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Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices

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  • Keles, Dogan
  • Genoese, Massimo
  • Möst, Dominik
  • Fichtner, Wolf

Abstract

This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices.

Suggested Citation

  • Keles, Dogan & Genoese, Massimo & Möst, Dominik & Fichtner, Wolf, 2012. "Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices," Energy Economics, Elsevier, vol. 34(4), pages 1012-1032.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:4:p:1012-1032
    DOI: 10.1016/j.eneco.2011.08.012
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Paraschiv, Florentina, 2013. "Price Dynamics in Electricity Markets," Working Papers on Finance 1314, University of St. Gallen, School of Finance.
    2. repec:dui:wpaper:1502 is not listed on IDEAS
    3. Gaudard, Ludovic, 2015. "Pumped-storage project: A short to long term investment analysis including climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 91-99.
    4. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    5. repec:eee:eneeco:v:68:y:2017:i:c:p:283-302 is not listed on IDEAS
    6. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    7. repec:dui:wpaper:1318 is not listed on IDEAS
    8. Florian Ziel & Rick Steinert & Sven Husmann, 2014. "Efficient Modeling and Forecasting of the Electricity Spot Price," Papers 1402.7027, arXiv.org, revised Oct 2014.
    9. Christian Pape & Arne Vogler & Oliver Woll & Christoph Weber, 2017. "Forecasting the distributions of hourly electricity spot prices," EWL Working Papers 1705, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised May 2017.
    10. Keles, Dogan & Genoese, Massimo & Möst, Dominik & Ortlieb, Sebastian & Fichtner, Wolf, 2013. "A combined modeling approach for wind power feed-in and electricity spot prices," Energy Policy, Elsevier, vol. 59(C), pages 213-225.
    11. Kovacevic, Raimund M. & Paraschiv, Florentina, 2012. "Medium-term Planning for Thermal Electricity Production," Working Papers on Finance 1220, University of St. Gallen, School of Finance.
    12. Sergey Voronin & Jarmo Partanen, 2013. "Price Forecasting in the Day-Ahead Energy Market by an Iterative Method with Separate Normal Price and Price Spike Frameworks," Energies, MDPI, Open Access Journal, vol. 6(11), pages 1-24, November.
    13. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    14. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    15. Rick Steinert & Florian Ziel, 2018. "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures," Papers 1801.10583, arXiv.org.
    16. Ehrlich, Lars G. & Klamka, Jonas & Wolf, André, 2015. "The potential of decentralized power-to-heat as a flexibility option for the german electricity system: A microeconomic perspective," Energy Policy, Elsevier, vol. 87(C), pages 417-428.
    17. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    18. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    19. Ziel, Florian & Steinert, Rick & Husmann, Sven, 2015. "Efficient modeling and forecasting of electricity spot prices," Energy Economics, Elsevier, vol. 47(C), pages 98-111.
    20. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    21. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.

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