IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v99y2016icp647-653.html
   My bibliography  Save this article

Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell

Author

Listed:
  • Bilal, Muhammad
  • Birkelund, Yngve
  • Homola, Matthew
  • Virk, Muhammad Shakeel

Abstract

Nygårdsfjell, a complex terrain near Norwegian-Swedish border, is characterized by its significant wind resources. The feasibility of using mesoscale winds as input to microscale model is studied in this work. The main objective is to take into account the actual terrain effects on wind flow over complex terrain. First set of mesoscale winds are modelled with Weather Research and Forecasting (WRF) numerical tool whereas second set of mesoscale winds are taken from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) data system. WindSim, a computational fluid dynamics based numerical solver is used as microscale modelling tool. The results suggest that the performance of microscale model is largely dependent upon the quality of mesoscale winds as input. The proposed coupled models achieve improvements in wind speed modelling, especially during cold weather. WRF-WindSim coupling showed better results than MERRA-WindSim coupling in all three test cases, as root mean square error (RMSE) decreased by 70.9% for the February case, 61.5% for October and 14.4% for June case respectively. Raw mesoscale winds from the WRF model were also more correct than the mesoscale winds from MERRA data set when extracted directly at the wind turbine by decreasing the RMSE by 62.6% for the February case, 62.7% for October and 23.7% for June case respectively. The difference of RMSE values between the mesoscale winds directly at wind turbine versus the coupled meso-microscale model outputs are not conclusive enough to indicate any specific trend.

Suggested Citation

  • Bilal, Muhammad & Birkelund, Yngve & Homola, Matthew & Virk, Muhammad Shakeel, 2016. "Wind over complex terrain – Microscale modelling with two types of mesoscale winds at Nygårdsfjell," Renewable Energy, Elsevier, vol. 99(C), pages 647-653.
  • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:647-653
    DOI: 10.1016/j.renene.2016.07.042
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116306383
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2016.07.042?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    2. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    3. Staffell, Iain & Green, Richard, 2014. "How does wind farm performance decline with age?," Renewable Energy, Elsevier, vol. 66(C), pages 775-786.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Spatial and temporal assessments of complementarity for renewable energy resources in China," Energy, Elsevier, vol. 177(C), pages 262-275.
    2. Renko Buhr & Hassan Kassem & Gerald Steinfeld & Michael Alletto & Björn Witha & Martin Dörenkämper, 2021. "A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification," Energies, MDPI, vol. 14(4), pages 1-22, February.
    3. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2020. "Spatial and temporal correlation analysis of wind power between different provinces in China," Energy, Elsevier, vol. 191(C).
    4. Liu, Zhenqing & Diao, Zheng & Ishihara, Takeshi, 2019. "Study of the flow fields over simplified topographies with different roughness conditions using large eddy simulations," Renewable Energy, Elsevier, vol. 136(C), pages 968-992.
    5. Lattawan Niyomtham & Charoenporn Lertsathittanakorn & Jompob Waewsak & Yves Gagnon, 2022. "Mesoscale/Microscale and CFD Modeling for Wind Resource Assessment: Application to the Andaman Coast of Southern Thailand," Energies, MDPI, vol. 15(9), pages 1-19, April.
    6. Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
    7. Durán, Pablo & Meiβner, Cathérine & Casso, Pau, 2020. "A new meso-microscale coupled modelling framework for wind resource assessment: A validation study," Renewable Energy, Elsevier, vol. 160(C), pages 538-554.
    8. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    9. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "A refined wind farm parameterization for the weather research and forecasting model," Applied Energy, Elsevier, vol. 306(PB).
    10. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    2. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    3. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    4. Andresen, Gorm B. & Søndergaard, Anders A. & Greiner, Martin, 2015. "Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis," Energy, Elsevier, vol. 93(P1), pages 1074-1088.
    5. Olauson, Jon & Bergkvist, Mikael, 2015. "Modelling the Swedish wind power production using MERRA reanalysis data," Renewable Energy, Elsevier, vol. 76(C), pages 717-725.
    6. Frank, Christopher W. & Pospichal, Bernhard & Wahl, Sabrina & Keller, Jan D. & Hense, Andreas & Crewell, Susanne, 2020. "The added value of high resolution regional reanalyses for wind power applications," Renewable Energy, Elsevier, vol. 148(C), pages 1094-1109.
    7. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    8. Ritter, Matthias & Deckert, Lars, 2015. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," SFB 649 Discussion Papers 2015-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Ritter, Matthias & Deckert, Lars, 2017. "Site assessment, turbine selection, and local feed-in tariffs through the wind energy index," Applied Energy, Elsevier, vol. 185(P2), pages 1087-1099.
    10. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    11. Yip, Chak Man Andrew & Gunturu, Udaya Bhaskar & Stenchikov, Georgiy L., 2016. "Wind resource characterization in the Arabian Peninsula," Applied Energy, Elsevier, vol. 164(C), pages 826-836.
    12. Hdidouan, Daniel & Staffell, Iain, 2017. "The impact of climate change on the levelised cost of wind energy," Renewable Energy, Elsevier, vol. 101(C), pages 575-592.
    13. Sharp, Ed & Dodds, Paul & Barrett, Mark & Spataru, Catalina, 2015. "Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information," Renewable Energy, Elsevier, vol. 77(C), pages 527-538.
    14. Erik Möllerström & Sean Gregory & Aromal Sugathan, 2021. "Improvement of AEP Predictions with Time for Swedish Wind Farms," Energies, MDPI, vol. 14(12), pages 1-12, June.
    15. Watts, David & Durán, Pablo & Flores, Yarela, 2017. "How does El Niño Southern Oscillation impact the wind resource in Chile? A techno-economical assessment of the influence of El Niño and La Niña on the wind power," Renewable Energy, Elsevier, vol. 103(C), pages 128-142.
    16. Cradden, Lucy C. & McDermott, Frank & Zubiate, Laura & Sweeney, Conor & O'Malley, Mark, 2017. "A 34-year simulation of wind generation potential for Ireland and the impact of large-scale atmospheric pressure patterns," Renewable Energy, Elsevier, vol. 106(C), pages 165-176.
    17. Jäger, Tobias & McKenna, Russell & Fichtner, Wolf, 2015. "Onshore wind energy in Baden-Württemberg: a bottom-up economic assessment of the socio-technical potential," Working Paper Series in Production and Energy 7, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    18. Akintayo T. Abolude & Wen Zhou, 2018. "A Comparative Computational Fluid Dynamic Study on the Effects of Terrain Type on Hub-Height Wind Aerodynamic Properties," Energies, MDPI, vol. 12(1), pages 1-14, December.
    19. Francisco Haces-Fernandez, 2020. "GoWInD: Wind Energy Spatiotemporal Assessment and Characterization of End-of-Life Activities," Energies, MDPI, vol. 13(22), pages 1-20, November.
    20. Abdollahzadeh, Hadi & Atashgar, Karim & Abbasi, Morteza, 2016. "Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups," Renewable Energy, Elsevier, vol. 88(C), pages 247-261.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:99:y:2016:i:c:p:647-653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.