Articles | Volume 10, issue 6
https://doi.org/10.5194/wes-10-1167-2025
© Author(s) 2025. 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-10-1167-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Jul 2025
Research article |
| 01 Jul 2025
| 01 Jul 2025
Investigating the relationship between simulation parameters and flow variables in simulating atmospheric gravity waves for wind energy applications
Delft University of Technology, Department of Flow Physics and Technology, Faculty of Aerospace Engineering, Delft, the Netherlands
Dries Allaerts
Delft University of Technology, Department of Flow Physics and Technology, Faculty of Aerospace Engineering, Delft, the Netherlands
deceased, 10 October 2024
Simon J. Watson
Delft University of Technology, Department of Flow Physics and Technology, Faculty of Aerospace Engineering, Delft, the Netherlands
Matthew J. Churchfield
National Renewable Energy Laboratory, Golden, CO, USA
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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.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
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We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Andreas Knauer, Lutz Mütschard, Matt Churchfield, and Senu Sirnivas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-86, https://doi.org/10.5194/wes-2025-86, 2025
Manuscript not accepted for further review
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Wake losses can reach up to 20 % of the power production in an offshore wind park. In simulations with the DTU 10-MW turbine the rotor is operated at slight off-design rotor speeds to manipulate wake turbulence development. An increased rotor speed establishes earlier high turbulence levels and increases turbulent mixing resulting in an increased power production. For a two-turbine array, the overall power production may increase up to 13 %.
Ali Eftekhari Milani, Donatella Zappalá, Francesco Castellani, and Simon Watson
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-62, https://doi.org/10.5194/wes-2025-62, 2025
Revised manuscript under review for WES
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This paper proposes a data-driven approach to simulate wind turbine sensor time series, such as temperature and pressure signals, describing the behaviour of a wind turbine component as it degrades through time up to the failure point. It allows for the simulation of new failure events or the replication of a given failure under different conditions. The results show that the synthetic signals generated using this approach improve the performance of fault detection and prognosis methods.
Dachuan Feng and Simon Watson
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-52, https://doi.org/10.5194/wes-2025-52, 2025
Preprint under review for WES
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Weather effects drive wind turbines loads and performance to be different from those under mean atmospheric conditions. However, the influence of unsteady atmospheric phenomena on wake behavior remains unclear. This paper explores how atmospheric gravity waves—large-scale wave-like patterns caused by topographical features—affect meandering motions and turbulence generation in the wake region. The outputs of this paper can be used to guide wake modeling in realistic atmospheric flows.
Branko Kosović, Sukanta Basu, Jacob Berg, Larry K. Berg, Sue E. Haupt, Xiaoli G. Larsén, Joachim Peinke, Richard J. A. M. Stevens, Paul Veers, and Simon Watson
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-42, https://doi.org/10.5194/wes-2025-42, 2025
Preprint under review for WES
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Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.
Oriol Cayon, Simon Watson, and Roland Schmehl
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-182, https://doi.org/10.5194/wes-2024-182, 2025
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This study demonstrates how kites used to generate wind energy can act as sensors to measure wind conditions and system behaviour. By combining data from existing sensors, such as those measuring position, speed, and forces on the tether, a sensor fusion technique accurately estimates wind conditions and kite performance. This approach can be integrated into control systems to help optimise energy generation and enhance the reliability of these systems in changing wind conditions.
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.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
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We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Livia Brandetti, Sebastiaan Paul Mulders, Roberto Merino-Martinez, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 9, 471–493, https://doi.org/10.5194/wes-9-471-2024, https://doi.org/10.5194/wes-9-471-2024, 2024
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This research presents a multi-objective optimisation approach to balance vertical-axis wind turbine (VAWT) performance and noise, comparing the combined wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller with a baseline. Psychoacoustic annoyance is used as a novel metric for human perception of wind turbine noise. Results showcase the WSE–TSR tracking controller’s potential in trading off the considered objectives, thereby fostering the deployment of VAWTs in urban areas.
Livia Brandetti, Sebastiaan Paul Mulders, Yichao Liu, Simon Watson, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1553–1573, https://doi.org/10.5194/wes-8-1553-2023, https://doi.org/10.5194/wes-8-1553-2023, 2023
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This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω2. Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.
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.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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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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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.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
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This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
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Bedassa R. Cheneka, Simon J. Watson, and Sukanta Basu
Wind Energ. Sci., 5, 1731–1741, https://doi.org/10.5194/wes-5-1731-2020, https://doi.org/10.5194/wes-5-1731-2020, 2020
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Wind power ramps have important characteristics for the planning and integration of wind power production into electricity. We present a new and simple algorithm that detects wind power ramp characteristics. The algorithm classifies wind power production into ramp-ups, ramp-downs, and no-ramps; and it can detect wind power ramp characteristics that show a temporal increasing (decreasing) power capacity.
Mark Schelbergen, Peter C. Kalverla, Roland Schmehl, and Simon J. Watson
Wind Energ. Sci., 5, 1097–1120, https://doi.org/10.5194/wes-5-1097-2020, https://doi.org/10.5194/wes-5-1097-2020, 2020
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We have presented a methodology for including multiple wind profile shapes in a wind resource description that are identified using a data-driven approach. These shapes go beyond the height range for which conventional wind profile relationships are developed. Moreover, they include non-monotonic shapes such as low-level jets. We demonstrated this methodology for an on- and offshore reference location using DOWA data and efficiently estimated the annual energy production of a pumping AWE system.
Luis A. Martínez-Tossas, Jennifer Annoni, Paul A. Fleming, and Matthew J. Churchfield
Wind Energ. Sci., 4, 127–138, https://doi.org/10.5194/wes-4-127-2019, https://doi.org/10.5194/wes-4-127-2019, 2019
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A new control-oriented model is developed to compute the wake of a wind turbine under yaw. The model uses a simplified version of the Navier–Stokes equation with assumptions. Good agreement is found between the model-proposed and large eddy simulations of a wind turbine in yaw.
Jessica M. Tomaszewski, Julie K. Lundquist, Matthew J. Churchfield, and Patrick J. Moriarty
Wind Energ. Sci., 3, 833–843, https://doi.org/10.5194/wes-3-833-2018, https://doi.org/10.5194/wes-3-833-2018, 2018
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Wind energy development has increased rapidly in rural locations of the United States, areas that also serve general aviation airports. The spinning rotor of a wind turbine creates an area of increased turbulence, and we question if this turbulent air could pose rolling hazards for light aircraft flying behind turbines. We analyze high-resolution simulations of wind flowing past a turbine to quantify the rolling risk and find that wind turbines pose no significant roll hazards to light aircraft.
Jeffrey D. Mirocha, Matthew J. Churchfield, Domingo Muñoz-Esparza, Raj K. Rai, Yan Feng, Branko Kosović, Sue Ellen Haupt, Barbara Brown, Brandon L. Ennis, Caroline Draxl, Javier Sanz Rodrigo, William J. Shaw, Larry K. Berg, Patrick J. Moriarty, Rodman R. Linn, Veerabhadra R. Kotamarthi, Ramesh Balakrishnan, Joel W. Cline, Michael C. Robinson, and Shreyas Ananthan
Wind Energ. Sci., 3, 589–613, https://doi.org/10.5194/wes-3-589-2018, https://doi.org/10.5194/wes-3-589-2018, 2018
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This paper validates the use of idealized large-eddy simulations with periodic lateral boundary conditions to provide boundary-layer flow quantities of interest for wind energy applications. Sensitivities to model formulation, forcing parameter values, and grid configurations were also examined, both to ascertain the robustness of the technique and to characterize inherent uncertainties, as required for the evaluation of more general wind plant flow simulation approaches under development.
Paul Fleming, Jennifer Annoni, Matthew Churchfield, Luis A. Martinez-Tossas, Kenny Gruchalla, Michael Lawson, and Patrick Moriarty
Wind Energ. Sci., 3, 243–255, https://doi.org/10.5194/wes-3-243-2018, https://doi.org/10.5194/wes-3-243-2018, 2018
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This paper investigates the role of flow structures in wind farm control through yaw misalignment. A pair of counter-rotating vortices is shown to be important in deforming the shape of the wake. Further, we demonstrate that the vortex structures created in wake steering can enable a greater change power generation than currently modeled in control-oriented models. We propose that wind farm controllers can be made more effective if designed to take advantage of these effects.
Cian J. Desmond, Simon J. Watson, Christiane Montavon, and Jimmy Murphy
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2017-34, https://doi.org/10.5194/wes-2017-34, 2017
Revised manuscript not accepted
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The flow over densely forested terrain under neutral and non-neutral conditions is considered using commercially available Computational Fluid Dynamics software. Results are validated against data from a site in North-Eastern France. It is shown that the effects of both neutral and stable atmospheric stratifications can be modelled numerically using state of the art methodologies whilst unstable stratifications remain elusive.
Javier Sanz Rodrigo, Matthew Churchfield, and Branko Kosovic
Wind Energ. Sci., 2, 35–54, https://doi.org/10.5194/wes-2-35-2017, https://doi.org/10.5194/wes-2-35-2017, 2017
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The series of GABLS model intercomparison benchmarks is revisited in the context of wind energy atmospheric boundary layer (ABL) models. GABLS 1 and 2 are used for verification purposes. Then GABLS 3 is used to develop a methodology for using realistic mesoscale forcing for microscale ABL models. The method also uses profile nudging to dynamically reduce the bias. Different data assimilation strategies are discussed based on typical instrumentation setups of wind energy campaigns.
J. K. Lundquist, M. J. Churchfield, S. Lee, and A. Clifton
Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, https://doi.org/10.5194/amt-8-907-2015, 2015
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Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications like wind energy, but their use often relies on assuming homogeneity in the wind. Using numerical simulations of stable flow past a wind turbine, we quantify the error expected because of the inhomogeneity of the flow. Large errors (30%) in winds are found near the wind turbine, but by three rotor diameters downwind, errors in the horizontal components have decreased to 15% of the inflow.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
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Sarah McElman, Amrit Shankar Verma, and Andrew Goupee
Wind Energ. Sci., 10, 1529–1550, https://doi.org/10.5194/wes-10-1529-2025, https://doi.org/10.5194/wes-10-1529-2025, 2025
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Bernard Postema, Remco A. Verzijlbergh, Pim van Dorp, Peter Baas, and Harm J. J. Jonker
Wind Energ. Sci., 10, 1471–1484, https://doi.org/10.5194/wes-10-1471-2025, https://doi.org/10.5194/wes-10-1471-2025, 2025
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Atmospheric large-eddy simulation is a technique that simulates weather conditions in high detail and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm without having to simulate 10 years of weather and instead simulating much less (1 year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude and that wind observations can be included as well to add more insight.
Lindsay M. Sheridan, Dmitry Duplyakin, Caleb Phillips, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, and Larry K. Berg
Wind Energ. Sci., 10, 1451–1470, https://doi.org/10.5194/wes-10-1451-2025, https://doi.org/10.5194/wes-10-1451-2025, 2025
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A total of 12 months of onsite wind measurement is standard for correcting model-based long-term wind speed estimates for utility-scale wind farms; however, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities for distributed wind projects. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to long-term wind speed estimates.
Cristina Lozej Archer
Wind Energ. Sci., 10, 1433–1438, https://doi.org/10.5194/wes-10-1433-2025, https://doi.org/10.5194/wes-10-1433-2025, 2025
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Two approximate analytical expressions are derived, one for the variance of wind speed and the other for turbulence intensity, based on one simple assumption: that the turbulent fluctuations in wind are small with respect to the mean. The formulations perform well when applied to the observations from the American WAKE experimeNt (AWAKEN) field campaign conducted in 2023.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
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Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Adriel J. Carvalho, Francisco L. Albuquerque Neto, and Denisson Q. Oliveira
Wind Energ. Sci., 10, 971–986, https://doi.org/10.5194/wes-10-971-2025, https://doi.org/10.5194/wes-10-971-2025, 2025
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Wind profilers are important to the wind power industry since they capture wind velocity and direction at higher altitudes than meteorological masts. Although some studies have investigated their performance in different scenarios, this paper covers a gap in knowledge by investigating and comparing their performance under rain events. This investigation is important since the data collected support strategic decisions in the wind power industry, where high data availability in all situations is critical.
Ye Liu, Timothy W. Juliano, Raghavendra Krishnamurthy, Brian J. Gaudet, and Jungmin Lee
Wind Energ. Sci., 10, 483–495, https://doi.org/10.5194/wes-10-483-2025, https://doi.org/10.5194/wes-10-483-2025, 2025
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Our study reveals how different weather patterns influence wind conditions off the US West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient.
Arjun Anantharaman, Jörge Schneemann, Frauke Theuer, Laurent Beaudet, Valentin Bernard, Paul Deglaire, and Martin Kühn
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-20, https://doi.org/10.5194/wes-2025-20, 2025
Revised manuscript accepted for WES
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The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are important to analyse for both existing and planned wind farms. We developed a new methodology to quantify how much the reductions in wind speed behind a farm can affect the loads on turbines which are tens of kilometers downstream. We found a 2.5 % increase in the turbine loads and discuss how further measurements could add to the design standards of future wind farms.
Tsvetelina Ivanova, Sara Porchetta, Sophia Buckingham, Gertjan Glabeke, Jeroen van Beeck, and Wim Munters
Wind Energ. Sci., 10, 245–268, https://doi.org/10.5194/wes-10-245-2025, https://doi.org/10.5194/wes-10-245-2025, 2025
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This study explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.
Laura Bianco, Reagan Mendeke, Jake Lindblom, Irina V. Djalalova, David D. Turner, and James M. Wilczak
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-133, https://doi.org/10.5194/wes-2024-133, 2024
Revised manuscript accepted for WES
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Including more renewable energy into the electric grid is a critical part of the strategy to mitigate climate change. Reliable numerical weather prediction (NWP) models need to be able to predict the intrinsic nature of weather-dependent resources, such as wind ramp events, as wind energy could quickly be available in abundance or temporarily cease to exist. We assess the ability of the operational High Resolution Rapid Refresh NWP model to forecast wind ramp events in two most recent versions.
Jonathan Minz, Axel Kleidon, and Nsilulu T. Mbungu
Wind Energ. Sci., 9, 2147–2169, https://doi.org/10.5194/wes-9-2147-2024, https://doi.org/10.5194/wes-9-2147-2024, 2024
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Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.
Harish Baki, Sukanta Basu, and George Lavidas
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-99, https://doi.org/10.5194/wes-2024-99, 2024
Revised manuscript accepted for WES
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Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing ramp-down events. Using the Weather Research and Forecasting model, we analyzed various modeling configurations and found that initial and boundary conditions, domain configuration, and wind farm parameterization significantly influence simulations. Our findings show such extreme events can be forecasted one day in advance, helping manage wind power more efficiently for a stable, reliable energy supply.
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.
Sebastiano Stipa, Mehtab Ahmed Khan, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1647–1668, https://doi.org/10.5194/wes-9-1647-2024, https://doi.org/10.5194/wes-9-1647-2024, 2024
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We introduce a novel way to model the impact of atmospheric gravity waves (AGWs) on wind farms using high-fidelity simulations while significantly reducing computational costs. The proposed approach is validated across different atmospheric stability conditions, and implications of neglecting AGWs when predicting wind farm power are assessed. This work advances our understanding of the interaction of wind farms with the free atmosphere, ultimately facilitating cost-effective research.
Abdul Haseeb Syed and Jakob Mann
Wind Energ. Sci., 9, 1381–1391, https://doi.org/10.5194/wes-9-1381-2024, https://doi.org/10.5194/wes-9-1381-2024, 2024
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Wind flow consists of swirling patterns of air called eddies, some as big as many kilometers across, while others are as small as just a few meters. This paper introduces a method to simulate these large swirling patterns on a flat grid. Using these simulations we can better figure out how these large eddies affect big wind turbines in terms of loads and forces.
Sara Müller, Xiaoli Guo Larsén, and David Robert Verelst
Wind Energ. Sci., 9, 1153–1171, https://doi.org/10.5194/wes-9-1153-2024, https://doi.org/10.5194/wes-9-1153-2024, 2024
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Tropical cyclone winds are challenging for wind turbines. We analyze a tropical cyclone before landfall in a mesoscale model. The simulated wind speeds and storm structure are sensitive to the boundary parametrization. However, independent of the boundary layer parametrization, the median change in wind speed and wind direction with height is small relative to wind turbine design standards. Strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 1123–1152, https://doi.org/10.5194/wes-9-1123-2024, https://doi.org/10.5194/wes-9-1123-2024, 2024
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This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at modeling turbine–wake and wind farm–gravity wave interactions, as well as local and global blockage effects. Comparisons against large eddy simulations show that the MSC model offers a valid contribution towards advancing our understanding of the coupled wind farm–atmosphere interaction, helping refining power estimation methodologies for existing and future wind farm sites.
David Rosencrans, Julie K. Lundquist, Mike Optis, Alex Rybchuk, Nicola Bodini, and Michael Rossol
Wind Energ. Sci., 9, 555–583, https://doi.org/10.5194/wes-9-555-2024, https://doi.org/10.5194/wes-9-555-2024, 2024
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The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
Mohammad Golam Mostafa Khan and Mohammed Rafiuddin Ahmed
Wind Energ. Sci., 8, 1277–1298, https://doi.org/10.5194/wes-8-1277-2023, https://doi.org/10.5194/wes-8-1277-2023, 2023
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A robust technique for wind resource assessment with a Bayesian approach for estimating Weibull parameters is proposed. Research conducted using seven sites' data in the tropical region from 1° N to 21° S revealed that the three-parameter (3-p) Weibull distribution with a non-zero shift parameter is a better fit for wind data that have a higher percentage of low wind speeds. Wind data with higher wind speeds are a special case of the 3-p distribution. This approach gives accurate results.
Sue Ellen Haupt, Branko Kosović, Larry K. Berg, Colleen M. Kaul, Matthew Churchfield, Jeffrey Mirocha, Dries Allaerts, Thomas Brummet, Shannon Davis, Amy DeCastro, Susan Dettling, Caroline Draxl, David John Gagne, Patrick Hawbecker, Pankaj Jha, Timothy Juliano, William Lassman, Eliot Quon, Raj K. Rai, Michael Robinson, William Shaw, and Regis Thedin
Wind Energ. Sci., 8, 1251–1275, https://doi.org/10.5194/wes-8-1251-2023, https://doi.org/10.5194/wes-8-1251-2023, 2023
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
Miguel Sanchez Gomez, Julie K. Lundquist, Jeffrey D. Mirocha, and Robert S. Arthur
Wind Energ. Sci., 8, 1049–1069, https://doi.org/10.5194/wes-8-1049-2023, https://doi.org/10.5194/wes-8-1049-2023, 2023
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The wind slows down as it approaches a wind plant; this phenomenon is called blockage. As a result, the turbines in the wind plant produce less power than initially anticipated. We investigate wind plant blockage for two atmospheric conditions. Blockage is larger for a wind plant compared to a stand-alone turbine. Also, blockage increases with atmospheric stability. Blockage is amplified by the vertical transport of horizontal momentum as the wind approaches the front-row turbines in the array.
Stephanie Redfern, Mike Optis, Geng Xia, and Caroline Draxl
Wind Energ. Sci., 8, 1–23, https://doi.org/10.5194/wes-8-1-2023, https://doi.org/10.5194/wes-8-1-2023, 2023
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As wind farm developments expand offshore, accurate forecasting of winds above coastal waters is rising in importance. Weather models rely on various inputs to generate their forecasts, one of which is sea surface temperature (SST). In this study, we evaluate how the SST data set used in the Weather Research and Forecasting model may influence wind characterization and find meaningful differences between model output when different SST products are used.
Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager
Wind Energ. Sci., 7, 2497–2512, https://doi.org/10.5194/wes-7-2497-2022, https://doi.org/10.5194/wes-7-2497-2022, 2022
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When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Hugo Rubio, Martin Kühn, and Julia Gottschall
Wind Energ. Sci., 7, 2433–2455, https://doi.org/10.5194/wes-7-2433-2022, https://doi.org/10.5194/wes-7-2433-2022, 2022
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A proper development of offshore wind farms requires the accurate description of atmospheric phenomena like low-level jets. In this study, we evaluate the capabilities and limitations of numerical models to characterize the main jets' properties in the southern Baltic Sea. For this, a comparison against ship-mounted lidar measurements from the NEWA Ferry Lidar Experiment has been implemented, allowing the investigation of the model's capabilities under different temporal and spatial constraints.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
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The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
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The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Frauke Theuer, Andreas Rott, Jörge Schneemann, Lueder von Bremen, and Martin Kühn
Wind Energ. Sci., 7, 2099–2116, https://doi.org/10.5194/wes-7-2099-2022, https://doi.org/10.5194/wes-7-2099-2022, 2022
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Remote-sensing-based approaches have shown potential for minute-scale forecasting and need to be further developed towards an operational use. In this work we extend a lidar-based forecast to an observer-based probabilistic power forecast by combining it with a SCADA-based method. We further aggregate individual turbine power using a copula approach. We found that the observer-based forecast benefits from combining lidar and SCADA data and can outperform persistence for unstable stratification.
Adithya Vemuri, Sophia Buckingham, Wim Munters, Jan Helsen, and Jeroen van Beeck
Wind Energ. Sci., 7, 1869–1888, https://doi.org/10.5194/wes-7-1869-2022, https://doi.org/10.5194/wes-7-1869-2022, 2022
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The sensitivity of the WRF mesoscale modeling framework in accurately representing and predicting wind-farm-level environmental variables for three extreme weather events over the Belgian North Sea is investigated in this study. The overall results indicate highly sensitive simulation results to the type and combination of physics parameterizations and the type of the weather phenomena, with indications that scale-aware physics parameterizations better reproduce wind-related variables.
Cited articles
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Short summary
To guide realistic atmospheric gravity wave simulations, we study flow over a two-dimensional hill and through a wind farm canopy, examining optimal domain size and damping layer setup. Wave properties based on non-dimensional numbers determine the optimal domain and damping parameters. Accurate solutions require the domain length to exceed the effective horizontal wavelength, height, and damping thickness to equal the vertical wavelength and non-dimensional damping strength between 1 and 10.
To guide realistic atmospheric gravity wave simulations, we study flow over a two-dimensional...
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