An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid
Abstract Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to en...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13012 |
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| author | Ying Han Yujing Hou Luoyi Li Weifeng Meng Qi Li Weirong Chen |
| author_facet | Ying Han Yujing Hou Luoyi Li Weifeng Meng Qi Li Weirong Chen |
| author_sort | Ying Han |
| collection | DOAJ |
| description | Abstract Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy. |
| format | Article |
| id | doaj-art-378dc8e2f84b436e87d55c2a17a5b257 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-378dc8e2f84b436e87d55c2a17a5b2572025-01-30T12:15:53ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118163624363910.1049/rpg2.13012An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgridYing Han0Yujing Hou1Luoyi Li2Weifeng Meng3Qi Li4Weirong Chen5School of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaSchool of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaSchool of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaSchool of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaSchool of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaSchool of Electrical Engineering Southwest Jiaotong University Chengdu Sichuan Province ChinaAbstract Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy.https://doi.org/10.1049/rpg2.13012cost reductiondurabilityenergy management systemsfuel cellshybrid renewable energy systemsmicrogrids |
| spellingShingle | Ying Han Yujing Hou Luoyi Li Weifeng Meng Qi Li Weirong Chen An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid IET Renewable Power Generation cost reduction durability energy management systems fuel cells hybrid renewable energy systems microgrids |
| title | An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid |
| title_full | An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid |
| title_fullStr | An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid |
| title_full_unstemmed | An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid |
| title_short | An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid |
| title_sort | optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero carbon dc microgrid |
| topic | cost reduction durability energy management systems fuel cells hybrid renewable energy systems microgrids |
| url | https://doi.org/10.1049/rpg2.13012 |
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