Research on Energy Management and Scheduling Algorithm of Lithium Battery Energy Storage System Based on Artificial Intelligence

In this paper, the energy management and scheduling algorithm of lithium battery energy storage system (ESS) based on artificial intelligence (AI) is studied, aiming at improving the energy efficiency of wind farm and the flexibility of power grid. Aiming at the intermittence and unpredictability of wind energy, this study introduces a framework for AI algorithms that incorporates wind power forecasting, grid demand response and ESS state monitoring. Through deep learning models such as LSTM and GRU, the accurate prediction of wind farm output and power grid load demand is realized. At the same time, the running state of ESS is monitored by CNN, and the charging and discharging strategy is optimized by reinforcement learning algorithm DDPG to minimize the running cost and prolong the battery life. Simulation analysis and experimental verification show that compared with traditional methods, AI method has obvious advantages in energy management efficiency, scheduling response speed and cost saving. The study presented in this paper offers a solid scientific foundation and technical assistance for the integration of large-scale wind power into the grid and the development of intelligent power systems.