An improved particle swarm optimization algorithm for unit commitment

This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles’ information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The convergence property of the proposed IPSO method is analyzed using standard results from the dynamic system theory and some guidelines are derived for proper algorithm parameter selection. A new adaptive strategy for choosing parameters is also proposed to assure convergence of IPSO method, and the proposed algorithm adopts the orthogonal design to generate initial population that are scattered uniformly over feasible solution space. Furthermore, this method combines relaxation technique to zero-one variable and penalty function method to transform the problem to a nonlinear continuous variable optimization one by taking into account more constraints. The feasibility of the proposed method is demonstrated from 10 to 100 unit systems, and the test results are compared with those obtained by Evolutionary Programming (EP) and Genetic Algorithm (GA) in terms of solution quality and convergence properties. The simulation results show that the proposed method is capable of obtaining higher quality solutions.