Are Advanced Wind Flow Models More Accurate Brower ......©2012 AWS Truepower, LLC Conclusions • No single model performed best at all sites • On the whole, NWP‐based models

Albany, New York | Barcelona, Spain | Bangalore, India | awstruepower.com | +1 877‐899‐3463

©2012 AWS Truepower, LLC

Philippe Beaucage, PhDSenior Research Scientist

Michael C. Brower, PhDChief Technical Officer

Brazil Wind Power Conference 2012

Are Advanced Wind Flow Models More Accurate?A Test of Four Models



AlbanyNew York, USA

BarcelonaSpain

BangaloreIndia

• Established in 1983• 105 staff in three main offices• Project roles in over 80 countries• Opening office in Curitiba (Fall 2012)

• Partners: Camargo‐Schubert (Curitiba), Aires Renovables (Buenos Aires)

• Partnering with industry and government to advance renewable energy worldwide

• Consulting in atmospheric science, meteorology, environment, and engineering

• Involved in design and assessment of >60,000 MW of wind projects

AWS Truepower in Brief

CuritibaBrazil



Why Do Wind Flow Modeling?

• Extrapolates from a few wind resource measurements to an entire wind farm

• Allows optimization of the plant layout• Doing it well is essential for accurate

energy production estimates



Physics‐Based Numerical Models

• Solve some subset of the primitive equations governing atmospheric conditions

• Typically use finite‐element methods, sometimes in combination with spectral methods

• A wide variety of models have been developed reflecting – Available computer power– The developer’s understanding of the physical processes most relevant to wind energy



Mass conservation:

Momentumconservation (Navier-Stokes):

Energy conservation:

Equation of state:

0 udtd

dPdQdTcp

RTP

1 2 3 4

N‐S Terms

1: Advection

2: Gravity force

3: Coriolis force

4: Pressure force

5: Viscous stress

6: Friction force

5

fFPuguutu

112

6

The Primitive Equations



Key Challenges in Numerical Modeling

• Spatial resolution must be ~50 m or better over a domain ~25 km or larger

• Must simulate a wide range of wind conditions for accurate energy production estimation

• Should simulate all relevant physical processes –but which are relevant?

• More advanced models require much more computer time than simpler models. But are they worth the cost?



Jackson‐Hunt*

• Fast, linearized, steady‐state N‐S 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. 929‐955.



WAsP Example:Orographic Influence on Wind Speed

WAsP (Riso

e)



“CFD” Models

• Most are Reynolds‐averaged Navier‐Stokes (RANS) solvers (e.g., Meteodyn WT, WindSim)

• Non‐linear; can simulate re‐circulations, flow separations, other effects of steep terrain

• Include turbulence parameterization• Usually assume constant, homogeneous boundary conditions

• Do not simulate thermal gradients or stability (buoyancy)



CFD Example: Recirculation Behind a Ridge

Source: WindSim, Vector AS



• Full time‐varying 3D physical model of the atmosphere. Examples: WRF, MASS, KAMM, ARPS…

• Solve all the Primitive Equations, including energy balance, surface exchanges, phase transitions, with turbulence parameterization

• Require 100‐1000x as much computer time as linear models; usually done on a high‐performance computing cluster

• Can be coupled with linear models to improve resolution, reduce runtime

• Current research: Coupled large eddy simulations to resolve the larger scales of turbulence

Numerical Weather Prediction (NWP) Models



NWP Example: Island Blocking and Channeling



NWP Modeling: Gravity Wave



Research Outline• Objective: Determine how much accuracy improves (if any) with

more advanced models• Other research* has produced mixed results for Jackson‐Hunt and

CFD models. NWP‐based models rarely studied.• AWS Truepower assessed 4 leading models at 4 sites with a total of

26 masts• Round‐robin approach:

– Use one mast to predict the mean wind speed at the other masts– Calculate errors (predicted minus observed mean speed)– Repeat with other masts

• Compile root‐mean‐square error (RMSE) for all models and cases

*For summary, see Wind Resource Assessment: A Practical Guide to Developing a Wind Project (Wiley, 2012), chapter 13.



Models• Jackson‐Hunt: WASP (Risoe National Laboratory)• RANS CFD: Meteodyn WT (Meteodyn)• Coupled NWP‐Linear: SiteWind (AWS Truepower)• Coupled NWP‐LES: ARPS (Oklahoma University)

Case StudiesCase Terrain Land Cover No. Masts1 Simple Grass and trees 82 Moderate Grass and shrubs 63 Complex Coastal Mostly forest 34 Complex Forest 9



Results By CaseRoot‐Mean‐Square Error (m/s)

Model 1 2 3 4 AllWASP 0.26 0.34 1.15 0.74 0.62Meteodyn WT(neutral stability) 0.50 0.46 1.07 0.95 0.76

Meteodyn WT(mixed stability) 0.41 0.55 1.09 0.97 0.76

SiteWind 0.10 0.39 0.56 0.67 0.48NWP/LES 0.28 0.49 0.57 0.49 0.45



Conclusions• 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– This shows that wind flow in complex terrain cannot be solved by the same methods used to model air flow over wings and through jet engines

– The atmosphere is never in equilibrium, and you cannot ignore the effects of vertical and horizontal temperature gradients

Documents

Are Advanced Wind Flow Models More Accurate Brower ......©2012 AWS Truepower, LLC Conclusions • No single model performed best at all sites • On the whole, NWP‐based models