Gold eagle optimization algorithm to solve short term hydrothermal scheduling problem

Short-term hydrothermal generation scheduling (SHS), which accounts for diverse hydraulic and electrical restrictions, is a challenging non-convex optimization challenge. The interconnection of cascaded reservoirs and the valve-point effects of thermal units significantly complicate the process of identifying an effective solution. The purpose of the HTS is to assess the best power distribution over a certain time period to minimize overall energy production costs. The proposed methodology is subsequently evaluated on two established hydrothermal systems, and the outcomes are validated through several meta-heuristic methodologies. This work offers an effective resolution to the SHS issue by the application of a golden eagle optimizing (GEO) algorithm, which produces optimal scheduling outcomes by adjusting speed at various phases of the helical path during searching. They have a greater inclination to navigate and seek prey during the first phases of searching and a heightened tendency to engage in attacks during the concluding phases. A golden eagle calibrates these two elements to capture optimal food within the quickest practicable timeframe. The results indicate that the suggested method has significant resilience and surpasses other leading algorithms regarding solution quality. The proposed method primarily focuses on generation scheduling and minimizing fuel costs in thermal systems, yielding competitive outcomes with reduced computing demands. The fuel expenditure of a thermal plant, incorporating four sequential hydro plants, is $908,222.44 per hour, which is lower than the fuel costs indicated by other established adaptive methodologies in the modeling outcomes. The computing time of the suggested method is significantly lower than that currently used techniques.