GenCost 2024-25 Final report Paul Graham, Jenny Hayward and James Foster July 2025   Contact Paul Graham +61 2 4960 6061 paul.graham@csiro.au Citation Graham, P., Hayward, J. and Foster J. 2025, GenCost 2024-25: Final report, 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. Contents Foreword vii Acknowledgements viii Executive summary ix 1 Introduction 15 1.1 Scope of the GenCost project and reporting 15 1.2 The GenCost mailing list 16 1.3 Summary of feedback on the Consultation draft 16 2 Nuclear: additional evidence and analysis on three topics 25 2.1 Nuclear capital recovery period and long operational life 25 2.2 Nuclear capacity factor range 30 2.3 Nuclear development lead times 32 3 Current technology costs 35 3.1 Current cost definition 35 3.2 Capital cost source 37 3.3 Current generation technology capital costs 37 3.4 Current storage technology capital costs 39 4 Scenario narratives and data assumptions 43 4.1 Scenario narratives 43 5 Projection results 45 5.1 Short-term and long-term inflationary pressures 45 5.2 Global generation mix 47 5.3 Changes in capital cost projections 49 6 Levelised cost of electricity analysis 68 6.1 Purpose and limitations of LCOE 68 6.2 LCOE estimates 69 6.3 Storage requirements underpinning variable renewable costs 79 Appendix A Global and local learning model 82 Appendix B Data tables 85 Appendix C Data assumptions 98 Appendix D Frequently asked questions 105 Appendix E Technology inclusion principles 123 Shortened forms 126 References 129   Figures Figure 2 1 Costs for long-lived multi-stage projects and the subsequent cost reduction achieved for electricity consumers. 28 Figure 2 2 Historical capacity factors for black and brown coal in Australian electricity generation (NEM states) 32 Figure 2 3 Relationship between the level of democracy, regions and construction times since 2011 34 Figure 3 1 Comparison of current capital cost estimates with previous reports (FYB) 38 Figure 3 2 Year on year change in current capital costs of selected technologies in the past 3 years (in real terms) 39 Figure 3 3 Capital costs of storage technologies in $/kWh (total cost basis) 40 Figure 3 4 Capital costs of storage technologies in $/kW (total cost basis) 41 Figure 5 1 Projected global electricity generation mix in 2030 and 2050 by scenario 48 Figure 5 2 Global hydrogen production by technology and scenario, Mt 49 Figure 5 3 Projected capital costs for black coal ultra-supercritical by scenario compared to 2023-24 projections 50 Figure 5 4 Projected capital costs for black coal with CCS by scenario compared to 2023-24 projections 51 Figure 5 5 Projected capital costs for gas combined cycle by scenario compared to 2023-24 projections 52 Figure 5 6 Projected capital costs for gas with CCS by scenario compared to 2023-24 projections 53 Figure 5 7 Projected capital costs for gas open cycle (small) by scenario compared to 2023-24 projections 54 Figure 5 8 Projected capital costs for nuclear SMR by scenario compared to 2023-24 projections 55 Figure 5 9 Projected capital costs for large-scale nuclear by scenario compared to 2023-24 projections 56 Figure 5 10 Projected capital costs for solar thermal with 14 hours storage compared to 2023-24 projections 57 Figure 5 11 Projected capital costs for large-scale solar PV by scenario compared to 2023-24 projections 58 Figure 5 12 Projected capital costs for rooftop solar PV by scenario compared to 2023-24 projections 59 Figure 5 13 Projected capital costs for onshore wind by scenario compared to 2023-24 projections 60 Figure 5 14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2023-24 projections 61 Figure 5 15 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant) 62 Figure 5 16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario 63 Figure 5 17 Projected technology capital costs under the Current policies scenario compared to 2023-24 projections 64 Figure 5 18 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2023-24 projections 65 Figure 5 19 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2023-24 projections 66 Figure 5 20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2023-24 67 Figure 6 1 Range of generation and storage capacity deployed in 2030 across the 9 weather year counterfactuals in NEM plus Western Australia 73 Figure 6 2 Levelised costs of achieving 60%, 70%, 80% and 90% annual variable renewable energy shares in the NEM in 2024 and 2030 74 Figure 6 3 Calculated LCOE by technology and category for 2024 76 Figure 6 4 Calculated LCOE by technology and category for 2030 77 Figure 6 5 Calculated LCOE by technology and category for 2040 78 Figure 6 6 Calculated LCOE by technology and category for 2050 79 Figure 6 7 2030 NEM maximum demand, demand at lowest renewable generation and generation capacity under 90% variable renewable generation share 80 Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation 83 Tables Table 2 1 The reduction in nuclear LCOE resulting from a 60- or 100-year capital recovery period compared to a 30-year capital recovery period, ignoring extension costs 27 Table 3 1 Suggested FOAK premium by technology 37 Table 4 1 Summary of scenarios and their key assumptions 44 Table 6 1 Questions the LCOE data are designed to answer 70 Table 6 2 Committed investments by category included in the 2024 cost of integrating variable renewables 72 Apx Table A.1 Cost breakdown of offshore wind 84 Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario 86 Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario 87 Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario 88 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) 89 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) 90 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) 91 Apx Table B.7 Pumped hydro storage cost data by duration, by scenario, total cost basis 92 Apx Table B.8 Storage current cost data by source, total cost basis 93 Apx Table B.9 Data assumptions for LCOE calculations 94 Apx Table B.10 Electricity generation technology LCOE projections data, 2023-24 $/MWh 96 Apx Table B.11 Hydrogen electrolyser cost projections by scenario and technology, $/kW 97 Apx Table C.1 Assumed technology learning rates that vary by scenario 98 Apx Table C.2 Assumed technology learning rates that are the same under all scenarios 100 Apx Table C.3 Hydrogen demand assumptions by scenario in 2050 102 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. 103 Apx Table D.1 Comparison of limiting factors applied in academic literature to the calculation of variable renewable integration costs and the GenCost approach 116 Apx Table E.1 Examples of considering global or domestic significance 124 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. However, if these additional costs are significant, we make an exception. Two exceptions that this report includes are the additional system costs required to make solar PV and wind generation reliable and the carbon dioxide pipeline and storage costs of projects with carbon capture and storage. The narrower and simpler scope of LCOE cost data means that readers should be cautious about drawing strong conclusions about the electricity system from such data. In particular, electricity systems will always require a diversity of resources to deliver all of their functions and so no single technology will meet all the system’s needs regardless of its relative cost position. Acknowledgements This report has benefited from feedback provided by electricity sector stakeholders in February 2025 on a Consultation Draft version of this report that was made available in December 2024. The authors greatly appreciate the time stakeholders have given to support the project. 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 Aurecon. Based on these updated current capital costs, the report provides projections of future changes in costs consistent with updated global electricity scenarios which incorporate different levels of achievement of global climate policy ambition. Levelised costs of electricity (LCOEs) are also included and provide a summary of the relative competitiveness of generation technologies. ‘Firming’ or integration costs of variable renewables In this report, where we make a comparison between the costs of variable renewables such as solar PV and wind and the costs of other technologies we include the cost of firming those renewables which we call integration costs. These are the additional costs of ensuring supply is reliable when using intermittent energy sources. These integration costs are itemised in the report and include storage, transmission, system security and spilled energy. Summary of feedback on the consultation draft While feedback has been dominated by nuclear related topics in the past two GenCost cycles, the 2024-25 consultation draft feedback was more diverse with the majority of feedback covering pumped hydro, electrolysers, wind, solar PV and solar thermal. This indicates a shift in stakeholder focus towards technologies currently planned for investment or under construction. The most impactful changes to the report arising from feedback and the availability of new data are: • Inclusion of an installation cost escalation factor to account for the impact of Australia’s rising real construction costs on the electricity sector. This is supported by new Oxford Economics Australia analysis published by AEMO. • A reduction in the rate of decline in rooftop solar PV costs recognising that the existing industry is already mature and so installation costs will be slower to fall for rooftop solar than in the large-scale solar sector. • Inclusion of the cost of new work camps for construction workers in onshore wind generation capital costs. • An assumed three year pause in gas technology cost reductions, reflecting a lack of gas generation manufacturing capacity relative to technology demand. • Aligning nuclear SMR costs with the recently announced Canadian Darlington project. These new costs were within the range previously projected by GenCost but are significant as they will represent the first commercial scale western project to provide a cost evidence base. • An increase in the average cost of capital financing from 6% to 7% to align with the rate used by AEMO and Infrastructure Australia. This impacts the levelised cost of electricity data provided in the report by increasing the cost of delivered energy from all technologies (because they now have higher loan or return to equity repayments). Together, these changes have increased technology costs compared to previous GenCost reports but there has been no significant change to the relative competitive position of technologies. Additional analysis on three key nuclear generation topics Based on public discussion of GenCost’s approach to nuclear generation since the 2023-24 final report release, the three most common areas of contention are: • The capital recovery period should be calculated over the entire operational life (e.g. 60 years), and not the industry standard of 30 years used in GenCost • Due to US experience, capacity factors of below 93% should not be considered (GenCost uses the range 53% to 89%) • The nuclear development lead time should be 10 to 15 years, not 15 years or greater as proposed by GenCost. Additional evidence and analysis of these topics has been provided in this report. There was no substantive feedback from the 2024-25 consultation to warrant changes in this analysis. Nuclear technology’s long operational life Nuclear advocates have asked for greater recognition of the potential cost advantages of nuclear technology’s long operational life and CSIRO has calculated those cost advantages for the first time in 2024-25. Our finding is that there are no unique cost advantages arising from nuclear technology’s long operational life. Similar cost savings are achievable from shorter lived technologies, even accounting for the fact that shorter lived technologies need to be built twice to achieve the same operational life. There are several reasons for the lack of an economic advantage from longer operational life. Substantial refurbishment costs are required, and without this new investment nuclear cannot achieve safe long operational life. When renewables are completely rebuilt to achieve a similar project life to nuclear, they are rebuilt at significantly lower cost due to ongoing technological improvements whereas large-scale nuclear technology costs are not improving to any significant extent owing to their maturity. Also, due to the long lead time in nuclear deployment, the limited cost reductions achieved in the second half of nuclear technology’s operational life, when the original capital investment is no longer being repaid, are not available until more than 45 years from now, significantly reducing their value to consumers compared to other options which can and are being deployed in the present time. Nuclear generation capacity factors GenCost has always provided a capacity factor range for every generation technology rather than a single point estimate. However, nuclear advocates would prefer GenCost only consider a single value of 93% which is the average capacity factor achieved in the United States. To be clear GenCost agrees that high capacity factors of around 90% are achievable for nuclear generation. However, a prudent investor (government or private) must prepare for all plausible eventualities. The observed global average capacity factor for nuclear generation is 80% and 10% of nuclear generation is operating at below 60%. This is because circumstances vary widely between countries and even within a country there is a merit order for generation dispatch. On international data alone, the proposition of only considering a 93% capacity factor is not supported by the evidence. However, our preference is to always use Australian data where it is available. In Australia, we have more than 100 years of experience with operating baseload generation, not nuclear but coal. Some black and brown coal plants operate at close to 90% capacity factor but the average for all coal in the past decade is 59%. On this basis a single point estimate of 93% does not adequately capture the plausible range achievable in Australia. GenCost bases its capacity factor assumptions for all baseload technologies – coal, gas, and nuclear – on the Australian evidence, applying a maximum of 89% and minimum of 53%. The minimum is based on the same formula that we apply to renewables (the minimum capacity factor for new build generation is assumed to be 10% below the average capacity factor of existing equivalent generation). Nuclear development lead time The development lead time includes the construction period plus all of the preconstruction activities such as planning, permitting and financing. Many stakeholders have agreed with the GenCost estimate of at least 15 years lead time for nuclear generation. Those stakeholders that are more optimistic cite two alternative sources, the International Atomic Energy Agency (IAEA) who have an estimate of 10 to 15 years and the recent completion of a nuclear project in the United Arab Emirates (UAE) which had a 12-year lead time. Both estimates are in relation to building nuclear for the first time. This report provides additional analysis of nuclear lead times to examine this issue more closely. Recent construction times and their relationship with the level of democracy in that country are examined. In the last 5 years, median construction time has increased to 8.2 years compared to 6 years when the IAEA first made their estimate in 2015 . This increase in construction time cannot be explained by the pandemic because median construction times were longer in the two years preceding the pandemic (8.6 and 9.8 years). Note that most of the historical construction time data is dominated by countries with established nuclear industries and so may be optimistic for a first-time country. There is some statistical evidence for the impact of the degree of democracy on nuclear lead times. Pakistan, China and the UAE have had the fastest construction times in the last decade with average construction times of 6 to 8 years, but their democracy index scores are low. Finland, South Korea, the United States (US) and India all had construction times 10 years or longer with high democracy scores. The two Western democracies in this list, Finland and the US had construction times of 17 and 21 years respectively which is significantly longer than the Asian democracies. Another factor which is correlated with shorter construction times is the existence of an ongoing building program rather than long intervals between projects. Given the direction of construction data available after the report’s release, the IAEA range of 10-15 years should likely be reinterpreted as 12 to 17 years to allow for the extra 2 years median construction time which now prevails. The lower part of this new range, 12 years, would be consistent with the UAE experience. Australia is not likely to be able to repeat the UAE experience because our level of consultation will be consistent with our higher level of democracy and the experience of other Western democracies. As such, at least 15 years remains the most plausible lead time. 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, the 2022-23 GenCost report observed an average 20% increase in technology costs. For each of the two years following that observation, 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 capital costs of onshore wind generation technology increased by a further 8% in 2023-24 and another 6% in 2024-25, while large-scale solar PV has fallen by 8% in consecutive years (ES Figure 0-1). The wind cost increase includes a one-off 4% increase to take account of work camp costs not previously included in wind capital cost estimates. Large-scale battery costs improved the most in 2024-25, falling by 20% in 2024-25. The cost of gas turbine technologies is still increasing significantly. GenCost includes hydrogen fuel readiness as a standard feature in gas generation, but this only has a negligible impact on capital costs. ES Figure 0-1 Year on year change in current capital costs of selected technologies in the past 3 years (in real terms) The cost of electricity technologies compared LCOE is the total unit costs a generator must recover over its economic life to meet all its costs including a return on investment. Each input to the LCOE calculation has a high and low assumption to create an LCOE range for each technology (ES Figure 0-2). The LCOE cost range for variable renewables (solar PV and wind) with integration costs is the lowest of all new-build technologies in 2030 and at a similar range with black coal in 2024. The lower end of the cost range of gas generation is also competitive. Black coal and gas are high emissions technologies which, if used to deliver the majority of Australia’s power supply, are not consistent with Australia’s current climate change policies . If we exclude high emissions generation options, the next most competitive generation technologies are solar thermal, gas with carbon capture and storage, large-scale nuclear and coal with carbon capture and storage. ES Figure 0-2 Calculated LCOE by technology and category for 2024 and 2030 While solar thermal costs are low, given the need to access better solar resources further from load centres, they will face additional transmission costs compared to coal, gas and nuclear. Directly calculating these costs was not in scope but could add around $14/MWh to solar thermal costs based on transmission costs that were calculated for solar PV and wind. Nuclear SMR costs improve significantly by 2030 but remain significantly higher cost than these other alternatives (ES Figure 0-2). For clarity, neither type of nuclear generation can be operational by 2030. Developers will need to purchase the technology in the 2030s sometime after pre-construction tasks are completed. Around 8 years of construction would then follow before full operation can be achieved. As such, the inclusion of large-scale and SMR nuclear in the cost comparisons is only as a point where investment could be considered. A practical operation date would be the mid-2040s by which time the costs of other technologies will have fallen further. Renewable and storage technologies also have development lead times, but their deep development pipeline of projects means that there are new projects reaching the point of financial close each year. While LCOE us useful for ranking technology costs, 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. 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. Following the release of the 2023-24 final report there are still three key areas of disagreement with nuclear advocates. To address these topics, additional evidence and analysis are presented on nuclear capacity factors, lead times and the value of a long project life in Section 2. The report provides an overview of updates to current costs in Section 3. This section draws significantly on updates to current costs provided in Aurecon (2025) and further information can be found in their report. The global scenario narratives and data assumptions for the projection modelling are outlined in Section 4. Capital cost projection results are reported in Section 5 and LCOE results in Section 6. CSIRO’s cost projection methodology is discussed in Appendix A. Appendix B provides data tables for those projections which can also be downloaded from CSIRO’s Data Access Portal . 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. 1.1.1 CSIRO and AEMO roles AEMO and CSIRO jointly fund the GenCost project by combining their own resources. AEMO commissioned Aurecon to provide an update of the current cost and performance characteristics of electricity generation, storage and hydrogen technologies (Aurecon, 2025). This report focusses on capital costs, but the Aurecon report provides a wider variety of data such as operating and maintenance costs and energy efficiency. Some of these other data types are used in levelised cost of electricity calculations in Section 6. For the 2024-25 report, AEMO also commissioned Oxford Economics Australia (2025) to provide installation cost escalation factors. Project management, capital cost projections (presented in 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, we have included a longer discussion on some topics related to nuclear energy (Section 2). We have also added historical data to most of the capital cost projections to give readers a better sense of what cost trends existed prior to the projection period. Another small change is that open cycle gas generation technology now has an explicit hydrogen blending ratio and are the only type of open cycle gas technology included given current trends in investment in this technology. The installation cost escalation factors developed by Oxford Economics Australia (2025) are also a new feature applied to the capital cost projections. 1.2 The GenCost mailing list The GenCost project would not be possible without the input of stakeholders. No single person or organisation is able to follow the evolution of all technologies in detail. We rely on the collective deep expertise of the energy community to review our work before publication to improve its quality. To that end the project maintains a mailing list to share draft outputs with interested parties. The mailing list is open to all. To join, use the contact details on the back of this report to request your inclusion. Some draft GenCost outputs are also circulated via AEMO’s Forecasting Reference Group mailing list which is also open to join via their website. 1.3 Summary of feedback on the Consultation draft 1.3.1 Technology specific items Nuclear Frontier economics report Frontier Economics released a report on 13th December 2024 which was widely anticipated to provide some new information about the cost of nuclear generation in Australia’s electricity system. The report was reviewed to determine if it included any useful information on nuclear costs. Our conclusion is that the report is a valuable contribution to understanding the outcomes for the electricity sector from a combination of three factors: delayed emissions reduction, lower economic growth and increased nuclear generation. However, the report cannot be used to draw any conclusions about the cost of nuclear generation relative to other generation options. The reason it has limited applicability in determining the relative costs of generation options is that the scenario design employed in the analysis does not allow for this comparison to be made. By combining three factors together in a single scenario, it is not possible to determine whether the addition of nuclear generation increased or decreased system costs relative to the alternative scenario (AEMO’s Step Change). There is no doubt that some of the three factors included did result in reduced system costs but, unless each factor is studied individually in their own scenario, their specific individual contributions cannot be determined from the data presented. The study only provides one other scenario, which clarifies the role of economic growth only, not the other two factors. A second issue is that the report does not adequately consider first-of-a-kind (FOAK) costs which developers of nuclear in Australia would inevitably face due to our lack of experience in deploying the technology. The report initially considers a 10% premium but then removes that premium by the time nuclear is developed. Based on global experience, a premium of over 100% is more appropriate for the first plant. See section 3.1.1 for more discussion and sources of data on FOAK premiums. Capacity factor assumption In reaction to GenCost’s use of historical coal capacity factors, as the basis for the potential future range of nuclear generation capacity factors in Australia, it was suggested that historical brown coal capacity factors would be most appropriate. The consultation draft had erroneously referred only to black coal capacity factors. However, the capacity factor range applied is in fact inclusive of brown coal. This clarification has been made in the body of the report. However, regarding the suggestion to only use brown coal capacity factors, this proposal was not accepted. Brown coal is confined to only one state in Australia and as such is regarded as too narrow a source of data for a national capacity factor range. Near term nuclear SMR project Credible costs for the Darlington nuclear SMR project became available with the announcement of its go ahead in May 2025. GenCost has chosen to align nuclear SMR costs with the $C20.9 billion 1200MW project. These new costs were within the range previously projected by GenCost for the late 2020s but nonetheless is a significant development as it will represent the first commercial scale western project to provide a cost evidence base for that technology. Construction time An individual stakeholder provided data which showed that democracies have been able to have fast nuclear construction times in the past. However, the point GenCost makes is that through environmental, industrial relations and safety regulations, countries with relatively high levels of democracy have increased their oversight of not just nuclear but all project development across the economy and consequently, can no longer build projects at the rate they did last century. Additional consultation The Australian Academy of Science (AAS) and the Academy of Technological Sciences and Engineering (ATSE) convened a Chatham House rules workshop in Canberra on Wednesday 17 July 2024, providing input to the GenCost team on nuclear energy in Australia. Coal Benchmark data A stakeholder suggested GenCost use some specific historical benchmark data for ultra-supercritical coal generation technology capital costs. In the absence of any Australian coal-fired power plant projects for almost 20 years, Aurecon used commercially available reputable software “Thermoflow” for costing. The software is well recognised by industry for thermal power plant modelling and costing. The software includes the capability to normalise cost for the Australian context. It should be noted that construction of coal plants in the developed world is very limited and hence there remains supply chain issues along with limited availability of contractors to build them. Therefore, escalating costs of projects built many years ago may not be appropriate in the current market. Land and development cost assumptions A stakeholder had concerns about the land and development costs associated with coal. Coal generation projects require large land areas to accommodate ash dams, coal stockpile, evaporation ponds, water treatment and waste treatment. The development timeframe and approval process is also lengthy. A 20% share of capital for the cost of land and development is an estimate only and may vary depending upon location, type of land, coal delivery requirements, transport and logistics development and its proximity to the grid. Gas Capital costs Stakeholders reported that gas generation technologies are in high demand relative to manufacturing capacity and as a result orders were taking longer than normal to fill. This observation was confirmed from other industry data sources. While this excess demand persists, it will be unlikely that gas generation technology costs fall. Consequently, the report introduces a three year pause before we see any real reductions in gas technology costs. Renewables (general) Spillage, storage and transmission costs Some stakeholders found difficulty reconciling the ranges given for spillage, storage and transmission costs in the text with the relevant graph (Figure 6 2). The graphed costs are lower because they are for the National Electricity Market (NEM) whereas the text refers to the average of the NEM and south west Western Australian Wholesale Electricity Market (WEM). Due to the isolation of that grid transmission costs are lower since there are fewer opportunities to diversify renewable resources by connecting to other grids. However, the lack of diversity means that spillage costs and storage costs are higher in the WEM. This means that NEM+WEM average costs for spillage and storage are higher than shown in the charts. This has been clarified in the report body. Capacity factor assumption Some stakeholders observed a recent bad year for renewable capacity factors while also observing that they were within the range used by GenCost. Year on year variability is to be expected in renewable capacity factor performance and is not a basis for determining the plausible long-term range. Rooftop and large-scale solar PV Rate of decrease in installation costs It was requested that the modelling approach be updated to separately model the installation costs of rooftop solar PV and large-scale solar PV with the view that rooftop solar PV installation costs were unlikely to continue to fall as fast as large-scale solar PV given the different levels of maturity of the two sectors. This was accepted and has been implemented for this report. The new learning rates for both types of solar PV are reported in Appendix C. Wind Work camp costs and a reminder about locational cost factor data Stakeholders remain concerned about the cost of wind projects. Some of the variance in wind costs relate to differences between states and these are captured in the locational cost factors which are separately provided by Aurecon and used in AEMO modelling when applying the GenCost data. All costs in GenCost are for a Victorian project not more than 200km from Melbourne’s port. The locational cost factors are required to adjust costs to be relevant to other states. However, in discussions with stakeholders it was identified that the increasing remoteness of wind projects and their workforce needed means that they typically need to construct a work camp as part of the project costs. This additional cost was added to current and future wind capital costs, increasing them by approximately 4%. Solar thermal Deeper analysis Solar thermal stakeholders would like deeper analysis on solar thermal including more detail on its role globally, inclusion as a storage technology, inclusion in firmed renewables calculations and for multiple configurations (storage to power ratios) to be included. These are significant requests given the lack of real Australian project data and the difficulty of modelling solar thermal given it simultaneously intersects two technological categories without any standard configuration of those characteristics. Under current resources these actions are out of scope for GenCost. Economic life assumption At 25 years in the consultation draft, the economic life was inconsistent with the Aurecon (2025) report and has been updated to 30 years. Consistency between Aurecon (2025) and Fichtner Engineering (2023) report A number of consistency issues have been checked and actioned where appropriate. Installation costs method While not prompted by stakeholders, the broader consideration of installation costs in this report led to solar thermal technologies having their own learning rate for installation costs consistent with other technologies. Previously only one learning rate applied to the whole capital cost. This approach was implemented for the first time in this report. Pumped hydro Class of sites included Some stakeholders felt that costs for pumped hydro energy storage should be based on a better class of sites. The GenCost project prefers sites that have stronger engineering and cost data over theoretical high quality sites with no commercial quality data associated with them. It is accepted that this limits the quality of sites included. It is noted that some high-quality sites may be difficult to develop due to the social, environmental or cultural values associated with them. Land and development cost assumption Aurecon’s land and development costs includes the cost of environmental offsets. Stakeholders suggest that land is more likely to be leased. The Aurecon approach is to include this in the capital cost, but a leasing arrangement would mean the cost shifts to the operating and maintenance component. Consistency of the cost and storage duration relationship The costs presented in GenCost for the 10, 24 and 48 hour options do not follow the expected trend in dollar per kW costs with the 24 hour duration being lower cost than the 10 hour duration project (which would be expected to be lowest cost per unit of power output due to its smaller storage reservoir). The 10 hour project is however, based on the most recent relevant project of that duration under construction rather than a theoretical cost curve. It is accepted that using real project costs rather than a theoretical curve will at times result in unexpected trends. The unexpected trend reflects some economies of scale in pumped hydro due to relatively fixed costs such as water licencing, transmission connection and access roads. Inclusion of additional 160 hour storage duration projects Snowy 2.0 which has 160 hours storage duration is not included in GenCost because it is an existing project under construction. Stakeholders have requested additional new projects of this duration be included in GenCost. This will be considered for future GenCost publications but could not be actioned for 2024-25. Appendix E provides the principles applied in determining which technologies to include in GenCost. A-CAES Consistency with public international project data Additional information has been added to the Aurecon report to ensure the basis of Australian A-CAES costs are understood. Natural cavern and vessel costs are very different. Some public costs cannot be relied upon because they lack detail about their scope (i.e. project boundaries, contingencies, subsidies and other assumptions). Finally, there are always significant differences in deployment costs between countries owing to different labour costs, productivity, equipment suppliers and experience. LAES Requested inclusion It was requested that liquid air energy storage (LAES) be included in GenCost. It has not been included in this report but may be considered for future reports. Appendix E provides the principles applied in determining which technologies to include in GenCost. Electrolysers Consistency with Australian projects There was a concern that current electrolyser costs in GenCost are below current Australian project costs. Aurecon cost estimates are based on selected Australian projects at various stages of development. A lower level of project development maturity will lead to a higher cost uncertainty (lower cost accuracy). Aurecon’s cost estimates do not include contingency. Other infrastructure costs not included in Aurecon’s cost estimate are hydrogen compression, storage, transportation, power generation, transmission, grid connection, bulk water source and supply to site and water treatment plant wastewater stream disposal and upgrade of port facilities. Electricity price accuracy for electrolysers There is a concern that simplified global models of electricity supply cannot capture the premium associated with using renewable electricity to supply electrolyser with energy that can be used to market their product as green hydrogen. It is accepted that GALLM, the electricity model employed in GenCost, is not sufficiently detailed to capture the cost of electricity to electrolysers at the accuracy that more temporally disaggregated national and precinct level electricity models can. This technical limitation diminishes as source of inaccuracy the further into the projection period. Electrolysers generally require less reliable (and consequently lower cost) electricity supply than the bulk of customers as their capital costs fall. Electrolyser learning rate assumptions Consistent with the view that electrolyser projects have not proceeded at the rate expected in the short term some stakeholders have suggested that electrolyser learning rates should be revised downwards, presenting literature which suggests technologies with high levels of complexity have lower learning potential. This report has not reduced learning rates on the basis that the range assumed across the scenarios (see Appendix C) already accommodates this view. However, in previous projections GenCost did assume cost reductions (in addition to the learning curve function) would be occur from the scaling up of electrolyser projects. This was implemented as a means of recognising the gigawatt scale of some proposed projects. However, cost reductions from large scale deployment so far have not significantly materialised. Consequently, this component of the projection approach has been removed. Inclusion of solid oxide electrolysers It was requested that solid oxide electrolysers be included in GenCost. They are higher cost but more energy efficient. It has not been included in this report but may be considered for future reports. Appendix E provides the principles applied in determining which technologies to include in GenCost. 1.3.2 General items Capital cost projection method Inclusion of installation cost escalation A new methodology was implemented in this report which increases the installation cost component in line with expected real increases in construction costs (after any reduction in installation costs due to other learning and innovation related factors). This new methodology was not flagged in the consultation draft because the data on real construction cost changes did not become available until after the consultation draft was published. The method is based on data from Oxford Economics Australia (2025). A description of the new approach appears at the beginning of Section 5. In this context, productivity improvements can offset rising labour costs. However, as some stakeholders have pointed out, many projects lack allowances for training and apprenticeships. This limits the both the number of qualified workers and the experience that can be carried from one project to another. Mature technology cost reduction assumption Since project inception, GenCost has included a general technology cost reduction rate for mature technologies which would otherwise not experience any cost reduction since they no longer experience learning by deployment effects that apply to newer or less mature technologies. The rationale for these cost reductions is primarily the fact that historically commodities fall in cost (in real terms) over time. Since the pandemic and subsequent global supply chain crisis the indices used to calculate the factor no longer indicate any ongoing cost reduction trend. As the global manufacturing sector works towards decarbonisation, some relevant commodities such as metals and minerals and other inputs such as cement are likely to increase in cost as they gradually adopt alternative low emissions production methods. As such, recent experience and expected developments in various material inputs no longer support the concept of falling costs for mature technologies. In acknowledgement of these developments which support a stakeholder request, the mature technology cost reduction factor has been removed from the projection method. Scope Definition of a new project GenCost has limited its scope to only considering costs for new build generation, storage and electrolyser projects. Some stakeholders believe an extension of capacity at an existing site should be considered a new build. GenCost does not accept this. Extension of capacity at an existing site, even where the main structures are separate, must necessarily take advantage of some existing infrastructure and is therefore not a fair comparison with completely new build projects. Use of existing sites should be considered as part of Australia’s electricity options. However, site specific cost estimates are outside of the scope of GenCost since it involves a different (higher) level of accuracy and engagement with existing owners of the site. Such studies are best led or commissioned by the site owner. AEMO publishes a range of cost information on existing sites in its regular Inputs and Assumption Workbook series (see AEMO, 2025 for latest workbook). Capital cost projections Inclusion of component detail A request was made for capital cost projections to be provided broken down by equipment and installation cost components. This level of detail is not required by any modelling teams for which the data is targeted but rather would be for the purpose of interrogating the projections more deeply. This request is not supported as it would represent a scope increase which is to be avoided to maintain the sustainability of the project. GenCost is not designed to describe technologies in detail or represent a detailed bottom-up projection process. Where local installation costs are included in the modelling process, this is primarily for the purpose of recognising differences in technology deployment and subsequent cost reduction between countries rather than to be an accurate estimate of technology component shares. The current exception to that is batteries where we do describe the components in this report and this reflects the shared role of the battery equipment component across the electricity and transport sectors. Levelised cost of electricity (LCOE) Discount rate assumption The discount rate or the weighted average cost of capital used in the LCOE formula has increased from 6% (used consistently since 2018) to 7%. This change is to align with AEMO and Infrastructure Australia. As a consequence of this change, the LCOE or delivered cost of energy for all technologies are higher than the consultation draft and all previous GenCost reports which used the lower value. This represents higher loan repayments or return to equity costs that must be recovered. Data and method transparency for renewable integration costs A request raised across several consultation cycles is for more detail and background information on the system modelling carried out to estimate variable renewable integration costs. The modelling tool used to carry out these calculations is not suitable for general release. It is also common that stakeholder submissions will attempt to calculate the cost of renewables integration using overly simplified ‘back of the envelope’ type approaches. The electricity system generation mix cannot be solved for a least cost outcome outside of a model (using a linear or mixed integer programming approach). There is only a relatively small number of researchers who are interested in the least cost generation mix but who also know how to and have the resource to build and operate appropriate electricity system models. To address both of these issues of transparency and lack of expertise and resources, work has commenced on a model and data set that could be more readily shared with other researchers and the results of this work will be discussed in the GenCost 2025-26 cycle. Inclusion of technology degradation A method was proposed by a stakeholder for incorporating technology degradation in the levelised cost of electricity formula. The method proposed averaging the initial and final capacity factors which is effectively a linearisation of the typical non-linear decline rate. This is accepted as a reasonable approximation at face value. One exception would be solar PV where the degradation occurs to the direct current panel capacity and would be ameliorated partially by the difference between the panel and inverter sizes. Wherever the topic of extending LCOE to include other factors has emerged the general philosophy of GenCost is to avoid adding additional factors in favour of simplicity if those factors they do not make a significant contribution to costs. However, the greatest concern is that capacity factors in GenCost already include degradation because they are based on ten years of data from the full range of projects in the NEM, many of which would already be significantly degraded. Consequently, the method has not been taken up. Inclusion of marginal loss factors It was requested that marginal loss factors (MLFs) be included in LCOE estimates driven by a concern that renewables are significantly impacted by this issue relative to other technologies due to their more distributed deployment away from strong parts of the existing transmission grid. MLFs are the electricity losses in the transmission network between a generator and the market's Regional Reference Node. MLFs are site specific by nature since they are based on location in the network. The LCOEs presented in GenCost are not site specific in the sense that they are for new build technologies but with no specific location. Furthermore, the grid will change over time to strengthen areas where new generation is being built but may also at other times weaken . Therefore, while the issue is acknowledged as significant, it is neither practical nor desirable in the long run to include MLFs. 2 Nuclear: additional evidence and analysis on three topics Based on public discussion of GenCost’s approach to nuclear generation since the 2023-24 final report release, the three most common areas of contention with CSIRO analysis are that: • The capital recovery period should be calculated over the entire operational life (e.g. 60 years), and not the industry standard of 30 years used in GenCost • Due to US experience, capacity factors of below 93% should not be considered (GenCost uses the range 53% to 89%) • The lead time should be 10 to 15 years, not 15 years or greater. Additional evidence and analysis of these topics is provided in the following discussion. 2.1 Nuclear capital recovery period and long operational life Stakeholders have raised direct government ownership as a serious proposal. Based on feedback received throughout the course of the GenCost project, it is assumed the primary intent of government ownership is to unlock the potential benefits of nuclear technology’s long operational life with a longer capital recovery period than might be achievable with private ownership and funding. In the following analysis we examine two potential ways in which government ownership might be able to unlock potential benefits of long operational life by: • Accessing longer-term capital recovery periods not available to the private sector, and, • Maintaining the same 30-year capital recovery period but acknowledging the lower generation costs in the remainder of the operational life in the assessment of levelised costs of electricity. With this knowledge, a government owner could choose to smooth out the average cost of electricity over time from nuclear generation. Alternatively, they might simply be able to weather the first 30 years of high-cost generation more sustainably than a private sector investor because governments can carry losses through debt for long periods of time. Our analysis of these two financial strategies for using the longer operational life of nuclear to create cost savings from government ownership finds that: • Long-term operation of nuclear is not costless. Extension costs are incurred and are significant. • Long operational life provides no major financial benefit to electricity customers relative to shorter-lived technologies. Taking account of extension costs, long operational life confers an average cost reduction of 8% to nuclear power relative to the costs that are calculated when only considering the standard 30-year private sector financial arrangements. However, there are three important limitations to this benefit: – Other technologies can achieve similar benefits. Our analysis includes examples where onshore wind and solar PV are initially built and then completely rebuilt at the 25 to 30 year mark to achieve a total 50 to 60 year project life. Alternatively, we could build a nuclear project and incur normal extension costs at the 40-year mark. Both types of projects involve re-investment costs during their life, although for the renewable projects the reinvestment is more substantial than nuclear relative to the initial investment. However, overall, renewables achieve a similar cost reduction of 5% when considered over a 50 to 60 year life because their costs are falling over time making their second investment lower than the first. – Time erodes most of the benefit of long operational life. The present value of the cost reduction that is available from lower costs in the second half of nuclear technology’s long operational life fades to less than half when we consider the cost of the delay before first nuclear generation can commence. – It is unclear how customers would be awarded benefits of future lower cost operation. The current electricity market design does not pass through the costs of the lowest cost generation – instead the benefits are captured as profits to owners. The material below provides more detail on how these conclusions were reached. 2.1.1 Cost advantage of accessing longer-term capital recovery ignoring extension costs In analysing the impact of longer-term capital recovery, for simplicity, we will initially ignore life extension costs. These are covered in the next section. The below analysis only changes one assumption about nuclear projects: the capital recovery period. If the capital recovery period is changed from 30 years to a number that reflects the full operational life, then the annual cost of capital recovery will be lower. However, the scale of cost reduction is not proportional to the increase in capital recovery period. For example, doubling the capital recovery period from 30 years to 60 years does not halve the levelised cost of electricity (LCOE) from nuclear. The main reason is that a longer capital recovery period results in the payment of more interest. A 60-year loan will incur around 131% more interest than a 30-year loan and this increases the total amount (principle plus interest) that must be repaid . Another reason is that while capital is the largest component of LCOE, nuclear generation has other non-capital costs which are not impacted by the longer capital recovery period. As a result of these factors, a 60-year capital recovery period results in only an 9-12% reduction in LCOE compared to a 30-year period, depending on the technology type (Table 1). Stakeholders have proposed operational lives of nuclear plants of up to 100 years. To avoid any doubts about the benefits of longer capital recovery periods, Table 1 reports the cost savings for an operational life of 60 years and a more speculative 100 years to demonstrate that the benefits of very long capital recovery periods do not proportionally improve with length. The data shows that nuclear SMR receives slightly more benefit. However, this is because its capital costs are higher and consequently capital recovery costs are a larger portion of total LCOE. Table 2 1 The reduction in nuclear LCOE resulting from a 60- or 100-year capital recovery period compared to a 30-year capital recovery period, ignoring extension costs New period Type 2024 2024 2030 2030 2040 2040 2050 2050 Low High Low High Low High Low High 60 Nuclear SMR 11% 11% 10% 10% 10% 10% 10% 10% 60 Nuclear large-scale 9% 9% 9% 9% 9% 9% 9% 10% 100 Nuclear SMR 12% 12% 11% 12% 11% 12% 11% 11% 100 Nuclear large-scale 10% 10% 10% 10% 10% 11% 10% 11% 2.1.2 Impact of accessing lower costs after the 30-year capital recovery period An alternative proposal for capturing the potential benefits of the longer operational life of nuclear is to go through the standard 30-year capital recovery period and reap the benefits of capital cost free operation thereafter. To go a step further, proponents have said that failure to recognise this opportunity for low-cost operation is a major flaw of LCOE analysis which is overly focussed on the investor’s perspective and not the long-term value to the consumer. To address this viewpoint and work through this concept the following analysis will focus only on large-scale nuclear and a 60-year operation period. Value to customers To determine the value to customers we deconstruct the timeline of costs to consumers of large-scale nuclear generation over the entire 60-year period of operation. In the first 30 years, the cost to consumers including capital recovery is $173-288/MWh (based on a purchase in 2030). For the remaining 30 years (31 to 60), assuming the plant requires no life extension investment, there would be zero capital recovery costs, only the normal operating and maintenance (O&M) and fuel cost of $36-56/MWh, reflecting GenCost uranium fuel cost assumptions in 2050 (see the first line in Figure 2 1). However, the assumption of no life extension costs is an oversimplification. Nuclear generation typically requires a major investment to extend life from 40 years to 60 years. Based on IEA (2019) these costs are estimated at A$2765/kW or $49-86/MWh when this refurbishment cost is recovered over the remaining 20 years of life . For simplicity, these costs have been applied from year 41 to 60 in line two of Figure 2 1 assuming uninterrupted generation (together with the existing fuel and O&M costs). In practice, there might be a period where generation needs to be offline for a few years to complete the installations associated with the life extension. Taking these two nuclear cost examples with and without life extension costs, it is clear that lower costs are available in the years 31 to 60. However, on the downside the consumer must wait 31 years before this is available. This has less value to consumers than if it is available to consumers now. This delay in cost reduction makes the total value of the project to consumers unclear. To determine what value the whole timeline of costs has to consumers we need to convert the costs in all years to a common value. To do this, we have calculated the constant cost to consumers that would be equivalent (using a present value approach) to the uneven timeline of costs over the two or three different cost intervals. From a present value point of view, the no life extension cost timeline which includes 30 years of no additional costs in years 31 to 60 is estimated to be equivalent to a constant cost to consumers of $157-261/MWh which is an 9% reduction in costs relative to a single 30-year generation project . The with life extension cost timeline, which includes 10 years of no additional refurbishment costs and 20 years of life extension capital costs, is estimated to be equivalent to a constant cost to consumers of $160-265/MWh, which is an 8% reduction in costs relative the costs for a single 30-year generation project. Figure 2 1 Costs for long-lived multi-stage projects and the subsequent cost reduction achieved for electricity consumers. 2.1.3 Allowing other technologies to benefit from multi-stage costing While nuclear has an inherently longer operational life it is not without additional investment and not completely unique. Coal technologies have an operational life of around 50 years. However, it is too early to be able to say what the total operational life is of more recent technologies such as solar PV and onshore wind. Solar PV panels will have degraded by year 30 but could go on generating for many more years at lower output. If it is advisable to replace panels due to degradation or damage, the underlying mounting system may still be viable beyond year 30. However, data for this will not be available until more projects reach the end of their capital recovery period . Similarly, some parts of the mounting system or other groundwork for wind turbines may have some residual value but are yet unknown. Parts of the existing transmission connection are likely to be viable for at least 50 years. Leaving aside the potential to re-use some elements of solar PV and onshore wind after their capital recovery period, since the data is not yet available, the analysis will focus on another major opportunity for second stage cost reduction which is to completely rebuild at a lower cost. The complete rebuild costs are available from GenCost because it provides LCOEs for each decade to 2050. Solar PV has a capital recovery period of 30 years. Consequently, we have designed a 60-year project where the solar PV plant is completely rebuilt and operated for another 30 years (years 31 to 60). Onshore wind has a capital recovery period of 25 years. Consequently, we create a 50-year project where the technology is completely rebuilt and continues to operate for the years 26 to 50. The costs for both solar PV and onshore wind have a long history of declining. On global weighted average, the levelised cost of generation from solar PV reduced 90% and onshore wind by 71% in the 13 years to 2023. Therefore, the rebuild of both technologies can reasonably be expected to be lower cost than the initial project. To calculate the benefit of the second period of lower costs, in the same way that we did for nuclear generation, we convert the two stages of costs in the full 50- and 60-year lifetimes to a constant value. The full timeline costs of the 60-year solar PV project, including a complete rebuild in the second half, is estimated to be equivalent to a constant cost to consumers of $45-77/MWh which is a 5% reduction in costs relative to a single 30-year project. The full timeline costs of the 50-year onshore wind project including a complete rebuilt in the second half is estimated to be equivalent to a constant cost to consumers of $75-125/MWh which is also a 5% reduction in costs relative to a single 25-year project. The solar PV and onshore wind 50– 60-year projects can be implemented immediately because of the existing pipeline of well-advanced projects. However, any Australian nuclear project would be at least 15 years away before first generation. It is therefore not a level playing field to measure the delayed present value benefits of generation from a nuclear project with that of a similar length solar PV or onshore wind project deployed now. The benefits of the nuclear project are devalued by the 15-year delay. For example, $100 today is only worth $39 if you have to wait 15 years to receive it (using the same annual real discount rate as the analysis above). As such, the 8% savings associated with a 60-year nuclear project are worth less than half their value when the 15 year delay before generation can commence is accounted for. 2.1.4 Challenges in passing through lower costs in the post-capital recovery period Whether it is nuclear or some other technology, passing on the lower costs associated with the second half of a long-lived multi-stage project will be challenging. Australia’s current electricity generation system is designed so that the wholesale price reflects the balance between demand and supply. When in excess supply, prices may be below costs of production. When in tight supply, prices can be many times higher than costs of production. Furthermore, the same price is awarded to all generators - there is only one market clearing price. The clearing price is set by the bid price of the last generator required to be dispatched to meet demand. The fact that all generation before that was bid at a lower price is not factored into prices charged to consumers to recover costs of supply. When an electricity system is growing, the expectation is that market prices will need to be at least as high as that needed for private investors to be sufficiently motivated to invest, otherwise the required new capacity will not be delivered. This is why the LCOE, which is a measure of the costs that investors need to recover to be economically viable, can be thought of as an indicator of future electricity prices. It remains only a partial indicator because other changes in supply and demand (such as capacity retirements, fuel price changes and strong weather changes) in any given year add noise to this underlying investment signal . In this context, if the government owns nuclear and would like to pass on cost reductions estimated above (either as an average 8% lower cost for all 60 years or by waiting until year 31 and passing on lower generation cost from that point forward (see Figure 2 1)) it is not clear what mechanism it would use to do that. If the share of nuclear power is only minor (e.g. 20% or less) then it is unlikely second-stage nuclear generation costs will set the market price because many other sources of generation will be required on top of that to clear the market. The lower cost of nuclear generation will not be experienced by consumers in the market price. Rather, it will be experienced by the government owner as higher profits. A new mechanism would be required to pass on profits through the tax system. Furthermore, if demand is growing, then to ensure sufficient new supply is invested in, the electricity price must reflect the cost of new investment. This is another barrier to consumers being able to access the lower costs of the second stage of nuclear generation. 2.2 Nuclear capacity factor range Some stakeholders have posited that if nuclear generation can achieve high capacity factors of 93% in the United States then that is the sole capacity factor that GenCost should be using rather than a range. Australia has no history of nuclear electricity generation but has more than 100 years of experience in operating black and brown coal generation in the same baseload power role. GenCost uses a range of 53% to 89%. 89% represents the best performance of black and brown coal in a recent ten-year period (2011 to 2021). 53% is 10% below the average capacity factor of black and brown coal of 59% over the same period. We use the same approach to setting high and low values for all technologies based on the same ten-year sample. The difference between large-scale nuclear costs at 93% and 89% capacity factor is an additional $5/MWh. This impact of the difference in assumptions for the high capacity factor range is negligible. In this context, the objection from stakeholders appears to be that GenCost is acknowledging that the capacity factor could be much lower than 90%. The sensitivity of some stakeholders to recognising this possibility is likely because, given the high capital cost of nuclear generation, the cost of generation could be very high if the capacity factor is low. Other technologies such as solar PV and wind have much lower capacity factors, but their capital costs are low and so the implications of low capacity factors are not as significant for them. International data shows that nuclear generation did experience average capacity factors of 60% in the 1970s and 1980s. This has increased to 80% in more recent decades. However, even in 2023, some 10% of reactors were still operating at 60% capacity factor or below (World Nuclear Association, 2024). Besides selected overseas experience, one reason for stakeholder insistence that capacity factors will be high could be confusion over the difference between the availability of a plant and the capacity factor. The availability factor is the percentage of the time over which a technology could generate electricity after accounting for required down time for maintenance or other outages. The capacity factor is the realised percentage of time generating at full capacity in a year which is influenced by the availability factor but also by market circumstances. Market circumstances include: • Decreasing demand at night and all day during the milder seasons of spring and autumn means a large portion of generators must ramp down generation at these times. This combined with a traditionally high share of coal generation in Australia means a reasonable proportion of coal is subject to this ramp down (that is, decreasing output is not confined only to the traditional flexible plant types such as hydro and gas). In other countries there may be a higher share of these flexible plant or the ability to export which creates a protective buffer against this need to ramp down. Other countries may also have a less variable daily and seasonal load curve due to higher year-round heating or cooling loads. • Since the mid-2010s, low-cost solar PV has reduced daytime and more broadly clear-weather-day demand. However, this does not appear to have substantially impacted average capacity factors (Figure 2 2). This is perhaps because coal retirements have allowed for less ramping down for the remaining coal fleet at other times of the day and year. Coal plants have also defended their minimum load by using negative price bids to stay operating during low demand periods. In states where wind generation is high, they can have a similar impact to solar by reducing other types of generation during periods of high wind availability. Figure 2 2 Historical capacity factors for black and brown coal in Australian electricity generation (NEM states) The capacity factor that nuclear might be able to achieve due to market circumstances will depend on the types and scale of technologies already deployed in that market and the shape of the daily and seasonal load curves at the time that nuclear is deployed (from the mid-2040s). While AEMO’s Integrated System Plan (AEMO, 2024) makes it clear this period will be dominated by solar PV and wind under current government policy, other generation mixes are possible under other policies, should they change. Rather than second guess this future generation mix, it is both appropriate and prudent to acknowledge that nuclear generation could face the same or other new market challenges resulting in a lower capacity factor consistent with the experience of Australian black and brown coal and some global regions with existing nuclear generation. 2.3 Nuclear development lead times GenCost has estimated that nuclear generation in Australia will have a lead time of at least 15 years. While many stakeholders agree with this assessment the main criticism is that it is partially at odds with the International Atomic Energy Agency’s Milestones in the Development of a National Infrastructure for Nuclear Power report (IAEA, 2024). The Milestones report provides a step-by-step guide to how to set up a new nuclear industry for countries previously without nuclear generation. However, it does not provide any timeline for each individual step nor any working or past evidence for their proposed 10-15 year timeframe. Given the estimate for lead time was originally published in 2015 (and the range not updated in 2024) it could be inferred that the timeline was at least based on recent construction times in the period leading up to 2015. Construction is the last stage of the lead time after other planning, safety Iicencing, financing and other approvals have been completed. In the decade leading up to the release of the estimate in 2015 the median construction time was 6 years and fairly stable . In the last 5 years median construction time has increased to 8.2 years. This increase cannot be explained by the pandemic because construction times were longer in the two years preceding the pandemic (8.6 and 9.8 years). Note that this historical construction time data is dominated by countries with established nuclear industries and so may be optimistic for a first-time country. The IAEA do not explicitly state what characteristic of a country puts them at the high or low end of their range. The degree of community consultation is one obvious factor. High levels of consultation tend to occur in democracies. This could be in the form of standard guidelines for community consultation that an institution in charge of planning approvals is obliged to follow. It could also encompass electoral processes where governments in favour of or against a nuclear project face elections (if the project is partisan in that country). There is some statistical evidence for the impact of the degree of democracy on nuclear lead times. A democracy index is published by The Economist Intelligence Unit. An index score of 8.01 to 10 (out of 10) indicates a full democracy, while countries that fall between 6.01 and 8.01 are considered flawed democracies. Countries that score lower on the index than 6.01 are not considered democracies. Australia’s score in 2023 was 8.66. We only have readily accessible data on construction times, not the total lead time. Considering the data since 2011, Pakistan and China have had the fastest construction times in the last decade with average construction times of 6 years, but their democracy index scores are 3.25 and 2.12 respectively (Figure 2 3). The United Arab Emirates (UAE) achieved 8 years construction with a democracy score of 3.01. Finland, South Korea, the United States (US) and India all had construction times 10 years or longer with democracy scores of 9.30, 8.09, 7.85 and 7.18 respectively. The two Western democracies in this list, Finland and the US had construction times of 17 and 21 years which is significantly longer than the Asian democracies. This matches with other analyses of the differences in Asian nuclear construction by authors such as Ingersoll et al. (2020) who noted that litigious responses to problems onsite are extremely rare in those cultures. There are some exceptions in the data. Iran and Russia have low democracy scores but construction times longer than ten years. Also, Japan has a high democracy score and a low construction time but has not built any new projects in the last ten years. If they did, they may face longer delays for any new projects due to the ongoing political fallout of the Fukushima accident. Figure 2 3 Relationship between the level of democracy, regions and construction times since 2011 Another factor associated with shorter construction times is ongoing building programs. Both China and Pakistan built multiple nuclear projects in the last decade. It is likely that democratic consultation and construction experience both play into achievable construction times. Given the direction of construction data available after the report’s initial estimate, the IAEA total lead time range of 10-15 years should likely be updated to 12 to 17 years to allow for the extra 2 years median construction time which now prevails. The lower part of this new range, 12 years, would be consistent with the UAE experience (completed in 2020) which is one of the highest profile first-time nuclear developer countries in recent years. GenCost maintains that the UAE 12-year timeframe is unlikely to be achievable in Australia primarily because Australia is a democracy and therefore it will likely have processes that require greater consultation than in the UAE. Furthermore, the data indicates that Western democracies consistently take longer to complete nuclear projects than other regions. It is therefore appropriate to conclude that Australia is likely to have a lead time in the middle to top end of the (updated) IAEA range with significant risk it could be even longer. Note that GenCost continues to use a six-year construction time in levelised cost of electricity calculations (based on Lazard (2023)). The reasons for this approach, despite the discussion above, is that GenCost only presents nth of a kind technology costs for all technologies. See Section 3.11 of this report for more discussion on the difference between first-of-a-kind and nth-of-a-kind technology costs. 3 Current technology costs 3.1 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 capacity . 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 Aurecon (2025). Aurecon (2025) also provide more detail on specific definitions of the scope of cost categories included. Aurecon 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. Aurecon 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. Aurecon (2025) also provides detailed information on the boundary of capital costs such as what development costs are included, ambient temperature, distance to fuel source, water availability and many other considerations. 3.1.1 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 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. Therefore, we can only warn stakeholders that some projects will cost significantly more than projected in Section 5. 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 • 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. GenCost has previously declined to offer a suggested FOAK premium because they are very difficult to forecast. However, we also observed that some stakeholders have used CSIRO’s NOAK costs and not assigned an appropriate FOAK premium. It is judged that the lesser harm may be found in at least providing a suggested FOAK premium (Table 3 1). To develop the premium the value of 120% has been applied to large scale nuclear based on Flyvbjerg 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 3 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% 3.2 Capital cost source AEMO commissioned Aurecon (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 Aurecon (2025) which represents a July estimate and so it is consistent with either the beginning of the financial year 2024-25 or the middle of 2024. Aurecon provides several measures of project capacity (e.g., rated, seasonal). We use the net capacity at 25oC to determine $/kW costs. Aurecon states that the uncertainty range of their data is +/- 30% . Technologies not included in Aurecon (2025) are typically those which are not being deployed in Australia but are otherwise of interest for modelling or policy purposes. For these other technologies we have applied an inflationary factor to last year’s estimate based on a bundle of consumer price indices applied to knowledge of the relative mix of imported and local content for each technology. Where cost estimates are based on technologies not deployed recently and recent inflationary factors are not therefore observable, GenCost has added a cost factor which is then removed over time. 3.3 Current generation technology capital costs Figure 3 1 provides capital costs for selected technologies since the project’s inception in 2018. All costs are expressed in real 2024-25 Australian dollars, represent overnight costs and do not include any available subsidies. Whilst there had been some steady declines over the years for technologies such as solar PV and offshore wind, costs increased for many technologies in the past three years 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 are also recovering from this development at different rates. The change in current costs over the past three years indicates a general easing of inflationary pressures across most technologies (Figure 3 2). We will discuss storage in more detail in the next section, but overall solar PV and battery storage have weathered the inflationary period the best of all technologies. Other technologies are still experiencing real cost increases but at a reduced rate compared to the previous two years. Figure 3 1 Comparison of current capital cost estimates with previous reports (FYB) Figure 3 2 Year on year change in current capital costs of selected technologies in the past 3 years (in real terms) 3.4 Current storage technology capital costs Updated and previous capital costs are provided on a total cost basis for various durations of batteries, adiabatic compressed air energy storage (A-CAES) and pumped hydro energy storage (PHES) in $/kW and $/kWh. Battery durations of 12 hours and 24 hours have been added in 2024-25. 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 kWh . 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 3 3). The downward trend flattens somewhat with batteries since its power component, mostly inverters, is relatively small but adding more batteries increases capital cost. However, the hydroelectric turbine in a PHES project is a large capital expense while adding more reservoir is less costly. As a result, PHES capital costs fall steeply with more storage duration. Note that these $/kWh costs are not for energy delivered but rather a capacity of storage. GenCost does not present levelised costs of storage (LCOS) which are on an energy delivered basis. However, LCOS estimates are available from the CSIRO (2023) Renewable Energy Storage Roadmap. While A-CAES appears to have a relatively higher capital cost at present, it is mainly competing with pumped hydro for longer duration storage applications. PHES is not expected to improve in costs and may be more distant to some locations. Storage capital costs in $/kW increase as storage duration increases because additional storage duration adds costs without adding any additional power capacity to the project (Figure 3 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 6). Figure 3 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 Aurecon battery costs are presented on a usable capacity basis such that the depth of discharge is 100% . Aurecon (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 a 5% lower battery cost for a 1-hour duration battery, scaling down to a 1% cost reduction for 8 hours duration and negligible for longer durations. PHES is more difficult to co-locate. Battery costs (battery and balance of plant in total) have decreased significantly by 11% to 36% depending on the duration. Figure 3 4 Capital costs of storage technologies in $/kW (total cost basis) PHES current cost estimates have increased by 12% for 24-hour duration projects and by 15% for 48-hour duration projects . The increases in PHES costs are partially due to higher construction costs associated with the global inflationary pressures but also increasing familiarity with PHES developments in Australia. 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. A-CAES is not yet integrated into our projection methodology and so its future costs are not presented in this report. While some components are mature, their deployment is not widespread relative to other options. Aurecon (2025) has provided a 24-hour duration cavern storage A-CAES project cost. A cost for vessel storage is also provided by Aurecon for 12-hour duration but is not reported here given its high cost. It appears that cavern will be the preferred storage method where possible given the cost advantage. Concentrating solar thermal (CST) is another technology incorporating storage but it is reported as a generation technology in Section 6. 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. 4 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. 4.1 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. 4.1.1 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 policies . 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. 4.1.2 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. 4.1.3 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 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 4 1 Summary of scenarios and their key assumptions Key drivers Global NZE by 2050 Global NZE post 2050 Current policies IEA WEO scenario alignment Net zero emission by 2050 Announced pledges scenario Stated policies scenario CO2 pricing / climate policy Consistent with 1.5 degrees world Based on NDCs and announced targets Based on current policies only Renewable energy targets and forced builds / accelerated retirement High reflecting confidence in renewable energy Renewable energy policies extended as needed Current renewable energy policies Demand / Electrification High Medium-high Medium Learning rates1 Stronger Normal maturity path Weaker Renewable resource & other renewable constraints2 Less constrained Existing constraint assumptions More constrained than existing assumptions Decentralisation Less constrained rooftop solar photovoltaics (PV)2 Existing rooftop solar PV constraints2 More constrained rooftop solar PV constraints2 1 The learning rate is the potential change in costs for each doubling of cumulative deployment, not the rate of change in costs over time. See 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. 5 Projection results All projections start from a current cost and the primary source of 2024 costs is Aurecon (2025) with data gathered from other sources where otherwise not available in that report. While we have used the trends in price indices of selected goods to inform our analysis, all projections remain in real terms. That is, all projected cost changes after 2024 are in addition to the general level of inflation. 5.1 Short-term and long-term inflationary pressures 5.1.1 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 three to ten years. 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 either 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 capacity ). The current cost update indicates inflationary pressures are weakening for most technologies and the cost of some technologies such as solar PV and batteries are falling again. 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, particularly for the Global NZE by 2050 and Global NZE post 2050 scenarios. This is partly because of the low global clean technology base (from which non-linear growth is more feasible) but also because governments and industry often use the turning of a decade as a target date for achieving energy targets. The Current policies scenario requires less growth in technology deployment and as such, for that scenario only, 2027 remains the date at which we assume most technology costs resume their pre-pandemic modelled pathway. In response to feedback, this report includes two exceptions which is that onshore wind costs do not return to their normal path until 2035 and gas technologies experience a three year delay before their costs start falling in real terms in line with other technologies. Of all the technologies that are currently in high demand, onshore wind and gas technology capital costs were impacted the most and have demonstrated to be the slowest to recover. It is therefore appropriate to give them a separate pathway. The trajectory for wind reflects that cost increases are getting gradually smaller, while the trajectory for gas is different because there has been limited sign of slowing in their cost increases. A consequence of this modelling approach is that the near term cost reductions to either 2027 or 2030 (or later for onshore wind and gas) mostly do not reflect learning. Rather, they are predominantly the slow unwinding of inflationary pressures that have temporarily placed costs above the underlying learning curve. Solar PV, batteries, fuel cells and offshore wind have already passed through the global inflationary event and their costs now follow the standard learning curve trajectory. 5.1.2 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 Aurecon (2025) and inflate that proportion of costs by the real land cost index that is published in Mott MacDonald (2023) . 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 Aurecon (2025). This report is the first time such assumptions have been applied to the projections and is responsible for higher technology costs than previous publications. 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, which have limited prospect of installation cost reductions, are the most impacted by this new escalation factor (e.g., gas and coal technologies). 5.2 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 5 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 transformation . 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 5 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 deceased relative to the 2023 World Energy Outlook. Figure 5 1 Projected global electricity generation mix in 2030 and 2050 by scenario The technology categories displayed are more aggregated than in the model to improve clarity. Solar includes solar thermal and solar photovoltaics. Current policies has the lowest non-hydro renewable share at 52% 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 increases the higher the climate policy ambition of the scenario with a range of 11% to 13% across the scenarios by 2030 but declines to 5% to 7% by 2050 reflecting its relatively slower installation rate as electrification causes demand to grow rapidly in the 2030s and 2040s. Figure 5 2 Global hydrogen production by technology and scenario, Mt 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 2% of generation by 2050. Offshore wind features strongly in this scenario at 17% of generation by 2050. Renewables other than hydro, biomass, wind and solar are 2% 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. 5.3 Changes in capital cost projections This section discusses the changes in cost projections to 2055 compared to the 2023-24 projections. For mature technologies, differences mainly reflect a change in current costs and the introduction into the projections of an installation cost escalator which was not included in the 2023-24 projections (Oxford Economics Australia, 2025). The 2023-24 projections for mature technologies also included an assumed annual rate of cost reduction for mature technologies post-2027 or 2030 (depending on the scenario) of 0.35%. This assumption has been discontinued as it is no longer supported by the historical data. Overall, the updated approach leads to higher capital costs in the long run relative to the 2023-24 projections. 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. Generally, capital costs for less mature technologies are higher than in 2023-24. Data tables for the full range of technology projections are provided in Appendix B and can be downloaded from CSIRO’s Data Access Portal . 5.3.1 Black coal ultra-supercritical The updated cost of black coal ultra-supercritical plant in 2024 has been sourced from Aurecon (2025). Prior to 2023-24, the black coal capital cost had previously been based on a supercritical plant. However, an ultra-supercritical technology is the most plausible type given Australia’s net zero by 2050 target. From 2024, the capital cost is assumed to return to levels consistent with ultra-supercritical prior to the COVID-19 pandemic by 2027 in Current policies and by 2030 in the Global NZE scenarios, reflecting our approach for incorporating current inflationary pressures outlined at the beginning of this section. 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. Figure 5 3 Projected capital costs for black coal ultra-supercritical by scenario compared to 2023-24 projections 5.3.2 Coal with CCS The capital cost of black coal with CCS from 2024 to 2027 in Current policies or 2024 to 2030 in the Global NZE scenarios 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 2023-24 projections, significantly less CCS is deployed globally. This is mainly because the ongoing cost reductions achieved by solar PV have increased its share, reducing the share of CCS in electricity generation. Cost reductions up to 2027 or 2030 are not technology related but rather represent the weakening of short-term inflationary pressures. 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. Figure 5 4 Projected capital costs for black coal with CCS by scenario compared to 2023-24 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 differences in the timing of reductions. As with Current policies, equipment cost reductions are not significant enough to offset and land and installation cost increases in the Global NZE scenarios. A first of a kind premium, in addition to the costs shown, will likely apply when cola with CCS is deployed in Australia for the first time. 5.3.3 Gas combined cycle Aurecon (2025) have included an increase in gas combined cycle costs for 2024 which followed similar increases the previous two years. CSIRO has assumed no change in real costs for the next return three years and then a return to previous cost levels by 2030 in Current policies and 2033 in the Global NZE scenarios. After the return to normal period, because gas combined cycle is classed as a mature technology for projection purposes, its change in capital cost is governed 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. Figure 5 5 Projected capital costs for gas combined cycle by scenario compared to 2023-24 projections 5.3.4 Gas with CCS The current cost for gas with CCS has been revised upwards for the 2024-25 projections reflecting the increase in gas combined cycle capital costs. 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 in 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 similar total costs across all scenarios by 2050. Apart from the introduction of increasing installation costs, the flatter outlook for CCS costs compared to the 2023-24 projections is because the lower costs of technologies such as solar PV and batteries has meant a lower share of CCS. Less deployment limits the amount of cost reduction that can be achieved. The IEA CCS database indicates there are over 100 planned electricity related projects which are yet to make a financial investment decision and two under construction. While there have been previously constructed plants in operation, none are currently 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. Figure 5 6 Projected capital costs for gas with CCS by scenario compared to 2023-24 projections 5.3.5 Gas open cycle (small and large) Figure 5 7 shows the 2024-25 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%. This assumption of hydrogen readiness adds a negligible premium to gas open cycle capital costs. The Aurecon (2025) report provides additional details for the unit sizes and total plant capacity that defines the small and large sizes. After a three year period where costs are flat or slightly increasing, capital costs are reverted back to previous costs in 2030 and 2033, depending on the scenario in line with the approach taken for other gas technologies. 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 Figure 5 7 Projected capital costs for gas open cycle (small) by scenario compared to 2023-24 projections 5.3.6 Nuclear SMR The projections start at the 2024 capital cost of around $29,600/kW which was based on a well-documented and costed project that did not go ahead in the United States. 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. Known projects have been pushed further into the future, beyond the 2020s (Global Energy Monitor, 2024a) relative to the 2023-24 projections. Later deployment of some nuclear SMR projects means it takes longer for capital cost reductions due to learning-by-doing and economies of scale to materialise. 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. Figure 5 8 Projected capital costs for nuclear SMR by scenario compared to 2023-24 projections 5.3.7 Large-scale nuclear Like other technologies, large-scale nuclear capital costs are assumed to return to their underlying costs, before the current global inflationary cycle, by 2027 in Current Policies and by 2030 in the Global NZE scenarios. 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 2027 or 2030 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. 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 5 9 Projected capital costs for large-scale nuclear by scenario compared to 2023-24 projections 5.3.8 Solar thermal The starting cost for solar thermal has been updated by Aurecon (2025) drawing on Fichtner Engineering (2023) which includes a change to the baseline configuration, with a storage duration of 16 hours. Changes relative to the projections since 2023-24 include a small increase in current year costs, inclusion of a local learning component for consistency with other learning technologies and increases in installation costs that apply to all technologies. The installation cost increases have had the largest impact. While higher than 2023-24, the capital cost projections diverge by a similar amount according to their scenario with the greatest cost reductions projected to be stronger the greater the global climate policy ambition. 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 6. Figure 5 10 Projected capital costs for solar thermal with 14 hours storage compared to 2023-24 projections 5.3.9 Large-scale solar PV Large-scale solar PV costs have been revised downwards for 2024-25 based on Aurecon (2025) indicating solar PV production costs are recovering more rapidly than previously projected from global inflationary pressures. As a result of the ongoing 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 is 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. Cost outcomes across the three scenarios project a capital cost range of $640/kW to $890/kW. The final minimum cost level for solar PV is one of the most 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. Figure 5 11 Projected capital costs for large-scale solar PV by scenario compared to 2023-24 projections 5.3.10 Rooftop solar PV The current costs for rooftop solar PV systems are lower than was projected for 2024 in the 2023-24 GenCost report. The price aligns to a 7kW system, but it should be noted that rooftop solar PV is sold across a broad range of prices . 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. Overall, this revised treatment of installation costs is the main point of difference with the 2023-24 projections which assumed a common installation learning rate and no general increase in installation costs. Figure 5 12 Projected capital costs for rooftop solar PV by scenario compared to 2023-24 projections 5.3.11 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 Aurecon (2025) data indicates that the rate of increase is slowing. However, the most recent 6% increase in current costs also includes the assumption that new wind generation projects will need to include the cost work camps. Without this new assumption the year-on-year cost increase would have been only 2%. 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 (five years later than other technologies). As such, wind costs are higher for longer throughout that period. 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. The higher current costs, inclusion of increasing local installation costs and workcamp costs has meant that the 2024-25 projections are higher than all 2023-24 projections. Figure 5 13 Projected capital costs for onshore wind by scenario compared to 2023-24 projections 5.3.12 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 metres 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 5 14 presents projections for both fixed and floating compared to 2023-24. The current costs for both types of offshore wind are provided in Aurecon (2025). The updated current capital costs are lower than projected in 2023-24 for fixed offshore wind and higher than projected for floating offshore wind. Post 2024, 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, 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 5 14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2023-24 projections A key feature of the updated projections is a lower rate of cost reduction over time, particularly for fixed offshore wind, relative to the 2023-24 projections. This reflects lower resource availability for floating offshore wind and the impact of continued reductions in solar PV technology costs. Floating offshore wind is deployed more widely than fixed offshore wind and therefore results in proportionally higher cost reductions in the Global NZE scenarios. However, floating offshore wind has a low level of deployment in Current policies leading to a flat outlook for costs. 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. 5.3.13 Battery storage Current costs of battery storage fell faster than projected in 2023-24 and the updated projections allow for a diversity of outcomes ranging from a continuation of the current rate of cost reduction to a slow rate of reduction. The costs shown in Figure 5 15 are for a 2-hour duration battery (total battery cost including battery and balance of plant). Given the 2024 cost reduction takes batteries back to their pre-pandemic levels we do not impose any additional reduction beyond the learning projected by the modelling. Figure 5 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. Aurecon (2025) has included current costs for small-scale batteries, designed to be installed in homes. They are estimated at $13,500 for a 5kW/10kWh system or $1350/kWh, including installation. This is more than twice the cost of large-scale battery projects per kWh. 5.3.14 Pumped hydro energy storage Pumped hydro energy storage (PHES) is assumed to be a mature technology and receives the same increase in installation costs as other technologies which is the main driving for the increasing cost trend post 2030. Unlike the other technologies, all three scenarios assume costs return to normal by 2030 (rather than in 2027 for Current policies). This reflects the longer lead time for PHES projects which means it is unlikely that global inflationary trends will result in different cost trajectories before 2030. Site variability is also a great source of variation in PHES costs and is separately addressed by Aureon (2025) and AEMO external to GenCost Figure 5 16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario The cost trajectory shown in Figure 5 16 is for a 24-hour duration storage design. Costs for 10-hour and 48-hour durations are also included in this report (Appendix B). 5.3.15 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 2024 with the exception to fuel cells. Reflecting the infrequency with which these technologies are built, the increases for some technologies mostly represent theoretical increases in costs if they had been built based on the general increase in infrastructure building costs. The downward trend to either 2027 or 2030 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 5 17 Projected technology capital costs under the Current policies scenario compared to 2023-24 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 2027 reflect co-learning from other CCS technologies which are deployed in electricity generation and in other sectors. There is also no significant deployment of fuel cells, tidal or wave technology reflecting the lack of climate policy ambition. The major difference with the 2023-24 projections is that fuel cells were deployed in those projections. The continued cost increases in fuel cells together with cost decreases in other technologies such as solar PV and batteries is responsible for this change. Global NZE by 2050 Biomass with CCS is adopted in the Global NZE by 2050 scenario but can only achieve learning in the CCS component of the plant. Cost reductions reflect learning from its own deployment and co-learning from deployment of CCS in other electricity generation, hydrogen production and other industry sectors. Biomass with CCS is an important technology in some global climate abatement scenarios if the electricity sector is required to produce negative abatement for other sectors. However, we are not able to model that scenario with GALLME. GALLME only models the electricity sector and from that perspective alone, biomass with CCS is a relatively high-cost technology. Wave energy is deployed at a minor level in the 2050s and tidal/ocean current in the late 2040s. Fuel cells are not deployed. The higher costs in most cases relative to 2023-24 are the result of higher installation costs for some technologies and a lack of deployment in favour of more mature technologies such as solar PV and wind. Figure 5 18 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2023-24 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. Again, the majority of cost reductions reflect co-learning from deployment of other types of CCS generation or use of CCS in other applications. Both scenarios have significant deployment of steam methane reforming with CCS which brings down the cost of all CCS technologies sooner compared to Current policies. Fuel cells are deployed in the 2040s but wave energy and tidal/ocean current not deployed. Higher costs relative to 2023-24 reflect lack of deployment due to the increasing gap between costs of these technologies and more mature renewables. Figure 5 19 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2023-24 projections 5.3.16 Hydrogen electrolysers Hydrogen electrolyser costs have decreased in 2024 for proton-exchange membrane (PEM) electrolysers but increased for alkaline electrolysers based on Aurecon (2025). Alkaline electrolysers remain lower cost than PEM electrolysers but their costs are now much 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. In 2023-24 and other previous GenCost reports we assumed that PEM and Alkaline cost would converge over time. However, the updated projections provide slightly more separated cost paths for the two technologies based on their differences in balance of plant. Updated analysis of balance of plant costs has also assisted in providing a more divergent cost range which better reflects future uncertainty. Previous projections also made allowances for economies of scale to recognise the huge scale of electrolyser projects that were being proposed. However, while larger projects have been deployed there has been no significant decrease in costs associated with them. Consequently, this element of the projection approach has been removed. Electrolyser deployment is being supported by a substantial number of hydrogen supply and end-use subsidised deployments globally and in Australia. Experience with other emerging technologies indicates that this type of globally coincident technology deployment activity can lead to a scale-up in technology manufacturing capacity which supports cost reductions. 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. By 2055 the projected cost range for PEM electrolysers is $746/kW to $1601/kW. The range of alkaline electrolysers is $710/kW to $1525/kW. Figure 5 20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2023-24 6 Levelised cost of electricity analysis 6.1 Purpose and limitations of LCOE Levelised cost of electricity (LCOE) data is an electricity generation technology comparison metric. It is the total unit costs a generator must recover to meet all its costs including a return on investment . Modelling studies such as AEMO’s Integrated System Plan (AEMO, 2024) do not require or use LCOE data . LCOE is a simple screening tool for quickly determining the relative competitiveness of electricity generation technologies. It is not a substitute for detailed project cashflow analysis or electricity system modelling which both provide more realistic representations of electricity generation project operational costs and performance. Furthermore, in the GenCost 2018 report and a supplementary report on methods for calculating the additional costs of renewables (Graham, 2018), we described several issues and concerns in calculating and interpreting levelised cost of electricity. These include: • The standard LCOE method does not take into account the additional costs associated with each technology and in particular the significant integration costs of variable renewable electricity generation technologies. • The standard LCOE applies the same discount rate across all technologies even though fossil fuel technologies face a greater risk of being impacted by the introduction of current or new state or commonwealth climate change policies. • The standard LCOE does not recognise that electricity generation technologies have different roles in the system. Some technologies are operated less frequently, increasing their LCOE, but are valued for their ability to quickly make their capacity available at peak times. In Graham (2018), after reviewing several alternatives from the global literature, a new method for addressing the first dot point was proposed that calculated and added integration costs unique to variable renewables. That new method was implemented in the 2020-21 GenCost report and updated results from that method are included in this report. For an overview of the method see GenCost 2020-21 Section 5.1. To address the issues not associated with additional cost of renewables, we: • Separate and group together peaking technologies, flexible technologies and variable technologies • Included, up until the 2022-23 GenCost report, additional LCOE calculations for baseload fossil fuel technologies which added a climate policy risk premium of 5% based on Jacobs (2017). This information has been discontinued because the estimated risk premium is now considered inadequate to capture climate policy risk in a meaningful way. 6.2 LCOE estimates 6.2.1 Framework for calculating variable renewable integration costs LCOE is typically used to compare the cost of one or more standalone projects on a common basis for a particular year (assuming they can all be built overnight, even if they have construction times varying from one to several years ). Technically, all electricity generation projects need other generation capacity to provide reliable electricity, even those that are dispatchable. Besides their inherent dispatchability, a key reason why the integration costs for dispatchable technologies are low is because they can rely on the flexibility of existing generation capacity to fill in at times when they are not generating or to add to generation during peak periods when they may already be at full production. The main difference with variable renewables is that existing capacity may not be enough to ensure reliable supply as the share of variable renewables grows. It may be enough when variable renewables are in the minority share of generation. However, it is not enough when they are in the majority because, to achieve their majority, significant existing flexible generation must be retired to make way for variable renewable generation. To calculate the integration cost of variable renewables, we therefore start by allowing them free access to any existing flexible capacity (that has not retired). Next, we need to add the cost of any extra capacity the project needs to deliver reliable electricity. Prior to the 2023-24 GenCost report, the focus was on calculating the integration costs for 2030 and the calculation allowed renewable projects to use any capacity that was expected to be built by that time at no cost. While this approach is strictly correct for answering the question of what integration costs are relevant for someone investing in a project in 2030, feedback from stakeholders indicated an appetite to consider the investor’s perspective at an earlier point in time when the electricity system is less developed. Consequently, this report includes integration costs for renewables in 2024 in addition to 2030 (the 2023-24 report showed 2023). Another concern of stakeholders is that the integration costs should include specific projects such as Snowy 2.0 and various committed or under construction transmission projects so that the community can understand how they are impacting the cost of electricity from variable renewables. Prior to 2030, there are many projects that are already committed by regulatory processes and government sponsored investments. After 2030, the investment landscape is less constrained. In 2024, there are only negligible amounts of home battery systems and electric vehicles. Consequently, the high voltage system can only use storage that it builds for itself in 2024. The purpose of GenCost is to provide key input data, primarily capital costs, to the electricity modelling community so that they can investigate complex questions about the electricity sector up to the year 2055. LCOE data can only answer a narrow range of questions. It is provided for the purpose of giving stakeholders who may not have access to modelling resources an indication of the relative cost of different technologies on a common basis. To avoid any confusion, Table 6 1 defines the question that is answered by the 2024 and 2030 LCOE data. Note that LCOE data for 2040 and 2050 is also provided, but without renewable integration costs. This reduces the computational burden for the GenCost project and recognises that, by the 2040s, if renewables are taken up, then most renewable integration resources will already be in place. If the LCOE does not answer a stakeholder’s question, then they may need to commission their own modelling study. Making data available that can be used in modelling studies is the primary goal of GenCost. Table 6 1 Questions the LCOE data are designed to answer LCOE data Question answered 2024 variable renewables LCOE with integration costs Assuming any existing capacity available in 2024 is free but insufficient to provide reliable supply, what is the total unit cost an investor must recover to deliver a project that provides reliable electricity supply in 2024 from a combination of variable renewable generation, transmission, storage and other resources, including the cost of currently committed or under construction projects? 2024 LCOE of all other generation technologies Assuming any existing capacity available in 2024 is free and sufficient to support reliable integration, what is the total unit cost an investor must recover to deliver a project that provides electricity supply in 2024? 2030 variable renewables LCOE with integration costs Assuming any existing capacity available in 2030 is free but insufficient to provide reliable supply, what is the total unit cost an investor must recover to deliver a project that provides reliable electricity supply in 2030 from a combination of variable renewable generation, transmission, storage and other resources? 2030 LCOE of all other generation technologies Assuming any existing capacity available in 2030 is free and sufficient to support reliable integration, what is the total unit cost an investor must recover to deliver a project that provides electricity supply in 2030? 6.2.2 Key assumptions We calculate the integration costs of renewables in 2024 and 2030 imposing large-scale variable renewable energy (VRE) shares of 60% to 90% which will require additional capacity over and above that already existing in the electricity system to ensure reliable supply. An electricity system model is applied to determine the optimal investment to support each VRE share. In practice, although wave, tidal/current and offshore wind are available as variable renewable technologies, onshore wind and large-scale solar PV are the only variable renewables deployed in the modelling due to their cost competitiveness . Victorian legislation creates a mandate for offshore wind generation, but this does not come into place until after 2030 and so is outside the scope of our analysis. The VRE share does not include rooftop solar PV. The impact of rooftop solar PV is accounted for, however, in the demand load shape as is the impact of other customer energy resources. Virtual Power Plants (VPPs) and electric vehicles are negligible in 2024. However, in 2030, a portion of customer-owned battery resources are assumed to be available to support the wholesale generation sector consistent with the approach taken in the AEMO ISP (AEMO, 2024). The standard LCOE formula requires an assumption of a capacity factor. Our approach in this report is to provide a high and low assumption for the capacity factor (which we report in Appendix B) in order to create an LCOE range . Stakeholders have previously indicated they prefer a range rather than a single estimate of LCOE. However, it is important to note that these capacity factors are not used at all in the modelling of renewable integration costs. When modelling renewable integration costs, we use the variable renewable energy production traces published by AEMO for its Integrated System Plan (ISP). We incorporate the uncertainty in variable renewable production by modelling nine different weather years, 2011 to 2019, and the results represent the highest cost outcome from these alternate weather years. The model covers the NEM, the South West Interconnected System (SWIS) in Western Australia (WA) and the remainder of WA. Northern Territory (NT) is not included in the results as it represents an outlier given its isolation and small size. 2024 represents the current electricity system. In 2030, we project forward including all existing state renewable energy targets resulting in a 54% renewable share and 47% variable renewable share in Australia ex-NT (both excluding rooftop PV). The share fluctuates a few percent depending on the nine weather years. The counterfactual VRE share reflects the impact of existing state renewable targets, planned state retirements of coal capacity in the case of WA and an already existing high VRE share in South Australia. In both 2024 and 2030, New South Wales, Queensland, Victoria and the SWIS are the main jurisdictions that are impacted by imposing the 60% to 90% VRE shares given that Tasmania and South Australia are already dominated by renewables such that the business as usual (BAU) already includes much of the necessary capacity to support high VRE shares. The NEM is an interconnected system, so we are also interested in how those states support each other and the overall costs for the NEM. The VRE share is applied in each state at the same time, but individual states can exceed the share if it is economic to do so. As we implement higher variable renewable energy shares, we must forcibly retire coal plant (only as a modelling assumption) as meeting the variable renewable share and the minimum load requirements on coal plant would otherwise eventually become infeasible . Snowy 2.0 ($12 billion) and battery of the nation ($1.7 billion) pumped hydro projects are assumed to be committed with construction complete before 2030 in the BAU, as well as various transmission expansion projects already flagged by the ISP process to be necessary before 2030 (Table 6 2). The NSW target for an additional 2 GW of at least 8 hours duration storage is also assumed to be committed and complete by 2030 together with the Kurri Kurri gas peaking plant . For the 2024 calculations, we abstract from reality and assume these projects can be completed immediately so that the cost of these committed projects is included in the current cost of integrating variable renewables . These costs are included regardless of the VRE share. Pumped hydro, battery and peaking plant costs are sourced either directly from the project source or estimated via GenCost capital costs. Transmission costs are from AEMO (2023b). For the 2030 investor, all of these projects are considered free capacity in the same way that existing capacity now is free for the 2024 analysis. This approach is consistent with the aim of the LCOE analysis (Table 6 1). Table 6 2 Committed investments by category included in the 2024 cost of integrating variable renewables Category $billion Transmission 15.9 Storage 19.2 Peaking gas 1.0 For 2024, the initial generation capacity is as it is today. For 2030, the capacity needs to be increased from today due to growing demand. In the nine weather year counterfactuals, the model does not choose to build any new fossil fuel-based generation capacity by 2030 (Figure 6 1). Pumped hydro storage is also the same. The main investment response to demand growth and the different weather years is to vary wind capacity by up to 2.6GW, solar PV capacity by 3.0GW and large-scale batteries (VPP capacity is fixed) by 0.7 GW. The capacities shown have been compared with the AEMO ISP 2030 capacity projections (AEMO, 2024). The NEM coal retirements to 2030 are slower than Step Change (2024 release) and the overall demand and renewable generation is lower. Reflecting the change in relative fortunes for solar PV, solar PV capacity is preferred over wind by 2030 but only slightly. Wind, was preferred in the ISP . The NEM and WA total variable renewable shares are 49% and 38% on average across the weather years. Figure 6 1 Range of generation and storage capacity deployed in 2030 across the 9 weather year counterfactuals in NEM plus Western Australia The costs of VRE share scenarios were compared against the same counterfactual weather year to determine the additional integration costs of achieving higher VRE shares. We use the maximum cost across all weather years as the resulting integration cost on the basis that the maximum cost represents a system that has been planned to be reliable across the worst outcomes from weather variation. The results, shown in Figure 6 2, include storage, transmission, spillage and synchronous condenser costs where applicable. The integration costs are flat with increasing variable renewable share in the 2024 results. This is because the cost of the committed storage and transmission infrastructure can be spread over more of the additional renewable generation the greater the required variable renewable share. It is appreciated that this result is somewhat counterintuitive as we normally understand that VRE integration costs increase with the VRE share. However, the result is valid and what can be learned from this result is that planned transmission and storage capacity is being built with higher electricity demand and subsequently higher volumes of variable renewable generation in mind. As the system reaches those higher VRE generation levels, the normal relationship between VRE share and costs (the higher the share the higher the costs) should resume. Across the different VRE shares, the cost of variable renewable generation in 2024 is $149/MWh on average in the NEM. This is 57% higher than average costs in 2030 for 60% VRE, but only 36% higher than average costs for 2030 for 90% VRE. Around a third of the higher costs are due to investors having to pay 2024 instead of 2030 technology costs (technology costs are falling over time). The remainder is due to the cost of the pre-2030 committed projects which must be paid for in the 2024 analysis, but are considered free existing capacity for investors in 2030 (in the same way that anything built pre-2024 is free existing capacity for 2024 investors). The use of 2024 technology costs in 2024, as well as applying committed project costs to lower VRE generation than these projects were intended to support, means these results represent the highest cost for achieving these VRE shares. In reality, the transition to these VRE shares would occur over several years at higher volumes and there would be access to lower costs as technologies improve over time (see the projections in Section 5). Figure 6 2 Levelised costs of achieving 60%, 70%, 80% and 90% annual variable renewable energy shares in the NEM in 2024 and 2030 Variable renewable integration costs in 2024 are dominated by storage and transmission. Synchronous condenser costs are relatively minor reflecting that gas generation capacity remains high relative to 2024 demand and can mostly fulfill this role alongside other existing synchronous generation such as hydro (but less so coal which needs to increasingly be retired because coal’s minimum run requirements make it incompatible with higher VRE shares). In 2030, with higher generation, synchronous condensers can play a larger role and expenditure is more significant. Storage is less significant by 2030 reflecting the value of investments made pre-2030 in the NEM. In the 2030 results, the shares of the supporting technologies shifts with the VRE share but not necessarily in a predictable fashion. This reflects that at each VRE share, a different combination of resources are needed. For example, other (non-REZ) transmission is less important at low VRE shares but becomes increasingly important at higher shares. Storage can shift variable renewable generation to a different time period. Transmission supports access to a greater diversity of variable renewable generation by accessing resources in other regions which can help smooth supply, reducing the need for storage. Spillage is a side-effect of over building VRE capacity to increase its minimum production levels . Given the low cost of VRE capacity, deploying VRE capacity to a level where energy is spilled is a valid alternative to expenditure on storage and transmission. As transmission, storage and VRE capacity costs are updated, their share of integration costs will change as they are partially in competition with each other. REZ expansion costs are required at similar levels for each additional 10% increase in VRE share in each state and across years. New South Wales and Victoria tend to attract the most transmission expenditure reflecting their central location in the NEM and access to pumped hydro storage. Variable renewable integration costs are similar in WA but with a heavy reliance on storage and more spilled energy reflecting the limited ability to connect, via transmission, to more varied sources of renewable energy. Costs in different states or regions are averaged out at the aggregate level (NEM + WA) in calculating integration costs for comparison with other technologies (and so will differ from Figure 6 2 which is only the NEM result). The cost of REZ transmission expansions adds an average $10.3/MWh in 2024 and $10.2/MWh in 2030, as the VRE share increases from 60% to 90%. Other transmission costs add $12.6/MWh in 2024 and $4.2/MWh in 2030. Storage costs add an average $20.5/MWh in 2024 and $12.2/MWh in 2030. Spillage costs peak at the 90% VRE share at $14.0/MWh in 2024 and $9.7/MWh in 2030. 6.2.3 Variable renewables with and without integration costs The results for the additional costs of increasing variable renewable shares are used to update and extend our LCOE comparison figures. We expand the results for 2024 and 2030 to include a combined wind and solar PV category for different VRE shares. Integration costs to support renewables are estimated at $48/MWh to $64/MWh in 2024 and $23/MWh to $40/MWh in 2030 depending on the VRE share (Figure 6 3 and Figure 6 4). Onshore wind and solar PV without integration costs such as transmission and storage are the lowest cost generation technologies by a significant margin. These can only be added to the system in a minority share before integration costs become significant and must be added. Offshore wind is higher cost than onshore wind but competitive with other alternative low emission generation technologies. Its higher capacity factor could result in lower storage costs and it tends to have a higher potential contribution during peak demand times. Integration costs have only been calculated for onshore wind in this report given it remains the lowest cost form of wind generation. The LCOE cost range for variable renewables (solar PV and wind) with integration costs is the lowest of all new-build technologies in 2030 and at a similar range with black coal in 2024. The lower end of the cost range of gas generation is also competitive. To achieve the lower end of the range for coal and gas a high capacity factor must be achieved and low cost fuel sourced. Deploying coal and gas for delivery of the majority of Australia’s electricity supply is not consistent with Australia’s national and state climate policies. If we exclude these high emission generation options, the next most competitive generation technologies are solar thermal, gas with carbon capture and storage (CCS) and large-scale nuclear. 6.2.4 Peaking technologies The peaking technology category includes two sizes for gas turbines, a gas reciprocating engine and a hydrogen reciprocating engine. Fuel comprises the majority of costs, but the lower capital costs of the larger gas turbine make it the most competitive. Reciprocating engines have higher efficiency and consequently, for applications with relatively higher capacity factors and where a smaller unit size is required, they can be the lower cost choice. All of the gas technologies include the ability to run on a mix of hydrogen and natural gas, but the costs shown are calculated for 100% natural gas. Hydrogen peaking plant are higher cost at present and include the cost of 100% hydrogen fuel. However, their capital and fuel costs are expected to fall over time. This technology has zero direct greenhouse gas emissions, but may involve some upstream emissions, depending on the hydrogen production process. Figure 6 3 Calculated LCOE by technology and category for 2024 6.2.5 Flexible technologies Large-scale nuclear, nuclear SMR, solar thermal, black coal, brown coal and gas-based generation technologies fall into the category of technologies that are designed to deliver energy for the majority of the year (specifically 53% to 89% in the capacity factor assumptions for most technologies and 57% to 71% for solar thermal based on ITP Thermal (2024) with this exception made because higher capacity factors to do not improve costs any further for this technology). This technology category is the next most competitive technology group after variable renewables (with or without integration costs). The reduction in fossil fuel generation costs between 2024 and 2030, is not a result of technological improvement. It represents a reduction in fuel prices and capital costs which were impacted by global inflationary pressures that peaked in 2022. Of the fossil fuel technologies, it is difficult to say which is more competitive as it depends on the price outcome achieved in contracts for long-term fuel supply. Also, using fossil fuels without carbon capture and storage makes them high emission technologies which makes them incompatible with national and state emission targets. Figure 6 4 Calculated LCOE by technology and category for 2030 Low emission flexible technologies are more viable under current climate change policies. In this category, solar thermal is the most competitive technology. 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 the analysis for solar PV and wind, additional transmission costs could add around $14/MWh. Gas with CCS is the next most competitive after solar thermal by 2030. Large-scale nuclear is only 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 in this category, but their cost range becomes more competitive over time. 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 factor 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%. Figure 6 5 Calculated LCOE by technology and category for 2040 Figure 6 6 Calculated LCOE by technology and category for 2050 6.3 Storage requirements underpinning variable renewable costs In both formal and informal feedback, a common concern is whether GenCost LCOE calculations have accounted for enough storage or other back-up generation capacity to deliver a steady supply from variable renewables. Ensuring all costs are accounted for is important when comparing costs with other low emission technologies such as nuclear which are capable of providing steady supply. Intuitively, high variable renewable systems will need other capacity to supply electricity for extended periods when variable renewable production is low. This observation might lead some to conclude that the system will need to build the equivalent capacity of long-duration storage or other flexible and peaking plant (in addition to the original variable renewable capacity). However, such a conclusion would substantially overestimate storage capacity requirements. Variable renewables have a low capacity factor, which means their actual generation over the year expressed as a percentage of their potential generation as defined by their rated capacity, is low (e.g., 20% to 40%). The average capacity factor of coal dominated electricity supply in Australia is around 60%. As a result, to deliver the equivalent energy of current coal-fired generation, the system needs to install around two times the capacity of variable renewables. If the system were to also build the equivalent capacity of storage, peaking and other flexible plant then the system now has around four times the capacity needed compared to a coal dominated system. For a number of reasons, this scale of capacity development is not necessary to replace coal. The most important factor is that while we are changing the generation source, maximum demand has not changed. Maximum demand is the maximum load that the system has to meet in a given year. Maximum demand typically occurs during heat waves in warmer climates (which is most of Australia) and in winter during extended cold periods in cooler climates (e.g., Tasmania). The combined capacity of storage, peaking and other flexible generation only needs to be sufficient to meet maximum demand. In a high variable renewable system, maximum demand will be significantly lower than the capacity of variable renewables installed. Instead of installing storage on a kW for kW basis, to ensure maximum demand is met, we only need to install a fraction of a kW of storage for each kW of variable renewables. The exact ratio depends on two other factors as well. First, we are very rarely building a completely new electricity system (except in greenfield off-grid areas). Existing electricity systems have existing peaking and flexible generation. This reduces the amount of new capacity that needs to be built. This is true for coal generation or any other new capacity as it is for variable renewable generation. All new capacity relies on being supported by existing generation capacity to meet demand. Second, as the variable renewable generation share increases, summer or winter peaking events may not represent the most critical day for back-up generation. For example, during a summer peaking event day, solar PV generation will have been high earlier in the day and consequently storages are relatively full and available to deliver into the evening peak period. A more challenging period for variable renewable systems might be on a lower demand day when cloud cover is high and wind speed is low. These days with low renewable generation and low charge to storages could see the greatest demands on storage, peaking and other flexible capacity. As such, it may be that the demand level on these low renewable generation days is a more important benchmark in setting the amount of additional back-up capacity required. Figure 6 7 2030 NEM maximum demand, demand at lowest renewable generation and generation capacity under 90% variable renewable generation share The modelling approach applied accounts for all of these factors across nine historical weather years. The result is that, in 2030, the NEM needs to have 0.3kW to 0.4kW storage capacity for each kW of variable renewable generation installed . Showing the most extreme case of 90% variable renewable share for the NEM, Figure 6 7 shows maximum annual demand, demand when renewable generation is lowest, storage capacity, peaking capacity, other flexible capacity and total variable renewable generation capacity. The data shows that: • Demand at the point of lowest renewable generation is substantially lower than maximum demand and can mostly be met by non-storage technologies (although in this example renewable generation is not zero and can still contribute). • Existing and new flexible capacity is very similar to maximum demand and can meet demand mostly without renewable generation being available. However, there is often a small amount of variable renewable generation available at peak demand events somewhere in the NEM (typically wind generation if the peak occurs outside of daylight hours such as in the evening or early morning). • Flexible capacity exceeds demand at minimum renewable generation. • The required existing and new flexible capacity to support variable renewables is a fraction of total variable renewable capacity. Appendix A Global and local learning model A.1 GALLM The Global and Local Learning Models (GALLMs) for electricity (GALLME) and transport (GALLMT) are described briefly here. More detail can be found in several 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. Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation However, technologies that do not have a standard unit size and can be used in a variety of applications tend to have a higher learning rate for longer (Wilson, 2012). This is the case for solar photovoltaics, batteries and historically for gas turbines. Technologies are made up of components and different components can be at different levels of maturity and thus have different learning rates. Different parts of a technology can be developed and sold in different markets (global vs. regional/local) which can impact 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 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 Aurecon (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. Appendix B Data tables The following tables provide data behind the figures presented in this document. The year 2024 is mostly sourced from Aurecon (2025) and is aligned to July which represents either the middle of that calendar year or the beginning of the 2024-25 financial year. As discussed in Section 3, 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 3 includes suggested FOAK premiums. Furthermore, capital costs are for a location not greater than 200km from the Victorian metropolitan area. Aurecon 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. 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 2024 6037 12263 9321 2455 2426 1310 5802 1980 2071 8916 24366 1463 1336 6769 3351 4710 8362 15547 29667 12979 8067 8984 2025 5757 11696 8804 2455 2426 1305 5802 1980 2071 8641 23188 1344 1314 6692 3221 4697 8351 13956 28183 11234 7710 8858 2026 5489 11146 8311 2455 2426 1301 5802 1980 2071 8372 22060 1234 1293 6621 3094 4685 8339 12482 26749 9656 7371 8738 2027 5319 10790 7998 2388 2327 1296 5679 1975 2074 8202 21340 1155 1273 6564 2972 4673 8326 11560 25388 8688 7075 8667 2028 5251 10632 7869 2253 2127 1292 5433 1966 2081 8136 21048 1144 1255 6526 2859 4664 8314 11136 24539 8249 7096 8656 2029 5274 10660 7904 2050 1828 1287 5064 1952 2092 8166 21136 1134 1239 6500 2750 4658 8304 11150 23998 8256 7104 8694 2030 5299 10696 7942 1917 1630 1283 4822 1945 2102 8198 21237 1123 1227 6480 2646 4654 8299 11166 23925 8264 7115 8736 2031 5323 10733 7980 1854 1534 1278 4706 1945 2110 8230 21338 1113 1216 6465 2546 4656 8299 11181 23850 8272 7125 8778 2032 5343 10766 8014 1859 1538 1274 4717 1952 2118 8260 21429 1103 1208 6457 2449 4659 8302 11195 23952 8279 7154 8815 2033 5361 10799 8047 1865 1543 1269 4728 1959 2125 8288 21515 1093 1203 6453 2356 4663 8305 11207 24048 8285 7183 8851 2034 5380 10831 8079 1870 1548 1265 4738 1965 2132 8316 21600 1083 1199 6455 2266 4667 8285 11220 24145 8291 7215 8886 2035 5399 10863 8111 1876 1552 1260 4749 1972 2140 8345 21686 1073 1195 6454 2206 4670 8255 11232 24241 8296 7246 8922 2036 5419 10896 8144 1882 1557 1256 4760 1979 2147 8373 21773 1063 1190 6458 2175 4674 8215 11245 24339 8302 7279 8958 2037 5440 10930 8177 1887 1562 1252 4771 1986 2154 8402 21861 1053 1186 6460 2171 4678 8186 11257 24438 8307 7311 8994 2038 5461 10963 8210 1893 1567 1247 4782 1993 2162 8432 21950 1043 1182 6463 2168 4682 8157 11270 24170 8313 7344 9031 2039 5482 10995 8244 1899 1572 1243 4791 2000 2170 8462 22037 1034 1178 6466 2166 4686 8127 11283 22245 8319 7377 9068 2040 5500 11020 8275 1904 1576 1239 4796 2006 2176 8488 22114 1024 1175 6470 2163 4690 8108 11295 19646 8324 7406 9102 2041 5517 11040 8302 1909 1579 1234 4798 2011 2182 8511 22181 1015 1172 6473 2160 4694 8099 11305 17398 8330 7428 9132 2042 5533 11050 8325 1912 1582 1230 4793 2016 2187 8530 22233 1005 1169 6406 2157 4697 8102 11315 16791 8336 7446 9158 2043 5547 11050 8349 1916 1585 1226 4779 2020 2192 8549 22276 996 1166 6286 2152 4701 8105 11324 16838 8341 7460 9184 2044 5561 11043 8373 1919 1588 1221 4758 2025 2197 8568 22312 987 1164 6125 2147 4704 8108 11334 16886 8347 7477 9210 2045 5573 11038 8397 1923 1591 1217 4738 2029 2202 8588 22350 978 1161 6016 2143 4708 8111 11343 16934 8353 7494 9236 2046 5586 11039 8421 1926 1594 1213 4724 2034 2207 8607 22394 969 1159 5947 2139 4711 8114 11353 16983 8359 7499 9263 2047 5599 11036 8445 1930 1597 1208 4707 2039 2212 8627 22435 960 1157 5903 2136 4714 8117 11362 17032 8364 7482 9289 2048 5611 11031 8469 1934 1600 1204 4687 2043 2217 8647 22473 951 1149 5865 2131 4718 8119 11372 17081 8370 7436 9316 2049 5623 11024 8494 1937 1603 1200 4665 2048 2222 8667 22510 942 1135 5824 2119 4721 8122 11381 17130 8376 7373 9343 2050 5635 11026 8517 1940 1606 1196 4653 2052 2227 8685 22554 934 1122 5799 2108 4724 8125 11390 17176 8382 7332 9368 2051 5647 11030 8538 1943 1608 1196 4645 2056 2231 8701 22597 925 1109 5789 2088 4727 8127 11399 17219 8388 7297 9391 2052 5659 11040 8558 1946 1610 1187 4642 2060 2235 8716 22644 917 1103 5793 2078 4731 8129 11407 17259 8394 7296 9413 2053 5670 11045 8578 1948 1612 1187 4636 2063 2239 8731 22687 908 1095 5732 2058 4733 8131 11415 17299 8400 7281 9435 2054 5681 11056 8598 1951 1614 1179 4635 2067 2243 8746 22736 900 1093 5670 2051 4736 8133 11423 17340 8406 7286 9457 2055 5686 11059 8608 1952 1615 1179 4632 2069 2245 8754 22758 891 1092 5606 2043 4738 8134 11427 17360 8409 7282 9468 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 2024 6037 12263 9321 2455 2426 1310 5802 1980 2071 8916 24366 1463 1336 6769 3351 4710 8362 15547 29667 12979 8067 8984 2025 5900 11988 9064 2455 2426 1305 5802 1980 2071 8787 23891 1371 1307 6633 3211 4395 7655 14712 28183 12044 7626 8929 2026 5768 11718 8819 2455 2426 1301 5802 1980 2071 8661 23431 1285 1278 6505 3075 4095 7008 13917 26749 11164 7212 8881 2027 5652 11462 8598 2417 2369 1296 5732 1980 2077 8548 22909 1207 1250 6391 2946 3818 6416 13179 25388 10362 6796 8851 2028 5558 11232 8413 2341 2253 1292 5591 1982 2088 8458 22399 1137 1223 6294 2829 3565 5874 12499 24014 9634 6405 8854 2029 5477 11019 8250 2227 2080 1287 5381 1983 2105 8381 21902 1074 1196 6206 2719 3333 5377 11861 22632 8962 6037 8876 2030 5441 10901 8169 2113 1906 1283 5170 1985 2122 8351 21563 1027 1172 6152 2616 3180 4923 11458 20700 8538 5753 8919 2031 5443 10869 8157 1999 1733 1278 4959 1986 2140 8360 21559 989 1146 6125 2520 3101 4507 11275 19298 8341 5806 8972 2032 5481 10920 8214 1926 1620 1274 4824 1991 2155 8407 21711 932 1128 6131 2431 3091 4126 11299 18397 8356 5851 9035 2033 5517 10969 8268 1893 1567 1269 4763 1998 2168 8451 21852 878 1115 6140 2353 3080 3777 11323 18517 8370 5893 9094 2034 5553 11018 8322 1902 1573 1265 4777 2009 2180 8468 21994 828 1102 5990 2289 3070 3458 11347 18638 8384 5935 9154 2035 5589 11015 8377 1910 1580 1260 4740 2019 2191 8447 22087 780 1088 5787 2276 3063 3166 11372 17848 8398 5978 9214 2036 5626 11006 8432 1919 1588 1256 4697 2030 2203 8427 22174 761 1075 5568 2217 3060 2898 11397 17055 8413 6021 9275 2037 5664 10992 8489 1927 1595 1252 4649 2041 2215 8436 22258 742 1062 5468 2179 3060 2895 11422 16257 8428 6065 9337 2038 5702 11028 8546 1936 1602 1247 4648 2053 2227 8482 22392 728 1050 5392 2141 3062 2893 11448 16366 8443 6110 9400 2039 5741 11028 8604 1945 1610 1243 4613 2064 2239 8529 22492 719 1037 5305 2129 3064 2891 11474 16477 8458 6156 9463 2040 5777 11028 8658 1953 1616 1239 4580 2074 2251 8573 22587 713 1025 5244 2123 3067 2891 11499 16582 8473 6199 9524 2041 5812 11026 8710 1961 1623 1234 4547 2084 2261 8613 22677 705 1013 5194 2115 3071 2891 11523 16680 8489 6239 9580 2042 5844 11059 8758 1968 1628 1230 4552 2093 2271 8650 22797 698 1001 5152 2108 3076 2892 11546 16773 8504 6277 9633 2043 5876 11096 8807 1974 1634 1226 4558 2102 2281 8687 22921 690 989 5117 2103 3080 2892 11569 16866 8520 6315 9687 2044 5909 11133 8856 1981 1639 1221 4566 2111 2291 8699 23047 683 977 5088 2100 3085 2893 11593 16960 8536 6353 9741 2045 5942 11172 8905 1988 1645 1217 4575 2120 2301 8712 23176 677 965 5064 2097 3089 2893 11616 17055 8552 6392 9795 2046 5975 11212 8955 1995 1651 1213 4584 2130 2311 8724 23306 671 954 5044 2095 3094 2894 11640 17150 8122 6432 9850 2047 6009 11253 9006 2002 1656 1208 4593 2139 2321 8763 23438 665 942 5027 2091 3098 2894 11664 17247 7692 6471 9906 2048 6043 11293 9057 2009 1662 1204 4602 2148 2331 8801 23570 659 931 5010 2084 3103 2895 11688 17345 7260 6493 9962 2049 6078 11334 9108 2016 1668 1200 4612 2158 2341 8840 23705 652 920 4986 2072 3107 2895 11581 17444 7125 6491 10019 2050 6111 11374 9159 2023 1674 1196 4621 2167 2351 8878 23835 647 909 4972 2065 3112 2896 11473 17540 6989 6488 10074 2051 6144 11411 9208 2029 1679 1196 4629 2176 2361 8893 23963 643 898 4951 2058 3117 2897 11305 17634 6853 6501 10128 2052 6176 11447 9255 2035 1684 1187 4636 2185 2370 8906 24087 643 887 4945 2057 3123 2898 11267 17725 6866 6538 10180 2053 6208 11480 9303 2041 1689 1187 4640 2193 2380 8919 24209 640 877 4923 2052 3128 2899 11194 17817 6880 6575 10233 2054 6240 11513 9352 2048 1694 1179 4645 2202 2389 8954 24332 640 866 4918 2052 3134 2901 11181 17910 6894 6612 10287 2055 6256 11529 9376 2051 1697 1179 4646 2206 2394 8972 24393 639 856 4907 2050 3137 2901 11157 17956 6900 6631 10313 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 2024 6037 12263 9321 2455 2426 1310 5802 1980 2071 8916 24366 1463 1336 6769 3351 4710 8362 15547 29667 12979 8067 8984 2025 5896 11984 9060 2455 2426 1305 5802 1980 2071 8784 23770 1373 1314 6648 3208 4699 7894 14709 28183 12041 7622 8924 2026 5761 11709 8807 2455 2426 1301 5802 1980 2071 8654 23196 1288 1292 6534 3069 4688 7442 13909 26749 11157 7203 8869 2027 5634 11442 8571 2416 2368 1296 5730 1979 2075 8531 22659 1210 1271 6431 2936 4679 7017 13162 25388 10348 6784 8823 2028 5525 11194 8364 2338 2250 1292 5587 1977 2083 8423 22194 1140 1250 6341 2814 4676 6618 12469 23983 9611 6390 8801 2029 5425 10959 8172 2220 2074 1287 5372 1973 2094 8326 21768 1075 1229 6258 2700 4675 6243 11817 22550 8929 6019 8792 2030 5372 10822 8065 2102 1897 1283 5157 1970 2106 8276 21537 1030 1208 6191 2592 4679 6004 11403 20559 8497 5736 8806 2031 5360 10774 8034 1985 1721 1278 4942 1966 2118 8269 21480 1005 1190 6135 2492 4686 5890 11209 18438 8293 5762 8836 2032 5388 10812 8074 1908 1605 1274 4802 1966 2128 8303 21589 998 1177 6108 2400 4694 5896 11225 16799 8302 5787 8881 2033 5413 10850 8113 1873 1550 1269 4739 1970 2138 8336 21693 985 1165 6094 2320 4702 5902 11240 16175 8309 5812 8924 2034 5440 10888 8153 1880 1556 1265 4750 1978 2147 8369 21798 955 1156 6100 2255 4709 5867 11256 16228 8317 5830 8968 2035 5467 10920 8193 1887 1561 1260 4756 1987 2156 8404 21899 926 1145 6103 2239 4716 5832 11272 16228 8325 5829 9012 2036 5494 10952 8234 1893 1567 1256 4761 1995 2164 8438 22000 912 1139 6121 2207 4724 5798 11287 16201 8333 5818 9057 2037 5522 10986 8275 1900 1572 1252 4767 2003 2173 8474 22104 897 1132 6013 2204 4632 5713 11303 16148 8341 5821 9102 2038 5550 11026 8317 1907 1578 1247 4780 2012 2183 8509 22216 886 1125 5964 2190 4518 5608 11319 16176 8349 5843 9148 2039 5578 11045 8359 1914 1584 1243 4772 2020 2192 8546 22307 877 1118 5928 2168 4382 5408 11336 16220 8357 5876 9195 2040 5604 11047 8398 1920 1589 1239 4749 2028 2200 8578 22377 868 1111 5886 2144 4334 5288 11351 16273 8365 5906 9238 2041 5628 11044 8434 1926 1593 1234 4724 2035 2208 8607 22437 860 1106 5857 2129 4283 5165 11365 16320 8373 5933 9277 2042 5649 11059 8466 1930 1597 1230 4719 2041 2214 8632 22510 852 1103 5833 2120 4233 5118 11377 16371 8382 5958 9312 2043 5671 11086 8499 1935 1601 1226 4726 2047 2221 8658 22596 843 1100 5779 2111 4177 5065 11390 16433 8390 5983 9348 2044 5692 11076 8531 1939 1605 1221 4697 2053 2228 8684 22645 835 1097 5702 2105 4122 5013 11403 16496 8378 5947 9384 2045 5714 11064 8564 1944 1609 1217 4665 2059 2234 8710 22693 827 1094 5606 2099 4069 4964 11416 16560 8367 5904 9420 2046 5736 11052 8597 1949 1613 1213 4634 2065 2241 8736 22741 820 1092 5540 2095 4016 4913 11429 16624 8356 5857 9456 2047 5759 11078 8630 1954 1617 1208 4639 2072 2248 8762 22827 812 1090 5495 2092 3971 4871 11442 16688 8364 5866 9493 2048 5781 11106 8664 1958 1621 1204 4646 2078 2255 8789 22916 805 1088 5465 2089 3933 4835 11455 16753 8373 5877 9530 2049 5803 11134 8698 1963 1624 1200 4654 2084 2262 8815 23005 797 1084 5437 2088 3903 4806 11469 16818 8381 5857 9567 2050 5825 11160 8730 1968 1628 1196 4660 2090 2268 8840 23090 790 1073 5405 2087 3888 4791 11481 16880 8390 5840 9602 2051 5845 11184 8761 1972 1632 1196 4665 2096 2274 8864 23172 784 1063 5385 2085 3875 4777 11494 16940 8398 5819 9636 2052 5865 11206 8790 1976 1635 1187 4669 2101 2280 8886 23249 780 1052 5370 2085 3870 4771 11505 16997 8407 5832 9668 2053 5885 11218 8819 1979 1638 1187 4664 2107 2286 8908 23316 774 1042 5369 2084 3856 4756 11517 17053 8415 5836 9701 2054 5904 11230 8849 1983 1641 1179 4658 2112 2291 8931 23384 770 1031 5327 2085 3851 4750 11529 17111 8424 5849 9733 2055 5914 11230 8864 1985 1643 1179 4650 2114 2294 8942 23413 765 1021 5294 2084 3844 4742 11535 17139 8428 5851 9750 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 2024 910 910 910 326 326 326 584 584 584 608 608 608 314 314 314 294 294 294 2025 860 806 753 321 300 278 539 507 475 580 543 507 309 288 268 271 255 239 2026 812 721 629 316 280 244 497 441 385 553 491 428 304 269 235 250 222 194 2027 770 651 532 312 265 218 458 386 314 530 449 368 300 255 210 230 194 158 2028 722 588 455 309 254 198 413 335 256 504 412 319 297 244 191 207 168 129 2029 713 553 393 307 245 184 407 308 209 499 390 281 294 235 176 204 155 105 2030 691 544 346 298 239 175 393 305 171 484 383 254 287 230 168 197 153 86 2031 669 534 335 290 234 166 379 301 168 468 375 244 278 224 160 190 151 84 2032 659 525 323 282 228 158 377 296 164 460 368 235 270 219 152 189 149 82 2033 648 515 311 274 223 151 375 292 160 450 360 225 262 214 145 188 147 80 2034 634 506 301 266 218 143 368 288 157 440 353 217 255 209 138 185 145 79 2035 620 497 292 258 213 137 362 284 156 429 346 209 247 204 131 181 143 78 2036 605 488 284 251 208 130 355 280 154 418 339 202 240 199 125 178 141 77 2037 591 479 277 244 203 124 348 277 154 407 333 195 233 194 119 174 139 77 2038 578 471 271 237 198 118 341 273 153 398 326 189 227 190 113 171 137 77 2039 565 463 265 229 193 112 335 269 153 387 320 184 220 185 107 168 135 76 2040 557 457 264 227 192 112 331 266 153 382 317 183 217 184 107 165 133 76 2041 553 452 264 224 190 111 329 262 152 379 313 183 214 182 107 164 131 76 2042 549 447 264 222 189 111 326 258 152 376 310 183 213 181 107 163 129 76 2043 541 443 263 220 188 111 320 254 152 371 308 183 211 180 106 160 127 76 2044 535 438 263 219 188 111 316 251 152 368 305 182 210 180 106 158 125 76 2045 531 434 263 218 187 111 313 247 152 365 303 182 209 179 106 156 123 76 2046 528 430 263 217 187 111 310 243 152 363 300 183 208 179 106 155 122 76 2047 525 426 264 216 186 111 309 240 152 361 298 183 207 178 106 154 120 76 2048 523 422 264 216 186 111 307 236 153 360 296 183 206 178 106 153 118 76 2049 521 419 264 215 186 111 305 233 153 359 294 183 206 178 107 153 116 76 2050 519 415 264 215 186 111 304 230 153 358 292 183 206 178 107 152 115 76 2051 520 412 265 215 186 112 305 226 153 358 291 184 206 178 107 152 113 77 2052 517 408 265 214 185 112 303 223 153 356 289 184 205 177 107 151 111 77 2053 518 405 266 215 186 112 303 220 154 357 287 184 205 178 107 152 110 77 2054 516 402 266 214 185 112 302 216 154 356 285 184 205 177 107 151 108 77 2055 516 413 267 214 185 112 302 228 155 356 291 185 205 177 107 151 114 77 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 2024 423 423 423 274 274 274 149 149 149 344 344 344 266 266 266 78 78 78 2025 406 380 355 269 251 234 137 129 121 333 311 290 261 244 227 72 68 63 2026 391 346 302 264 234 204 126 112 98 322 286 249 256 227 198 66 59 51 2027 378 320 263 261 222 183 116 98 80 314 267 219 253 215 177 61 51 42 2028 363 297 231 258 212 166 105 85 65 305 250 195 251 206 161 55 44 34 2029 360 283 207 256 205 154 103 78 53 302 239 177 248 199 149 54 41 28 2030 349 277 190 249 200 146 100 77 43 294 234 164 242 194 142 52 40 23 2031 338 271 182 242 195 139 96 76 43 285 229 157 235 189 135 50 40 22 2032 331 266 174 235 191 132 96 75 42 278 224 150 228 185 128 50 39 22 2033 323 260 166 228 186 126 95 74 41 271 219 143 221 180 122 50 39 21 2034 315 254 159 222 181 120 93 73 40 263 214 137 214 176 116 49 38 21 2035 306 249 153 215 177 114 91 72 39 256 209 131 208 171 110 48 38 21 2036 298 244 147 209 173 108 90 71 39 249 204 125 202 167 105 47 37 20 2037 290 239 142 203 169 103 88 70 39 242 200 120 196 163 100 46 36 20 2038 283 234 137 197 165 98 86 69 39 235 195 115 190 159 95 45 36 20 2039 275 229 132 191 161 93 85 68 39 228 191 110 184 155 90 44 35 20 2040 271 226 131 188 159 93 83 67 38 225 189 110 182 154 90 43 35 20 2041 269 224 131 186 158 93 83 66 38 223 187 110 180 153 90 43 34 20 2042 267 222 131 184 157 92 82 65 38 221 186 109 178 152 89 43 34 20 2043 264 220 131 183 156 92 81 64 38 219 185 109 177 151 89 42 33 20 2044 261 219 131 182 156 92 80 63 38 217 184 109 176 151 89 41 33 20 2045 260 217 131 181 155 92 79 62 38 216 183 109 175 150 89 41 32 20 2046 258 216 131 180 155 92 78 61 38 215 182 109 174 150 89 41 32 20 2047 257 215 131 179 155 92 78 60 38 214 181 109 173 149 89 40 31 20 2048 256 214 131 179 154 92 77 60 38 213 180 109 173 149 89 40 31 20 2049 255 213 131 179 154 92 77 59 39 213 179 109 173 149 89 40 31 20 2050 255 212 131 178 154 92 77 58 38 212 179 109 172 149 89 40 30 20 2051 255 211 131 178 154 93 77 57 39 212 179 110 172 149 90 40 30 20 2052 254 210 131 178 154 93 76 56 39 211 178 110 172 149 90 40 29 20 2053 254 209 132 178 154 93 76 55 39 212 178 110 172 149 90 40 29 20 2054 253 208 132 177 154 93 76 54 39 211 177 110 171 148 90 40 28 20 2055 253 211 132 177 154 93 76 57 39 211 178 110 171 149 90 40 30 20 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 2024 318 318 318 266 266 266 52 52 52 292 292 292 266 266 266 26 26 26 2025 309 289 269 261 244 227 48 45 42 285 266 248 261 244 227 24 23 21 2026 300 266 232 256 227 198 44 39 34 278 247 215 256 227 198 22 20 17 2027 294 249 205 253 215 177 41 34 28 274 232 191 253 215 177 20 17 14 2028 287 235 184 251 206 161 37 30 23 269 220 172 251 206 161 18 15 11 2029 284 226 167 248 199 149 36 27 19 266 212 158 248 199 149 18 14 9 2030 276 221 157 242 194 142 35 27 15 259 207 149 242 194 142 17 13 8 2031 268 216 150 235 189 135 33 27 15 251 202 142 235 189 135 17 13 7 2032 261 211 143 228 185 128 33 26 15 244 198 135 228 185 128 17 13 7 2033 254 206 136 221 180 122 33 26 14 238 193 129 221 180 122 17 13 7 2034 247 201 130 214 176 116 32 25 14 231 188 123 214 176 116 16 13 7 2035 240 196 124 208 171 110 32 25 14 224 184 117 208 171 110 16 13 7 2036 233 192 118 202 167 105 31 25 14 218 180 112 202 167 105 16 12 7 2037 227 188 113 196 163 100 31 24 14 211 175 106 196 163 100 15 12 7 2038 220 183 108 190 159 95 30 24 13 205 171 101 190 159 95 15 12 7 2039 214 179 104 184 155 90 29 24 13 199 167 97 184 155 90 15 12 7 2040 211 177 103 182 154 90 29 23 13 196 166 96 182 154 90 14 12 7 2041 209 176 103 180 153 90 29 23 13 194 164 96 180 153 90 14 11 7 2042 207 175 103 178 152 89 29 23 13 193 163 96 178 152 89 14 11 7 2043 205 174 103 177 151 89 28 22 13 191 162 96 177 151 89 14 11 7 2044 203 173 103 176 151 89 28 22 13 190 162 96 176 151 89 14 11 7 2045 202 172 103 175 150 89 27 22 13 189 161 96 175 150 89 14 11 7 2046 201 171 103 174 150 89 27 21 13 188 160 96 174 150 89 14 11 7 2047 200 170 103 173 149 89 27 21 13 187 160 96 173 149 89 14 10 7 2048 200 170 103 173 149 89 27 21 13 186 159 96 173 149 89 13 10 7 2049 199 169 103 173 149 89 27 20 13 186 159 96 173 149 89 13 10 7 2050 199 169 103 172 149 89 27 20 13 185 159 96 172 149 89 13 10 7 2051 199 169 103 172 149 90 27 20 13 186 159 96 172 149 90 13 10 7 2052 198 168 103 172 149 90 26 19 13 185 158 96 172 149 90 13 10 7 2053 198 168 103 172 149 90 27 19 13 185 158 96 172 149 90 13 10 7 2054 198 167 103 171 148 90 26 19 13 185 158 97 171 148 90 13 9 7 2055 198 169 104 171 149 90 26 20 14 185 159 97 171 149 90 13 10 7 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 10hrs 24hrs 48hrs 10hrs 24hrs 48hrs 10hrs 24hrs 48hrs 10hrs 24hrs 48hrs 10hrs 24hrs 48hrs 2024 7677 6496 7822 7677 6496 7822 7677 6496 7822 768 271 163 768 271 163 768 271 163 2025 7530 6372 7673 7530 6372 7673 7530 6372 7673 753 266 160 753 266 160 753 266 160 2026 7387 6251 7527 7387 6251 7527 7387 6251 7527 739 260 157 739 260 157 739 260 157 2027 7244 6130 7381 7244 6130 7381 7244 6130 7381 724 255 154 724 255 154 724 255 154 2028 7094 6003 7228 7094 6003 7228 7094 6003 7228 709 250 151 709 250 151 709 250 151 2029 6944 5876 7075 6944 5876 7075 6944 5876 7075 694 245 147 694 245 147 694 245 147 2030 6794 5749 6922 6794 5749 6922 6794 5749 6922 679 240 144 679 240 144 679 240 144 2031 6833 5782 6962 6839 5787 6968 6863 5808 6968 683 241 145 684 241 145 686 242 145 2032 6862 5807 6992 6874 5817 7004 6916 5852 7004 686 242 146 687 242 146 692 244 146 2033 6893 5833 7024 6911 5848 7042 6969 5898 7042 689 243 146 691 244 147 697 246 147 2034 6924 5859 7055 6949 5880 7080 7028 5947 7080 692 244 147 695 245 148 703 248 148 2035 6954 5885 7086 6987 5912 7119 7087 5997 7119 695 245 148 699 246 148 709 250 148 2036 6985 5911 7117 7025 5945 7158 7147 6048 7158 698 246 148 703 248 149 715 252 149 2037 7016 5937 7149 7064 5978 7198 7208 6100 7198 702 247 149 706 249 150 721 254 150 2038 7047 5964 7181 7103 6011 7238 7270 6152 7238 705 248 150 710 250 151 727 256 151 2039 7079 5991 7213 7143 6045 7278 7332 6205 7278 708 250 150 714 252 152 733 259 152 2040 7111 6018 7246 7183 6079 7319 7396 6258 7319 711 251 151 718 253 152 740 261 152 2041 7134 6037 7269 7214 6105 7350 7450 6304 7350 713 252 151 721 254 153 745 263 153 2042 7157 6056 7292 7245 6131 7382 7504 6350 7382 716 252 152 724 255 154 750 265 154 2043 7180 6076 7316 7276 6157 7414 7559 6397 7414 718 253 152 728 257 154 756 267 154 2044 7203 6095 7339 7307 6184 7446 7615 6444 7446 720 254 153 731 258 155 761 268 155 2045 7226 6115 7363 7339 6210 7478 7671 6491 7478 723 255 153 734 259 156 767 270 156 2046 7250 6135 7387 7371 6237 7510 7727 6539 7510 725 256 154 737 260 156 773 272 156 2047 7273 6155 7411 7403 6264 7543 7785 6587 7543 727 256 154 740 261 157 778 274 157 2048 7297 6175 7435 7435 6291 7575 7842 6636 7575 730 257 155 743 262 158 784 277 158 2049 7320 6195 7459 7467 6319 7608 7900 6685 7608 732 258 155 747 263 159 790 279 159 2050 7344 6215 7483 7499 6346 7641 7959 6735 7641 734 259 156 750 264 159 796 281 159 2051 7364 6231 7503 7528 6370 7670 8014 6781 7670 736 260 156 753 265 160 801 283 160 2052 7383 6248 7523 7556 6394 7699 8069 6828 7699 738 260 157 756 266 160 807 285 160 2053 7403 6265 7543 7585 6418 7728 8124 6875 7728 740 261 157 758 267 161 812 286 161 2054 7423 6281 7563 7613 6442 7757 8180 6922 7757 742 262 158 761 268 162 818 288 162 2055 7443 6298 7584 7642 6467 7787 8237 6970 7787 744 262 158 764 269 162 824 290 162 Apx Table B.8 Storage current cost data by source, total cost basis $/kWh $/kW Aurecon 2019-20 Aurecon 2020-21 Aurecon 2021-22 Aurecon 2022-23 Aurecon 2023-24 Aurecon 2024-25 GenCost 2019-20 AEMO ISP Dec 2021 AEMO ISP Jun 2022/CSIRO Aurecon 2019-20 Aurecon 2020-21 Aurecon 2021-22 Aurecon 2022-23 Aurecon 2023-24 Aurecon 2024-25 GenCost 2019-20 AEMO ISP Dec 2021 AEMO ISP Jun 2022/CSIRO Battery (1hr) 1195 958 906 1024 1048 910 - - - 1195 958 906 1024 1048 910 - - - Battery (2hrs) 752 642 603 741 758 608 - - - 1504 1284 1205 1481 1517 1216 - - - Battery (4hrs) 594 510 476 601 614 423 - - - 2375 2041 1903 2406 2457 1691 - - - Battery (8hrs) 539 450 418 534 538 344 - - - 4309 3601 3340 4273 4308 2748 - - - Battery (12hrs) - - - - 496 885 - - - - - - - 11910 10617 - - - Battery (24hrs) - - - - 443 751 - - - - - - - 21272 18032 - - - PHES (10hrs) - - - - - 768 - - - - - - - - 7677 - - - A-CAES (12hrs) - - - 386 - - - - - - - - 4626 - - - - - PHES (12hrs) - - - - - - 213 226 240 - - - - - - 2561 3167 3167 A-CAES (24hrs) - - - - 305 316 - - - - - - - 7326 7585 - - - PHES (24hrs) - - - - 242 271 158 147 157 - - - - 6030 6496 3796 4132 4140 PHES (48hrs) - - - - 142 163 89 111 118 - - - - 7078 7822 4252 6208 6219 Notes: Batteries are large scale. Small scale batteries for home use with 2-hour duration are estimated at $1350/kWh (Aurecon, 2025). 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% 22.5 8.0 8.7 5802 13.5 89% 5802 19.8 53% Gas combined cycle 25 2.0 51% 15.0 4.1 0.0 2455 13.5 89% 2455 19.8 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 2426 13.5 20% 2426 19.8 20% Gas open cycle (large) 25 1.5 33% 14.1 8.1 0.0 1310 13.5 20% 1310 19.8 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 1980 13.5 20% 1980 19.8 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2071 40.7 20% 2071 41.9 20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.3 12263 3.1 89% 12263 4.6 53% Black coal 30 2.0 42% 64.9 4.7 0.0 6037 3.1 89% 6037 4.6 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 9321 0.6 89% 9321 0.7 53% Nuclear SMR 30 4.4 33% 200 5.3 0.0 29667 1.1 89% 29667 1.3 53% Nuclear large-scale 30 5.8 33% 200 5.3 0.0 8984 1.1 89% 8984 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 1463 0.0 32% 1463 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 3351 0.0 48% 3351 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 4710 0.0 52% 4710 0.0 40% 2030 Gas with CCS 25 2.0 44% 22.5 8.0 8.7 4822 9.4 89% 5170 16.5 53% Gas combined cycle 25 2.0 51% 15.0 4.1 0.0 1917 9.4 89% 2113 16.5 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 1630 9.4 20% 1906 16.5 20% Gas open cycle (large) 25 1.5 33% 14.1 8.1 0.0 1630 9.4 20% 1906 16.5 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 1945 9.4 20% 1985 16.5 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2122 37.7 20% 2102 40.9 20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.3 10696 3.1 89% 10901 5.5 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5299 3.1 89% 5441 5.5 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 7942 0.7 89% 8169 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 20700 0.8 89% 23925 1.0 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 8736 0.8 89% 8919 1.0 53% Solar thermal 30 1.8 100% 124.2 0.0 0.0 8276 0.0 71% 8614 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 1027 0.0 32% 1123 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 2616 0.0 48% 2646 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 3180 0.0 54% 4654 0.0 40% 2040 Gas with CCS 25 2.0 44% 22.5 8.0 8.7 4580 9.2 89% 4796 16.1 53% Gas combined cycle 25 2.0 51% 15.0 4.1 0.0 1904 9.2 89% 1953 16.1 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 1576 9.2 20% 1616 16.1 20% Gas open cycle (large) 25 1.5 33% 14.1 8.1 0.0 1576 9.2 20% 1616 16.1 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 2006 9.2 20% 2074 16.1 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2251 31.0 20% 2176 36.6 20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.3 11020 2.9 89% 11028 4.6 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5500 2.9 89% 5777 4.6 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 8275 0.7 89% 8658 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 16582 0.5 89% 19646 0.7 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 9102 0.5 89% 9524 0.7 53% Solar thermal 30 1.8 100% 124.2 0.0 0.0 7055 0.0 71% 8600 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 713 0.0 32% 1024 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 2123 0.0 48% 2163 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 3067 0.0 57% 4690 0.0 40% 2050 Gas with CCS 25 2.0 44% 22.5 8.0 8.7 4621 9.2 89% 4653 16.1 53% Gas combined cycle 25 2.0 51% 15.0 4.1 0.0 1940 9.2 89% 2023 16.1 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 1606 9.2 20% 1674 16.1 20% Gas open cycle (large) 25 1.5 33% 14.1 8.1 0.0 1606 9.2 20% 1674 16.1 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 2052 9.2 20% 2167 16.1 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2351 28.5 20% 2227 35.8 20% Black coal with CCS 30 2.0 30% 94.8 8.9 14.3 11026 2.9 89% 11374 4.6 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5635 2.9 89% 6111 4.6 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 8517 0.7 89% 9159 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 17176 0.5 89% 17540 0.7 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 9368 0.5 89% 10074 0.7 53% Solar thermal 30 1.8 100% 124.2 0.0 0.0 6688 0.0 71% 7709 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 647 0.0 32% 934 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 2065 0.0 48% 2108 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 3112 0.0 61% 4724 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%. Apx Table B.10 Electricity generation technology LCOE projections data, 2023-24 $/MWh Category Assumption Technology 2024 2030 2040 2050 Low High Low High Low High Low High Peaking 20% load Gas open cycle (small) 293 356 209 295 204 275 205 278 Gas open cycle (large) 233 301 206 298 201 278 203 281 Gas reciprocating 249 304 211 276 212 277 215 281 H2 reciprocating 586 599 554 589 485 544 462 538 Flexible load, high emission Black coal 111 178 103 174 103 173 105 179 Brown coal 148 240 129 214 134 225 138 237 Gas 133 199 97 169 95 163 96 164 Flexible load, low emission Black coal with CCS 217 342 200 326 201 317 201 324 Gas with CCS 204 307 158 266 153 255 153 252 Nuclear SMR 456 757 328 619 268 516 276 467 Nuclear large-scale 180 293 173 288 175 300 179 314 Solar thermal 140 175 140 183 122 182 117 166 Variable Standalone Solar photovoltaic 48 80 35 63 25 59 24 54 Wind onshore 80 132 64 107 53 89 52 87 Wind offshore (fixed) 147 191 108 189 100 191 94 192 Variable with integration costs Wind & solar PV combined 60% VRE share 120 168 76 116 70% VRE share 116 165 80 119 80% VRE share 118 168 83 124 90% VRE share 125 176 90 131 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 2024 2706 2840 2706 2840 2706 2840 2025 2626 2756 2482 2605 2513 2638 2026 2548 2675 2276 2389 2333 2449 2027 2473 2596 2087 2190 2167 2274 2028 2400 2519 1914 2009 2012 2112 2029 2329 2444 1755 1842 1868 1961 2030 2260 2372 1609 1689 1735 1821 2031 2193 2302 1476 1549 1611 1691 2032 2128 2234 1353 1421 1496 1570 2033 2065 2168 1241 1303 1389 1458 2034 2004 2103 1138 1195 1290 1354 2035 1945 2041 1063 1116 1221 1281 2036 1887 1981 1031 1082 1202 1262 2037 1831 1922 944 991 1207 1267 2038 1777 1865 921 966 1209 1269 2039 1724 1810 913 958 1143 1199 2040 1673 1757 903 948 1116 1171 2041 1680 1763 888 932 1110 1165 2042 1663 1746 862 905 1096 1150 2043 1658 1741 844 886 1074 1127 2044 1653 1736 830 871 1069 1122 2045 1644 1726 814 855 1055 1108 2046 1638 1720 790 829 1052 1104 2047 1592 1671 764 802 1043 1095 2048 1551 1628 745 782 1043 1095 2049 1533 1609 731 768 1039 1091 2050 1514 1589 721 756 1034 1085 2051 1519 1595 725 761 1038 1089 2052 1515 1590 716 752 1034 1086 2053 1521 1596 721 756 1038 1090 2054 1520 1595 706 741 1038 1090 2055 1525 1601 710 746 1042 1094 Appendix C 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 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 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 Aurecon (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 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 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 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 fall . 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 EUW 21 2 23 22 NA FSU 25 NA NA NA NA IND 7 21 18 4 302 JPN 16 1 12 11 10 LAM 25 NA NA NA NA MEA 21 NA NA NA NA NAM 30 NA NA NA NA PAO 11 1 8 8 15.5 SEA 14 3 32 8 NA NA means the resource is greater than projected electricity demand. The regions are Africa (AFR), Australia (AUS), China (CHI), Eastern Europe (EUE), Former Soviet Union (FSU), India (IND), Japan (JPN), Latin America (LAM), Middle East (MEA), North America (NAM), OECD Pacific (PAO), Rest of Asia (SEA), and Western Europe (EUW) Power plant technology operating and maintenance (O&M) costs, plant efficiencies and fossil fuel emission factors were obtained from (Aurecon, 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). Appendix D 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 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 2024-25 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 projects . 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 Aurecon 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 Aurecon (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. 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? Aurecon (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. Aurecon (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. 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 Aurecon (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. 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% . 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 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. 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? Aurecon’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. Aurecon (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. The costs for project and land procurement are highly variable and project specific. For the purposes of this report, and outlining development and land costs for a general project within each technology category, a simplified approach must be taken. Land and development costs are calculated as a percentage of capital equipment, and as a result, absolute values associated with these costs will change for those technologies whose equipment capital costs have changed. These costs do not include any applicable fees, such as fees paid to councils, local authorities, electrical connection fee etc. An indicative estimate has been determined based on a percentage of CAPEX estimate for each technology from recent projects, and experience with development processes.” 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. 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 time . 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 do other studies find higher costs than GenCost for integrating variable renewables in the electricity system? Stakeholders have forwarded research which they believe arrives at a different result to GenCost on the cost of integrating renewables and requested that GenCost adopt their methodology or justify why GenCost arrives at different results. In reviewing these studies, which in some cases appear in peer reviewed journals, it became evident that there were several common limiting factors which explain why they find higher variable renewable integration costs. These include: • Requiring that the variable renewable share be 100% or that all electricity sector emissions be completely eliminated. There is no such requirement in Australia under our net zero emission policy. Furthermore, going to 100% variable renewables would require the non-sensical step of shutting down existing non-variable renewable generation such as the existing Snowy hydro scheme and biomass generation. This approach denies renewables access to peaking plant such as open cycle gas turbines which are the most efficient technology for managing long periods of low renewable production but only result in residual emissions of a few percent compared to current electricity sector emissions. • Limiting the types of storage technologies available to the system (e.g., only allowing batteries to participate rather than all storage options). • Limiting the duration of storage technologies available to the system (e.g., only including one possible storage duration). • Limiting access of the system to realistically diverse renewable profiles (e.g., using just one profile for solar and one for wind). • Imposing inertia and system security constraints but only allowing a limited range of technologies to supply these services. • Ignoring the availability of existing generation capacity in the system. To be clear, none of the studies reviewed included all of these limiting factors but they all included at least one. The following table matches the common limiting factors to the published work. The table focuses on Idel (2022) because it was forwarded by more than one stakeholder and on Cross et al (2023) of Blueprint Institute because it is the most recent example specific to Australia. In September 2024, the DOE (2024) republished research by Baik et al (2021) which some stakeholders also brought to the attention of GenCost and so we include this as well. It is our expectation that were these limiting factors not imposed, the results of their analysis of the cost of integrating variable renewables would be lower and likely similar to GenCost. For example, when Idel (2022) removes the requirement for a 100% variable renewable share, decreasing it to 95%, system cost estimates halve in the German and Texas case studies. In the case of Texas, the cost was $97/MWh which is inside the range of costs estimated by GenCost despite the higher VRE share and limits on storage technologies. Like Idel (2022), the Baik et al (2021) research published in DOE (2024) initially sets up a scenario where solar and wind can only access battery storage to meet demand. No gas peaking plants are allowed creating an artificially high cost scenario. Baik et al (2021) then only allows nuclear, CCS, hydrogen or biofuels as additional firming options and finds the system cheaper under all of those combinations. The problem with this approach is that the initial system may have been cheaper had the gas peaking plant been allowed. Consequently, it is difficult to ascertain if adding any of the other resources – nuclear, CCS, hydrogen and biofuels would have reduced costs further. All of these other options for firming are more expensive than peaking gas. Baik et al (2021) also makes the error of including only one type of storage technology – batteries. Gilmore et al (2023) published research which provided an estimate of the impact on the cost of electricity from a high VRE system of only including batteries in the storage options. They found a battery-only scenario increased costs by 35% compared to a system that also allowed pumped hydro storage. Gilmore et al (2023) also finds costs within the range estimated by GenCost. One stakeholder submission argued that it is necessary to assume that renewables can provide baseload power sources like coal and gas. To be clear, GenCost is not targeting the production of baseload power as the point of comparison. Australia’s electricity system load is not flat. The cost of integrated VRE presented in GenCost is for delivery of reliable power to meet the system load. Apx Table D.1 Comparison of limiting factors applied in academic literature to the calculation of variable renewable integration costs and the GenCost approach Limiting factor Idel (2022) Cross et al (2023) of Blueprint Institute Baik et al (2021) reported in DOE (2024) GenCost Requiring 100% variable renewable share The main analysis upon which conclusions are based assumes 100% VRE. A 95% VRE sensitivity that was included results in very different outcomes. Focus on 90% and 99% calculated on the basis of VRE plus existing renewable share combined (VRE share not separately provided) 100% renewables with batteries or lesser shares of renewables with either nuclear, CCS, hydrogen or biofuel. Gas peaking plant disallowed Considers 60%, 70%, 80% and 90% VRE shares Limiting storage technologies Only batteries are included Only batteries are included Only batteries are included Lithium batteries, flow batteries, compressed air and pumped hydro storage included Limiting the duration of storage technologies Only 3-hour batteries are allowed Only 4-hour batteries are allowed Multiple battery durations allowed lithium-ion batteries at 1, 2, 4, or 8 hours; flow batteries at 4, 8, 12 or 24; compressed air at 8, 12, 24 or 48; and pumped hydro at 6, 8 12, 24 or 48 hours. The 168-hour Snowy 2.0 pumped hydro project is also included Limiting diversity of renewable profiles Single profile each for solar and wind Single profile for solar and wind per state Range of Californian profiles Profiles for a wide range of Australian Renewable Energy Zones included Limiting technologies that can meet system security requirements NA Synchronous generators only, but pumped hydro excluded NA Synchronous condensers, grid forming batteries and synchronous generators all available to be deployed However, CSIRO acknowledges that there will be circumstances where flat or baseload power is required such as in direct contracts to grid connected industrial facilities such as aluminium smelters or the industrial off-grid sector (e.g., mining). In these circumstances, it is likely that VRE will be more costly than it is when undertaking the task of supplying general residential and commercial customer demand. There is published research available on this topic based on CSIRO modelling (ClimateWorks and ClimateKic, 2023). The challenge and opportunity for Australia’s industrial sector is whether it can access low emission industrial electricity supply at lower costs than our international competitors. This will depend not just on the generation technologies selected but on other factors such as relative labour and installation costs (Graham and Havas, 2023). D.4.8 Why are integration costs not increasing with VRE share in 2024 but increase in the 2030 results? Stakeholders requested that all of the currently committed transmission and storage projects in Australia be included in any assessment of current VRE integration costs. This request arises from some stakeholder views that the costs of integrating VRE may be high and none of the costs already committed should be left out when undertaking the assessment, regardless of the VRE share being targeted. However, not all of those committed transmission and storage projects are strictly necessary to reach lower VRE shares at current demand. They are being built in anticipation of high renewable electricity supply and system demand. Consequently, the integration costs from these projects are high at low VRE shares because the investment is more than is necessary for a moderate increase in VRE share to meet 2024 demand. However, as we increase the VRE share these new investments are better utilised, decreasing the calculated costs of integration. The same problem does not arise in 2030 because, following the same methodology we apply in 2024, existing capacity is not included in the LCOE, only committed projects and anything additional needed (as assessed by the modelling framework). Without the forced inclusion of a block of committed project expenditure in the 2024 calculation, the 2030 result conforms to expectations of higher integration costs as the VRE share increases. In reality, the calculated 2024 VRE LCOE costs with integration will not be experienced by the electricity sector. Variable renewable generation will be deployed progressively (rather than in a single year) and likely at lower costs as cost reductions resume following recovery from recent global inflationary pressures. Electricity demand is expected to increase given the key role of electrification in decarbonising Australia’s economy and this increase in volume will increase the volume of renewable generation to improve the utilisation of the planned integration assets. In this sense, the 2024 LCOE results could be considered an upper bound if variable renewable technology cost reductions never occur again and electricity demand is flat. LCOE is not a tool that is designed to capture transitional costs. LCOE places all costs in a single year. Stakeholders who wish to explore system costs over multiple time periods will need to review existing multi-year modelling studies or commission new modelling that uses a multi-year framework. The information GenCost publishes on capital costs over time is targeted at providing the information needed for others to conduct multi-year modelling studies. It is not designed to provide those studies directly. LCOE data published by GenCost provides an indication of what those deeper modelling studies might find regarding technology competitiveness. D.4.9 Why do other studies show the cost of storage increasing more rapidly with higher VRE share? If storage is provided to an electricity system as the only technology available for variable renewables to meet electricity demand reliably, then the cost of storage increases exponentially as the VRE share increases. However, this is not a least cost system for integrating variable renewables. A least cost system uses a combination of storage of varying durations, peaking generation technology , (based on either natural gas, renewable gas or hydrogen) hydro if it is available and transmission (to source diverse renewables that complement each other). In particular, peaking generation technology is a more cost effective means to provide generation in so-called ‘renewable droughts’. When peaking plants are made available to an electricity system with increasing VRE share, the power ratio of storage to renewable capacity tends to plateau at the 80-90% VRE share rather than continue to increase (as is otherwise found in studies where peaking generation technology are not made available). Transmission and spilling electricity also reduce the need for more storage. In summary, modelling studies that find an exponential increase in storage costs as the VRE share increases have artificially constrained the options available to support variable renewables. D.4.10 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.4.11 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. D.4.12 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.4.13 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. 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.4.14 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.4.15 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. D.4.16 Why does GenCost only conduct LCOE analysis instead of system cost to society analysis? Some stakeholders believe GenCost is obligated to provide a system cost to society analysis. The stated purpose of GenCost is to provide essential capital cost information for the modelling community to use in their own system cost studies. There are several Australian researchers and consultants capable of delivering such studies. CSIRO has significant experience in conducting whole of electricity system studies and can therefore say with confidence that such a study would increase the annual budget of GenCost by around five- to ten-fold. It is therefore not a simple extension. Substantially expanding the scope of GenCost or creating a new separate project to accommodate stakeholder interest in whole-of-system studies is not planned at present. However, CSIRO does operate in this field and new separate research of this type is likely to be available in the future. D.4.17 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.4.18 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 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. Appendix E 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. 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) 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. 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 kW Kilowatt kWh Kilowatt hour LAES Liquid Air Energy Storage LCOE 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 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 References Association for the Advancement of Cost Engineering (AACE). 1991, Conducting technical and economic evaluations – as applied for the process and utility industries, Recommended Practice No. 16R‐90, AACE International. 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