Enhanced Automatic Generation Control in Multiarea Power Systems: Crow Search Optimized Cascade FOPI-TIDDN Controller With Integrated Renewable Solar Thermal Models and HVDC Lines

Abstract

As renewable energy sources (RES) are increasingly unified into multiarea power systems, automatic generation control (AGC) faces challenges such as frequency instability, longer settling times, and higher overshoot. While existing optimization techniques like Firefly (FF) and gray wolf (GW) suffer from slow convergence and local optima trapping, conventional controllers like FOPI, PIDN, TIDN, and TIDDN struggle to maintain stability under fluctuating load conditions. Fractional-Order Proportional-Integral with Tilt Integral Double Derivative and Filter (FOPI-TIDDN), a novel cascade controller optimized using the crow search (CS) algorithm, is proposed in this paper to overcome these issues. Furthermore, a two-area AGC framework incorporates realistic dish-Stirling solar thermal systems (RDSTS) and parabolic trough solar thermal plants (PTSTP), and the effects of these systems are examined under different patterns of solar insolation. Additionally, the study assesses how high voltage direct current (HVDC) tie-lines contribute to increased system stability. According to simulation data, the FOPI-TIDDN controller works noticeably better than others in terms of improved frequency regulation, faster settling time, and less overshoot. Compared to FF and GW approaches, the CS algorithm guarantees faster convergence. An ideal fixed-random solar insolation method and HVDC integration also improve system performance. The suggested method enhances renewable-integrated power systems' resilience, efficiency, and stability.