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The role of air density in wind energy assessment – A case study from Germany

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  • Jung, Christopher
  • Schindler, Dirk

Abstract

The statistical air density distribution was modeled on a high-spatial resolution scale (200 m × 200 m) and the error by using constant standard air density was estimated using Germany as study area. Daily mean air temperature and air pressure time series of 144 meteorological measuring stations operated in the period 1979–2014 were used to calculate air density in the very common hub height for newly installed wind turbines of 140 m. The parameters of the statistical air density distributions were mapped for the whole of Germany. By applying a 2.4 MW power curve and the wind speed-wind shear model, study area-wide annual energy yield was calculated assuming constant standard air density and using the modeled air density distributions. The results from the comparison of the energy yields demonstrate that the total area with energy yield >7.0 GWh/yr is slightly smaller (0.7%) when air density is considered to be variable. Based on the results of this study, the influence of air density on the wind energy yield of low elevation coastal sites and high elevation mountain sites can now be quantified in the study area. This will contribute to a more efficient use of the wind resource.

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  • Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    • Handle: RePEc:eee:energy:v:171:y:2019:i:c:p:385-392
    DOI: 10.1016/j.energy.2019.01.041
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    1. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    2. Nugent, Daniel & Sovacool, Benjamin K., 2014. "Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey," Energy Policy, Elsevier, vol. 65(C), pages 229-244.
    3. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    4. Amirinia, Gholamreza & Kamranzad, Bahareh & Mafi, Somayeh, 2017. "Wind and wave energy potential in southern Caspian Sea using uncertainty analysis," Energy, Elsevier, vol. 120(C), pages 332-345.
    5. Zhang, Jie & Chowdhury, Souma & Messac, Achille & Castillo, Luciano, 2013. "A Multivariate and Multimodal Wind Distribution model," Renewable Energy, Elsevier, vol. 51(C), pages 436-447.
    6. Jung, Christopher & Schindler, Dirk, 2018. "On the inter-annual variability of wind energy generation – A case study from Germany," Applied Energy, Elsevier, vol. 230(C), pages 845-854.
    7. Leonie Grau & Christopher Jung & Dirk Schindler, 2017. "On the Annual Cycle of Meteorological and Geographical Potential of Wind Energy: A Case Study from Southwest Germany," Sustainability, MDPI, vol. 9(7), pages 1-11, July.
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