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Optimization of Natural Gas Transport Pipeline Network Layout: a New Methodology Based on Dominance Degree Model

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Optimization of Natural Gas Transport Pipeline Network Layout: a New Methodology Based on Dominance Degree Model Transport ISSN 1648-4142 / eISSN 1648-3480 1 2017 Volume 00: 1–8 53 2 http://dx.doi.org/10.0000/00000000.0000.000001 54 3 55 4 56 5 OPTIMIZATION OF NATURAL GAS TRANSPORT PIPELINE NETWORK LAYOUT: 57 6 A NEW METHODOLOGY BASED ON DOMINANCE DEGREE MODEL 58 7 59 8 60 Zhenjun ZHU 1,∗,ChaoxuSUN2,JunZENG1 and Guowei CHEN 3 9 61 10 1 Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing, China 62 11 2 Zhejiang Provincial Natural Gas Development Co. Ltd, Hangzhou, China 63 12 3 Zhejiang Electric power construction Co. Ltd, Ningbo, China 64 13 65 Submitted 23 January 2017; resubmitted 13 April 2017; accepted 20 April 2017 14 66 15 67 Abstract. Atthephaseof13thfive-yearplaninChina,naturalgaswillplayanimportantroleinenergyrevolution.With 16 the growth of consumption, natural gas infrastructures will become hot spots of future investment and pipeline network 68 17 construction will also usher in a period of rapid development. Therefore, it is of great theoretical and practical significance 69 18 to study layout methods of transport pipeline network. This paper takes natural gas transport pipeline network as a research 70 19 object, introduces dominance degree to analyse benefits of pipeline projects. Then, this paper proposes Dominance Degree 71 Model (DDM) of transport pipeline projects based on Potential Model (PM) and Economic Potential Theory (EPT). Ac- 20 72 cording to DDM of gas transport pipeline projects, layout methods of pipeline network are put forward, which is simple 21 and easy to obtain the overall optimal solution and ensure maximum comprehensive benefits. What’s more, construction 73 22 sequences of gas transport pipeline projects can be also determined. Finally, the model is applied to a real case of natural gas 74 23 transport pipeline projects in Zhejiang Province, China. The calculation results suggest that the model should deal with the 75 24 transport pipeline network layout problem well, which have important implications for other potential pipeline networks 76 not only in the Zhejiang Province but also throughout China and beyond. 25 77 26 Keywords: natural gas, transport pipeline network, dominance degree model, potential model, economic potential theory, 78 27 layout method 79 28 80 29 81 30 82 1. Introduction or expanded. So when we make the pipeline network lay- 31 83 out, market supply and demand, benefits and construction 32 As an efficient and cleaner energy, natural gas is an im- 84 costs of pipeline projects’ investments, etc. – should be con- 33 portantbridgetomakeenergyconsumptiontransittolow- 85 sidered to determine a reasonable layout scheme. 34 carbon (Gillingham et al., 2009). According to 13th five- 86 A number of studies have been conducted on layout opti- 35 year plan and National Energy Development Strategy Plan 87 mization with respect to natural gas transport pipeline net- 36 88 of China, with respect to the proportion of primary energy work, where most of this research has focused on mathe- 37 89 consumption, natural gas demand will continue to increase, matical optimization. For example, Edgar et al. (1978)used 38 90 its share will reach 10% by the year of 2020. Continued Generalized Reduced Gradient Method to optimize natu- 39 growth in consumption is bound to promote the construc- ral gas transport pipeline network system. Bhaskaran and 91 40 tion of natural gas infrastructures, coverage area of trunk Salzborn (1979) studied the optimal design problem of gas 92 41 pipelines will in further expansion. Thus, gas transport and gathering pipeline network, which was divided into three 93 42 distribution network will be improved and facilities in dif- sub-problem: system layout, node locations, and the diam- 94 43 ferent pipe network will also achieve interoperability grad- eter distribution. Pedrycz et al. (1992)examinedthede- 95 44 ually. sign and application of neural networks in the context of a 96 45 Natural gas transport pipeline network shoulders the specific decision-making problem, the selection of an ap- 97 46 task of gas supply, which plays a significant role in im- propriate layout for natural gas distribution system. Singh 98 47 proving the overall socio-economic benefits for the region. and Nain (2012), Sanaye and Mahmoudimehr (2013)ap- 99 48 However, pipeline projects’ investment is large and pay- plied genetic algorithm to pipeline system optimization 100 49 back period is long, which cannot be easily reconstructed and simulated natural gas transport pipeline network. Üster 101 50 and Dilaveroğlu (2014) developed an integrated large-scale 102 51 103 *Corresponding author. E-mail: [email protected]. mixed-integer nonlinear optimization model to determine 52 104 Copyright © 2017 The Author(s) Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unre- stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. [vgtuart2x 2017/10/19 v0.0.1] tran000001.tex 2017/10/20 14:40 p. 2/8 2 Z. Zhu et al. Optimization of natural gas transport pipeline network layout: A new methodology based on dominance degree model 1 Table 1. The methodology analysis 56 2 Methods Advantages Limitations 57 3 Graph theory ∙ easy access to computer program processing; ∙ only solving the network layout among known fixed 58 4 ∙ higher computing efficiency; points; 59 5 ∙ solving the shortest tree problem within a large scale ∙ project investment costs are not considered; 60 network effectively ∙ solutions can only be used as the initial network layout 6 61 7 Dynamic ∙ solving optimization problems with multiple ∙ not suitable for dealing with large-scale network 62 programming decision-making variables systems, ‘dimension obstacles’ exist in solving 8 63 Neural network ∙ solving optimization problems with multiple ∙ only obtaining the local optimal solution 9 64 method decision-making variables 10 65 Genetic algorithm ∙ higher adaptability, which can overcome the ∙ lower computing efficiency; 11 difficulties in solving nonlinear optimization ∙ no effective quantitative analysis concerning 66 12 algorithm precision, feasibility and computational 67 13 complexity 68 14 Complex method ∙ the algorithm is simple and suitable for dealing with ∙ unable to deal with multi-variable, multi-constraint 69 15 constrained optimization problems optimization problems 70 16 71 17 pipelines in the network. Schmidt et al. (2015)presented paper, which can not only determine the optimal layout 72 18 stationary neuro-linguistic programming type models of scheme, but also obtain construction sequences of gas 73 19 gas networks that are primarily designed to include detailed transport pipeline projects, which ensure maximum com- 74 20 nonlinear physics in the final optimization steps for mid- prehensive benefits of pipeline network. Specifically, we 75 21 term planning problems. An and Peng (2016)usedriskcost combine Potential Model (PM) and Economic Potential 76 22 functions to study the synchronization of the minimum Theory (EPT) to establish the Dominance Degree Model 77 23 risk loss and total cost of natural gas pipeline networks at (DDM) for pipeline projects, and use dominance degree to 78 24 the planning stage. However, these methods are essentially measure comprehensive benefits of pipeline projects. Then, 79 25 solving network structure to satisfy a given criterion, a local a layout method based on the DDM for natural gas trans- 80 26 optimal solution can be only obtained. port pipeline network is proposed, which can search out the 81 27 Minimum Spanning Tree Method (MSTM) and Dy- most advantageous pipeline project successively through 82 28 namic Programming (DP) are the most commonly used so- cyclecalculations.Thecalculationprocessissimpleand 83 29 lutions. MSTM abstracts pipeline network into undirected easy to obtain the overall optimal solution. To verify valid- 84 30 network, including classic solutions of Dijkstra Algorithms, ity of the method, natural gas transport pipeline projects in 85 31 Kruskal Algorithms, Steiner Algorithms (Graham and Hell, Zhejiang Province are taken as a case study. 86 32 1985). Compared to solutions of traditional graph theory, 87 33 these algorithms adopt computer programs for processing, 2. Dominancedegreeofpipelineprojects 88 34 whose operation efficiency is relatively high, Steiner Al- 89 35 gorithms effectively solve to the shortest path problem of Dominance generally refers to the advantageous form that 90 36 large-scale network (Han and Lim, 2010;Sniedovich,2006; one party can overcome or overwhelm its counterpart. 91 37 Zhou, 2004). However, the above three algorithms do not Dominance degree is an index, which can be used to re- 92 38 consider investment costs of pipeline projects, whose re- flect and compare the degree of the pros and cons among 93 39 sults can be only regarded as the initial pipeline network system elements (Shamsie, 2003). Due to differences in lo- 94 40 layout. DP can deal the optimization problem of multiple cation, economy and geographical conditions, transport 95 41 decision-making variables, while ‘dimension obstacles’ ex- pipeline projects have made a tremendous impact on social 96 42 ist in solving, that is computation will increase exponen- and economic benefits and influenced dominance degree of 97 43 tially with growth of the number of variables. When the network layout. Therefore, this paper uses dominance de- 98 44 dimension of this problem increases
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