Multi-objective Optimization Of Microgrid Based On Improved Particle Swarm Algorithm

In the face of the increasing energy shortage in the world,the development of more energy-saving,more reliable,more reasonable and cleaner power supply is a feasible strategy to face the energy crisis.Microgrids are flexible,efficient,energy-saving and environmentally friendly,which can fully reduce the voltage supply and improve the reliability of power supply,so the research of microgrids is very relevant and it is important to optimize the scheduling of microgrids.In this paper,the main components of the microgrid are firstly introduced,and the wind power plant,photovoltaic power plant,diesel generator and energy storage battery are selected as the main components of the microgrid,and the four main units are analyzed,and the corresponding objective functions and constraints are set to form the microgrid scheduling model.The subsequent prediction of wind power and photovoltaic units and the corresponding load is carried out by the improved LSTM of quantum particle swarm.Based on the established microgrid optimal scheduling model and forecast data,the scheduling model is optimized and compared by the improved multi-objective particle swarm algorithm,and various microgrid scheduling strategies are set for analysis.In the improvement of multi-objective particle swarm algorithm,the basic multi-objective particle swarm algorithm is analyzed,and the idea of good point set proposed by mathematician Hua Luogeng is used to improve the multi-objective particle swarm algorithm for the shortcomings of generating the final optimal solution when the initial value is not good.The improved multi-objective particle swarm algorithm has a more uniform distribution of particles when generating the initial particle population,and the initial values are more perfect and applicable.The improved multi-objective particle swarm algorithm is used in the model optimization session to find the optimal solution and make a comparison,which verifies the superiority of the improved multi-objective particle swarm algorithm in dealing with the optimal scheduling problem of microgrid.