IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v147y2010i2d10.1007_s10957-010-9725-y.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-010-9725-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10957-010-9725-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ron Bird & Harry Liem & Susan Thorp, 2012. "The Tortoise and the Hare: Risk Premium Versus Alternative Asset Portfolios," Working Paper Series 16, The Paul Woolley Centre for Capital Market Dysfunctionality, University of Technology, Sydney.
    2. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2014. "Bank regulation and international financial stability: A case against the 2006 Basel framework for controlling tail risk in trading books," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 107-130.
    3. Pengyu Wei & Zuo Quan Xu, 2021. "Dynamic growth-optimum portfolio choice under risk control," Papers 2112.14451, arXiv.org.
    4. Taras Bodnar & Yarema Okhrin & Valdemar Vitlinskyy & Taras Zabolotskyy, 2018. "Determination and estimation of risk aversion coefficients," Computational Management Science, Springer, vol. 15(2), pages 297-317, June.
    5. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    6. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," Journal of Banking & Finance, Elsevier, vol. 126(C).
    7. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    8. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
    9. Bing Liang & Hyuna Park, 2007. "Risk Measures for Hedge Funds: a Cross‐sectional Approach," European Financial Management, European Financial Management Association, vol. 13(2), pages 333-370, March.
    10. Guan, Guohui & Liang, Zongxia, 2016. "Optimal management of DC pension plan under loss aversion and Value-at-Risk constraints," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 224-237.
    11. Omid Momen & Akbar Esfahanipour & Abbas Seifi, 2020. "A robust behavioral portfolio selection: model with investor attitudes and biases," Operational Research, Springer, vol. 20(1), pages 427-446, March.
    12. Jinyu Zhou & Jigao Yan & Dongya Cheng, 2024. "Strong consistency of tail value-at-risk estimator and corresponding general results under widely orthant dependent samples," Statistical Papers, Springer, vol. 65(6), pages 3357-3394, August.
    13. Wolfgang Kürsten & Mario Brandtner, 2009. "Kohärente Risikomessung versus individuelle Akzeptanzmengen — Anmerkungen zum impliziten Risikoverständnis des “Conditional Value-at-Risk”," Schmalenbach Journal of Business Research, Springer, vol. 61(4), pages 358-381, June.
    14. Righi, Marcelo Brutti & Borenstein, Denis, 2018. "A simulation comparison of risk measures for portfolio optimization," Finance Research Letters, Elsevier, vol. 24(C), pages 105-112.
    15. Massimiliano Amarante, 2016. "A representation of risk measures," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 39(1), pages 95-103, April.
    16. Jinghan Zhang & Henry Xie & Xinhao Zhang & Kunpeng Liu, 2024. "Enhancing Risk Assessment in Transformers with Loss-at-Risk Functions," Papers 2411.02558, arXiv.org.
    17. Anthony Hatherley & Jamie Alcock, 2007. "Portfolio construction incorporating asymmetric dependence structures: a user's guide," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 47(3), pages 447-472, September.
    18. Tongyao Wang & Qitong Pan & Weiping Wu & Jianjun Gao & Ke Zhou, 2024. "Dynamic Mean–Variance Portfolio Optimization with Value-at-Risk Constraint in Continuous Time," Mathematics, MDPI, vol. 12(14), pages 1-17, July.
    19. Taras Bodnar & Mathias Lindholm & Erik Thorsén & Joanna Tyrcha, 2021. "Quantile-based optimal portfolio selection," Computational Management Science, Springer, vol. 18(3), pages 299-324, July.
    20. Marcelo Brutti Righi & Paulo Sergio Ceretta, 2015. "Shortfall Deviation Risk: An alternative to risk measurement," Papers 1501.02007, arXiv.org, revised May 2016.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joptap:v:147:y:2010:i:2:d:10.1007_s10957-010-9725-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.