Validation of nominations in gas network optimization: models, methods, and solutions

Pfetsch, Marc E.; Fügenschuh, Armin; Geißler, Björn; Geißler, Nina; Gollmer, Ralf; Hiller, Benjamin; Humpola, Jesco; Koch, Thorsten; Lehmann, Thomas; Martín, Alexander; Morsi, Antonio; Rövekamp, Jessica; Schewe, Lars; Schmidt, Martin; Schultz, Rüdiger; Schwarz, R.; Schweiger, Jonas; Stangl, Claudia; Steinbach, Marc C.; Vigerske, Stefan; Willert, Bernhard M.

doi:10.1080/10556788.2014.888426

Cited by 102 publications

(68 citation statements)

References 44 publications

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“…The continuous reformulations yield comparably robust results, optimal values of almost the same quality on our test set, and tend to be solvable within shorter solution times. Together with the techniques developed in Pfetsch et al (2015), Schmidt (2013), Schmidt et al (2013), and Schmidt et al (2015b), this article provides a complete continuous reformulation of the discrete-continuous problem of stationary gas transport optimization. Additionally, first promising numerical results on a large-scale network underpin the practical usability of continuous reformulations for entire networks.…”

Section: Resultsmentioning

confidence: 99%

“…We will see that the continuous reformulations work quite well in comparison to MINLP approaches. As mentioned in the introduction, one main contribution of this paper is thatin combination with techniques presented in Pfetsch et al (2015), Schmidt (2013), Schmidt et al (2013) and Schmidt et al (2015b)-our approach allows the purely continuous reformulation of genuinely discrete-continuous models of entire gas networks. In order to demonstrate that this is also viable in practice, in the sense that one can compute locally optimal values of the corresponding MINLPs, we present some promising first results.…”

Section: Computational Studymentioning

confidence: 99%

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Computational optimization of gas compressor stations: MINLP models versus continuous reformulations

et al. 2016

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When considering cost-optimal operation of gas transport networks, compressor stations play the most important role. Proper modeling of these stations leads to nonconvex mixed-integer nonlinear optimization problems. In this article, we give an isothermal and stationary description of compressor stations, state MINLP and GDP models for operating a single station, and discuss several continuous reformulations of the problem. The applicability and relevance of different model formulations, especially of those without discrete variables, is demonstrated by a computational study on both academic examples and real-world instances. In addition, we provide preliminary computational results for an entire network.

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Section: Resultsmentioning

confidence: 99%

Section: Computational Studymentioning

confidence: 99%

Computational optimization of gas compressor stations: MINLP models versus continuous reformulations

et al. 2016

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“…These features make the solution of the model extremely difficult. Pfetsch et al [10] made a detailed review on the NLP for the operation optimization problem of natural gas pipelines.…”

Section: Introductionmentioning

confidence: 99%

“…However, the GRG method is easily entrapped into the local optimum due to the gradient search strategy. Many improved LP methods have a solid mathematical background [10] and the ability to find the global optimum, but they are not designed for solving the NLP problem. Moreover, none of the above three deterministic methods are designed for solving the resulting MINLP model related to some special optimization problems of natural gas pipelines [4].…”

Section: Introductionmentioning

confidence: 99%

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

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et al. 2018

Mathematical Problems in Engineering

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Operation optimization of natural gas pipelines has received increasing attentions, due to such advantages as maximizing the operating economic benefit and the gas delivery amount. This paper provides a review on the most relevant research progress related to the steady-state operation optimization models of natural gas pipelines as well as corresponding solution methods based on stochastic optimization algorithms. The existing operation optimization model of the natural gas pipeline is a mixed-integer nonlinear programming (MINLP) model involving a nonconvex feasible region and mixing of continuous, discrete, and integer optimization variables, which represents an extremely difficult problem to be solved by use of optimization algorithms. A survey on the state of the art demonstrates that many stochastic algorithms show better performance of solving such optimization models due to their advantages of handling discrete variables and of high computation efficiency over classical deterministic optimization algorithms. The essential progress mainly with regard to the applications of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) algorithms, and their extensions is summarized. The performances of these algorithms are compared in terms of the quality of optimization results and the computation efficiency. Furthermore, the research challenges of improving the optimization model, enhancing the stochastic algorithms, developing an online optimization technology, researching the transient optimization, and studying operation optimization of the integrated energy network are discussed.

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“…On the basis of these models, several simulation and optimization algorithms have been proposed. We refer to [1][2][3] and the references therein for more information. With GasLib, we provide a set of network instances that can be used to test and compare such models and the algorithms for solving them.…”

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confidence: 99%

GasLib—A Library of Gas Network Instances

Schmidt

Aßmann

Burlacu

et al. 2017

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Abstract: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 (extensible markup language) format. In this paper, we explain this format and present the instances that are available in the library.Data Set: http://gaslib.zib.de Data Set License: CC BY 3.0 Keywords: gas transport; networks; problem instances; mixed-integer nonlinear optimization; GasLib MSC: 90-08; 90C90; 90B10 SummaryThe mathematical simulation and optimization of gas transport through pipeline systems is an important field of research with a large practical impact. Over the last decades, many different mathematical models on different levels of accuracy for different components of gas networks have been developed. On the basis of these models, several simulation and optimization algorithms have been proposed. We refer to [1][2][3] and the references therein for more information. With GasLib, we provide a set of network instances that can be used to test and compare such models and the algorithms for solving them.

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Validation of nominations in gas network optimization: models, methods, and solutions

Cited by 102 publications

References 44 publications

Computational optimization of gas compressor stations: MINLP models versus continuous reformulations

Computational optimization of gas compressor stations: MINLP models versus continuous reformulations

Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

GasLib—A Library of Gas Network Instances

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