A deep learning model for gas storage optimization

Nicolas Curin, Michael Kettler, Xi Kleisinger-Yu, Vlatka Komaric, Thomas Krabichler, Josef Teichmann and Hanna Wutte

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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.

Date: 2021-02, Revised 2021-03
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-rmg
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Citations: View citations in EconPapers (2)

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