An improved material-inspired generation algorithm for load frequency control in EV-integrated power systems

The increasing penetration of electric vehicles (EVs) and interconnected multi-area power systems has intensified the complexity of load frequency control (LFC), necessitating robust and high-precision optimization techniques for controller tuning. Conventional metaheuristic algorithms often suffer from premature convergence and inconsistent performance when applied to nonlinear, multi-parameter control problems. To address these limitations, This paper proposes an Improved Material Generation Algorithm (IMGA) for optimal tuning of a cascaded PDn–PI controller to enhance LFC performance in a two-area interconnected power system with aggregated EV fleets. The IMGA incorporates a Quadratic Interpolation Process (QIP) to improve population diversity, convergence accuracy, and robustness, thereby overcoming the premature convergence limitations of the standard MGA. The proposed control framework is evaluated under multiple operating scenarios, including with and without EV participation and under different load disturbance conditions. Comprehensive comparative studies against several optimizers such as the standard MGA, Particle Swarm Optimization (PSO), Quadratic Interpolation Optimization (QIO), Shin Cosh Optimization (SCHO), and War Strategy Optimizer (WSO) demonstrate the superior performance of IMGA in terms of convergence speed, solution quality, and statistical robustness. Simulation results confirm that the IMGA-based controller significantly reduces frequency deviations and tie-line power oscillations, achieving up to 5.49% improvement in average IAE and 3.65% reduction in standard deviation. The IMGA achieves improvements of 40.42% over PSO, 20.64% over QIO, 51.47% over SCHO, and 80.62% over WSO in terms of the average IAE value. These results highlight the effectiveness of the proposed IMGA-based cascaded control strategy for reliable frequency regulation in EV-integrated power systems.