###### Abstract
To address the limitations of LFC under diverse operating conditions, superior advanced control strategies must be developed to enhance dynamic performance and robustness. Notably, among existing advanced control methods, sliding mode control (SMC) stands out for its inherent robustness against parametric uncertainties and external disturbances [23; 24; 25]. Furthermore, SMC achieves rapid transient response, accommodates nonlinear dynamics, and allows customization through diverse sliding surface designs for multi-objective optimization. Consequently, SMC has been widely adopted in nonlinear control domains such as aircraft and robotic systems, demonstrating superior performance. In power systems, SMC can be synergistically integrated with other control architectures to enhance grid stability. [PERSON] and [PERSON] synthesized a novel sliding mode fuzzy logic controller that significantly improves dynamic performance and stability in interconnected systems [26]. While conventional SMC is widely utilized due to its robustness and simplicity, its asymptotic convergence to equilibrium points limits transient performance. In contrast,terminal sliding mode controllers (TSMC) enable finite-time convergence, substantially accelerating settling speeds. This breakthrough has spurred extensive research. To mitigate chattering effects, [PERSON]; [PERSON]; [PERSON] and [PERSON] designed an intelligent TSMC based on PSO while maintaining disturbance rejection capabilities [27]. Fractional-order TSMC (FO-TSMC) achieved uniform voltage regulation under wide-ranging load variations and input voltage fluctuation [28]. Fast terminal sliding mode control (FTSMC) further enhances convergence rates near equilibrium by incorporating linear terms into sliding surfaces, outperforming conventional TSMC in dynamic response. And a novel global sliding mode control method was proposed to make the system robust globally in [29; 30] and the Lyapunov stability theory was employed to analyze stability [31]. In [32], a learning-based load frequency control approach was proposed to balance generation costs and frequency stability. In [33], a novel algorithm was designed based on an event-triggered mechanism, which can cause the frequency deviation of the power system to converge to a bounded value. In [34], a novel approach based on multi-event collaborative triggering mechanism was proposed, improving system reliability and computational efficiency. And a model-free adaptive dynamic programming (ADP) algorithm was designed, utilizing measured data to learn optimal control gains in [35]. Error serves as a critical metric for evaluating algorithm performance [36] and a novel algorithm deep neural networks trained by the twin-delayed deep deterministic gradient reinforcement learning policy was designed, significantly reducing steady-state error in [37]. In [38], an optimized approach combining model order reduction techniques with teaching learning-based optimization was proposed, achieving a 56.8% reduction in integral square error compared to conventional methods. In [39], a novel algorithm was designed based on particle swarm optimisation and deep artificial neural network, significantly reducing mean square error and improving the response speed. Based on the above analysis, extensive studies have been conducted on LFC for interconnected power systems with energy storage devices. However, these studies have not incorporated high renewable penetration scenarios. Furthermore, LFC methodologies based on PID controllers, advanced control strategies, and SMC with its extensions have been thoroughly investigated. In contrast, adaptive global fast terminal sliding mode control (AGFTSMC) exhibits exceptional robustness against system parametric uncertainties and external disturbances, while guaranteeing fast and precise finite-time convergence of system states to the equilibrium point.