This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.

The compact design of modern wind farms means that turbines are located in the wake over a certain amount of time. This leads to reduced power and increased loads on the turbine in the wake. Currently, research has been dedicated to reduce or avoid these effects. One approach is wake-steering, where a yaw misalignment is introduced in the upstream wind turbine. Due to the intentional misalignment of upstream turbines, their wake flow can be forced around the downstream turbines, thus increasing park energy output. Such a control scheme reduces the turbulence seen by the downstream turbine but introduces additional load variation to the turbine that is misaligned. Within the scope of this investigation, a generic multi body simulation model is simulated for various yaw misalignments. The time series of the calculated loads are combined with the wind speed distribution of a reference site over 20 years to investigate the effects of yaw misalignments on the turbines main bearing loads. It is shown that damage equivalent loads increase with yaw misalignment within the range considered. Especially the vertical in-plane force, bending and tilt moment acting on the main bearing are sensitive to yaw misalignments. Furthermore, it is found that the change of load due to yaw misalignments is not symmetrical. The results of this investigation are a primary step and can be further combined with distributions of yaw misalignments for a study regarding specific load distributions and load cycles.

Storing energy is a major challenge in achieving a 100 % renewable energy system. One promising approach is the production of green hydrogen from wind power. This work proposes a method for optimizing the design of wind–hydrogen systems for existing onshore wind farms in order to achieve the lowest possible levelized cost of hydrogen (LCoH). This is done by the application of a novel Python-based optimization model that iteratively determines the optimal electrolyzer position and distribution mode of hydrogen for given wind farm layouts. The model includes the costs of all required infrastructure components. It considers peripheral factors such as existing and new roads, necessary power cables and pipelines, wage and fuel costs for truck transportation, and the distance to the point of demand (POD). Based on the results, a decision can be made whether to distribute the hydrogen to the POD by truck or pipeline. For a 23.4 MW onshore wind farm in Germany, a minimal LCoH of EUR 4.58 ${{\mathrm{kg}}_{{\mathrm{H}}_{\mathrm{2}}}}^{-\mathrm{1}}$ at an annual hydrogen production of 241.4 ${\mathrm{t}}_{{\mathrm{H}}_{\mathrm{2}}}{\mathrm{a}}^{-\mathrm{1}}$ is computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, and the distance to the POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between the rated power of the electrolyzer and the wind farm lies at around 10 %, with a resulting capacity factor of 78 % for the given case. The new model can be used by system planners and researchers to improve and accelerate the planning process for wind–hydrogen systems. Additionally, the economic efficiency, hence competitiveness, of wind–hydrogen systems is increased, which contributes to an urgently needed accelerated expansion of electrolyzers. The results of the influencing parameters on the LCoH will help to set development goals and indicate a path towards a cost-competitive green wind–hydrogen system.

With the increasing demand for greener, sustainable, and economical energy sources, wind energy has proven to be a potential sustainable source of energy. The trend development of wind turbines tends to increase rotor diameter and tower height to capture more energy. The bigger, lighter, and more flexible structure is more sensitive to smaller excitations. To make sure that the dynamic behavior of the wind turbine structure will not influence the stability of the system and to further optimize the structure, a fully detailed analysis of the entire wind turbine structure is crucial. Since the fatigue and the excitation of the structure are highly depending on the aerodynamic forces, it is important to take blade–tower interactions into consideration in the design of large-scale wind turbines. In this work, an aeroelastic model that describes the interaction between the blade and the tower of a horizontal axis wind turbine (HAWT) is presented. The high-fidelity fluid–structure interaction (FSI) model is developed by coupling a computational fluid dynamics (CFD) solver with a finite element (FE) solver to investigate the response of a multi-megawatt wind turbine structure. The results of the computational simulation showed that the dynamic response of the tower is highly dependent on the rotor azimuthal position. Furthermore, rotation of the blades in front of the tower causes not only aerodynamic forces on the blades but also a sudden reduction in the rotor aerodynamic torque by 2.3 % three times per revolution.

Fixed feed-in tariffs based on the Renewable Energy Act grant secure revenues from selling electricity for wind turbine operators in Germany. Anyhow, the level of federal financial support is being reduced consecutively. Plant operators must trade self-sufficiently in the future and hence generate revenue by selling electricity directly on electricity markets. Therefore, uncertain future market price developments will influence investment considerations and may lead to stagnation in the expansion of renewable energies. This study estimates future revenue potentials of non-subsidized wind turbines in Germany to reduce this risk. The paper introduces and analyses a forecasting model that generates electricity price time series suited for revenue estimation of wind turbines based on the electricity exchange market. Revenues from the capacity market are neglected. The model is based on openly accessible data and applies a merit-order approach in combination with a simple agent-based approach to forecast long-term day-ahead prices at an hourly resolution. The hourly generation profile of wind turbines can be mapped over several years in conjunction with fluctuations in the electricity price. Levelized revenue of energy is used to assess both dynamic variables (electricity supply and price). The merit-order effect from the expansion of renewables as well as the phasing out of nuclear energy and coal are assessed in a scenario analysis. Based on the assumptions made, the opposing effects could result in a constant average price level for Germany over the next 20 years. The influence of emission prices is considered in a sensitivity analysis and correlates with the share of fossil generation capacities in the generation mix. In a brief case study, it was observed that current average wind turbines are not able to yield financial profit over their lifetime without additional subsidies for the given case. This underlines a need for technical development and new business models like power purchase agreements. The model results can be used for setting and negotiating appropriate terms, such as energy price schedule or penalties for those agreements.