Work- Package 1: "DEFINITION OF PREDICTION REQUIREMENTS
Work-Package 5:
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"Prediction Models for
Offshore Wind Farms".
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WP Leader:
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Univ. OLDENBURG (Detlev Heinemann).
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Partners Involved:
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ARIA,
ARMINES, CCLRC/RAL, CENER, CIEMAT, DTU, OVERSPEED, RISOE
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Objectives:
This Work-Package aims to develop forecasting models for offshore wind farms.
In a given area of interest, offshore wind parks are currently located in the
5 to 10 km zones from coast. Considering the surface covered by wind farms,
the resolution of the wind grid needed is about 1 km. The data available for
offshore applications will be measurements at the level of wind farm as well
as Numerical Weather Predictions (NWP) provided by a meteorological system.
The downscaling of NWPs to the level of wind farm involves several difficulties;
for example due to the variable roughness of sea surface that depends on wind
speed. This Work-Package focuses on the specificities of the meteorological
conditions offshore. The roughness is very low, and the thermal state of the
atmosphere, the stability, is for long periods very different from the near
neutral case expected onshore. Additionally, the low roughness increases the
influence of stability on the wind speed profile dramatically. It is of interest
to investigate the parameters that influence the wind speed offshore most. In
a first step purely NWPs are evaluated against measurements.
Of special interest is the evaluation of meteorological forecasts by estimation
of wind speed on a kilometric grid from Satellite Radar data. Radar images from
open waters contain information about wind speed and wind direction. In contrast
to mast measurements, the absolute accuracy is quite poor (rmse ~2 m/s), but
spatial information for a large region can be delivered. This spatial information
about wind speed and direction can be used e.g. to validate shadowing within
the windfarm. Patterns of wind field around the wind farm can be defined from
Satellite images. It will be examined how this high-resolution information can
be integrated in either physical or statistical prediction models.
The ultimate objective is to develop statistical (i.e. artificial intelligence
based ones) and physical models able to perform accurately for the offshore
case. The physical models should be able to describe the vertical wind profile
for vertical refinement of the wind speed. Emphasis is given in modelling spatio-temporal
characteristics in large offshore wind farms. Offshore installed capacity will
be very concentrated. Therefore large power fluctuations will occur much more
often then onshore.
Description of Work:
Task 5.1: Impact of high-resolution meteorological forecasts.
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Task 5.2: Contribution of satellite-radar information.
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Task 5.3: Development of physical & statistical
models
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Task 5.4: Modelling spatio-temporal characteristics
in large offshore wind farms
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