Voltage Situation Prediction Of Low-voltage Stations Considering Scale Electric Heating And Photovoltaic Access

In order to win the "Blue Sky Defense War" and promote the continuous transformation of social energy use models to clean and low-carbon,it is of great significance to develop and utilize new energy sources and realize electric energy substitution.Due to the implementation and implementation of photovoltaic poverty alleviation policies,low-voltage station areas are showing a trend of high-density access.At the same time,in order to achieve clean heating,the country vigorously promotes the electric heating policy,especially in rural areas showing the phenomenon of large-scale access to electric heating equipment.The electricity consumption characteristics of electric heating are affected by the weather and region,especially after interacting with photovoltaics,they will have related overall characteristics.The superimposed influence of the rapidly growing two types of equipment on the grid voltage is bound to be a prominent problem,plus the level of intelligent rural grid Low,lack of technical means and management measures,resulting in the inability to quickly perceive the grid operation situation and become more passive in the grid operation management.Therefore,in order to control the operation situation of the grid under the large-scale photovoltaic and electric heating access,it is necessary to urgently improve the operation situation awareness and prediction capabilities of the station area to make the safety management of the low-voltage station area more active,intelligent and safe.The specific work of this paper is as follows:Firstly,the types of electric heating are analyzed in detail,and the direct heating and thermal storage in the decentralized electric heating are the main research objects;the house heat demand model is established based on actual meteorological data and thermodynamic principles,and the direct heating and thermal storage are combined.Electric heating behavior characteristics,deduced two types of electric heating in the "peak-valley electricity price" operation mode and "photovoltaic + electric heating" operation mode electricity consumption model,for the analysis of distributed electric heating and photovoltaic access low-voltage stations The influence of the district on the grid voltage and the improvement of the ability to predict the voltage of the district will lay the foundation.Secondly,in order to analyze the impact of electric heating and photovoltaic access on the voltage of the low-voltage station area,a set of voltage statistical indicators for analyzing the impact of electric heating and photovoltaics on voltage is proposed;from modeling methods,simulation functions,and user-defined extensions Explains the basic principles of Open DSS(Open Distribution System Simulator),using its photovoltaic modules to obtain photovoltaic power;combining different configurations and operating modes of electric heating,using Open DSS running power flow function to analyze the impact of electric heating and photovoltaic access to low-voltage stations,and obtain various voltage statistics indicators,to provide data preparation for the realization of power grid situation prediction and safety management.Finally,a situation prediction method for low-voltage station areas driven by actual weather data based on deep neural network algorithms is proposed,which can predict the operation situation of low-voltage station areas and give quantitative evaluation index values for the operation situation of low-voltage station areas.Wind speed and light intensity 9-dimensional meteorological characteristics and a multilayer neural network model with various voltage statistical indicators as the output,continuously adjust the model parameters to obtain the optimal model;through the example simulation,it can be verified that the proposed method is accurate and effective It reduces the complexity of processing and calculating data with the mechanism model.