Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model
Abstract 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 h...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Smart Grid |
| Online Access: | https://doi.org/10.1049/stg2.12080 |
| _version_ | 1811166324091518976 |
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| author | Tao Chen Ciwei Gao Zhengqin Wang Hao Ming Meng Song Xingyu Yan |
| author_facet | Tao Chen Ciwei Gao Zhengqin Wang Hao Ming Meng Song Xingyu Yan |
| author_sort | Tao Chen |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-10T15:50:33Z |
| format | Article |
| id | doaj.art-4e35bca8a8104ff39cccb9a933cfd343 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-10T15:50:33Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-4e35bca8a8104ff39cccb9a933cfd3432023-02-11T12:27:25ZengWileyIET Smart Grid2515-29472023-02-0161283710.1049/stg2.12080Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery modelTao Chen0Ciwei Gao1Zhengqin Wang2Hao Ming3Meng Song4Xingyu Yan5Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaJiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast University Nanjing ChinaSchool of Electrical Engineering Southeast University Nanjing ChinaAbstract 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.https://doi.org/10.1049/stg2.12080 |
| spellingShingle | Tao Chen Ciwei Gao Zhengqin Wang Hao Ming Meng Song Xingyu Yan Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model IET Smart Grid |
| title | Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| title_full | Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| title_fullStr | Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| title_full_unstemmed | Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| title_short | Intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| title_sort | intelligent energy management of low carbon hybrid energy system with solid oxide fuel cell and accurate battery model |
| url | https://doi.org/10.1049/stg2.12080 |
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