Residents' Electricity Consumption Behavior And Evolutionary Game Under Intelligent Demand Response

With the State Council promulgated the new power reform No.9 document,China officially opened the prelude to the power market reform.At the same time,with the rapid development of smart grids,demand-side resources have become increasingly prominent in the electricity market.Resident users,as an important part of the demand side,in order to guide them to actively participate in the demand response,the power sales company needs to conduct research on its power consumption behavior in order to reasonably formulate price signals or incentive mechanisms.In addition,due to the limitations and blindness of the individual users themselves,they cannot fully understand the overall situation of the dispatch.At the same time,the randomness of their behavioral processes will also bring unstable factors to the traditional demand response analysis.As a result,evolutionary game theory is needed to solve this series of problems.The main research work of this thesis is as follows:Firstly,taking the electricity behavior of a single resident user as the research object,a framework for modeling the electricity consumption behavior of a single household user is given,including the following three modules.The similar day extraction module is based on the daily feature vector method to simulate the influence of internal and external factors on the electricity consumption behavior of single households.The single resident user's electricity behavior analysis module is modeled using statistics to simulate the historical operation of household appliances.The user's electricity consumption forecasting module predicts the electricity consumption behavior of the single-family resident user.In the end,the validity of the established model was verified by software simulation.Secondly,the electricity consumption behavior of the group residents is further studied as the research object,and a modeling framework for electricity consumption behavior of the group residents is established,including the following three modules.The family category analysis module is based on fuzzy C-means clustering analysis,which reflects the influence of family members' differences on residents' electricity consumption behavior.The typical household electricity behavior analysis module analyzes typical household electricity consumption in various types.Based on the central limit theorem and the Monte Carlo method,the community residents' electricity consumption prediction module simulates the types of household appliances,the power of household appliances,and the time difference between electricity consumption in each household.In the end,the accuracy and feasibility of the established model are verified by software simulation.Finally,this thesis studies the smart demand-side response technology based on the evolutionary game of residents' electricity consumption behavior.Based on the intelligent demand response analysis framework based on residents' electricity consumption behavior,a two-party two-strategy,a three-party two-strategy and a two-party three-strategy evolutionary game mathematical model were established.A symmetrical and an asymmetry evolutionary game model among the group of residents were established,depends on whether or not taking the differences of residents into consideration.Futhermore,a multi-strategy asymmetric evolutionary game model among group residents was established considering the diversification of electricity sales programs and smart electricity participation.Eventually,the simulation analysis of the resident user's intelligent demand response participation in three situations is conducted to verify the effectiveness of the evolutionary game model.The research work of this thesis proposes a method to analyze the electricity behavior of residents.The established evolutionary game model can intuitively reflect the changing trend of residents' electricity consumption decision after the electricity price is formulated,and can provide a theoretical reference for the electricity sales company to forecast and formulate the electricity price for residents' users.