Metaheuristics for Natural Gas Pipeline Networks

Living reference work entry

Abstract

In this chapter an overview on metaheuristic algorithms that have been very successful on tackling a particular class of natural gas pipeline network optimization problems is presented. In particular, the problem of minimizing fuel consumption incurred by the compressor stations driving natural gas in pipeline networks is addressed. This problem has been studied from different angles over the past few years by virtue of its tremendous economical impact. First, a general mathematical framework for this class of problems is presented. After establishing the most relevant model properties and fundamental network topologies, which are key factors for choosing an appropriate solution technique, current state-of-the-art metaheuristics are presented for handling different versions of this problem. This work concludes by highlighting the most relevant and important challenges of this very exciting area of research in natural gas transportation networks.

Keywords

Natural gas transmission systems Pipeline optimization Nonlinear programming Mixed-integer nonlinear programming Tabu search Ant colony optimization Simulated annealing Particle swarm optimization 

Notes

Acknowledgements

The research of the author was supported by the Mexican Council for Science and Technology, grant CONACyT CB-2011-01-166397. The author would also like to thank the editors for their helpful remarks and suggestions.

References

  1. 1.
    Aalto H (2008) Optimal control of natural gas pipeline networks: a real-time, model-based, receding horizon optimisation approach. VDM Verlag, SaarbrückenGoogle Scholar
  2. 2.
    Anglard P, David P (1988) Hierarchical steady state optimization of very large gas pipelines. In: Proceedings of the 20th PSIG annual meeting, TorontoGoogle Scholar
  3. 3.
    Borraz-Sánchez C, Haugland D (2011) Minimizing fuel cost in gas transmission networks by dynamic programming and adaptive discretization. Comput Ind Eng 61(2):364–372CrossRefGoogle Scholar
  4. 4.
    Borraz-Sánchez C, Ríos-Mercado RZ (2005) A hybrid meta-heuristic approach for natural gas pipeline network optimization. In: Blesa MJ, Blum C, Roli A, Sampels M (eds) Hybrid metaheuristics. Springer, Berlin, pp 54–65CrossRefGoogle Scholar
  5. 5.
    Borraz-Sánchez C, Ríos-Mercado RZ (2009) Improving the operation of pipeline systems on cyclic structures by tabu search. Comput Chem Eng 33(1):58–64CrossRefGoogle Scholar
  6. 6.
    Carter RG (1998) Pipeline optimization: dynamic programming after 30 years. In: Proceedings of the 30th PSIG annual meeting, DenverGoogle Scholar
  7. 7.
    Chebouba A, Yalaoui F, Smati A, Amodeo L, Younsi K, Tairi A (2009) Optimization of natural gas pipeline transportation using ant colony optimization. Comput Oper Res 36(6):1916–1923CrossRefMATHGoogle Scholar
  8. 8.
    Cobos-Zaleta D, Ríos-Mercado RZ (2002) A MINLP model for minimizing fuel consumption on natural gas pipeline networks. In: Proceedings of the XI Latin-Ibero-American conference on operations research, ConcepciónGoogle Scholar
  9. 9.
    De Wolf D, Smeers Y (2000) The gas transmission problem solved by an extension of the simplex algorithm. Manag Sci 46(11):1454–1465CrossRefMATHGoogle Scholar
  10. 10.
    Domschke P, Geißler B, Kolb O, Lang J, Martin A, Morsi A (2011) Combination of nonlinear and linear optimization of transient gas networks. INFORMS J Comput 23(4):605–617MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Ehrhardt K, Steinbach MC (2005) Nonlinear optimization in gas networks. In: Bock HG, Kostina E, Phu HX, Rannacher R (eds) Modeling, simulation and optimization of complex processes. Springer, Berlin, pp 139–148CrossRefGoogle Scholar
  12. 12.
    Flores-Villarreal HJ, Ríos-Mercado RZ (2003) Computational experience with a GRG method for minimizing fuel consumption on cyclic natural gas networks. In: Mastorakis NE, Stathopulos IA, Manikopoulos C, Antoniou GE, Mladenov VM, Gonos IF (eds) Computational methods in circuits and systems applications. WSEAS Press, Athens, pp 90–94Google Scholar
  13. 13.
    Jin L, Wojtanowocz AK (2010) Optimization of large gas pipeline network – a case study in China. J Can Pet Technol 49(4):36–43CrossRefGoogle Scholar
  14. 14.
    Ke SL, Ti HC (2000) Transient analysis of isothermal gas flow in pipeline network. Chem Eng J 76(2):169–177CrossRefGoogle Scholar
  15. 15.
    Lall HS, Percell PB (1990) A dynamic programming based gas pipeline optimizer. In: Bensoussan A, Lions JL (eds) Analysis and optimization of systems. Lecture notes in control and information sciences, vol 144. Springer, Berlin, pp 123–132Google Scholar
  16. 16.
    Larson RE, Wismer DA (1971) Hierarchical control of transient flow in natural gas pipeline networks. In: Proceedings of the IFAC symposium on distributed parameter systems, Banff,Google Scholar
  17. 17.
    Luongo CA, Gilmour BJ, Schroeder DW (1989) Optimization in natural gas transmission networks: a tool to improve operational efficiency. Technical report. Stoner Associates, Inc., HoustonGoogle Scholar
  18. 18.
    Mahlke D, Martin A, Moritz S (2007) A simulated annealing algorithm for transient optimization in gas networks. Math Methods Oper Res 66(1):99–115MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Mantri VB, Preston LB, Pringle CS (1985) Transient optimization of a natural gas pipeline system. In: Proceedings of the 17th PSIG annual meeting, AlbuquerqueGoogle Scholar
  20. 20.
    Martin A, Möller M, Moritz S (2006) Mixed integer models for the stationary case of gas network optimization. Math Program 105(2–3):563–582MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Osiadacz AJ (1994) Dynamic optimization of high pressure gas networks using hierarchical systems theory. In: Proceedings of the 26th PSIG annual meeting, San DiegoGoogle Scholar
  22. 22.
    Osiadacz AJ (1998) Hierarchical control of transient flow in natural gas pipeline systems. Int Trans Oper Res 5(4):285–302CrossRefGoogle Scholar
  23. 23.
    Osiadacz AJ, Bell DJ (1986) A simplified algorithm for optimization of large-scale gas networks. Optim Control Appl Methods 7(1):95–104CrossRefMATHGoogle Scholar
  24. 24.
    Osiadacz AJ, Chaczykowski M (2001) Comparison of isothermal and non-isothermal pipeline gas flow models. Chem Eng J 81(1):41–51CrossRefGoogle Scholar
  25. 25.
    Osiadacz AJ, Swierczewski S (1994) Optimal control of gas transportation systems. In: Proceedings of the 3rd IEEE conference on control applications, Glasgow, pp 795–796. ISBN:0-7803-1872-2Google Scholar
  26. 26.
    Percell PB, Ryan MJ (1987) Steady-state optimization of gas pipeline network operation. In: Proceedings of the 19th PSIG annual meeting, TulsaGoogle Scholar
  27. 27.
    Pratt KF, Wilson JG (1984) Optimisation of the operation of gas transmission systems. Trans Inst Meas Control 6(5):261–269CrossRefGoogle Scholar
  28. 28.
    Ríos-Mercado RZ (2002) Natural gas pipeline optimization. In: Pardalos PM, Resende MGC (eds) Handbook of applied optimization, chap 18.8.3. Oxford University Press, New York, pp 813–825Google Scholar
  29. 29.
    Ríos-Mercado RZ, Borraz-Sánchez C (2015) Optimization problems in natural gas transportation systems: a state-of-the-art review. Appl Energy 147:536–555CrossRefGoogle Scholar
  30. 30.
    Ríos-Mercado RZ, Wu S, Scott LR, Boyd EA (2002) A reduction technique for natural gas transmission network optimization problems. Ann Oper Res 117(1–4):217–234CrossRefMATHGoogle Scholar
  31. 31.
    Ríos-Mercado RZ, Kim S, Boyd EA (2006) Efficient operation of natural gas transmission systems: a network-based heuristic for cyclic structures. Comput Oper Res 33(8):2323–2351CrossRefMATHGoogle Scholar
  32. 32.
    Schmidt M, Steinbach MC, Willert BM (2015) High detail stationary optimization models for gas networks. Optim Eng 16(1):131–164MathSciNetCrossRefGoogle Scholar
  33. 33.
    Suresh K, Ghosh S, Kundu D, Sen A, Das S, Abraham A (2008) Inertia-adaptive particle swarm optimizer for improved global search. In: Proceedings of the eighth international conference on intelligent systems design and applications. IEEE Computer Society, Los Alamitos, pp 253–258Google Scholar
  34. 34.
    Tabkhi F, Pibouleau L, Hernandez-Rodriguez G, Azzaro-Pantel C, Domenech S (2010) Improving the performance of natural gas pipeline networks fuel consumption minimization problems. AIChE J 56(4):946–964Google Scholar
  35. 35.
    Tao WQ, Ti HC (1998) Transient analysis of gas pipeline network. Chem Eng J 69(1):47–52CrossRefGoogle Scholar
  36. 36.
    Wong PJ, Larson RE (1968a) Optimization of natural-gas pipeline systems via dynamic programming. IEEE Trans Autom Control AC–13(5):475–481Google Scholar
  37. 37.
    Wong PJ, Larson RE (1968b) Optimization of tree-structured natural-gas transmission networks. J Math Anal Appl 24(3):613–626MathSciNetCrossRefGoogle Scholar
  38. 38.
    Wu S, Ríos-Mercado RZ, Boyd EA, Scott LR (2000) Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Math Comput Model 31(2–3):197–220CrossRefGoogle Scholar
  39. 39.
    Wu X, Li C, Jia W, He Y (2014) Optimal operation of trunk natural gas pipelines via an inertia-adaptive particle swarm optimization algorithm. J Nat Gas Sci Eng 21:10–18CrossRefGoogle Scholar
  40. 40.
    Zheng QP, Rebennack S, Iliadis NA, Pardalos PM (2010) Optimization models in the natural gas industry. In: Rebennack S, Pardalos PM, Pereira MVF, Iliadis NA (eds) Handbook of power systems I, energy systems. Springer, Berlin, pp 121–148Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate Program in Systems EngineeringUniversidad Autónoma de Nuevo León (UANL)San Nicolás de los GarzaMexico

Personalised recommendations