Monthly Hydropower Scheduling of Cascaded Reservoirs Using Genetic Algorithm with Simulation Procedure

Abstract This study introduces an innovative approach for the monthly hydropower scheduling of cascaded reservoirs in the Jinsha River, China, utilizing a Genetic Algorithm (GA) with a simulation procedure. The proposed methodology incorporates a field-leveling (FL) procedure within the GA framework, employing push-and-pull strategies to enhance the efficiency and quality of feasible solutions, particularly when guided by spillage minimization. The study sequentially optimizes firm yield, total energy, and spillage in order of priority. Comparative analyses with a Sequential Quadratic Programming (SQP) model demonstrate the superiority of GA in achieving an 8.3% improvement in firm yield at the highest priority despite higher spillage. Additionally, the study explores the convergence behavior of the GA procedure, highlighting its efficiency. The results emphasize the significance of spillage minimization in FL for preserving firm yield and present a comprehensive analysis of the scheduling outcomes for different hydrological scenarios, providing valuable insights for optimizing hydropower generation in cascaded reservoir systems.