To increase the total wind farm power output, the wind farm layout needs to be optimized. The power output of a wind turbine depends on the incoming velocity, while the velocity is influenced by the wake of the upstream wind turbines. Wind Farm Layout Optimization (WFLO) problems makes use of the so called low fidelity wake models, which predicts the velocity downstream of a turbine. The analytical Jensen wake model with a top hat velocity wake profile is commonly used to perform the WFLO during the preliminary design phase of a wind farm. However a top hat velocity profile is not an accurate depiction
of the actual velocity profile downstream of the turbine wake. To get a more accurate wake profile the model needs to be extended. To improve the wake model, the role of the stability of the Atmospheric Boundary Layer (ABL) on the development of the turbine wake is analyzed using the software openFOAM and SOWFA. It is noticed that the analytical Jensen-Gaussian wake model is in better agreement with measurement data than the Jensen top hat wake model. It is verified that it is necessary to include the added Turbulence Intensity (T.I) induced by the wind turbine. For the Jensen- Gaussian wake model, the Gao turbulence model gives results that are in good agreement with the experimental data. The Jensen-Gaussian wake model is extended to be used inside a wind farm with multiple wakes. The power output for a row of 10 Vestas V-80 turbines in the Horns rev Wind farm is computed. Using the equivalent velocity by weighted area averaging over a discretized wake turbine-cross section in combination with the power curve, the power output of a turbine can be computed. Using the energy superposition method the equivalent velocity for alligned turbines can be computed. Comparison with measurement data shows that there still is a difference between the results from the wake model and the measurement data. To further improve the Jensen-Gaussian wake model it is important to take into account the effect of the stability of the ABL on the wake. The different stabilities for an offshore ABL are simulated with SOWFA and the turbine wakes are computed. The different wake recovery rates and elliptical shapes due to the stability of the ABL are included in the extended model.
Using the offshore Horns rev wind farm data, the extended Jensen-Gaussian model in combination with the mixed-discrete Particle Swarm Optimization (MDPSO), the WFLO is carried out. The WFLO predicts that it is important to take the stability of the different models into account. However it is concluded that the improvement cannot be quantified, due to the uncertainties in the computation in the power output of each wind turbine.