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A mean field game model for optimal trading in the intraday electricity market

Author

Listed:
  • Sema Coskun

    (RPTU Kaiserslautern-Landau)

  • Ralf Korn

    (RPTU Kaiserslautern-Landau
    Fraunhofer ITWM)

Abstract

In this study, we provide a simple one period mean-field-games setting for the joint optimal trading problem for electricity producers in the electricity markets. Based on the Markowitz mean-variance approach from stock trading, we consider a decision problem of an electricity provider when determining the optimal fractions of production that should be traded in the day-ahead and in the intraday markets. Moreover, all such providers are related by a ranking criterion and each one wants to perform as good as possible in this ranking. We first start with a simple model where only the price risk in the intraday market is present and subsequently extend the problem to the cases involving either production and/or demand uncertainty. The key technique is to reduce the optimality conditions to a first order non-linear ordinary differential equation. We will illustrate our findings by various numerical examples. Our findings will in particular be important for electricity producers using renewable resources.

Suggested Citation

  • Sema Coskun & Ralf Korn, 2025. "A mean field game model for optimal trading in the intraday electricity market," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 48(1), pages 269-299, June.
    • Handle: RePEc:spr:decfin:v:48:y:2025:i:1:d:10.1007_s10203-024-00445-1
    DOI: 10.1007/s10203-024-00445-1
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    References listed on IDEAS

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    1. Kiesel, Rüdiger & Paraschiv, Florentina, 2017. "Econometric analysis of 15-minute intraday electricity prices," Energy Economics, Elsevier, vol. 64(C), pages 77-90.
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    4. Olivier F'eron & Peter Tankov & Laura Tinsi, 2020. "Price formation and optimal trading in intraday electricity markets," Papers 2009.04786, arXiv.org, revised Jun 2021.
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    9. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
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