Meteorological Controls on Wind Turbine Wakes

Barthelmie, R.J.; Hansen, Kurt Schaldemose; Pryor, Sara C.

doi:10.1109/jproc.2012.2204029

Cited by 81 publications

(64 citation statements)

References 27 publications

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“…Measurements of annual average wake losses at some offshore wind power plants in European waters have been in the range of 10% to 20% (Barthelmie 2013;Hansen et al 2012) based on available wake measurement data. As part of the MA WEA wake analysis, NREL researchers examined the array efficiency (100% ideal efficiency -% wake losses) for the three delineation alternatives in Figure 4.…”

Section: Wake Loss Summarymentioning

confidence: 99%

“…Studies have shown that the turbulence intensity of the wind flow can have a significant effect on the wake losses in offshore wind power plants (Barthelmie et al 2013;Hansen et al 2012). Measurements of power production and wakes in European offshore wind power plants have verified that wake losses are typically greatest for low turbulence intensity wind flow conditions and lowest for high turbulence intensity wind flow conditions.…”

Section: Effect Of Turbulence Intensity On Wake Lossesmentioning

confidence: 99%

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Assessment of Offshore Wind Energy Leasing Areas for the BOEM Massachusetts Wind Energy Area

Musiał¹,

Parker²,

Fields³

et al. 2013

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Section: Wake Loss Summarymentioning

confidence: 99%

Section: Effect Of Turbulence Intensity On Wake Lossesmentioning

confidence: 99%

Assessment of Offshore Wind Energy Leasing Areas for the BOEM Massachusetts Wind Energy Area

Musiał¹,

Parker²,

Fields³

et al. 2013

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“…Multiple factors impact the IAV of net AEP from operating wind farms including but not limited to curtailment for system operation and/or WT maintenance (Clifton et al, 2016), WT wake losses (Clifton et al, 2016;Barthelmie et al, 2013), and 10 wind speed variability. Here we focus on this last factor.…”

Section: Simulationsmentioning

confidence: 99%

“…Typical wind turbine induced wakes losses for onshore wind farms are often estimated to be ≤ 5% (Staid et al, 2018), while 25 those offshore are frequently in excess of 10% (Barthelmie et al, 2013). Onshore availability typically exceeds 98% (Carroll et al, 2017), but tends to decrease with WT age (Olauson et al, 2017).…”

Section: 2018) Annual Gross Capacity Factors (Cf) For Grid 15mentioning

confidence: 99%

“…25 Accurate quantification of the wind resource and the P50(AEP) and P90(AEP) presents a significant challenge to current models (Zhang et al, 2015), and even small uncertainties in modelled wind speeds cause major uncertainties in P50 (AEP) and P90(AEP) and significantly impact the cost of investment capital in new wind projects (Tindal, 2011;Clifton et al, 2016). Capital investments by the wind energy industry within the United States of America during 2016 are estimated at $14.5 billion (Dykes et al, 2017), while estimates of investment in the European offshore wind energy are projected to be 30 between $90-124 billion over the period 2013-2020(Gatzert and Kosub, 2016.…”

Section: Introductionmentioning

confidence: 99%

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Inter-annual variability of wind climates and wind turbine annual energy production

Pryor¹,

Shepherd²,

Barthelmie³

2018

Preprint

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Abstract.Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50 th and 90 th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind 10 turbine hub-heights is poorly constrained and maybe overestimated by the 6% standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the longterm wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the 15 power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6%. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90% of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. 20Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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A new turbulence model for offshore wind turbine standards

et al. 2013

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Correct turbulence intensity modeling is crucial for fatigue load estimation for wind turbine structural design. It is well known that the International Electrotechnical Commission 61400-3 Normal Turbulence Model recommended for offshore wind turbine design is not representative of offshore wind conditions. A new model is urgently needed as offshore wind energy is rapidly developing worldwide. After evaluating the suitability of the Normal Turbulence Model at three sites in Asia, Europe and the USA, it is found that wind-wave interaction and stability correction should be taken into account in modeling the offshore turbulence intensity and wind speed relationship. Therefore, a new turbulence intensity model, which models wind-wave interaction with the Charnock equation and adjusts for the influence of atmospheric stability through empirical turbulence scaling functions for the unstable atmospheric boundary layer, was developed. The new model is physically based and is tested against observations from the three sites. It shows better performance than the Normal Turbulence Model and hence is recommended to replace the Normal Turbulence Model. For model application, only two parameters are required, which are defined herein to represent offshore sites with high, medium and low turbulence intensities.

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Meteorological Controls on Wind Turbine Wakes

Cited by 81 publications

References 27 publications

Assessment of Offshore Wind Energy Leasing Areas for the BOEM Massachusetts Wind Energy Area

Assessment of Offshore Wind Energy Leasing Areas for the BOEM Massachusetts Wind Energy Area

Inter-annual variability of wind climates and wind turbine annual energy production

A new turbulence model for offshore wind turbine standards

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