Chaotic African vultures optimization based non-linear FOPID controller for frequency regulation in standalone microgrid system

Abstract Microgrids (MGs) are crucial in enhancing energy security, reducing carbon footprints, and promoting socio-economic growth in areas where traditional grid infrastructure is impractical or cost-prohibitive. These autonomous systems offer a robust and sustainable solution to energy accessibility challenges in remote areas. This study introduces a Chaotic-map enhanced African vultures optimization (CAVO) algorithm to optimally tune a non-linear fractional order PID (NLFOPID) controller for frequency regulation in a biodiesel-based standalone microgrid (SMG) network. The proposed algorithm integrates a chaotic logistic map into the African vultures optimization (AVO) framework to strengthen global search ability and maintain population diversity. The performance of the CAVO-tuned NLFOPID controller is evaluated under three different operating scenarios and compared with conventional PI, PID, and FOPID controllers, as well as standard optimization techniques including AVO, Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). The robustness of the suggested controller is also assessed under varying system parameters. Further, the proposed technique is validated through the OPAL-RT platform. The simulation results reveal that the CAVO-tuned NLFOPID controller achieves the lowest integral time absolute error (ITAE) of 0.0578, outperforming PSO, WOA, and AVO by 28.55%, 21.78%, and 16.47%, respectively.