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A bilevel programming approach to price decoupling in Pay-as-Clear markets, with application to day-ahead electricity markets

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  • Frangioni, Antonio
  • Lacalandra, Fabrizio

Abstract

Motivated by the recent crisis of the European electricity markets, we propose the concept of Segmented Pay-as-Clear (SPaC) market, introducing a new family of market clearing problems that achieve a degree of decoupling between groups of participants. This requires a relatively straightforward modification of the standard PaC model and retains its crucial features by providing both long- and short-term sound price signals. The approach is based on dynamically partitioning demand across the segmented markets, where the partitioning is endogenous, i.e., controlled by the model variables, and is chosen to minimise the total system cost. The thusly modified model leads to solving Bilevel Programming problems, or more generally Mathematical Programs with Complementarity Constraints; these have a higher computational complexity than those corresponding to the standard PaC, but in the same ballpark as the models routinely used in real-world Day Ahead Markets (DAMs) to represent “nonstandard” requirements, e.g., the unique buying price in the Italian DAM. Thus, SPaC models should still be solvable in a time compatible with market operation with appropriate algorithmic tools. Like all market models, SPaC is not immune to strategic bidding techniques, but some theoretical results indicate that, under the right conditions, the effect of these could be limited. An initial experimental analysis of the proposed models, carried out through Agent Based simulations, seems to indicate a good potential for significant system cost reductions and an effective decoupling of the two markets.

Suggested Citation

  • Frangioni, Antonio & Lacalandra, Fabrizio, 2024. "A bilevel programming approach to price decoupling in Pay-as-Clear markets, with application to day-ahead electricity markets," European Journal of Operational Research, Elsevier, vol. 319(1), pages 316-331.
  • Handle: RePEc:eee:ejores:v:319:y:2024:i:1:p:316-331
    DOI: 10.1016/j.ejor.2024.06.018
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    References listed on IDEAS

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    1. Steven A. Gabriel & Antonio J. Conejo & J. David Fuller & Benjamin F. Hobbs & Carlos Ruiz, 2013. "Complementarity Modeling in Energy Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4419-6123-5, April.
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    4. Mehdi Madani & Mathieu Van Vyve, 2017. "A MIP framework for non-convex uniform price day-ahead electricity auctions," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 263-284, March.
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    6. W. Ackooij & I. Danti Lopez & A. Frangioni & F. Lacalandra & M. Tahanan, 2018. "Large-scale unit commitment under uncertainty: an updated literature survey," Annals of Operations Research, Springer, vol. 271(1), pages 11-85, December.
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