Research On Data Analysis And Routing Strategy Of Power Router

With the rapid development of new energy sources such as solar energy,wind energy,nuclear energy and biomass energy,energy Internet technology is gradually becoming well known,in the meantime,researches on power router,which is the core equipment of energy Internet,have increased sharply.This paper aims at the data analysis and routing strategy inside the power router,and studies the time-of-use electricity price data prediction and household electricity optimization strategy.On this basis,the routing strategy of the power router is optimized to minimize the total transmission loss.For the time-sharing electricity price data,a time series analysis method is used in this study to determine the time period of electricity price data and eliminate the cyclical influence.A zero-mean stationary time series can be obtained by zero-averaging the electricity price data.Then the auto-regressive moving average(ARMA)model is established,and the sample autocorrelation coefficient and partial autocorrelation coefficient of the electricity price data are calculated to identify the model.The hypothesis test is used to judge the tailing and censored property of the autocorrelation coefficient and the partial autocorrelation coefficient,the type and order of the model can be determined.Finally,the parameters of ARMA(2,3)are calculated by the least square method and applied in the model to predict the electricity price in the future.After obtaining the predicted data of the caller price,the household power router needs to manage the household power policy according to the characteristics of the household electrical equipment.This study analyzes the power generation equipment,energy storage equipment and power equipment of the home microgrid component,and establishes the household electricity model according to the operating characteristics of the power equipment.On the basis of the electricity consumption model,the influences of power generation equipment and energy storage equipment are added to establish a minimizing electricity consumption model.Then,the model is solved by greedy method.Combined with the predicted electricity price data,the final scheduling result is obtained so that the electricity cost is reduced on the basis of electricity price,and the total amount of electricity used in the area is calculated by the power consumption of the microgrid user.Finally,according to the energy storage and conversion loss of the power router and the power transmission loss of the DC transmission line,the power transmission loss model of the energy internet is established.Then the structural model of energy internet is designed based on the home microgrid unit,and the improved Dijkstra algorithm is proposed for the optimization goal,and the power transmission loss minimization model is solved to obtain the optimal routing strategy.Finally,the experimental verification and analysis of two different cases are carried out.The energy loss of the improved algorithm and Dijkstra algorithm are compared to verify the feasibility of the improved algorithm.