| dc.description.abstract | The rapid development of the offshore wind industry has led to more wind farm clusters worldwide. In 2022, the Norwegian government announced an ambitious plan to develop 30 GW of offshore wind capacity by 2040. Sørlige Nordsjø II (SN2) is among the first areas to benefit from this plan. The layout suggestion for SN2 is based on three subareas of 1.5 GW each, resulting in a total capacity of 4.5 GW. These subareas are separated by a 5 km buffer zone to allow for sufficient wake recovery. Each subarea will be operated by a different consortium, making SN2 an interesting area for studying wind farm interferences and farm-to-farm wake assessment.
The thesis employs wind statistics from the NORA3 database, which comprises 29 years of hourly data collected between 1992 and 2020. The spatial resolution is 3 km, while the temporal resolution is 1 h. These statistics are employed for the IEA 15 MW wind turbine model. Utilising the engineering wake models FLORIS, PyWake, and Vind AI, along with the analytical wake models Jensen, GCH (Gaussian Curl Hybrid), and TurbOPark, the wake losses in the wind farm are analysed. GCH is found to predict the lowest wake losses, followed by Jensen and lastly, TurbOPark is found to predict the highest wake loss values. It is also found that PyWake and Vind AI predict fairly similar results, while FLORIS predicts slightly lower wake loss values. Further, the effectiveness of the buffer zone for wake recovery and the wind potential of each subarea are discussed. It is found that the buffer zone is inadequate for full wake recovery. Considering wake losses, subarea 1 experiences the least impact from the other subareas. Lastly, the potential for increasing wind farm capacity in SN2 is evaluated. | |
