The core of the innovation lies in the hybridization of two distinct metaheuristic algorithms. Particle Swarm Optimization is known for its rapid convergence but is prone to getting trapped in local optima, while Grey Wolf Optimization offers robust global exploration but can be slower in refining solutions. By integrating the hierarchical leadership mechanism of GWO with the velocity-update mechanism of PSO, the researchers created a unified optimizer that balances global exploration with local exploitation. This hybrid approach allows the algorithm to navigate complex, nonlinear search spaces more effectively, identifying optimal controller gains that minimize frequency deviations and tie-line power oscillations in interconnected power areas.

The study models an unequal two-area power system where Area 1 consists of thermal, hydro, and electric vehicle units, while Area 2 integrates wind, photovoltaic, diesel, and hydrogen-based energy storage. This configuration reflects the complexity of modern grids, where diverse generation sources and storage technologies must work in concert to maintain balance. The researchers used MATLAB/Simulink to simulate the system, incorporating transfer function models for each component, including an aqua electrolyzer and fuel cell for hydrogen storage. The objective was to minimize the Integral of Time-weighted Absolute Error, a metric that penalizes prolonged frequency excursions, which are operationally damaging to grid stability.

When tested under step load disturbances in Area 1, the hybrid GWO-PSO algorithm demonstrated remarkable performance. It achieved the lowest objective value with an ITAE of 0.29888, representing a 71% reduction compared to the PSO-only approach and a 28.1% reduction compared to the Slime Mould Algorithm. In terms of time-domain response, the hybrid controller reduced the frequency settling time to 4.6 seconds, outperforming both PSO and SMA. The frequency in Area 1 remained between 55.5 Hz and 62.5 Hz, while Area 2 exhibited the lowest peak frequency of 60.5 Hz among all tested optimizers, indicating superior damping of inter-area coupling effects. These results suggest that the hybrid algorithm can more effectively manage the dynamic interactions between different power sources and storage units.

The integration of hydrogen storage via an aqua electrolyzer and fuel cell played a significant role in the system’s stability. During grid imbalances, the electrolyzer acts as a controllable load, absorbing excess power to produce hydrogen, which can later be converted back to electricity by the fuel cell. Under the GWO-PSO tuning, the electrolyzer delivered a peak hydrogen production of 1.7 × 10⁻³ kg/s within two seconds of the disturbance, providing rapid grid support. This dual-phase behavior, characterized by a high-amplitude transient response followed by sustained steady-state production, effectively combines the fast initial response of PSO-optimized systems with the stable long-term output of SMA-optimized systems, enhancing the overall resilience of the power system.

Electric vehicle units were also included as a fast-acting resource in the control loop. The study found that EVs have a material impact on steady-state frequency regulation, helping to eradicate persistent frequency offsets that other storage technologies might not address. An ablation study revealed that disabling the EV units led to a non-decaying offset in Area 1 frequency, resulting in a significantly higher ITAE value. This highlights the importance of treating EV fleets as first-class resources in AGC design, rather than merely as secondary loads. The hybrid controller effectively coordinated the EVs and hydrogen storage to ensure that the system returned to nominal frequency quickly and accurately, even under significant disturbances.

Further analysis under simultaneous step load disturbances in both areas showed that the GWO-PSO controller maintained robust performance. The frequency settling time was reduced to 5.0 seconds, compared to 7.0 seconds for PSO and 6.0 seconds for SMA. The tie-line power settled within 3.8 seconds, demonstrating the controller’s ability to manage inter-area power flows efficiently. The hybrid algorithm consistently selected higher proportional and integral gains than PSO, indicating that it identifies more aggressive yet stable control actions. This allows the system to recover swiftly without sacrificing steady-state accuracy, which is crucial for maintaining reliability in renewable-dominated grids.

Sensitivity analyses confirmed the robustness of the proposed controller under varying conditions. When renewable penetration and system inertia were perturbed by ±20%, the controller remained stable, with performance declining gradually rather than suddenly. The system was found to be more sensitive to inertia variations than to load-damping uncertainties, but it handled both types of perturbations effectively. Additionally, the controller showed low sensitivity to load disturbance amplitudes, with ITAE values varying by less than 1% across different disturbance levels. This consistency suggests that the hybrid GWO-PSO approach is well-suited for real-world applications where grid conditions are constantly changing.

The computational complexity of the hybrid algorithm was also evaluated. A full optimization run took approximately 2.21 hours, dominated by the cost of simulation rather than algorithmic overhead. However, since this tuning is performed offline, it does not impact real-time AGC operations. The convergence characteristics of the hybrid algorithm were superior to its components, achieving the lowest final ITAE while maintaining a convergence speed comparable to GWO alone. This indicates that the hybridization provides genuine benefits in solution quality without compromising convergence speed, making it a practical tool for grid operators seeking to enhance frequency regulation in complex, multi-source power systems.

In conclusion, the study demonstrates that hybrid metaheuristic tuning offers a viable path toward more resilient frequency regulation in renewable-integrated power systems. By combining the strengths of GWO and PSO, the proposed algorithm effectively optimizes PID controller gains to handle the dynamic complexities of modern grids. The integration of diverse generation sources and storage technologies, along with the careful tuning of control parameters, ensures that the system can maintain stability and efficiency even under significant disturbances. This research provides valuable insights for grid operators and engineers working to integrate higher percentages of renewable energy into the power grid, offering a practical and deployable solution for maintaining grid stability in an increasingly complex energy landscape.

Subject of Research: Optimization of automatic generation control in renewable-integrated power systems using a hybrid GWO-PSO algorithm

Article Title: Optimization of automatic generation control in renewable-integrated power systems using a hybrid GWO–PSO algorithm

Article References: Uddin, M. I., Imam, A. A., Alabdullah, F. T., Messini, E. M. B., Alyamani, N., & Almohammedali, M. A. (2026). Optimization of automatic generation control in renewable-integrated power systems using a hybrid GWO–PSO algorithm. Results in Engineering, 32, Article 113439. https://doi.org/10.1016/j.rineng.2026.113439

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113439

Keywords: automatic generation control, renewable energy, power systems, hybrid optimization, PID controller, frequency stability, hydrogen storage, electric vehicles, metaheuristic algorithms, grid resilience, Optimization, automatic