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Research on intelligent photovoltaic charging station energy testing system based on machine learning
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Fei Wei1, Di Xu1, Xin Chen1, Zhaojie Zhang1, Xue Liu1, Dongdong Zhang2
Renewable Energy Resources | 2025, 43(2) : 268 - 274
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Renewable Energy Resources | 2025, 43(2): 268-274
Research on intelligent photovoltaic charging station energy testing system based on machine learning
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Fei Wei1, Di Xu1, Xin Chen1, Zhaojie Zhang1, Xue Liu1, Dongdong Zhang2
Affiliations
  • 1 State Grid Tianjin Marketing Service Center Tianjin 300220 China
  • 2 Nanjing Institute of Technology Nanjing 211167 China
Published: 2025-02-20
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This article proposes an intelligent charging station energy scheduling system based on machine learning, which is applied to public fast charging station microgrids equipped with photovoltaic systems and energy storage systems using secondary life electric vehicle batteries. The energy dispatch system can be used to address the uncertainty of energy demand for electric vehicles and the power gap between grid connection and fast charging services. In addition, this article uses machine learning methods to automatically synthesize suitable energy scheduling systems based on fuzzy rules. The energy dispatch system proposed in this article considers different electric vehicle fleets and photovoltaic scales, providing a reference for the optimal scale of photovoltaic systems and the effectiveness of nanogrid systems. Finally, in the experiment, a mixed deterministic stochastic process was used to simulate the energy demand of electric vehicles, which showed an improvement in performance compared to the optimal benchmark solution. This indicates that the system can more effectively handle the energy demand uncertainty of electric vehicles and the power gap between grid connection and fast charging services.

machine learning  /  intelligent charging station  /  energy scheduling  /  photovoltaic system
Fei Wei, Di Xu, Xin Chen, Zhaojie Zhang, Xue Liu, Dongdong Zhang. Research on intelligent photovoltaic charging station energy testing system based on machine learning[J]. Renewable Energy Resources, 2025 , 43 (2) : 268 -274 .
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  • Receive Date：2024-05-06
  • Online Date：2025-07-18
  • Published：2025-02-20
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  • Received：2024-05-06
Funding
Affiliations
    1 State Grid Tianjin Marketing Service Center Tianjin 300220 China
    2 Nanjing Institute of Technology Nanjing 211167 China
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红菇属 Russula 17 8.13
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