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Technologies status and perspectives of Power-to-Gas in connection with seasonal underground storage
(2019)
To support a rational and efficient use of electrical energy in residential and industrial environments, Non-Intrusive Load Monitoring (NILM) provides several techniques to identify state and power consumption profiles of connected appliances. Design requirements for such systems include a low hardware and installations costs for residential, reliability and high-availability for industrial purposes, while keeping invasive interventions into the electrical infrastructure to a minimum. This work introduces a reference hardware setup that allows an in depth analysis of electrical energy consumption in industrial environments. To identify appliances and their consumption profile, appropriate identification algorithms are developed by the NILM community. To enable an evaluation of these algorithms on industrial appliances, we introduce the Laboratory-measured IndustriaL Appliance Characteristics (LILAC) dataset: 1302 measurements from one, two, and three concurrently running appliances of 15 appliance types, measured with the introduced testbed. To allow in-depth appliance consumption analysis, measurements were carried out with a sampling rate of 50 kHz and 16-bit amplitude resolution for voltage and current signals. We show in experiments that signal signatures, contained in the measurement data, allows one to distinguish the single measured electrical appliances with a baseline machine learning approach of nearly 100% accuracy.
Die Energiewende führt zu neuen Herausforderungen für Verteilungsnetzbetreiber hinsichtlich der Erbringung von Systemdienstleistungen, der Integrationsfähigkeit weiterer Erzeugungsanlagen und Lasten sowie bei der Gewährleistung einer hohen Versorgungssicherheit. Die Deckung der steigenden Blindleistungsbedarfe seitens der Netzbetriebsmittel, Verbraucher und Erzeuger gewinnt durch den Wegfall der Großkraftwerke für Netzbetreiber zunehmend an Bedeutung.Das abgeschlossene und vom BMWi geförderte Projekt SyNErgie (Laufzeit von 03/2015 bis 05/2018) beschäftigt sich mit der Entwicklung von Blindleistungsmanagementsystemen für Mittelspannungsnetze (MS-Netze). Ziel dabeiist es, das bisher ungenutzte, freie Blindleistungspotenzial betrieblicher Kompensationsanlagen und dezentraler Erzeugungsanlagen (allg.: Q-Quellen) zu nutzen, um die Blindleistungsänderungsfähigkeit 1 eines Verteilungsnetzes zu erhöhen. Diese Veröffentlichung stellt ausgewählte Einzelergebnisse und Erfahrungen des Projektes vor, welche u. a. über zahlreiche Messungen inMS-Netzen bei Firmen mit Anschlusspunkt in der MS-Ebene, Netzsimulationen und mathematische Modelle abgeleitet wurden.
Power-to-Methane as one part of Power-to-Gas has been recognized globally as one of the key elements for the transition towards a sustainable energy system. While plants that produce methane catalytically have been in operation for a long time, biological methanation has just reached industrial pilot scale and near-term commercial application. The growing importance of the biological method is reflected by an increasing number of scientific articles describing novel approaches to improve this technology. However, these studies are difficult to compare because they lack a coherent nomenclature. In this article, we present a comprehensive set of parameters allowing the characterization and comparison of various biological methanation processes. To identify relevant parameters needed for a proper description of this technology, we summarized existing literature and defined system boundaries for Power-to-Methane process steps. On this basis, we derive system parameters providing information on the methanation system, its performance, the biology and cost aspects. As a result, three different standards are provided as a blueprint matrix for use in academia and industry applicable to both, biological and catalytic methanation. Hence, this review attempts to set the standards for a comprehensive description of biological and chemical methanation processes.
Im Rahmen des Forschungsprojektes MAGGIE, das am Beispiel des historischen Quartiers Margaretenau in Regensburg innovative Lösungen für modernes und bezahlbares Wohnen erforscht, soll über eine solaraktive Fassade Sonnenwärme im Bestandsmauerwerk eingespeichert werden, um so den Energiebedarf des Gebäudes zu reduzieren.
In this thesis, a concept for the automated use of network planning processes, which is part of the decision support system developed in the framework of the EU project CrossEnergy, is presented. This publication focuses on the automatic processing of input data and subsequent network modeling as well as the coordination of network extension measures for the systematic solution of static voltage and current problems. Different variants are calculated in order to be able to compare the design technologies.
This paper presents a convolutional neural network (CNN)which can be used for forecasting electricity load profiles 36 hours intothe future. In contrast to well established CNN architectures, the inputdata is one-dimensional. A parameter scanning of network parameters isconducted in order to gain information about the influence of the kernelsize, number of filters, and dense size. The results show that a goodforecast quality can already be achieved with basic CNN architectures.The method works not only for smooth sum loads of many hundredconsumers, but also for the load of apartment buildings
The rope curve line of a tensioned rope can be described by means of the catenary curve. Opposed to that, the curved line of a free bent rope cannot be described by an analytical function. Practical applications of free bending are for example at tail ropes at the bottom of shaft in rope drives with traction sheaves. The question whether the maximum diameter of rope loop is small enough for the diameter of the shaft is highly interesting. In [1] a method was presented to calculate the curved line of free bent ropes numerically by help of energy methods. An analytical description of rope curve line would be very helpful. Beginning with the structure of a rope curve line of tensioned rope (catenary curve) and considering the influence of bending stiffness, the structure of an analytical equation for the curve line of a free bent rope will be developed. The main focus of this paper is to develop and to describe the structure of such an analytical equation. To get a first idea about the values of the constants in that analytical equation a few test results were evaluated. But these equations consider the static rope behavior only. Due to dynamic effects in the rope while running through the loop at the bottom of a shaft, pendulousness of the tail rope occurs.
Mobile cranes are regularly operated in regions which experience ultra deep operating temperatures of down to -60?C (-76?F). In safety regulated work environments crane operations will be suspended simply because the lowest wire rope working temperature stated in the applicable standards is -40?C/F. Examples of ultra deep temperature wire rope application are shown in Figure 1.1 and 1.3. It should be noted that this paper is written in conjunction with a paper written by the co-author Ulrich Briem titled “Fatigue Behaviour of Rope Wires”, presented and published in unison with this one. In order to analyze the behaviour under ultra deep temperature conditions, tests on wire rope as well as on rope wires were carried out. In the following, static tensile and bending test results with rope and rope wires will be reported, which were carried out in conditions of down to -95?C (-139?F) as well as at room temperature. The conclusion is that the results of these tests can be adopted to crane wire rope as well.
As part of current design standards, the Battelle Two-Curve Model (BTCM) is still widely used to predict and secure ductile crack arrest in gas transmission pipelines. For modern linepipe steels and rich natural gases or CO2 mixtures, the BTCM might lead to incorrect predictions. On the one hand, it suffers from the insufficient description of the individual physical processes in the pipe material and fluid itself. Furthermore, the model does not account for fluid-structure-interaction (FSI) effects during simultaneous running-ductile fracture (RDF) and mixture decompression. Numerical FSI models allow for a more sophisticated, coupled analysis of the driving forces for the failure of pipelines. This paper deals with the development of an FSI model for the coupled prediction of 3D pressure profiles acting on the inner pipe wall during crack propagation. The coupled Euler-Lagrange (CEL) method is used to link the fluid and structure models. In a Lagrange formulation, the modified Bai-Wierzbicki (MBW) model describes the plastic deformation and ductile fracture as a function of the underlying stress/strain conditions. The fluid behavior is calculated in a 3D model space by Euler equations and the GERG-2008 reference equation of state (EOS). The coupled CEL model is used to predict the RDF in small-diameter pipe sections for different fluid mixtures. The calculated 3D pressure distributions ahead and behind the running crack tip (CT) significantly differ in axial and circumferential directions depending on the mixture composition. The predicted FSI between the pipe wall and fluid decompression in 3D CEL/FSI model provides reliable knowledge about the pressure loading of the pipeline during RDF.