Study On Incentive Strategies Of Day-ahead And Real-time Demand Response Of Residents Users Based On Flexibility Analysis

Renewable energy has been developing rapidly in recent years because of its superior clean performance.However,the flexibility and absorption capacity of the power system is greatly influenced by the uncertainty and fluctuation characteristics of wind power and photovoltaic power generation,leading to the serious phenomenon of"three abandonment”.To achieve the goal of being "carbon neutral",the flexibility of the system needs to be further enhanced.With the deepening of power market reform and the rapid popularization of smart homes,demand response(DR)of residential users has become an important way for the load side to participate in the flexible interaction of the power grid and promote the consumption of renewable energy.However,due to a large number of residential users,dispersed distribution,and differences in power consumption behaviors,the strategy of DR incentive pricing based on user characteristics need to be further studied.Therefore,it is of great significance to classify and evaluate the flexibility of residential users according to their electrical characteristics,and then to develop personalized day-ahead and real-time dual-scale electricity price strategy,which can improve the participation of users in DR and enhance the consumption capacity and flexibility of the system for renewable energy.This dissertation studied the electricity price strategy in three aspects: residential user classification and DR flexibility analysis,day-ahead multi-hierarchical and time-of-use(TOU)electricity price model,and real-time flexible electricity pricing for residential users considering the forecasting error of renewable energy.It provides a reference for analyzing the DR capacity of residential users and formulating personalized pricing strategies,and thereby promoting the consumption of renewable energy in the power system.Specific research contents are as follows:Aiming at the problem that it is difficult to quantify the user’s flexibility,this study establishes the flexibility evaluation index of DR and proposes a flexibility evaluation method for user’s DR based on the DR scheduling model.According to the high-dimensional residential users’ electricity load data,the load curve features were extracted to reduce the dimension,and then the AP clustering algorithm was used to classify the users,and thereby generating aggregators by aggregating users with similar electricity characteristics.Considering the constraints of uncontrollable loads,interruptible loads,and translational loads of various users,a day-ahead economic scheduling model based on the dispatchable capacity of users is established to maximize the aggregator’s revenue.The flexibility evaluation index of residential user DR can be constructed from the perspectives of response quantity,response speed,and response frequency,and then the change of electricity consumption before and after the response can be obtained according to the dispatching results to calculate the DR flexibility.The effectiveness of the proposed method is verified by a modified IEEE 33 node distribution system.The results show that the theoretical and practical flexibility of all kinds of power consumption characteristics are the same relative size,and the actual flexibility is slightly less than the theoretical flexibility.Users with large power consumption and peak period of power consumption coinciding with the system peak period have higher flexibility,and these users participate in DR more times with greater DR capacity and potential.Aiming at the problem of determining personalized electricity prices for users with different flexibility,this study establishes a method of dividing the peak and valley segments of TOU electricity price based on trapezoidal density function and develops an optimization model of TOU electricity price considering price elasticity and user satisfaction.On this basis,an optimization model of multi-hierarchical TOU electricity price is built for the smart grid where contains multiple levels of residents with the target customers flexibility participating in DR.A Newton-Ralph method improved multi-objective Particle Swarm Optimization algorithm(MOPSO)is introduced to solve the multi-objective optimization problem to get the optimal TOU electricity price strategy for different users.The example analysis shows that the DR willingness and response income of users is significantly improved under the multi-hierarchical TOU strategy,and the overall flexibility of the system is improved simultaneously.Compared with the unified TOU price strategy,the refinement of user classification TOU price can further motivate users to increase their participation in DR,thus improving the flexibility of the power system.Besides,the synchronous optimization of electricity price among multiple aggregators considering the cooperative and competitive relationship among different aggregators,which makes the model more consistent with the market rules.Aiming at the problem that power generation can’t be consumed in real-time because of the prediction error of wind power and photovoltaic power generation caused by weather factors,this study develops a real-time rolling optimization scheduling model of DR considering user satisfaction.The renewable energy prediction error Copula function is fitted based on the historical scenery output data and obtain error scenarios by probability sampling.It analyzes the forecast error cost of renewable energy using the thermal power unit compensation mechanism and calculates the thermal power unit absorption forecast error cost.A real-time rolling optimization scheduling model is established to synergistically absorb the prediction error through the flexibility of the unit and user DA.The real-time electricity price can be calculated based on the dispatching results,and the real-time and day-ahead dual-scale electricity price strategy is synthesized by combining the hierarchical TOU day-ahead incentive price to calculate the change of user DR flexibility.The example analysis shows that the participation of user DA in real-time scheduling can help absorb the prediction error,and the cost is reduced compared with the system that only considers flexible units.Real-time electricity price is the supplement and correction of day-ahead multi-hierarchy TOU electricity price on the real-time scale.It considers the constraints of grid power flow,which can realize the double improvement of flexibility and economy of the high-proportion renewable energy power system.