Articles | Volume 9, issue 7
https://doi.org/10.5194/wes-9-1595-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1595-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jul 2024
Research article |
| 29 Jul 2024
| 29 Jul 2024
Dynamic performance of a passively self-adjusting floating wind farm layout to increase the annual energy production
Mohammad Youssef Mahfouz
CORRESPONDING AUTHOR
Stuttgart Wind Energy, University of Stuttgart, Allmandring 5B, 70569 Stuttgart, Germany
Ericka Lozon
National Renewable Energy Laboratory (NREL), 15013 Denver W Pkwy, Golden, CO 80401, USA
Matthew Hall
National Renewable Energy Laboratory (NREL), 15013 Denver W Pkwy, Golden, CO 80401, USA
Po Wen Cheng
Stuttgart Wind Energy, University of Stuttgart, Allmandring 5B, 70569 Stuttgart, Germany
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Cited articles
Baker, N. F., Stanley, A. P., Thomas, J. J., Ning, A., and Dykes, K.: Best practices for wake model and optimization algorithm selection in wind farm layout optimization, in: AIAA Scitech 2019 Forum, San Diego, California, 2019, https://doi.org/10.2514/6.2019-0540, 2019. a, b
Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, https://doi.org/10.1017/jfm.2016.595, 2016. a, b
Bodini, N. and Optis, M.: Operational-based annual energy production uncertainty: are its components actually uncorrelated?, Wind Energ. Sci., 5, 1435–1448, https://doi.org/10.5194/wes-5-1435-2020, 2020. a
Crespo, A. and Hernández, J.: Turbulence characteristics in wind-turbine wakes, J. Wind Eng. Ind. Aerod., 61, 71–85, https://doi.org/10.1016/0167-6105(95)00033-X, 1996. a
Doubrawa, P., Annoni, J., Jonkman, J., and Ghate, A.: Optimization-based calibration of FAST.Farm parameters against SOWFA, in: Wind Energy Symposium, Kissimmee, Florida, 2018, 1–16, https://doi.org/10.2514/6.2018-0512, 2018. a
Fleming, P., Gebraad, P. M., Lee, S., van Wingerden, J.-W., Johnson, K., Churchfield, M., Michalakes, J., Spalart, P., and Moriarty, P.: Simulation comparison of wake mitigation control strategies for a two-turbine case, Wind Energy, 18, 2135–2143, https://doi.org/10.1002/we.1810, 2015. a
Fleming, P. A., Ning, A., Gebraad, P. M. O., and Dykes, K.: Wind plant system engineering through optimization of layout and yaw control, Wind Energy, 19, 329–344, https://doi.org/10.1002/we.1836, 2016. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G. E., Abbas, N. J., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G. N., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: IEA Wind TCP Task 37: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, Tech. rep., International Energy Agency, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: last access: 11 July 2024), 2020. a
Gill, P. E., Murray, W., and Saunders, M. A.: SNOPT: An SQP algorithm for large-scale constrained optimization, SIAM Review, 47, 99–131, https://doi.org/10.1137/S0036144504446096, 2005. a
Gill, P. E., Murray, W., and Saunders, M. a.: User's Guide for SNOPT Version 7, Tech. Rep., https://web.stanford.edu/group/SOL/guides/sndoc7.pdf (last access: 11 July 2024), 2008. a
Hall, M., Housner, S., Sirnivas, S., and Wilson, S.: MoorPy (Quasi-Static Mooring Analysis in Python), DOE Code [software], https://doi.org/10.11578/dc.20210726.1, 2021. a
Johlas, H.: Simulating the Effects of Floating Platforms, Tilted Rotors, and Breaking Waves for Offshore Wind Turbines, https://doi.org/10.7275/24291287, 2021. a
Jonkman, J. and Shaler, K.: FAST.Farm User's Guide and Theory Manual, Tech Report, https://www.nrel.gov/docs/fy21osti/78485.pdf (last access: 11 July 2024), 2020. a
Jonkman, J., Doubrawa, P., Hamilton, N., Annoni, J., and Fleming, P.: Validation of FAST.Farm Against Large-Eddy Simulations, J. Phys. Conf. Ser., 1037, 062005, https://doi.org/10.1088/1742-6596/1037/6/062005, 2018. a
Kheirabadi, A. C. and Nagamune, R.: Modeling and power optimization of floating offshore wind farms with yaw and induction-based turbine repositioning, Proceedings of the American Control Conference, Philadelphia, PA, USA, 10–12 July 2019, 5458–5463, https://doi.org/10.23919/acc.2019.8814600, 2019. a, b
Kheirabadi, A. C. and Nagamune, R.: Real-time relocation of floating offshore wind turbine platforms for wind farm efficiency maximization: An assessment of feasibility and steady-state potential, Ocean Eng., 208, 107445, https://doi.org/10.1016/j.oceaneng.2020.107445, 2020. a
King, J., Fleming, P., King, R., Martínez-Tossas, L. A., Bay, C. J., Mudafort, R., and Simley, E.: Control-oriented model for secondary effects of wake steering, Wind Energ. Sci., 6, 701–714, https://doi.org/10.5194/wes-6-701-2021, 2021. a
Mahfouz, M. Y.: SWE-UniStuttgart/FloatingWAYS: Beta Version (v0.2.0-beta), Zenodo [code and data set], https://doi.org/10.5281/zenodo.8370977, 2023a. a
Mahfouz, M. Y.: A passively self-adjusting floating wind farm layout design [video], https://doi.org/10.5446/63167, 2023b. a, b
Mahfouz, M. Y., Salari, M., Vigara, F., Hernandez, S., Molins, C., Trubat, P., Bredmose, H., and Pegalajar-Jurado, A.: D1.3. Public design and FAST models of the two 15MW floater-turbine concepts, Zenodo, https://doi.org/10.5281/zenodo.4385727, 2020. a, b
Mahfouz, M. Y., Molins, C., Trubat, P., Hernández, S., Vigara, F., Pegalajar-Jurado, A., Bredmose, H., and Salari, M.: Response of the International Energy Agency (IEA) Wind 15 MW WindCrete and Activefloat floating wind turbines to wind and second-order waves, Wind Energy Science, 6, 867–873, https://doi.org/10.5194/wes-6-867-2021, 2021. a, b, c
Mahfouz, M. Y., Hall, M., and Cheng, P. W.: A parametric study of the mooring system design parameters to reduce wake losses in a floating wind farm, J. Phys. Conf. Ser., 2265, 42004, https://doi.org/10.1088/1742-6596/2265/4/042004, 2022. a, b
Mann, J.: Wind field simulation, Probabilist. Eng. Mech., 13, 269–282, https://doi.org/10.1016/s0266-8920(97)00036-2, 1998. a
Nanos, E. M., Bottasso, C. L., Tamaro, S., Manolas, D. I., and Riziotis, V. A.: Vertical wake deflection for floating wind turbines by differential ballast control, Wind Energ. Sci., 7, 1641–1660, https://doi.org/10.5194/wes-7-1641-2022, 2022. a
Niayifar, A. and Porté-Agel, F.: Analytical modeling of wind farms: A new approach for power prediction, Energies, 9, 741, https://doi.org/10.3390/en9090741, 2016. a
NREL: FLORIS. Version 3.1, GitHub [code], https://github.com/NREL/floris, 2023. a
Perez-Moreno, S. S., Dykes, K., Merz, K. O., and Zaaijer, M. B.: Multidisciplinary design analysis and optimisation of a reference offshore wind plant, J. Phys. Conf. Ser., 1037, https://doi.org/10.1088/1742-6596/1037/4/042004, 2018. a
Ramos-García, N., González Horcas, S., Pegalajar-Jurado, A., Kontos, S., and Bredmose, H.: Investigation of the floating IEA wind 15-MW RWT using vortex methods Part II: Wake impact on downstream turbines under turbulent inflow, Wind Energy, 25, 1434–1463, https://doi.org/10.1002/we.2738, 2022a. a
Ramos-García, N., Kontos, S., Pegalajar-Jurado, A., González Horcas, S., and Bredmose, H.: Investigation of the floating IEA Wind 15 MW RWT using vortex methods Part I: Flow regimes and wake recovery, Wind Energy, 25, 468–504, https://doi.org/10.1002/we.2682, 2022b. a, b
Ramos-García, N., González-horcas, S., Pegalajar-jurado, A., Gözcü, O., Bredmose, H., Özinan, U., Mahfouz, M. Y., Fontanella, A., Facchinetti, A., and Belloli, M.: D1.5: Methods for nonlinear wave forcing and wakes, Tech. Rep. March 2022, https://corewind.eu/wp-content/uploads/files/delivery-docs/D1.5.pdf (last access: 11 July 2024), 2023. a
Rivera-Arreba, I., Li, Z., Yang, X., and Bachynski-Polić, E. E.: Comparison of the dynamic wake meandering model against large eddy simulation for horizontal and vertical steering of wind turbine wakes, Renew. Energ., 221, 119807, https://doi.org/10.1016/j.renene.2023.119807, 2024. a
Rodrigues, S. F., Teixeira Pinto, R., Soleimanzadeh, M., Bosman, P. A., and Bauer, P.: Wake losses optimization of offshore wind farms with moveable floating wind turbines, Energ. Convers. Manage., 89, 933–941, https://doi.org/10.1016/j.enconman.2014.11.005, 2015. a, b
Short summary
As climate change increasingly impacts our daily lives, a transition towards cleaner energy is needed. With all the growth in floating offshore wind and the planned floating wind farms (FWFs) in the next few years, we urgently need new techniques and methodologies to accommodate the differences between the fixed bottom and FWFs. This paper presents a novel methodology to decrease aerodynamic losses inside an FWF by passively relocating the downwind floating wind turbines out of the wakes.
As climate change increasingly impacts our daily lives, a transition towards cleaner energy is...
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