Efficient load frequency control in multi-source interconnected power systems using an innovative intelligent control framework

The goal of this paper is to develop an innovative intelligent controller, called TID-IC, to improve the efficiency and stability of multi-area multi-source power systems. The paper represents the first integration of brain emotional learning with tilted integral derivative control in load frequency control applications, representing a revolutionary step towards intelligent power system management. The equilibrium optimizer is used to tune the parameters of the TID-IC. This optimization algorithm is chosen for its ability to navigate the TID-IC controller's complex parameter space, providing an appropriate balance between exploration and exploitation capabilities crucial for dynamic power system environments. The performance evaluation of the proposed controller focuses on a two-area multi-source interconnected power system. Within this system, each area includes conventional power generation units like thermal, gas, and hydraulic plants, alongside renewable energy sources such as wind and solar. Considering system nonlinearities, parameter uncertainties, physical constraints, communication time delays, load perturbations, and variations in renewable energy sources, simulation results demonstrate the effective management of the load frequency control problem by the proposed controller. The TID-IC's effec-tiveness and superiority are further supported by a comparison of its performance against several established control techniques. Our findings demonstrate the TID-IC controller's superior performance in reducing frequency deviations, improving system robustness, reducing performance index values, and offering better disturbance rejection capability, outperforming conventional PID and other advanced controllers in diverse operational scenarios. These achievements point to a promising application of the TID-IC in real-world multi-source inter-connected power systems, representing significant progress in managing the complexities and uncertainties inherent in modern energy grids. Consequently, this research contributes to the development of more reliable and efficient energy grids, vital for integrating renewable energy sources and meeting the growing demands for sustainable power solutions.