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Data-driven structural modeling of electricity price dynamics

Author

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  • Mahler, Valentin
  • Girard, Robin
  • Kariniotakis, Georges

Abstract

In many countries, electricity prices on day-ahead auction markets result from a market clearing designed to maximize social welfare. For each hour of the day, the market price can be represented as the intersection of a supply and demand curve. Structural market models reflect this price formation mechanism and are widely used in prospective studies guiding long-term decisions (e.g. investments and market design). However, simulating the supply curve in these models proves challenging since estimating the sell orders it comprises (i.e. offer prices and corresponding quantities) typically requires formulating numerous techno-economic hypotheses about power system assets and the behaviors of market participants. Due to imperfect competition, real market prices differ from the theoretical optimum, but modeling this difference is not straightforward. The objective of this work is to propose a model to simulate prices on day-ahead markets that account for the optimal economic dispatch of generation units, while also making use of historical day-ahead market prices. Inferring from historical data is especially important when not all information is made public (e.g. bidding strategies) or due to difficulty in accurately accounting for qualitative notions in quantitative models (e.g. market power). In this paper we propose a method for the parametrization of sell orders associated with production units. The estimation algorithm for this parametrization makes it possible to mitigate the requirement for analytic formulation of all of the above-mentioned aspects and to take advantage of the ever-increasing volume of available data on power systems (e.g. technical and market data). Parametrized orders also offer the possibility to account for various factors in a modular fashion, such as the strategic behavior of market participants. The proposed approach is validated using data related to the French day-ahead market and power system, for the period from 2015 to 2018.

Suggested Citation

  • Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
    • Handle: RePEc:eee:eneeco:v:107:y:2022:i:c:s0140988322000032
    DOI: 10.1016/j.eneco.2022.105811
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    References listed on IDEAS

    as
    1. Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. "The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform," Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.
    2. Mayer, Klaus & Trück, Stefan, 2018. "Electricity markets around the world," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 77-100.
    3. Abujarad, Saleh Y. & Mustafa, M.W. & Jamian, J.J., 2017. "Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 215-223.
    4. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    5. 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.
    6. Ziel, Florian & Steinert, Rick, 2018. "Probabilistic mid- and long-term electricity price forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 251-266.
    7. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Solbrekke, Ida Marie, 2018. "A review of modelling tools for energy and electricity systems with large shares of variable renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 440-459.
    8. Beran, Philip & Pape, Christian & Weber, Christoph, 2019. "Modelling German electricity wholesale spot prices with a parsimonious fundamental model – Validation & application," Utilities Policy, Elsevier, vol. 58(C), pages 27-39.
    9. 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.
    10. Wiese, Frauke & Schlecht, Ingmar & Bunke, Wolf-Dieter & Gerbaulet, Clemens & Hirth, Lion & Jahn, Martin & Kunz, Friedrich & Lorenz, Casimir & Mühlenpfordt, Jonathan & Reimann, Juliane & Schill, Wolf-P, 2019. "Open Power System Data – Frictionless data for electricity system modelling," Applied Energy, Elsevier, vol. 236(C), pages 401-409.
    11. Sánchez de la Nieta, A.A. & Contreras, J., 2020. "Quantifying the effect of renewable generation on day–ahead electricity market prices: The Spanish case," Energy Economics, Elsevier, vol. 90(C).
    12. Piotr F. Borowski, 2020. "Zonal and Nodal Models of Energy Market in European Union," Energies, MDPI, vol. 13(16), pages 1-21, August.
    13. Alasseur, C. & Féron, O., 2018. "Structural price model for coupled electricity markets," Energy Economics, Elsevier, vol. 75(C), pages 104-119.
    14. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    15. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    16. Sergei Kulakov, 2020. "X-Model: Further Development and Possible Modifications," Forecasting, MDPI, vol. 2(1), pages 1-16, February.
    17. repec:dau:papers:123456789/11500 is not listed on IDEAS
    18. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    19. Lion Hirth, 2018. "What caused the drop in European electricity prices? A factor decomposition analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    20. Gross, Robert & Blyth, William & Heptonstall, Philip, 2010. "Risks, revenues and investment in electricity generation: Why policy needs to look beyond costs," Energy Economics, Elsevier, vol. 32(4), pages 796-804, July.
    21. Florian Ziel & Rick Steinert, 2017. "Probabilistic Mid- and Long-Term Electricity Price Forecasting," Papers 1703.10806, arXiv.org, revised May 2018.
    22. John R. Birge & Ali Hortaçsu & J. Michael Pavlin, 2017. "Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market," Operations Research, INFORMS, vol. 65(4), pages 837-855, August.
    23. Parkinson, Thomas W., 2004. "Market Price Risks of Merchant Generation," The Electricity Journal, Elsevier, vol. 17(4), pages 33-48, May.
    24. 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.
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    More about this item

    Keywords

    Day-ahead markets; Electricity prices; Structural market model; Prospective studies; Power systems;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C57 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Econometrics of Games and Auctions
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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