Facing the economic challenges of significant frequency regulation wear and tear on thermal power units and short energy storage lifespan in thermal-energy storage combined systems participating in grid primary frequency regulation (PFR)

this paper proposes a novel hybrid energy storage system (HESS) control strategy based on Newton-Raphson optimization algorithm(NRBO)-VMD and a fuzzy neural network(FNN)for PFR. In the primary power allocation stage

the high inertia and slow response of thermal power units prevent them from promptly responding to the high-frequency components of PFR signals

leading to increased mechanical stress. To address the distinct response characteristics of thermal units and HESS

an NRBO-VMD based decomposition method for PFR signals is proposed

enabling a flexible system response to grid frequency deviations.Within the HESS

an adaptive coordinated control strategy and a State of Charge (SOC) self-recovery strategy are introduced. These strategies autonomously adjust the virtual inertia and droop coefficients based on the depth of frequency regulation and the real-time SOC.Furthermore

a FNN is constructed to perform secondary refinement of the internal power distribution within the HESS.Finally

simulations under various operational conditions demonstrate that the proposed strategy effectively mitigates frequent power adjustments of the thermal unit during PFR

adaptively achieves optimal power decomposition and distribution

maintains the flywheel energy storage’s SOC within an optimal range

and ensures the long-term stable operation of the HESS.