## 1 Introduction  
Joint control strategies of wind storage systems play a crucial role in enhancing the competitiveness and regulation of wind power in high-penetration markets [1, 2]. Energy storage systems achieve dispatchability comparable to that of conventional units by regulating the active output of wind power to track the dispatch schedule value [3, 4, 5]. Given energy storage's cost and capacity constraints, optimizing the wind storage control strategy to improve output accuracy and system economic efficiency is essential. Much previous research has been conducted on the joint control strategy of wind storage systems. It is mainly divided into two categories: One is the direct control strategy. That is, real-time section control is carried out according to the deviation between the actual output of the current wind farm and the planned value, which mainly includes mode decomposition [6], proportional-integral-derivative (PID) control [7], fuzzy control [8], Fourier transform [9], and so on. The direct control strategy can quickly respond to errors and has strong engineering practicability. However, in the case of large fluctuations in wind power, it is easy to cause problems such as the over-charging and over-discharging of the energy storage system and frequent regulation, which affects the final control effect and economy. The second category is the process optimization control strategy. Aimingat the wind power output error in the control period, this kind of control strategy establishes a mathematical model with the number of energy storage orders and throughput as the objective function and uses the optimizer to solve it. This type of control strategy continuously corrects the energy storage output during the control period through rolling calculations [10; 11], effectively reducing the number of energy storage actions and having high control accuracy. However, the solver may not converge in complex environments, which affects the control effect. If proportional-integral (PI) control is used when the solver does not converge, the stability of control can be improved to a certain extent. Therefore, the data after control of the above two control methods can be used as samples, and the excellent control characteristics of both can be learned using deep learning methods, which can enhance the stability and comprehensive performance of control. Firstly, wind power has high stochasticity and volatility [3]. Deep learning can deal with uncertainty problems well, and it performs well in the research of power system state estimation [12], transient stability [13], fault detection [14], and automatic generation control (AGC) [15]. In the research of wind power generation, deep reinforcement learning extracts uncertain relationships between inputs and outputs from massive, high-dimensional data from wind farms, which significantly reduces the omission of crucial information, and improves the coordination and economy of the wind storage system scheduling as well as the grid connection [16; 17]. In addition, deep learning can also cope well with wind power uncertainty [18]. The above research shows that deep learning has applicability and advancement for wind storage combined control strategy research. Secondly, there exists a certain time series of wind power. Among many neural network algorithms, both the long short-term memory network (LSTM) and gated recurrent unit (GRU) can handle time-series data well and are widely used in wind power prediction [19], wind speed prediction [20], and short-term load prediction [21]. In wind power prediction, the prediction performance of the two is equivalent, but compared with the LSTM network, the structure of the GRU is more concise, and the training speed is faster [21]. In addition, the bidirectional gated recurrent unit (BiGRU) can process the time series data in both directions and fully extract the connection before and after the time series data, which has been shown to improve the model's accuracy compared to GRU [22; 23].