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Optimal day-ahead offering strategy for large producers based on market price response learning

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  • Alcántara, Antonio
  • Ruiz, Carlos
Abstract
In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO’s market share may imply that its offering strategy can alter the marginal price formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose an optimization-based methodology to summarize each GENCO’s step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the resulting market price and the energy block offering prices is modeled through a probabilistic forecasting tool: a Distributional Neural Network, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy via a set of linear constraints. Finally, this predictive model is embedded in the stochastic optimization model employing a constraint learning approach. Results show how allowing the GENCO to deviate from its true marginal costs renders significant changes in its profits and the marginal price of the market. Additionally, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy can effective in a real-world market context.

Suggested Citation

  • Alcántara, Antonio & Ruiz, Carlos, 2024. "Optimal day-ahead offering strategy for large producers based on market price response learning," European Journal of Operational Research, Elsevier, vol. 319(3), pages 891-907.
    • Handle: RePEc:eee:ejores:v:319:y:2024:i:3:p:891-907
    DOI: 10.1016/j.ejor.2024.06.038
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    References listed on IDEAS

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    1. Brusaferri, Alessandro & Matteucci, Matteo & Portolani, Pietro & Vitali, Andrea, 2019. "Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices," Applied Energy, Elsevier, vol. 250(C), pages 1158-1175.
    2. Bjørndal, Endre & Bjørndal, Mette Helene & Coniglio, Stefano & Körner, Marc-Fabian & Leinauer, Christina & Weibelzahl, Martin, 2023. "Energy storage operation and electricity market design: On the market power of monopolistic storage operators," European Journal of Operational Research, Elsevier, vol. 307(2), pages 887-909.
    3. Fajemisin, Adejuyigbe O. & Maragno, Donato & den Hertog, Dick, 2024. "Optimization with constraint learning: A framework and survey," European Journal of Operational Research, Elsevier, vol. 314(1), pages 1-14.
    4. Román Pérez-Santalla & Miguel Carrión & Carlos Ruiz, 2022. "Optimal pricing for electricity retailers based on data-driven consumers’ price-response," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 430-464, October.
    5. Devine, Mel T. & Siddiqui, Sauleh, 2023. "Strategic investment decisions in an oligopoly with a competitive fringe: An equilibrium problem with equilibrium constraints approach," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1473-1494.
    6. Chen, Kaixuan & Lin, Jin & Song, Yonghua, 2019. "Trading strategy optimization for a prosumer in continuous double auction-based peer-to-peer market: A prediction-integration model," Applied Energy, Elsevier, vol. 242(C), pages 1121-1133.
    7. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    8. Pandžić, Hrvoje & Morales, Juan M. & Conejo, Antonio J. & Kuzle, Igor, 2013. "Offering model for a virtual power plant based on stochastic programming," Applied Energy, Elsevier, vol. 105(C), pages 282-292.
    9. Ricardo M. Lima & Antonio J. Conejo & Loïc Giraldi & Olivier Le Maître & Ibrahim Hoteit & Omar M. Knio, 2022. "Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1795-1818, May.
    10. Islyaev, Suren & Date, Paresh, 2015. "Electricity futures price models: Calibration and forecasting," European Journal of Operational Research, Elsevier, vol. 247(1), pages 144-154.
    11. Rom'an P'erez-Santalla & Miguel Carri'on & Carlos Ruiz, 2021. "Optimal pricing for electricity retailers based on data-driven consumers' price-response," Papers 2110.02735, arXiv.org, revised Feb 2022.
    12. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
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