Data-driven optimal scheduling for underground space based integrated hydrogen energy system

Integrated hydrogen energy systems (IHESs) have attracted extensive attention in mitigating climate problems. As a kind of large-scale hydrogen storage device, underground hydrogen storage (UHS) can be introduced into IHES to balance the seasonal energy mismatch, while bringing challenges to optimal operation of IHES due to the complex geological structure and uncertain hydrodynamics. To address this problem, a deep deterministic policy gradient (DDPG)-based optimal scheduling method for underground space based IHES is proposed. The energy management problem is formulated as a Markov decision process to characterize the interaction between environmental states and policy. Based on DDPG theory, the actor-critic structure is applied to approximate deterministic policy and actor-value function. Through policy iteration and actor-critic network training, the operation of UHS and other energy conversion devices can be adaptively optimised, which is driven by real-time response data instead of accurate system models. Finally, the effectiveness of the proposed optimal scheduling method and the benefits of underground space are verified through time-domain simulations.