Articles | Volume 9, issue 5
https://doi.org/10.5194/wes-9-1123-2024
© Author(s) 2024. 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-9-1123-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2024
Research article |
| 08 May 2024
| 08 May 2024
The multi-scale coupled model: a new framework capturing wind farm–atmosphere interaction and global blockage effects
School of Engineering, University of British Columbia–Okanagan, Kelowna, Canada
Arjun Ajay
School of Engineering, University of British Columbia–Okanagan, Kelowna, Canada
Dries Allaerts
Aerospace Engineering, TU Delft, Delft, the Netherlands
Joshua Brinkerhoff
School of Engineering, University of British Columbia–Okanagan, Kelowna, Canada
Related authors
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 2301–2332, https://doi.org/10.5194/wes-9-2301-2024, https://doi.org/10.5194/wes-9-2301-2024, 2024
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models, demonstrating its utility for future large-scale wind farm simulations.
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
Short summary
Short summary
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.
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
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 2301–2332, https://doi.org/10.5194/wes-9-2301-2024, https://doi.org/10.5194/wes-9-2301-2024, 2024
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models, demonstrating its utility for future large-scale wind farm simulations.
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
Short summary
Short summary
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.
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
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
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
Short summary
Short summary
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.
Marcus Becker, Bastian Ritter, Bart Doekemeijer, Daan van der Hoek, Ulrich Konigorski, Dries Allaerts, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2163–2179, https://doi.org/10.5194/wes-7-2163-2022, https://doi.org/10.5194/wes-7-2163-2022, 2022
Short summary
Short summary
In this paper we present a revised dynamic control-oriented wind farm model. The model can simulate turbine wake behaviour in heterogeneous and changing wind conditions at a very low computational cost. It utilizes a three-dimensional turbine wake model which also allows capturing vertical wind speed differences. The model could be used to maximise the power generation of with farms, even during events like a wind direction change. It is publicly available and open for further development.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Atmospheric physics
Linking large-scale weather patterns to observed and modeled turbine hub-height winds offshore of the US West Coast
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
Estimating the technical wind energy potential of Kansas that incorporates the effect of regional wind resource depletion by wind turbines
Analyzing the performance of vertical wind profilers in rain events
Large-eddy simulation of an atmospheric bore and associated gravity wave effects on wind farm performance in the Southern Great Plains
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
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)
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Adriel J. Carvalho, Francisco Albuquerque Leite Neto, and Denisson Q. Oliveira
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-132, https://doi.org/10.5194/wes-2024-132, 2024
Revised manuscript accepted for WES
Short summary
Short summary
The wind profilers are very important to wind power industry since they can capture wind velocity and direction at higher altitudes. Although there are some literature investigating their performance in different scenarios, this paper covers a gap investigating and comparing their performance under rain events. This investigation is important since the data collected support strategic decisions in wind power industry, demanding a high availability in all situations.
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. Discuss., https://doi.org/10.5194/wes-2024-84, https://doi.org/10.5194/wes-2024-84, 2024
Revised manuscript accepted for WES
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Ainslie, J.: Calculating the flowfield in the wake of wind turbines, J. o Wind Eng. Indust. Aerodynam., 27, 213–224, https://doi.org/10.1016/0167-6105(88)90037-2, 1988. a
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, https://doi.org/10.1017/jfm.2017.11, 2017. a, b
Allaerts, D. and Meyers, J.: Gravity Waves and Wind-Farm Efficiency in Neutral and Stable Conditions, Bound.-Lay. Meteorol., 166, 269–299, https://doi.org/10.1007/s10546-017-0307-5, 2018. a
Allaerts, D., Broucke, S. V., van Lipzig, N., and Meyers, J.: Annual impact of wind-farm gravity waves on the Belgian-Dutch offshore wind-farm cluster, J. Phys.: Conf. Ser., 1037, 072006, https://doi.org/10.1088/1742-6596/1037/7/072006, 2018. a
Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a, b
Blondel, F. and Cathelain, M.: An alternative form of the super-Gaussian wind turbine wake model, Wind Energy Science, 5, 1225–1236, https://doi.org/10.5194/wes-5-1225-2020, 2020. a, b
Branlard, E. and Gaunaa, M.: Cylindrical vortex wake model: right cylinder, Wind Energy, 18, 1973–1987, https://doi.org/10.1002/we.1800, 2014. a, b, c, d
Branlard, E., Quon, E., Forsting, A. R. M., King, J., and Moriarty, P.: Wind farm blockage effects: comparison of different engineering models, J. Phys.: Conf. Ser., 1618, 062036, https://doi.org/10.1088/1742-6596/1618/6/062036, 2020. a, b
Brogna, R., Feng, J., Sørensen, J. N., Shen, W. Z., and Porté-Agel, F.: A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain, Appl. Energy, 259, 114189, https://doi.org/10.1016/j.apenergy.2019.114189, 2020. a
Businger, J. A.: Transfer of Momentum and Heat in the Planetary Boundary Layer, in: Proceedings of the Symposium on Arctic Heat Budget and Atmospheric Circulation, Lake Arrowhead, The Rand Corporation, 305–332, https://www.rand.org/pubs/research_memoranda/RM5233.html (last access: 3 May 2024), 1966. a
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulations of fully developed wind-turbine array boundary layers, Phys. Fluids, 22, 015110, https://doi.org/10.1063/1.3291077, 2010. a, b
Centurelli, G., Vollmer, L., Schmidt, J., Dörenkämper, M., Schröder, M., Lukassen, L. J., and Peinke, J.: Evaluating Global Blockage engineering parametrizations with LES, J. Phys.: Conf. Ser., 1934, 012021, https://doi.org/10.1088/1742-6596/1934/1/012021, 2021. a
Crespo, A. and Hernandez, J.: Turbulence characteristics in wind-turbine wakes, J. Wind Eng. Indust. Aerodynam., 61, 71–85, https://doi.org/10.1016/0167-6105(95)00033-X, 1996. a
Devesse, K., Lanzilao, L., Jamaer, S., van Lipzig, N., and Meyers, J.: Including realistic upper atmospheres in a wind-farm gravity-wave model, Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, 2022. a
Devesse, K., Lanzilao, L., and Meyers, J.: A meso-micro atmospheric perturbation model for wind farm blockage, arXiv [preprint], https://doi.org/10.48550/arXiv.2310.18748, 2023. a
Etling, D.: Modelling the vertical ABL structure, World Scientific, 45–86, https://doi.org/10.1142/9789814447164_0003, 1996. a
Farrell, A., King, J., Draxl, C., Mudafort, R., Hamilton, N., Bay, C. J., Fleming, P., and Simley, E.: Design and analysis of a wake model for spatially heterogeneous flow, Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, 2021. a, b
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., Højstrup, J., and Thøgersen, M.: Analytical modelling of wind speed deficit in large offshore wind farms, Wind Energy, 9, 39–53, https://doi.org/10.1002/we.189, 2006. a
Gill, A. E.: Chapter Four – Equations Satisfied by a Moving Fluid, in: Atmosphere–Ocean Dynamics, vol. 30 of International Geophysics, Academic Press, 63–94, https://doi.org/10.1016/S0074-6142(08)60029-7, 1982. a
Gribben, B. J. and Hawkes, G. S.: A potential flow model for wind turbine induction and wind farm blockage, Systems and Engineering Technology, https://www.fnc.co.uk/media/o5eosxas/a-potential-flow-model-for-wind-turbine-induction-and-wind (last access: 3 May 2024), 2019. a, b
Jensen, N.: A note on wind generator interaction, no. 2411 in Risø-M, Risø National Laboratory, ISBN 87-550-0971-9, 1983. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5MW Reference Wind Turbine for Offshore System Development, NREL – National Renewable Energy Laboratory, https://doi.org/10.2172/947422, 2009. a, b
Katić, I., Højstrup, J., and Jensen, N.: A Simple Model for Cluster Efficiency, in: European Wind Energy Association Conference and Exhibition, EWEC '86, edited by: Palz, W. and Sesto, E., A. Raguzzi, 407–410, https://backend.orbit.dtu.dk/ws/portalfiles/portal/106427419/A_Simple_Model_for_Cluster_Efficiency_EWEC_86_.pdf (last access: 3 May 2024), 1987. a, b
Lanzilao, L. and Meyers, J.: A new wake-merging method for wind-farm power prediction in the presence of heterogeneous background velocity fields, Wind Energy, 25, 237–259, https://doi.org/10.1002/we.2669, 2022. a, b
Lanzilao, L. and Meyers, J.: An Improved Fringe-Region Technique for the Representation of Gravity Waves in Large Eddy Simulation with Application to Wind Farms, Bound.-Lay. Meteorol., 183, 567–593, https://doi.org/10.1007/s10546-022-00772-z, 2023. a, b, c
Lanzilao, L. and Meyers, J.: A parametric large-eddy simulation study of wind-farm blockage and gravity waves in conventionally neutral boundary layers, J. Fluid Mech., 979, A54, https://doi.org/10.1017/jfm.2023.1088, 2024. a
Larsen, G.: A simple wake calculation procedure, Tech. Rep. Risø-M-2760 Risø, Tech. rep., Risø DTU National Laboratory for Sustainable Energy, https://orbit.dtu.dk/en/publications/a-simple-wake-calculation-procedure (last access: 3 May 2024), 1988. a
Lin, Y.-L.: Mesoscale Dynamics, Cambridge University Press, ISBN 9780511619649, https://doi.org/10.1017/CBO9780511619649, 2007. a, b
Lissaman, P. B. S.: Energy Effectiveness of Arbitrary Arrays of Wind Turbines, J. Energy, 3, 323–328, https://doi.org/10.2514/3.62441, 1979. a
Meneveau, C.: The top-down model of wind farm boundary layers and its applications, J. Turbul., 13, N7, https://doi.org/10.1080/14685248.2012.663092, 2011. a, b
Meyers, J. and Meneveau, C.: Large Eddy Simulations of Large Wind-Turbine Arrays in the Atmospheric Boundary Layer, ARC, ISBN 978-1-60086-959-4, https://doi.org/10.2514/6.2010-827, 2010. a
Monin, A. and Obukhov, A.: Basic laws of turbulent mixing in the surface layer of the atmosphere, Tr. Akad. Nauk SSSR Geophiz. Inst., 151, 163–187, 1954. a
Nappo, C. J. (Ed.): Copyright, in: vol. 102 ofInternational Geophysics, Academic Press, https://doi.org/10.1016/B978-0-12-385223-6.00014-8, 2012. a
Nieuwstadt, F. T. M.: On the solution of the stationary, baroclinic Ekman-layer equations with a finite boundary-layer height, Bound.-Lay. Meteorol., 26, 377–390, https://doi.org/10.1007/BF00119534, 1983. a, b
Nishino, T. and Dunstan, T. D.: Two-scale momentum theory for time-dependent modelling of large wind farms, J. Fluid Mech., 894, A2, https://doi.org/10.1017/jfm.2020.252, 2020. a
Nygaard, N. G., Steen, S., Poulsen, L., and Pedersen, J. G.: Modelling cluster wakes and wind farm blockage, J. Phys.: Conf. Ser., 1618, 062072, https://doi.org/10.1088/1742-6596/1618/6/062072, 2020. a
Nygaard, N. G., Poulsen, L., Svensson, E., and Grønnegaard Pedersen, J.: Large-scale benchmarking of wake models for offshore wind farms, J. Phys.: Conf. Ser., 2265, 022008, https://doi.org/10.1088/1742-6596/2265/2/022008, 2022. a
OpenWind: Theoretical basis and validation, AWS Truepower, Version 1.3., Tech. rep., UL Renewables, https://openwind.ul-renewables.com/index.html (last access: 3 May 2024), 2010. a
Ørsted: Ørsted presents update on its long-term financial targets, https://orsted.com/en/company-announcement-list/2019/10/1937002 (last access: 3 May 2024), 2019. a
Panofsky, H. A.: Determination of stress from wind and temperature measurements, Q. J. Roy. Meteorol. Soc. 89, 85–94, https://doi.org/10.1002/qj.49708937906, 1963. a
Rampanelli, G. and Zardi, D.: A Method to Determine the Capping Inversion of the Convective Boundary Layer, J. Appl. Meteorol., 43, 925–933, https://doi.org/10.1175/1520-0450(2004)043<0925:AMTDTC>2.0.CO;2, 2004. a, b, c
Saad, Y. and Schultz, M. H.: GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems, SIAM J. Scient. Stat. Comput., 7, 856–869, https://doi.org/10.1137/0907058, 1986. a
Segalini, A.: An analytical model of wind-farm blockage, J. Renew. Sustain. Energ., 13, 033307, https://doi.org/10.1063/5.0046680, 2021. a
Shapiro, C., Starke, G., Meneveau, C., and Gayme, D.: A Wake Modeling Paradigm for Wind Farm Design and Control, Energies, 12, 2956, https://doi.org/10.3390/en12152956, 2019. a, b, c
Smith, R. B.: Linear theory of stratified hydrostatic flow past an isolated mountain, Tellus, 32, 348–364, https://doi.org/10.3402/tellusa.v32i4.10590, 1980. a, b
Smith, R. B.: Interacting Mountain Waves and Boundary Layers, J. Atmos. Sci., 64, 594–607, https://doi.org/10.1175/JAS3836.1, 2007. a
Smith, R. B.: The wind farm pressure field, Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, 2024. a
Starke, G. M., Meneveau, C., King, J. R., and Gayme, D. F.: The area localized coupled model for analytical mean flow prediction in arbitrary wind farm geometries, J. Renew. Sustain. Energ., 13, 033305, https://doi.org/10.1063/5.0042573, 2021. a
Stevens, R., Gayme, D., and Meneveau, C.: Coupled wake boundary layer model of wind-farms, J. Renew. Sustain. Energ., 7, 023115, https://doi.org/10.1063/1.4915287, 2015. a, b
Stevens, R. J. A. M.: Dependence of optimal wind turbine spacing on wind farm length, Wind Energy, 19, 651–663, https://doi.org/10.1002/we.1857, 2016. a
Stipa, S., Ajay, A., and Brinkerhoff, J.: Toolbox fOr Stratified Convective Atmospheres (TOSCA), OSF [code], https://doi.org/10.17605/OSF.IO/Q4VAF, 2023. a
Stipa, S., Ajay, A., Allaerts, D., and Brinkerhoff, J.: TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows, Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, 2024. a, b, c, d
Troldborg, N. and Meyer Forsting, A.: A simple model of the wind turbine induction zone derived from numerical simulations, Wind Energy, 20, 2011–2020, https://doi.org/10.1002/we.2137, 2017. a
Voutsinas, S., Rados, K., and Zervos, A.: On the Analysis of Wake Effects in Wind Parks, Wind Eng., 14, 204–219, 1990. a
Wu, K. L. and Porté-Agel, F.: Flow Adjustment Inside and Around Large Finite-Size Wind Farms, Energies, 10, 2164, https://doi.org/10.3390/en10122164, 2017. a
Short summary
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.
This paper introduces the multi-scale coupled (MSC) model, an engineering framework aimed at...
Altmetrics
Final-revised paper
Preprint