Analysis Of Power Consumption Characteristics Of Urban Residential Users And Study On Cooperative Optimization Strategy

With the constant improvement of residents’ income,the total quantity of resident household appliances in our country has reached a high level.At present,due to the lack of scientific and comprehensive strategic support,there are serious problems of waste and low energy utilization rate in the daily electricity consumption of Chinese residents.Household electricity consumption has become an important cause of peak load in the power grid.Household appliances load has the characteristics of diverse response resources,strong controllability,large potential of aggregation,and there is a large space for optimization.Therefore,optimizing the electricity consumption methods of residential users is of great significance for alleviating the power supply pressure of the power system,improving energy utilization efficiency,and achieving coordinated development of electricity,economy,and environment.Based on the above background,this paper conducts research on the electricity consumption characteristics and collaborative optimization strategies of urban residential user devices.Firstly,this paper focuses on the collaborative optimization of urban residential users’ electrical equipment,introduces the research background and current status of residential users’ electrical characteristics and collaborative optimization strategies for electrical equipment,clarifies the research background and significance of the topic,and sorts out the research status and development status of residential users’ electrical characteristics and collaborative optimization strategies for electrical equipment at home and abroad.Next,the panoramic architecture of urban residential user equipment is constructed,and the components of urban residential user electrical equipment are introduced.The characteristics of equipment load of residential users are analyzed and the load of main household appliances is classified and modeled.The model of photovoltaic power output and energy storage battery is established according to the working characteristics of photovoltaic power supply and energy storage battery to provide theoretical support for the subsequent research on collaborative optimization strategies of urban resident user devices.Then,focusing on the optimization scheduling problem of individual residents,the Bayesian neural network is introduced into the analysis of the consumption behavior of residential users,and a low-carbon economic scheduling model is proposed in which the controllable resources of individual residents participated in the collaborative interaction of the power grid.Concretely,considering the time characteristics of residential load and its correlation with external environmental factors,the probability model of residents’ flexible resource energy consumption is constructed based on Bayesian network,and the time series characteristics of residential users’ electricity consumption behavior are analyzed to realize the load comfort modeling of residential appliances considering the time series characteristics;Simultaneously introducing real-time carbon emission factors and considering constraints such as user comfort,a low-carbon economic dispatch model for residential flexible resources considering load timing characteristics is proposed;And through numerical examples,it is verified that the proposed optimization model can effectively reduce carbon emissions on the user side while improving user electricity economy.Finally,focusing on the optimal dispatching of residential load clusters,considering the carbon emission reduction factors,this paper proposes an optimization model of residential load aggregators’ power purchase considering master-slave game and demand response.Concretely,a comprehensive electricity price model considering real-time carbon emission factors is constructed to quantify the responsibility of power consumers for carbon emissions,and based on this,an optimization model of residential load aggregators’ electricity purchase considering master-slave game and demand response is proposed.The model takes the power supplier as the leader,and its optimization objective is to guide the load aggregator to carry out peak cutting and valley filling at the lowest possible cost.Each load aggregator as the follower,its optimization goal is to reduce electricity consumption.The model is solved by combining genetic algorithm and backward induction.The results show that the two-layer master-slave game model can mobilize the enthusiasm of residential users for energy conservation and emission reduction,and improve the economy of both the power supply side and the user side at the same time.