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State of the Art and Trends in Wind Resource Assessment

Author

Listed:
  • Oliver Probst

    (Physics Department, Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, N.L., CP64849, Mexico)

  • Diego Cárdenas

    (Physics Department, Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, N.L., CP64849, Mexico)

Abstract

Given the significant rise of the utilization of wind energy the accurate assessment of the wind potential is becoming increasingly important. Direct applications of wind assessment techniques include the creation of wind maps on a local scale (typically 5 20 km) and the micrositing of wind turbines, the estimation of vertical wind speed variations, prospecting on a regional scale (>100 km), estimation of the long-term wind resource at a given site, and forecasting. The measurement of wind speed and direction still widely relies on cup anemometers, though sonic anemometers are becoming increasingly popular. Moreover, remote sensing by Doppler techniques using the backscattering of either sonic beams (SODAR) or light (LIDAR) allowing for vertical profiling well beyond hub height are quickly moving into the mainstream. Local wind maps are based on the predicted modification of the regional wind flow pattern by the local atmospheric boundary layer which in turn depends on both topographic and roughness features and the measured wind rose obtained from one or several measurement towers within the boundaries of the planned development site. Initial models were based on linearized versions of the Navier-Stokes equations, whereas more recently full CFD models have been applied to wind farm micrositing. Linear models tend to perform well for terrain slopes lower than about 25% and have the advantage of short execution times. Long-term performance is frequently estimated from correlations with nearby reference stations with concurrent information and continuous time series over a period of at least 10 years. Simple methods consider only point-to-point linear correlations; more advanced methods like multiple regression techniques and methods based on the theory of distributions will be discussed. Both for early prospecting in regions where only scarce or unreliable reference information is available, wind flow modeling on a larger scale (mesoscale) is becoming increasingly popular.

Suggested Citation

  • Oliver Probst & Diego Cárdenas, 2010. "State of the Art and Trends in Wind Resource Assessment," Energies, MDPI, vol. 3(6), pages 1-55, June.
    • Handle: RePEc:gam:jeners:v:3:y:2010:i:6:p:1087-1141:d:8562
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    References listed on IDEAS

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    1. Kiranoudis, C. T. & Voros, N. G. & Maroulis, Z. B., 2001. "Short-cut design of wind farms," Energy Policy, Elsevier, vol. 29(7), pages 567-578, June.
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    1. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    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. He-Yau Kang & Meng-Chan Hung & W. L. Pearn & Amy H. I. Lee & Mei-Sung Kang, 2011. "An Integrated Multi-Criteria Decision Making Model for Evaluating Wind Farm Performance," Energies, MDPI, vol. 4(11), pages 1-25, November.
    4. 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.
    5. Yang, Xiaolei & Milliren, Christopher & Kistner, Matt & Hogg, Christopher & Marr, Jeff & Shen, Lian & Sotiropoulos, Fotis, 2021. "High-fidelity simulations and field measurements for characterizing wind fields in a utility-scale wind farm," Applied Energy, Elsevier, vol. 281(C).
    6. Souma Chowdhury & Ali Mehmani & Jie Zhang & Achille Messac, 2016. "Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes," Energies, MDPI, vol. 9(5), pages 1-31, May.
    7. José V. P. Miguel & Eliane A. Fadigas & Ildo L. Sauer, 2019. "The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment," Energies, MDPI, vol. 12(19), pages 1-15, September.
    8. Carta, José A. & Cabrera, Pedro & Matías, José M. & Castellano, Fernando, 2015. "Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study," Applied Energy, Elsevier, vol. 158(C), pages 490-507.
    9. Mifsud, Michael D. & Sant, Tonio & Farrugia, Robert N., 2018. "A comparison of Measure-Correlate-Predict Methodologies using LiDAR as a candidate site measurement device for the Mediterranean Island of Malta," Renewable Energy, Elsevier, vol. 127(C), pages 947-959.
    10. Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2018. "Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods," Energies, MDPI, vol. 11(6), pages 1-17, June.
    11. Majidi Nezhad, M. & Groppi, D. & Marzialetti, P. & Fusilli, L. & Laneve, G. & Cumo, F. & Garcia, D. Astiaso, 2019. "Wind energy potential analysis using Sentinel-1 satellite: A review and a case study on Mediterranean islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 499-513.
    12. Muche, Thomas & Pohl, Ralf & Höge, Christin, 2016. "Economically optimal configuration of onshore horizontal axis wind turbines," Renewable Energy, Elsevier, vol. 90(C), pages 469-480.
    13. Khan, Komal S. & Tariq, Muhammad, 2018. "Wind resource assessment using SODAR and meteorological mast – A case study of Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2443-2449.
    14. Radünz, William Corrêa & Sakagami, Yoshiaki & Haas, Reinaldo & Petry, Adriane Prisco & Passos, Júlio César & Miqueletti, Mayara & Dias, Eduardo, 2021. "Influence of atmospheric stability on wind farm performance in complex terrain," Applied Energy, Elsevier, vol. 282(PA).
    15. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    16. Osvaldo Rodríguez & Jesús A del Río & Oscar A Jaramillo & Manuel Martínez, 2015. "Wind Power Error Estimation in Resource Assessments," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-13, May.
    17. Dongheon Shin & Kyungnam Ko, 2019. "Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement," Energies, MDPI, vol. 12(6), pages 1-15, March.
    18. José F. Herbert-Acero & Oliver Probst & Pierre-Elouan Réthoré & Gunner Chr. Larsen & Krystel K. Castillo-Villar, 2014. "A Review of Methodological Approaches for the Design and Optimization of Wind Farms," Energies, MDPI, vol. 7(11), pages 1-87, October.

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