Research On Identification And Analysis Of Impact Factors For Peak Demand Reduction Under The Time Of Use Programs

With the development of national smart grid and the promotion of electricity market reform,the role demand side is gradually changing in essence.By participating in demand response programs,power consumers gradually move from passive to active direction,becoming an important participant in maintaining the safe,stable and highquality operation of power grid,improving the efficiency of power grid operation,promoting the use of renewable energy and energy saving as well as emission reduction.As a significant part of demand response concept,time of use(TOU)has been widely used due to its low control cost and stable user participation rate.It has become an effective means for power companies to guide customers to change their consumption patterns,thereby avoiding risks and maximizing market benefits.A large number of TOU programs have been carried out in many countries.However,the implementation effect of the projects usually fails to achieve the expected goals.Especially for residential TOU programs,the effect of different projects varies a lot,but the reasons behind the differences are still unclear.The identification and analysis of the impact factors of peak demand reduction(PDR)effect under the time of use can help explain the reasons for the differences in the implementation effect of various programs,provide key and effective information for power companies and policy makers,and play an important role in the improvement of existing TOU programs as well as the planning and design of new projects in the future.In order to comprehensively explore the impact factors of peak demand reduction,a PDR quantifying model based on difference-in-difference model is proposed to describe the PDR characteristics for a single customer.Then,all the customers are divided into three groups according to their PDR distribution features using K-means algorithm.For the purpose of analyzing the relationship between load patterns and the PDR,an adoptive DBSCAN algorithm is presented in the paper to extract the load patterns of customers and the chi-square test is adopted to conduct the analysis.After that,an enhanced Apriori algorithm is presented to explore the impacts factors of PDR covering four categories of household characteristics including dwelling characteristics,socio-demographic,appliances and heating and attitudes towards energy.The analysis results of a case study illustrate that PDR level cannot be obtained simply based on the appliance's ownership and its usage habits.Socio-demographic information of households should be taken into consideration together;Internet connection and good house insulation contribute to the increase of PDR levels.Moreover,the percentage of renewable generation for households also show relationship with PDR.Finally,this paper introduces a concept of users' electricity consuming behavior driving based on community interactive information feedback.And then based on the questionnaire data collected in Qinhuangdao city,this paper discusses the response behavior of residential users under the influence of different community information feedback and the application of community interactive information feedback to enhance the PDR levels of users participating in TOU program is analyzed further.The proposed analysis framework and findings will associate retailer to improve the benefits of TOU programs and guide policy makers to design more efficient energy saving policies for residents in the future.