Published January 15, 2025 | Version 1
Journal article Open

Resolving three-dimensional wind velocity fields with sequential wind-Doppler LiDAR for wind energy in the complex terrain - Gotthard Pass, Switzerland

  • 1. Laboratory of Cryospheric Sciences, Environmental Engineering Institute, Ecole polytechnique federale de Lausanne (EPFL), Sion, Valais, 1950, Switzerland
  • 2. Snow Processes, Swiss Institute for Snow and Avalanche Research (SLF), Davos, Graubünden, 7260, Switzerland

Description

We have developed a new method of using an existing measurement device to improve the information we can gather about the changes in the wind as they pass over a mountain pass. Five wind turbines were constructed in 2020 on top of a prominent Swiss mountain pass in the Gotthard Wind Park. It is known that these mountain winds are much stronger due to an effect called wind channelling, allowing the wind turbines to generate more energy. With our new measurement method, we can visualise the airflow through the wind park with just one sensor by applying an algorithm that can identify similar wind conditions at different times. Our measurement campaign during the Summer of 2023 allowed us to study several different flow patterns and identify some complex flow structures that affect the efficiency of the wind turbine. We show for each turbine, specifically for Northerly and Southerly winds, how efficient their energy production is, and explain with our detailed wind flow measurements, how these can arise due to the surrounding mountains.

Files

openreseurope-5-20665.pdf

Files (6.7 MB)

Name Size Download all
md5:1b4bf75b145e8cb923e734ef6719ca14
6.7 MB Preview Download

Additional details

References

  • Elgendi M, AlMallahi M, Abdelkhalig A (2023). A review of wind turbines in complex terrain. Int J Thermofluids. doi:10.1016/j.ijft.2023.100289
  • Hyvärinen A, Segalini A (2017). Effects from complex terrain on wind-turbine performance. J Energy Resour Technol. doi:10.1115/1.4036048
  • (2018). Energy strategy 2050 once the new energy act is in force.
  • Meyer L, Koller S, Froidevaux P (2022). Windpotenzial Schweiz 2022: Schlussberich zum Windpotenzial Schweiz 2022.
  • Qosja S, Rolle R, Gebremedhin A (2022). Solving the bottleneck issue of energy supply. Case study of a wind power plant. Int J Web Eng Technol. doi:10.15157/IJITIS.2022.5.2.874-891
  • Spiess H, Lobsiger-Kägi E, Carabias-Hütter V (2015). Future acceptance of wind energy production: exploring future local acceptance of wind energy production in a Swiss Alpine Region. Technol Forecast Soc Change. doi:10.1016/j.techfore.2015.06.042
  • Vignali S, Lörcher F, Hegglin D (2022). A predictive flight-altitude model for avoiding future conflicts between an emblematic raptor and wind energy development in the Swiss Alps. R Soc Open Sci. doi:10.1098/rsos.211041
  • Apostol D, Palmer J, Pasqualetti M (2016). The renewable energy landscape: preserving scenic values in our sustainable future. doi:10.4324/9781315618463
  • Bradley S, Strehz A, Emeis S (2015). Remote sensing winds in complex terrain - a review. Meteorologische Zeitschrift. doi:10.1127/metz/2015/0640
  • Lange J, Mann J, Berg J (2017). For wind turbines in complex terrain, the devil is in the detail. Environ Res Lett. doi:10.1088/1748-9326/aa81db
  • Bechmann A, Sørensen NN, Berg J (2011). The bolund experiment, part II: blind comparison of microscale flow models. Boundary-Layer Meteorol. doi:10.1007/s10546-011-9637-x
  • Berg J, Mann J, Bechmann A (2011). The bolund experiment, part I: flow over a steep, three-dimensional hill. Boundary-Layer Meteorol. doi:10.1007/s10546-011-9636-y
  • Vassberg JC, Tinoco EN, Mani M (2008). Abridged summary of the third AIAA computational fluid dynamics drag prediction workshop. J Aircraft. doi:10.2514/1.30572
  • Menke R, Vasiljević N, Mann J (2019). Characterization of flow recirculation zones at the Perdigão site using multi-lidar measurements. Atmos Chem Phys. doi:10.5194/acp-19-2713-2019
  • Wildmann N, Kigle S, Gerz T (2018). Coplanar lidar measurement of a single wind energy converter wake in distinct atmospheric stability regimes at the Perdigão 2017 experiment. J Phys Conf Ser. doi:10.1088/1742-6596/1037/5/052006
  • Wenz F, Langner J, Lutz T (2022). Impact of the wind field at the complex-terrain site perdigão on the surface pressure fluctuations of a wind turbine. Wind Energy Sci. doi:10.5194/wes-7-1321-2022
  • Venkatraman K, Hågbo TO, Buckingham S (2023). Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão. Wind Energy Sci. doi:10.5194/wes-8-85-2023
  • Wagner J, Gerz T, Wildmann N (2019). Long-term simulation of the boundary layer flow over the double-ridge site during the Perdigão 2017 field campaign. Atmos Chem Phys. doi:10.5194/acp-19-1129-2019
  • Barthelmie RJ, Pryor SC, Wildmann N (2018). Wind turbine wake characterization in complex terrain via integrated Doppler lidar data from the Perdigão experiment. J Phys Conf Ser. doi:10.1088/1742-6596/1037/5/052022
  • Barthelmie RJ, Pryor SC (2019). Impact of local meteorology on wake characteristics at Perdigão. J Phys Conf Ser. doi:10.1088/1742-6596/1256/1/012007
  • Volkert H, Gutermann T (2007). Inter-domain cooperation for mesoscale atmospheric laboratories: the mesoscale alpine programme as a rich study case. Quart J Royal Meteorol Soc. doi:10.1002/qj.95
  • Flamant C, Richard E, Schär C (2004). The wake south of the Alps: dynamics and structure of the lee-side flox and secondary potential vorticity banners. Quart J Royal Meteorol Soc. doi:10.1256/qj.03.17
  • Schär C, Sprenger M, Lüthi D (2003). Structure and dynamics of an Alpine potential-vorticity banner. Quart J Royal Meteorol Soc. doi:10.1256/qj.02.47
  • Walser A, Schär C (2004). Convection-resolving precipitation forecasting and its predictability in Alpine river catchments. Journal of Hydrology. doi:10.1016/j.jhydrol.2003.11.035
  • Walser A, Lüthi D, Schär C (2004). Predictability of precipitation in a cloud-resolving model. Mon Weather Rev. doi:10.1175/1520-0493(2004)132<0560:POPIAC>2.0.CO;2
  • Mackenzie H, Dyson J (2017). Short term forecasting of wind power plant generation for system stability and provision of ancillary services. Wind Integration Forum Proceedings.
  • Risan A, Lund JA, Chang CY (2018). Wind in complex terrain—lidar measurements for evaluation of CFD simulations. Remote Sensing. doi:10.3390/rs10010059
  • Bingöl F, Mann J, Foussekis D (2009). Conically scanning lidar error in complex terrain. Meteorologische Zeitschrift. doi:10.1127/0941-2948/2009/0368
  • Wagner R, Bejdic J (2014). WINDCUBE+ FCR test at Hrgud, Bosnia and Herzegovina.
  • Kristianti F, Dujardin J, Gerber F (2023). Combining weather station data and short-term LiDAR deployment to estimate wind energy potential with machine learning: a case study from the swiss alps. Boundary-Layer Meteorol. doi:10.1007/s10546-023-00808-y
  • Dujardin J, Lehning M (2022). Wind-topo: downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning. Q J R Meteorol Soc. doi:10.1002/qj.4265
  • Melani PF, Di Pietro F, Motta M (2023). A critical analysis of the uncertainty in the production estimation of wind parks in complex terrains. Renew Sustain Energy Rev. doi:10.1016/j.rser.2023.113339
  • Baker WE, Atlas R, Cardinali C (2014). Lidar-measured wind profiles: the missing link in the global observing system. Bull Am Meteorol Soc. doi:10.1175/BAMS-D-12-00164.1
  • Panofsky HA, Ming Z (1983). Characteristics of wind profiles over complex terrain. Journal of Wind Engineering and Industrial Aerodynamics. doi:10.1016/0167-6105(83)90188-5
  • Clifton A, Clive P, Gottschall J (2018). IEA wind task 32: wind lidar identifying and mitigating barriers to the adoption of wind lidar. Remote Sens. doi:10.3390/rs10030406
  • Clifton A, Barber S, Stökl A (2022). Research challenges and needs for the deployment of wind energy in hilly and mountainous regions. Wind Energ Sci. doi:10.5194/wes-7-2231-2022
  • Fuertes FC, Iungo GV, Porté-Agel F (2014). 3D turbulence measurements using three synchronous wind lidars: validation against sonic anemometry. J Atmos Ocean Technol. doi:10.1175/JTECH-D-13-00206.1
  • Boquet M, Thobois L (2012). Wind resource assessment campaign with lidars and met mast in large and complex sites.
  • Lang S, McKeogh E (2011). LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes. Remote Sens. doi:10.3390/rs3091871
  • Barthelmie RJ, Folkerts L, Ormel FT (2003). Offshore wind turbine wakes measured by sodar. J Atmos Ocean Technol. doi:10.1175/1520-0426(2003)20<466:OWTWMB>2.0.CO;2
  • Crescenti GH (1997). A look back on two decades of doppler sodar comparison studies. Bull Am Meteorol Soc. doi:10.1175/1520-0477(1997)078<0651:ALBOTD>2.0.CO;2
  • (2024). Description of automated stations — slf.ch.
  • (2024). Measurement values and measuring networks - MeteoSwiss — meteoswiss.admin.ch.
  • Kröpfli D, Schlegel T, Geissmann M (2022). Windatlas schweiz: Jahresmittel der modellierten windgeschwindigkeit und windrichtung.
  • (2024). Föhnindex - MeteoSchweiz.
  • (null). HALO PHOTONICS | StreamLine series - Product — halo-photonics.com.
  • van Schaik B, Lehning M, Huwald H (2024). Sequential wind-doppler lidar wind profile measurements on the Gotthard pass in Switzerland - Summer 2023. Open Research Europe, Zenodo.