Solving Highly Detailed Gas Transport MINLPs: Block Separability and Penalty Alternating Direction Methods

Björn Geißler (), Antonio Morsi (), Lars Schewe () and Martin Schmidt ()
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Björn Geißler: Friedrich-Alexander-Universität Erlangen-Nürnberg, Discrete Optimization, 91058 Erlangen, Germany
Antonio Morsi: Friedrich-Alexander-Universität Erlangen-Nürnberg, Discrete Optimization, 91058 Erlangen, Germany
Lars Schewe: Friedrich-Alexander-Universität Erlangen-Nürnberg, Discrete Optimization, 91058 Erlangen, Germany
Martin Schmidt: Friedrich-Alexander-Universität Erlangen-Nürnberg, Discrete Optimization, 91058 Erlangen, Germany; Energie Campus Nürnberg, 90429 Nürnberg, Germany

INFORMS Journal on Computing, 2018, vol. 30, issue 2, 309-323

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 large-scale, 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.

Keywords: nonconvex mixed-integer nonlinear optimization; penalty methods; alternating direction methods; block separability; gas transport (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:30:y:2018:i:2:p:309-323

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