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On the construction of hourly price forward curves for electricity prices

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Listed:
  • Rüdiger Kiesel

    (University Duisburg-Essen
    University of Oslo)

  • Florentina Paraschiv

    (Norwegian University of Science and Technology)

  • Audun Sætherø

    (University Duisburg-Essen)

Abstract

There are several approaches in the literature for the derivation of price forward curves (PFCs) which distinguish among each other by the procedure employed for the derivation of seasonality shapes, smoothing technique and by the design of the optimization procedure. However, a comparative study to highlight the strengths and weaknesses of different methods is missing. For the construction of PFCs we typically incorporate the information about market expectation from the observed futures prices and the deterministic seasonal effects of electricity prices. In most existing approaches, the seasonality shape is fitted to historically observed spot prices, and it is an exogenous input to the optimization procedure. As seasonal effects on electricity prices differ between markets, our model allows a more general and flexible definition of the seasonality shape. In this study, we propose an alternative calibration procedure for the seasonality shape, where the level of futures as well as historical spot prices are simultaneously taken into account in a joint optimization approach. We discuss comparatively the features of existing methods for PFCs, and highlight the advantages of our optimization procedure.

Suggested Citation

  • Rüdiger Kiesel & Florentina Paraschiv & Audun Sætherø, 2019. "On the construction of hourly price forward curves for electricity prices," Computational Management Science, Springer, vol. 16(1), pages 345-369, February.
    • Handle: RePEc:spr:comgts:v:16:y:2019:i:1:d:10.1007_s10287-018-0300-6
    DOI: 10.1007/s10287-018-0300-6
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    References listed on IDEAS

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    1. Fleten, Stein-Erik & Lemming, Jacob, 2003. "Constructing forward price curves in electricity markets," Energy Economics, Elsevier, vol. 25(5), pages 409-424, September.
    2. Paraschiv, Florentina & Bunn, Derek & Westgaard, Sjur, 2016. "Estimation and Application of Fully Parametric Multifactor Quantile Regression with Dynamic Coefficients," Working Papers on Finance 1607, University of St. Gallen, School of Finance.
    3. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    4. Patrick Hagan & Graeme West, 2006. "Interpolation Methods for Curve Construction," Applied Mathematical Finance, Taylor & Francis Journals, vol. 13(2), pages 89-129.
    5. Caldana, Ruggero & Fusai, Gianluca & Roncoroni, Andrea, 2017. "Electricity forward curves with thin granularity: Theory and empirical evidence in the hourly EPEXspot market," European Journal of Operational Research, Elsevier, vol. 261(2), pages 715-734.
    6. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    7. Paraschiv, Florentina & Fleten, Stein-Erik & Schürle, Michael, 2015. "A spot-forward model for electricity prices with regime shifts," Energy Economics, Elsevier, vol. 47(C), pages 142-153.
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    Cited by:

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    2. Giorgia Callegaro & Andrea Mazzoran & Carlo Sgarra, 2019. "A Self-Exciting Modelling Framework for Forward Prices in Power Markets," Papers 1910.13286, arXiv.org.

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