IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5454-d627376.html
   My bibliography  Save this article

Evolution of Fundamental Price Determination within Electricity Market Simulations

Author

Listed:
  • Lothar Wyrwoll

    (Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, 52062 Aachen, Germany)

  • Moritz Nobis

    (Trianel GmbH, 52070 Aachen, Germany)

  • Stephan Raths

    (Amprion GmbH, 44263 Dortmund, Germany)

  • Albert Moser

    (Institute for High Voltage Equipment and Grids, Digitalization and Energy Economics (IAEW), RWTH Aachen University, 52062 Aachen, Germany)

Abstract

Electricity prices are the key instrument for coordinating electricity markets. For long-term market analyses, price determination based on fundamental unit commitment simulations is required. Within the European wholesale market, electricity prices result from a market clearing, which finds a welfare-optimal price–quantity tuple considering a coupling of multiple market areas with limited transmission capacity. With increasing exchange capacities in Europe, the precise modeling of the market coupling is required. Many market simulation models use multi-stage approaches with a separation of market coupling and price determination. In this paper, we analyze a new single-stage approach that combines both steps and theoretically and empirically demonstrate its precision by a backtest. For this purpose, we compare a simulated versus a historical electricity price distribution. Moreover, we explain the necessary adjustments for future regulatory developments of the European electricity market regarding flow-based market coupling and propose a concept for the application of future regulatory developments. We demonstrate further developments using a future scenario.

Suggested Citation

  • Lothar Wyrwoll & Moritz Nobis & Stephan Raths & Albert Moser, 2021. "Evolution of Fundamental Price Determination within Electricity Market Simulations," Energies, MDPI, vol. 14(17), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5454-:d:627376
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5454/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/17/5454/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael Metzger & Mathias Duckheim & Marco Franken & Hans Joerg Heger & Matthias Huber & Markus Knittel & Till Kolster & Martin Kueppers & Carola Meier & Dieter Most & Simon Paulus & Lothar Wyrwoll & , 2021. "Pathways toward a Decarbonized Future—Impact on Security of Supply and System Stability in a Sustainable German Energy System," Energies, MDPI, vol. 14(3), pages 1-28, January.
    2. Peter Cramton, 2017. "Electricity market design," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 33(4), pages 589-612.
    3. 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.
    4. Schönheit, David & Dierstein, Constantin & Möst, Dominik, 2021. "Do minimum trading capacities for the cross-zonal exchange of electricity lead to welfare losses?," Energy Policy, Elsevier, vol. 149(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Schmitt, Carlo & Schumann, Klemens & Kollenda, Katharina & Blank, Andreas & Rebenaque, Olivier & Dronne, Théo & Martin, Arnault & Vassilopoulos, Philippe & Roques, Fabien & Moser, Albert, 2022. "How will local energy markets influence the pan-European day-ahead market and transmission systems? A case study for local markets in France and Germany," Applied Energy, Elsevier, vol. 325(C).

    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. Prokhorov, Oleksandr & Dreisbach, Dina, 2022. "The impact of renewables on the incidents of negative prices in the energy spot markets," Energy Policy, Elsevier, vol. 167(C).
    2. Doering, Kenji & Sendelbach, Luke & Steinschneider, Scott & Lindsay Anderson, C., 2021. "The effects of wind generation and other market determinants on price spikes," Applied Energy, Elsevier, vol. 300(C).
    3. Lavička, Hynek & Kracík, Jiří, 2020. "Fluctuation analysis of electric power loads in Europe: Correlation multifractality vs. Distribution function multifractality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    4. Keppler, Jan Horst & Quemin, Simon & Saguan, Marcelo, 2022. "Why the sustainable provision of low-carbon electricity needs hybrid markets," Energy Policy, Elsevier, vol. 171(C).
    5. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    6. 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.
    7. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    8. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    9. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    10. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    11. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    12. Peter Cramton, 2022. "Fostering Resiliency with Good Market Design: Lessons from Texas," ECONtribute Discussion Papers Series 145, University of Bonn and University of Cologne, Germany.
    13. Raviv, Eran & Bouwman, Kees E. & van Dijk, Dick, 2015. "Forecasting day-ahead electricity prices: Utilizing hourly prices," Energy Economics, Elsevier, vol. 50(C), pages 227-239.
    14. Dongxiao Niu & Yi Liang & Wei-Chiang Hong, 2017. "Wind Speed Forecasting Based on EMD and GRNN Optimized by FOA," Energies, MDPI, vol. 10(12), pages 1-18, December.
    15. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    16. Zonggui Yao & Chen Wang, 2018. "A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting," Sustainability, MDPI, vol. 10(5), pages 1-33, May.
    17. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    18. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    19. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.
    20. Bobinaite Viktorija & Zuters Jānis, 2016. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia," Economics and Business, Sciendo, vol. 29(1), pages 12-26, August.

    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:gam:jeners:v:14:y:2021:i:17:p:5454-:d:627376. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.