Optimizing Reactive Power Dispatch in Electrical Networks Using a Hybrid Artificial Rabbits and Gradient-Based optimization

Abstract

The optimal reactive power dispatch (ORPD) problem is a critical factor in maintaining the safe and efficient operation of electric networks. Due to its mixed-variable nonlinear nature, addressing this problem requires an appropriate optimization algorithm. In this study, a novel approach based on the combination of Artificial Rabbits Optimization (ARO) and gradient-based optimization (GBO) algorithms was proposed to solve the ORPD problem in electric networks. The performance of the proposed AROGBO algorithm were verified on 7 numerical optimization test functions. To evaluate the effectiveness of this hybrid AROGBO technique, standard IEEE-30, IEEE-57 bus, and IEEE-118 bus test systems were employed, with two objective functions tested for each system: minimum total power loss and minimum total voltage deviations. The AROGBO, ARO, and GBO algorithms were utilized in each case to determine the optimal values of the generator voltage, transformer tap changer positions, and reactive power compensation values. A comprehensive comparison was made between the results obtained from the hybrid AROGBO algorithm, standard ARO and GBO algorithms, and other metaheuristic optimization techniques. The simulation attainments verify the accuracy and stability of the proposed AROGBO methodology in solving the ORPD problem.