Optimization Of Household Electricity Consumption Based On Real-time Electricity

In order to achieve the double-carbon goal,develop clean energy,and actively promote the development of distributed energy on the user side,this thesis takes time-of-use electricity price and real-time electricity price policy as examples to study the household electricity optimization strategy based on photovoltaic energy storage system.The main contents of this thesis are as follows:（1）Power consumption optimization of smart home equipment.Firstly,the household electricity consumption and electricity price are analyzed,and the electrical equipment of smart home is determined.According to the schedulability of smart home equipment,smart home equipment is divided into flexible load and rigid load,and flexible load is divided into interruptible load and uninterruptible load according to whether smart home equipment can be interrupted or not,and the load energy consumption model is established.Then,under the timeof-use electricity price and real-time electricity price,the user cost,user satisfaction and peakvalley difference rate are taken as the optimization objectives,and the genetic algorithm is used to solve the problem,so as to optimize the scheduling of electrical equipment.（2）Research on scheduling strategy and capacity allocation of household photovoltaic energy storage system.The structure of household photovoltaic energy storage system is analyzed,and a bi-level optimal allocation model of household energy storage capacity considering battery life loss is proposed.This model is divided into external model and internal model.The objective function of the internal model is the annual maximum net profit within the planned time,and the objective function of the external model is the daily maximum profit.The internal and external models determine the optimal energy storage allocation scheme through the interaction between operating cost and battery capacity.On the premise of not reducing the battery life,the daily profit of the system is maximized,and finally the particle swarm optimization algorithm is used to solve this model.（3）Analysis and simulation of typical daily PV energy storage system operation.In this thesis,six typical days in a year are selected,and examples are simulated under time-of-use electricity price and real-time electricity price,respectively.The operation conditions of photovoltaic energy storage system in different typical days and different capacities are obtained,and the optimal energy storage capacity is configured on the premise of the highest profit for users.Therefore,the method in this thesis can reasonably configure the capacity of energy storage battery and maximize the user’s income.