Optimization Of Economic Dispatch Of Microgrid Based On Particle Swarm Algorithm

Recently,the problems of energy depletion and environmental pollution have become more and more serious,and the microgrid composed of various distributed power sources has been vigorously developed and used.However,due to the different structural and output characteristics of each distributed power source,some power sources are affected by external factors.Therefore,the microgrid has instability and volatility,which also brings difficulties to the management of distributed power output.The economic dispatch optimization of micro-grid is also an important trend in the research of micro-grid today,which is of practical significance.We choose the particle swarm algorithm with strong global optimization ability,simple algorithm and high efficiency among the intelligent algorithms to study,and in view of the shortcomings of basic particle swarm algorithm that are easy to fall into premature and local optimality,we propose an improved particle swarm algorithm to correct The microgrid system is optimized for economic dispatch.In the research of particle swarm optimization algorithm,it is found that it has the disadvantages of falling into premature and local optimum.The first reason is that although the inertia weight factor is constant,although linearly decreasing,it is not conducive to the convergence of the algorithm.Once falling into the local optimum,it is still difficult.Jump out;The second reason is that the original learning mode is "passive" in nature,which causes the particles to fail to jump out after finding the local optimal result in the search process.Aiming at these two problems of particle swarm algorithm,the inertia weight is randomly generated by introducing random inertia weight coefficients to improve the global search ability and increase the convergence speed,and the dynamically changing learning factor and time flight factor are used to improve the self-learning ability of particles and the interaction with the population.Communication and cooperation capabilities,thereby improving the local search capabilities of particles and the speed and accuracy of convergence,using the above two points to propose an improved particle swarm algorithm,and after experiments,it is verified that the improved particle swarm algorithm is more excellent.In terms of model establishment,firstly,the composition and working principle of each distributed power supply are introduced,and corresponding mathematical function models are established according to their characteristics,and the principles and strategies of dispatching optimization for microgrid economy are formulated,and a dynamic electricity price model is introduced.In this paper,a multi-objective function including economic cost and environmental governance cost optimization is proposed.Finally,the corresponding constraint conditions are formulated according to the output characteristics of the distributed power sources in the microgrid and the system balance,and the microgrid optimization dispatching model of this article is established.The optimized scheduling model is simulated on Matlab software,and the related results of the basic particle swarm algorithm and the improved particle swarm algorithm are obtained.Through the method of comparative analysis,it is concluded that under the optimized scheduling of the improved particle swarm algorithm,it is more reasonable and excellent in terms of power stability,economy of microgrid operation,and environmental governance cost.The use value also proves that the improved particle swarm algorithm has better convergence and accuracy.