Demand response declaration method and system based on user feedback and electricity price index

Abstract

The invention relates to a demand response declaration method and system based on user feedback and an electricity price index. The method comprises the following steps: screening out a user whose user type and response type satisfy a current demand response as a participating user; constructing an electricity price and electricity quantity relation model and an electricity price and electricity quantity index model according to the historical demand response; predicting market electricity price and electricity quantity meeting the current demand response according to the electricity price and electricity quantity relation model and the electricity price and electricity quantity index model; according to the predicted market electricity price and electric quantity, constructing a declaration model by taking aggregator income maximization as a target; determining a user income distribution method after declaration according to the declaration model according to the stage of the aggregator participating in the demand response; constructing a user satisfaction evaluation system, and determining the user satisfaction corresponding to the user income distribution method; and when the user satisfaction does not meet the preset threshold, updating the electricity price and electricity quantity relation model and the electricity price and electricity quantity index model until the user satisfaction meets the preset threshold.

Description

Technical Field

[0001] The present application relates to the technical field of power demand response, and in particular to a demand response declaration method and system based on user feedback and electricity price index. Background Art

[0002] With the continuous development of the electricity market, the proportion of new energy is also increasing year by year. The country has introduced a series of policies to encourage the construction of virtual power plants, and to reasonably allocate users' resources based on the maximization of user interests. By using the electricity price mechanism, users are encouraged to actively manage their own loads by adjusting electricity prices, actively reduce loads during peak loads, and maximize energy consumption during low loads. The vigorous development of new energy power supply has reduced the peak and average values ​​of electricity demand, thereby reducing user costs.

[0003] As an important interactive resource under the smart grid framework, demand response plays a positive role in maintaining system stability, reducing grid company investment, and promoting the consumption of new energy. As the coupling degree of multi-energy loads on the user side is deepening, and the potential of adjustable resources and user energy preferences are closely related to the efficient operation of the system, the accurate modeling of user participation in demand response benefits, the selection of benefit distribution mechanisms, and the analysis of the game process are of great significance for continuing to promote the reform of the electricity market and the construction of a healthy electricity spot market.

[0004] Foreign power markets started early and developed rapidly. They have done a lot of research on demand response, using knowledge from different fields such as mathematical programming, probability theory, and game theory. As the power market develops, related research has followed closely. The research points are also diverse. Some focus on economic benefits; some focus on new energy consumption; some focus on energy storage; and some focus on power grids and reactive power.

[0005] The current demand response business model is a three-tier structure of trading center-aggregator-user. The demand response is set to adopt an invitation system, that is, during peak electricity consumption, due to tight power supply, the trading center issues an invitation to seek to provide more available electricity in the market to try to make up for the electricity gap; and during low electricity consumption, due to surplus power supply capacity, the trading center issues an invitation to seek to consume more electricity in the market to ensure the economic operation of the power grid. The invitation system is set to be released in the morning three days before the response execution day. After the demand is released, each load aggregator and wholesale user decides on the declared capacity and declared price, and completes the declaration one day before the peak load regulation. Then, in the market clearing process, it is called in order from low to high according to the declared price. If the bid is the same, it is called in order before the deadline and the most recent declaration time until the response capacity demand is met. The clearing capacity is based on the full amount of winning bids, and the response is executed the next day; after the response is executed and before the result appeal, the response evaluation will be conducted and the settlement will be made.

[0006] Since many market rules are imperfect at this stage, when declaring actual demand response, traders usually make declarations based on their experience, which makes it difficult to meet the needs of users. Summary of the invention

[0007] In order to at least to some extent overcome the problem in the related art that in actual demand response declaration, traders make declarations based on experience, which makes it difficult to meet user-side needs, the present application provides a demand response declaration method and system based on user feedback and electricity price index.

[0008] The scheme of this application is as follows:

[0009] According to a first aspect of an embodiment of the present application, a demand response declaration method based on user feedback and electricity price index is provided, comprising:

[0010] Screen out users whose user types and response types meet the current demand response as participating users;

[0011] Construct an electricity price and quantity relationship model and an electricity price and quantity index model based on historical demand response;

[0012] Predict the market electricity price and quantity that meet the current demand response based on the electricity price and quantity relationship model and the electricity price and quantity index model;

[0013] According to the predicted market electricity price and electricity volume, a declaration model is constructed with the goal of maximizing the aggregator's profit; the constraints of the declaration model are user load capacity constraints and user output time constraints;

[0014] Determine the method of allocating user benefits after reporting according to the reporting model according to the stage of the aggregator's participation in demand response;

[0015] Constructing a user satisfaction evaluation system to determine the user satisfaction corresponding to the user benefit distribution method;

[0016] When the user satisfaction does not meet the preset threshold, the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model are updated until the user satisfaction meets the preset threshold.

[0017] Preferably, the electricity price and quantity relationship model is constructed based on historical demand response, including:

[0018] Construct the electricity price and quantity relationship model based on the elasticity coefficient:

[0019]

[0020] Among them, ε represents the elasticity coefficient; ΔP represents the change in the amount of electricity P, and Δq represents the change in the electricity price q;

[0021] The elasticity matrix E is constructed based on the elasticity coefficient to represent the user's electricity demand for period j when the electricity price in period i changes:

[0022]

[0023] Let P 0 is the initial charge, q 0 is the initial electricity price, then the electricity price and quantity relationship model is expressed as:

[0024]

[0025] Preferably, an electricity price and electricity quantity index model is constructed based on historical demand response, including:

[0026] Determine the exponential function relationship between the change in electricity quantity and the change in electricity price, and build an electricity price and electricity quantity index model:

[0027]

[0028] Among them, P min and P max Respectively represent the minimum and maximum power consumption of the user.

[0029] Preferably, a declaration model is constructed based on the predicted market electricity price and electricity volume with the goal of maximizing the aggregator's revenue, including:

[0030]

[0031] Among them, y represents the total revenue, P represents the predicted market electricity price and quantity, m represents the total number of users, n represents the peak load period, α ij represents the peak load of the jth user, z ij Indicates whether user j participates in peak load regulation in time period i;

[0032]

[0033] The user load capacity constraint is:

[0034] The user output time period constraint is: ij ∈z j ;

[0035] in, represents the maximum load of user j in time period i; z j Indicates the period in which user j participates in demand response.

[0036] Preferably, according to the stage at which the aggregator participates in the demand response, a method for distributing user benefits after the declaration according to the declaration model is determined, including:

[0037] In the early stages of aggregators’ participation in demand response, the benefits are divided equally among users.

[0038] Preferably, according to the stage at which the aggregator participates in the demand response, determining the method for distributing user benefits after the declaration according to the declaration model further includes:

[0039] In the medium term when the aggregator participates in demand response, the benefits to users are distributed according to the capacity ratio.

[0040] Preferably, according to the stage at which the aggregator participates in the demand response, determining the method for distributing user benefits after the declaration according to the declaration model further includes:

[0041] In the mature stage of aggregators' participation in demand response, the benefits to users are distributed according to quantitative indicators.

[0042] Preferably, the user's benefits are distributed according to quantitative indicators, including:

[0043] Establishing quantitative indicators; the quantitative indicators include the number of times users participate in the demand response market, response capacity and user scale;

[0044] Assign weights to each quantitative indicator;

[0045] The user's income is distributed according to the quantitative indicators after the weights are assigned.

[0046] Preferably, determining the user satisfaction corresponding to the user benefit distribution method includes:

[0047] Construct user satisfaction function:

[0048]

[0049] Where z represents the user's satisfaction, ∑r(i) is the demand response benefit actually obtained by user i, and ∑Er(i) is the expected benefit of user i.

[0050] According to a second aspect of an embodiment of the present application, a demand response declaration system based on user feedback and electricity price index is provided, including:

[0051] Processor and memory;

[0052] The processor and the memory are connected via a communication bus:

[0053] Wherein, the processor is used to call and execute the program stored in the memory;

[0054] The memory is used to store a program, and the program is used to at least execute a demand response declaration method based on user feedback and electricity price index as described in any one of the above items.

[0055] The technical solution provided by this application may have the following beneficial effects:

[0056] The demand response declaration method based on user feedback and electricity price index in the present application includes: screening out users whose user types and response types meet the current demand response as participating users; constructing an electricity price and electricity quantity relationship model and an electricity price and electricity quantity index model according to historical demand responses; predicting the market electricity price and electricity quantity that meets the current demand response according to the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model; constructing a declaration model based on the predicted market electricity price and electricity quantity with the goal of maximizing the aggregator's profit; the constraints of the declaration model are user load capacity constraints and user output period constraints; determining a user profit distribution method after declaration according to the declaration model according to the stage at which the aggregator participates in the demand response; constructing a user satisfaction evaluation system to determine the user satisfaction corresponding to the user profit distribution method; when the user satisfaction does not meet the preset threshold, updating the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model until the user satisfaction meets the preset threshold.

[0057] This technical solution can flexibly respond to market changes, and accurately guide users to reduce electricity consumption during peak hours and balance the load on the power grid by dynamically adjusting electricity prices. At the same time, the adjustable user benefit distribution ensures that each user receives a reasonable return based on their energy-saving contribution, motivating users to actively participate. Incorporating user feedback makes the strategy closer to actual needs, and continuously optimizes and improves user satisfaction. In general, this technical solution plays a role in promoting energy conservation and emission reduction, improving energy efficiency, enhancing user participation, and achieving a win-win situation in economic and social benefits. In addition, this technical solution has the ability to continuously optimize, and because it attaches importance to user feedback, it can adapt to market changes and meet user needs.

[0058] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 It is a flowchart of a demand response declaration method based on user feedback and electricity price index provided by an embodiment of the present application;

[0061] Figure 2 This is a schematic diagram of a demand response transaction architecture provided by an embodiment of the present application;

[0062] Figure 3 This is a schematic diagram of dividing user types and response types provided by an embodiment of the present application;

[0063] Figure 4 It is a structural diagram of a demand response reporting system based on user feedback and electricity price index provided by an embodiment of the present application.

[0064] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0065] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0066] Figure 1 is a flow chart of a demand response declaration method based on user feedback and electricity price index provided by an embodiment of the present application, with reference to Figure 1 , a demand response declaration method based on user feedback and electricity price index, comprising:

[0067] S11: Screen out users whose user types and response types meet the current demand response as participating users;

[0068] S12: Constructing an electricity price and electricity quantity relationship model and an electricity price and electricity quantity index model based on historical demand response;

[0069] S13: predicting the market electricity price and electricity quantity that meet the current demand response based on the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model;

[0070] S14: Based on the predicted market electricity price and electricity volume, a declaration model is constructed with the goal of maximizing the aggregator's revenue; the constraints of the declaration model are user load capacity constraints and user output time constraints;

[0071] S15: Determine the method of allocating user benefits after declaration according to the declaration model based on the stage of the aggregator participating in the demand response;

[0072] S16: Build a user satisfaction evaluation system to determine the user satisfaction corresponding to the user benefit distribution method;

[0073] S17: When the user satisfaction does not meet the preset threshold, the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model are updated until the user satisfaction meets the preset threshold.

[0074] It should be noted that, refer to Figure 2The current demand response business model is a three-layer structure of trading center-aggregator-user. Since wholesale users themselves do not have the ability to organize other users to participate in demand response, retail users must form an alliance with aggregators if they want to obtain the benefits of demand response. Because aggregators are not users in the park, they cannot provide any of the response capacity they declare. In essence, they are just an alliance agent composed of many users. They provide the alliance with the binding force of the agreement by signing contracts with users, and redistribute the benefits obtained, and then distribute them to the users under their names. This means that at the lower level of the game, a cooperative game with transferable payments is formed under the name of each load aggregator, that is, multiple cooperative game patterns are formed at the lower level. At this point, a two-layer game model with Bayes game as the upper layer and several cooperative games as the lower layer has been constructed.

[0075] Mutual feedback between aggregators and users is the most direct way to improve the model. Figure 3 , the users themselves are classified according to their response methods and load types. Then the user behaviors are counted according to the historical declaration and clearing situations. Correspondingly, the user satisfaction also reflects whether the model can provide effective strategies for users.

[0076] Figure 3 This means that the upper limit of the aggregator's responsive capacity is randomly determined. For users, they only need to choose a suitable aggregator, and the user's response capacity will directly affect the aggregator's benefits in participating in the demand response game.

[0077] Price-based demand response mainly guides users to adjust their electricity consumption habits by setting electricity prices at different time periods. There are usually four methods to describe the relationship between user electricity consumption and electricity prices. The most effective and commonly used method is to establish an electricity price elasticity matrix.

[0078] Specifically, a price-volume relationship model is constructed based on historical demand response, including:

[0079] Construct the electricity price and quantity relationship model based on the elasticity coefficient:

[0080]

[0081] Among them, ε represents the elasticity coefficient; ΔP represents the change in the amount of electricity P, and Δq represents the change in the electricity price q;

[0082] The elasticity matrix E is constructed based on the elasticity coefficient to represent the user's electricity demand for period j when the electricity price in period i changes:

[0083]

[0084] Let P 0 is the initial charge, q 0is the initial electricity price, then the electricity price and quantity relationship model is expressed as:

[0085]

[0086] As can be seen from the above formula, there is a linear relationship between load changes and electricity price changes. In real life, there is load saturation. When electricity prices fluctuate within a normal range, users actively participate in demand response to change electricity consumption; when the change in electricity prices is too high, it will be more difficult to guide users to use electricity through linear price-based demand response. Usually, the formulation of peak and valley electricity prices in time-of-use electricity prices is related to normal electricity prices, and they fluctuate within a certain range. As shown below:

[0087]

[0088] Among them, D f , D P , D g Respectively represent the peak, normal and valley electricity prices; r f , r g They are the upward fluctuation coefficient of peak electricity price and the downward fluctuation coefficient of valley electricity price respectively.

[0089] Considering that there are basic load and saturation load, if a linear relationship is used to represent the relationship between electricity quantity and electricity price, it cannot actually reflect the characteristics of demand response. Therefore, according to the actual situation, the change in electricity quantity and the change in electricity price can be described as an exponential function relationship. According to the historical demand response, the electricity price and electricity quantity index model is constructed, including:

[0090] Determine the exponential function relationship between the change in electricity quantity and the change in electricity price, and build an electricity price and electricity quantity index model:

[0091]

[0092] Among them, P min and P max They represent the user's minimum and maximum power consumption, namely the base load and saturation load, respectively, where the saturation load is the peak value of the typical daytime load.

[0093] The aggregator formulates electricity sales strategies for virtual power plants with the goal of maximizing profits and distributes prices to users; users, as followers, combine their actual electricity consumption plans and, while ensuring that their own surplus is maximized, report the values ​​of shiftable load and reducible load to the virtual power plant; after receiving the electricity consumption reports from users, the aggregator responds to the market according to actual conditions and demand, optimizes the data reported by users, and formulates reporting strategies.

[0094] It should be noted that the declaration model is constructed based on the predicted market electricity price and electricity volume with the goal of maximizing the aggregator's revenue, including:

[0095]

[0096] Among them, y represents the total revenue, P represents the predicted market electricity price and quantity, m represents the total number of users, n represents the peak load period, α ij represents the peak load of the jth user, z ij Indicates whether user j participates in peak load regulation in time period i;

[0097]

[0098] The user load capacity constraint is:

[0099] The user output time period constraint is: ij ∈z j ;

[0100] in, represents the maximum load of user j in time period i; z j Indicates the period in which user j participates in demand response.

[0101] According to the stage of the aggregator's participation in demand response, the method of distributing user benefits after declaration according to the declaration model is determined, including:

[0102] In the early stages of aggregators’ participation in demand response, the benefits are divided equally among users.

[0103] In the medium term when the aggregator participates in demand response, the benefits to users are distributed according to the capacity ratio.

[0104] In the mature stage of aggregators' participation in demand response, the benefits to users are distributed according to quantitative indicators.

[0105] In the early stage of aggregators' participation in demand response, when historical user data collection was insufficient, an equal distribution method was adopted. However, due to differences in user size and peak-shaving capabilities, the equal distribution method will always reduce the benefits of some users, causing their dissatisfaction to gradually increase over time, so the distribution method needs to be optimized in the later stage.

[0106] In the middle period when aggregators participate in demand response, compared with the equal distribution method, the allocation according to capacity ratio will reduce the overall dissatisfaction of users to a certain extent. However, in the example, due to the idealized condition of complete rationality of users, the loss rate of user numbers has not been significantly improved. In general, it can be considered that within a sufficiently long time scale, the mechanical allocation method according to capacity ratio will still lose all users until the aggregator is delisted. From the perspective of the concept of nucleus in game theory, it means that the allocation strategies under the first two allocation methods must not be in the nucleus, that is, the strategy elements are not elements within the scope of the nucleus.

[0107] In the mature stage of aggregators participating in demand response, according to the market utility redistribution model, after quantifying the evaluation indicators, the hierarchical analysis method is used to determine the weight of each indicator. Finally, the redistribution coefficient and distribution result of the market utility of thermal power units are obtained, so as to further encourage users to participate in the demand response power market. The hierarchical analysis method quantifies the indicator weights through various means. Specifically, the user's benefits are distributed according to the quantitative indicators, including:

[0108] Establish quantitative indicators; quantitative indicators include the number of users participating in the demand response market, response capacity and user scale;

[0109] Assign weights to each quantitative indicator;

[0110] The user's income is distributed according to the quantitative indicators after the weights are assigned.

[0111] In specific practice, a hierarchical model can be constructed. The indicator structure for determining the allocation capacity is divided into two levels, the first level is the allocation coefficient, and the second level is the indicator level.

[0112] Then construct the judgment matrix. First normalize the historical demand response data. Then calculate the three indicators L 1 , L 2 , L 3 , the calculation formula is as follows:

[0113]

[0114] in, represents the number of times user i participates in demand response, K 1 represents the total number of demand response markets, and n represents the total number of users.

[0115] L 2 , L 3 Calculation method and L 1 similar.

[0116] Determination of weight vector: Usually according to the principle of equal proportion distribution, the weight is 1 / 3.

[0117] In order to evaluate the demand response declaration strategy and eliminate the uncertainty of the results caused by irrationality in the game, it is assumed that users are completely rational and a dissatisfaction evaluation system is established to continuously update the model.

[0118] Determine the user satisfaction corresponding to the user benefit distribution method, including:

[0119] Construct a user satisfaction function:

[0120]

[0121] Where z represents the user's satisfaction, ∑r(i) is the demand response benefit actually obtained by user i, and ∑Er(i) is the expected benefit of user i.

[0122] In specific practice, the threshold for evaluating user satisfaction can be set to 0.5, that is, when z>0.5, it means that the user is satisfied, and when z≤0.5, it means that the user is dissatisfied.

[0123] Embodiment 2

[0124] A demand response reporting system based on user feedback and electricity price index, referring to Figure 4 ,include:

[0125] Processor 21 and memory 22;

[0126] The processor 21 and the memory 22 are connected via a communication bus:

[0127] The processor 21 is used to call and execute the program stored in the memory 22;

[0128] The memory 22 is used to store a program, and the program is used to execute at least one of the demand response declaration methods based on user feedback and electricity price index in the above embodiments.

[0129] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0130] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0131] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0132] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0133] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0135] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0137] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A demand response declaration method based on user feedback and electricity price index, characterized in that: include: Screen out users whose user types and response types meet the current demand response as participating users; Construct an electricity price and quantity relationship model and an electricity price and quantity index model based on historical demand response; Predict the market electricity price and quantity that meet the current demand response based on the electricity price and quantity relationship model and the electricity price and quantity index model; According to the predicted market electricity price and electricity volume, a declaration model is constructed with the goal of maximizing the aggregator's profit; the constraints of the declaration model are user load capacity constraints and user output time constraints; Determine the method of allocating user benefits after reporting according to the reporting model according to the stage of the aggregator's participation in demand response; Constructing a user satisfaction evaluation system to determine the user satisfaction corresponding to the user benefit distribution method; When the user satisfaction does not meet the preset threshold, the electricity price and electricity quantity relationship model and the electricity price and electricity quantity index model are updated until the user satisfaction meets the preset threshold.

2. The method according to claim 1, characterized in that The electricity price and quantity relationship model is constructed based on historical demand response, including: Construct the electricity price and quantity relationship model based on the elasticity coefficient: Among them, ε represents the elasticity coefficient; ΔP represents the change in the amount of electricity P, and Δq represents the change in the electricity price q; The elasticity matrix E is constructed based on the elasticity coefficient to represent the user's electricity demand for period j when the electricity price in period i changes: Let P 0 is the initial charge, q 0 is the initial electricity price, then the electricity price and quantity relationship model is expressed as:

3. The method according to claim 2, characterized in that The electricity price and quantity index model is constructed based on historical demand response, including: Determine the exponential function relationship between the change in electricity quantity and the change in electricity price, and build an electricity price and electricity quantity index model: Among them, P min and P max Respectively represent the minimum and maximum power consumption of the user.

4. The method according to claim 1, characterized in that: According to the predicted market electricity price and electricity volume, a declaration model is constructed with the goal of maximizing the aggregator's revenue, including: Among them, y represents the total revenue, P represents the predicted market electricity price and quantity, m represents the total number of users, n represents the peak load period, α ij represents the peak load of the jth user, z ij Indicates whether user j participates in peak load regulation in time period i; The user load capacity constraint is: The user output time period constraint is: ij ∈z j ; in, represents the maximum load of user j in time period i; z j Indicates the period in which user j participates in demand response.

5. The method according to claim 1, characterized in that According to the stage of the aggregator participating in the demand response, determine the user benefit distribution method after the declaration according to the declaration model, including: In the early stages of aggregators’ participation in demand response, the benefits are divided equally among users.

6. The method according to claim 1, characterized in that Determining the method of distributing user benefits after reporting according to the reporting model according to the stage at which the aggregator participates in the demand response, and also including: In the medium term when the aggregator participates in demand response, the benefits to users are distributed according to the capacity ratio.

7. The method according to claim 1, characterized in that Determining the method of distributing user benefits after reporting according to the reporting model according to the stage at which the aggregator participates in the demand response, and also including: In the mature stage of aggregators' participation in demand response, the benefits to users are distributed according to quantitative indicators.

8. The method according to claim 7, characterized in that The benefits to users are distributed according to quantitative indicators, including: Establishing quantitative indicators; the quantitative indicators include the number of times users participate in the demand response market, response capacity and user scale; Assign weights to each quantitative indicator; The user's income is distributed according to the quantitative indicators after the weights are assigned.

9. The method according to claim 1, characterized in that: Determining the user satisfaction corresponding to the user benefit distribution method includes: Construct a user satisfaction function: Where z represents the user's satisfaction, ∑r(i) is the demand response benefit actually obtained by user i, and ∑Er(i) is the expected benefit of user i.

10. A demand response declaration system based on user feedback and electricity price index, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is used at least to execute a demand response declaration method based on user feedback and electricity price index as described in any one of claims 1-9.

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