Quantum-behaved Particle Swarm Optimization for Multiple-fuel-constrained Generation Scheduling of Power System

Abstract

This research proposes a quantum-behaved particle swarm optimization with a multiplier updating technique (QPSO-MU) for the multiple-fuel-constrained generation scheduling of power system. The quantum-behaved particle swarm optimization (QPSO) equips with a migration can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed algorithm integrates the QPSO and the MU that has merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the power economic dispatch problem of the multiple-fuel-constrained generation scheduling. Numerical results of two test systems indicate that the proposed algorithm is more suitable than previous approaches in the practical economic dispatch for the multiple-fuel-constrained generation scheduling of power system.