Multi-objective optimal power flow with wind-solar-small hydro power using non-dominated sorting genghis khan shark optimizer

In this paper, an enhanced version of the Non-dominated Sorting Genghis Khan Shark Optimizer (NSGKSO) is proposed to tackle complex multi-objective optimization problems, with a specific focus on the Optimal Power Flow (OPF) problem. The optimization incorporates renewable energy sources such as solar, wind, and small-hydro power, which introduce significant variability and complexity into the system. The proposed NSGKSO is based on the widely used NSGA-II framework but incorporates novel mechanisms to improve convergence and solution diversity. We evaluated the performance of NSGKSO on several OPF test cases and compared its results with other state-of-the-art algorithms. Our experimental results demonstrate that NSGKSO achieves a better balance between convergence speed and diversity preservation, making it well-suited for solving multi-objective OPF problems in the presence of renewable energy sources.