Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2181-2022
© Author(s) 2022. 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-7-2181-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2022
Research article |
| 03 Nov 2022
| 03 Nov 2022
FarmConners market showcase results: wind farm flow control considering electricity prices
Konstanze Kölle
CORRESPONDING AUTHOR
SINTEF Energy Research, Trondheim, Norway
Tuhfe Göçmen
DTU Wind and Energy Systems, Technical University of Denmark, Roskilde/Lyngby, Denmark
Irene Eguinoa
Wind Energy Department, Centro Nacional de Energías Renovables (CENER), Sarriguren, Spain
Leonardo Andrés Alcayaga Román
DTU Wind and Energy Systems, Technical University of Denmark, Roskilde/Lyngby, Denmark
Maria Aparicio-Sanchez
Wind Energy Department, Centro Nacional de Energías Renovables (CENER), Sarriguren, Spain
Ju Feng
DTU Wind and Energy Systems, Technical University of Denmark, Roskilde/Lyngby, Denmark
Johan Meyers
Mechanical Engineering, KU Leuven, Leuven, Belgium
Vasilis Pettas
Stuttgart Wind Energy (SWE), University of Stuttgart, Stuttgart, Germany
Ishaan Sood
Mechanical Engineering, KU Leuven, Leuven, Belgium
Related authors
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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
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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.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
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Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Théo Delvaux and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-110, https://doi.org/10.5194/wes-2024-110, 2024
Revised manuscript under review for WES
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The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity wind farm simulations (LES) for a wide variety of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that the optimal wind farm operating point is reached at lower regimes. Therefore, we proposed three simple approaches with which thrust significantly decreases with only limited impact on power.
Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 1811–1826, https://doi.org/10.5194/wes-9-1811-2024, https://doi.org/10.5194/wes-9-1811-2024, 2024
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Leading-edge erosion (LEE) can impact wind turbine aerodynamics and wind farm efficiency. This study couples LEE prediction, aerodynamic loss modeling, and wind farm flow modeling to show that LEE's effects on wake dynamics can affect overall energy production. Without preventive initiatives, the effects of LEE increase over time, resulting in significant annual energy production (AEP) loss.
Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-137, https://doi.org/10.5194/gmd-2024-137, 2024
Revised manuscript under review for GMD
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known `anomalous’ event.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
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In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-96, https://doi.org/10.5194/wes-2024-96, 2024
Preprint under review for WES
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This research develops a new method for assessing Hybrid Power Plants (HPPs) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art Energy Management System (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-79, https://doi.org/10.5194/wes-2024-79, 2024
Revised manuscript accepted for WES
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Traditionally, the aerodynamic loss of wind farm efficiency is classified into ‘wake loss’ and ‘farm blockage loss’. This study, using high-fidelity simulations, shows neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called ’turbine-scale efficiency’ and ‘farm-scale efficiency’ to better describe turbine-wake effects and farm-atmosphere interactions. This study suggests the importance of better modelling ‘farm-scale loss’ in future studies.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
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Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
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Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
Jaime Liew, Tuhfe Göçmen, Alan W. H. Lio, and Gunner Chr. Larsen
Wind Energ. Sci., 8, 1387–1402, https://doi.org/10.5194/wes-8-1387-2023, https://doi.org/10.5194/wes-8-1387-2023, 2023
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We present recent research on dynamically modelling wind farm wakes and integrating these enhancements into the wind farm simulator, HAWC2Farm. The simulation methodology is showcased by recreating dynamic scenarios observed in the Lillgrund offshore wind farm. We successfully recreate scenarios with turning winds, turbine shutdown events, and wake deflection events. The research provides opportunities to better identify wake interactions in wind farms, allowing for more reliable designs.
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
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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.
Iñaki Sandua-Fernández, Felipe Vittori, Raquel Martín-San-Román, Irene Eguinoa, and José Azcona-Armendáriz
Wind Energ. Sci., 8, 277–288, https://doi.org/10.5194/wes-8-277-2023, https://doi.org/10.5194/wes-8-277-2023, 2023
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This work analyses in detail the causes of the yaw drift in floating offshore wind turbines with a single-point-mooring system induced by an upwind wind turbine. The ability of an individual pitch control strategy based on yaw misalignment is demonstrated through simulations using the NREL 5 MW wind turbine mounted on a single-point-mooring version of the DeepCwind OC4 floating platform. This effect is considered to be relevant for all single-point-moored concepts.
Camilla Marie Nyborg, Andreas Fischer, Pierre-Elouan Réthoré, and Ju Feng
Wind Energ. Sci., 8, 255–276, https://doi.org/10.5194/wes-8-255-2023, https://doi.org/10.5194/wes-8-255-2023, 2023
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Our article presents a way of optimizing the wind farm operation by keeping the emitted noise level below a defined limit while maximizing the power output. This is done by switching between noise reducing operational modes. The method has been developed by using two different noise models, one more advanced than the other, to study the advantages of each model. Furthermore, the optimization method is applied to different wind farm cases.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
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This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
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In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Felipe Vittori, José Azcona, Irene Eguinoa, Oscar Pires, Alberto Rodríguez, Álex Morató, Carlos Garrido, and Cian Desmond
Wind Energ. Sci., 7, 2149–2161, https://doi.org/10.5194/wes-7-2149-2022, https://doi.org/10.5194/wes-7-2149-2022, 2022
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This paper describes the results of a wave tank test campaign of a scaled SATH 10 MW INNWIND floating platform. The software-in-the-loop (SiL) hybrid method was used to include the wind turbine thrust and the in-plane rotor moments. Experimental results are compared with a numerical model developed in OpenFAST of the floating wind turbine. The results are discussed, identifying limitations of the numerical models and obtaining conclusions on how to improve them.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
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The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
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Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
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In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
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
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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
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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.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
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We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
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This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio
Wind Energ. Sci., 6, 111–129, https://doi.org/10.5194/wes-6-111-2021, https://doi.org/10.5194/wes-6-111-2021, 2021
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Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
Leonardo Alcayaga
Atmos. Meas. Tech., 13, 6237–6254, https://doi.org/10.5194/amt-13-6237-2020, https://doi.org/10.5194/amt-13-6237-2020, 2020
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Wind lidars present advantages over meteorological masts, including simultaneous multipoint observations, flexibility in measuring geometry, and reduced installation cost. But wind lidars come with the cost of increased complexity in terms of data quality and analysis. The common carrier-to-noise ratio and median filters are compared to the DBSCAN clustering algorithm to find improved data quality and recovery rate.
Paul Hulsman, Søren Juhl Andersen, and Tuhfe Göçmen
Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, https://doi.org/10.5194/wes-5-309-2020, 2020
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We aim to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion, are built using high-fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°.
Thomas Duc, Olivier Coupiac, Nicolas Girard, Gregor Giebel, and Tuhfe Göçmen
Wind Energ. Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, https://doi.org/10.5194/wes-4-287-2019, 2019
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Wind turbine wake recovery is very sensitive to ambient atmospheric conditions. This paper presents a way of including a local turbulence intensity estimation from SCADA into the Jensen wake model to improve its accuracy. This new model procedure is used to optimize power production of an operating wind farm and shows that some gains can be expected even if uncertainties remain high. These optimized settings are to be implemented in a field test campaign in the scope of the SMARTEOLE project.
Wim Munters and Johan Meyers
Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, https://doi.org/10.5194/wes-3-409-2018, 2018
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Wake interactions in wind farms result in power losses for downstream turbines. We aim to mitigate these losses through coordinated control of the induced slowdown of the wind by each turbine. We further analyze results from earlier work towards the utilization of such control strategies in practice. Coherent vortex shedding is identified and mimicked by a sinusoidal control. The latter is shown to increase power in downstream turbines and is robust to turbine spacing and turbulence intensity.
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
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Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Carl R. Shapiro, Johan Meyers, Charles Meneveau, and Dennice F. Gayme
Wind Energ. Sci., 3, 11–24, https://doi.org/10.5194/wes-3-11-2018, https://doi.org/10.5194/wes-3-11-2018, 2018
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We investigate the capability of wind farms to track a power reference signal to help ensure reliable power grid operations. The wind farm controller is based on a simple dynamic wind farm model and tested using high-fidelity simulations. We find that the dynamic nature of the wind farm model is vital for tracking the power signal, and the controlled wind farm would pass industry performance tests in most cases.
Maria Aparicio-Sanchez, Alvaro Gonzalez-Salcedp, Sugoi Gomez-Iradi, and Xabier Munduate
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2016-50, https://doi.org/10.5194/wes-2016-50, 2016
Preprint withdrawn
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The use of the flaps is one of the options to reduce the equivalent loads or to increase the power in large wind turbines. This paper presents a methodology to simulate the performance of the blades with these aerodynamic devices. This method presents important advantages with respect to CFD in terms of computational effort and leads to similar results.
Vahid S. Bokharaie, Pieter Bauweraerts, and Johan Meyers
Wind Energ. Sci., 1, 311–325, https://doi.org/10.5194/wes-1-311-2016, https://doi.org/10.5194/wes-1-311-2016, 2016
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Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. We propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the Jensen model.
Related subject area
Thematic area: Dynamics and control | Topic: Wind farm control
Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant
Evaluating the potential of a wake steering co-design for wind farm layout optimization through a tailored genetic algorithm
On the importance of wind predictions in wake steering optimization
On the power and control of a misaligned rotor – beyond the cosine law
Dynamic wind farm flow control using free-vortex wake models
The value of wake steering wind farm flow control in US energy markets
Load assessment of a wind farm considering negative and positive yaw misalignment for wake steering
Towards real-time optimal control of wind farms using large-eddy simulations
Sensitivity analysis of wake steering optimisation for wind farm power maximisation
The dynamic coupling between the pulse wake mixing strategy and floating wind turbines
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
Wind farm flow control: prospects and challenges
Large-eddy simulation of a wind-turbine array subjected to active yaw control
The revised FLORIDyn model: implementation of heterogeneous flow and the Gaussian wake
Multifidelity multiobjective optimization for wake-steering strategies
Evaluation of different power tracking operating strategies considering turbine loading and power dynamics
A physically interpretable data-driven surrogate model for wake steering
Experimental analysis of the effect of dynamic induction control on a wind turbine wake
Peter Bachant, Peter Ireland, Brian Burrows, Chi Qiao, James Duncan, Danian Zheng, and Mohit Dua
Wind Energ. Sci., 9, 2235–2259, https://doi.org/10.5194/wes-9-2235-2024, https://doi.org/10.5194/wes-9-2235-2024, 2024
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Intentional misalignment of upstream turbines in wind plants in order to steer wakes away from downstream turbines has been a topic of research interest for years but has not yet achieved widespread commercial adoption. We deploy one such wake steering system to a utility-scale wind plant and then create a model to predict plant behavior and enable successful control. We apply calibrations to a physics-based model and use machine learning to correct its outputs to improve predictive capability.
Matteo Baricchio, Pieter M. O. Gebraad, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 2113–2132, https://doi.org/10.5194/wes-9-2113-2024, https://doi.org/10.5194/wes-9-2113-2024, 2024
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Wake steering can be integrated into wind farm layout optimization through a co-design approach. This study estimates the potential of this method for a wide range of realistic conditions, adopting a tailored genetic algorithm and novel geometric yaw relations. A gain in the annual energy yield between 0.3 % and 0.4 % is obtained for a 16-tubrine farm, and a multi-objective implementation is used to limit loss in the case that wake steering is not used during farm operation.
Elie Kadoche, Pascal Bianchi, Florence Carton, Philippe Ciblat, and Damien Ernst
Wind Energ. Sci., 9, 1577–1594, https://doi.org/10.5194/wes-9-1577-2024, https://doi.org/10.5194/wes-9-1577-2024, 2024
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This work proposes a new wind farm controller based on wind predictions and conducts a synthetic sensitivity analysis of wake steering and the variations of the wind direction. For wind turbines that can rotate from −15 to 15° every 10 min, if the wind direction changes by more than 7.34° every 10 min, it is important to consider future wind data in a steady-state yaw control optimization.
Simone Tamaro, Filippo Campagnolo, and Carlo L. Bottasso
Wind Energ. Sci., 9, 1547–1575, https://doi.org/10.5194/wes-9-1547-2024, https://doi.org/10.5194/wes-9-1547-2024, 2024
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We develop a new simple model to predict power losses incurred by a wind turbine when it yaws out of the wind. The model reveals the effects of a number of rotor design parameters and how the turbine is governed when it yaws. The model exhibits an excellent agreement with large eddy simulations and wind tunnel measurements. We showcase the capabilities of the model by deriving the power-optimal yaw strategy for a single turbine and for a cluster of wake-interacting turbines.
Maarten J. van den Broek, Marcus Becker, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 721–740, https://doi.org/10.5194/wes-9-721-2024, https://doi.org/10.5194/wes-9-721-2024, 2024
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Wind turbine wakes negatively affect wind farm performance as they impinge on downstream rotors. Wake steering reduces these losses by redirecting wakes using yaw misalignment of the upstream rotor. We develop a novel control strategy based on model predictions to implement wake steering under time-varying conditions. The controller is tested in a high-fidelity simulation environment and improves wind farm power output compared to a state-of-the-art reference controller.
Eric Simley, Dev Millstein, Seongeun Jeong, and Paul Fleming
Wind Energ. Sci., 9, 219–234, https://doi.org/10.5194/wes-9-219-2024, https://doi.org/10.5194/wes-9-219-2024, 2024
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Wake steering is a wind farm control technology in which turbines are misaligned with the wind to deflect their wakes away from downstream turbines, increasing total power production. In this paper, we use a wind farm control model and historical electricity prices to assess the potential increase in market value from wake steering for 15 US wind plants. For most plants, we find that the relative increase in revenue from wake steering exceeds the relative increase in energy production.
Regis Thedin, Garrett Barter, Jason Jonkman, Rafael Mudafort, Christopher J. Bay, Kelsey Shaler, and Jasper Kreeft
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-6, https://doi.org/10.5194/wes-2024-6, 2024
Revised manuscript accepted for WES
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This work investigates asymmetries in terms of power performance and fatigue loading on a 5-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintenance good power gains and show that partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
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Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
Filippo Gori, Sylvain Laizet, and Andrew Wynn
Wind Energ. Sci., 8, 1425–1451, https://doi.org/10.5194/wes-8-1425-2023, https://doi.org/10.5194/wes-8-1425-2023, 2023
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Wake steering is a promising strategy to increase the power output of modern wind farms by mitigating the negative effects of aerodynamic interaction among turbines. As farm layouts grow in size to meet renewable targets, the complexity of wake steering optimisation increases too. With the objective of enabling robust and predictable wake steering solutions, this study investigates the sensitivity of wake steering optimisation for three different farm layouts with increasing complexity levels.
Daniel van den Berg, Delphine de Tavernier, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 849–864, https://doi.org/10.5194/wes-8-849-2023, https://doi.org/10.5194/wes-8-849-2023, 2023
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Wind turbines placed in farms interact with their wake, lowering the power production of the wind farm. This can be mitigated using so-called wake mixing techniques. This work investigates the coupling between the pulse wake mixing technique and the motion of floating wind turbines using the pulse. Frequency response experiments and time domain simulations show that extra movement is undesired and that the
optimalexcitation frequency is heavily platform dependent.
Balthazar Arnoldus Maria Sengers, Gerald Steinfeld, Paul Hulsman, and Martin Kühn
Wind Energ. Sci., 8, 747–770, https://doi.org/10.5194/wes-8-747-2023, https://doi.org/10.5194/wes-8-747-2023, 2023
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The optimal misalignment angles for wake steering are determined using wake models. Although mostly analytical, data-driven models have recently shown promising results. This study validates a previously proposed data-driven model with results from a field experiment using lidar measurements. In a comparison with a state-of-the-art analytical model, it shows systematically more accurate estimates of the available power. Also when using only commonly available input data, it gives good results.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
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We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Mou Lin and Fernando Porté-Agel
Wind Energ. Sci., 7, 2215–2230, https://doi.org/10.5194/wes-7-2215-2022, https://doi.org/10.5194/wes-7-2215-2022, 2022
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Large-eddy simulation (LES) is a widely used method to study wind turbine flow. To save computational resources, the turbine-inducing forces in LES are often modelled by parametrisations. We validate three widely used turbine parametrisations in LES in different yaw and offset configurations with wind tunnel measurements, and we find that, in comparison with other parametrisations, the blade element actuator disk model strikes a good balance of accuracy and computational cost.
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
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In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Julian Quick, Ryan N. King, Garrett Barter, and Peter E. Hamlington
Wind Energ. Sci., 7, 1941–1955, https://doi.org/10.5194/wes-7-1941-2022, https://doi.org/10.5194/wes-7-1941-2022, 2022
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Wake steering is an emerging wind power plant control strategy where upstream turbines are intentionally yawed out of alignment with the incoming wind, thereby steering wakes away from downstream turbines. Trade-offs between the gains in power production and fatigue loads induced by this control strategy are the subject of continuing investigation. In this study, we present an optimization approach for efficiently exploring the trade-offs between power and loading during wake steering.
Florian Pöschke and Horst Schulte
Wind Energ. Sci., 7, 1593–1604, https://doi.org/10.5194/wes-7-1593-2022, https://doi.org/10.5194/wes-7-1593-2022, 2022
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The paper compares two different strategies for wind turbine control when following a power command. A model-based control scheme for a 5 MW wind turbine is designed, and a comparison in terms of the mechanical loading and the attainable power dynamics is drawn based on simulation studies. Reduced-order models suitable for integration into an upper-level control design are discussed. The dependence of the turbine behavior on the chosen strategy is illustrated and analyzed.
Balthazar Arnoldus Maria Sengers, Matthias Zech, Pim Jacobs, Gerald Steinfeld, and Martin Kühn
Wind Energ. Sci., 7, 1455–1470, https://doi.org/10.5194/wes-7-1455-2022, https://doi.org/10.5194/wes-7-1455-2022, 2022
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Wake steering aims to redirect the wake away from a downstream turbine. This study explores the potential of a data-driven surrogate model whose equations can be interpreted physically. It estimates wake characteristics from measurable input variables by utilizing a simple linear model. The model shows encouraging results in estimating available power in the far wake, with significant improvements over currently used analytical models in conditions where wake steering is deemed most effective.
Daan van der Hoek, Joeri Frederik, Ming Huang, Fulvio Scarano, Carlos Simao Ferreira, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 1305–1320, https://doi.org/10.5194/wes-7-1305-2022, https://doi.org/10.5194/wes-7-1305-2022, 2022
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The paper presents a wind tunnel experiment where dynamic induction control was implemented on a small-scale turbine. By periodically changing the pitch angle of the blades, the low-velocity turbine wake is perturbed, and hence it recovers at a faster rate. Small particles were released in the flow and subsequently recorded with a set of high-speed cameras. This allowed us to reconstruct the flow behind the turbine and investigate the effect of dynamic induction control on the wake.
Cited articles
Andersson, L. E., Anaya-Lara, O., Tande, J. O., Merz, K. O., and Imsland, L.:
Wind farm control – Part I: A review on control system concepts and
structures, IET Renew. Power Gen., 15, 2085–2108,
https://doi.org/10.1049/rpg2.12160, 2021. a
Astrain Juangarcia, D., Eguinoa, I., and Knudsen, T.: Derating a single wind
farm turbine for reducing its wake and fatigue, J. Phys. Conf. Ser., 1037, 032039, https://doi.org/10.1088/1742-6596/1037/3/032039,
2018. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine
wakes, Renew. Energ., 70, 116–123,
https://doi.org/10.1016/j.renene.2014.01.002, 2014. a, b, c, d
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, c
Blondel, F. and Cathelain, M.: An alternative form of the super-Gaussian wind turbine wake model, Wind Energ. Sci., 5, 1225–1236, https://doi.org/10.5194/wes-5-1225-2020, 2020. a
Bossanyi, E.: Optimising yaw control at wind farm level, J. Phys. Conf. Ser., 1222, 012023, https://doi.org/10.1088/1742-6596/1222/1/012023,
2019. a
Bossanyi, E. A.: Further load reductions with individual pitch control, Wind
Energy, 8, 481–485, https://doi.org/10.1002/we.166, 2005. 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, b, c, d
Dimitrov, N., Kelly, M. C., Vignaroli, A., and Berg, J.: From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases, Wind Energ. Sci., 3, 767–790, https://doi.org/10.5194/wes-3-767-2018, 2018. a, b
Doekemeijer, B. M., van der Hoek, D., and van Wingerden, J. W.: Closed-loop
model-based wind farm control using FLORIS under time-varying inflow
conditions, Renew. Energ., 156, 719–730,
https://doi.org/10.1016/j.renene.2020.04.007, 2020. a
Eguinoa, I., Göçmen, T., Garcia-Rosa, P. B., Das, K., Petrović, V. I.,
Kölle, K., Manjock, A., Koivisto, M. J., and Smailes, M.: Wind farm flow
control oriented to electricity markets and grid integration: initial
perspective analysis, Advanced Control for Applications, 3, e80,
https://doi.org/10.1002/adc2.80, 2021. a, b, c, d
FarmConners Market Showcases Wiki: https://farmconners-market-showcase.readthedocs.io/en/latest/contact_us.html, last access: 26 October 2022. a
Fleming, P., King, J., Bay, C. J., Simley, E., Mudafort, R., Hamilton, N.,
Farrell, A., and Martinez-Tossas, L.: Overview of FLORIS updates, J. Phys. Conf. Ser., 1618, 022028,
https://doi.org/10.1088/1742-6596/1618/2/022028, 2020. a
Gambier, A.: Pitch Control of Three Bladed Large Wind Energy Converters – A
Review, Energies, 14, 8083, https://doi.org/10.3390/en14238083, 2021. a
Gebraad, P. M. O., Teeuwisse, F. W., van Wingerden, J. W., Fleming, P. A.,
Ruben, S. D., Marden, J. R., and Pao, L. Y.: Wind plant power optimization
through yaw control using a parametric model for wake effects – a CFD
simulation study, Wind Energy, 19, 95–114,
https://doi.org/10.1002/we.1822, 2016. a
Göçmen, T., Campagnolo, F., Duc, T., Eguinoa, I., Andersen, S. J., Petrović, V., Imširović, L., Braunbehrens, R., Liew, J., Baungaard, M., van der Laan, M. P., Qian, G., Aparicio-Sanchez, M., González-Lope, R., Dighe, V. V., Becker, M., van den Broek, M. J., van Wingerden, J.-W., Stock, A., Cole, M., Ruisi, R., Bossanyi, E., Requate, N., Strnad, S., Schmidt, J., Vollmer, L., Sood, I., and Meyers, J.: FarmConners wind farm flow control benchmark – Part 1: Blind test results, Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, 2022a. a
Göçmen, T., Kölle, K., and Eguinoa, I.: FarmConners Market Showcases
Repository, Zenodo [code], https://doi.org/10.5281/zenodo.6203808, 2022b. a
Houck, D. R.: Review of wake management techniques for wind turbines, Wind
Energy, 25, 195–220, https://doi.org/10.1002/we.2668, 2022. a
Howland, M. F.: Wind farm yaw control set-point optimization under model
parameter uncertainty, J. Renew. Sustain. Ener., 13,
043303, https://doi.org/10.1063/5.0051071, 2021. a
Hulsman, P., Andersen, S. J., and Göçmen, T.: Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations, Wind Energ. Sci., 5, 309–329, https://doi.org/10.5194/wes-5-309-2020, 2020. a
Jonkman, J. M. and Buhl, M. L. J.: FAST User's Guide – Updated August 2005,
Tech. Rep. 6, National Renew. Energ. Laboratory (NREL), Golden, CO,
https://doi.org/10.2172/15020796, 2005. a
Kanellas, P., Das, K., Gea-Bermudez, J., and Sørensen, P.: Balancing
Tool Chain: Balancing and automatic control in North Sea Countries in 2020,
2030 and 2050, Tech. rep.,
https://orbit.dtu.dk/en/publications/balancing-tool-chain-balancing-and-automatic-control-in-north-sea (last access: 26 October 2022),
2020. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster efficiency, in: European Wind Energy Association Conference and Exhibition, 6–8 October 1986, vol. 1, 407–410, https://orbit.dtu.dk/files/106427419/A_Simple_Model_for_Cluster_Efficiency_EWEC_86_.pdf (last access: 26 October 2022), 1986. a, b
Kheirabadi, A. C. and Nagamune, R.: A quantitative review of wind farm control
with the objective of wind farm power maximization,
J. Wind Eng. Ind. Aerod., 192, 45–73,
https://doi.org/10.1016/j.jweia.2019.06.015, 2019. a
Kretschmer, M., Pettas, V., and Cheng, P. W.: Effects of Wind Farm
Down-Regulation in the Offshore Wind Farm Alpha Ventus, ASME 2019 2nd
International Offshore Wind Technical Conference, 3–6 November 2019,
https://doi.org/10.1115/IOWTC2019-7554, 2019. a
Lanzilao, L. and Meyers, J.: A new wake‐merging method for wind‐farm power
prediction in the presence of heterogeneous background velocity fields, Wind
Energy, 25, 237–259, https://doi.org/10.1002/we.2669, 2021. a, b, c
Meyers, J., Bottasso, C., Dykes, K., Fleming, P., Gebraad, P., Giebel, G., Göçmen, T., and van Wingerden, J.-W.: Wind farm flow control: prospects and challenges, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-24, in review, 2022. a
Pedersen, M. M., van der Laan, P., Friis-Møller, M., Rinker, J., and
Réthoré, P.-E.: DTUWindEnergy/PyWake: PyWake, Zenodo [code], https://doi.org/10.5281/zenodo.2562662, 2019. a, b
Pettas, V. and Cheng, P. W.: Down-regulation and individual blade control as
lifetime extension enablers, J. Phys. Conf. Ser., 1102,
012026, https://doi.org/10.1088/1742-6596/1102/1/012026, 2018. a
Pettas, V. and Cheng, P. W.: Towards flexible wind turbine operation :
Optimization with flexible control, Wind Energy Science Conference 2021
(WESC 2021), Zenodo [data set], https://doi.org/10.5281/ZENODO.5017956, 2021. a, b
Pettas, V., Salari, M., Schlipf, D., and Cheng, P. W.: Investigation on the
potential of individual blade control for lifetime extension, J. Phys. Conf. Ser., 1037, 032006,
https://doi.org/10.1088/1742-6596/1037/3/032006, 2018. a
Quick, J., King, J., King, R. N., Hamlington, P. E., and Dykes, K.: Wake steering optimization under uncertainty, Wind Energ. Sci., 5, 413–426, https://doi.org/10.5194/wes-5-413-2020, 2020. a
Rott, A., Doekemeijer, B., Seifert, J. K., van Wingerden, J.-W., and Kühn, M.: Robust active wake control in consideration of wind direction variability and uncertainty, Wind Energ. Sci., 3, 869–882, https://doi.org/10.5194/wes-3-869-2018, 2018. a
Siemens Gamesa A/S: Siemens Gamesa Power Boost technology,
brochure;
https://www.siemensgamesa.com/products-and-services/offshore/wind-turbine-sg-14-222-dd (last access: 6 June 2022),
2020. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering controller with wind direction variability, Wind Energ. Sci., 5, 451–468, https://doi.org/10.5194/wes-5-451-2020, 2020. a
Simley, E., Fleming, P., Girard, N., Alloin, L., Godefroy, E., and Duc, T.: Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance, Wind Energ. Sci., 6, 1427–1453, https://doi.org/10.5194/wes-6-1427-2021, 2021. a, b
Sood, I. and Meyers, J.: Validation of an analytical optimization framework for
wind farm wake steering applications, AIAA SCITECH 2022 Forum, 3–7 January 2022,
https://doi.org/10.2514/6.2022-1920, 2022. a
TotalControl: Reference Wind Power Plant, Tech. Rep. TotalControl
Deliverable D1.3,
https://orbit.dtu.dk/en/publications/reference-wind-power-plant-d103 (last access: 26 October 2022),
2018. a
van Beek, M. T., Viré, A., and Andersen, S. J.: Sensitivity and Uncertainty of
the FLORIS Model Applied on the Lillgrund Wind Farm, Energies, 14, 1293
https://doi.org/10.3390/en14051293, 2021. a
van den Hoek, D. C. and Kanev, S.: Reducing Wind Turbine Loads with
Down-Regulation, Tech. Rep. September, ECN E–17-032,
http://resolver.tudelft.nl/uuid:fc6658f8-106b-4cf0-8d67-50c99cb895aa (last access: 26 October 2022),
2017. a
van Wingerden, J. W., Fleming, P. A., Göçmen, T., Eguinoa, I.,
Doekemeijer, B. M., Dykes, K., Lawson, M., Simley, E., King, J., Astrain, D.,
Iribas, M., Bottasso, C. L., Meyers, J., Raach, S., Kölle, K., and Giebel,
G.: Expert Elicitation on Wind Farm Control, J. Phys. Conf. Ser., 1618, 022025,
https://doi.org/10.1088/1742-6596/1618/2/022025, 2020. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng,
Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R.,
Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro,
A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors:
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
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The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices....
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