Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis

Abstract

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems).