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Electricity pool prices: long-term uncertainty characterization for futures-market trading and risk management

Author

Listed:
  • A J Conejo

    (University of Castilla—La Mancha)

  • F J Nogales

    (Universidad Carlos III de Madrid)

  • M Carrión

    (University of Castilla—La Mancha)

  • J M Morales

    (University of Castilla—La Mancha)

Abstract

Organized trading for electricity includes both the pool and the futures market. Pool prices are volatile while the prices of the futures-market products are comparatively more stable. Thus, futures-market products constitute hedging instruments to reduce the risk suffered by any market agent. Electricity market agents engage in both pool and futures market transactions seeking to maximize their respective profits/utilities for a given risk level on profit variability. To make informed decisions, the market agent must gather as much accurate information as possible on the pool prices covering the whole time horizon spanned by the futures-market product. This paper provides a novel technique to represent conveniently the uncertainty associated with pool prices during long- or medium-term horizons through a set of scenarios, that is, pool price realizations. The proposed technique uses the prices of the futures-market products as long-term explanatory variables and exploits the short-term structure of the pool prices.

Suggested Citation

  • A J Conejo & F J Nogales & M Carrión & J M Morales, 2010. "Electricity pool prices: long-term uncertainty characterization for futures-market trading and risk management," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(2), pages 235-245, February.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:2:d:10.1057_jors.2008.140
    DOI: 10.1057/jors.2008.140
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    References listed on IDEAS

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    1. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
    2. 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.
    3. repec:bla:jfinan:v:59:y:2004:i:4:p:1877-1900 is not listed on IDEAS
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    1. Fitiwi, Desta Z. & de Cuadra, F. & Olmos, L. & Rivier, M., 2015. "A new approach of clustering operational states for power network expansion planning problems dealing with RES (renewable energy source) generation operational variability and uncertainty," Energy, Elsevier, vol. 90(P2), pages 1360-1376.
    2. Fanelli, Viviana & Maddalena, Lucia & Musti, Silvana, 2016. "Modelling electricity futures prices using seasonal path-dependent volatility," Applied Energy, Elsevier, vol. 173(C), pages 92-102.
    3. Zhongkai Feng & Wenjing Niu & Sen Wang & Chuntian Cheng & Zhenguo Song, 2019. "Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones," Energies, MDPI, vol. 12(11), pages 1-17, June.
    4. García-Martos, Carolina & Rodríguez, Julio & Sánchez, María Jesús, 2011. "Forecasting electricity prices and their volatilities using Unobserved Components," Energy Economics, Elsevier, vol. 33(6), pages 1227-1239.
    5. Orhan Altuğ Karabiber & George Xydis, 2019. "Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods," Energies, MDPI, vol. 12(5), pages 1-29, March.

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