Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2022
Research article |
| 08 Dec 2022
| 08 Dec 2022
Predicting power ramps from joint distributions of future wind speeds
Thomas Muschinski
CORRESPONDING AUTHOR
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Moritz N. Lang
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Georg J. Mayr
Department of Atmospheric and Cryospheric Sciences, Universität Innsbruck, Innsbruck, Austria
Jakob W. Messner
MeteoServe Wetterdienst GmbH, Innsbruck, Austria
Achim Zeileis
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Thorsten Simon
Department of Statistics, Universität Innsbruck, Innsbruck, Austria
Department of Mathematics, Universität Innsbruck, Innsbruck, Austria
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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.
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
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.
The power generated by offshore wind farms can vary greatly within a couple of hours, and...
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