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Risk Measures in Multi-Horizon Scenario Trees

In: Handbook of Risk Management in Energy Production and Trading

Author

Listed:
  • Adrian S. Werner

    (SINTEF Technology and Society)

  • Alois Pichler

    (Norwegian University of Science and Technology (NTNU))

  • Kjetil T. Midthun

    (SINTEF)

  • Lars Hellemo

    (SINTEF)

  • Asgeir Tomasgard

    (Norwegian University of Science and Technology (NTNU))

Abstract

Production assurance requirements are used to ensure that the operation of natural gas transportation networks is robust with respect to flow and production disruptions. They also affect strategies for optimal infrastructure investments. Motivated by a combined investment and operational optimization model for natural gas transport, we describe how to address such requirements through risk measure formulations such as Average Value-at-Risk. The large number of operational scenarios required for a meaningful analysis of the risk measures creates a computational challenge. A new scenario tree structure, multi-horizon scenario trees, can improve computational tractability. We investigate properties of the risk measures such as time consistency for such scenario trees and illustrate this discussion with a stylized example.

Suggested Citation

  • Adrian S. Werner & Alois Pichler & Kjetil T. Midthun & Lars Hellemo & Asgeir Tomasgard, 2013. "Risk Measures in Multi-Horizon Scenario Trees," International Series in Operations Research & Management Science, in: Raimund M. Kovacevic & Georg Ch. Pflug & Maria Teresa Vespucci (ed.), Handbook of Risk Management in Energy Production and Trading, edition 127, chapter 0, pages 177-201, Springer.
    • Handle: RePEc:spr:isochp:978-1-4614-9035-7_8
    DOI: 10.1007/978-1-4614-9035-7_8
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    Citations

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    Cited by:

    1. Martin Glanzer & Georg Ch. Pflug, 2020. "Multiscale stochastic optimization: modeling aspects and scenario generation," Computational Optimization and Applications, Springer, vol. 75(1), pages 1-34, January.
    2. Francesca Maggioni & Elisabetta Allevi & Asgeir Tomasgard, 2020. "Bounds in multi-horizon stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 605-625, September.
    3. Giovanni Pantuso & Trine K. Boomsma, 2020. "On the number of stages in multistage stochastic programs," Annals of Operations Research, Springer, vol. 292(2), pages 581-603, September.
    4. Wim Ackooij & Debora Daniela Escobar & Martin Glanzer & Georg Ch. Pflug, 2020. "Distributionally robust optimization with multiple time scales: valuation of a thermal power plant," Computational Management Science, Springer, vol. 17(3), pages 357-385, October.

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