Geoscience Australia and Monash University have produced a series of renewable energy capacity factor maps of Australia. Solar photovoltaic, concentrated solar power, wind (150 m hub height) and hybrid wind and solar capacity factor maps are included in this web service. Solar Photovoltaic capacity factor map The minimum capacity factor is <10% and the maximum is 25%. The map is derived from Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). Concentrated Solar Power capacity factor map The minimum capacity factor is 52% and the maximum is 62%. The map is derived from Bureau of Meteorology (2020) data. Minimum exposure cut-off values used are from International Renewable Energy Agency (2012) and Wang (2019). The scientific colour map is sourced from Crameri (2018). Wind (150 m hub height) capacity factor map The minimum capacity factor is <15% and the maximum is 42%. The map is derived from Global Modeling and Assimilation Office (2015) and DNV GL (2016) data. The scientific colour map is sourced from Crameri (2018). Hybrid Wind and Solar capacity factor maps Nine hybrid wind and solar maps are available, divided into 10% intervals of wind to solar ratio (eg. (wind 40% : solar 60%), (wind 50% : solar 50%), (wind 60% : solar 40%) etc.). The maps show the capacity factor available for electrolysis. Wind and solar plants might be oversized to increase the overall running time of the hydrogen plant allowing the investor to reduce electrolyser capital expenditures for the same amount of output. Calculations also include curtailment (or capping) of excess electricity when more electricity is generated than required to operate the electrolyser. The minimum and maximum capacity factors vary relative to a map’s specified wind to solar ratio. A wind to solar ratio of 50:50 produces the highest available capacity factor of 64%. The maps are derived from Global Modeling and Assimilation Office (2015), DNV GL (2016) and Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). Disclaimer The capacity factor maps are derived from modelling output and not all locations are validated. Geoscience Australia does not guarantee the accuracy of the maps, data, and visualizations presented, and accepts no responsibility for any consequence of their use. Capacity factor values shown in the maps should not be relied upon in an absolute sense when making a commercial decision. Rather they should be strictly interpreted as indicative. Users are urged to exercise caution when using the information and data contained. If you have found an error in this dataset, please let us know by contacting clientservices@ga.gov.au.

Geoscience Australia and Monash University have produced a series of renewable energy capacity factor maps of Australia. Solar photovoltaic, concentrated solar power, wind (150 m hub height) and hybrid wind and solar capacity factor maps are included in this web service. Solar Photovoltaic capacity factor map The minimum capacity factor is <10% and the maximum is 25%. The map is derived from Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). Concentrated Solar Power capacity factor map The minimum capacity factor is 52% and the maximum is 62%. The map is derived from Bureau of Meteorology (2020) data. Minimum exposure cut-off values used are from International Renewable Energy Agency (2012) and Wang (2019). The scientific colour map is sourced from Crameri (2018). Wind (150 m hub height) capacity factor map The minimum capacity factor is <15% and the maximum is 42%. The map is derived from Global Modeling and Assimilation Office (2015) and DNV GL (2016) data. The scientific colour map is sourced from Crameri (2018). Hybrid Wind and Solar capacity factor maps Nine hybrid wind and solar maps are available, divided into 10% intervals of wind to solar ratio (eg. (wind 40% : solar 60%), (wind 50% : solar 50%), (wind 60% : solar 40%) etc.). The maps show the capacity factor available for electrolysis. Wind and solar plants might be oversized to increase the overall running time of the hydrogen plant allowing the investor to reduce electrolyser capital expenditures for the same amount of output. Calculations also include curtailment (or capping) of excess electricity when more electricity is generated than required to operate the electrolyser. The minimum and maximum capacity factors vary relative to a map’s specified wind to solar ratio. A wind to solar ratio of 50:50 produces the highest available capacity factor of 64%. The maps are derived from Global Modeling and Assimilation Office (2015), DNV GL (2016) and Bureau of Meteorology (2020) data. The scientific colour map is sourced from Crameri (2018). Disclaimer The capacity factor maps are derived from modelling output and not all locations are validated. Geoscience Australia does not guarantee the accuracy of the maps, data, and visualizations presented, and accepts no responsibility for any consequence of their use. Capacity factor values shown in the maps should not be relied upon in an absolute sense when making a commercial decision. Rather they should be strictly interpreted as indicative. Users are urged to exercise caution when using the information and data contained. If you have found an error in this dataset, please let us know by contacting clientservices@ga.gov.au.

Monash University under commission of Geoscience Australia produced an offshore wind capacity factor map assessed at a 150m hub height applying the Bureau of Meteorology 10 year (2009-2018) “Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia” (BARRA) hindcast model. The wind capacity factor has been calculated using the bounding curve of all scaled power curves for wind turbines available within the Open Energy Platform as of 2021. Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines and are available in this web map service.

Monash University under commission of Geoscience Australia produced an offshore wind capacity factor map assessed at a 150m hub height applying the Bureau of Meteorology 10 year (2009-2018) BARRA hindcast model. The wind capacity factor has been calculated using the bounding curve of all scaled power curves for wind turbines available within the Open Energy Platform as of 2021. Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines.

<div>Steelmaking value chains are economically important to Australia, but the need to decarbonize traditional steel-making processes could disrupt existing supply lines. Hydrogen-based iron and steel production offers one pathway for reducing the carbon intensity of steel. The opportunities and challenges presented by this technology, for Australia, are obscured as its cost competitiveness depends on the interaction between multiple industrial processes, including feedstock requirements, storage options, and the availability of infrastructure. To address these problems, we have developed the Green Steel Economic Fairways Mapper. This mapping tool enables user-driven assessments of the green iron or steel resource potential across Australia. The tool optimizes system capacities for renewable energy generation, battery storage, hydrogen electrolysis, and hydrogen storage to estimate the levelized costs of green steel and how these costs vary regionally. Here, we present examples of analysis and integration with other geospatial datasets. Our model compares favourably to previously published cost estimates while also providing granular, spatial considerations of resource potential. Examples demonstrate that the tool that can be used to inform decision-making in the development of actions to de-risk green steel development within Australia.</div>

<div>Global steel demand is forecast to grow in the coming decades with continued development across Asia and Africa. Over the same period, the International Energy Agency suggests that the carbon intensity of steel production will need to decrease rapidly to align with projected pathways to net zero emissions by 2050. Balancing these competing priorities is a challenge that could shift global steelmaking business models. With abundant resources of both iron ore and metallurgical coal, Australia has benefited significantly from traditional steelmaking value chains. In the face of potential disruption, how should Australia navigate the challenges and opportunities accompanying the transition to ’green’ steel? How can geoscience help to identify and leverage Australia’s specific advantages?&nbsp;</div><div><br></div><div>The Green Steel Economic Fairways Mapper is a free, online tool that models the costs of hydrogen-based green ironmaking and steelmaking and maps how these costs vary across Australia. Developed through collaboration between Geoscience Australia and Monash University, it represents a novel approach to model multiple interconnected resource facilities. Following the Economic Fairways approach, the Mapper combines large-scale infrastructure and geoscience datasets to provide a high-level, geospatial analysis of the economic viability of hypothetical green steel projects. In doing so it creates a new capability within Australia—filling the void before the detail and expense of feasibility studies—to understand the broad contours of the decarbonization challenge, and to inform early-stage decision making in the pursuit of low-carbon steel. In this seminar, we introduce the Green Steel Economic Fairways Mapper, demonstrate its capabilities, and discuss some of the insights it reveals.&nbsp;</div>

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>The global push towards decarbonisation may disrupt existing steelmaking supply chains. While this presents opportunities for Australia, it remains an open question as to what the country’s role should be within new international value chains. Here, we examine green steel production and export strategies between Australia and Japan, comparing different exports from raw material feedstocks to end products. We assess five scenarios in 2030, involving Australian exports of green steel, hot briquetted iron, green ammonia, liquid hydrogen, and/or iron ore pellets. The export of iron ore pellets for Japanese processing using offshore wind is most expensive (~AU$1500/tonne). Although, direct steel production is most economical (~AU$1000/tonne) due to lower energy costs from holistic system optimisation, exporting hot-briquetted iron or HBI (~AU$1032/tonne) balances Australia’s resources with Japan’s steel manufacturing expertise. The liquid hydrogen and ammonia pathways incur substantial energy losses from conversion and reconversion processes, making them less competitive. Trade partnerships across the value chain enhance sustainability and economic feasibility of international green steel manufacturing.</div><div><br></div><div><strong>Citation: </strong>Wang, C., Walsh, S.D.C., Haynes, M.W., Weng, Z. & Feitz, A., 2024. Green steel supply chain options between Australia and Japan. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://doi.org/10.26186/149413</div>

<div>This data submission was provided by the Offshore Energy Pty Ltd (also known as SOTS) to the Commonwealth under clause 8.3 of the Exploration Licence (D2024-46036). The data is Commercial-in-Confidence. </div><div><br></div><div>Under a Memorandum of Understanding dated 30 November 2022 between the Department of Climate Change, Energy, the Environment and Water (the department) and Geoscience Australia (GA) it was agreed that the management and sharing of offshore wind energy research and exploration data will be provided to the Commonwealth. This may be a one-off situation until new Legislation is developed. In the meantime, Exploration Licence conditions require the Offshore Energy Pty Limited (also known as SOTS) to submit data to the Commonwealth.</div><div><br></div><div>A SOTS Data Sharing Exchange of Letters was signed on March 2024 (D2024-46035) which outlined the terms under which data will be accepted and managed by GA on behalf of the Commonwealth.</div><div><br></div><div>The data was submitted to GA using the National Seabed Mapping (NSM) Data submission tool using a temporary Amazon s3 bucket. Data was received under three categories (Geophysical & Geotechnical; Meteorological &; Oceans,&nbsp;and Marine Ecosystems &; Ecological - totalling 29.5 TB (1.2 million files). A data listing can be found in the SOTS Exploration Licence data directory (D2024-22376).&nbsp;</div><div><br></div><div><br></div><div><br></div><div><strong>The department and GA have agreed that GA will:</strong></div><div><em>accept the data on behalf of the department; and</em></div><div><em>store the data in its data management platform and make it available to the department as agreed between us.</em></div><div><br></div><div><em>GA will, acting on behalf of the department, accept the data directly from SOTS.</em></div><div><br></div><div><strong>GA acknowledges that:</strong></div><div><em>the data is provided to the Commonwealth on the terms and conditions of the Exploration Licence;</em></div><div><em>GA accepts the data on those same terms, including in relation to confidentiality and intellectual property; and </em></div><div><em>this exchange of letters does not affect the ownership of intellectual property rights applicable to the data.</em></div><div><br></div><div><strong>GA will store the data securely in its information technology system, with internal access restricted on a need-to-know basis.</strong></div><div><br></div><div><strong>GA acknowledges that it holds and manages the data on behalf of the department for the purposes of the Archives Act 1983 (Cth) and it will consult with the department before destroying any data to ensure the department remains compliant with relevant information management legislation.</strong></div><div><br></div><div><strong>GA must not publish or disclose the data to a third party without SOTS’ prior written consent.</strong></div><div><em>If GA wishes to disclose the data to another Commonwealth agency, it must take all reasonable steps to protect confidentiality including by notifying the receiving agency that the data is confidential and requiring it to accept the data on the same terms as the Exploration Licence.</em></div><div><br></div><div><em>If GA wishes to use or disclose the data outside the terms of the Exploration Licence, it will enter into its own arrangements with SOTS.</em></div><div><em>GA will immediately inform the department if data is accessed, used, and/or disclosed except in accordance with the Exploration Licence and this letter.</em></div><div> </div><div>GA will immediately inform the department if data is accessed, used and/or disclosed except in accordance with the Exploration Licence and the Data Sharing Exchange of Letters.</div><div><br></div><div>Following discussions between NSM staff, the Director of Data Services and the Manager, Data Governance and Catalogue it was agreed that the Geoscience Australia Information Archive (GAIA) would be the best solution to fulfill the requirements of the Data Sharing Exchange of Letters and to ensure the enduring value of the data to the Commonwealth.</div><div><br></div><div>GA Roles and Responsibilities have been outlined in the Lineage and in D2024-46051.</div>

<div>Steelmaking value chains are economically important to Australia, but the need to decarbonize traditional steel-making processes could disrupt existing supply lines. Hydrogen-based iron and steel production offers one pathway for reducing the carbon intensity of steel. Here, we present maps assessing the costs of hydrogen-based direct reduction of iron oxides (to produce hot briquetted iron), optionally coupled with steelmaking in an electric arc furnace (i.e. the H2-DRI-EAF value chain). Developed as part of the Exploring for the Future program and in collaboration with Monash University, these models build off the functionality of the Green Steel Economic Fairways Mapper (beta release), with additional enhancements to the modelling algorithm to reflect constant furnace operation, the incorporation of costings to transport the produced hot briquetted iron or steel to domestic ports, and the optimisation of facility capacities. The capacity of facilities (including solar and wind generation, proton exchange membrane [PEM] electolysis, battery storage, and hydrogen storage tanks) are determined by the 1 Mtpa production target and the local availability of renewable energy resources, as modelled according to 2019 data sourced from the Renewables.Ninja (https://www.renewables.ninja/; Pfenninger & Staffell, 2016; Staffell & Pfenninger, 2016). The high-resolution (approximately 5.5 km pixels) maps reflect our preferred technology cost assumptions (see Wang et al., 2023) for the year 2025. Iron concentrate feedstocks are assumed to cost AU$150 per tonne, reflecting approximate costs for 65 % Fe pellets as derived from magnetite ores. Conversions to USD assume US$1.00 = AU$0.73.</div><div><br></div><div>Geoscience Australia's Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia's geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia's transition to a low emissions economy, strong resources and agriculture sectors, and economic opportunities and social benefits for Australia's regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government.</div>

Monash University under commission of Geoscience Australia produced an offshore wind capacity factor map assessed at a 150m hub height applying the Bureau of Meteorology 10 year (2009-2018) “Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia” (BARRA) hindcast model. The wind capacity factor has been calculated using the bounding curve of all scaled power curves for wind turbines available within the Open Energy Platform as of 2021. Average wind capacity factor values were also calculated for the Vestas V126 3.45MW and the GE V130 3.2MW wind turbines and are available in this web map service.