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This paper presents a security control method of Grid energy storage based on neural network model. The clean energy consumption effect of hybrid ESS was studied through a load forecasting method based on improved RNN (Recurrent Neural Network). Based on the current mainstream deep learning architecture, deep RNNs with different ring kernels were established to optimize the hybrid ESS model. The research results indicate that the curve obtained by this method is smoother after peak shaving and valley filling. The planned variance of this method is 43.037, which is 7.37% lower than the load variance of the literature method. It improves the stability of the distribution network operation and the absorption of photovoltaic and wind energy, reducing the cost of exceeding the limit of battery losses. The optimized operation status of microgrids can reduce costs, improve the security of microgrid systems, and better meet the proposed optimization goals.

Research on safety control method of power grid energy storage system based on neural network model