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Benchmarking time series based forecasting models for electricity balancing market prices

Author

Listed:
  • Gro Klaeboe

    (Norwegian University of Science and Technology)

  • Anders Lund Eriksrud

    (Norwegian University of Science and Technology)

  • Stein-Erik Fleten

    (Norwegian University of Science and Technology)

Abstract

In the trade-off between bidding in the day-ahead electricity market and the real time balancing market, producers need good forecasts for balancing market prices to make informed decisions. A range of earlier published models for forecasting of balancing market prices, including a few extensions, is benchmarked. The models are benchmarked both for one hour-ahead and day-ahead forecast, and both point and interval forecasts are compared. None of the benchmarked models produce informative day-ahead point forecasts, suggesting that information available before the closing of the day-ahead market is effciently reflected in the day-ahead market price rather than the balancing market price. Evaluation of the interval forecasts reveals that models without balancing state information overestimate variance, making them unsuitable for scenario generation.

Suggested Citation

  • Gro Klaeboe & Anders Lund Eriksrud & Stein-Erik Fleten, 2013. "Benchmarking time series based forecasting models for electricity balancing market prices," Working Papers 2013-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    • Handle: RePEc:gwc:wpaper:2013-006
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    File URL: https://www2.gwu.edu/~forcpgm/2013-006.pdf
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    References listed on IDEAS

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    1. Skytte, Klaus, 1999. "The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis," Energy Economics, Elsevier, vol. 21(4), pages 295-308, August.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Jean Michel Glachant & Marcello Saguan, 2007. "An Institutional Frame to Compare Alternative Market Designs in EU Electricity Balancing," Working Papers EPRG 0711, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. Jean Michel Glachant & Marcelo Saguan, 2007. "An Institutional Frame to Compare Alternative Market Designs in e U Electricity Balancing," Working Papers 0701, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
    5. Möller, Christoph & Rachev, Svetlozar T. & Fabozzi, Frank J., 2011. "Balancing energy strategies in electricity portfolio management," Energy Economics, Elsevier, vol. 33(1), pages 2-11, January.
    6. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    7. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    8. van der Veen, Reinier A.C. & Abbasy, Alireza & Hakvoort, Rudi A., 2012. "Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets," Energy Economics, Elsevier, vol. 34(4), pages 874-881.
    9. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, University Library of Munich, Germany, revised 13 Nov 2003.
    10. Willemain, Thomas R. & Smart, Charles N. & Shockor, Joseph H. & DeSautels, Philip A., 1994. "Forecasting intermittent demand in manufacturing: a comparative evaluation of Croston's method," International Journal of Forecasting, Elsevier, vol. 10(4), pages 529-538, December.
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    Cited by:

    1. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
    2. Litjens, G.B.M.A. & Worrell, E. & van Sark, W.G.J.H.M., 2018. "Economic benefits of combining self-consumption enhancement with frequency restoration reserves provision by photovoltaic-battery systems," Applied Energy, Elsevier, vol. 223(C), pages 172-187.
    3. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    4. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    5. 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.

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    More about this item

    Keywords

    Federal Reserve; Forecast Evaluation; Survey of Professional Forecasts; Business Cycle; Mahalanobis Distance;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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