Life Cycle Cost Analysis of a Floating Wind Farm Located in the Norwegian Sea

This thesis aims to investigate the levelized cost of energy of an offshore floating wind farm, as well as evaluate its financial feasibility. Thus, the research question is as follows: How to estimate the life cycle costs of a floating wind farm off the coast of Norway? The investigated wind farm is located off the coast of Norway, more specifically in the Troll field area west of Bergen. This area has a water depth of 325 m and a distance to shore of 65 km. The wind farm is set to consist of 50 wind turbines and has a lifespan of 25 years. The OC4 Deepwind semisubmersible floater developed by the National Renewable Energy Laboratory, complemented with a 15 MW turbine, is used as the research model. To find the capital expenditures of the planned wind farm, the Offshore Renewables Balance-of-system and Installation Tool is used, while the operational expenditures are calculated based on the theoretical energy output. The total levelized cost of energy of the wind farm is calculated to be 100.69 $/MWh. Capital expenditure is the most prominent cost and constitutes 63.1 expenditures constitute the remaining 36.9 lifespan, capacity factor, and project discount rate are the factors with the most potential to influence the levelized cost of energy. The financial calculations show that the wind farm is not economically feasible as it has a computed net present value of negative$ 561 900 000. Finally, novel offshore wind energy solutions involving the utilization of shared substructures and mooring lines have been studied, and the findings suggest the possibility of a diminished levelized cost of energy

Life Cycle Cost Analysis of a Floating Wind Farm Located in the Norwegian Sea

This thesis aims to investigate the levelized cost of energy of an offshore floating wind farm, as well as evaluate its financial feasibility. Thus, the research question is as follows: How to estimate the life cycle costs of a floating wind farm off the coast of Norway? The investigated wind farm is located off the coast of Norway, more specifically in the Troll field area west of Bergen. This area has a water depth of 325 m and a distance to shore of 65 km. The wind farm is set to consist of 50 wind turbines and has a lifespan of 25 years. The OC4 Deepwind semisubmersible floater developed by the National Renewable Energy Laboratory, complemented with a 15 MW turbine, is used as the research model. To find the capital expenditures of the planned wind farm, the Offshore Renewables Balance-of-system and Installation Tool is used, while the operational expenditures are calculated based on the theoretical energy output. The total levelized cost of energy of the wind farm is calculated to be 100.69 $/MWh. Capital expenditure is the most prominent cost and constitutes 63.1 expenditures constitute the remaining 36.9 lifespan, capacity factor, and project discount rate are the factors with the most potential to influence the levelized cost of energy. The financial calculations show that the wind farm is not economically feasible as it has a computed net present value of negative$ 561 900 000. Finally, novel offshore wind energy solutions involving the utilization of shared substructures and mooring lines have been studied, and the findings suggest the possibility of a diminished levelized cost of energy

Life Cycle Cost Analysis of a Floating Wind Farm Located in the Norwegian Sea

This thesis aims to investigate the levelized cost of energy of an offshore floating wind farm, as well as evaluate its financial feasibility. Thus, the research question is as follows: How to estimate the life cycle costs of a floating wind farm off the coast of Norway? The investigated wind farm is located off the coast of Norway, more specifically in the Troll field area west of Bergen. This area has a water depth of 325 m and a distance to shore of 65 km. The wind farm is set to consist of 50 wind turbines and has a lifespan of 25 years. The OC4 Deepwind semisubmersible floater developed by the National Renewable Energy Laboratory, complemented with a 15 MW turbine, is used as the research model. To find the capital expenditures of the planned wind farm, the Offshore Renewables Balance-of-system and Installation Tool is used, while the operational expenditures are calculated based on the theoretical energy output. The total levelized cost of energy of the wind farm is calculated to be 100.69 $/MWh. Capital expenditure is the most prominent cost and constitutes 63.1$ 561 900 000. Finally, novel offshore wind energy solutions involving the utilization of shared substructures and mooring lines have been studied, and the findings suggest the possibility of a diminished levelized cost of energy

Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process.publishedVersio

Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network

Numerical simulations in coupled aero-hydro-servo-elastic codes are known to be a challenge for design and analysis of offshore wind turbine systems because of the large number of design load cases involved in checking the ultimate and fatigue limit states. To alleviate the simulation burden, machine learning methods can be useful. This article investigates the effect of machine learning methods on predicting the mooring line tension of a spar floating wind turbine. The OC3 Hywind wind turbine with a spar-buoy foundation and three mooring lines is selected and simulated with SIMA. A total of 32 sea states with irregular waves are considered. Artificial neural works with different constructions were applied to reproduce the time history of mooring tensions. The best performing network provides a strong average correlation of 71% and consists of two hidden layers with 35 neurons, using the Bayesian regularisation backpropagation algorithm. Sea states applied in the network training are predicted with greater accuracy than sea states used for validation of the network. The correlation coefficient is primarily higher for sea states with lower significant wave height and peak period. One sea state with a significant wave height of 5 meters and a peak period of 9 seconds has an average extreme value deviation for all mooring lines of 0.46%. Results from the study illustrate the potential of incorporating artificial neural networks in the mooring design process