Wind Flow Modeling: Are computationally intensive models more accurate?

Transcription

1 June 23 rd, 2015 Wind Flow Modeling: Are computationally intensive models more accurate? Philippe Beaucage, PhD Lead Research Scientist Michael C. Brower, PhD President & Chief Technical Officer Jose Vidal, MSc Consulting Services Europe & Latin America Manager

2 Calgary Canada Albany New York (HQ) Warsaw Poland (*) Denver Colorado Barcelona Spain Istanbul Turkey (*) Rio de Janeiro Brazil Bangalore India Company Snapshot Renewable energy professionals providing technical consulting, software, data, and advisory services for the wind and solar energy markets. Established in 1983; over 30 years of renewable energy industry experience Assessed over 100,000 MW Project roles in over 80 countries Over 100 professional staff Offices around the globe and *with partnerships in Poland and Turkey

3 Why Do Wind Flow Modeling? Extrapolation from a few points to an entire wind farm Allows optimization of the plant layout Doing it well is essential for accurate energy production estimates

4 Physics-Based Numerical Models Navier-Stokes equations i. Mass conservation : ii. Momentum conservation : d dt u t u u u g 2 u P Ff iii. Energy conservation : dt c p dq dp + Equation of state : P RT (ideal gas law) FORCES 1: Advection 2: Gravity 3: Coriolis 4: Pressure-gradient 5: Viscous stress 6: Friction

5 Key Challenges in Wind Flow Modeling Spatial resolution must be ~50 m or better over a domain ~25 km or larger Must simulate a wide range of atmospheric conditions for accurate energy production estimation More advanced models require much more computer time than simpler models. But are they worth the cost?

6 Jackson-Hunt* (Linear) Models Fast, linearized, steady-state Navier-Stokes solver (e.g., WAsP) Assumes terrain is a small perturbation on a constant background wind field Infers the regional wind climate based on point measurements Reverses process to extrapolate to other points Includes obstacle, surface roughness modules *Jackson, P.S. and J.C.R. Hunt (1975). "Turbulent Wind Flow over Low Hill". Quart. J. R. Met. Soc., vol. 101, pp Source: Risoe National Lab

7 WAsP Example: Orographic Influence on Wind Speed Source: Risoe National Lab

8 CFD Models Most are Reynolds-averaged Navier-Stokes (RANS) solvers (e.g., Meteodyn WT, WindSim, Fluent, STAR-CCM+) Non-linear; can simulate re-circulations, flow separations, other effects of steep terrain Like JH models, most ignore energy balance, i.e., thermal stability (buoyancy), horizontal temperature gradients Usually assume constant, homogeneous boundary conditions, iterate to convergence

9 CFD/RANS Example: Recirculation Behind a Ridge Source: WindSim, Vector AS

10 Mesoscale Numerical Weather Prediction Models Step 1 Step 2 Numerical Weather Prediction (NWP) Model Microscale Wind Flow Model Full time-varying 3D physical model of the atmosphere. Examples: WRF, ARPS, COAMPS Solve all the primitive equations, including energy balance, surface exchanges, and phase transitions, with turbulence parameterization Require x as much computer time as linear models; usually done on a high-performance computing cluster Typically, mass-consistent or linear (Jackson-Hunt) wind flow model

11 Vertical Cross Sections

12 Sampling of Prior Research Authors Models Compared No. Sites Mast Pairs Distance Scale (km) Conclusion Cabezon et al (CENER) Berge et al (Kjeller Vindteknik) Bechmann et al (Risoe) Bolund experiment Corbett et al (DNV/GL) WAsP, RANS- CFD WAsP, RANS- CFD Linear, RANS- CFD, LES Linear, RANS- CFD RANS-CFD 70% smaller error WAsP 0-40% smaller error 1 8 ~ RANS-CFD ~30-40% smaller error N/A RANS-CFD ~20% smaller error

13 Models Jackson-Hunt: WASP (Risoe) RANS CFD: Meteodyn WT (Meteodyn) NWP-Mass Consistent: SiteWind (AWS Truepower) NWP-LES: ARPS (AWS Truepower & Oklahoma Univ.) Projects Group 1: Four US projects initially studied in 2011 (Beaucage et al. Wind Energy, 2014) 25 masts, 74 independent mast pairs Group 2: Five additional US and Spanish projects 28 masts, 70 independent mast pairs Meta-analysis of combined groups yields greater statistical reliability: 9 sites, 53 masts, 144 mast pairs

14 Case Studies Project Group Terrain Land Cover No. Masts 1 1 Simple Grass, some trees Moderate Grass, shrubs Complex coastal Mostly forest Complex Forest Complex Grass, shrubs Moderate Grass, trees Moderate Grass, trees Simple Grass Simple Grass, shrubs 7

15 Validation: Round-robin approach Red reference mast Blue target masts 1) Adjust wind speed map using one reference mast, 2) Calculate the bias for each reference-target pair of masts (i,j) where i j, 3) Repeat using a different reference mast

16 RMSE (m/s) RMSE by Project Group 1 Group JH RANS NWP-MC NWP-LES P1 P2 P3 P4 P5 P6 P7 P8 P9 All Project ID

17 RMSE (m/s) 0.9 By Terrain Complexity JH RANS NWP-MC Simple Moderate Complex Terrain Complexity

18 RMSE (m/s) Microscale model dominates By Distance Mesoscale NWP model dominates JH RANS-CFD NWP-MC Distance (km)

19 AWEA WRA Workshop (2013): Blind Comparison Study These results are taken from the presentation of G. Poulos and V. Kumar (2013) at the AWEA WRA workshop, Investigating the Wind Flow Modeling Experiment

20 Conclusion No single model performed best at all sites On the whole, NWP based models performed better than CFD and WASP type models by a substantial margin Overall, CFD did not perform better than WASP!! Supports other research reporting mixed findings The atmosphere is never in equilibrium, and you cannot ignore the effects of vertical and horizontal temperature gradients Need more extensive validation

21 Thank You awstruepower.com Philippe Beaucage