Explore the vast range of titles available.

A simple techno-economic model for determining wind power production and costs related to the development of floating offshore wind power is proposed. The model is a further extension of the minimalistic prediction model for fixed-bottom wind farms previously developed by two of the authors. In the extended version, costs associated with the deployment of floating structures, such as floaters, mooring lines, and anchors, including additional installation and operational expenses, are taken into account. This paper gives an overview of the costs of the various components of different types of floating wind power installations, and using actual wind climate and bathymetry data for the North Sea, the model is employed to map the annual energy production and levelized cost of energy (LCoE) for floating wind farms located in the North Sea.

Calculations of annual energy productions of wind farms are normally very computing demanding as they require simulations of the wind flow field inside the wind farms for a range of ambient wind conditions and directions. Although there exists many advanced computing tools for atmospheric flows, which, in principle, cope with all flow situations, most wind power developers rely their work on simplified engineering models based on analytical approaches and superposition of the flow behind a single row of wind turbines. An alternative to wake modeling is the fully developed wind farm array boundary layer model, which assumes that the wind farm is so large, that the wind field inside the wind farm is in equilibrium with the flow field of the ambient atmospheric boundary layer. Such a model was recently further developed by the authors using a simple correction for coping with finite-sized wind farms. The purpose of the present work is to extend further the finiteness correction formula and validate the model by comparing results to actual production data and to results from other simulation models, such as the Jensen, Gaussian and TurbOPark engineering models. In spite of the simplicity of the proposed model, it outperforms the other models, achieving results within 5% accuracy as compared with full-scale data from existing wind farms.