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

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

WoS Article

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogenelectric 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 hydrogenelectric 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.

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogenelectric 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 hydrogenelectric 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.

Deep reinforcement learning; Demand response; Hydrogen energy; Information uncertainty; Smart grid

Deep reinforcement learning; Demand response; Hydrogen energy; Information uncertainty; Smart grid

Shi, Tao; Xu, Chang; Dong, Wenhao; Zhou, Hangyu; Bokhari, Awais; Klemes, Jiri Jaromir; Han, Ning

2024

01.11.2023

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

0360-5442

Energy

282

1

United Kingdom of Great Britain and Northern Ireland

11

https://www.sciencedirect.com/science/article/pii/S0360544223015682?via%3Dihub