Adaptive Frilled Lizard Optimization for Enhancing Distribution Feeder Performance with Renewables and Charging Stations

The growing integration of renewable energy sources and electric vehicle (EV) fast-charging stations (FCSs) in power distribution networks introduces challenges such as increased power losses, voltage instability, and operational complexity.To address these issues, this study proposes Adaptive Frilled Lizard Optimization (A-FLO), an enhanced metaheuristic algorithm inspired by the predatory behaviour of frilled lizards.Unlike conventional optimizers with static control parameters, A-FLO introduces adaptive mechanisms that dynamically adjust exploration and exploitation strategies through modified hunting and climbing behaviours.A-FLO is employed to optimize the placement and sizing of distributed generators (DGs) and FCSs in electrical distribution networks (EDNs) under high EV penetration.The results demonstrate that A-FLO effectively reduces power losses, improves voltage profiles, and enhances voltage stability in IEEE 33-bus, 69-bus and 118-bus EDNs.Comparative evaluations with recent metaheuristic algorithms confirm A-FLO's superior convergence reliability, solution quality, and scalability.The proposed method shows strong potential for real-world smart grid applications involving the coordinated planning of renewable energy and EV infrastructure.