Solving probabilistic optimal power flow with renewable energy sources in distribution networks using fire hawk optimizer

Abstract

The main contribution in this article is proposing a modern meta-heuristic algorithm for solving nonlinear, nonconvex, and complex OPF problems effectively in distribution networks, considering RES. The Fire Hawk Optimizer (FHO) algorithm is proposed in this work. The considered objective function (OF) is the combined minimization of generating cost, active power loss (Plosses), and load bus voltage deviation (VD). The proposed strategy is validated using the standard IEEE 69-bus benchmark. The obtained results are compared with various recent meta-heuristic techniques such as Capuchin Search Algorithm (CSA), Bear Smell Search Algorithm (BSSA), Black Widow Optimization (BWO) Algorithm, Walrus Optimization Algorithm (WaOA), and Prairie Dog Optimization (PDO) Algorithm. One-way ANOVA and the Tukey test, two common statistical tests, are used to compare algorithms effectively. The FHO shows the best and superior performance with minimum mean cost and standard deviation values of 51.2711 and 5.1131 respectively. This demonstrates that FHO results are more consistent and less diverse. The FHO shows a lower computational time of (25.3241 s) as compared with other strategies. The statistical results confirm the proposed approach's reliability, stability, and consistency. The proposed method provided reliable, high-quality results with faster computing times. Additionally, it has a significant convergence that makes it superior to solving OPF problems in distribution networks with variable RES and NRES.