Adaptively optimal energy management for integrated hydrogen energy systems

Abstract

Integrated hydrogen energy systems (IHESs) have become attractive alternatives to cope with the depletion of fossil fuels and increasingly severe climate change problems. This paper proposes an adaptively optimal energy scheduling method based on deep deterministic policy gradient (DDPG) to improve the operational efficiency of IHES. The optimal scheduling problem is formulated as a Markov decision process problem with action space, environmental states, and action-value function. The DDPG-based optimal energy management algorithm with actor-critic structure is proposed based on policy gradients and neural networks. Through actor-critic network training and policy iteration, the energy management scheme can be adaptively optimized according to the dynamic responses of IHES. The benefits of the proposed algorithm are analysed through time-domain simulations, and the scheduling robustness under different uncertain conditions is verified.