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Reliably model of microwind power energy output under real conditions in France suburban area

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  • Ramenah, H.
  • Tanougast, C.

Abstract

Micro-wind turbine are now specially designed for rural or urban environment and one of the main advantages of such turbine is that it can be propelled by a wind speed as low as 3 m/s. However, due to terrain roughness in urban environments wind flow is reduced compared to open spaces reducing power output and increasing payback time on capital investment. Well mounting turbines in urban areas may provide the perfect opportunity for onsite generation from wind power. In this paper, we investigate the performance of a micro-wind turbine in a complex urban area and show that due to long time period and very subtile onsite measurements the ideal position for the wind turbine can be determined. Well measured data, wind speed, power output at this particular location are approximated by the Weibull function. The considered model is tested and validated at an urban landscape location in Metz City, France, where an anemometry is positioned at adjacent to the turbine and the instrumentation is positioned specific to its surrounding location and, record wind turbine data thanks to real time wireless communications. Technical data including wind speed and output power were analyzed and reported allowing to provide an reliable estimation of the wind energy potential in an urban location.

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  • Ramenah, H. & Tanougast, C., 2016. "Reliably model of microwind power energy output under real conditions in France suburban area," Renewable Energy, Elsevier, vol. 91(C), pages 1-10.
    • Handle: RePEc:eee:renene:v:91:y:2016:i:c:p:1-10
    DOI: 10.1016/j.renene.2015.11.019
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    1. Kam, Olle Michel & Noël, Stéphane & Ramenah, Harry & Kasser, Pierre & Tanougast, Camel, 2021. "Comparative Weibull distribution methods for reliable global solar irradiance assessment in France areas," Renewable Energy, Elsevier, vol. 165(P1), pages 194-210.
    2. Ba, Moustapha & Ramenah, Harry & Tanougast, Camel, 2018. "Forseeing energy photovoltaic output determination by a statistical model using real module temperature in the north east of France," Renewable Energy, Elsevier, vol. 119(C), pages 935-948.
    3. Efthimiou, G.C. & Kumar, P. & Giannissi, S.G. & Feiz, A.A. & Andronopoulos, S., 2019. "Prediction of the wind speed probabilities in the atmospheric surface layer," Renewable Energy, Elsevier, vol. 132(C), pages 921-930.

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