An ICEEMDAN-based collaborative optimization control for wind-hydrogen-electrochemical energy storage under multiple application scenarios

Abstract

An improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-based collaborative optimization control strategy of wind-hydrogen-electrochemical energy storage coupled system with the interconversion characteristics between hydrogen with electricity under multiple application scenarios is introduced in this paper. After identifying the grid-connected wind power based on the ICEEMDAN algorithm, the normalized maximum discrepancy of the modal functions divides the high-frequency modal components into the fluctuating power smoothed by lithium iron phosphate batteries and hydrogen storage, with wind power curtailment from grid connection being consumed by electrolysis of water for hydrogen in alkaline electrolyzers. Another novelty is a collaborative optimization strategy for hydrogen-electrochemical energy storage under two application scenarios, comparing the smoothing effect and the ability to eliminate wind curtailment with different energy storage schemes. Demonstrate the method's effectiveness through the certain operational data from a Chinese wind farm. Simulation results indicate that the coupled system results in 19.45% and 7.79% cost reduction compared to other schemes, and the collaborative optimization control strategy achieves complete wind curtailment, which further improves the capacity of consuming curtailed wind power while smoothing fluctuations and providing certain engineering application value.