## 5 Experiments and Results
### Experiment on Standard Test Case
#### 5.2.2 Three test cases  
Case 1: Test of a Random Day  
A workday in summer with heavy load is chosen to be tested. During the experiment procedure, we set the load property \(\lambda=2\) for summer, \(\mu=1\) for workday and the minimum load similarity margin \(s_{\text{min}}=0.95\). \begin{table}
\begin{tabular}{c c} \hline
**Device** & **Setting** \\ \hline ULTC & 70/10 kV, \(-\)10\% to 10\% regulation with 32 steps \\ C\({}_{\text{S}}\) & Substation capacitors: 2.5 Mvar each \\ C\({}_{\text{F}}\) & Feeder capacitors: 1.5 Mvar each \\ \hline \end{tabular}
\end{table} 
Table 2: Configuration of reactive power devices. \begin{table}
\begin{tabular}{c c} \hline
**Load Type** & **Node** \\ \hline Residential load & 3, 6, 8, 14 \\ Commercial load & 4, 5, 7, 9, 13 \\ Industrial load & 2, 10, 11, 12 \\ \hline \end{tabular}
\end{table} 
Table 3: Load types. During the experiment, the maximum load similarity is \(s(t_{\text{max}})=0.9684>s_{\text{min}}\), so the historical RPO dispatching scheme of date \(t_{\text{max}}\) can be used on the tested day without fine adjustment. Most of the reactive power control device action sequences by BDO and MGA are the same, except some actions of C\({}_{\text{F1}}\) and C\({}_{\text{F2}}\) at several points shown in Figure 8a,b. The optimization results of the selected day are shown in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Loss of MGA (MWh)** & **Loss of BDO (MWh)** & **Similarity** & **Error (\%)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Optimization result of MGA (multi-population genetic algorithm) and BDO (big data reactive power optimization) without fine adjustment. Figure 7: Distribution of load similarity and loss error. Based on the results of BDO and MGA, as shown in Table 5, the comparison of the two methods can be presented as follows. In the aspect of active power loss, the BDO method achieves a little larger active power loss than the MGA method. The loss error is 2.76%, which is acceptable in engineering application under undemanding condition. In the aspect of device action times, the BDO method can spend less action times than the MGA method, which can prolong the service life of the devices. In the aspect of computation time, the BDO method can achieve the optimization result within 0.5 s, while the computation time of MGA method lasts as long as 141.6 s. It is concluded that the BDO method  
Comparing the optimization results of BDO method and MGA method, the differences appear at the feeder capacitor units C\({}_{\text{F1}}\) and C\({}_{\text{F2}}\), as shown in Figure 8a,b. The BDO method shows less action times at C\({}_{\text{F1}}\) and presents less compensation capacity at C\({}_{\text{F2}}\) compared with the MGA method. Case 2: Test of Some Random Days  
In order to compare performances of BDO and MGA, 20 random days are chosen to be tested. The optimization results are shown in Table 6, in which the losses of BDO (a) and BDO (b) stand for the losses before and after the application of control variables fine adjustment method, respectively. As shown in Table 6, there are five days requiring fine adjustment with similarities smaller than 0.95 and another 15 days obtaining the optimization results simply by similarity matching. The largest loss error is 0.2241%, which means the BDO method can obtain a similar result to the MGA method. There are negative loss errors, which mean the BDO method may obtain a more excellent result than the MGA method. Case 3: Test of Typical Days  
Based on the load grouping process shown in Figure 3, typical days of different categories are chosen to be tested among workdays and weekends in different seasons. Both the BDO method and the MGA method are used to obtain the optimization results. As shown in Table 7, though the losses by the BDO method are a little larger than those by the MGA method, it is acceptable within the range of allowable error.