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

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.