Automatic generation control of a nuclear-renewable hybrid power system using optimal PID controller

This study investigates the Automatic Generation Control (AGC) of a nuclear-renewable hybrid power system using optimized PID controllers combined with Superconducting Magnetic Energy Storage (SMES). The system is modeled in MATLAB Simulink using state-of-the-art transfer functions, with the pressurized water reactor (PWR) simulated using a point kinetics model to accurately capture dynamic behavior. Six load variation scenarios involving changes of 100–200 MW in the NPP and 5–20 MW in the renewables are analyzed to assess system stability. Optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) are employed to fine-tune the PID gains. The results show improved frequency control: the NPP stays within 49.99–50.14 Hz, an improvement over the previous range of 49.97–50.48 Hz, while the renewables stabilize at 49.99–50.05 Hz, a significant improvement from 46.98–72.30 Hz. These findings confirm improved grid stability and control during sudden load changes. • Proposed an AGC scheme for interconnected nuclear-renewable hybrid grids. • Simulated nuclear PWR reactor using point kinetics model in MATLAB/Simulink. • Optimized PID controller tuned with GWO, GA, and PSO algorithms. • Integrated SMES to significantly enhance grid resilience and stability. • Achieved reduced frequency deviation and settling time under varying load conditions.