IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v197y2009i3p1041-1050.html
   My bibliography  Save this article

Electricity swing options: Behavioral models and pricing

Author

Listed:
  • Pflug, Georg C.
  • Broussev, Nikola

Abstract

Electricity swing options are supply contracts for power, which give the owner the right to change the required delivery on short time notice. It gives more flexibility than fixed base load or peak load contracts. The name "option" is a bit misleading, since it gives the owner multiple exercise rights at many different time horizons with exercise amounts on a continuous scale. We look at the problem to determine a rational ask price for such a contract from the viewpoint of the contract seller. The pricing of these contracts differs drastically from the pricing of financial options. First, peculiar properties arise from the non-storability of the underlying (the energy) and therefore the impossibility to hedge with the underlying, hedging is only possible with some future contracts. Second, the behavior of the owner plays an important role. Based on some behavioral model for the option holder, we develop a game-theoretic model, which allows to identify the equilibrium price. Besides some theoretical results, we present some numerical results which clarify the dependence of the asked price on the amount of flexibility offered in the swing option.

Suggested Citation

  • Pflug, Georg C. & Broussev, Nikola, 2009. "Electricity swing options: Behavioral models and pricing," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1041-1050, September.
    • Handle: RePEc:eee:ejores:v:197:y:2009:i:3:p:1041-1050
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(08)00275-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Thompson, Andrew C., 1995. "Valuation of Path-Dependent Contingent Claims with Multiple Exercise Decisions over Time: The Case of Take-or-Pay," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(2), pages 271-293, June.
    3. Patrick Jaillet & Ehud I. Ronn & Stathis Tompaidis, 2004. "Valuation of Commodity-Based Swing Options," Management Science, INFORMS, vol. 50(7), pages 909-921, July.
    4. N. Meinshausen & B. M. Hambly, 2004. "Monte Carlo Methods For The Valuation Of Multiple‐Exercise Options," Mathematical Finance, Wiley Blackwell, vol. 14(4), pages 557-583, October.
    5. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    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. Alexander, Carol & Deng, Jun & Zou, Bin, 2023. "Hedging with automatic liquidation and leverage selection on bitcoin futures," European Journal of Operational Research, Elsevier, vol. 306(1), pages 478-493.
    2. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    3. Kovacevic, Raimund M. & Pflug, Georg Ch., 2014. "Electricity swing option pricing by stochastic bilevel optimization: A survey and new approaches," European Journal of Operational Research, Elsevier, vol. 237(2), pages 389-403.
    4. Huang, Zhouchun & Zheng, Qipeng Phil, 2020. "A multistage stochastic programming approach for preventive maintenance scheduling of GENCOs with natural gas contract," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1036-1051.
    5. Mathias Barkhagen & Jörgen Blomvall, 2016. "Modeling and evaluation of the option book hedging problem using stochastic programming," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 259-273, February.
    6. Ghaffari, Reza & Venkatesh, Bala, 2015. "Network constrained model for options based reserve procurement by wind generators using binomial tree," Renewable Energy, Elsevier, vol. 80(C), pages 348-358.
    7. 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.
    8. Barro, Diana & Consigli, Giorgio & Varun, Vivek, 2022. "A stochastic programming model for dynamic portfolio management with financial derivatives," Journal of Banking & Finance, Elsevier, vol. 140(C).
    9. John Moriarty & Jan Palczewski, 2019. "Imbalance Market Real Options and the Valuation of Storage in Future Energy Systems," Risks, MDPI, vol. 7(2), pages 1-30, April.

    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. Dong, Wenfeng & Kang, Boda, 2019. "Analysis of a multiple year gas sales agreement with make-up, carry-forward and indexation," Energy Economics, Elsevier, vol. 79(C), pages 76-96.
    2. Kovacevic, Raimund M. & Pflug, Georg Ch., 2014. "Electricity swing option pricing by stochastic bilevel optimization: A survey and new approaches," European Journal of Operational Research, Elsevier, vol. 237(2), pages 389-403.
    3. Nicolas Essis-Breton & Patrice Gaillardetz, 2020. "Fast Lower and Upper Estimates for the Price of Constrained Multiple Exercise American Options by Single Pass Lookahead Search and Nearest-Neighbor Martingale," Papers 2002.11258, arXiv.org.
    4. Kourouvakalis, Stylianos, 2008. "Méthodes numériques pour la valorisation d'options swings et autres problèmes sur les matières premières," Economics Thesis from University Paris Dauphine, Paris Dauphine University, number 123456789/116 edited by Geman, Hélyette.
    5. Liu, Xiaoran & Ronn, Ehud I., 2020. "Using the binomial model for the valuation of real options in computing optimal subsidies for Chinese renewable energy investments," Energy Economics, Elsevier, vol. 87(C).
    6. Christian Bender, 2011. "Dual pricing of multi-exercise options under volume constraints," Finance and Stochastics, Springer, vol. 15(1), pages 1-26, January.
    7. Cartea, Álvaro & Williams, Thomas, 2008. "UK gas markets: The market price of risk and applications to multiple interruptible supply contracts," Energy Economics, Elsevier, vol. 30(3), pages 829-846, May.
    8. John Schoenmakers, 2012. "A pure martingale dual for multiple stopping," Finance and Stochastics, Springer, vol. 16(2), pages 319-334, April.
    9. R. Mark Reesor & T. James Marshall, 2020. "Forest of Stochastic Trees: A Method for Valuing Multiple Exercise Options," JRFM, MDPI, vol. 13(5), pages 1-31, May.
    10. Hendrik Kohrs & Hermann Mühlichen & Benjamin R. Auer & Frank Schuhmacher, 2019. "Pricing and risk of swing contracts in natural gas markets," Review of Derivatives Research, Springer, vol. 22(1), pages 77-167, April.
    11. Vincent Lemaire & Gilles Pag`es & Christian Yeo, 2023. "Swing contract pricing: with and without Neural Networks," Papers 2306.03822, arXiv.org, revised Mar 2024.
    12. Secomandi, Nicola & Seppi, Duane J., 2014. "Real Options and Merchant Operations of Energy and Other Commodities," Foundations and Trends(R) in Technology, Information and Operations Management, now publishers, vol. 6(3-4), pages 161-331, July.
    13. Soren Christensen & Albrecht Irle & Stephan Jurgens, 2012. "Optimal multiple stopping with random waiting times," Papers 1205.1966, arXiv.org.
    14. John Ery & Loris Michel, 2021. "Solving optimal stopping problems with Deep Q-Learning," Papers 2101.09682, arXiv.org, revised Jun 2024.
    15. Belomestny, Denis & Kolodko, Anastasia & Schoenmakers, John G. M., 2009. "Regression methods for stochastic control problems and their convergence analysis," SFB 649 Discussion Papers 2009-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Rodrigo S. Targino & Gareth W. Peters & Georgy Sofronov & Pavel V. Shevchenko, 2017. "Optimal Exercise Strategies for Operational Risk Insurance via Multiple Stopping Times," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 487-518, June.
    17. Hanfeld, Marc & Schlüter, Stephan, 2016. "Operating a swing option on today's gas markets: How least squares Monte Carlo works and why it is beneficial," FAU Discussion Papers in Economics 10/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    18. J. Lars Kirkby & Shi-Jie Deng, 2019. "Swing Option Pricing By Dynamic Programming With B-Spline Density Projection," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(08), pages 1-53, December.
    19. Jain, Shashi & Roelofs, Ferry & Oosterlee, Cornelis W., 2013. "Valuing modular nuclear power plants in finite time decision horizon," Energy Economics, Elsevier, vol. 36(C), pages 625-636.
    20. Hahn, Warren J. & Dyer, James S., 2008. "Discrete time modeling of mean-reverting stochastic processes for real option valuation," European Journal of Operational Research, Elsevier, vol. 184(2), pages 534-548, January.

    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:eee:ejores:v:197:y:2009:i:3:p:1041-1050. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.