| dc.description.abstract | With the shift to carbon neutral energy global wind electricity production is rapidly increasing. Leading to larger and larger wind turbines and wind farms. As wind conditions change it is important to understand how production at wind farms will be affected by the local wind conditions, to optimize the layout and production of the wind farm. PyWake is a tool that can be used to simulate flow fields and energy production at wind farms.
This thesis aims to gather data and information about offshore wind farms, then use the information gathered to make models capable of running simulations with PyWake. Results from the simulations will be compared with the production of the actual wind farms.
To estimate the annual energy production (AEP) the NOJ wake deficit model have been used. To make the layout of the wind farms being studied, the 4COffshore global map have been used. Publicly accessible data have been found and used to make models of the turbines at the different wind farms. And Global Wind Atlas have been used to gather information about the wind conditions needed to make the models.
The offshore wind farms Borssele I & II, Borssele III & IV, the Borssele wind farm zone, Borkum Riffgrund II and Hornsea Project 2 have been modelled in this thesis. The NOJ wake deficit model have been used to estimate the AEP, and that have been compared to information about the actual production at the different wind farms. As in previous studies the NOJ model estimates a higher AEP than the actual AEP. The models are also shown to be compatible with different PyWake functions. | |
