challenges in modelling offshore wind – how to address them using observation

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Challenges in modelling offshore wind – how to address them using observation Idar Barstad [email protected]

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Challenges in modelling offshore wind – how to address them using observation. Idar Barstad [email protected]. Improvements of resource estimates and forecast of energy yield rely primarily on the quality of numerical models and their input data. - PowerPoint PPT Presentation

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Page 1: Challenges in modelling offshore wind – how to address them using observation

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Challenges in modelling offshore wind – how to address them using observation

Idar Barstad

[email protected]

Page 2: Challenges in modelling offshore wind – how to address them using observation

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Improvements of resource estimates and forecast of energy yield rely primarily on the quality of numerical models and their input data.

There are many ways to set up the model suite, and the numerical model tool is normally tailored for generic use.

The system can sometimes be inherently unpredictable

Page 3: Challenges in modelling offshore wind – how to address them using observation

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Future wind power potential in Europe

Barstad et al. (2012), J. Renewable Energy

- Arpege/Ifs T159L60c3-- (1972-2001) N/F--(2020-2049) – R[1-4] (A1B)

SST = ERA40 + delta from CGCMs(variability from ERA40=>get more realistic sea ice)

[ Barstad et al. (2008), Clim.Dyn. ]

Page 4: Challenges in modelling offshore wind – how to address them using observation

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Current wind climateWind speed at 100m ::Annual average (1972-2001)

Solid line=8.5 m/s

Page 5: Challenges in modelling offshore wind – how to address them using observation

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Future power potential(2020-2049)

Fractional power potential in reference to (1972-2001)

Black line=1.0

Page 6: Challenges in modelling offshore wind – how to address them using observation

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Example 12UTC 29Feb 2008

The effect of surface waves

Page 7: Challenges in modelling offshore wind – how to address them using observation

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WRF roughness length (m) after 12 h

Colour scale for roughness length / m Colour scale for roughness length difference / m

No coupling Two-way coupling Difference

Work by: Alastair Jenkins, Alok Gupta, John Michalakes (NREL), Idar Barstad

Page 8: Challenges in modelling offshore wind – how to address them using observation

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The effect on U10 (wind speed) in WRF

2-way coupling :: Significant impact on the wind field!

After 12 hrs simulation

Page 9: Challenges in modelling offshore wind – how to address them using observation

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High resolution downscaling (9-3km)

0

2

-2

BIAS (Obs-ERAI)

BIAS (Obs-3km)

BIAS (Obs-9km)

Page 10: Challenges in modelling offshore wind – how to address them using observation

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High resolution downscaling (9-3km)

Qscatassimilation

STD

0

2

-2

Coastal effects

RMSE

BIAS

Stations along the Norwegian Coast

Page 11: Challenges in modelling offshore wind – how to address them using observation

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(Courtesy M Zagar, Vestas

BIAS

Page 12: Challenges in modelling offshore wind – how to address them using observation

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Inversion - Vosper’s regime diagramWeak inversion

Vosper (2004)

Strong inversion

Strong inversion

Hm/PBLH

Valentia Irland

- 6700 cases over 10 yrs (2000-2010)

- Conditions for lee waves

15-20% of the time

Page 13: Challenges in modelling offshore wind – how to address them using observation

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Drag from turbines in a single cell, distributed over several layers

- > works on both the TKE and the momentum eqs.

(Blahak et al. 2010)

Wind turbine drag in WRF from V3.3

Page 14: Challenges in modelling offshore wind – how to address them using observation

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Fitch et al. (2012); MWR

Q:

How sensitive is the power output to the atmospheric characteristics?

Simulation of a wind farm100 x 5MW

wind turbines

Page 15: Challenges in modelling offshore wind – how to address them using observation

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Generation of pressure gradients by wind farmGeneration of pressure gradients by wind farm::- θ increases with height under typical stable conditions- θ increases with height under typical stable conditions- As air lifted over farm, lower θ air brought up from below- As air lifted over farm, lower θ air brought up from below- This creates cold anomaly aloft and thus high pressure anomaly below - This creates cold anomaly aloft and thus high pressure anomaly below – –> pressure gradients deflect wind.> pressure gradients deflect wind.

Slide 3

top viewtop view

side viewside view

Typical θ-profile over sea: Typical θ-profile over sea:

The principle:

Page 16: Challenges in modelling offshore wind – how to address them using observation

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Slide 11

Full model• WRF 1 km model• Ideal set-up• turbine drag 5MW

top view

Idealized study –sensitivity to upstream parameters

Work by: Fitch and Barstad

Page 17: Challenges in modelling offshore wind – how to address them using observation

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(9km-3km-1km)

Demonstration of the WRF-turbine dragat Dogger bank

15 x15 km with a 5 MW turbine in each grid cell

Page 18: Challenges in modelling offshore wind – how to address them using observation

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1km domainwind and wind speed

1km domainwind, wind speed &

vorticity

Dogger bank wind farm

Conclusions: - 10% effect on wind speed(up to 60% on power)- Long wakes

(15 x15 km with a 5 MW turbine in each grid cell)

Page 19: Challenges in modelling offshore wind – how to address them using observation

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40km

20km

Reduced model –”single farm”Same model as in Smith (2009)

Fra

ctio

nal w

ind

redu

ctio

n

-50 0 50

Page 20: Challenges in modelling offshore wind – how to address them using observation

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Reduced model – “single farm”

The effect of the inversion strength

double inversion strength

Page 21: Challenges in modelling offshore wind – how to address them using observation

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Two farms – reduced model

Present in all runs (Lx=10km)

Second farm (Lx=10km)

Utop=15 m/sUBL=8.5 m/sH=500mdth/th=0.01

Page 22: Challenges in modelling offshore wind – how to address them using observation

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Two farms – reduced model

Present in all runs (Lx=10km)

Second farm (Lx=10km)

Page 23: Challenges in modelling offshore wind – how to address them using observation

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Conclusions• Models may produce data, but we have to be

critical to their results

• Models may be tailored to your specific needs -> talk to an expert!

• Observational campaigns should be design to address scientific questions. Do we have these questions?

Page 24: Challenges in modelling offshore wind – how to address them using observation

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NOAA / MODIS 23 MAR 2010, Aleutians Islands

Thank you for your attention!

[email protected]

Alaska