6588
2017
eng
4
2
article
0
--
--
--
GasLib – A Library of Gas Network Instances
Data
10.3390/data2040040
no
Martin Schmidt
Regine Kossick
Denis Assmann
Robert Burlacu
Jesco Humpola
Imke Joormann
Nikolaos Kanelakis
Thorsten Koch
Djamal Oucherif
Marc Pfetsch
Lars Schewe
Robert Schwarz
Matthias Sirvent
Mathematical Optimization
Koch, Thorsten
MODAL-GasLab
TRR154-A04
MODAL-Gesamt
6217
eng
reportzib
0
--
2017-08-03
--
The SCIP Optimization Suite 4.0
The SCIP Optimization Suite is a powerful collection of optimization software that consists of the branch-cut-and-price framework and mixed-integer programming solver SCIP, the linear programming solver SoPlex, the modeling language Zimpl, the parallelization framework UG, and the generic branch-cut-and-price solver GCG. Additionally, it features the extensions SCIP-Jack for solving Steiner tree problems, PolySCIP for solving multi-objective problems, and SCIP-SDP for solving mixed-integer semidefinite programs. The SCIP Optimization Suite has been continuously developed and has now reached version 4.0. The goal of this report is to present the recent changes to the collection. We not only describe the theoretical basis, but focus on implementation aspects and their computational consequences.
1438-0064
urn:nbn:de:0297-zib-62170
Stephen J. Maher
Ambros Gleixner
Tobias Fischer
Tristan Gally
Gerald Gamrath
Ambros Gleixner
Robert Lion Gottwald
Gregor Hendel
Thorsten Koch
Marco Lübbecke
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Sebastian Schenker
Robert Schwarz
Felipe Serrano
Yuji Shinano
Dieter Weninger
Jonas T. Witt
Jakob Witzig
ZIB-Report
17-12
Computer aspects of numerical algorithms
Linear programming
Mixed integer programming
Nonconvex programming, global optimization
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Gottwald, Robert
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6217/scipoptsuite-40.pdf
https://opus4.kobv.de/opus4-zib/files/6217/scipoptsuite-401.pdf
1782
eng
reportzib
0
2013-03-07
2013-03-07
--
Mathematical Optimization for Challenging Network Planning Problems in Unbundled Liberalized Gas Markets
The recently imposed new gas market liberalization rules in Germany lead to a change of business of gas network operators.
While previously network operator and gas vendor where united, they were forced to split up into independent companies.
The network has to be open to any other gas trader at the same conditions, and free network capacities have to be identified and publicly offered in a non-discriminatory way.
We show that these new paradigms lead to new and challenging mathematical optimization problems.
In order to solve them and to provide meaningful results for practice, all aspects of the underlying problems, such as combinatorics, stochasticity, uncertainty, and nonlinearity, have to be addressed.
With such special-tailored solvers, free network capacities and topological network extensions can, for instance, be determined.
1438-0064
urn:nbn:de:0297-zib-17821
10.1007/s12667-013-0099-8
Appeared in: Energy Systems 5 (2014) 449-473
Armin Fügenschuh
Jonas Schweiger
Björn Geißler
Ralf Gollmer
Christine Hayn
Rene Henrion
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Alexander Martin
Radoslava Mirkov
Antonio Morsi
Werner Römisch
Jessica Rövekamp
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Claudia Stangl
Marc Steinbach
Bernhard Willert
ZIB-Report
13-13
eng
uncontrolled
Gas Market Liberalization
eng
uncontrolled
Entry-Exit Model
eng
uncontrolled
Gas Network Access Regulation
eng
uncontrolled
Mixed-Integer Nonlinear Nonconvex Stochastic Optimization
Energy resources (see also 84.60.-h Direct energy conversion and storage)
Network models, deterministic
Mixed integer programming
Nonlinear programming
Applications of mathematical programming
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
https://opus4.kobv.de/opus4-zib/files/1782/ZR-13-13.pdf
1234
eng
reportzib
0
2011-03-15
2011-03-15
--
Gas Network Topology Optimization for Upcoming Market Requirements
Gas distribution networks are complex structures that consist of
passive pipes, and active, controllable elements such as valves and
compressors. Controlling such network means to find a suitable setting
for all active components such that a nominated amount of gas can be
transmitted from entries to exits through the network, without
violating physical or operational constraints. The control of a
large-scale gas network is a challenging task from a practical point
of view. In most companies the actual controlling process is supported
by means of computer software that is able to simulate the flow of the
gas. However, the active settings have to be set manually within such
simulation software. The solution quality thus depends on the
experience of a human planner.
When the gas network is insufficient for the transport then topology
extensions come into play. Here a set of new pipes or active elements
is determined such that the extended network admits a feasible control
again. The question again is how to select these extensions and where
to place them such that the total extension costs are
minimal. Industrial practice is again to use the same simulation
software, determine extensions by experience, add them to the virtual
network, and then try to find a feasible control of the active
elements. The validity of this approach now depends even more on the
human planner.
Another weakness of this manual simulation-based approach is that it
cannot establish infeasibility of a certain gas nomination, unless all
settings of the active elements are tried. Moreover, it is impossible
to find a cost-optimal network extension in this way.
In order to overcome these shortcomings of the manual planning
approach we present a new approach, rigorously based on mathematical
optimization. Hereto we describe a model for finding feasible
controls and then extend this model such that topology extensions can
additionally and simultaneously be covered. Numerical results for real-world instances are presented and
discussed.
11-09
urn:nbn:de:0297-zib-12348
10.1109/EEM.2011.5953035
Appeared in: Engery Market (EEM), 2011 8th International Conference on the European, Zagreb, 25-27 May 2011, pp. 346-351
Armin Fügenschuh
-empty- (Opus4 user: )
Benjamin Hiller
Jonas Schweiger
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Robert Schwarz
Jonas Schweiger
Jácint Szabó
ZIB-Report
11-09
deu
uncontrolled
Mathematical Optimization
deu
uncontrolled
Gas Distribution Networks
deu
uncontrolled
Topology Planning
Flow control and optimization
Network models, deterministic
Mixed integer programming
Nonlinear programming
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
https://opus4.kobv.de/opus4-zib/files/1234/ZR-11-09.pdf
4718
2014
eng
article
Taylor & Francis
0
--
--
--
Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions
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 feasibility problem. The first phase consists of four distinct algorithms applying linear, and methods for complementarity constraints to compute possible settings for the discrete decisions. The second phase employs a precise continuous programming model of the gas network. Using this setup, we are able to compute high quality solutions to real-world industrial instances that are significantly larger than networks that have appeared in the mathematical programming literature before.
Optimization Methods and Software
10.1080/10556788.2014.888426
yes
urn:nbn:de:0297-zib-16531
Robert Schwarz
Marc Pfetsch
Armin Fügenschuh
Björn Geißler
Nina Geißler
Ralf Gollmer
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Alexander Martin
Antonio Morsi
Jessica Rövekamp
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Claudia Stangl
Marc Steinbach
Stefan Vigerske
Bernhard Willert
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
Vigerske, Stefan
Schweiger, Jonas
4738
2013
eng
449
473
3
5
article
Springer Berlin Heidelberg
Berlin
0
--
--
--
Mathematical optimization for challenging network planning problems in unbundled liberalized gas markets
The recently imposed new gas market liberalization rules in Germany lead to a change of business of gas network operators. While previously network operator and gas vendor were united, they were forced to split up into independent companies. The network has to be open to any other gas trader at the same conditions, and free network capacities have to be identified and publicly offered in a non-discriminatory way. We discuss how these changing paradigms lead to new and challenging mathematical optimization problems. This includes the validation of nominations, that asks for the decision if the network’s capacity is sufficient to transport a specific amount of flow, the verification of booked capacities and the detection of available freely allocable capacities, and the topological extension of the network with new pipelines or compressors in order to increase its capacity. In order to solve each of these problems and to provide meaningful results for the practice, a mixture of different mathematical aspects have to be addressed, such as combinatorics, stochasticity, uncertainty, and nonlinearity. Currently, no numerical solver is available that can deal with such blended problems out-of-the-box. The main goal of our research is to develop such a solver, that moreover is able to solve instances of realistic size. In this article, we describe the main ingredients of our prototypical software implementations.
Energy Systems
10.1007/s12667-013-0099-8
yes
urn:nbn:de:0297-zib-17821
Armin Fügenschuh
Humpola
Björn Geißler
Ralf Gollmer
Christine Hayn
René Henrion
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Alexander Martin
Radoslava Mirkov
Antonio Morsi
Werner Römisch
Jessica Rövekamp
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Claudia Stangl
Marc Steinbach
Bernhard Willert
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
5795
2015
eng
article
0
2015-11-25
--
--
GasLib - A Library of Gas Network Instances
The development of mathematical simulation and optimization models and algorithms for solving gas transport problems is an active field of research. In order to test and compare these models and algorithms, gas network instances together with demand data are needed. The goal of GasLib is to provide a set of publicly available gas network instances that can be used by researchers in the field of gas transport. The advantages are that researchers save time by using these instances and that different models and algorithms can be compared on the same specified test sets. The library instances are encoded in an XML format. In this paper, we explain this format and present the instances that are available in the library.
Optimization Online
urn:nbn:de:0297-zib-57950
no
http://www.optimization-online.org/DB_FILE/2015/11/5216.pdf
Jesco Humpola
Robert Schwarz
Imke Joormann
Djamal Oucherif
Marc Pfetsch
Lars Schewe
Martin Schmidt
Robert Schwarz
Mathematical Optimization
MODAL-GasLab
ForNe
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/5795/humpola2015gaslib.pdf
4832
2011
eng
346
351
conferenceobject
0
--
--
--
Gas Network Topology Optimization for Upcoming Market Requirements
Gas distribution networks are complex structures that consist of passive pipes, and active, controllable elements such as valves and compressors. Controlling such network means to find a suitable setting for all active components such that a nominated amount of gas can be transmitted from entries to exits through the network, without violating physical or operational constraints. The control of a large-scale gas network is a challenging task from a practical point of view. In most companies the actual controlling process is supported by means of computer software that is able to simulate the flow of the gas. However, the active settings have to be set manually within such simulation software. The solution quality thus depends on the experience of a human planner. When the gas network is insufficient for the transport then topology extensions come into play. Here a set of new pipes or active elements is determined such that the extended network admits a feasible control again. The question again is how to select these extensions and where to place them such that the total extension costs are minimal. Industrial practice is again to use the same simulation software, determine extensions by experience, add them to the virtual network, and then try to find a feasible control of the active elements. The validity of this approach now depends even more on the human planner. Another weakness of this manual simulation-based approach is that it cannot establish infeasibility of a certain gas nomination, unless all settings of the active elements are tried. Moreover, it is impossible to find a cost-optimal network extension in this way. In order to overcome these shortcomings of the manual planning approach we present a new approach, rigorously based on mathematical optimization. Hereto we describe a model for finding feasible controls and then extend this model such that topology extensions can additionally and simultaneously be covered. Numerical results for real-world instances are presented and discussed.
International Conference on the European Energy Market (EEM)
10.1109/EEM.2011.5953035
yes
urn:nbn:de:0297-zib-12348
Jonas Schweiger
Armin Fügenschuh
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Robert Schwarz
Jonas Schweiger
Jacint Szabo
Mathematical Optimization
Mathematical Optimization Methods
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
6719
2018
eng
339
346
conferenceobject
0
--
2018-05-26
--
Finding Maximum Minimum Cost Flows to Evaluate Gas Network Capacities
In this article we consider the following problem arising in the context of scenario generation to evaluate the transport capacity of gas networks: In the Uncapacitated Maximum Minimum Cost Flow Problem (UMMCF) we are given a flow network where each arc has an associated nonnegative length and infinite capacity. Additionally, for each source and each sink a lower and an upper bound on its supply and demand are known, respectively. The goal is to find values for the supplies and demands respecting these bounds, such that the optimal value of the induced Minimum Cost Flow Problem is maximized, i.e., to determine a scenario with maximum transportmoment. In this article we propose two linear bilevel optimization models for UMMCF, introduce a greedy-style heuristic, and report on our first computational experiment.
Operations Research Proceedings 2017
10.1007/978-3-319-89920-6_46
978-3-319-89919-0
yes
urn:nbn:de:0297-zib-61519
2017-11-01
Kai Hoppmann
Kai Hoppmann
Robert Schwarz
Mathematical Optimization
Hoppmann, Kai
MODAL-GasLab
MODAL-Gesamt
Energy Network Optimization
Schwarz, Robert
6151
eng
reportzib
0
--
2016-12-22
--
Using Bilevel Optimization to find Severe Transport Situations in Gas Transmission Networks
In the context of gas transmission in decoupled entry-exit systems, many approaches to determine the network capacity are based on the evaluation of realistic and severe transport situations. In this paper, we review the Reference Point Method, which is an algorithm used in practice to generate a set of scenarios using the so-called transport moment as a measure for severity. We introduce a new algorithm for finding severe transport situations that considers an actual routing of the flow through the network and is designed to handle issues arising from cyclic structures in a more dynamical manner. Further, in order to better approximate the physics of gas, an alternative, potential based flow formulation is proposed. The report concludes with a case study based on data from the benchmark library GasLib.
1438-0064
urn:nbn:de:0297-zib-61519
Kai Hennig
Kai Hennig
Robert Schwarz
ZIB-Report
16-68
eng
uncontrolled
bilevel optimization
eng
uncontrolled
gas networks
eng
uncontrolled
network flow
CALCULUS OF VARIATIONS AND OPTIMAL CONTROL; OPTIMIZATION [See also 34H05, 34K35, 65Kxx, 90Cxx, 93-XX]
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
GAME THEORY, ECONOMICS, SOCIAL AND BEHAVIORAL SCIENCES
Mathematical Optimization
Hoppmann, Kai
MODAL-GasLab
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6151/ZR-16-68.pdf
6156
eng
reportzib
0
--
2016-12-22
--
Optimal Looping of Pipelines in Gas Networks
In this paper, we compare several approaches for the problem of gas network expansions using loops, that is, to build new pipelines in parallel to existing ones. We present different model formulations for the problem of continuous loop expansions as well as discrete loop expansions. We then analyze problem properties, such as the structure and convexity of the underlying feasible regions. The paper concludes with a computational study comparing the continuous and the discrete formulations.
1438-0064
urn:nbn:de:0297-zib-61564
Ralf Lenz
Ralf Lenz
Robert Schwarz
ZIB-Report
16-67
eng
uncontrolled
Mixed Integer Nonlinear Programming
deu
uncontrolled
Gas Network Applications
deu
uncontrolled
Expansion Planning
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Lenz, Ralf
MODAL-GasLab
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6156/ZR-16-67.pdf
3267
2011
2011
eng
105
114
conferenceobject
VDI-Verlag, Düsseldorf
0
--
--
--
Optimierung Technischer Kapazitäten in Gasnetzen
Optimierung in der Energiewirtschaft
VDI-Berichte 2157
urn:nbn:de:0297-zib-15121
no
Alexander Martin
Björn Geißler
Christine Heyn
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Antonio Morsi
Marc Pfetsch
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Marc Steinbach
Bernhard Willert
Mathematical Optimization
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
5349
2015
eng
SIAM-MOS series on Optimization
bookpart
0
--
--
--
Computational results for validation of nominations
The different approaches to solve the validation of nomination problem presented in the previous chapters are evaluated computationally in this chapter. Each approach is analyzed individually, as well as the complete solvers for these problems. We demonstrate that the presented approaches can successfully solve large-scale real-world instances.
Evaluating Gas Network Capacities
9781611973686
yes
Benjamin Hiller
Jesco Humpola
Jesco Humpola
Thomas Lehmann
Ralf Lenz
Antonio Morsi
Marc Pfetsch
Lars Schewe
Martin Schmidt
Robert Schwarz
Jonas Schweiger
Claudia Stangl
Bernhard Willert
Mathematical Optimization
Hiller, Benjamin
Lenz, Ralf
MODAL-GasLab
ForNe
TRR154-A04
MODAL-Gesamt
Schweiger, Jonas
5345
2015
eng
SIAM-MOS series on Optimization
bookpart
0
--
--
--
The Specialized MINLP Approach
We propose an approach to solve the validation of nominations problem using mixed-integer nonlinear programming (MINLP) methods. Our approach handles both the discrete settings and the nonlinear aspects of gas physics. Our main contribution is an innovative coupling of mixed-integer (linear) programming (MILP) methods with nonlinear programming (NLP) that exploits the special structure of a suitable approximation of gas physics, resulting in a global optimization method for this type of problem.
Evaluating Gas Network Capacities
9781611973686
yes
Jesco Humpola
Jesco Humpola
Armin Fügenschuh
Benjamin Hiller
Thorsten Koch
Thomas Lehmann
Ralf Lenz
Robert Schwarz
Jonas Schweiger
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
Lenz, Ralf
MODAL-GasLab
ForNe
TRR154-A04
MODAL-Gesamt
Schweiger, Jonas
7840
2020
eng
161
179
78
article
0
2020-05-13
--
--
On the relation between the extended supporting hyperplane algorithm and Kelley’s cutting plane algorithm
Recently, Kronqvist et al. (J Global Optim 64(2):249–272, 2016) rediscovered the supporting hyperplane algorithm of Veinott (Oper Res 15(1):147–152, 1967) and demonstrated its computational benefits for solving convex mixed integer nonlinear programs. In this paper we derive the algorithm from a geometric point of view. This enables us to show that the supporting hyperplane algorithm is equivalent to Kelley’s cutting plane algorithm (J Soc Ind Appl Math 8(4):703–712, 1960) applied to a particular reformulation of the problem. As a result, we extend the applicability of the supporting hyperplane algorithm to convex problems represented by a class of general, not necessarily convex nor differentiable, functions.
Journal of Global Optimization
10.1007/s10898-020-00906-y
yes
urn:nbn:de:0297-zib-73253
Felipe Serrano
Felipe Serrano
Robert Schwarz
Ambros Gleixner
Mathematical Optimization
Gleixner, Ambros
Serrano, Felipe
MODAL-SynLab
MODAL-Gesamt
Schwarz, Robert
EnBA-M
7325
eng
reportzib
0
--
2019-05-20
--
On the Relation between the Extended Supporting Hyperplane Algorithm and Kelley’s Cutting Plane Algorithm
Recently, Kronqvist et al. (2016) rediscovered the supporting hyperplane algorithm of Veinott (1967) and demonstrated its computational benefits for solving convex mixed-integer nonlinear programs. In this paper we derive the algorithm from a geometric point of view. This enables us to show that the supporting hyperplane algorithm is equivalent to Kelley's cutting plane algorithm applied to a particular reformulation of the problem. As a result, we extend the applicability of the supporting hyperplane algorithm to convex problems represented by general, not necessarily convex, differentiable functions that satisfy a mild condition.
1438-0064
urn:nbn:de:0297-zib-73253
Felipe Serrano
Felipe Serrano
Robert Schwarz
Ambros Gleixner
ZIB-Report
19-18
Mathematical Optimization
Mathematical Optimization Methods
Gleixner, Ambros
Serrano, Felipe
MODAL-SynLab
MODAL-Gesamt
Schwarz, Robert
EnBA-M
https://opus4.kobv.de/opus4-zib/files/7325/gauge.pdf
6743
eng
reportzib
0
--
2018-03-01
--
A System to Evaluate Gas Network Capacities: Concepts and Implementation
In 2005 the European Union liberalized the gas market with a disruptive change and decoupled trading of natural gas from its transport. The gas is now transported by independent so-called transmissions system operators or TSOs. The market model established by the European Union views the gas transmission network as a black box, providing shippers (gas traders and consumers) the opportunity to transport gas from any entry to any exit. TSOs are required to offer the maximum possible capacities at each entry and exit such that any resulting gas flow can be realized by the network. The revenue from selling these capacities more than one billion Euro in Germany alone, but overestimating the capacity might compromise the security of supply. Therefore, evaluating the available transport capacities is extremely important to the TSOs.
This is a report on a large project in mathematical optimization, set out to develop a new toolset for evaluating gas network capacities. The goals and the challenges as they occurred in the project are described, as well as the developments and design decisions taken to meet the requirements.
1438-0064
urn:nbn:de:0297-zib-67438
An earlier version of this report is available as ZR 17-03 at
https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6193.
Benjamin Hiller
Jonas Schweiger
Thorsten Koch
Lars Schewe
Robert Schwarz
Jonas Schweiger
ZIB-Report
18-11
Case-oriented studies
Applications of mathematical programming
Mathematical Optimization
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
TRR154-A04
MODAL-Gesamt
Schweiger, Jonas
Energy Network Optimization
Schwarz, Robert
https://opus4.kobv.de/opus4-zib/files/6743/ZR-18-11.pdf
6730
2018
eng
797
808
3
270
article
0
--
--
--
A System to Evaluate Gas Network Capacities: Concepts and Implementation
In 2005 the European Union liberalized the gas market with a disruptive change
and decoupled trading of natural gas from its transport. The gas is now trans-
ported by independent so-called transmissions system operators or TSOs. The
market model established by the European Union views the gas transmission
network as a black box, providing shippers (gas traders and consumers) the
opportunity to transport gas from any entry to any exit. TSOs are required
to offer the maximum possible capacities at each entry and exit such that any
resulting gas flow can be realized by the network. The revenue from selling these
capacities more than one billion Euro in Germany alone, but overestimating the
capacity might compromise the security of supply. Therefore, evaluating the
available transport capacities is extremely important to the TSOs.
This is a report on a large project in mathematical optimization, set out
to develop a new toolset for evaluating gas network capacities. The goals and
the challenges as they occurred in the project are described, as well as the
developments and design decisions taken to meet the requirements.
European Journal of Operational Research
yes
urn:nbn:de:0297-zib-67438
2018-02-18
Benjamin Hiller
Jonas Schweiger
Thorsten Koch
Lars Schewe
Robert Schwarz
Jonas Schweiger
Mathematical Optimization
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
TRR154-A04
MODAL-Gesamt
Schweiger, Jonas
Energy Network Optimization
Schwarz, Robert
1653
eng
reportzib
0
2012-11-19
2012-11-19
--
Validation of Nominations in Gas Network Optimization: Models, Methods, and Solutions
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.
1438-0064
urn:nbn:de:0297-zib-16531
10.1080/10556788.2014.888426
Appeared in: Optimization Methods and Software 30 (2015) pp. 15-53
Marc Pfetsch
Jonas Schweiger
Armin Fügenschuh
Björn Geißler
Nina Geißler
Ralf Gollmer
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Alexander Martin
Antonio Morsi
Jessica Rövekamp
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Claudia Stangl
Marc Steinbach
Stefan Vigerske
Bernhard Willert
ZIB-Report
12-41
Software
Computer Applications
Computational methods
Mixed integer programming
Nonlinear programming
Applications of mathematical programming
Mathematical Optimization
Fügenschuh, Armin
Hiller, Benjamin
Koch, Thorsten
Vigerske, Stefan
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
https://opus4.kobv.de/opus4-zib/files/1653/ZR-12-41.pdf
https://opus4.kobv.de/opus4-zib/files/1653/ZR-12-41_revised.pdf
6193
eng
reportzib
0
--
2017-02-09
--
A System to Evaluate Gas Network Capacities: Concepts and Implementation
Since 2005, the gas market in the European Union is liberalized and the trading of natural gas is decoupled from its transport. The transport is done by so-called transmissions system operators or TSOs. The market model established by the European Union views the gas transmission network as a black box, providing shippers (gas traders and consumers) the opportunity to transport gas from any entry to any exit. TSOs are required to offer maximum independent capacities at each entry and exit such that the resulting gas flows can be realized by the network without compromising security of supply. Therefore, evaluating the available transport capacities is extremely important to the TSOs.
This paper gives an overview of the toolset for evaluating gas network capacities that has been developed within the ForNe project, a joint research project of seven research partners initiated by Open Grid Europe, Germany's biggest TSO. While most of the relevant mathematics is described in the book "Evaluating Gas Network Capacities", this article sketches the system as a whole, describes some developments that have taken place recently, and gives some details about the current implementation.
1438-0064
urn:nbn:de:0297-zib-61931
A revised and extended version is available as ZIB-Report 18-11.
Benjamin Hiller
Benjamin Hiller
Thorsten Koch
Lars Schewe
Robert Schwarz
Jonas Schweiger
ZIB-Report
17-03
eng
uncontrolled
operations research in energy
eng
uncontrolled
gas network optimization
eng
uncontrolled
entry-exit model
eng
uncontrolled
freely allocable capacity
eng
uncontrolled
large-scale mixed-integer nonlinear programming
Computer Applications
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
TRR154-A04
MODAL-Gesamt
Schweiger, Jonas
https://opus4.kobv.de/opus4-zib/files/6193/euro2016_eepa_FORNE_2017-02-09_ZR.pdf
1512
deu
reportzib
0
2012-04-19
2012-04-19
--
Optimierung Technischer Kapazitäten in Gasnetzen
Die mittel- und längerfristige Planung für den Gastransport hat sich durch
Änderungen in den regulatorischen Rahmenbedingungen stark verkompliziert.
Kernpunkt ist die Trennung von Gashandel und -transport. Dieser Artikel
diskutiert die hieraus resultierenden mathematischen Planungsprobleme,
welche als Validierung von Nominierungen und Buchungen, Bestimmung der
technischen Kapazität und Topologieplanung bezeichnet werden. Diese
mathematischen Optimierungsprobleme werden vorgestellt und Lösungsansätze
skizziert.
1438-0064
urn:nbn:de:0297-zib-15121
Alexander Martin
Benjamin Hiller
Björn Geißler
Christine Hayn
Benjamin Hiller
Jesco Humpola
Thorsten Koch
Thomas Lehmann
Antonio Morsi
Marc Pfetsch
Lars Schewe
Martin Schmidt
Rüdiger Schultz
Robert Schwarz
Jonas Schweiger
Marc Steinbach
Bernhard Willert
ZIB-Report
11-56
deu
uncontrolled
Gasnetzplanung
deu
uncontrolled
Technische Kapazitäten
deu
uncontrolled
Nominierungsvalidierung
deu
uncontrolled
Buchungsvalidierung
deu
uncontrolled
Topologieplanung
Computer Applications
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Hiller, Benjamin
Koch, Thorsten
MODAL-GasLab
ForNe
MODAL-Gesamt
Schweiger, Jonas
https://opus4.kobv.de/opus4-zib/files/1512/ZR-11-56.pdf
6134
eng
reportzib
0
--
2016-01-12
--
PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite
SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOpt, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.
1438-0064
urn:nbn:de:0297-zib-61348
10.1007/978-3-319-42432-3_37
Appeared in: Mathematical Software – ICMS 2016, Volume 9725, Pages 301-307
Stephen J. Maher
Matthias Miltenberger
Matthias Miltenberger
João Pedro Pedroso
Daniel Rehfeldt
Robert Schwarz
Felipe Serrano
ZIB-Report
16-64
eng
uncontrolled
SCIP, Mathematical optimization, Python, Modeling
Computer Applications
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Mathematical Optimization Methods
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
ASTfSCM
MIP-ZIBOPT
MODAL-GasLab
MODAL-SynLab
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6134/PySCIPOpt.pdf
6045
2016
eng
301
307
9725
conferenceobject
Springer
0
--
--
--
PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite
SCIP is a solver for a wide variety of mathematical optimization problems. It is written in C and extendable due to its plug-in based design. However, dealing with all C specifics when extending SCIP can be detrimental to development and testing of new ideas. This paper attempts to provide a remedy by introducing PySCIPOpt, a Python interface to SCIP that enables users to write new SCIP code entirely in Python. We demonstrate how to intuitively model mixed-integer linear and quadratic optimization problems and moreover provide examples on how new Python plug-ins can be added to SCIP.
Mathematical Software – ICMS 2016
10.1007/978-3-319-42432-3_37
yes
Lecture Notes in Computer Science
urn:nbn:de:0297-zib-61348
Stephen J. Maher
Daniel Rehfeldt
Matthias Miltenberger
João Pedro Pedroso
Daniel Rehfeldt
Robert Schwarz
Felipe Serrano
Mathematical Optimization
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
MODAL-GasLab
MODAL-SynLab
MODAL-Gesamt
5767
eng
reportzib
0
--
2016-02-26
--
The SCIP Optimization Suite 3.2
The SCIP Optimization Suite is a software toolbox for generating and solving various classes of mathematical optimization problems. Its major components are the modeling language ZIMPL, the linear programming solver SoPlex, the constraint integer programming framework and mixed-integer linear and nonlinear programming solver SCIP, the UG framework for parallelization of branch-and-bound-based solvers, and the generic branch-cut-and-price solver GCG. It has been used in many applications from both academia and industry and is one of the leading non-commercial solvers.
This paper highlights the new features of version 3.2 of the SCIP Optimization Suite. Version 3.2 was released in July 2015. This release comes with new presolving steps, primal heuristics, and branching rules within SCIP. In addition, version 3.2 includes a reoptimization feature and improved handling of quadratic constraints and special ordered sets. SoPlex can now solve LPs exactly over the rational number and performance improvements have been achieved by exploiting sparsity in more situations. UG has been tested successfully on 80,000 cores. A major new feature of UG is the functionality to parallelize a customized SCIP solver. GCG has been enhanced with a new separator, new primal heuristics, and improved column management. Finally, new and improved extensions of SCIP are presented, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs.
1438-0064
urn:nbn:de:0297-zib-57675
no
Gerald Gamrath
Gerald Gamrath
Tobias Fischer
Tristan Gally
Ambros Gleixner
Gregor Hendel
Thorsten Koch
Stephen J. Maher
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Sebastian Schenker
Robert Schwarz
Felipe Serrano
Yuji Shinano
Stefan Vigerske
Dieter Weninger
Michael Winkler
Jonas T. Witt
Jakob Witzig
ZIB-Report
15-60
eng
uncontrolled
mixed-integer linear and nonlinear programming
eng
uncontrolled
MIP solver
eng
uncontrolled
MINLP solver
eng
uncontrolled
linear programming
eng
uncontrolled
LP solver
eng
uncontrolled
simplex method
eng
uncontrolled
modeling
eng
uncontrolled
parallel branch-and-bound
eng
uncontrolled
branch-cut-and-price framework
eng
uncontrolled
generic column generation
eng
uncontrolled
Steiner tree solver
eng
uncontrolled
multi-criteria optimization
eng
uncontrolled
mixed-integer semidefinite programming
Parallel computation
Linear programming
Integer programming
Mixed integer programming
Nonlinear programming
Applications of mathematical programming
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Vigerske, Stefan
Winkler, Michael
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
CRC1026
Siemens
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/5767/scipopt-32.pdf