Supervisorer: Lektor Niels-Erik Clausen, DTU Vindenergi - Postdoc Antonio Manuel Pegalajar Jurado, DTU Vindenergi
Eksaminator: Bjarne Kallesøe, Siemens Gamesa Renewable Energy
Titel: Development of an LCOE Optimization Tool used for the Siting and Planning of Hybrid Floating Wind and Wave Farms
Offshore floating hybrid wind and wave energy is a young technology that still needs to be scaled up. To ensure its competitiveness in the sustainable energy market, the total costs of the energy production must be reduced by either improving the technology or maximizing the farm’s efficiency. The key indicator to be minimized is the Levelized Cost of Energy, subject of optimization in the model developed in this work.
A full energy production and costs model has been built for the hybrid P80 concept designed by the company Floating Power Plant A/S. The technical aspects involved in the model include the wakes and waves shadow, the power output conversion values of both wind and waves generators, as well as all the expenditures and economical factors involved in the project: CAPEX, OPEX, and discount rate. The site-dependent parameters considered are: the wind and wave resources, the bathymetry, and the distances to shore, to the harbors and to the grid. A Particle Swarm Optimization algorithm consummates the tool, taking as decision variables the layout, the offshore substation position, and the export cable choice. It was found that more sophisticated algorithms are needed to find consistent results in this kind of optimization process.
The model has been applied off the west coast of Ireland in a site of interest for the company. With a layout of 25 platforms in 5 rows, the LCOE was minimized to a value of 116.56 AC/MWh when using the considered base parameters. The main vessel routes, the area’s grid strength, and the visual impact of the farm were also studied. It was then found that lower costs of about 85 AC/MWh can be reached in the short-term, and the room for improvement in the structure’s design and materials was highlighted, with an LCOE reduction potential of up to a 32%. It is also proved that a model of this kind serves usefully as a preliminary analysis, but the uncertainty estimate of an 11% indicates that site-specific studies and measurements are essential.