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Für den geforderten – und von der Deutschen Forschungsgemeinschaft (DFG) geförderten – Open-Access-Transformationsprozess der deutschen, wissenschaftlichen Publikationslandschaft braucht es neue Formen der Zusammenarbeit zwischen Wissenschaft und Verlagen. Bereits seit 2011 wurden mit Unterstützung seitens der DFG in Deutschland die sogenannten Allianz-Lizenzen zwischen Bibliotheken und Verlagen verhandelt, in denen weitreichende Rechte hinsichtlich der Open-Access-Archivierung verankert sind: Autorinnen und Autoren aber auch die sie vertretenden Einrichtungen dürfen Artikel, die in lizenzierten Zeitschriften erschienen sind, ohne oder mit nur kurzer Embargofrist in geeigneten Repositorien ihrer Wahl frei zugänglich machen. Aufbauend auf diese Open-Access-Komponenten zeigt das DFG-geförderte Projekt „DeepGreen“ ein mögliches neues Modell der Zusammenarbeit mit Verlagen auf: DeepGreen setzt auf die automatisierte Verteilung von Artikeldaten von Verlagen an Repositorien und will disziplinübergreifend einen Großteil jener wissenschaftlichen Publikationen aus Fachzeitschriften, die unter lizenzrechtlichen Kontexten frei zugänglich online gehen dürften, auch tatsächlich online abrufbar machen.
Erprobte DeepGreen von 2016 bis Ende 2017 prototypisch die Machbarkeit der Zielstellung, will das Projekt in der zweiten Projektphase (2018-2020) den (möglichst stark) automatisierten Workflow gemeinsam mit Verlagen, berechtigten Bibliotheken und anderen Einrichtungen etablieren. Technischer Baustein ist eine zentrale, intermediäre Datenverteilstation, die die automatische und rechtssichere Ablieferung von Metadaten inklusive der Volltexte aus Verlagshand direkt an dazu berechtigte institutionelle Repositorien gewährleistet. Erreicht werden soll ein bundesweiter Service, der auf verbindlichen Absprachen mit Verlagen und Bibliotheken fußt und (zunächst) die Bedingungen der Allianz-Lizenzen umsetzt. Gleichzeitig wird die Übertragbarkeit des DeepGreen-Ansatzes auf weitere Lizenzkontexte (FID-Lizenzen, Konsortiallizenzen, Gold-Open-Access-Vereinbarungen) geprüft. Eine zusätzliche Ausbaustufe stellt die Überlegung zur automatisierten Ablieferung an Fachrepositorien und Forschungsinformationssysteme dar, die ebenfalls geplant wird.
Das nationale Projektkonsortium besteht aus den zwei Bibliotheksverbünden Kooperativer Bibliotheksverbund Berlin-Brandenburg (KOBV) und Bibliotheksverbund Bayern (BVB), zwei Universitätsbibliotheken, der Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) und der Technische Universität Berlin (TU Berlin), zusätzlich der Bayerischen Staatsbibliothek (BSB) und einer außeruniversitären Forschungseinrichtung - dem Helmholtz Open Science Koordinationsbüro am Deutschen GeoForschungsZentrum (GFZ).
Das Folgeprojekt beginnt am 01. August 2018. Hier vorliegend finden Sie den Projektantrag zum Nachlesen.
In 2005 the European Union liberalized the gas market with a disruptive change and decoupled trading of natural gas from its transport. The gas is now transported by independent so-called transmissions system operators or TSOs. The market model established by the European Union views the gas transmission network as a black box, providing shippers (gas traders and consumers) the opportunity to transport gas from any entry to any exit. TSOs are required to offer the maximum possible capacities at each entry and exit such that any resulting gas flow can be realized by the network. The revenue from selling these capacities more than one billion Euro in Germany alone, but overestimating the capacity might compromise the security of supply. Therefore, evaluating the available transport capacities is extremely important to the TSOs.
This is a report on a large project in mathematical optimization, set out to develop a new toolset for evaluating gas network capacities. The goals and the challenges as they occurred in the project are described, as well as the developments and design decisions taken to meet the requirements.
Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions
to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.
Borne out of a surprising variety of practical applications, the maximum-weight connected subgraph problem has attracted considerable interest during the past years. This interest has not only led to notable research on theoretical properties, but has also brought about several (exact) solvers-with steadily increasing performance. Continuing along this path, the following article introduces several new algorithms such as reduction techniques and heuristics and describes their integration into an exact solver. The new methods are evaluated with respect to both their theoretical and practical properties. Notably, the new exact framework allows to solve common problem instances from the literature faster than all previous approaches. Moreover, one large-scale benchmark instance from the 11th DIMACS Challenge can be solved for the first time to optimality and the primal-dual gap for two other ones can be significantly reduced.
In 2005 the European Union liberalized the gas market with a disruptive change
and decoupled trading of natural gas from its transport. The gas is now trans-
ported by independent so-called transmissions system operators or TSOs. The
market model established by the European Union views the gas transmission
network as a black box, providing shippers (gas traders and consumers) the
opportunity to transport gas from any entry to any exit. TSOs are required
to offer the maximum possible capacities at each entry and exit such that any
resulting gas flow can be realized by the network. The revenue from selling these
capacities more than one billion Euro in Germany alone, but overestimating the
capacity might compromise the security of supply. Therefore, evaluating the
available transport capacities is extremely important to the TSOs.
This is a report on a large project in mathematical optimization, set out
to develop a new toolset for evaluating gas network capacities. The goals and
the challenges as they occurred in the project are described, as well as the
developments and design decisions taken to meet the requirements.
The Steiner tree problem in graphs is a classical problem that commonly arises in practical applications as one of many variants. Although the different Steiner tree problem variants are usually strongly related, solution approaches employed so far have been prevalently problem-specific. Against this backdrop, the solver SCIP-Jack was created as a general-purpose framework that can be used to solve the classical Steiner tree problem and 11 of its variants. This versatility is achieved by transforming various problem variants into a general form and solving them by using a state-of-the-art MIP-framework. Furthermore, SCIP-Jack includes various newly developed algorithmic components such as preprocessing routines and heuristics. The result is a high-performance solver that can be employed in massively parallel environments and is capable of solving previously unsolved instances. After the introduction of SCIP-Jack at the 2014 DIMACS Challenge on Steiner problems, the overall performance of the solver has considerably improved. This article provides an overview on the current state.
The stability of flows in porous media plays a vital role in transiting energy supply from natural gas to hydrogen, especially for estimating the usability of existing underground gas storage infrastructures. Thus, this research aims to analyze the interface stability of the tangential-velocity discontinuity between two compressible gases by using Darcy's model to include the porosity effect. The results shown in this research will be a basis for considering whether underground gas storages in porous material can be used to store hydrogen. We show the relation between the Mach number M, the viscosity \mu, and the porosity \epsilon on the stability of the interface. This interface stability affects gases' withdrawal and injection processes, thus will help us to determine the velocity which with gas can be extracted and injected into the storage effectively. By imposing solid walls along the flow direction, the critical values of these parameters regarding the stability of the interface are smaller than when considering no walls. The consideration of bounded flows approaches the problem more realistically. In particular, this analysis plays a vital role when considering two-dimensional gas flows in storages and pipes.
Deep learning for spatio-temporal supply anddemand forecasting in natural gas transmission networks
(2021)
Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. Germany's high-pressure gas pipeline network is roughly 40,000 km long, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25km/h, an adequate high-precision, high-frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio-temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high-pressure transmission network. Experiments show that our model effectively captures comprehensive spatio-temporal correlations through modeling gas networks and consistently outperforms state-of-the-art benchmarks on real-world data sets by at least 21%.
The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.
MILP. Try. Repeat.
(2021)