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Maximizing the storage capacity of gas networks: a global MINLP approach

Robert Burlacu(robert.burlacu***at***fau.de)
Herbert Egger(egger***at***mathematik.tu-darmstadt.de)
Martin Groß(martin.gross***at***oms.rwth-aachen.de)
Alexander Martin(alexander.martin***at***fau.de)
Marc E. Pfetsch(pfetsch***at***mathematik.tu-darmstadt.de)
Lars Schewe(lars.schewe***at***fau.de)
Mathias Sirvent(mathias.sirvent***at***fau.de)
Martin Skutella(martin.skutella***at***tu-berlin.de)

Abstract: In this paper, we study the transient optimization of gas networks, focusing in particular on maximizing the storage capacity of the network. We include nonlinear gas physics and active elements such as valves and compressors, which due to their switching lead to discrete decisions. The former is described by a model derived from the Euler equations that is given by a coupled system of nonlinear parabolic partial differential equations (PDEs). We tackle the resulting mathematical optimization problem by a first-discretize-then-optimize approach. To this end, we introduce a new discretization of the underlying system of parabolic PDEs and prove well-posedness for the resulting nonlinear discretized system. Endowed with this discretization, we model the problem of maximizing the storage capacity as a non-convex mixed-integer nonlinear problem (MINLP). For the numerical solution of the MINLP, we algorithmically extend a well-known relaxation approach that has already been used very successfully in the field of stationary gas network optimization. This method allows us to solve the problem to global optimality by iteratively solving a series of mixed-integer problems (MIPs). Finally, we present two case studies that illustrate the applicability of our approach.

Keywords: Mixed-Integer Nonlinear Programming, Transient Gas Transport Optimization, Storage Capacity Maximization, Power-to-Gas, First-Discretize-Then-Optimize

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Applications -- Science and Engineering (Optimization of Systems modeled by PDEs )

Citation: Transregio-CRC 154, https://opus4.kobv.de/opus4-trr154/frontdoor/index/index/docId/221

Download: [PDF]

Entry Submitted: 06/30/2018
Entry Accepted: 07/01/2018
Entry Last Modified: 06/30/2018

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