Research On Operation Optimization Of Wind And Solar Fuel Storage Microgrid System Based On Improved Particle Swarm Optimization

Under the background of environmental protection and increasingly scarce resources,the use of renewable energy technologies has further developed.Among them,microgrid technology is highly sought after for its high flexibility,low cost,and low pollution.At the same time,in order to overcome the problems of poor power quality and abandoned wind and electricity caused by renewable energy in microgrid,it is the focus of research to use advanced control methods and intelligent optimization algorithms to perform energy scheduling.The main research contents of this article are as follows:First,the development status and working principle of the fan,photovoltaic cell,energy storage battery,diesel generator and other components in the micro-grid are introduced.The corresponding mathematical model is established and selected reasonably,and specific parameters are given.This paper introduces MPPT maximum power tracking technology for wind and solar power generation,and provides a theoretical basis for subsequent calculation of wind and solar output.Secondly,the working principles and characteristics of the currently widely used intelligent optimization algorithms are described in detail,and two improved methods are given for the particle swarm algorithm used in this paper.The test functions are used to compare the optimization capabilities of several algorithms,and the most optimal ones are finally selected.The CPSO algorithm is suitable for this paper.The working principles and characteristics of the three prediction algorithms used to predict the temperature,wind speed,and load data used in this article are introduced.Finally,the most suitable prediction algorithm is selected.Third,taking a living park as an example,a microgrid system for storing wind and solar fuel is created,and the objective function with the lowest operating cost and corresponding constraints are given.Using the three prediction methods described above to process and predict the light intensity data in the park,compared with the actual value,the prediction algorithm that is most suitable for this article is the BP neural network,and then this method is used to calculate the wind speed,light intensity,Temperature and load data are predicted.Finally,an improved particle swarm optimization algorithm is used to optimize the analysis of the capacity configuration of each device andthe sum ofthe hourly cost of the microgrid system in a typical 24-hour hour.Then the concept of multi-objective optimization problem is introduced,and the principles and characteristics of traditional multi-objective optimization algorithm and evolutionary multi-objective algorithm are introduced.Based on this,the multi-objective particle swarm optimization algorithm in evolutionary multi-objective algorithm is improved.Establish the objective function with the lowest emission pollution,and form a multi-objective mathematical model together with the function with the lowest operating cost.Combined with the improved multi-objective particle swarm optimization algorithm to analyze the previous actual case,and verify the superiorityof the improved multi-objective optimization algorithm Sex.Finally,a relatively complete design scheme is given for the control system of the microgrid control.Detailed design schemes are given for the overall structure,hardware design,and software design of the system.Based on the Microsoft Visual Studio 2015 development platform,the design is made using C # language.A set of microgrid operation monitoring system,the work flow chart andhuman-computerinteraction interface of eachlink are given.