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Stochastic and Risk Management Models and Solution Algorithm for Natural Gas Transmission Network Expansion and LNG Terminal Location Planning

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  • Qipeng P. Zheng

    (University of Florida)

  • Panos M. Pardalos

    (University of Florida)

Abstract

Due to the increasing demands for natural gas, it is playing a more important role in the energy system, and its system expansion planning is drawing more attentions. In this paper, we propose expansion planning models which include both natural gas transmission network expansion and LNG (Liquified Natural Gas) terminals location planning. These models take into account the uncertainties of demands and supplies in the future, which make the models stochastic mixed integer programs with discrete subproblems. Also we consider risk control in our models by including probabilistic constraints, such as a limit on CVaR (Conditional Value at Risk). In order to solve large-scale problems, especially with a large number of scenarios, we propose the embedded Benders decomposition algorithm, which applies Benders cuts in both first and second stages, to tackle the discrete subproblems. Numerical results show that our algorithm is efficient for large scale stochastic natural gas transportation system expansion planning problems.

Suggested Citation

  • Qipeng P. Zheng & Panos M. Pardalos, 2010. "Stochastic and Risk Management Models and Solution Algorithm for Natural Gas Transmission Network Expansion and LNG Terminal Location Planning," Journal of Optimization Theory and Applications, Springer, vol. 147(2), pages 337-357, November.
    • Handle: RePEc:spr:joptap:v:147:y:2010:i:2:d:10.1007_s10957-010-9725-y
    DOI: 10.1007/s10957-010-9725-y
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    References listed on IDEAS

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    1. Gordon J. Alexander & Alexandre M. Baptista, 2004. "A Comparison of VaR and CVaR Constraints on Portfolio Selection with the Mean-Variance Model," Management Science, INFORMS, vol. 50(9), pages 1261-1273, September.
    2. Lewis Ntaimo, 2010. "Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse," Operations Research, INFORMS, vol. 58(1), pages 229-243, February.
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    Cited by:

    1. Markéta Mikolajková-Alifov & Frank Pettersson & Margareta Björklund-Sänkiaho & Henrik Saxén, 2019. "A Model of Optimal Gas Supply to a Set of Distributed Consumers," Energies, MDPI, vol. 12(3), pages 1-27, January.
    2. Munoz, Francisco D. & van der Weijde, Adriaan Hendrik & Hobbs, Benjamin F. & Watson, Jean-Paul, 2017. "Does risk aversion affect transmission and generation planning? A Western North America case study," Energy Economics, Elsevier, vol. 64(C), pages 213-225.
    3. Huang, Yuping & Zheng, Qipeng P. & Fan, Neng & Aminian, Kashy, 2014. "Optimal scheduling for enhanced coal bed methane production through CO2 injection," Applied Energy, Elsevier, vol. 113(C), pages 1475-1483.
    4. Qipeng Zheng & Jianhui Wang & Panos Pardalos & Yongpei Guan, 2013. "A decomposition approach to the two-stage stochastic unit commitment problem," Annals of Operations Research, Springer, vol. 210(1), pages 387-410, November.
    5. Marte Fodstad & Ruud Egging & Kjetil Midthun & Asgeir Tomasgard, 2016. "Stochastic Modeling of Natural Gas Infrastructure Development in Europe under Demand Uncertainty," The Energy Journal, , vol. 37(3_suppl), pages 5-32, December.
    6. Adrian Werner, Kristin Tolstad Uggen, Marte Fodstad, Arnt-Gunnar Lium, and Ruud Egging, 2014. "Stochastic Mixed-Integer Programming for Integrated Portfolio Planning in the LNG Supply Chain," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).

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