In this chapter, a methodology has been presented to obtain the dynamic response of a FOWT to different load cases and to assess its performance considering different wind and wave conditions. A simplified model has been built using MATLAB and the system response has been evaluated for the surge, heave and pitch motions. The results have been compared with FAST, which is a well-known complex tool to model and simulate wind turbines. An overall good agreement has been found in the comparison of the structural properties computed by both models. Furthermore, the main motions and system’s dynamics could be captured by the simpler model with an acceptable accuracy. The power generated by the FOWT has been computed for an environment with regular waves and steady wind as well as a load case consisting of turbulent wind and irregular waves. It has been found that even for the most extreme wind and wave combination the power loss experienced by the FOWT is less than 1% or 1.1%, respectively the load case studied. Furthermore, the performance of the FOWT has been evaluated for three offshore locations with their specific environmental conditions. Surge and pitch motions are governed by the mean wind speed, whereas the hub acceleration is influenced strongly by the wave height.
The response in heave is only of small magnitude for all three locations, which is typical for a Spar-type FOWT. The peak response has been obtained for all three degrees of freedom at rated wind speed, when the controller starts to reduce the thrust coefficient. Among the offshore locations, the largest motions appear at West of Barra, where the harshest environmental conditions exist, with 29.5m for surge and 5.9°for pitch motion. The highest value for the hub acceleration has also been obtained at West of Barra with 6.6m/s2. Despite the large motions, no significant loss in energy generation for the FOWT has been found. The difference is smaller than 1% for all three sites.
The highest capacity factor has been reached at West of Barra with up to 75%, which exceeds current BOWFs. This large capacity factors demon- strate the high power performance of the OC3-Hywind Spar FOWT and also coincides with the values achieved by the Hywind floating wind farm. Besides that, it shows that floating offshore wind could be a suitable com- plement to base load power generation. Finally, a sensitivity analysis has been used to evaluate the effect of different threshold limits, such as hub ac- celeration and platform pitch, on the performance of the FOWT. Lowering the threshold limits in order to increase the safe operation of the FOWT results in a nonlinear decrease of the capacity factor and nonlinear increase of downtime. The optimal selection of threshold limits should be a trade-off between system reliability and energy generation.
5
Technical-economic
assessment of floating
offshore wind farms
5.1 Introduction
Floating offshore wind turbines represent a competitive new energy solution by having the ability to harness the best possible wind resources without depth constraints and applying larger wind turbines to increase power gen- eration [12]. Furthermore, the ability to mount the turbine on the floating substructure dockside and to tow the fully assembled structure by tug boats to the offshore site provides a significant potential for cost reduction along the life cycle, because expensive heavy lift jack-up vessels are avoided [10]. However, since only a few prototypes have been constructed so far, there is a lack of information on the cost structure and potential LCOE values of large scale FOWFs. Myhr et al. [131] have estimated in 2013 the LCOE for a number of different FOWT concepts made of steel and supporting a 5MW wind turbine. The findings have shown LCOE values ranging between 106.3e/MWh and 287.8e/MWh, which appear unfavorable in comparison to the cost of current BOWFs [132]. Further research has been proposed to investigate possible cost reductions and to study the impact of different site conditions. Castro et al. [133] have developed in 2013 a methodology for the economic evaluation of FOWFs. The emphasis has been more on the model- ing of the life cycle cost and less on the computation of the power generation.
For instance, the power losses due to the wake effect in the wind farm have not been considered. Ebenhoch et al. [134] have calculated in 2015 the LCOE of a FOWF based on a monolithic Spar buoy concept. The LCOE obtained at 175.5e/MWh has been significantly higher than estimated benchmark values for bottom-fixed structures in shallow waters [134]. The high LCOE value may has been due to the lack of information on the cost structure of FOWFs and several assumptions that have been made in the LCOE estima- tion. For instance, the operation and maintenance costs have been based on estimations for BOWF and the decommissioning cost has been considered as a percentage of the capital expenses. Hence, the advantages that FOWTs provide to reduce costs in these life cycle phases have not been taken into account [28]. Besides that, the energy generation and losses in the system have been based on gross load factors and efficiency rates from literature and have not been optimized for the specific location [135].
Following the work done and the proposal for further investigation, the aim of this chapter is to provide a comprehensive LCOE calculation for commer- cial scale FOWFs based on cost data provided by industrial and academic FOWT concept developers. The LCOE computation involves both a detailed life cycle cost and energy loss calculation of the system. Furthermore, three different FOWT concepts are analyzed, namely Semi-submersible, TLP and Spar, representing the most promising designs in the sector. Besides that, concrete as well as steel structures are included to represent both manufac- turing materials. The calculation is performed for three different offshore locations to study the effect of metocean conditions on the LCOE. More- over, FOWTs with a rated capacity of 10MW are considered to represent the trend towards larger offshore wind turbines. A sensitivity analysis of 325 input parameters is performed to identify the ones that most influence the LCOE, which provides an useful insight for developers and researchers for further cost reductions.
The analysis is performed by using the tool FOWAT. A description of the tool is given in the Appendix B.1 and the LCOE methodology is presented next.