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Overview of six commercial and research wake models for large offshore wind farms

EWEA Conference Copenhagen, Denmark April 18, 2012. Overview of six commercial and research wake models for large offshore wind farms. Philippe BeaucagE , SR. RESEARCH SCIENTIST Michael Brower, chief technical officer Nick Robinson, Director of OpenWind Charles Alonge, R&D SPECIALIST.

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Overview of six commercial and research wake models for large offshore wind farms

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  1. EWEA Conference Copenhagen, Denmark April 18, 2012 Overview of six commercial and research wake models for large offshore wind farms Philippe BeaucagE, SR. RESEARCH SCIENTIST Michael Brower, chief technical officer Nick Robinson, Director of OpenWind Charles Alonge, R&D SPECIALIST

  2. Presentation Overview • Background • Validation of different modeling approaches: • Standard wake models • Hybrid model using IBL approach • Linear CFD-RANS model • Non-linear CFD-RANS model • Large-Eddy Simulations • Results • Nysted wind farm • Horns Rev wind farm • Conclusions • Acknowledgments • J. Garza and colleagues at DONG Energy • R. Barthelmie at Indiana University • DONG Energy, Vattenfall and E.On • P.-E. Réthoré at the Risø /DTU

  3. Background • Wind power projects have been steadily growing in size. • 1000 MW + wind farms are now being proposed. • Standard wake models (Modified Park, Eddy Viscosity, etc.) used in engineering were developed two decades ago for modest-size wind farms. • They do not account for the interaction between multiple turbine wakes, or between wakes and the planetary boundary layer (PBL) • both of which become relatively more important in deep arrays i.e. large wind farms. → Serious doubts regarding the validity of standard wake models applied to large wind farms (“deep arrays”)

  4. Background GOAL: To validate and improve our estimated wake losses and energy productions. QUESTIONS: How to capture the interaction between multiple turbine wakes (deep array effect) as well as between wakes and the PBL? METHODOLOGY : Validate different modeling approaches • Standard engineering wake models • Hybrid model using IBL approach • Linear CFD-RANS model • Non-linear CFD-RANS model • Large-Eddy Simulations PBL = Planetary Boundary Layer

  5. Wind Turbine Aerodynamic: Actuator Disk Theory • Vdisk = (1-a) · V0 • Vwake = (1-2a) · V0 • a ≡ axial induction factor

  6. Overview of wake modeling approaches • Standard wake models • Park / Modified Park (Jensen 1983, Katic et al. 1986) • Eddy Viscosity (Ainslie 1988) • Hybrid wake model based on IBL approaches • Deep-Array Wake Model (Brower and Robinson 2009) • Large Wind Farm Model (Schlez and Neubert 2009) • Physics-based models • Numerical Weather Prediction (NWP) • Computational Fluid Dynamics (CFD) • Reynolds-Averaged Navier-Stokes(RANS) • Detached-Eddy Simulation (DES) • Large-Eddy Simulation (LES) Turbine parameterization • Actuator disk model (Rankine-Froude, BEM, etc.) • Actuator line model • etc.

  7. Wake Models

  8. Park model • Based on a balance of momentum to model single wakes (Jensen 1983, Katic 1986) • Assumes an initial velocity deficit immediately behind the turbine rotor, calculated from the turbine’s thrust coefficient (Ct) and an empirically determined wake-decay constant (k) • The wake-decay constant sets the linear rate of expansion of the wake with distance downstream

  9. Eddy Viscosity model • Based on Navier-Stokes equations with simplifying assumptions (Ainslie 1988) • No pressure gradient term; • Beyond 5 rotor diameter downstream the wake profile is roughly Gaussian and the centerline deficit decays monotonically; • Etc. • Valid only at distances farther than ~ 2-3 rotor diameters downstream of a turbine. • The model runs fast on any PC → suitable for turbine layout optimization. • An industry standard for calculating wake losses

  10. Deep Array Wake Model (DAWM) • Hybrid model based on internal boundary layer (IBL) growth and Eddy Viscosity (Brower and Robinson 2009) • Assign a roughness to each turbine and assume that an internal boundary layer develops at the bottom and top of the turbine rotor (based on Frandsen 2007). • Couple the IBL growth model with Eddy Viscosity • The model runs fast on any PC → suitable for turbine layout optimization • An industry standard for calculating wake losses (openWind). http://www.daviddarling.info/encyclopedia/B/AE_blades.html

  11. Fuga model • Linear RANS model + actuator disk developed by Risø/DTU • Designed for sites with homogeneous terrain and roughness • Fully integrated within WAsP Garza, J. et al. (2011). “Evaluation of two novel wake models in offshore wind farms ". Proceedings from the EWEA Offshore conference, 29 Nov. - 1 Dec 2011. 10 p.

  12. WindModeller model • RANS model using a k- turbulence closure. • Based on the commercial RANS software Ansys CFX. • Added of an actuator disk to model wakes. • Does not take atmospheric stability into account (at the moment) Garza, J. et al. (2011). “Evaluation of two novel wake models in offshore wind farms ". Proceedings from the EWEA Offshore conference, 29 Nov. - 1 Dec 2011. 10 p.

  13. Advanced Regional Prediction System (ARPS) • Numerical Weather Prediction (NWP) model and Large-Eddy Simulation (LES) • Fully compressible, non-hydrostatic Navier-Stokes equations • Dynamic model • Conservation of mass, momentum and energy • Complete suite of physics parameterization schemes • 1.5-order turbulence closure: k-l model • Initial and boundary conditions provided by a) an external data source (e.g. NAM analyses) or b) an atmospheric sounding.

  14. Actuator Disk Implementation in ARPS Based on the actuator disk theory (Adams et al. 2007, Réthoré et al. 2008) , a wind turbine is modeled as : • Drag force due to the thrust force that a turbine exert on the upwind flow. • Source of turbulent kinetic energy representing the sub-grid scale turbulence due to the turbine-induced wakes. It includes the effects of the blade tip, blade shed and root vortices. • Ct(|u|) = thrust coefficient, • Cp(|u|) = power coefficient, • u = wind speed vectors, •  = air density, • A = area swept by the blades.

  15. Nysted Wind Farm • Number of turbines : 72 • Array: 8 × 9 • Turbine: 2.3 MW • Rated capacity: 166 MW • Rotor diameter (D): 82.4 m • Hub Height: 69 m • Distance between turbines: 7D • Water depth: 6-14 m • Distance from land: 10 km Nysted, Denmark

  16. Results: Nysted • The Park and EV model significantly overestimates the production (underestimates the wake loss) beginning at about the 4th column from the front. • DAWM performs very well. • All models show better accuracy over 30° wide sector than 5°.

  17. Horns Rev Wind Farm • Number of turbines : 80 • Array: 8 × 10 • Turbine: 2 MW • Rated capacity: 160 MW • Rotor diameter (D): 80 m • Hub Height: 70 m • Distance between turbines: 7D • Water depth: 6-14 m • Distance from land: 14-20 km Horns Rev, Denmark

  18. Results: Horns Rev (Part 1) • The Park and EV model significantly overestimates the production (underestimates the wake loss) beginning at about the 4th column from the front. • DAWM performs very well. • ARPS performs relatively well but not for the 8 m/s case.

  19. Results: Horns Rev (part 2) • The Fuga and WindModeller models align very well with the observed normalized power production within the third column of turbines . • The Park model performs better at Horns Rev than it did at Nysted but, as with the EV model, the profile remains relatively flat after the fourth column • The Jensen and Fuga models show better accuracy over 30° wide sector than 5°. These results were kindly provided by Garza et al. (2011). “Evaluation of two novel wake models in offshore wind farms”. Proceedings from the EWEA Offshore conference, 29 Nov. - 1 Dec 2011. 10 p.

  20. ARPS → Large-Eddy Simulation wind speed

  21. ARPS → Large-Eddy Simulation wind speed m/s

  22. Conclusion • The Park and Eddy Viscosity models works well within the first 3 columns. However, they are typically not able to capture the wake losses beyond the 3rd column from the front (→ deep array effect). • DAWM captures the wake losses in large array much better than either the EV or Park model. • The Fuga and Windmodeller models also showed promise, though a full comparison was not possible. • ARPS performed reasonably well for these initial tests and merits further research • Need more detailed power production data with concurrent meteorological conditions (e.g. IEA WakeBench experiment) • Acknowledgments • J. Garza and colleagues at DONG Energy • R. Barthelmie at Indiana University • DONG Energy, Vattenfall and E.On • P.-E. Réthoré at the Risø /DTU

  23. Thank you for your attention!

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