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

1. 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

2. 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 1

3. Contents 1. Description of the Project Objectives Summary of the site and instrumentation 2. Methodology • Definition • Characterization and classes 3. Results and comparison Stability evaluation methods Production 4. Conclusion 2

4. 1. Description of the project - Objectives • Objectives: • Enhancing the consideration of the atmospheric stability by facilitating its use in wind power assessment ->presented in this session • Developing new MCP method taking into account the atmospheric stability -> under development • Enhancing the supervision of the wind farms production -> application of objective 1 • 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 ressourcemodelling, Integration of atmosphericstability in wind power assessmentthrough CFD modeling, O.Texier, Maia Eolis) • 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. 3

5. 1. Description of the project - InstrumentationSite description: Bovée-sur-Barboure 4

6. 1. Description of the project - InstrumentationMast set 5

7. 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. 6

8. 2. MethodologyCharacterization of the atmospheric stability Evaluation of typical numbers : • Richardson number • Monin-Obukhov length • Use of the mean gradients (temperature, wind) • Use of the measured vertical turbulent fluxes 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 Collection of data and characterization of atmospheric stability: 7

9. 2. MethodologyStability classes (1/2) • Richardson number • 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 8

10. 2. MethodologyStability classes (2/2) • Turner classification: 7 stability classes from stable to unstable (1 to 7). Use of the wind speed and radiation index (cloud cover, sun position). • Fluctuation: Vertical speed fluctuation was examined but did not lead to efficient and consistent results 9

11. 3. ResultsSummary Variable Method Data collected on site and weather station Stability is evaluated with several methods. Data is sorted according to evaluated stability conditions. 10

12. 3. ResultsComparison of the methods Percentage of coherence between M1 and others methods 11

13. 3. ResultsComparison with production • Flow computations with MeteodynWT: • Software dedicated to the wind resource assessment • Adaptation of the stability classes of MeteodynWT,0 to 9, with the Pasquill, Turner and Richardson classes 12

14. 3. ResultsComparison with production • 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 13

15. 3. ResultsComparison with production • 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 14

16. 4. Conclusion, validation, limitation Good consistency of the stability assessment methods and stability distribution High improvement of the wind power production assessment, 2% of the NMAE on BO Soon Integrated in Meteodyn WT • 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 • … Conclusions: 15

17. References [1] Turner, Journal of AppliedMeteorology1992, Vol 31, p83-91, [2] Businger et al., 1971, flux profile relationships in the atmosphericboundary 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 16

18. Thank you for your attention! Contacts: otexier@maiaeolis.fr info@meteodyn.com 17

20. MethodologyFlow calculations – CFD tool • Complete resolution of the 3D RANS equations • This CFD model solves the steady isotherm incompressible Reynolds Averaged Navier-Stokes equations. • Advanced modeling of the forest canopy • Sink terms in the momentum conservation equations for • the cells lying inside the forested volumes. • Takes into account the atmospheric stability • The 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. meteodynWT - CFD software dedicated to wind resource assessment 18

21. Function of Richardson Number 19 Integration of stability effect in CFD MeteodynWT