# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Comparison and Analysis of Dynamic Reconfiguration Results  
To assess the feasibility, effectiveness, and superiority of the proposed fuzzy C-means clustering time division method and the improved EO algorithm for active distribution network reconstruction, three schemes are set up for comparative analysis:  
Scheme 1: No network reconstruction, disconnect all contact switches at all times. Scheme 2: Static reconstruction using the improved EO algorithm proposed in this study, i.e., performing reconstruction once per day. Scheme 3: Dynamic reconstruction optimization based on the method proposed in this study. The reconstruction results for each scheme are presented in Table 2.  
From the comparison between Scheme 1 and Scheme 2 in Table 2, it is evident that without any reconstruction strategy, the distribution network remains in its initial state, with no section switches in operation. This lack of optimization leads to high energy consumption and significant voltage deviation. However, after applying the improved EO algorithm, system performance improves substantially. Netw \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Time Interval** &
\begin{tabular}{c} **Disconnected** \\ **Branches** \\ \end{tabular} & **Loss/kWh** & **Voltage Deviation/p.u.** \\ \hline Scheme 1 & All time & s33-s34-s35-s36-s37 & 1320.4 & 25.1 \\ \hline Scheme 2 & All time & s7-s14-s9-s36-s37 & 961.7 & 18.6 \\ \hline \multirow{5}{*}{Scheme 3} & 00:00–08:00 & s7-s34-s9-s31-s37 & \multirow{5}{*}{729.3} & \multirow{5}{*}{16.1} \\  & 08:00–13:00 & s7-s14-s9-s31-s37 & \multirow{5}{*}{729.3} & \multirow{5}{*}{16.1} \\  & 13:00–17:00 & s7-s14-s10-s32-s28 & & \\ \cline{1-1}  & 17:00–21:00 & s7-s14-s9-s32-s30 & & \\ \cline{1-1}  & 21:00–24:00 & s7-s14-s9-s32-s28 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison and Analysis of Dynamic Reconfiguration Results in IEEE33. Figure 7: Period division result. about 27.2%, from 1320.4 kWh to 961.7 kWh, and voltage deviation reduces by 25.9%, from 25.1 p.u. to 18.6 p.u. This shows that even with a single optimization per cycle, the improved EO algorithm can achieve notable performance gains. Further comparison of Scheme 2 and Scheme 3 reveals that while Scheme 2 effectively reduces network loss and voltage deviation, its static nature limits the potential for dynamic topology adjustment within the allowed switching range. Scheme 3, however, introduces a load period clustering method, dividing the day into representative periods and optimizing reconstruction for each. As a result, network loss further decreases to 729.3 kWh, a 24% reduction compared to Scheme 2, while voltage deviation drops to 16.1 p.u., a 13% improvement, demonstrating the advantages of dynamic reconstruction. In summary, the proposed method outperforms Scheme 1 and Scheme 2 in reducing distribution network losses, improving node voltage deviations, and enhancing power quality. This indicates that the dynamic reconstruction strategy not only optimizes power flow distribution but also enhances the consumption capacity of distributed generation, balancing active and switching losses, and improving the system's economy and reliability. The changes in network loss values for each period in the three schemes are shown in Figure 8.  
From Figure 8, it is evident that Scheme 3 significantly reduces network loss compared to Scheme 1 and Scheme 2 in all time periods. The most substantial reduction occurs during the 19-21 h period, which is the peak load period of the day. During this time, residential, commercial, and industrial loads reach their highest levels, while photovoltaic output drops to nearly zero, and wind turbines are unable to fully meet the demand, leading to line overload and increased network losses. The dynamic reconstruction in Scheme 3 effectively mitigates these losses, improving the overall economic efficiency of the system. Figures 9-11 show the voltage distribution at each node across the time periods for the three schemes.