Solving highly detailed gas transport MINLPs: Block separability and penalty alternating direction methods

Björn Geißler, Antonio Morsi, Lars Schewe, Martin Schmidt

Research output: Contribution to journalArticlepeer-review

Abstract

Detailed modeling of gas transport problems leads to nonlinear and nonconvex mixed-integer optimization or feasibility models (MINLPs) because both the incorporation of discrete controls of the network and accurate physical and technical modeling are required to achieve practical solutions. Hence, ignoring certain parts of the physics model is not valid for practice. In the present contribution we extend an approach based on linear relaxations of the underlying nonlinearities by tailored model reformulation techniques yielding block-separable MINLPs. This combination of techniques allows us to apply a penalty alternating direction method and thus to solve highly detailed MINLPs for largescale, real-world instances. The practical strength of the proposed method is demonstrated by a computational study in which we apply the method to instances from steady-state gas transport including both pooling effects with respect to the mixing of gases of different composition and a highly detailed compressor station model.

Original languageEnglish
Pages (from-to)309-323
Number of pages15
JournalINFORMS Journal on Computing
Volume30
Issue number2
DOIs
Publication statusPublished - 30 Apr 2018

Keywords

  • Alternating direction methods
  • Block separability
  • Gas transport
  • Nonconvex mixed-integer nonlinear optimization
  • Penalty methods

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