The most basic requirement of power economic dispatch is to reduce energy consumption, improve operating efficiency and achieve optimal operation on the premise of ensuring the security of electricity supply. Therefore, the economic dispatch plays a crucial role in the safe operation of the power system. Based on the fundamental theory of particle swarm optimization and quantum mechanism, this thesis presents two improved algorithms of quantum-behaved particle swarm optimization, which are applied to the problem solving of single-target economic dispatch. The thesis is mainly devoted to the following aspects:First, a novel algorithm of quantum-behaved particle swarm concerning the socialist characteristics of Cauchy distribution is introduced. Then the algorithm is tested through basic test function. As it turns out, the improved algorithm has better convergence and faster iterative speed.Second, since the Sobol sequence of random sequence has more uniform sampling than the uniform distribution, an algorithm of the quantum-behaved particle swarm optimization based on random Sobol sequence is presented with a bounded mutation operator in order to increase the diversity of particles. The test result indicates that the algorithm has good convergence, high speed and high accuracy.Finally, based on the analysis of the mathematical model, the improved algorithms are employed to solve the economic dispatch problem. A penalty function is adopted to handle the constraint conditions of power balance so as to construct more suitable fitness function, and the calculation procedure has been elaborated in the thesis. Then these algorithms are applied to13units,15units and 40units of two groups. The simulation result proves that the algorithms are both effective and feasible. |