Optimal Power Flow using a Novel Harris Hawk Optimization Algorithm to Minimize Fuel Cost and Power loss

A novel nature inspired population-based Harris Hawk Optimization (HHO) method for solving optimal power flow (OPF) problem is proposed in this paper. OPF is a non-linear, highly coupled non-convex constrained optimization problem that minimizes the objective function considering both equality and inequality constraints of the system. In this work, HHO is employed as the main optimizer to adjust the control variables namely real power dispatch by minimizing the fitness function. The proffered method is a single objective approach that aims to minimize the selective objective such as fuel cost or losses of the system by meeting the growing electric demands. Thus, the dynamic nature of chasing pattern of hawks and escaping pattern of prey, results in the global optimal solution. The proposed method is coded using MATLAB software and the effectiveness of method is tested in standard IEEE-30 bus system. The results obtained under selective objective shows that the fuel cost and losses of the system are minimized compared to other well-known intelligence algorithms such as Bat, Butter-Fly (BF), Ant-Lion Optimizer (ALO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). It is inferred that the proposed method outperforms in terms of global search ability and convergence property.