New developments on thermal stability

in Meteodyn WT

K. Fahssis, C.Bezault, D.Delaunay

Stability effects modeling in Meteodyn WT

Validation studies

Integrating stability effects in the AEP estimation

Contents

Challenges for introducing thermal stability in a long term statistical assessment:

Great number of meso-scale configurations Transient nature of thermal stability (diurnal cycle) Correlation with wind speed and direction Integration of the thermal stability effects at micro-scale

Two approaches for the numerical methods

1/ NS equations + Heat transport equation

Combining transient meso and micro scale computations Ground model (albedo, ground temperature, conductivity, soil humidity) Radiative fluxes (solar, infra-red) Selection of a limited number of « homogeneous events »

2/ NS equations + Turbulent length scale profiles

Steady NS equations solved for given direction and stability class Statistical analysis of the triplet (wind direction and speed, stability class) A stability class defines a turbulent length scale profile and inlet boundary conditions

Thermal stability and AEP assessment

Reynolds Averaged Navier Stokes equations - Stationary flow

Closure of the system (turbulence modeling):

Equations

0

i

i

x

u

0''

ijii

j

j

i

jij

ij Fuux

u

x

u

xx

P

x

uu

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j

j

itji x

u

x

uuu ''

TT Lk 2/1where

j

j

i

j

j

iTk

T

T

jk

T

jk

jj

x

U

x

U

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UP

kL

C

x

k

xP

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2

Equations

Transport equation for the turbulent kinetic energy

lSL mT232

Evaluation of the turbulent length scale:

- Consideration of the thermal stratification

- Models of Yamada (1983) and Arritt (1987)

085.0:16.0

)2231.0)(1(

)2341.0)(1912.0(96.1:16.0

mif

ifif

ififmif

SR

RR

RRSR

zll /1/1/1 0

ifR Flux Richardson number

Equations

Log – linear law profiles on homogeneous terrain

Validations

2D hill: Experiment of Ross et al. (2004)Boundary-Layer Meteorol. 113, 427-459

Experiment: neutral Experiment: stable

h = Hcos²(p x/L)H = 229 m (full-scale) L = 1000 mz0 = 1 m (canopy model)

WT neutral

WT stable: LMO=400 m

Validations

Integrating the thermal stratification

in AEP assessment

Meteorological Data

Time series Speed/direction joint frequencies Speed/direction/stability joint frequencies

Thermal stability and AEP assessment

Wind speed coefficientsTurbulence intensityWind shearWind direction

Orography map

Roughness map

Met masts and wind turbines locations

Wind flow computation: one direction sector one stability class

AEP , IEC export

Integration Process

On site Turbulence measurements

(LIDAR, SODAR, met mast)

Standard deviation of:Vertical wind speedHorizontal wind speedHorizontal wind directionHeat and momentum vertical fluxes

Stability Classes

On site Gradient measurements

(met mast, LIDAR, SODAR)

10-min mean values of:Mean wind speedMean air temperature

Richardson Number Obukhov Length

Stability Classes

Regional data(weather station, meso-scale data)

Mean Wind speedSolar radiation (daily)Snow (daily)Hour, season

Stability Classes

Time series of wind speed, direction, stability class Tables of joint frequency tables speed/direction/stability

Thermal stability and AEP assessment

AEP assessment of a wind farmin the North-East of France

Speed Coefficients for 3 stability classes

Wind direction: 60 deg

unstable : LMO = - 80 m neutral stable : LMO = 500 m

Thermal stability and AEP assessment

Wind Direction 60 degRoughness length = 6 cm – 65 cm

Wind profiles at the met mast(Maïa Eolis measurements)

Thermal stability and AEP assessment

Stability Class LMO (m) Mean hourly

production (kWh)

Frequency Contribution(MWh/an)

Unstable - 80 4900 0.07 2575

Slightly unstable - 500 5250 0.18 8278

Neutral 10000 5480 0.45 21602

Slightly stable 1500 4300 0.12 4520

Stable 800 2600 0.11 2505

Very stable 300 1530 0.07 938

TOTAL - - 1.00 41040

Thermal stability and AEP assessment

Stability effects: Works in progress

- Analysis of Hovsore and Horns Rev profiles (A.Peña, 2009)- New sites by Maïa Eolis (multiple 80 m masts)- Calibrating LMO inside WT code as a function of « experimental » LMO

- Most relevant parameters from a statistical point of view- Application to the Meteodyn forecast module

Acknowledgements

French Environment and Energy Management Agency Research funding

French Ministry for Research Research funding

Maïa Eolis On site measurements and scientific partnership

Conclusion

THANK YOU FOR YOUR ATTENTION!