Articles | Volume 8, issue 8
https://doi.org/10.5194/wes-8-1235-2023
© Author(s) 2023. 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-8-1235-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Aug 2023
Research article |
| 01 Aug 2023
| 01 Aug 2023
Stochastic gradient descent for wind farm optimization
Julian Quick
CORRESPONDING AUTHOR
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Pierre-Elouan Rethore
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mads Mølgaard Pedersen
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Rafael Valotta Rodrigues
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mikkel Friis-Møller
Technical University of Denmark, Risø National Laboratory for Sustainable Energy, Frederiksborgvej 399, 4000 Roskilde, Denmark
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Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, https://doi.org/10.5194/wes-9-883-2024, 2024
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This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
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Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
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The use of wind energy has been growing over the last few decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods for analysis of small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem in a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.
Yuriy Marykovskiy, Thomas Clark, Justin Day, Marcus Wiens, Charles Henderson, Julian Quick, Imad Abdallah, Anna Maria Sempreviva, Jean-Paul Calbimonte, Eleni Chatzi, and Sarah Barber
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This paper delves into the crucial task of transforming raw data into actionable knowledge which can be used by advanced artificial intelligence systems – a challenge that spans various domains, industries, and scientific fields amid their digital transformation journey. This article underscores the significance of cross-industry collaboration and learning, drawing insights from sectors leading in digitalisation, and provides strategic guidance for further development in this area.
Juan Pablo Murcia Leon, Hajar Habbou, Mikkel Friis-Møller, Megha Gupta, Rujie Zhu, and Kaushik Das
Wind Energ. Sci., 9, 759–776, https://doi.org/10.5194/wes-9-759-2024, https://doi.org/10.5194/wes-9-759-2024, 2024
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A methodology for an early design of hybrid power plants (wind, solar, PV, and Li-ion battery storage) consisting of a nested optimization that sizes the components and internal operation optimization. Traditional designs that minimize the levelized cost of energy give worse business cases and do not include storage. Optimal operation balances the increasing revenues and faster battery degradation. Battery degradation and replacement costs are needed to estimate the viability of hybrid projects.
Javier Criado Risco, Rafael Valotta Rodrigues, Mikkel Friis-Møller, Julian Quick, Mads Mølgaard Pedersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 585–600, https://doi.org/10.5194/wes-9-585-2024, https://doi.org/10.5194/wes-9-585-2024, 2024
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Wind energy developers frequently have to face some spatial restrictions at the time of designing a new wind farm due to different reasons, such as the existence of protected natural areas around the wind farm location, fishing routes, and the presence of buildings. Wind farm design has to account for these restricted areas, but sometimes this is not straightforward to achieve. We have developed a methodology that allows for different inclusion and exclusion areas in the optimization framework.
Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre-Elouan Réthoré
Wind Energ. Sci., 9, 321–341, https://doi.org/10.5194/wes-9-321-2024, https://doi.org/10.5194/wes-9-321-2024, 2024
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Jens Visbech, Tuhfe Göçmen, Özge Sinem Özçakmak, Alexander Meyer Forsting, Ásta Hannesdóttir, and Pierre-Elouan Réthoré
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-128, https://doi.org/10.5194/wes-2023-128, 2023
Revised manuscript accepted for WES
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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.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
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Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
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.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
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Mads M. Pedersen and Gunner C. Larsen
Wind Energ. Sci., 5, 1551–1566, https://doi.org/10.5194/wes-5-1551-2020, https://doi.org/10.5194/wes-5-1551-2020, 2020
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In this paper, the influence of optimal wind farm control and optimal wind farm layout is investigated in terms of power production. The capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm, Lillgrund. It shows that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.
Maarten Paul van der Laan, Søren Juhl Andersen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 4, 645–651, https://doi.org/10.5194/wes-4-645-2019, https://doi.org/10.5194/wes-4-645-2019, 2019
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Wind farm layouts are designed by simple engineering wake models, which are fast to compute but also include a high uncertainty. Higher-fidelity models, such as Reynolds-averaged Navier–Stokes, can be used to verify optimized wind farm layouts, although the computational costs are high due to the large number of cases that are needed to calculate the annual energy production. This article presents a new wind turbine control method to speed up the high-fidelity simulations by a factor of 2–3.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Gunner Christian Larsen
Wind Energ. Sci., 4, 303–323, https://doi.org/10.5194/wes-4-303-2019, https://doi.org/10.5194/wes-4-303-2019, 2019
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In this paper, detailed inflow information extracted from measurements is used to improve the accuracy of simulated wind turbine fatigue loads. Inflow information from nearby met masts is utilised as well as information from a blade-mounted flow sensor in combination with a method to compensate for the disturbance to the flow caused by the presence of the wind turbine.
Mads Mølgaard Pedersen, Torben Juul Larsen, Helge Aagaard Madsen, and Søren Juhl Andersen
Wind Energ. Sci., 3, 121–138, https://doi.org/10.5194/wes-3-121-2018, https://doi.org/10.5194/wes-3-121-2018, 2018
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Mads M. Pedersen, Torben J. Larsen, Helge Aa. Madsen, and Gunner Chr. Larsen
Wind Energ. Sci., 2, 547–567, https://doi.org/10.5194/wes-2-547-2017, https://doi.org/10.5194/wes-2-547-2017, 2017
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Dalibor Cavar, Pierre-Elouan Réthoré, Andreas Bechmann, Niels N. Sørensen, Benjamin Martinez, Frederik Zahle, Jacob Berg, and Mark C. Kelly
Wind Energ. Sci., 1, 55–70, https://doi.org/10.5194/wes-1-55-2016, https://doi.org/10.5194/wes-1-55-2016, 2016
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Feasibility of a freely available CFD tool, OpenFOAM, in calculating flows of general relevance to the wind industry is investigated by comparing several aspects of its performance to a well-established in-house EllipSys3D solver. The comparison is focused on CFD solver demands regarding grid generation process and computational time.
The quality and accuracy of the achieved results are investigated by conducting the computations using identical/similar solver parameters and numerical setups..
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Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
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Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
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This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
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We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
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This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
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Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
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In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
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Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
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Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
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Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
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Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
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Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
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The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
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Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
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Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
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This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Cassia Maria Leme Beu and Eduardo Landulfo
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-104, https://doi.org/10.5194/wes-2023-104, 2023
Revised manuscript accepted for WES
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Extrapolating the wind profile for complex terrain through the Long Short-Term Memory outperformed the traditional Power Law methodology, which due to its universal nature cannot capture local features, as the machine learning methodology does. Moreover, considering the importance of investigating the wind potential and the needs for alternative energy sources, it is motivating to find out that a short observational campaign can produce better results than the traditional techniques.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
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This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
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The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
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The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
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.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
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A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
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Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
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Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
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We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
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The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
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We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
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Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
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Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
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Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
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Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
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This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
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.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
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A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Yulong Ma, Cristina L. Archer, and Ahmadreza Vasel-Be-Hagh
Wind Energ. Sci., 7, 2407–2431, https://doi.org/10.5194/wes-7-2407-2022, https://doi.org/10.5194/wes-7-2407-2022, 2022
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Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
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We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Turið Poulsen, Bárður A. Niclasen, Gregor Giebel, and Hans Georg Beyer
Wind Energ. Sci., 7, 2335–2350, https://doi.org/10.5194/wes-7-2335-2022, https://doi.org/10.5194/wes-7-2335-2022, 2022
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Wind power is cheap and environmentally friendly, but it has a disadvantage: it is a variable power source. Because wind is not blowing everywhere simultaneously, optimal placement of wind farms can reduce the fluctuations.
This is explored for a small isolated area. Combining wind farms reduces wind power fluctuations for timescales up to 1–2 d. By optimally placing four wind farms, the hourly fluctuations are reduced by 15 %. These wind farms are located distant from each other.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
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This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Xiaodong Zhang and Anand Natarajan
Wind Energ. Sci., 7, 2135–2148, https://doi.org/10.5194/wes-7-2135-2022, https://doi.org/10.5194/wes-7-2135-2022, 2022
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Joint probability distribution of 10 min mean wind speed and the standard deviation is proposed using the Gaussian mixture model and has been shown to agree well with 15 years of measurements. The environmental contour with a 50-year return period (extreme turbulence) is estimated. The results from the model could be taken as inputs for structural reliability analysis and uncertainty quantification of wind turbine design loads.
Alex Rybchuk, Timothy W. Juliano, Julie K. Lundquist, David Rosencrans, Nicola Bodini, and Mike Optis
Wind Energ. Sci., 7, 2085–2098, https://doi.org/10.5194/wes-7-2085-2022, https://doi.org/10.5194/wes-7-2085-2022, 2022
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
Lindsay M. Sheridan, Raghu Krishnamurthy, Gabriel García Medina, Brian J. Gaudet, William I. Gustafson Jr., Alicia M. Mahon, William J. Shaw, Rob K. Newsom, Mikhail Pekour, and Zhaoqing Yang
Wind Energ. Sci., 7, 2059–2084, https://doi.org/10.5194/wes-7-2059-2022, https://doi.org/10.5194/wes-7-2059-2022, 2022
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Using observations from lidar buoys, five reanalysis and analysis models that support the wind energy community are validated offshore and at rotor-level heights along the California Pacific coast. The models are found to underestimate the observed wind resource. Occasions of large model error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production, and mischaracterization of the diurnal wind speed cycle in summer months.
Sebastian Brune and Jan D. Keller
Wind Energ. Sci., 7, 1905–1918, https://doi.org/10.5194/wes-7-1905-2022, https://doi.org/10.5194/wes-7-1905-2022, 2022
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A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.
Rieska Mawarni Putri, Etienne Cheynet, Charlotte Obhrai, and Jasna Bogunovic Jakobsen
Wind Energ. Sci., 7, 1693–1710, https://doi.org/10.5194/wes-7-1693-2022, https://doi.org/10.5194/wes-7-1693-2022, 2022
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As offshore wind turbines' sizes are increasing, thorough knowledge of wind characteristics in the marine atmospheric boundary layer (MABL) is becoming crucial to help improve offshore wind turbine design and reliability. The present study discusses the wind characteristics at the first offshore wind farm, Vindeby, and compares them with the wind measurements at the FINO1 platform. Consistent wind characteristics are found between Vindeby measurements and the FINO1 measurements.
Pradip Zamre and Thorsten Lutz
Wind Energ. Sci., 7, 1661–1677, https://doi.org/10.5194/wes-7-1661-2022, https://doi.org/10.5194/wes-7-1661-2022, 2022
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To get more insight into the influence of the urban-terrain flow on the performance of the rooftop-mounted two-bladed Darrieus vertical-axis wind turbine, scale resolving simulations are performed for a generic wind turbine in realistic terrain under turbulent conditions. It is found that the turbulence and skewed nature of the flow near rooftop locations have a positive impact on the performance of the wind turbine.
Eduardo Weide Luiz and Stephanie Fiedler
Wind Energ. Sci., 7, 1575–1591, https://doi.org/10.5194/wes-7-1575-2022, https://doi.org/10.5194/wes-7-1575-2022, 2022
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This work analyses a meteorological event, called nocturnal low-level jets (NLLJs), defined as high wind speeds relatively close to the surface. There were positive and negative impacts from NLLJs. While NLLJs increased the mean power production, they also increased the variability in the wind with height. Our results imply that long NLLJ events are also larger, affecting many wind turbines at the same time. Short NLLJ events are more local, having stronger effects on power variability.
Patrick Letzgus, Giorgia Guma, and Thorsten Lutz
Wind Energ. Sci., 7, 1551–1573, https://doi.org/10.5194/wes-7-1551-2022, https://doi.org/10.5194/wes-7-1551-2022, 2022
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The research article presents the results of a study of highly resolved numerical simulations of a wind energy test site in complex terrain that is currently under construction in the Swabian Alps in southern Germany. The numerical results emphasised the importance of considering orography, vegetation, and thermal stratification in numerical simulations to resolve the wind field decently. In this way, the effects on loads, power, and wake of the wind turbine can also be predicted well.
Niels Troldborg, Søren J. Andersen, Emily L. Hodgson, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 1527–1532, https://doi.org/10.5194/wes-7-1527-2022, https://doi.org/10.5194/wes-7-1527-2022, 2022
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This article shows that the power performance of a wind turbine may be very different in flat and complex terrain. This is an important finding because it shows that the power output of a given wind turbine is governed by not only the available wind at the position of the turbine but also how the ambient flow develops in the region behind the turbine.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
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In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to annual energy production (AEP) estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. It is therefore vital to consider overall AEP when evaluating simulation accuracies.
Louis de Montera, Henrick Berger, Romain Husson, Pascal Appelghem, Laurent Guerlou, and Mauricio Fragoso
Wind Energ. Sci., 7, 1441–1453, https://doi.org/10.5194/wes-7-1441-2022, https://doi.org/10.5194/wes-7-1441-2022, 2022
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A novel method for estimating offshore wind resources at turbine hub height with synthetic aperture radar (SAR) satellites is presented. The machine learning algorithm uses as input geometrical parameters of the SAR sensors and parameters related to atmospheric stability. It is trained with Doppler wind lidar vertical profiles. The extractable wind power accuracy up to 200 m is within 3 %, and SAR can resolve the coastal wind gradient, unlike the Weather Research and Forecasting numerical mode.
Florian Wenz, Judith Langner, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 7, 1321–1340, https://doi.org/10.5194/wes-7-1321-2022, https://doi.org/10.5194/wes-7-1321-2022, 2022
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To get a better understanding of the influence of the terrain flow on the unsteady pressure distributions on the wind turbine surface, a fully resolved turbine was simulated in the complex terrain of Perdigão, Portugal. It was found that the pressure fluctuations at the tower caused by vortex shedding are significantly hampered by the terrain flow, while the pressure fluctuations caused by the blade–tower interaction are hardly changed.
Rad Haghi and Curran Crawford
Wind Energ. Sci., 7, 1289–1304, https://doi.org/10.5194/wes-7-1289-2022, https://doi.org/10.5194/wes-7-1289-2022, 2022
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Based on the IEC standards, a limited number of simulations is sufficient to calculate the extreme and fatigue loads on a wind turbine. However, this means inaccuracy in the output statistics. This paper aims to build a surrogate model on blade element momentum aerodynamic model simulation output employing non-intrusive polynomial chaos expansion. The surrogate model is then used in a large number of Monte Carlo simulations to provide an accurate statistical estimate of the loads.
Cited articles
Alibrahim, H. and Ludwig, S. A.: Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization, in: 2021 IEEE Congress on Evolutionary Computation (CEC), 28 June–1 July 2021, Kraków, Poland, 1551–1559, https://doi.org/10.1109/CEC45853.2021.9504761, 2021. a
Allen, J., King, R., and Barter, G.: Wind farm simulation and layout
optimization in complex terrain, J. Phys.: Conf. Ser., 1452, 012066, https://doi.org/10.1088/1742-6596/1452/1/012066, 2020. a, b
Annoni, J., Fleming, P., Scholbrock, A., Roadman, J., Dana, S., Adcock, C.,
Porte-Agel, F., Raach, S., Haizmann, F., and Schlipf, D.: Analysis of
control-oriented wake modeling tools using lidar field results, Wind Energ.
Sci., 3, 819–831, https://doi.org/10.5194/wes-3-819-2018, 2018. a
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, 7–11 January 2019, San Diego, California, USA, p. 0540, https://doi.org/10.2514/6.2019-0540, 2019. a
Bastankhah, M. and Porté-Agel, F.: A new analytical model for wind-turbine wakes, Renew. Energy, 70, 116–123, https://doi.org/10.1016/j.renene.2014.01.002, 2014. a
Byrd, R. H., Hansen, S. L., Nocedal, J., and Singer, Y.: A stochastic
quasi-Newton method for large-scale optimization, SIAM J. Optimiz., 26, 1008–1031, https://doi.org/10.1137/140954362, 2016. a
Ciavarra, A. W., Rodrigues, R. V., Dykes, K., and Réthoré, P.-E.: Wind farm optimization with multiple hub heights using gradient-based methods, J. Phys.: Conf. Ser., 2265, 022012, https://doi.org/10.1088/1742-6596/2265/2/022012, 2022. a, b, c
Clark, C. E., Barter, G., Shaler, K., and DuPont, B.: Reliability-based layout optimization in offshore wind energy systems, Wind Energy, 25, 125–148, https://doi.org/10.1002/we.2664, 2022. a
Criado Risco, J., Valotta Rodrigues, R., Friis-Møller, M., Quick, J., Mølgaard Pedersen, M., and Réthoré, P.-E.: Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-5, in review, 2023. a
Croonenbroeck, C. and Hennecke, D.: A comparison of optimizers in a unified
standard for optimization on wind farm layout optimization, Energy, 216,
119244, https://doi.org/10.1016/j.energy.2020.119244, 2021. a
De, S., Hampton, J., Maute, K., and Doostan, A.: Topology optimization under
uncertainty using a stochastic gradient-based approach, Struct. Multidiscip. Optimiz., 62, 2255–2278, https://doi.org/10.1007/s00158-020-02599-z, 2020. a
Denkowski, M. and Neubig, G.: Stronger Baselines for Trustable Results in
Neural Machine Translation, in: Proceedings of the First Workshop on Neural
Machine Translation, Association for Computational Linguistics, Vancouver, 18–27, https://doi.org/10.18653/v1/W17-3203, 2017. a
Duc, T., Coupiac, O., Girard, N., Giebel, G., and Göçmen, T.: Local
turbulence parameterization improves the Jensen wake model and its
implementation for power optimization of an operating wind farm, Wind Energ.
Sci., 4, 287–302, https://doi.org/10.5194/wes-4-287-2019, 2019. a
Feng, J. and Shen, W. Z.: Solving the wind farm layout optimization problem
using random search algorithm, Renew. Energy, 78, 182–192,
https://doi.org/10.1016/j.renene.2015.01.005, 2015. a
Fischereit, J., Schaldemose Hansen, K., Larsén, X. G., van der Laan, M. P., Réthoré, P.-E., and Murcia Leon, J. P.: Comparing and validating
intra-farm and farm-to-farm wakes across different mesoscale and
high-resolution wake models, Wind Energ. Sci., 7, 1069–1091,
https://doi.org/10.5194/wes-7-1069-2022, 2022. a
Fleming, P. A., Stanley, A. P., Bay, C. J., King, J., Simley, E., Doekemeijer, B. M., and Mudafort, R.: Serial-Refine Method for Fast Wake-Steering Yaw Optimization, J. Phys.: Conf. Ser., 2265, 032109, https://doi.org/10.1088/1742-6596/2265/3/032109, 2022. a, b
Gebraad, P., Thomas, J. J., Ning, A., Fleming, P., and Dykes, K.: Maximization of the annual energy production of wind power plants by optimization of layout and yaw-based wake control, Wind Energy, 20, 97–107,
https://doi.org/10.1002/we.1993, 2017. a, b
Göçmen, T. and Giebel, G.: Estimation of turbulence intensity using
rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms, Renew. Energy, 99, 524–532, https://doi.org/10.1016/j.renene.2016.07.038, 2016. a
Godinho, M. and Castro, R.: Comparative performance of AI methods for wind
power forecast in Portugal, Wind Energy, 24, 39–53, https://doi.org/10.1002/we.2556,
2021. a
Graf, P., Dykes, K., Scott, G., Fields, J., Lunacek, M., Quick, J., and
Rethore, P.-E.: Wind farm turbine type and placement optimization, J. Phys.: Conf. Ser., 753, 062004, https://doi.org/10.1088/1742-6596/753/6/062004, 2016. a
Guirguis, D., Romero, D. A., and Amon, C. H.: Toward efficient optimization of wind farm layouts: Utilizing exact gradient information, Appl. Energy, 179, 110–123, https://doi.org/10.1016/j.apenergy.2016.06.101, 2016. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., G'erard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. a
Hasager, C. B., Rasmussen, L., Peña, A., Jensen, L. E., and Réthoré, P.-E.: Wind farm wake: The Horns Rev photo case, Energies,
6, 696–716, https://doi.org/10.3390/en6020696, 2013. a
Herbert-Acero, J. F., Probst, O., Réthoré, P.-E., Larsen, G. C., and
Castillo-Villar, K. K.: A review of methodological approaches for the design
and optimization of wind farms, Energies, 7, 6930–7016,
https://doi.org/10.3390/en7116930, 2014. a
Howland, M. F., Ghate, A. S., Quesada, J. B., Pena Martínez, J. J., Zhong, W., Larrañaga, F. P., Lele, S. K., and Dabiri, J. O.: Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions, Wind Energ. Sci., 7, 345–365, https://doi.org/10.5194/wes-7-345-2022, 2022. a
Hussain, M. N., Shaukat, N., Ahmad, A., Abid, M., Hashmi, A., Rajabi, Z., and
Tariq, M. A. U. R.: Micro-Siting of Wind Turbines in an Optimal Wind Farm
Area Using Teaching–Learning-Based Optimization Technique, Sustainability,
14, 8846, https://doi.org/10.3390/su14148846, 2022. a
International Electrotechnical Commission: IEC 61400-12-1 Wind Turbines-Part 12-1: Power Performance Measurements of Electricity Producing Wind Turbines, IEC – International Electrotechinal Commission, Geneva, Switzerland, 1, https://webstore.iec.ch/publication/68499 (last access: 31 July 2023), 2005. a
Kervadec, H., Dolz, J., Yuan, J., Desrosiers, C., Granger, E., and Ayed, I. B.: Constrained deep networks: Lagrangian optimization via log-barrier
extensions, arXiv [preprint], arXiv:1904.04205, https://doi.org/10.48550/arXiv.1904.04205, 2019. a, b
Ketkar, N.: Stochastic gradient descent, in: Deep learning with Python, Springer, 113–132, https://doi.org/10.1007/978-1-4842-2766-4_8, 2017. a
King, R., Glaws, A., Geraci, G., and Eldred, M. S.: A probabilistic approach to estimating wind farm annual energy production with bayesian quadrature, in: AIAA Scitech 2020 Forum, 6–10 January 2020, Orlando, Florida, USA, p. 1951, https://doi.org/10.2514/6.2020-1951, 2020. a
King, R. N., Dykes, K., Graf, P., and Hamlington, P. E.: Optimization of wind
plant layouts using an adjoint approach, Wind Energ. Sci., 2, 115–131,
https://doi.org/10.5194/wes-2-115-2017, 2017. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv
[preprint], arXiv:1412.6980, https://doi.org/10.48550/arXiv.1412.6980, 2014. a
Kölle, K., Göçmen, T., Eguinoa, I., Alcayaga Román, L. A.,
Aparicio-Sanchez, M., Feng, J., Meyers, J., Pettas, V., and Sood, I.:
FarmConners market showcase results: wind farm flow control considering
electricity prices, Wind Energ. Sci., 7, 2181–2200,
https://doi.org/10.5194/wes-7-2181-2022, 2022. a
Lam, R., Poloczek, M., Frazier, P., and Willcox, K. E.: Advances in Bayesian
optimization with applications in aerospace engineering, in: 2018 AIAA
Non-Deterministic Approaches Conference, 8–12 January 2018, Kissimmee, Florida, p. 1656, https://doi.org/10.2514/6.2018-1656, 2018. a, b
Li, J. and Zhang, M.: Data-based approach for wing shape design optimization,
Aerospace Sci. Technol., 112, 106639, https://doi.org/10.1016/j.ast.2021.106639, 2021. a, b
Liu, J., Rong, Y., Takác, M., and Huang, J.: On the acceleration of l-bfgs with second-order information and stochastic batches, arXiv [preprint], arXiv:1807.05328, https://doi.org/10.48550/arXiv.1807.05328, 2018. a
Márquez-Neila, P., Salzmann, M., and Fua, P.: Imposing hard constraints on deep networks: Promises and limitations, arXiv [preprint], arXiv:1706.02025, https://doi.org/10.48550/arXiv.1706.02025, 2017. a, b
Moritz, P., Nishihara, R., and Jordan, M.: A linearly-convergent stochastic
L-BFGS algorithm, in: Artificial Intelligence and Statistics, PMLR, 249–258, https://proceedings.mlr.press/v51/moritz16.html (last access: 31 July 2023), 2016. a
Murcia, J., Réthoré, P.-E., Natarajan, A., and Sørensen, J. D.: How many model evaluations are required to predict the AEP of a wind power
plant?, J. Phys.: Conf. Ser., 625, 012030, https://doi.org/10.1088/1742-6596/625/1/012030, 2015. a
Najafabadi, M. M., Khoshgoftaar, T. M., Villanustre, F., and Holt, J.:
Large-scale distributed l-bfgs, J. Big Data, 4, 1–17, https://doi.org/10.1186/s40537-017-0084-5, 2017. a
Najd, A. H., Goksu, G., and Hammood, H. F.: Pitch angle control using neural
network in wind turbines, Mater. Sci. Eng., 928, 022118,
https://doi.org/10.1088/1757-899X/928/2/022118, 2020. a
Ning., A., Dykes., K., and Quick, J.: Systems engineering and optimization of
wind turbines and power plants, Wind Energy Modeling and Simulation – Volume 2: Turbine and System, Institution of Engineering and Technology, 235–292, https://doi.org/10.1049/pbpo125g_ch7, 2020. a
Padrón, A. S., Thomas, J., Stanley, A. P., Alonso, J. J., and Ning, A.:
Polynomial chaos to efficiently compute the annual energy production in wind
farm layout optimization, Wind Energ. Sci., 4, 211–231,
https://doi.org/10.5194/wes-4-211-2019, 2019. 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
Qian, N.: On the momentum term in gradient descent learning algorithms, Neural Networks, 12, 145–151, https://doi.org/10.1016/S0893-6080(98)00116-6, 1999. a
Quick, J.: Data Used for Article: Stochastic Gradient Descent for Wind Farm Optimization, Zenodo [data set], https://doi.org/10.5281/zenodo.8202150, 2023. 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
Riedmiller, M. and Braun, H.: A direct adaptive method for faster
backpropagation learning: The RPROP algorithm, in: IEEE international conference on neural networks, 28 March–1 April 1993, San Francisco, California, USA, 586–591, https://doi.org/10.1109/ICNN.1993.298623, 1993. a
Riva, R., Liew, J., Friis-Møller, M., Dimitrov, N., Barlas, E.,
Réthoré, P.-E., and Beržonskis, A.: Wind farm layout optimization with load constraints using surrogate modelling, J. Phys.: Conf. Ser., 1618, 042035, https://doi.org/10.1088/1742-6596/1618/4/042035, 2020. a, b, c
Rodrigues, R. V., Friis-Møller, M., Dykes, K., Pollini, N., and Jensen, M.: A surrogate model of offshore wind farm annual energy production to support financial valuation, J. Phys.: Conf. Ser., 2265, 022003, https://doi.org/10.1088/1742-6596/2265/2/022003, 2022. a
Rodrigues, R. V., Pedersen, M. M., Schøler, J. P., Quick, J., and Réthoré, P.: Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-61, in review, 2023. a
Roy, S. K. and Harandi, M.: Constrained Stochastic Gradient Descent: The Good
Practice, in: 2017 International Conference on Digital Image Computing:
Techniques and Applications (DICTA), 29 November–1 December 2017, Sydney, NSW, Australia, 1–8, https://doi.org/10.1109/DICTA.2017.8227420, 2017. a
Ruder, S.: An overview of gradient descent optimization algorithms, arXiv [preprint], https://doi.org/10.48550/ARXIV.1609.04747, 2016. a
Saint-Drenan, Y.-M., Besseau, R., Jansen, M., Staffell, I., Troccoli, A.,
Dubus, L., Schmidt, J., Gruber, K., Simões, S. G., and Heier, S.: A
parametric model for wind turbine power curves incorporating environmental
conditions, Renew. Energy, 157, 754–768, https://doi.org/10.1016/j.renene.2020.04.123, 2020. a
Samorani, M.: The wind farm layout optimization problem, Handbook of wind power systems, Springer, 21–38, https://doi.org/10.1007/978-3-642-41080-2_2, 2013. a
Sanderse, B.: Aerodynamics of wind turbine wakes, US Department of Energy
Office of Scientific and Technical Information, https://www.osti.gov/etdeweb/biblio/21162007 (last access: 31 July 2023), 2009. a
Simley, E., Millstein, D., Jeong, S., and Fleming, P.: The value of wake steering wind farm control in U.S. energy markets, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2023-12, in review, 2023. a
Sivanantham, G. and Gopalakrishnan, S.: Stochastic Gradient Descent
Optimization Model for Demand Response in a Connected Microgrid, KSII
Transactions on Internet and Information Systems (TIIS), 16, 97–115,
https://doi.org/10.3837/tiis.2022.01.006, 2022. a
Stanley, A. P., Roberts, O., King, J., and Bay, C. J.: Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant, Wind Energ. Sci., 6, 1143–1167,
https://doi.org/10.5194/wes-6-1143-2021, 2021. a
Stengel, K., Glaws, A., Hettinger, D., and King, R. N.: Adversarial
super-resolution of climatological wind and solar data, P. Natl. Acad. Sci. USA, 117, 16805–16815, https://doi.org/10.1073/pnas.1918964117, 2020. a
Technical University of Denmark: Sophia HPC Cluster,
https://doi.org/10.57940/FAFC-6M81, 2019. a
Tian, Y., Zhang, Y., and Zhang, H.: Recent Advances in Stochastic Gradient
Descent in Deep Learning, Mathematics, 11, 682, https://doi.org/10.3390/math11030682,
2023. a
van der Laan, M. P., García-Santiago, O., Kelly, M., Meyer Forsting, A., Dubreuil-Boisclair, C., Sponheim Seim, K., Imberger, M., Peña, A., Sørensen, N. N., and Réthoréé, P.-E.: A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling, Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, 2023. a
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. Meth., 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wu, N., Kenway, G., Mader, C. A., Jasa, J., and Martins, J. R.: pyOptSparse: A Python framework for large-scale constrained nonlinear optimization of sparse systems, J. Open Sour. Softw., 5, 2564, https://doi.org/10.21105/joss.02564,
2020. a, b, c
You, K., Long, M., Wang, J., and Jordan, M. I.: How does learning rate decay
help modern neural networks?, arXiv [preprint], arXiv:1908.01878,
https://doi.org/10.48550/arXiv.1908.01878, 2019. a
Zhang, C., Kramer, S. C., Angeloudis, A., Zhang, J., Lin, X., and Piggott,
M. D.: Improving tidal turbine array performance through the optimisation of
layout and yaw angles, Int. Mar. Energ. J., 5, 273–280,
https://doi.org/10.36688/imej.5.273-280, 2022. a
Zhang, J. and Zhao, X.: Wind farm wake modeling based on deep convolutional
conditional generative adversarial network, Energy, 238, 121747,
https://doi.org/10.1016/j.energy.2021.121747, 2022.
a
Zhang, Z., Santoni, C., Herges, T., Sotiropoulos, F., and Khosronejad, A.:
Time-averaged wind turbine wake flow field prediction using autoencoder
convolutional neural networks, Energies, 15, 41, https://doi.org/10.3390/en15010041, 2021. a
Zilong, T. and Wei, D. X.: Layout optimization of offshore wind farm
considering spatially inhomogeneous wave loads, Appl. Energy, 306, 117947, https://doi.org/10.1016/j.apenergy.2021.117947, 2022. a
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Wind turbine positions are often optimized to avoid wake losses. These losses depend on...
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