## 4 Results and Discussions
### The Results Obtained by the Two Algorithms in Case 2  
In this subsection, the number of WTs placed in the IEEE-85 is the same as the first case, which is 2 WTs. However, the WTs, in this case, offer the capability of generating both active and reactive power. Note that, in this case, the scale of the considered problem has been increased due to the addition of more variables needed for achieving the optimal solution. Specifically, the set of optimal solutions, in this case, consists of six variables instead of only four in Case 1.  
Figure 6 shows the fitness values resulting from the two applied algorithms after 50 trial runs. In the figure, WOA provides a better search capability while reaching more optimal fitness values than OOA. Moreover, the fluctuation of the results among trial runs is substantially lower than OOA.  
The best convergences among 50 trial runs of the two applied algorithms are presented in Figure 7. While the WOA only requires around 20 iterations to reach the optimal power loss value, the OOA cannot achieve the same result in the best trial runs.  
The comparison between the two applied algorithms in different criteria, including LST. Loss, Aver. Loss, HST. Loss, and STD is presented in Figure 8. In the figure, the LST  
Figure 6: The results obtained by the two algorithms after 50 trial runs.  
Loss achieved by the WOA is 30.64586 kW which is lower than that of OOA 0.083 kW, corresponding to 0.27%. Similar to Case 1, the difference in the first criterion between the two applied algorithms is not that much, but the remaining criteria start to show clear differences on performance and stability. For instance, the values of Aver. Loss and HST. Loss obtained by WOA are all significantly lower than those resulting from the OOA. The better percentages of the WOA over OOA on the two criteria are 20.14% for the Aver. Loss and 25.15% for the HST. Loss. Lastly, the evaluation of the STD criterion also indicates that the WOA still provides outstanding stability over the OOA by 65.73%, regardless of the increase in the complexity degree in this case.  
Figure 9 describes the voltage profile of the whole system in Case 2 under two circumstances: a Base case without WTs and two other cases with WTs. It is easy to observe that the placement of the WTs has brought a noticeable improvement in voltage magnitude at all nodes in the whole network compared to the original configuration. However, the superiority of WOA over OOA in this circumstance is a little. For instance, the WOA can only result in better voltage magnitudes over the OOA at nodes 5, 6, and 14-16, whereas the voltage improvement at other nodes is slightly lower than the OOA. The following terms can explain this phenomenon: (1) The main objective function considered is to reduce the power loss, not to improve the voltage profile; (2) Although both the OOA and WOA result in the same optimal positions of WT, the sizes of WTs found by the OOA and WOA are slightly different. By observing Table 2, the active and re  
Figure 8: The comparison between the two algorithms on different criteria in Case 2.  
Figure 7: The best convergence curves, obtained by the two algorithms in Case 2.  
OOA for the WT at node 60 is noticeably larger than the similar values resulting from the WOA. Mainly, the OOA found 1236.0772 kW and 765.0688 kvar for the WT placed at node 60, while those of WOA are 1200.4984 kW and 744.0021 kvar, respectively. Due to these higher values, the OOA brings a better voltage improvement at many nodes thanks to the larger reactive power injected into the grid over the WOA, but the improvement is tiny.