# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 5 Case Studies
### Optimal Dispatching Results of the Integrated Energy System
The experiments without data augmentation contain only existing data for training, and the rest of the model parameters are identical to those of the model with data augmentation. After the offline training of the model, the trained model can directly generate online scheduling decisions based on the real-time status of the IES. Tables 5 and 6 show the total power imbalance and the total cost during a week and a weekend for the summer, winter, and transition seasons, respectively. It can be seen that, after the data expansion, due to the sufficient sample size, the intelligences can be better trained with a low power imbalance most of the time while having a lower running cost, whereas the method without data expansion exhibits a higher power imbalance in scenarios that have never been seen, and these results are essential, because a larger power imbalance can lead to blackouts, primarily if the main grid defines the output limits. In Table 6, the method without data expansion has a lower total cost for some of the test days than the method with data expansion; this is because it does not respect the power balance, which leads to a large amount of power purchases from the grid, which reduces the total cost, but this is not justified. To further analyze the reasons for this phenomenon, this paper uses a typical day within a summer week as an example for further analysis. Figure 10a,b show the scheduling results for typical summer intra-weekdays with and without data expansion. As can be seen from the figures, the method with data expansion is more inclined to draw power from the \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Test Day**} & \multicolumn{3}{c}{**Without Data Expansion**} & \multicolumn{3}{c}{**With Data Expansion**} \\ \cline{2-7}  & **Trans.** & **Sum.** & **Win.** & **Trans.** & **Sum.** & **Win.** \\ \hline Weekday & 10,894.05 & 6389.72 & 11,192.66 & 6583.06 & 7557.53 & 10,805.92 \\ \hline Weekend & 13,270.10 & 10,140.78 & 9138.18 & 12,796.00 & 5626.50 & 9502.25 \\ \hline \end{tabular}
\end{table} 
Table 4: DG units information. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Units**} & \multirow{2}{*}{\(a[\textbf{S/kW}^{2}]\)} & \multirow{2}{*}{\(b[\textbf{S/kW}]\)} & \multirow{2}{*}{\(c[\textbf{S}]\)} & \multirow{2}{*}{\(\underline{\textbf{P}}^{G}[\textbf{kW}]\)} & \multirow{2}{*}{\(\overline{\textbf{P}}^{G}[\textbf{kW}]\)} & \multirow{2}{*}{\(\overline{\textbf{R}}\textbf{U}[\textbf{kW}]\)} & \multirow{2}{*}{\(\mathbf{RD}[\textbf{kW}]\)} \\ \hline DG1 & 0.0034 & 3 & 30 & 10 & 150 & 100 & 100 \\ \hline DG2 & 0.001 & 10 & 40 & 50 & 375 & 100 & 100 \\ \hline DG3 & 0.001 & 15 & 70 & 100 & 500 & 200 & 200 \\ \hline \end{tabular}
\end{table} 
Table 4: DG units information. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Test Day**} & \multicolumn{3}{c}{**Without Data Expansion**} & \multicolumn{3}{c}{**With Data Expansion**} \\ \cline{2-7}  & **Trans.** & **Sum.** & **Win.** & **Trans.** & **Sum.** & **Win.** \\ \hline Weekday & 259.84 & 141.22 & 361.65 & 156.08 & 4.31 & 0.00 \\ \hline Weekend & 252.52 & 354.40 & 124.81 & 3.84 & 0.00 & 1.70 \\ \hline \end{tabular}
\end{table} 
Table 5: Power imbalance in different scenarios (kW). grid and charge the battery at night during the low-peak hours of electricity consumption. In contrast, the method without data expansion generates power. It tells it to the grid through unit 1, which is a time when the price of electricity is low; thus, the behavior is not economical. Also, DG3 is unreasonably invoked for power generation at 8:00 A.M. and 11:00 A.M., resulting in higher costs. In the second half of the scheduling cycle, the method with and without data expansion can effectively utilize energy storage for configuration due to sufficient learning training, prioritize the excess power generated by the PV for storage, sell the excess to the grid, and prioritize the use of batteries for energy supply when the PV output is insufficient.