High detail stationary optimization models for gas networks

Schmidt, Martin; Steinbach, Marc C.; Willert, Bernhard M.

doi:10.1007/s11081-014-9246-x

Cited by 76 publications

(62 citation statements)

References 34 publications

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“…In accordance with our general network (Schmidt et al 2015c), a compressor station is seen as an arc a = (u, v) of a directed network graph G = (V, A). The boundary conditions at which the compressor station a has to operate consist of a mass flow range,…”

Section: Boundary Conditionsmentioning

confidence: 94%

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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: Boundary Conditionsmentioning

confidence: 94%

“…Full details can be found in Schmidt et al (2015c) where models of all network elements have been developed. For later reference, the models are presented in constraint form, with constraint functions being denoted by c and super-indexed with an abbreviated name indicating the semantics of the constraint.…”

Section: Problem Descriptionmentioning

confidence: 99%

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

et al. 2016

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“…The centrifugal compressor uses fans to create a vacuum and is used to boost natural gas with relatively lower pressure and in large quantity. Due to the centrifugal compressor can boost the significant higher amount of gas and requires less maintenance than the reciprocating compressor, almost all the trunk natural gas pipelines are equipped with the centrifugal compressor [37]. Hence, in this paper, we only review the significant progress regarding the constraints of centrifugal compressors.…”

Section: Constraintsmentioning

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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“…Thus, the goal of the TSOs is to transport the gas in a way such that all contracts with entry and exit customers are satisfied and that the operation of the compressor stations is as cheap as possible. The reader interested in more details about gas network optimization is referred to, e.g., Fügenschuh et al (2013), Koch et al (2015, Pfetsch et al (2015), , Schmidt et al (2013Schmidt et al ( ), (2015. Due to the combination of highly nonlinear and nonconvex phenomena of gas dynamics in pipes and other network elements as well as the switching of controllable elements, e.g., the (de-)activation of compressors, the problem of validation of nominations leads to nonlinear and nonconvex mixed-integer models that are hard to solve for real-world instances.…”

Section: Nonsmooth Problems In Gas Transportmentioning

confidence: 99%

High detail stationary optimization models for gas networks

Cited by 76 publications

References 34 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

An interior-point method for nonlinear optimization problems with locatable and separable nonsmoothness

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