GasLib

GasLib. A library of gas network instances. Natural gas is one of the most important energy resources worldwide. In Europe it accounts for about 25% of the primary energy consumption and is distributed through a pipeline network with a total length of more than 100,000 km. The reliable and efficient operation of these pipeline networks is a permanent challenge for the gas transmission operators. Indeed, the liberalization of the European and German gas market poses novel and difficult planning problems for gas transmission network operators. They are obliged to offer as much freely allocable capacity as possible. Freely allocable capacities enable gas shippers (usually gas traders or bulk consumers) to feed in or withdraw gas at their entries and exits without having to care where the gas is withdrawn or fed in, respectively. When offering transmission capacities, the transmission system operator has to ensure free allocability, which means that all gas flow situations that may result from nominating these capacities can be realized by the given gas transmission network. This requirement can hardly be verified with existing simulation-based planning methods. This observation led to the implementation of a research project Technical Capacities of Gas Networks, funded by the Federal Ministry of Economics and Technology. One main goal of this project has been to develop and implement mathematical optimization-based methods for checking realizability of stationary gas flow situations. GasLib is a collection of technical gas network descriptions as well as contract-based nomination data (gas flow and pressure specifications at entries and exits). This collection is based on real-world network data from the gas transport company Open Grid Europe GmbH. The data are distorted in order to yield a realistic gas network that is significantly different from the original. The goal of GasLib is to promote research on gas networks by providing a set of large and realistic benchmark instances.


References in zbMATH (referenced in 24 articles )

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  1. Burlacu, Robert; Geißler, Björn; Schewe, Lars: Solving mixed-integer nonlinear programmes using adaptively refined mixed-integer linear programmes (2020)
  2. Fokken, Eike; Göttlich, Simone; Kolb, Oliver: Optimal control of compressor stations in a coupled gas-to-power network (2020)
  3. Benner, Peter; Grundel, Sara; Himpe, Christian; Huck, Christoph; Streubel, Tom; Tischendorf, Caren: Gas network benchmark models (2019)
  4. Burlacu, Robert; Egger, Herbert; Groß, Martin; Martin, Alexander; Pfetsch, Marc E.; Schewe, Lars; Sirvent, Mathias; Skutella, Martin: Maximizing the storage capacity of gas networks: a global MINLP approach (2019)
  5. Cay, Pelin; Mancilla, Camilo; Storer, Robert H.; Zuluaga, Luis F.: Operational decisions for multi-period industrial gas pipeline networks under uncertainty (2019)
  6. Fokken, E.; Göttlich, S.; Kolb, O.: Modeling and simulation of gas networks coupled to power grids (2019)
  7. Habeck, Oliver; Pfetsch, Marc E.; Ulbrich, Stefan: Global optimization of mixed-integer ODE constrained network problems using the example of stationary gas transport (2019)
  8. Hante, Falk M.; Schmidt, Martin: Complementarity-based nonlinear programming techniques for optimal mixing in gas networks (2019)
  9. Schmidt, Martin; Sirvent, Mathias; Wollner, Winnifried: A decomposition method for MINLPs with Lipschitz continuous nonlinearities (2019)
  10. Gugat, Martin; Schuster, Michael; Weber, Gerhard-Wilhelm: Stationary gas networks with compressor control and random loads: optimization with probabilistic constraints (2018)
  11. Hoppmann, Kai; Schwarz, Robert: Finding maximum minimum cost flows to evaluate gas network capacities (2018)
  12. Mehrmann, Volker; Schmidt, Martin; Stolwijk, Jeroen J.: Model and discretization error adaptivity within stationary gas transport optimization (2018)
  13. Schweiger, Jonas: Exploiting structure in non-convex quadratic optimization and gas network planning under uncertainty (2018)
  14. Schweiger, Jonas; Liers, Frauke: A decomposition approach for optimal gas network extension with a finite set of demand scenarios (2018)
  15. Streubel, Tom; Strohm, Christian; Trunschke, Philipp; Tischendorf, Caren: Generic construction and efficient evaluation of flow network DAEs and their derivatives in the context of gas networks (2018)
  16. Walther, Tom; Hiller, Benjamin; Saitenmacher, René: Polyhedral 3D models for compressors in gas networks (2018)
  17. Borraz-Sánchez, Conrado; Bent, Russell; Backhaus, Scott; Hijazi, Hassan; Van Hentenryck, Pascal: Convex relaxations for gas expansion planning (2016)
  18. Rose, Daniel; Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: Computational optimization of gas compressor stations: MINLP models versus continuous reformulations (2016)
  19. Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.: High detail stationary optimization models for gas networks: validation and results (2016)
  20. Schweiger, Jonas: Gas network extension planning for multiple demand scenarios (2016)

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