Data-driven Optimal Scheduling for Underground Space Based Integrated Hydrogen Energy System

Integrated hydrogen energy systems (IHESs) have attracted extensive attention in miti-gating climate problems. As a kind of large-scale hydrogen storage device, undergroundhydrogen storage (UHS) can be introduced into IHES to balance the seasonal energy mis-match, while bringing challenges to optimal operation of IHES due to the complex geolog-ical structure and uncertain hydrodynamics. To address this problem, a deep deterministicpolicy gradient (DDPG)-based optimal scheduling method for underground space basedIHES is proposed. The energy management problem is formulated as a Markov decisionprocess to characterize the interaction between environmental states and policy. Based onDDPG theory, the actor-critic structure is applied to approximate deterministic policy andactor-value function. Through policy iteration and actor-critic network training, the oper-ation of UHS and other energy conversion devices can be adaptively optimised, which isdriven by real-time response data instead of accurate system models. Finally, the effective-ness of the proposed optimal scheduling method and the benefits of underground spaceare verified through time-domain simulations.