GenCost 2025-26
Consultation draft
Paul Graham and Jenny Hayward
December 2025
Australia’s National
Science Agency
GenCost 2025-26
Consultation draft
Paul Graham and Jenny Hayward
December 2025
Contact
Paul Graham
+61 2 4960 6061
paul.graham@csiro.au
Citation
Graham, P. and Hayward, J. 2025, GenCost 2025-26: Consultation draft, CSIRO, Australia.
Acknowledgement
CSIRO acknowledges the Traditional Owners of the lands that we live and work on across Australia and pays its respect to Elders past and present
Copyright
© Commonwealth Scientific and Industrial Research Organisation 2025. 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.
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Contents
Foreword vii
Consultation viii
Executive summary ......................................................................................................................... ix
1 Introduction ...................................................................................................................... 12
1.1 Scope of the GenCost project and reporting....................................................... 12
1.2 The GenCost mailing list ...................................................................................... 13
2 Current technology costs .................................................................................................. 14
2.1 Current cost definition ........................................................................................ 14
2.2 Capital cost source............................................................................................... 16
2.3 Current generation technology capital costs ...................................................... 16
2.4 Current storage technology capital costs ............................................................ 18
3 Scenario narratives and data assumptions ....................................................................... 21
3.1 Scenario narratives .............................................................................................. 21
4 Projection results .............................................................................................................. 23
4.1 Short-term and long-term inflationary pressures ............................................... 23
4.2 Global generation mix ......................................................................................... 25
4.3 Changes in capital cost projections ..................................................................... 27
5 Levelised cost of electricity analysis ................................................................................. 46
5.1 LCOE definition .................................................................................................... 46
5.2 Change in method for estimating the cost of reliable high VRE share generation ............................................................................................................................. 46
5.3 SLCOE scenarios ................................................................................................... 47
5.4 SLCOE estimates .................................................................................................. 49
5.5 LCOE estimates .................................................................................................... 58 Global and local learning model .......................................................................... 61
Data tables ........................................................................................................... 64
Data assumptions ................................................................................................ 78
Frequently asked questions................................................................................. 85
Technology inclusion principles........................................................................... 98
Shortened forms ......................................................................................................................... 101
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References ........................................................................................................................... 104
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Figures
Figure 2-1 Comparison of current capital cost estimates with previous reports (2025-26 $A, FYB) ....................................................................................................................................................... 17
Figure 2-2 Year on year change in current capital costs of selected technologies in the past four years (in real terms) ...................................................................................................................... 17
Figure 2-3 Capital costs of storage technologies in $/kWh (total cost basis) ............................... 19
Figure 2-4 Capital costs of storage technologies in $/kW (total cost basis) ................................. 20
Figure 4-1 Projected global electricity generation mix in 2030 and 2050 by scenario ................ 26
Figure 4-2 Global hydrogen production by technology and scenario, Mt .................................... 26
Figure 4-3 Projected capital costs for black coal ultra-supercritical by scenario compared to 2024-25 projections ...................................................................................................................... 28
Figure 4-4 Projected capital costs for black coal with CCS by scenario compared to 2024-25 projections .................................................................................................................................... 29
Figure 4-5 Projected capital costs for gas combined cycle by scenario compared to 2024-25 projections .................................................................................................................................... 30
Figure 4-6 Projected capital costs for gas with CCS by scenario compared to 2024-25 projections ....................................................................................................................................................... 31
Figure 4-7 Projected capital costs for gas open cycle (small) by scenario compared to 2024-25 projections .................................................................................................................................... 32
Figure 4-8 Projected capital costs for nuclear SMR by scenario compared to 2024-25 projections ....................................................................................................................................................... 33
Figure 4-9 Projected capital costs for large-scale nuclear by scenario compared to 2024-25 projections .................................................................................................................................... 34
Figure 4-10 Projected capital costs for solar thermal with 16 hours storage compared to 2024-25 projections ............................................................................................................................... 35
Figure 4-11 Projected capital costs for large-scale solar PV by scenario compared to 2024-25 projections .................................................................................................................................... 36
Figure 4-12 Projected capital costs for rooftop solar PV by scenario compared to 2024-25 projections .................................................................................................................................... 37
Figure 4-13 Projected capital costs for onshore wind by scenario compared to 2024-25 projections .................................................................................................................................... 38
Figure 4-14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2024-25 projections ...................................................................................................................... 39
Figure 4-15 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant) ........................................................................................................................... 40
Figure 4-16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario ... 41
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Figure 4-17 Projected technology capital costs under the Current policies scenario compared to 2024-25 projections ...................................................................................................................... 42
Figure 4-18 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2024-25 projections ................................................................................................ 43
Figure 4-19 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2024-25 projections ................................................................................................ 44
Figure 4-20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2024-25 ................................................................................................................... 45
Figure 5-1 The projected 2030 large-scale generation share (left) and SLCOE by cost component (right) consistent with the 82% renewables by 2030 policy ......................................................... 51
Figure 5-2 The projected generation mix in 2050 by emissions intensity target and allowed technology. .................................................................................................................................... 53
Figure 5-3 The projected SLCOE in 2050 in the NEM by emission intensity target and technology allowed .......................................................................................................................................... 55
Figure 5-4 The breakdown of SLCOE in 2050 in the NEM by cost component for mature technology only scenario .............................................................................................................. 56
Figure 5-5 Average and marginal cost of abatement to achieve lower emissions intensity targets in 2050 compared to whole of economy abatement costs .......................................................... 57
Figure 5-6 Calculated LCOE range by technology and SLCOE for 2030 ........................................ 59
Figure 5-7 Calculated LCOE range by technology and SLCOE range for 2050 .............................. 60
Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation .............................................................................. 62
Tables
Table 2-1 Suggested FOAK premium by technology ..................................................................... 16
Table 3-1 Summary of scenarios and their key assumptions ....................................................... 22
Table 5-1 2050 full fuel cycle greenhouse gas emissions intensity target scenarios and their meaning......................................................................................................................................... 49
Apx Table A.1 Cost breakdown of offshore wind ......................................................................... 63
Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario ............................................................................................................................ 65
Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario ........................................................................................................................... 66
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Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario ........................................................................................................................ 67
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) .......................................................................... 68
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) .......................................................................... 69
Apx Table B.6 Twelve- and twenty-four hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW) ........................................................... 70
Apx Table B.7 Pumped hydro storage cost data by duration, by scenario, total cost basis ......... 71
Apx Table B.8 Historical storage cost data, total cost basis ......................................................... 72
Apx Table B.9 Data assumptions for LCOE calculations ................................................................ 73
Apx Table B.10 Electricity generation technology LCOE projections data, 2025-26 $/MWh ....... 75
Apx Table B.11 Hydrogen electrolyser cost projections by scenario and technology, $/kW ....... 76
Apx Table B.12 System levelised cost of electricity by cost component, $/MWh ....................... 77
Apx Table C.1 Assumed technology learning rates that vary by scenario .................................... 78
Apx Table C.2 Assumed technology learning rates that are the same under all scenarios .......... 80
Apx Table C.3 Hydrogen demand assumptions by scenario in 2050 ............................................ 82
Apx Table C.4 Maximum renewable generation shares in the year 2050 under the Current policies scenario, except for offshore wind which is in GW of installed capacity. ....................... 83
Apx Table E.1 Examples of considering global or domestic significance ...................................... 99
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Foreword
Assumptions about the cost of electricity generation and storage technologies are a key input to any electricity system planning exercise in Australia or around the world. The primary role of GenCost is to provide capital cost data for the electricity modelling and planning community. The project delivers the capital cost data with an emphasis on stakeholder consultation, recognising that no single organisation can be completely across the changing circumstances of all relevant technologies.
A secondary goal of the project is to provide an indicator of what the capital cost data means for the cost of delivered electricity and the relative competitiveness of generation technologies. This function is delivered by calculating a metric called the levelised cost of electricity (LCOE) which is the minimum per unit price that a project requires to pay back its investment and running costs over its life. LCOE typically only consider a small number of core project details with the more minor or unique costs of each project ignored so that costs are calculated on a simple and common basis.
GenCost also provides a system levelised cost of electricity (SLCOE) which is the average cost of electricity from a bundle of electricity generation and storage technologies that together meet all the requirements of the electricity system. Electricity systems will always require a diversity of resources to deliver all their functions and so no single technology will meet all the system’s needs regardless of its relative cost position.
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Consultation
This consultation draft is being provided for stakeholders to consider and provide feedback. For instructions on how to provide feedback and the consultation schedule, please go to https://www.aemo.com.au/consultations/current-and-closed-consultations/draft-2026-forecasting-assumptions-update.
GenCost 2025-26 | ix
Executive summary
Technological change in electricity generation is a global effort that is strongly linked to global climate change policy ambitions. While the rate of change remains uncertain and the level of commitment of each country varies over time, in broad terms, there is continued support for collective action limiting global average temperature increases. 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 PV) 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.
Purpose and scope
GenCost is a collaboration between CSIRO and AEMO to deliver an annual process of updating the capital costs of electricity generation, energy storage and hydrogen production technologies 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 guide decision making, given technology costs change each year. This is the seventh 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 GHD. 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.
New method for estimating integration costs
This report revises the methodology for estimating the integration costs of renewables and other technologies into the electricity system based on stakeholder feedback. Through various submissions over several years, stakeholders requested that the methodology be revised in two ways:
1.
Use a System Levelised Cost of Electricity (SLCOE) approach. A SLCOE takes the cost of electricity directly from an electricity system model by dividing all system costs from multiple existing and new technology deployments by the total useful electricity supply in a given year. This concept is also equivalent to the average annual unit cost of electricity for a given electricity system.
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2.
Provide greater transparency with regard to the data inputs and the modelling system used.
Separate from these two items the new method also seeks to make electricity system modelling more accessible to stakeholders. To address these objectives, a new open source electricity system model and data set was created to calculate SLCOE, replacing the previous method for estimating the integration costs for solar PV and wind.
The key findings from applying the new costing method are
•
The average cost of electricity in the NEM consistent with meeting the 2030 82% renewables target is projected to be $91/MWh including transmission or $81/MWh for wholesale generation cost only.
•
To determine the cost of electricity in 2050, we must first determine the efficient contribution of the electricity sector to achieving the net zero by 2050 policy target since that target alone does not define the emissions level for the electricity sector.
•
In a whole of economy effort to reach net zero by 2050, the modelling found that it will not be efficient to eliminate all emissions from the electricity sector. It will be more efficient to undertake further abatement elsewhere in the economy.
•
The efficient range of emissions intensity of the electricity sector lies somewhere between 0.02tCO2e/MWh to 0.05tCO2e/MWh depending on the uncertainty in the whole of economy abatement cost.
•
Achieving the electricity sector’s efficient role in whole of economy net zero abatement is projected to result in electricity costs in 2050 of between $135/MWh to $148/MWh in the NEM inclusive of new transmission costs or $115/MWh to $124/MWh measured as wholesale generation costs only. For context, in 2024-25, the historical average NEM volume weighted generation price is estimated to be slightly higher than the top end of this range at $129/MWh.
•
The combination of solar PV, onshore wind, storage and either natural gas or hydrogen was the least cost technology mix in all cases examined with the addition of carbon capture and storage, offshore wind and nuclear leading to higher average electricity costs. These outcomes are based on average costs. Offshore wind has a much wider capital cost uncertainty range and so could perform better under alternative cost scenarios not explored.
•
Achieving weak or no progress in reducing electricity sector emissions in the period between 2030 and 2050 is not efficient for achieving net zero because electricity sector emissions reduction is substantially lower cost than emissions reduction elsewhere in the economy.
These cost estimates do not guarantee future generation prices. Changes in generation prices are also subject to:
•
Supply-demand imbalance as a result of too much or too little deployment relative to demand growth and retirements.
•
Fuel price and weather volatility.
GenCost 2025-26 | xi
•
The level of competition amongst suppliers.
These additional drivers of generation price formation can lead to prices significantly lower or higher than the underlying cost of the system and can take many years to correct due to the long lead times for capacity deployment. Generation prices are currently around 33% of retail prices. Transmission is around 7%, distribution around 34% with the remainder made up of metering, retail services and government programs.
Key changes in capital costs in the past year
The COVID-19 pandemic led to global supply chain constraints which impacted the prices of raw materials needed in technology manufacturing and freight costs. Consequently, past reports have observed consecutive increases in technology costs. For some technologies, the inflationary pressures have progressively eased but the results remain mixed. Technologies have been affected differently because they each have a unique set of material inputs and supply chains.
The biggest recent increases have been in coal and gas open cycle costs. This reflects general increases in gas turbine and steam turbine costs. Battery costs have performed the best in terms of delivering consecutive cost reductions. Onshore wind costs are showing tentative signs of stabilising after experiencing the largest increase in 2022-23.
ES Figure 0-1 Year on year change in current capital costs of selected technologies in the past four years (in real terms)
17%13%16%9%35%20%-2%14%6%-8%8%2%4%11%19%-8%6%-20%13%0%32%9%-5%-15%Black coalGas combinedcycleGas open cycle(large)Large scale solarPVWind (onshore)Large scalebattery (2hr)2022-232023-242024-252025-26
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1 Introduction
Current and projected electricity generation, storage and hydrogen 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 GHD (2025) and further information can be found in their report. The global scenario narratives are outlined in Section 3. Capital cost projection results are reported in Section 4 and LCOE and SLCOE results in Section 5. CSIRO’s cost projection methodology is discussed in Appendix A including key global data assumptions. Appendix B provides data tables and this data can also be downloaded from CSIRO’s Data Access Portal1. A set of technology selection and data quality principles has been included in Appendix C. Feedback on these principles is always welcome.
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, storage and hydrogen technology 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. Appendix E discusses some technology inclusion principles. GenCost does not seek to describe the set of electricity generation and storage technologies included in detail. The report provided by GHD (2025) does include more detailed technology specifications and commentary.
1.1.1 CSIRO and AEMO roles
AEMO and CSIRO jointly fund the GenCost project by combining their own resources. AEMO commissioned GHD to provide an update of the current cost and performance characteristics of electricity generation, storage and hydrogen technologies (GHD, 2025). This report focusses on
1 Search GenCost at https://data.csiro.au/collections
GenCost 2025-26 | 13
capital costs, but the GHD 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.
Project management, capital cost projections (presented in Section 4), LCOE estimates (Section 5) and development of this report are primarily the responsibility of CSIRO.
1.1.2 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, the main innovation is an update of the method for calculating technology integration costs. The method used up until now was designed in 2018 and its revision and new results are discussed in Section 5. The new method accounts for feedback we received about the previous method and the development of the new method has been separately published in Graham et al. (2025).
1.2 The GenCost mailing list
The GenCost project would not be possible without the input of stakeholders. No single person or organisation can 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.
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2 Current technology costs
2.1 Current cost definition
Our definition of current capital costs is 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 capacity2. 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. In these cases, lower cost quotes will become known 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 GHD (2025). GHD (2025) also provide more detail on specific definitions of the scope of cost categories included. GHD cost estimates are provided for Australia in Australian dollars. They represent the capital costs for a location not greater than 200km from the Victorian metropolitan area. GHD provide adjustments for costs for different regions of the NEM. Site conditions will also impact costs to varying degrees, depending on the technology. CSIRO adjusts the data when used in global modelling to take account of differences in costs in different global regions.
GHD (2025) also provides detailed information on the boundary of capital costs such as what development costs are included, ambient temperature, distance to fuel source and many other considerations.
2.1.1 First-of-a-kind cost premiums
When building a technology that has a degree of novelty, capital cost estimates typically underestimate the realised cost of installation. This is sometimes called an optimism factor or first-of-a-kind (FOAK) costs. These costs are reduced with more installations. The industry term for the point when costs are no longer impacted by the immaturity of the development supply chain is
2 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.
GenCost 2025-26 | 15
nth-of-a-kind (NOAK). The cost estimates in GenCost are mostly on a NOAK basis. This is not because all technologies have mature supply chains but rather because it is too difficult to objectively estimate the FOAK premium that should be applied. It is only observable after a proponent fails to deliver the first project for the cost they had planned. Even then it is difficult to separate optimism from ordinary changes in circumstances, particularly for projects that have long total development times. These cost increases will sometimes be found through the process of more detailed engineering and feasibility studies prior to final investment decisions but may not be shared publicly.
EIA (2023) applies FOAK premiums of up to 25% to their technology costs. AACE (1991) recommends applying different levels of contingency based on the Technology Readiness Level ranging from 10% to up to 70%. In practice, we can find examples of projects that have cost around 100% more than planned such as the Vogtle large-scale nuclear plant in the US and the Snowy 2.0 pumped hydro project in Australia. Flyvbjerg and Gardner (2023) report that the global average cost overrun for nuclear, hydro, wind and solar are 120%, 75%, 13% and 1%, respectively. As such, while special circumstances may have occurred in specific cases, generally, FOAK premiums should be part of normal expectations for estimating the cost of deploying less mature or large technology projects in the future.
The technologies most at risk of FOAK cost premiums in Australia are:
•
Offshore wind
•
Large-scale nuclear
•
Small modular reactor (SMR) nuclear
•
Solar thermal
•
Coal, gas or biomass with carbon capture and storage
•
Wave, tidal and ocean current technologies.
Given the size and unique site conditions of most pumped hydro projects they may also continue to be at risk of cost overruns. However, given these projects are relatively rare, in practice there is not as much difference between a FOAK and NOAK costing.
Technologies that are currently being regularly deployed in Australia such as onshore wind, solar PV, batteries and gas generation are least likely to be impacted. Technologies that have been deployed before and are globally commercially mature may still be subject to FOAK premiums due to large intervals since the last deployment leading to loss of skills, new designs which create uncertainty or new licensing requirements, project size and unique site conditions.
It is likely that 2024 nuclear SMR costs includes some FOAK costs given it was based on a FOAK in the US project. However, the first commercial project is proceeding in Canada at Darlington and costs are reduced from the 2024 level to match that project’s costings which include the assumption that they will build each of the four proposed units at lower cost than the previous unit. Regardless of how successful Canada is in reducing costs for each unit build, Australia would still experience a FOAK premium if that technology were to be built for the first time here.
FOAK premiums are very difficult to forecast. However, given stakeholder interest in the technologies listed above, there is a need for an estimate of the FOAK premium (Table 2-1). To develop the premium the value of 120% has been applied to large scale nuclear based on Flyvbjerg
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and Gardner (2023). The remaining premiums are based on observing the ratio between this large scale nuclear premium and its construction time and applying that ratio to the other technology’s construction times. Effectively we are proposing that technologies that take longer to build will face higher FOAK premiums as they are more complex to plan. We then halve the premium for the second project and assume the third and subsequent projects are not impacted by a FOAK premium.
Table 2-1 Suggested FOAK premium by technology
Construction time Premium Technology
Years
First project
Second project Gas with CCS
2.0
42%
21% Black coal with CCS
2.0
42%
21% Nuclear SMR
4.4
92%
46% Nuclear large-scale
5.8
120%
60% Solar thermal
1.8
37%
18% Wind offshore
3.0
63%
31%
2.2 Capital cost source
AEMO commissioned GHD (2025) 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 GHD (2025) which represents a July estimate and so it is consistent with either the beginning of the financial year 2025-26 or the middle of 2025.
Nuclear technologies are not included in GHD (2025). These are sourced separately by CSIRO.
2.3 Current generation technology capital costs
Figure 2-1 provides capital costs for selected technologies since the project’s inception in 2018. All costs are expressed in real 2025-26 Australian dollars, represent overnight costs and do not include any available subsidies.
Costs increased for many technologies from 2022 owing to the global supply chain constraints following the COVID-19 pandemic which also increased freight and raw material costs. Technologies were impacted differently given different input materials and manufacturing regions and are recovering from this development at different rates. The change in current costs over the past four years indicates an easing of inflationary pressures for solar PV, wind and batteries while coal and gas technology costs have recently increased significantly (Figure 2-2). Coal and gas costs are not developed from project data but rather using standard industry software since there are insufficient projects in Australia. In particular, existing coal the projects are very old and therefore unreliable for estimating current costs. The latest updates to industry software included a large upwards revision in gas turbine and steam turbine costs.
GenCost 2025-26 | 17
Figure 2-1 Comparison of current capital cost estimates with previous reports (2025-26 $A, FYB)
Figure 2-2 Year on year change in current capital costs of selected technologies in the past four years (in real terms)
0
2000
4000
6000
8000
10000
12000
14000
Black coal Black coal
with CCS
Gas
combined
cycle
Gas open
cycle (large)
Gas with CCS Large scale
solar PV
Solar thermal
(14hrs)
Wind
2025-26 $/kW
2018 2019 2020 2021 2022 2023 2024 2025
17%
13%
16%
9%
35%
20%
-2%
14%
6%
-8%
8%
4% 2%
11%
19%
-8%
6%
-20%
13%
0%
32%
9%
-5%
-15%
Black coal Gas combined
cycle
Gas open cycle
(large)
Large scale solar
PV
Wind (onshore) Large scale
battery (2hr)
2022-23 2023-24 2024-25 2025-26
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2.4 Current storage technology capital costs
Updated and previous capital costs are provided on a total cost basis for various durations3 of batteries and pumped hydro energy storage (PHES) in $/kW and $/kWh. GenCost only provides projections for batteries and PHES. Current costs of compressed air energy storage are included in GHD (2025).
None of these capital costs provide enough information to be able to say one technology is more competitive than the other. Capital costs are only one factor. Additional cost factors include energy input costs (where not already included), utilisation rate, round trip efficiency, operating costs and design life.
Total cost basis means that the costs are calculated by taking the total project costs divided by the capacity in kW or kWh4. 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).
Note that these $/kWh costs are not for energy delivered but rather a capacity of storage. GenCost does not present levelised costs of storage (LCOS) which are on an energy delivered basis. However, LCOS estimates are available from the CSIRO (2023) Renewable Energy Storage Roadmap.
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 deployed in low storage duration applications, while PHES is deployed in high duration applications. A combination of durations may be required by the system depending on the operation of other generation in the system, particularly the scale of variable renewable generation and peaking plant (see Section 5).
3 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
4 Component costs basis is when the power and storage components are separately costed and must be added together to calculate the total project cost.
GenCost 2025-26 | 19
Figure 2-3 Capital costs of storage technologies in $/kWh (total cost basis)
Depth of discharge in batteries can be an important constraint on use. However, all GHD battery costs are presented on a usable capacity basis such that the depth of discharge is 100%5. GHD (2025) 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 around a 5% 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.
Battery costs (battery and balance of plant in total) have decreased significantly by 11% to 16% depending on the duration.
5 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%.
0200400600800100012002025-26 $/kWh2019202020212022202320242025
20 | CSIRO Australia’s National Science Agency
Figure 2-4 Capital costs of storage technologies in $/kW (total cost basis)
PHES current cost estimates have decreased 34% to 55%. The decreases represent a reassessment of costs rather than a change in the technology. See GHD (2025) for details on how the costs were prepared. It is important to note that PHES has 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.
Concentrating solar thermal (CST) is another technology incorporating storage but it is reported as a generation technology in Section 5. It incorporates built-in long-duration energy storage. Direct comparison with the other electricity storage technologies is complicated by the fact that a CST system also collects its own solar energy. Direct comparison with other storage technologies via calculation of the LCOS can be found in CSIRO’s Renewable Energy Storage Roadmap (CSIRO, 2023), but is outside the scope of GenCost.
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3 Scenario narratives and data assumptions
The global scenario narratives included in GenCost have not changed since GenCost 2022-23 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 2024 World Energy Outlook (IEA, 2024a) 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. The final GenCost report will update to the 2025 outlook.
3.1.1 Current policies
The Current policies scenario includes existing climate policies as at mid-2024 and does not assume that all government targets will be met. The implementation of climate policies in the modelling includes a combination of carbon prices and other climate policies6. This scenario has the strongest constraints applied with respect to global variable renewable energy resources and the slowest technology learning rates. 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 governments meeting their Nationally Determined Contributions (NDCs) and longer-term net zero emission targets, which provides the investment signal necessary to deploy low emission technologies. 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 a strong climate policy consistent with maintaining temperature increases of 1.5 degrees of warming and achieving net zero emissions by 2050
6 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.
22 | CSIRO Australia’s National Science Agency
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 Current policies Global NZE post 2050 Global NZE by 2050 IEA WEO scenario alignment Stated policies scenario Announced pledges scenario Net zero emission by 2050
CO2 pricing / climate policy
Based on current policies only
Based on NDCs and announced targets
Consistent with 1.5 degrees world Renewable energy targets and forced builds / accelerated retirement Current renewable energy policies Renewable energy policies extended as needed High reflecting confidence in renewable energy
Demand / Electrification
Medium
Medium-high
High Learning rates1 Weaker Normal maturity path Stronger
Renewable resource & other renewable constraints2
More constrained than existing assumptions
Existing constraint assumptions
Less constrained Decentralisation More constrained rooftop solar PV constraints2 Existing rooftop solar PV constraints2 Less constrained rooftop solar photovoltaics (PV)2
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 Appendix C for assumed learning rates.
2 Existing large-scale and rooftop solar PV renewable generation constraints are as shown in Apx Table C.4.
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4 Projection results
All projections start from a current cost and the primary source of 2025 costs is GHD (2025) with data gathered from other sources where otherwise not available in that report. All projections are in real terms. That is, all projected cost changes after 2025 are in addition to the general level of inflation.
4.1 Short-term and long-term inflationary pressures
4.1.1 Short term equipment costs
In recent 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 a trajectory consistent with learning-by-doing and economies of scale.
CSIRO has explored a number of resources to understand cost increases already embedded in technology costs and to 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 decade. It is not appropriate to project long-term future costs directly from the top of a price bubble, otherwise all future costs will permanently embed what may be temporary market features.
It is acknowledged that some stakeholders believe the price bubble is not a price bubble but rather a permanent feature that will be built into all future costs. However, to sustain real price increases, supply needs to be either constrained by a cartel (or other persistent market power) 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 capacity7). The current cost update indicates inflationary pressures are weakening for at least some technologies.
7 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.
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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 of the GenCost 2022-23: Final report for more discussion on this topic). However, it is considered that the period to 2030 will likely experience extra strong technology deployment. 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.
Our current view is that it may take longer than 2030 for technologies to return to a more normal level of costs. This report assumes that if a technology has not already started to show strong signs of recovery it will not return to their normal cost path until 2035. This includes technologies such as onshore wind, coal, gas and nuclear. Note that to achieve a recovery most technologies need only stay constant in nominal prices. This delivers real cost reduction of around 2-3% per year.
A consequence of this modelling approach is that the near-term cost reductions that are shown in the following pages mostly do not reflect learning. Rather, they are predominantly the slow unwinding of inflationary pressures that have temporarily placed costs above the underlying cost curve. Solar PV, batteries, fuel cells and offshore wind have already passed through the global inflationary event and their costs now follow the underlying learning curve cost trajectory.
4.1.2 Long term land and construction costs
Two exceptions where scarcity is a factor and is expected to lead to ongoing real increases in costs is land and construction costs. 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 GHD (2025) and inflate that proportion of costs by the real land cost index that is published in Mott MacDonald (2023)8. 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 was first included in the GenCost 2022-23: Final report.
Information on future real construction costs become available in a February 2025 report from Oxford Economics Australia (2025), commissioned by AEMO. The data indicates that while construction costs are expected to ease in the short term, a longer term trend of rising real construction costs is projected owing primarily to above inflation growth in construction workers’ wages and, to a lesser extent, constrained supply of quarry and cement materials. The construction cost escalation factors estimated by Oxford Economics Australia are applied to the installation cost proportion of capital costs which is sourced from GHD (2025). Note that, this escalation factor is applied after learning. That is, it is still possible for developers to be more productive or innovative at installing some technologies while at the same time facing increases in real costs for some installation components (such as labour). Consequently, mature technologies,
8 It is referred to as an easement cost index in that document.
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which have limited prospect of installation cost reductions, are the most impacted by this new escalation factor (e.g., gas and coal technologies).
4.2 Global generation mix
The rate of global 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.
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 transformation9. 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 production technology in each scenario indicating the Global NZE scenarios are assumed to experience a significant growth in electrolysis hydrogen production. Note that the IEA’s estimates of hydrogen demand have decreased relative to the 2023 World Energy Outlook.
Current policies has the lowest non-hydro renewable share at 53% 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 emissions intensity. In absolute capacity terms, nuclear is 9% of generation in 2030 but declines to 4% to 5% by 2050 reflecting its relatively slower installation rate as electrification causes demand to grow rapidly in the 2030s and 2040s.
9 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.
26 | CSIRO Australia’s National Science Agency
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.
Figure 4-2 Global hydrogen production by technology and scenario, Mt
0
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Current policies Global NZE post
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Generation (TWh)
BECCS Coal Coal CCS Gas
Gas CCS Hydro Nuclear Oil
Solar Wind onshore Wind offshore Other renewables
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Electrolysis Steam methane reforming Steam methane reforming with CCS
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The Global NZE by 2050 scenario is close to but not completely zero emissions by 2050. All generation from fossil fuel sources is with CCS accounting for 3% of generation by 2050. Offshore wind features strongly in this scenario at 21% of generation by 2050. Renewables other than hydro, biomass, wind and solar are 6% of generation in 2050. The greater deployment of renewables and CCS leads to lower renewable and CCS costs. CCS costs are also impacted by the use of CCS in hydrogen production and other industries.
4.3 Changes in capital cost projections
This section discusses the changes in cost projections to 2055 compared to the 2024-25 projections. For mature technologies, differences mainly reflect any changes in current costs, an assumed return to normal costs by 2035 and land and construction costs increases thereafter.
Less mature technologies include learning components in addition to the land and construction cost escalators. For technologies with high learning potential, the cost reduction from learning more than offsets the escalation factors for most of the projection period. For those with lower learning potential, the cost changes may cancel one another out.
Data tables for the full range of technology projections are provided in Appendix B and can be downloaded from CSIRO’s Data Access Portal10.
4.3.1 Black coal ultra-supercritical
The updated cost of black coal ultra-supercritical plant in 2024 has been sourced from GHD (2025). This included a substantial increase based on updates to standard software used to model coal generation capital costs. From 2025, the capital cost is assumed to return to levels consistent with ultra-supercritical prior to the COVID-19 pandemic by 2035 adjusted for changes in construction costs. Black coal ultra-supercritical is treated in the projections as a learning technology. However, global new building of ultra-supercritical coal is limited due to climate change policies and the learning rate is low. The outlook for costs in all scenarios is increasing due to increasing land and installation costs. Installation costs are rising faster the stronger the climate policy ambition of the scenario reflecting a stronger rate of electricity sector construction activity.
10 Search GenCost at https://data.csiro.au/collections
28 | CSIRO Australia’s National Science Agency
Figure 4-3 Projected capital costs for black coal ultra-supercritical by scenario compared to 2024-25 projections
4.3.2 Coal with CCS
The capital cost of black coal with CCS from 2025 to 2035 has been updated according to the approach outlined in the beginning of this section. Thereafter, the capital cost of the mature parts of the plant reflects assumed land and installation cost increases. For the CCS components, in addition to these changes in land and installation costs, changes in equipment costs are a function of global deployment of gas and coal with CCS, steam methane reforming with CCS and other industry applications of CCS. Compared to the 2024-25 projections, global CCS deployment has not significantly changed.
Current policies has no uptake of steam methane reforming with CCS in hydrogen production. Consequently, any equipment cost reductions 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 non-electricity industries, there is only a negligible amount of generation from black coal with CCS throughout the projection period.
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Figure 4-4 Projected capital costs for black coal with CCS by scenario compared to 2024-25 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). The total CCS 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 equipment cost reduction by 2050 but with stronger local construction cost increases in Global NZE by 2050. In Current policies, equipment cost reductions are not significant, but installation cost increases are lower than the Global NZE scenarios.
A first of a kind premium, in addition to the costs shown, will likely apply when coal with CCS is deployed in Australia for the first time.
4.3.3 Gas combined cycle
GHD (2025) have included negligible real change in gas combined cycle costs for 2025 which is a change compared to real increases the previous two years. During 2026 to 2035 costs are assumed to slowly return to normal. 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 only by assumed increases in land and installation costs for all scenarios. Consistent with the need for greater construction activity the stronger the climate policy ambition, combined cycle gas costs are highest in Global NZE by 2050.
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Figure 4-5 Projected capital costs for gas combined cycle by scenario compared to 2024-25 projections
4.3.4 Gas with CCS
The current cost for gas with CCS has been revised upwards for the 2025-26 projections and decline to 2035 based on our return to normal assumptions during this period. The relativities between the scenarios reflect the changes in land and installation costs increases and 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, the equipment component of gas with CCS is lower by 2050 in those scenarios and this results in total costs being lower in the late 2030s and early 2040s. In the same period, CCS equipment costs are highest cost where CCS deployment is lowest. However, by 2050, installation costs have increased the most in Global NZE by 2050 and the least in Current policies. The proportionally offsetting sources of cost changes result in a narrow range of costs across all scenarios by 2050.
The IEA CCS database11 indicates there are over 100 planned electricity related projects which are yet to make a financial investment decision and around 9 under construction. Around 6 are operational. Given the current state of the pipeline of projects, significant global deployment of CCS is not expected until after 2030.
A first of a kind premium, in addition to the costs shown, will likely apply when gas with CCS is deployed in Australia for the first time.
11 CCUS Projects Database - Data product - IEA
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Figure 4-6 Projected capital costs for gas with CCS by scenario compared to 2024-25 projections
4.3.5 Gas open cycle (small and large)
Figure 4-7 shows the 2025-26 cost projections for small and large open cycle gas turbines. All new gas turbine projects are expected to include the capability for hydrogen blending and eventual conversion to hydrogen firing when hydrogen supply becomes more readily available and lower cost. This is in addition to the existing ability to use liquid fuels such as diesel or renewable diesel. However, it is possible that some plants will only ever use natural gas during their life. It depends on the market conditions and climate policy during their operation. The small open cycle gas technology is designed with a maximum 35% hydrogen blend. The large size is designed for 10%. However, GHD also provides costs for higher and lower blends at both sizes. This assumption of hydrogen readiness adds a negligible premium to gas open cycle capital costs.
The GHD (2025) report provides additional details for the unit sizes and total plant capacity that defines the small and large sizes. Gas open cycle costs are increasing rapidly due to demand outpacing manufacturing supply capability. Costs are not projected to return to normal until 2035. For the remainder of the projection period, there are no improvements in equipment costs because of the maturity of the technology, and so the assumed land and installation cost increases result in a rising trend in costs. Capital costs are highest under the Global NZE scenarios reflecting the higher installation costs associated with the greater construction activity of those scenarios
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Figure 4-7 Projected capital costs for gas open cycle (small) by scenario compared to 2024-25 projections
4.3.6 Nuclear SMR
For the next five years, costs are based on the planned Darlington SMR project in Canada which consists of four 300MW units for a total cost of C$20.9b. Costs are expected to be highest for the first unit but lower for each subsequent unit and this is captured in the cost trajectory. Unlike large-scale nuclear, to convert Darlington nuclear SMR costs to Australian dollars the method only included an exchange rate conversion. That is, no allowance has been made for differences in construction costs between Canada and Australia. The difference in approach is justified based on the high level of commercial immaturity of nuclear SMR outweighing any other uncertainties in the cost estimate.
The rate of cost reductions after the Darlington project is calculated as function of deployment of other global nuclear SMR projects, to a greater or lesser degree depending on the global scenario and some known projects. Capital costs only improve in the 2040s for the Current policies scenario due to a lack of additional deployment of projects in the 2030s. The Global NZE scenarios achieve a greater level of deployment of nuclear SMR in the 2030s owing to a stronger commitment to addressing climate change.
Nuclear SMR equipment cost reductions may be partly driven by modular manufacturing processes. Modular plants reduce the number of unique inputs that need to be manufactured. Assumed increases in land and installation costs are responsible for increases in Australian nuclear SMR costs in the 2040s and 2050s.
A first of a kind premium, in addition to the costs shown, will likely apply when nuclear SMR is deployed in Australia for the first time.
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GenCost 2025-26 | 33
Figure 4-8 Projected capital costs for nuclear SMR by scenario compared to 2024-25 projections
4.3.7 Large-scale nuclear
Given Australia has no experience building large scale nuclear, we base costs on South Korean nuclear building costs adjusted for the relative costs of building ultra-supercritical coal in each country. Given the cost of building ultra-supercritical coal in Australia has increased, nuclear costs are also revised upwards. From a more direct perspective, given all steam turbine costs have gone up, then nuclear costs are also impacted. However, like other technologies, large-scale nuclear capital costs are assumed to return to their underlying costs by 2035.
Large-scale nuclear is treated as a mature technology and therefore is not assigned any learning rate whereby cost reductions are achieved as a function of deployment. Instead, large-scale nuclear costs increase after 2035 due to the assumed increases in land and installation costs that impact all technologies.
There is some uncertainty in the literature about whether large-scale nuclear is a learning technology or not. There are many new designs for nuclear generation and so it is not a settled technology in the way we might consider steam turbines. Even settled technologies still incrementally change. However, our reluctance to assign a learning rate to large-scale nuclear reflects two issues. First, an assigned learning rate would have little impact because it is difficult for any mature technology to double its global capacity which is the required trigger to achieve an assigned learning rate (see Appendix A for an explanation of the learning rate function). Second, new designs for large-scale nuclear have not always delivered cost reductions. Therefore, our projection reflects a nuclear industry that mostly consolidates construction around proven designs.
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34 | CSIRO Australia’s National Science Agency
A first of a kind premium, in addition to the costs shown, will likely apply when large-scale nuclear is deployed in Australia for the first time.
Figure 4-9 Projected capital costs for large-scale nuclear by scenario compared to 2024-25 projections
4.3.8 Solar thermal
The starting cost for solar thermal has been increased by GHD (2025) and this represents the main difference in costs relative to 2024-25. A first of a kind premium, in addition to the costs shown, will likely apply when solar thermal is deployed in Australia for the first time.
Solar thermal systems consist of the combination of solar mirror field, thermal storage and power blocks that are sized in varying ratios according to the location and market signals that prevail. Each such configuration will have a different capital cost. As a consequence, the baseline configuration represented in the capital cost projection data is not the same as the configurations used to calculate the LCOEs in Section 5.
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GenCost 2025-26 | 35
Figure 4-10 Projected capital costs for solar thermal with 16 hours storage compared to 2024-25 projections
4.3.9 Large-scale solar PV
Large-scale solar PV costs have been revised upwards for 2025-26 based on GHD (2025). This represents a reversal of the last two years of gains but likely reflects cost volatility rather than a new trend. As a result of past cost reductions for this technology, unlike other technologies, we do not impose any additional cost reduction related to recovery from the global inflationary pressures. All cost reductions in the projection are due to learning through deployment. Current policies has the lowest global share of solar PV generation and therefore the highest cost trajectory. In the Global NZE scenarios, there is faster technology deployment to meet stronger climate policies leading to proportionally higher cost reductions. All scenarios include increases in installation costs in Australia and this narrows the differences between the scenarios slightly over time. Installation costs are assumed to grow faster the stronger the global climate policy ambition due to stronger construction activity.
The final minimum cost level for solar PV is difficult to predict because, unlike other technologies, and notwithstanding recent inflationary pressures, the historical learning rate for solar PV has not significantly slowed. The modular nature of solar PV appears to be the main point of difference in explaining this characteristic.
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Figure 4-11 Projected capital costs for large-scale solar PV by scenario compared to 2024-25 projections
4.3.10 Rooftop solar PV
The current costs for rooftop solar PV systems are lower than was projected for 2025 in the 2024-25 GenCost report. Rooftop solar PV is sold across a broad range of prices12 and consequently this data is best interpreted as a mean and may not align with the lowest cost systems available. The cost is before available subsidies and on the basis of the direct current power rating of the system whereas large-scale solar PV and all other generation technologies are on an alternating current power rating basis.
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. However, the rate of capital cost reduction in each scenario is slower than large-scale solar PV because we have assumed a low learning rate on the installation or local learning component for rooftop solar. This reflects that Australia already has a very high degree of experience in installing rooftop solar so there are less opportunities to reduce the cost of installation compared to large-scale solar PV. Installation costs are also impacted by the general increase in installation costs that apply to all technologies.
12 The Cost of Solar Panels - Solar Panel Price | Solar Choice
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GenCost 2025-26 | 37
Figure 4-12 Projected capital costs for rooftop solar PV by scenario compared to 2024-25 projections
4.3.11 Onshore wind
As the historical data indicates onshore wind is one of the technologies which has been most impacted by recent global inflationary pressures. The updated GHD (2025) data indicates that the costs pressures are stabilising.
To recognise the more difficult circumstances for the onshore wind industry locally and globally, our assumption is that capital costs of onshore wind will not return to its normal cost path until 2035 in all scenarios. After 2035, wind costs are projected to decline only a modest amount. Global equipment cost reductions from learning are offset by local increases in land and installation costs. While equipment costs fall the most in stronger climate policy ambition scenarios, these scenarios also experience the strongest increase in installation costs due to greater construction activity. Consequently, these global and local changes in costs tend to offset one another resulting in little difference between the three scenarios by 2055.
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Figure 4-13 Projected capital costs for onshore wind by scenario compared to 2024-25 projections
4.3.12 Fixed and floating offshore wind
Fixed and floating offshore wind are represented separately in the 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 onshore siting conflicts. Fixed offshore wind is the lowest cost offshore technology, but its maximum deployment is limited by access to seas of a maximum depth of around 50-60 metres13 and any navigation, marine conservation or aesthetic 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 4-14 presents projections for both fixed and floating compared to the 2024-25 projection. The current costs for both types of offshore wind are provided in GHD (2025). The updated current capital costs are lower than projected in 2024-25 for floating offshore wind and higher than projected for fixed offshore wind. Post 2025, offshore wind capital costs are not adjusted for inflationary pressures in the same way as other technologies because fixed offshore wind has already recovered based on the average global data which informs the historical series. However,
13 This is more an economic than absolute technical limit.
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it is likely that technology prices are higher for some regions and manufacturers. Australia is not likely to deploy offshore wind before 2030 and therefore GenCost will continue to be required to rely on global sources of offshore wind cost data until then. A first of a kind premium, in addition to the costs shown, will likely apply when offshore wind is deployed in Australia for the first time.
Figure 4-14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2024-25 projections
Floating offshore wind projections are lower relative to the 2024-25 projections and this reflect higher projected global deployment. Fixed offshore wind is higher but this mostly reflects the higher current cost rather than a change in global deployment. Floating offshore wind is deployed more widely than fixed offshore wind and therefore results in proportionally higher cost reductions in the Global NZE scenarios.
Offshore wind is not as impacted as other technologies on land costs but does require some onshore land to connect to the grid. Offshore wind costs are impacted by the new assumptions with regards to increasing installation costs.
4.3.13 Battery storage
Current 2025 costs of battery storage fell in line with the fastest cost reduction projected in 2024-25 and the updated cost projections continue to allow for a diversity of outcomes after 2025 ranging from a continuation of the current rate of cost reduction to a slow rate of reduction. The costs shown in Figure 4-15 are for a 2-hour duration battery (total battery cost including battery and balance of plant). Given the 2025 cost reduction takes batteries back to below their pre-pandemic levels we do not impose any additional reduction beyond the learning projected by the modelling.
0100020003000400050006000700080009000100002015202020252030203520402045205020552025-26 $/kW2025-26 Global NZE by 2050 (fixed)2024-25 Global NZE by 2050 (fixed)2025-26 Global NZE post 2050 (fixed)2024-25 Global NZE post 2050 (fixed)2025-26 Current policies (fixed)2024-25 Current policies (fixed)2025-26 Global NZE by 2050 (floating)2024-25 Global NZE by 2050 (floating)2025-26 Global NZE post 2050 (floating)2024-25 Global NZE post 2050 (floating)2025-26 Current policies (floating)2024-25 Current policies (floating)History
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Figure 4-15 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant)
The projections use different learning rates by scenario to reflect the uncertainty as to whether they will be able to continue to achieve their high historical cost reduction rates (notwithstanding the pandemic period). Historical cost reductions have mainly been achieved through deployment in industries other than electricity such as in consumer electronics and electric vehicles. Global electric vehicle uptake has been updated with inputs from the 2024 IEA World Energy Outlook. While these other uses are important, small- and large-scale stationary electricity system applications are growing globally. Under the three global scenarios, batteries have a large future role to play in supporting variable renewables alongside other storage and flexible generation options and in growing electric vehicle deployment.
Battery deployment is strongest in the Global NZE by 2050 scenario reflecting stronger deployment of variable renewables, which increases electricity sector storage requirements. Together with an assumed high learning rate this leads to the fastest cost reduction. The remaining scenarios have more moderate cost reductions reflecting a reduced requirement for stationary storage and assumed lower learning rates. All projections are impacted by assumed increases in installation costs. However, for batteries, the learning effects more than offset this factor leading to declining cost trajectories.
A breakdown of battery pack and balance of plant costs for various storage durations are provided in Appendix B.
GHD (2025) has included current costs for small-scale batteries, designed to be installed in homes. They are estimated at $11,000 for a 5kW/10kWh system or $1100/kWh, including installation but excluding subsidies. This is around twice the cost of large-scale battery projects per kWh.
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However, larger household batteries are achieving lower per kWh costs more consistent with large-scale costs.
4.3.14 Pumped hydro energy storage
GHD (2025) has provided a reassessment of pumped hydro energy storage (PHES) costs and this is the main driver of differences in the cost outlook compared to 2024-25. See GHD (2025) for more discussion. PHES is a mature technology and receives the same increase in installation costs as other technologies which is the main driver for the increasing cost trend post 2030. Unlike the other technologies, all three scenarios assume costs return to normal by 2030 (rather than in 2035). This reflects the already large downgrade in costs in 2025. Site variability is also a great source of variation in PHES costs and is separately addressed by GHD (2025) and AEMO external to GenCost.
The cost trajectory shown in Figure 4-16 is for a 24-hour duration storage design. Costs for 10-hour, 48-hour and 160-hour durations are also included in this report (Appendix B).
Figure 4-16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario
4.3.15 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 which have generally experienced an increase in capital costs for 2025 with the exception of fuel cells. Reflecting the infrequency with which these technologies are built, the increases for some technologies mostly represent theoretical increases
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in costs if they had been built based on the general increase in infrastructure building costs. The downward trend to 2035 has been included using the same methodology for the technologies above. The projections also include increasing land and installation costs for biomass with CCS and fuel cells (wave and tidal/ocean current are excluded due to insufficient data).
Figure 4-17 Projected technology capital costs under the Current policies scenario compared to 2024-25 projections
Current policies
Biomass with CCS is deployed at a negligible level in the Current policies scenario because the climate policy ambition is not strong enough to incentivise significant deployment. Cost reductions after 2035 reflect co-learning from other CCS technologies which are deployed in electricity generation and in other sectors but these are more than offset by increasing installation costs. There is also no significant deployment of fuel cells, tidal or wave technology reflecting the lack of climate policy ambition. Fuel cells are lower owing to updated current costs.
Global NZE by 2050
Biomass with CCS has a low level of adoption in the Global NZE by 2050 scenario and this is scenario has the strongest increase in installation costs. 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.
Wave energy is deployed at a modest level in the 2040s and tidal/ocean current in the 2050s leading to some costs reductions during those periods. Fuel cells are lower owing to updated current costs.
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Figure 4-18 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2024-25 projections
Global NZE post 2050
Biomass with CCS is deployed at a slightly higher level than Global NZE by 2050 resulting in slightly more cost reduction. Both scenarios have significant deployment of steam methane reforming with CCS which brings down the cost of all CCS technologies sooner compared to Current policies. There is no significant deployment of other technologies.
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Figure 4-19 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2024-25 projections
4.3.16 Hydrogen electrolysers
Hydrogen electrolyser costs have decreased in 2025 for both proton-exchange membrane (PEM) and alkaline electrolysers based on GHD (2025). Alkaline electrolysers remain lower cost than PEM electrolysers but their costs are becoming closer together.
The key advantage of PEM electrolysers is their 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.
Deployment of electrolysers and subsequent cost reductions are projected to be greatest in the Global NZE by 2050 scenario with the least change expected in Current policies.
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Figure 4-20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2024-25
0500100015002000250030003500400045005000202020252030203520402045205020552025-26 $/kW2025-26 Current policies Alkaline2025-26 Current policies PEM2025-26 Global NZE by 2050 Alkaline2025-26 Global NZE by 2050 PEM2025-26 Global NZE post 2050 Alkaline2025-26 Global NZE post 2050 PEM2024-25 Current policies Alkaline2024-25 Current policies PEM2024-25 Global NZE by 2050 Alkaline2024-25 Global NZE by 2050 PEM2024-25 Global NZE post 2050 Alkaline2024-25 Global NZE post 2050 PEM
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5 Levelised cost of electricity analysis
5.1 LCOE definition
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 investment14. Modelling studies such as AEMO’s Integrated System Plan (AEMO, 2024) do not require or use LCOE data15. 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.
The standard method of calculating LCOE does not include all of the additional costs required to deliver reliable electricity supply from variable renewable energy (VRE) generation, particularly when the share of VRE generation is high. The two key VRE technologies are wind and solar photovoltaics (PV). To address this issue of additional VRE costs, since 2019, GenCost has deployed a two-step methodology which separately calculates the VRE integration costs in an electricity system model and then adds them back into the standard LCOE for wind and solar photovoltaics. This method was devised after a thorough literature review in Graham (2018) but is due for an update.
5.2 Change in method for estimating the cost of reliable high VRE share generation
This 2025-26 GenCost report revises the methodology for estimating the cost of high VRE electricity systems in response to stakeholder feedback. Through various submissions over several years, stakeholders requested that the methodology be revised in two ways:
1.
Use a System Levelised Cost of Electricity (SLCOE) approach. A SLCOE takes the cost of electricity directly from an electricity system model by dividing all system costs from multiple existing and new technology deployments by the total useful electricity supply in a given year. This concept is also equivalent to the average annual unit cost of electricity for a given electricity system boundary16. Calculation of SLCOE is more direct than the previous approach which required setting up a baseline and identifying additional integration costs by calculating differences in costs for alternative levels of VRE generation share. Another advantage is that SLCOE also provides a system perspective which inherently requires a
14For a description the LCOE formula and the application of the formula go to CSIRO’s Data Access Portal and download the latest Excel file that accompanies this report. CSIRO Data Access Portal
15 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.
16 In this case we are concerned with generation and transmission
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bundle of technologies. This is a more useful perspective than comparing single technologies against one another using LCOE since very few systems use a single technology (the exception being remote power systems but even these are increasingly combining multiple technologies.)
2.
Provide greater transparency with regard to the data inputs and the modelling system used.
Separate from these two key items of stakeholder feedback, it was also observed over several years that while public interest in the future generation mix remains high, and the GenCost project’s primary goal is to support electricity system modelling by providing cost data, the amount of published electricity system modelling had not increased. It is hypothesised that the high complexity and cost of creating and applying commercial electricity system modelling tools may have been a significant contributor to this outcome. With this context, and to address all three issues simultaneously, a new open source electricity system model was created to calculate SLCOE, replacing the previous method for estimating the integration costs for solar PV and wind.
The new electricity system model is a simplified version of the larger commercial models used by CSIRO and other organisations. With the needs of other potential users in mind, the model was designed to be as short and fast solving as possible whilst not significantly reducing accuracy regarding the estimation of electricity costs and the generation mix. The final model design and the simplifications considered in the development phase are described in Graham et al. (2025). The model is of the National Electricity Market (NEM), excluding Western Australia and Northern Territory. The exclusion of these regions is a simplification in itself but also reflects the fact that these jurisdictions do not provide enough publicly available information about their electricity systems to easily construct an open source model.
Additional model details, software code and all input data can be downloaded at https://data.csiro.au/collection/csiro:71289. The model has been named Simple Electricity Model or SEM. Despite the goal of simplification, the model still requires access to and knowledge of specialised software and solvers and is targeted at users with a university level of knowledge who will likely have most success in following the equations and code. This is because universities typically provide education on linear programming across a wide variety of degree courses as well as access to free software and solver licences. Simpler spreadsheet type models or calculations were considered as an alternative but ultimately are not accurate enough to be relied on to estimate system costs due to their inability to consider and optimise all of the available system configurations.
5.3 SLCOE scenarios
Since 2019 GenCost has estimated the cost of integrating VRE at 60% to 90% shares initially for 2030 and then adding the current year more recently. With the new SLCOE method we have taken the opportunity to update the scenarios. The new analysis focusses on the 82% renewable target in 203017 and an emissions intensity target range in 2050 that supports achieving the policy of net
17 https://www.dcceew.gov.au/climate-change/emissions-reduction/net-zero/electricity-and-energy-sector-plan
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zero emissions by 2050. These were judged to be the most relevant electricity systems to be costed in Australia’s current policy environment. Stakeholders interested in other scenarios can explore them with the open source model.
The estimation of the 2030 SLCOE excludes all new technology deployment other than renewables and their supporting technologies for two reasons. Firstly, the current policy is specifically targeting renewables. Secondly, under normal project development lead times, there is not enough time for any other low emissions technology to contribute to new generation capacity in 2030. High emission technologies are not relevant to achieving current 2030 policy targets. However, even if those policies were ignored, mature high emissions technologies would also be constrained by development lead times.
The 2050 SLCOE considers either mature firmed renewables only or mature firm renewables plus any of three groups of technologies: floating and fixed offshore wind, coal and gas with carbon capture and storage and large-scale and small modular reactor nuclear. Each additional technology group is modelled separately because they each are assumed to require two first-of-a-kind (FOAK) projects to be built before they can access the standard technology costs. As such each technology group represents three unique scenarios. It would not be advisable to design a scenario where all technologies are built as it would be prohibitively expensive to build FOAK projects for all technologies. After building the two FOAK projects in each of the technology group scenarios the model determines whether to build any more of that technology at the standard cost based on whether additional capacity of that technology contributes to achieving a least cost electricity system.
The 2050 scenarios must meet emission intensity targets of 0tCO2e/MWh up to 0.20tCO2e/MWh. On their own these numbers mean little and so Table 5-1 provides more context and scenario names. It is appropriate to explore a range of electricity sector emissions intensity targets because there is no strict policy on the degree to which any sector must decarbonise in a net zero emissions policy world. To minimise greenhouse gas emissions abatement costs, ideally emission reduction takes place in each sector up to the point where no less expensive abatement may be found in any other sectors, including offset sectors such as land use, land use change and forestry. The value of 0.02tCO2e/MWh is most closely aligned with AEMO’s Step Change scenario in 2024 Integrated System Plan (ISP) modelling results, after adjusting for the ISP only reporting direct emissions and GenCost modelling being on a full fuel cycle basis (that is, inclusive of fugitive emissions in coal and gas extraction). Other emissions intensity scenarios serve as minimum and maximum costs, helping to define the range of possible electricity systems costs in 2050.
AEMO currently makes available 13 historical weather years of demand and renewable production data. It is possible to simulate all of these. However, for the sake of brevity and because for reliability purposes sufficient renewables and other supporting technologies must be deployed to deal with the worst weather conditions, all scenarios only use the single most costly weather year. Graham et al. (2025) provides more detail on the distribution of costs across weather years.
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Table 5-1 2050 full fuel cycle greenhouse gas emissions intensity target scenarios and their meaning 2050 emissions intensity target scenario Scenario name Meaning 0.20tCO2e/MWh NoProgressToNetZero Consistent with achieving the 2030 82% renewables target policy target and holding the emissions intensity constant to 2050. This is not consistent with achieving net zero by 2050 but serves as a reference case cost against which reducing the emissions intensity beyond 2030 can be measured.
0.10tCO2e/MWh
WeakNetZero
A halving of the 2030 emissions intensity by 2050. Only weakly consistent with the Australia’s net zero by 2050 policy, requiring significantly more abatement in non-electricity sectors. 0.05tCO2e/MWh ModerateNetZero A 75% reduction in the 2030 emissions intensity by 2050. In the range of what is required for the electricity sector to meet Australia’s net zero by 2050 policy
0.02tCO2e/MWh
StrongNetZero
The emissions intensity consistent with AEMO’s 2024 Integrated System Plan Step Change scenario. Likely to be what is required to meet Australia’s net zero by 2050 policy 0tCO2e/MWh ZeroEmissions Completely eliminating emissions from the electricity sector. This likely exceeds what is required to meet Australia’s net zero by 2050 policy but serves to define the maximum cost of electricity associated with emissions abatement.
5.4 SLCOE estimates
5.4.1 Data alignment
For this consultation draft, the results below are based on the GenCost 2024-25 final report generation and storage costs and transmission costs from AEMO’s Final 2025 Inputs, Assumptions and Scenarios Report. For the final GenCost 2025-26 report the generation and storage costs will be aligned with the costs in that report (i.e. the final 2025-26 cost estimates after consultation).
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5.4.2 Interpretation of costs from SLCOE and LCOE
Neither SLCOE or LCOE estimates guarantee a future wholesale electricity generation price outcome. They indicate the breakeven price required for investors to make a return on their portfolio or individual investments respectively. SLCOE is the more accurate indicator of the two because it includes the integration costs and operational requirements of the entire system whereas LCOE does not with the much simpler calculation only including a limited set of standard cost and technical inputs
Neither of these cost estimates guarantee future electricity prices. Changes in electricity prices are also subject to18:
•
Supply-demand imbalance as a result of too much or too little deployment relative to demand growth and retirements.
•
Fuel price and weather volatility.
•
The level of competition amongst suppliers.
These additional drivers of price formation can lead to prices significantly lower or higher than the underlying cost of the system and can take many years to correct due to the long lead times for capacity deployment.
Lead times are impacted by many factors such as the maturity of technology, approval and development processes, general uncertainty, the supply contract market and confidence in government policy directions. Construction times also vary significantly between technologies.
Given this background the SLCOE and LCOE data in GenCost are an indicator of the minimum price needed for investors to enter the market of their own accord ignoring any other uncertainties or external influences. However, should current or future electricity prices be much lower or higher than the indicated breakeven SLCOE, this outcome is likely to have been caused by excess or insufficient new capacity in the market whose root causes may span several years.
5.4.3 SLCOE results and the ISP
The SLCOE results will cover some similar ground to the Integrated System Plan (ISP) in that they present a future generation mix for the NEM. Where results differ, GenCost advises that the ISP should be given greater weight. The SLCOE results are based on a simplified electricity model. The results are designed to be reasonably accurate but, by design, are substantially less sophisticated than the multi-model state-of-the-art framework deployed in the ISP.
5.4.4 2030 SLCOE results
The 82% renewables by 2030 target includes customer generated electricity (rooftop solar PV). The 2030 modelling takes as given the 2030 rooftop solar PV consistent with the Step Change scenario published by AEMO in their 2025 final Inputs and Assumptions Workbook (the
18 Retailers also have hedging costs, market fees and ancillary services costs associated with their purchase of wholesale electricity. However, we do not go into those topics here as we are focussed with the main underlying drivers of generation price changes.
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Workbook). This reduces the renewable generation share that must be met by large scale generation to around 65% (or 78% when considered as a share of large-scale generation only). The SLCOE for 2030 is the average system cost to meet this large scale generation sector target. The least-cost share of generation sources selected by the modelling is shown on the left-hand-side of Figure 5-1.
The model may use, without any new capital cost, any existing or committed generation capacity as listed in the same AEMO Workbook. Otherwise, it must build new renewable generation, storage, transmission and incur any other cost required to meet the target such as connection costs and ongoing fuel and operating and maintenance costs for existing and new generation capacity. These other costs are sourced from either GenCost or the Workbook. The individual cost components and their contribution to SLCOE are shown on the right hand side of Figure 5-1. The SLCOE in 2030 is $91/MWh in the NEM inclusive of new transmission or $81/MWh for the generation sector only costs19. For context, the AEMC (2025) projected wholesale generation costs of just over $90/MWh in 2030, however, this included hedging costs which is a premium paid to avoid exposure to possible higher prices.
VRE-– variable renewable generation; IBR - Inverter-based resources cost such as deployment of synchronous condensers and grid-forming batteries; REZ – renewable energy zone. O&M – operating and maintenance costs
Figure 5-1 The projected 2030 large-scale generation share (left) and SLCOE by cost component (right) consistent with the 82% renewables by 2030 policy
New wind and solar PV capital costs are the bulk of new costs. The next highest cost category is fuel for existing coal and gas generation. Total transmission costs for connecting to renewable zones and other network strengthening are the next largest category followed by storage.
19 This is a point estimate using the mid range scenarios of fuel and technology cost projections available across GenCost and AEMO’s Input and Assumptions Workbook.
0%10%20%30%40%50%60%70%80%90%100%Generation shareCoalGasSolar PVWindHydro0102030405060708090100Cost2025-26 $/MWhOthertransmissionREZtransmissionFuelConnectionIBR costO&MStorageVRE capital
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Connection costs and inverter-based resources costs associated with maintaining system inertia are the smallest categories20.
5.4.5 2050 SLCOE results
By 2050, we assume all current generation is retired and only currently existing or committed hydro, pumped hydro and transmission remains. Rooftop solar PV, home batteries and vehicle-to-grid batteries are aligned to the Step Change scenario. In contrast to the 2030 SLCOE, it is assumed that in 2050 there is time available to deploy non-mature low emission technologies to assist in meeting the net zero by 2050 policy.
The three technology groups considered in the analysis are floating and fixed offshore wind, carbon capture and storage (CCS) applied to either coal or gas generation and large-scale and small modular reactor nuclear. However, to be eligible to deploy these technologies at the costs published in Appendix B of this report, each of these three technologies must first build a minimum of two projects at a cost premium consistent with the discussion of first-of-a-kind premiums in Section 2 of this report. The premiums applied are consistent with those presented in Table 2-1. In other words, there is an initial additional cost which must be paid to establish the required workforce, skills and supply chains when commencing a program of building technologies that Australia have not previously been deployed.
The least cost generation mix in 2050 for each emission intensity target and for either mature technologies only or mature technologies plus one of three technology groups not previously deployed in Australia are shown in Figure 5-2. In NoProgressToNetZero, with an emissions intensity 0.20tCO2e/MWh, the 2050 technology mix would retain a significant amount of natural gas and only build new coal when the renewable share has less onshore wind and solar PV. This occurs in the scenarios where FOAK offshore wind, CCS and nuclear projects are required to be built. The modelling chooses not to take up any additional offshore wind, CCS or nuclear beyond the required initial FOAK projects.
20 Previous versions of GenCost were also able to split out the costs associated with spillage (the loss of capacity factor when renewables are unable to deliver their generation either due to congestion or lack of demand). However under the SLCOE measure, all costs are calculated against useful generation only (that is excluding spilled generation). Consequently, spillage costs inflate all cost categories rather than appear as their own category.
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Mature renewables includes solar PV, onshore wind and hydro. FOAK – first of a kind. CCS – carbon capture and storage.
Figure 5-2 The projected generation mix in 2050 by emissions intensity target and allowed technology.
In WeakNetZero (at 0.10tCO2e/MWh) the gas share of generation is more than halved and there is no expansion of offshore wind, CCS or nuclear beyond the FOAK projects. Solar PV and offshore wind gain all of the market share lost by gas and coal relative to NoProgressToNetZero. In ModerateNetZero (at 0.05tCO2e/MWh), the same trend occurs with solar PV and onshore wind replacing natural gas with no competition from other generation sources.
In StrongNetZero (at 0.02tCO2e/MWh) the trend changes. Gas is too emission intensive to continue providing 7% of generation that it did under ModerateNetZero. However, gas cannot be cost effectively replaced with only solar PV and onshore wind because this would increase storage costs. Instead, the model deploys an alternative flexible generation technology in the form of hydrogen generation. The model also chooses to deploy additional CCS and nuclear beyond the required initial FOAK projects.
When the emission intensity target is set to zero in ZeroEmissions, CCS cannot contribute as GenCost only includes a version of CCS technology with 90% capture which cannot be emission free21. When deploying only mature technologies or offshore wind, renewable generation combines with hydrogen generation to deliver the required zero emission outcome. If the FOAK nuclear projects are first deployed, the model chooses to extend nuclear generation from of 5% to 7% of total generation in ZeroEmissions.
21 100% capture technology is theoretically possible but has additional costs.
0%10%20%30%40%50%60%70%80%90%100%Mature tech. only+ offshore wind+ CCS+ nuclearMature tech. only+ offshore wind+ CCS+ nuclearMature tech. only+ offshore wind+ CCS+ nuclearMature tech. only+ offshore wind+ CCS+ nuclearMature tech. only+ offshore wind+ CCS+ nuclearNoProgressToNetZeroWeakNetZeroModerateNetZeroStrongNetZeroZeroEmissionsOffshore wind FOAKCCS FOAKNuclear FOAKOffshore windCCSNuclearMature renewablesHydrogenNatural gasCoal
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The expansion of CCS or nuclear in the StrongNetZero and ZeroEmissions scenarios does not mean that greater deployment of those technologies is least cost. The reason we see these changes in deployment of technologies is because of the path dependency of transmission choices. When a CCS or nuclear plant is established in one region of the NEM, this changes the topology of the transmission system which can increase the cost of expanding transmission elsewhere due to the difference in the scale and ratio of transmission costs required to access renewables. However, if CCS and nuclear is not first deployed then the transmission system aligns with accessing cost competitive renewables. We need to compare the costs of these different generation and transmission systems to determine what it least cost across the scenarios explored. To this end, the SLCOE results for all scenarios are shown in Figure 5-3.
The SLCOE results for 2050 are all higher cost than in 2030 because whereas the 2030 modelling met demand using a significant amount of existing capacity, the 2050 modelling only allows for existing long lived transmission and hydro technology, assuming all other large scale generation technology that exists today has retired22. For this reason, the 2050 estimates overstate the average cost of generation in 2050 by assuming there will be no significant existing generation capacity, but the estimates can be considered an upper bound on average costs23.
The SLCOE results show that deploying mature technology only (solar PV, wind, gas and storage) is the least cost generation mix in 2050 for all emission intensity levels modelled. For the FOAK technologies, the CCS and offshore wind scenarios have very similar costs with CCS being only slightly lower cost than offshore wind. Nuclear is consistently the highest cost. The gap between mature renewables with gas and storage and the alternative low emission technology options narrows, the lower the emission intensity target. These outcomes are based on average costs. Offshore wind has a much wider cost uncertainty range and so could perform better under alternative cost scenarios not explored.
22 This is not strictly true because most solar PV built now or in the last few years will remain operating in 2050 due to their 30 year life.
23 In practice, the partial existing capacity that would normally be available to meet 2050 demand will be developed and paid for through generation in the decades leading up to 2050 and building out this investment pathway is how a more commercial grade electricity system model would estimate electricity costs over time. However, in this simplified modelling approach we only include selected long-lived existing resources and other resources needed to meet demand in 2050 are built in 2050 and paid for in the decades that follow (through amortisation).
GenCost 2025-26 | 55
Figure 5-3 The projected SLCOE in 2050 in the NEM by emission intensity target and technology allowed
There is a clear trend that decreasing emissions increases 2050 electricity costs. This reflects that as the emissions intensity declines, more zero emissions technology must be deployed, supported by more storage and more transmission as well as more expensive fuels (such as hydrogen) in some cases (StrongNetZero and ZeroEmissions).
The breakdown of costs for each emissions intensity scenario is shown in Figure 5-4. The fuel cost falls as the gas share of generation declines but increases for the last two scenarios as hydrogen enters the generation mix with hydrogen fuel being higher cost than gas on an energy unit basis. Baseload fossil fuel costs also fall as they are removed from the generation mix as the emission intensity declines. Almost all other cost categories increase as the emissions intensity falls. However, the increase in connection costs, inverter-based resource costs and operating and maintenance costs are relatively minor. The biggest increases are in storage and transmission costs.
0204060801001201401601802002025-26 $/MWhMaturetechnology onlyMature +offshore windMature + CCSMature + nuclear
56 | CSIRO Australia’s National Science Agency
VRE-– variable renewable generation; IBR - Inverter-based resources cost such as deployment of synchronous condensers and grid-forming batteries; REZ – renewable energy zone. O&M – operating and maintenance costs
Figure 5-4 The breakdown of SLCOE in 2050 in the NEM by cost component for mature technology only scenario
As discussed in the scenario descriptions, in theory the highest of the projected costs in 2050 need only be experienced if lower cost abatement is not available elsewhere in the economy outside of the electricity sector since there is no specific requirement for the electricity sector to eliminate all emissions. To shed some light on this issue, the average and marginal (incremental) cost of abatement of each emissions intensity level was calculated relative to NoProgressToNetZero (Figure 5-5). This can be compared to the expected cost of abatement across the whole economy to reach net zero which is published by Infrastructure Australia24. They publish a cost of abatement range in 2050 of $304 to $497/tCO2e (adjusted to 2025 dollars).
Based on the whole of economy cost of abatement range, it is not economically efficient for the electricity sector to achieve zero emissions. While the average cost of abatement of achieving zero emissions is lower than the whole of economy cost range, this is only because there are low abatement costs for removing the first 90% of those emissions. However, removing the last 10% of emissions is very high costs as indicated by the marginal abatement cost for the ZeroEmissions scenario.
The StrongNetZero scenario or an emissions intensity level of 0.02tCO2e/MWh is efficient for achieving net zero if the whole of economy cost of abatement is at the highest range but it inefficient if it is in the lower range. That is, at the low range whole of economy abatement cost it would be more efficient to achieve further abatement outside of the electricity sector. In this case it would be efficient for the electricity sector to target an emissions intensity of 0.05tCO2e/MWh in
24 https://www.infrastructureaustralia.gov.au/publications/valuing-emissions-economic-analysis
0204060801001201401601802025-26 $/MWhOther transmissionREZ transmissionFuelConnectionIBR costO&MStorageBaseload fossil capitalPeaking capitalVRE capital
GenCost 2025-26 | 57
the ModerateNetZero scenario or slightly below that given its marginal cost of abatement is below the whole of economy range.
In summary, examining the cost of abatement indicates that:
•
In a whole of economy effort to reach net zero by 2050, the modelling result indicate that it will not be efficient to eliminate all emissions from the electricity sector. It will be more efficient to undertake further abatement elsewhere in the economy
•
The efficient range of emissions intensity of the electricity sector lies somewhere between 0.02tCO2e/MWh to 0.05tCO2e/MWh depending on the uncertainty in the whole of economy abatement cost.
•
Achieving the electricity sector’s efficient role in whole of economy net zero abatement is projected to result in electricity costs of between $135/MWh to $148/MWh in the NEM inclusive of new transmission costs or $115/MWh to $124/MWh measured as wholesale generation costs only. For context, in 2024-25, the average NEM volume weighted generation price is estimated to be slightly higher than the top end of this range at $129/MWh.
•
Achieving weak or no progress in reducing electricity sector emissions in the period between 2030 and 2050 is not efficient for achieving net zero because electricity sector emissions reduction is substantially lower cost than emissions reduction elsewhere in the economy.
Figure 5-5 Average and marginal cost of abatement to achieve lower emissions intensity targets in 2050 compared to whole of economy abatement costs
0100200300400500600700800900WeakNetZeroModerateNetZeroStrongNetZeroZeroEmissions2025-26 $/tCO2eAverage costMarginal costWhole of economy cost highWhole of economy cost low
58 | CSIRO Australia’s National Science Agency
5.4.6 Comparative analysis of cost results
There is a surprising range of estimates of the cost of electricity from systems with a high share of weather dependent renewable generation. These studies have been separately reviewed in Graham (2025) where costs have been estimated in the range of below $70/MWh to over $1000/MWh. The research finds in each case where system average costs were reported to be high, it was observed that the modelling had excluded key resources required to keep costs low. Typical exclusions included: only one type of storage technology allowed, only one duration of storage technology allowed, peaking technology not allowed and a narrow set or single type of renewable generation allowed. When resources are made available, renewable generation is lowest cost when sourced from both solar PV and wind in different locations and combined with multiple storage technologies and gas or hydrogen peaking plant. Graham (2025) concludes that the observed exclusions do not appear to be valid for the NEM which has all of these resources available.
5.5 LCOE estimates
In addition to the SLCOE estimates provided in the previous section, the LCOE for individual technologies is also calculated and represents the breakeven price needed for each technology to achieve a reasonable return on investment. Appendix B includes the LCOE for additional years and technologies, however in this section we focus on a selected set of technologies for consistency with the SLCOE modelling.
Figure 5-6 shows the LCOE results for 2030 and the estimated SLCOE for 2030 is added for context. As discussed in the SLCOE section, given normal development lead times, there is insufficient time before 2030 to deploy technologies other than those already in the development pipeline which consists primarily of solar PV, wind, gas and storage (batteries and pumped hydro). However, the LCOE data indicates that were it possible to develop other technologies they are not expected to breakeven under the 2030 electricity system if wholesale electricity prices are close to the estimated SLCOE. CCS, nuclear, solar thermal and offshore wind are above the estimated 2030 SLCOE.
GenCost 2025-26 | 59
LCOE excludes integration costs. SLCOE includes integration costs of the least cost technology mix
Figure 5-6 Calculated LCOE range by technology and SLCOE for 2030
Figure 5-7 shows the LCOE results for 2050. Here they have been compared to the electricity system cost range identified in the SLCOE analysis as being the efficient range for the electricity sector to contribute to achieving net zero. By 2050, the costs of most technologies other than coal and gas have fallen. Solar PV and onshore wind (without integration costs) are lowest cost relative to the SLCOE estimates. The SLCOE analysis, which does include the renewable integration costs, indicates that solar PV and wind will deliver the majority of electricity supply supported by storage, transmission and either gas or hydrogen or a combination of both.
New black coal, while competitive at this SLCOE range is not relevant for deployment since electricity system modelling shows it cannot efficiently contribute to achieving net zero (that is, it would increase the cost of achieving net zero across the economy). New gas is also in the competitive range and unlike coal, the SLCOE analysis indicates it will play a role in achieving net zero emissions contributing a 3% to 7% share of generation.
Solar thermal is competitive relative to other technologies and inside the SLCOE range. However, given the need to access better solar resources which are further from load centres, solar thermal will be subject to additional transmission costs compared to coal, gas and nuclear which have not been directly accounted for. Based on previous GenCost analysis for solar PV and wind, additional transmission costs could add around $10-20/MWh. Some offshore wind is also in the competitive range however the modelling used the average cost of offshore wind. Lower range offshore wind costs appear to be competitive but were not explored in the modelling and so need more analysis.
01002003004005006007002025-26 $/MWhLCOE rangeSLCOE
60 | CSIRO Australia’s National Science Agency
LCOE excludes integration costs. SLCOE includes integration costs of the least cost technology mix
Figure 5-7 Calculated LCOE range by technology and SLCOE range for 2050
Gas with CCS is the next most competitive after solar thermal and offshore wind. Large-scale nuclear is slightly higher in cost than gas with CCS. Black coal with CCS occupies a similar cost range to nuclear. Nuclear small modular reactors (SMRs) are the highest cost. 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 fuel cost assumptions). Coal, gas and nuclear technologies would all have to be successful in operating at 89% capacity factor25 to achieve the lower end of the cost range when historically coal, which has been the main baseload energy source in Australia’s largest states, has only achieved an average of around 60%.
25 The lowest cost flexible plant in the system will typically be able to operate at this high capacity factor. However, this will be challenging for new plant to achieve. Older existing plant, with their capital costs mostly paid down and access to existing low cost fuel sources, are typically the lowest cost generation units. New generation units entering the market must recover their capital costs and tend to have less favourable fuel contracts due to competition with export markets.
01002003004005002025-26 $/MWhLCOE rangeSLCOE lowSLCOE high
GenCost 2025-26 | 61
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 publications (Hayward and Graham,
2017; Hayward and Graham, 2013; Hayward, Foster, Graham and 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 and 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 states 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%
(McDonald and Schrattenholzer, 2001). The transition between learning rates based on technology
uptake is illustrated in Apx Figure A.1.
𝐼𝐶 = 𝐼𝐶0 ×
𝐶𝐶
𝐶𝐶0
−𝑏
,
or equivalently log 𝐼𝐶 = log 𝐼𝐶0 − 𝑏(log 𝐶𝐶 − log 𝐶𝐶0 )
𝐿𝑅 = 100 × 1 − 2−𝑏
62 | CSIRO Australia’s National Science Agency
Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation
However, technologies that are modular and as a result 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 the relative cost reductions given each region will have a different level of demand for a technology.
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
GenCost 2025-26 | 63
capacity) is determined at each time step. See Hayward and Graham (2013) and Hayward et al. (2017) for more information. Both models run from the year 2006 to 2100. However, results are only reported from the present year to 2055.
A.1.3 Offshore wind
Offshore wind has been divided into fixed and floating foundation technologies. IRENA (2024) and Stehly and 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 GHD (2025) 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.
64 | CSIRO Australia’s National Science Agency
Data tables
The following tables provide data behind the figures presented in this document.
The year 2025 is mostly sourced from GHD (2025) and is aligned to July which represents either the middle of that calendar year or the beginning of the 2025-26 financial year.
As discussed in Section 2, the data is not intended to include FOAK costs. Therefore, for technologies not recently constructed in Australia, the cost of the first plant may be higher than estimated here. Section 2 includes suggested FOAK premiums.
Furthermore, capital costs are for a location not greater than 200km from the Victorian metropolitan area. GHD provide data for adjusting costs for different locations in the NEM. Site conditions will also impact costs to varying degrees, depending on the technology.
All capital costs are for the alternating current power rating of the equipment with the exception of rooftop solar which is on a direct current basis. Power is also on a net basis after auxiliary loads. Capital costs are before any subsidies that may be available.
GenCost 2025-26 | 65
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 (16hrs) Wind Offshore wind fixed Offshore wind floating Wave Nuclear SMR Tidal /ocean current Fuel cell Nuclear large-scale
$/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW
2025
6946
12941
10725
2497
2940
1764
6962
2022
2648
9016
25712
1621
1216
7562
3248
5433
8325
15842
30290
12420
7000
10332 2026 6756 12685 10400 2427 2824 1694 6707 2014 2619 8631 25277 1536 1192 7416 3123 5358 7222 15330 28775 11951 6940 10153
2027
6585
12465
10108
2360
2716
1629
6463
2010
2594
8387
24850
1456
1171
7306
3004
5306
6504
14835
26703
11500
6881
10000 2028 6436 12284 9852 2298 2566 1540 6234 2009 2573 8287 24430 1380 1156 7244 2896 5279 6171 14356 25179 11066 6822 9877
2029
6297
12118
9613
2238
2427
1456
6016
2009
2554
8316
24017
1307
1145
7209
2793
5271
6164
13893
23998
10649
6763
9764 2030 6164 11961 9385 2180 2296 1377 5807 2010 2537 8349 23611 1239 1135 7197 2697 5268 6163 13444 23925 10247 6705 9658
2031
6033
11805
9161
2124
2173
1304
5606
2011
2519
8381
23211
1174
1127
7196
2607
5269
6166
13010
23850
9860
6648
9552 2032 5902 11644 8938 2069 2056 1233 5410 2011 2501 8411 22819 1113 1121 7191 2525 5275 6172 12590 23952 9488 6591 9442
2033
5772
11481
8717
2015
1946
1167
5220
2010
2482
8439
22433
1055
1117
7121
2453
5280
6166
12183
24048
9130
6534
9330 2034 5644 11320 8501 1962 1842 1105 5037 2010 2463 8467 22054 1000 1115 6999 2395 5285 6136 11790 24145 8786 6478 9220
2035
5567
11220
8371
1929
1776
1066
4913
2012
2454
8495
21681
947
1113
6833
2384
5289
6088
11409
24241
8454
6423
9159 2036 5541 11176 8325 1916 1747 1048 4842 2017 2453 8524 21749 942 1110 6699 2353 5292 6038 11489 24339 8493 6450 9149
2037
5563
11196
8362
1922
1752
1051
4830
2024
2462
8553
21815
938
1108
6593
2350
5294
5998
11502
24438
8499
6470
9189 2038 5587 11227 8400 1928 1757 1054 4828 2031 2471 8582 21893 931 1106 6522 2347 5297 5964 11515 24170 8504 6484 9229
2039
5610
11268
8437
1934
1762
1057
4835
2038
2480
8612
21980
925
1104
6477
2345
5299
5934
11528
22245
8510
6492
9269 2040 5631 11299 8472 1939 1767 1060 4834 2045 2488 8638 22053 918 1103 6456 2343 5301 5918 11539 19646 8516 6502 9306
2041
5649
11311
8503
1944
1770
1062
4819
2050
2494
8661
22105
912
1102
6438
2340
5304
5913
11550
17398
8522
6514
9339 2042 5666 11310 8531 1948 1773 1064 4794 2055 2500 8680 22139 908 1100 6428 2337 5308 5917 11560 16791 8528 6529 9368
2043
5683
11307
8558
1952
1776
1066
4768
2060
2506
8700
22173
904
1099
6406
2332
5312
5921
11569
16838
8534
6542
9398 2044 5699 11296 8586 1955 1780 1068 4734 2065 2512 8719 22198 900 1098 6402 2328 5317 5926 11579 16886 8540 6558 9428
2045
5715
11277
8615
1959
1783
1070
4693
2070
2518
8739
22217
896
1097
6381
2324
5321
5931
11589
16934
8545
6574
9457 2046 5732 11254 8643 1963 1786 1071 4648 2075 2524 8758 22233 892 1096 6366 2320 5326 5936 11598 16983 8551 6588 9487
2047
5750
11241
8671
1967
1789
1073
4613
2080
2530
8778
22257
889
1096
6308
2317
5330
5940
11608
17032
8557
6594
9517 2048 5768 11237 8700 1971 1792 1075 4585 2085 2537 8798 22290 875 1095 6232 2313 5334 5945 11618 17081 8563 6592 9548
2049
5786
11240
8729
1974
1795
1077
4563
2090
2543
8818
22330
863
1093
6139
2300
5339
5950
11628
17130
8569
6583
9578 2050 5803 11254 8756 1978 1798 1079 4553 2095 2548 8836 22378 851 1091 6089 2290 5344 5955 11637 17176 8576 6579 9607
2051
5820
11270
8782
1981
1801
1081
4545
2099
2554
8853
22426
838
1083
6050
2268
5348
5959
11646
17219
8582
6567
9634 2052 5835 11293 8806 1984 1803 1082 4544 2103 2558 8869 22478 827 1077 6038 2256 5353 5964 11654 17259 8588 6566 9660
2053
5848
11308
8830
1987
1806
1083
4536
2107
2563
8884
22522
800
1068
6006
2237
5357
5968
11662
17299
8594
6536
9685 2054 5861 11330 8855 1990 1808 1085 4534 2111 2568 8899 22574 786 1066 5997 2230 5361 5972 11671 17340 8600 6523 9711
2055
5866
11337
8867
1991
1809
1085
4530
2113
2571
8907
22596
771
1064
5983
2223
5363
5974
11675
17360
8603
6502
9724
66 | CSIRO Australia’s National Science Agency
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 (16hrs) Wind Offshore wind fixed Offshore wind floating Wave Nuclear SMR Tidal /ocean current Fuel cell Nuclear large-scale
$/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW
2025
6946
12941
10725
2497
2914
1764
6962
2022
2648
9016
25712
1621
1216
7562
3248
5433
8325
15842
30290
12420
7000
10332 2026 6769 12703 10419 2429 2773 1679 6707 2017 2622 8828 25450 1382 1184 7259 3100 4882 6880 15376 28112 11991 6805 10171
2027
6617
12514
10156
2366
2641
1599
6467
2016
2602
8660
25190
1171
1157
6969
2961
4375
5614
14923
26040
11577
6616
10046 2028 6498 12387 9946 2309 2524 1528 6248 2022 2590 8516 24933 996 1136 6907 2835 3928 4590 14484 24157 11177 6433 9967
2029
6396
12291
9765
2256
2418
1464
6043
2030
2581
8388
24679
848
1117
6877
2718
3532
3757
14057
22444
10791
6254
9912 2030 6296 12196 9588 2204 2319 1404 5846 2039 2574 8292 24427 743 1101 6869 2608 3281 3257 13643 20271 10418 6080 9858
2031
6188
12083
9399
2152
2199
1331
5651
2046
2563
8224
24178
677
1085
6872
2505
3159
3028
13242
18697
10058
5911
9790 2032 6074 11952 9200 2099 2084 1262 5458 2050 2550 8191 23932 652 1073 6873 2410 3153 3021 12852 17682 9711 5747 9708
2033
5957
11815
8999
2048
1975
1196
5270
2054
2536
8159
23688
642
1066
6856
2326
3146
3014
12474
17809
9375
5587
9620 2034 5843 11680 8803 1997 1872 1133 5089 2058 2522 8131 23446 641 1064 6670 2258 3139 3008 12106 17936 9052 5432 9533
2035
5782
11517
8695
1967
1808
1095
4887
2064
2517
8088
23207
623
1061
6495
2238
3134
3002
11750
17383
8739
5281
9499 2036 5772 11416 8674 1957 1781 1078 4742 2073 2522 8072 23264 606 1060 6288 2194 3132 3000 11643 16826 8605 5317 9516
2037
5814
11380
8739
1966
1788
1083
4655
2085
2536
8079
23317
589
1059
6230
2176
3132
2999
11669
16266
8621
5353
9585 2038 5857 11438 8806 1975 1796 1088 4653 2097 2551 8123 23461 588 1060 6185 2161 3133 3000 11695 16386 8636 5391 9656
2039
5900
11494
8873
1985
1804
1092
4648
2109
2566
8167
23605
586
1062
6149
2155
3136
3002
11722
16507
8652
5429
9727 2040 5941 11551 8937 1994 1811 1097 4645 2120 2579 8205 23745 584 1058 6122 2153 3139 3004 11747 16622 8668 5465 9795
2041
5981
11609
8998
2002
1818
1101
4646
2131
2592
8239
23881
582
1047
6101
2153
3144
3008
11772
16732
8684
5499
9859 2042 6018 11675 9055 2009 1824 1104 4655 2141 2604 8271 24021 580 1030 6085 2153 3149 3012 11795 16835 8700 5530 9920
2043
6055
11744
9114
2016
1830
1108
4666
2151
2616
8301
24164
578
1009
6074
2153
3155
3017
11819
16940
8271
5554
9982 2044 6093 11815 9173 2023 1836 1112 4679 2160 2628 8332 24310 576 994 6067 2152 3161 3021 11843 17045 7842 5578 10044
2045
6131
11890
9232
2031
1842
1115
4695
2171
2640
8360
24460
575
980
6064
2151
3167
3026
11867
17152
7403
5604
10107 2046 6169 11966 9292 2038 1848 1119 4712 2181 2653 8395 24613 573 974 6064 2149 3174 3031 11891 17259 7408 5636 10171
2047
6208
12045
9353
2046
1855
1123
4729
2191
2665
8429
24768
572
969
6066
2147
3180
3036
11916
17368
7413
5669
10235 2048 6247 12124 9414 2054 1861 1127 4747 2201 2678 8466 24925 570 970 6072 2145 3186 3041 11729 17478 7428 5698 10299
2049
6287
12203
9476
2061
1867
1131
4765
2212
2691
8501
25083
567
945
6079
2142
3191
3046
11454
17589
7289
5653
10365 2050 6326 12282 9537 2069 1874 1134 4782 2222 2703 8534 25238 564 920 6076 2141 3197 3051 11178 17698 7150 5607 10429
2051
6364
12358
9597
2076
1879
1138
4798
2232
2715
8566
25390
559
891
6107
2136
3204
3056
11066
17804
7011
5560
10491 2052 6401 12433 9655 2083 1885 1141 4814 2241 2726 8600 25538 558 890 6072 2136 3211 3062 11042 17908 7025 5587 10553
2053
6439
12506
9714
2090
1891
1145
4827
2251
2738
8630
25685
555
880
6103
2130
3218
3068
10990
18013
7039
5612
10615 2054 6476 12580 9773 2096 1896 1148 4841 2260 2750 8661 25834 555 875 6070 2131 3226 3074 10983 18119 7053 5640 10677
2055
6495
12615
9803
2100
1899
1150
4847
2265
2756
8674
25907
553
868
6102
2128
3229
3077
10965
18172
7060
5653
10708
GenCost 2025-26 | 67
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 (16hrs) Wind Offshore wind fixed Offshore wind floating Wave Nuclear (SMR) Tidal /ocean current Fuel cell Nuclear large-scale
$/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW $/kW
2025
6946
12941
10725
2497
2914
1764
6962
2022
2648
9016
25712
1621
1216
7562
3248
5433
8325
15842
30290
12420
7000
10332 2026 6761 12687 10406 2428 2771 1678 6703 2015 2620 8854 25321 1381 1191 7474 3108 5404 7796 15367 28775 11983 6804 10159
2027
6596
12472
10121
2362
2637
1597
6456
2012
2596
8705
24937
1187
1171
7417
2976
5383
7297
14906
26703
11561
6613
10013 2028 6456 12303 9879 2301 2516 1523 6227 2012 2578 8574 24558 1066 1155 7395 2855 5369 6839 14460 24554 11155 6428 9902
2029
6329
12156
9658
2243
2405
1456
6010
2015
2562
8453
24184
989
1141
7384
2742
5360
6415
14026
22351
10762
6248
9808 2030 6207 12015 9446 2188 2303 1394 5802 2019 2548 8382 23817 930 1126 7372 2636 5306 6103 13606 20114 10384 6073 9718
2031
6084
11869
9233
2133
2181
1320
5600
2021
2532
8327
23455
872
1114
7346
2536
5186
5878
13198
18498
10018
5903
9624 2032 5957 11713 9016 2078 2064 1250 5402 2022 2515 8320 23098 831 1104 7321 2445 5020 5706 12802 17453 9666 5738 9522
2033
5832
11556
8802
2025
1954
1183
5211
2023
2498
8319
22747
806
1095
7204
2365
4886
5558
12418
17544
9326
5577
9418 2034 5710 11402 8593 1973 1851 1121 5027 2024 2481 8352 22401 788 1087 7046 2301 4804 5454 12046 17636 8998 5421 9315
2035
5639
11321
8471
1941
1786
1081
4912
2028
2473
8385
22061
772
1080
6852
2285
4758
5418
11685
17171
8681
5269
9264 2036 5619 11312 8434 1929 1758 1064 4865 2034 2475 8420 22177 759 1075 6735 2252 4718 5388 11532 16704 8524 5244 9264
2037
5650
11375
8481
1936
1764
1068
4882
2043
2485
8454
22297
749
1070
6658
2244
4684
5362
11548
16236
8533
5230
9314 2038 5680 11439 8529 1943 1770 1071 4901 2052 2496 8489 22418 742 1065 6620 2235 4654 5340 11564 16324 8541 5228 9365
2039
5711
11434
8577
1950
1776
1075
4857
2061
2507
8525
22477
734
1061
6608
2217
4624
5241
11581
16414
8549
5223
9416 2040 5740 11425 8622 1957 1781 1078 4812 2069 2517 8557 22528 725 1059 6618 2201 4597 5144 11596 16497 8558 5225 9464
2041
5767
11407
8663
1963
1786
1081
4760
2077
2526
8586
22565
717
1056
6632
2182
4573
5049
11610
16574
8566
5230
9508 2042 5792 11452 8702 1968 1790 1084 4768 2083 2534 8611 22656 709 1055 6595 2169 4556 5037 11623 16644 8574 5247 9548
2043
5816
11498
8740
1973
1794
1086
4776
2090
2542
8637
22748
704
1054
6490
2160
4541
5027
11636
16715
8583
5260
9589 2044 5841 11498 8779 1978 1798 1089 4743 2096 2550 8662 22798 698 1054 6357 2154 4528 5018 11650 16787 8591 5267 9630
2045
5866
11495
8818
1983
1802
1091
4707
2103
2558
8688
22845
691
1053
6234
2148
4517
5010
11663
16858
8600
5252
9671 2046 5892 11491 8857 1988 1807 1094 4670 2110 2567 8714 22893 685 1054 6163 2144 4507 5004 11676 16931 8609 5222 9713
2047
5917
11539
8897
1993
1811
1096
4679
2117
2575
8741
22988
679
1054
6088
2141
4498
4998
11690
17004
8617
5184
9754 2048 5942 11590 8937 1998 1815 1099 4691 2124 2583 8767 23087 675 1055 6032 2139 4491 4994 11703 17077 8626 5162 9797
2049
5968
11642
8977
2003
1819
1102
4703
2130
2592
8794
23187
668
1054
5975
2138
4479
4986
11717
17151
8635
5158
9839 2050 5993 11692 9015 2008 1823 1104 4714 2137 2600 8819 23283 664 1054 5949 2137 4472 4982 11730 17223 8644 5171 9880
2051
6017
11735
9053
2013
1827
1106
4720
2143
2607
8843
23370
660
1054
5936
2136
4458
4972
11742
17291
8652
5184
9919 2052 6040 11775 9088 2017 1831 1108 4724 2149 2614 8865 23454 659 1055 5942 2137 4454 4970 11754 17357 8661 5198 9957
2053
6063
11806
9125
2021
1834
1110
4720
2155
2621
8887
23528
656
1055
5926
2136
4438
4958
11767
17423
8670
5194
9995 2054 6086 11842 9161 2025 1838 1113 4721 2161 2629 8910 23608 655 1056 5920 2137 4434 4956 11779 17489 8679 5193 10033
2055
6097
11855
9179
2027
1839
1114
4717
2164
2632
8921
23644
654
1057
5906
2137
4426
4951
11785
17523
8684
5183
10052
68 | CSIRO Australia’s National Science Agency
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
2025
778
778
778
310
310
311
467
467
467
525
525
525
290
290
290
235
235
235 2026 760 717 673 306 289 272 455 428 401
514 485 456 285 270 254 229 215 202
2027
747
668
590
302
273
244
445
395
346
505
453
401
282
255
227
224
199
174 2028 740 630 521 299 260 221 441 370 300
500 429 357 279 243 206 221 186 151
2029
734
599
464
297
251
205
438
349
260
497
409
322
277
234
191
220
175
130 2030 717 589 420 289 243 195 428 345 225
484 400 295 269 227 182 215 173 113
2031
698
578
407
281
236
186
418
341
222
471
392
284
262
220
173
210
171
111 2032 689 567 394 273 229 177 417 337 218
463 383 274 254 214 165 209 169 109
2033
676
556
380
265
222
168
411
334
212
453
375
263
247
207
157
206
167
106 2034 647 546 369 257 216 160 390 330 209
435 366 254 240 201 149 196 165 105
2035
627
535
359
250
210
152
378
326
207
422
359
246
233
195
142
189
163
104 2036 611 526 350 243 203 145 369 322 205
411 351 238 226 189 135 185 161 103
2037
597
516
343
236
197
138
361
319
205
400
343
231
219
184
128
181
160
103 2038 585 506 336 229 192 131 356 315 204
391 336 224 213 178 122 178 158 102
2039
573
497
329
222
186
125
351
311
204
382
329
218
206
173
116
176
156
102 2040 568 492 329 219 185 125 348 308 204
378 326 218 204 172 116 174 154 102
2041
562
487
328
217
183
124
345
304
204
374
322
218
202
170
116
173
152
102 2042 558 482 328 215 182 124 343 300 204
372 320 218 200 169 115 172 150 102
2043
555
478
329
213
181
124
341
297
205
369
317
218
198
169
115
171
148
102 2044 552 474 329 212 181 124 340 293 205
367 314 218 197 168 115 170 147 103
2045
550
469
329
211
180
124
339
289
205
366
312
218
196
167
115
170
145
103 2046 548 465 330 210 180 124 338 286 206
364 310 218 195 167 115 169 143 103
2047
547
462
330
209
179
124
337
282
206
363
308
218
195
166
115
169
141
103 2048 545 458 331 209 179 124 336 279 207
362 306 219 194 166 115 168 139 104
2049
541
454
332
208
179
124
332
275
208
360
304
219
194
166
115
166
138
104 2050 537 451 332 208 179 124 329 272 208
358 302 219 193 166 116 164 136 104
2051
538
447
333
208
179
125
329
269
209
358
300
220
193
166
116
165
134
104 2052 534 444 334 207 178 125 326 265 209 356 298 221 193 166 116 163 133 105
2053
535
440
335
208
179
125
327
262
210
356
297
221
193
166
116
163
131
105 2054 532 437 336 207 178 125 325 259 211 355 295 222 192 166 116 162 129 106
2055
533
446
337
207
178
125
325
268
212
355
300
222
192
166
116
163
134
106
GenCost 2025-26 | 69
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
2025
385
385
385
265
265
265
120
120
120
308
308
308
245
245
245
63
63
63 2026 377 356 335 260 246 232 117 110 103
301 285 268 240 227 214 61 58 54
2027
371
333
296
257
232
207
114
101
89
297
268
238
237
214
191
60
53
47 2028 367 316 265 254 221 188 113 95 77
294 254 214 235 204 174 59 50 40
2029
364
302
241
252
213
174
112
89
66
292
243
196
233
197
161
59
47
35 2030 355 295 223 246 207 166 109 88 58
284 237 183 227 191 153 57 46 30
2031
345
288
214
238
201
158
107
87
57
276
231
175
220
185
146
56
46
30 2032 338 281 206 231 195 150 106 86 56
269 225 168 213 180 138 56 45 29
2033
330
274
197
225
189
143
105
85
54
262
219
160
207
174
132
55
45
28 2034 318 267 189 218 183 136 100 84 53
253 213 153 201 169 125 52 44 28
2035
308
261
182
212
178
129
96
83
53
246
207
147
195
164
119
50
44
28 2036 299 254 175 206 172 123 94 82 52
239 202 141 189 159 113 49 43 27
2037
291
248
169
199
167
117
92
81
52
232
196
135
184
154
108
48
42
27 2038 284 242 163 194 162 111 91 80 52
226 191 130 178 149 102 47 42 27
2039
277
236
158
188
157
106
89
79
52
220
186
124
173
145
97
47
41
27 2040 274 234 157 185 156 105 88 78 52
217 184 124 171 144 97 46 41 27
2041
271
232
157
183
155
105
88
77
52
215
183
124
169
142
97
46
40
27 2042 269 230 157 182 154 105 87 76 52
213 182 124 167 142 97 46 40 27
2043
267
228
157
180
153
105
87
75
52
211
180
124
166
141
96
45
39
27 2044 265 227 157 179 152 105 86 74 52
210 179 124 165 140 96 45 39 27
2045
264
225
157
178
152
105
86
73
52
209
178
124
164
140
96
45
38
27 2046 263 224 157 177 151 105 86 73 52
208 177 124 163 139 96 45 38 27
2047
262
223
157
177
151
105
86
72
52
207
177
124
163
139
96
45
37
27 2048 262 222 157 176 151 105 85 71 53
207 176 124 162 139 96 45 37 27
2049
260
221
157
176
151
105
84
70
53
206
175
124
162
139
96
44
37
28 2050 259 220 158 175 151 105 83 69 53
205 175 124 161 139 97 44 36 28
2051
259
219
158
176
151
105
83
68
53
205
174
124
162
139
97
44
36
28 2052 258 218 158 175 150 105 83 67 53 204 174 125 161 138 97 43 35 28
2053
258
217
159
175
151
105
83
66
53
204
173
125
161
139
97
43
35
28 2054 257 216 159 175 150 105 82 66 54 204 173 125 161 138 97 43 34 28
2055
257
218
159
175
150
106
83
68
54
204
174
125
161
138
97
43
35
28
70 | CSIRO Australia’s National Science Agency
Apx Table B.6 Twelve- and twenty-four hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW)
Battery storage (12 hrs)
Battery storage (24 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
2025
287
287
287
245
245
245
42
42
42
266
266
266
245
245
245
21
21
21 2026 281 266 250 240 227 214 41 38 36
261 246 232 240 227 214 20 19 18
2027
277
250
222
237
214
191
40
35
31
257
232
207
237
214
191
20
18
16 2028 274 237 201 235 204 174 39 33 27
255 221 187 235 204 174 20 17 13
2029
272
228
184
233
197
161
39
31
23
252
212
172
233
197
161
20
16
12 2030 265 222 173 227 191 153 38 31 20
246 206 163 227 191 153 19 15 10
2031
257
216
165
220
185
146
37
31
20
239
200
155
220
185
146
19
15
10 2032 251 210 158 213 180 138 37 30 19
232 195 148 213 180 138 19 15 10
2033
244
204
151
207
174
132
37
30
19
226
189
141
207
174
132
18
15
9 2034 236 198 144 201 169 125 35 29 19
218 183 134 201 169 125 17 15 9
2035
229
193
137
195
164
119
34
29
18
212
178
128
195
164
119
17
15
9 2036 222 187 131 189 159 113 33 29 18
206 173 122 189 159 113 16 14 9
2037
216
182
126
184
154
108
32
28
18
200
168
117
184
154
108
16
14
9 2038 210 177 120 178 149 102 32 28 18
194 163 111 178 149 102 16 14 9
2039
204
172
115
173
145
97
31
28
18
188
159
106
173
145
97
16
14
9 2040 201 171 115 171 144 97 31 27 18
186 157 106 171 144 97 15 14 9
2041
199
169
115
169
142
97
31
27
18
184
156
106
169
142
97
15
13
9 2042 198 168 115 167 142 97 30 27 18
182 155 106 167 142 97 15 13 9
2043
196
167
115
166
141
96
30
26
18
181
154
106
166
141
96
15
13
9 2044 195 166 115 165 140 96 30 26 18
180 153 105 165 140 96 15 13 9
2045
194
165
115
164
140
96
30
26
18
179
153
105
164
140
96
15
13
9 2046 193 165 115 163 139 96 30 25 18
178 152 105 163 139 96 15 13 9
2047
192
164
115
163
139
96
30
25
18
178
152
105
163
139
96
15
12
9 2048 192 164 115 162 139 96 30 25 18
177 151 106 162 139 96 15 12 9
2049
191
163
115
162
139
96
29
24
18
176
151
106
162
139
96
15
12
9 2050 190 163 115 161 139 97 29 24 18
176 151 106 161 139 97 15 12 9
2051
191
162
115
162
139
97
29
24
18
176
151
106
162
139
97
15
12
9 2052 190 162 115 161 138 97 29 23 19 175 150 106 161 138 97 14 12 9
2053
190
162
116
161
139
97
29
23
19
176
150
106
161
139
97
14
12
9 2054 189 161 116 161 138 97 29 23 19 175 150 106 161 138 97 14 11 9
2055
189
162
116
161
138
97
29
24
19
175
150
107
161
138
97
14
12
9
GenCost 2025-26 | 71
Apx Table B.7 Pumped hydro storage cost data by duration, by scenario, total cost basis
$/kW
$/kWh
Current policies Global NZE post 2050 Global NZE by 2050
Current policies Global NZE post 2050
Global NZE by 2050
10hrs
24hrs
48hrs
160hrs
10hrs
24hrs
48hrs
160hrs
10hrs
24hrs
48hrs
160hrs
10hrs
24hrs
48hrs
160hrs
10hrs
24hrs
48hrs
160hrs
10hrs
24hrs
48hrs
160hrs 2025 3535 4364 5192 8879 3535 4364 5192 8879 3535 4364 5192 8879
354 182 108 55 354 182 108 55 354 182 108 55
2026
3468
4280
5093
8709
3468
4280
5093
8709
3468
4280
5093
8709
347
178
106
54
347
178
106
54
347
178
106
54 2027 3402 4199 4996 8544 3402 4199 4996 8544 3402 4199 4996 8544
340 175 104 53 340 175 104 53 340 175 104 53
2028
3336
4118
4899
8378
3336
4118
4899
8378
3336
4118
4899
8378
334
172
102
52
334
172
102
52
334
172
102
52 2029 3267 4032 4798 8205 3267 4032 4798 8205 3267 4032 4798 8205
327 168 100 51 327 168 100 51 327 168 100 51
2030
3198
3947
4696
8031
3198
3947
4696
8031
3198
3947
4696
7131
320
164
98
50
320
164
98
50
320
164
98
45 2031 3215 3968 4721 8074 3218 3972 4726 8081 3228 3985 4726 7199
321 165 98 50 322 165 98 51 323 166 98 45
2032
3228
3984
4741
8107
3233
3991
4749
8121
3252
4014
4749
7251
323
166
99
51
323
166
99
51
325
167
99
45 2033 3242 4001 4761 8142 3250 4011 4772 8161 3275 4043 4772 7304
324 167 99 51 325 167 99 51 328 168 99 46
2034
3255
4018
4781
8175
3266
4032
4797
8203
3301
4075
4797
7361
326
167
100
51
327
168
100
51
330
170
100
46 2035 3269 4035 4800 8209 3283 4052 4822 8245 3327 4107 4822 7420
327 168 100 51 328 169 100 52 333 171 100 46
2036
3282
4051
4820
8243
3300
4073
4846
8288
3354
4140
4846
7479
328
169
100
52
330
170
101
52
335
172
101
47 2037 3296 4068 4841 8278 3317 4095 4872 8331 3381 4173 4872 7539
330 170 101 52 332 171 101 52 338 174 101 47
2038
3310
4086
4861
8313
3335
4116
4897
8375
3408
4206
4897
7599
331
170
101
52
333
171
102
52
341
175
102
47 2039 3324 4103 4882 8349 3352 4138 4923 8419 3436 4240 4923 7661
332 171 102 52 335 172 103 53 344 177 103 48
2040
3339
4121
4903
8384
3370
4160
4949
8464
3464
4275
4949
7723
334
172
102
52
337
173
103
53
346
178
103
48 2041 3349 4133 4918 8410 3384 4176 4969 8498 3487 4304 4969 7776
335 172 102 53 338 174 104 53 349 179 104 49
2042
3359
4145
4932
8435
3397
4193
4989
8532
3511
4334
4989
7830
336
173
103
53
340
175
104
53
351
181
104
49 2043 3369 4158 4947 8460 3411 4210 5009 8566 3535 4364 5009 7884
337 173 103 53 341 175 104 54 354 182 104 49
2044
3379
4171
4962
8486
3425
4227
5029
8601
3560
4394
5029
7938
338
174
103
53
342
176
105
54
356
183
105
50 2045 3389 4183 4977 8512 3439 4244 5050 8636 3585 4424 5050 7993
339 174 104 53 344 177 105 54 358 184 105 50
2046
3399
4196
4992
8537
3453
4261
5070
8671
3609
4455
5070
8048
340
175
104
53
345
178
106
54
361
186
106
50 2047 3410 4209 5007 8563 3467 4279 5091 8706 3635 4486 5091 8105
341 175 104 54 347 178 106 54 363 187 106 51
2048
3420
4221
5023
8589
3481
4296
5112
8742
3660
4517
5112
8161
342
176
105
54
348
179
106
55
366
188
106
51 2049 3431 4234 5038 8616 3495 4314 5133 8777 3686 4549 5133 8218
343 176 105 54 349 180 107 55 369 190 107 51
2050
3441
4247
5053
8642
3509
4331
5154
8813
3711
4581
5154
8276
344
177
105
54
351
180
107
55
371
191
107
52 2051 3450 4258 5066 8663 3522 4347 5172 8844 3735 4611 5172 8329
345 177 106 54 352 181 108 55 374 192 108 52
2052
3458
4268
5079
8685
3534
4362
5190
8875
3760
4640
5190
8383
346
178
106
54
353
182
108
55
376
193
108
52 2053 3467 4279 5091 8707 3547 4377 5208 8907 3784 4670 5208 8438
347 178 106 54 355 182 109 56 378 195 109 53
2054
3476
4290
5104
8729
3559
4393
5227
8938
3809
4701
5227
8492
348
179
106
55
356
183
109
56
381
196
109
53 2055 3484 4301 5117 8750 3572 4409 5245 8970 3833 4731 5245 8548
348 179 107 55 357 184 109 56 383 197 109 53
72 | CSIRO Australia’s National Science Agency
Apx Table B.8 Historical storage cost data, total cost basis
$/kW $/kWh
Battery (1hr) Battery (2hrs) Battery (4hrs) Battery (8hrs) PHES (6hrs) PHES (8hrs) PHES (10hrs) PHES (12hrs) PHES (24hrs) PHES (48hrs) PHES 160hrs) Battery (1hr) Battery (2hrs) Battery (4hrs) Battery (8hrs) PHES (6hrs) PHES (8hrs) PHES (10hrs) PHES (12hrs) PHES (24hrs) PHES (48hrs) PHES 160hrs)
2019
1535
2424
4404
2283
2461
2614
3875
4341
768
606
551
308
249
177
131
88
2020 977 1310 2086 3676 2877 3004
3292 4232 6358
977 655 521 460 387 303
222 142 128
2021
942
1257
1942
3407
2771
3006
3234
4219
6337
942
629
485
426
387
315
226
147
133
2022 1046 1513 2454 4360 3245 3526
3786 4949 7434
1046 756 614 545 481 392
281 184 165
2023
1069
1549
2510
4400
4037
4387
4617
6156
7226
1069
775
627
550
635
517
363
242
170
2024 929 1241 1727 2809
7837
6632 7985
929 621 432 351
768
271 196
2025
778
1050
1540
2464
3535
4364
5192
8879
778
525
385
308
354
182
130
55
Notes: Batteries are large scale. Small scale batteries of 10kWh for home use with 2-hour duration are estimated at $1100/kWh before subsidies (GHD, 2025). However, lower costs may be available for larger household battery sizes.
GenCost 2025-26 | 73
Apx Table B.9 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
2024
Years
Years
$/kW
$/MWh
$/MWh
$/kW
$/GJ
$/kW
$/GJ
Gas with CCS 25 2.0 44% 46.0 8.0 8.6
6962 13.3 89%
6962 19.5 53%
Gas combined cycle
25
2.0
51%
36.0
5.0
0.0
2497
13.3
89%
2497
19.5
53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0
2886 13.3 20%
2886 19.5 20%
Gas open cycle (large)
25
1.5
33%
26.0
10.0
0.0
2886
13.3
20%
2886
19.5
20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0
2022 13.3 20%
2022 19.5 20%
Hydrogen reciprocating
25
1.0
32%
33.0
0.0
0.0
2648
40.7
20%
2648
41.9
20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.1
12941 3.0 89%
12941 4.5 53%
Black coal
30
2.0
42%
64.9
4.7
0.0
6946
3.0
89%
6946
4.5
53% Brown coal 30 4.0 32% 69.0 5.3 0.0
10725 0.6 89%
10725 0.7 53%
Nuclear SMR
30
4.4
33%
200
5.3
0.0
30290
1.1
89%
30290
1.3
53% Nuclear large-scale 30 5.8 33% 200 5.3 0.0 10332 1.1 89% 10332 1.3 53%
Solar thermal
30
1.8
100%
124.2
0.0
0.0
8278
0.0
71%
8179
0.0
57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 1621 0.0 32% 1621 0.0 19%
Wind onshore
25
1.0
100%
29.0
0.0
0.0
3248
0.0
48%
3248
0.0
29% Wind offshore (fixed) 25 3.0 100% 175.0 0.0 0.0
5433 0.0 52%
5433 0.0 40%
2030
Gas with CCS 25 2.0 44% 46.0 8.0 8.6
5807 9.4 89%
5846 16.5 53%
Gas combined cycle
25
2.0
51%
36.0
5.0
0.0
2180
9.4
89%
2204
16.5
53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0
2296 9.4 20%
2319 16.5 20%
Gas open cycle (large)
25
1.5
33%
26.0
10.0
0.0
1377
9.4
20%
1404
16.5
20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0
2010 9.4 20%
2039 16.5 20%
Hydrogen reciprocating
25
1.0
32%
33.0
0.0
0.0
2574
37.7
20%
2537
40.9
20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.1
11961 3.1 89%
12196 5.5 53%
Black coal
30
2.0
42%
64.9
4.7
0.0
6164
3.1
89%
6296
5.5
53% Brown coal 30 4.0 32% 69.0 5.3 0.0
9385 0.7 89%
9588 0.7 53%
Nuclear SMR
30
4.4
33%
200.0
5.3
0.0
20271
0.8
89%
23925
1.0
53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 9658 0.8 89% 9858 1.0 53%
Solar thermal
30
1.8
100%
124.2
0.0
0.0
9020
0.0
71%
9338
0.0
57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0
743 0.0 32%
1239 0.0 19%
Wind onshore
25
1.0
100%
29.0
0.0
0.0
2608
0.0
48%
2697
0.0
29% Wind offshore (fixed) 25 3.0 100% 175.0 0.0 0.0
3281 0.0 54%
5268 0.0 40%
74 | CSIRO Australia’s National Science Agency
2040
Gas with CCS 25 2.0 44% 46.0 8.0 8.6
4645 9.2 89%
4834 16.1 53%
Gas combined cycle
25
2.0
51%
36.0
5.0
0.0
1939
9.2
89%
1994
16.1
53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0
1767 9.2 20%
1811 16.1 20%
Gas open cycle (large)
25
1.5
33%
26.0
10.0
0.0
1060
9.2
20%
1097
16.1
20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0
2045 9.2 20%
2120 16.1 20%
Hydrogen reciprocating
25
1.0
32%
33.0
0.0
0.0
2579
31.0
20%
2488
36.6
20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.1
11299 2.9 89%
11551 4.6 53%
Black coal
30
2.0
42%
64.9
4.7
0.0
5631
2.9
89%
5941
4.6
53% Brown coal 30 4.0 32% 69.0 5.3 0.0
8472 0.7 89%
8937 0.7 53%
Nuclear SMR
30
4.4
33%
200.0
5.3
0.0
16622
0.5
89%
19646
0.7
53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 9306 0.5 89% 9795 0.7 53%
Solar thermal
30
1.8
100%
124.2
0.0
0.0
8040
0.0
71%
8377
0.0
57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0
584 0.0 32%
918 0.0 19%
Wind onshore
25
1.0
100%
29.0
0.0
0.0
2153
0.0
48%
2343
0.0
29% Wind offshore (fixed) 25 3.0 100% 175.0 0.0 0.0
3139 0.0 57%
5301 0.0 40%
2050
Gas with CCS 25 2.0 44% 46.0 8.0 8.6
4782 9.2 89%
4553 16.1 53%
Gas combined cycle
25
2.0
51%
36.0
5.0
0.0
1978
9.2
89%
2069
16.1
53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0
1798 9.2 20%
1874 16.1 20%
Gas open cycle (large)
25
1.5
33%
26.0
10.0
0.0
1079
9.2
20%
1134
16.1
20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0
2095 9.2 20%
2222 16.1 20%
Hydrogen reciprocating
25
1.0
32%
33.0
0.0
0.0
2703
28.5
20%
2548
35.8
20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.1
11254 2.9 89%
12282 4.6 53%
Black coal
30
2.0
42%
64.9
4.7
0.0
5803
2.9
89%
6326
4.6
53% Brown coal 30 4.0 32% 69.0 5.3 0.0
8756 0.7 89%
9537 0.7 53%
Nuclear SMR
30
4.4
33%
200.0
5.3
0.0
17176
0.5
89%
17698
0.7
53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0
9607 0.5 89%
10429 0.7 53%
Solar thermal
30
1.8
100%
124.2
0.0
0.0
7978
0.0
71%
7900
0.0
57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0
564 0.0 32%
851 0.0 19%
Wind onshore
25
1.0
100%
29.0
0.0
0.0
2141
0.0
48%
2290
0.0
29% Wind offshore (fixed) 25 3.0 100% 175.0 0.0 0.0
3197 0.0 61%
5344 0.0 40%
Notes: Economic life is the design life or the period of financing. Total operational life, with refurbishment expenses, is not included in the LCOE calculation but is used in electricity system modelling to understand natural retirement dates. Large-scale solar PV is single axis tracking. The real discount rate for all technologies is 7%.
GenCost 2025-26 | 75
Apx Table B.10 Electricity generation technology LCOE projections data, 2025-26 $/MWh Category Technology 2025
2030
2040
2050
Low High Low High Low High Low High
Peaking 20% load
Gas open cycle (small)
316
377
245
317
214
285
216
289
Gas open cycle (large) 325 392 201 280 182 258 183 260
Gas reciprocating
249
303
214
278
214
279
217
284
H2 reciprocating 616 629 578 611 503 561 481 555
Flexible load, high emission
Black coal
121
195
113
191
105
176
107
183
Brown coal 167 272 149 246 137 232 141 245
Gas
135
203
104
176
99
169
100
170 Flexible load, low emission Black coal with CCS 224 354 214 351 204 328 204 342
Gas with CCS
219
333
173
286
157
261
158
255
Nuclear SMR 465 772 322 619 268 516 276 470
Nuclear large-scale
200
328
187
312
178
307
183
323 Solar thermal 140 175 151 196 136 178 136 170
Variable
Solar photovoltaic
52
88
26
69
22
53
21
50
Wind onshore 78 129 64 109 54 96 54 94
Wind offshore (fixed)
164
213
110
208
101
209
96
210
76 | CSIRO Australia’s National Science Agency
Apx Table B.11 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
2025
2100
2730
2100
2730
2100
2730 2026 2058 2675 1818 2363 1873 2435
2027
2017
2622
1589
2066
1692
2199 2028 1977 2569 1395 1813 1530 1989
2029
1937
2518
1228
1597
1387
1803 2030 1898 2468 1081 1406 1258 1635
2031
1860
2418
1067
1388
1242
1614 2032 1861 2420 1068 1388 1234 1604
2033
1796
2334
1030
1339
1231
1600 2034 1742 2264 999 1299 1218 1583
2035
1713
2227
983
1278
1222
1589 2036 1688 2194 968 1259 1226 1594
2037
1630
2119
935
1216
1231
1600 2038 1587 2063 910 1184 1194 1553
2039
1553
2020
891
1159
1190
1547 2040 1527 1985 876 1139 1150 1495
2041
1506
1957
864
1123
1135
1476 2042 1488 1934 854 1110 1125 1463
2043
1474
1916
845
1099
1098
1427 2044 1462 1900 839 1090 1092 1419
2045
1434
1865
823
1070
1078
1401 2046 1385 1800 795 1033 1077 1400
2047
1349
1753
774
1006
1072
1393 2048 1321 1718 758 986 1072 1394
2049
1300
1690
746
970
1070
1392 2050 1284 1669 737 958 1059 1377
2051
1292
1680
741
964
1063
1382 2052 1279 1663 734 954 1060 1378
2053
1287
1673
738
960
1064
1383 2054 1265 1644 726 944 1065 1384
2055
1273
1655
730
949
1069
1389
GenCost 2025-26 | 77
Apx Table B.12 System levelised cost of electricity by cost component, $/MWh
2030 2050
82% renewable target Moderate net zero Strong net zero
VRE capital
43.7
56.7
60.2 Peaking capital 0.0 8.9 8.7
Storage
7.5
15.2
17.0 O&M 6.4 10.2 10.3
IBR cost
2.4
2.2
2.3 Connection 4.5 7.4 7.8
Fuel
16.6
14.1
17.8 REZ transmission 5.7 8.6 9.4
Other transmission
4.0
11.6
14.6 Total - generation 81.2 114.7 124.0
Total - generation and transmission
90.9
135.0
148.1
VRE-– variable renewable generation; IBR - Inverter-based resources cost such as deployment of synchronous condensers and grid-forming batteries; REZ – renewable energy zone. O&M – operating and maintenance costs
78 | CSIRO Australia’s National Science Agency
Data assumptions
C.1 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 Apx Table C.1 and Apx. Table C.2 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.
Apx Table C.1 Assumed technology learning rates that vary by scenario Technology Scenario Component LR 1 (%) LR 2 (%) LR 3 (%) References Photovoltaics
Current policies
G
20
30
13
(IEA 2021, IRENA, 2023, Fraunhofer ISE, 2015) Rooftop BOP
L
17.5
8.5
4.5 Large scale BOP
L
17.5
17.5
17.5 Photovoltaics
Global NZE by 2050
G
20
30
23 Rooftop BOP
L
17.5
17.5
8.5 Large scale BOP
L
17.5
17.5
17.5 Photovoltaics
Global NZE post 2050
G
20
30
23 Rooftop BOP
L
17.5
8.5
4.5 Large scale BOP
L
20
10
10 Electrolysis
Current policies
G
10
5
5
(Schmidt et al., 2017, IEA 2024b)
L
8
8
8
GenCost 2025-26 | 79
Electrolysis
Global NZE by 2050
G
18
18
9
L
8
8
8 Electrolysis
Global NZE post 2050
G
10
5
5
L
8
8
8 Ocean
Current policies
G
10
5
5
(IEA, 2021)
Global NZE by 2050
G
20
10
10
Global NZE post 2050
G
14
7
7 Fixed offshore wind
Current policies
G
10
5
5
(Samadi, 2018; Zwaan, et al. 2012; Voormolen et al. 2016; IEA, 2021) Fixed offshore wind
Global NZE by 2050
G
20
10
10 Fixed offshore wind
Global NZE post 2050
G
15
8
8 Floating offshore wind
Current policies
G
10
5
5
G
10
5
5 Floating offshore wind
Global NZE by 2050
G
20
10
10
G
20
10
10 Floating offshore wind
Global NZE post 2050
G
15
8
8
G
15
7.5
7.5 Utility scale energy storage – Li-ion
Current policies
G
7.5
7.5
7.5
(Grübler et al., 1999; McDonald and Schrattenholzer, 2001)
L
7.5
7.5
7.5 Utility scale energy storage – Li-ion
Global NZE post 2050
G
10
10
10
L
10
10
10 Utility scale energy storage – Li-ion
Global NZE by 2050
G
15
15
15
L
15
15
15 Onshore wind
Current policies
G
4.3
4.3
4.3
(IEA, 2021; Hayward & Graham, 2013)
L
9.8
4.8
2.8
Global NZE post 2050
G
4.3
4.3
4.3
80 | CSIRO Australia’s National Science Agency
L
9.8
4.8
2.8
Global NZE by 2050
G
4.3
4.3
4.3
L
11.3
11.3
11.3
While solar photovoltaics are implemented with separate learning rates for large scale and rooftop balance of plant (BOP), inverters are not included in the BOP nor 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 GHD (2025) 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.
Apx Table C.2 Assumed technology learning rates that are the same under all scenarios Technology Component LR 1 (%) LR 2 (%) LR 3 (%) References Coal, supercritical
-
-
-
Coal, ultra-supercritical
G
2
2
2
(IEA, 2008; Neij, 2008) Coal/Gas/Biomass with CCS
G
20
10
5
(EPRI 2010; Rubin et al., 2007)
L
20
10
5
As above + (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Gas peaking plant
-
-
-
Gas combined cycle
-
-
-
Nuclear
G
-
-
(IEA, 2008) Nuclear SMR
G
20
10
5
(Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Diesel/oil-based generation
-
-
-
Reciprocating engines
-
-
-
Hydro and pumped hydro
-
-
-
Biomass
G
5
5
5
(IEA, 2008; Neij, 2008) Concentrating solar thermal (CST)
G
14.6
7
7
(Hayward & Graham, 2013)
L
14.6
7
7
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CHP
-
-
-
Conventional geothermal
G
8
8
8
(Hayward & Graham, 2013)
L
20
20
20
(Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Fuel cells
G
20
10
10
(Neij, 2008; Schoots, Kramer, & van der Zwaan, 2010) Steam methane reforming with CCS
G
20
10
5
(EPRI, 2010; Rubin et al., 2007)
L
20
10
5
As above + (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001)
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. Apx. Figure C.1 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 Apx. Table C.2.
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.
C.2 Electricity demand and electrification
Various elements of underlying electricity demand are sourced from the World Energy Outlook (IEA, 2021; IEA, 2022; IEA, 2023). 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.
C.2.1 Global vehicle electrification
Global adoption of electric vehicles (EVs) is projected using an adoption curve calibrated to correspond to Global NZE by 2050 scenario from the IEA World Energy Outlook. The shape of the adoption curve varies by vehicle type, where cars and light commercial vehicles (LCV) have faster
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rates of adoption, followed by medium commercial vehicles (MCV) and buses. The adoption rate is applied to new vehicle sales shares.
C.3 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 Proton Exchange Membrane (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 Apx. Table C.3 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.
Apx Table C.3 Hydrogen demand assumptions by scenario in 2050 Scenario Total hydrogen demand (Mt) Current policies 132
Global NZE post 2050
274 Global NZE by 2050 366
C.4 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 fuel-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
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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.
C.5 Resource constraints
The availability of suitable sites for renewable energy farms, available rooftop space for rooftop solar 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 (Apx. Table C.4) (see Government of India, 2016, Edmonds, et al., 2013 and Hayward and Graham, 2017 for more information on sources). With the exception of rooftop solar 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.
C.6 Other data assumptions
GALLME international black coal and gas prices are based on (IEA, 2023) 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 fuel prices must fall26. 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.
Apx Table C.4 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 % CST % 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
26 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.
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Region Rooftop PV % Large scale PV % CST % Onshore wind % Fixed offshore wind GW 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 (GHD, 2025) (IEA, 2016b) (IEA, 2015), capacity factors from (IRENA, 2023) (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) (Energy Information Administration, 2017a) (Energy Information Administration, 2017b) (GWEC) (IEA, 2016a) (World Nuclear Association, 2017) (Schmidt, Hawkes, Gambhir, & Staffell, 2017) (Cavanagh, et al., 2015).
New capacity that was installed in 2023 was sourced from (IRENA, 2024) (Global Energy Monitor, 2024a) (Global Energy Monitor, 2024b) and (Global Energy Monitor, 2024c).
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Frequently asked questions
The following list of questions represents a summary of the most commonly asked questions in relation to methods and assumptions applied in GenCost.
D.1 Process
D.1.1 Why does GenCost not immediately change its report when provided with new advice from experts?
The GenCost report undertakes a significant stakeholder consultation process, but it is not a consensus process and the response to feedback is based on its quality, not who provided it. This process is consistent with the objectivity and scientific approach that stakeholders expect of CSIRO.
There have been suggestions from some stakeholders that because some information was provided by an expert or group of experts it should have been accepted and acted upon immediately. This is not sufficient grounds for making a change to the GenCost report. Changes to the GenCost report need to be based on public evidence and reason. They cannot be based on assertions alone, no matter the qualifications and experience of the individual or group of individuals providing input.
GenCost reserves the right to test the quality of any evidence provided. There are widely varying qualities of data and evidence provided in the consultation process. Stakeholders should consider the many issues that can impact the quality of evidence when providing it such as the appropriateness of methodologies used to develop the data, stated or unstated vested interests behind the data development, and the level of inherent proof the evidence represents (e.g., correlation versus causation, opinion versus verifiable data).
Finally, CSIRO reserves the right to prioritise the issues and evidence it chooses to investigate. Not every topic raised will be fully investigated in the year the feedback is received. We prioritise issues based on their relevance, the weight of feedback received, and the technical challenges associated with investigating the topic in a way that meets our own standards.
D.2 Scenarios
D.2.1 Why are disruptive events and bifurcations excluded from the scenarios?
It is acknowledged that the future evolution of major drivers of the global energy system will not be smooth, particularly considering the recent pandemic and Ukraine war impacts on the energy sector. GenCost provides relatively smooth projections of capital costs over time compared to what is likely to occur. This reflects our understanding that very few end-users of the capital cost projections would like to access results that include major discontinuities. More volatility in inputs
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will lead to more volatility in all model outputs. Such volatility can interfere with the interpretation of models which are often seeking to answer separate questions about the evolution of the system by reading into the changes in the modelling results. As such, our judgment is that adding more realism does not add value in this case.
D.2.2 Why is no sensitivity analysis conducted and presented?
The staff delivering GenCost have many decades of experience in energy and electricity system modelling. They understand which parameters in the model have the greatest impact on model outcomes. The scenarios have been designed to explore those parameters that are the most uncertain and impactful (within a plausible range) so that they provide a set of results that represent the likely range of outcomes. The possible range of outcomes is wider and could be calculated. However, our understanding of end-user needs is that they require outputs that align with globally accepted literature on the likely range of major drivers such as global climate policy, learning rates and resource constraints. Should our understanding of the likely range of any of these factors change, the scenarios will be updated.
D.3 Capital costs
D.3.1 What did you base your large-scale nuclear costs on?
The GenCost 2023-24 final report provides a detailed discussion of the method for estimating large-scale nuclear costs in Section 2.5
D.3.2 Why have the estimates for nuclear SMR capital costs increased so much since 2022?
The GenCost 2023-24 final report provides a detailed discussion of the history of estimating nuclear SMR costs in Section 2.4. This report has adopted the project cost for the Darlington nuclear SMR project as its primary source current and near term costs.
D.3.3 Do you assume Australia continues to rely on overseas technology suppliers or are you assuming Australia develops its own original equipment manufacturing capability?
The context of this question is the concern that reliance on overseas manufacturers makes Australia vulnerable to non-competitive market pricing (e.g., the dominance of China), delayed access to technology because of competing buyers or represents a security of supply risk in the event of conflict in or with supplying countries. In this context, some government policies have provided international partnership support and direct grants for critical minerals projects27.
27 https://www.industry.gov.au/publications/critical-minerals-strategy-2023-2030/our-focus-areas/2-attracting-investment-and-building-international-partnerships
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Whilst GenCost will continue to monitor these developments, the equipment component of capital cost estimates remains based on the best available representative technology cost deployment in Australia with equipment supplied from anywhere in the world that meets our standards.
D.3.4 Why does GenCost persist with the view that technology costs will fall over time when there are many factors that will keep technology costs high?
In the GenCost 2022-23 final report, research was outlined that indicated that there is no historical precedence for the real cost of commodities increasing indefinitely in real terms. Most periods of high prices resolve themselves within 4 years. Longer-term commodity price super cycles do occur but are shallower and are associated with changes in global economic growth. There is no suggestion from stakeholders that the world is in a major economic growth cycle. It was also argued in GenCost 2022-23 that global manufacturing will not need to be endlessly scaled up. Rather global technology capacity forecasts indicate that technology manufacturing capacity will need to grow to 2030, but after that point will be able to meet mostly linear demand for additional capacity without significant additional scale-up.
Stakeholders have raised the following additional points on this topic:
•
That the energy sector may have a different inflationary path to the economy in general
•
That GenCost needs to prove that the world is not in a new commodity super cycle
•
That concentration of manufacturing in China will lead to non-competitive behaviour and high prices for those products, particularly solar
•
That demand for energy technologies will remain non-linear for a long time because of delays in Australian deployment.
The current uncertainty in global manufacturing is acknowledged and makes forecasting at this time in history very challenging. The global inflationary event triggered by the pandemic is a significant structural break. Based on the evidence available of similar events, the approach taken has been to assume a reasonably quicker resolution of high technology prices with some lingering effects for 3 to 6 years, the length depending on the scenario.
The data on technology project costs from GHD and various commodities price inputs to those technologies indicates (so far) that the evidence is in alignment with our approach. Some costs have already fallen in real terms. Some are still rising but the rate of increase is significantly lower. The evidence from GHD (2025) points to cost pressures easing. Commodity price reporting also indicates cost pressures have eased in raw material markets such as lithium.
Based on this data, it does not appear energy is on a different path to the rest of the economy. Solar panels produced predominantly by China who have market power are recovering better than others and their price increase was more modest to begin with.
Regarding the expected linear growth rates in technology deployment, this refers to the global technology deployment and the required global manufacturing capacity to meet this growth. Australia’s technology deployment rate, while important to us as Australians, has very little impact on the scale or cost of global technology manufacturing.
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Notwithstanding these points, our projection methodology assumes increasing land and installation costs (in real terms). These exceptions are due to the scarcity of land and suitably qualified construction labour. This assumption means that the costs of some technologies (particularly mature technologies) increases for significant parts of the projection period.
D.3.5 Why is the uncertainty in the data not emphasised more?
GHD (2025) provide an uncertainty range of +/- 30% for their capital costs. To reduce this uncertainty, their analysis would have to be performed on a specific project. The GenCost project requires general data, not specific project data, that can be used in national level modelling studies. GHD (2025) also provide factors to convert the general costs to specific locations in the National Electricity Market (NEM). In that context, GenCost data is based on transporting and installing equipment not more than 200km from Melbourne but can be converted to other locations. An important aspect for GenCost is that all data is on a common basis.
Some stakeholders have requested that we emphasise this uncertainty in capital costs more in the text and diagrams. The main purpose of GenCost has always been to provide data which can be used in modelling studies. While there are stochastic modelling frameworks, the majority of electricity system models used in Australia are deterministic. In simple terms, this means they use single data points without any probability information attached to them. Therefore, GenCost capital cost outputs, which focus on providing scenarios to explore uncertainty rather than probability ranges, remain appropriate for the end-use they are created for.
LCOE data is specifically designed for the non-modelling community. In this case, we take a different approach. LCOE data is always presented as a range representing the plausible maximum and minimum costs. We also provide ranges for key inputs to the LCOE calculations such as capital costs, fuel costs and capacity factors.
D.3.6 Why include an advanced ultra-supercritical pulverised coal instead of cheaper, less efficient plant designs?
Some stakeholders take a view that although Australia has national and state net zero emissions policies by 2050, the highest greenhouse gas emitting options should remain on the table. The deployment of new coal has low plausibility given its high emissions intensity. A high efficiency design brings it closer to being plausible by reducing its emissions. Perhaps the most plausible scenario for building new coal consistent with meeting the net zero emissions by 2050 target would be to later retrofit coal generation with carbon capture and storage. Carbon capture and storage imposes a very significant fuel efficiency loss on the coal generator. In this context, it is even more important to start from a high efficiency coal generation technology.
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D.4 LCOE
D.4.1 Why is the economic life used in LCOE calculations instead of the full operational life?
The LCOE calculation converts all upfront and ongoing costs to annual costs which is then divided by annual production. The capital cost component of a technology is converted to an annual repayment to the debt and equity providers. The annual repayment amount is determined using the economic life and the weighted average cost of capital. The economic life is shorter than the asset life for some technologies such as coal, nuclear and hydro. Some stakeholders have queried why this is so.
Debt and equity providers require a shorter payback period than the total asset life for some technologies to avoid the risk that part of the equipment might fail or might need new investment (sometimes called refurbishment or extension costs) to keep operating safely and reliably. To determine the economic life, debt and equity providers might look to the warranties provided with the equipment. They might also look at the typical timing of refurbishments or life extensions for that technology. The economic life is an input provided by the engineering firm that AEMO commissions each year as an input to GenCost.
Some stakeholders suggested that coal and nuclear could access special financing arrangements to move the economic life closer to the asset life. However, our preference is not to introduce special arrangements for technologies where there is limited Australian evidence. A common approach to the LCOE calculation is important to maintain comparability. The 2024-25 report does explore the impact of longer capital recovery periods in Section 2. It finds there is no significant benefit from the longer operational life of nuclear relative to shorter-lived technologies whose costs have been falling over time.
Determining the economic life of storage is more complex because the cycle life comes into play in determining the life of some components. The cycle life and intended use of the storage device might also be something debt and equity providers are also interested in to set the repayment date. Batteries in GenCost are costed for a project which has purchased a 20-year warranty on the battery (this warranty is costed as part of the ongoing operating and maintenance cost – see GHD (2025) for more information on this).
It should also be noted that cycle life is often calculated in the academic literature based on a full charge and discharge and is tested over a shorter period than would occur in practice. It is not clear how well deployed storage projects will match the lab tests. Their operation may be more prone to partial discharge, preferring to save some charge for higher priced periods. That is, they will bid parts of their storage capacity at different prices. Time will tell how this bidding behaviour will impact their cycle life, but it is a reasonable expectation that practical operation will be less damaging to batteries than the lab tests.
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D.4.2 Coal and nuclear plants are capable of very high capacity factors, why do LCOE calculations not always reflect this?
Stakeholders are sometimes not aware of the difference between the availability factor, which is how often a plant will be technically available to generate electricity and the capacity factor which is how often they typically generate electricity after the effects of competition or other market constraints which limit generation.
In the last ten years in Australia, baseload generators have had an average capacity factor of 59% (see Appendix D GenCost 2022-23 final report). The simple reason for this outcome is that most baseload plants need to reduce production at night and in milder seasons when demand is lowest. There are individual generators that do achieve around 90%. These are a minority of plants which have a fuel cost advantage which allows them to keep running at full production during low demand periods by underbidding other generators for the right to keep generating at a high level.
GenCost LCOE calculations allow for the fact that a new baseload generator might achieve a capacity factor of up to 89% based on the maximum achieved by black and brown coal generators. At the low end of the range a capacity factor of 53% is assumed for new black coal, brown coal or nuclear generators which is equivalent to achieving 10% below the average capacity factor for black and brown coal. Around 10% of nuclear generators globally run at less than 60% capacity factor and many have run at over 90%28. However, we prefer to use Australian data for the plausible baseload plant operation data because it is consistent with our electricity load curve while other countries may have very different loads. For example, some equatorial and northern regions with hotter and colder climates have higher rates of air conditioning in buildings leading to flatter electricity loads (where either electricity or combined heat and power are the energy source).
Higher penetration of renewables, which have a zero fuel cost, could make it difficult for new baseload plant to achieve high capacity factors depending on the scale of demand overall. Ultimately, we do not know what new coal or nuclear will be competing with in the future. The key principle though is to acknowledge a plausible range rather than assume only the best outcome for new build capacity factors.
D.4.3 Why do LCOE calculations not use the lowest historical capacity factors for the low range assumptions?
For all existing technologies there are some generators that are performing poorly relative to what might be expected, and these represent the low range of historical capacity factors which were examined in Appendix D of the GenCost 2022-23 report. The data does not reveal why some projects are performing below expectations, but it could represent older technologies or, for renewables, sites that did not live up to expectations in terms of the renewable resource. GenCost LCOE capacity factor low range assumptions are developed on the basis that new entrant technologies will not be deployed if they cannot perform close to the current average capacity factor performance. Investors would prefer to avoid such projects in preference for more
28 https://world-nuclear.org/our-association/publications/world-nuclear-performance-report/global-nuclear-industry-performance
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attractive investment options. Accordingly, we apply a common rule across renewables, coal, nuclear and gas that the minimum capacity factor for new plant is 10% below the previous ten years average capacity factor for that technology or its nearest equivalent grouping (baseload technologies are treated as one group).
D.4.4 Why were all potential cost factors not included in the LCOE calculations?
While each technology has its own specific characteristics the goal of the LCOE calculation is to use a common formula to calculate costs so that that observed differences in costs are due to a small set of key differences in the technology, namely: capital costs, fuel costs, fuel efficiency, operating and maintenance costs, economic life and construction time. However, often stakeholders request that other special topics be included in the calculations. Items requested to be added to the LCOE analysis by stakeholders include:
•
Plant decommissioning and recycling costs
•
Deeper pre-development costs
•
Technology degradation
•
Whole-of-life emissions
•
Savings from developing on a brownfield site
•
Various environmental impacts
•
Energy in manufacturing costs
•
Public acceptance barriers
•
National security impacts
•
Extreme climate events
•
Connection costs
•
Marginal loss factors.
Adding these additional parameters would greatly expand the physical and time boundary of the generic generation projects assumed in GenCost and require more complicated formulas to implement. Our current understanding is that few of the topics presented in the feedback have a large enough impact on LCOE to warrant a change in the boundary or formula. That is, it would add complexity and cost to the project without significantly changing the outcome of the comparisons. Some factors, like marginal loss factors are significant but are too unpredictable at this stage of the energy transition.
We do acknowledge that taking account of brownfield project characteristics would make a difference in costs. This is because brownfield projects can avoid some development costs associated with site selection, grid connection and land. However, brownfield projects are outside our stated scope for GenCost of greenfield or new build projects. The study of brownfield projects is always site-specific and more resource intensive and for these reasons less generally comparable to other options. Their inclusion would essentially amount to bringing “one-off” projects into the analysis. This is inconsistent with our goal of providing a general comparison metric. Some brownfield project costs are included in AEMO’s publicly accessible forecasting input data.
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There are two exceptions in the past where GenCost added new technology cost elements. These are CO2 storage costs for carbon capture and storage technologies and integration costs for variable renewables. In both cases, the impact of these additional elements is significant and justifies modification of the standard approach to LCOE calculation.
Given that GenCost does not account for all potential additional project costs such as those captured in the list above, real projects are likely to cost more than indicated by the LCOE. Consequently, investors must do their own deeper studies to discover these. Likewise, investors who are interested in brownfield project development will need to source this information elsewhere (e.g., check AEMO publications) or do their own analysis.
Energy used in manufacturing costs are accounted for in capital costs. Notwithstanding the current difficulties in manufacturer profitability following the global supply chain crunch, to remain solvent, manufacturers must recover these costs (as with all other costs), in the long term, by building them into their technology prices. Also, the more that global economies track and potentially price greenhouse gas emissions, the greater the incidence of lifecycle greenhouse gas emissions of projects being built into technology prices. Planned carbon border adjustment mechanisms are an example of this.
D.4.5 What is the boundary of development costs? Is it only costs from the point of contracting a developer before commencing construction?
GHD’s reports and data break down the capital cost into three components: equipment, land and development and installation costs. Development costs are captured in the land and development segment. GHD (2025) provides this definition of the land and development cost component:
“The development and land costs for a generation or storage project typically include the following components:
•
Legal and technical advisory costs
•
Financing and insurance (no interest during construction considered)
•
Project administration, grid connection studies, and agreements
•
Permits and licences, approvals (development, environmental, etc)
•
Land procurement and applications.”
D.4.6 How is interest lost during construction included in GenCost?
The type of capital cost data included in GenCost is called overnight capital costs. That is, it is the cost if you built it overnight. Consequently, to make the costs more realistic, interest lost during the construction period needs to be added when using this data.
Interest lost during construction is added differently depending on how the data is being used. When overnight capital cost data is being used in an energy system model, information is provided to the model about the construction time. The time discounting function within the system model accounts for the interest lost during construction in the time delay between investment expenditure and when the project is fully operational.
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When overnight capital cost data is being used in an LCOE calculation a different approach is used. LCOE calculations must average all costs into a single year of electricity production and so the time during construction does not exist as a concept. However, there are several ways in which the interest lost can be added to an LCOE. GenCost uses the simplest way which is to increase the capital cost by the assumed discount rate raised to the power of the construction time29. There are more sophisticated ways to do this which account for developer plans for drawing down the financing during construction depending on the arrival time of different plant parts and payment for each component. These more detailed approaches are appropriate for real project planning but require tailored calculations for each technology and a cash flow model approach. The cashflow approach tracks payments over each year of construction plus economic life before averaging them into a single yearly cost (dividing total expenditure including the construction period by total production including periods of zero production during the construction period). The simpler approach is more efficient (requires just a few cells of calculations and fewer input data), but the latter is more accurate. The simpler approach tends to overestimate interest lost during construction as it assumes all funds need to be drawn down at the beginning of construction.
D.4.7 Why are the cost of government renewable subsidies not included in the LCOE calculations for variable renewables with integration costs?
The cost of government subsidies for variable renewables, in whatever form they take, are not included as a cost because all of the variable renewable costs applied in the modelling are without subsidy. In other words, because we do not subtract any subsidies from the cost of variable renewable generation, it is not necessary to add those subsidies back in as a cost to society. The GenCost estimates of the cost of integrating variable renewables are without any government subsidies.
D.4.8 Why is a value of 100% applied to the fuel efficiency of renewables in the LCOE formula?
For our purposes there is no practical limit to supply of solar and wind power and its cost as a fuel is free. Since the fuel price applied is zero, any value for renewable energy efficiency other than zero would work in the fuel cost formula (and avoid division by zero) where fuel cost equals FuelPrice÷FuelEfficiency. We choose 1 or 100% for simplicity. This is not to say that the energy conversion efficiency of renewable generation technologies is 100%, or irrelevant, or not accounted for. The conversion efficiency of solar irradiance and wind to electricity is accounted for in the capital cost. Manufacturers apply a nameplate plant capacity in watts to the equipment they sell based on exposure to representative wind speeds or solar irradiance and this reflects the energy conversion efficiency of the plant. Conversion efficiency is also partially captured in land costs which reflect the scarcity of sites with the required renewable resources to operate at nameplate capacity.
29 GenCost readers who have downloaded the Appendix tables from CSIRO’s Data Access Portal should be able to find this step in the cell formula under the Capital component of the LCOE calculation
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D.4.9 Why do you apply only one discount rate or weighted average cost of capital to all technologies?
This question may arise in the context of stakeholder concerns that some projects might be government funded and receive a lower financing rate and that should be included. While GenCost recognises that governments have in the past and may choose in the future to provide lower cost financing to selected projects, GenCost makes no specific assumptions about who will invest in a technology project.
Another factor guiding our approach is that we wish to compare technologies on a common basis wherever that approach does not lead to an unwanted distortion. In most cases, that can be achieved but there are exceptions. In some cases, we need to apply a different formula or method to different technologies to capture important additional costs such as adding reliability costs for variable renewables or carbon dioxide storage costs for CCS technologies (see D.4.4 for a longer discussion of what additional costs we have chosen to include).
Previous versions of GenCost also applied a cost of capital premium to fossil fuel technologies due to their additional climate policy risk. However, our judgement was that although that risk is real and ongoing, we were no longer able to find a cost of capital premium that adequately captured that risk. Instead, wherever we present high emission fossil fuel technology costs we simply state that investment in these technologies may not be consistent with government emission targets.
In conclusion, our judgment is that, in the case of the cost of capital, applying the same rate to every technology is the most informative and least distortionary approach for levelised cost of electricity. Other modelling exercises may take an alternative approach. However, our LCOE data is not likely to be an input to any detailed electricity system modelling. Rather LCOE data is simply an indicator of the potential direction of the results from more detailed modelling.
D.4.10 Why did you take the maximum and average of existing generator prices to create the high and low range new build coal prices?
Our goal is to explore the high and low range for total coal generation costs in the LCOE calculations. To do this we include high and low ranges for the various inputs to coal generation costs such as capacity factors, capital costs and coal fuel costs.
We require coal prices for new-build projects which are different to coal prices that are received by existing generation sites. Some existing generators receive low coal prices because they may have captured an adjacent coal mine with no competing rail line to export markets. Alternatively, if they are competing with export markets, they are more likely to have developed a favourable long-term contract to manage high price risk. New-build projects will start their life by competing with export markets for supply of coal.
High and low coal prices are sourced from the AEMO Inputs and Assumptions workbook. The June 2022 Inputs and assumptions workbook provided coal prices for new build and existing coal generators. Reflecting the issues discussed above, average new build coal prices were two and half times higher than the minimum existing generator coal prices. For GenCost 2022-23, our methodology for selecting coal prices to use in GenCost was to take the minimum and maximum of only the new build coal prices.
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After June 2022, AEMO has no longer published new build coal prices. This reflects the bipartisan policies of net zero emissions by 2050 which make it unlikely that new coal can be developed in Australia. AEMO continued to publish coal prices, but only for existing generators which remain in the system.
To create the high and low range for new build coal prices post-2022-23 GenCost had to apply a new methodology based on the only available data which was coal prices for existing generators. Knowing that new build coal prices are at least as high as that for existing generators, for the maximum, GenCost simply takes the maximum of existing generator prices.
However, for the minimum new build coal prices, taking the minimum of existing generator prices is not appropriate. CSIRO developed a new methodology, using the only available data from AEMO on coal prices for existing generators, to extrapolate the low-cost range. This methodology takes into account that new-build coal generation projects cannot achieve the same low prices as existing generators, hence why the low coal prices are averaged. The average of the lowest coal price trajectory for existing generators tends to be two to three times the minimum coal price for those generators, which maintains the previously observed relationship between existing generator and new build coal prices.
IEA coal prices are used in the global modelling which underpins the capital cost projections. A different source is justified on the basis that the global modelling requires a consistent set of global fuel prices by major global region which is not available from AEMO which only provides Australian data.
D.4.11 Why do you not include high and low ranges for economic life?
Economic life is in some cases set by a warranty. This is the case for batteries. In other cases, it represents long standing practice in the financing of utility assets which are unlikely to vary significantly between Australian projects. While many stakeholders have provided evidence for variation in asset lives, there has been little evidence provided on variation in economic life or warranties or loan periods. At this stage, there is not enough information to form a basis for a high and low range for economic life as an input to the LCOE calculations. See D.4.1 for a discussion on the differences between economic and asset life.
D.4.12 Why are your low range capacity factors for coal and renewables closer to the historical average capacity factor?
In the GenCost 2022-23, report capacity factors from the previous ten years were reviewed to inform our choices about capacity factors in the LCOE calculations. Stakeholders have noted that the low range capacity factor applied is close to the ten-year average capacity factor. In fact, the approach to set the low range value for new-build generators is to use a value 10% below the average capacity. Our reasoning is that new projects are less likely to proceed if their capacity factor is significantly lower than the market average. The same method is applied for renewables as for coal to develop the low range capacity factor assumption.
For the high capacity factor assumption, the highest capacity factor achieved over a ten year period is applied. Given these are new-build, it is appropriate to be less conservative on the high range assumption. Again, the approach is the same for coal and renewables.
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D.4.13 If GenCost shows renewables are cheaper, why are electricity prices higher in Australia and in countries transitioning to renewables?
GenCost calculates the breakeven cost of electricity needed for investors to recover their capital, fuel and operating costs, including a reasonable return on investment. This is an indicator of the electricity price needed to encourage new investment, but it does not control the electricity price. Electricity prices are controlled by the balance of supply and demand. If supply is tight relative to demand, then prices go up. If supply is significantly more than demand, then prices go down. Changes in fossil fuel prices are another source of volatility. Price increases in recent years are a combination of lack of supply and fuel price volatility.
In 2022, global natural gas supply constraints, triggered by sanctions on Russia due to the Ukraine war, together with unplanned coal plant outages caused a price spike in Australia that is still reverberating through the electricity system. The prices of other electricity systems around the world were also impacted by the rising global fossil fuel prices and constrained supply of gas.
In Australia, retailers, experiencing these conditions, secured electricity supply contracts for 2023-24 and factored these higher prices in. A decrease in gas prices or growth in new supply capacity (net of retirements) can put downward pressure on market prices. However, there is no guarantee that either of these forces will maintain downward pressure on prices. If gas prices rise again or capacity is retired faster than it is rebuilt, then prices will increase again regardless of the cost of new entrant capacity.
The quality of both renewables and fossil fuel resources varies substantially around the world as do the pace of transition to lower emission sources, the degree of state ownership, subsidies, age of generation fleet and market incentives for building new capacity. As a result, due to the variety of differences in circumstances and the impact of supply and demand imbalances, there are no clear causal relationships that can be concluded from a simple correlation analysis of electricity prices and the energy source used by country or region.
D.4.14 If nuclear has such high capital costs why do they have such low-cost nuclear electricity overseas?
New large-scale nuclear costs are significantly lower than nuclear SMR but both represent moderate- to high-cost sources of electricity generation. This result could be perceived as out of step with overseas experience where some countries enjoy low-cost nuclear electricity. There are two reasons for this seemingly inconsistent result.
The first is that new generation technology electricity costs have only weak transferability between countries. While the technology can be identical, electricity generation costs vary widely between countries due to differences in installation, maintenance and fuel costs in each country. There are also unknown or known subsidies and different levels of state versus private ownership which impact the costs that ultimately get passed to electricity customers.
The second issue is that observations of low-cost nuclear electricity overseas are in most cases referring to historical rather than new projects which could have been funded by governments or whose capital costs have already been recovered by investors. Either of these circumstances could mean that those existing nuclear plants are charging lower than the electricity price that would be
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required to recover the costs of new commercial nuclear deployment. Such prices are not available to countries that do not have existing nuclear generation such as Australia.
In summary, given overseas new generation electricity costs are not easily transferable and may be referring to assets that are not seeking to recover costs equivalent to a commercial new-build nuclear plant, there may be no meaningful comparison that can be made between overseas nuclear electricity prices and the costs that Australia could be presented with in building new nuclear.
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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.
E.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 PV, 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.
E.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.
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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.
E.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 E.1 Examples of considering global or domestic significance 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 No Yes None currently. A previous example was enhanced geothermal, but domestic interest in this technology declined
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)
E.4 Input data quality level is reasonable
Input data quality types generally fall into five 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)
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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, if a technology is included 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 paper studies are used. Confidential data as a primary information source is not used since, by definition, it cannot be validated by stakeholders. However, confidential sources could provide some guidance in interpreting public sources.
E.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 and 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 are similar.
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Shortened forms Abbreviation Meaning AAS Australian Academy of Science
A-CAES
Adiabatic Compressed Air Energy Storage AE Alkaline electrolysis
AEMO
Australian Energy Market Operator ATSE Academy of Technological Sciences and Engineering
BAU
Business as usual BOP Balance of plant
CCS
Carbon capture and storage CCUS Carbon capture, utilisation and storage
CHP
Combined heat and power CIS Capacity Investment Scheme
CO2
Carbon dioxide CSIRO Commonwealth Scientific and Industrial Research Organisation
CST
Concentrated solar thermal EV Electric vehicle
FOAK
First-of-a-kind 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
IAEA
International Atomic Energy Agency IEA International Energy Agency
ISP
Integrated System plan
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Abbreviation Meaning kW Kilowatt
kWh
Kilowatt hour LAES Liquid Air Energy Storage
LCOE
Levelised Cost of Electricity SLCOE System Levelised Cost of Electricity
LCOS
Levelised cost of storage LCV Light commercial vehicle
MCV
Medium commercial vehicle MLF Marginal Loss Factor
Li-ion
Lithium-ion LR Learning Rate
Mt
Million tonnes MW Megawatt
MWh
Megawatt hour NDC Nationally Determined Contribution
NEM
National Electricity Market NOAK Nth-of-a-kind
NSW
New South Wales NT Northern Territory
NZE
Net zero emissions O&M Operations and Maintenance
OECD
Organisation for Economic Cooperation and Development PEM Proton-exchange membrane
PHES
Pumped hydro energy storage PV Photovoltaic
REZ
Renewable Energy Zone SMR Small modular reactor
STEPS
Stated Policies Scenario SWIS South-West Interconnected System
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Abbreviation Meaning
TWh
Terawatt hour UAE United Arab Emirates
USC
Ultra-supercritical VPP Virtual Power Plant
VRE
Variable Renewable Energy WA Western Australia
WEM
Western Electricity Market WEO World Energy Outlook
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