A modified Lyrebird optimizer with cascaded PDn-PI controller for LFC in multi-area system including electric vehicles

– Electric vehicles (EVs) are becoming integrated into the power grid, providing ancillary services through their energy storage and active power control. This paper presents an enhanced Modified Lyrebird Optimizer (MLO) designed for a cascaded PDn-PI controller to manage Load Frequency Control (LFC) in areas with non-reheat thermal plants and EV fleets. The MLO incorporates Diversity Maintenance Learning Strategy (DMLS) and Memory-Based Learning Strategy (MBLS) to support Lyrebird populations and enhance exploration. The MLO is verified via several scenarios, considering different step load disruptions and the inherent properties of EVs. The proposed MLO significantly enhances the objective function by 18.91%, with a 24.03% mean objective score and 78.43% standard deviation compared to the standard LO. Also, several comparisons are implemented with other published controllers’ outcomes. The comparison confirms the strength of MLO, since it provides minimum fitness function as well as minimum over, under shoots and settling times values in contrast with GWO based I/PI/ PI–(1+‏DD) controllers. The MLO-tuned PDn-PI controller enhances transient performance and dynamic EV behavior, proving a promising solution for modern LFC challenges in smart grid environments, despite parameter uncertainties.