IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v77y2015icp291-299.html
   My bibliography  Save this article

Combining the VAS 3D interpolation method and Wind Atlas methodology to produce a high-resolution wind resource map for the Czech Republic

Author

Listed:
  • Hanslian, David
  • Hošek, Jiří

Abstract

This paper describes a method that was applied as a part of the creation of a wind map for the Czech Republic. The method (abbreviated VAS/WindAtlas) combines an interpolation method (VAS) with the Wind Atlas methodology applied by using the microscale model WAsP. While WAsP eliminates the site-specific effects around measurement sites to provide generalised wind conditions (GWC), VAS interpolates the GWC over the entire domain and takes into account the general increase of wind speed with altitude. As a result, an altitude-dependent generalised wind map is provided. Then, a final high-resolution calculation is performed by WAsP. The VAS/WindAtlas method is considerably more simplified and less computationally demanding than approaches employing more complex numerical models, but it requires a sufficiently dense network of wind measurements, a thorough data quality evaluation and careful corrections that compensate for the known limitations of the applied data and methods. A comparison of the original wind map with new independent wind measurements, which were obtained after the wind map calculation, was performed with a detailed analysis of the expected errors and uncertainties at the validation sites. The presented VAS/WindAtlas method proved to be good solution to estimate wind resources of Czech Republic.

Suggested Citation

  • Hanslian, David & Hošek, Jiří, 2015. "Combining the VAS 3D interpolation method and Wind Atlas methodology to produce a high-resolution wind resource map for the Czech Republic," Renewable Energy, Elsevier, vol. 77(C), pages 291-299.
    • Handle: RePEc:eee:renene:v:77:y:2015:i:c:p:291-299
    DOI: 10.1016/j.renene.2014.12.013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114008398
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.12.013?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. Tar, Károly & Farkas, István & Rózsavölgyi, Kornél, 2011. "Climatic conditions for operation of wind turbines in Hungary," Renewable Energy, Elsevier, vol. 36(2), pages 510-518.
    2. Nicolas Gasset & Mathieu Landry & Yves Gagnon, 2012. "A Comparison of Wind Flow Models for Wind Resource Assessment in Wind Energy Applications," Energies, MDPI, vol. 5(11), pages 1-35, October.
    3. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: Introduction and statistical analysis," Renewable Energy, Elsevier, vol. 33(6), pages 1237-1250.
    4. Al-Yahyai, Sultan & Charabi, Yassine & Al-Badi, Abdullah & Gastli, Adel, 2012. "Nested ensemble NWP approach for wind energy assessment," Renewable Energy, Elsevier, vol. 37(1), pages 150-160.
    5. Jonathon Sumner & Christophe Sibuet Watters & Christian Masson, 2010. "CFD in Wind Energy: The Virtual, Multiscale Wind Tunnel," Energies, MDPI, vol. 3(5), pages 1-25, May.
    6. Cellura, M. & Cirrincione, G. & Marvuglia, A. & Miraoui, A., 2008. "Wind speed spatial estimation for energy planning in Sicily: A neural kriging application," Renewable Energy, Elsevier, vol. 33(6), pages 1251-1266.
    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. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    2. Rabbani, Rabab & Zeeshan, Muhammad, 2022. "Impact of policy changes on financial viability of wind power plants in Pakistan," Renewable Energy, Elsevier, vol. 193(C), pages 789-806.
    3. Samuel Van Ackere & Greet Van Eetvelde & David Schillebeeckx & Enrica Papa & Karel Van Wyngene & Lieven Vandevelde, 2015. "Wind Resource Mapping Using Landscape Roughness and Spatial Interpolation Methods," Energies, MDPI, vol. 8(8), pages 1-22, August.
    4. Tang, Xiao-Yu & Zhao, Shumian & Fan, Bo & Peinke, Joachim & Stoevesandt, Bernhard, 2019. "Micro-scale wind resource assessment in complex terrain based on CFD coupled measurement from multiple masts," Applied Energy, Elsevier, vol. 238(C), pages 806-815.
    5. Gualtieri, Giovanni, 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site," Renewable Energy, Elsevier, vol. 120(C), pages 457-467.
    6. Cheng, Xu & Yan, Bowen & Zhou, Xuhong & Yang, Qingshan & Huang, Guoqing & Su, Yanwen & Yang, Wei & Jiang, Yan, 2024. "Wind resource assessment at mountainous wind farm: Fusion of RANS and vertical multi-point on-site measured wind field data," Applied Energy, Elsevier, vol. 363(C).

    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. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    2. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    3. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    4. Petinrin, J.O. & Shaaban, Mohamed, 2015. "Renewable energy for continuous energy sustainability in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 967-981.
    5. González-Longatt, Francisco & Medina, Humberto & Serrano González, Javier, 2015. "Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 1-16.
    6. Maciej J. Nowak & Agnieszka Brelik & Anna Oleńczuk-Paszel & Monika Śpiewak-Szyjka & Justyna Przedańska, 2023. "Spatial Conflicts concerning Wind Power Plants—A Case Study of Spatial Plans in Poland," Energies, MDPI, vol. 16(2), pages 1-20, January.
    7. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
    8. Collados-Lara, Antonio-Juan & Baena-Ruiz, Leticia & Pulido-Velazquez, David & Pardo-Igúzquiza, Eulogio, 2022. "Data-driven mapping of hourly wind speed and its potential energy resources: A sensitivity analysis," Renewable Energy, Elsevier, vol. 199(C), pages 87-102.
    9. Rubin, Ofir David, 2010. "Equilibrium pricing in electricity markets with wind power," ISU General Staff Papers 201001010800002361, Iowa State University, Department of Economics.
    10. Soulis, Konstantinos X. & Manolakos, Dimitris & Ntavou, Erika & Kosmadakis, George, 2022. "A geospatial analysis approach for the operational assessment of solar ORC systems. Case study: Performance evaluation of a two-stage solar ORC engine in Greece," Renewable Energy, Elsevier, vol. 181(C), pages 116-128.
    11. Hur, J. & Baldick, R., 2016. "A new merit function to accommodate high wind power penetration of WGRs (wind generating resources)," Energy, Elsevier, vol. 108(C), pages 34-40.
    12. Leer, Donald & Chang, Byungik & Chen, Gerald & Carr, David & Starcher, Kenneth & Issa, Roy, 2013. "Windtane contour map of the state of Texas," Renewable Energy, Elsevier, vol. 58(C), pages 140-150.
    13. Beccali, M. & Cirrincione, G. & Marvuglia, A. & Serporta, C., 2010. "Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor," Applied Energy, Elsevier, vol. 87(3), pages 884-893, March.
    14. Tar, Károly & Farkas, István & Rózsavölgyi, Kornél, 2011. "Climatic conditions for operation of wind turbines in Hungary," Renewable Energy, Elsevier, vol. 36(2), pages 510-518.
    15. Qiaomu Zhu & Jinfu Chen & Lin Zhu & Xianzhong Duan & Yilu Liu, 2018. "Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach," Energies, MDPI, vol. 11(4), pages 1-18, March.
    16. Cellura, Maurizio & Guarino, Francesco & Longo, Sonia & Mistretta, Marina, 2015. "Different energy balances for the redesign of nearly net zero energy buildings: An Italian case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 100-112.
    17. Ouammi, Ahmed & Sacile, Roberto & Zejli, Driss & Mimet, Abdelaziz & Benchrifa, Rachid, 2010. "Sustainability of a wind power plant: Application to different Moroccan sites," Energy, Elsevier, vol. 35(10), pages 4226-4236.
    18. Lagorse, Jeremy & Paire, Damien & Miraoui, Abdellatif, 2010. "A multi-agent system for energy management of distributed power sources," Renewable Energy, Elsevier, vol. 35(1), pages 174-182.
    19. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    20. Calif, Rudy & Emilion, Richard & Soubdhan, Ted, 2011. "Classification of wind speed distributions using a mixture of Dirichlet distributions," Renewable Energy, Elsevier, vol. 36(11), pages 3091-3097.

    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:77:y:2015:i:c:p:291-299. 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.