## 5 Numerical Computational Analysis  
This paper constructs a 220 kV grid system with a renewable energy generation cluster shown in Figure 4 based on the IEEE39 node system. The regional grid consists of five conventional power plants and four REG stations (PV1, PV2, W1 and W2). According to the 2020 data of PV plants and wind farms in a certain area, PV1 is a photovoltaic power plant with an installed capacity of 30 MW; PV2 is a photovoltaic power plant with an installed capacity of 90 MW; W1 is a wind power plant with an installed capacity of 90 MW; W2 is a wind power plant with an installed capacity of 120 MW. Detailed data is in Appendix A. The installed capacity of each REG station within the power generation cluster is shown in Table 1.  
The REG cluster can transmit power to the grid through the section, and the new energy transmission section is shown in Figure 5. The upper limit of the power transmission from the section is set at 270 MW. In larger grids, the fluctuations in load at the nodes are relatively small. After clustering and analyzing the historical operating data of the REG cluster, a subsequent analysis was carried out for a typical operating mode in which the upside of the REG cluster load \(P_{j,t}^{Lf}\) was 4 MW. Assuming that the actual power delivered by the section is 249 MW at a certain time, the actual operating capacity of each REG station and the expected output of the next period are shown in Table 2. \begin{table}
\begin{tabular}{c c} \hline
**REG Station** & **Installed Capacity/MW** \\ \hline PV1 & 30 \\ PV2 & 90 \\ W1 & 90 \\ W2 & 120 \\ \hline \end{tabular}
\end{table} 
Table 1: Installed capacity of each new energy station. According to the prediction error model of each REG station (detailed data is in Appendix A), the probability density function of the predicted REG power error was obtained, as shown in Figure 6.  
According to the predicted output of each REG station and its probability density function, the output realization probability of each REG station can be obtained, as shown in Figure 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**REG Station** & **Actual Power Output** & **Expected Output of Next Period** \\ \hline PV1 & 24 & 30 \\ PV2 & 81 & 87 \\ W1 & 72 & 80 \\ W2 & 90 & 109 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Actual power output of each REG station/MW. Figure 5: A 220 kV power grid system with a new REG cluster. Figure 6: Probability density function of the predicted REG power error. At this point, the section margin \(P_{l}^{SM}\) is 21 MW. According to the section margin allocation function, the objective is to maximize the probability that all REG stations in the REG cluster can meet the output command after the margin allocation. The active output of each REG station adjusted to the section margin requirements is calculated and the results are shown in Table 3.  
At this point, the probability that the new output instructions for each REG station will be met after the section margin allocation is at its highest, allowing the section margin to be fully utilized. We present a comparison of the method used in this paper with other section margin allocation methods. The average allocation method is method 1. The proportional allocation method according to the installed capacity of REG stations is method 2 [19]. The allocation method based on the principle of minimum wind and light resources abandonment is method 3 [20]. Each allocation method and the probability of all REG stations being able to complete their output orders when using each allocation method is shown in Table 4.  
A comparison of the data in Table 4 shows that the section margin allocation method proposed in this paper, which considers the randomness of source and load power, has