Australia’s National Science Agency GenCost 2024-25 Consultation draft Paul Graham, Jenny Hayward and James Foster December 2024 Contact Paul Graham +61 2 4960 6061 paul.graham@csiro.au Citation Graham, P., Hayward, J. and Foster J. 2024, GenCost 2024-25: Consultation draft, CSIRO, Australia. Acknowledgement CSIRO acknowledges the Traditional Owners of the lands that we live and work on across Australia and pays its respect to Elders past and present Copyright © Commonwealth Scientific and Industrial Research Organisation 2024. 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. GenCost 2024-25 | i Contents Consultation process......................................................................................................................vii Executive summary .......................................................................................................................viii 1 Introduction ...................................................................................................................... 13 1.1 Scope of the GenCost project and reporting....................................................... 13 1.2 The GenCost mailing list ...................................................................................... 14 1.3 Consultation ........................................................................................................ 14 2 Nuclear: additional evidence and analysis on three topics.............................................. 15 2.1 Nuclear capital recovery period and long operational life.................................. 15 2.2 Nuclear capacity factor range ............................................................................. 21 2.3 Nuclear development lead times ........................................................................ 23 3 Current technology costs.................................................................................................. 26 3.1 Current cost definition ........................................................................................ 26 3.2 Capital cost source............................................................................................... 27 3.3 Current generation technology capital costs ...................................................... 28 3.4 Current storage technology capital costs............................................................ 29 4 Scenario narratives and data assumptions....................................................................... 33 4.1 Scenario narratives.............................................................................................. 33 5 Projection results.............................................................................................................. 35 5.1 Short-term inflationary pressures ....................................................................... 35 5.2 Global generation mix ......................................................................................... 36 5.3 Changes in capital cost projections ..................................................................... 38 6 Levelised cost of electricity analysis ................................................................................. 57 6.1 Purpose and limitations of LCOE ......................................................................... 57 6.2 LCOE estimates .................................................................................................... 58 6.3 Storage requirements underpinning variable renewable costs.......................... 67 Global and local learning model.......................................................................... 70 Data tables........................................................................................................... 73 Data assumptions ................................................................................................ 86 Frequently asked questions................................................................................. 93 Technology inclusion principles......................................................................... 110 ii | CSIRO Australia’s National Science Agency Shortened forms ......................................................................................................................... 113 References ........................................................................................................................... 116 GenCost 2024-25 | iii Figures Figure 2-1 Costs for long-lived multi-stage projects and the subsequent cost reduction achieved for electricity consumers. ............................................................................................................. 19 Figure 2-2 Historical capacity factors for black coal in Australian electricity generation (NEM states)............................................................................................................................................ 22 Figure 2-3 Relationship between the level of democracy, regions and construction times since 2011............................................................................................................................................... 24 Figure 3-1 Comparison of current capital cost estimates with previous reports (FYB)................ 28 Figure 3-2 Year on year change in current capital costs of selected technologies in the past 3 years (in real terms) ...................................................................................................................... 29 Figure 3-3 Capital costs of storage technologies in $/kWh (total cost basis)............................... 30 Figure 3-4 Capital costs of storage technologies in $/kW (total cost basis)................................. 31 Figure 5-1 Projected global electricity generation mix in 2030 and 2050 by scenario ................ 37 Figure 5-2 Global hydrogen production by technology and scenario, Mt.................................... 38 Figure 5-3 Projected capital costs for black coal ultra-supercritical by scenario compared to 2023-24 projections ...................................................................................................................... 39 Figure 5-4 Projected capital costs for black coal with CCS by scenario compared to 2023-24 projections .................................................................................................................................... 40 Figure 5-5 Projected capital costs for gas combined cycle by scenario compared to 2023-24 projections .................................................................................................................................... 41 Figure 5-6 Projected capital costs for gas with CCS by scenario compared to 2023-24 projections ....................................................................................................................................................... 42 Figure 5-7 Projected capital costs for gas open cycle (small) by scenario compared to 2023-24 projections .................................................................................................................................... 43 Figure 5-8 Projected capital costs for nuclear SMR by scenario compared to 2023-24 projections ....................................................................................................................................................... 44 Figure 5-9 Projected capital costs for large-scale nuclear by scenario compared to 2023-24 projections .................................................................................................................................... 45 Figure 5-10 Projected capital costs for solar thermal with 14 hours storage compared to 202324 projections ............................................................................................................................... 46 Figure 5-11 Projected capital costs for large-scale solar PV by scenario compared to 2023-24 projections .................................................................................................................................... 47 Figure 5-12 Projected capital costs for rooftop solar PV by scenario compared to 2023-24 projections .................................................................................................................................... 48 Figure 5-13 Projected capital costs for onshore wind by scenario compared to 2023-24 projections .................................................................................................................................... 49 iv | CSIRO Australia’s National Science Agency Figure 5-14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2023-24 projections ...................................................................................................................... 50 Figure 5-15 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant) ........................................................................................................................... 51 Figure 5-16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario ... 52 Figure 5-17 Projected technology capital costs under the Current policies scenario compared to 2023-24 projections ...................................................................................................................... 53 Figure 5-18 Projected technology capital costs under the Global NZE by 2050 scenario compared to 2023-24 projections ................................................................................................ 54 Figure 5-19 Projected technology capital costs under the Global NZE post 2050 scenario compared to 2023-24 projections ................................................................................................ 55 Figure 5-20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2023-24 ................................................................................................................... 56 Figure 6-1 Range of generation and storage capacity deployed in 2030 across the 9 weather year counterfactuals in NEM plus Western Australia................................................................... 62 Figure 6-2 Levelised costs of achieving 60%, 70%, 80% and 90% annual variable renewable energy shares in the NEM in 2024 and 2030................................................................................ 63 Figure 6-3 Calculated LCOE by technology and category for 2024............................................... 65 Figure 6-4 Calculated LCOE by technology and category for 2030............................................... 66 Figure 6-5 Calculated LCOE by technology and category for 2040............................................... 67 Figure 6-6 Calculated LCOE by technology and category for 2050............................................... 67 Figure 6-7 2030 NEM maximum demand, demand at lowest renewable generation and generation capacity under 90% variable renewable generation share........................................ 69 Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation .............................................................................. 71 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....................... 17 Table 4-1 Summary of scenarios and their key assumptions ....................................................... 34 Table 6-1 Questions the LCOE data are designed to answer........................................................ 59 Table 6-2 Committed investments by category included in the 2023 cost of integrating variable renewables.................................................................................................................................... 61 Apx Table A.1 Cost breakdown of offshore wind ......................................................................... 72 GenCost 2024-25 | v Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario ............................................................................................................................ 74 Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario ........................................................................................................................... 75 Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario ........................................................................................................................ 76 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) .......................................................................... 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) .......................................................................... 78 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)........................................................... 79 Apx Table B.7 Pumped hydro storage cost data by duration, all scenarios, total cost basis ....... 80 Apx Table B.8 Storage current cost data by source, total cost basis............................................ 81 Apx Table B.9 Data assumptions for LCOE calculations................................................................ 82 Apx Table B.10 Electricity generation technology LCOE projections data, 2023-24 $/MWh....... 84 Apx Table B.11 Hydrogen electrolyser cost projections by scenario and technology, $/kW....... 85 Apx Table C.1 Assumed technology learning rates that vary by scenario.................................... 86 Apx Table C.2 Assumed technology learning rates that are the same under all scenarios.......... 88 Apx Table C.3 Hydrogen demand assumptions by scenario in 2050............................................ 90 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........................ 91 Apx Table D.1 Comparison of limiting factors applied in academic literature to the calculation of variable renewable integration costs and the GenCost approach ............................................. 103 Apx Table E.1 Examples of considering global or domestic significance.................................... 111 vi | CSIRO Australia’s National Science Agency Consultation process This report is provided for the purposes of stakeholder review. Feedback received will be used to improve content and produce a final GenCost 2024-25 report mid-2025. Feedback can be provided at: https://aemo.com.au/consultations/current-and-closed-consultations/2025-iasr GenCost 2024-25 | vii 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 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. 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: viii | CSIRO Australia’s National Science Agency  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 consultation draft. 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. 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 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 around 45 years from now, significantly reducing their value to consumers compared to other options which can be deployed now. 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 fact is that the 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 coal plants operate at close to 90% capacity factor but the average for black 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 GenCost 2024-25 | ix 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) had a 12 year lead time. Both estimates are in relation to building nuclear for the first time. This consultation draft provides additional analysis of nuclear lead times to examine this issue more closely. We examine recent construction times and their relationship with the level of democracy in that country. In the last 5 years, median construction time has increased to 8.2 years compared to 6 years when the IAEA 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 1015 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 in 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. x| CSIRO Australia’s National Science Agency The capital costs of onshore wind generation technology increased by a further 8% in 2023-24 and another 2% in 2024-25 while large-scale solar PV has fallen by 8% in consecutive years (ES Figure 0-1). Large-scale battery costs improved the most in 2024-25 falling by 20% in 2024-25. Gas turbine technologies are still increasing but this is because GenCost is now including hydrogen fuel readiness as a standard feature. This has increased gas technology capital costs but recognises the reality that gas generation is more likely to be deployed with multiple fuel options. 2022-23 2023-24 2024-25 35% 24% 13% 9% 20% -2% 14% -8% 8% 2%4% 11% -8% 2% -20% Black coal Gas combined cycle Large scale solar PV Wind (onshore) Large scale battery (2hr) 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 2024 and 2030. The cost range overlaps with the lower end of the cost range for coal and gas generation. These are high emission technologies which, if used to deliver the majority of Australia’s power supply, are not consistent with Australia’s current climate change policies1. If we exclude high emission 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. 1 Although most modelling indicates that gas is likely to continue to be utilised and constructed for some time yet as a peaking technology which supports the grid but with low contribution to total electricity produced. AEMO analysis of electricity systems consistent with net zero by 2050 can be accessed at: https://aemo.com.au/consultations/current-and-closed-consultations/draft-2024-isp-consultation GenCost 2024-25 | xi 1002003004005006002024-25$/MWh1002003004005006002024-25$/MWh Black coal Gas Black coal with CCS Gas with CCS Nuclear SMR Nuclear large-scale Solar thermal Solar PV and wind with firming Black coal Gas Black coal with CCS Gas with CCS Nuclear SMR Nuclear large-scale Solar thermal Solar PV and wind with firming 2024 2030 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 preconstruction tasks are completed. At least 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 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. xii | CSIRO Australia’s National Science Agency 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 (2024b) 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 Portal2. 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 2 Search GenCost at https://data.csiro.au/collections GenCost 2024-25 | 13 of electricity generation, storage and hydrogen technologies (Aurecon, 2024b). 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. 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. 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 Consultation This report is provided for the purposes of stakeholder review. Feedback received will be used to improve content and produce a final GenCost 2024-25 report in the second quarter of 2025. While the release of the consultation draft represents our main annual consultation process, CSIRO also participates in various additional consultations throughout the year. 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. 14 | CSIRO Australia’s National Science Agency 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 The role of GenCost is to make fair comparisons of technologies on a common costing basis. Governments can and do subsidise technologies and our consistent approach since project inception is to exclude any subsidies in our analysis. However, stakeholders have raised direct government ownership as a serious proposal. Consequently, it is difficult to avoid the consideration of subsidies which are irrevocably intertwined with any government ownership. Governments have access to several financing abilities which are not available to the private sector and whether these are made available to all technologies or selectively to some technologies, they represent a subsidy. The first advantage of government ownership is that it could apply the lower interest rates which are available to governments to the investment. This is a clear subsidy which would benefit any technology it is applied to3. If access to lower interest rates were the only intent of government ownership of nuclear then this would simply represent a subsidy and require no further investigation by GenCost. However, based on feedback received throughout the course of the GenCost project4, it is assumed the primary intent of government ownership is not low interest rates but rather to unlock the potential benefits of nuclear technology’s long operational life with a longer capital recovery period. We therefore ignore the lower interest rate aspect of government ownership and focus on the issue of financing and long operational life. 3 Lower interest rates provide the most benefit to technologies with a higher proportion of capital costs in total costs. If low interest rates were offered to all technologies as a technology neutral government subsidy, renewables and nuclear would achieve close to the same proportional cost reduction since they both have around a 90% share of capital in their total cost of generation. Technologies with higher fuel costs such as gas generation would receive a lower proportional benefit but would still experience reduced costs. 4 The Liberal-National Coalition has not yet specifically stated exactly why and how government ownership is justified or will benefit nuclear generation. However, GenCost has received many submissions requesting we ignore the standard 30-year financing period and apply capital recovery over the whole operational life of nuclear, which could be achieved under government ownership. Financing projects for longer than 30 years it not generally available to the private sector without government guarantees, even for technologies with operational lives longer than 30 years. GenCost 2024-25 | 15 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 9% 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 7% 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. 16 | CSIRO Australia’s National Science Agency 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 130% more interest than a 30-year loan and this increases the total amount (principle plus interest) that must be repaid5. 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 11-16% 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 30year 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 13% 13% 13% 13% 12% 13% 12% 13% 60 Nuclear large-scale 11% 11% 11% 11% 11% 11% 11% 11% 100 Nuclear SMR 15% 16% 15% 15% 14% 15% 14% 15% 100 Nuclear large-scale 12% 13% 13% 13% 13% 13% 13% 13% 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. 5 The exact amount of interest depends on the detailed schedule of payments and addition of interest. This estimate is based on a simple annual model. Fixed and variable interest rates are also a source of uncertainty. GenCost 2024-25 | 17 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 $150-245/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 $2765/kW or $43-80/MWh when this refurbishment cost is recovered over the remaining 20 years of life6. 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 go offline for a longer than normal maintenance period 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 $133-217/MWh which is an 11% reduction in costs relative to a single 30-year generation project7. 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 $136-222/MWh which is a 9% reduction in costs relative the costs for a single 30-year generation project. 6 Similar to our approach to calculating original large-scale nuclear capital costs, the life extension costs for this technology were aligned with those in South Korea and scaled up to recognise the known differences in South Korean and Australian generation construction costs based on the cost of building a common coal technology type. 7 This aligns perfectly with the costs reductions that were calculated to be achievable from the 60-year capital recovery period in the previous section which also did not include life extension costs. The perfect alignment reflects that fact that interest cost on capital and the present value of future payments are based on the same central concept of the change in the time value of money. 18 | CSIRO Australia’s National Science Agency First interval is the cost under normal capital recovery Subsequent intervals are costs if the same project site Cost reduction achieved by accounting for second arrangements continues to generate generation period based on calculating a single cost with the equivalent present value of the two (or three) Nuclear with no $150-245/MWh $36-56/MWh intervals Nuclear with $150-245/MWh $36-56/MWh $80-129/MWh Solar PV rebuilt $43-73/MWh $19-43/MWh Onshore wind $70-116/MWh $43-75/MWh extensioncosts11% extensioncosts9% at30years7% rebuiltat25years7% Year 0 10 20 30 40 50 60 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 period8. 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 60year 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 8 Aurecon (2024b) suggest a possible 40-year technical life for solar and 30 to 35 years for onshore wind. GenCost 2024-25 | 19 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 which is of benefit to consumers. To calculate the benefit of the second period of lower costs, in the same way that we did for nuclear generation, we convert the costs in the full 50-and 60-year lifetimes to a common 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 $40-68/MWh which is a 7% 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 $65-108/MWh which is also a 7% 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 $44 if you have to wait 15 years to receive it (using the same annual real discount rate as the analysis above). As such, the 9% savings associated with a 60-year nuclear project are worth less than half their value when delayed 15 years before generation can commence. 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 20 | CSIRO Australia’s National Science Agency demand (such as capacity retirements, fuel price changes and strong weather changes) in any given year add noise to this underlying investment signal9. In this context, if the government owns nuclear and would like to pass on cost reductions estimated above (either as an average 9% 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 US 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 coal generation in the same baseload power role. GenCost uses a range of 53% to 89%. 89% represents the best performance of black coal in a recent ten-year period (2011 to 2021). 53% is 10% below the average capacity factor of black 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). 9 There are also a number of other unique market features which enhance or mute market price signals to investors. The Reliability Obligation acts as a backstop mechanism if market prices are not expected to deliver the required capacity on time. Also, price caps prevent a full expression of market supply tightness. GenCost 2024-25 | 21 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-weatherday demand. However, this does not appear to have substantially impacted average capacity factors (Figure 2-2), 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. 1.00 0.80 0.60 0.40 0.00 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Max Min Average 0.20 Figure 2-2 Historical capacity factors for black 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 22 | CSIRO Australia’s National Science Agency and seasonal load curves at the time that nuclear is deployed (from the 2040s). While AEMO’s Integrated System Plan 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 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 2015). 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 report was released in 2015 it could be inferred that the timeline was at least based on recent construction times at the time of writing. 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 report in 2015 the median construction time was 6 years and fairly stable10. 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. 10 This data is from the IAEA’s own annual World Nuclear Performance Report. We include only the latest 2024 report in the reference list but studied the annual reports for 2015 and other nearby years to come to this conclusion GenCost 2024-25 | 23 ChinaFinlandIndiaIranSouthKoreaPakistanRussiaUnitedArabEmiratesUnitedStates012345678910DemocracyscoreWesterndemocraciesAsiandemocraciesLowscoringdemocraciesChinaFinlandIndiaIranSouthKoreaPakistanRussiaUnitedArabEmiratesUnitedStates012345678910DemocracyscoreWesterndemocraciesAsiandemocraciesLowscoringdemocracies 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. 0 5 10 15 20 25 Years of construction 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 democratic consultation and construction experience both play into achievable construction times. Given the direction of construction data available after the report’s release, 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 24 | CSIRO Australia’s National Science Agency 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. GenCost 2024-25 | 25 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 capacity11. 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 (2024b). Aurecon (2024b) 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 (2024b) 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 firstof- 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 11 This is not strictly true of all models but is most true of long-term investment models. In other models, investment costs are converted to an annuity (adjusted for different economic lifetimes), or additional capital costs may be added later in a project timeline for replacement of key components. 26 | CSIRO Australia’s National Science Agency 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. As such, while special circumstances occurred in each case, we cannot rule out FOAK premiums of 100% applying to other 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. 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, and unique site conditions. It is likely that 2023 nuclear SMR costs include some FOAK costs. The extent of that FOAK component will not be completely transparent until further projects proceed. The first commercial project did not proceed, and the next may be some years ahead. Further complicating matters, the first commercial project to almost proceed coincided with a large global inflationary event. We can remove all the identifiable inflationary impacts based on a costing that was available before the pandemic, but cannot reliably identify the FOAK share of the cost increase. 3.2 Capital cost source AEMO commissioned Aurecon (2024b) 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 (2024b) which represents a July estimate and so it is consistent with either the beginning of the financial year 2024-25 or the middle of GenCost 2024-25 | 27 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 (2024b) 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. 14000 12000 10000 8000 2024-25 $/kW 6000 4000 2000 0 Black coal with CCS Gas with CCS Gas reciprocating Large scale solar PV Wind Offshore wind fixed 2018 2019 2020 2021 2022 2023 2024 Figure 3-1 Comparison of current capital cost estimates with previous reports (FYB) 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 two 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 all technologies (Figure 3-2). 28 | CSIRO Australia’s National Science Agency 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 mostly still experiencing real cost increases but at a reduced rate compared to the previous two years. 2022-23 2023-24 2024-25 35% 24% 13% 9% 20% -2% 14% -8% 8% 2%4% 11% -8% 2% -20% Black coal Gas combined cycle Large scale solar PV Wind (onshore) Large scale battery (2hr) 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 durations12 of batteries, adiabatic compressed air energy storage (A-CAES) and pumped hydro energy storage (PHES) in $/kW and $/kWh. Battery durations of 24 hours and 48 hours have been added for the first time. 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), 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 kWh13. 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. 12 The storage duration used throughout this report refers to the maximum duration for which the storage technology can discharge at maximum rated power. However, it is important to note that every storage technology can discharge for longer by doing so at a rate lower than their maximum rated power 13 Component costs basis is when the power and storage components are separately costed and must be added together to calculate the total project cost. GenCost 2024-25 | 29 Note that these $/kWh costs are not for energy delivered but rather a capacity of storage. GenCost does not present levelised costs of storage. However, these are available from the CSIRO (2023) Renewable Energy Storage Roadmap. While A-CAES 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 depending on the operation of other generation in the system, particularly the scale of variable renewable generation and peaking plant (see Section 5). 1400 1200 1000 800 600 400 200 0 Battery (1hr) Battery (2hrs) Battery (4hrs) Battery (8hrs) Battery (24hrs) Battery (48hrs) PHES (10hrs) A-CAES (12hrs) PHES (12hrs) A-CAES (24hrs) PHES (24hrs) PHES (48hrs) GenCost 2019-20 Aurecon 2019-20 AEMO ISP Dec 20Aurecon 2020-21 21 AEMO ISP Jun 2022/CSIRO Aurecon 2021-22 Aurecon 2022-23 Aurecon 2023-24 Aurecon 2024-25 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%14. Aurecon (2024b) 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% 2024-25 $/kWh 14 The batteries in this publication have additional capacity which is not usable (e.g., there is typically a minimum 20% state of charge). This unusable capacity is not counted in the capacity of the battery or in any expression of its costs. When other publications include this unusable capacity the depth of discharge is less than 100%. 30 | CSIRO Australia’s National Science Agency lower battery cost for a 1-hour duration battery, scaling down to a 1% cost reduction for 8 hours duration and negligible for longer durations. PHES is more difficult to co-locate. Battery costs (battery and balance of plant in total) have decreased significantly by 11% to 36% depending on the duration. 25000 050001000015000200002024-25$/kW Battery Battery Battery Battery Battery Battery PHES A-CAES PHES A-CAES PHES PHES (1hr) (2hrs) (4hrs) (8hrs) (24hrs) (48hrs) (10hrs) (12hrs) (12hrs) (24hrs) (24hrs) (48hrs) GenCost 2019-20 AEMO ISP Dec 2021 Aurecon 2019-20 Aurecon 2020-21 Aurecon 2021-22 Aurecon 2022-23 Aurecon 2023-24 Aurecon 2024-25 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 projects15. 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 (2024b) 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 15 The PHES capital costs used in this report are based on taking the mid-point of the range provided by Aurecon (2024b). Percentage differences will be higher or lower for projects at different ends of that range. GenCost 2024-25 | 31 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 Levelised Cost of Storage (LCOS) can be found in CSIRO’s Renewable Energy Storage Roadmap (CSIRO, 2023), but is outside the scope of GenCost. 32 | CSIRO Australia’s National Science Agency 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 2023 World Energy Outlook (IEA, 2023) scenario matching to the Stated Policies scenario, Announced Pledges Scenario respectively and Net Zero Emissions by 2050. Various elements, such as the degree of vehicle electrification and hydrogen production, are also consistent with the IEA scenarios. The final GenCost 2024-25 report will update the scenario data to align with the IEA’s 2024 World Energy Outlook. However, the timing of its release does not allow for inclusion in the Consultation draft. 4.1.1 Current policies The Current policies scenario includes existing climate policies as at mid-2023 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 policies16. 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. 16 The application of a combination of carbon prices and other non-carbon price policies is consistent with the approach applied by the IEA. While we directly apply the IEAs published carbon prices, we design our own implementation of non-carbon price policies to ensure we match the emissions outcomes in the relevant IEA scenario. Structural differences between GALLM and the IEA’s models means that we cannot implement the exact same non-carbon price policies. Even if our models were the same, the IEA’s description of non-carbon price policies is insufficiently detailed to apply directly. GenCost 2024-25 | 33 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 KeydriversGlobalNZEby2050GlobalNZEpost2050Currentpolicies IEA WEO scenario Net zero emission by 2050 Announced pledges Stated policies scenario alignment scenario CO2 pricing / climate Consistent with 1.5 Based on NDCs and Based on current policies policy degrees world announced targets only Renewable energy targets High reflecting confidence Renewable energy policies Current renewable energy and forced builds / in renewable energy extended as needed policies accelerated retirement Demand / Electrification High Medium-high Medium Learning rates1 Stronger Normal maturity path Weaker Renewable resource & Less constrained Existing constraint More constrained than other renewable assumptions existing assumptions constraints2 Decentralisation Less constrained rooftop Existing rooftop solar PV More constrained rooftop solar photovoltaics (PV)2 constraints2 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. 34 | CSIRO Australia’s National Science Agency Projection results 5.1 Short-term inflationary pressures 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 those projected by learning-by-doing and economies of scale. CSIRO has explored a number of resources to understand cost increases already embedded in technology costs and 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 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 capacity17). 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 17 If the world ramps up to X GW per year technology manufacturing capacity by a certain date, then, without expanding manufacturing capacity any further, it can meet any future capacity target after that date up to the value of bX (where b is the years since the manufacturing capacity was established). The future capacity target would need to include all capacity needed to meet growth as well as replace retiring plant. GenCost 2024-25 | 35 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 costs resume their pre-pandemic modelled pathway. In response to feedback, this report includes an exception which is that onshore wind costs do not return to their normal path until 2035. Of all the technologies that are currently in high demand, onshore wind capital costs were impacted the most and have demonstrated to be the slowest to recover. It is therefore appropriate to give onshore wind a separate pathway. A consequence of this modelling approach is that all of the cost reductions to either 2027 or 2030 (or 2035 for onshore wind) 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. An exception to the resumption of a modelled cost path after 2027, 2030 or 2035 is that the projection has been adjusted to recognise that land may be a source of ongoing input scarcity. Land costs generally make up 2% to 9% of generation, storage and electrolyser capital costs. The projections take the land share of capital costs provided in Aurecon (2024b) and inflate that proportion of costs by the real land cost index that is published in Mott MacDonald (2023)18. This common land cost index provides some consistency between the treatment of land costs between transmission, generation and storage assets in AEMO’s modelling. The inclusion of a specific land cost inflator is a recent feature, first included in GenCost 2022-23: Final report. All projections start from a current cost and the primary source of 2024 costs is Aurecon (2024b) 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. 5.2 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. 18 It is referred to as an easement cost index in that document. 36 | CSIRO Australia’s National Science Agency 90000 Generation (TWh) 80000 70000 60000 50000 40000 30000 20000 10000 0 Current policies Global NZE by Global NZE post Current policies Global NZE by Global NZE post 2050 2050 2050 2050 2030 2050 BECCS Coal Coal CCS Gas Gas CCS Hydro Nuclear Oil Solar Wind onshore Wind offshore Other renewables 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 electrification because it is a slower decarbonisation pathway than the other scenarios considered. However, it has the least energy efficiency and industry transformation19. 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. Current policies has the lowest non-hydro renewable share at 48% 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 2050. The Global NZE by 2050 scenario is close to but not completely zero emissions by 2050. All generation from fossil fuel sources is with CCS accounting for 3% of generation by 2050. Offshore wind features strongly in this scenario at 23% of generation by 2050. Renewables other than 19 Economies can reduce their emissions by reducing the activity of emission intensive sectors and increasing the activity of low emission sectors. This is not the same as improving the energy efficiency of an emissions intensive sector. Industry transformation can also be driven by changes in consumer preferences away from emissions intensive products. GenCost 2024-25 | 37 hydro, biomass, wind and solar are 1% of generation in 2050. The greater deployment of renewables and CCS leads to lower renewable and CCS costs. 500 450 Hydrogen production (Mt) 400 350 300 250 200 150 100 50 0 CurrentpoliciesGlobalNZEby2050GlobalNZEpost2050CurrentpoliciesGlobalNZEby2050GlobalNZEpost205020302050 Electrolysis Steam methane reforming Steam methane reforming with CCS Figure 5-2 Global hydrogen production by technology and scenario, Mt 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, where the current costs have not changed and the assumed improvement rate after 2027 or 2030 (depending on the scenario) is very similar, their projection pathways often overlap. The land cost inflator results in a 56% increase in land costs by 2050 but this is only a small portion of capital costs. Before the land cost inflation is added, the assumed annual rate of cost reduction for mature technologies post-2027 or 2030 (depending on the scenario) is 0.35% (the same as previous reports given the rate is based on a long-term historical trend). The method for calculating the reduction rate for mature technologies is outlined in Appendix A. Data tables for the full range of technology projections are provided in Appendix B and can be downloaded from CSIRO’s Data Access Portal20. 5.3.1 Black coal ultra-supercritical The updated cost of black coal ultra-supercritical plant in 2024 has been sourced from Aurecon (2024b). Prior to 2023-24, the black coal capital cost had previously been based on a supercritical 20 Search GenCost at https://data.csiro.au/collections 38 | CSIRO Australia’s National Science Agency 2024-25 $/kW 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 to the Current policies scenario and the learning rate is low. The outlook for costs in all scenarios is flat, with a slight increase due to increasing land costs. 7000 6000 5000 4000 3000 2000 1000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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 current 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 in a similar manner as mature technologies, but with differences to take account of its unique set of inputs. Thereafter, the capital cost of the mature parts of the plant improves at the mature technology cost improvement rate. For the CCS components, the cost reductions are a function of global deployment of gas and coal with CCS, steam methane reforming with CCS and other industry applications of CCS. Compared to the 202324 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 current inflationary pressures. Current policies has no uptake of steam methane reforming with CCS in hydrogen production. Consequently, cost reduction from the late 2030s is mainly driven by the deployment of CCS in other industries. While black coal with CCS benefits from co-learning from deployment of CCS in GenCost 2024-25 | 39 non-electricity industries, there is only a negligible amount of generation from black coal with CCS throughout the projection period. 14000 800010000120002024-25$/kW 6000 4000 2000 0 2015 2024-25 Glo2023-24 GloHistory 2020 bal NZE by 2050 bal NZE by 2050 2025 2030 2024-25 Gl2023-24 Gl2035 obal NZE post 2050 obal NZE post 2050 2040 2045 2024-25 C2023-24 C2050 urrent policies urrent policies 2055 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 learning and cost reduction by 2050 but with differences in the timing of reductions. 5.3.3 Gas combined cycle Aurecon (2024b) have included an increase in gas combined cycle costs for 2024 but this needs to be interpreted with caution because the 2024 capital cost update includes a premium for hydrogen readiness that was not included in previous data. CSIRO has imposed an assumed return to previous costs levels by 2027 in Current policies and 2030 in the Global NZE scenarios while still maintaining the premium for hydrogen readiness. After the return to normal period, because gas combined cycle is classed as a mature technology for projection purposes, its change in capital cost is governed by our assumed cost improvement rate for mature technologies together with a land cost increase for all scenarios. Consequently, the rate of improvement is constant across the Current policies, Global NZE by 2050 and Global NZE post 2050 scenarios. 40 | CSIRO Australia’s National Science Agency 3000 10001500200025002024-25$/kW 500 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History Figure 5-5 Projected capital costs for gas combined cycle by scenario compared to 2023-24 projections 5.3.4 Gas with CCS The current cost for gas with CCS has been revised upwards for the 2023-24 projections reflecting the increase in gas combined cycle capital costs. The relativities between the scenarios reflect the differences in global deployment in electricity generation, hydrogen production and other industry uses of CCS. Global NZE by 2050 and Global NZE post 2050 have the highest total deployment of all CCS technologies. Subsequently, gas with CCS is lower by 2050 in those scenarios. Conversely, CCS has the highest cost in Current policies where CCS deployment is lowest. The projection reductions in the cost of CCS are much less than in the 2023-24 projections because the low cost of solar PV while other technology costs have increased has meant a greater share of solar and lower share of CCS. Less deployment limits the amount of cost reduction that can be achieved. The IEA CCS database21 indicates there are over 100 planned electricity related projects which are yet to make a financial investment decision, six under construction and five completed. The advanced projects are for smaller volumes and/or low capture rates. Given the current state of the pipeline of projects, significant global deployment of CCS is not expected until after 2030. 21 CCUS Projects Database -Data product -IEA GenCost 2024-25 | 41 7000 200030004000500060002024-25$/kW 1000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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. Detail of previous costs are not included because the technology design has changed, impacting the costs. That is, 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. This is in addition to the existing ability to use liquid fuels such as diesel or renewable diesel. The small open cycle gas technology is designed with a maximum 35% hydrogen blend. The large size is designed for 10%. Aurecon (2024b) provides additional details for the unit sizes and total plant capacity that defines the small and large sizes. Given this is a new technology type with no historical series, no provision is made for reverting back to previous costs over time. Aside from assumed increasing land costs, open cycle gas is classed as a mature technology for projection purposes and as a result its change in capital costs is also governed by our assumed cost improvement rate for mature technologies. Consequently, the rate of improvement is constant across the scenarios. 42 | CSIRO Australia’s National Science Agency 3000 2500 2023-24 $/kW Small 2000 1500 Large 1000 500 0 2020 2025 2030 2035 2040 2045 2050 2055 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies 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 updated 2024 capital cost of around $29,600/kW. Cost reductions to 2027 in Current policies and to 2030 in the Global NZE scenarios are an assumed unwinding of global inflationary premiums following the COVID-19 pandemic. Cost reductions after those times are due to learning. The nuclear SMR capital cost results are higher for longer in the updated projections compared to 2023-24 because updates have been made to the expected dates of deployment for projects, which have been pushed further into the future, beyond the 2020s (Global Energy Monitor, 2024a). Later deployment of nuclear SMR means it takes longer for capital cost reductions due to learning-by-doing and economies of scale to materialize. Capital costs only improve slightly for the Current policies scenario due to a low deployment of projects in the 2030s followed by a later stage of deployment in the 2040s. Global NZE by 2050 achieves a similar level of deployment as Current policies but with deployment commencing 10 years earlier due to a stronger commitment to addressing climate change. In Global NZE post 2050, the less competitive renewables means that nuclear SMR can deploy a little further into the late 2030s than under Global NZE by 2050. As a result, this scenario has the largest deployment of nuclear SMR with subsequently lower cost reductions through the learning rate assumptions which may be partly driven by modular manufacturing processes. Modular plants reduce the number of unique inputs that need to be manufactured. Capital costs are between approximately $12,000/kW and $16,000/kW across the scenarios by the 2040s. GenCost 2024-25 | 43 35000 10000150002000025000300002024-25$/kW 5000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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 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 decline after 2027 or 2030 at the pre-determined annual cost reduction rate assigned to all mature 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 around proven designs. 44 | CSIRO Australia’s National Science Agency 10000 80009000 7000 2024-25 $/kW 6000 5000 4000 3000 2000 1000 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History Figure 5-9 Projected capital costs for large-scale nuclear by scenario compared to 2023-24 projections 5.3.8 Solar thermal The starting cost for solar thermal has been updated by Aurecon (2024b) drawing on Fichtner Engineering (2023) which includes a change to the baseline configuration, with a storage duration of 16 hours. The small increase in current year costs is the main cause for changes in the projection compared to 2023-24. Otherwise, the projections diverge by a similar amount according to their scenario with the greatest cost reductions projected to be stronger the greater the global climate policy ambition. Solar thermal systems consist of the combination of solar mirror field, thermal storage and power blocks that are sized in varying ratios according to the location and market signals that prevail. Each such configuration will have a different capital cost. As a consequence, the baseline configuration represented in the capital cost projection data is not the same as the configurations used to calculate the LCOEs in Section 6. GenCost 2024-25 | 45 10000 30004000500060007000800090002024-25$/kW 2000 1000 0 2015 2024-25 Glo2023-24 GloHistory 2020 2025 bal NZE by 2050 bal NZE by 2050 2030 2024-25 Gl2023-24 Gl2035 obal NZE post 2050 obal NZE post 2050 2040 2045 2024-25 C2023-24 C2050 2055 urrent policies urrent policies 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 (2024b) indicating solar PV production costs are recovering more rapidly than projected from global inflationary pressures. As a result of the ongoing cost reductions for this technology, we do not impose any additional cost reduction related to recovery from the global inflationary pressures. All cost reduction in the projection is due to learning through deployment. The 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. The differences are most prominent in the mid2030s whereas deployment in the next few years is less divergent across the scenarios. Cost outcomes across the three scenarios project a capital cost range of $550/kW to $800/kW. The final minimum cost level for solar PV is one of the most difficult to predict because, unlike other technologies, and notwithstanding recent inflationary pressures, the historical learning rate for solar PV has not slowed. The modular nature of solar PV appears to be the main point of difference in explaining this characteristic. 46 | CSIRO Australia’s National Science Agency 2500 5001000150020002024-25$/kW 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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 202324 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 prices22. This data is best interpreted as a mean and may not align with the lowest cost systems available. Rooftop solar PV benefits from co-learning with the components in common with large scale PV generation and is also impacted by the same drivers for variable renewable generation deployment across scenarios. As a result, we can observe similar trends in the rate of capital cost reduction in each scenario as for large-scale solar PV. 22 The Cost of Solar Panels -Solar Panel Price | Solar Choice GenCost 2024-25 | 47 2500 5001000150020002024-25$/kW 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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. Costs have risen around 36% since the beginning of the pandemic. Equipment costs appear to account for around 40% of that increase with the remainder reflecting other factors such as local installation23. The updated Aurecon (2024b) data indicates that the rate of increase is slowing. 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 be reduced with greater global climate policy ambition and subsequent deployment. Land cost increases are assumed which will partially offset these reductions. Cost reductions are strongest under Global NZE by 2050 resulting in a range of around $1830/kW to $1910/kW by 2055. 23 Based on analysis of Vestas average selling price adjusted for Australian dollars. 48 | CSIRO Australia’s National Science Agency 3500 2000250030002024-25$/kW 1500 1000 500 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 2024-25 Global NZE post 2050 2024-25 Current policies 2023-24 Global NZE by 2050 2023-24 Global NZE post 2050 2023-24 Current policies History 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 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 metres24 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 (2024b). The updated 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 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 so 24 This is more an economic than absolute technical limit. GenCost 2024-25 | 49 GenCost will continue to be required to rely on global sources of offshore wind cost data until then. 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 constraints 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. 0100020003000400050006000700080009000100002024-25$/kW 2015 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Global NZE by 2050 (fixed) 2024-25 Global NZE post 2050 (fixed) 2024-25 Current policies (fixed) 2024-25 Global NZE by 2050 (floating) 2024-25 Global NZE post 2050 (floating) 2024-25 Current policies (floating) History 2023-24 Global NZE by 2050 (fixed) 2023-24 Global NZE post 2050 (fixed) 2023-24 Current policies (fixed) 2023-24 Global NZE by 2050 (floating) 2023-24 Global NZE post 2050 (floating) 2023-24 Current policies (floating) Figure 5-14 Projected capital costs for fixed and floating offshore wind by scenario compared to 2023-24 projections 5.3.13 Battery storage The projections for batteries include a 11% to 36% decrease in total costs (depending on the duration) which is faster than the 2023-24 projections. The costs shown in Figure 5-15 are for a 2hour duration battery (total battery cost including battery and balance of plant). Given the 2024 cost reduction takes batteries back to their pre-pandemic levels we do not impose any additional reduction beyond the learning projected by the modelling. The projections use different learning rates by scenario to reflect the uncertainty as to whether they will be able to continue to achieve their high historical cost reduction rates (notwithstanding the pandemic period). Historical cost reductions have mainly been achieved through deployment in industries other than electricity such as in consumer electronics and electric vehicles. However, small-and large-scale stationary electricity system applications are growing globally. Under the three global scenarios, batteries have a large future role to play in supporting variable renewables 50 | CSIRO Australia’s National Science Agency alongside other storage and flexible generation options and in growing electric vehicle deployment. The projected future change in the total cost of battery projects is shown in Figure 5-15. 1200 20040060080010002024-25$/kWh 0 2015 2020 2025 2030 2035 2040 2045 2050 2055 2023-24 Current policies 2024-25 Current policies History 2023-24 Global NZE post 2050 2024-25 Global NZE post 2050 2023-24 Global NZE by 2050 2024-25 Global NZE by 2050 Figure 5-15 Projected total capital costs for 2-hour duration batteries by scenario (battery and balance of plant) Battery deployment is strongest in the Global NZE by 2050 scenario reflecting stronger deployment of variable renewables, which increases electricity sector storage requirements. 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. A breakdown of battery pack and balance of plant costs for various storage durations are provided in Appendix B. Aurecon (2024b) 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 is assumed to be a mature technology and receives the same assumed improvement rate as other mature technologies. Unlike the other technologies, all three scenarios assume costs return to normal by 2030 (rather than in 2027 for Current policies). This reflects the longer lead time for PHES projects which means it is unlikely the level of global climate ambition will result in different cost trajectories before 2030. Site variability is more likely a greater source of variation in costs. GenCost 2024-25 | 51 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). 7000 50006000 W4000 2024-25 $/k3000 2000 1000 0 2015 2024-25 Glo2023-24 GloHistory 2020 bal NZE by 2050 bal NZE by 2050 2025 2030 2024-25 Gl2023-24 Gl2035 obal NZE post 2050 obal NZE post 2050 2040 2045 2024-25 C2023-24 C2050 urrent policies urrent policies 2055 Figure 5-16 Projected capital costs for pumped hydro energy storage (24-hour) by scenario 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 costs. 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. 52 | CSIRO Australia’s National Science Agency 30000 25000 2024-25 $/kW 20000 15000 10000 5000 0 2020 2025 2030 2035 2040 2045 2050 2055 Tidal/Ocean current Biomass with CCS 2023-24 Wave Fuel cell 2023-24 Tidal/Ocean current 2023-24 Biomass with CCS Wave 2023-24 Fuel cell Figure 5-17 Projected technology capital costs under the Current policies scenario compared to 2023-24 projections Global NZE by 2050 Biomass with CCS is adopted in the Global NZE by 2050 scenario but can only achieve learning in the CCS component of the plant. Cost reductions reflect learning from its own deployment and co- learning from deployment of CCS in other electricity generation, hydrogen production and other industry sectors. Biomass with CCS is an important technology in some global climate abatement scenarios if the electricity sector is required to produce negative abatement for other sectors. However, we are not able to model that scenario with GALLME. GALLME only models the electricity sector and from that perspective alone, biomass with CCS is a relatively high-cost technology. Wave energy is deployed at a minor level in the 2050s. Fuel cells and tidal/ocean current generation are not deployed. The higher costs (reflecting lower deployment) relative to 2023-24 are the result of ongoing cost increases for these technologies relative to more mature technologies such as solar PV whose costs have decreased. GenCost 2024-25 | 53 30000 5000100001500020000250002024-25$/kW 0 2020 2025 2030 2035 2040 2045 2050 2055 Tidal/Ocean current Biomass with CCS 2023-24 Wave Fuel cell 2023-24 Tidal/Ocean current 2023-24 Biomass with CCS Wave 2023-24 Fuel cell 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 only 10% of the level of Global NZE by 2050 but achieves similar 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 gas with CCS generation and steam methane reforming with CCS which brings down the cost of all CCS technologies sooner compared to Current policies. Tida/ocean current energy is deployed at a minor level in the 2050s. Fuel cells are deployed in the 2040s but wave energy is not. Higher costs relative to 2023-24 reflect the increasing gap between costs of these technologies and more mature renewables. 54 | CSIRO Australia’s National Science Agency 30000 5000100001500020000250002024-25$/kW 0 2020 2025 2030 2035 2040 2045 2050 2055 Tidal/Ocean current Biomass with CCS 2023-24 Wave Fuel cell 2023-24 Tidal/Ocean current 2023-24 Biomass with CCS Wave 2023-24 Fuel cell 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 (2024b). 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 sperate 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 reflect future uncertainty. 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 GenCost 2024-25 | 55 technologies indicates that this type of globally coincident technology deployment activity can lead to a scale-up in manufacturing which supports cost reductions through economies of scale. 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 $816/kW to $1613/kW. The range of alkaline electrolysers is $435/kW to $1138/kW. 3500 500100015002000250030002024-25$/kW 0 2020 2025 2030 2035 2040 2045 2050 2055 2024-25 Current policies Alkaline 2024-25 Global NZE by 2050 Alkaline 2024-25 Global NZE post 2050 Alkaline 2023-24 Current policies Alkaline 2023-24 Global NZE by 2050 Alkaline 2023-24 Global NZE post 2050 Alkaline 2024-25 Current policies PEM 2024-25 Global NZE by 2050 PEM 2024-25 Global NZE post 2050 PEM 2023-24 Current policies PEM 2023-24 Global NZE by 2050 PEM 2023-24 Global NZE post 2050 PEM Figure 5-20 Projected technology capital costs for alkaline and PEM electrolysers by scenario, compared to 2023-24 56 | CSIRO Australia’s National Science Agency 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 investment25. Modelling studies such as AEMO’s Integrated System Plan do not require or use LCOE data26. 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, we proposed a new method for addressing the first dot point – inclusion of integration costs unique to variable renewables. That new method was implemented in the 2020-21 GenCost report and we update results from that method in 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 25For a description the LCOE formula and the application of the formula go to CSIRO’s Data Access Portal and download the latest Excel file that accompanies this report. CSIRO Data Access Portal 26 LCOE is a measure of the long run marginal cost of generation which could partly inform generator bidding behaviour in a model of the electricity dispatch system. However, in such cases, it would be expected that the LCOE calculation would be internal to the modelling framework to ensure consistency with other model inputs rather than drawn from separate source material. GenCost 2024-25 | 57  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 years27). 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. 27 Interest lost during construction is added so that the advantage given by projects that take less time to build is recognised. 58 | CSIRO Australia’s National Science Agency 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 LCOEdataQuestionanswered 2024 variable renewables LCOE with integration costs 2024 LCOE of all other generation technologies 2030 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? Assuming any existing capacity available in 2023 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? 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 Assuming any existing capacity available in 2030 is free and sufficient to other generation support reliable integration, what is the total unit cost an investor must technologies recover to deliver a project that provides electricity supply in 2030? GenCost 2024-25 | 59 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%28 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 competitiveness29. Victorian legislation creates are 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. 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 range30. Stakeholders have previously indicated they prefer a range rather than a single estimate of LCOE. However, it is important to note that these capacity factors are not used at all in the modelling of renewable integration costs. When modelling renewable integration costs, we use the variable renewable energy production traces published by AEMO for its Integrated System Plan. We incorporate the uncertainty in variable renewable production by modelling nine different weather years, 2011 to 2019, and the results represent the highest cost outcome from these alternate weather years. The model covers the NEM, the South West Interconnected System (SWIS) in Western Australia (WA) and the remainder of WA. Northern Territory (NT) is not 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 65% renewable share and 57% variable renewable share in Australia ex-NT31 (both excluding rooftop PV). The share fluctuates a few percent 28 Above 90% VRE share is of limited interest because it would mean forcibly retiring other non-variable renewables such as hydro and biomass which would not be optimal for the system. Also there is no current requirement for the electricity system to be emissions free. For example, a 95% emissions free electricity system could still be consistent with meeting Australia’s 2050 net zero emission goal. 29 This does not preclude other types of projects proceeding in reality but is a reflection of modelling inputs in 2024 and 2030. 30 The capacity factor range assigned to new build technologies is based on a formula which uses the ten-year average capacity factors. For the high range, we use the high range of historically achieved capacity factors. However, the low range capacity factor assumption is closer to the average capacity factor rather than the lowest case. Specifically, we assume the low range value is 10% below the average on the basis that if a project cannot achieve a capacity factor at least that level it is unlikely to proceed as a new investment. Appendix D of the GenCost 2022-23: Final report provided a discussion of historical capacity factors upon which the data in this report is based. 31 We do not include the impact of the Capacity Investment Scheme which is a national policy for achieving 82% renewables by 2030. In the June 2022 ISP, the 82% renewables policy was consistent with 65% large-scale VRE share with the remainder of renewable share made up of hydro, biomass and rooftop solar PV (which represents small-scale VRE). As such, most of the large-scale VRE shares explored in GenCost exceed government policy to 2030 except the 60% case. We exclude the CIS policy so that the 60% case can remain and the trend in progression of costs from 60% to 90% can still be observed. 60 | CSIRO Australia’s National Science Agency 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 states 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 infeasible32. Snowy 2.0 ($12 billion) and battery of the nation ($3.3 billion) pumped hydro projects are assumed to be committed with construction complete before 2030 in the BAU, as well as various transmission expansion projects already flagged by the June 2022 ISP process to be necessary before 2030 (Table 6-2). The NSW target for an additional 2 GW of at least 8 hours duration storage is also assumed to be committed and complete by 2030 together with the Kurri Kurri gas peaking plant33. For the 2024 calculations, we abstract from reality and assume these projects can be completed immediately so that the cost of these committed projects is included in the current cost of integrating variable renewables34. These costs are included regardless of the VRE share. Pumped hydro, battery and peaking plant costs are sourced either directly from the project source or AEMO inputs and assumptions workbook (AEMO, 2023a). Transmission costs are from AEMO (2023b). For the 2030 investor, all of these projects are considered free capacity in the same way that existing capacity now is free for the 2024 analysis. This approach is consistent with the aim of the LCOE analysis (Table 6-1). Table 6-2 Committed investments by category included in the 2023 cost of integrating variable renewables Category$billion Transmission 15.9 Storage 22.9 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). 32 The model would be unable to simultaneously meet the minimum VRE share and the minimum run requirements of coal plant which are around 30% to 50% of rated capacity. There have been experiments in Australia to determine whether some coal plant could switch off completely rather have a minimum run constraint. However, currently, not enough is known about this mode of operating coal generation to include it in the modelling. 33 The Tallawarra B gas-fired generation project is already in operation and is not included. 34 This is necessary because the LCOE methodology is designed to annualise all project costs into a single year. It is not well suited to costing a progression of projects over multiple years. Multi-year investment problems can be studied more appropriately in intertemporal electricity system models. GenCost 2024-25 | 61 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 4.8GW, solar PV capacity by 3.9GW and large-scale batteries (VPP capacity is fixed) by 1.4GW. The capacities shown have been compared with the AEMO ISP 2030 capacity projections. The NEM coal retirements to 2030 are aligned with Step Change (June 2022 release) but the overall demand and renewable generation is lower. Wind capacity is preferred over solar PV by 2030. However, this preference is stronger in the ISP35. The NEM and WA total variable renewable shares are 57% and 52% on average across the weather years. NEM+ WA 25 20 GW 15 10 5 0 Wind Solar PV CCGT Peaking gas & liquids Black coal Brown coal Hydro Pumped hydro VPP and large-scale battery 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 35 This outcome only relates to 2030 and large-scale generation. When rooftop solar PV is included and as solar PV costs fall faster in the projections, a closer share of wind and solar PV is likely to emerge in the long run as reflected in the global generation mix in Figure 5-1 62 | CSIRO Australia’s National Science Agency 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 $125/MWh on average in the NEM. This is 58% higher than average costs in 2030 for 60% VRE, but only 18% 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). 140 140 NEM -2030 NEM -2024 120 120 100 100 $/MWh 60%VRE70%VRE80%VRE90%VRE 80 $/MWh 80 40 40 20 20 0 60% VRE 70% VRE 80% VRE 90% VRE 0 Generation Spillage REZ transmission Generation Spillage REZ transmission Other transmission Synchronous condensers Storage Other transmission Synchronous condensers Storage 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 2023 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. 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 60 60 GenCost 2024-25 | 63 over building VRE capacity to increase its minimum production levels36. Given the low cost of VRE capacity, this 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 given the limited ability to connect, via transmission, the various isolated systems in that state. Higher or lower costs in different states or regions are averaged out at the aggregate level for the NEM and WA. The cost of REZ transmission expansions adds an average $8.20/MWh in 2024 and $5.30/MWh in 2030, as the VRE share increases from 60% to 90%. Other transmission costs add $10.00/MWh in 2024 and $4.20/MWh in 2030. Storage costs add an average $19.20/MWh in 2024 and $10.70/MWh in 2030. Spillage costs peak at the 90% VRE share at $8.70/MWh in 2024 and $15.10/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 $42/MWh to $48/MWh in 2024 and $20/MWh to $50/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 and its higher capacity factor could result in lower storage costs. Integration costs have only been calculated for onshore wind in this report given it remains the lowest cost form of wind generation. The cost range for variable renewables with integration costs is the lowest of all new-build technology capable of supplying reliable electricity in 2024 and 2030. The cost range overlaps slightly with the lower end of the cost range for high emission coal and gas generation. However, the lower end of the range for coal and gas is only achievable if they can deliver a high capacity factor and source low cost fuel. Their deployment is also not consistent with Australia’s net zero by 2050 target. If we exclude high emission generation options, the next most competitive generation technologies are solar thermal, gas with carbon capture and storage (CCS) and large- scale nuclear. 36 The spilled electricity cost is calculated as the LCOE of the variable renewable generation equipment when calculated via total additional generation minus the LCOE when calculated on the basis of useful generation only (defined as the minimum additional generation needed to meet the next 10% increment of VRE share). 64 | CSIRO Australia’s National Science Agency 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. 100200300400500600700 2024-25 A$/MWh Gas turbine smallGas turbine largeGas reciprocatingH2 reciprocatingPeaking 20% load Black coalBrown coalGasFlexible load, high emission Black coal with CCSGas with CCSSolar thermalNuclear SMRNuclear large-scaleFlexible load, low emission Solar PVWind onshoreWind offshore (fixed) Standalone Variable 60% VRE share70% VRE share80% VRE shareWind & solar PV combined 90% VRE share Variable with integration costs 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 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. GenCost 2024-25 | 65 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. 100200300400500600 2024-25 A$/MWh Gas turbine smallGas turbine largeGas reciprocatingH2 reciprocatingPeaking 20% load Black coalBrown coalGasFlexible load, high emission Black coal with CCSGas with CCSSolar thermalNuclear SMRNuclear large-scaleFlexible load, low emission Standalone Variable Solar PVWind onshoreWind offshore (fixed) Wind & solar PV combined Variable with integration costs 60% VRE share70% VRE share80% VRE share90% VRE share 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. 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 factor37 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%. 37 The lowest cost flexible plant in the system will typically be able to operate at this high capacity factor. However, this will be challenging for new plant to achieve. Older existing plant, with their capital costs mostly paid down and access to existing low cost fuel sources, are typically the lowest cost generation units. New generation units entering the market must recover their capital costs and tend to have less favourable fuel contracts. 66 | CSIRO Australia’s National Science Agency 050100150200250300350400450GasturbinesmallGasturbinelargeGasreciprocatingH2reciprocatingBlackcoalBrowncoalGasBlackcoalwithCCSGaswithCCSSolarthermalNuclearSMRNuclearlarge-scaleSolarPVWindonshoreWindoffshore(fixed) StandalonePeaking20%loadFlexibleload,highemissionFlexibleload,lowemissionVariable050100150200250300350400450GasturbinesmallGasturbinelargeGasreciprocatingH2reciprocatingBlackcoalBrowncoalGasBlackcoalwithCCSGaswithCCSSolarthermalNuclearSMRNuclearlarge-scaleSolarPVWindonshoreWindoffshore(fixed) StandalonePeaking20%loadFlexibleload,highemissionFlexibleload,lowemissionVariable 2024-25 A$/MWh Figure 6-5 Calculated LCOE by technology and category for 2040 050100150200250300350GasturbinesmallGasturbinelargeGasreciprocatingH2reciprocatingBlackcoalBrowncoalGasBlackcoalwithCCSGaswithCCSSolarthermalNuclearSMRNuclearlarge-scaleSolarPVWindonshoreWindoffshore(fixed) StandalonePeaking20%loadFlexibleload,highemissionFlexibleload,lowemissionVariable 2024-25 A$/MWh 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 GenCost 2024-25 | 67 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 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 low demand level on these low renewable generation days is a more important benchmark in setting the amount of additional back-up capacity required. 68 | CSIRO Australia’s National Science Agency 70 0102030405060GW Peak Demand Demand @ min. renewable generation Dispatchable capacity Variable renewable capacity Peaking gas & liquids Hydro Coal and gas Storage 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 0.28kW to 0.41kW storage capacity for each kW of variable renewable generation installed38. 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 generation39 is substantially lower than maximum demand and can mostly be met by non-storage technologies (although in this example renewable generation is not zero and can still contribute)  Existing and new flexible capacity is very slightly lower than maximum demand. This indicates that there is some variable renewable generation available at peak demand events in at least one state of the NEM (mostly likely wind generation if the peak occurs outside of daylight hours such as in the evening or early morning)  Flexible capacity exceeds demand at minimum renewable generation  The required existing and new flexible capacity to support variable renewables is a fraction of total variable renewable capacity. 38 This ratio may change as storage and transmission are partial competitors and as such the storage ratio could increase if transmission becomes relatively more expensive. There has been a drift upwards in the ratio projected over the past few years of analysis. 39 Calculated as sum of coincident NEM state demand. GenCost 2024-25 | 69 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: −𝑏 𝐶𝐶 𝐼𝐶 = 𝐼𝐶0 × @ A , 𝐶𝐶0 or equivalently log(𝐼𝐶) = log( 𝐼𝐶0 ) − 𝑏(log(𝐶𝐶) − log(𝐶𝐶0)) 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: × (1 − 2−𝑏) 𝐿𝑅 = 100 (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. 70 | CSIRO Australia’s National Science Agency Apx Figure A.1 Schematic of changes in the learning rate as a technology progresses through its development stages after commercialisation However, technologies that 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 GenCost 2024-25 | 71 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 Mature technologies and the “basket of costs” There are three main drivers of mature technology costs: imported materials and equipment, domestic materials and equipment, and labour. The indices of these drivers over the last 20 years (ABS data) combined with the split in capital cost of mature technologies between imported equipment, domestic equipment and labour (BREE, 2012) was used to calculate an average rate of change in technology costs: -0.35%. This value has been applied as an annual capital cost reduction factor to mature technologies and to operating and maintenance costs. A.1.4 Offshore wind Offshore wind has been divided into fixed and floating foundation technologies. IRENA (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 (2024b) resulting in the values as shown in Apx Table A.1. Apx Table A.1 Cost breakdown of offshore wind CostcomponentFixedoffshorewind($/kW)Floatingoffshorewind($/kW) Foundation 597 2393 Remainder of cost 4065 4065 Total cost 4662 6459 The learning of all offshore wind components (i.e., “Remainder of cost” components) except for the foundations are shared among both offshore wind technologies. The floating foundations used in floating offshore wind have a learning rate, but the fixed foundations used in fixed offshore wind have no learning rate. 72 | CSIRO Australia’s National Science Agency Data tables The following tables provide data behind the figures presented in this document. The year 2024 is mostly sourced from Aurecon (2024b) and is aligned to July which represents either the middle of that calendar year or the beginning of the 2024-25 financial year. As discussed in Section 3, the data is not intended to include FOAK costs. Therefore, for technologies not recently constructed in Australia, the cost of the first plant may be higher than estimated here. Furthermore, capital costs are for a location not greater than 200km from the Victorian metropolitan area. Aurecon provide data for adjusting costs for different locations in the NEM. Site conditions will also impact costs to varying degrees, depending on the technology. GenCost 2024-25 | 73 Apx Table B.1 Current and projected generation technology capital costs under the Current policies scenario BlackcoalBlackcoalwithCCSBrowncoalGascombinedcycleGasopencycle(small) Gasopencycle(large) GaswithCCSGasreciprocatingHydrogenreciprocatingBiomass(smallscale) BiomasswithCCS(largescale) LargescalesolarPVRooftopsolarpanelsSolarthermal(16hrs)WindOffshorewindfixedOffshorewindfloatingWaveNuclearSMRTidal/oceancurrentFuelcellNuclearlargescale$/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 3223 4710 8362 15547 29667 12979 8067 8984 5781 11667 8782 2300 2428 1311 5404 1982 2073 8954 23149 1414 1287 6633 3113 4699 8345 13986 26958 11257 7710 8834 2026 5533 11091 8264 2144 2431 1312 5025 1984 2075 8995 21975 1369 1242 6482 3006 4687 8335 12531 24231 9690 7371 8687 2027 5363 10684 7907 2034 2426 1310 4765 1980 2071 9009 21152 1322 1198 6337 2900 4673 8323 11610 21414 8725 7075 8567 2028 5284 10474 7727 1977 2421 1307 4634 1976 2067 9023 20729 1284 1163 6209 2797 4658 8306 11177 21436 8279 7085 8500 2029 5292 10452 7712 1973 2416 1305 4624 1972 2063 9037 20688 1250 1131 6084 2698 4643 8291 11181 21301 8279 7087 8483 5301 10435 7698 1969 2412 1302 4617 1968 2059 9052 20651 1224 1106 5973 2603 4629 8282 11186 21168 8279 7087 8467 2031 5309 10421 7684 1966 2407 1300 4614 1965 2055 9067 20618 1191 1075 5872 2511 4615 8279 11190 21035 8279 7086 8452 2032 5316 10408 7670 1962 2403 1297 4611 1961 2051 9082 20586 1164 1050 5780 2423 4600 8282 11195 21071 8279 7096 8436 2033 5323 10397 7656 1959 2399 1295 4610 1957 2047 9097 20556 1140 1027 5696 2338 4586 8286 11199 21107 8279 7107 8421 2034 5330 10382 7642 1955 2394 1293 4605 1954 2044 9113 20524 1131 1017 5618 2231 4572 8289 11204 21144 8279 7121 8406 5338 10368 7629 1952 2390 1291 4601 1951 2040 9129 20492 1123 1009 5545 2152 4558 8291 11209 21181 8279 7134 8392 2036 5346 10342 7616 1948 2386 1288 4586 1947 2037 9146 20449 1115 1000 5476 2094 4544 8286 11214 21219 8279 7148 8377 2037 5354 10312 7603 1945 2382 1286 4565 1944 2033 9162 20402 1104 989 5411 2085 4530 8262 11219 21257 8279 7162 8363 2038 5362 10273 7590 1942 2378 1284 4537 1941 2030 9179 20347 1090 975 5350 2074 4516 8219 11224 20979 8279 7176 8349 2039 5370 10236 7578 1939 2374 1282 4510 1938 2027 9196 20294 1062 949 5291 2061 4503 8167 11229 19538 8279 7190 8336 5378 10202 7566 1935 2371 1280 4486 1934 2023 9214 20244 1016 907 5217 2047 4489 8134 11234 17735 8279 7205 8322 2041 5380 10159 7544 1930 2364 1276 4460 1929 2017 9219 20171 962 859 5125 2034 4474 8117 11236 16222 8279 7207 8298 2042 5382 10122 7522 1924 2357 1272 4440 1923 2012 9224 20105 921 821 5020 2025 4459 8117 11238 15874 8279 7194 8273 2043 5383 10084 7499 1918 2350 1269 4419 1917 2006 9230 20038 895 798 4922 2018 4444 8117 11239 15883 8279 7166 8249 2044 5383 10043 7477 1913 2343 1265 4395 1912 2000 9235 19968 875 780 4830 2013 4429 8115 11241 15892 8279 7131 8225 5382 9998 7456 1907 2336 1261 4367 1906 1994 9240 19894 857 763 4743 2007 4414 8113 11242 15901 8279 7100 8201 2046 5380 9953 7434 1902 2329 1258 4340 1901 1988 9245 19821 840 748 4661 2001 4399 8112 11244 15911 8279 7077 8177 2047 5379 9909 7412 1896 2322 1254 4312 1895 1982 9251 19748 825 735 4583 1993 4385 8111 11246 15920 8279 7057 8153 2048 5378 9868 7390 1891 2316 1250 4289 1890 1976 9256 19679 816 726 4510 1985 4370 8111 11247 15929 8279 7039 8129 2049 5377 9832 7369 1885 2309 1247 4268 1884 1971 9262 19614 810 720 4439 1974 4355 8111 11249 15938 8279 7021 8105 5376 9810 7356 1882 2305 1243 4257 1881 1967 9267 19575 807 718 4388 1967 4346 8112 11250 15948 8279 7012 8091 2051 5372 9776 7330 1875 2297 1243 4242 1874 1960 9267 19506 804 715 4337 1961 4330 8110 11250 15948 8279 6990 8063 2052 5370 9754 7313 1871 2291 1234 4233 1870 1956 9267 19462 803 714 4287 1958 4320 8110 11250 15948 8279 6976 8044 2053 5366 9712 7279 1862 2281 1234 4216 1861 1947 9267 19374 801 712 4237 1953 4300 8108 11250 15948 8279 6947 8007 2054 5364 9691 7262 1858 2275 1226 4208 1857 1942 9267 19331 800 711 4187 1950 4290 8107 11250 15948 8279 6932 7988 5362 9670 7245 1853 2270 1226 4200 1852 1938 9267 19287 798 710 4139 1947 4280 8106 11250 15948 8279 6917 7969 74 | CSIRO Australia’s National Science Agency Apx Table B.2 Current and projected generation technology capital costs under the Global NZE by 2050 scenario BlackcoalBlackcoalwithCCSBrowncoalGascombinedcycleGasopencycle(small) Gasopencycle(large) GaswithCCSGasreciprocatingHydrogenreciprocatingBiomass(smallscale) BiomasswithCCS(largescale) LargescalesolarPVRooftopsolarpanelsSolarthermal(16hrs)WindOffshorewindfixedOffshorewindfloatingWaveNuclearSMRTidal/oceancurrentFuelcellNuclearlargescale$/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 3223 4710 8362 15547 29667 12979 8067 8984 5919 11962 9048 2380 2428 1311 5599 1982 2073 8954 23891 1391 1266 6499 3091 4633 8168 14738 28093 12065 7626 8912 2026 5804 11669 8782 2304 2431 1312 5401 1984 2075 8995 23431 1332 1209 6251 2962 4569 8006 13958 26510 11194 7212 8842 2027 5674 11348 8498 2220 2426 1310 5194 1980 2071 9009 22909 1283 1163 6056 2837 4519 7884 13210 24838 10386 6796 8746 2028 5547 11036 8223 2137 2421 1307 4996 1976 2067 9023 22399 1231 1115 5868 2716 4476 7777 12501 23162 9636 6405 8652 2029 5423 10734 7958 2053 2416 1305 4805 1972 2063 9037 21902 1184 1071 5653 2601 4441 7686 11831 21481 8940 6037 8559 5344 10529 7779 1996 2412 1302 4678 1968 2059 9052 21563 1141 1031 5437 2491 4409 7601 11398 20359 8494 5753 8493 2031 5310 10421 7684 1966 2407 1300 4614 1965 2055 9067 21529 1105 997 5251 2386 4382 7529 11387 19233 8279 5754 8452 2032 5319 10408 7670 1962 2403 1297 4611 1961 2051 9082 21495 1080 974 5115 2285 4358 7467 11376 18273 8279 5762 8436 2033 5328 10397 7656 1959 2399 1295 4610 1957 2047 9097 21464 1062 956 4986 2188 4337 7413 11365 18304 8279 5772 8421 2034 5337 10382 7642 1955 2394 1293 4605 1954 2044 9113 21430 1000 900 4878 2095 4318 7366 11354 18336 8279 5782 8406 5347 10294 7629 1952 2390 1291 4530 1951 2040 9129 21321 911 818 4767 2030 4301 7325 11343 17453 8279 5794 8392 2036 5356 10206 7616 1948 2386 1288 4455 1947 2037 9146 21212 812 729 4674 1990 4286 7288 11332 16567 8279 5805 8377 2037 5366 10120 7603 1945 2382 1286 4381 1944 2033 9162 21106 756 677 4579 1975 4271 7256 11321 15678 8279 5816 8363 2038 5376 10109 7590 1942 2378 1284 4379 1941 2030 9179 21077 720 645 4491 1962 4258 7227 11311 15707 8279 5827 8349 2039 5386 10054 7578 1939 2374 1282 4335 1938 2027 9196 21003 693 619 4380 1950 4246 7201 11300 15737 8279 5839 8336 5396 10001 7566 1935 2371 1280 4292 1934 2023 9214 20931 671 599 4269 1940 4235 7178 11290 15767 8279 5851 8322 2041 5399 9934 7544 1930 2364 1276 4243 1929 2017 9219 20831 653 583 4142 1929 4223 7154 11276 15775 8279 5854 8298 2042 5402 9911 7522 1924 2357 1272 4237 1923 2012 9224 20776 639 570 4034 1920 4211 7133 11262 15784 8279 5855 8273 2043 5405 9886 7499 1918 2350 1269 4229 1917 2006 9230 20720 628 560 3921 1913 4200 7114 11248 15793 8279 5854 8249 2044 5408 9862 7477 1913 2343 1265 4221 1912 2000 9235 20664 619 551 3831 1907 4190 7096 11234 15802 8279 5850 8225 5411 9837 7456 1907 2336 1261 4213 1906 1994 9240 20608 610 543 3747 1901 4180 7080 11220 15811 8279 5847 8201 2046 5415 9815 7434 1902 2329 1258 4206 1901 1988 9245 20554 603 537 3678 1895 4170 7065 11206 15821 8279 5846 8177 2047 5418 9786 7412 1896 2322 1254 4194 1895 1982 9251 20493 596 530 3613 1888 4161 7051 11192 15830 8279 5844 8153 2048 5421 9745 7390 1891 2316 1250 4169 1890 1976 9256 20421 591 526 3552 1881 4151 7037 11178 15839 8279 5844 8129 2049 5424 9699 7369 1885 2309 1247 4141 1884 1971 9262 20344 578 514 3493 1873 4142 7020 11164 15848 8279 5843 8105 5427 9670 7356 1882 2305 1243 4122 1881 1967 9267 20296 569 505 3444 1868 4136 7008 11080 15857 8279 5844 8091 2051 5427 9636 7330 1875 2297 1243 4107 1874 1960 9267 20224 557 496 3395 1858 4126 6925 10916 15857 8279 5833 8063 2052 5427 9615 7313 1871 2291 1234 4100 1870 1956 9267 20179 556 494 3347 1852 4120 6849 10837 15857 8279 5823 8044 2053 5427 9575 7279 1862 2281 1234 4085 1861 1947 9267 20090 553 492 3300 1841 4108 6710 10720 15857 8279 5799 8007 2054 5427 9555 7262 1858 2275 1226 4078 1857 1942 9267 20045 552 491 3253 1837 4103 6645 10681 15857 8279 5785 7988 5427 9536 7245 1853 2270 1226 4071 1852 1938 9267 20001 551 490 3207 1833 4097 6581 10643 15857 8279 5771 7969 GenCost 2024-25 | 75 Apx Table B.3 Current and projected generation technology capital costs under the Global NZE post 2050 scenario BlackcoalBlackcoalwithCCSBrowncoalGascombinedcycleGasopencycle(small) Gasopencycle(large) GaswithCCSGasreciprocatingHydrogenreciprocatingBiomass(smallscale) BiomasswithCCS(largescale) LargescalesolarPVRooftopsolarpanelsSolarthermal(16hrs)WindOffshorewindfixedOffshorewindfloatingWaveNuclear(SMR) Tidal/oceancurrentFuelcellNuclearlargescale$/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 3223 4710 8362 15547 29667 12979 8067 8984 5919 11962 9048 2380 2428 1311 5599 1982 2073 8954 23753 1403 1277 6499 3099 4691 8358 14738 28093 12065 7622 8834 2026 5804 11669 8782 2304 2431 1312 5401 1984 2075 8994 23154 1351 1226 6251 2978 4673 8356 13958 26510 11194 7203 8687 2027 5674 11348 8498 2220 2426 1310 5194 1980 2071 9007 22502 1302 1181 6056 2860 4649 8023 13210 24838 10386 6784 8567 2028 5547 11036 8223 2137 2421 1307 4996 1976 2067 9021 21869 1258 1139 5919 2746 4581 7679 12501 23162 9636 6390 8500 2029 5423 10734 7958 2053 2416 1305 4805 1972 2063 9035 21255 1217 1101 5807 2637 4485 7239 11831 21481 8940 6019 8483 5344 10529 7779 1996 2412 1302 4678 1968 2059 9049 20840 1183 1069 5666 2533 4379 7033 11398 20359 8494 5736 8467 2031 5310 10421 7684 1966 2407 1300 4614 1965 2055 9064 20618 1148 1036 5497 2432 4305 6821 11190 19183 8279 5724 8452 2032 5319 10408 7670 1962 2403 1297 4611 1961 2051 9079 20586 1122 1012 5314 2336 4265 6694 11195 18173 8279 5707 8436 2033 5328 10397 7656 1959 2399 1295 4610 1957 2047 9094 20556 1101 992 5154 2243 4239 6654 11199 18154 8279 5692 8421 2034 5337 10382 7642 1955 2394 1293 4605 1954 2044 9110 20524 1066 958 5021 2154 4226 6630 11204 18186 8279 5690 8406 5347 10368 7629 1952 2390 1291 4601 1951 2040 9126 20492 1017 914 4913 2094 4202 6573 11209 17344 8279 5701 8392 2036 5356 10354 7616 1948 2386 1288 4597 1947 2037 9142 20461 963 864 4832 2063 4185 6504 11214 16500 8279 5711 8377 2037 5366 10311 7603 1945 2382 1286 4565 1944 2033 9159 20401 930 833 4758 2057 4146 6443 11219 14878 8279 5722 8363 2038 5376 10269 7590 1942 2378 1284 4533 1941 2030 9175 20343 905 810 4694 2052 4097 6404 11224 14128 8279 5732 8349 2039 5386 10204 7578 1939 2374 1282 4479 1938 2027 9193 20262 877 784 4625 2045 4023 6355 11229 13031 8279 5743 8336 5396 10172 7566 1935 2371 1280 4457 1934 2023 9210 20214 843 753 4570 2039 3961 6190 11234 12710 8279 5754 8322 2041 5399 10127 7544 1930 2364 1276 4429 1929 2017 9215 20140 808 721 4501 2033 3908 6036 11236 12372 8279 5756 8298 2042 5402 10104 7522 1924 2357 1272 4423 1923 2012 9220 20087 780 696 4431 2029 3868 5892 11238 12379 8279 5749 8273 2043 5405 10081 7499 1918 2350 1269 4416 1917 2006 9225 20035 761 679 4357 2024 3833 5852 11239 12386 8215 5734 8249 2044 5408 10038 7477 1913 2343 1265 4390 1912 2000 9230 19963 747 666 4288 2020 3803 5818 11241 12393 8151 5695 8225 5411 9969 7456 1907 2336 1261 4340 1906 1994 9235 19866 733 653 4227 2017 3775 5788 11242 12400 8080 5660 8201 2046 5415 9901 7434 1902 2329 1258 4289 1901 1988 9240 19769 721 642 4155 2013 3751 5762 11244 12407 8073 5596 8177 2047 5418 9844 7412 1896 2322 1254 4250 1895 1982 9246 19684 711 632 4073 2009 3729 5739 11246 12414 8065 5516 8153 2048 5421 9807 7390 1891 2316 1250 4230 1890 1976 9251 19619 703 626 3975 2006 3709 5719 11247 12421 8065 5438 8129 2049 5424 9762 7369 1885 2309 1247 4201 1884 1971 9256 19545 694 617 3881 2003 3691 5700 11249 12428 7947 5393 8105 5427 9734 7356 1882 2305 1243 4184 1881 1967 9261 19500 688 612 3814 2001 3679 5684 11250 12436 7829 5387 8091 2051 5427 9695 7330 1875 2297 1243 4164 1874 1960 9261 19427 681 605 3748 1995 3658 5673 11250 12436 7711 5374 8063 2052 5427 9673 7313 1871 2291 1234 4155 1870 1956 9261 19382 680 604 3684 1990 3642 5653 11250 12436 7711 5366 8044 2053 5427 9630 7279 1862 2281 1234 4138 1861 1947 9261 19294 677 602 3620 1981 3610 5637 11250 12436 7711 5349 8007 2054 5427 9610 7262 1858 2275 1226 4131 1857 1942 9261 19251 676 601 3558 1977 3595 5608 11250 12436 7711 5341 7988 5427 9590 7245 1853 2270 1226 4123 1852 1938 9261 19209 675 600 3497 1973 3580 5593 11250 12436 7711 5333 7969 76 | CSIRO Australia’s National Science Agency Apx Table B.4 One-and two-hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW) Batterystorage(1hr)Batterystorage(2hrs) TotalBatteryBOPTotalBatteryBOP Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 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 889 836 784 319 310 302 570 526 482 593 563 533 307 299 290 287 265 243 2026 864 788 713 313 296 280 551 492 433 577 532 487 301 285 269 277 247 217 2027 848 755 663 307 284 262 541 471 401 567 510 453 295 273 252 272 237 201 2028 833 728 622 302 274 246 531 454 376 557 491 425 290 263 236 267 228 189 2029 824 708 592 298 266 234 525 442 358 550 478 405 286 256 225 264 222 180 2030 780 667 531 290 257 228 490 410 302 525 453 371 279 247 219 246 206 152 2031 737 654 522 282 249 223 455 406 299 499 442 364 271 239 214 228 204 150 2032 724 641 513 274 240 217 450 401 296 489 432 357 263 231 209 226 201 148 2033 709 623 505 266 232 212 443 391 293 478 419 350 255 223 203 222 196 147 2034 696 606 498 259 224 207 437 382 291 467 407 344 248 215 198 219 191 146 2035 685 592 491 251 217 202 433 375 289 458 396 338 241 208 193 217 188 145 2036 674 579 484 244 210 197 430 369 288 449 386 333 234 201 188 215 185 144 2037 664 566 478 237 203 192 426 363 286 441 376 327 228 194 184 213 182 143 2038 654 554 473 231 196 187 423 358 286 433 367 322 221 188 179 212 179 143 2039 644 543 467 224 189 182 420 354 285 425 358 317 214 181 175 210 177 142 2040 640 539 466 223 188 182 418 350 284 422 355 316 213 180 174 209 175 142 2041 636 534 465 221 187 182 415 346 283 419 353 315 212 179 174 208 173 142 2042 633 530 464 220 187 181 413 343 282 417 350 315 211 179 173 207 172 141 2043 630 526 463 219 186 181 411 340 282 415 348 314 209 178 173 206 170 141 2044 628 523 462 218 185 181 410 338 281 413 346 313 209 177 173 205 169 141 2045 625 520 461 217 185 180 408 335 280 412 344 313 208 177 172 204 168 140 2046 623 518 460 217 184 180 406 333 280 410 343 312 207 176 172 203 167 140 2047 621 515 459 216 184 180 405 332 279 409 341 312 207 176 172 202 166 140 2048 619 513 458 215 183 180 404 330 279 408 340 311 206 175 172 202 165 139 2049 618 512 458 215 183 180 403 328 278 407 339 311 206 175 172 201 164 139 2050 617 509 457 215 183 180 402 326 278 406 338 311 205 175 172 201 163 139 2051 617 509 457 215 183 180 402 326 278 406 338 311 205 175 172 201 163 139 2052 613 506 457 214 182 179 399 324 277 404 336 310 205 174 171 200 162 139 2053 613 506 457 214 182 179 399 324 277 404 336 310 205 174 171 200 162 139 2054 611 504 456 213 182 179 397 323 277 402 335 310 204 174 171 199 161 138 2055 611 504 456 213 182 179 397 323 277 402 335 310 204 174 171 199 161 138 GenCost 2024-25 | 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) Batterystorage(4hrs)Batterystorage(8hrs) TotalBatteryBOPTotalBatteryBOP Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 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 413 394 376 268 260 253 145 134 123 336 323 310 260 253 246 76 70 64 2026 402 373 345 262 248 235 140 125 110 327 306 285 254 241 228 73 65 58 2027 395 358 321 257 238 219 138 120 102 321 293 266 249 231 212 72 63 53 2028 388 344 301 253 229 206 135 115 95 315 282 249 245 222 199 71 60 50 2029 383 335 287 249 223 196 133 112 91 311 274 237 242 216 190 70 59 47 2030 367 319 268 242 215 191 124 104 77 300 263 225 235 208 185 65 54 40 2031 351 311 262 235 208 186 115 103 76 288 255 220 228 201 180 60 54 40 2032 343 302 256 229 201 181 114 102 75 281 247 215 222 194 176 60 53 39 2033 334 293 251 222 194 177 112 99 74 274 239 210 215 188 171 59 52 39 2034 326 284 246 216 187 172 111 97 74 267 232 205 209 181 167 58 50 38 2035 319 276 241 209 181 168 110 95 73 260 224 201 203 175 163 57 50 38 2036 312 268 236 203 175 164 109 93 73 254 218 196 197 169 158 57 49 38 2037 305 260 232 198 169 160 108 92 72 247 211 192 191 163 154 56 48 38 2038 299 253 228 192 163 155 107 90 72 241 205 188 186 158 150 56 47 38 2039 292 247 223 186 157 152 106 89 72 235 199 184 180 152 147 55 47 37 2040 290 245 223 185 156 151 105 88 72 234 197 183 179 151 146 55 46 37 2041 288 243 222 184 156 151 105 87 71 232 196 183 177 150 146 55 46 37 2042 287 241 221 183 155 150 104 86 71 231 195 182 177 150 145 54 45 37 2043 285 240 221 182 154 150 104 86 71 230 194 182 176 149 145 54 45 37 2044 284 239 221 181 154 150 103 85 71 229 193 182 175 149 145 54 44 37 2045 283 238 220 180 153 150 103 84 71 228 192 181 174 148 145 54 44 37 2046 282 237 220 180 153 149 102 84 70 227 191 181 174 148 144 53 44 37 2047 281 236 219 179 152 149 102 83 70 226 191 181 173 147 144 53 43 37 2048 280 235 219 179 152 149 102 83 70 225 190 181 173 147 144 53 43 37 2049 279 234 219 178 152 149 101 83 70 225 190 180 172 147 144 53 43 36 2050 279 233 219 178 151 149 101 82 70 225 189 180 172 146 144 53 43 36 2051 279 233 219 178 151 149 101 82 70 225 189 180 172 146 144 53 43 36 2052 278 233 218 177 151 149 100 82 70 224 188 180 171 146 144 52 42 36 2053 278 233 218 177 151 149 100 82 70 224 188 180 171 146 144 52 42 36 2054 277 232 218 177 151 148 100 81 70 223 188 180 171 146 143 52 42 36 2055 277 232 218 177 151 148 100 81 70 223 188 180 171 146 143 52 42 36 78 | CSIRO Australia’s National Science Agency Apx Table B.6 Twelve-and twenty-four hour battery cost data by storage duration, component and total costs (multiply by duration to convert to $/kW) Batterystorage(12hrs)Batterystorage(24hrs) TotalBatteryBOPTotalBatteryBOP Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global Current Global Global policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by policies NZE post NZE by 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 2050 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 310 299 288 260 252 245 51 47 43 285 276 267 260 252 245 25 23 21 2026 302 284 266 254 241 227 49 44 38 278 262 247 254 241 227 24 22 19 2027 297 272 248 249 230 212 48 42 35 273 251 230 249 230 212 24 21 18 2028 291 262 232 244 222 199 47 40 33 268 242 216 244 222 199 23 20 17 2029 288 254 221 241 215 189 46 39 32 264 235 205 241 215 189 23 20 16 2030 278 244 211 234 208 185 43 36 27 256 226 198 234 208 185 22 18 13 2031 268 236 206 228 201 180 40 36 26 248 219 193 228 201 180 20 18 13 2032 261 229 201 221 194 175 40 35 26 241 211 188 221 194 175 20 18 13 2033 253 221 196 214 187 171 39 34 26 234 204 184 214 187 171 19 17 13 2034 247 214 192 208 181 166 38 34 26 227 197 179 208 181 166 19 17 13 2035 240 207 187 202 174 162 38 33 25 221 191 175 202 174 162 19 16 13 2036 234 201 183 196 168 158 38 32 25 215 184 170 196 168 158 19 16 13 2037 228 194 179 190 162 154 37 32 25 209 178 166 190 162 154 19 16 13 2038 222 188 175 185 157 150 37 31 25 203 172 162 185 157 150 18 16 12 2039 216 182 171 179 151 146 37 31 25 197 167 158 179 151 146 18 15 12 2040 214 181 170 178 150 145 36 31 25 196 166 158 178 150 145 18 15 12 2041 213 180 170 177 150 145 36 30 25 195 165 157 177 150 145 18 15 12 2042 212 179 169 176 149 145 36 30 25 194 164 157 176 149 145 18 15 12 2043 210 178 169 175 148 144 36 30 25 193 163 157 175 148 144 18 15 12 2044 210 177 168 174 148 144 36 29 24 192 162 156 174 148 144 18 15 12 2045 209 176 168 173 147 144 35 29 24 191 162 156 173 147 144 18 15 12 2046 208 176 168 173 147 144 35 29 24 190 161 156 173 147 144 18 15 12 2047 207 175 168 172 146 143 35 29 24 190 161 155 172 146 143 18 14 12 2048 207 175 167 172 146 143 35 29 24 189 160 155 172 146 143 18 14 12 2049 206 174 167 171 146 143 35 29 24 189 160 155 171 146 143 17 14 12 2050 206 174 167 171 145 143 35 28 24 188 160 155 171 145 143 17 14 12 2051 206 174 167 171 145 143 35 28 24 188 160 155 171 145 143 17 14 12 2052 205 173 167 170 145 143 35 28 24 188 159 155 170 145 143 17 14 12 2053 205 173 167 170 145 143 35 28 24 188 159 155 170 145 143 17 14 12 2054 204 173 167 170 145 142 35 28 24 187 159 155 170 145 142 17 14 12 2055 204 173 167 170 145 142 35 28 24 187 159 155 170 145 142 17 14 12 GenCost 2024-25 | 79 Apx Table B.7 Pumped hydro storage cost data by duration, all scenarios, total cost basis $/kW$/kWh 10hrs 24hrs 48hrs 10hrs 24hrs 48hrs 2024 7677 6496 7822 768 271 163 7493 6341 7635 749 264 159 2026 7310 6186 7448 731 258 155 2027 7119 6024 7254 712 251 151 2028 6929 5863 7060 693 244 147 2029 6739 5702 6866 674 238 143 6548 5541 6672 655 231 139 2031 6540 5534 6664 654 231 139 2032 6532 5527 6656 653 230 139 2033 6524 5521 6647 652 230 138 2034 6516 5514 6640 652 230 138 6509 5508 6632 651 229 138 2036 6501 5501 6624 650 229 138 2037 6493 5495 6616 649 229 138 2038 6486 5488 6608 649 229 138 2039 6478 5482 6601 648 228 138 6471 5476 6593 647 228 137 2041 6461 5467 6583 646 228 137 2042 6451 5459 6573 645 227 137 2043 6441 5451 6563 644 227 137 2044 6431 5442 6553 643 227 137 6421 5434 6543 642 226 136 2046 6412 5426 6533 641 226 136 2047 6402 5417 6523 640 226 136 2048 6392 5409 6513 639 225 136 2049 6382 5401 6503 638 225 135 6372 5392 6493 637 225 135 2051 6362 5383 6482 636 224 135 2052 6351 5374 6471 635 224 135 2053 6340 5365 6460 634 224 135 2054 6329 5356 6449 633 223 134 6318 5347 6438 632 223 134 80 | CSIRO Australia’s National Science Agency Apx Table B.8 Storage current cost data by source, total cost basis $/kWh$/kWAurecon2019-20Aurecon2020-21Aurecon2021-22Aurecon2022-23Aurecon2023-24Aurecon2024-25GenCost2019-20AEMOISPDec2021AEMOISPJun2022/CSIROAurecon2019-20Aurecon2020-21Aurecon2021-22Aurecon2022-23Aurecon2023-24Aurecon2024-25GenCost2019-20AEMOISPDec2021AEMOISPJun2022/CSIRO Battery 1195 958 906 1024 1048 910 ---1195 958 906 1024 1048 910 --- (1hr) Battery 752 642 603 741 758 608 ---1504 1284 1205 1481 1517 1216 --- (2hrs) Battery 594 510 476 601 614 423 ---2375 2041 1903 2406 2457 1691 --- (4hrs) Battery 539 450 418 534 538 344 ---4309 3601 3340 4273 4308 2748 --- (8hrs) Battery ----496 442 -------11910 10617 --- (24hrs) Battery ----443 376 -------21272 18032 --- (48hrs) PHES -----768 --------7677 --- (10hrs) A-CAES ---386 --------4626 ----- (12hrs) PHES ------213 226 275 ------2561 2711 3295 (12hrs) A-CAES ----305 316 -------7326 7585 --- (24hrs) PHES ----242 271 158 147 179 ----6030 6496 3796 3537 4307 (24hrs) PHES ----142 163 89 111 135 ----7078 7822 4252 5313 6470 (48hrs) Notes: Batteries are large scale. Small scale batteries for home use with 2-hour duration are estimated at $1350/kWh (Aurecon, 2024b). GenCost 2024-25 | 81 Apx Table B.9 Data assumptions for LCOE calculations ConstantLowassumptionHighassumption Economic Construction Efficiency O&M O&M CO2 Capital Fuel Capacity Capital Fuel Capacity life time fixed variable storage factor factor 2024 Years Years $/kW $/MWh $/MWh $/kW $/GJ $/kW $/GJ Gas with CCS 25 1.5 44% 22.5 8.0 1.9 5802 13.5 89% 5802 19.8 53% Gas combined cycle 25 1.5 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 1.3 33% 14.1 8.1 0.0 943 13.5 20% 943 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 40.9 20% 2071 43.2 20% Black coal with CCS 30 2.0 30% 94.8 8.9 4.1 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 8655 1.1 89% 8655 1.3 53% Solar thermal 25 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 3223 0.0 48% 3223 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 1.5 44% 22.5 8.0 1.9 4678 8.0 89% 4617 14.4 53% Gas combined cycle 25 1.5 51% 15.0 4.1 0.0 1996 8.0 89% 1969 14.4 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 2412 8.0 20% 2412 14.4 20% Gas open cycle (large) 25 1.3 33% 14.1 8.1 0.0 1302 8.0 20% 1302 14.4 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 1968 8.0 20% 1968 14.4 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2059 35.4 20% 2059 38.6 20% Black coal with CCS 30 2.0 30% 94.8 8.9 4.1 10529 2.8 89% 10435 4.3 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5301 2.8 89% 5344 4.3 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 7779 0.7 89% 7698 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 20359 0.8 89% 21168 1.0 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 8467 0.8 89% 8493 1.0 53% Solar thermal 25 1.8 100% 124.2 0.0 0.0 7315 0.0 71% 7939 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 1141 0.0 32% 1224 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 2491 0.0 48% 2603 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 4409 0.0 54% 4629 0.0 40% 82 | CSIRO Australia’s National Science Agency 2040 Gas with CCS 25 1.5 44% 22.5 8.0 1.9 4292 7.9 89% 4486 15.8 53% Gas combined cycle 25 1.5 51% 15.0 4.1 0.0 1935 7.9 89% 1935 15.8 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 2371 7.9 20% 2371 15.8 20% Gas open cycle (large) 25 1.3 33% 14.1 8.1 0.0 1280 7.9 20% 1280 15.8 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 1934 7.9 20% 1934 15.8 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 2023 24.5 20% 2023 29.1 20% Black coal with CCS 30 2.0 30% 94.8 8.9 4.1 10001 2.6 89% 10202 3.9 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5378 2.6 89% 5396 3.9 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 7566 0.7 89% 7566 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 12710 0.5 89% 17735 0.7 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 8322 0.5 89% 8322 0.7 53% Solar thermal 25 1.8 100% 124.2 0.0 0.0 5743 0.0 71% 6935 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 671 0.0 32% 1016 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 1940 0.0 48% 2047 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 4235 0.0 57% 4489 0.0 40% 2050 Gas with CCS 25 1.5 44% 22.5 8.0 1.9 4122 7.9 89% 4257 15.8 53% Gas combined cycle 25 1.5 51% 15.0 4.1 0.0 1882 7.9 89% 1882 15.8 53% Gas open cycle (small) 25 1.5 36% 17.4 16.1 0.0 2305 7.9 20% 2305 15.8 20% Gas open cycle (large) 25 1.3 33% 14.1 8.1 0.0 1243 7.9 20% 1243 15.8 20% Gas reciprocating 25 1.1 41% 29.4 8.5 0.0 1881 7.9 20% 1881 15.8 20% Hydrogen reciprocating 25 1.0 32% 33.0 0.0 0.0 1967 17.8 20% 1967 22.7 20% Black coal with CCS 30 2.0 30% 94.8 8.9 4.1 9670 2.6 89% 9810 3.9 53% Black coal 30 2.0 42% 64.9 4.7 0.0 5376 2.6 89% 5427 3.9 53% Brown coal 30 4.0 32% 69.0 5.3 0.0 7356 0.7 89% 7356 0.7 53% Nuclear SMR 30 4.4 33% 200.0 5.3 0.0 12436 0.5 89% 15948 0.7 53% Nuclear large-scale 30 5.8 33% 200.0 5.3 0.0 8091 0.5 89% 8091 0.7 53% Solar thermal 25 1.8 100% 124.2 0.0 0.0 4633 0.0 71% 5833 0.0 57% Large scale solar PV 30 0.5 100% 12.0 0.0 0.0 569 0.0 32% 807 0.0 19% Wind onshore 25 1.0 100% 28.0 0.0 0.0 1868 0.0 48% 1967 0.0 29% Wind offshore (fixed) 25 3.0 100% 174.6 0.0 0.0 4136 0.0 61% 4346 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 discount rate for all technologies is 5.99%. GenCost 2024-25 | 83 Apx Table B.10 Electricity generation technology LCOE projections data, 2023-24 $/MWh CategoryAssumptionTechnology2024203020402050 Low High Low High Low High Low High Peaking 20% load Gas open cycle (small) 279 342 224 287 221 300 217 297 Gas open cycle (large) 207 275 165 234 163 248 161 247 Gas reciprocating 238 294 190 245 187 257 184 254 H2 reciprocating 577 603 514 550 390 442 312 367 Flexible load, high emission Black coal 102 164 92 149 91 147 91 147 Brown coal 131 211 113 180 111 178 109 174 Gas 128 192 85 145 83 155 82 154 Flexible load, low emission Black coal with CCS 190 303 169 268 161 260 157 253 Gas with CCS 187 284 130 217 125 227 123 223 Nuclear SMR 400 663 285 487 189 414 186 378 Nuclear large-scale 155 252 150 245 145 238 142 233 Solar thermal 134 168 121 164 99 146 84 127 Variable Standalone Solar photovoltaic 43 73 35 62 22 53 19 43 Wind onshore 70 116 56 96 45 78 43 75 Wind offshore (fixed) 135 175 124 173 114 169 105 165 Variable with integration costs Wind & solar PV combined 60% VRE share 101 142 67 105 70% VRE share 98 141 72 113 80% VRE share 100 143 80 122 90% VRE share 106 150 94 137 84 | CSIRO Australia’s National Science Agency Apx Table B.11 Hydrogen electrolyser cost projections by scenario and technology, $/kW CurrentpoliciesGlobalNZEby2050GlobalNZEpost2050 Alkaline PEM Alkaline PEM Alkaline PEM 2024 2706 2840 2706 2840 2706 2840 2571 2734 2429 2619 2523 2700 2026 2443 2632 2182 2415 2353 2567 2027 2314 2526 1953 2221 2188 2433 2028 2193 2424 1749 2042 2035 2307 2029 2078 2327 1566 1878 1892 2187 1968 2233 1402 1727 1760 2073 2031 1865 2144 1256 1588 1636 1965 2032 1767 2058 1124 1460 1522 1863 2033 1675 1975 1007 1343 1415 1766 2034 1587 1896 902 1235 1316 1675 1571 1900 859 1206 1285 1664 2036 1547 1893 811 1166 1262 1662 2037 1523 1885 765 1125 1240 1661 2038 1478 1851 726 1093 1215 1654 2039 1429 1809 690 1061 1191 1648 1356 1737 658 1034 1146 1611 2041 1342 1738 629 1009 1126 1608 2042 1325 1734 603 987 1088 1577 2043 1310 1733 580 968 1053 1549 2044 1285 1717 559 950 1024 1529 1268 1713 540 934 1000 1513 2046 1243 1695 522 919 978 1501 2047 1227 1690 506 905 959 1491 2048 1205 1676 486 884 941 1483 2049 1169 1642 467 862 924 1475 1144 1621 446 837 909 1469 2051 1144 1621 446 837 909 1469 2052 1140 1616 441 826 907 1465 2053 1140 1616 441 826 907 1465 2054 1138 1613 435 815 904 1461 1138 1613 435 815 904 1461 GenCost 2024-25 | 85 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 TechnologyScenarioComponentLR1(%) LR2(%) References Photovoltaics Current policies G 30 13 (IEA 2021, IRENA, 2022, L -17 Fraunhofer ISE, 2015) Photovoltaics Global NZE by 2050 G 30 23 L -17 Photovoltaics Global NZE post 2050 G 30 23 L -17 Electrolysis Current policies G 10 5 (Schmidt et al., 2017, IEA L -8 2024) Electrolysis Global NZE by 2050 G 18 9 L -8 Electrolysis Global NZE post 2050 G 10 5 L -8 Ocean Current policies G 10 5 (IEA, 2021) Global NZE by 2050 G 20 10 86 | CSIRO Australia’s National Science Agency Global NZE post 2050 G 14 7 Fixed offshore wind Fixed offshore wind Current policies Global NZE by 2050 G G 10 20 5 10 (Samadi, 2018; Zwaan, et al. 2012; Voormolen et al. 2016; IEA, 2021) Fixed offshore wind Global NZE post 2050 G 15 8 Floating offshore wind Current policies G 10 5 G 10 5 Floating offshore wind Global NZE by 2050 G 20 10 G 20 10 Floating offshore wind Global NZE post 2050 G 15 8 G 15 7.5 Utility scale energy storage – Li-ion Current policies G -7.5 (Grübler et al., 1999; McDonald and Schrattenholzer, 2001) L -7.5 Utility scale energy storage – Li-ion Global NZE post 2050 G -10 L -10 Utility scale energy storage – Li-ion Global NZE by 2050 G -15 L -15 Solar photovoltaics is listed as one technology with global and local components however there are two separate PV plant technologies in GALLME. Rooftop PV includes solar photovoltaic modules, and the local learning component is the balance of plant (BOP). Large-scale PV also include modules and BOP. However, a discount of 25% is given to the BOP to take into account economies of scale in building a large-scale versus rooftop PV plant. Inverters are not given a learning rate instead they are given a constant cost reduction, which is based on historical data. The potential for local learning means that technology costs are different in different regions in the same time period. This has been of particular note for technology costs in China, which can be substantially lower than other regions. GALLME uses inputs from Aurecon (2024b) 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 GenCost 2024-25 | 87 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 (%) References Coal, supercritical --- Coal, ultra- supercritical G -2 (IEA, 2008; Neij, 2008) Coal/Gas/Biomass with CCS G 20 10 (EPRI 2010; Rubin et al., 2007) L 20 10 As above + (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Gas peaking plant --- Gas combined cycle --- Nuclear G --(IEA, 2008) Nuclear SMR G 20 10 (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Diesel/oil-based generation --- Reciprocating engines --- Hydro and pumped hydro --- Biomass G -5 (IEA, 2008; Neij, 2008) Concentrating solar thermal (CST) G 14.6 7 (Hayward & Graham, 2013) Onshore wind G -4.3 (IEA, 2021; Hayward & Graham, 2013) L -9.8 As above CHP --- Conventional geothermal G -8 (Hayward & Graham, 2013) L 20 20 (Grübler et al., 1999; Hayward & Graham, 2013; McDonald and Schrattenholzer, 2001) Fuel cells G 20 10 (Neij, 2008; Schoots, Kramer, & van der Zwaan, 2010) Steam methane reforming with CCS G 20 10 (EPRI, 2010; Rubin et al., 2007) L 20 10 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 88 | CSIRO Australia’s National Science Agency 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. GenCost 2024-25 | 89 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 ScenarioTotalhydrogendemand(Mt) Current policies 117 Global NZE post 2050 251 Global NZE by 2050 428 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. 90 | CSIRO Australia’s National Science Agency 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 fall40. 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. RegionRooftopPV% LargescalePV% CST% Onshorewind% FixedoffshorewindGW 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 88 15.5 SEA 14 3 32 8 NA 40 In the long run, fossil fuel prices will fluctuate due to cycles of demand and supply imbalances. However, underlying these fluctuations, prices should track the cost of production given the competitive nature of commodity markets. This relationship holds whether demand is falling or rising over the long run. GenCost 2024-25 | 91 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, 2024b)(Aurecon, 2023a) (Aurecon, 2022) (Aurecon, 2021) (IEA, 2016b) (IEA, 2015), capacity factors from (IRENA, 2022) (IEA, 2015) (CO2CRC, 2015) and historical technology installed capacities from (IEA , 2008) (Gas Turbine World, 2009) (Gas Turbine World, 2010) (Gas Turbine World, 2011) (Gas Turbine World, 2012) (Gas Turbine World, 2013) (UN, 2015a) (UN, 2015b) (US Energy Information Administration, 2017a) (US Energy Information Administration, 2017b) (GWEC) (IEA, 2016a) (World Nuclear Association, 2017) (Schmidt, Hawkes, Gambhir, & Staffell, 2017) (Cavanagh, et al., 2015). 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). 92 | CSIRO Australia’s National Science Agency 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 GenCost 2024-25 | 93 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 judgement 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 D.3.3 Why did you use the capital cost of a single failed project in the United States for your representative nuclear SMR cost (the UAMPS Carbon Free Power Project)? While there are several currently existing and proposed SMR projects, only the UAMPS project has been willing to provide an open and reliable costing for their project. Costings for projects not built often turn out to be optimistic or marketing pieces and for those reasons are not considered reliable. The UAMPS project is deemed to be reliable because the developers were prepared to financially commit and there would have been financial consequences if they had provided lower than achievable estimates and then tried to proceed at a higher cost. Their subscription model for power produced meant they had to agree to a cost up front. If they underestimated costs, they would be liable for the shortfall. In contrast, there are no financial consequences for manufacturers who supply unrealistically low estimates for technologies they are not committed to both build and sell the power from themselves. While many submissions have in the past requested GenCost use different data, no evidence was provided for an alternative project with 94 | CSIRO Australia’s National Science Agency data quality equal to or better than the UAMPS project. All other suggested costs were vendor estimates for projects the vendor has not committed to directly build or own which we regard as low quality data. D.3.4 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 projects41. 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.3.5 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 41 https://www.industry.gov.au/publications/critical-minerals-strategy-2023-2030/our-focus-areas/2-attracting-investment-and-buildinginternational- partnerships GenCost 2024-25 | 95 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 (2024b) 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. D.3.6 Why is the uncertainty in the data not emphasised more? Aurecon (2024b) 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 (2024b) also provide factors to convert the general costs to specific locations in the National Electricity Market. 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.3.7 Why include an advanced ultra-supercritical pulverised coal instead of cheaper, less efficient plant designs? Some stakeholders take a view that although Australia has bipartisan commitment for net zero emissions by 2050, the highest greenhouse gas emitting options should remain on the table. The deployment of new coal has low plausibility given its high emission intensity. A high efficiency 96 | CSIRO Australia’s National Science Agency design brings it closer to being plausible. 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 (2024b) 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 GenCost 2024-25 | 97 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 typically brown coal plants which have a significant 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 coal generators. At the low end of the range a capacity factor of 53% is assumed for new black coal or nuclear generators which is equivalent to achieving 10% below the average capacity factor for black coal. Around 10% of nuclear generators globally run at less than 60% capacity factor and 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. 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. 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 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 98 | CSIRO Australia’s National Science Agency 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 none of the topics presented in the feedback have a large enough impact on LCOE to warrant a change in the boundary or formula (and no quantitative evidence of their significance was provided). That is, it would add complexity and cost to the project without significantly changing the outcome of the comparisons. One exception is 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. GenCost 2024-25 | 99 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. 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 (2024b) 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  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. 100 | CSIRO Australia’s National Science Agency 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 time42. 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). 42 GenCost readers who have downloaded the Appendix tables from CSIRO’s Data Access Portal should be able to find this step in the cell formula under the Capital component of the LCOE calculation GenCost 2024-25 | 101  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. No gas peaking plants are allowed creating an artificially high cost scenario. Baik (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 would have been cheaper had the gas peaking plant been allowed. Thereafter, it is unlikely that adding any of the other resources – nuclear, CCS, hydrogen and biofuels would have reduced costs. 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 baseload43 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. 43 It is also worth noting that baseload generation which is taken to mean almost constant production except for periods of maintenance by this stakeholder, is something that happens at a very small minority of plants in Australia with the average historical capacity factor of coal plants being around 60%. 102 | CSIRO Australia’s National Science Agency 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 (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 GenCost 2024-25 | 103 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 2023 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 104 | CSIRO Australia’s National Science Agency 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 technology44, (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 44 Such as a gas turbine or reciprocating engine GenCost 2024-25 | 105 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 judgement 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 greenfield 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 (greenfield) projects which are different to coal prices that are received by existing projects. 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 greenfield and existing coal 106 | CSIRO Australia’s National Science Agency generators. Reflecting the issues discussed above, average greenfield 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 greenfield coal prices. After June 2022, AEMO has no longer published greenfield 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 greenfield coal prices, GenCost 2023-24 had to apply a new methodology based on the only available data which was coal prices for existing generators. Knowing that greenfield coal prices are at least as high as that for existing generators, for the maximum, GenCost 2023-24 simply takes the maximum of existing generator prices. However, for the minimum greenfield 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 greenfield 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 will not go ahead if their capacity factor is too GenCost 2024-25 | 107 low. 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 studies45 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-ofsystem 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 202324 and factored these higher prices in. While additional renewable supply has, in some regions, lowered wholesale electricity prices, customers may not immediately feel the impact due to existing higher priced supply contracts. There is no guarantee that renewables or any other new 45 See for example these projects: https://www.energynetworks.com.au/projects/electricity-network-transformation-roadmap/ and https://www.transgrid.com.au/about-us/network/network-planning/energy-vision. 108 | CSIRO Australia’s National Science Agency entrant technology will maintain downward pressure on prices. If 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. GenCost 2024-25 | 109 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. 110 | CSIRO Australia’s National Science Agency 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 GloballysignificantDomesticallysignificantExamples 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 economics have meant there is no current domestic interest in this technology No No Emerging technologies that have yet to receive commercial interest (e.g., fusion) or have no commercial prospects due to changing circumstances (e.g., new brown coal) E.4 Input data quality level is reasonable Input data quality types generally fall into 5 categories in order of highest (A) to lowest (E) confidence in Australian costs A. Domestically observable projects (this might be through public data or data held by engineering and construction firms) B. Extrapolations of domestic or global projects (e.g., observed 2-hour battery re-costed to a 4-hour battery, gas reciprocating engine extrapolated to a hydrogen reciprocating engine) GenCost 2024-25 | 111 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. 112 | CSIRO Australia’s National Science Agency Shortened forms AbbreviationMeaning AAS Australian Academy of Science A-CAES Adiabatic Compressed Air Energy Storage ABS Australian Bureau of Statistics AE Alkaline electrolysis AEMO Australian Energy Market Operator ATSE Academy of Technological Sciences and Engineering BAU Business as usual BOP Balance of plant CCGT Combined cycle gas turbine 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 GenCost 2024-25 | 113 AbbreviationMeaningAbbreviationMeaning IEA International Energy Agency ISP Integrated System plan kW Kilowatt kWh Kilowatt hour LCOE Levelised Cost of Electricity LCOS Levelised cost of storage Light commercial vehicle MCV Medium commercial vehicle Li-ion Lithium-ion LR Learning Rate Mt Million tonnes MW Megawatt MWh Megawatt hour NDC Nationally Determined Contribution NEM National Electricity Market 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 114 | CSIRO Australia’s National Science Agency AbbreviationMeaningAbbreviationMeaning USC Ultra-supercritical VPP Virtual Power Plant VRE Variable Renewable Energy WA Western Australia WEO World Energy Outlook GenCost 2024-25 | 115 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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Zwaan, B., Rivera Tinoco, R., Lensink, S. and Van den Oosterkamp, P. 2012, Cost reductions for offshore wind power: Exploring the balance between scaling, learning and R&D, Renewable Energy, vol. 41, pp 389-393 120 | CSIRO Australia’s National Science Agency The GenCost project is a partnership of CSIRO and AEMO. As Australia’s national science agency and innovation catalyst, CSIRO is solving the greatest challenges through innovative science and technology. CSIRO. Unlocking a better future for everyone. Contact us 1300 363 400 +61 3 9545 2176 www.csiro.au/en/contact For further information Energy Paul Graham +61 2 4960 6061 paul.graham@csiro.au csiro.au/energy ’