Results of the Integration of Atmospheric Stability in Wind Power Assessment through CFD Modeling

Speaker: Olivier TEXIER (Maia Eolis)

Co-authors:

Céline BEZAULT, Jean-Claude Houbart (Meteodyn)

Nicolas GIRARD, Stéphanie PHAM (Maia Eolis)

With the support of the ADEME, French Environment and Energy Management Agency

Starting Point

The main factors impacting wind flows are:the orography, the roughness and the thermal structure of the atmosphere.

Since wind statistics are based over years, the latter is generally considered as neutral.

Limitations of this Hypothesis:

• On sites with low average wind speed

 • On offshore sites

• For short periods

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Contents

1. Description of the ProjectObjectivesSummary of the site and instrumentation

2. MethodologyDefinitionCharacterization and classes

3. Results and comparisonStability evaluation methodsProduction

4. Conclusion

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1. Description of the project - Objectives

Project carried out by Maia Eolis and Meteodyn with the support of the ADEME (French Environment and Energy Management Agency) from June 2009 to June 2011.

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Objectives:1. Enhancing the consideration of the atmospheric stability by facilitating its use in wind power

assessment ->presented in this session

2. Developing new MCP method taking into account the atmospheric stability -> under development

3. Enhancing the supervision of the wind farms production -> application of objective 1

4. Developing the Wind power production prediction by including atmospheric stability in CFD modeling -> integrated in Meteodyn Forecast, not presented here

Two sites with two operating wind-farms in the east of France were selected: Vaudeville-le-Haut and Bovée-sur-Barboure.

Only one site was kept for the methodology due to high difficulties for the roughness calibration (see EWEC 2010, session advanced ressource modelling, Integration of atmospheric stability in wind power assessment through CFD modeling, O.Texier, Maia Eolis)

1. Description of the project - InstrumentationSite description: Bovée-sur-Barboure

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1. Description of the project - InstrumentationMast set

2. MethodologyDefinition of the atmospheric stability

Potential Temperature (θ): The potential temperature of a parcel of fluid at Pressure P is the temperature that the parcel would acquire adiabatically brought to a standard reference pressure P0, usually 1000 millibars

If dθ/dz >0 : vertical moves , structure stable

If dθ/dz =0 : structure neutral

If dθ/dz < 0 : vertical moves , structure unstable.

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2. MethodologyCharacterization of the atmospheric stability

Collection of data and characterization of atmospheric stability:

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Richardson number Monin-Obukhov length

• Use of the mean gradients (temperature, wind)• Use of the measured vertical turbulent fluxes

Evaluation of typical numbers :

Classification of data in different stability classes : Pasquill, Turner classes

Examination of fluctuations : Garatt, Mahrt relations

Garatt: Fluctuation of the vertical component of the wind speed using the standard deviation Marhrt: Fluctuation of the horizontal component of the wind speed using the standard deviation

2. MethodologyStability classes (1/2)

Class Stratification characterization Richardson number(Ri)A Very unstable <=-10B Unstable -10<Ri<=-2.5C Slightly unstable -2.5<Ri<=-0.6D Neutral -0.06<Ri<=0.2E Slightly stable 0.2<=Ri<=1F Stable 1<Ri<=3G Very stable Ri>3

Wind speed(10m)

DAYDirect Solar Radiation W/m2 NIGHT

m/sRS≥ 600 (High Radiation)

300<RS<600 (Moderated Radiation)

RS≤300(Low Radiation)

Cloudy / Very cloudy sky(cov.>4/8)

Clear / partly cloudy sky (cov.≤4/8)

<2 A A-B B F F2-3 A-B B C E F3-5 B B-C C D E5-6 C C-D D D D>6 C D D D D

Class Stratification characterization

A Very unstable

B Unstable

C Slightly unstable

D Neutral

E Stable

F Very stable

Pasquill classification: 6 stability classes from unstable to stable (A to F) . Use the radiation and cloud cover on a meteorological station completed by the wind speed measurement

• Richardson number

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Wind-speed

Net Radiation Index

DAY Night

m/s 4 3 2 1 0 -1 -20-0.7 A A B C D F G

0.8-1.8 A B B C D F G1.9-2.8 A B C D D E F2.9-3.3 B B C D D E F3.4-3.8 B B C D D D E3.9-4.8 B C C D D D E4.9-5.4 C C D D D D E5.5-5.9 C C D D D D D

>=6 C D D D D D D

2. MethodologyStability classes (2/2)

Sun Postion(°) Insolation Value of te radiation

Index « IN »

60<Θ Forte 4

35< Θ ≤ 60 Moyenne 3

15 < Θ ≤35 Légère 2

Θ ≤15 Faible 1

Class Stratification characterization

A Very unstableB UnstableC Slightly unstableD NeutralE Slightly stableF StableG Very stable

Turner classification: 7 stability classes from stable to unstable (1 to 7). Use of the wind speed and radiation index (cloud cover, sun position).

9 Fluctuation: Vertical speed fluctuation was examined but did not lead to efficient and consistent results

Wind-speed

Temp. Cloud cover

Solar Radiation

Sun position

Richardson Number

Pasquill classes

Turner classes

Vertical speed fluctuations

3. ResultsSummary

Data collected on site and weather station

Stability is evaluated with several methods.

Data is sorted according to evaluated stability conditions.

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Method

Variable

Name Methods

M1 Richardson number (10-80m)

M2 Richardson number (10-40m)

M3 Pasquill classes with « Saint Dizier » weather station

M4Pasquill classes with « Nancy Ochey » weather station

M5 Turner classes with « Saint Dizier » weather station

M6 Turner classes with « Nancy Ochey » weather station

3. ResultsComparison of the methods

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Percentage of coherence between M1 and others methods

A + B C D E F+G0

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20

30

40

50

60

70Stability classes distribution according to the method used

Stability classes

%

M1 M2 M3 M4 M5 M6

M1 92.21% 82.08% 84.39% 77.79% 78.68%

Name Methods

M1 Richardson number (10-80m)

M2 Richardson number (10-40m)

M3 Pasquill classes with « Saint Dizier » weather station

M4Pasquill classes with « Nancy Ochey » weather station

M5 Turner classes with « Saint Dizier » weather station

M6 Turner classes with « Nancy Ochey » weather station

3. ResultsComparison with production

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Flow computations with Meteodyn WT :

• Software dedicated to the wind resource assessment

• Adaptation of the stability classes of Meteodyn WT, 0 to 9, with the Pasquill, Turner and Richardson classes

3. ResultsComparison with production

BoutonnierReal

ProductionPasquil Turner Richardson

100%

Neutral

Production (kWh) 2 500 075 2 492 685 2 500 082 2 499 473 2 788 893

Difference % of real production 0.3% 0.0% 0.0% -11.6%

Bias % installed capacity 0.1% 0.0% 0.0% -2.2%

NMAE en %

of installed capacity6.6% 6.5% 6.6% 8.5%

NRMSE %

of the installed capacity11.2% 11.2% 11.2% 13.9%

Use of data where: All the turbines are operating All the data are available at the met mast, All the data are available at the weather stations

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3. ResultsComparison with production

BoutonnierReal

ProductionPasquil Turner Richardson

100%

Neutral

Production (kWh) 2 500 075 2 492 685 2 500 082 2 499 473 2 788 893

Difference % of real production 0.3% 0.0% 0.0% -11.6%

Bias % installed capacity 0.1% 0.0% 0.0% -2.2%

NMAE en %

of installed capacity6.6% 6.5% 6.6% 8.5%

NRMSE %

of the installed capacity11.2% 11.2% 11.2% 13.9%

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Use of data where: All the turbines are operating All the data are available at the met mast, All the data are available at the weather stations

4. Conclusion, validation, limitation

Conclusions:

Limitation – Future validations:

• Only one site was available for the validation. It will extend with number of users

• Need of precise temperature sensors for Richardson’s method (0.1°C)

• Other points of uncertainty and way of improvement :

• roughness calibration,

• power conversion with shear and turbulence-> can be now integrated with WT

• …

1. Good consistency of the stability assessment methods and stability distribution

2. High improvement of the wind power production assessment, 2% of the NMAE on BO

3. Soon Integrated in Meteodyn WT

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References

[1] Turner, Journal of Applied Meteorology 1992, Vol 31, p83-91,

[2] Businger et al., 1971, flux profile relationships in the atmospheric boundary layer, J.Atmos.Sci 28 , 181 - 189

[3] H. Madsen. A Protocol for Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models. Technical University of Danemark, ANEMOS project, 2004

[4] Meteodyn WT technical documentation

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Thank you for your attention!

Contacts:

otexier@maiaeolis.fr

info@meteodyn.com

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MethodologyFlow calculations – CFD tool

meteodyn WT - CFD software dedicated to wind resource assessment

• Complete resolution of the 3D RANS equationsThis CFD model solves the steady isotherm incompressible Reynolds Averaged Navier-Stokes equations.

• Advanced modeling of the forest canopySink terms in the momentum conservation equations forthe cells lying inside the forested volumes.

• Takes into account the atmospheric stabilityThe non-linear Reynolds stress tensor is modeled by a one-equation closure scheme : k-L model, developed by Yamada and Arritt, dedicated to atmospheric boundary layer, including several thermal stability conditions.

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Integration of stability effect in CFD Meteodyn WT

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i

j

j

itji x

u

x

uuu ''

TT Lk 2/1

lSL mT232 zll /1/1/1 0

Function of Richardson Number

HV Réelle S1 S2 S4 S7

 Production

(kWh) 2 570 478 2 882 588 2 887 843 2 886 239 3 005 441

ecart en % de prod

réelle   -11.1% -11.3% -11.3% -16.9%

Biais en % de capacité

installé   -2.5% -2.5% -2.5% -3.4%

NMAE en % de

capacité installée   8.0% 7.9% 7.9% 9.9%

NRMSEen % de

capacité installé   12.7% 12.6% 12.6% 15.4%