# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Generality Verification Based on the IEEE 69-Node System  
In the IEEE 33-node system, the lines are relatively short, and the topology is simple, meaning the fluctuations of photovoltaics and wind turbines may have a relatively small impact on local voltage. However, in the IEEE 69-node system, due to longer lines and more branches, the fluctuation of distributed power sources may have a more significant effect on voltage and power flow. To demonstrate the generality and effectiveness of the proposed method, this Section presents simulations based on the IEEE 69-node system. Figure 12: Comparison of convergence curves of different algorithms. The power grid topology and node numbering are shown in Figure 13, with a reference power of 10 MVA and a reference voltage of 12.47 kV. The partial line impedance and load data for the nodes are provided in reference [13]. Wind turbines are connected to nodes 5, 33, and 64, while photovoltaics are connected to nodes 14 and 50. The parameters for photovoltaics and wind turbines are consistent with those used in Section 5.1. The distribution network reconstruction based on the IEEE 69-bus system is similar to the IEEE 33-bus system reconstruction. This paper uses the proposed improved fuzzy C-means clustering algorithm to divide the reconstruction period of 24 h into five time periods: 00:00-08:00, 08:00-14:00, 14:00-17:00, and 17:00-24:00. The time division results of different node systems vary due to differences in network topology, voltage and power constraints, and load response characteristics [44; 45]. First, in terms of network topology, the IEEE 69-node system has longer lines and more branches, meaning the fluctuation of distributed power sources has a more significant impact on voltage and power flow. Second, regarding voltage and power constraints, the IEEE 33-node system may experience a small voltage deviation during certain periods, which does not require frequent reconstruction. In contrast, the IEEE 69-node system may experience voltage violations during the same period, necessitating the division into independent periods for optimization. Lastly, differences in load response characteristics among different node systems affect how loads and distributed power sources interact. Consequently, even with the same output from photovoltaic and wind turbines, the impact on system operation varies, leading to different time division results between the IEEE 33-bus and IEEE 69-bus systems. This difference reflects the network characteristics and demonstrates the adaptability of the proposed time division method in various scenarios. This Section adopts the same setting method as the previous Section. The simulation results of the reconstruction scheme are summarized in Table 4. Figure 14 shows the trend of network loss changes for the three schemes at different time periods, and Figures 15-17 depict the dynamic fluctuation of voltage at each node of the system. Figure 13: The topology of the IEEE 69-bus distribution system. Figure 14: Network loss at each time period under different schemes. Figure 15: Voltage levels of each node in scheme 1 based on the IEEE 69-bus system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Time Interval** & **Disconnected Branches** & **Loss/kWh** & **Voltage Deviation/p.u.** \\ \hline Scheme 1 & All time & s69-s70-s71-s72-s73 & 1412.4 & 44.2 \\ \hline Scheme 2 & All time & s12-s18-s58-s61-s69 & 1127.5 & 35.9 \\ \hline  & 00:00–08:00 & s14-s47-s50-s69-s70 & & \\  & 08:00–14:00 & s10-s19-s26-s71-s73 & & \\ Scheme 3 & 14:00–17:00 & s8-s19-s26-s36-s66 & 846.7 & 26.3 \\  & 17:00–21:00 & s17-s25-s59-s67-s73 & & \\  & 21:00–24:00 & s17-s25-s58-s67-s73 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison and Analysis of Dynamic Reconfiguration Results in IEEE64. From the data in Table 4, it is evident that Scheme 3 outperforms Scheme 1 and Scheme 2 in terms of optimization. The total active power loss decreased from 1412.4 kWh before reconstruction to 846.7 kWh, representing a reduction of approximately 40.0%.