A Novel Harris Hawks Optimization Algorithm for Optimal Power Flow in Power Systems with Renewable Energy Integration

Abstract

Introduction: The integration of renewable energy sources (RESs) into power systems introduces significant challenges to solving Optimal Power Flow (OPF) problems due to their intermittent and uncertain nature. These challenges demand robust optimization methods capable of handling system non-linearity and variability.

Objectives: This study aims to develop an efficient and reliable optimization approach for solving the OPF problem in power systems with high-RES penetration, focusing on minimizing generation costs and improving overall system performance.

Methods: A novel Harris Hawks Optimization (HHO) algorithm, inspired by the cooperative hunting strategy of Harris hawks, is proposed. The algorithm incorporates stochastic modelling of RESs and is tested on the IEEE 30-bus system. Its performance is compared against Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

Results Simulation results show that the HHO algorithm achieves faster convergence, higher solution quality, and greater robustness compared to PSO and GA. It significantly reduces generation costs and effectively handles the uncertainty of RESs.

Conclusions: The proposed HHO-based approach offers a robust and efficient solution for OPF in renewable-rich power systems, contributing to more sustainable and reliable energy management.