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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.
Chemische Energiespeicher
(2017)
Im Zuge der Energiewende werden die erneuerbaren Energien zur tragenden Säule unserer Stromversorgung. Bis 2025 sollen bundesweit 40-45 % des Stroms aus erneuerbaren Quellen stammen, bis 2050 sogar 80 %. Dabei wird der erzeugte Strom hauptsächlich aus Wind und Sonne stammen. Im Norden der Bundesrepublik wird Wind die Erzeugung dominieren, während im Süden, insbesondere in Bayern der Strom vornehmlich aus Photovoltaik gewonnen werden wird. Um die Ziele der Energiewende zu erreichen, wird neben den ausreichenden Erzeugungskapazitäten auch eine leistungsfähige Infrastruktur zur Verteilung und Nutzung des erneuerbaren Stroms notwendig sein. Planbarkeit und Sicherung der Versorgungsqualität bei zunehmenden Anteilen von volatiler Erzeugung werden dabei zu einer immer größeren Herausforderung. Das Projekt neos hat im engen Verbund mit den beteiligten Partnern wichtige tagesaktuelle Fragestellungen wissenschaftlich erörtert und untersucht, die sich durch die laufende Transformation des Energiesystems und den rasanten Ausbau der Anlagen zur erneuerbaren Energieerzeugung den Projektpartnern stellen.
Das vom BMWi geförderte Forschungsprojekt SyNErgie (03/2015 bis 05/2018) beschäftigt sich mit der Weiterentwicklung von Netzplanungsprozessen [1]. Im Fokus steht dabei die Modellierung von Blindleistungsflüssen und die dezentrale Nutzung von Blindleistungsquellen (z. B. Wechselrichter, Ladedrosseln, Kondensatoren) in Mittelspannungsnetzen, über welche der Blindleistungshaushalt letztendlich beeinflusst werden soll. Im Rahmen dieses Beitrags wird anhand eines realen Mittelspannungsnetzes (MS-Netz) gezeigt, welche Modellierungsschritte zur Erstellung eines möglichst realitätsnahen Simulationsabbilds nötig sind. Dies ist notwendig, um der Verteilnetzplanung eine robuste Planungsgrundlage für die Ableitung von Entscheidungen im Rahmen eines Blindleistungsmanagements zur Verfügung zu stellen.
Das vom BMWi geförderte Forschungsprojekt SyNErgie (03/2015 bis 05/2018) beschäftigt sich mit der Weiterentwicklung von Netzplanungsprozessen [1]. Im Fokus steht dabei die Modellierung von Blindleistungsflüssen und die dezentrale Nutzung von Blindleistungsquellen (z. B. Wechselrichter, Ladedrosseln, Kondensatoren) in Mittelspannungsnetzen, über welche der Blindleistungshaushalt letztendlich beeinflusst werden soll. Diese Veröffentlichung konzentriert sich auf die konzipierte Erweiterung des Netzplanungsprozesses zur Einbindung von Blindleistungsquellen im Verteilungsnetz in ein Blindleistungsmanagement. Deren einzelne Phasen werden erläutert und anschließend die Netz-Sensitivitäts- und -Restriktionsanalyse anhand eines realen Mittelspannungsnetzes demonstriert. Abschließend werden mögliche Abschwächungs- und Verstärkungseffekte im Hinblick auf einen Blindleistungsabruf über mehrere Spannungsebenen hinweg diskutiert.
Monitoring of structures and related decisions based on cost optimization are discussed in this contribution. Many research publications and experimental data are currently available on inspection and on monitoring and they represent the outcome of the remarkable work done. Not all the topics of interest are widely debated and implemented in standards. The current state-of-practice in standards is summarized and aspects to be included in future recommendations are proposed. The implementation of risk-based decisions is illustrated in a case study dealing with a stadium roof in Northern Italy. Snow actions are important especially in northern and mountainous regions where heavy snowfalls and related accumulation result to considerable loads. As the roof structure fails to comply with the requirements of Eurocodes, a permanent monitoring system has been installed to allow for real time evaluation of the reliability level of the structure. The system supplies the necessary information supporting immediate decisions on closure of the stadium in case of an extraordinary snow load. Cost-optimal decisions regarding the closure of the stadium are analysed based on a limit state function, on probabilistic models for the influencing parameters including measurement uncertainty, economic losses related to closure and failure consequences. The results demonstrate the potential of the use of the monitoring systems and probabilistic reliability analysis in order to support decisions and highlight the need for their implementation in future standards.
This paper deals with the locomotion by tilting sequences of shape-variable compliant tensegrity structures. The shape of these structures is controlled by manipulating their prestress state. The tensegrity structure is tilting as consequence of a suitable variation of its shape. By multiple repetition of such tilting sequences a motion is generated. Quasi-static considerations for the considered structures are presented in order to estimate the actuation parameters. For a proper number of actuators this quasi-static approach enables an analytical calculation of the actuation parameters of the structure in order to control the geometrical configuration as required. As an example a two-dimensional tensegrity structure which is in contact with a horizontal plane due to gravity is considered. By successive tilting sequences a uniaxial motion results. The excitation of the structure is calculated for a given change of shape with the quasi-static analysis. The according results are compared with transient dynamic simulations. Qualitative conclusions about the motion behavior and the usability of the quasi-static approach are given.
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.
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.