Natural gas storage valuation using deep reinforcement learning

Energy trading involves physically handling energy commodities such as oil, natural gas, and coal. These trading activities are enabled by a complex global network of energy conversion assets such as pipelines, refineries, and storage facilities. The profitability of energy trading relies on efficiently managing these assets' operational capacity constraints. The competitive management of these assets is called merchant operations. Energy merchant companies acquire conversion assets to support their trading activities. Therefore, the proper valuation of these assets is crucial. In practice, energy merchant companies rely on heuristic methods that produce deterministic operating policies. Deterministic policies are unable to capture the so-called embedded optionality in the energy conversion assets. In this thesis, we tackle the problem of natural gas storage valuation using a novel deep reinforcement learning (DRL) algorithm called soft actor-critic (SAC). SAC utilizes entropy regularization to improve policy exploration and stability. Our results show that SAC has learned an effective operating policy during training while other state-of-the-art DRL algorithms couldn't do so in our problem.