A Multi-Time Scale Optimal Scheduling Strategy for the Electro-Hydrogen Coupling System Based on the Modified TCN-PPO

Dongsen Li, Kang Qian (), Yiyue Xu, Jiangshan Zhou, Zhangfan Wang, Yufei Peng and Qiang Xing
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Dongsen Li: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Kang Qian: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Yiyue Xu: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Jiangshan Zhou: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Zhangfan Wang: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Yufei Peng: China Energy Engineering Group Jiangsu Power Design Institute Co., Ltd., Nanjing 211100, China
Qiang Xing: College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Energies, 2025, vol. 18, issue 8, 1-22

Abstract: The regional integrated energy system, centered on electro-hydrogen technology, serves as a crucial mechanism for advancing the utilization of a high proportion of renewable energy and achieving the low-carbon transition of the energy system. In this context, a multi-time scale optimization model for distributed electro-hydrogen coupling systems is proposed, utilizing an enhanced deep reinforcement learning (DRL) method. Firstly, considering the comprehensive operation cost and real-time deviations, the optimization model of day-ahead and real-time multi-time scale electro-hydrogen coupling system is constructed. Secondly, A dynamic perception model of environmental information is established based on a time convolutional network (TCN) to achieve multi-time scale feature capture of the coupling system and to improve the ability of the agents to perceive the environment of the coupling system. Then, the proposed optimization model is transformed into the Markov decision process (MDP), and a modified Proximal Policy Optimization (PPO) algorithm is introduced to achieve optimal solutions. Finally, case studies are conducted to analyze the electro-hydrogen coupling system in a specific region. The case studies verify the effectiveness of deep reinforcement learning and the electro-hydrogen coupling system in new energy consumption.

Keywords: electro-hydrogen coupling system; multi-time scale; deep reinforcement learning; Markov decision process; time convolutional network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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