Comprehensive utilization of mesoscale modelling for wind energy applications
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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 Division Risø DTU. Introduction.

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Comprehensive utilization of mesoscale modelling for wind energy applications

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Comprehensive utilization of mesoscale modelling for wind energy applications

Comprehensive utilization of mesoscalemodelling 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 Division

Risø DTU


Introduction

Introduction

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

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

ComprehensiveadjOxford English Dictionary

1. complete; including all, or nearly all elements, aspects etc.

2. of or relating to understanding

3. ...

Comprehensive (1st meaning: complete)

Wind resource assessment Poster ID 156

Wind power forecastingPoster ID 153

Extreme wind climate assessment Talk

Mesoscale variability of wind Talk

‘Tall’ wind profilesTalk

Flow over forestTalk

Wind power integration

Wind farm wakes: their impacts on climate

Wind turbine icing forecasting and climate

Wind and wave climate studies

...


Introduction1

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


Routes from mesoscale model to site

Routes from mesoscale model to site

local roughness corrections

mesoscale model output

estimates of site conditions

Meso ‘local’ corrections

micro local corrections


Routes from mesoscale model to site1

Routes from mesoscale model to site

local roughness corrections

mesoscale model output

estimates of site conditions

Meso ‘local’ corrections

micro local corrections

 direct


Routes from mesoscale model to site2

Routes from mesoscale model to site

local roughness corrections

mesoscale model output

estimates of site conditions

Meso ‘local’ corrections

micro local corrections

 direct

 micro corrections only


Routes from mesoscale model to site3

Routes from mesoscale model to site

local roughness corrections

mesoscale model output

estimates of site conditions

Meso ‘local’ corrections

micro local corrections

 direct

 micro corrections only

 meso & micro corrections


Links in the model chain

Links in the model chain

Mesocale model fields and output

h, z0

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

Post-processing

Generalization

Badger et al (2010)

  • corrections for

  • orography

  • roughness

account for ‘local’ mesoscale effects

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

  • application of

  • M-O similarity theory

  • geostrophic drag law

Application

account for ‘local’ microscale effects at site

WAsP

WAsP Engineering

u at site


Comprehensive utilization of mesoscale modelling for wind energy applications

Mesoscale ‘local’ corrections

due to orography

max +18%

min +4%

due to roughness

max +1%

min - 6%

Site in northern Spain

Microscale local corrections

due to orography

max +60%

min +10%

due to roughness

max 0%

min - 6%


Verification of mean wind speed

Verification of mean wind speed

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

Site in northern Spain

normalized wind speed

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

method

 direct

 micro corrections only

 meso & micro corrections


Verification of mean power density

Verification of mean power density

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

Site in northern Spain

normalized power density

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

method

 direct

 micro corrections only

 meso & micro corrections


Importance of microscale a motivation

Importance of microscale... a motivation

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

50 km

10 km

5 km

50 km

328 W/m2

378 W/m2

324 W/m2

378 W/m2

2.5 km

0.1 km

505 W/m2

641 W/m2

323 W/m2

378 W/m2

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


Application at high resolution

Application at high resolution

Wind climate

WAsP

Extreme wind climate

WAsP Engineering


New modes of model verification

New modes of model verification

Pulsed LIDAR wind measurement to 600 m

Floors et al (2010)

100 m

600 m

100 m

Separate error contribution into mesoscale and microscale parts?

Microscale influence tending to reduce with height.


New modes of model verification1

New modes of model verification

Comparison of surface layer fluxes from sonics

Peña and Hahmann (2011)

u*, WRF v sonic

heat flux, WRF v sonic

1/L, WRF v sonic

Learn characteristic errors in surface fluxes.

B-L schemes may give unexpected velocity profiles.

Surface fluxes and theory for alternative profiles.


Advancing the links in the model chain

Advancing the links in the model chain

Gryning et al 2007

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 chain1

Advancing the links in the model chain

  • 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 LIDAR

via aerosols’ backscatter

z [m]

B-L height from WRF

B-L height [m]

Julian day

Peña et al (2010)

time of day (LST)

Hahmann 2011


Advancing the links in the model chain2

Advancing the links in the model chain

< > at 70 m

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

Peña and Hahmann (2011)

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 < >


Summary and conclusions

Summary and conclusions

  • 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.


  • Thank you for your attention jaba@risoe dtu dk

    Thank you for your [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


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