## 5 Case Study
### Analysis of Control Effects  
The evaluation indexes are RMSE, ABS_MAX, KD, KDB, KS, KSH, K1%, TD, TDA, and TDB. The specific meanings of each index are in the abbreviations. Tables 4 and 5 show the effect of wind power and energy storage outputs and the comparison of energy storage output under five control strategies. Table 4 shows that the five control strategies effectively reduce the assessment power of the wind farm and improve the tracking planning ability of the wind storage combined system. However, the wind storage joint control strategy based on TPA-BiGRU performs the best, and it can control the control deviation of the wind storage combined system in a small range. The average value of RMSE is only 0.79%, with high control accuracy. The assessment rate (KSH) is only 10.15%, and the assessed electric quantity (KD) of the wind farm is reduced from the original 3466.96 MWh to 31.76 MWh. Table 5 shows that the wind storage joint control strategy based on TPA-BiGRU has the least average regulated electricity quantity (TDA), which is only 146.48 MWh, and all the storage regulations are better than the other networks. It can be seen from the two tables that the wind storage joint control strategy based on TPA-BiGRU has better control accuracy and stability and is more suitable for the operating conditions of wind farms than the other four control strategies. Table 6 shows the assessment results of each control strategy. It can be seen from the table that the K1 assessment index of the wind storage joint control strategy based on TPA-BiGRU has the largest number of 100% qualified days, which exceeds the other four control strategies. It shows that the wind storage joint control strategy based on TPA-BiGRU has a better and more stable control effect than the other four control strategies. To verify the robustness and reliability of the results of the study, this paper uses the following methodology to validate the experimental results: \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Control Strategy**} & \multicolumn{3}{c}{**K1 Qualifying Days**} \\ \cline{2-4}  & **1440 min** & **1350–1430 min** & **<1350 min** \\ \hline Before control & 0 & 10 & 21 \\ TPA-BiGRU & 8 & 23 & 0 \\ TPA-BiLSTM & 5 & 26 & 0 \\ BiGRU & 3 & 28 & 0 \\ AROCS & 3 & 28 & 0 \\ PI & 0 & 24 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Assessment results of 5 control strategies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Control Strategy** & **Range of RMSE** & **Average of RMSE** & **KD (MWh)** & **KS (min) *** & **KSH** \\ \hline Before control & 3.82–10.5\% & 7.85\% & 3466.96 & 2619 & 82.76\% \\ TPA-BiGRU & 0.53–0.97\% & 0.79\% & 31.76 & 302 & 10.15\% \\ TPA-BiLSTM & 0.5–1.24\% & 0.88\% & 36.65 & 317 & 10.65\% \\ BiGRU & 0.53–1.33\% & 0.98\% & 40.67 & 402 & 13.51\% \\ AROCS & 0.54–2.67\% & 1.69\% & 82.48 & 433 & 14.55\% \\ PI & 0.86–4.3\% & 2.57\% & 273.46 & 637 & 21.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of control effects of wind and storage outputs. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control Strategy** & **Total Number of Energy** & **The Average Number of** & **TD (MWh)** & **TDA (MWh)** \\ \cline{2-4}  & **Storage Actions** & **Energy Storage Actions** & **TD (MWh)** & **TDA (MWh)** \\ \hline TPA-BiGRU & 8110 & 261.61 & 4540.77 & 146.48 \\ TPA-BiLSTM & 9028 & 291.23 & 4675.45 & 150.82 \\ BiGRU & 11,180 & 360.65 & 4920.41 & 158.72 \\ AROCS & 11,286 & 364.06 & 4858.65 & 156.73 \\ PI & 16,612 & 535.87 & 6108.60 & 197.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of energy storage regulation. Statistical significance tests: A paired \(t\)-test (\(\alpha\) = 0.05) is performed on the prediction error of TPA-BiGRU versus the baseline model (BiGRU) over 100 trials, and the results confirmed a significant difference in the RMSE distributions (\(p\) < 0.01), validating the reliability of the proposed model. 2.