Online energy management optimization of hybrid energy storage microgrid with reversible solid oxide cell: A model-based study
Microgrids (MGs) that contain a reversible solid oxide cell (rSOC) system and battery energy storage system (BESS) are gaining prominence in terminal load supply and renewable energy consumption. However, the economy and durability are highly dependent on the reliability of its energy management sys...
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| Published in: | Journal of cleaner production Vol. 423; p. 138663 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
15.10.2023
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| ISSN: | 0959-6526, 1879-1786 |
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| Abstract | Microgrids (MGs) that contain a reversible solid oxide cell (rSOC) system and battery energy storage system (BESS) are gaining prominence in terminal load supply and renewable energy consumption. However, the economy and durability are highly dependent on the reliability of its energy management system (EMS). Herein, a model-based online optimal control strategy is proposed to obtain optimal schedule decisions for EMS. Firstly, the aging of BESS and the efficiency of the rSOC system are considered simultaneously, and are modeled in detail. Subsequently, the optimal energy management (OEM) is formulated as a multi-objective optimization problem based on the constructed model. On this basis, to improve the efficiency and accuracy of the optimization search, a novel multi-objective evolutionary algorithm is proposed and verified, which hybridizes the non-dominated sorting genetic algorithm-II and tabu search, and incorporates improved crossover and mutation operations. Afterwards, data from a commercial user in Ningxia, China was used for the case study. The results show that the proposed control strategy can effectively slow down the aging of BESS and improve the efficiency of the rSOC system compared to the rule-based control strategy. Furthermore, compare with the rule-based control and economic dispatching strategy, the MG obtained a higher self-sufficiency rate (93.28%) and a shorter payback period (14.57 years) under the proposed OEM strategy. |
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| AbstractList | Microgrids (MGs) that contain a reversible solid oxide cell (rSOC) system and battery energy storage system (BESS) are gaining prominence in terminal load supply and renewable energy consumption. However, the economy and durability are highly dependent on the reliability of its energy management system (EMS). Herein, a model-based online optimal control strategy is proposed to obtain optimal schedule decisions for EMS. Firstly, the aging of BESS and the efficiency of the rSOC system are considered simultaneously, and are modeled in detail. Subsequently, the optimal energy management (OEM) is formulated as a multi-objective optimization problem based on the constructed model. On this basis, to improve the efficiency and accuracy of the optimization search, a novel multi-objective evolutionary algorithm is proposed and verified, which hybridizes the non-dominated sorting genetic algorithm-II and tabu search, and incorporates improved crossover and mutation operations. Afterwards, data from a commercial user in Ningxia, China was used for the case study. The results show that the proposed control strategy can effectively slow down the aging of BESS and improve the efficiency of the rSOC system compared to the rule-based control strategy. Furthermore, compare with the rule-based control and economic dispatching strategy, the MG obtained a higher self-sufficiency rate (93.28%) and a shorter payback period (14.57 years) under the proposed OEM strategy. |
| ArticleNumber | 138663 |
| Author | Kupecki, Jakub Deng, Zhonghua Xia, Zhiping Fu, Xiaowei Liu, Xingbo Jin, Bing Zhou, Yihuan Li, Xi |
| Author_xml | – sequence: 1 givenname: Yihuan surname: Zhou fullname: Zhou, Yihuan organization: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science & Technology, 430074, Wuhan, Hubei, China – sequence: 2 givenname: Zhiping surname: Xia fullname: Xia, Zhiping organization: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science & Technology, 430074, Wuhan, Hubei, China – sequence: 3 givenname: Xingbo surname: Liu fullname: Liu, Xingbo organization: Hubei Huazhong Electric Power Technology Development Co., Ltd, 430074, Wuhan, Hubei, China – sequence: 4 givenname: Zhonghua surname: Deng fullname: Deng, Zhonghua organization: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science & Technology, 430074, Wuhan, Hubei, China – sequence: 5 givenname: Xiaowei surname: Fu fullname: Fu, Xiaowei organization: Shenzhen Huazhong University of Science and Technology Research Institute, 518055, Shenzhen, Guangdong, China – sequence: 6 givenname: Jakub orcidid: 0000-0003-2736-3031 surname: Kupecki fullname: Kupecki, Jakub organization: Center for Hydrogen Technologies (CTH2), Institute of Power Engineering, Augustowka 36, 02-981, Warsaw, Poland – sequence: 7 givenname: Bing surname: Jin fullname: Jin, Bing organization: Hubei Huazhong Electric Power Technology Development Co., Ltd, 430074, Wuhan, Hubei, China – sequence: 8 givenname: Xi orcidid: 0000-0001-6396-792X surname: Li fullname: Li, Xi email: lixi_wh@126.com organization: School of Artificial Intelligence and Automation, Key Laboratory of Image Processing and Intelligent Control of Education Ministry, Huazhong University of Science & Technology, 430074, Wuhan, Hubei, China |
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| Keywords | Reversible solid oxide cell Multi-objective optimization Microgrid Energy management system Optimal energy management |
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