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Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions

Marc Pfetsch (pfetsch***at***mathematik.tu-darmstadt.de)
Armin Fügenschuh (fuegenschuh***at***zib.de)
Björn Geißler (bjoern.geissler***at***math.uni-erlangen.de)
Nina Geißler (Nina.Geissler***at***open-grid-europe.com)
Ralf Gollmer (ralf.gollmer***at***uni-due.de)
Benjamin Hiller (hiller***at***zib.de)
Jesco Humpola (humpola***at***zib.de)
Thorsten Koch (koch***at***zib.de)
Thomas Lehmann (lehmann***at***zib.de)
Alexander Martin (Alexander.Martin***at***math.uni-erlangen.de)
Antonio Morsi (Antonio.Morsi***at***math.uni-erlangen.de)
Jessica Rövekamp (Jessica.Roevekamp***at***open-grid-europe.com)
Lars Schewe (lars.schewe***at***math.uni-erlangen.de)
Martin Schmidt (mschmidt***at***ifam.uni-hannover.de)
Rüdiger Schultz (ruediger.schultz***at***uni-due.de)
Robert Schwarz (schwarz***at***zib.de)
Jonas Schweiger (schweiger***at***zib.de)
Claudia Stangl (claudia.stangl***at***uni-due.de)
Marc Steinbach (mcs***at***ifam.uni-hannover.de)
Stefan Vigerske (vigerske***at***zib.de)
Bernhard Willert (willert***at***ifam.uni-hannover.de)

Abstract: In this article we investigate methods to solve a fundamental task in gas transportation, namely the validation of nomination problem: Given a gas transmission network consisting of passive pipelines and active, controllable elements and given an amount of gas at every entry and exit point of the network, find operational settings for all active elements such that there exists a network state meeting all physical, technical, and legal constraints. We describe a two-stage approach to solve the resulting complex and numerically difficult mixed-integer non-convex nonlinear feasibility problem. The first phase consists of four distinct algorithms facilitating mixed-integer linear, mixed-integer nonlinear, reduced nonlinear, and complementarity constrained methods to compute possible settings for the discrete decisions. The second phase employs a precise continuous nonlinear programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances whose size is significantly larger than networks that have appeared in the literature previously.

Keywords: mixed integer nonlinear programming, gas transport optimization, real-world instances

Category 1: Applications -- Science and Engineering

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 3: Nonlinear Optimization (Systems governed by Differential Equations Optimization )


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Entry Submitted: 11/30/2012
Entry Accepted: 11/30/2012
Entry Last Modified: 11/17/2013

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