Optimization Of Regional Integrated Energy System Considering Meteorological Information

With the improvement of the level of industrialization and the continuous improvement of new energy policies,the number of distributed power stations based on wind power and photovoltaic has gradually increased.Distributed power station uses local new energy to generate electricity,which can effectively reduce the use of fossil energy and reduce carbon emissions.However,its large randomness and strong volatility bring new problems to the stability of power grid.The integrated energy system includes electricity,heat,gas and other energy sources.By strengthening the coupling between various energy sources,the energy utilization rate can be improved,and the shortcomings of traditional distributed power stations such as large randomness and strong fluctuation can be improved to a certain extent.Therefore,the in-depth study of integrated energy system is of great significance to the development of energy field.This paper takes integrated energy system as the research object,discusses the influence of meteorological information on distributed power generation and load,studies the distributed power generation output and load prediction model of integrated energy system considering meteorological information,and solves the related problems of low-carbon optimal scheduling of integrated energy system.The main research contents are as follows:Firstly,the prediction model of integrated energy system considering meteorological information is studied.In this paper,the influence of meteorological information on distributed power generation and load in integrated energy system is analyzed by Pearson correlation coefficient method according to the collected wind power output and load data and corresponding meteorological information data.According to the corresponding conclusions,the prediction model of particle swarm optimization neural network algorithm considering meteorological information is established.Through the prediction simulation of a typical day of integrated energy system in winter,it is proved that the algorithm has faster response speed and higher prediction accuracy.Secondly,the low-carbon economic optimization scheduling model of integrated energy system is established.Under the condition of accurate prediction of the integrated energy system output,the system can effectively improve the reliability of operation,so considering the meteorological information related predicted on the basis of the prediction model for integrated energy system in the operation of the economy and the contradiction between the low carbon,set the P2 G,electric boiler and carbon trading of integrated energy system optimization models of low-carbon economy,discuss the influences of carbon trading on wind power given rate,and the particle swarm optimization bacterial foraging algorithm applied in the integrated energy system operation,the analysis of different carbon trading price on the result of energy conversion components run.According to the simulation results,the integrated energy system considering carbon trading can effectively improve the wind power consumption level of the system,and can give consideration to both economy and low carbon.