Study On Intelligent Control Strategy Of Household Intelligent Electricity Under Electric Power Replacement

In recent years,China's foggy problem is becoming increasingly serious,and power replacement is an effective measure to alleviate the problem of air pollution.With the advance of power replacement and the development of intelligent electricity related technologies,it is possible to optimize the control of residential appliances.Through the analysis of the electricity behavior of the users on the residents side,and then put forward the corresponding optimization strategy of electricity,we can achieve the purpose of cutting the peak and filling the new energy.This thesis firstly analyzed the structure,interaction mode and implementation mechanism of intelligent electricity consumption based on the two power supply and demand model of the traditional time-based tariff mechanism and the electricity market.And then analyzed the characteristics of electricity behavior of home users,and extracted the main influencing factors that affect the behavior of household users.On the basis of this analysis,the forecasting method of user's electricity behavior based on support vector regression machine was proposed,and the simulation experiment proved that this method can predict the starting time of different users' electrical appliances accurately.Based on the analysis and forecasting of user behavior,this thesis also studied the optimal control strategy of home intelligent electricity under the mechanism of traditional time-of-use electricity price.This thesis analyzed the existing research results of intelligent power optimization strategy,put forward the concept of relevance degree of household appliances and established the correlation degree matrix of household appliances.Then,the cost minimization algorithm was established with the goal of the minimum cost of electricity for the user.The simulation results showed that the algorithm can effectively reduce the user's electricity cost and improved the user's load curve while ensuring the normal power consumption of the user.Finally,this thesis presented a new power supply model in the electricity market environment.The user licensed the electrical control to the load aggregator and received subsidies from the loader aggregator,while the load polymerizer could unified control of a large number of the same kind of load.In this thesis,a load group control strategy based on genetic algorithm was proposed,and an electric vehicle was taken as an example to simulate the algorithm.The simulation results showed that the algorithm can achieve the goal of eliminating the peak and filling the new energy under the premise of ensuring that the users' electricity consumption is not affected.