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Interests of regional modelisation for wind power forecasting

Authors
Publication Date
Keywords
  • Wind Power Forecasting
  • Gfs Model
  • Wrf Model
  • Physical
  • Chemical
  • Mathematical & Earth Sciences :: Earth Sciences & Physical Geography [G02]
  • Physique
  • Chimie
  • Mathématiques & Sciences De La Terre :: Sciences De La Terre & Géographie Physique [G02]
Disciplines
  • Earth Science
  • Ecology

Abstract

European policies have decided to reduce the greenhouse gas emissions of 20% and to reach 20% of renewable power production by 2020. Increasing wind power is one of the numerous solutions to reach these goals. However, this kind of energy production depends on the meteorological conditions and gives it an intermittent behaviour. The wind speed variations cause voltage and frequency fluctuations that are unacceptable for the power grid. Therefore, forecasting production will become essential with the aim of integrating this kind of energy production into the power grid. We have developed and compared two forecasting models which give as outputs the wind power production every 15 minutes over the Belgian territory: the first one uses the outputs from the global model GFS (available at a horizontal resolution of 0.5° every 3h) and the second one uses the regional climate model WRF-NMM (using a horizontal resolution of 4km). Both of these models predict the wind speed and transform wind speed into wind power production, using a power curve which depends on the wind turbines and their characteristics. The first model using the GFS outputs is not precise enough in space and time to correctly forecast the wind speed in punctual wind farms. That is why we apply some specific tunings on these forecasts. These tunings depend on the air density, the wind direction and the stability of the air mass. The second model using the WRF-NMM outputs runs over the Belgian territory. Initial conditions are forced by the GFS outputs at 0.5° and WRF computes a physical based spatio-temporal downscaling of the meteorological variables. The outputs have a spatial resolution of 4 km and a time resolution of 15 minutes. Some tunings are also needed to adjust the wind power forecasts by comparison to the wind power observations. We present here some results of both models and the interest of using a regional model for more precise wind power forecasting.

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