Energy Scheduling of Renewable Energy Large-scale Hydrogen Production System Based on Deep Deterministic Strategy Gradient Algorithm

In order to realize the full utilization of renewable energy, reduce the investment costs of the rectifying and grid-connected equipment, lower the costs of the hydrogen production from the electrolytic water, and realize the large-scale hydrogen production from the renewable energy, this paper establishes an off-grid renewable energy large-scale hydrogen production system (H2-RES), Taking the energy management of H2-RES as the research object, the optimization objective is set as the economy and security of the system. First, the simulation environment of the H2-RES system is built, and the control strategy is given. Then, an intelligent energy scheduling strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. The agent obtained through a long-term and massive training and learning with the DDPG algorithm may realize the intelligent real-time online energy scheduling. By comparing with the Deep Q Network (DQN), the PSO and the traditional control strategy in terms of economy and security, it is verified that the application of the DDPG algorithm to the energy management of the H2-RES is able to obtain higher economic benefits, to absorb more renewable resources, and to ensure the security of the system, having stronger academic significance and engineering value.