Optimization of storage hydropower operation using metaheuristic algorithms: a comparative study of the Budhi Gandaki Hydropower Project

Reservoir optimization is essential for maximizing the utilization of water resources. Assessing multiple optimization algorithms helps identify effective approaches to balance competing needs, such as maximizing power production, meeting environmental needs, water supply, and minimizing spills. This study investigates the performance of seven independent and hybrid metaheuristic algorithms (MHAs), including the football team training algorithm (unexplored in reservoir optimization problems), and evaluates their performance. As a test case, the Budhi Gandaki Hydropower Project (BGHEP) is analyzed for energy maximization considering physical and operational constraints. The results indicate that all MHAs provide consistently superior performance, with the hybrid genetic algorithm–particle swarm optimization showing superior performance among MHAs. Hybridization has improved the performance of individual algorithms in terms of computational efficiency and reliability. The maximum increase of dry energy and total energy is around 31 and 4%, respectively, compared with the non-optimized case. Results highlight the importance of using different MHAs for optimizing reservoir operation for sustainable water management.