Enhancing power flow optimization with FACTS devices and wind power integration through a leader Genghis khan shark optimizer

This study introduces an enhanced variant of the Genghis Khan Shark Optimizer (GKSO) algorithm, designed to improve its search performance when solving the Optimal Power Flow (OPF) problem, particularly in power systems incorporating Flexible AC Transmission Systems (FACTS) devices and wind energy sources. The proposed approach, referred to as the Enhanced Genghis Khan Shark Optimizer (EGKSO), addresses the limitations of the original GKSO, which, despite its simplicity and effectiveness, may suffer from premature convergence and entrapment in local optima. To overcome these shortcomings, EGKSO incorporates significant improvements in both the exploration and exploitation phases of the optimization process. Exploration is enhanced through a stochastic mutation mechanism that promotes the discovery of new regions in the search space, while exploitation is refined using an adaptive strategy that intensifies the search around promising solutions. This combined mechanism improves the balance between global exploration and local exploitation, leading to more efficient convergence toward the global optimum. The performance of EGKSO is first evaluated using the CEC’17 benchmark functions and compared with several well-established meta-heuristic algorithms, including Artificial Ecosystem-Based Optimization (AEO), Marine Predators Algorithm (MPA), Manta Ray Foraging Optimization (MRFO), Hunter-Prey Optimization (HPO), and the original GKSO. The proposed algorithm is then validated on a complex and highly constrained OPF problem involving the integration of FACTS devices namely Thyristor Controlled Phase Shifter (TCPS), Thyristor-Controlled Series Compensator (TCSC), and Static VAR Compensator (SVC) as well as wind power generation. The results demonstrate that EGKSO achieves superior performance in terms of convergence speed, solution accuracy, and robustness when addressing complex OPF scenarios. These findings confirm the effectiveness of the proposed algorithm and highlight its potential for solving real-world power system optimization problems.