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Natural gas storage valuation and optimization: A real options application

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  • Matt Thompson
  • Matt Davison
  • Henning Rasmussen

Abstract

In this article, we present an algorithm for the valuation and optimal operation of natural gas storage facilities. Real options theory is used to derive nonlinear partial‐integro‐differential equations (PIDEs), the solution of which give both valuation and optimal operating strategies for these facilities. The equations are designed to incorporate a wide class of spot price models that can exhibit the same time‐dependent, mean‐reverting dynamics, and price spikes as those observed in most energy markets. Particular attention is paid to the operational characteristics of real storage units. These characteristics include working gas capacities, variable deliverability and injection rates, and cycling limitations. We illustrate the model with a numerical example of a salt cavern storage facility that clearly shows how a gas storage facility is like a financial straddle with both put and call properties. Depending on the amount of gas in storage the relative influence of the put and call components vary. © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009

Suggested Citation

  • Matt Thompson & Matt Davison & Henning Rasmussen, 2009. "Natural gas storage valuation and optimization: A real options application," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 226-238, April.
    • Handle: RePEc:wly:navres:v:56:y:2009:i:3:p:226-238
    DOI: 10.1002/nav.20327
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    Cited by:

    1. Guoming Lai & Mulan X. Wang & Sunder Kekre & Alan Scheller-Wolf & Nicola Secomandi, 2011. "Valuation of Storage at a Liquefied Natural Gas Terminal," Operations Research, INFORMS, vol. 59(3), pages 602-616, June.
    2. Yangfang (Helen) Zhou & Alan Scheller-Wolf & Nicola Secomandi & Stephen Smith, 2016. "Electricity Trading and Negative Prices: Storage vs. Disposal," Management Science, INFORMS, vol. 62(3), pages 880-898, March.
    3. Ren, Xiaohang & Lu, Zudi & Cheng, Cheng & Shi, Yukun & Shen, Jian, 2019. "On dynamic linkages of the state natural gas markets in the USA: Evidence from an empirical spatio-temporal network quantile analysis," Energy Economics, Elsevier, vol. 80(C), pages 234-252.
    4. Daniel R. Jiang & Warren B. Powell, 2015. "Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 27(3), pages 525-543, August.
    5. Konstantina Valogianni & Wolfgang Ketter & John Collins & Dmitry Zhdanov, 2020. "Sustainable Electric Vehicle Charging using Adaptive Pricing," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1550-1572, June.
    6. Yangfang (Helen) Zhou & Alan Scheller‐Wolf & Nicola Secomandi & Stephen Smith, 2019. "Managing Wind‐Based Electricity Generation in the Presence of Storage and Transmission Capacity," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 970-989, April.
    7. Lin Zhao & Sweder van Wijnbergen, 2015. "Asset Pricing in Incomplete Markets: Valuing Gas Storage Capacity," Tinbergen Institute Discussion Papers 15-104/VI/DSF95, Tinbergen Institute.
    8. Selvaprabu Nadarajah & François Margot & Nicola Secomandi, 2015. "Relaxations of Approximate Linear Programs for the Real Option Management of Commodity Storage," Management Science, INFORMS, vol. 61(12), pages 3054-3076, December.
    9. Mengying Xue & Tianhu Deng & Zuo‐Jun Max Shen, 2019. "Optimizing natural gas pipeline transmission with nonuniform elevation: A new initialization approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(7), pages 547-564, October.
    10. Nemat Safarov & Colin Atkinson, 2016. "Natural gas-fired power plants valuation and optimisation under Levy copulas and regime-switching," Papers 1607.01207, arXiv.org, revised Jul 2016.
    11. Guoming Lai & François Margot & Nicola Secomandi, 2010. "An Approximate Dynamic Programming Approach to Benchmark Practice-Based Heuristics for Natural Gas Storage Valuation," Operations Research, INFORMS, vol. 58(3), pages 564-582, June.
    12. Nicola Secomandi, 2015. "Merchant Commodity Storage Practice Revisited," Operations Research, INFORMS, vol. 63(5), pages 1131-1143, October.
    13. Nicolas Curin & Michael Kettler & Xi Kleisinger-Yu & Vlatka Komaric & Thomas Krabichler & Josef Teichmann & Hanna Wutte, 2021. "A deep learning model for gas storage optimization," Papers 2102.01980, arXiv.org, revised Mar 2021.
    14. Nicola Secomandi & Guoming Lai & François Margot & Alan Scheller-Wolf & Duane J. Seppi, 2015. "Merchant Commodity Storage and Term-Structure Model Error," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 302-320, July.
    15. Mason, Charles F. & Wilmot, Neil A., 2020. "Jumps in the convenience yield of crude oil," Resource and Energy Economics, Elsevier, vol. 60(C).
    16. Löhndorf, Nils & Wozabal, David, 2021. "Gas storage valuation in incomplete markets," European Journal of Operational Research, Elsevier, vol. 288(1), pages 318-330.
    17. Nicolas Curin & Michael Kettler & Xi Kleisinger-Yu & Vlatka Komaric & Thomas Krabichler & Josef Teichmann & Hanna Wutte, 2021. "A deep learning model for gas storage optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1021-1037, December.
    18. Ikonnikova, Svetlana A. & del Carpio Neyra, Victor & Berdysheva, Sofia, 2022. "Investment choices and production dynamics: The role of price expectations, financial deficit, and production constraints," Journal of Economics and Business, Elsevier, vol. 120(C).
    19. A. Lust & K.-H. Waldmann, 2019. "A general storage model with applications to energy systems," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 71-97, March.
    20. Szabó, Dávid Zoltán & Duck, Peter & Johnson, Paul, 2020. "Optimal trading of imbalance options for power systems using an energy storage device," European Journal of Operational Research, Elsevier, vol. 285(1), pages 3-22.

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