2018
DOI: 10.5194/wes-3-651-2018
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Interannual variability of wind climates and wind turbine annual energy production

Abstract: Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standa… Show more

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Cited by 42 publications
(24 citation statements)
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“…Wind speeds range from 8.6 to 10 m s −1 at KGRR (MI), KMKX (WI), KBUF (NY) and KDVN (IA) ( Table 2). The wind speed distributions at these five of the six locations exhibit relatively good qualitative agreement with a priori expectations (see wind resource maps available at https://windexchange.energy.gov/ maps-data/324, last access: 15 January 2020) and estimates from simulations for 2002-2016 with the Weather Research and Forecasting model (Pryor et al, 2018) for 12 km grid cells containing the nominal wind field locations that indicate mean annual wind speeds of 6.5 m s −1 at KSFX (ID) and 8.4-9.0 m s −1 (KGRR (MI), KMKX (WI), KBUF (NY) and KDVN (IA)). However, wind speeds derived from radar observations from KMAF (TX) are relatively low (mean value of 5.9 m s −1 ) and exhibit a relatively low frequency of observations above 13 m s −1 (2.2 %).…”
Section: Resultssupporting
confidence: 64%
See 1 more Smart Citation
“…Wind speeds range from 8.6 to 10 m s −1 at KGRR (MI), KMKX (WI), KBUF (NY) and KDVN (IA) ( Table 2). The wind speed distributions at these five of the six locations exhibit relatively good qualitative agreement with a priori expectations (see wind resource maps available at https://windexchange.energy.gov/ maps-data/324, last access: 15 January 2020) and estimates from simulations for 2002-2016 with the Weather Research and Forecasting model (Pryor et al, 2018) for 12 km grid cells containing the nominal wind field locations that indicate mean annual wind speeds of 6.5 m s −1 at KSFX (ID) and 8.4-9.0 m s −1 (KGRR (MI), KMKX (WI), KBUF (NY) and KDVN (IA)). However, wind speeds derived from radar observations from KMAF (TX) are relatively low (mean value of 5.9 m s −1 ) and exhibit a relatively low frequency of observations above 13 m s −1 (2.2 %).…”
Section: Resultssupporting
confidence: 64%
“…In 2017 wind turbines (WTs) provided 6 % of total electricity generation in the United States of America (USA) (U.S. Energy Information Administration, 2018) and there are over 50 000 WTs operating in the USA today (Pryor et al, 2019). WTs are subject to harsh operating conditions during their 20-25-year lifetimes, including extreme winds, impacts from heavy rain, hailstones and snow, and intense ultraviolet light exposure that can lead to material damage (Keegan et al, 2013).…”
Section: Introduction and Objectivesmentioning
confidence: 99%
“…Meteorological measurements of atmospheric stability are uncommon in offshore wind farms. Different methods for the derivation of stability exist (see Rodrigo et al, 2015 for an overview). We applied the bulk Richardson method from profile measurements according to Emeis (2018) based on the tropical observations of Grachev and Fairall (1997).…”
Section: Atmospheric Stability and Meteorological Datamentioning
confidence: 99%
“…Table 4 presents the capacity factors in percentages at different hub heights. It is observed that the highest capacity factor was obtained in 50 m hub height (Pryor et al, 2018).…”
Section: Estimation Of Capacity Factormentioning
confidence: 94%