## 5 Example Analysis
### Result Analysis of Virtual Power Plant Construction  
In this paper, combining with the daily average time-series characteristic curve of different resources in each area, the resource selection in each region are carried out with the improved bat algorithm based on priority selection. The four elements in the objective function set represent the daily average cost, daily load characteristics, DG consumption degree, and regional aggregation degree, respectively. The value of each objective function is expressed in scientific notation as \(a^{*}10^{*}b\), and the pareto optimal solution set containing 11 solutions is finally obtained, as shown in Table 2. (The objective function set omits \(10^{*}b\), and the pareto optimal solution set only lists the area resources which are not involved in the construction). By calculating the membership degree of the objective function of the 11 solutions, the weight of each objective function is calculated, and the priority selection of each solution is finally obtained. Through the calculation, the variance weights of the four objective functions are 0.21, 0.18, 0.32, and 0.29, respectively. The priority selection of each scheme is obtained by adding the membership degree of each objective function through the weight, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Resources 1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline
**DG** & 1 & 1 & 1 & 1 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 1 \\
**ES** & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 1 \\
**DR** & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of resources in each area. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Serial Number** & **Objective Function Set** & **Area Resources Not Involved in the Construction** \\ \hline
1 & (2.5, 8.7, 3.3, 1.8) & \(X_{1}^{\text{ES}},X_{1}^{\text{DR}},X_{2}^{\text{DG}},X_{5}^{\text{ES}},X_{7}^{ \text{DR}},X_{9}^{\text{DR}},X_{11}^{\text{ES}}\) \\
2 & (2.5, 7.3, 3.3, 1.8) & \(X_{1}^{\text{ES}},X_{4}^{\text{DR}},X_{9}^{\text{DR}},X_{10}^{\text{DR}},X_{10}^{ \text{ES}},X_{1  
The 9 th scheme solution is based on the detailed division of decision areas. In order to verify the validity of this method, this scheme is compared with the VPP construction scheme based on the preliminary division of decision areas (the number of each area is shown in Figure 6). The comparison example changes the division of decision area, but the multi-objective optimization model and the solution algorithm remain unchanged. The compromise optimal solution of the comparison example is shown in Table 5 (\(\checkmark\) represents the area resources involved in the construction). The comparison of the two different multi-objective construction models of VPP, which correspond to different decision area division methods, is shown in Table 6.  
It can be seen from Tables 5 and 6, F1, F2, F3 and F4 represent the daily average cost, daily load characteristics, DG consumption degree, and regional aggregation degree, respectively. The smaller \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **F1** & **F2** & **F3** & **F4** \\ \hline
**Detailed division** & 19,044 & 3.52\% & 20,505 & 1,133,120 \\
**Preliminary division** & 19,121 & 5.89\% & 20,406 & 1,487,020 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of objective function sets. Figure 10: Daily characteristics curve of various resources. \begin{table}
\begin{tabular}{c c} \hline \hline
**Resource Type** & **Complementary Coefficient** \\ \hline WP, PV & 0.2375 \\ WP, ES & 0.3100 \\ WP, DR & 0.3341 \\ PV, ES & 0.3116 \\ PV, DR & 0.2534 \\ ES, DR & 0.3167 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Complementary coefficients of various resources. the values of F1, F2 and F4 are, the better the model construction effect is, while F3 is the opposite.