Optimizing Large-Scale Linear Energy System Problems with Block Diagonal Structure by Using Parallel Interior-Point Methods
- Resource Type
- Authors
- Michael R. Bussieck; Thorsten Koch; Dmitry Khabi; Ambros M. Gleixner; Daniel Rehfeldt; Thomas Breuer; Frederik Fiand; Karl-Kiên Cao; Manuel Wetzel; Felix Cebulla; Hans Christian Gils
- Source
- Cham : Springer International Publishing, Operations Research Proceedings 641-647 (2018). doi:10.1007/978-3-319-89920-6_85
Operations Research Proceedings 2017 / Kliewer, Natalia (Editor) ; Cham : Springer International Publishing, 2018, Chapter 85 ; ISSN: 0721-5924=2197-9294 ; ISBN: 978-3-319-89919-0=978-3-319-89920-6 ; doi:10.1007/978-3-319-89920-6
Operations Research Proceedings 2017 / Kliewer, Natalia (Editor) ; Cham : Springer International Publishing, 2018, Chapter 85 ; ISSN: 0721-5924=2197-9294 ; ISBN: 978-3-319-89919-0=978-3-319-89920-6 ; doi:10.1007/978-3-319-89920-6International Conference on Operations Research, Berlin, Germany, 2017-09-06-2017-09-08
Operations Research Proceedings ISBN: 9783319899190
OR
- Subject
- Linear programming
Scale (ratio)
Interface (Java)
Computer science
020209 energy
02 engineering and technology
energy system models
Solver
Supercomputer
block structures
parallel solvers
Computational science
high performance computing
020401 chemical engineering
0202 electrical engineering, electronic engineering, information engineering
Stochastic optimization
0204 chemical engineering
Massively parallel
Interior point method
linear optimization
- Language
- English
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.