Optimal Power System Operation by a Novel Efficient Starfish Optimization Algorithm

Abstract

Optimal Power Flow (OPF) in contemporary power systems is inherently complex due to nonlinear interactions, multiple constraints, and dynamic operational conditions. This study introduces an innovative optimization framework based on the Starfish Optimization (SFO) algorithm, specifically designed to address multiple objectives concurrently: reducing fuel costs, enhancing voltage stability, improving voltage profiles, and minimizing both active and reactive power losses. The IEEE 30bus test system is employed as case study, with its performance rigorously compared against Moth Flame Optimization (MFO) and Jellyfish Search (JS) algorithms. Comprehensive simulations performed in MATLAB 2021b demonstrate that SFO consistently achieves faster convergence and higher solution quality. The results approve the robustness, practical applicability, and adaptability of the SFO-based OPF framework for real-world power system operations. It significantly impacts fuel costs, which decrease by $11.3 \%$, as well as the voltage deviation improves by $90.14 \%$, and the voltage stability index is increased by $33.22 \%$, and the active power losses decrease by $50.65 \%$.This study highlights the potential of advanced metaheuristic algorithms to optimize complex electrical networks efficiently, offering system operators a reliable and effective tool to simultaneously improve both economic and technical performance in modern power systems.