IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v131y2024ics0140988324000513.html
   My bibliography  Save this article

EuroMod: Modelling European power markets with improved price granularity

Author

Listed:
  • Mendes, Carla
  • Staffell, Iain
  • Green, Richard

Abstract

Electricity system models are widely used to study future designs of power markets. They are commonly used to represent electricity dispatch decisions but struggle to reproduce realistic variation in prices. We show that current assumption of generators bidding their average variable cost (AVC) underestimates the spread and volatility of hourly wholesale prices.While existing models can accurately estimate the revenues of conventional (thermal) generators, they fail with the revenues of next-generation technologies such as storage and merchant transmission. Imperfect competition makes market prices differ from the theoretical optimum. In this paper we present a bottom-up electricity market model for Europe called EuroMod: a deterministic linear optimization in GAMS which models generation, storage and transmission dispatch at hourly resolution for European markets connected using net transfer capacities. Two additions are tested for their ability to improve wholesale price formation: a simple modification to the short-run marginal cost approach that allows generators to make bids which diverge from AVC; and a post-optimizer transformation of prices with respect to demand net of renewables. These corrections improve both the representation of prices and dispatch decisions across European markets, and reduce errors by 40% for prices, 6% for power station revenues, between 24% to 33% for energy storage profits, and 43% for the median arbitrage value of interconnectors when compared to traditional linear models.

Suggested Citation

  • Mendes, Carla & Staffell, Iain & Green, Richard, 2024. "EuroMod: Modelling European power markets with improved price granularity," Energy Economics, Elsevier, vol. 131(C).
  • Handle: RePEc:eee:eneeco:v:131:y:2024:i:c:s0140988324000513
    DOI: 10.1016/j.eneco.2024.107343
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324000513
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2024.107343?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 search for a different version of it.

    References listed on IDEAS

    as
    1. McConnell, Dylan & Hearps, Patrick & Eales, Dominic & Sandiford, Mike & Dunn, Rebecca & Wright, Matthew & Bateman, Lachlan, 2013. "Retrospective modeling of the merit-order effect on wholesale electricity prices from distributed photovoltaic generation in the Australian National Electricity Market," Energy Policy, Elsevier, vol. 58(C), pages 17-27.
    2. Richard Green & Iain Staffell, 2021. "The contribution of taxes, subsidies and regulations to British electricity decarbonisation," Working Papers EPRG2105, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Huppmann, Daniel & Siddiqui, Sauleh, 2018. "An exact solution method for binary equilibrium problems with compensation and the power market uplift problem," European Journal of Operational Research, Elsevier, vol. 266(2), pages 622-638.
    4. 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.
    5. Valentin Mahler & Robin Girard & Georges Kariniotakis, 2022. "Data-driven structural modeling of electricity price dynamics," Post-Print hal-03542564, HAL.
    6. Schlecht, Ingmar & Weigt, Hannes, 2014. "Swissmod - a model of the Swiss electricity market," Working papers 2014/04, Faculty of Business and Economics - University of Basel.
    7. 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.
    8. 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.
    9. 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).
    10. Iain Staffell & Stefan Pfenninger & Nathan Johnson, 2023. "A global model of hourly space heating and cooling demand at multiple spatial scales," Nature Energy, Nature, vol. 8(12), pages 1328-1344, December.
    11. 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.
    12. Deane, J.P. & Drayton, G. & Ó Gallachóir, B.P., 2014. "The impact of sub-hourly modelling in power systems with significant levels of renewable generation," Applied Energy, Elsevier, vol. 113(C), pages 152-158.
    13. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    14. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    15. Jonas Egerer & Clemens Gerbaulet & Richard Ihlenburg & Friedrich Kunz & Benjamin Reinhard & Christian von Hirschhausen & Alexander Weber & Jens Weibezahn, 2014. "Electricity Sector Data for Policy-Relevant Modeling: Data Documentation and Applications to the German and European Electricity Markets," Data Documentation 72, DIW Berlin, German Institute for Economic Research.
    16. Pfenninger, Stefan & DeCarolis, Joseph & Hirth, Lion & Quoilin, Sylvain & Staffell, Iain, 2017. "The importance of open data and software: Is energy research lagging behind?," Energy Policy, Elsevier, vol. 101(C), pages 211-215.
    17. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    18. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(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. Biancardi, Andrea & Mendes, Carla & Staffell, Iain, 2024. "Battery electricity storage as both a complement and substitute for cross-border interconnection," Energy Policy, Elsevier, vol. 189(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. Mahler, Valentin & Girard, Robin & Kariniotakis, Georges, 2022. "Data-driven structural modeling of electricity price dynamics," Energy Economics, Elsevier, vol. 107(C).
    2. 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.
    3. 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.
    4. Alexis Tantet & Marc Stéfanon & Philippe Drobinski & Jordi Badosa & Silvia Concettini & Anna Cretì & Claudia D’Ambrosio & Dimitri Thomopulos & Peter Tankov, 2019. "e 4 clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy," Energies, MDPI, vol. 12(22), pages 1-37, November.
    5. Lombardi, Francesco & Rocco, Matteo Vincenzo & Colombo, Emanuela, 2019. "A multi-layer energy modelling methodology to assess the impact of heat-electricity integration strategies: The case of the residential cooking sector in Italy," Energy, Elsevier, vol. 170(C), pages 1249-1260.
    6. Zappa, William & Junginger, Martin & van den Broek, Machteld, 2021. "Can liberalised electricity markets support decarbonised portfolios in line with the Paris Agreement? A case study of Central Western Europe," Energy Policy, Elsevier, vol. 149(C).
    7. Thomaßen, Georg & Redl, Christian & Bruckner, Thomas, 2022. "Will the energy-only market collapse? On market dynamics in low-carbon electricity systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    8. Valentin Mahler & Robin Girard & Georges Kariniotakis, 2021. "Data-driven Structural Modeling of Electricity Price Dynamics," Working Papers hal-03445396, HAL.
    9. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. 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).
    11. Germeshausen, Robert & Wölfing, Nikolas, 2019. "How marginal is lignite? Two simple approaches to determine price-setting technologies in power markets," ZEW Discussion Papers 19-031, ZEW - Leibniz Centre for European Economic Research.
    12. Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
    13. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Nybø, Astrid, 2020. "Transitioning remote Arctic settlements to renewable energy systems – A modelling study of Longyearbyen, Svalbard," Applied Energy, Elsevier, vol. 258(C).
    14. Heilmann, Erik, 2023. "The impact of transparency policies on local flexibility markets in electric distribution networks," Utilities Policy, Elsevier, vol. 83(C).
    15. Gyanwali, Khem & Komiyama, Ryoichi & Fujii, Yasumasa, 2020. "Representing hydropower in the dynamic power sector model and assessing clean energy deployment in the power generation mix of Nepal," Energy, Elsevier, vol. 202(C).
    16. Gabrielli, Paolo & Aboutalebi, Reyhaneh & Sansavini, Giovanni, 2022. "Mitigating financial risk of corporate power purchase agreements via portfolio optimization," Energy Economics, Elsevier, vol. 109(C).
    17. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    18. Matthias Greiml & Florian Fritz & Josef Steinegger & Theresa Schlömicher & Nicholas Wolf Williams & Negar Zaghi & Thomas Kienberger, 2022. "Modelling and Simulation/Optimization of Austria’s National Multi-Energy System with a High Degree of Spatial and Temporal Resolution," Energies, MDPI, vol. 15(10), pages 1-33, May.
    19. 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).
    20. Spodniak, Petr & Bertsch, Valentin, 2017. "Determinants of power spreads in electricity futures markets: A multinational analysis," Papers WP580, Economic and Social Research Institute (ESRI).

    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:131:y:2024:i:c:s0140988324000513. 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.