Spatial optimality and temporal variability in Australia’s wind resource

Andrew Gunn; Roger Dargaville; Christian Jakob; Shayne McGregor

doi:10.1088/1748-9326/ad0253

Spatial optimality and temporal variability in Australia’s wind resource

Andrew Gunn, Roger Dargaville, Christian Jakob, Shayne McGregor

Research output: Contribution to journal › Article › Research › peer-review

7 Citations (Scopus)

Abstract

To meet electricity demand using renewable energy supply, wind farm locations should be chosen to minimise variability in output, especially at night when solar photovoltaics cannot be relied upon. Wind farm location must balance grid-proximity, resource potential, and wind correlation between farms. A top-down planning approach for farm locations can mitigate demand unmet by wind supply, yet the present Australian wind energy market has bottom-up short-term planning. Here we show a computationally tractable method for optimising farm locations to maximise total supply. We find that Australia’s currently operational and planned wind farms produce less power with more variability than a hypothetical optimal set of farms with equivalent capacity within 100 km of the Australian Energy Market Operator grid. Regardless of the superior output, this hypothetical set is still subject to variability due to large-scale weather correlated with climate modes (i.e. El Niño). We study multiple scenarios and highlight several internationally transferable planning implications.

Original language	English
Article number	114048
Number of pages	8
Journal	Environmental Research Letters
Volume	18
Issue number	11
DOIs	https://doi.org/10.1088/1748-9326/ad0253
Publication status	Published - 27 Oct 2023

Keywords

climate variability
optimisation
wind power

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1088/1748-9326/ad0253Licence: CC BY

Cite this

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title = "Spatial optimality and temporal variability in Australia{\textquoteright}s wind resource",

abstract = "To meet electricity demand using renewable energy supply, wind farm locations should be chosen to minimise variability in output, especially at night when solar photovoltaics cannot be relied upon. Wind farm location must balance grid-proximity, resource potential, and wind correlation between farms. A top-down planning approach for farm locations can mitigate demand unmet by wind supply, yet the present Australian wind energy market has bottom-up short-term planning. Here we show a computationally tractable method for optimising farm locations to maximise total supply. We find that Australia{\textquoteright}s currently operational and planned wind farms produce less power with more variability than a hypothetical optimal set of farms with equivalent capacity within 100 km of the Australian Energy Market Operator grid. Regardless of the superior output, this hypothetical set is still subject to variability due to large-scale weather correlated with climate modes (i.e. El Ni{\~n}o). We study multiple scenarios and highlight several internationally transferable planning implications.",

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note = "Funding Information: This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government. We thank Julie Hughes at Paragon Media for collated data on planned wind farms in Australia. C J and S M acknowledge funding from the Australian Research Council through the Centre of Excellence for Climate Extremes (#CE170100023). Publisher Copyright: {\textcopyright} 2023 The Author(s). Published by IOP Publishing Ltd",

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N1 - Funding Information: This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI Australia), an NCRIS enabled capability supported by the Australian Government. We thank Julie Hughes at Paragon Media for collated data on planned wind farms in Australia. C J and S M acknowledge funding from the Australian Research Council through the Centre of Excellence for Climate Extremes (#CE170100023). Publisher Copyright: © 2023 The Author(s). Published by IOP Publishing Ltd

PY - 2023/10/27

Y1 - 2023/10/27

N2 - To meet electricity demand using renewable energy supply, wind farm locations should be chosen to minimise variability in output, especially at night when solar photovoltaics cannot be relied upon. Wind farm location must balance grid-proximity, resource potential, and wind correlation between farms. A top-down planning approach for farm locations can mitigate demand unmet by wind supply, yet the present Australian wind energy market has bottom-up short-term planning. Here we show a computationally tractable method for optimising farm locations to maximise total supply. We find that Australia’s currently operational and planned wind farms produce less power with more variability than a hypothetical optimal set of farms with equivalent capacity within 100 km of the Australian Energy Market Operator grid. Regardless of the superior output, this hypothetical set is still subject to variability due to large-scale weather correlated with climate modes (i.e. El Niño). We study multiple scenarios and highlight several internationally transferable planning implications.

AB - To meet electricity demand using renewable energy supply, wind farm locations should be chosen to minimise variability in output, especially at night when solar photovoltaics cannot be relied upon. Wind farm location must balance grid-proximity, resource potential, and wind correlation between farms. A top-down planning approach for farm locations can mitigate demand unmet by wind supply, yet the present Australian wind energy market has bottom-up short-term planning. Here we show a computationally tractable method for optimising farm locations to maximise total supply. We find that Australia’s currently operational and planned wind farms produce less power with more variability than a hypothetical optimal set of farms with equivalent capacity within 100 km of the Australian Energy Market Operator grid. Regardless of the superior output, this hypothetical set is still subject to variability due to large-scale weather correlated with climate modes (i.e. El Niño). We study multiple scenarios and highlight several internationally transferable planning implications.

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JO - Environmental Research Letters

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ER -

Spatial optimality and temporal variability in Australia’s wind resource

Abstract

Keywords

UN SDGs

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ARC Centre of Excellence for Climate Extremes

Cite this

Spatial optimality and temporal variability in Australia’s wind resource

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Projects

ARC Centre of Excellence for Climate Extremes

Cite this