The application of the improved Particle Swarm Optimization on dynamic economic dispatch of power system with wind farms

Wind power technology has gradually matured as the development of new energy technology. The introduction of wind power brings significant impacts to the dynamic economic dispatch of power system. The goal of this study is to address the randomness, volatility and intermittent of wind power. To achieve this goal, a dynamic economic dispatch model compatible with the wind farm power system is firstly built. Based on the model, an algorithm is proposed to solve the problem. Firstly, we calculate the spinning reserve needed based on the probability distribution of wind power forecast error. The unit commitment is checked based on the priority list in this step. Secondly, we optimize results under various constraints using simplified gradient method combined with the improved Particle Swarm Optimization (PSO) algorithm. A preliminary solution from certain iterations using simplified gradient method is taken as one of the initial particle swarm. Further, we introduce the simulated annealing (SA) algorithm to avoid the local optimum solution and the premature convergence.