Articles | Volume 10, issue 10
https://doi.org/10.5194/wes-10-2435-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2435-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Wind resources of southeast Australia during peak electricity demand days
Claire L. Vincent
CORRESPONDING AUTHOR
School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Melbourne, VIC, Australia
The ARC Centre of Excellence for the Weather of the 21st Century, Melbourne, VIC, Australia
Adam Nahar
Weather Routing inc., Glens Falls, New York, United States of America
formerly at: The University of Melbourne, Melbourne, VIC, Australia
Kelvin Say
School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Melbourne, VIC, Australia
Melbourne Climate Futures, The University of Melbourne, Melbourne, VIC, Australia
Related authors
Claire L. Vincent and Andrew J. Dowdy
Atmos. Chem. Phys., 24, 10209–10223, https://doi.org/10.5194/acp-24-10209-2024, https://doi.org/10.5194/acp-24-10209-2024, 2024
Short summary
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We investigate how wind speed at the height of a wind turbine changes during El Niño and La Niña years and with season and time of day in southeastern Australia. We found that El Niño and La Niña can cause average wind speed differences of around 1 m s-1 in some regions. The highest wind speeds occur in the afternoon or evening around mountains or the coast and during the night for inland areas. The results help show how placement of wind turbines can help balance electricity generation.
Mathieu Pichault, Claire Vincent, Grant Skidmore, and Jason Monty
Wind Energ. Sci., 6, 131–147, https://doi.org/10.5194/wes-6-131-2021, https://doi.org/10.5194/wes-6-131-2021, 2021
Short summary
Short summary
This paper assesses the behaviour and causality of sudden variations in wind power generation over a short period of time, also called "ramp events". It is shown, amongst other things, that ramps at the study site are mostly associated with frontal activity. Overall, the research contributes to a better understanding of the drivers and behaviours of wind power ramps at the wind farm scale, beneficial to ramp forecasting and ramp modelling.
Claire L. Vincent and Andrew J. Dowdy
Atmos. Chem. Phys., 24, 10209–10223, https://doi.org/10.5194/acp-24-10209-2024, https://doi.org/10.5194/acp-24-10209-2024, 2024
Short summary
Short summary
We investigate how wind speed at the height of a wind turbine changes during El Niño and La Niña years and with season and time of day in southeastern Australia. We found that El Niño and La Niña can cause average wind speed differences of around 1 m s-1 in some regions. The highest wind speeds occur in the afternoon or evening around mountains or the coast and during the night for inland areas. The results help show how placement of wind turbines can help balance electricity generation.
Mathieu Pichault, Claire Vincent, Grant Skidmore, and Jason Monty
Wind Energ. Sci., 6, 131–147, https://doi.org/10.5194/wes-6-131-2021, https://doi.org/10.5194/wes-6-131-2021, 2021
Short summary
Short summary
This paper assesses the behaviour and causality of sudden variations in wind power generation over a short period of time, also called "ramp events". It is shown, amongst other things, that ramps at the study site are mostly associated with frontal activity. Overall, the research contributes to a better understanding of the drivers and behaviours of wind power ramps at the wind farm scale, beneficial to ramp forecasting and ramp modelling.
Cited articles
AEC: Australia's ENERGY FUTURE: 55 BY 35 – Electrification and Heat, https://www.energycouncil.com.au/media/qn3cwx4m/electrification-and-heat.pdf (last access: 13 October 2025), 2022. a
AEMO: Forecasting Approach – Electricity Demand Forecasting Methodology, https://aemo.com.au/-/media/files/electricity/nem/planning_and_forecasting/nem_esoo/2023/forecasting-approach_electricity-demand-forecasting-methodology_final.pdf (last access: 13 October 2025), 2023. a
AEMO: Quarterly Energy Dynamics Q2 2024, https://aemo.com.au/-/media/files/major-publications/qed/2024/qed-q2-2024.pdf (last access: 13 October 2025), 2024. a
AEMO: Quarterly Energy Dynamics Q4 2024, https://aemo.com.au/-/media/files/major-publications/qed/2024/qed-q4-2024.pdf (last access: 13 October 2025), 2025. a
Ashcroft, L. C., Pezza, A. B., and Simmonds, I.: Cold events over southern Australia: Synoptic climatology and hemispheric structure, J. Climate, 22, 6679–6698, https://doi.org/10.1175/2009JCLI2997.1, 2009. a, b
The Australian Bureau of Meteorology: Bureau's Atmospheric high-resolution Regional Reanalysis for Australia (BARRA), The Australian Bureau of Meteorology [data set], https://doi.org/10.4225/41/5993927b50f53, 2025. a
Brown, A., Vincent, C., Lane, T., Short, E., and Nguyen, H.: Scatterometer estimates of the tropical sea-breeze circulation near Darwin, with comparison to regional models, Q. J. Roy. Meteor. Soc., 143, 2818–2831, https://doi.org/10.1002/qj.3131, 2017. a, b
Brown, A., Dowdy, A., and Lane, T. P.: Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia, Nat. Hazards Earth Syst. Sci., 24, 3225–3243, https://doi.org/10.5194/nhess-24-3225-2024, 2024. a
Davis, N. N., Badger, J., Hahmann, A. N., Hansen, B. O., Mortensen, N. G., Kelly, M., Larsén, X. G., Olsen, B. T., Floors, R., Lizcano, G., Casso, P., Oriol Lacave, A. B., Bauwens, I., Knight, O. J., van Loon, A. P., Fox, R., Parvanyan, T., Hansen, S. B. K., Heathfield, D., Onninen, M., and Drummond, R.: The Global Wind Atlas: A high-resolution dataset of climatologies and associated web-based application, B. Am. Meteorol. Soc., 104, E1507–E1525, https://doi.org/10.1175/BAMS-D-21-0075.1, 2023. a
Department of Climate Change, Energy, the Environment and Water, The Australian Government: Australia's offshore wind areas, https://www.dcceew.gov.au/energy/renewable/offshore-wind/areas (last access: 16 January 2025), 2024. a
Gunn, A., Dargaville, R., Jakob, C., and McGregor, S.: Spatial optimality and temporal variability in Australia's wind resource, Environ. Res. Lett., 18, 114048, https://doi.org/10.1088/1748-9326/ad0253, 2023. a
Henderson, C. R., Reeder, M. J., Parker, T. J., Quinting, J. F., and Jakob, C.: Summer Heatwaves in Southeastern Australia, Q. J. Roy. Meteor. Soc., 150, 4285–4305, https://doi.org/10.1002/qj.4816, 2024. a
Huang, Q., Reeder, M. J., Jakob, C., King, M. J., and Su, C. H.: The life cycle of the heatwave boundary layer identified from commercial aircraft observations at Melbourne Airport (Australia), Q. J. Roy. Meteor. Soc., 149, 3440–3454, https://doi.org/10.1002/qj.4566, 2023. a
Liu, Y. and Bai, J.: Daily Variation and Regional Differences in Wind Power Output during Heat and Cold Wave Days in China, International Transactions on Electrical Energy Systems, 2023, https://doi.org/10.1155/2023/8828093, 2023. a
Napoli, C. D., Barnard, C., Prudhomme, C., Cloke, H. L., and Pappenberger, F.: ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis, Geoscience Data Journal, 8, 2–10, https://doi.org/10.1002/gdj3.102, 2021. a
Ohlendorf, N. and Schill, W.-P.: Frequency and duration of low-wind-power events in Germany, Environ. Res. Lett., 15, 084045, https://doi.org/10.1088/1748-9326/ab91e9, 2020. a
OpenElectricity: National Electricity Market Energy Consumption, https://explore.openelectricity.org.au/, last access: 13 October 2025. a
Osczevski, R. and Bluestein, M.: The new wind chill equivalent temperature chart, B. Am. Meteorol. Soc., 86, 1453–1458, https://doi.org/10.1175/BAMS-86-10-1453, 2005. a
Pezza, A. B., van Rensch, P., and Cai, W.: Severe heat waves in Southern Australia: Synoptic climatology and large scale connections, Clim. Dynam., 38, 209–224, https://doi.org/10.1007/s00382-011-1016-2, 2012. a
Pichault, M., Vincent, C., Skidmore, G., and Monty, J.: Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes, Wind Energ. Sci., 6, 131–147, https://doi.org/10.5194/wes-6-131-2021, 2021. a
Reeder, M. J., Spengler, T., and Musgrave, R.: Rossby waves, extreme fronts, and wildfires in southeastern Australia, Geophys. Res. Lett., 42, 2015–2023, https://doi.org/10.1002/2015GL063125, 2015. a
Restel, L. and Say, K.: Counteracting the duck curve: Prosumage with time-varying import and export electricity tariffs, Energy Policy, 198, 114461, https://doi.org/10.1016/j.enpol.2024.114461, 2025. a
Richardson, D., Pitman, A. J., and Ridder, N. N.: Climate influence on compound solar and wind droughts in Australia, npj Climate and Atmospheric Science, 6, 184, https://doi.org/10.1038/s41612-023-00507-y, 2023. a
Simmonds, I., Keay, K., and Bye, J. A. T.: Identification and climatology of Southern Hemisphere mobile fronts in a modern reanalysis, J. Climate, 25, 1945–1962, https://doi.org/10.1175/JCLI-D-11-00100.1, 2012. a
Simshauser, P. and Wild, P.: Rooftop Solar PV, Coal Plant Inflexibility and the Minimum Load Problem, The Energy Journal, 46, 93–118, https://doi.org/10.1177/01956574241283732, 2025. a
Su, C.-H., Eizenberg, N., Jakob, D., Fox-Hughes, P., Steinle, P., White, C. J., and Franklin, C.: BARRA v1.0: kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains, Geosci. Model Dev., 14, 4357–4378, https://doi.org/10.5194/gmd-14-4357-2021, 2021. a
Vincent, C., Kelvin, S., and Nahar, A.: Lists of modelled hot and cold high-electricity demand days in Victoria, Australia, Zenodo [data set], https://doi.org/10.5281/zenodo.15493355, 2025. a, b, c
Wei, J., Wang, B., Luo, J. J., Li, C., and Yuan, C.: Synoptic characteristics of heatwave events in Australia during austral summer of 1950/1951–2019/2020, Int. J. Climatol., 43, 5662–5680, https://doi.org/10.1002/joc.8166, 2023. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Elsevier Science & Technology, Chantilly, United States, ISBN 9780123850232, 2011. a
Wind Turbine Models: Compare power curves of wind turbines, https://en.wind-turbine-models.com/powercurves, last access: 11 August 2025. a
Xia, G., Draxl, C., Optis, M., and Redfern, S.: Detecting and characterizing simulated sea breezes over the US northeastern coast with implications for offshore wind energy, Wind Energ. Sci., 7, 815–829, https://doi.org/10.5194/wes-7-815-2022, 2022. a
Short summary
The most important days for wind energy to make a large contribution to the electricity supply are when electricity demand is high. We examined the wind resource of southeast Australia on these days. We found that most hot high-demand days are influenced by a similar weather pattern, while cold high-demand days can be cold, wet, and windy or associated with widespread light winds. These results are important when considering the types of weather that could influence future wind energy.
The most important days for wind energy to make a large contribution to the electricity supply...
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