# An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation
## 3 Case Studies
### Case Study in an Offline Mode  
The proposed strategy is first verified in an offline mode. Four cases using different strategies and filtering methods are applied and compared. Details are shown in Table 1.  
In case 1, no segmentation is applied. In the other three cases, load curve segmentation strategy mentioned in Section 2 is utilized. A filtering signal (blue line shown in Figure 4) is first calculated from load time series using filtering methods 1 or 2, and then thresholded to obtain separation points. Cases 2 and 3 use integrated load curve, the former follows filtering method 1 and the latter uses method 2. Figures 4 and 5 illustrate separation results in cases 2 to 4. Time stamps above the threshold line, i.e., horizontal dashed line, are extracted as separation points. The vertical dashed lines indicate boundaries between segments. There is a slight difference in segmentation results by the two filtering methods, as in Figure 4. Figure 5 shows that case 4 produces more segments than case 2 and 3 because it uses all three load curves independently for segmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & **Segmentation** & **Filtering Method Used** & **Use Integrated** & **Number of** \\  & **Applied** & **in Segmentation** & **Curve** & **Segments** \\ \hline Case 1 & No & – & – & 24 \\ Case 2 & Yes & method 1 as in (1) & Yes & 6 \\ Case 3 & Yes & method 2 as in (2) & Yes & 7 \\ Case 4 & Yes & method 2 as in (2) & No & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters for case 1 to 4.  
Figure 3: Illustration of three load curves (**a–c**) and integrated one (**d**).  
Optimization is afterwards conducted based on the average load in each load segment. As shown in Figure 6, network losses are slightly higher in case 2. Table 2 also shows that case 2 produces the highest losses while case 4 gives the lowest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & **Total Losses** & **Top Changes** & **Capacitor** & **Computation** \\  & **(kWh)** & **(Times)** & **Changes (Times)** & **Time (s)** \\ \hline Case 1 & 2782.9 & 17 & 3 & 2744.99 \\ Case 2 & 2879.0 & 8 & 6 & 602.38 \\ Case 3 & 2771.9 & 7 & 2 & 638.28 \\ Case 4 & 2759.1 & 10 & 3 & 881.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network losses, device adjustment times and time consumed by cases 1 to 4.  
Figure 4: Segmentation of integrated load curve in case 2 (**a**) and case 3 (**b**).  
Figure 5: Segmentation of load curves in case 4. Each figure corresponds to segmentation for each curve, i.e., load curve 1 (**a**), curve 2 (**b**) and curve 3 (**c**), then all boundaries shown as vertical dashed lines are combined as separation time points.  
The tap changes of one regulator in four cases are illustrated in Figure 7. It is clear that case 1 presents most frequent tap changes, and cases 2 and 3 the least. The tap changing times have positive correlation with the segment number involved.  
Table 2 also shows that case 1 consumed three times of computation time compared with other cases. Lowest network loss presented in case 4 is at the sacrifice of adjusting times. Cases 2 and 3 are comparable with regard to time efficiency while case 3 presented better performance on network losses, indicating the merits of filtering method 2.  
It could be concluded in this part that the segmentation strategies based on integrated load curve highly improved computational efficiency and at the same time gave satisfactory optimization performance. In addition, filtering method 2 is slightly better than method 1 in terms of obtained network losses.  
Figure 6: Time series of network losses by cases 1 to 4.  
Figure 7: Tap position changes of one voltage regulator for case 1 (**a**), case 2 (**b**), case 3 (**c**) and case 4 (**d**).