Electric Power System Active Dispatch Based On Improved Particle Swarm Optimization

The fundamental task of the electric power industry is taking the security as a center,in an adequate and rational use of energy and ability to run the equipment,under the conditions to ensure safe and economic electricity generation.Regarding such important energy transformation of the electric power system,enhances the necessity of its operating efficiency, realizes its movement optimizes is obvious.The targets of power market operations are:promote competition between power plans, optimize costs,make daily plans,save energy,protect environment etc.on the basis of safe and stable power system operation.Particle swarm optimization(Partical Swarm Optimization-PSO) algorithm is a new theory based on swarm intelligence evolution of computing technology.This algorithm is proposed by American psychologist Kennedy and Electronics Engineers,Eberhart who were inspired by the foraging behavior of birds.Its easy implementation,high accuracy and fast convergence advantages attracted academic attention,and to solve real problems demonstrated its superiority. Currently,it has been widely applied to function optimization,neural network training,fuzzy system control,and other genetic algorithm applications.Since the standard PSO algorithm in the optimization process shown by certain questions:(1) Parameter control:a range of issues,how to select the appropriate parameters to achieve optimal results.(2) the lack of dynamic adjustment of the speed:climbing capability is not strong, sometimes up to a certain accuracy,it's hard to find a better solution.(3) Early:Particle Swarm premature convergence,so that optimization stagnation.(4) The late slow convergence.Therefore, we need to improve the standard particle swarm algorithm in order to reach the best results.This paper presents an improved particle swarm algorithm,using the method of improving algorithm parameters,the particle swarm optimization algorithm inertia weight and acceleration constants has been improved.The algorithm was applied to optimize the function,and compared to the standard particle swarm optimization algorithm,genetic algorithms,results prove this method is very effective.Then the improved particle swarm algorithm was applied to active power system optimization problems,Active for optimal scheduling of power system,this paper presents an improved particle swarm algorithm,which takes into account the volume of coal consumption of thermal power plants,pollutant emissions,as well as Lines loss.The multi-objective optimal problem can be transformed into single-objective optimal problem by means of respectively solving the various single-objective optimal problems and the definition of the objective of the individual membership function.The cost of power generation is minimized on the whole.This algorithm is based on the standard particle swarm optimization,has improved its parameter and limited the speed of its search.Apply it to 3 unit models of the power system,simulation results show that the algorithm has saved the time of searching,has fast convergence and the advantages of high accuracy.Finally,the improved particle swarm algorithm is used for simulation optimal scheduling of Jiaozuo grid,the cost of power generation on the Jiaozuo grid has been saved:the current situation has been improved.