Leveraging chaos-enhanced turbulent water flow optimization for hydrothermal scheduling to reduce fuel costs

Due to rising energy needs and the need to reduce fuel prices while meeting operating restrictions, the Short-Term Hydrothermal Scheduling (STHTS) problem is a major difficulty in modern power generation. STHTS’s simultaneous linear and nonlinear limitations and objectives hinder its resolution. This research adds chaotic dynamics to the Turbulent Water Flow Optimization (TWFO) algorithm to tackle these issues, creating the Chaotic TWFO algorithm. Chaos expedites search space exploration, reduces premature convergence, and boosts convergence speed and reliability. The fourth chaotic map improves CTWFO performance on IEEE CEC-06 2019 benchmark functions. The Taguchi method has been adopted to select the best parameters of CTWFO. The technique is then applied to three STHTS test systems, yielding optimal running costs of $922,073.35, $41,442.36, and $162,162.24 for examples 1, 2, and 3. A novel repair mechanism meets reservoir storage limits. A comparative study with earlier methods, statistical assessments, and box plot visualizations over 30 runs demonstrates the algorithm’s efficacy, consistency, and robustness, making it a reliable hydrothermal power system optimization tool.