Optimal operation of trunk natural gas pipelines via an inertia-adaptive particle swarm optimization algorithm

Wu, Xue-Bing; Li, Changjun; Jia, Wenlong; He, Yufa

doi:10.1016/j.jngse.2014.07.028

Cited by 55 publications

(33 citation statements)

References 15 publications

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“…The values 1 and −1 in the matrix refer to the upstream and downstream nodes of the element, respectively. The value 0 indicates that there is no connection between the node and the element [15,24]. Thus, all the inflow and outflow elements that connect with a specified node can be found by searching a fixed column of the resulting node-element matrix.…”

Section: The Optimal Operation Model Of Natural Gas Pipelinesmentioning

confidence: 99%

“…The total gas delivery amount reflects whether the pipelines and related facilities have been sufficiently utilized or not [15,18]. Constrained by gas supply plans, the gas delivery amount is usually fixed in a specific range.…”

Section: Objective Functionsmentioning

confidence: 99%

“…The operation economic benefit is defined as the difference between the gas sales income and the costs associated with the gas purchasing, pipeline's operating, management, and compressors running costs [15,29]. This objective function is given by…”

Section: Objective Functionsmentioning

confidence: 99%

“…More recently, development of multiobjective optimization algorithms offers a new way to simultaneously optimize two or more objectives by use of a unified optimization model. Wu et al [15] proposed a biobjective optimization model that considers the maximum gas delivery amount and the maximum operating benefit by use of a weighted hybrid objective function, which is essentially a single-objective optimization model. Unlike the weighted method, the Pareto optimality provides a method to determine the optimal solutions from a set of candidate multiobjective solutions [35].…”

Section: Objective Functionsmentioning

confidence: 99%

“…Recently, a number of successful industry projects associated with applications of stochastic algorithms have been reported, including applications of the Genetic Algorithm (GA) [12,13], Ant Colony Optimization (ACO) [13], Simulated Annealing (SA) optimization [14], Particle Swarm Optimization (PSO) [15], and their extensions. These achievements show prospective ways of efficient solving the operation optimization model of natural gas pipelines.…”

Section: Introductionmentioning

confidence: 99%

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

He³

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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Section: The Optimal Operation Model Of Natural Gas Pipelinesmentioning

confidence: 99%

Section: Objective Functionsmentioning

confidence: 99%

Section: Objective Functionsmentioning

confidence: 99%

Section: Objective Functionsmentioning

confidence: 99%