Due to the complexity and uncertainty involved in the process of making power from wind, more and more advanced tools are being developed to maintain the sustainability and the growing trend of the wind industry. Prior to the development of a wind farm project, measured data are provided by limited installed wind masts at the site under investigation and by other nearby weather stations. Therefore, the wind resource assessment depends on the uncertainties and limitations of those measurements. To improve the reliability and limit the risks, weather prediction forecasting models can be employed in parallel with measurements, to investigate the local wind map and the potential wind power. Nevertheless, the physics involved at the inter-turbine or smaller scales cannot be captured by mesoscale modelling. To obtain predictions of such scales, high resolution mesoscale models are coupled with micro-scale computational fluid dynamics (CFD) simulations. Results of neutral atmospheric stability over a defined wind sector have been averaged in time and extracted from mesoscale simulations using the Weather Research and Forecasting model (WRF) with a very fine resolution (150 m). Those results were used to provide the inlet conditions of the micro-scale CFD simulations which were performed using the open-source CFD software OpenFOAM. The predicted time-averaged atmospheric flow within the Egmond aan Zee wind farm is compared for both numerical approaches. The wind farm’s total power estimations are compared to operational SCADA data.
Original languageEnglish
DOIs
Publication statusPublished - Mar 2017
EventResource Assessment 2017 - Edinburgh , United Kingdom
Duration: 16 Mar 201717 Mar 2017
https://windeurope.org/workshops/resource-assessment-2017/

Conference

ConferenceResource Assessment 2017
CountryUnited Kingdom
CityEdinburgh
Period16/03/1717/03/17
Internet address

    Research areas

  • CFD, Mesoscale modelling, WRF, Wind Energy, Wind Farm, OpenFOAM, Offshore wind energy, Wake effects, Wind measurement, SCADA, RANS

ID: 30552054