Enhanced Power Plant Generation Management through an Improved Grasshopper Optimization Algorithm

This study introduces an improved algorithm called modified Grasshopper Optimization Algorithm [Modified-GOA (M-GOA)], designed to prevent the Grasshopper Optimization Algorithm (GOA) from getting trapped in local optima. The findings demonstrate that even small modifications can significantly enhance an optimization algorithm’s performance. To evaluate M-GOA’s effectiveness, it was applied to the optimization problem of multireservoir hydropower systems under baseline conditions and two future periods, capturing a range of climate change scenarios, namely RCP2.6, RCP4.5, and RCP8.5. The results indicate that M-GOA outperforms both GOA and particle swarm optimization (PSO) in achieving optimal objective function values, with a significantly lower standard deviation across five operating modes. In the Seimareh single-reservoir system, M-GOA achieves 93% of the total energy production capacity, compared to 70% and 91% for GOA and PSO, respectively. Additionally, M-GOA exhibits strong performance in multireservoir systems, consistently yielding low objective function values under various conditions. The algorithm demonstrates high adaptability to diverse climatic scenarios and long-term resource planning, highlighting its flexibility and efficiency in complex optimization tasks.