Offshore wind farm cluster wakes as observed by long-range-scanning wind lidar measurements and mesoscale modeling

被引:41
作者
Canadillas, Beatriz [1 ,2 ]
Beckenbauer, Maximilian [1 ]
Trujillo, Juan J. [2 ]
Dorenkamper, Martin [3 ]
Foreman, Richard [2 ]
Neumann, Thomas [2 ]
Lampert, Astrid [1 ]
机构
[1] Tech Univ Carolo Wilhelmina Braunschweig, Inst Flight Guidance, Braunschweig, Germany
[2] UL Int GmbH, Renewables, Oldenburg, Germany
[3] Fraunhofer Inst Wind Energy Syst, Oldenburg, Germany
关键词
TURBINE WAKES; IMPACT; ATLAS;
D O I
10.5194/wes-7-1241-2022
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
As part of the ongoing X-Wakes research project, a 5-month wake-measurement campaign was conducted using a scanning lidar installed amongst a cluster of offshore wind farms in the German Bight. The main objectives of this study are (1) to demonstrate the performance of such a system and thus quantify cluster wake effects reliably and (2) to obtain experimental data to validate the cluster wake effect simulated by the flow models involved in the project. Due to the lack of free wind flow for the wake flow directions, wind speeds obtained from a mesoscale model (without any wind farm parameterization) for the same time period were used as a reference to estimate the wind speed deficit caused by the wind farm wakes under different wind directions and atmospheric stabilities. For wind farm waked wind directions, the lidar data show that the wind speed is reduced up to 30 % at a wind speed of about 10 m s(-1), depending on atmospheric stability and distance to the wind farm. For illustrating the spatial extent of cluster wakes, an airborne dataset obtained during the scanning wind lidar campaign is used and compared with the mesoscale model with wind farm parameterization and the scanning lidar. A comparison with the results of the model with a wind farm parameterization and the scanning lidar data reveals a relatively good agreement in neutral and unstable conditions (within about 2 % for the wind speed), whereas in stable conditions the largest discrepancies between the model and measurements are found. The comparative multi-sensor and model approach proves to be an efficient way to analyze the complex flow situation in a modern offshore wind cluster, where phenomena at different length scales and timescales need to be addressed.
引用
收藏
页码:1241 / 1262
页数:22
相关论文
共 65 条
[1]   Wind Farm Wakes from SAR and Doppler Radar [J].
Ahsbahs, Tobias ;
Nygaard, Nicolai Gayle ;
Newcombe, Alexander ;
Badger, Merete .
REMOTE SENSING, 2020, 12 (03)
[2]   Applications of satellite winds for the offshore wind farm site Anholt [J].
Ahsbahs, Tobias ;
Badger, Merete ;
Volker, Patrick ;
Hansen, Kurt S. ;
Hasager, Charlotte B. .
WIND ENERGY SCIENCE, 2018, 3 (02) :573-588
[3]   Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data [J].
Aitken, Matthew L. ;
Banta, Robert M. ;
Pichugina, Yelena L. ;
Lundquist, Julie K. .
JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, 2014, 31 (04) :765-787
[4]   Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials [J].
Akhtar, Naveed ;
Geyer, Beate ;
Rockel, Burkhardt ;
Sommer, Philipp S. ;
Schrum, Corinna .
SCIENTIFIC REPORTS, 2021, 11 (01)
[5]  
[Anonymous], 2021, NCAR USERS PAGE WRF, DOI [10.5065/D6MK6B4K, DOI 10.5065/D6MK6B4K]
[6]  
[Anonymous], 2017, IEC61400121
[7]   Two Corrections for Turbulent Kinetic Energy Generated by Wind Farms in the WRF Model [J].
Archer, Cristina L. ;
Wu, Sicheng ;
Ma, Yulong ;
Jimenez, Pedro A. .
MONTHLY WEATHER REVIEW, 2020, 148 (12) :4823-4835
[8]   Characterizing Wake Turbulence with Staring Lidar Measurements [J].
Bastine, D. ;
Waechter, M. ;
Peinke, J. ;
Trabucchi, D. ;
Kuehn, M. .
WAKE CONFERENCE 2015, 2015, 625
[9]   Light detection and ranging measurements of wake dynamics Part I: One-dimensional Scanning [J].
Bingol, Ferhat ;
Mann, Jakob ;
Larsen, Gunner C. .
WIND ENERGY, 2010, 13 (01) :51-61
[10]   Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars [J].
Borraccino, Antoine ;
Courtney, Michael ;
Wagner, Rozenn .
REMOTE SENSING, 2016, 8 (11)