IDEAS home Printed from https://ideas.repec.org/a/sae/enejou/v35y2014i4p61-78.html
   My bibliography  Save this article

Understanding the Determinants of Electricity Prices and the Impact of the German Nuclear Moratorium in 2011

Author

Listed:
  • Stefan Thoenes

Abstract

This paper shows how the effect of fuel prices varies with the level of electricity demand. It analyzes the relationship between daily prices of electricity, natural gas and carbon emission allowances with a semiparametric varying smooth coefficient cointegration model. Different electricity generation technologies have distinct fuel price dependencies, which allows estimating the structure of the power plant portfolio by exploiting market prices. The semiparametric model indicates a technology switch from coal to gas at roughly 85% of maximum demand. This model is used to analyze the market impact of the nuclear moratorium by the German Government in March 2011. Futures prices of electricity, natural gas and emission allowances are used to show that the market efficiently accounts for the suspended capacity and correctly expects that several nuclear plants will not be switched on after the moratorium.

Suggested Citation

  • Stefan Thoenes, 2014. "Understanding the Determinants of Electricity Prices and the Impact of the German Nuclear Moratorium in 2011," The Energy Journal, , vol. 35(4), pages 61-78, October.
    • Handle: RePEc:sae:enejou:v:35:y:2014:i:4:p:61-78
    DOI: 10.5547/01956574.35.4.3
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5547/01956574.35.4.3
    Download Restriction: no
    File URL: https://libkey.io/10.5547/01956574.35.4.3?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. Kanamura, Takashi & Ohashi, Kazuhiko, 2007. "A structural model for electricity prices with spikes: Measurement of spike risk and optimal policies for hydropower plant operation," Energy Economics, Elsevier, vol. 29(5), pages 1010-1032, September.
    Full references (including those not matched with items on IDEAS)

    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. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    3. Kanamura, Takashi & O[combining macron]hashi, Kazuhiko, 2008. "On transition probabilities of regime switching in electricity prices," Energy Economics, Elsevier, vol. 30(3), pages 1158-1172, May.
    4. Stefan Thoenes, 2014. "Understanding the Determinants of Electricity Prices and the Impact of the German Nuclear Moratorium in 2011," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    5. Ding Ding, 2022. "The impacts of carbon pricing on the electricity market in Japan," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    6. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
    7. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    8. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.
    9. Huisman, R. & Mahieu, R.J. & Schlichter, F., 2009. "Electricity portfolio management : Optimal peak/off-peak allocations," Other publications TiSEM f880b2e6-c56c-483c-9334-9, Tilburg University, School of Economics and Management.
    10. Shin S. Ikeda, 2013. "An Empirical Market Microstructure Analysis of the Implied Spread Cost in the Japanese Day-Ahead Electricity Market," GRIPS Discussion Papers 12-22, National Graduate Institute for Policy Studies.
    11. Angelica Gianfreda, 2010. "Volatility and Volume Effects in European Electricity Spot Markets," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 39(1‐2), pages 47-63, February.
    12. Shin S. Ikeda, 2015. "Illiquidity in the Japanese Day-Ahead Electricity Market," GRIPS Discussion Papers 15-04, National Graduate Institute for Policy Studies.
    13. Füss, Roland & Mahringer, Steffen & Prokopczuk, Marcel, 2015. "Electricity derivatives pricing with forward-looking information," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 34-57.
    14. Huisman, Ronald & Mahieu, Ronald & Schlichter, Felix, 2009. "Electricity portfolio management: Optimal peak/off-peak allocations," Energy Economics, Elsevier, vol. 31(1), pages 169-174, January.
    15. Rangga Handika & Chi Truong & Stefan Trueck & Rafal Weron, 2014. "Modelling price spikes in electricity markets - the impact of load, weather and capacity," HSC Research Reports HSC/14/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    16. Thoenes, Stefan, 2011. "Understanding the Determinants of Electricity Prices and the Impact of the German Nuclear Moratorium in 2011," EWI Working Papers 2011-6, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    17. Kanamura, Takashi, 2009. "A supply and demand based volatility model for energy prices," Energy Economics, Elsevier, vol. 31(5), pages 736-747, September.
    18. Muche, Thomas, 2009. "A real option-based simulation model to evaluate investments in pump storage plants," Energy Policy, Elsevier, vol. 37(11), pages 4851-4862, November.
    19. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    20. Miguel Pinhão & Miguel Fonseca & Ricardo Covas, 2022. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve," Mathematics, MDPI, vol. 10(12), pages 1-20, June.

    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:sae:enejou:v:35:y:2014:i:4:p:61-78. 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: SAGE Publications (email available below). General contact details of provider: .

    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.