This Title All WIREs
How to cite this WIREs title:
WIREs Energy Environ.
Impact Factor: 3.803

Quantifying the variability of wind energy

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Wind by its very nature is a variable element. Its variation is different on different timescales and spatially its magnitude can change dramatically depending on local climatology and terrain. This has implications in a variety of sectors, not least in the wind energy sector. The accuracy of weather forecasting models has increased significantly in the last few decades and these models are able to give an insight into variability on the hourly and daily timescales. On shorter timescales, predicting chaotic turbulent fluctuations is far more challenging. Similarly, the ability to make seasonal forecasts is extremely limited. General circulation models (GCMs) can give insights into possible future decadal fluctuations, but there are still large uncertainties. Observational data can give useful information concerning variation on a variety of timescales, but data quality and spatial coverage can be variable. An understanding of local scale spatial variations in wind is extremely important in wind farm siting. In the last 40 years, there have been significant advances in predicting these variations using computer models, although there remain significant challenges in understanding the behavior of the wind in certain environments. Both the spatial and temporal variations of wind are important considerations when wind power is integrated into electricity networks, and this will become an ever more important consideration as wind generation makes an increasing contribution to our global energy needs. WIREs Energy Environ 2014, 3:330–342. doi: 10.1002/wene.95 This article is categorized under: Wind Power > Climate and Environment Energy Infrastructure > Economics and Policy
Projected changes in wind speed across Brazil by 2091–2100 under the IPCC A2 emissions scenario relative to the 1961–1990 baseline. (Reprinted with permission from Ref . Copyright 2010 Elsevier)
[ Normal View | Magnified View ]
Robust coefficient of variability (RCoV) across the USA. (Reprinted with permission from Ref . Copyright 2012 under the Creative Commons Attribution 3.0 License)
[ Normal View | Magnified View ]
Trends in observed surface wind speeds (in m/s per annum). Reprinted with permission from Ref . Copyright 2012 Elsevier)
[ Normal View | Magnified View ]
Histogram of observed wind speeds with three theoretical distributions fitted. W, Weibull; WW, bi‐modal Weibull; TNW, singly truncated from below normal Weibull. (Reprinted with permission from Ref . Copyright 2007 Elsevier)
[ Normal View | Magnified View ]
Power spectrum density of wind speeds based on data collected at STFC‐RAL, UK.
[ Normal View | Magnified View ]
The Van der Hoven spectrum of wind speeds at Brookhaven National Laboratory, USA.
[ Normal View | Magnified View ]
(a) Average correlation length and (b) autocorrelation time inferred from ERA‐40 data over a 44‐year period. (Reprinted with permission from Ref . Copyright 2008 under the Creative Commons Attribution 3.0 License)
[ Normal View | Magnified View ]
Correlations (ρ) between pairs of wind farm sites in Texas compared with sites in Europe. Best fit exponential decay curves are shown for the two regions. The decay curves have the form ρ ∝ exp(−Distance/D), where D is the characteristic decay distance (305 km for Texas and 641 km for Europe). (Reprinted with permission from Ref . Copyright 2010 Elsevier)
[ Normal View | Magnified View ]

Browse by Topic

Wind Power > Climate and Environment
Energy Infrastructure > Economics and Policy

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts