Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia

Altaee, A; Khlaifat, N; Zhou, J; Huang, Y

Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia

Altaee, A

Khlaifat, N Zhou, J Huang, Y

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Publication Type:: Journal Article
Citation:: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Issue Date:: 2020-09-22

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Field	Value	Language
dc.contributor.author	Altaee, A https://orcid.org/0000-0001-9764-3974
dc.contributor.author	Khlaifat, N
dc.contributor.author	Zhou, J
dc.contributor.author	Huang, Y
dc.date.accessioned	2020-09-23T09:44:42Z
dc.date.available	2020-09-23T09:44:42Z
dc.date.issued	2020-09-22
dc.identifier.citation	Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
dc.identifier.uri	http://hdl.handle.net/10453/142833
dc.description.abstract	Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources.
dc.language	en
dc.relation.ispartof	Energy Sources, Part A: Recovery, Utilization, and Environmental Effects
dc.relation.isbasedon	10.1080/15567036.2020.1822956
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	“This is an Accepted Manuscript of an article published by Taylor & Francis in [Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020], available online: https://www.tandfonline.com/doi/full/10.1080/15567036.2020.1822956]
dc.title	Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia
dc.type	Journal Article
utslib.location.activity	Australia
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2021-09-22T00:00:00+1000Z
dc.date.updated	2020-09-23T09:43:46Z

Abstract:

Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources.

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http://hdl.handle.net/10453/142833