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A deep learning model for gas storage optimization

Author

Listed:
  • Nicolas Curin
  • Michael Kettler
  • Xi Kleisinger-Yu
  • Vlatka Komaric
  • Thomas Krabichler
  • Josef Teichmann
  • Hanna Wutte

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," Papers 2102.01980, arXiv.org, revised Mar 2021.
    • Handle: RePEc:arx:papers:2102.01980
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    References listed on IDEAS

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    1. Matt Thompson & Matt Davison & Henning Rasmussen, 2009. "Natural gas storage valuation and optimization: A real options application," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 226-238, April.
    2. repec:aen:journl:2011v32-01-a08 is not listed on IDEAS
    3. Rene Carmona & Michael Ludkovski, 2010. "Valuation of energy storage: an optimal switching approach," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 359-374.
    4. Thompson, Matt, 2016. "Natural gas storage valuation, optimization, market and credit risk management," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 26-44.
    5. Roberto Daluiso & Emanuele Nastasi & Andrea Pallavicini & Giulio Sartorelli, 2020. "Pricing commodity swing options," Papers 2001.08906, arXiv.org.
    6. Patrick Hénaff & Ismail Laachir & Francesco Russo, 2018. "Gas Storage Valuation and Hedging: A Quantification of Model Risk," IJFS, MDPI, vol. 6(1), pages 1-27, March.
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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.
    2. Tapio Behrndt & Ren-Raw Chen, 2022. "A New Look at the Swing Contract: From Linear Programming to Particle Swarm Optimization," JRFM, MDPI, vol. 15(6), pages 1-20, May.
    3. Hussain, Altaf & Pan, Peng-Zhi & Hussain, Javid & Feng, Yujie & Zheng, Qingsong, 2025. "Data-driven machine learning models for predicting deliverability of underground natural gas storage in aquifer and depleted reservoirs," Energy, Elsevier, vol. 319(C).

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