Enhanced Dynamic Response Stabilization in Next Generation Power Systems using an Advanced Optimized Nonlinear Cascade Iλ-PDμ-MPC Control Framework

The growing demand for electricity due to industrialization and modern lifestyles necessitates the integration of renewable energy sources (RESs) into electricity distribution networks. While RESs such as solar photovoltaic and wind turbines contribute to sustainable energy, their intermittent and uncertain nature characteristics poses serious challenges to frequency regulation and overall system stability. This study addresses these issues by proposing a hybrid power system (HPS) of distributed grid framework incorporating conventional generation, RESs, and advanced energy storage systems (ESSs) to attain a satisfactory performance by enhance frequency stability and power quality. In this paper, a robust and intelligent nonlinear-cascaded Fractional Order Integral Proportional Derivative with Model Predictive Control (Iλ-PDμ-MPC) controller-based efficient load frequency control (LFC) strategy is proposed to coordinate power flow, regulate frequency deviations, and mitigate the adverse effects of load fluctuations, renewable intermittency, and the significant influence of modern energy sources. To achieve optimal controller tuning and superior dynamic performance, a computational intelligence technique based modified improved fitness dependent optimizer (MIFDO) is employed, for improving system resilience against uncertainties, load fluctuations, and high RES penetration in a multi-sources integrated hybrid power system. Simulation results confirm that the proposed control scheme significantly reduces frequency deviations, minimizes settling time, and enhances the overall stability and resilience of the hybrid distributed power system under high RES penetration. The proposed intelligent control strategy effectively manages power generation, coordination, and frequency regulation within power system, ensuring reliable and smooth system operation under varying load and generation conditions. This research emphasizes the role of energy storage systems (ESSs) and mobile electric vehicles (MEVs) as flexible energy storage solutions, demonstrating their contribution to system stability, reliability and resilience. Simulation results demonstrate that, our proposed strategy reduces the settling time (TS) for integral squared error (ISE), integral time squared error (ITSE), integral absolute error (IAE), and integral time weighted absolute error (ITAE) by 48.73%, 59.84%, 59.42%, and 65.02%, respectively. The Iλ-PDμ-MPC controller reduces undershoot (USH) by 99.30% to PIDF, 83.33% to TIDF, and 77.78% to I-TDF. It also significantly decreases overshoot (OSH) for ITAE, IAE, ITSE, and ISE by 94.55%, 92.85%, 95%, and 97.84%, respectively. It achieves a mean computational time of 2.41s. The overall results show that, the proposed approach enhances the system's dynamic performance and maintains system frequency near 50Hz, under arising disturbances from integrating sources and load variations.