Similar to other renewable energy sources, wind energy is characterized by a low power density. Hence, for wind energy to make considerable contributions to the world's overall energy supply, large wind farms (on- and offshore) consisting of arrays of ever larger wind turbines are being envisioned and built. From a fluid mechanics perspective, wind farms encompass turbulent flow phenomena occurring at many spatial and temporal scales. Of particular interest to understanding mean power extraction and fluctuations in wind farms are the scales ranging from 1 to 10 m that comprise the wakes behind individual wind turbines, to motions reaching 100 m to kilometers in scale, inherently associated with the atmospheric boundary layer. In this review, we summarize current understanding of these flow phenomena (particularly mean and second-order statistics) through field studies, wind tunnel experiments, large-eddy simulations, and analytical modeling, emphasizing the most relevant features for wind farm design and operation.


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Literature Cited

  1. Abkar M, Porté-Agel F. 2013. The effect of free-atmosphere stratification on boundary-layer flow and power output from very large wind farms. Energies 6:2338–61 [Google Scholar]
  2. Abkar M, Porté-Agel F. 2015a. A new wind-farm parameterization for large-scale atmospheric models. J. Renew. Sustain. Energy 7:013121 [Google Scholar]
  3. Abkar M, Porté-Agel F. 2015b. Influence of atmospheric stability on wind-turbine wakes: a large-eddy simulation study. Phys. Fluids 27:035104 [Google Scholar]
  4. Abkar M, Sharifi A, Porté-Agel F. 2016. Wake flow in a wind farm during a diurnal cycle. J. Turbul. 17:420–41 [Google Scholar]
  5. Ainslie JF. 1988. Calculating the flow field in the wake of wind turbines. J. Wind Eng. Ind. Aerodyn. 27:213–24 [Google Scholar]
  6. Allaerts D, Meyers J. 2015. Large eddy simulation of a large wind-turbine array in a conventionally neutral atmospheric boundary layer. Phys. Fluids 27:065108 [Google Scholar]
  7. Apt J. 2007. The spectrum of power from wind turbines. J. Power Sources 169:369–74 [Google Scholar]
  8. Archer CL, Mirzaeisefat S, Lee S. 2013. Quantifying the sensitivity of wind farm performance to array layout options using large-eddy simulation. J. Geophys. Res. 40:4963–70 [Google Scholar]
  9. Baidya-Roy S, Pacala SW, Walko RL. 2004. Can large scale wind farms affect local meteorology?. J. Geophys. Res. 109:D19101 [Google Scholar]
  10. Barrie DB, Kirk-Davidoff DB. 2010. Weather response to a large wind turbine array. Atmos. Chem. Phys. 10:769–75 [Google Scholar]
  11. Barthelmie RJ, Frandsen ST, Nielsen MN, Pryor SC, Rethore PE, Jørgensen HE. 2007. Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm. Wind Energy 10:517–28 [Google Scholar]
  12. Barthelmie RJ, Frandsen ST, Rathmann O, Hansen K, Politis E. et al. 2011. Flow and wakes in large wind farms: final report for UpWind WP8 Rep. Risø-R-1765(EN) Eur. Comm.
  13. Barthelmie RJ, Hansen K, Frandsen ST, Rathmann O, Schepers JG. et al. 2009. Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy 12:431–44 [Google Scholar]
  14. Barthelmie RJ, Jensen LE. 2010. Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm. Wind Energy 13:573–86 [Google Scholar]
  15. Barthelmie RJ, Larsen G, Pryor S, Jørgensen H, Bergström H. et al. 2004. ENDOW (EfficieNt Development of Offshore Wind farms): modelling wake and boundary layer interactions. Wind Energy 7:225–45 [Google Scholar]
  16. Barthelmie RJ, Pryor SC, Frandsen ST, Hansen KS, Schepers JG. et al. 2010. Quantifying the impact of wind turbine wakes on power output at offshore wind farms. J. Atmos. Ocean. Technol. 27:1302–17 [Google Scholar]
  17. Bastankhah M, Porté-Agel F. 2014. A new analytical model for wind-turbine wakes. Renew. Energy 70:116–23 [Google Scholar]
  18. Bastine D, Wächter M, Peinke J, Trabucchi D, Kühn M. 2015. Characterizing wake turbulence with staring lidar measurements. J. Phys. Conf. Ser. 625:012006 [Google Scholar]
  19. Beaucage P, Robinson N, Brower M, Alonge C. 2012. Overview of six commercial and research wake models for large offshore wind farms. Proc. Eur. Wind Energy Assoc. Conf.95–99 Brussels: Eur. Wind Energy Assoc. [Google Scholar]
  20. Bingöl F, Mann J, Larsen GC. 2010. Light detection and ranging measurements of wake dynamics. Part I: one-dimensional scanning. Wind Energy 13:51–61 [Google Scholar]
  21. Bossanyi EA. 2003a. Individual blade pitch control for load reduction. Wind Energy 6:119–28 [Google Scholar]
  22. Bossanyi EA. 2003b. Wind turbine control for load reduction. Wind Energy 6:229–44 [Google Scholar]
  23. Bossuyt J, Howland MF, Meneveau C, Meyers J. 2016. Measuring power output intermittency and unsteady loading in a micro wind farm model Presented at Wind Energy Symp., AIAA SciTech, 34th San Diego, CA: AIAA Pap. 2016–1992
  24. Bottasso CL, Cacciola S, Schreiber J. 2015. A wake detector for wind farm control. J. Phys. Conf. Ser. 625:012007 [Google Scholar]
  25. Bottasso CL, Campagnolo F, Petrović V. 2014a. Wind tunnel testing of scaled wind turbine models: beyond aerodynamics. J. Wind Eng. Ind. Aerodyn. 127:11–28 [Google Scholar]
  26. Bottasso CL, Pizzinelli P, Riboldi CED, Tasca L. 2014b. LiDAR-enabled model predictive control of wind turbines with real-time capabilities. Renew. Energy 71:442–52 [Google Scholar]
  27. Brower MC, Robinson NM. 2012. The openWind deep-array wake model: development and validation Rep., AWS Truepower Albany, NY:
  28. Burton T, Sharpe D, Jenkins N, Bossanyi E. 2001. Wind Energy Handbook New York: Wiley
  29. Businger JA, Wyngaard JC, Izumi Y, Bradley EF. 1971. Flux-profile relationships in the atmospheric surface layer. J. Atmos. Sci. 28:181–89 [Google Scholar]
  30. Cal RB, Lebrón J, Castillo L, Kang HS, Meneveau C. 2010. Experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer. J. Renew. Sustain. Energy 2:013106 [Google Scholar]
  31. Calaf M, Meneveau C, Meyers J. 2010. Large eddy simulations of fully developed wind-turbine array boundary layers. Phys. Fluids 22:015110 [Google Scholar]
  32. Campagnolo F, Bottasso C, Bettini P. 2014. Design, manufacturing and characterization of aero-elastically scaled wind turbine blades for testing active and passive load alleviation techniques within a ABL wind tunnel. J. Phys. Conf. Ser. 524:012061 [Google Scholar]
  33. Chamorro L, Porté-Agel F. 2009. A wind-tunnel investigation of wind-turbine wakes: boundary-layer turbulence effects. Bound. Layer Meteorol. 132:129–49 [Google Scholar]
  34. Chamorro LP, Porté-Agel F. 2010. Effects of thermal stability and incoming boundary-layer flow characteristics on wind-turbine wakes: a wind-tunnel study. Bound. Layer Meteorol. 136:515–33 [Google Scholar]
  35. Chowdhury S, Zhang J, Messac A, Castillo L. 2013. Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions. Renew. Energy 52:273–82 [Google Scholar]
  36. Churchfield MJ, Lee S, Michalakes J, Moriarty PJ. 2012a. A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics. J. Turbul.13N14
  37. Churchfield MJ, Lee S, Moriarty PJ. 2013. Adding complex terrain and stable atmospheric condition capability to the OpenFOAM-based flow solver of the simulator for on/offshore wind farm applications (SOWFA) Presented at Symp. OpenFOAM Wind Energy , 1st. Oldenburg, Ger.:
  38. Churchfield MJ, Lee S, Moriarty PJ, Hao Y, Lackner M. et al. 2015. A comparison of the dynamic wake meandering model, large-eddy simulation, and field data at the Egmond aan Zee offshore wind plant Presented at Wind Energy Symp., AIAA SciTech , 33rd. Kissimmee, FL: AIAA Pap. 2015–0724
  39. Churchfield MJ, Lee S, Moriarty PJ, Martínez LA, Leonardi S. et al. 2012b. A large-eddy simulation of wind-plant aerodynamics Presented at AIAA Aerosp. Sci. Meet. , 50th. Nashville, TN: AIAA Pap. 2012–0537
  40. Creech A, Früh WG, Maguire AE. 2015. Simulations of an offshore wind farm using large-eddy simulation and a torque-controlled actuator disc model. Surv. Geophys. 36:427–81 [Google Scholar]
  41. Crespo A, Hernández J. 1996. Turbulence characteristics in wind-turbine wakes. J. Wind Eng. Ind. Aerodyn. 61:71–85 [Google Scholar]
  42. Crespo A, Hernández J, Frandsen S. 1999. Survey of modelling methods for wind turbine wakes and wind farms. Wind Energy 2:1–24 [Google Scholar]
  43. Dahlberg JA. 2009. Assessment of the Lillgrund wind farm: power performance, wake effects LG Pilot Rep. Vattenfall Vindkraft AB: Stockholm https://corporate.vattenfall.se/globalassets/sverige/om-vattenfall/om-oss/var-verksamhet/vindkraft/lillgrund/assessment.pdf
  44. Duckworth A, Barthelmie RJ. 2008. Investigation and validation of wind turbine wake models. Wind Eng. 32:459–75 [Google Scholar]
  45. Emami A, Noghreh P. 2010. New approach on optimization in placement of wind turbines within wind farm by genetic algorithms. Renew. Energy 35:1559–64 [Google Scholar]
  46. Emeis S. 2010. A simple analytical wind PARK model considering atmospheric stability. Wind Energy 13:459–69 [Google Scholar]
  47. Emeis S, Harris M, Banta R. 2007. Boundary-layer anemometry by optical remote sensing for wind energy applications. Meteor. Z. 16:337–47 [Google Scholar]
  48. Eriksson O, Lindvall J, Breton SP, Ivanell S. 2015. Wake downstream of the Lillgrund wind farm: a comparison between LES using the actuator disc method and a wind farm parametrization in WRF. J. Phys. Conf. Ser. 625:012028 [Google Scholar]
  49. Fitch AC, Olson JB, Lundquist JK. 2013. Parameterization of wind farms in climate models. J. Wind Eng Ind. Aerodyn. 26:6439–58 [Google Scholar]
  50. Fitch AC, Olson JB, Lundquist JK, Dudhia J, Gupta AK. et al. 2012. Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Weather Rev. 140:3017–38 [Google Scholar]
  51. Frandsen S. 1992. On the wind speed reduction in the center of large clusters of wind turbines. J. Wind Eng. Ind. Aerodyn. 39:251–65 [Google Scholar]
  52. Frandsen S, Barthelmie R, Pryor S, Rathmann O, Larsen S. et al. 2006. Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy 9:39–53 [Google Scholar]
  53. Frandsen S, Thøgersen ML. 1999. Integrated fatigue loading for wind turbines in wind farms by combining ambient turbulence and wakes. J. Wind Eng. 23:327–40 [Google Scholar]
  54. Garratt JR. 1992. The Atmospheric Boundary Layer Cambridge, UK: Cambridge Univ. Press
  55. Gayme D, Chakrabortty A. 2012. Impact of wind farm placement on inter-area oscillations in large power systems. Proc. 2012 Am. Control Conf.3038–43 New York: IEEE [Google Scholar]
  56. Gebraad PMO, Teeuwisse FW, van Wingerden JW, Fleming PA, Ruben SD. et al. 2016. Wind plant power optimization through yaw control using a parametric model for wake effects—a CFD simulation study. Wind Energy 19:95–114 [Google Scholar]
  57. Goit JP, Meyers J. 2015a. Effect of Ekman layer on windfarm roughness and displacement height. Direct and Large-Eddy Simulation IX J Fröhlich, H Kuerten, BJ Guerts, V Armenio 423–34 New York: Springer [Google Scholar]
  58. Goit JP, Meyers J. 2015b. Optimal control of energy extraction in wind-farm boundary layers. J. Fluid Mech. 768:5–50 [Google Scholar]
  59. Goit J, Munters W, Meyers J. 2016. Optimal coordinated control of power extraction in LES of a wind farm with entrance effects. Energies 9:29 [Google Scholar]
  60. Hamilton N, Kang HS, Meneveau C, Cal RB. 2012. Statistical analysis of kinetic energy entrainment in a model wind turbine array boundary layer. J. Renew. Sustain. Energy 4:063105 [Google Scholar]
  61. Hansen KS, Barthelmie RJ, Jensen LE, Sommer A. 2012. The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy 15:183–96 [Google Scholar]
  62. Hansen KS, Réthoré PE, Palma J, Hevia BG, Prospathopoulos J. et al. 2015. Simulation of wake effects between two wind farms. J. Phys. Conf. Ser. 625:012008 [Google Scholar]
  63. Herbert GMJ, Iniyan S, Sreevalsan E, Rajapandian S. 2007. A review of wind energy technologies. Renew. Sustain. Energy Rev. 11:1117–45 [Google Scholar]
  64. Herbert-Acero JF, Probst O, Réthoré PE, Larsen GC, Castillo-Villar KK. 2014. A review of methodological approaches for the design and optimization of wind farms. Energies 7:6930–7016 [Google Scholar]
  65. Herp J, Poulsen UF, Greiner M. 2015. Wind farm power optimization including flow variability. Renew. Energy 81:173–81 [Google Scholar]
  66. Huang J, Bou-Zeid E. 2013. Turbulence and vertical fluxes in the stable atmospheric boundary layer. Part I: a large-eddy simulation study. J. Atmos. Sci. 70:1513–27 [Google Scholar]
  67. IEC (Int. Electrotech. Comm.) 2005. Wind turbines—Part 1: design requirements Int. Stand. IEC61400-1 , 3rd ed.. IEC Geneva:
  68. Iungo GV, Porté-Agel F. 2014. Volumetric scans of wind turbine wakes performed with three simultaneous wind LiDARs under different atmospheric stability regimes. J. Phys. Conf. Ser. 524:012164 [Google Scholar]
  69. Iungo GV, Wu YT, Porté-Agel F. 2013. Field measurements of wind turbine wakes with lidars. J. Atmos. Ocean. Technol. 30:274–87 [Google Scholar]
  70. Ivanell S. 2009. Numerical computations of wind turbine wakes PhD Thesis, Dep. Mech. Gotland Univ. Stockholm:
  71. Jacobson MZ, Archer CL. 2012. Saturation wind power potential and its implications for wind energy. PNAS 109:15679–84 [Google Scholar]
  72. Jacobson MZ, Archer CL, Kempton W. 2014. Taming hurricanes with arrays of offshore wind turbines. Nat. Clim. Change 4:195–200 [Google Scholar]
  73. Jensen NO. 1983. A note on wind generator interaction Rep. Risø-M-2411, Risø Natl. Lab. Roskilde, Den.:
  74. Jeon S, Kim B, Huh J. 2015. Comparison and verification of wake models in an onshore wind farm considering single wake condition of the 2 MW wind turbine. Energies 93:1769–77 [Google Scholar]
  75. Jiménez A, Crespo A, Migoya E, Garcia J. 2007. Advances in large-eddy simulation of a wind turbine wake. J. Phys. Conf. Ser. 75:012041 [Google Scholar]
  76. Johnson KE, Fingersh LJ, Balas MJ, Pao LY. 2004. Methods for increasing region 2 power capture on a variable-speed wind turbine. ASME. J. Sol. Energy Eng. 126:1092–100 [Google Scholar]
  77. Kaimal JC, Wyngaard JC, Haugen DA, Coté OR, Izumi Y. et al. 1976. Turbulence structure in the convective boundary layer. J. Atmos. Sci. 33:2152–69 [Google Scholar]
  78. Katić I, Højstrup J, Jensen NO. 1986. A simple model for cluster efficiency. Proc. Eur. Wind Energy Assoc. Conf. Exhib. W Palz, E Sesto 407–10 Freiburg, Ger.: Int. Solar Energy Soc. [Google Scholar]
  79. Katul GG, Konings AG, Porporato A. 2011. Mean velocity profile in a sheared and thermally stratified atmospheric boundary layer. Phys. Rev. Lett. 107:268502 [Google Scholar]
  80. Katzenstein W, Fertig E, Apt J. 2010. The variability of interconnected wind plants. Energy Policy 38:4400–10 [Google Scholar]
  81. Keck RE, de Maré M, Churchfield MJ, Lee S, Larsen G, Madsen HA. 2014. On atmospheric stability in the dynamic wake meandering model. Wind Energy 17:1689–710 [Google Scholar]
  82. Keck RE, de Maré M, Churchfield MJ, Lee S, Larsen G, Madsen HA. 2015. Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build-up in a row of wind turbines. Wind Energy 18:111–32 [Google Scholar]
  83. Keith D, DeCarolis J, Denkenberger D, Lenschow D, Malyshev S. et al. 2004. The influence of large-scale wind power on global climate. PNAS 101:16115–20 [Google Scholar]
  84. Khanna S, Brasseur JG. 1998. Three-dimensional buoyancy and shear-induced local structure of the atmospheric boundary layer. J. Atmos. Sci. 55:710–43 [Google Scholar]
  85. Kinzel M, Mulligan Q, Dabiri JO. 2012. Energy exchange in an array of vertical-axis wind turbines. J. Turbul. 13:N38 [Google Scholar]
  86. Krogstad , Eriksen PE. 2013. “Blind test” calculations of the performance and wake development for a model wind turbine. Renew. Energy 50:325–33 [Google Scholar]
  87. Krogstad , Sætran L, Adaramola MS. 2015. “Blind test 3” calculations of the performance and wake development behind two in-line and offset model wind turbines. J. Fluids Struct. 52:65–80 [Google Scholar]
  88. Kusiak A, Song Z. 2010. Design of wind farm layout for maximum wind energy capture. Renew. Energy 35:685–94 [Google Scholar]
  89. Larsen GC. 1988. A simple wake calculation procedure Rep. Risø-M-2760, Risø Natl. Lab. Roskilde, Den.:
  90. Larsen GC, Madsen HA, Bingöl F, Mann J, Ott S. et al. 2007. Dynamic wake meandering modeling Rep. Risø-R-1607(EN), Risø Natl. Lab. Roskilde, Den.:
  91. Larsen GC, Madsen HA, Thomsen K, Larsen TJ. 2008. Wake meandering: a pragmatic approach. Wind Energy 11:377–95 [Google Scholar]
  92. Larsen TJ, Madsen HA, Larsen GC, Hansen KS. 2013. Validation of the dynamic wake meander model for loads and power production in the Egmond aan Zee wind farm. Wind Energy 16:605–24 [Google Scholar]
  93. Lebron J, Castillo L, Meneveau C. 2012. Experimental study of the kinetic energy budget in a wind turbine streamtube. J. Turbul. 13:N43 [Google Scholar]
  94. Lee S, Churchfield M, Moriarty P, Jonkman J, Michalakes J. 2012. Atmospheric and wake turbulence impacts on wind turbine fatigue loadings Presented at AIAA Aerosp. Sci. Meet. , 50th. AIAA: Pap. 2012–0540
  95. Lignarolo LEM, Mehta D, Stevens RJAM, Emre Yilmaz A, van Kuik G. et al. 2016. Validation of four LES and a vortex model against stereo-PIV measurements in the near wake of an actuator disc and a wind turbine. Renew. Energy 94:510–23 [Google Scholar]
  96. Lissaman PBS. 1979. Energy effectiveness of arbitrary arrays of wind turbines. J. Energy 3:323–28 [Google Scholar]
  97. Lu H, Porté-Agel F. 2011. Large-eddy simulation of a very large wind farm in a stable atmospheric boundary layer. Phys. Fluids 23:065101 [Google Scholar]
  98. Machefaux E, Larsen GC, Troldborg N, Gaunaa M, Rettenmeier A. 2015. Empirical modeling of single-wake advection and expansion using full-scale pulsed lidar-based measurements. Wind Energy 18:2085–103 [Google Scholar]
  99. Machefaux E, Larsen GC, Troldborg N, Hansen KS, Angelou N. et al. 2016. Investigation of wake interaction using full-scale lidar measurements and large eddy simulation. Wind Energy 191535–51 [Google Scholar]
  100. Mahrt L. 2014. Stably stratified atmospheric boundary layers. Annu. Rev. Fluid Mech. 46:23–45 [Google Scholar]
  101. Mann J. 1998. Wind field simulation. Probab. Eng. Mech. 13:269–82 [Google Scholar]
  102. Markfort CD, Zhang W, Porté-Agel F. 2012. Turbulent flow and scalar transport through and over aligned and staggered wind farms. J. Turbul. 13:N33 [Google Scholar]
  103. Marmidis G, Lazarou S, Pyrgioti E. 2008. Optimal placement of wind turbines in a wind park using Monte Carlo simulation. Renew. Energy 33:1455–60 [Google Scholar]
  104. Martínez-Tossas LA, Stevens RJAM, Meneveau C. 2016. Wind farm large-eddy simulations on very coarse grid resolutions using an actuator line model Presented at Wind Energy Symp., AIAA SciTech , 34th. San Diego: AIAA Pap. 2016–1261
  105. Marusic I, Monty J, Hultmark M, Smits A. 2013. On the logarithmic region in wall turbulence. J. Fluid Mech. 716:R3 [Google Scholar]
  106. Medici D, Alfredsson PH. 2006. Measurement on a wind turbine wake: 3D effects and bluff body vortex shedding. Wind Energy 9:219–36 [Google Scholar]
  107. Mehta D, van Zuijlen AH, Koren B, Holierhoek JG, Bijl H. 2014. Large eddy simulation of wind farm aerodynamics: a review. J. Wind Eng. Ind. Aerodyn. 133:1–17 [Google Scholar]
  108. Meneveau C. 2012. The top-down model of wind farm boundary layers and its applications. J. Turbul. 13:N7 [Google Scholar]
  109. Meyers J, Meneveau C. 2012. Optimal turbine spacing in fully developed wind farm boundary layers. Wind Energy 15:305–17 [Google Scholar]
  110. Meyers J, Meneveau C. 2013. Flow visualization using momentum and energy transport tubes and applications to turbulent flow in wind farms. J. Fluid Mech. 715:335–58 [Google Scholar]
  111. Mikkelsen T, Angelou N, Hansen K, Sjöholm M, Harris M. et al. 2013. A spinner-integrated wind lidar for enhanced wind turbine control. Wind Energy 16:625–43 [Google Scholar]
  112. Milan P, Wächter M, Peinke J. 2013. A study of the performance benefits of closely-spaced lateral wind farm configurations. Phys. Rev. Lett. 110:138701 [Google Scholar]
  113. Miller LM, Brunsell NA, Mechem DB, Gansa F, Monaghan AJ. et al. 2015. Two methods for estimating limits to large-scale wind power generation. PNAS 112:11169–74 [Google Scholar]
  114. Miller LM, Gans F, Kleidon A. 2011. Estimating maximum global land surface wind power extractability and associated climatic consequences. Earth Syst. Dyn. 2:1–12 [Google Scholar]
  115. Mironov DV, Sullivan PP. 2016. Second-moment budgets and mixing intensity in the stably stratified atmospheric boundary layer over thermally heterogeneous surfaces. J. Atmos. Sci. 73:449–64 [Google Scholar]
  116. Morales A, Wächter M, Peinke J. 2012. Characterization of wind turbulence by higher-order statistics. Wind Energy 15:391–406 [Google Scholar]
  117. Moriarty P, Rodrigo JS, Gancarski P, Chuchfield M, Naughton JW. et al. 2014. IEA-Task 31 WAKEBENCH: towards a protocol for wind farm flow model evaluation. Part 2: wind farm wake models. J. Phys. Conf. Ser. 524:012185 [Google Scholar]
  118. Munters W, Meneveau C, Meyers J. 2016. Turbulent inflow precursor method with time-varying direction for large-eddy simulations and applications to wind farms. Bound. Layer Meteorol. 159:305–28 [Google Scholar]
  119. Mur-Amada J, Bayod-Rújula AA. 2007. Characterization of spectral density of wind farm power output Presented at Int. Conf. Elect. Power Qual. Util. , 9th. Barcelona
  120. Newman BG. 1977. The spacing of wind turbines in large arrays. Energy Convers. 16:169–71 [Google Scholar]
  121. Niayifar A, Porté-Agel F. 2015. A new analytical model for wind farm power prediction. J. Phys. Conf. Ser. 625:012039 [Google Scholar]
  122. Nilsson K, Ivanell S, Hansen KS, Mikkelsen R, Sørensen JN. et al. 2015. Large-eddy simulations of the Lillgrund wind farm. Wind Energy 18:449–67 [Google Scholar]
  123. Nygaard NG. 2014. Wakes in very large wind farms and the effect of neighbouring wind farms. J. Phys. Conf. Ser. 524:012162 [Google Scholar]
  124. Peña A, Gryning SE, Hasager C. 2008. Measurements and modelling of the wind speed profile in the marine atmospheric boundary layer. Bound. Layer Meteorol. 129:479–95 [Google Scholar]
  125. Peña A, Gryning SE, Hasager C. 2010. Comparing mixing-length models of the diabatic wind profile over homogeneous terrain. Theor. Appl. Climatol. 100:325–35 [Google Scholar]
  126. Peña A, Rathmann O. 2014. Atmospheric stability-dependent infinite wind-farm models and the wake-decay coefficient. Wind Energy 17:1269–85 [Google Scholar]
  127. Peña A, Réthoré PE, Hasager CB, Hansen KS. 2013. Results of wake simulations at the Horns Rev I and Lillgrund wind farms using the modified Park model Rep. 0026, DTU Wind Energy Roskilde, Den.:
  128. Pierella F, Krogstad , Sætran L. 2014. Blind test 2 calculations for two in-line model wind turbines where the downstream turbine operates at various rotational speeds. Renew. Energy 70:62–77 [Google Scholar]
  129. Politis ES, Prospathopoulos J, Cabezon D, Hansen KS, Chaviaropoulos P, Barthelmie RJ. 2012. Modeling wake effects in large wind farms in complex terrain: the problem, the methods and the issues. Wind Energy 15:161–82 [Google Scholar]
  130. Porté-Agel F, Wu YT, Chen CH. 2013. A numerical study of the effects of wind direction on turbine wakes and power losses in a large wind farm. Energies 6:5297–313 [Google Scholar]
  131. Quarton D, Ainslie J. 1990. Turbulence in wind turbine wakes. J. Wind Eng. 14:15–23 [Google Scholar]
  132. Rathmann O, Barthelmie R, Frandsen S. 2006. Turbine wake model for wind resource software Presented at Wind Energy Conf. Exhib. Athens:
  133. Rodrigo JS, Gancarski P, Arroyo RC, Moriarty P, Chuchfield M. et al. 2014. IEA-Task 31 WAKEBENCH: towards a protocol for wind farm flow model evaluation. Part 1: Flow-over-terrain models. J. Phys. Conf. Ser. 524:012105 [Google Scholar]
  134. Rossby C, Montgomery R. 1935. The layers of frictional influence in wind and ocean currents. Papers in Physical Oceanography and Meteorology 31–101 Cambridge, MA: MIT Press [Google Scholar]
  135. Saavedra-Moreno B, Salcedo-Sanz S, Paniagua-Tineo A, Prieto L, Portilla-Figueras A. 2011. Seeding evolutionary algorithms with heuristics for optimal wind turbines positioning in wind farms. Renew. Energy 36:2838–44 [Google Scholar]
  136. Sanderse B, van der Pijl SP, Koren B. 2011. Review of computational fluid dynamics for wind turbine wake aerodynamics. Wind Energy 14:799–819 [Google Scholar]
  137. Sarlak H, Meneveau C, Sørensen JN. 2015. Role of subgrid-scale modeling in large eddy simulation of wind turbine wake interactions. Renew. Energy 77:386–99 [Google Scholar]
  138. Schepers J, Boorsma K, Cho T, Gomez-Iradi S, Schaffarczyk P. et al. 2007. Final report of IEA Task 29, Mexnext (phase 1): analysis of MEXICO wind tunnel measurements Rep. ECN-E12-004 ECN Amsterdam:
  139. Schepers JG, Boorsma K, Gomez-Iradi S, Schaffarczyk P, Madsen HA. et al. 2014. Final report of IEA Wind Task 29: Mexnext (Phase 2) Rep. ECN-E14-060 ECN Amsterdam:
  140. Sescu A, Meneveau C. 2015. Large eddy simulation and single column modeling of thermally stratified wind-turbine arrays for fully developed, stationary atmospheric conditions. J. Atmos. Ocean. Technol. 32:1144–62 [Google Scholar]
  141. Shah SK, Bou-Zeid E. 2014. Direct numerical simulations of turbulent Ekman layers with increasing static stability: modifications to the bulk structure and second-order statistics. J. Fluid Mech. 760:494–539 [Google Scholar]
  142. Sharma V, Calaf M, Lehning M, Parlange MB. 2016. Time-adaptive wind turbine model for an LES framework. Wind Energy 19:939–52 [Google Scholar]
  143. Sim C, Basu S, Manuel L. 2012. On space-time resolution in inflow representations for wind turbine load analysis. Energies 5:2071–92 [Google Scholar]
  144. Sjöholm M, Mikkelsen T, Kristensen L, Mann J, Kirkegaard P. et al. 2010. Spectral analysis of wind turbulence measured by a Doppler Lidar for velocity fine structure and coherence studies Presented at Int. Symp. Adv. Bound. Layer Remote Sensing , 15th. Paris
  145. Smalikho IN, Banakh VA, Pichugina YL, Brewer WA, Banta RM. et al. 2013. Lidar investigation of atmosphere effect on a wind turbine wake. J. Atmos. Ocean. Technol. 30:2554–70 [Google Scholar]
  146. Snel H. 1998. Review of the present status of rotor aerodynamics. Wind Energy 1:46–49 [Google Scholar]
  147. Son E, Lee S, Hwang B, Lee S. 2014. Characteristics of turbine spacing in a wind farm using an optimal design process. Renew. Energy 65:245–49 [Google Scholar]
  148. Sørensen JN. 2011. Aerodynamic aspects of wind energy conversion. Annu. Rev. Fluid Mech. 43:427–48 [Google Scholar]
  149. Sørensen P, Cutululis NA, Vigueras-Rodríguez A, Jensen LE, Hjerrild J. et al. 2007. Power fluctuations from large wind farms. IEEE Trans. Power Syst. 22:958–65 [Google Scholar]
  150. Sørensen P, Hansen AD, Rosas PAC. 2002. Wind models for simulation of power fluctuations from wind farms. J. Wind Eng. Ind. Aerodyn. 90:1381–402 [Google Scholar]
  151. Stevens RJAM. 2016. Dependence of optimal wind-turbine spacing on wind-farm length. Wind Energy 19:651–63 [Google Scholar]
  152. Stevens RJAM, Gayme DF, Meneveau C. 2014a. Large eddy simulation studies of the effects of alignment and wind farm length. J. Renew. Sustain. Energy 6:023105 [Google Scholar]
  153. Stevens RJAM, Gayme DF, Meneveau C. 2015. Coupled wake boundary layer model of wind-farms. J. Renew. Sustain. Energy 7:023115 [Google Scholar]
  154. Stevens RJAM, Gayme DF, Meneveau C. 2016a. Effects of turbine spacing on the power output of extended wind-farms. Wind Energy 19:359–70 [Google Scholar]
  155. Stevens RJAM, Gayme DF, Meneveau C. 2016b. Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind farms. Wind Energy 192023–40 [Google Scholar]
  156. Stevens RJAM, Graham J, Meneveau C. 2014b. A concurrent precursor inflow method for large eddy simulations and applications to finite length wind farms. Renew. Energy 68:46–50 [Google Scholar]
  157. Stevens RJAM, Meneveau C. 2014. Temporal structure of aggregate power fluctuations in large-eddy simulations of extended wind farms. J. Renew. Sustain. Energy 6:043102 [Google Scholar]
  158. Stevens RJAM, Wilczek M, Meneveau C. 2014c. Large eddy simulation study of the logarithmic law for high-order moments in turbulent boundary layers. J. Fluid Mech. 757:888–907 [Google Scholar]
  159. Storey RC, Cater JE, Norris SE. 2016. Large eddy simulation of turbine loading and performance in a wind farm. Renew. Energy 95:31–42 [Google Scholar]
  160. Stull RB. 1988. An Introduction to Boundary Layer Meteorology Boston: Kluwer Acad.
  161. Talbot C, Bou-Zeid E, Smith J. 2012. Nested mesoscale large-eddy simulations with WRF: performance in real test cases. J. Hydrometeorol. 13:1421–41 [Google Scholar]
  162. Tarroja B, Mueller F, Eichman JD, Brouwer J, Samuelsen S. 2011. Spatial and temporal analysis of electric wind generation intermittency and dynamics. Renew. Energy 36:3424–32 [Google Scholar]
  163. Tennekes H, Lumley JL. 1972. A First Course in Turbulence Cambridge, MA: MIT Press
  164. Thiringer T. 2006. Frequency scanning for power system property determination—applied to a wind power grid. IEEE Trans. Power Syst. 21:702–8 [Google Scholar]
  165. Troldborg N, Larsen GC, Madsen HA, Hansen KS, Sørensen JN, Mikkelsen R. 2011. Numerical simulations of wake interaction between two wind turbines at various inflow conditions. Wind Energy 14:859–76 [Google Scholar]
  166. Troldborg N, Sørensen J, Mikkelsen R. 2010. Numerical simulations of wake characteristics of a wind turbine in uniform inflow. Wind Energy 13:86–99 [Google Scholar]
  167. Trujillo JJ, Bingöl F, Larsen GC, Mann J, Kühn M. 2011. Light detection and ranging measurements of wake dynamics. Part II: two-dimensional scanning. Wind Energy 14:61–75 [Google Scholar]
  168. van der Laan MP, Hansen KS, Sørensen NN, Réthoré PE. 2015. Predicting wind farm wake interaction with RANS: an investigation of the Coriolis force. J. Phys. Conf. Ser. 625:012026 [Google Scholar]
  169. van Kuik GAM, Peinke J, Nijssen R, Lekou D, Mann J. et al. 2016. Long-term research challenges in wind energy: a research agenda by the European Academy of Wind Energy. Wind Energy Sci. 1:1–39 [Google Scholar]
  170. Vautard R, Thais F, Tobin I, Bréon FM, de Lavergne JGD. et al. 2014. Regional climate model simulations indicate limited climatic impacts by operational and planned European wind farms. Nat. Commun. 5:3196 [Google Scholar]
  171. VerHulst C, Meneveau C. 2014. Large eddy simulation study of the kinetic energy entrainment by energetic turbulent flow structures in large wind farms. Phys. Fluids 26:025113 [Google Scholar]
  172. VerHulst C, Meneveau C. 2015. Altering kinetic energy entrainment in large eddy simulations of large wind farms using unconventional wind turbine actuator forcing. Energies 8:370–86 [Google Scholar]
  173. Vermeer LJ, Sørensen JN, Crespo A. 2003. Wind turbine wake aerodynamics. Prog. Aerosp. Sci. 39:467–510 [Google Scholar]
  174. Voutsinas SG, Rados KG, Zervos A. 1990. On the analysis of wake effects in wind parks. J. Wind Eng. Ind. Aerodyn. 14:204–19 [Google Scholar]
  175. Walker K, Adams N, Gribben B, Gellatly B, Nygaard NG. et al. 2016. An evaluation of the predictive accuracy of wake effects models for offshore wind farms. Wind Energy 19:979–96 [Google Scholar]
  176. Wang C, Prinn RG. 2010. Potential climatic impacts and reliability of very large-scale wind farms. Atmos. Chem. Phys. 10:2053–61 [Google Scholar]
  177. Wu X. 2017 Inflow turbulence generation methods. Annu. Rev. Fluid Mech. 49:23–49 [Google Scholar]
  178. Wu YT, Porté-Agel F. 2011. Large-eddy simulation of wind-turbine wakes: evaluation of turbine parametrisations. Bound. Layer Meteorol. 138:345–66 [Google Scholar]
  179. Wu YT, Porté-Agel F. 2015. Modeling turbine wakes and power losses within a wind farm using LES: an application to the Horns Rev offshore wind farm. Renew. Energy 75:945–55 [Google Scholar]
  180. Xie S, Archer C. 2015. Self-similarity and turbulence characteristics of wind turbine wakes via large-eddy simulation. Wind Energy 18:1815–38 [Google Scholar]
  181. Yang D, Meneveau C, Shen L. 2014. Large-eddy simulation of offshore wind farm. Phys. Fluids 26:025101 [Google Scholar]
  182. Yang X, Sotiropoulos F. 2013. On the predictive capabilities of LES-actuator disk model in simulating turbulence past wind turbines and farms. Proc. 2013 Am. Control Conf.2878–83 New York: IEEE [Google Scholar]
  183. Yang X, Sotiropoulos F. 2016. Analytical model for predicting the performance of arbitrary size and layout wind-farms. Wind Energy 19:1239–48 [Google Scholar]
  184. Zhang W, Markfort CD, Porté-Agel F. 2012. Near-wake flow structure downwind of a wind turbine in a turbulent boundary layer. Exp. Fluids 52:1219–35 [Google Scholar]
  185. Zhang W, Markfort CD, Porté-Agel F. 2013. Wind-turbine wakes in a convective boundary layer: a wind-tunnel study. Bound. Layer Meteorol. 146:161–79 [Google Scholar]
  186. Zhou L, Tian Y, Baidya-Roy S, Thorncroft C, Bosart LF, Hu Y. 2012. Impacts of wind farms on land surface temperature. Nat. Clim. Change 2:539–43 [Google Scholar]

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