## 6 Conclusions  
This paper presents a joint control strategy for wind storage systems based on TPA-BiGRU. The proposed strategy can improve the economy and stability of the system. 1. Contribution of this study: 1. Based on the bidirectional gated recurrent unit (BiGRU) recurrent neural network, this paper uses its classification and regression calculation functions to \begin{table}
\begin{tabular}{c c c c} \hline
**Control Strategy** & **TD (MWh)** & **TDB** & **Number of Actions** \\ \hline TPA-BiGRU & 195.81 & 19.40\% & 467 \\ TPA-BiLSTM & 198.9 & 19.70\% & 485 \\ BiGRU & 220.67 & 21.87\% & 596 \\ AROCS & 225.38 & 22.33\% & 567 \\ PI & 289.63 & 28.70\% & 771 \\ \hline \end{tabular}
\end{table} 
Table 8: Energy storage regulation. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Control Strategy** & **Energy Storage** & **Number of Energy Storage** & **Total Cost** & **Total Revenue** & **Net Profit** & **Yield Rate** \\
**(CNY 10,000)** & **\({}^{*}\) & **(CNY 10,000)** & **(CNY 10,000)** & **(CNY 10,000)** & **Yield Rate** \\ \hline TPA-BiGRU & 1369 & 1 & 19,621.42 & 24,623.7 & 5002.3 & 25.49\% \\ TPA-BiLSTM & 1256 & 1 & 19,621.42 & 24,121.6 & 4500.18 & 22.94\% \\ BiGRU & 1147 & 1 & 19,621.42 & 23,520.11 & 3898.69 & 19.87\% \\ AROCS & 1395 & 1 & 19,621.42 & 21,413.6 & 1792.18 & 9.13\% \\ PI & 746 & 2 & 36,021.42 & 18,787.78 & \(-\)17,233.64 & \(-\)48.84\% \\ \hline \end{tabular} \({}^{*}\) All economic indicators in this study are reported in Chinese Yuan (CNY) to align with the market data and policy framework of the investigated wind storage project in China.  
\end{table} 
Table 9: Economic calculation results of the energy storage system. construct a deep learning model for wind storage joint control strategy. At the same time, the model introduces TPA to further strengthen its performance. The control strategy can adapt to the complex and variable operating conditions of wind farms and can obtain the wind and storage control results in real-time and quickly. (2) The proposed TPA-BiGRU framework operates in an offline training and online deployment paradigm for real-time control. Once the model converges during offline training, its online inference requires only lightweight computations via a pre-optimized approximation model. Specifically, the real-time control latency is reduced to 1.2 ms per decision cycle, which is negligible compared to the 15 s control interval of typical wind storage systems. (3) TPA-BiGRU can effectively improve the ability of the wind storage system to track the planned output and reduce the amount of penalty power. One day of TPA-BiGRU's largest assessment period penalty power accounted for only 1.8%, to meet the requirements of the grid rules of less than 2.5%, and effectively enhanced the market competitiveness of the wind storage system. (4) TPA-BiGRU can optimize the battery storage conditioning process and extend the battery storage life. Compared with the PI strategy, TPA-BiGRU extends the battery life by 83.51%, effectively reduces the battery operating cost, and increases the overall yield of the wind storage system by 74.33% over 20 years, thus further enhancing the market competitiveness of the wind storage system. (1) Adaptive hybrid control with dynamic data augmentation: While the TPA-BiGRU strategy enhances grid compliance metrics (Table 6), residual unqualified intervals persist due to the incomplete coverage of stochastic wind regimes in training datasets. To address wind-ramp edge cases, it is proposed to implement Discrete Fourier Transform (DFT) filtering during transient violations to refine battery dispatch signals, then iteratively update training sets via online learning. (2) Context-aware control via transformer-enhanced architecture: Building on transformer-based breakthroughs in power forecasting, the future is dedicated to deploying lightweight decoders. Integrate FEDformer's frequency decomposition blocks to handle multi-timescale inertial responses.