A deep learning model for gas storage optimization

Nicolas Curin (), Michael Kettler (), Xi Kleisinger-Yu (), Vlatka Komaric (), Thomas Krabichler (), Josef Teichmann () and Hanna Wutte ()
Additional contact information
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

Decisions in Economics and Finance, 2021, vol. 44, issue 2, No 24, 1037 pages

Abstract: 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.

Keywords: Deep hedging; Gas storage; Least-squares Monte-Carlo; Optimization; Quantitative risk management; 65K99; 91G60 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10203-021-00363-6

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