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Structural price model for coupled electricity markets

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  • Alasseur, C.
  • Féron, O.

Abstract

We propose a new structural model that can compute the electricity spot and forward prices in two coupled markets with limited interconnection and multiple fuels. We choose a structural approach in order to represent some key characteristics of electricity spot prices such as their link to fuel prices, consumption level and production fleet. With this model, explicit formulas are also available for forward prices and other derivatives. We give some illustrative results of the behaviour of spot, forward and transmission rights prices.

Suggested Citation

  • Alasseur, C. & Féron, O., 2018. "Structural price model for coupled electricity markets," Energy Economics, Elsevier, vol. 75(C), pages 104-119.
    • Handle: RePEc:eee:eneeco:v:75:y:2018:i:c:p:104-119
    DOI: 10.1016/j.eneco.2018.07.018
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    References listed on IDEAS

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    1. Coulon, Michael & Powell, Warren B. & Sircar, Ronnie, 2013. "A model for hedging load and price risk in the Texas electricity market," Energy Economics, Elsevier, vol. 40(C), pages 976-988.
    2. 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.
    3. 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.
    4. A Ciarreta and A Zarraga, 2015. "Analysis of mean and volatility price transmissions in the MIBEL and EPEX electricity spot markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    5. Mahringer, Steffen & Fuess, Roland & Prokopczuk, Marcel, 2015. "Electricity Market Coupling and the Pricing of Transmission Rights: An Option-based Approach," Working Papers on Finance 1512, University of St. Gallen, School of Finance.
    6. S. Moazeni & M. Coulon & I. Arciniegas Rueda & B. Song & W.B. Powell, 2016. "A non-parametric structural hybrid modeling approach for electricity prices," Quantitative Finance, Taylor & Francis Journals, vol. 16(2), pages 213-230, February.
    7. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    8. Kallabis, Thomas & Pape, Christian & Weber, Christoph, 2016. "The plunge in German electricity futures prices – Analysis using a parsimonious fundamental model," Energy Policy, Elsevier, vol. 95(C), pages 280-290.
    9. repec:dau:papers:123456789/14506 is not listed on IDEAS
    10. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    11. Gianfreda, Angelica & Parisio, Lucia & Pelagatti, Matteo, 2016. "Revisiting long-run relations in power markets with high RES penetration," Energy Policy, Elsevier, vol. 94(C), pages 432-445.
    12. Aïd, René & Campi, Luciano & Langrené, Nicolas & Pham, Huyên, 2014. "A probabilistic numerical method for optimal multiple switching problems in high dimension," LSE Research Online Documents on Economics 63011, London School of Economics and Political Science, LSE Library.
    13. René Aïd & Luciano Campi & Nicolas Langrené & Huyên Pham, 2014. "A probabilistic numerical method for optimal multiple switching problems in high dimension," Post-Print hal-02294328, HAL.
    14. de Menezes, Lilian M. & Houllier, Melanie A., 2016. "Reassessing the integration of European electricity markets: A fractional cointegration analysis," Energy Economics, Elsevier, vol. 53(C), pages 132-150.
    15. 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.
    16. de Menezes, Lilian M. & Houllier, Melanie A., 2015. "Germany's nuclear power plant closures and the integration of electricity markets in Europe," Energy Policy, Elsevier, vol. 85(C), pages 357-368.
    17. Rene Carmona & Michael Coulon & Daniel Schwarz, 2012. "Electricity price modeling and asset valuation: a multi-fuel structural approach," Papers 1205.2299, arXiv.org.
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    Citations

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    Cited by:

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    2. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    3. Saez, Yago & Mochon, Asuncion & Corona, Luis & Isasi, Pedro, 2019. "Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region," Energy Policy, Elsevier, vol. 132(C), pages 549-566.
    4. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    5. Valentin Mahler & Robin Girard & Georges Kariniotakis, 2021. "Data-driven Structural Modeling of Electricity Price Dynamics," Working Papers hal-03445396, HAL.
    6. Benatia, David, 2022. "Ring the alarm! Electricity markets, renewables, and the pandemic," Energy Economics, Elsevier, vol. 106(C).
    7. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
    8. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
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    11. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Konstandatos, Otto & Rai, Alan, 2021. "Wind generation and the dynamics of electricity prices in Australia," Energy Economics, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Energy markets; Structural models; Derivatives pricing; Electricity forwards; Interconnection;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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