Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-141-2024
© 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-9-141-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jan 2024
Research article |
| 18 Jan 2024
| 18 Jan 2024
Drivers for optimum sizing of wind turbines for offshore wind farms
Wind Energy Group, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Michiel Zaaijer
Wind Energy Group, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Dominic von Terzi
Wind Energy Group, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
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Mihir Kishore Mehta, Michiel Zaaijer, and Dominic von Terzi
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Preprint under review for WES
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In a subsidy-free era, there is a need to optimize turbines to maximize the revenue of the farm instead of minimizing the LCoE. A wind farm-level modeling framework with a simplified market model to optimize the size of wind turbines to maximize revenue-based metrics like IRR/NPV. The results show that the optimum turbine size is driven mainly by the choice of the economic metric and the market price scenario, with an LCoE-optimized design already performing well w.r.t. metrics like IRR.
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Next-generation wind turbines are the largest rotating machines ever built, experiencing local flow Mach where the incompressibility assumption is violated, and even transonic flow can occur. This study assesses the transonic features over the FFA-W3-211 wind turbine tip airfoil for selected industrial test cases, defines the subsonic-supersonic flow threshold, and evaluates the Reynolds number effects on transonic flow occurrence. Shock wave occurrence is also depicted.
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In a subsidy-free era, there is a need to optimize turbines to maximize the revenue of the farm instead of minimizing the LCoE. A wind farm-level modeling framework with a simplified market model to optimize the size of wind turbines to maximize revenue-based metrics like IRR/NPV. The results show that the optimum turbine size is driven mainly by the choice of the economic metric and the market price scenario, with an LCoE-optimized design already performing well w.r.t. metrics like IRR.
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The paper investigates the influence of the rain drop diameter on the formation of erosion damage and its implication for the erosion-safe mode (ESM). By building an erosion damage model that incorporates several drop-size effects, it is found that large droplets are significantly more erosive than small droplets. It is shown that the performance of the ESM is significantly increased when drop-size effects are correctly accounted for. A method to derive optimal ESM strategies is given as well.
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Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-10, https://doi.org/10.5194/wes-2024-10, 2024
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When standing still without a nacelle or blades, the vibrations on the wind turbine tower are a concern to its structural health. This study finds that the air which flows around the tower recirculates behind the tower, forming so-called wakes. This wakes initiates the vibration, and the movement itself keeps the vibration increasing or decreasing depending on the wind speed. The current study uses a methodology called Force-partitioning to analyse this in depth.
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Rain droplets damage wind turbine blades due to the high impact speed at the tip. In this study, it is found that rain droplets and wind turbine blades interact aerodynamically. The rain droplets slow down and deform close to the blade. A model from another field of study was adapted and validated to study this process in detail. This effect reduced the predicted erosion damage by up to 50 %, primarily affecting smaller drops. It is shown how the slowdown effect can influence erosion mitigation.
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This paper investigates the effect of wind farm layout on the performance of offshore wind farms. A regular farm layout is compared to optimised irregular layouts. The irregular layouts have higher annual energy production, and the power production is less sensitive to wind direction. However, turbine towers require thicker walls to counteract increased fatigue due to increased turbulence levels in the farm. The study shows that layout optimisation can be used to maintain high-yield performance.
Erik Quaeghebeur, René Bos, and Michiel B. Zaaijer
Wind Energ. Sci., 6, 815–839, https://doi.org/10.5194/wes-6-815-2021, https://doi.org/10.5194/wes-6-815-2021, 2021
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We present a technique to support the optimal layout (placement) of wind turbines in a wind farm. It efficiently determines good directions and distances for moving turbines. An improved layout reduces production losses and so makes the farm project economically more attractive. Compared to most existing techniques, our approach requires less time. This allows wind farm designers to explore more alternatives and provides the flexibility to adapt the layout to site-specific requirements.
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Meteorological and oceanic datasets are fundamental to the modeling of offshore wind farms. Data quality issues in one such dataset led us to conduct a study to establish whether such issues are more generally present in these datasets. The answer is yes and users should be aware of this. We therefore also investigated how such issues can be avoided. The result is a set of techniques and recommendations for dataset producers, leading to substantial quality improvements with limited extra effort.
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Integrating a tuned liquid multi-column damping (TLMCD) into a floating offshore wind turbine (FOWT) is challenging. The synergy between the TLMCD, the turbine controller, and substructure dynamics affects the FOWT's performance and cost. A control co-design optimization framework is developed to optimize the substructure, the TLMCD, and the blade pitch controller simultaneously. The results show that the optimization can significantly enhance FOWT system performance.
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Cited articles
Andrew Ning, S., Damiani, R., and Moriarty, P. J.: Objectives and constraints for wind turbine optimization, J. Sol Energ. Eng., 136, 041010, https://doi.org/10.1115/1.4027693, 2014. a
Ashuri, T., Zaaijer, M. B., Martins, J. R., and Zhang, J.: Multidisciplinary design optimization of large wind turbines – Technical, economic, and design challenges, Energ. Convers. Manage., 123, 56–70, https://doi.org/10.1016/J.ENCONMAN.2016.06.004, 2016. a, b, c
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Eenergy, 70, 116–123, 2014. a
Bortolotti, P., Tarres, H. C., Dykes, K. L., Merz, K., Sethuraman, L., Verelst, D., and Zahle, F.: IEA Wind TCP Task 37: Systems Engineering in Wind Energy – WP2.1 Reference Wind Turbines, OSTI.GOV, https://doi.org/10.2172/1529216, 2019a. a
Bortolotti, P., Berry, D. S., Murray, R., Gaertner, E., Jenne, D. S., Damiani, R. R., Barter, G. E., and Dykes, K. L.: A Detailed Wind Turbine Blade Cost Model, OSTI.GOV, https://doi.org/10.2172/1529217, 2019b. a
Bortolotti, P., Bay, C., Barter, G., Gaertner, E., Dykes, K., McWilliam, M., Friis-Moller, M., Molgaard Pedersen, M., and Zahle, F.: System Modeling Frameworks for Wind Turbines and Plants: Review and Requirements Specifications, Tech. Rep. March, NREL, https://www.nrel.gov/docs/fy22osti/82621.pdf (last access: 15 October 2023), 2022. a
Chehouri, A., Younes, R., Ilinca, A., and Perron, J.: Review of performance optimization techniques applied to wind turbines, Appl. Energy, 142, 361–388, https://doi.org/10.1016/J.APENERGY.2014.12.043, 2015. a
Dykes, K.: Optimization of Wind Farm Design for Objectives Beyond LCOE, J. Phys.: Conf. Ser., 1618, 042039, https://doi.org/10.1088/1742-6596/1618/4/042039, 2020. a
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, F., Anderson, B., Barter, G., Abbas, N., Meng, F., Bortolotti, P., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition of the IEA Wind 15-Megawatt Offshore Reference Wind Turbine, Tech. Rep. March, NREL, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 7 November 2023), 2020. a
Griffith, D. T. and Johanns, W.: Carbon Design Studies for Large Blades: Performance and Cost Tradeoffs for the Sandia 100-meter Wind Turbine Blade, in: 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 8–11 April 2013, Boston, Massachusetts, https://doi.org/10.2514/6.2013-1554, 2013. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a, b
IRENA – International Renewable Energy Agency: Future of wind: Deployment, investment, technology, grid integration and socio-economic aspects (A Global Energy Transformation paper), Tech. rep., ISBN 978-92-9260-155-3, https://www.irena.org/-/media/files/irena/agency/publication/2019/oct/irena_future_of_wind_2019.pdf (last access: 10 February 2023), 2019. a, b
Kaiser, M. J. and Snyder, B.: Offshore wind energy cost modeling: installation and decommissioning, in: vol. 85, Springer Science & Business Media, ISBN 9781447124870, 2012. a
Lantz, E., Hand, M., and Wiser, R.: WREF 2012: Past and Future Cost of Wind Energy, OSTI.GOV, https://www.osti.gov/biblio/1061391 (last access: 14 March 2023), 2012. a
Lensink, S. and Pisca, I.: Costs of offshore wind energy 2018, https://www.pbl.nl/sites/default/files/downloads/pbl-2019-costs-of-offshore-wind-energy-2018_3623.pdf (last access: 4 March 2023), 2019. a
Mehta, M.: mihir0210/WINDOW_static: Turbine sizing for electricity and hydrogen production (v3.0_turbine_sizing), Zenodo [code], https://doi.org/10.5281/zenodo.8380355, 2023. a, b, c
NREL: DrivetrainSE – WISDEM 2.0 documentation, https://wisdem.readthedocs.io/en/master/wisdem/drivetrainse/index.html (last access: 15 October 2023), 2015. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake (v1.0.10), Zenodo [code], https://doi.org/10.5281/zenodo.2562662, 2019. 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, 042004, https://doi.org/10.1088/1742-6596/1037/4/042004, 2018. a
Rijkswaterstaat: Hollandse Kust (Zuid) wind farm zone including Offshore Wind Farm Luchterduinen (Lud), https://www.noordzeeloket.nl/en/functions-and-use/offshore-wind-energy/free-passage-shared-use/hollandse-kust-zuid-wind-farm-zone-including/ (last access: 24 October 2023), 2021. a
RVO – Rijksdienst voor Ondernemend Nederland: Hollandse Kust (zuid) Wind Farm Zone (Wind Farm Sites III & IV), Tech. rep., https://offshorewind.rvo.nl/file/download/ce29c209-818e-4727-9c44-68c4902e7caa/1540220273rvo hkz iii and iv maindocument_october_2018_lowres_web.pdf (last access: 24 October 2023), 2018. a
Sanchez Perez Moreno, S.: A guideline for selecting MDAO workflows with an application in offshore wind energy, PhD thesis, Delft University of Technology, Delft, ISBN 9789055841745, https://doi.org/10.4233/uuid:ea1b4101-0e55-4abe-9539-ae5d81cf9f65, 2019. a, b
Serafeim, G., Manolas, D., Riziotis, V., and Chaviaropoulos, P.: Multidisciplinary aeroelastic optimization of a 10 MW-scale wind turbine rotor targeting to reduced LCoE, J. Phys.: Conf. Ser., 2265, 042051, https://doi.org/10.1088/1742-6596/2265/4/042051, 2022. a
Shields, M., Beiter, P., Nunemaker, J., Cooperman, A., and Duffy, P.: Impacts of turbine and plant upsizing on the levelized cost of energy for offshore wind, Appl. Energy, 298, 117189, https://doi.org/10.1016/J.APENERGY.2021.117189, 2021. a, b, c, d
Sieros, G., Chaviaropoulos, P., Sørensen, J. D., Bulder, B. H., and Jamieson, P.: Upscaling wind turbines: theoretical and practical aspects and their impact on the cost of energy, Wind Energy, 15, 3–17, https://doi.org/10.1002/WE.527, 2012. a
Smart, G., Smith, A., Warner, E., Sperstad, I. B., Prinsen, B., and Lacal-Arantegui, R.: IEA Wind Task 26: Offshore Wind Farm Baseline Documentation, OSTI.GOV, https://doi.org/10.2172/1259255, 2016. a, b
Tanmay, T.: Multi-disciplinary Optimization of Rotor Nacelle Assemblies for Offshore Wind Farms, MS thesis, Delft University of Technology, Delft, https://repository.tudelft.nl/islandora/object/uuid:d7e2b321-adf8-4a64-97b5-7bad5f644a1f?collection=education (last access: 9 November 2023), 2018. a
Todd Griffith, D. and Johanns, W.: Large Blade Manufacturing Cost Studies Using the Sandia Blade Manufacturing Cost Tool and Sandia 100-meter Blades, Tech. Rep. March, SANDIA, https://energy.sandia.gov/wp-content/gallery/uploads/dlm_uploads/SAND_SNLLargeBladeManufacturingCostTrendsAnalysis_SAND2013-2734.pdf (last access: 10 March 2023), 2013. a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., et al.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Wind & water works: Dutch Offshore Wind Guide: Your guide to Dutch offshore wind policy, technologies and innovations, Tech. rep., https://www.rvo.nl/sites/default/files/2021/10/Dutch Offshore Wind Guide 2022.pdf (last access: 20 February 2023), 2022. a
Zaaijer, M.: Great expectations for offshore wind turbines, PhD thesis, TU Delft, Delft, ISBN 9789053357521, https://repository.tudelft.nl/islandora/object/uuid%3Afd689ba2-3c5f-4e7c-9ccd-55ddbf1679bd (last access: 9 November 2023), 2013. a
Short summary
Turbines are becoming larger. However, it is important to understand the key drivers of turbine design and explore the possibility of a global optimum, beyond which further upscaling might not reduce the cost of energy. This study explores, for a typical farm, the entire turbine design space with respect to rated power and rotor diameter. The results show a global optimum that is subject to various modeling uncertainties, farm design conditions, and policies with respect to wind farm tendering.
Turbines are becoming larger. However, it is important to understand the key drivers of turbine...
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