Optimal Power Flow using Hybrid Metaheuristic Algorithms with RES and EV Integration

The growing integration of renewable energy sources (RES) and electric vehicles (EVs) in modern power systems introduces new challenges in solving Optimal Power Flow (OPF) problems due to the uncertainty and variability of generation and demand. This paper proposes a novel hybrid metaheuristic algorithm based on Harris Hawks Optimization (HHO) and Salp Swarm Algorithm (SSA) to solve the OPF problem in systems with wind, solar, and V2G-enabled EVs. The objective is to minimize total fuel cost, reduce emissions, and improve voltage profile while satisfying system operational constraints such as power balance, generator limits, voltage bounds, and line flow limits. The performance of the proposed method is tested on standard and modified IEEE 57-bus systems. Simulation results demonstrate that the hybrid HHO-SSA algorithm outperforms individual HHO, SSA optimization techniques and conventional optimization methods such as GA and PSO in terms of convergence speed, cost reduction, and voltage stability, especially under high RES and EV penetration scenarios. Results obtained with the proposed strategy shows 14.8%, 19.7% and 4.9% improvement in minimization of total fuel cost, emission reduction and voltage profile improvement respectively compared to current ongoing research.