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A deep learning model for gas storage optimization

Author

Listed:
  • Nicolas Curin

    (Axpo Solutions AG)

  • Michael Kettler

    (Axpo Solutions AG)

  • Xi Kleisinger-Yu

    (ETH Zürich)

  • Vlatka Komaric

    (Axpo Solutions AG)

  • Thomas Krabichler

    (Eastern Switzerland University of Applied Sciences)

  • Josef Teichmann

    (ETH Zürich)

  • Hanna Wutte

    (ETH Zürich)

Abstract

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.

Suggested Citation

  • Nicolas Curin & Michael Kettler & Xi Kleisinger-Yu & Vlatka Komaric & Thomas Krabichler & Josef Teichmann & Hanna Wutte, 2021. "A deep learning model for gas storage optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1021-1037, December.
    • Handle: RePEc:spr:decfin:v:44:y:2021:i:2:d:10.1007_s10203-021-00363-6
    DOI: 10.1007/s10203-021-00363-6
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    References listed on IDEAS

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    Cited by:

    1. Vo Thanh, Hung & Zamanyad, Aiyoub & Safaei-Farouji, Majid & Ashraf, Umar & Hemeng, Zhang, 2022. "Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites," Renewable Energy, Elsevier, vol. 200(C), pages 169-184.

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