Research On Dual Game Energy Use Optimization Based On Behavioral Analysis Of Resident Users

Under the background of the " carbon peaking and carbon neutrality" strategy,the new power system dominated by new energy sources experiences a significant reduction in the regulation capacity of the power generation side due to its randomness and volatility.Therefore,the management of demand-side resources becomes increasingly important.With the implementation of demand response on the residential user side becoming more feasible,the grid company needs to analyze the electricity usage characteristics and behavioral differences of residential users to develop a reasonable demand response strategy that guides users to actively participate in response and maintain grid stability.As the electricity market reform advances,facing the conflicts of interest between the grid company and various types of residential users,the traditional single-objective optimization is difficult to solve the multi-party decision-making and interest optimization.Moreover,residential users are not entirely rational,and their stochastic decision-making behavior also makes traditional demand response optimization less applicable.Therefore,this paper uses game theory to solve this series of problems and focuses on the following research work:(1)A study on electrical behavior analysis for residential users.Firstly,a module is developed to extract similar days of electricity consumption behavior of residential users by analyzing the characteristics of similar days,and typical load curves of each resident are extracted from historical load curves of similar days through data preprocessing and density clustering methods.Secondly,the K-means++clustering method is applied to analyze the residential electricity consumption data and evaluate the clustering results.Then,the peak-valley membership of each period was constructed according to the semi-gradient membership function,and the peak-valley membership of different types of residential load was divided by fuzzy clustering.Finally,the data of residential load are processed and analyzed by an example.(2)Establish a residential user demand response and energy optimization model based on dual game theory.Considering the diversity of residential user decisionmaking,and the power grid company formulates multiple electricity price packages for users to choose from.Residential users can select and change their electricity plans according to the electricity price packages.The conflict of interest between the power grid company and resident users is constructed as a master-slave game model.Considering that residential users have limited rationality,an evolutionary game is used to construct a package selection model that more realistically depicts residential user decision-making behavior.In this game mechanism,the upper-level power grid company adjusts the electricity price packages through the game,and the lower-level residents select packages and optimize energy use through the game.Through comparative analysis of examples,the results show that the benefits of both sides of the game have been improved,achieving the goal of peak shaving,valley filling,and reducing power grid fluctuations,while also reducing carbon emissions,verifying the effectiveness of this method.