Abstract:in Wiley InterScience (www.interscience.wiley.com).As the gas industry has developed, gas pipeline networks have evolved over decades into very complex systems. A typical network today might consist of thousands of pipes, dozens of stations, and many other devices, such as valves and regulators. Inside each station, there can be several groups of compressor units of various vintages that were installed as the capacity of the system expanded. The compressor stations typically consume about 3-5% of the transport… Show more
“…In [9] mixed integer models for the stationary case of gas network are proposed but without operating diagram constraints, feature included in our work, and for networks without cycles, which is not the case of Spanish gas network. Other related works are presented in [10] and [11].…”
Managing a gas transport network is a complex problem because of the number of possibilities of routing the gas through the pipes. The most important aim in this kind of systems is to fulfill the demand within the pressure bounds, independently of its associated costs. However, in the present work some cost drivers are also taken into account by means of different objective functions in order to manage the network in an efficient way. This work deals with mathematical modeling and optimization of gas transport networks, where a two-stage procedure is proposed. In the first stage, optimization algorithms based on mathematical programming are applied to make some decisions (whether to activate compressor stations, control valves and other control elements) and gives an initial solution to the second stage. This last stage, which is based on control theory techniques, refines the solution to obtain more accurate results. Due to the reduced complexity in each stage, both can be solved within reasonable runtimes for relatively large gas networks. Based on the mathematical methods involved, a software called GANESO TM has been developed.
“…In [9] mixed integer models for the stationary case of gas network are proposed but without operating diagram constraints, feature included in our work, and for networks without cycles, which is not the case of Spanish gas network. Other related works are presented in [10] and [11].…”
Managing a gas transport network is a complex problem because of the number of possibilities of routing the gas through the pipes. The most important aim in this kind of systems is to fulfill the demand within the pressure bounds, independently of its associated costs. However, in the present work some cost drivers are also taken into account by means of different objective functions in order to manage the network in an efficient way. This work deals with mathematical modeling and optimization of gas transport networks, where a two-stage procedure is proposed. In the first stage, optimization algorithms based on mathematical programming are applied to make some decisions (whether to activate compressor stations, control valves and other control elements) and gives an initial solution to the second stage. This last stage, which is based on control theory techniques, refines the solution to obtain more accurate results. Due to the reduced complexity in each stage, both can be solved within reasonable runtimes for relatively large gas networks. Based on the mathematical methods involved, a software called GANESO TM has been developed.
“…This explains why they have been adopted in this study, where two algorithms for dealing with continuous and mixedinteger problems are presented. Then, the numerical procedures are illustrated by chemical engineering problems often used in the literature as benchmarks: three biobjective ones (ammonia synthesis reactor, Babu and Angira [4], alkylation plant, Jones [25] and natural gas transportation network, Tabkhi et al [51]), a structural mixed-integer problem (Papalexandri and Dimkou [37]) and three multi-objective problems (Williams-Otto process, Chakraborti et al [9], new product development in the pharmaceutical industry, Blau et al [6] and economic and environmental study of the HDA process, Douglas [17]). After the complete set of solutions is displayed in the form of a Pareto front, the best solution is identified by means of multiple choice decision making (MCDM) procedures.…”
“…In an outstanding work done by Wu et al [6], a set of polynomial correlations including the surge and stonewall curves were developed to describe the feasible domain of a centrifugal compressor, thus overcoming the defects of the previous idealized compressor model. These constraints given by (11) have already been widely adopted in gas pipeline optimization models [27,30,38] in the past fifteen years. In reality, these constraints are similar to the compressor models embedded in some wellknown gas pipeline simulation tools, such as the Pipeline Studio and Synergi Pipeline Simulator [39].…”
Section: Constraintsmentioning
confidence: 99%
“…Thus, minimizing the fuel cost would greatly contribute to the economic benefit. The minimum fuel cost problem (MFCP) represents the most popular topic in the field of the optimal operation of natural gas pipelines since 1968 [5,30]. The MFCP objective function can be extracted from (4) and expressed by the following [6,12,31]:…”
Section: Objective Functionsmentioning
confidence: 99%
“…Equality constraints mainly represent the governing equations of gas flowing in pipelines, which include the mass balance, pressure, and temperature equations. The equality constraints are given by following equations [15,30]:…”
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.