Optimizing Large-Scale Linear Energy System Problems with Block Diagonal Structure by Using Parallel Interior-Point Methods

Thomas Breuer, Michael Bussieck, Karl-Kiên Cao, Felix Cebulla, Frederik Fiand, Hans Christian Gils, Ambros Gleixner, Dmitry Khabi, Thorsten Koch, Daniel Rehfeldt () and Manuel Wetzel ()
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Thomas Breuer: Forschungszentrum Jülich GmbH
Michael Bussieck: GAMS Software GmbH
Karl-Kiên Cao: German Aerospace Center (DLR)
Felix Cebulla: German Aerospace Center (DLR)
Frederik Fiand: GAMS Software GmbH
Hans Christian Gils: German Aerospace Center (DLR)
Ambros Gleixner: Zuse Institute Berlin/Technical University Berlin
Dmitry Khabi: High Performance Computing Center Stuttgart (HLRS)
Thorsten Koch: Zuse Institute Berlin/Technical University Berlin
Daniel Rehfeldt: Zuse Institute Berlin/Technical University Berlin
Manuel Wetzel: German Aerospace Center (DLR)

A chapter in Operations Research Proceedings 2017, 2018, pp 641-647 from Springer

Abstract: Abstract Current linear energy system models (ESM) acquiring to provide sufficient detail and reliability frequently bring along problems of both high intricacy and increasing scale. Unfortunately, the size and complexity of these problems often prove to be intractable even for commercial state-of-the-art linear programming solvers. This article describes an interdisciplinary approach to exploit the intrinsic structure of these large-scale linear problems to be able to solve them on massively parallel high-performance computers. A key aspect are extensions to the parallel interior-point solver PIPS-IPM originally developed for stochastic optimization problems. Furthermore, a newly developed GAMS interface to the solver as well as some GAMS language extensions to model block-structured problems will be described.

Keywords: Energy system models; Linear programming; Interior-point methods; Parallelization; High performance computing (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-3-319-89920-6_85

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