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Grid-scale electrical energy storage (EES) is a key component in cost-effective transition scenarios to renewable energy sources. The requirement of scalability favors EES approaches such as pumped-storage hydroelectricity (PSH) or compressed-air energy storage (CAES), which utilize the cheap and abundant storage materials water and air, respectively. To overcome the site restriction and low volumetric energy densities attributed to PSH and CAES, liquid-air energy storage (LAES) has been devised; however, it suffers from a rather small round-trip efficiency (RTE) and challenging storage conditions. Aiming to overcome these drawbacks, a novel system for EES is developed using solidified air (i.e., clathrate hydrate of air) as the storable phase of air. A reference plant for solidified-air energy storage (SAES) is conceptualized and modeled thermodynamically using the software CoolProp for water and air as well as empirical data and first-order approximations for the solidified air (SA). The reference plant exhibits a RTE of 52% and a volumetric storage density of 47 kWh per m3 of SA. While this energy density relates to only one half of that in LAES plants, the modeled RTE of SAES is comparable already. Since improved thermal management and the use of thermodynamic promoters can further increase the RTEs in SAES, the technical potential of SAES is in place already. Yet, for a successful implementation of the concept - in addition to economic aspects - questions regarding the stability of SA must be first clarified and challenges related to the processing of SA resolved.
In dieser Arbeit wird ein Luft-Clathrat Energiespeicher entworfen, der die Speicherung von elektrischer Energie bei einer Speicherdauer von etwa einem Tag ermöglichen soll. Als Speichermedium dient Luft. Die Luft wird zuerst komprimiert (Einspeichern der elektrischen Energie) und anschließend bei Temperaturen oberhalb des Wasser-Gefrierpunkts (mindestens 274 K, Drücke ab etwa 170 bar) zu Luft-Clathrat synthetisiert. Gespeichert wird das Luft-Clathrat in einem offenen Tank, der aufgrund des Selbstkonservierungseffekts bei moderaten Bedingungen von 1 bar und bei einer Temperatur von 271 K gehalten werden kann. Durch das Hinzufügen von Promotern zum Luft-Clathrat, lässt sich das Luft-Clathrat bei niedrigeren Drücken (< 50 bar) und bei leicht höheren Temperaturen (ca. 280 K) synthetisieren.
Bei den Druck- und Flüssigluftspeichern handelt es sich um ähnliche Speicher, da bei diesen Luft ebenfalls als Speichermedium dient. Vorteile des Luft-Clathrat Energiespeichers gegenüber den Druck- und Flüssigluftspeichern liegen darin, dass dieser bei milderen Bedingungen (Druck, Temperatur) die Luft speichert und für die Speicherung der Luft keine Kaverne benötigt, wie es bei den Druckluftspeichern der Fall ist.
In der folgenden Masterarbeit werden drei Systeme des Luft-Clathrat Energiespeichers thermodynamisch modelliert. Das isotherme und isentrope System dienen als ideale Systeme, während das reale System an die Wirklichkeit angenähert wird. Für das reale System wird bei reinem Luft-Clathrat ein Zykluswirkungsgrad von ca. 17 %, eine gravimetrische Energiedichte von ca. 22 Wh/kg und eine volumetrische Energiedichte von ca. 21 kWh/m3 erreicht. Mit Promotern konnte der Zykluswirkungsgrad auf ca. 47 % bei einer gravimetrischen Energiedichte von ca. 11 Wh/kg und einer volumetrischen Energiedichte von ca. 10 kWh/m3 erhöht werden.
With the emergence of the recent Industry 4.0 movement, data integration is now also being driven along the production line, made possible primarily by the use of established concepts of intelligent supply chains, such as the digital avatars. Digital avatars – sometimes also called Digital Twins or more broadly Cyber-Physical Systems (CPS) – are already successfully used in holistic systems for intelligent transport ecosystems, similar to the use of Big Data and artificial intelligence technologies interwoven with modern production and supply chains. The goal of this paper is to describe how data from interwoven, autonomous and intelligent supply chains can be integrated into the diverse data ecosystems of the Industry 4.0, influenced by a multitude of data exchange formats and varied data schemas. In this paper, we describe how a framework for supporting SMEs was established in the Lake Constance region and describe a demonstrator sprung from the framework. The demonstrator project’s goal is to exhibit and compare two different approaches towards optimisation of manufacturing lines. The first approach is based upon static optimisation of production demand, i.e. exact or heuristic algorithms are used to plan and optimise the assignment of orders to individual machines. In the second scenario, we use real-time situational awareness – implemented as digital avatar – to assign local intelligence to jobs and raw materials in order to compare the results to the traditional planning methods of scenario one. The results are generated using event-discrete simulation and are compared to common (heuristic) job scheduling algorithms.