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

Author:
Grassi, Stefano   Junghans, Sven   Raubal, Martin  


Journal:
Applied Energy


Issue Date:
2014


Abstract(summary):

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 50 m 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. (C) 2014 Elsevier Ltd. All rights reserved.


Page:
827-837


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