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GasLib—A Library of Gas Network Instances

Author

Listed:
  • Martin Schmidt

    (Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
    Energie Campus Nürnberg, Fürther Str. 250, 90429 Nürnberg, Germany)

  • Denis Aßmann

    (Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany)

  • Robert Burlacu

    (Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany)

  • Jesco Humpola

    (Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany)

  • Imke Joormann

    (Institut für Mathematische Optimierung, Technische Universität Braunschweig, Universitätsplatz 2, 38106 Braunschweig, Germany)

  • Nikolaos Kanelakis

    (School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece)

  • Thorsten Koch

    (Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany)

  • Djamal Oucherif

    (Institut für Angewandte Mathematik, Leibniz Universität Hannover, Welfengarten 1, 30167 Hannover, Germany)

  • Marc E. Pfetsch

    (Department of Mathematics, Technische Universität Darmstadt, Dolivostr. 15, 64293 Darmstadt, Germany)

  • Lars Schewe

    (Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
    Energie Campus Nürnberg, Fürther Str. 250, 90429 Nürnberg, Germany)

  • Robert Schwarz

    (Zuse Institut Berlin, Takustr. 7, 14195 Berlin, Germany)

  • Mathias Sirvent

    (Discrete Optimization, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany)

Abstract

The development of mathematical simulation and optimization models and algorithms for solving gas transport problems is an active field of research. In order to test and compare these models and algorithms, gas network instances together with demand data are needed. The goal of GasLib is to provide a set of publicly available gas network instances that can be used by researchers in the field of gas transport. The advantages are that researchers save time by using these instances and that different models and algorithms can be compared on the same specified test sets. The library instances are encoded in an XML (extensible markup language) format. In this paper, we explain this format and present the instances that are available in the library.

Suggested Citation

  • Martin Schmidt & Denis Aßmann & Robert Burlacu & Jesco Humpola & Imke Joormann & Nikolaos Kanelakis & Thorsten Koch & Djamal Oucherif & Marc E. Pfetsch & Lars Schewe & Robert Schwarz & Mathias Sirvent, 2017. "GasLib—A Library of Gas Network Instances," Data, MDPI, vol. 2(4), pages 1-18, December.
    • Handle: RePEc:gam:jdataj:v:2:y:2017:i:4:p:40-:d:121266
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    References listed on IDEAS

    as
    1. DE WOLF, Daniel & SMEERS, Yves, 2000. "The gas transmission problem solved by an extension of the simplex algorithm," LIDAM Reprints CORE 1489, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Daniel Rose & Martin Schmidt & Marc C. Steinbach & Bernhard M. Willert, 2016. "Computational optimization of gas compressor stations: MINLP models versus continuous reformulations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 83(3), pages 409-444, June.
    3. Conrado Borraz-Sánchez & Russell Bent & Scott Backhaus & Hassan Hijazi & Pascal Van Hentenryck, 2016. "Convex Relaxations for Gas Expansion Planning," INFORMS Journal on Computing, INFORMS, vol. 28(4), pages 645-656, November.
    4. Daniel De Wolf & Yves Smeers, 2000. "The Gas Transmission Problem Solved by an Extension of the Simplex Algorithm," Management Science, INFORMS, vol. 46(11), pages 1454-1465, November.
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