• Energy Research
• 7. Clean energy close
• 13. Climate action close
• DE close
• UA close
• Technical University of Munich close

AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.

The presented Solid Fuel Entrained Flow Gasification Database contains complete datasets on individual solid fuels considering the most important physical properties and conversion behavior analysis necessary for the simulation of pressurized high temperature entrained flow gasification. This includes their composition (proximate- and ultimate analysis, XRF of ash at 200, 450, 815 and 1500 °C) as well as physical properties such as original mineral phase composition (XRD), density (true and tap) and particle size distribution. The database also contains data on fuel behavior such as heating values, swelling factors, fragmentation index, slag viscosity, ash melting behavior and ash mineral phase evolution during heat-up and cool-down. Moreover, the database provides model parameters describing their pyrolysis behavior, gasification kinetics including product gas inhibition, thermal deactivation and surface area development. Chars produced and gasified in pressurized high temperature entrained flow reactors like the PiTER (located at the Chair of Energy Systems of the Technical University of Munich) and the KIVAN (operated by the Institute of Energy Process Engineering and Chemical Engineering of the TU Bergakademie Freiberg) were analyzed in thermogravimetric and structural analyzer. Since these reactors are designed for temperatures up to 1800 °C (PiTER) and pressures up to 100 bar (KIVAN), the resulting model parameters are relevant for the simulation of industrial scale applications. In order to validate the applied models for entrained flow gasification kinetics, the database refers to several publications describing the models and experimental setups as well as providing additional experimental data points.

We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.

AbstractState of the art technologies like air-to-water heat pumps have become popular because of their high efficiencies and their supply by electricity rather than by gas or oil. As a direct result of this supply substitution and due to high load- shifting potential, electricity demand of heat pumps have to be taken into account into energy management strategies of the overall building. In this paper a new controller is discussed, which has predictive and adaptive properties and is able to optimize the running-times of non-modulating heat pumps.

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.

Abstract. The concept of wake steering on wind farms for power maximization has gained significant popularity over the last decade. Recent field trials described in the literature not only demonstrate the real potential of wake steering on commercial wind farms but also show that wake steering does not yet consistently lead to an increase in energy production for all inflow conditions. Moreover, a recent survey among experts shows that validation of the concept currently remains the largest barrier to adoption. In response, this article presents the results of a field experiment investigating wake steering in three-turbine arrays at an onshore wind farm in Italy. This experiment was performed as part of the European CL-Windcon project. While important, this experiment excludes an analysis of the structural loads and focuses solely on the effects of wake steering on power production. The measurements show increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions too. In addition to the gains achieved through wake steering at downstream turbines, more interesting to note is that a significant share in gains is from the upstream turbines, showing an increased power production of the yawed turbine itself compared to baseline operation for some wind directions. Furthermore, the surrogate model, while capturing the general trends of wake interaction, lacks the details necessary to accurately represent the measurements. This article supports the notion that further research is necessary, notably on the topics of wind farm modeling and experiment design, before wake steering will lead to consistent energy gains on commercial wind farms.

In this paper, the performances of two iron-based syngas-fueled chemical looping (SCL) systems for hydrogen (H2) and electricity production, with carbon dioxide (CO2) capture, using different reactor configurations were evaluated and compared. The first investigated system was based on a moving bed reactor configuration (SCL-MB) while the second used a fluidized bed reactor configuration (SCL-FB). Two modes of operation of the SCL systems were considered, namely, the H2 production mode, when H2 was the desired product from the system, and the combustion mode, when only electricity was produced. The SCL systems were modeled and simulated using Aspen Plus software. The results showed that the SCL system based on a moving bed reactor configuration is more efficient than the looping system with a fluidized bed reactor configuration. The H2 production efficiency of the SCL-MB system was 11 % points higher than that achieved in the SCL-FB system (55.1 % compared to 44.0 %). When configured to produce only electricity, the net electrical efficiency of the SCL-MB system was 1.4 % points higher than that of the SCL-FB system (39.9 % compared to 38.5 %). Further, the results showed that the two chemical looping systems could achieve >99 % carbon capture efficiency and emit ~2 kg CO2/MWh, which is significantly lower than the emission rate of conventional coal gasification-based plants for H2 and/or electricity generation with CO2 capture.