• Energy Research

Abstract. 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.

AbstractWind energy is an essential source of renewable energy. However, to compete with conventional energy sources, energy needs to be produced at low costs. An ideal situation would be to have no costly, unscheduled maintenance, preferably. Currently, O&M are half of the yearly expenses. The O&M costs are kept low by scheduled and reactive maintenance. An alternative is predictive maintenance. This method aims to act before any critical and costly repair is required. Additionally, the component is used to its full potential. However, such a strategy requires a damage indication, similar to one provided by a condition monitoring system (CMS). This paper investigates if Supervisory Control and Data Acquisition (SCADA) can be used as a damage indicator and CMS. Since 2006, every wind turbine is obliged to use such a SCADA-system. SCADA records a 10-minute average, maximum, minimum, and standard deviation of multiple technical information channels. Analytics can use those data to determine the normal behavior and a prediction model of the wind turbine. The authors investigated statistical and data mining methods to predict main bearing faults. The methods indicated a defect of up to 6 months before its maintenance.

Abstract. Uncoordinated extension of renewable energy sources (RES) disregarding local demand structures leads to increased loads on the transmission grid and overall economic losses. One approach to solve this problem is to support the local power consumption by local power generation without making use of the transmission network. Therefore, the actual physical coverage of local demand with local supply is to be investigated instead of a yearly net power balance. Rural municipalities are an ideal starting point to establish such self-sufficient power supply systems on the basis of RES as they have a high RES potential in combination with low demand loads. Fluctuating feed-in of wind and solar power and peaks in demand loads can be balanced by bioenergy as flexible power generation capacity. In contrast to highly resource dependent wind and solar power, biomass can be stored and power generation from biomass can be controlled flexibly. To assess the potential of electricity from biomass, this study analyses the agricultural structure of the rural municipalities. The objective of this study is to assess what kind of agricultural structure might be advantageous for flexible power generation from bioenergy, hence balancing fluctuation RES feed-in and power demand. The results from this structural assessment of rural municipalities can help for analysing further municipalities to identify potentials at first sight without costly individual analysis. Heat and fuel sectors are neglected. A methodology is introduced to model time series of wind, PV and biomass power with a 15-minute resolution. It is evaluated to which degree it is possible to cover the local demand in power supply with bioenergy as flexible power generation capacity in the identified clusters. The results indicate that bioenergy is generally suitable to cover the gap between local power demand and supply. Waste products from animal farming are far more effective for biomass power production than from agricultural farming. Low population densities raise the potential for self-sufficiency in the power sector because of low demand loads. Further improvement of the model is needed concerning the clustering approach and for the approximation of installed wind and PV power capacities.

Abstract. 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 kgH2-1 at an annual hydrogen production of 241.4 tH2a-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.

Abstract. 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 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 23.8 MW onshore wind farm in Germany, minimal LCoH of 4.58 € kg−1H2 at an annual hydrogen production of 241.4 tH2 a−1 are computed. These results are significantly affected by the position of the electrolyzer, the distribution mode, varying wind farm and electrolyzer sizes, as well as distance to POD. The influence of the ratio of electrolyzer power to wind farm power is also investigated. The ideal ratio between rated power of electrolyzer and wind farm lies at around 10 % and 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 LCoH will help to set development goals and indicate a path towards cost-competitive green wind-hydrogen.

Abstract. In order to identify holistically better drivetrain concepts for onshore application, their operational behaviour needs to be considered at an early design phase. In this paper, a validated approach for estimating drivetrain concept-specific risk of unplanned maintenance based on open access data is presented. Uncertain influencing factors are described with distribution functions. This way, the poor data availability in the early design phase can be used to give an indication about the concept’s choice influence on the unplanned operational turbine behaviour. In order to get representative comparisons, Monte Carlo method is applied. This makes it possible to model the life of a fictional wind turbine based on the derived distributions. Technical availability and drivetrain influenced unplanned maintenance effort are defined as evaluation criteria. The latter is constituted by labour, material, and equipment expenses. By calculating the range of fluctuation of the evaluation criteria mean values, this approach offers an indication about the inherent risk in the operational phase induced by the drivetrain concept choice. This approach shows that open access data or expert estimations are sufficient for comparing different drivetrain concepts over the operational phase in an early design stage. The approach is applied on the five most common state-of-the-art drivetrain concepts. The comparison shows that the drivetrain concept without a gearbox and with a permanent magnet synchronous generator performs the best in terms of absolute unplanned maintenance effort over the lifetime as well as on the inherent risk. For future research, the influence of the maintenance strategy as well as site and park specific impacts on the unplanned concept behaviour should be included. For adapting this method to new concepts, a physically based approach could be developed which would make it possible to estimate probability distributions for the uncertain factors. Nevertheless, this approach will help to identify holistically better drivetrain concepts by being able to estimate the inherent risks in the operational phase.

Abstract. 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.