Technical-economic analysis, modeling and optimization of floating offshore wind farms

  1. LERCH, MARKUS JÜRGEN
Dirigida por:
  1. Mikel De Prada Gil Director/a
  2. Climent Molins Borrell Codirector/a

Universidad de defensa: Universitat Politècnica de Catalunya (UPC)

Fecha de defensa: 09 de marzo de 2020

Tribunal:
  1. Wen Cheng Presidente/a
  2. Oriol Gomis Bellmunt Secretario/a
  3. Almudena Filgueira-Vizoso Vocal

Tipo: Tesis

Teseo: 152047 DIALNET

Resumen

The offshore wind sector has grown significantly during the last decades driven by the increasing demand for clean energy and to reach defined energy targets based on renewable energies. As the wind speeds tend to be faster and steadier offshore, wind farms at sea can reach higher capacity factors compared to their onshore counterparts. Furthermore, fewer restrictions regarding land use, visual impact, and noise favors the application of this technology. However, most of today's offshore wind farms use bottom-fixed foundations that limit their feasible application to shallow water depths. Floating substructures for offshore wind turbines are a suitable solution to harness the full potential of offshore wind as they have less constraints to water depths and soil conditions and can be applied from shallow to deep waters. As several floating offshore wind turbine (FOWT) concepts have been successfully tested in wave tanks and prototypes have been proven in open seas, floating offshore wind is now moving towards the commercial phase with the first floating offshore wind farm (FOWF) commissioned in 2017 and several more are projected to be constructed in 2020. This transition increases the need for comprehensive tools that allow to model the complete system and to predict its behavior as well as to assess the performance for different locations. The aim of this thesis is to analyze from a technical and economic perspective commercial scale FOWFs. This includes the modeling of FOWTs and the study of their dynamic behavior as well as the economic assessment of different FOWT concepts. The optimization of the electrical layout is also addressed in this thesis. The first model developed is applied to analyze the performance of a Spar type FOWT. The model is tested with different load cases and compared to a reference model. The results of both models show an overall good agreement. Afterwards, the developed model is applied to study the behavior of the FOWT with respect to three different offshore sites. Even at the site with the harshest conditions and largest motions, no significant loss in energy generation is measured, which demonstrates the good performance of this concept. The second model is used to perform a technical-economic assessment of commercial scale FOWFs. It includes a comprehensive LCOE methodology based on a life cycle cost estimation as well as the computation of the energy yield. The model is applied to three FOWT concepts located at three different sites and considering a 500MW wind farm configuration. The findings indicate that FOWTs are a high competitive solution and energy can be produced at an equal or lower LCOE compared to bottom-fixed offshore wind or ocean energy technologies. Furthermore, a sensitivity analysis is performed to identify the key parameters that have a significant influence on the LCOE and which can be essential for further cost reductions. The last model is aimed to optimize the electrical layout of FOWFs based on the particle swarm optimization theory. The model is validated against a reference model at first and is then used to optimize the inter-array cable routing of a 500MW FOWF. The obtained electrical layout results in a reduction of the power cable costs and a decrease of the energy losses. Finally, the use of different power cable configurations is studied and it is shown that the use of solely dynamic power cables in comparison to combined dynamic and static cables results in decreased acquisition and installation costs due to the avoidance of cost-intensive submarine joints and additional installation activities.