Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1455-2021
© Author(s) 2021. 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-6-1455-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus
Vasilis Pettas
CORRESPONDING AUTHOR
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Matthias Kretschmer
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Andrew Clifton
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Po Wen Cheng
Stuttgart Wind Energy (SWE), University of Stuttgart, Allmandring 5b, 70569 Stuttgart, Germany
Related authors
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
Short summary
Short summary
We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
Short summary
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Mohammad Youssef Mahfouz, Ericka Lozon, Matthew Hall, and Po Wen Cheng
Wind Energ. Sci., 9, 1595–1615, https://doi.org/10.5194/wes-9-1595-2024, https://doi.org/10.5194/wes-9-1595-2024, 2024
Short summary
Short summary
As climate change increasingly impacts our daily lives, a transition towards cleaner energy is needed. With all the growth in floating offshore wind and the planned floating wind farms (FWFs) in the next few years, we urgently need new techniques and methodologies to accommodate the differences between the fixed bottom and FWFs. This paper presents a novel methodology to decrease aerodynamic losses inside an FWF by passively relocating the downwind floating wind turbines out of the wakes.
Fiona Dominique Lüdecke, Martin Schmid, and Po Wen Cheng
Wind Energ. Sci., 9, 1527–1545, https://doi.org/10.5194/wes-9-1527-2024, https://doi.org/10.5194/wes-9-1527-2024, 2024
Short summary
Short summary
Large direct-drive wind turbines, with a multi-megawatt power rating, face design challenges. Moving towards a more system-oriented design approach could potentially reduce mass and costs. Exploiting the full design space, though, may invoke interaction mechanisms, which have been neglected in the past. Based on coupled simulations, this work derives a better understanding of the electro-mechanical interaction mechanisms and identifies potential for design relevance.
Qi Pan, Dexing Liu, Feng Guo, and Po Wen Cheng
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-44, https://doi.org/10.5194/wes-2024-44, 2024
Preprint withdrawn
Short summary
Short summary
The floating wind market is striving to scale up from a handful of prototypes to gigawatt-scale capacity, despite facing barriers of high costs in the deep-sea deployment. Shared mooring is promising in reducing material costs. This paper introduces a comprehensive design methodology for reliable shared mooring line configurations, and reveals their potential for cost-saving and power enhancement. These findings contribute to achieving cost-effective solutions for floating wind farms.
Wei Yu, Sheng Tao Zhou, Frank Lemmer, and Po Wen Cheng
Wind Energ. Sci., 9, 1053–1068, https://doi.org/10.5194/wes-9-1053-2024, https://doi.org/10.5194/wes-9-1053-2024, 2024
Short summary
Short summary
Integrating a tuned liquid multi-column damping (TLMCD) into a floating offshore wind turbine (FOWT) is challenging. The synergy between the TLMCD, the turbine controller, and substructure dynamics affects the FOWT's performance and cost. A control co-design optimization framework is developed to optimize the substructure, the TLMCD, and the blade pitch controller simultaneously. The results show that the optimization can significantly enhance FOWT system performance.
Christian W. Schulz, Stefan Netzband, Umut Özinan, Po Wen Cheng, and Moustafa Abdel-Maksoud
Wind Energ. Sci., 9, 665–695, https://doi.org/10.5194/wes-9-665-2024, https://doi.org/10.5194/wes-9-665-2024, 2024
Short summary
Short summary
Understanding the underlying physical phenomena of the aerodynamics of floating offshore wind turbines (FOWTs) is crucial for successful simulations. No consensus has been reached in the research community on which unsteady aerodynamic phenomena are relevant and how much they can influence the loads acting on a FOWT. This work contributes to the understanding and characterisation of such unsteady phenomena using a novel experimental approach and comprehensive numerical investigations.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 8, 149–171, https://doi.org/10.5194/wes-8-149-2023, https://doi.org/10.5194/wes-8-149-2023, 2023
Short summary
Short summary
The benefits of lidar-assisted control are evaluated using both the Mann model and Kaimal model-based 4D turbulence, considering the variation of turbulence parameters. Simulations are performed for the above-rated mean wind speed, using the NREL 5.0 MW reference wind turbine and a four-beam lidar system. Using lidar-assisted control reduces the variations in rotor speed, pitch rate, tower base fore–aft bending moment, and electrical power significantly.
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
Short summary
Short summary
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.
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.
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Matthias Kretschmer, Jason Jonkman, Vasilis Pettas, and Po Wen Cheng
Wind Energ. Sci., 6, 1247–1262, https://doi.org/10.5194/wes-6-1247-2021, https://doi.org/10.5194/wes-6-1247-2021, 2021
Short summary
Short summary
We perform a validation of the new simulation tool FAST.Farm for the prediction of power output and structural loads in single wake conditions with respect to measurement data from the offshore wind farm alpha ventus. With a new wake-added turbulence functionality added to FAST.Farm, good agreement between simulations and measurements is achieved for the considered quantities. We hereby give insights into load characteristics of an offshore wind turbine subjected to single wake conditions.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
Short summary
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Yiyin Chen, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 6, 61–91, https://doi.org/10.5194/wes-6-61-2021, https://doi.org/10.5194/wes-6-61-2021, 2021
Short summary
Short summary
Wind evolution is currently of high interest, mainly due to the development of lidar-assisted wind turbine control (LAC). Moreover, 4D stochastic wind field simulations can be made possible by integrating wind evolution into 3D simulations to provide a more realistic simulation environment for LAC. Motivated by these factors, we investigate the potential of Gaussian process regression in the parameterization of a two-parameter wind evolution model using data of two nacelle-mounted lidars.
Cited articles
Ahsbahs, T., Nygaard, N. G., Newcombe, A., and Badger, M.: Wind Farm Wakes from SAR and Doppler Radar, Remote Sens.-Basel, 12, 462, https://doi.org/10.3390/rs12030462, 2020. a
Alpha Ventus: Alpha Ventus homepage, Fraunhofer-Gesellschaft, available at: https://www.alpha-ventus.de/english, last access:
1 December 2020. a
Bundesamt für Seeschifffahrt und Hydrographie (BSH): BSH data, available at: https://www.bsh.de/EN/DATA/data_node.html, last access: 1 December 2020b. a
Cañadillas, B., Foreman, R., Barth, V., Siedersleben, S., Lampert, A., Platis, A., Djath, B., Schulz-Stellenfleth, J., Bange, J., Emeis, S., and Neumann, T.: Offshore wind farm wake recovery: Airborne measurements and its representation in engineering models, Wind Energy, 23, 1249–1265, https://doi.org/10.1002/we.2484, 2020. a
Christiansen, M. B. and Hasager, C. B.: Wake effects of large offshore wind farms identified from satellite SAR, Remote Sens. Environ., 98, 251–268, https://doi.org/10.1016/j.rse.2005.07.009, 2005. a
Emeis, S.: A simple analytical wind park model considering atmospheric stability, Wind Energy, 13, 459–469, https://doi.org/10.1002/we.367, 2009. a
FINO1: FINO1 – Research Platform in the North and Baltic Seas No. 1, Forschungs- und Entwicklungszentrum Fachhochschule Kiel, available at: https://www.fino1.de/en/, last access: 1 December 2020. a
Frandsen, S. T.: Turbulence and turbulence- generated structural loading in wind turbine clusters, PhD thesis, Risoe National Laboratory, available at: https://backend.orbit.dtu.dk/ws/portalfiles/portal/12674798/ris_r_1188.pdf (last acccess: 1 December 2020), 2007. a
Hansen, K. S., Réthoré, P.-E., Palma, J., Hevia, B. G.,
Prospathopoulos, J., Peña, A., Ott, S., Schepers, G., Palomares, A.,
van der Laan, M. P., and Volker, P.: Simulation of wake effects between two
wind farms, J. Phys. Conf. Ser., 625, 012008, https://doi.org/10.1088/1742-6596/625/1/012008, 2015. a
Hübler, C., Gebhardt, C. G., and Rolfes, R.: Development of a comprehensive database of scattering environmental conditions and simulation constraints for offshore wind turbines, Wind Energ. Sci., 2, 491–505, https://doi.org/10.5194/wes-2-491-2017, 2017. a
International Electrotechnical Commission (IEC): International Standard
IEC61400-1: wind turbines–part 1: design guidelines, 4th edn., Standard, IEC, Geneva, Switzerland, 2019. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development,
Tech. rep., National Renewable Energy Laboratory (NREL), Golden, CO,
https://doi.org/10.2172/947422, available at: http://www.osti.gov/servlets/purl/947422-nhrlni/ (last access: 1 December 2020), 2009. a
Katic, I., Højstrup, J., and Jensen, N.: A Simple Model for Cluster
Efficiency, in: European Wind Energy Association Conference and Exhibition,
407–410, 6–8 October 1986, Rome, Italy, 1987. a
Kretschmer, M., Schwede, F., Faerron Guzmán, R., Lott, S., and Cheng, P. W.: Influence of atmospheric stability on the load spectra of wind turbines at alpha ventus, J. Phys. Conf. Ser., 1037, 052009, https://doi.org/10.1088/1742-6596/1037/5/052009, 2018. a
Kretschmer, M., Pettas, V., and Cheng, P. W.: Effects of Wind Farm Down-Regulation in the Offshore Wind Farm Alpha Ventus, in: ASME 2019 2nd International Offshore Wind Technical Conference, American Society of Mechanical Engineers, St. Julian's, Malta, https://doi.org/10.1115/IOWTC2019-7554, 2019. a
Kretschmer, M., Jonkman, J., Pettas, V., and Cheng, P. W.: FAST.Farm load
validation for single wake situations at alpha ventus, Wind Energ. Sci., 6,
1247–1262, https://doi.org/10.5194/wes-6-1247-2021, 2021. a
Lu, H. and Porté-Agel, F.: On the Impact of Wind Farms on a Convective Atmospheric Boundary Layer, Bound.-Lay. Meteorol., 157, 81–96, https://doi.org/10.1007/s10546-015-0049-1, 2015. a
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs and consequences of wind turbine wake effects arising from uncoordinated wind energy development, Nature Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019. a
Merkur Offshore: Merkur Offshore homepage, available at: https://www.merkur-offshore.com/, last access: 1 December 2020. a
Mittelmeier, N., Allin, J., Blodau, T., Trabucchi, D., Steinfeld, G., Rott, A., and Kühn, M.: An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects, Wind Energ. Sci., 2, 477–490, https://doi.org/10.5194/wes-2-477-2017, 2017. a
Nygaard, N. G., Steen, S. T., 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
Ortensi, M., Fruhman, R., and Neumann, T.: Long-term Effects of Wakes from Offshore Wind Farms on Wind Conditions at FINO1, Tech. Rep. November, UL white paper, available at: https://aws-dewi.ul.com/knowledge-center/technical-papers/, 1 December 2020. a
Ørsted: Borkum Riffgrund 1 by Orsted, available at: https://orsted.de/en/offshore-windenergie/unsere-offshore-windparks-nordsee/borkum-riffgrund-1, last access: 1 December 2020. a
Platis, A., Siedersleben, S. K., Bange, J., Lampert, A., Bärfuss, K., Hankers, R., Cañadillas, B., Foreman, R., Schulz-Stellenfleth, J., Djath, B., Neumann, T., and Emeis, S.: First in situ evidence of wakes in the far field behind offshore wind farms, Sci. Rep.-UK, 8, 2163, https://doi.org/10.1038/s41598-018-20389-y, 2018. a
Platis, A., Hundhausen, M., Mauz, M., Siedersleben, S., Lampert, A.,
Bärfuss, K., Djath, B., Schulz-Stellenfleth, J., Canadillas, B.,
Neumann, T., Emeis, S., and Bange, J.: Evaluation of a simple analytical model
for offshore wind farm wake recovery by in situ data and Weather Research and
Forecasting simulations, Wind Energy, 24, 212–228, https://doi.org/10.1002/we.2568, 2020.
a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-Turbine and Wind-Farm Flows: A Review, Bound.-Lay. Meteorol., 174, 1–59, https://doi.org/10.1007/s10546-019-00473-0, 2020. a
Pryor, S. C., Shepherd, T. J., and Barthelmie, R. J.: Interannual variability of wind climates and wind turbine annual energy production, Wind Energ. Sci., 3, 651–665, https://doi.org/10.5194/wes-3-651-2018, 2018. a
Quaeghebeur, E. and Zaaijer, M. B.: How to improve the state of the art in metocean measurement datasets, Wind Energ. Sci., 5, 285–308, https://doi.org/10.5194/wes-5-285-2020, 2020. a
Quarton, D. C. and Ainslie, J. F.: Turbulence in Wind Turbine Wakes, Wind Engineering, 14, 15–23, 1990. a
Research at Alpha Ventus (RAVE): German research initiative Research at alpha ventus (RAVE), available at: https://www.rave-offshore.de/en/start.html, last access: 1 December 2020. a
Ronda, R. J., Wijnant, I. L., and Stepek, A.: Inter-annual wind speed variability on the North Sea, Tech. rep., TR-360, Royal Netherlands Meteorological Institute, available at: https://cdn.knmi.nl/knmi/pdf/bibliotheek/knmipubTR/TR360.pdf (last access: 1 December 2020), 2017. a
Schneemann, J., Rott, A., Dörenkämper, M., Steinfeld, G., and Kühn, M.: Cluster wakes impact on a far-distant offshore wind farm's power, Wind Energ. Sci., 5, 29–49, https://doi.org/10.5194/wes-5-29-2020, 2020. a
Trianel Windkraftwerk Borkum: Trianel Borkum I homepage, available at: http://www.trianel-borkum.de/en/home/, last access: 1 December 2020. a
Wu, K. 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 study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
This study aims to quantify the effect of inter-farm interactions based on long-term measurement...
Altmetrics
Final-revised paper
Preprint