## 4 Results and Discussions
### Analysis of Day-Ahead Optimization  
During the day-ahead scheduling phase, three different cases are preliminarily optimized based on 24-h forecast data at a 1-h time scale to arrange electricity purchase plans, ESS, or shared energy storage charging/discharging schedules, serving as a reference for intraday optimization strategies. Notably, the CO-CSES not only generates charging/discharging scheduling plans for each AC subnetwork but also produces a day-ahead SOC trajectory, ensuring that the total SOC of the entire CSES remains within safe limits throughout. As a comparison, the CL-CSES uses the total charging/discharging power of each AC subnetwork obtained from CO-CSES optimization to represent the source-load difference and allocates the total 12 MWh shared energy storage capacity proportionally based on this difference. The results are shown in Table 7.  
Figure 4: Forecasted RES and load power in each subnetwork. (**a**) At 1-h time scale for day-ahead scheduling; (**b**) At 15-min time scale for intraday scheduling. From Table 7, it can be observed that there are significant differences among the AC subnetworks. The AC1 subnetwork with a source-load difference of 6.9709 MWh receives a relatively low shared energy storage allocation proportion of 22.39%. The AC2 subnetwork has the highest source-load difference, accounting for 42.07% and, thus, receives the largest shared energy storage capacity allocation. And the capacity of the AC3 subnetwork is in the middle. This capacity allocation reflects CL-CSES's consideration of the actual energy demand of each subnetwork. Table 8 presents a comparative analysis of various costs across the three cases after day-ahead optimization. The data indicate that Case 3 (CO-CSES) achieves a total operational cost of CNY 40,097.62, representing significant reductions of CNY 4069.44 (9.21%) and CNY 3873.07 (8.81%) compared to Case 1 (IE) and Case 2 (CL-CSES), respectively. A detailed analysis of the cost components shows that the electricity purchase cost of Case 3 is CNY 4533.89 (6.45%) and CNY 4167.37 (5.96%) less compared to Case 1 and Case 2, respectively. These results demonstrate that the CO-CSES can adjust the shared energy storage allocation scheme dynamically according to the source-load characteristics of different subnetworks, reducing reliance on external grids while maintaining the same total energy storage capacity. Table 9 provides an analysis of RES utilization rates across different AC subnetworks for each case. The data confirm that Case 3 demonstrates notable advantages in overall RES utilization. Specifically, for the AC1 subnetwork, Case 3 achieves 99.57% RES utilization, which is 5.02% and 4.80% higher than Case 1 and Case 2, respectively; for the AC2 subnetwork, Case 3 improves by 5.84% and 2.46% over the other cases; while for the AC3 subnetwork, all cases achieve high utilization rates (all above 98%). These results indicate that the CO-CSES, through centralized coordination of shared energy storage across subnetworks, can achieve higher RES utilization. The improvement is more significant for subnetworks with obvious source-load mismatch (e.g., AC1 and AC2). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & \
\begin{tabular}{c} **Source–Load** \\ **Difference (MWh)** \\ \end{tabular} & **Allocation Ratio** &
\begin{tabular}{c} **Allocated Capacity** \\ **(MWh)** \\ \end{tabular} \\ \hline AC1 & 6.9709 & 0.2239 & 2.6868 \\ AC2 & 13.0971 & 0.4207 & 5.0480 \\ AC3 & 11.0660 & 0.3554 & 4.2652 \\ \hline Total & 31.1340 & 1.0000 & 12.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: ESS capacity allocation of CL-CSES.