Optimization Of Photovoltaic System And Domestic Energy Demand Scheduling

Energy is the foundation of social welfare,economic and developmental conditions.Compared with other renewable energy sources(RES),photovoltaic technology continues to grow rapidly,with the advantage of replacing some of the electricity and less pollution.Nevertheless,the mismatch between intermittent solar energy,installation characteristics and residential load distribution suggests the need to provide solutions for energy flexibility,demand and supply / storage management.The main purpose of this paper is to use energy management systems combined with battery systems to address the instability of photovoltaic systems.At the same time,the author analyzes a case of a house in Brighton,England,which was collected by the author when she participated in the British exchange student project.The main contents of the study are as follows:(1)Establish a Simulink Photovoltaic power generation simulation model.The effects of temperature and solar radiation on photovoltaic power generation were studied by varying the values of temperature and solar radiation.(2)Photovoltaic power generation forecast by hourly.Analysis shows that climate type,temperature and solar radiation have an impact on photovoltaic power generation.In this paper,the method of temperature combined with solar radiation is used to select similar days.At the same time,the similar days and BP neural network are used to predict photovoltaic power generation.The results show that using the method of temperature combined with solar radiation to select similar days for PV generation prediction can reduce the error to less than 5% when there are enough samples.(3)Domestic energy management.Based on the battery energy storage system,the optimal management of energy demand is carried out by using the time-of-use price(TOU)and the priority division of household electric load.The system is implemented by Matlab programming,using the GUI as the user interface,and conducting household energy demand scheduling tests for household energy for one day,three days,and one working day.The results show that the system can reasonably allocate energy usage,peak shaving,reduce the user’s demand for electricity from large power grids,and combine time-of-use electricity prices to save electricity purchase expenses.Finally,the effectiveness of the study was verified by the photovoltaic cabin in Brighton,England,reducing the prediction error to 5%.Meanwhile,the energy management system rationally allocates the use of energy,reducing the user’s purchase of electricity.