A novel weight-improved particle swarm optimization algorithm for optimal power flow and economic load dispatch problems

The optimization problems of optimal power flow (OPF) and the economic load dispatch (ELD) with valve-point effects in power systems are recently solved by some types of artificial intelligent (AI) algorithms. In this paper, based on improving the function of weight parameters, we present a novel weight-improved particle swarm optimization (WIPSO) method for computing two above problems. To evaluate the accuracy, convergence speed and applicability of the proposed method, we first compare the OPF results of proposed method to those of traditional particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), ant colony optimization (ACO) methods on the standard IEEE 30-bus system. We second compare our results of ELD problem to those of classical evolutionary programming (CEP), improved fast evolutionary programming (IFEP), and various improved particle swarm optimization methods on the IEEE 13-unit and 40-unit systems. The tested results indicate that the proposed method is more efficient than the others in terms of total fuel costs, total losses and computational times. Thus it can be applied to large power systems.