Stochastic optimization models for offshore wind farm maintenance

The world is fast moving away from fossil fuel, to a more renewable and sustainable energy future.The offshore wind industry is a major player in the drive for renewable energy. In order for sustainability to be achieved,the cost of operating and maintaining an offshore wind farm has to be minimized. The O&M cost of an offshore wind farm accounts for roughly 20% to 30% of the total lifetime cost of a wind farm.
In this thesis report,a stochastic model is formulated in integer programming for a single wind farm with twenty turbines, ten feasible routes and a set of periods.The model is developed to handle both small and large data sets from a wind farm. Several case scenarios were considered in order to test the performance of the model. Simulation results proved that the model can solve smaller data sets in fewer minutes by arriving at an optimum solution, while it takes longer runtime in solving larger data sets,with feasible solutions.In addition,the result of the simulated cases at a runtime of 10mins, showed that the model can be used as a decision making tool for maintenance scheduling.The model is able to determine which turbine should be maintained in a set period, giving the right data set.