A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty
With the ever-increasing awareness of worldwide greenhouse gas emissions, traditional diesel-driven ships are gradually being replaced by renewable energy ships. Zero-carbon power sources, such as photovoltaic (PV) power generation, are progressively integrated into electric ships. However, the unce...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525003928 |
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| author | Huasong Fang Huayue Zhang Shuli Wen Zhong Li Zhilin Zeng Miao Zhu Pengfeng Lin |
| author_facet | Huasong Fang Huayue Zhang Shuli Wen Zhong Li Zhilin Zeng Miao Zhu Pengfeng Lin |
| author_sort | Huasong Fang |
| collection | DOAJ |
| description | With the ever-increasing awareness of worldwide greenhouse gas emissions, traditional diesel-driven ships are gradually being replaced by renewable energy ships. Zero-carbon power sources, such as photovoltaic (PV) power generation, are progressively integrated into electric ships. However, the uncertainty associated with onboard PV generation has become a critical factor limiting effective energy management on alternative energy ships. To address this issue, this paper proposes a real-time energy management optimization method based on reinforcement learning, specifically tailored to handle PV uncertainty and dynamic load variations during navigation. The proposed algorithm optimizes the energy flow between the onboard diesel generator and the energy storage system in real-time, aiming to minimize fuel consumption and enhance operational stability. Real-world shipboard microgrid data is utilized to perform case studies. Simulation results indicate that fuel consumption under the proposed approach is only 90.32% and 94.57% of that in scenarios without PV systems and traditional robust optimization methods, respectively. Moreover, the method effectively stabilizes the state of charge within a safe operational range of [0.2, 0.8], which is helpful for energy storage lifespan. |
| format | Article |
| id | doaj-art-f2bbf88cb66a41a596bc86f53113e5ce |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-f2bbf88cb66a41a596bc86f53113e5ce2025-08-20T03:41:22ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011084410.1016/j.ijepes.2025.110844A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertaintyHuasong Fang0Huayue Zhang1Shuli Wen2Zhong Li3Zhilin Zeng4Miao Zhu5Pengfeng Lin6School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Wuhan Ship Electric Propulsion Equipment Research Institute, Wuhan 430200, ChinaCollege of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China; Corresponding authors.School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Corresponding authors.Shanghai Marine Equipment Research Institute, Shanghai 200031, ChinaShanghai Marine Equipment Research Institute, Shanghai 200031, ChinaSchool of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaWith the ever-increasing awareness of worldwide greenhouse gas emissions, traditional diesel-driven ships are gradually being replaced by renewable energy ships. Zero-carbon power sources, such as photovoltaic (PV) power generation, are progressively integrated into electric ships. However, the uncertainty associated with onboard PV generation has become a critical factor limiting effective energy management on alternative energy ships. To address this issue, this paper proposes a real-time energy management optimization method based on reinforcement learning, specifically tailored to handle PV uncertainty and dynamic load variations during navigation. The proposed algorithm optimizes the energy flow between the onboard diesel generator and the energy storage system in real-time, aiming to minimize fuel consumption and enhance operational stability. Real-world shipboard microgrid data is utilized to perform case studies. Simulation results indicate that fuel consumption under the proposed approach is only 90.32% and 94.57% of that in scenarios without PV systems and traditional robust optimization methods, respectively. Moreover, the method effectively stabilizes the state of charge within a safe operational range of [0.2, 0.8], which is helpful for energy storage lifespan.http://www.sciencedirect.com/science/article/pii/S0142061525003928Reinforcement learningEnergy managementAll-electric shipPV uncertainty |
| spellingShingle | Huasong Fang Huayue Zhang Shuli Wen Zhong Li Zhilin Zeng Miao Zhu Pengfeng Lin A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty International Journal of Electrical Power & Energy Systems Reinforcement learning Energy management All-electric ship PV uncertainty |
| title | A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty |
| title_full | A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty |
| title_fullStr | A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty |
| title_full_unstemmed | A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty |
| title_short | A reinforcement learning based real-time energy management method for mobile microgrid considering photovoltaic uncertainty |
| title_sort | reinforcement learning based real time energy management method for mobile microgrid considering photovoltaic uncertainty |
| topic | Reinforcement learning Energy management All-electric ship PV uncertainty |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525003928 |
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