• Energy Research

Abstract Offshore wind energy is a seminal technology to achieve the goals set for renewable energy deployment. However, today's offshore wind energy projects are mostly not yet sufficiently competitive. The optimization of offshore wind turbine substructures with regard to costs and reliability is a promising approach to increase competitiveness. Today, interdisciplinary analyses considering sophisticated engineering models and their complex economic effects are not widespread. Existing approaches are deterministic. This research gap is addressed by combining an aero-elastic wind turbine model with an economic viability model for probabilistic investment analyses. The impact of different monopile designs on the stochastic cost-efficiency of an offshore wind farm is investigated. Monopiles are varied with regard to diameters and wall thicknesses creating designs with increased lifetimes but higher capital expenditures (durable designs) and vice versa (cheaper designs). For each substructure, the aero-elastic wind turbine model yields distributions for the fatigue lifetime and electricity yield and different capital expenditures, which are applied to the economic viability model. For other components, e.g. blades, constant lifetimes and costs are assumed. The results indicate that the gain of increased stochastic lifetimes exceeds the benefit of reduced initial costs, if the overall lifetime is not governed by other turbine components' lifetimes.

Abstract Offshore wind energy is a seminal technology to achieve the goals set for renewable energy deployment. However, today's offshore wind energy projects are mostly not yet sufficiently competitive. The optimization of offshore wind turbine substructures with regard to costs and reliability is a promising approach to increase competitiveness. Today, interdisciplinary analyses considering sophisticated engineering models and their complex economic effects are not widespread. Existing approaches are deterministic. This research gap is addressed by combining an aero-elastic wind turbine model with an economic viability model for probabilistic investment analyses. The impact of different monopile designs on the stochastic cost-efficiency of an offshore wind farm is investigated. Monopiles are varied with regard to diameters and wall thicknesses creating designs with increased lifetimes but higher capital expenditures (durable designs) and vice versa (cheaper designs). For each substructure, the aero-elastic wind turbine model yields distributions for the fatigue lifetime and electricity yield and different capital expenditures, which are applied to the economic viability model. For other components, e.g. blades, constant lifetimes and costs are assumed. The results indicate that the gain of increased stochastic lifetimes exceeds the benefit of reduced initial costs, if the overall lifetime is not governed by other turbine components' lifetimes.

Fatigue damage is a design-driving phenomenon for substructures of offshore wind turbines. However, fatigue design based on numerical simulations is quite uncertain. One main reason for this uncertainty is scattering offshore conditions combined with a limited number of simulations (samples). According to current standards, environmental conditions are sampled using a deterministic grid of the most important environmental conditions (e.g., wind speed and direction, significant wave height, and wave period). Recently, there has been some effort to reduce the inherent uncertainty of damage calculations due to limited data by applying other sampling concepts. Still, the investigation of this uncertainty and of methods to reduce it is a subject of ongoing research. In this work, two improved sampling concepts—previously proposed by the authors and reducing the uncertainty due to limited sampling—are validated. The use of strain measurement data enables a realistic estimate of the inherent uncertainty due to limited samples, as numerical effects, etc., are excluded. Furthermore, an extensive data set of three years of data of two turbines of the Belgian wind farm Northwind is available. It is demonstrated that two previously developed sampling methods are generally valid. For a broad range of model types (i.e., input dimensions as well as degrees of non-linearity), they outperform standard sampling concepts such as deterministic grid sampling or Monte Carlo sampling. Hence, they can reduce the uncertainty while keeping the sampling effort constant, or vice versa.

Fatigue damage is a design-driving phenomenon for substructures of offshore wind turbines. However, fatigue design based on numerical simulations is quite uncertain. One main reason for this uncertainty is scattering offshore conditions combined with a limited number of simulations (samples). According to current standards, environmental conditions are sampled using a deterministic grid of the most important environmental conditions (e.g., wind speed and direction, significant wave height, and wave period). Recently, there has been some effort to reduce the inherent uncertainty of damage calculations due to limited data by applying other sampling concepts. Still, the investigation of this uncertainty and of methods to reduce it is a subject of ongoing research. In this work, two improved sampling concepts—previously proposed by the authors and reducing the uncertainty due to limited sampling—are validated. The use of strain measurement data enables a realistic estimate of the inherent uncertainty due to limited samples, as numerical effects, etc., are excluded. Furthermore, an extensive data set of three years of data of two turbines of the Belgian wind farm Northwind is available. It is demonstrated that two previously developed sampling methods are generally valid. For a broad range of model types (i.e., input dimensions as well as degrees of non-linearity), they outperform standard sampling concepts such as deterministic grid sampling or Monte Carlo sampling. Hence, they can reduce the uncertainty while keeping the sampling effort constant, or vice versa.

Designing jacket substructures for offshore wind turbines demands numerous time domain simulations to face different combinations of wind, wave, and current states. Regarding sophisticated design methods incorporating structural optimization algorithms, a load set reduction is highly desirable. To obtain knowledge about the required size of the design load set, a study on fatigue limit state load sets is conducted, which addresses mainly two aspects. The first one is a statistical evaluation of random subsets derived from probabilistic load sets with realistic environmental data obtained from the research platform FINO3. A full set comprising 2048 load simulations is gradually reduced to subsets and the results are compared to each other. The second aspect is a systematic load set reduction with the assumption of unidirectional wind, waves, and current. Firstly, critical directions are determined. Then, unidirectional load sets are systematically reduced. The corresponding damages are compared to those obtained from probabilistic load sets for eight test structures. It is shown that the omission of wind-, wave-, and current-misalignment does not necessarily imply an excessive simplification, if considered wisely. The outcome of this study can be used to decrease the numerical effort of the jacket design process and the levelized costs of energy.

Designing jacket substructures for offshore wind turbines demands numerous time domain simulations to face different combinations of wind, wave, and current states. Regarding sophisticated design methods incorporating structural optimization algorithms, a load set reduction is highly desirable. To obtain knowledge about the required size of the design load set, a study on fatigue limit state load sets is conducted, which addresses mainly two aspects. The first one is a statistical evaluation of random subsets derived from probabilistic load sets with realistic environmental data obtained from the research platform FINO3. A full set comprising 2048 load simulations is gradually reduced to subsets and the results are compared to each other. The second aspect is a systematic load set reduction with the assumption of unidirectional wind, waves, and current. Firstly, critical directions are determined. Then, unidirectional load sets are systematically reduced. The corresponding damages are compared to those obtained from probabilistic load sets for eight test structures. It is shown that the omission of wind-, wave-, and current-misalignment does not necessarily imply an excessive simplification, if considered wisely. The outcome of this study can be used to decrease the numerical effort of the jacket design process and the levelized costs of energy.

Abstract Although uncertainties are present in all real parameters, numerical calculations of the structural behaviour of offshore wind turbines are usually conducted with deterministic values. However, with this approach, optimisation processes can be misleading and reliability levels cannot be calculated. The reasons for deterministic calculations are high computing times of probabilistic approaches and the lack of knowledge about the scatter of data. For deterministic approaches, more complex models with higher computing times are possible, although they, are less generally valid. Therefore, it is useful to identify the most influential parameters that have to be treated in a probabilistic manner using sensitivity analyses is valuable. Contrary to rudimentary sensitivity approaches being used in offshore wind energy so far, this paper presents a new four-step sensitivity analysis reducing the probabilistic parameter subset step by step and aiming to achieve a compromise between computing time and complexity. It can be shown that for different substructures and different load cases, only a small parameter subset is influential and many other inputs can be regarded as deterministic without losing accuracy. However, attention must be paid to the slight differences among substructures. Therefore, it must be highlighted that not all results are general.

Abstract Although uncertainties are present in all real parameters, numerical calculations of the structural behaviour of offshore wind turbines are usually conducted with deterministic values. However, with this approach, optimisation processes can be misleading and reliability levels cannot be calculated. The reasons for deterministic calculations are high computing times of probabilistic approaches and the lack of knowledge about the scatter of data. For deterministic approaches, more complex models with higher computing times are possible, although they, are less generally valid. Therefore, it is useful to identify the most influential parameters that have to be treated in a probabilistic manner using sensitivity analyses is valuable. Contrary to rudimentary sensitivity approaches being used in offshore wind energy so far, this paper presents a new four-step sensitivity analysis reducing the probabilistic parameter subset step by step and aiming to achieve a compromise between computing time and complexity. It can be shown that for different substructures and different load cases, only a small parameter subset is influential and many other inputs can be regarded as deterministic without losing accuracy. However, attention must be paid to the slight differences among substructures. Therefore, it must be highlighted that not all results are general.