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Wind Farms in Complex Terrain: Numerical Simulation of Wind and Wakes for Optimized Micrositing

Wind Farms in Complex Terrain: Numerical Simulation of Wind and Wakes for Optimized Micrositing. S. Jafari, N. Chokani , R.S. Abhari ETH Zürich, Switzerland. Overview. Motivation Objectives Modeling Approach: Immersed Wind Turbine Model Validation and Results Single Wake Multiple Wake

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Wind Farms in Complex Terrain: Numerical Simulation of Wind and Wakes for Optimized Micrositing

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  1. Wind Farms in Complex Terrain: Numerical Simulation of Wind and Wakes for Optimized Micrositing S. Jafari, N. Chokani, R.S. Abhari ETH Zürich, Switzerland

  2. Overview • Motivation • Objectives • Modeling Approach: Immersed Wind Turbine Model • Validation and Results • Single Wake • Multiple Wake • Wind farm in complex terrain • Summary and Conclusion

  3. Motivation • Wind turbines operating in wakes of upwind wind turbines may have • 30-40 % power losses compared to upstream turbine • up to 80% larger fatigue loads than upstream turbine • Most models fail in predictions when more complex inflow or ground features are present • Interaction of topography with wake and wind flow not addressed satisfactorily to date • Microscale wind and wake flow must be simultaneously simulated to be able to: • Account for change in inflow wind due to terrain effects • Assess effect of elevated turbulence on wake’s evolution • Investigate interaction of wake with adverse/favorable pressure gradients caused by topography

  4. Objectives • Develop computationally efficient wake model to be embedded in RANS solver used for microscale wind simulations with comparable grid requirements • Perform simultaneous simulations of microscale wind and wind turbine wake • Validate and evaluate predictions of flow field and power performance in wind farms

  5. Numerical Approach • Turbines modeled using immersed wind turbine model (IWTM), embedded in LEC’s RANS solver, MULTI3 • Turbine represented as streamtube defined based on turbine operating point • Near wake modeled, velocity and turbulent field mapped at the end of inviscid expansion of wake, but far wake resolved on computational grid • Boundary conditions imposed on Cartesian grid using immersed boundary method

  6. Validation: Microscale Wind • Prediction of wind speed compared with field measurements over Askervein (moderate terrain, Jafari et al., 2011) and Bolund Hill (complex terrain, Jafari et al., 2012) • Good agreement observed for both cases, up- and downstream of hill Bolund Hill, 270o Askervein Hill

  7. Mean Flow of Single Wake • Predicted evolution of wake in good agreement with wind tunnel experiments, (Hassan,1992) • Maximum 12% difference between predicted and measured wind speed

  8. Turbulence Intensity in Single Wake • At x=2.5D, two peaks observed in turbulence intensity profile as expected • Evolution of turbulence intensity captured well both qualitatively and qualitatively

  9. Single Wake: Full-scale Measurement • Predictions compared with full-scale measurements at Sexbierum wind farm • 5.4 MW farm consisting of 18 turbines, D=30 m • Maximum deficit underestimated by 20% • Wake width predicted well

  10. Validation: Multiple Wakes • Interactions of multiple wakes examined for offshore wind farms • Horns Rev (offshore, Denmark), 80 Vestas V2.8-80 • Lillgrund (offshore, Sweden), 48 Siemens SWT-2.3-93 27 28 30 • Power loss in array and sensitivity to wind direction captured for all cases

  11. Wind Farm in Complex Terrain • 23.7 MW Mont Crosin wind farm located in Jura region, Switzerland (complex terrain) consisting of 16 turbines with hub heights of 45 and 95 m • SCADA data collected and analyzed over one and a half years period 270o

  12. Simulation Set-Up • Upstream conditions of wind speed and turbulence and specified based on: • Long-term mesoscale [Weather Research Forecast Model (WRF)] simulations performed over Switzerland, Jafari et al., 2012 • Measurements using LEC’s nacelle mounted probe, Mansour et al., 2013 Computational grid for wind direction 170o Dominant wind direction from south-west quadrant

  13. Mont Crosin Wind Farm: Results 260o • Impact of terrain on local wind evident • Performance of turbine 14 decreases relative to turbine 13 up to 65% 90 m AGL 260o 230o 45 m AGL

  14. Summary & Future Work • Simultaneous simulation of microscale wind and wakes accomplished with computationally efficient wind turbine model • Model evaluated with broad range of test cases including wind tunnel/field experiments, onshore/offshore, and flat/complex terrain • IWTM brings grid requirements for wake simulations closer to microscale wind and facilitates use of Computational Fluid Dynamics for micrositing in complex terrain

  15. Thank you.

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