Metaheuristic‐Based Load Frequency Control of Multi‐Source Renewable Systems With SMES and AC‐DC Parallel Tie‐Lines

ABSTRACT Maintaining frequency stability in interconnected, renewable‐rich power systems is challenging due to low inertia and stochastic generation. This paper introduces a hybrid evolutionary exploration algorithm (HEEA) that combines the genetic algorithm (GA) and grey wolf optimiser (GWO) to co‐tune PID controller gains and superconducting magnetic energy storage (SMES) parameters in a two‐area, multi‐source system linked by parallel AC–DC tie‐lines. Compared to a triple‐PID controller without SMES (best classical baseline), HEEA with SMES reduces the settling time by 84.4% in Area 1 (6.8813 to 1.0732 s), 64.6% in Area 2 (5.6571 to 2.0008 s) and 53.0% for tie‐line power (5.0692 to 2.3798 s). Against contemporary metaheuristics (PSO, GA, GWO, SCA, ABC, WOA, DBOA and HHO), HEEA achieves the lowest ITAE (0.0003797) with faster convergence and improved overshoot/undershoot across 70 runs. Sensitivity tests variations of the parameter and renewable fluctuations under 25% and 50% confirm robustness. This approach enables grid operators to achieve faster recovery and tighter frequency regulation in renewable‐dominated multi‐area systems using AC–DC tie‐lines and SMES.