Abstract
Natural gas has been increasingly used as a source of energy and presents itself as a strong trend for the future. In this context, regarding the high cost of installing pipelines, the design of gas networks requires highlight quality solutions, relating not only financial indicators but also reliability and security concerning demand. Thus, this paper proposes an approach for the design of natural gas networks under conditions of uncertainty of load evolution over a time horizon. A predefined network topology is assumed, where the pipe diameters define the design variables. We propose a Multiobjective Variable Neighborhood Search (MOVNS)-based algorithm, which is evaluated considering a set of test instances defined from the TSPLIB library data. The proposed methodology is also applied to a real case study being the results compared to those obtained by three engineers of a gas company with six years of experience on average. The solutions are investigated from a dominance analysis perspective, considering the criteria: installation cost, minimum gas pressure, feasibility rate, average cost of failure, and sensitivity. The results indicate solutions relatively different from those obtained by the engineers, presenting more robust and safe networks under conditions of uncertainties of load evolution.
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References
Arya, A. K. (2022). A critical review on optimization parameters and techniques for gas pipeline operation profitability. Journal of Petroleum Exploration and Production Technology, pp 1–25. https://doi.org/10.1007/s13202-022-01490-5
Boyd, I., Surry, P. D., & Radcliffe, N. (1994). Constrained gas network pipe sizing with genetic algorithms. Technical Report EPCC-TR94, Parallel Computing Center, Edinburgh.
Campelo, F. (2018). Lecture notes on design and analysis of experiments. Available online at: http://git.io/v3Kh8. Version 2.12; Creative Commons BY-NC-SA 4.0.
de Wolf, D., & Smeers, Y. (1996). Optimal dimensioning of pipe networks with application to gas transmission networks. Operations Research, 44(4), 596–608.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Demissie, A., Zhu, W., & Belachew, C. T. (2017). A multi-objective optimization model for gas pipeline operations. Computers & Chemical Engineering, 100, 94–103.
Duarte, A., Pantrigo, J. J., Pardo, E. G., & Mladenovic, N. (2015). Multi-objective variable neighborhood search: An application to combinatorial optimization problems. Journal of Global Optimization, 63, 515–536.
Duarte, H. M., Goldbarg, E. F. G., & Goldbarg, M. C. (2006). A tabu search algorithm for optimization of gas distribution networks. European Conference on Evolutionary Computation in Combinatorial Optimization, 18, 37–48.
El Kafazi, I., & Bannari, R. (2019). Multiobjective scheduling-based energy management system considering renewable energy and energy storage systems: A case study and experimental result. Journal of Control, Automation and Electrical Systems, 30(6), 1030–1040.
El-Mahdy, O. F. M., Ahmed, M. E. H., & Metwalli, S. (2010). Computer aided optimization of natural gas pipe networks using genetic algorithm. Applied Soft Computing, 45(10), 1141–1150.
Goldbarg, E., Castro, M., & Goldbarg, M. (2006). A transgenetic algorithm for the gas network pipe sizing problem. Computational Methods, 1, 893–904.
Hansen, C. T., Madsen, K., & Nielsen, H. B. (1991). Optimization of pipe networks. Mathematical Programming, 52, 45–58.
Hari, S. K. K., Sundar, K., Srinivasan, S., Zlotnik, A., & Bent, R. (2021). Operation of natural gas pipeline networks with storage under transient flow conditions. IEEE Transactions on Control Systems Technology, 30, 667–679.
Jiao, K., Wang, P., Wang, Y., Yu, B., Bai, B., Shao, Q., & Wang, X. (2021). Study on the multi-objective optimization of reliability and operating cost for natural gas pipeline network. Oil & Gas Science and Technology-Revue d’IFP Energies nouvelles, 76, 42.
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. In Proceedings of the American Mathematical Society (vol. 7, pp. 48–50). American Mathematical Society.
Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Springer Science & Business Media.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097–1100.
Mohajeri, A., Mahdavi, I., Amiri, N. M., & Tafazzoli, R. (2012). Optimization of tree-structured gas distribution network using ant colony optimization: A case study. International Journal of Engineering, 25(2), 141–158.
Mohajeri, A., Mahdavi, I., & Mahdavi-Amiri, N. (2012). Optimal pipe diameter sizing in a tree-structured gas network: A case study. International Journal of Industrial and Systems Engineering, 12(3), 346–368.
Montgomery, D. C., & Runger, G. C. (2013). Applied Statistics and Probability for Engineers (6th ed.). Wiley.
Ottoni, L. T. C., & Batista, L. S. (2020). Proposta de uma abordagem multiobjetivo para o projeto de dimensionamento de redes de gás natural. In Anais do Congresso Brasileiro de Automática (CBA), 2, 1–7.
R Core Team. (2019). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Ramos, E. S., & Batista, L. S. (2020). Natural gas pipeline network expansion under load-evolution uncertainty based on multi-criteria analysis. Applied Soft Computing, 96(2), 106697.
Reinelt, G. (1991). TSPLIB - a traveling salesman problem library. ORSA Journal on Computing, 3(4), 376–384.
Rothfarb, B., Frank, H., Rosenbaum, D., Steiglitz, K., & Kleitman, D. J. (1970). Optimal design of offshore natural-gas pipeline systems. Operations Research, 18(6), 992–1020.
Simpson, A. R., Dandy, G. C., & Murphy, L. J. (1994). Genetic algorithms compared to other techniques for pipe optimization. Journal of Water Resources Planning and Management, 120(4), 423–443.
Su, H., Zio, E., Zhang, J., Li, X., Chi, L., Fan, L., & Zhang, Z. (2019). A method for the multi-objective optimization of the operation of natural gas pipeline networks considering supply reliability and operation efficiency. Computers & Chemical Engineering, 131, 106584.
Surry, P. D., Radcliffe, N. J., and Boyd, I. D. (1995). A multi-objective approach to constrained optimisation of gas supply networks: the COMOGA method. In AISB Workshop on Evolutionary Computing, volume 993 of Lecture Notes in Computer Science (pp. 166–180). Springer.
Torkinejad, M., Mahdavi, I., Amiri, N. M., & Esfahani, M. S. (2019). Topology design and component selection in an urban gas network: simultaneous optimization approach. Journal of Pipeline Systems Engineering and Practice, 10(1), 04018035.
Tukey, J. W. (1953). The problem of multiple comparisons. Unpublished manuscript, Princeton University.
Vedik, B., Kumar, R., Deshmukh, R., Verma, S., & Shiva, C. K. (2021). Renewable energy-based load frequency stabilization of interconnected power systems using quasi-oppositional dragonfly algorithm. Journal of Control, Automation and Electrical Systems, 32(1), 227–243.
Zecchin, A. C., Simpson, A. R., Maier, H. R., Leonard, M., Roberts, A. J., & Berrisford, M. J. (2006). Application of two ant colony optimisation algorithms to water distribution system optimisation. Mathematical and Computer Modelling, 44(5–6), 451–468.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.
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The authors are grateful to CAPES, CNPq, FAPEMIG, UFMG and UFBA.
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Ottoni, L.T.C., Batista, L.S. Multicriteria Analysis of Natural Gas Network Pipe Sizing Design Under Load-Evolution Uncertainty. J Control Autom Electr Syst 33, 1860–1873 (2022). https://doi.org/10.1007/s40313-022-00932-z
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DOI: https://doi.org/10.1007/s40313-022-00932-z