Optimization of automatic generation control in renewable-integrated power systems using a hybrid GWO–PSO algorithm
Year: 2026
Type: article
Abstract: The growing penetration of low-inertia renewable generation makes automatic generation control (AGC) increasingly difficult, as it widens frequency deviations and tie-line power mismatches during load disturbances. This paper proposes a hybrid Grey Wolf Optimizer-Particle Swarm Optimization (GWO-PSO) algorithm to tune a proportional-integral-derivative (PID) controller for AGC, combining the global exploration of GWO with the local exploitation of PSO. The controller is tested on an unequal two-area renewable-integrated power system modeled in MATLAB/Simulink, in which Area 1 contains thermal, hydro, and electric vehicle (EV) units, and Area 2 integrates wind, photovoltaic, and diesel generation together with an aqua electrolyzer and fuel cell for hydrogen-based storage. Rate limiters and dead bands are included to reflect realistic dynamics, and the Integral of Time-weighted Absolute Error (ITAE) is used as the objective function. The proposed optimizer is benchmarked against PSO and the Slime Mould Algorithm (SMA) under step load disturbances applied to one area and to both areas simultaneously. For a step load in Area 1, GWO-PSO achieves a frequency settling time of 4.6 s, faster than PSO (5 s) and SMA (4.8 s), and the lowest ITAE of 0.29888, an improvement of 71% over PSO (1.0315) and 28.1% over SMA (0.41569) under the single-area condition. Under the two-area step load disturbance conditions, SMA achieves the lowest ITAE and the smallest undershoot, while GWO-PSO enables the system to have the fastest settling time and the most balanced dynamic response overall . The aqua electrolyzer and fuel cell provide complementary support under GWO-PSO, combining a fast transient hydrogen response with sustained steady-state production. The results show that the hybrid GWO-PSO offers an effective and robust solution for frequency and tie-line power regulation in multi-source, renewable-integrated power systems.
Source: Results in Engineering
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,940
OpenAlex ID: W7220905979