Research on Control Strategy of Hybrid Superconducting Energy Storage Based on Adaptive Dynamic Programming

Frequent charging and discharging of the battery will seriously shorten the battery life, thus increasing the power fluctuation in the distribution network. In this paper, a microgrid energy storage model combining superconducting magnetic energy storage (SMES) and battery energy storage technology is proposed. At the same time, the energy storage efficiency and the application scenario of superconducting energy storage are analyzed. In order to optimize the performance of the proposed microgrid energy storage model, reinforcement learning algorithm is used to solve the optimization strategy, and the feasibility of the energy storage model is verified by simulation analysis. The results show that the hybrid energy storage system is more conducive to the stable operation of power grid.