IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v144y2025ics0140988325001392.html
   My bibliography  Save this article

Coordinated bidding in sequential electricity markets: Effects of price-making

Author

Listed:
  • Miskiw, Kim K.
  • Kraft, Emil
  • Fleten, Stein-Erik

Abstract

As the uncertainty and time granularity of short-term electricity markets increase and as intraday trading gains importance, deriving good trading decisions becomes increasingly complex. This paper analyses the potential benefit of coordinating bids in three sequential electricity markets using a three-stage stochastic optimisation. The modelled markets include a typical European market setting consisting of a balancing reserve, a day-ahead, and an intraday market. Due to limited intraday market liquidity, the trading strategies also take price impacts into account. The results indicate that coordinated bidding can increase profitability, with the extent of gains depending on the price impacts. In a case study with a biomass and photovoltaic portfolio operating in Germany, we find that coordinated bidding increases the average revenue by around 18% over all analysed type days. As renewable generation continues to increase, trading strategies that coordinate bids across markets are expected to become increasingly important.

Suggested Citation

  • Miskiw, Kim K. & Kraft, Emil & Fleten, Stein-Erik, 2025. "Coordinated bidding in sequential electricity markets: Effects of price-making," Energy Economics, Elsevier, vol. 144(C).
    • Handle: RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001392
    DOI: 10.1016/j.eneco.2025.108316
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325001392
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2025.108316?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
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    2. Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023. "Stochastic optimization of trading strategies in sequential electricity markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
    3. Boomsma, Trine Krogh & Juul, Nina & Fleten, Stein-Erik, 2014. "Bidding in sequential electricity markets: The Nordic case," European Journal of Operational Research, Elsevier, vol. 238(3), pages 797-809.
    4. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    5. Gregory Steeger & Timo Lohmann & Steffen Rebennack, 2018. "Strategic bidding for a price-maker hydroelectric producer: Stochastic dual dynamic programming and Lagrangian relaxation," IISE Transactions, Taylor & Francis Journals, vol. 50(11), pages 929-942, November.
    6. Finnah, Benedikt & Gönsch, Jochen & Ziel, Florian, 2022. "Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 301(2), pages 726-746.
    7. Wozabal, David & Rameseder, Gunther, 2020. "Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity," European Journal of Operational Research, Elsevier, vol. 280(2), pages 639-655.
    8. Keles, Dogan & Dehler-Holland, Joris, 2022. "Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming," Energy Economics, Elsevier, vol. 106(C).
    9. 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.
    10. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    11. Koch, Christopher & Hirth, Lion, 2019. "Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany's electricity system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    12. Xiaolin Ayón & María Ángeles Moreno & Julio Usaola, 2017. "Aggregators’ Optimal Bidding Strategy in Sequential Day-Ahead and Intraday Electricity Spot Markets," Energies, MDPI, vol. 10(4), pages 1-20, April.
    13. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    14. Gro Klæboe & Jørgen Braathen & Anders Lund Eriksrud & Stein-Erik Fleten, 2022. "Day-ahead market bidding taking the balancing power market into account," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 683-703, October.
    15. Ottesen, Stig Ødegaard & Tomasgard, Asgeir & Fleten, Stein-Erik, 2018. "Multi market bidding strategies for demand side flexibility aggregators in electricity markets," Energy, Elsevier, vol. 149(C), pages 120-134.
    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. Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023. "Stochastic optimization of trading strategies in sequential electricity markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
    2. Finhold, E. & Gärtner, C. & Grindel, R. & Heller, T. & Leithäuser, N. & Röger, E. & Schirra, F., 2023. "Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study," Applied Energy, Elsevier, vol. 352(C).
    3. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    4. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    5. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    6. Thomas Kuppelwieser & David Wozabal, 2023. "Intraday power trading: toward an arms race in weather forecasting?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 57-83, March.
    7. Fraunholz, Christoph & Miskiw, Kim K. & Kraft, Emil & Fichtner, Wolf & Weber, Christoph, 2021. "On the role of risk aversion and market design in capacity expansion planning," Working Paper Series in Production and Energy 62, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    8. Siddique, Muhammad Bilal & Keles, Dogan & Scheller, Fabian & Nielsen, Per Sieverts, 2024. "Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach," Energy Economics, Elsevier, vol. 136(C).
    9. Kim, Seokwoo & Choi, Dong Gu, 2024. "A sample robust optimal bidding model for a virtual power plant," European Journal of Operational Research, Elsevier, vol. 316(3), pages 1101-1113.
    10. Klyve, Øyvind Sommer & Klæboe, Gro & Nygård, Magnus Moe & Marstein, Erik Stensrud, 2023. "Limiting imbalance settlement costs from variable renewable energy sources in the Nordics: Internal balancing vs. balancing market participation," Applied Energy, Elsevier, vol. 350(C).
    11. Feng, Wenxiu & Ruiz Mora, Carlos & Nogales Martín, Francisco Javier, 2024. "Predictive day-ahead offering for renewable generators in uncertain spot and balancing markets," DES - Working Papers. Statistics and Econometrics. WS 44216, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    13. Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
    14. Bayona, Anna & Manzano, Carolina, 2024. "Competition in schedules with cursed traders," Journal of Economic Theory, Elsevier, vol. 222(C).
    15. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2020. "Strategic offering of a flexible producer in day-ahead and intraday power markets," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1136-1153.
    16. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 267-286.
    17. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    18. Nickelsen, Daniel & Müller, Gernot, 2025. "Bayesian hierarchical probabilistic forecasting of intraday electricity prices," Applied Energy, Elsevier, vol. 380(C).
    19. Finnah, Benedikt & Gönsch, Jochen & Ziel, Florian, 2022. "Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 301(2), pages 726-746.
    20. Simon Hirsch & Florian Ziel, 2022. "Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution," Papers 2211.13002, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

    Statistics

    Access and download statistics

    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:eee:eneeco:v:144:y:2025:i:c:s0140988325001392. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.