Multireservoir system operation optimization by hybrid quantum-behaved particle swarm optimization and heuristic constraint handling technique

Abstract

Generally, the multireservoir system operation optimization (MSOO) is classified as a large-scale and multi-stage optimization problem with a set of complex constraints. Here, the goal of MSOO is chosen to determine the optimal operation policy of all the reservoirs to minimize the energy deficit of electrical system. In order to effectively resolve this problem, a hybrid quantum-behaved particle swarm optimization (HQPSO) is developed in this study. In HQPSO, the external archive set conserving the elite particles is used to provide multiple search directions for various agents; the modified evolution strategy and mutation operator are used to enhance the convergence rate of the swarm; while a practical heuristic constraint handling method is employed to address the complex physical constraints imposed on all the hydropower reservoirs. The simulations of 12 benchmark functions indicate that HQPSO can produce better results than several existing evolutionary methods. Then, two multireservoir systems are chosen to verify the performance of the proposed method. The results show that compared with the conventional methods, the HQPSO method can obtain scheduling results with better performances in reducing the energy deficits of power system. Hence, this paper provides an effective tool for the complex multireservoir system operation problem.