Research On Demand Side Management Strategy Of Smart Grid Based On Load Clustering Analysis

Intelligence and informatization have become the inevitable trend of the development of power grid industry.As an important part of smart grid construction,power demand side management is an important way to implement load management.It can realize the optimal scheduling of load resources and generation side resources,and effectively respond to the growing demand of power consumption load.As an important economic measure in demand side management,time of use price has been promoted and developed in China.With the continuous construction of smart grid and intelligent measurement system,more and more users participate in the response to time-of-use priceprice.Different load characteristics of users have different response to time-of-use priceprice.It is necessary to establish a time of use price adjustment mechanism to meet the demand of different users.Based on this,this paper proposes to establish a time-of-use price selection model based on customer satisfaction evaluation.Firstly,the user load characteristics collected by smart meters are analyzed,and the improved k-means clustering method is used to mine the user load characteristics.The potential of users with different characteristics to participate in demand side response is analyzed to determine the implementation object of demand side response time of use price.Then,the overall load information of users is analyzed.The membership function method is used to divide the peak valley period of the user load.The electricity price is determined based on the peak valley price ratio coefficient,and is pushed to the user through the intelligent meter.Finally,the customer satisfaction with different pricing strategies is calculated by simulating the response of users to different electricity prices.The results of satisfaction can provide a reference for power grid enterprises to understand consumers’ preference for tou pricing strategy and participate in the process of demand response.It has guiding significance for the formulation of time of use price in smart grid.