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Optimization of natural gas transport pipeline network layout: a new methodology based on dominance degree model

    Zhenjun Zhu Affiliation
    ; Chaoxu Sun Affiliation
    ; Jun Zeng Affiliation
    ; Guowei Chen Affiliation
DOI: https://doi.org/10.3846/transport.2018.145

Abstract

At the phase of 13-th five-year plan in China, natural gas will play an important role in energy revolution. With the growth of consumption, natural gas infrastructures will become hot spots of future investment and pipeline network construction will also usher in a period of rapid development. Therefore, it is of great theoretical and practical significance to study layout methods of transport pipeline network. This paper takes natural gas transport pipeline network as a research object, introduces dominance degree to analyse benefits of pipeline projects. Then, this paper proposes Dominance Degree Model (DDM) of transport pipeline projects based on Potential Model (PM) and Economic Potential Theory (EPT). According to DDM of gas transport pipeline projects, layout methods of pipeline network are put forward, which is simple and easy to obtain the overall optimal solution and ensure maximum comprehensive benefits. What’s more, construction sequences of gas transport pipeline projects can be also determined. Finally, the model is applied to a real case of natural gas transport pipeline projects in Zhejiang Province, China. The calculation results suggest that the model should deal with the transport pipeline network layout problem well, which have important implications for other potential pipeline networks not only in the Zhejiang Province but also throughout China and beyond.

Keyword : natural gas, transport pipeline network, dominance degree model, potential model, economic potential theory, layout method

How to Cite
Zhu, Z., Sun, C., Zeng, J., & Chen, G. (2018). Optimization of natural gas transport pipeline network layout: a new methodology based on dominance degree model. Transport, 33(1), 143-150. https://doi.org/10.3846/transport.2018.145
Published in Issue
Jan 26, 2018
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

An, J.; Peng, S. 2016. Layout optimization of natural gas network planning: Synchronizing minimum risk loss with total cost, Journal of Natural Gas Science and Engineering 33: 255–263. https://doi.org/10.1016/j.jngse.2016.05.017

Bellman, R. E.; Dreyfus, S. E. 2016. Applied Dynamic Programming. Princeton University Press. 390 p.

Bhaskaran, S.; Salzborn, F. 1979. Optimal diameter assignment for gas pipeline networks, The Journal of the Australian Mathematical Society. Series B. Applied Mathematics 21(2):129–144.
https://doi.org/10.1017/S0334270000002009

Edgar, T. F.; Himmelblau, D. M.; Bickel, T. C. 1978. Optimal design of gas transmission networks, Society of Petroleum Engineers Journal 18(2). https://doi.org/10.2118/6034-PA

Chang, S. J. 2008. A cultural and philosophical perspective on Korea’s education reform: a critical way to maintain Korea’s economic momentum, Academic Paper Series 3(2): 1–11.

Graham, R. L.; Hell, P. 1985. On the history of the minimum spanning tree problem, Annals of the History of Computing 7(1): 43–57. https://doi.org/10.1109/MAHC.1985.10011

Gillingham, K.; Newell, R. G.; Palmer, K. 2009. Energy efficiency economics and policy, Annual Review of Resource Economics 1: 597–620.
https://doi.org/10.1146/annurev.resource.102308.124234

Han, D.-M.; Lim, J.-H. 2010. Design and implementation of smart home energy management systems based on ZigBee, IEEE Transactions on Consumer Electronics 56(3): 1417–1425. https://doi.org/10.1109/TCE.2010.5606278

Lu, H.; Taur, Y. 2006. An analytic potential model for symmetric and asymmetric DG MOSFETs, IEEE Transactions on Electron Devices 53(5): 1161–1168.
https://doi.org/10.1109/TED.2006.872093

Mátyás, L. 1998. The gravity model: some econometric considerations, The World Economy 21(3): 397–401.
https://doi.org/10.1111/1467-9701.00136

Novoselov, K. S.; Geim, A. K.; Morozov S. V.; Jiang, D.; Zhang, Y.; Dubonos, S. V.; Grigorieva, I. V.; Firsov, A. A. 2004. Electric field effect in atomically thin carbon films, Science. 306(5696): 666–669.
https://doi.org/10.1126/science.1102896

Pedrycz, W.; Davidson, J.; Goulter, I. 1992. Neural network based decision model, used for design of rural natural gas systems, in IEEE International Conference on Fuzzy Systems1992, 8–12 March 1992, San Diego, CA, US, 1219–1226. https://doi.org/10.1109/FUZZY.1992.258651

Shamsie, J. 2003. The context of dominance: an industry-driven framework for exploiting reputation, Strategic Management Journal 24(3): 199–215. https://doi.org/10.1002/smj.291

Sniedovich, M. 2006. Dijkstra’s algorithm revisited: the dynamic programming connexion, Control and Cybernetics 35(3): 599–620.

Sanaye, S; Mahmoudimehr, J. 2013. Optimal design of a natural gas transmission network layout, Chemical Engineering Research and Design 91(12): 2465–2476.
https://doi.org/10.1016/j.cherd.2013.04.005

Singh, R. R.; Nain, P. K. S. 2012. Optimization of natural gas pipeline design and its total cost using GA, International Journal of Scientific and Research Publications 2(8): 1–10.

Schmidt, M.; Steinbach, M. C.; Willert, B. M. 2015. High detail stationary optimization models for gas networks, Optimization and Engineering 16(1): 131–164.
https://doi.org/10.1007/s11081-014-9246-x

Üster, H.; Dilaveroğlu, S. 2014. Optimization for design and operation of natural gas transmission networks, Applied Energy 133: 56–59.
https://doi.org/10.1016/j.apenergy.2014.06.042

Zhou, H. 2004. Efficient Steiner tree construction based on spanning graphs, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 23(5): 704–710. https://doi.org/10.1109/TCAD.2004.826557