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) “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>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>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>

<div>GeoInsight was an 18-month pilot project developed in the latter part of Geoscience Australia’s Exploring for the Future Program (2016–2024). The aim of this pilot was to develop a new approach to communicating geological information to non-technical audiences, that is, non-geoscience professionals. The pilot was developed using a human-centred design approach in which user needs were forefront considerations. Interviews and testing found that users wanted a simple and fast, plain-language experience which provided basic information and provided pathways for further research. GeoInsight’s vision is to be an accessible experience that curates information and data from across Geoscience Australia, helping users make decisions and refine their research approach, quickly and confidently.</div><div><br></div><div>In the first iteration of GeoInsight, selected products for energy, minerals, water, and complementary information from Geoscience Australia’s Data Discovery Portal and Data and Publications Catalogue were examined to (1) gauge the relevance of the information they contain for non-geoscientists and, (2) determine how best to deliver this information for effective use by non-technical audiences.</div><div><br></div><div>This Record documents the technical details of the methods used for summarising energy commodities for GeoInsight. These methods were devised to convey current production and future production/extraction potential quickly and efficiently for regions across the Australian continent. Evaluated energy commodities include oil and gas, hydrogen and geological hydrogen storage, uranium and thorium, coal (black and brown), geothermal energy, and renewable energy. Carbon storage, a decarbonisation enabler, was also addressed under the energy theme.</div><div><br></div><div>This document contains two sections:</div><div><strong>Production Summary:</strong> To showcase where energy resources are being produced in different regions of Australia. The source datasets provide a snapshot of energy production activities at the time of publication. </div><div><strong>Potential Summary:</strong> To highlight, at first glance, the likelihood that future energy production and decarbonisation initiatives may occur in different regions of Australia. The source datasets provide a snapshot of future energy potential at the time of publication.</div><div><br></div><div>Any updates to the methodology used in GeoInsight will be accompanied by updates to this document, including a change log.</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 net zero emissions, strong, sustainable 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><div><br></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. 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>