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The distributed integration of renewable energy sources has reduced the stability of synchronous operations in power systems and diminished clock reliability, ultimately impacting the effectiveness and precision of centralized management. To address this issue, this paper designs a TSN switch traffic scheduling structure and model that integrates queueing buffers. This design not only meets the delay jitter requirements for ST and SR traffic flows but also reduces the end-to-end transmission delay of BE flows, thereby enhancing the determinism of the electric power network. Furthermore, a method for calculating the minimum scheduling time slot is proposed to maximize the sampling period of ST flows, minimizing their bandwidth usage. A deterministic traffic scheduling algorithm based on Particle Swarm Optimization is also introduced, which controls the number of data frames entering the CQF queue from different buffers during each time slot. This provides finer-grained traffic shaping and reduces the reserved bandwidth for SR flows. Experimental results show that the proposed method can reduce the maximum end-to-end delay of BE messages by 16.52% to 75.76%.

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A Electric Power Deterministic Network Traffic Scheduling Method Based on TSN Queueing Buffer Scheduling

  • 会议论文

  • 发表日期：

    2026年

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  • Dong Liu,
  • Liangliang Yu,
  • Lei Jin,
  • Shanqi Zheng,
  • Peng Lin

摘要

The distributed integration of renewable energy sources has reduced the stability of synchronous operations in power systems and diminished clock reliability, ultimately impacting the effectiveness and precision of centralized management. To address this issue, this paper designs a TSN switch traffic scheduling structure and model that integrates queueing buffers. This design not only meets the delay jitter requirements for ST and SR traffic flows but also reduces the end-to-end transmission delay of BE flows, thereby enhancing the determinism of the electric power network. Furthermore, a method for calculating the minimum scheduling time slot is proposed to maximize the sampling period of ST flows, minimizing their bandwidth usage. A deterministic traffic scheduling algorithm based on Particle Swarm Optimization is also introduced, which controls the number of data frames entering the CQF queue from different buffers during each time slot. This provides finer-grained traffic shaping and reduces the reserved bandwidth for SR flows. Experimental results show that the proposed method can reduce the maximum end-to-end delay of BE messages by 16.52% to 75.76%.