Metaheuristic-Based Load Frequency Control for EVs-Integrated Hybrid Power Systems with Delays

This paper proposed a load frequency control (LFC) to ensure power quality of a two-area interconnected hybrid power system (PS) involving integrated electric vehicles (EVs) and communication delay. The control framework consists of three optimization-based proportional-integralderivative (PID) controllers named genetic algorithm (GA-PID), particle swarm optimization (PSO-PID), and grey wolf optimization (GWO-PID) controllers, and fractional order proportional-integral-derivative (FOPID) for improving the system's stability and performance. The performance of the proposed controllers for handling the challenges arising from EV integration, communication delay, and renewable energy sources (RESs) intermittency has been assessed. A thorough performance analysis is subsequently carried out according to the simulation results and various error indices, including integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE). The findings show that the proposed GA-PID controller performs better dynamically than the PSO-PID, GWO-PID, and FOPID controllers in terms of robustness and convergence rate. The results suggest the improvement of LFC in power systems through optimization algorithms.