Abstract
Load frequency control (LFC) is critical for maintaining stability in interconnected power systems, addressing frequency deviations and tie-line power fluctuations due to system disturbances. Existing methods often face challenges, including limited robustness, poor adaptability to dynamic conditions, and early convergence in optimization. This paper introduces a novel application of the sinh cosh optimizer (SCHO) to design proportional-integral (PI) controllers for a hybrid photovoltaic (PV) and thermal generator-based two-area power system. The SCHO algorithm's balanced exploration and exploitation mechanisms enable effective tuning of PI controllers, overcoming challenges such as local minima entrapment and limited convergence speeds observed in conventional metaheuristics. Comprehensive simulations validate the proposed approach, demonstrating superior performance across various metrics. The SCHO-based PI controller achieves faster settling times (e.g., 1.6231 s and 2.4615 s for frequency deviations in Area 1 and Area 2, respectively) and enhanced robustness under parameter variations and solar radiation fluctuations. Additionally, comparisons with the controllers based on the salp swarm algorithm, whale optimization algorithm, and firefly algorithm confirm its significant advantages, including a 25-50% improvement in integral error indices (IAE, ITAE, ISE, ITSE). These results highlight the SCHO-based PI controller's effectiveness and reliability in modern power systems with hybrid and renewable energy sources.