IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v51y2003i4p602-612.html
   My bibliography  Save this article

Market Offering Strategies for Hydroelectric Generators

Author

Listed:
  • G. Pritchard

    (Department of Statistics, University of Auckland, Private Bag 92019, Auckland, NewZealand)

  • G. Zakeri

    (Department of Engineering Science, University of Auckland, Private Bag 92019, Auckland, NewZealand)

Abstract

This paper considers the problem of offering electricity produced by a series of hydroelectric reservoirs to a pool-type central market. The market model is a simplified version of the New Zealand wholesale electricity market, with prices modelled by a first-order Markov process. The demand for electricity is not explicitly modelled. The hydroelectric generator is assumed to be unable to influence market prices (i.e., to be a price-taker). We discuss the resulting stochastic dynamic program, methods for its solution, and the explicit optimal offer curves that it produces. It is shown that the utility function is monotone increasing with respect to both reservoir level and current price; however, the optimal offer curves need not be monotone. This is shown by example. Numerical results are provided.

Suggested Citation

  • G. Pritchard & G. Zakeri, 2003. "Market Offering Strategies for Hydroelectric Generators," Operations Research, INFORMS, vol. 51(4), pages 602-612, August.
  • Handle: RePEc:inm:oropre:v:51:y:2003:i:4:p:602-612
    DOI: 10.1287/opre.51.4.602.16097
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.51.4.602.16097
    Download Restriction: no

    File URL: https://libkey.io/10.1287/opre.51.4.602.16097?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
    ---><---

    References listed on IDEAS

    as
    1. Philip J. Neame & Andrew B. Philpott & Geoffrey Pritchard, 2003. "Offer Stack Optimization in Electricity Pool Markets," Operations Research, INFORMS, vol. 51(3), pages 397-408, June.
    2. Deb, Rajat & Albert, Richard & Hsue, Lie-Long & Brown, Nicholas, 2000. "How to Incorporate Volatility and Risk in Electricity Price Forecasting," The Electricity Journal, Elsevier, vol. 13(4), pages 65-75, May.
    3. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    4. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    5. Keppo, Jussi & Rasanen, Mika, 1999. "Pricing of electricity tariffs in competitive markets," Energy Economics, Elsevier, vol. 21(3), pages 213-223, June.
    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. van Ackere, Ann & Ochoa, Patricia, 2010. "Managing a hydro-energy reservoir: A policy approach," Energy Policy, Elsevier, vol. 38(11), pages 7299-7311, November.
    2. Fleten, Stein-Erik & Haugstvedt, Daniel & Steinsbø, Jens Arne & Belsnes, Michael & Fleischmann, Franziska, 2011. "Bidding hydropower generation: Integrating short- and long-term scheduling," MPRA Paper 44450, University Library of Munich, Germany.
    3. Boomsma, Trine Krogh & Juul, Nina & Fleten, Stein-Erik, 2014. "Bidding in sequential electricity markets: The Nordic case," European Journal of Operational Research, Elsevier, vol. 238(3), pages 797-809.
    4. Ellen Krohn Aasgård, 2017. "Hydropower Bidding Using Linearized Start-Ups," Energies, MDPI, vol. 10(12), pages 1-13, 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. Unterschultz, James R., 2000. "New Instruments For Co-Ordination And Risk Sharing Within The Canadian Beef Industry," Project Report Series 24046, University of Alberta, Department of Resource Economics and Environmental Sociology.
    2. Chiarella, Carl & Kang, Boda & Nikitopoulos, Christina Sklibosios & Tô, Thuy-Duong, 2013. "Humps in the volatility structure of the crude oil futures market: New evidence," Energy Economics, Elsevier, vol. 40(C), pages 989-1000.
    3. Bujar Huskaj & Marcus Nossman, 2013. "A Term Structure Model for VIX Futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 33(5), pages 421-442, May.
    4. Back, Janis & Prokopczuk, Marcel & Rudolf, Markus, 2013. "Seasonality and the valuation of commodity options," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 273-290.
    5. Xiao, Chang & Florescu, Ionut & Zhou, Jinsheng, 2020. "A comparison of pricing models for mineral rights: Copper mine in China," Resources Policy, Elsevier, vol. 65(C).
    6. Peilun He & Karol Binkowski & Nino Kordzakhia & Pavel Shevchenko, 2021. "On Modelling of Crude Oil Futures in a Bivariate State-Space Framework," Papers 2108.01886, arXiv.org.
    7. Gil-Bazo, Javier, 2005. "Market imperfections, discount factors and stochastic dominance: an empirical analysis with oil-linked derivatives," DEE - Working Papers. Business Economics. WB wb055013, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    8. Max F. Schöne & Stefan Spinler, 2017. "A four-factor stochastic volatility model of commodity prices," Review of Derivatives Research, Springer, vol. 20(2), pages 135-165, July.
    9. Karol Binkowski & Peilun He & Nino Kordzakhia & Pavel Shevchenko, 2021. "On the Parameter Estimation in the Schwartz-Smiths Two-Factor Model," Papers 2108.01881, arXiv.org.
    10. de Jong, C.M. & Huisman, R., 2002. "Option Formulas for Mean-Reverting Power Prices with Spikes," ERIM Report Series Research in Management ERS-2002-96-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    11. Benjamin Cheng & Christina Nikitopoulos-Sklibosios & Erik Schlogl, 2015. "Pricing of Long-dated Commodity Derivatives with Stochastic Volatility and Stochastic Interest Rates," Research Paper Series 366, Quantitative Finance Research Centre, University of Technology, Sydney.
    12. Isabel Figuerola‐Ferretti & Alejandro Rodríguez & Eduardo Schwartz, 2021. "Oil price analysts' forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1351-1374, September.
    13. Richter, Martin & Sørensen, Carsten, 2002. "Stochastic Volatility and Seasonality in Commodity Futures and Options: The Case of Soybeans," Working Papers 2002-4, Copenhagen Business School, Department of Finance.
    14. Ke Du, 2013. "Commodity Derivative Pricing Under the Benchmark Approach," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2013, January-A.
    15. Chad E. Hart & Sergio H. Lence & Dermot J. Hayes & Na Jin, 2016. "Price Mean Reversion, Seasonality, and Options Markets," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(3), pages 707-725.
    16. Calum G. Turvey, 2006. "Managing food industry business and financial risks with commodity-linked credit instruments," Agribusiness, John Wiley & Sons, Ltd., vol. 22(4), pages 523-545.
    17. Philippe Raimbourg & Paul Zimmermann, 2022. "Is normal backwardation normal? Valuing financial futures with a local index-rate covariance," Post-Print hal-04011013, HAL.
    18. Iivo Vehvilainen, 2002. "Basics of electricity derivative pricing in competitive markets," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 45-60.
    19. Ederington, Louis H. & Fernando, Chitru S. & Hoelscher, Seth A. & Lee, Thomas K. & Linn, Scott C., 2019. "Characteristics of petroleum product prices: A survey," Journal of Commodity Markets, Elsevier, vol. 14(C), pages 1-15.
    20. Ke Tang, 2012. "Time-varying long-run mean of commodity prices and the modeling of futures term structures," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 781-790, April.

    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:inm:oropre:v:51:y:2003:i:4:p:602-612. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.