Articles | Volume 10, issue 8
https://doi.org/10.5194/wes-10-1575-2025
© 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-10-1575-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Aug 2025
Research article |
| 06 Aug 2025
| 06 Aug 2025
Modeling frontal low-level jets and associated extreme wind power ramps over the North Sea
Geosciences and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Sukanta Basu
Atmospheric Sciences Research Center, State University of New York, Albany, NY, USA
Department of Environmental and Sustainable Engineering, University at Albany, Albany, NY, USA
George Lavidas
Marine Renewable Energies Lab, Offshore Engineering, Hydraulic Engineering Department, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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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.
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.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
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Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
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.
Patrick Hawbecker, Sukanta Basu, and Lance Manuel
Wind Energ. Sci., 3, 203–219, https://doi.org/10.5194/wes-3-203-2018, https://doi.org/10.5194/wes-3-203-2018, 2018
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Quantifying tropical-cyclone-generated waves in extreme-value-derived design for offshore wind
Estimating long-term annual energy production from shorter-time-series data: methods and verification with a 10-year large-eddy simulation of a large offshore wind farm
Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment
Brief communication: A note on the variance of wind speed and turbulence intensity
Investigating the relationship between simulation parameters and flow variables in simulating atmospheric gravity waves for wind energy applications
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the southern Great Plains
Analyzing the performance of vertical wind profilers in rain events
How do convective cold pools influence the boundary-layer atmosphere near two wind turbines in northern Germany?
Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast
The impact of far-reaching offshore cluster wakes on wind turbine fatigue loads
Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms
Evaluating the ability of the operational High Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) to forecast wind ramp events in the US Great Plains
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Mesoscale weather systems and associated potential wind power variations in a midlatitude sea strait (Kattegat)
A large-eddy simulation (LES) model for wind-farm-induced atmospheric gravity wave effects inside conventionally neutral boundary layers
Simulating low-frequency wind fluctuations
Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
Bayesian method for estimating Weibull parameters for wind resource assessment in a tropical region: a comparison between two-parameter and three-parameter Weibull distributions
Lessons learned in coupling atmospheric models across scales for onshore and offshore wind energy
Investigating the physical mechanisms that modify wind plant blockage in stable boundary layers
Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic
Lifetime prediction of turbine blades using global precipitation products from satellites
Evaluation of low-level jets in the southern Baltic Sea: a comparison between ship-based lidar observational data and numerical models
Predicting power ramps from joint distributions of future wind speeds
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Research challenges and needs for the deployment of wind energy in hilly and mountainous regions
Observer-based power forecast of individual and aggregated offshore wind turbines
Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)
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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This paper investigates the treatment of tropical-cyclone-generated waves in metocean models and in statistical evaluation for offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for the design and operation of offshore projects on the Atlantic coast of the United States.
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.
Mehtab Ahmed Khan, Dries Allaerts, Simon J. Watson, and Matthew J. Churchfield
Wind Energ. Sci., 10, 1167–1185, https://doi.org/10.5194/wes-10-1167-2025, https://doi.org/10.5194/wes-10-1167-2025, 2025
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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.
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.
Jeffrey D. Thayer, Gerard Kilroy, and Norman Wildmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-38, https://doi.org/10.5194/wes-2025-38, 2025
Revised manuscript accepted for WES
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With increasing wind energy in the German energy grid, it is crucial to better understand how different types of weather (including thunderstorms) can impact wind turbines and the surrounding atmosphere. We find rapid wind changes associated with the leading edge of thunderstorm outflows within the height range of wind turbines that would quickly increase wind power output, with longer-lasting changes in the near-surface atmosphere that would affect subsequent wind turbine operations.
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.
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.
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Short summary
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 1 d in advance, helping manage wind power more efficiently for a stable, reliable energy supply.
Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing...
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