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Research Article Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: a New Methodology Based on Monte Carlo Method

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Research Article Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: a New Methodology Based on Monte Carlo Method Hindawi Journal of Advanced Transportation Volume 2018, Article ID 9213648, 10 pages https://doi.org/10.1155/2018/9213648 Research Article Uncertainty Analysis for Natural Gas Transport Pipeline Network Layout: A New Methodology Based on Monte Carlo Method Jun Zeng ,1,2 Chaoxu Sun ,3 Zhenjun Zhu ,1,4 Jiangling Wu,5 and Hongsheng Chen6 1 School of Transportation, Southeast University, Nanjing 211189, China 2DepartmentofCivil,ArchitecturalandEnvironmentalEngineering,TeUniversityofTexasatAustin,Austin,TX78712,USA 3Zhejiang Provincial Natural Gas Development Co. Ltd., Hangzhou 310052, China 4Department of City and Regional Planning, University of California, Berkeley, Berkeley, CA 94720, USA 5School of Civil Engineering and Architecture, Henan University, Kaifeng 475004, China 6School of Architecture, Southeast University, Nanjing 210096, China Correspondence should be addressed to Zhenjun Zhu; [email protected] Received 2 November 2017; Revised 6 April 2018; Accepted 10 April 2018; Published 23 May 2018 Academic Editor: Zhi-Chun Li Copyright © 2018 Jun Zeng et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Natural gas plays an increasing important role in the China’s energy revolution. Te rapid market development and refned government regulation demand improvements in the natural gas transport pipeline network. Terefore, it is of great theoretical and practical signifcance to conduct a study regarding the layout of pipeline networks. To refect the comprehensive benefts of pipeline projects and obtain global optimal solution, this study introduces the dominance degree model (DDM). Aiming at optimizing the layout of natural gas transport pipeline networks, this paper studies the uncertainty of the DDM and the corresponding method for network layout. Tis study proposes an uncertainty analysis based on the Monte Carlo method to quantify the uncertainty of the DDM and its infuential factors. Finally, the methodology is appliedtotherealcaseofanaturalgastransportpipelineproject in Zhejiang Province, China. Te calculation results suggest that the methodology appropriately addresses the problem of pipeline network layout for natural gas transport. Tis has important implications for other potential pipeline networks not only in the Zhejiang Province but also throughout China and beyond. 1. Introduction the overall regional socioeconomic conditions. Although pipeline projects require a large investment and have a long Natural gas is an efcient and clean energy source that can be payback period, the gas market is exhibiting a dynamic utilised in the production of low-carbon energy consumption development trend [2]. Terefore, when planning the pipeline [1]. According to China’s energy development strategic plan network, the benefts from investments on pipeline projects, and natural gas transport pipeline network plan, the share of construction costs, other infuential factors, and the impact natural gas in the primary energy consumption will continue of uncertainty should be considered to determine the optimal to increase up to 10% by 2020, while the total length of layout scheme. natural gas pipelines is planned to reach 104,000 km. Driven Previous studies on the layout of natural gas transport by the continued growth of consumption and infrastructure pipeline networks have proposed several methods based on strengthening, the trunk pipeline coverage will be further pipeline network topology, such as graph theory, dynamic expanded and the regional gas transport pipeline network programming, neural networks, genetic algorithms, and will be improved. complex methods [3, 4]. Tese methods essentially solve the Te natural gas transport pipeline network is tasked with network structure to satisfy a given criterion and focus on gas distribution, which plays a signifcant role in improving mathematical optimization [5, 6]. Te minimum spanning 2 Journal of Advanced Transportation Table 1: Advantages and limitations of existing methods. Methods Advantages Limitations Easy access to computer program Only solves the network layout among known fxed processing; higher computing efciency; points; project investment costs are not considered; Graph Teory efectively solving the shortest tree problem solutions can only be used as the initial network within a large scale network layout Not suitable for dealing with large-scale network Solving optimization problems with Dynamic Programming systems, dimension obstacles exist in solution multiple decision-making variables process Solving optimization problems with Neural Network Method Only obtaining the local optimal solution multiple decision-making variables Lower computing efciency; no efective quantitative Higher adaptability that can overcome the Genetic Algorithm analysis concerning algorithm precision, feasibility difculties of solving nonlinear optimization and computational complexity Te algorithm is simple and suitable for Unable to deal with multi-variable, multi-constraint Complex Method dealing with constrained optimization optimization problems problems tree method (MSTM) and dynamic programming (DP) are layout of the natural gas transport pipeline network have not themostcommonlyusedsolutions.MSTMabstractsthe been reported. pipeline network into an undirected network, including the Terefore, this study uses the dominance degree model classic solutions of the Dijkstra, Kruskal, and Steiner algo- (DDM) of pipeline projects and the corresponding layout rithms [7, 8]. Compared to traditional graph theory solutions, method, which considers socioeconomic benefts and con- these algorithms are implemented by computer programs for struction costs. Te layout method based on the DDM is processing and have a relatively higher operational efciency. simple: it provides a global optimal solution to obtain the Te Steiner algorithms are efective in solving the shortest comprehensive benefts of the pipeline network. Terefore, path problem of a large-scale network [9–11]. However, the by analyzing the uncertain infuential factors of the DDM, three algorithms mentioned above do not consider the invest- this study proposes an innovative uncertainty analysis of ment costs of pipeline projects, and their results can only be the natural gas transport pipeline network layout based on regarded as the initial pipeline network layout. DP can deal the Monte Carlo method. Tis proposed method uses the with the optimization problem of multiple decision-making Monte Carlo method and sensitivity analysis to determine the variables. However, dimension obstacles exist during the impact of uncertainty factors on the model results. Tis can solution process. Specifcally, the computation will increase quantify the uncertainty and its infuence and thus strengthen exponentially as the number of variables grows. When the the practicability of the DDM and function as a future dimension of this problem increases to a certain extent, the referencefortheoptimallayoutofthepipelinenetwork. problem cannot be solved [12]. Tus, the current commonly Finally, to verify the validity of the methodology, natural gas used methods have certain advantages and limitations, as transport pipeline projects in Zhejiang Province, China, are presented in Table 1. In summary, the optimization of the taken as a case study. natural gas transport pipeline network is a multiobjective nonlinear programming problem, which should consider the 2. Dominance Degree Model and uncertainty caused by the gas market and costs. However, this problem cannot be solved easily and efectively by using the Its Layout Method abovementioned methods. Te dominance degree model (DDM) of pipeline projects is With regard to uncertainty and network layout, pre- a new method that was developed to optimize the pipeline vious studies have mainly focused on transportation and network layout [20]. Tis method used the dominance degree logistics [13–15]. For example, regarding uncertainty and to refect the comprehensive benefts of transport pipeline trafc network layout, Partriksson used a stochastic bilevel projects by combining the potential model (PM) and eco- programming model to solve the optimal transportation nomic potential theory (EPT) to build the dominance degree network layout scheme based on the uncertainty of demand model (DDM) for pipeline projects. [16]. Yin et al. [17, 18] studied the urban road network Te DDM of pipeline projects embodies the comprehen- layout methods under the impact of demand uncertainty sive socioeconomic benefts of the projects. By comparing andproposedsensitivity-based,scenario-based,andmin- the dominance degree of pipeline projects when applying the max optimization models. Zhang et al. [19] investigated the DDM to the layout of natural gas transport pipeline network, joint optimization problem of the green vehicle scheduling the optimal layout scheme and construction sequences are and routing problem in time-varying trafc networks and determined, which will leverage the advantages of pipeline developed a corresponding joint optimization model. How- projects to obtain the maximum socioeconomic benefts of ever, extensive studies on uncertainty associated with and the the pipeline network. Journal of Advanced Transportation 3 Table 2: Calibration for the value of ��. determined by referring to engineering data within the study � � � � � area. � is the number of crossings of the th type,
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