## 3 Designing Control Strategy and Model  
### Joint Control Strategy of Wind and Storage Based on TPA-BiGRU  
The control process of the wind storage joint control strategy based on advanced rolling optimization control and PI control is divided into two parts. The first part is to determine the type of energy storage action. Both control strategies input the relevant data of wind farms and energy storage and judge the operation of energy storage by calculation. Only when the current control deviation is outside the dead zone does the energy storage need to be charged and discharged. Otherwise, energy storage will not act. The second part calculates the specific value of the charge and discharge of the energy storage. If it has been judged that the energy storage output needs to be adjusted, the adjustment power of the energy storage is determined by using the data of the first part.  
In this paper, the proposed wind storage joint control strategy uses three trained TPA-BiGRU models to realize the judgment and calculation functions in the above links to obtain a new wind storage joint control strategy. The function of the first link is to distinguish the action state of energy storage, that is, charging, discharging, and inaction, which can be realized by using a three-classification model. The function of the second link is to calculate the specific value of the charge and discharge power of the energy storage, which can be realized by using two regression calculation models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Indices** & \multicolumn{3}{c}{**Requirements**} \\ \hline \multirow{8}{*}{Wind turbine units} & & Installed capacity & 10 min change value & 1 min change value \\  & & (MW) & (MW) & (MW) \\ \cline{3-5}  & Maximum power & \(<\)30 & 10 & 3 \\  & variation limit & 30–150 & \(P_{N}/3\) * & \(P_{N}/10\) \\  & & \(>\)150 & 50 & 15 \\ \cline{2-5}  & Control accuracy & & \(\leq 3\%P_{N}\) & \\  & Response time & & \(\leq 120\) s & \\  & Overshoot & & \(\leq 10\%\) & \\ \hline \multirow{2}{*}{Traditional units} & 15 min power & \multirow{2}{*}{\(\pm 2.5\%\)} & \multirow{2}{*}{\(\mathrm{\SIUnitSymbolMicro}P_{N}\) is the installed capacity of the wind farm.} & \multirow{2}{*}{\(\mathrm{\SIUnitSymbolMicro}P_{N}\) is the installed capacity of the wind farm.  
Firstly, the energy storage action state is judged by the classification network. A control calculation is performed on each sampling point, and the relevant data at the judgment time are composed of the original data according to the input requirements and standardized. The average and variance of the variables are calculated when training the network. The processed data are input into the classification network to obtain the energy storage control state after the one-hot encoding with a length of 3. After decoding, three control states of energy storage are finally obtained, namely, \(-1\) (charging), \(0\) (inaction), and \(1\) (discharging). Then, the regression network calculates the regulated power value under the energy storage's charge and discharge state. When the energy storage state is in action, the input samples are fed again into the charging or discharging power regression network for calculation, and the network output is anti-standardized to obtain the energy storage charging or discharging power value.