Research On Time-of-Use Electricity Price Optimization Based On User Electricity Consumption Characteristics

With the construction of smart grids,the role of demand side resources in the competitive market is being re-recognized.Demand response refers to users responding according to price signals and incentive mechanisms.The TOU price can effectively guide users to use electricity and adjust system load through price signals.A reasonable TOU price allows the user to actively change the power consumption,so as to achieve the purpose of load shifting.Due to the rapid development of intelligent measurement systems in power grids in China,users are increasingly sensitive to the reflection of TOU price.Different types of power users have different responses to TOU price.Therefore,it is necessary to analyze the response characteristics of different types of power users to TOU prices,and establish a reasonable adjustment mechanism.according to their respective characteristics.The main work is as follows:Firstly,processing and analyzing the collected power data.Based on the traditional K-means clustering algorithm,the improved K-means algorithm is obtained.Power users are clustered using the improved K-means algorithm to obtain different types of power users.Secondly,the basic theory of price elasticity of electricity demand is introduced.And the characteristics of different types of power users' response to TOU price are analyzed.Thirdly,the method of dividing the peak and valley time is introduced.The fuzzy membership function is used to calculate the peak-valley membership degree at each time point.According to the calculated peak-valley membership degree,the time period is divided by fuzzy clustering,and finally the peak and valley time division result is obtained.For reducing the Peak valley difference as well as peak load value and enhancing user satisfaction,a multi-objective optimization model is constructed.The principle and basic steps of the NSGA-II algorithm are introduced to solve the multi-objective optimization problem.Finally,the TOU price optimization model is verified by an example.Two division methods are adopted for the time division,which are divided according to the whole social load and the classification of user load.By comparing single-objective optimization,it is better to divide the scheme according to the whole social period.Comparing the single target and the multi-objective,it is concluded that although the multi-objective is not as good as the single target in the relevant indicators,the multi-objective can fully respond to the TOU price,and can fully take into account the user's electricity satisfaction.Therefore,the multi-objective optimization scheme is superior to the single-objective optimization scheme.