Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model

In this study, an intelligent energy management method is introduced to deal with the hydrogen-dominant hybrid energy system with low carbon consideration. Specially, both the new type fuel cell, solid oxide fuel cell, and chemical battery are subtly modelled to construct a high-efficient hybrid energy system, in which the thermodynamics feature and accurate battery model characteristics, as well as low carbon effect, are considered. Because the hybrid energy system incorporates various complex dynamic operation features that are hard to capture via conventional operation strategy, an energy management method based on deep reinforcement learning techniques is proposed to guide the intelligent operation with self-adaptive performance. In the simulation, it is observed that highly efficient use of hydrogen in the hybrid energy system with the aid of chemical battery could achieve good economic benefit, as well as low carbon advantages. Powered by the gas and chemical energy coupling storage effect and state-of-the-art machine learning methods, the proposed intelligent energy management strategy can benefit more renewable energy adoption and guarantee the ultimate environmental friendly low carbon ecosystem in the long-term future.

Automated summary

In this study, an intelligent energy management method is proposed for a hydrogen-dominant hybrid energy system with low carbon consideration. The method utilizes solid oxide fuel cells and chemical batteries to construct a high-efficient hybrid energy system, taking into account thermodynamics features, accurate battery model characteristics, and low carbon effects. The energy management method based on deep reinforcement learning techniques guides intelligent operation, achieving efficient use of hydrogen and economic as well as low carbon benefits.