Nationwide Assessment of Potential Output from Wind-Powered Generators

Justus, C. G.; Hargraves, W. R.; Yalçin, Ali

doi:10.1175/1520-0450(1976)015<0673:naopof>2.0.co;2

Cited by 253 publications

(125 citation statements)

References 2 publications

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“…When these certainty ratings are taken into consideration, the WPD estimated at 50 m is well within the bounds of uncertainty. Justus et al (1976) observed that across the US, the shape parameter for Weibull distribution of wind speeds varies between 1.1 and 2.7 and the mean value is 2.0. As discussed above, a larger shape parameter is used for larger wind speed regions.…”

Section: Comparison With Nrel Map At 50 Mmentioning

confidence: 99%

Characterization of wind power resource in the United States

Gunturu

Schlosser

2012

Atmos. Chem. Phys.

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Abstract. Wind resource in the continental and offshore United States has been reconstructed and characterized using metrics that describe, apart from abundance, its availability, persistence and intermittency. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) boundary layer flux data has been used to construct wind profile at 50 m, 80 m, 100 m, 120 m turbine hub heights. The wind power density (WPD) estimates at 50 m are qualitatively similar to those in the US wind atlas developed by the National Renewable Energy Laboratory (NREL), but quantitatively a class less in some regions, but are within the limits of uncertainty. The wind speeds at 80 m were quantitatively and qualitatively close to the NREL wind map. The possible reasons for overestimation by NREL have been discussed. For long tailed distributions like those of the WPD, the mean is an overestimation and median is suggested for summary representation of the wind resource.The impact of raising the wind turbine hub height on metrics of abundance, persistence, variability and intermittency is analyzed. There is a general increase in availability and abundance of wind resource but there is an increase in intermittency in terms of level crossing rate in low resource regions.

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Section: Comparison With Nrel Map At 50 Mmentioning

confidence: 99%

Characterization of wind power resource in the United States

Gunturu

Schlosser

2012

Atmos. Chem. Phys.

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“…As seen from the literature, much concentration has been given to Weibull function because it is found to give fit to the observed wind speed data both at the surface [4] [5] and in the upper air [6]. Wentink compared the Weibull functions with other distributions like Plank's frequency distribution, Rayleigh Distribution and Gamma distribution [7].…”

Section: Introductionmentioning

confidence: 99%

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Parajuli¹

2016

EPE

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“…The aforementioned function is utilized to denote the probability of wind speed variations wherein significant relationship between Mean Wind Speed (MWS), wind speed standard deviation and also shape parameter and scale factor of Weibull distribution function are all extracted [40]. Weibull distribution function has been exploited in several typical sites to observe the hourly change in MWS [52][53][54]. Since year-to-year prediction of MWS is very difficult based on annual horizon, the wind speed variations over a year can be well expressed with PDF.…”

Section: Wind Prediction Proceduresmentioning

confidence: 99%

On the management of wind power intermittency

Rahimi

Rabiee

Aghaei

et al. 2013

Renewable and Sustainable Energy Reviews

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Nowadays, the world is encountering severe challenges in the energy generation sector. Environmental issues like climate change, global warming and Green House Gases (GHGs) and also social issues like dramatic increase in global population and increasing energy demand are the main causes of global concerns about energy resource management. In this regard, Renewable Energy Sources (RESs) are the suitable substitution to replace the conventional generating units that emit GHGs due to the use of fossil fuels. Among all RESs, wind energy seems to be promising for generating emission-free electrical energy. However, it is naturally unpredictable due to its intermittency which leads to s ome technical problems such as generation imbalance as well as optimal reserve allocation. This paper investigates the solutions to compensate wind intermittency through introducing various technolo gies such as Pumped-Hydro Storage (PH S) units, Plug-in Hybrid Electric Vehicles (PHEVs), solar energy and other electric storages like batteries. Abstract-Nowadays, the world is encountering severe challenges in the energy generation sector. Environmental issues like climate change, global warming and Green House Gases

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Nationwide Assessment of Potential Output from Wind-Powered Generators

Cited by 253 publications

References 2 publications

Characterization of wind power resource in the United States

Characterization of wind power resource in the United States

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

On the management of wind power intermittency

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