Multiple cluster scattering theory and collaborative control for wave power optimization

Ocean waves provide a promising source of renewable energy, but no large-scale wave power technology has yet demonstrated high efficiency and competitive costs.

Main challenges for achieving this are that the optimization algorithms of wave power farms require fast hydrodynamics models, but all fast hydrodynamics models for large farms are too approximate; that control methods can greatly enhance the performance, but in general require wave forecasting which is difficult to obtain in reality; and that the cost functions used in the optimization should take into account not only the power output which is the standard today, but instead optimize upon multiple objectives such as lifetime costs, electricity quality, and constraints on the devices.The proposed project addresses these problem with new approaches to modelling and optimizing large-scale wave power systems.

A multiple cluster scattering theory will be developed and validated based on the world-leading hydrodynamics model developed by the applicant.

A collaborative machine learning control method will enhance the performance of the farm, based on promising preliminary work.

The model will be combined with state-of-the-art optimization algorithms and economical models with unique input in the economical cost function.

The resulting accurate and computationally fast model will be used to find efficient and competitive configurations of large-scale conversion of ocean waves to electricity.