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Assessment of the wake effect on the energy production of onshore wind farms using GIS

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  • Grassi, Stefano
  • Junghans, Sven
  • Raubal, Martin

Abstract

In this study, we propose a method to estimate the mean annual energy production of a wind farm with a Geographic Information System (GIS). GIS allows for spatial modeling in many fields and has recently been applied in the field of renewable energy. The geographic features of a wind park are represented using spatial data such as topography, land cover, and wind resource. Wind resource layers contain data of the 16 wind directions, in which the wind rose is divided, including the frequency of the wind direction, the mean annual wind speed and the annual Weibull parameters k and C) estimated at 50m height. The wind turbines are represented by points including information about the roughness of the surrounding terrain. Roughness is calculated within a GIS process that models the variation of the land cover over the year around the wind turbine position. The mean annual energy production is calculated coupling the technical characteristics of the wind turbines models with the wind resource. In addition, the wake effect between wind turbines has been included. A parameter called “reduced efficiency coefficient” has been introduced to assess the impact of the layout of wind farm on the annual energy production in respect to the change of the wind direction. The reduced efficiency coefficient shows that relatively regular wind farm layouts designed for exploiting the wind speed blowing from the prevailing wind direction can cause significant energy losses. In particular, when the wind comes from directions perpendicular to the prevailing one, the wind turbines waste up to 60% of the available energy. The method has been tested, comparing the actual annual energy production of four wind farms in Kansas (U.S.) with the estimated mean annual energy production. The validation demonstrated an average underestimation of 3.56% of the annual energy production and an average underestimation of 1.11% of the capacity factor. The results are encouraging and the developed process enables the quantification of the annual energy production with low uncertainties.

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  • Grassi, Stefano & Junghans, Sven & Raubal, Martin, 2014. "Assessment of the wake effect on the energy production of onshore wind farms using GIS," Applied Energy, Elsevier, vol. 136(C), pages 827-837.
    • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:827-837
    DOI: 10.1016/j.apenergy.2014.05.066
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    9. Davide Astolfi & Francesco Castellani & Ludovico Terzi, 2018. "Wind Turbine Power Curve Upgrades," Energies, MDPI, vol. 11(5), pages 1-17, May.
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    11. Zhong, Hongmin & Du, Pingan & Tang, Fangning & Wang, Li, 2015. "Lagrangian dynamic large-eddy simulation of wind turbine near wakes combined with an actuator line method," Applied Energy, Elsevier, vol. 144(C), pages 224-233.
    12. Lin, Jian Wei & Zhu, Wei Jun & Shen, Wen Zhong, 2022. "New engineering wake model for wind farm applications," Renewable Energy, Elsevier, vol. 198(C), pages 1354-1363.
    13. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    14. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    15. Böhme, Gustavo S. & Fadigas, Eliane A. & Gimenes, André L.V. & Tassinari, Carlos E.M., 2018. "Wake effect measurement in complex terrain - A case study in Brazilian wind farms," Energy, Elsevier, vol. 161(C), pages 277-283.
    16. Sahoo, K. & Hawkins, G.L. & Yao, X.A. & Samples, K. & Mani, S., 2016. "GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: A case study with cotton stalks in the Southeastern US," Applied Energy, Elsevier, vol. 182(C), pages 260-273.
    17. Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2016. "Optimization of wind turbine layout position in a wind farm using a newly-developed two-dimensional wake model," Applied Energy, Elsevier, vol. 174(C), pages 192-200.
    18. Ji, Ling & Li, Jiahui & Sun, Lijian & Wang, Shuai & Guo, Junhong & Xie, Yulei & Wang, Xander, 2024. "China's onshore wind energy potential in the context of climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    19. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
    20. Sergio Velázquez Medina & José A. Carta & Ulises Portero Ajenjo, 2019. "Performance Sensitivity of a Wind Farm Power Curve Model to Different Signals of the Input Layer of ANNs: Case Studies in the Canary Islands," Complexity, Hindawi, vol. 2019, pages 1-11, March.

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