacity to bring down costs available to Australia. Lowest cost gas with CCS is subject to accessing gas supply at the lower end of the range assumed (see Appendix B for assumptions). Both technology would also have to be successful in operating at 89% capacity factor to achieve the lower end of the cost range.

Figure 54 Calculated LCOE by technology and category for 2030

Figure 55 Calculated LCOE by technology and category for 2040


Figure 56 Calculated LCOE by technology and category for 2050
5.2 Storage requirements underpinning variable renewable costs
In both formal and informal feedback, a common concern is whether GenCost LCOE calculations have accounted for enough storage or other back-up to deliver a steady supply from variable renewables. Ensuring all costs are accounted for is important when comparing costs with other low emission technologies such as nuclear SMR which can provide steady supply. Intuitively, high variable renewable systems will need something else to supply electricity for extended periods when variable renewable production is low. This observation might lead some to conclude that the system will need to build the equivalent capacity of long duration storage or other flexible and peaking plant (in addition to the original variable renewable capacity). However, such a conclusion would substantially overestimate storage capacity requirements.
Variable renewables have a low capacity factor, which means their actual generation over the year expressed as a percentage of their potential generation as defined by their rated capacity, is low (e.g., 20% to 40%). As a result, to deliver the equivalent energy of a coal generator, the system needs to install around three times the variable renewable capacity. If the system were to also build the equivalent capacity of storage, peaking and other flexible plant then the system now has six times the capacity needed when coal is deployed. For a number of reasons this scale of capacity development is not necessary.
The most important factor to remember is that while we are changing the generation source, maximum demand has not changed. Maximum demand is the maximum load that the system has to meet in a given year. It typically occurs during heat waves in warmer climates (which is most of Australia) and in winter during extended cold periods in cooler climates (e.g., Tasmania). The combined capacity of storage, peaking and other flexible generation only needs to be sufficient to meet maximum demand. In a high variable renewable system, maximum demand will be significantly lower than the capacity of variable renewables installed. So instead of installing storage on a kW per kW basis, to ensure maximum demand is met, we only need to install a fraction of a kW of storage for each kW of variable renewables. The exact ratio depends on two other factors as well.
The first is that we are very rarely building a completely new electricity system (except in new off grid areas). Existing electricity systems will have existing peaking and flexible generation. This reduces the amount of new capacity that needs to be built. This is as true for coal generation or any other new capacity as it is for variable renewable generation. All new capacity relies on being supported by existing generation capacity to meet demand.
The second factor is that, as the variable renewable generation share increases, summer or winter peaking events may not represent the most critical day for back-up generation. For example, during a summer peaking event day, solar PV generation will have been high earlier in the day and consequently storages are relatively full and available to deliver into the evening peak period. A more challenging period for variable renewable system might be on a lower demand day when cloud cover is high and wind speed is low. These days with low renewable generation and low charge to storages could see the greatest demands on storage, peaking and other flexible capacity. As such it may be that the low demand level on these low renewable generation days is a more important benchmark in setting the amount of additional back-up capacity required.
The modelling approach applied accounts for all of these factors across nine historical weather years. The result we find is that, in 2030, the NEM needs to have 0.26kW to 0.39kW storage capacity for each kW of variable renewable generation installed28. Showing the most extreme case of 90% variable renewable share for the NEM, Figure 57 show the maximum annual demand, demand when renewable generation is lowest, storage capacity, peaking capacity, other flexible capacity and total variable renewable generation capacity.
The data shows that:
* Demand at the point of lowest renewable generation29 is substantially lower than maximum demand and can mostly be met by non-storage technologies (although in this example renewable generation is not zero and can still contribute)
* Existing and new flexible capacity is very slightly lower than maximum demand. This indicates that there is some variable renewable generation available at peak demand events in at least one state of the NEM (mostly likely wind generation if the peak occurs outside of daylight hours such as in the evening or early morning).
* Flexible capacity exceeds demand at minimum renewable generation.
* The required existing and new flexible capacity to support variable renewables is a fraction of total variable renewable capacity.

Figure 57 2030 NEM maximum demand, demand at lowest renewable generation and generation capacity under 90% variable renewable generation share
Appendix A  Global and local learning model
A.1 GALLM
The Global and Local Learning Models (GALLMs) for electricity (GALLME) and transport (GALLMT) are described briefly here. More detail can be found in several existing publications (Hayward & Graham, 2017) (Hayward & Graham, 2013) (Hayward, Foster, Graham, & Reedman, 2017).
A.1.1 Endogenous technology learning
Technology cost reductions due to ‘learning-by-doing’ were first observed in the 1930s for aeroplane construction (Wright, 1936) and have since been observed and measured for a wide range of technologies and processes (McDonald & Schrattenholzer, 2001). Cost reductions due to this phenomenon are normally shown via the equation:

where IC is the unit investment cost at CC cumulative capacity and IC0 is the cost of the first unit at CC0 cumulative capacity. The learning index b satisfies 0 < b < 1 and it determines the learning rate which is calculated as:

(typically quoted as a percentage ranging from 0 to 50%) and the progress ratio is given by PR=100-LR. All three quantities express a measure of the decline in unit cost with learning or experience. This relationship says that for each doubling in cumulative capacity of a technology, its investment cost will fall by the learning rate (Hayward & Graham, 2013). Learning rates can be measured by examining the change in unit cost with cumulative capacity of a technology over time. 
Typically, emerging technologies have a higher learning rate (15–20%), which reduces once the technology has at least a 5% market share and is considered to be at the intermediate stage (to approximately 10%). Once a technology is considered mature, the learning rate tends to be 0–5% (Schrattenholzer and McDonald, 2001). The transition between learning rates based on technology uptake is illustrated in Apx Figure A.1.

Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation
However, technologies that do not have a standard unit size and can be used in a variety of applications tend to have a higher learning rate for longer (Wilson, 2012). This is the case for solar photovoltaics, batteries and historically for gas turbines.
Technologies are made up of components and different components can be at different levels of maturity and thus have different learning rates. Different parts of a technology can be developed and sold in different markets (global vs. regional/local) which can impact on the cost reductions as each region will have a different level of demand for a technology and this will affect its uptake. 
A.1.2 The modelling framework
To project the future cost of a technology using experience curves, the future level of cumulative capacity/uptake needs to be known. However, this is dependent on the costs. The GALLM models solve this problem by simultaneously projecting both the cost and uptake of the technologies. The optimisation problem includes constraints such as government policies, demand for electricity or transport, capacity of existing technologies, exogenous costs such as for fossil fuels and limits on resources (e.g., rooftops for solar photovoltaics). The models have been divided into 13 regions and each region has unique assumptions and data for the above listed constraints. The regions have been based on Organisation for Economic Co-operation Development (OECD) regions (with some variation to look more closely at some countries of interest) and are: Africa, Australia, China, Eastern Europe, Western Europe, Former Soviet Union, India, Japan, Latin America, Middle East, North America, OECD Pacific, Rest of Asia and Pacific. 
The objective of the model is to minimise the total system costs while meeting demand and all constraints. The model is solved as a mixed integer linear program. The experience curves are segmented into step functions and the location on the experience curves (i.e., cost vs. cumulative capacity) is determined at each time step. See (Hayward & Graham, 2013) and (Hayward, Foster, Graham, & Reedman, 2017) for more information. Both models run from the year 2006 to 2100. However, results are only reported from the present year to 2050.
A.1.3 Mature technologies and the “basket of costs”
There are three main drivers of mature technology costs: imported materials and equipment, domestic materials and equipment, and labour. The indices of these drivers over the last 20 years (ABS data) combined with the split in capital cost of mature technologies between imported equipment, domestic equipment and labour (Bureau of Resource and Energy Economics (BREE), 2012) was used to calculate an average rate of change in technology costs: - 0.35%. This value has been applied as an annual capital cost reduction factor to mature technologies and to operating and maintenance costs. 
A.1.4 Offshore wind
Offshore wind has been divided into fixed and floating foundation technologies. IRENA (2022) and Stehly & Duffy (2021) provided a breakdown of the cost of all components of both fixed and floating offshore wind, which allowed us to separate out the cost of the foundations from the remainder of the cost components. This division in costs was then applied to the current Australian costs from Aurecon (2023) resulting in the values as shown in Apx Table A.1.
Apx Table A.1 Cost breakdown of offshore wind
Cost componentFixed offshore wind ($/kW)Floating offshore wind ($/kW)Foundation5972393Remainder of cost40654065Total cost46626459The learning of all offshore wind components (i.e. “Remainder of cost” components) except for the foundations are shared among both offshore wind technologies. The floating foundations used in floating offshore wind have a learning rate, but the fixed foundations used in fixed offshore wind have no learning rate.


Appendix B  Data tables
The following tables provide data behind the figures presented in this document.
The year 2022 is mostly sourced from Aurecon (2023) and is aligned to the middle of that calendar year or the beginning of the 2022-23 financial year.

Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario
Black coalBlack coal with CCSBrown coalGas combined cycleGas open cycle (small)Gas open cycle (large)Gas with CCSGas reciprocatingHydrogen reciprocatingBiomass (small scale)Biomass with CCS (large scale)Large scale solar PVRooftop solar panelsSolar thermal (15hrs)WindOffshore wind fixedOffshore wind floatingWaveNuclear (SMR)Tidal /ocean currentFuel cell$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW202253981104081801766149994343542004243878252193515721454652526425682787211662-866397872023496410513774517061593100243401759214086662112115161400647826445480759110987-81588612202448901038076091699154096343311759213384962080214071307633925195288715410869-80587979202548251026574871694149092643291762212983402051713011223620223985103673710756-79597389202647611015373681690144288843281765212681892024111971150605022844924634410645-7862684420274673992672191671139784743071750211278981971011411092591121524834600810447-7717640420284608974471121654136980642901735210176711928411031042578920564799579010291-7605611720294566960670481639135680542791719209175061896010911009566919964818568510176-75245672203045589597703516361354803427917162087751918934105897755621989480356571018018167752452712031455095887023163313518024279171320847532189091038956546519834789564310184181997524486420324542957970111630134980042801710208075451888410229395376197847745641101891823175244721203345359571699916281347799428017072077755818860101092652941974475956311019318264752445652034452795636987162513457984281170420737572188369999135218197247455600101981829775144386203545209428697616221342796415617012070758618685984897514719694730556710202183317503424220364512927469641620134079540141699206676011851697188450801967471655441020718365749240652037450591016953161713387943851169620637615183279478615016196447025543102121840174923894203844989035694216141336793379416932060763018246922837495619624688554610217184367492371120394491896369321612133479137311691205776451816087679748981960467355481022218473748735762040448488966921161013327903673168820547660180798397644826195946595550102271851074823491204144718827690116051328788362016832048766517983791723474019564644554910228185207477343720424458878168811600132478635891678204276691791075969546421953462855481023018531747734142043444587446861159613207833567167320367674178467246654550194946135547102321854374733405204444328713684115911316781355116692030767917789706649446419454597554510233185547469340720454419868268211586131377935351664202476831773169563943831940458255431023518565746434092046440786576801158213097763525165920187688176806916344306193745665541102361857674643409204743948633678215771305774351616542012769217630687630423319334551554010238185877463340520484381860967621573130177235071649200676971758068362641641931453655411023918599746333992049436985826743156812977703495164520017702175276796224099192845215541102411861074573394205043618566673115651295768348816421997770717496676619405119274511554110242186227451339120514346853467071560129176834731636199077071743267461640021925449555401024218622744533832052433685136692155612887623464163219857707173916726153955192444855540102421862274443379205343158474666115491282762344816241976770717310669612390819224464553810242186227443336920544305845566451545127975734411621197277071727166761038621921445355381024218622744333642055429584366629154212767573434161719677707172316666093816192044435537102421862274433359Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario
Black coalBlack coal with CCSBrown coalGas combined cycleGas open cycle (small)Gas open cycle (large)Gas with CCSGas reciprocatingHydrogen reciprocatingBiomass (small scale)Biomass with CCS (large scale)Large scale solar PVRooftop solar panelsSolar thermal (15hrs)WindOffshore wind fixedOffshore wind floatingWaveNuclear (SMR)Tidal /ocean currentFuel cell$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW202253981104081801766149994343542004243878252193515721454652526425682787211662-866397872023496410513774517061593100243291759214086662112115161400636826444888677110987-81588612202449281038076771704156497843231761213384962080214241313615625214498610710869-80657914202549001026576201705153895543241766212983402051713481241590124034131548810756-79727264202648721015375671706151393243251772212681892024112871184566722903793493310645-78816670202748301001174891702148390643141772211680151990512271129548721813481443010527-7790610320284788987174131697145488043021772210778451957611691077531420773194397910410-7701558520294747973373381693142685442901772209776801925311141028511619782931357410294-76125111203046689639720816721392828428317522089757119031107198849171913275533131021910939755447922031460095887099165113668024279173220847522189091034954474618802662318588509733752446182032454295797011163013498004280171020807492188841014933462018792646317676658523752445772033453595716999162813477994280170720777443188609899084501186626363172663873097524452820344527956369871625134579842811704207373521883696888744011840262730835749730675244454203545209294697616221342796402517012070727418551923846429818072619299349797317752443622036451290236964162013407953766169920667219182638657954211177926112904431273297504427220374505875069531617133879435061696206372061797479573441231759260429033734734274814199203844988740694216141336793350316932060722017949736683404117442598290232347357745841352039449187316932161213347913502169120577234179266906423938173125932901280173717455407720404484872269211610133279035021688205472481790465361038351720258929022426738674554030204144718702690116051328788349616832048725317857623584372017092584290121007390745539872042445885406881160013247863351167820427257176675985623622170025792900181973957399396120434445837668611596132078332041673203672611747657854435201693257429001575739973283944204444328209684115911316781305516692030726617282561529343916872569289915757403696839342045441981836821158613137793044166420247270172295495183362168025642899157574086656392420464407815968011582130977630341659201872741717953950933001672256028981572741263593915204743948137678215771305774302716542012727917131530500324116642555275315607417635139062048438181166762157313017723021164920067283170845214923186165625492560154474216351389220494369809567431568129777030151645200172881703851548631321648254323481528742662203872205043618083673115651295768301216421997729217010513483308716422539228015217431609038552051434680586707156012917683004163619907292169545114813043163825332250151774315960383220524336804266921556128876230001632198572921691751048029991637253022401515743159603818205343158009666115491282762299016241976729216842507478295616352524222315117431596037922054430579926645154512797572985162119727292168055064772913163425202216151074315960378020554295797666291542127675729811617196772921676850547528711633251722091508743159603768Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario
Black coalBlack coal with CCSBrown coalGas combined cycleGas open cycle (small)Gas open cycle (large)Gas with CCSGas reciprocatingHydrogen reciprocatingBiomass (small scale)Biomass with CCS (large scale)Large scale solar PVRooftop solar panelsSolar thermal (15hrs)WindOffshore wind fixedOffshore wind floatingWaveNuclear (SMR)Tidal /ocean currentFuel cell$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW$/kW202253981104081801766149994343542004243878252193515721454652526425682787211662-866397872023496410513774517061593100243401759214086662112115161400636826445480759110987-81588612202448901038076091699154096343311759213384962080214241312615625195288715410869-80587979202548251026574871694149092643291762212983402051713481238590123985103673710756-79597389202647611015373681690144288843281765212681892024112871179566722844924634410645-78626844202747441001173361690142987743151766211680151990512271122548721734756597910527-7765631720284739987173261691142386543021769210778451957611691068536020674600564310410-7670583120294747973373381693142685442901772209776791925311141017525519674457533610294-75765383203046689639720816721392828428317522089757419031107197651241900435251281021914586751350582031460095887099165113668024279173220847529189091034942496818644284501710184134187482483820324542957970111630134980042801710208075361888410149224800185942434986101891234474824688203345359571699916281347799428017072077754218860989899465318524194495010193113567482453420344527956369871625134579842811704207375281883696887945291846415048691019810448748243602035452093186976162213427964049170120707515185759238364429184141154795102029612748242222036451290596964162013407953802169920667504182998657834353183740994737102078844748140532037450587936953161713387943548169620637514180177957184283183340714717102128137748038942038449887646942161413367933527169320607529179747406684223182840514704102177487747937282039449187496932161213347913521169120577536179457066364158182240174680976874747479360920404484873969211610133279035181688205475321792168761841051817398846609340747274793532204144718717690116051328788351116832048751817872676608404318123950463189277465747934792042445886956881160013247863504167820427512178236635973979180639224611853274677476345120434445867368611596132078334971673203675161777565258639111802389545928155746474723431204444328650684115911316781348916692030752117725639574384917993872457777947464746834192045441985986821158613137793453166420247525176476255623794179638474558744974637455340820464407853168011582130977634021659201875301755461355037281795382445427120746874433399204743948379678215771305774326616542012753417375604542365317933801452768057472743033892048438182556762157313017723159164920067538172245965353565179237814513650474777430337420494369813467431568129777030531645200175431707759152934801789376245016217748172963339205043618109673115651295768303716421997754717036586525341917873751449359427486716333082051434680716707156012917683017163619907547169665815213360178537284477594274867029328320524336805466921556128876230121632198575471692957851833011784371144655942748670293280205343158021666115491282762300216241976754716854571512324317833679444259427486700932752054430580046645154512797572997162119727547168175665073187178336644431594274866989327220554295798766291542127675729921617196775471678056250331311782364844205942748669693269Apx Table B.4 One and two hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW)
Battery storage (1 hr)Battery storage (2 hrs)TotalBatteryBOPTotalBatteryBOPCurrent policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh202293193193143143143150050050067367367341841841825525525520239369369364404404404964964966776776774274274272502502502024882882882413413413469469469637622602400377356237246246202583583583538838838844744744760257553937633329722724224220267937937933623623624314314315725324873502942482222382392027799743726373319302426424425556494442337260207219234235202878069966935928225242241741854145940432422917221723023120297616596213442492104174104115254283713112021442142262282030742623580330220175412403405510401344298179120212222224203172359252631619612840739639949437730828515987209218220203270458151930119112740338939247937030327215587207214217203368557151228718812639838338646336329925915386204211213203466656150527218412439437638044835629524614985202207210203564755149825818112438937037443235029123214784200204207203662854149124317712238536436841734428721914484197200203203761053348422917512238135836340133828320614283195197200203859952447822317212137735235739333227920013982193193197203959051547121816912037234635238732727619613782191190194204058150746521316711936834034638032227219113581189187191204157249945920916511836433434037431726818813381186183188204256649345320716411835932833537031326518613381184180184204356048644820516411835532332936631026218413281182177181204455047944220016211835131732435930525918013180180174178204554347343619616111734731131935430125517613080177171176204653646643019316011734230631334929725217413080175168173204753046042519115911733830130834529424917212979173165170204852445442019015911633429530334229024617112879171162167204951944841518815811633029029833828724316912879169159164205051444341018715811632628529333528424116912879167156162205151243740518715711632628028933428123816812779167153159205251243240018715711632527528433427823516812779166151156205351042739518515711632427027933227523216712779166148154205450942239018515711632426527433227223016712779166145151205550742139018415611632326527433127223016612679165145151Apx Table B.5 Four and eight hour battery cost data by storage duration, component and total costs  (multiply by duration to convert to $/kW)
Battery storage (4 hrs)Battery storage (8 hrs)TotalBatteryBOPTotalBatteryBOPCurrent policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050Current policiesGlobal NZE post 2050Global NZE by 2050$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh$/kWh202254654654641141141113513513548548548540440440481818120235495495494194194191301301304884884884124124127575752024517498477393370350123127128459438418386364343727474202548845241736932729212012512643339435936232128671737320264634123673432892431201231244113553103372832397472722027449376324330255203118121122403320269330250199737071202844034729132322817211711912039429724132222817172696920294263182613102021431161171183802692113092011437168682030411293235297178119114115116366244186296177119706767203139727120128415887113113114352223153283158876965662032382265198271155861121111123382181512701548669646520333682611962571528511010911032421414925715185686364203435325619324414985109107108310210147243148846762632035339251191231146841081051072962061452301458466616220363242461882181438310610310528220214421714283656061203731024218620514183105102103269199142204140826559602038303238183199138821041001022621951401981388164585920392972341811951368110398100257192139194136816357582040291230179190134811019698251189137189133806256572041287227177186132801009597247187136185132806155562042283225175185132809993952441851351841318061545520432812231731831318098919424218413418213180605354204427522017217813080979092237181132177130795952532045270217170175129799588902331801311741297959515220462672151681721297994868922917813017212879585051204726421316617112879938587227176129170127785749512048261211165169127799283862251751281681277856485020492592091631681277991828522317412716712678564749205025720716216712778908083221172126166126785547482051256205160166126788979822201711251651267855464720522562041591661267889788022017012416512678554547205325420215716512678897679219169124164125785544462054254201156165126788975782191681231641257855434520552532001561641257889757821816812316312578544345Apx Table B.6 Pumped hydro storage cost data by duration, all scenarios, total cost basis
$/kW$/kWh6hrs8hrs12hrs24hrs24hrs Tas48hrs48hrs Tas6hrs8hrs12hrs24hrs24hrs Tas48hrs48hrs Tas2022288831383369440427306616304341133524015797118552023283230773304431926786488298448139228118311413864202427773018324042352625636129264723852751801121356320252723295931774152257462382869463377270176109133622026266929003114407025246114281345437026517310713060202726122839304839842470598527534453632591701051275920282556277829823898241758562694435355254166103125582029250027162916381223635726263442634724916210112257203024432655285037262310559725744173402431599811955203124402652284737212307559025714073322381559611754203224372648284337172304558325684073312371559611654203324342645284037122302557725654063312371559611654203424312642283737082299557025624063312371559611654203524292639283337042296556425594053302361559611654203624262636283036992294555725564053302361549611654203724232633282736952291555125534043302361549611654203824202630282436912288554525514043292361549511654203924182627282136872286553925484033292351549511654204024152624281736832283553225454033282351549511554204124112620281336772280552425414023282351539511554204224082616280936722276551625374023282341539511553204324042612280536662273550725334013272341539511553204424002608280036602269549925294013272341539511553204523972604279636552266549025264003262331539511553204623932600279236492263548225223993262331529411453204723892596278736442259547425183993252331529411453204823862592278336382256546525143983252321529411453204923822589277936332252545725103983242321529411453205023782585277536272249544925063973242321519411453205123742580277036212245544025023963232311519411453205223702576276536152241543024983963232311519411353205323662571276136092237542124943953222301519311353205423622567275636022234541224893943212301509311352205523582563275135962230540224853943212301509311352Apx Table B.7 Storage current cost data by source, total cost basis
$/kWh$/kWAurecon 2019-20Aurecon 2020-21Aurecon 2021-22Aurecon 2022-23GenCost 2019-20AEMO ISP Dec 2021AEMO ISP Jun 2022/CSIROFichtner Engineering 2023Aurecon 2019-20Aurecon 2020-21Aurecon 2021-22Aurecon 2022-23GenCost 2019-20AEMO ISP Dec 2021AEMO ISP Jun 2022/CSIROFichtner Engineering 2023Battery (1hr)1092870823931----1092870823931----Battery (2hrs)687583547673----1375116610951346----Battery (4hrs)543464432546----2171185417292185----Battery (8hrs)492409379485----3939327130353882----PHES (8hrs)----275315392-----220325203138-A-CAES (12hrs)---350-------4203----PHES (12hrs)----195226281-----234127113369-CST (15hrs)-------435-------6525PHES (24hrs)----145147183-----346935374404-PHES (24hrs) Tasmania-----91114------21852730-PHES (48hrs)----81111138-----388753136616-PHES (48hrs) Tasmania-----5164------24683043-Notes: Batteries are large scale. Small scale batteries for home use with 2-hour duration are estimated at $1600/kWh (Aurecon, 2023).





Apx Table B.8 Data assumptions for LCOE calculations
ConstantLow assumptionHigh assumptionEconomic lifeConstruction timeEfficiencyO&M fixedO&M variableCO2 storageCapitalFuelCapacity factorCapitalFuelCapacity factorYearsYears$/kW$/MWh$/MWh$/kW$/GJ$/kW$/GJ2022Gas with CCS251.544%16.47.21.9435414.089%435420.053%Gas combined cycle251.551%10.93.70.0176614.089%176620.053%Gas open cycle (small)251.536%12.612.00.0149914.020%149920.020%Gas open cycle (large)251.333%10.27.30.094314.020%94320.020%Gas reciprocating251.141%24.17.60.0200414.020%200420.020%Hydrogen reciprocating251.032%33.00.00.0243814.620%243821.920%Black coal with CCS302.030%77.88.04.1110406.989%1104010.553%Black coal302.040%53.24.20.053986.989%539810.553%Brown coal304.032%69.05.30.081800.689%81800.753%Biomass (small scale)301.329%131.68.40.078250.589%78252.053%Large scale solar PV300.5100%17.00.00.015720.032%15720.019%Wind onshore251.0100%25.00.00.026420.048%26420.029%Wind offshore (fixed)253.0100%149.90.00.056820.052%56820.040%2030Gas with CCS251.544%16.47.21.942838.089%427916.853%Gas combined cycle251.551%10.93.70.016728.089%163616.853%Gas open cycle (small)251.536%12.612.00.013928.020%135416.820%Gas open cycle (large)251.333%10.27.30.08038.020%80316.820%Gas reciprocating251.141%24.17.60.017528.020%171616.820%Hydrogen reciprocating251.032%33.00.00.0208914.620%208721.920%Black coal with CCS302.030%77.88.04.196392.389%95974.053%Black coal302.040%53.24.20.046682.389%45584.053%Brown coal304.032%69.05.30.072080.789%70350.753%Biomass (small scale)301.329%131.68.40.075710.589%75192.053%Nuclear (SMR)303.035%200.05.30.0145860.589%181670.760%Large scale solar PV300.5100%17.00.00.010710.032%10580.019%Wind onshore251.0100%25.00.00.019130.048%19890.029%Wind offshore (fixed)253.0100%149.90.00.027550.054%48030.040%2040Gas with CCS251.544%16.47.21.935188.289%367317.953%Gas combined cycle251.551%10.93.70.016108.289%161017.953%Gas open cycle (small)251.536%12.612.00.013328.220%133217.920%Gas open cycle (large)251.333%10.27.30.07908.220%79017.920%Gas reciprocating251.141%24.17.60.016888.220%168817.920%Hydrogen reciprocating251.032%33.00.00.0205411.620%205417.420%Black coal with CCS302.030%77.88.04.187391.889%88964.053%Black coal302.040%53.24.20.044841.889%44844.053%Brown coal304.032%69.05.30.069210.789%69210.753%Biomass (small scale)301.329%131.68.40.072480.589%76602.053%Nuclear (SMR)303.040%200.05.30.073860.589%185100.753%Large scale solar PV300.5100%17.00.00.06530.032%8390.019%Wind onshore251.0100%25.00.00.017200.048%19590.029%Wind offshore (fixed)253.0100%149.90.00.025890.057%46590.040%2050Gas with CCS251.544%16.47.21.930128.289%348817.953%Gas combined cycle251.551%10.93.70.015658.289%156517.953%Gas open cycle (small)251.536%12.612.00.012958.220%129517.920%Gas open cycle (large)251.333%10.27.30.07688.220%76817.920%Gas reciprocating251.141%24.17.60.016428.220%164217.920%Hydrogen reciprocating251.032%33.00.00.0199710.220%199715.320%Black coal with CCS302.030%77.88.04.180831.889%85664.053%Black coal302.040%53.24.20.043611.889%43614.053%Brown coal304.032%69.05.30.067310.789%67310.753%Biomass (small scale)301.329%131.68.40.072920.589%77072.053%Nuclear (SMR)303.045%200.05.30.074310.589%186220.753%Large scale solar PV300.5100%17.00.00.05130.032%6760.019%Wind onshore251.0100%25.00.00.016420.048%19270.029%Wind offshore (fixed)253.0100%149.90.00.025390.061%45110.040%Notes: Large-scale solar PV is single axis tracking. The discount rate used for all technologies is 5.99% unless a risk premium of 5% is added.

Apx Table B.9 Electricity generation technology LCOE projections data, 2022-23 $/MWh
CategoryAssumptionTechnology2022203020402050LowHighLowHighLowHighLowHighPeaking 20% loadGas turbine small232292167254166263164262Gas turbine large210275138234139245138243Gas reciprocating240293175251173259171257H2 reciprocating298381282364246312228285Flexible 60-80% load, high emissionBlack coal129205811327413073128Brown coal117189106167104165101161Gas123180801558016280161Climate policy risk premiumBlack coal171275116191108188106185Brown coal204335183292177288173281Gas136200911749118191179Flexible 60-80% load, low emissionBlack coal with CCS220348150245135233128227Gas with CCS174257123229117227111223Nuclear (SMR)198349117399117401Biomass (small scale)110193107188104191105191VariableStandaloneSolar PV4881355823482041Wind onshore5896447540743873Wind offshore149194861707816772163Variable with integration costsWind & solar PV combined60% share659470% share679680% share689690% share71100

Apx Table B.10 Hydrogen electrolyser cost projections by scenario and technology, $/kW
Current policiesGlobal NZE by 2050Global NZE post 2050AlkalinePEMAlkalinePEMAlkalinePEM2022183730061837300618373006202316062628160626281606262820241369224113072139136922412025119120451086189111912035202610511866914167210511848202793316977751474933167420288461544663130083615152029787140557011467531372203072512784931010682124220316931163461890645112520326651058433785607101920336419624056925829232034614876381610558836203559579736053854175720365737253414745256852037557660325418511621203853260031136949756220395195462993254865092040497497287287461461204148648627327345145120424684682612614384382043456456250250423423204444444424024041141120454354352312313953952046418418223223383383204740740721521537237220483953952042043633632049375375195195355355205035535518218234234220513553551821823423422052353353180180342342205335335318018034234220543523521771773413412055352352177177341341

Appendix C  Technology inclusion principles
GenCost is not designed to be a comprehensive source of technology information. To manage the cost and timeliness of the project, we reserve the right to target our efforts on only those technologies we expect to be material, or that are otherwise informative. However, the range of potential futures is broad and as a result there is uncertainty about what technologies we need to include. 
The following principles have been established to provide the project with more guidance on considerations for including technology options.
C.1 Relevant to generation sector futures
The technology must have the potential to be deployed at significant scale now or in the future and is a generation technology, a supporting technology or otherwise could significantly impact the generation sector. The broad categories that are currently considered relevant are:
* Generation technologies
* Storage technologies
* Hydrogen technologies
* Consumer scale technologies (e.g., rooftop solar, batteries).
Auxiliary technologies such as synchronous condensers, statcoms and grid forming inverters are also relevant and important but their inclusion in energy system models is not common or standardised due to the limited representation of power quality issues in most electricity models. Where they have been included, results indicate they may not be financially significant enough to warrant inclusion. Also, inverters, which are relevant for synthetic inertia, are not distinct from some generation technologies which creates another challenge.
C.2 Transparent Australian data outputs are not available from other sources
Examples of technologies for which Australian data is already available from other sources includes:
* Operating generation technologies (i.e., specific information on projects that have already been deployed)
* Retrofit generation projects
* New build transmission.
Most of these are provided through separate AEMO publications and processes.
Other organisations publish information for new build Australian technologies but not with an equivalent level of transparency and consultation. New build cost projections also require more complex methodologies than observing the characteristics of existing projects. There is a distinct lack of transparency around these projection methodologies. Hence, the focus of GenCost is on new build technologies.
C.3 Has the potential to be either globally or domestically significant
A technology is significant if it can find a competitive niche in a domestic or global electricity market, and therefore has the potential to reach a significant scale of development.
Technologies can fall into four possible categories. Any technology that is neither globally nor domestically significant will not be included anywhere. Any other combination should be included in the global modelling. However, we may only choose to include domestically significant technologies in the current cost update which is subcontracted to an engineering firm.
Apx Table C.1 Examples of considering global or domestic signficance
Globally significantDomestically significantExamplesYesYesSolar PV, onshore and offshore wind YesNoNew large-scale hydro. No significant new sites expected to be developed in Australia
Conventional geothermal energy: Australia is relatively geothermally inactive
Large scale nuclear: scale is unsuitableNoYesNone currently. A previous example was enhanced geothermal, but economics have meant there is no current domestic interest in this technologyNoNoEmerging technologies that have yet to receive commercial interest (e.g., fusion) or have no commercial prospects due to changing circumstances (e.g., new brown coal)C.4 Input data quality level is reasonable
Input data quality types generally fall into 5 categories in order of highest (A) to lowest (E) confidence in Australian costs
A. Domestically observable projects (this might be through public data or data held by engineering and construction firms)
B. Extrapolations of domestic or global projects (e.g., observed 2-hour battery re-costed to a 4-hour battery, gas reciprocating engine extrapolated to a hydrogen reciprocating engine)
C. Globally observable projects
D. Broadly accepted costing software (e.g., ASPEN)
E. “Paper” studies (e.g., industry and academic reports and articles)
While paper studies are least preferred and would normally be rejected, where we need to include a technology because of its potential to be globally or domestically significant in the future, and that technology only has paper studies available as the highest quality available, then we will use paper studies. We will not use confidential data as a primary information source since by definition they cannot be validated by stakeholders. However, confidential sources could provide some guidance to interpreting public sources.
C.5 Mindful of model size limits in technology specificity
Owing to model size limits, we are mindful of not getting too specific about technologies but achieving good predictive power (called model parsimony). We often choose:
* A single set of parameters to represent a broad class (e.g., selecting the most common size)
* A leading design where there are multiple available (e.g., solar thermal tower has been selected over dish or linear Fresnel or single axis tracking solar PV over flat)
The approach to a technology’s specificity may be reviewed (e.g., two sizes of gas turbines have been added over time and offshore wind turbines have been split into fixed and floating). For a technology like storage, it has been necessary to include multiple durations for each storage as this property is too important to generalise. As it becomes clearer what the competitive duration niche is for each type of storage technology, it will be desirable to remove some durations. It might also be possible to generalise across storage technologies if their costs at some durations is similar.

Appendix D  Responses to feedback
D.1 Input prior to the IASR 2023 consultation
D.1.1 Following up on feedback from GenCost 2021-22
We received feedback from the biomass and solar thermal industries that their technologies were not being represented in a way that reflected how those technologies were likely to be deployed. In regard to biomass, it was requested that the technology should include waste heat utilisation. For solar thermal, we were advised that this technology is likely to be configured with a greater emphasis on nighttime generation and 15 hours duration. These technologies have been adjusted in the Aurecon (2023) report and those changes flowed through to this GenCost report.
D.1.2 October 2022 webinar
At the October 2022 webinar it was noted that the impact of inflationary pressures on technologies that are not currently being built could not be observed but this needs to be addressed. As a result, CSIRO developed a method and there are more details on our approach in the body of this report
D.1.3 Energy Policy Institute of Australian policy paper 3/2022
On 23rd September 2022 the Energy Policy Institute of Australia (EPIA) publicly released a paper called Future Australian Electricity Generation Costs – A review of CSIRO’s GenCost 2021-22 Report30. GenCost receives uninvited input and questions from stakeholders throughout the year outside of our planned stakeholder engagement processes. Stakeholders are encouraged to continue providing input - both invited and uninvited. We typically provide a summary of feedback received throughout the year and how it has been incorporated. However, the depth of input received in the EPIA paper is beyond what a summary could adequately address. Therefore, in this case, GenCost has provided a more detailed response in the form of a short report which is available at: https://publications.csiro.au/publications/publication/PIcsiro:EP2022-5083.
D.2 Feedback from the IASR 2023 consultation
Given there are a number of overlapping areas of feedback included in submissions to the consultation, for efficiency, the discussion below addresses broad themes rather than each specific item of input. Two specific items which have been addressed in the body of the report are to include A-CAES in our storage technologies and to update our CST costs to account for more recently available analysis. A-CAES is now included in our comparison of current storage costs (Section 2.4) and CST costs have been updated based on Fichtner Engineering (2023).
D.2.1 Capacity factor range and trend in LCOE calculations
Some submissions requested CSIRO review the capacity factor assumptions it uses in its LCOE calculations. It is important to note that CSIRO’s LCOE capacity factor assumptions are for the LCOE calculations only. They are not used by AEMO in any of their work31. The goal of the LCOE capacity factor assumptions is to help define the range of capacity factors and subsequent costs that might be experienced by a new build technology. The range should represent uncertainties regarding location, technology improvements and market operation. Location uncertainty is mostly relevant for variable renewables whose capacity factors are affected by local weather.
Technological uncertainty refers to changes in how a technology performs over time due to improvements which might mean for example, a longer operating time which improves the capacity factor. For example, if a wind turbine is better able to capture energy from low wind speeds or can access stronger winds by being built taller, it can operate for more hours per year. Finally, the market ultimately determines when and for how long a technology gets to operate and so this factor also cannot be ignored in determining a capacity factor range.
The capacity factor range assigned to new build technologies are designed to be higher than the historical range. This is based on the view that new build technologies may include some technical advancements on their historical predecessors which mean they do not enter at the low range. Consequently, their low range capacity factor assumption has been closer to the average capacity factor rather than the worst case. However, the high range assumption is closer to the historical high range with some exceptions and accounting for changes over time. Some additional text in the body of the report has been added to explain this approach as previous reports did not provide any significant discussion of this topic. The analysis below has led to changes in capacity factor assumptions which are incorporated in this report.
Review of data and proposed changes
The direction of feedback is that wind and solar capacity factors are too high and capacity factors for baseload generation such as coal too low. For large-scale solar PV the capacity factor range applied in the consultation draft for 2022 is 22% to 32% widening to 19% to 32% by 2050. For wind the range is 35% to 44% in 2022 and 35% to 50% in 2050. Apx Figure D.1 shows the historical range for existing solar PV and wind capacity in the NEM32.

Apx Figure D.1 Historical maximum, minimum and average capacity factors for existing NEM solar PV (left) and onshore wind (right) generation
The capacity factor for wind has been relative steady, however there is a notable decline in the capacity factor for solar PV. This could represent several factors including curtailment due to transmission congestion, self-curtailment due to increased negative price periods during times of solar PV generation and increased rainfall and cloudiness due to the dominance of the La Nina weather pattern in recent years33.
It is unclear if this declining capacity factor trend for solar PV will be temporary, stable or worsen. More solar PV capacity could worsen capacity factors through increased congestion. On the other hand, there are other factors which could improve capacity factors. There is a significant program of new transmission capacity planned to be built in Australia. There is also a significant pipeline of storage projects. The weather pattern is also forecast to shift back to El Nino in the short term which is less associated with rainfall on the east coast. Finally, coal fired power stations partly contribute to the prevalence of negative price periods because of their inability to reduce generation below their minimum run rate during periods of high renewable supply. Increased coal retirements might therefore potentially reduce those events depending on what they are replaced with.
The Australian NEM solar PV and wind capacity factor trends are somewhat at odds with global trends. IRENA (2022) published the capacity factor changes for the total global capacity (Apx Figure D.2). The global data shows a much flatter trend for solar PV and an increasing trend for onshore wind. Although Australia does have better than average solar resources, the range for solar PV indicates that the data may include smaller capacity solar PV systems which tend to have a lower capacity factor on average34. However, the range for wind is more comparable with the NEM.

Apx Figure D.2 Historical high, low and weighted average capacity factors for existing global solar PV (left) and onshore wind (right) generation.
Based on the historical data, 32% remains a plausible maximum capacity factor for solar PV. The previously assumed low range capacity factor of 22% is equal to the most recent average. However, our updated approach is to apply a discount of 10% to the historical average which results in a value of 19%. The previous value was 22% declining to 19% by 2030 and flat thereafter. As such, this updated approach brings forward the previous 2030 value to 2022.
The previous values for onshore wind were a low range of 35% and a high range of 44% in 2022 (increasing to 50% by 2050). The historical wind data suggests an average of 33% and high of 48%. The updated values are therefore a low range of 29% (applying the 10% discount) and high of 48%. International data suggests the potential for an improving wind capacity factor could be justified. However, given this has not been the case in Australia for the last decade, this range will no longer be adjusted over time. These changes represent a broadening out of the wind capacity factor assumptions of 4% at the high end and 6% at the low end (in 2022).
The historical NEM capacity factors for brown and black coal generation are shown in Apx Figure D.3. Although there is a near zero probability of Australian deployment of new coal plant given existing government policy, these capacity factors are a guide to what could be achieved by alternative low emission baseload generation were they to emerge35.
The data indicates that some coal plant are achieving up to 89% capacity factor. This is higher than the maximum of 80% applied in the consultation draft. The minimum capacity factor data is difficult to interpret because it includes plant which have been in the process of retiring and may not be operating under normal conditions. However, the average capacity factor for black coal is 57%. The average for brown coal is higher at 73%, reflecting their market bidding advantage associated with using a lower cost fuel. The average for all coal is 59% which is similar to the value of 60% used as the low range for flexible plant.
 
Apx Figure D.3 Historical maximum, minimum and average capacity factors for existing NEM brown coal (left) and black coal (right) generation.
The average capacity factor of black coal has been declining from around the introduction of solar PV. This demonstrates the widely expected competitive tension arising from the near zero short run marginal cost variable renewables interacting with coal plant that are unable to reduce their generation below a minimum threshold. However, it is also interesting to note that low capacity factors for coal plant have been a feature of that technology group since before renewables were a significant share.
Brown coal’s lower marginal cost appears to provide some protection relative to black coal to very low capacity factors. The competition faced by coal is expected to intensify as variable renewable capacity increases to meet state targets36 but will at other times improve as coal retires37 and more storage is deployed.
Based on the historical data, the maximum capacity factor for flexible plant is increased to 89% which is higher than the previous assumption of 80%. For the low range capacity factor assumption, if we average across brown and black coal average capacity factor and apply a 10% discount (the same as the approach for wind and solar PV) the new low range value is 53%. This widens the range from previous capacity factor values by 9% at the top and 7% at the bottom.
D.2.2 Long term materials and supply chain constraints and their impact on technology costs
While some submissions supported CSIRO’s assumption that technology costs would return to their normal pathway by 2027 or even sooner, several submissions supported sustaining higher prices for longer reflecting unresolved or extended supply chain constraints. In some cases, it was suggested that the method could single out technologies that rely on rarer inputs. Whether universal or for selected technologies, the central idea is that technology costs will remain relatively high due to ongoing tight supply of materials relative to demand growth.
CSIRO generally does not support this view. Sustained commodity prices would require some combination of:
* Strong global economic growth despite rising interest rates and slowing population growth in many countries or growth in demand for energy technologies that constantly outstrips supply
* A sustained war in the Ukraine and limited transition away from fossil fuels in affected regions. This would mean that fossil fuel supply from Russia continues to be low and its normal customers have failed to find significant opportunities to substitute alternative energy sources
* Insurmountable new entrant barriers in the mining sector such that, while product prices are highly profitable, companies are unable to commence new mining operations
Some of these factors are difficult to predict and none can be ruled out entirely. However, the historical evidence is that while there is significant volatility, commodity prices are flat to declining over the long run (measured over century scale data). The volatility is characterised by super cycles decades long as well as shorter term cycles lasting only 4 years on average. (Cashin and McDermott, 2002; Harvey et al, 2012).
When commodity price super cycles have occurred, they tended to be associated with periods of high global economic growth – that does not appear to be a feature of current and expected world conditions. As such, the central assumption of an end to inflationary pressures after a few years (i.e., a period of short cycle volatility) is reasonable in the context of historical experience. The argument for a longer cycle is based not around a period of high economic growth but growth specific to the clean energy technology sector. It is uncertain whether this would be enough to drive a super cycle as other parts of the economy may grow slower or decline, offsetting this source of faster demand growth.
The scale of deployment in clean energy technology relative to today is not grounds alone for sustained cost pressures. Linear growth, for example, is unlikely to support sustained price pressures. Once the relevant labour and materials markets have scale up to meet a strong period of growth, a linear period of growth implies it can meet all growth without any further expansion of supply capacity. Growth has to be non-linear to present an ongoing need to scale up supply capacity or a failure of supply to meet demand (triggering price rationing).
In the consultation draft we assumed that all scenarios experienced the same cost path until 2027, but we flagged that we would introduce more uncertainty in the final projections.
Having considered the feedback, the resolution of the price bubble has been extended to 2030 for Global NZE by 2050 and Global NZE post 2050. These two scenarios are about relatively stronger global climate policy ambition supported by large deployments of low emissions technologies. They will likely result in non-linear growth in demand for these technologies up until 2030. Globally, governments and industry have a tendency to set clean energy targets at the turning point of decades. As a result, 2030 is likely to result in a surge of activity to meet those targets. However, there are a number of projections, including those from the IEA, indicating the rate of deployment appears to be more linear and in some scenarios, slower than linear post-2030.
D.2.3 The appropriateness of treating past investment costs as sunk in 2030 LCOE calculations
A submission from Australian Resources Development (ARD) has indicated some stakeholder misunderstanding over how integration costs between different higher variable renewable energy shares relate to each other, and in addition, how calculation of those costs might be affected by the approach we apply whereby costs for all existing capacity in 2030 are treated as sunk. Putting these two issues together, ARD was concerned that the integration costs calculated for variable renewable shares failed to include the cost of moving from the current variable renewable share to the business as usual renewable share in 2030. To address this concern this explainer begins with the concept of sunk costs and then explores issues around the marginal cost of supply and how those costs change over time and with different variable renewable shares.
Sunk costs in the generation sector
Investment costs relate to the capital items of generation and storage infrastructure. These are considered to be sunk once they occur, by which we mean they are not immediately recoverable, for a variety of reasons:
* They cannot be returned to the seller unless there is a defect under warranty
* They can be sold to another party but that does not change their sunk status, it only changes the owner for which costs remain sunk
* The market does not owe the owner a reasonable return on investment
The sunk nature of the costs is in fact the reason why investors very carefully study the market before investing. If costs were not sunk then there would be no penalty for bad investments. Investors take the risk that future market prices will be high enough to provide a reasonable return on investment gradually over the economic life of the asset.
There are mechanisms external to the market which partially address investor risk. While all generation supply by units above 30MW is settled on the market, external contracts can buffet the market price volatility through long term contracts for supply. The AEMC provide an overview of the types of external contracts that are entered into, how they relate to the spot price and their relationship to the physical market38. An important point to remember is that while external contracts are a way of increasing the likelihood of achieving a reasonable return on investment, the average spot price and external contract prices should converge in the long run. That is, there is a limit on the premium buyers will pay for longer term supply if expectations about the future spot price are significantly lower than the contract market. Hence the general statement that the market does not owe the owner a reasonable return on investment remains true despite the existence of external contract markets.
It is also true that the market will at times over-reward investors, temporarily providing a greater than reasonable return on investment (if it were to continue). The NEM grid dispatch system sets the price each five minutes according to the last bid required to meet demand. All lower bids that were also accepted to meet demand receive the same price as the last bid accepted, regardless of their lower bid price or their actual costs of supply. However, these relatively high-priced periods do end for a number of reasons:
* Demand falls such that high bids are no longer needed
* New capacity enters the market which is able to compete with the higher bids
* Fuel or other input costs of existing generators decrease making it possible for them to lower their bids when sufficient competition encourages them to do so
Long term average prices and generation system costs
If, as we have discussed, the market does not have an inherent obligation to pay for current capacity, then what does the spot price represent? In the most immediate sense, the spot price represents the last bid required to clear the market – that is for demand to be only just met by total supply from all lower bids plus the last bid. However, the spot price also has an investment signalling function which plays out beyond the five-minute dispatch and governs its long-term average.
Investors observe the spot price over time and compare it to the costs of available generation or storage technologies. If the spot price is too low to provide confidence that a reasonable return on investment is achievable, then new investment does not proceed beyond those already committed. If this period of spot-prices-lower-than-new-investment-costs prevails over a long period, then no new capacity is entering while some existing capacity must eventually retire out of the current stock of capacity – either because a low spot price means they cannot recover short term costs (e.g., fuel, operating and maintenance) or because the plant’s technical life has ended. Eventually, this loss of capacity reduces the number of low bids available to meet demand thereby forcing up the spot price since higher bids must be accepted to meet demand.
If supply were perfectly elastic, the spot price need only go high enough to encourage the level of capacity required to lower the market bids to the level of the cost of new entrants. However, in reality, investors face delays in bringing capacity to market because:
* Infrastructure takes years to plan and construct,
* Investors may need to see a sustained higher price to give them confidence that prices will remain higher on average rather than reflecting a more temporary phenomenon
* Investors may also need to consider the actions of other investors who may also bring new capacity to the market.
If spot prices overshoot the cost of new capacity for these reasons, prices should return to a lower level once the new capacity enters and conditions become more competitive.
Overall, these dynamics mean that the spot price will under and overshoot the cost of new capacity, and these under and overshoot periods can last for years. However, in the long run, attrition of existing plant and competition between new entrants means that, on average, the spot price will ultimately reflect the cost of new capacity.
Implications for how to cost technology share scenarios
This market fundamentals discussion has explained why for any year that we wish the know the cost of achieving any given baseload or variable renewable generation share (as an indicator of the resulting average spot price trend), we only need to know the cost of the new entrants required to reliably deliver that generation technology mix.
In GenCost we take the year 2030 and observe the combination of renewables and supporting technology that must be built for four levels of variable renewable energy (VRE) share – 60%, 70%, 80%, 90%. – starting form a system that has already exceeded 50% VRE. ADR’s concern is that the costs to achieve 30%, 40% and 50% should also be added to those four VRE share costs. From the discussion above it should be clear that this is not the case. Each specific VRE share has its own cost which reflects the last new entrant needed to reach that share reliably, which will be a combination of renewable generation capacity, storage capacity, peaking capacity, renewable energy zone transmission and other additional transmission capacity. Some of these capacities increase proportionally with VRE share and some not. The results from the 2022 ISP modelling for the Progressive change and Step change scenarios provide useful indicators of the changing system needs with increasing VRE share of generation39.
The ratio of storage capacity needed to meet a growing VRE share of generation increases fairly proportionally up to around a 70% VRE share but levels out thereafter with growth peaking capacity becoming more important after that point. Little to no new renewable energy zone transmission capacity is needed to move from 20% to 60% VRE share of generation. However, to move beyond that REZ transmission expenditure needs to increase with VRE share. Conversely, at higher levels of VRE share other transmission capacity is not needed. However, it does increase at low to medium VRE shares.

Apx Figure D.4 Changes in storage, peaking plant and transmission deployment with changes in VRE share, Progressive change scenario


Apx Figure D.5 Changes in storage, peaking plant and transmission deployment with changes in VRE share, Step change scenario
Based on these modelling results we can build a picture of the marginal cost of the last new entrant required to achieve each VRE share in 10% increments (Table 1):
* At low VRE shares the marginal new entrant is renewables with a low ratio of storage
* At low-medium VRE share the marginal new entrant is renewables with a medium ratio of storage and significant additional transmission. 
* At medium-high VRE share the marginal new entrant is renewables with a high ratio of storage, new peaking plant and new REZ transmission and limited new transmission
* At a high VRE share the marginal new entrant is renewables with a high ratio storage (but potentially lower than previous entrants), new peaking plant and new REZ transmission
Apx Table C.1 Rate of new entrant investment required to increase VRE share reliably and efficiently
Change in VRE shareStoragePeaking plantREZ transmissionOther transmission20% to 30%Increase rateNone requiredNone requiredNone required30% to 40%Increase rateNone requiredNone requiredMay be required40% to 50%Increase rateNone requiredMay be requiredIncrease rate50% to 60%Increase rateMay be requiredIncrease rateIncrease rate60% to 70%Increase rateIncrease rateIncrease rateMaintain rate70% to 80%Maintain rateIncrease rateIncrease rateMaintain rate80% to 90%Decrease rateMaintain rateMaintain rateMaintain rateWhenever these VRE share transitions occur, the combined cost of these investments will be the main driver for electricity costs subject to any volatility at the time due to excess or tight capacity conditions. As discussed above, until the market corrects the price through attrition or new entrants, spot prices may under or overshoot the minimum cost of entry required for reliability for a period of time. A delay in making transmission available is one possible source of delay in new entrant competition. The cost of transmission is recovered separately from the generation spot market through regulated revenue recovery procedures. However, GenCost has included it as part of variable renewable integration costs given its importance to variable renewable deployment.

Shortened forms
AbbreviationMeaningABSAustralian Bureau of StatisticsAEAlkaline electrolysisAEMOAustralian Energy Market OperatorAPGTAustralian Power Generation TechnologyBAUBusiness as usualBECCSBioenergy carbon capture and storageBOPBalance of plantCCSCarbon capture and storageCCUSCarbon capture, utilisation and storageCHPCombined heat and powerCO2Carbon dioxideCPIConsumer price indexCSIROCommonwealth Scientific and Industrial Research OrganisationCSPConcentrated solar powerEVElectric vehicleGALLMGlobal and Local Learning ModelGALLMEGlobal and Local Learning Model Electricity GALLMTGlobal and Local Learning Model TransportGJGigajouleGWGigawattH2HydrogenhrsHoursIASRInputs, Assumptions and Scenarios ReportIEAInternational Energy AgencyIGCCIntegrated gasification combined cycleISPIntegrated System plankWKilowattkWhKilowatt hourLCOELevelised Cost of ElectricityLCVLight commercial vehicleMCVMedium commercial vehicleLi-ionLithium-ionLRLearning RateMtMillion tonnesMWMegawattMWhMegawatt hourNDCNationally Determined ContributionNEMNational Electricity MarketNSWNew South WalesNZENet zero emissionsO&MOperations and MaintenanceOECDOrganisation for Economic Cooperation and DevelopmentPEMProton-exchange membrane electrolysispfPulverised fuelPHESPumped hydro energy storagePVPhotovoltaicREZRenewable Energy ZoneSDSSustainable Development ScenarioSMRSmall modular reactorSTEPSStated PoliciesSWISSouth-West Interconnected SystemTWhTerawatt hourVPPVirtual Power PlantVREVariable Renewable EnergyWAWestern AustraliaWEOWorld Energy Outlook

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1 90% is about as high as variable renewable deployment is likely to need to go as increasing it further would result in the undesirable outcome of shutting down existing non-variable renewable generation from biomass and hydroelectric sources. Approximately 55% will be achieved in 2030 without any new policies (excluding Northern Territory).
2 Search GenCost at https://data.csiro.au/collections 
3 This is not strictly true of all models but is most true of long-term investment models. In other models, investment costs are converted to an annuity (adjusted for different economic lifetimes), or additional capital costs may be added later in a project timeline for replacement of key components.
4 The Australian Nuclear Science and Technology Organisation has joined an International Atomic Energy Agency project to appraise the costs of nuclear SMR. The project is due for completion in December 2024. Economic Appraisal of Small Modular Reactors Projects: Methodologies and Applications | IAEA. EFWG (2019) is the most recently available data source.
5 The storage duration used throughout this report refers to the maximum duration for which the storage technology can discharge at maximum rated power. However, it is important to note that every storage technology can discharge for longer by doing so at a rate lower than their maximum rated power
6 This data only became available after Aurecon had completed its analysis of solar thermal and so differs from their estimate of solar thermal capital costs which was presented in the consultation draft. Aurecon has therefore not been able to assess the Fichtner Engineering analysis. However, given the depth of the new analysis, the GenCost team has decided to use the new data as its current estimate of solar thermal capital costs.
7 Component costs basis is when the power and storage components are separately costed and must be added together to calculate the total project cost.
8 The batteries in this publication have additional capacity which is not usable (e.g., there is typically a minimum 20% state of charge). This unusable capacity is not counted in the capacity of the battery or in any expression of its costs. When other publications include this unusable capacity the depth of discharge is less than 100%.
9 The application of a combination of carbon prices and other non-carbon price policies is consistent with the approach applied by the IEA. While we directly apply the IEAs published carbon prices, we design our own implementation of non-carbon price policies to ensure we match the emissions outcomes in the relevant IEA scenario. Structural differences between GALLM and the IEA’s models means that we cannot implement the exact same non-carbon price policies. Even if our models were the same, the IEA’s description of non-carbon price policies is insufficiently detailed to apply directly.
10 In the long run, fossil fuel prices will fluctuate due to cycles of demand and supply imbalances. However, underlying these fluctuations, prices should track the cost of production given the competitive nature of commodity markets. This relationship holds whether demand is falling or rising over the long run.
11 If the world ramps up to X GW per year technology manufacturing capacity by a certain date, then, without expanding manufacturing capacity any further, it can meet any future capacity target after that date up to the value of bX (where b is the years since the manufacturing capacity was established). The future capacity target would need to include all capacity needed to meet growth as well as replace retiring plant.
12 It is referred to as an easement cost index in that document
13 https://www.aer.gov.au/system/files/AGN%20-%20Attachment%207.8A%20-%20BIS%20Oxford%20Input%20Cost%20Escalation%20Forecasts%20to%202025-26%20-%2013%20January%202021.pdf
14 Economies can reduce their emissions by reducing the activity of emission intensive sectors and increasing the activity of low emission sectors. This is not the same as improving the energy efficiency of an emissions intensive sector. Industry transformation can also be driven by changes in consumer preferences away from emissions intensive products. 
15 Search GenCost at https://data.csiro.au/collections 
16 CCUS Projects Database - Data product - IEA
17 The Cost of Solar Panels - Solar Panel Price | Solar Choice
18 This is more an economic than absolute technical limit
19 This improvement occurs generically for the capital cost of pumped hydro energy storage. However, any capital cost estimate is a mean of projects that may have a wide distribution of costs due to site conditions. It is possible that poorer site conditions may offset cost savings from improved drilling productivity.
20 LCOE is a measure of the long run marginal cost of generation which could partly inform generator bidding behaviour in a model of the electricity dispatch system. However, in such cases, it would be expected that the LCOE calculation would be internal to the modelling framework to ensure consistency with other model inputs rather than drawn from separate source material.
21 This year makes the most sense within the framework applied because there is enough time to plausibly reach high VRE shares but in the counterfactual or business as usual variable renewable shares are still expected to be below 60% in the larger states (although the total renewable share will be above that level). In the 2040s and 2050s, much of the existing flexible capacity in the system will retire due to end of asset life and be replaced with variable renewables (see AEMO ISP and other long-term modelling). As such, most of the additional costs will already be incurred in the counterfactual.
22 This does not preclude other types of projects proceeding in reality but is a reflection of modelling inputs in 2030.
23 The capacity factor range assigned to new build technologies are designed to be higher than the historical range. This is based on the view that new build technologies may include some technical advancements on their historical predecessors which mean they do not enter at the low range. Consequently, their low range capacity factor assumption is closer to the average capacity factor rather than the worst case. Specifically, we assume the low range value is 5% below the average. The high range assumption is that it equals the historical high range. Appendix D provides further discussion of historical capacity factors. Capacity factor ranges have been widened in this report compared to previous reports.
24 The model would be unable to simultaneously meet the minimum VRE share and the minimum run requirements of coal plant which are around 30% to 50% of rated capacity.
25 This outcome only relates to 2030 and large-scale generation. When rooftop solar PV is included and as solar PV costs fall faster in the projections, a closer share of wind and solar PV is likely to emerge in the long run as reflected in the global generation mix in Figure 41
26 https://media.epw.qld.gov.au/files/Queensland_Energy_and_Jobs_Plan.pdf
27 This risk premium has been applied in previous studies (e.g., the 2017 Finkel review modelling) but may not adequately represent the present difficulty in obtaining finance for fossil fuel projects.
28 This is higher than the ratio calculated in GenCost 2021-22. However, as discussed in Section 5.11, this likely reflects the updated modelling selecting a higher ratio of storage to avoid higher transmission costs since these two resource types are partially substitutable.
29 Calculated as sum of coincident NEM state demand.
30 At the time of writing, the EPIA report was available at this link: https://www.energypolicyinstitute.com.au/_files/ugd/874c49_34b5379865264b12bec396e5ae8ebcfe.pdf 
31 AEMO capacity factors are determined within their models for dispatchable plant and through half hourly production profiles specifically related to each existing plant, and for new plant, each renewable energy zone and weather year included in their modelling.
32 The data has removed projects that were built partway through the year such that the values represent only projects that operated for the whole year. Projects smaller than 30MW have also been excluded.
33 What is La Niña and how does it impact Australia? (bom.gov.au)
34 Smaller systems are less likely to have any sort of built-in sun-tracking which improves the capacity factor
35 Nuclear SMR and coal or gas with CCS are the main alternatives included in GenCost
36 Some submissions have requested that CSIRO ignore existing government policies that will accelerate deployment of variable renewable generation capacity, noting that the competitive impact of renewables on baseload generation is a dynamic that makes baseload generation more expensive and therefore difficult to examine as an alternative on a clean basis. While we acknowledge the point being made, CSIRO’s role is to provide information consistent with government policy.
37 Other coal retirements perhaps explains the improvement in the minimum brown coal capacity factor since 2017
38 https://www.aemc.gov.au/energy-system/electricity/electricity-market/spot-and-contract-markets.
39 Small scale rooftop solar generation is excluded from the calculation of VRE share since from the perspective of the large scale grid and what it needs to build, it only sees the demand it needs to meet after the impact of rooftop solar on behind the meter demand.
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