Search

As the number of renewable energy systems and the energy demand are growing simultaneously, many challenges arise for wind energy systems. One challenge is to respond flexibly to market prices while the numerous actors and the gap between supply and demand are leading to increased complexity in the energy market. Also, the reliability of wind energy system components and the full utilization of any unit’s service life are gaining importance. Current research is looking for ways to balance the many factors influencing the operation of a wind turbine while maintaining the key objective of maximizing monetary profit over the life cycle. Finding the optimal operating strategy for wind farms usually requires advanced optimization methods. An emerging method that eliminates complicated mathematical optimization is reinforcement learning (RL). RL is a type of machine learning able to learn through direct interaction with the environment. RL enables finding self-learned optimal adaptive operational strategies for wind energy systems with little computational effort. To test if RL is suitable for finding optimal operation strategies for long-term value maximization in wind energy systems, we analyze the benefit of reducing turbine power through effective derating on the optimal damage budget distribution of a given failure mode over the lifetime of a wind turbine. For this, we use pre-simulated damage progression data created from aero-elastic simulations for calculating the damages within the turbine blade depending on wind speed and power output reduction. The schematic RL problem formulation is shown in Figure 2. We assess the best way to set up the RL problem by analyzing the optimal state and reward formulations and comparing the performance of different algorithms with different hyperparameter settings. We present our results for a possible lifetime extension, and thus value maximation, through efficient derating. Further, we compare our results with the optimal planning results for the respective failure modes obtained with a mathematical optimization model and discuss advantages and drawbacks of RL versus traditional optimization approaches. As the benefits of RL are most obvious as operational decision-making gets more complex, we want to focus our future research on applying RL algorithms using deep neural networks and multi-agent algorithms to consider market prices and other factors influencing the optimal operation of a wind farm.

Waste heat recovery in the energy intensive industry is one of the most important measures for the mitigation of climate change. The utilization of just a fraction of the theoretically available waste heat potential would lead to a significant reduction of the primary energy consumption and hence a reduction of greenhouse gas emissions. The present study examines the integration of a packed bed thermal energy storage for waste heat recovery in the iron and steel industry. Along with the highly fluctuating availability of excess heat the main difficulty of waste heat recovery in industrial processes is the high amount of powder that is transported by the hot exhaust gases. Therefore, investigations focus on the pressure drop and powder hold-up in a packed bed thermal energy storage that is operated with a gas-powder two phase exhaust gas as heat transfer fluid with the ultimate goal to assess its suitability and robustness under such challenging operational conditions. The results indicate, that 98 % of the powder that is introduced into the system with the heat transfer fluid during charging accumulates in the packed bed. Remarkably, most of the powder hold-up in the packed bed is concentrated near the surface at which the heat transfer fluid enters the packed bed. When reversing the flow direction of the heat transfer fluid to discharge the storage with a clean single phase gas, this gas is not contaminated with the powder that has been accumulated in previous charging periods. Further, the radial distribution of the powder hold-up in the packed bed is observed to be even which indicates that there is no risk of random flow channel formation that could affect the thermal performance (storage capacity, thermal power rate) of the system. The results reinforce the great potential of packed bed thermal energy storage systems for waste heat recovery in the energy intensive industry., Comment: submitted to Applied Energy

In IETS Task XVIII Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry we are working on identifying technologies that can be applied in industry to increase the energy efficiency and ultimately reduce the emission of greenhouse gases. Subtask 2 of IETS Task XVIII focuses on Methods and Applications of Digital Twins (DT) to promote the application of DTs in industry, in order to improve energy efficiency and reduce GHG emissions. Subtask 2 has the following sub-objectives: - Overview of methods and applications of DTs and their requirements for different industry sectors - Analysis of the potential benefits of these methods, focusing on the impact on energy efficiency and GHG emissions reduction - Creation of an international, interdisciplinary network of research and industry Digital Twins have the potential to improve industrial energy systems considerably. Not surprisingly, there has been quite a hype around digital twins during the last years. Though, successful implementations of digital twins in industry are rare and the actual return-on-investment is hard to estimate. For this reason, many companies are still hesitant to employ digital twins. Within Subtask 2, a questionnaire was designed to collect information on the participant’s digital twin projects. The goal was to gain insight on the current state of digital twins in the participant’s companies, to learn about requirements, expectations, and the solutions they chose. The questionnaire was carried out by the Austrian Institute of Technology (AIT). The replies were analyzed by a team from AIT, TU Wien, AEE INTEC and Montanuniversität Leoben (MUL).

In IETS Task XVIII Digitalization, Artificial Intelligence and Related Technologies for Energy Efficiency and GHG Emissions Reduction in Industry we are working on identifying technologies that can be applied in industry to increase the energy efficiency and ultimately reduce the emission of greenhouse gases. Subtask 2 of IETS Task XVIII focuses on Methods and Applications of Digital Twins (DT) to promote the application of DTs in industry, in order to improve energy efficiency and reduce GHG emissions. Subtask 2 has the following sub-objectives: - Overview of methods and applications of DTs and their requirements for different industry sectors - Analysis of the potential benefits of these methods, focusing on the impact on energy efficiency and GHG emissions reduction - Creation of an international, interdisciplinary network of research and industry Digital Twins have the potential to improve industrial energy systems considerably. Not surprisingly, there has been quite a hype around digital twins during the last years. Though, successful implementations of digital twins in industry are rare and the actual return-on-investment is hard to estimate. For this reason, many companies are still hesitant to employ digital twins. As different definitions of DT exist and potential applications are often indistinct, a uniform description and definition for them is required. Within Subtask 2, an overview of this extensive topic is presented in the form of a Glossary. A literature review was conducted and findings from previous work were collected to clarify the terms and definitions around the topic digital twin., Das Ziel des IEA IETS Task 18, Subtask 2 ist es, ein internationales Netzwerk von Industrie, Technologieanbietern und Forschung zu schaffen, in dem Wissen und neueste Erkenntnisse über digitale Zwillinge für die Erhöhung von Energieeffizienz und die Reduktion von Treibhausgasen in der Industrie ausgetauscht werden. Digitale Zwillinge haben das Potenzial, industrielle Energiesysteme erheblich zu verbessern. Da es unterschiedliche Definitionen von digitaler Zwillingsbildung gibt und die möglichen Anwendungen oft unklar sind, ist eine einheitliche Beschreibung und Definition erforderlich. In Subtask 2 wurde ein Überblick über dieses umfangreiche Thema in Form eines Glossars gegeben. Zur Klärung der Begriffe und Definitionen rund um das Thema Digitaler Zwilling wurde eine Literaturrecherche durchgeführt und Erkenntnisse aus bisherigen Arbeiten zusammengetragen.

Diese Doktorarbeit befasst sich mit der Untersuchung von Modellierungsmethoden für die Kinetik von Gas-Feststoffreaktionen im Kontext von thermochemischer Energiespeicherung. Etablierte Modellierungsmethoden beruhen entweder auf starken Annahmen über die untersuchte Reaktion, was das Ergebnis verzerren kann, oder sie sind auf zwei unabhängige Variablen beschränkt, was die Komplexität von Gas-Feststoffreaktionen nicht abbilden kann. In dieser Arbeit wird eine datengetriebene Modellierungsmethode entwickelt: die TensorNPK (non-parametric kinetics) Methode. Sie benötigt keine Annahme abgesehen von der General Kinetic Equation und sie kann Modelle mit beliebig vielen unabhängigen Variablen aus experimentellen Daten generieren. Mit einer Literaturstudie wird ein blinder Fleck in der Theorie zu Gas-Feststoffreaktionen identifiziert: Das Fehlen validierter Modelle, die das Verhalten der Reaktionen in der Nähe des Reaktionsgleichgewichtes beschreiben. Dieses Verhalten ist von großer Relevanz für thermochemische Speicherprozesse, weil sie so nahe am Gleichgewicht wie möglich arbeiten müssen um höchstmögliche Effizienz zu erreichen. Mit der TensorNPK Methode können Erkenntnisse über die Kinetik gewonnen werden, auch wenn keine expliziten Modelle für Effekte in der Nähe des Reaktionsgleichgewichts verfügbar sind. In Zusammenarbeit mit Kollegen wurde die TensorNPK Methode in kinetischen Studien von mehreren Materialien, die für thermochemische Energiespeicherung relevant sind, eingesetzt. Die Performance der TensorNPK Methode in diesen Studien wird evaluiert und Implikationen für kinetische Studien werden hervorgehoben., This thesis investigates modeling methods for the kinetics of gas-solid reactions in the context of thermochemical energy storage (TCES). Established modeling methods require either strong assumptions about the reaction under consideration, which can introduce modeling bias, or are limited to only two independent variables, which does not reflect the complexity of gas-solid reactions. In this thesis, a data-driven modeling method is developed: the TensorNPK (non-parametric kinetics) method. It does require no assumption besides the General Kinetic Equation and it can derive models in any number of variables from experimental data. Through a literature study, a gap in the theory of gas-solid reaction kinetics is identified: the lack of validated models for the behavior in close proximity to the reaction equilibrium.The near-equilibrium kinetics are highly relevant for TCES because these processes need to operate as close to the equilibrium as possible to maximize efficiency. With the TensorNPK method, insights in the relevant kinetics can be gained without explicit models for near-equilibrium effects. In collaboration with colleagues, the TensorNPK method was applied in kinetic studies of various materials relevant for thermochemical energy storage. The performance of the TensorNPK method in these studies is evaluated and implications for kinetic modeling are highlighted.

The storage of industrial waste heat through thermochemical energy storage (TCES) shows high potential to reduce the dependency on fossil fuels. In this paper the capital cost investment of a TCES system utilizing fluidized bed reactors and the reaction system MgO/Mg(OH) 2 is estimated and a profitability analysis is performed. The study estimate is based on a simulation study that considers the mass and energy balance of the proposed preliminary heat storage and release processes utilizing fluidized bed reactors. Furthermore, transport, operation and maintenance as well as utility costs were estimated in order to evaluate the profitability of the system. It is concluded that for the selected boundary conditions, the specific investment costs per kW stored heat are approximately 900 &euro, /kW and that the systems should not be installed at sites where less than around 5 MW of waste heat are available. Finally, a sensitivity analysis was conducted, to identify the key process and economic parameters critical for a positive net present value.

Aufgrund steigendem Bewusstseins und neuer Gesetzgebung gewinnt W��rmer��ckgewinnung zur Steigerung der Energieeffizienz an Relevanz. Insbesondere in energiehungrigen Industriezweigen wie der Stahl- und Eisenindustrie stehen typischerweise eine Reihe von W��rmequellen mit hohem Temperaturniveau zur Verf��gung, die zur Erzeugung von Elektrizit��t genutzt werden k��nnen, wenn die W��rme nicht direkt verwertet werden kann. Mit Dampfkreisl��ufen kann die W��rme effizient in Elektrizit��t umgewandelt werden, aber diese ben��tigen einen kontinuierlichen W��rmestrom um Thermospannungen und W��rmedehnungs-Probleme in der Dampfturbine zu vermeiden. Wenn keine zus��tzlichen W��rmequellen eingesetzt werden sollen wird ein Element ben��tigt, das das diskontinuierliche Temperaturprofil der Abhitzequelle gl��ttet. In dieser Arbeit wird die Anwendung eines Fluidbett-Regenerators f��r die W��rmer��ckgewinnung in einem Brammengie��prozess untersucht. Ein Matlab Modell wurde entwickelt und der Einfluss von Konstruktionsparametern auf die Gl��ttung des Temperaturprofils und den Wirkungsgrad wurde untersucht um Optimierungspotenzial aus zu machen. Au��erdem wurde der Einfluss von einer Umkehr der Str��mungsrichtung des W��rmetransportmediums w��hrend dem Entladen des Regenerators untersucht. Die Ergebnisse deuten darauf hin, dass es m��glich sein sollte einen Regenerator zu konstruieren, der die Temperaturvariation des W��rmetransportmediums bei einem Massenstrom von 12 kg/s in einem Prozess mit einer Periodendauer von 1 h und einem Lastfaktor von 3:1 von 85K auf 11K reduziert. Dieser Regenerator h��tte die Abmessungen 14mx3mx3.5m, w��rde 92 t Partikel mit einem Durchmesser von 80 ��m und Rohre mit einer Gesamtoberfl��che von 440m2 beinhalten. Er w��rde 650 kWh mit einem Wirkungsgrad von in etwa 90% ��bertragen. Insbesondere die hohe W��rmekapazit��t und der hohe Wirkungsgrad sollten diese Technologie attraktiv f��r W��rmer��ckgewinnungs-Anwendungen machen, auch, wenn es sich erst zeigen muss, ob diese Werte auch in realen Regeneratoren erreicht werden k��nnen. Die Entwicklung eines Regelungs-Konzeptes w��rde die Kompetitivit��t von Fluidbett-Regeneratoren im Vergleich zu anderen Technologien noch weiter verbessern., Due to increasing awareness and recent legislation heat recovery is becoming more and more important, in an effort to increase energy efficiency. Especially in energy intensive industries like iron and steel production, numerous high-temperature excess heat sources are available that can be used to produce electricity, if no heat sinks are available to use the heat directly. Steam cycles offer efficient means to convert the heat into electricity, but they require continuous heat supply to avoid thermal stress and dilatation issues within the turbine. If no additional heat sources are to be used, an appliance to buffer the discontinuous excess heat is required to smooth the temperature profile. In this thesis the application of a fluidized-bed heat regenerator in the heat recovery from a steel casting process is investigated. A Matlab model has been developed and the influence of a range of construction parameters on the capacity to smooth the temperature profile and the efficiency of the regenerator was investigated to point out optimization potentials. Furthermore the effect of reversing the flow direction of the heat transfer medium during discharging is analyzed. Overall the simulation results suggest that it should be possible to build a regenerator that reduces the temperature variation of the working medium from 85K to 11K at a heat transfer medium mass flow of 12 kg/s in a process with a cycle duration of 1 h and a duty cycle of 3:1. The regenerator would have a size of 14mx3mx3.5m, contain 92 t particles with a mean diameter of 80 ��m and tubes with a total surface area of 440m2. This regenerator would transfer 650 kWh with a thermal efficiency of approximately 90%. Especially the high capacity and thermal efficiency should make this technology very attractive in heat recovery applications, though it remains to be seen if these values can actually be achieved in real-world regenerators. Regardless, further development, e.g. regarding a control mechanism, could reduce the size and material requirement of the regenerator further to increase the competitiveness of the fluidized-bed heat regenerator compared to other technologies.