Comprehensive utilization of mesoscale modelling for wind energy applications

Comprehensive utilization of mesoscale modelling for wind energy applications

Jake Badger, Andrea Hahmann, Xiaoli Guo Larsen, Alfredo Peña Diaz, Ekaterina Batchvarova, Sven-Erik Gryning, Rogier Floors, Hans Ejsing Jørgensen

Wind Energy DivisionRisø DTU

Risø DTU, Technical University of Denmark

Introduction Comprehensive adj Oxford English Dictionary

1. complete; including all, or nearly all elements, aspects etc.2. of or relating to understanding 3. ...

Comprehensive (1st meaning: complete)1. Wind resource assessment Poster ID 1562. Wind power forecasting Poster ID 1533. Extreme wind climate assessment Talk4. Mesoscale variability of wind Talk5. ‘Tall’ wind profiles Talk6. Flow over forest Talk 7. Wind power integration8. Wind farm wakes: their impacts on climate9. Wind turbine icing forecasting and climate10.Wind and wave climate studies11....

PREDICTABILITY OF WIND CONDITIONS17/3/2011, 11:00 - 12:30

SITING CHALLENGES16/3/2011, 11:00 - 12:30


Introduction

Comprehensive (2nd meaning: understanding)

There is a need to understand• limitations of mesoscale modelling• appropriate use of the modelling results

Need valid link between mesoscale modelling results and measurement

This allows: • application• verification

We can also verify against other meteorological quantities (not just wind) to:

• test performance of model• indicate new linkages between mesocale modelling, microscale modelling and

measurements


micro local corrections

local roughness corrections

mes

osca

le m

odel

out

put

estim

ates

of

site

con

ditio

ns

Routes from mesoscale model to site

Meso ‘local’ corrections




mes

osca

le m

odel

out

put

estim

ates

of

site

con

ditio

ns


direct





mes

osca

le m

odel

out

put

estim

ates

of

site

con

ditio

ns


direct micro corrections only





mes

osca

le m

odel

out

put

estim

ates

of

site

con

ditio

ns


direct micro corrections only meso & micro corrections



u*, L or u(z) or u(zj)

h, z0

u at site

account for ‘local’ mesoscale effects

Evaluate u for standard heights above flat terrain of standard roughness lengths

account for ‘local’ microscale effects at site

WAsP WAsP Engineering

Mesocale model fields and output

Post-processing

Generalization

Badger et al (2010)

Application

Links in the model chain

corrections for• orography• roughness

application of• M-O similarity

theory• geostrophic drag

law


Microscale local corrections

Mesoscale ‘local’ corrections

Site in northern Spain

due to orographymax +18%min +4%

due to orographymax +60%min +10%

due to roughnessmax +1%min - 6%

due to roughnessmax 0%min - 6%


Verification of mean wind speed

meso & micro corrections

u* Lmeso u* Luser u(z) u(zj)


Mesoscale ‘local’ corrections and microscale local corrections give best agreement with measurements

norm

aliz

ed w

ind

spee

d


method


Verification of mean power density

meso & micro corrections

u* Lmeso u* Luser u(z) u(zj)


Mesoscale ‘local’ corrections and microscale local corrections give best agreement with measurements

norm

aliz

ed p

ower

den

sity

method



Importance of microscale... a motivation

Wind resource (power density) at 50 m calculated at different resolutions

10 km 5 km

2.5 km 0.1 km

50 km

50 km

324 W/m2

378 W/m2

328 W/m2

378 W/m2

323 W/m2

378 W/m2

505 W/m2

641 W/m2

mean power density of total area mean power density for windiest 50% of area


Application at high resolution

Extreme wind climate

WAsP Engineering

Wind climate

WAsP


New modes of model verification

100 m

600 m

Pulsed LIDAR wind measurement to 600 m

Floors et al (2010)

100 m

• Separate error contribution into mesoscale and microscale parts?• Microscale influence tending to reduce with height.


New modes of model verification

1/L, WRF v sonic

Comparison of surface layer fluxes from sonics

Peña and Hahmann (2011)

heat flux, WRF v sonicu*, WRF v sonic

• Learn characteristic errors in surface fluxes.• B-L schemes may give unexpected velocity profiles.• Surface fluxes and theory for alternative profiles.


• Modelling profiles beyond the surface layer

After Gryning et al (2007), 3 characteristic lengths scales used to define boundary-layer profiles:

Neutral with baroclinicity term (Kelly 2011, pers.comm.) (stable and unstable profiles have corresponding

expressions)

Advancing the links in the model chain

Gryning et al 2007



Peña et al (2010)

Hahmann 2011

• Modelling profiles beyond the surface layer

After Gryning et al (2007), 3 characteristic lengths scales used to define boundary-layer profiles:

Neutral with baroclinicity term (Kelly 2011, pers.comm.) (stable and unstable profiles have corresponding

expressions)

B-L height from pulsed LIDARvia aerosols’ backscatter

Julian dayz

[m]

B-L height from WRF

time of day (LST)

B-L

hei

ght

[m]


Kelly and Gryning (2010) describe method to correct long-term profile according to long-term distribution of stability (pdf of 1/L).

Peña and Hahmann (2011) use WRF to evaluate long-term distribution of stability (pdf of 1/L) and thus give a long-term stability correction < >


Peña and Hahmann (2011)

< > at 70 m

• Correction of long-term profile will be useful in application of generalized wind climates at sites.


• Verification of mesoscale modelling applications in wind energy requires consideration of local unresolved effects.

• Valuable new model verification possible via application of new measurement technologies.

• New theory gives possibilities for advancing the mesoscale to microscale model chain.

• Understanding mesoscale model characteristics guides appropriate use of mesoscale model output:

• boundary layer parameterizations• surface layer and boundary layer properties

• Most appropriate use may not always be the most obvious.

• Verification is an essential part of model development loop.

Summary and conclusions


Thank you for your attention

[email protected]

Oral presentation sessions

PREDICTABILITY OF WIND CONDITIONS17/3/2011, 11:00 - 12:30

SITING CHALLENGES16/3/2011, 11:00 - 12:30

Poster session

Poster ID 156 on uncertainty mapping

Poster ID 153 on forecasting

Comprehensive utilization of mesoscale modelling for wind energy applications

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