Optimization method for grid-forming energy storage parameters based on big data and new energy inertia

In response to the problem that the virtual synchronous machine control technology in grid-forming energy storage provides inertia support for the system, but also causes power oscillation and reduces the power angle stability of the system, this paper explores a parameter optimization method for grid-forming energy storage based on big data and new energy inertia. This method achieves precise optimization of parameters for grid-forming energy storage systems by deeply mining and analyzing big data on new energy generation and grid operation, combined with the dynamic characteristics of new energy inertia. The experimental results show that through intelligent control strategies and optimization of system topology, this method can significantly improve the efficiency of energy storage systems. The frequency fluctuation amplitude is minimized after power shortage occurs in the system, and the system frequency can quickly recover and stabilize. The stability and reliability of the proposed method in new energy high penetration power grids have been verified through the above analysis.