Improving uncertainty estimates: Inter-annual variability in Ireland

Pullinger, David; Zhang, M; Hill, Nigel; Crutchley, T

doi:10.1088/1742-6596/926/1/012006

Cited by 4 publications

(7 citation statements)

References 2 publications

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“…The period required for 'convergence' of inter-annual variability estimates of annual mean wind speed was previously evaluated by computing the standard deviation of mean annual wind speeds using output from a reanalysis data 5 set of 35 years, and comparing that estimate with the estimate derived from truncated samples there-of. That study found σ converges on the long-term estimate to within +/-15% after 11 years (Pullinger et al, 2017), which implies that the simulations presented herein are of sufficient duration to adequately characterize IAV.…”

Section: Simulationssupporting

confidence: 52%

“…within 10 m of the ground) stations in Ireland ranged from 4.7 to 6.4% (Raftery et al, 1998). In a more recent analysis of surface observations from 16 stations also in Ireland collected over data periods of up 20 to 13 years, σ was reported to lie between 4.4 to 6.9% of the mean (Pullinger et al, 2017). Conversely, an analysis of monthly wind speeds at approximately 80-m over the period 1979-2014 from the North American Regional Reanalysis (NARR) data set found 'variations in the wind speed of up to 30%' at some existing wind turbine locations in the United States (Hamlington et al, 2015).…”

Section: Introductionmentioning

confidence: 98%

“…According to some estimates, IAV contributes "anywhere between 10 % and 25 %" of the overall uncertainty in project energy yield over a 10-year period (Pullinger et al, 2017). In the wind energy literature IAV is often represented using the standard deviation (σ) of 5 annual mean wind speeds to the long term mean value and is thus is often quoted as a percentage of the mean.…”

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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Section: Simulationssupporting

confidence: 52%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Pryor¹,

Shepherd²,

Barthelmie³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…In one of the first published studies on this topic, the IAV of mean wind speeds as described using the σ of annual values around the mean across five surface (i.e., within 10 m of the ground) stations in Ireland ranged from 4.7 % to 6.4 % (Raftery et al, 1998). In a more recent analysis of surface observations from 16 stations, also in Ireland, collected over data periods of up to 13 years, σ was reported to lie between 4.4 % and 6.9 % of the mean (Pullinger et al, 2017). Conversely, an analysis of monthly wind speeds at approximately 80 m over the period 1979-2014 from the North American Regional Reanalysis (NARR) data set found "variations in the wind speed of up to 30 %" at some existing wind turbine locations in the United States (Hamlington et al, 2015).…”

mentioning

confidence: 98%

“…Interannual variability (IAV) is used to describe the yearto-year variability in a given property. According to some estimates, IAV contributes "anywhere between 10 % and 25 %" of the overall uncertainty in project energy yield over a 10year period (Pullinger et al, 2017). In the wind energy literature IAV is often represented by assuming a Gaussian distribution for annual mean wind speeds and specifying the dispersion of values around that mean in terms of the standard deviation (σ ) of annual mean wind speeds to the longterm mean value.…”

mentioning

confidence: 99%

Interannual variability of wind climates and wind turbine annual energy production

2018

View full text Add to dashboard Cite

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 standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV 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 min wind speeds close to wind turbine hub heights and the 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 or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in 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. Further, the 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 preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

show abstract