Research on energy management of hydrogen electric coupling system based on deep reinforcement learning

Tao Shi, Chang Xu, Wenhao Dong, Hangyu Zhou, Awais Bokhari, Jiří Jaromír Klemeš and Ning Han

Energy, 2023, vol. 282, issue C

Abstract: In this paper, a deep reinforcement learning-based energy optimization management method for hydrogen-electric coupling system is proposed for the conversion and utilization and joint optimization operation of hydrogen, wind and solar energy forms considering information uncertainty on the demand side of smart grid. Based on the wind energy, photovoltaic energy generation and load forecast information, the method uses deep Q network to simulate the energy management strategy set of the hydrogen-electric coupling system, and obtains the optimal strategy through reinforcement learning to finally realize the optimal operation of the hydrogen-electric coupling system based on the demand response. Firstly, based on the energy management model, a research framework and equipment model for integrated energy systems is established. On the basis of fundamental theories of reinforcement learning framework, Q-learning algorithm and DQN algorithm, the empirical replay mechanism and freezing parameter mechanism to improve the performance of DQN are analyzed, and the energy management and optimization of integrated energy system is completed with the objective of economy. By comparing the performance of DQN algorithms with different parameters in integrated energy system energy management, the simulation results demonstrate the improvement of algorithm performance after inheriting the set of strategies, and verify the feasibility and superiority of deep reinforcement learning compared to genetic algorithm in integrated energy system energy management applications.

Keywords: Deep reinforcement learning; Demand response; Hydrogen energy; Information uncertainty; Smart grid (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223015682

DOI: 10.1016/j.energy.2023.128174

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