Optimal Configuration of Flywheel–Battery Hybrid Energy Storage System for Smoothing Wind–Solar Power Generating Fluctuation

The integration of energy storage systems is an effective solution to grid fluctuations caused by renewable energy sources such as wind power and solar power. This paper proposes a hybrid energy storage system (HESS) capacity optimization method combining flywheel and battery energy storage. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the original wind–solar power signal into a grid-connected signal and a leveling command signal. Low-pass filtering is then applied to separate the leveling command signal by frequency and assign it to the flywheel and battery of the HESS, respectively. Secondly, with the goal of minimizing the full lifecycle cost, a capacity optimization model for a flywheel–battery HESS aimed at minimizing wind–solar power fluctuation is established based on the particle swarm optimization (PSO) algorithm. Finally, a simulation analysis is conducted on a microgrid consisting of a 10 MW wind power generation system, a 10 MW solar power generation system, and a flywheel-battery HESS. The results show that the use of hybrid energy storage has a significant power smoothing effect, with a maximum power fluctuation rate of 3.2% in 1-min intervals and a maximum power fluctuation of less than 8% in 10-min intervals in most cases. Under the same stabilizing effect, the HESS reduces costs by 45.1% compared to single-battery energy storage.