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

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.

KEYWORDS:

• Probability density function
• wind energy
• wind potential
• statistical analysis
• Weibull distribution

Nomenclature

ID Station number	=
${\sigma }_U$	=	Standard deviation (m/s)
$s$	=	Skewness
$f\left(U\right)$	=	Probability density function
$F\left(U\right)$	=	Cumulative distribution function
$R^2$	=	Coefficient of determination
$RMSE$	=	The root mean square error
$BIC$	=	Schwarz’s Bayesian information criterion
$AIC$	=	Akaike information criterion
$PD$	=	The wind power density (W/m2)
$k$	=	Shape parameter (dimensionless)
$c$	=	Scale parameter (m/s)

Acknowledgments

The authors would like to acknowledge the Australian Government Bureau of Meteorology for supplying wind data. The authors would like to sincerely thank Isra University, Jordan for their financial support given through doctoral scholarship to the student Nour Khlaifat.

Additional information

Notes on contributors

Nour Khlaifat

Nour Khlaifat is currently a PhD student in the Faculty of   Engineering at the University of Technology Sydney in Australia. Her research interests include renewable energy technologies, wind engineering, computational fluid dynamics, MATLAB simulation, photovoltaics, and engineering thermodynamics.

Ali Altaee

Ali Altaee obtained his in the Environmental Engineering in 2004 from the University of Brighton in the UK, in the Environmental Engineering. He worked at Surrey University, University of New South Wales, Heriot Watt University, Abu Dhabi University, University of West of Scotland and University of Technology in Sydney. He also worked as a senior researcher at Doosan Heavy Industry and Constructions in water and energy department. Dr. Altaee has many publication in peer reviewed international journals, book chapters, conference papers and several patents and patent applications. He worked in the development of several technologies in renewable energy and water desalination including forward osmosis for desalination, forward osmosis for pretreatment of feed water to thermal processes and dual stage pressure retarded osmosis.

John Zhou

John L. Zhou is currently a Professor and the Director of Centre for Green Technology at University of Technology Sydney in Australia. His research expertise covers environmental analysis, remote sensing of vehicle emissions, pollution processes, advanced wastewater treatment, and energy recovery from waste.

Yuhan Huang

Yuhan Huang is currently a Postdoctoral Research Fellow in the School of Civil and Environmental Engineering at University of Technology Sydney in Australia. His research interests include vehicle emissions, air quality, computational fluid dynamics, internal combustion engines, and renewable fuels.