## 5 Implementation and Results
### Iranian 66 Bus Power Distribution Feeder  
In this section, the proposed framework for outage management is implemented on the Iranian 66 bus power distribution feeder. The topology of the Iranian distribution feeder is shown in Figure 9. In Figure 9, two depots and ten faults are considered. Furthermore, eight DGs are provided in the power distribution feeder. The important parameters of the DGs are presented in Table 5.  
The minimum and maximum of the penalty factor are considered for load shading programs like the first case study. The indexes of public hazard for Outages 2 and 7 are assumed to be 2 and for other outages are considered to be 1. The resources and time to repair the outages are listed in Table 6. The capacity of Crews 1, 2, 3 and 4 to carry the resources are assumed to be 30, 40, 30 and 30, respectively. The total maximum capacity of the demand response in each demand response bus \begin{table}
\begin{tabular}{c c} \hline
**Step Times** & **Damaged Component** \\ \hline
1 & * \\
2 & M4 \\
3 & M1-M3 \\
4 & M2-M5-M6 \\
5 & M7 \\ \hline \end{tabular}
\end{table} 
Table 4: The optimal priority list of the damaged components in the 34 bus case. Figure 8: The effect of EDRP on the served load in the 34 bus case. and the step cost of the EDRP are assumed to be similar to in the first case. The maximum travel time between damaged component (outages) is considered to be 2.6 (step time). In this case study, the bus numbers and outages are high, and the first-stage optimization is important. The results of the outage clustering are shown in Figure 10.  
According to Figure 9, the outages are clustered into two groups. Groups 1 and 2 are shown by green and blue, respectively. The important target of this stage is clustering the nearest outage to the depot. This target is considered besides the resources and expertise limitations of the crews. The results of the first stage are provided to the second stage to solve the \(SOMS_{\text{DGs}}^{DRPs}\) problem. In the first stage, the \(SOMS_{\text{DGs}}^{DRPs}\) optimization problem is solved to minimize the RRC time, DG cost, load shedding cost and EDRP cost. Furthermore, each cluster of the Iranian distribution feeder (green and blue color) is related to one depot. In this case study, two crews are assumed in each depot and the outages in each cluster should be repaired by crews of the targeted depot. The results of the RRC problem are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Damage**} & \multicolumn{4}{c}{**Repair Time (30 min Step)**} & \multirow{2}{*}{**Needed Resources**} \\ \cline{2-2} \cline{4-6}  & **Crew 1** & **Crew 2** & **Crew 3** & **Crew 4** & \\ \hline M 1 & 2.5 & 1.2 & 1.1 & 1.1 & 10 \\ M 2 & 3 & 2.3 & 2.1 & 2.2 & 8 \\ M 3 & 1.7 & 1.4 & 1.5 & 2 & 9 \\ M 4 & 1 & 2.1 & 1.2 & 1 & 6 \\ M 5 & 2 &  
According to Figure 10, each fault is assigned to only one crew. The optimal dispatch of the tasks has been performed, and the outages in bus 50 and 34 are assigned to Crew 1. Additionally, the outage in buses 3, 60 and 66 are referred to Crew 2. Furthermore, Crews 3 and 4 should repair the outage in buses 7, 36, 45, 62 and 63. The priority of the faults for repairing is determined by the public hazard value of the fault, the fault location, the number of loads that are affected by the fault, the crew expertise and other parameters and indexes. As mentioned in the above case study, the optimal number of resources that is assigned to each crew is important in the distribution system. Furthermore, assigning numerous resources to the crews is not a good strategy and causes the optimal crew performance to decrease. Therefore, the optimal number of resources to assign to each crew is shown in Figure 12.  
Figure 11: RRC schedule for Iranian 66 bus power distribution feeder. Figure 10: Outage clustering in Iranian 66 bus power distribution feeder. One of the important outputs of the model is the priority list of the outages for repair by crews.