Research On Microgrid Optimization Based On Improved Particle Swarm Optimization Algorithm

With the energy crisis and environmental pollution problems becoming more and more serious,the development and utilization of clean energy has become an inevitable choice for solving energy crisis and environmental pollution.The safety drawbacks of traditional power system power supply have made distributed power generation technology widely concerned.The microgrid is composed of a variety of distributed units,featuring high reliability,high energy utilization efficiency,flexible installation of distributed units,and the concept of energy saving and emission reduction.It can realize flexible operation of grid-connected mode and isolated network mode.It can effectively solve the defects of the traditional power grid,but the problems of micro-grid optimization scheduling,energy management,and operation control need to be resolved.Among them,the optimal scheduling of the system can improve the economic,stability and environmental protection of the system.Therefore,it is of practical value and significance to study the optimal operation of the microgrid.This thesis studies the optimization operation of microgrid,the main contents are as follows:Firstly,the operational characteristics of microgrid wind,light,storage,micro gas turbine and fuel cell are analyzed,and the corresponding mathematical model is established.The wind,light and load power prediction is the premise of system optimization scheduling.This thesis constructs an improved PSO-SVM prediction.Model,the combined kernel function of linear kernel function and radial basis kernel function is selected as the support vector machine(SVM)kernel function,and the penalty factor C and kernel of SVM model are adopted by particle swarm optimization(PSO)with adaptive weight coefficient and learning factor.The parameter ? is optimized.Using this model to predict wind,light and load short-term power,compared with SVM model and PSO-SVM model,improved PSO-SVM prediction model can improve prediction accuracy.The optimal operation problem of microgrid is a multi-objective,multi-constrained,multi-variable nonlinear optimization problem.Intelligent algorithms are widely concerned with their superiority,such as particle swarm optimization algorithm and genetic algorithm.Among them,the particle swarm optimization algorithm has the advantages of simplicity and good convergence.Aiming at the shortcomings of multi-objective particle swarm optimization(MOPSO),an improved MOPSO algorithm is proposed.Firstly,a binomial crossover operator is introduced as the second learning mode of particle updating.For external archive particles,the guiding particles are selected by the crowding distance;The redundant set with mutated particles is introduced into the elite archives to improve the diversity of external elite archives.Through the test examples,the results show that the improved MOPSO algorithm has better distribution and global search performance.Finally,the fuzzy model identification method is used to determine the optimal solution from the Pareto frontier solution set.For the microgrid system running on the grid,the function model and constraints are constructed with the economic cost,environmental cost and system operation risk as the target,and the scheduling strategy of the system connected to the grid is given.Based on the improved PSO-SVM model prediction and the short-term power data of wind,light and load,the improved MOPSO algorithm is used to solve the model.The validity of the model is verified by simulation.