- Featured in Physics
- Editors' Suggestion
- Access by Xinjiang University
Effect of layout on asymptotic boundary layer regime in deep wind farms
Phys. Rev. Fluids 3, 124603 – Published 5 December, 2018
DOI: https://doi.org/10.1103/PhysRevFluids.3.124603
Abstract
The power output of wind farms depends strongly on spatial turbine arrangement, and the resulting turbulent interactions with the atmospheric boundary layer. Wind farm layout optimization to maximize power output has matured for small clusters of turbines, with the help of analytical wake models. On the other hand, for large farms approaching a fully developed regime in which the integral power extraction by turbines is balanced through downward transport of the mean kinetic energy, the influence of turbine layout is much less understood. The main goal of this work is to study the effect of turbine layout on the power output for large wind farms approaching a fully developed regime. For this purpose we employ an experimental setup of a scaled wind farm with 100 porous disk models, of which 60 are instrumented with strain gauges. Our experiments cover a parametric space of 56 different layouts for which the turbine-area density is constant, focusing on different turbine arrangements including nonuniform spacings. The strain-gauge measurements are used to deduce surrogate power and unsteady loading on turbines for each layout. Our results indicate that the power asymptote at the end of the wind farm depends on the layout in different ways. Firstly, for layouts with a relatively uniform spacing we find that the power asymptote in the fully developed regime reaches approximately the same value, similarly to the prediction of available analytical models. Secondly, we show that the power asymptote in the fully developed regime can be lowered by inefficient turbine placement, for instance when a large number of the turbines are located in the near wake of upwind turbines. Thirdly, our experiments indicate that an uneven spacing between turbines can improve the overall power output for both the developing and fully developed part of large wind farms. Specifically, we find a higher power asymptote for a turbine layout with a significant streamwise uneven spacing (i.e., a large streamwise spacing between pairs of closely spaced rows that are slightly staggered). Our results thereby indicate that such a layout may promote beneficial flow interactions in the fully developed regime for conditions with a strongly prevailing wind direction.
Physics Subject Headings (PhySH)
Synopsis
Uneven Turbine Placement Improves Wind Farms
Wind-tunnel experiments show that uneven positioning of the turbines in a wind farm can improve its power output.
See more in Physics
Article Text
References (58)
- N. G. Nygaard, Wakes in very large wind farms and the effect of neighbouring wind farms, J. Phys.: Conf. Ser. 524, 012162 (2014).
- P. B. S. Lissaman, Energy effectiveness of arbitrary arrays of wind turbines, J. Energy 3, 323 (1979).
- I. Katic, J. Højstrup, and N. O. Jensen, A Simple Model for Cluster Efficiency, in European Wind Energy Association Conference and Exhibition, edited by W. Palz and E. Sesto (A. Raguzzi, 1987), pp. 407–410.
- M. Bastankhah and F. Porté-Agel, A new analytical model for wind-turbine wakes, Renewable Energy 70, 116 (2014).
- J. F. Herbert-Acero, O. Probst, P.-E. Réthoré, G. C. Larsen, and K. K. Castillo-Villar, A review of methodological approaches for the design and optimization of wind farms, Energies 7, 6930 (2014).
- J. Feng and W. Z. Shen, Solving the wind farm layout optimization problem using random search algorithm, Renewable Energy 78, 182 (2015).
- L. Parada, C. Herrera, P. Flores, and V. Parada, Wind farm layout optimization using a Gaussian-based wake model, Renewable Energy 107, 531 (2017).
- M. Beşkirli, İ. Koç, H. Haklı, and H. Kodaz, A new optimization algorithm for solving wind turbine placement problem: Binary artificial algae algorithm, Renewable Energy 121, 301 (2018).
- A. M. Abdelsalam and M. A. El-Shorbagy, Optimization of wind turbines siting in a wind farm using genetic algorithm based local search, Renewable Energy 123, 748 (2018).
- M. Calaf, C. Meneveau, and J. Meyers, Large eddy simulation study of fully developed wind-turbine array boundary layers, Phys. Fluids 22, 015110 (2010).
- V. S. Bokharaie, P. Bauweraerts, and J. Meyers, Wind-farm layout optimisation using a hybrid Jensen-LES approach, Wind Energy Science 1, 311 (2016).
- C. L. Archer, S. Mirzaeisefat, and S. Lee, Quantifying the sensitivity of wind farm performance to array layout options using large-eddy simulation, Geophys. Res. Lett. 40, 4963 (2013).
- L. P. Chamorro and F. Porté-Agel, Turbulent flow inside and above a wind farm: A wind-tunnel study, Energies 4, 1916 (2011).
- L. P. Chamorro, R. E. A. Arndt, and F. Sotiropoulos, Turbulent flow properties around a staggered wind farm, Boundary Layer Meteorol. 141, 349 (2011).
- J. Bossuyt, M. F. Howland, C. Meneveau, and J. Meyers, Measurement of unsteady loading and power output variability in a micro wind farm model in a wind tunnel, Exp. Fluids 58, 1 (2017).
- R. J. A. M. Stevens, D. F. Gayme, and C. Meneveau, Large eddy simulation studies of the effects of alignment and wind farm length, J. Renewable Sustainable Energy 6, 023105 (2014).
- R. J. A. M. Stevens, D. F. Gayme, and C. Meneveau, Effects of turbine spacing on the power output of extended wind-farms, Wind Energy 19, 359 (2016).
- K. L. Wu and F. Porté-Agel, Flow adjustment inside and around large finite-size wind farms, Energies 10, 2164 (2017).
- R. B. Cal, J. Lebrón, L. Castillo, H. S. Kang, and C. Meneveau, Experimental study of the horizontally averaged flow structure in a model wind-turbine array boundary layer, J. Renewable Sustainable Energy 2, 013106 (2010).
- C. VerHulst and C. Meneveau, Large eddy simulation study of the kinetic energy entrainment by energetic turbulent flow structures in large wind farms, Phys. Fluids 26, 025113 (2014).
- C. D. Markfort, W. Zhang, and F. Porté-Agel, Analytical model for mean flow and fluxes of momentum and energy in very large wind farms, Boundary Layer Meteorol. 166, 31 (2018).
- S. Frandsen, On the wind speed reduction in the center of large clusters of wind turbines, J. Wind Eng. Ind. Aerodyn. 39, 251 (1992).
- S. Frandsen, R. Barthelmie, S. Pryor, O. Rathmann, S. Larsen, J. Højstrup, and M. Thøgersen, Analytical modeling of wind speed deficit in large offshore wind farms, Wind Energy 9, 39 (2006).
- C. Meneveau, The top-down model of wind farm boundary layers and its applications, J. Turbul. 13(7), N7 (2012).
- X. Yang, S. Kang, and F. Sotiropoulos, Computational study and modeling of turbine spacing effects in infinite aligned wind farms, Phys. Fluids 24, 115107 (2012).
- J. Bossuyt, M. Howland, C. Meneveau, and J. Meyers, Measuring Power Output Intermittency and Unsteady Loading in a Micro Wind Farm Model, in 34th Wind Energy Symposium (AIAA SciTech Forum, 2016), p. 1992.
- T. Chatterjee and Y. T Peet, Contribution of large scale coherence to wind turbine power: A large eddy simulation study in periodic wind farms, Phys. Rev. Fluids 3, 034601 (2018).
- S. Taddei, C. Manes, and B. Ganapathisubramani, Characterisation of drag and wake properties of canopy patches immersed in turbulent boundary layers, J. Fluid Mech. 798, 27 (2016).
- S. McTavish, D. Feszty, and F. Nitzsche, An experimental and computational assessment of blockage effects on wind turbine wake development, Wind Energy 17, 1515 (2014).
- G. P. Corten, P. Schaak, and T. Hegberg, Turbine interaction in large offshore wind farms. Wind Tunnel Measurements. ECN report ECN-C-04-048, 2004.
- C. D. Markfort, W. Zhang, and F. Porté-Agel, Turbulent flow and scalar transport through and over aligned and staggered wind farms, J. Turbul. 13, N33 (2012).
- K. Charmanski, J. Turner, and M. Wosnik, Physical Model Study of the Wind Turbine Array Boundary Layer, in ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting Collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels (American Society of Mechanical Engineers, 2014).
- R. Theunissen, P. Housley, C. B. Allen, and C. Carey, Experimental verification of computational predictions in power generation variation with layout of offshore wind farms, Wind Energy 18, 1739 (2015).
- R. J. Barthelmie, S. T. Frandsen, O. Rathmann, K. S. Hansen, E. Politis, J. Prospathopoulos, J. G. Schepers, K. Rados, D. Cabezón, W. Schlez et al., Flow and Wakes in Large Wind Farms: Final Report for UpWind WP8, Technical Report, Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi, 2011.
- M. A. Miller, J. Kiefer, C. Westergaard, and M. Hultmark, Model wind turbines tested at full-scale similarity, J. Phys.: Conf. Ser. 753, 032018 (2016).
- D. Medici and P. H. Alfredsson, Measurements on a wind turbine wake: 3D effects and bluff body vortex shedding, Wind Energy 9, 219 (2006).
- M. Bastankhah and F. Porté-Agel, A new miniature wind turbine for wind tunnel experiments. Part I: Design and performance, Energies 10, 908 (2017).
- N. Coudou, S. Buckingham, L. Bricteux, and J. van Beeck, Experimental study on the wake meandering within a scale model wind farm subject to a wind-tunnel flow simulating an atmospheric boundary layer, Boundary Layer Meteorol. 167, 77 (2018).
- L. P. Chamorro, R. E. A. Arndt, and F. Sotiropoulos, Reynolds number dependence of turbulence statistics in the wake of wind turbines, Wind Energy 15, 733 (2012).
- S. Aubrun, S. Loyer, P. E. Hancock, and P. Hayden, Wind turbine wake properties: Comparison between a non-rotating simplified wind turbine model and a rotating model, J. Wind Eng. Ind. Aerodyn. 120, 1 (2013).
- R. Mikkelsen, Actuator disc methods applied to wind turbines, Ph.D. dissertation, Technical University of Denmark, 2003.
- I. P. Castro, Wake characteristics of two-dimensional perforated plates normal to an air-stream, J. Fluid Mech. 46, 599 (1971).
- H. C. Lim, I. P. Castro, and R. P. Hoxey, Bluff bodies in deep turbulent boundary layers: Reynolds-number issues, J. Fluid Mech. 571, 97 (2007).
- E. H. Camp and R. B. Cal, Mean kinetic energy transport and event classification in a model wind turbine array versus an array of porous disks: Energy budget and octant analysis, Phys. Rev. Fluids 1, 044404 (2016).
- H. S. Kang and C. Meneveau, Direct mechanical torque sensor for model wind turbines, Meas. Sci. Technol. 21, 105206 (2010).
- N. Tobin, H. Zhu, and L. P. Chamorro, Spectral behavior of the turbulence-driven power fluctuations of wind turbines, J. Turbul. 16, 832 (2015).
- J. Bossuyt, C. Meneveau, and J. Meyers, Wind farm power fluctuations and spatial sampling of turbulent boundary layers, J. Fluid Mech. 823, 329 (2017).
- J. Bossuyt, C. Meneveau, and J. Meyers, Large eddy simulation of a wind tunnel wind farm experiment with one hundred static turbine models, J. Phys.: Conf. Ser. 1037, 062006 (2018).
- K. M. Talluru, V. Kulandaivelu, N. Hutchins, and I. Marusic, A calibration technique to correct sensor drift issues in hot-wire anemometry, Meas. Sci. Technol. 25, 105304 (2014).
- Y.-T. Wu and F. Porté-Agel, Simulation of turbulent flow inside and above wind farms: Model validation and layout effects, Boundary Layer Meteorol. 146, 181 (2013).
- R. J. Barthelmie, O. Rathmann, S. T. Frandsen, K. S. Hansen, E. Politis, J. Prospathopoulos, K. Rados, D. Cabezón, W. Schlez, J. Phillips et al., Modelling and measurements of wakes in large wind farms, J. Phys.: Conf. Ser. 75, 012049 (2007).
- R. J. A. M. Stevens and C. Meneveau, Flow structure and turbulence in wind farms, Annu. Rev. Fluid Mech. 49, 311 (2017).
- R. J. A. M. Stevens, D. F. Gayme, and C. Meneveau, Generalized coupled wake boundary layer model: Applications and comparisons with field and LES data for two wind farms, Wind Energy 19, 2023 (2016).
- P. Sørensen, A. D. Hansen, and P. A. C. Rosas, Wind models for simulation of power fluctuations from wind farms, J. Wind Eng. Ind. Aerodyn. 90, 1381 (2002).
- R. J. A. M. Stevens and C. Meneveau, Temporal structure of aggregate power fluctuations in large-eddy simulations of extended wind-farms, J. Renewable Sustainable Energy 6, 043102 (2014).
- H. Liu, Y. Jin, N. Tobin, and L. P. Chamorro, Towards uncovering the structure of power fluctuations of wind farms, Phys. Rev. E 96, 063117 (2017).
- L. J. Lukassen, R. J. A. M. Stevens, C. Meneveau, and M. Wilczek, Modeling space-time correlations of velocity fluctuations in wind farms, Wind Energy 21, 474 (2018).
- J. Bossuyt, C. Meneveau, and J. Meyers, Wind tunnel experiment of a micro wind farm model (2018), doi: 10.5281/zenodo.1467411.