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
Related authors
Mohammad Youssef Mahfouz, Climent Molins, Pau Trubat, Sergio Hernández, Fernando Vigara, Antonio Pegalajar-Jurado, Henrik Bredmose, and Mohammad Salari
Wind Energ. Sci., 6, 867–883, https://doi.org/10.5194/wes-6-867-2021, https://doi.org/10.5194/wes-6-867-2021, 2021
Short summary
Short summary
This paper introduces the numerical models of two 15 MW floating offshore wind turbines (FOWTs) WindCrete and Activefloat. WindCrete is a spar floating platform designed by Universitat Politècnica de Catalunya, while Activefloat is a semi-submersible platform designed by Esteyco. The floaters are designed within the Horizon 2020 project COREWIND. Later in the paper, the responses of both models to wind and second-order waves are analysed with an emphasis on the effect of second-order waves.
Katarzyna Patryniak, Maurizio Collu, Jason Jonkman, Matthew Hall, Garrett Barter, Daniel Zalkind, and Andrea Coraddu
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-167, https://doi.org/10.5194/wes-2024-167, 2025
Revised manuscript under review for WES
Short summary
Short summary
This paper studies the Instantaneous Centre of Rotation (ICR) of Floating Offshore Wind Turbines (FOWTs). We present a method for computing the ICR and examine the correlations between the external loading, design features, ICR statistics, motions, and loads. We demonstrate how to apply the new insights to successfully modify the designs of the spar and semisubmersible FOWTs to reduce the loads in the moorings, the tower, and the blades, improving the ultimate strength and fatigue properties.
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci., 9, 1527–1545, https://doi.org/10.5194/wes-9-1527-2024, https://doi.org/10.5194/wes-9-1527-2024, 2024
Short summary
Short summary
Large direct-drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Qi Pan, Dexing Liu, Feng Guo, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-44, https://doi.org/10.5194/wes-2024-44, 2024
Preprint withdrawn
Short summary
Short summary
The floating wind market is striving to scale up from a handful of prototypes to gigawatt-scale capacity, despite facing barriers of high costs in the deep-sea deployment. Shared mooring is promising in reducing material costs. This paper introduces a comprehensive design methodology for reliable shared mooring line configurations, and reveals their potential for cost-saving and power enhancement. These findings contribute to achieving cost-effective solutions for floating wind farms.
Wei Yu, Sheng Tao Zhou, Frank Lemmer, and Po Wen Cheng
Wind Energ. Sci., 9, 1053–1068, https://doi.org/10.5194/wes-9-1053-2024, https://doi.org/10.5194/wes-9-1053-2024, 2024
Short summary
Short summary
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.
Christian W. Schulz, Stefan Netzband, Umut Özinan, Po Wen Cheng, and Moustafa Abdel-Maksoud
Wind Energ. Sci., 9, 665–695, https://doi.org/10.5194/wes-9-665-2024, https://doi.org/10.5194/wes-9-665-2024, 2024
Short summary
Short summary
Understanding the underlying physical phenomena of the aerodynamics of floating offshore wind turbines (FOWTs) is crucial for successful simulations. No consensus has been reached in the research community on which unsteady aerodynamic phenomena are relevant and how much they can influence the loads acting on a FOWT. This work contributes to the understanding and characterisation of such unsteady phenomena using a novel experimental approach and comprehensive numerical investigations.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 8, 149–171, https://doi.org/10.5194/wes-8-149-2023, https://doi.org/10.5194/wes-8-149-2023, 2023
Short summary
Short summary
The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence, considering the variation of turbulence parameters. Simulations are performed for the above-rated mean wind speed, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, tower base fore–aft bending moment, and electrical power significantly.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
Short summary
Short summary
We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
Mohammad Youssef Mahfouz, Climent Molins, Pau Trubat, Sergio Hernández, Fernando Vigara, Antonio Pegalajar-Jurado, Henrik Bredmose, and Mohammad Salari
Wind Energ. Sci., 6, 867–883, https://doi.org/10.5194/wes-6-867-2021, https://doi.org/10.5194/wes-6-867-2021, 2021
Short summary
Short summary
This paper introduces the numerical models of two 15 MW floating offshore wind turbines (FOWTs) WindCrete and Activefloat. WindCrete is a spar floating platform designed by Universitat Politècnica de Catalunya, while Activefloat is a semi-submersible platform designed by Esteyco. The floaters are designed within the Horizon 2020 project COREWIND. Later in the paper, the responses of both models to wind and second-order waves are analysed with an emphasis on the effect of second-order waves.
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
Short summary
Short summary
Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Martin Hofsäß, Dominique Bergmann, Jan Denzel, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-81, https://doi.org/10.5194/wes-2019-81, 2019
Revised manuscript not accepted
Short summary
Short summary
We needed a way to measure wind vectors and turbulence in complex, hard-to-access terrain. We equipped a model helicopter with a standard 3-D ultrasonic anemometer. Due to the hovering capabilities, stationary point measurements are possible. The first measurements were made in flat terrain. A 100 m high stationary wind measuring mast served as reference. The results were investigated in the time domain as well as in the frequency domain.
Steffen Raach, Bart Doekemeijer, Sjoerd Boersma, Jan-Willem van Wingerden, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-54, https://doi.org/10.5194/wes-2019-54, 2019
Publication in WES not foreseen
Short summary
Short summary
The presented work combines two control approaches of wake redirection control, feedforward wake redirection and feedback wake redirction. In our previous investigatins the lidar-assisted feedback control was studied and the advantages and disadvantages were discussed. The optimal yaw angles for the wind turbines are precomputed, the feedback takes care of uncertainties and disturbances. The concept is demonstrated in a high fidelity simulation model.
Kolja Müller and Po Wen Cheng
Wind Energ. Sci., 3, 149–162, https://doi.org/10.5194/wes-3-149-2018, https://doi.org/10.5194/wes-3-149-2018, 2018
Short summary
Short summary
An efficient and accurate Monte Carlo approach is presented to assess the lifetime fatigue loading on a floating offshore wind turbine accurately. This is typically challenging in simulation effort due to the many different combinations of relevant environmental conditions which need to be considered. The applied method uses quasi-random Sobol sequences and shows promising performance with respect to convergence and accuracy.
Steffen Raach, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 2, 257–267, https://doi.org/10.5194/wes-2-257-2017, https://doi.org/10.5194/wes-2-257-2017, 2017
Short summary
Short summary
This work provides a possible solution to closed-loop flow control in a wind farm.
The remote sensing technology, lidar, which is a laser-based measurement system, is used to obtain wind speed information behind a wind turbine. The measurements are processed using a model-based approach to estimate position information of the wake. The information is then used in a controller to redirect the wake to the desired position. Altogether, the concept aims to increase the power output of a wind farm.
Related subject area
Thematic area: Wind technologies | Topic: Offshore technology
Estimating microplastics emissions from offshore wind turbine blades in the Dutch North Sea
Gaussian Mixture autoencoder for uncertainty-aware damage identification in a Floating Offshore Wind Turbine
Experimental Validation of Parked Loads for a Floating Vertical Axis Wind Turbine: Wind-Wave Basin Tests
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads in extreme idling conditions
Spatio-Temporal Graph Neural Networks for Power Prediction in Offshore Wind Farms Using SCADA Data
A New Gridded Offshore Wind Profile Product for US Coasts Using Machine Learning and Satellite Observations
OC6 project Phase IV: validation of numerical models for novel floating offshore wind support structures
Quantifying the impact of modeling fidelity on different substructure concepts for floating offshore wind turbines – Part 1: Validation of the hydrodynamic module QBlade-Ocean
A new methodology for upscaling semi-submersible platforms for floating offshore wind turbines
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads
Design optimization of offshore wind jacket piles by assessing support structure orientation relative to metocean conditions
Comparison of optimal power production and operation of unmoored floating offshore wind turbines and energy ships
Marco Caboni, Anna Elisa Schwarz, Henk Slot, and Harald van der Mijle Meijer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-175, https://doi.org/10.5194/wes-2024-175, 2024
Revised manuscript accepted for WES
Short summary
Short summary
In this study, we assessed the total quantity of microplastics emitted by wind turbines currently operating in the Dutch North Sea. The estimate of microplastics currently emitted from offshore wind turbines in The Netherlands account for a very small portion of the total microplastics released offshore in The Netherlands, specifically less than one per mille.
Ana Fernandez-Navamuel, Nicolas Gorostidi, David Pardo, Vincenzo Nava, and Eleni Chatzi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-160, https://doi.org/10.5194/wes-2024-160, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This work employs Deep Neural Networks to identify damage in the mooring system of a Floating Offshore Wind Turbine (FOWT) using measurements from the platform response. We account for the effect of uncertainty in the assessment estimates caused by the existence of multiple solutions (different damage scenarios can cause the observed data). We describe the damage condition features using a distributional model based on a Gaussian Mixture, which captures the uncertainty in the predictions.
Md Sanower Hossain and D. Todd Griffith
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-156, https://doi.org/10.5194/wes-2024-156, 2024
Revised manuscript accepted for WES
Short summary
Short summary
The document presents an experimental study on the parked loads of floating vertical axis wind turbines (VAWTs) in a wind and waves basin, focusing on the effects of wind speed, solidity, and floating platform dynamics. Findings show that higher wind speed, and higher solidity generally increase the parked loads, while a floating platform introduces additional effects due to tilting. A semi-numerical model was also presented to predict the parked loads, which helps enhance VAWT design.
Will Wiley, Jason Jonkman, and Amy Robertson
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-130, https://doi.org/10.5194/wes-2024-130, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Numerical models, used to assess loads on floating offshore wind turbines, require many input parameters to describe air and water conditions, system properties, and load calculations. All parameters have some possible range, due to uncertainty and/or variations with time. The selected values can have important effects on the uncertainty in the resulting loads. This work identifies the input parameters that have the most impact on ultimate and fatigue loads for extreme storm load cases.
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-113, https://doi.org/10.5194/wes-2024-113, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study presents a novel model for predicting wind turbine power output at high temporal resolution in wind farms using a hybrid Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated within a Normal Behavior Model (NBM) framework, the model effectively identifies and analyzes power loss events.
James Frech, Korak Saha, Paige D. Lavin, Huai-Min Zhang, James Reagan, and Brandon Fung
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-77, https://doi.org/10.5194/wes-2024-77, 2024
Revised manuscript accepted for WES
Short summary
Short summary
A machine learning model is developed using lidar stations around the US coasts to extrapolate wind speed profiles up to the hub heights of wind turbines from surface wind speeds. Independent validation shows that our model vastly outperforms traditional methods for vertical wind extrapolation. We produce a new long-term gridded dataset of wind speed profiles from 20 to 200 m at 0.25°, 6-hourly resolution from 1987 to 2022 by applying this model to the NOAA/NCEI Blended Seawinds product.
Roger Bergua, Will Wiley, Amy Robertson, Jason Jonkman, Cédric Brun, Jean-Philippe Pineau, Quan Qian, Wen Maoshi, Alec Beardsell, Joshua Cutler, Fabio Pierella, Christian Anker Hansen, Wei Shi, Jie Fu, Lehan Hu, Prokopios Vlachogiannis, Christophe Peyrard, Christopher Simon Wright, Dallán Friel, Øyvind Waage Hanssen-Bauer, Carlos Renan dos Santos, Eelco Frickel, Hafizul Islam, Arjen Koop, Zhiqiang Hu, Jihuai Yang, Tristan Quideau, Violette Harnois, Kelsey Shaler, Stefan Netzband, Daniel Alarcón, Pau Trubat, Aengus Connolly, Seán B. Leen, and Oisín Conway
Wind Energ. Sci., 9, 1025–1051, https://doi.org/10.5194/wes-9-1025-2024, https://doi.org/10.5194/wes-9-1025-2024, 2024
Short summary
Short summary
This paper provides a comparison for a floating offshore wind turbine between the motion and loading estimated by numerical models and measurements. The floating support structure is a novel design that includes a counterweight to provide floating stability to the system. The comparison between numerical models and the measurements includes system motion, tower loads, mooring line loads, and loading within the floating support structure.
Robert Behrens de Luna, Sebastian Perez-Becker, Joseph Saverin, David Marten, Francesco Papi, Marie-Laure Ducasse, Félicien Bonnefoy, Alessandro Bianchini, and Christian-Oliver Paschereit
Wind Energ. Sci., 9, 623–649, https://doi.org/10.5194/wes-9-623-2024, https://doi.org/10.5194/wes-9-623-2024, 2024
Short summary
Short summary
A novel hydrodynamic module of QBlade is validated on three floating offshore wind turbine concepts with experiments and two widely used simulation tools. Further, a recently proposed method to enhance the prediction of slowly varying drift forces is adopted and tested in varying met-ocean conditions. The hydrodynamic capability of QBlade matches the current state of the art and demonstrates significant improvement regarding the prediction of slowly varying drift forces with the enhanced model.
Kaylie L. Roach, Matthew A. Lackner, and James F. Manwell
Wind Energ. Sci., 8, 1873–1891, https://doi.org/10.5194/wes-8-1873-2023, https://doi.org/10.5194/wes-8-1873-2023, 2023
Short summary
Short summary
This paper presents an upscaling methodology for floating offshore wind turbine platforms using two case studies. The offshore wind turbine industry is trending towards fewer, larger offshore wind turbines within a farm, which is motivated by the per unit cost of a wind farm (including installation, interconnection, and maintenance costs). The results show the platform steel mass to be favorable with upscaling.
Will Wiley, Jason Jonkman, Amy Robertson, and Kelsey Shaler
Wind Energ. Sci., 8, 1575–1595, https://doi.org/10.5194/wes-8-1575-2023, https://doi.org/10.5194/wes-8-1575-2023, 2023
Short summary
Short summary
A sensitivity analysis determined the modeling parameters for an operating floating offshore wind turbine with the biggest impact on the ultimate and fatigue loads. The loads were the most sensitive to the standard deviation of the wind speed. Ultimate and fatigue mooring loads were highly sensitive to the current speed; only the fatigue mooring loads were sensitive to wave parameters. The largest platform rotation was the most sensitive to the platform horizontal center of gravity.
Maciej M. Mroczek, Sanjay Raja Arwade, and Matthew A. Lackner
Wind Energ. Sci., 8, 807–817, https://doi.org/10.5194/wes-8-807-2023, https://doi.org/10.5194/wes-8-807-2023, 2023
Short summary
Short summary
Benefits of orientating a three-legged offshore wind jacket relative to the metocean conditions for pile design are assessed considering the International Energy Agency 15 MW reference turbine and a reference site off the coast of Massachusetts. Results, based on the considered conditions, show that the pile design can be optimized by orientating the jacket relative to the dominant wave direction. This design optimization can be used on offshore wind projects to provide cost and risk reductions.
Patrick Connolly and Curran Crawford
Wind Energ. Sci., 8, 725–746, https://doi.org/10.5194/wes-8-725-2023, https://doi.org/10.5194/wes-8-725-2023, 2023
Short summary
Short summary
Mobile offshore wind energy systems are a potential way of producing green fuels from the untapped wind resource that lies far offshore. Herein, computational models of two such systems were developed and verified. The models are able to predict the power output of each system based on wind condition inputs. Results show that both systems have merits and that, contrary to existing results, unmoored floating wind turbines may produce as much power as fixed ones, given the right conditions.
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...
Altmetrics
Final-revised paper
Preprint