The objective of this thesis is to build an optimization algorithm with the aim of optimizing layouts for two objective functions - Annual Energy Production (AEP) and Levelized Cost of Energy (LCoE), for large offshore wind farms. The algorithm considers the four main factors that are taken into account when creating a preliminary system design for an offshore wind farm. They are - the geographical location of the turbines, the hub height of the turbines, the type of the turbine, and the total number of turbines in the design space.
The annual energy production (AEP) of the wind farm is calculated using PyWake which uses the simple NOJDeficit wake model combined with the required superposition and blockage models to resolve wind turbine wakes. This AEP is then fed into TOPFARM, an economic solver developed at DTU which uses scaling factors to derive the total cost of the wind farm. Constant factors such as the discount rate, distance from shore, foundation type, and drivetrain type are also considered when deriving the total cost of the wind farm.
The results of this process are used to determine whether a system design is better than another. Several constraints are applied when changing each optimization variable to keep each iteration as realistic as possible. The boundary is assumed to be a square. Both algorithms arrive at similar results, with random search providing a much better solution with approximately a 40\% reduction in LCoE.