## 6 Case Studies
### Analysis of the Influence of Network Reconfiguration on Planning Results  
The impact of network reconfiguration on the planning results of \(DG\) in ADN is analyzed as follows.  
(1) Comparative analysis of Case 1 and Case 3  
In Case 1, the network reconfiguration is not considered and the deterministic planning model is adopted. Only the ADN planning model with \(DG\) is solved. In this planning scheme, WTGs are installed at nodes 7 and 14 with an installed capacity of 400 kW, and _PVG_s are installed at nodes 24 and 25 with an installed capacity of 400 kW. The maximum load in summer is 4000 kW, and the penetration rate of clean energy is 40.0%, meeting the requirements of ADN for high penetration rate of clean energy. The annual average comprehensive cost is USD 7.4136 million, including USD 250,300 for network loss, USD 1,862,800 for \(DG\), and USD 5,305,000 for power purchase from the superior power grid.  
In Case 3, network reconfiguration is considered, but deterministic planning model is used to plan the network. In this planning scheme, considering the network reconfiguration, the distribution network can change the network topology through the action of branch switches and tie switches, so as to optimize the network power flow. Therefore, under the premise of a certain load, the total installed capacity of \(DG\) is reduced. As shown in Table 1, the total installed capacity of \(DG\) is 1500 kW (penetration rate is 37.5%), which reduces 100 kW. Therefore, the total cost of \(DG\) is reduced by USD 89,700. By optimizing the power flow, the system power loss is reduced by 38.35%, which is of great significance to energy conservation. The power purchase cost decreased by USD 299,700. At the same time, because the planning scheme reconstructs the network of the system, the reconstruction cost is USD 38,400. It can be seen that this cost is low, because in the planning year, the load fluctuation is mainly reflected in seasonal changes, so the number of reconstructions  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bus** & **7** & **14** & **24** & **25** \\ \hline \multirow{2}{*}{Case 1} & Element Type & WTG & WTG & _PVG_ & _PVG_ \\  & Capacity (kW) & 400 & 400 & 400 & 400 \\  & Element Type & WTG & WTG & _PVG_ & _PVG_ \\  & Capacity (kW) & 400 & 400 & 600 & 400 \\  & Element Type & WTG & WTG & _PVG_ & _PVG_ \\  & Capacity (kW) & 200 & 300 & 600 & 400 \\  & Element Type & _WTG_ & _WTG_ & _PVG_ & _PVG_ \\  & Capacity (kW) & 300 & 300 & 600 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Planning scheme of Case 1 to Case 4.  
Figure 6: The cost of Case 1 to Case 4.  
is less. To sum up, the annual average comprehensive cost of the system decreased by USD 447,000.  
(2) Comparative analysis of Case 2 and Case 4  
Since both Case 2 and Case 4 adopt robust planning models, the system has a good adaptability to the uncertainty of load, wind power and photovoltaic, and the system does not need to be reconstructed frequently. Therefore, the total installed \(DG\) capacity of Case 2 and Case 4 is the same, which is 1800 kW. At the same time, the reconstruction cost of Case 4 is lower than that of Case 2, which is USD 19,200. However, the operation and maintenance cost of \(DG\) and the power purchase cost from the large power grid in Case 4 are slightly lower than that in Case 2. At the same time, the network loss cost of Case 4 is greatly reduced, and the annual average comprehensive cost is reduced by USD 147,500. Therefore, it is still advantageous to consider network reconfiguration when using robust programming model.  
Through the above comparison, it is found that network reconfiguration is considered in ADN planning, which is of great significance to reduce network loss, delay \(DG\) investment and make the distribution system more economical.