Natural Gas Storage Valuation Using Deep Reinforcement Learning

Masood Tadi, Milan Fičura and and Jiří Witzany

No 6.003, FFA Working Papers from Prague University of Economics and Business

Abstract: We study natural gas storage valuation under a stochastic futures term structure using deep reinforcement learning (DRL). The storage problem is formulated as a continuous-state, continuous-action Markov Decision Process and solved using the Deep Deterministic Policy Gradient (DDPG) algorithm with Prioritized Experience Replay (PER) buffer and a constraint-aware policy network. We benchmark the approach against intrinsic and rolling intrinsic strategies and find that DRL consistently outperforms intrinsic valuation and achieves competitive performance relative to rolling intrinsic in markets with jumps and seasonality. The results show that DRL provides a practical valuation framework that captures additional extrinsic value under realistic market dynamics and operational constraints.

Keywords: Natural Gas Storage; Rolling Intrinsic Valuation; Deep Reinforcement Learning (search for similar items in EconPapers)
Pages: 17 pages
Date: 2026-06-12, Revised 2026-06-12
New Economics Papers: this item is included in nep-ene
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