Study On Optimization Of Home Intelligent Electricity Consumption

With the improvement of people’s living standards,household appliances are increasing,and the random use of household appliances is becoming more and more uncontrolled,which leads to large fluctuations of load in some areas and frequent power shortage in power grid.To protect the unnecessary waste of electric energy,the behavior of demand side is very important,which can also reduce the occurrence of power accidents to a certain extent.According to the concept of demand side response,power grid companies formulate different electricity price strategies to encourage residents to respond,so that users can actively participate in the "peak shaving and valley filling" plan,so as to ensure the safe and stable operation of power grid and save electricity charges for families.First of all,according to the power demand of the equipment load in the operation cycle,the commonly used household electrical equipment is divided into interruptible load and non interruptible load,and its model is established.For two kinds of load,this paper deeply studies genetic algorithm and immune clonal algorithm.Both algorithms have better global search ability.In this paper,the starting power consumption time is taken as the variable to seek the optimal solution,and the optimal time for users to start working is obtained.Then,based on the strategy of time-of-use price,the model is built.Firstly,the electricity consumption time is optimized for the purpose of saving electricity consumption cost.It is found that the optimized load fluctuates greatly,which is very unfavorable to the operation of power grid.Therefore,the original objective function is improved,and a peak valley difference index is introduced to measure the load fluctuation of power grid.The two objective functions are normalized and a new objective function is established,which considers both economy and load fluctuation.Then the genetic algorithm and immune clonal algorithm are used to optimize,and the load distribution map and power consumption distribution map are obtained through MATLAB simulation.Comparing the size of electricity charge and peak valley difference,the two algorithms can be effectively optimized,but the immune clonal algorithm is better.Finally,based on the strategy of real-time electricity price,this paper sets the demand effect function according to the different needs of users for household electrical equipment,and formulates the daily real-time electricity price according to the demand effect function.The real-time electricity price changes in real time according to the needs of users.After the real-time price is obtained,the time of use price and the real-time price are still used to optimize the power consumption respectively.Through MATLAB simulation,the power consumption distribution map within one day is obtained.Compared with the size of the electricity charge and the peak valley difference,the two algorithms can be effectively optimized,but the optimization effect of immune clone algorithm is better.The purpose of this paper is that residents participate in the demand side response,while meeting the daily electricity consumption of residents,by changing their own electricity consumption habits,they can respond to different electricity pricing schemes implemented by power companies,which can not only reduce the electricity expenditure for families,but also smooth the load fluctuation of power grid to a certain extent,and enhance the security and stability of power grid operation.Finally,it is concluded that the optimization effect of immune clonal algorithm is higher than that of genetic algorithm,whether it is based on time of use price or real-time price.