Multi-objective Multi-Microgrid Economic Optimal Scheduling Based On Improved Particle Swarm Optimization

As human beings continue to develop natural resources,the gradual depletion of resources is a problem that has to be faced.Then,how to improve resource utilization has attracted more and more attention from experts and scholars.The combination of different distributed power sources in the microgrid system can greatly improve resource utilization and maintain the stability of the large power grid.As the scale of the microgrid system continues to expand,multiple microgrid systems emerge at the historic moment,making the systems connected Closer and more diverse scheduling methods.It is a very valuable question how to perform better power dispatching for multi-micro-grid systems,so that the overall system has better economy and environmental protection.With the development of intelligent algorithms,more solutions are provided for the economical operation of microgrid systems.This article builds a multi-micro-grid system model.In order to solve the multi-micro-grid system economic benefits,environmental protection benefits and diversified system scheduling problems,this article proposes an improved multi-objective particle swarm optimization algorithm(IM-MOPSO)Analyze and verify its feasibility,and provide concrete and feasible solutions for the multi-micro-network system economic optimization scheduling model.Firstly,the mathematical model of distributed power in multi-microgrid system is introduced.In this paper,the multi-microgrid system includes wind turbine,photovoltaic system,micro gas turbine,energy storage device and fuel cell.And analyze the cost of multi-micro-network system.Formulate control strategies for optimal operation of multi-micronets and objective functions under different operation modes.Then,an improved multi-objective particle swarm optimization algorithm(IM-MOPSO)is proposed.The particle swarm optimization algorithm has a simple structure and fast convergence speed,but it is prone to premature convergence,and the diversity and scalability will be damaged.This chapter makes corresponding improvements to the multi-objective particle swarm optimization algorithm.The global optimal solution selects the strategy of using the dominant tree to make the particles quickly fly to the optimal frontier;the update and maintenance strategy of the external reserve set is to specify the maximum and minimum size of the reserve set and Using the crowded distance method to maintain the reserve set can ensure the optimal solution diversity and extensibility of the external reserve set;the inertia factor and learning factor of the particle swarm algorithm have changed from static fixed values to dynamic changes,in order to make the algorithm in the early and late stages All have better search capabilities.Comparing the improved multi-objective particle swarm optimization algorithm with other algorithms,the results show that the overall performance of IM-MOPSO is superior to the comparison algorithm.Finally,the calculation example of the multi-micro-grid system is analyzed,mainly considering the grid-connected operation and the isolated network operation mode.Under the grid-connected operation mode,five objectives are proposed: the minimum economic cost of sub-microgrid 1,the minimum pollutant emission amount of sub-microgrid 2,the minimum total economic cost of multi-microgrid system,and the total value of multi-microgrid system The minimum value of pollutant discharge,the total economic cost of the multi-microgrid system and the compromise value of the total pollutant discharge.Under different decision goals,the multi-micro-grid system was optimized 24 hours a day,and compared with the multi-micro-grid system without electric energy exchange between sub-micro-grids,so as to draw conclusions.Under the solitary grid operation mode,the economic cost or pollutant discharge amount of a sub-microgrid in the multi-microgrid system is used as the decision goal to optimize the scheduling of the multi-microgrid system 24 hours a day.The results show that the use of IM-MOPSO to optimize the scheduling of the multi-micro-grid system in this article can improve the economics and environmental protection of the multi-micro-grid system,and ensure diverse scheduling needs.The paper has 51 pictures,27 tables and 81 references.