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Online decision making for trading wind energy

Author

Listed:
  • Miguel Angel Muñoz

    (University of Malaga)

  • Pierre Pinson

    (Imperial College London
    Technical University of Denmark)

  • Jalal Kazempour

    (Technical University of Denmark)

Abstract

We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning and optimization framework. In particular, we combine a component-wise adaptive variant of the gradient descent algorithm with recent advances in the feature-driven newsvendor model. This results in an online offering approach capable of leveraging data-rich environments, while adapting to the nonstationary characteristics of energy generation and electricity markets, also with a minimal computational burden. The performance of our approach is analyzed based on several numerical experiments, showing both better adaptability to nonstationary uncertain parameters and significant economic gains.

Suggested Citation

  • Miguel Angel Muñoz & Pierre Pinson & Jalal Kazempour, 2023. "Online decision making for trading wind energy," Computational Management Science, Springer, vol. 20(1), pages 1-31, December.
    • Handle: RePEc:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00462-2
    DOI: 10.1007/s10287-023-00462-2
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    References listed on IDEAS

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    1. Kraft, Emil & Russo, Marianna & Keles, Dogan & Bertsch, Valentin, 2023. "Stochastic optimization of trading strategies in sequential electricity markets," European Journal of Operational Research, Elsevier, vol. 308(1), pages 400-421.
    2. Bart P. G. Van Parys & Peyman Mohajerin Esfahani & Daniel Kuhn, 2021. "From Data to Decisions: Distributionally Robust Optimization Is Optimal," Management Science, INFORMS, vol. 67(6), pages 3387-3402, June.
    3. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, February.
    4. Francesca Maggioni & Matteo Cagnolari & Luca Bertazzi, 2019. "The value of the right distribution in stochastic programming with application to a Newsvendor problem," Computational Management Science, Springer, vol. 16(4), pages 739-758, October.
    5. Gah-Yi Ban & Cynthia Rudin, 2019. "The Big Data Newsvendor: Practical Insights from Machine Learning," Operations Research, INFORMS, vol. 67(1), pages 90-108, January.
    6. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
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    Cited by:

    1. Feng, Wenxiu & Ruiz Mora, Carlos & Nogales Martín, Francisco Javier, 2024. "Predictive day-ahead offering for renewable generators in uncertain spot and balancing markets," DES - Working Papers. Statistics and Econometrics. WS 44216, Universidad Carlos III de Madrid. Departamento de Estadística.

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