## 5 Conclusions  
The current study states the electricity distribution networks reconfiguration in the presence of DGs. The mentioned problem was in the form of an optimization problem, whose objective function is to minimize annual energy losses. Additionally, the constraints of the problem included power flow equations, the constraints of DGs, technical limitations of the network, and the reconfiguration planning model. In this model, constant power-current-impedance load models were assumed. The mentioned problem was mixed-integer nonlinear, and was solved by the CSA. Eventually, according to the numerical results, the mentioned solution algorithm was able to obtain the most optimal solution in terms of the number of convergence iterations and low computation time. Moreover, it has the lowest dispersion in its final response, where the standard deviation of its final response is around 0.96%. In the proposed scheme, there are changes in the performance of switches mounted on each distribution line every hour in order to minimize annual energy losses. DGs produce high active and reactive power; hence, the consumers are supplied locally and the power loss is minimized. Further, reducing the apparent power of the distribution substation, smoothing the voltage profile, reducing the voltage drop in the distribution lines, and reducing the power and energy losses is achievable when optimal reconfiguration of the distribution network is opted for along with reaching the optimal performance of DGs compared with network power flow studies. Therefore, in the proposed scheme, the energy losses will be reduced by about 700 MWh compared to the power flow studies. In  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solver** & \begin{tabular}{c} **Annual Energy** \\ **Loss (MWh)** \\ \end{tabular} & \begin{tabular}{c} **Convergence** \\ **Iteration** \\ \end{tabular} & \
\begin{tabular}{c} **Convergence** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation (\%)** \\ \end{tabular} \\ \hline CSA & 97.455 & 2936 & 128 & 0.96 \\ \hline KHO & 99.874 & 3567 & 174 & 1.48 \\ \hline GWO & 104.032 & 3974 & 198 & 1.97 \\ \hline TLBO & 109.257 & 4367 & 241 & 2.34 \\ \hline PSO & 113.228 & 4822 & 287 & 2.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Convergence status of the problem based on different solvers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Annual Energy Loss (MWh)** \\ \hline
1 & 802.27 \\
2 & 748.97 \\
3 & 97.455 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Annual energy loss (objective function value).  
addition, a smoother voltage profile is obtained in the proposed scheme compared to the power flow studies.  
Investigation, M.J. and Y.Z.; resources, Y.Z.; data curation, Y.Z.; supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
2023 Excellent Teaching Team Project of Jiangsu University's \"Blue Project\": \"Cloud Network Integration\" Application Teaching Team.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.