Frequency regulation of PV-reheat thermal power system via a novel hybrid educational competition optimizer with pattern search and cascaded PDN-PI controller

Maintaining a stable balance between generated power and load demand is a critical challenge in modern power systems, especially with the increasing integration of renewable energy sources like photovoltaic (PV) systems. This study introduces a novel hybrid educational competition optimizer with pattern search (hECO-PS) algorithm to optimally tune a cascaded proportional-derivative with filter and proportional-integral (PDN-PI) controller for load frequency control (LFC) in a two-area power system comprising a PV system and a reheat thermal power system. The proposed hECO-PS algorithm enhances both global exploration and local exploitation capabilities, resulting in superior convergence rates and solution accuracy. The controller's performance was evaluated under various scenarios, including a 10 % step load change and solar radiation variations, demonstrating significant improvements in frequency regulation. The hECO-PS tuned PDN-PI controller achieved a minimum integral of time-weighted absolute error (ITAE) value of 0.4464, outperforming conventional methods like the modified whale optimization algorithm and sea horse algorithm, which yielded ITAE values of 2.6198 and 0.8598, respectively. Furthermore, the proposed controller reduced settling time by up to 46 % and minimized overshoot by up to 40 %. These results confirm the efficacy of the proposed approach in enhancing system stability and reliability under dynamic operating conditions, suggesting it as a promising solution for LFC in modern power systems with high renewable energy penetration.