Research on Self-Decision Control Strategy in Distributed Photovoltaic Intelligent Monitoring Device
This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the dist...
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| Vydáno v: | IEEE International Conference on Automation, Electronics and Electrical Engineering (Online) s. 515 - 519 |
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| Hlavní autoři: | , , , , |
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| Jazyk: | angličtina |
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IEEE
27.12.2024
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| ISSN: | 2831-4549 |
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| Abstract | This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the distributed photovoltaic power generation process, this paper designs a self-decision control algorithm to achieve real-time monitoring and optimization control of photovoltaic components, inverters and grid interfaces through multi-agent collaboration. Each agent represents a different control unit, which is independent and cooperative with each other, and continuously trains under the reinforcement learning framework to improve the overall performance. The model verification based on the simulation platform shows that the algorithm can significantly improve the response speed and power generation efficiency of the photovoltaic system under different weather and load conditions. The data analysis results show that after adopting this control strategy, the output fluctuation of photovoltaic power generation is reduced by 15%, the inverter efficiency is improved by 12%, the system stability is enhanced, and the impact on the power grid is effectively reduced. In addition, the algorithm has good generalization performance and can be applied to large-scale distributed photovoltaic networks. |
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| AbstractList | This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic systems to improve the efficiency and stability of the system under variable environments. In order to cope with the uncertainty in the distributed photovoltaic power generation process, this paper designs a self-decision control algorithm to achieve real-time monitoring and optimization control of photovoltaic components, inverters and grid interfaces through multi-agent collaboration. Each agent represents a different control unit, which is independent and cooperative with each other, and continuously trains under the reinforcement learning framework to improve the overall performance. The model verification based on the simulation platform shows that the algorithm can significantly improve the response speed and power generation efficiency of the photovoltaic system under different weather and load conditions. The data analysis results show that after adopting this control strategy, the output fluctuation of photovoltaic power generation is reduced by 15%, the inverter efficiency is improved by 12%, the system stability is enhanced, and the impact on the power grid is effectively reduced. In addition, the algorithm has good generalization performance and can be applied to large-scale distributed photovoltaic networks. |
| Author | Guo, Yingjie Lei, Tinghao Wu, Yang Li, Yao Cai, Xinya |
| Author_xml | – sequence: 1 givenname: Tinghao surname: Lei fullname: Lei, Tinghao email: 850776032@qq.com organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China – sequence: 2 givenname: Xinya surname: Cai fullname: Cai, Xinya organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China – sequence: 3 givenname: Yang surname: Wu fullname: Wu, Yang organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China – sequence: 4 givenname: Yingjie surname: Guo fullname: Guo, Yingjie organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China – sequence: 5 givenname: Yao surname: Li fullname: Li, Yao organization: Zunyi Power Supply Bureau of Guizhou Power Grid Co., Ltd.,Zunyi,Guizhou,China |
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| Snippet | This study proposes an intelligent monitoring self-decision control strategy based on multi-agent reinforcement learning algorithm for distributed photovoltaic... |
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| SubjectTerms | Collaboration Distributed photovoltaic intelligent monitoring Inverters Monitoring multi-agent reinforcement learning algorithm Optimization Photovoltaic systems Power grids Power system stability Real-time systems Reinforcement learning self-decision-making control Stability analysis |
| Title | Research on Self-Decision Control Strategy in Distributed Photovoltaic Intelligent Monitoring Device |
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