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Trading power instead of energy in day-ahead electricity markets

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  • Philipsen, Rens
  • Morales-España, Germán
  • de Weerdt, Mathijs
  • de Vries, Laurens

Abstract

Day-ahead electricity markets are inefficient due to their coarse discretisation of time and their representation of electricity production and consumption in energy per time interval. This leads to excessive costs and infeasible schedules in the market clearing results. Some real-world systems have increased the resolution to improve accuracy, but this comes at a high computational cost. We propose an alternative, based on using linear power trajectories in the day-ahead scheduling process, which represent the momentary electricity production. Changing from a traditional energy-based to such a power-based formulation of the scheduling method can reduce cost by several percentage points, leading to a cost reduction of millions of euros in real-world systems on a yearly basis. Attempting to do so by increasing the resolution of the schedule would be accompanied by large increases in computational demands. Furthermore, we provide market design options to implement power-based bidding and pricing in day-ahead electricity markets, showing that pricing and market rules which encompass existing markets are readily available for implementation, and illustrate these with examples.

Suggested Citation

  • Philipsen, Rens & Morales-España, Germán & de Weerdt, Mathijs & de Vries, Laurens, 2019. "Trading power instead of energy in day-ahead electricity markets," Applied Energy, Elsevier, vol. 233, pages 802-815.
  • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:802-815
    DOI: 10.1016/j.apenergy.2018.09.205
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    References listed on IDEAS

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    Cited by:

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    2. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad, 2020. "Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market," Energy, Elsevier, vol. 210(C).
    3. 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).
    4. Chen, Peipei & Wu, Yi & Zou, Lele, 2019. "Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis," Energy, Elsevier, vol. 185(C), pages 423-436.
    5. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Virag, Ana & Gibescu, Madeleine, 2022. "Short term wholesale electricity market designs: A review of identified challenges and promising solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    6. Lina Silva-Rodriguez & Anibal Sanjab & Elena Fumagalli & Ana Virag & Madeleine Gibescu, 2020. "Short Term Electricity Market Designs: Identified Challenges and Promising Solutions," Papers 2011.04587, arXiv.org.
    7. Liu, Shuangquan & Xie, Mengfei, 2020. "Modeling the daily generation schedules in under-developed electricity markets with high-share renewables: A case study of Yunnan in China," Energy, Elsevier, vol. 201(C).
    8. Ibrahim Alotaibi & Mohammed A. Abido & Muhammad Khalid & Andrey V. Savkin, 2020. "A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources," Energies, MDPI, vol. 13(23), pages 1-41, November.
    9. Nycander, Elis & Morales-España, Germán & Söder, Lennart, 2022. "Power-based modelling of renewable variability in dispatch models with clustered time periods," Renewable Energy, Elsevier, vol. 186(C), pages 944-956.
    10. Samuli Honkapuro & Jasmin Jaanto & Salla Annala, 2023. "A Systematic Review of European Electricity Market Design Options," Energies, MDPI, vol. 16(9), pages 1-26, April.
    11. Kuttner, Leopold, 2022. "Integrated scheduling and bidding of power and reserve of energy resource aggregators with storage plants," Applied Energy, Elsevier, vol. 321(C).

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