Method and system for incentivizing user energy consumption considering anchoring-pulley effect

Abstract

The present application relates to the technical field of power grid new energy consumption, demand side management, and specifically discloses a method and system for stimulating user energy consumption considering anchoring-ratchet effect, comprising collecting relevant parameters; constructing an IDR double-layer stochastic optimization model; and inputting the collected relevant parameters into the IDR double-layer stochastic optimization model to obtain an optimized incentive price; and adjusting the incentive price of the unit response quantity released by the current IESP to the user based on the optimized incentive price. The method comprehensively measures the influence of user subjective psychology on user response behavior based on "anchoring effect" and "ratchet effect", and improves the prediction ability of IESP for the to-be-estimated parameters representing user subjective psychology, so as to enable IESP to formulate effective and economic incentive strategies, thereby guiding users to adjust energy demand, reducing peak-valley difference, improving the accuracy and effectiveness of load regulation, and achieving a win-win situation between users and IESP.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid new energy consumption, demand side management, and in particular to a method and system for encouraging users to use energy considering anchor-ratchet effect. BACKGROUND

[0002] To ensure the safe and stable operation of the power system, demand response (DR) emerges as the times require, but DR has a greater impact on the energy comfort of users, resulting in lower enthusiasm of users to participate in DR and smaller regulation potential; to further tap the response potential of users, integrated demand response (IDR) has become a research hotspot at present, which can not only realize the coordinated optimization of multiple energies, but also realize the benign interaction of the supply and demand of the same kind of energy, and has important significance for improving the flexibility of the power grid and ensuring the balance between supply and demand of the power system; the users participating in IDR on the demand side are induced by the integrated energy system provider (IESP) to change the demand for one or more energies through price or incentive methods, thereby affecting the demand for another or more energies; since incentive demand response can more effectively encourage users to actively participate in IDR, existing researches are mostly carried out around incentive IDR; in addition, to ensure that IESP can develop strategies to encourage users to achieve the expected response amount, accurate modeling of user response behavior has become one of the important works in the current research on incentive IDR.

[0003] In the existing research on incentive-based IDR that accurately models user response behavior, Sun Yi et al. in the 42nd issue of the 4th volume of the Chinese Journal of Electrical Engineering modeled the comfort loss caused by users changing their energy use habits when participating in IDR, constructed a user response model considering user energy use comfort and economic benefits, and predicted the response behavior of users when their own benefits are maximized. However, this model ignores the influence of user subjective psychology on user response behavior, resulting in the need to improve the accuracy of the model. In patent CN118674230B, a multi-dimensional elastic coefficient matrix is introduced to comprehensively characterize the spatio-temporal characteristics of IDR multi-user load and its contribution to system low-carbon operation, optimize the energy consumption mode on the user side, and promote the efficient use of energy and reduce carbon emissions. However, it does not consider the influence of user subjective psychology on their response behavior. In patent CN112180741A, a corresponding IESP is established for a specific subdivided user group, and corresponding energy conversion equipment and energy storage equipment are selected to meet the diversified energy needs of users. In addition, by subdividing the IESP of the user group, a corresponding demand response strategy is established. Although this improves the accuracy of the IESP model, it does not provide a parameter estimation method for the scenario where the to-be-estimated characteristic parameters of the user and the expected response quantity are in a nonlinear relationship to optimize the ability of the IESP to predict user response behavior, resulting in the IESP still being unable to develop the most effective and economic incentive measures. SUMMARY

[0004] To solve the above problems, the first object of the present application is to provide a method for incentivizing user energy use considering anchor-ratchet effect, which comprehensively measures the influence of user subjective psychology on user response behavior based on "anchor effect" and "ratchet effect", and improves the prediction ability of IESP for to-be-estimated parameters representing user subjective psychology, so that IESP can develop effective and economic incentive strategies to guide users to adjust their energy demand, reduce peak-valley difference, and improve the accuracy and effectiveness of load regulation, achieving a win-win situation for users and IESP.

[0005] The second object of the present application is to provide a system for incentivizing user energy use considering anchor-ratchet effect.

[0006] The first technical solution adopted by the present application is: a method for incentivizing user energy use considering anchor-ratchet effect, comprising the following steps:

[0007] S100: Collecting the type of IDR released by IESP at the current time, the incentive price released by IESP to the user in the last period, the unit penalty coefficient of uncompleted expected response quantity, and the type of response quantity provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the user's expectation of the incentive price;

[0008] S200: constructing an IDR double-layer stochastic optimization model based on the user response model considering the anchoring effect and the ratchet effect and the IESP benefit model; and inputting the type of IDR released by the IESP at the current time, the incentive price per unit of response quantity released by the IESP to the user at the last time period, the unit penalty coefficient of the uncompleted expected response quantity, and the type of the response quantity provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expected input of the user to the incentive price into the IDR double-layer stochastic optimization model to obtain an optimized incentive price;

[0009] S300: adjusting the incentive price per unit of response quantity released by the IESP to the user based on the optimized incentive price, thereby guiding the user to use energy at the peak and the valley to achieve the expected response quantity.

[0010] Preferably, the step S200 comprises: the IDR double-layer stochastic optimization model converting the double-layer stochastic optimization problem into a single-layer deterministic optimization problem for solving to obtain the optimized incentive price.

[0011] Preferably, the step S200 comprises the following sub-steps:

[0012] S210: establishing a user response model considering the user dissatisfaction and the economic benefit; and establishing a user response model considering the anchoring effect and the ratchet effect based on the user response model;

[0013] S220: considering the incentive cost and the penalty cost of the IESP, and establishing an IESP benefit model based on the minimum total cost;

[0014] S230: constructing an IDR double-layer stochastic optimization model based on the user response model considering the anchoring effect and the ratchet effect and the IESP benefit model.

[0015] Preferably, the user response model considering the user dissatisfaction and the economic benefit is represented by the following formula:

[0016]

[0017] In the formula, U i,k (x i,k ) is the benefit function of the user i at the kth time period; p i,k represents the incentive price per unit of response quantity released by the IESP to the user i at the kth time period; T is a transpose; diag() represents generating a diagonal matrix, and the diagonal elements in the diagonal matrix are the element values in the matrix in the parentheses in sequence; s IESP,k is the type of IDR released by the IESP at the kth time period; x i,k is the expected response quantity provided by the user i at the kth time period predicted by the IESP. is the comfort loss function of user i in the kth time period; θ i,k is the response willingness of user i in the kth time period; λ i,k is the lowest acceptable incentive price of user i in the kth time period to participate in IDR; s i,k is the type of response quantity provided by user i in the kth time period; is the maximum consumption quantity of user i in the kth time period; is the maximum curtailment quantity of user i in the kth time period.

[0018] Preferably, the step S210 comprises:

[0019] optimizing the user response model considering the anchoring effect of the user to obtain a user response model considering the anchoring effect of the user; and optimizing the user response model considering the anchoring effect of the user to obtain a user response model considering the anchoring effect and the ratchet effect of the user.

[0020] Preferably, the user response model considering the anchoring effect and the ratchet effect of the user is represented by the following formula:

[0021]

[0022] In the formula, U" i,k (x i,k ) is the benefit function of user i in the kth time period considering the anchoring effect and the ratchet effect; p i,k represents the incentive price of unit response quantity issued by IESP to user i in the kth time period; T is the transpose; diag() represents generating a diagonal matrix, the diagonal elements of which are the element values of the matrix in the parentheses in turn; s IESP,k is the type of IDR issued by IESP in the kth time period; x i,k is the expected response quantity provided by user i in the kth time period predicted by IESP; is the comfort loss function of user i in the kth time period; respectively represent the price elasticity coefficient and the lowest acceptable incentive price of user i in the kth time period considering the anchoring effect and the ratchet effect; s i,k is the type of response quantity provided by user i in the kth time period; E3 is a 3x3 order unit matrix; ε i,k is a parameter defined for the purpose of simplifying the formula; θ i,k is the response willingness of user i in the kth time period; E 3×1 is a 3x1 order matrix with all elements being 1; χ is the proportional coefficient of parameters b i,k , λ i,k ; b i,k is a parameter defined for the purpose of simplifying mathematical expressions; λ i,kthe minimum acceptable incentive price for user i to participate in IDR in the kth time period; the maximum consumption of user i in the kth time period; the maximum curtailment of user i in the kth time period; i the anchoring effect matrix.

[0023] Preferably, the expected response amount of user i provided by the IESP in the kth time period is calculated by:

[0024]

[0025] wherein x i,k the expected response amount of user i provided by the IESP in the kth time period; s IESP,k the IDR type published by the IESP in the kth time period; p i,k the incentive price of unit response amount published by the IESP to user i in the kth time period; s i,k the type of response amount provided by user i in the kth time period; respectively represent the price elasticity coefficient and the minimum acceptable incentive price of user i in the kth time period considering the anchoring effect and the ratchet effect; the ratchet coefficient in the model parameters modified considering the anchoring effect and the ratchet effect is estimated by the maximum likelihood theory and the Newton iteration method.

[0026] Preferably, the objective function of the IESP benefit model in the step S200 is represented by:

[0027]

[0028] wherein U IESP the response cost of the IESP in the kth time period; K is the total number of comprehensive demand response time periods; N is the number of users participating in comprehensive demand response; p i,k the incentive price of unit response amount published by the IESP to user i in the kth time period; s IESP,k the IDR type published by the IESP in the kth time period; x i,k the expected response amount of user i provided by the IESP in the kth time period; β is the penalty coefficient of unit electric, heat, and gas response amount deviation; T is the transpose; || represents taking the absolute value; ζ k the total deviation of the expected response amount and the actual response amount of electric, heat, and gas in the kth time period.

[0029] Preferably, the constraint conditions of the IESP benefit model include the power balance constraint of the IESP, the working condition constraint of the coupled device of the IESP, and the working condition constraint of the energy storage device of the IESP.

[0030] The second technical solution adopted by the present application is: a system for stimulating users to use energy by considering anchor-ratchet effect, comprising a collection module, an IDR double-layer random optimization model construction module and an optimization module.

[0031] The collection module is used to collect the IDR type published by IESP at the current time, the incentive price published by IESP to users in the last period, the unit penalty coefficient of uncompleted expected response amount, and the type of response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expectation of the user to the incentive price.

[0032] The IDR double-layer random optimization model construction module is used to construct an IDR double-layer random optimization model based on a user response model considering user anchor effect and ratchet effect and an IESP benefit model, and input the IDR type published by IESP at the current time, the incentive price published by IESP to users in the last period, the unit penalty coefficient of uncompleted expected response amount, and the type of response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expectation of the user to the incentive price into the IDR double-layer random optimization model, to obtain an optimized incentive price.

[0033] The optimization module is used to adjust the unit response amount incentive price published by the current IESP to users based on the optimized incentive price, so as to guide the energy demand of users at peaks and valleys, so as to achieve the expected response amount.

[0034] The beneficial effects of the above technical solution are:

[0035] (1) The present application comprehensively measures the influence of user subjective psychology on user response behavior based on "anchor effect" and "ratchet effect", and improves the prediction ability of IESP for the to-be-estimated parameters representing user subjective psychology, so as to enable IESP to formulate effective and economic incentive strategies, thereby guiding users to adjust energy demand, reducing peak-valley difference, improving the accuracy and effectiveness of load regulation, and improving the effectiveness and economy of comprehensive demand response strategies, achieving a win-win situation between IESP and users.

[0036] (2) The application considers the influence of the "anchoring effect" and "ratchet effect" in the subjective psychology of users on user response behavior; firstly, an anchoring effect matrix is established to effectively model the "anchoring effect" of users; secondly, a ratchet coefficient is introduced into the traditional IDR model to effectively model the "ratchet effect" of users; meanwhile, an IESP optimization model considering incomplete information is established, which solves the incomplete information caused by the unobservable user ratchet coefficient based on the maximum likelihood theory and Newton iteration principle; finally, a double-layer optimization problem with users and IESP as the main body is established based on the user response model considering the anchoring effect and ratchet effect of users and the IESP benefit model, and is converted into a single-layer optimization problem for solving.

[0037] (3) Compared with the prior art which has not fully considered the influence of the subjective psychology of users on user response behavior; the application optimizes the modeling of user response behavior by establishing an "anchoring effect" matrix and introducing a ratchet coefficient to represent the influence of the "anchoring effect" and "ratchet effect" of users on user response behavior, reduces the bias of the user response model, and improves the effectiveness and economy of the IESP incentive strategy; the application improves the accuracy of the traditional user response model of IDR so that the IESP can formulate more economical and effective incentive strategies.

[0038] (4) Compared with the prior art which has not effectively solved the parameter estimation problem in the nonlinear scenario of user response characteristics and user response quantity, the application proposes a parameter estimation method based on the maximum likelihood theory and Newton iteration principle, so that the IESP can more accurately estimate the user response characteristic parameters in the nonlinear relationship with the user response quantity, so that the IESP can more accurately formulate differentiated incentive strategies according to user response characteristics, and improve the accuracy of the incentive strategy. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 A flowchart of a method for incentivizing user energy consumption considering anchoring-ratchet effect provided for an embodiment of the application;

[0040] Figure 2 An IDR architecture diagram provided for an embodiment of the application;

[0041] Figure 3 An EH coupling diagram provided for an embodiment of the application. DETAILED DESCRIPTION

[0042] The embodiments of the application will be further described in conjunction with the drawings and examples. The detailed description of the following examples and drawings are used to exemplarily illustrate the principles of the application, but cannot be used to limit the scope of the application, that is, the application is not limited to the preferred embodiments described, and the scope of the application is defined by the claims.

[0043] In the description of the present application, it should be noted that, unless otherwise specified, the meaning of "a plurality of" is two or more; the terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance; the above-mentioned terms can be understood in the specific meaning in the present application by the person skilled in the art according to the specific circumstances.

[0044] Embodiment one

[0045] As Figure 1 shown, one embodiment of the present application provides a method for stimulating user energy consumption considering anchor-ratchet effect, comprising the following steps:

[0046] S100: collecting the IDR type published by IESP at the current time, the incentive price published by IESP to the user in the last period, the unit penalty coefficient of uncompleted expected response amount, and the type of response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expectation of the user to the incentive price.

[0047] S200: based on the user response model considering the anchor effect and the ratchet effect of the user and the IESP benefit model, constructing an IDR double-layer stochastic optimization model; and inputting the IDR type published by IESP at the current time, the incentive price published by IESP to the user in the last period, the unit penalty coefficient of uncompleted expected response amount, and the type of response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expectation of the user to the incentive price into the IDR double-layer stochastic optimization model, to obtain the optimized incentive price;

[0048] Constructing the IDR double-layer stochastic optimization model comprises the following sub-steps:

[0049] S210: establishing a user response model considering user dissatisfaction and economic benefit; and based on the user response model, establishing a user response model considering the anchor effect and the ratchet effect of the user;

[0050] (1) establishing a user response model considering user dissatisfaction and economic benefit;

[0051] The IDR model comprises a user response model and an IESP benefit model, and the architecture of the IDR model is as shown in Figure 2 In the IDR model, the user will make a response behavior according to the incentive price published by IESP in the current period, the loss of comfort degree and the response ability to maximize the benefit of the user in the current period, and the user response model considering user dissatisfaction and economic benefit (i.e. the benefit function of the user) is represented by formula (1):

[0052]

[0053] wherein U i,k )x i,k is the utility function of user i in the kth time period; is the transpose of p i,k ; p i,k represents the incentive price of unit response quantity of electricity, heat, and gas published by IESP to user i in the kth time period, p i,k = [p e,i,k , p h,i,k , p g,i,k ] T , p e,i,k , p h,i,k , and p g,i,k respectively represent the incentive price of unit response quantity of electricity, heat, and gas, T is the transpose; diag() represents generating a diagonal matrix, the diagonal elements of which are the element values in the matrix inside the parentheses in turn; s IESP,k is the IDR type published by IESP in the kth time period, the value of which is 1 when the IDR type is curtailment, and -1 when the IDR type is consumption; s IESP,k = [s IESP,e,k , s IESP,j,k , s IESP,g,k ] T , s IESP,e,k , s IESP,h,k , and s IESP,g,k respectively represent the electricity IDR type, heat IDR type, and gas IDR type published by IESP in the kth time period; x i,k is the expected response quantity predicted by IESP that user i will provide in the kth time period, x i,k = [x e,i,k , x h,i,k , x g,i,k ] T , x e,i,k , x h,i,k , and x g,i,k respectively represent the electricity, heat, and gas response quantity predicted by IESP that user i will provide in the kth time period, x i,k is a positive value indicating that user i will reduce the energy consumption in the kth time period, and x i,k is a negative value indicating that user i will increase the energy consumption in the kth time period; is the comfort loss function of user i in the kth time period, which represents the comfort loss of user i in the kth time period due to the change in energy consumption; is the transpose of x i,k ; θ i,k and λ i,k are parameters related to the characteristics of the consumer; θ i,k is the response willingness of user i in the kth time period, and the smaller the value is, the higher the enthusiasm of user i to participate in IDR is.i,k = [θ e,i,k , θ h,i,k , θ g,i,k ] T , θ e,i,k , θ h,i,k , θ g,i,k respectively represent the electricity response willingness, the heat response willingness, and the gas response willingness of the user i in the kth time period; λ i,k is the minimum incentive price acceptable to the user i in the kth time period for participating in the IDR, λ i,k = [λ e,i,k , λ h,i,k , λ g,i,k ] T , λ e,i,k , λ h,i,k , λ g,i,k respectively represent the minimum electricity incentive price, the minimum heat incentive price, and the minimum gas incentive price acceptable to the user i in the kth time period for participating in the IDR; is the transpose of λ i,k ; s i,k is the type of the response amount provided by the user i in the kth time period, and the absolute value thereof is 1, and the positive or negative value symbol thereof is consistent with x i,k ; s i,k = [s e,i,k , s h,i,k , s g,i,k ] T , s e,i,k , s h,i,k , s g,i,k respectively represent the electricity response type, the heat response type, and the gas response type provided by the user i in the kth time period; is the maximum consumption amount of the user i in the kth time period; is the maximum reduction amount of the user i in the kth time period; is the range of the response amount provided by the user i in the kth time period.

[0054] (2) establishing a user response model considering the anchoring effect and the ratchet effect of the user based on the user response model;

[0055] establishing the user response model considering the anchoring effect and the ratchet effect of the user includes: optimizing the user response model considering the anchoring effect of the user to obtain a user response model considering the anchoring effect of the user; and optimizing the user response model considering the anchoring effect of the user to obtain a user response model considering the anchoring effect and the ratchet effect of the user.

[0056] 1) the user response model considering the anchoring effect of the user;

[0057] In the IDR, the user will not only focus on the objective economic benefits when making a response behavior, but also focus on the subjective psychological feelings, which is shown as the user will compare the incentive price p i,k published by the IESP in the current period IESP with the incentive price p i,k-1 published by the IESP in the last period IESP i,k-1 If p i,k > p i,k-1 , the user will have a gap psychology, thinking that he has missed the period of maximizing economic benefits, thereby leading to the decrease of the response willingness in the current period; on the contrary, if p i,k < p i,k , the response willingness in the current period will increase; the quantitative formula of the "anchoring effect" of the user in the IDR is:

[0058]

[0059] In the formula, p represents the anchoring benefit function of the user i in the kth period; p represents the expected value of the user i to the incentive price in the kth period, which is the average value of the incentive prices in the 1st period to the (k-1)th period, respectively represent the expected value of the user i to the incentive prices of the electric energy, the thermal energy and the gas energy in the kth period; p i,k is the incentive price published by the IESP to the user i in the kth period; p i,k-1 represents the incentive price published by the IESP to the user i in the (k-1)th period; diag() represents generating a diagonal matrix, and the diagonal elements in the diagonal matrix are the element values in the matrix in the brackets; is the price anchoring coefficient, which represents the tolerance degree of the user to the fluctuation of the incentive price, and the greater the value is, the greater the influence of the incentive price in the (k-1)th period on the response behavior of the user in the kth period respectively represent the price anchoring coefficients of the electric energy, the thermal energy and the gas energy of the user i; s i,k is the type of the response amount provided by the user i in the kth period; x i,k is the expected response amount predicted by the IESP that the user i provides in the kth period.

[0060] By combining the same items, the formula (2) is rewritten in the following form:

[0061]

[0062] In the formula, p represents the anchoring benefit function of the user i in the kth period; p i,k is the incentive price published by the IESP to the user i in the kth period; s i,kthe type of response quantity provided by user i in period k; x i,k the expected response quantity provided by user i in period k predicted by IESP; the price anchoring coefficient for user i for electricity, heat, and gas; E3 is a 3x3 identity matrix; the expected incentive price for period k for user i; p i,k-1 the incentive price published by IESP to user i in period k-1.

[0063] After considering the "anchoring effect" of the user, the utility function of the user can be rewritten as follows:

[0064]

[0065] where U' i,k (x i,k ) is the utility function of user i in period k after considering the anchoring effect; p i,k represents the incentive price of the unit response quantity published by IESP to user i in period k; T is the transpose; s IESP,k is the type of IDR published by IESP in period k; x i,k is the expected response quantity provided by user i in period k predicted by IESP; is the anchoring utility function of user i in period k; is the comfort loss function of user i in period k.

[0066] Based on the improved utility function of the user, the expected response quantity of user i in period k is:

[0067]

[0068] Combining the term containing p i,k in equation (7), the formula for the expected response quantity of user i can be rewritten as follows:

[0069]

[0070] In addition, since the parameters b i,k and λ i,k have the same dimension, and both are known in period k, b i,k = χλ i,k can be defined, and the expected response quantity of the user can be further rewritten as follows:

[0071]

[0072] where x i,k is the expected response quantity provided by user i in period k predicted by IESP; θ i,kthe response willingness of user i in the kth period; E3 is a 3x3 order unit matrix; s IESP,k IDR type published by IESP in the kth period; s i,k the type of response amount provided by user i in the kth period; a parameter defined for simplifying mathematical expressions, and the calculation formula is shown in formula (4), respectively represent the electrical energy parameter, the thermal energy parameter and the gas energy parameter defined for simplifying mathematical expressions; p i,k incentive price published by IESP to user i in the kth period; λ i,k the minimum acceptable incentive price of user i participating in IDR in the kth period; χ is a parameter b i,k , λ i,k , b i,k a proportionality coefficient; b

[0073] Based on formulas (1)-(9), and combined with the operation properties of matrix, the user response model considering the "anchoring effect" of user is rewritten in the form as follows according to the present application:

[0074]

[0075] In the formula, U' i,k (x i,k ) is the benefit function of user i in the kth period considering the anchoring effect; p i,k represents the incentive price of unit response amount published by IESP to user i in the kth period; T is the transpose; diag() represents generating a diagonal matrix, and the diagonal elements in the diagonal matrix are the element values of the matrix in the parentheses in turn; s IESP,k IDR type published by IESP in the kth period; x i,k the expected response amount provided by user i in the kth period predicted by IESP; comfort loss function of user i in the kth period, which represents the comfort loss of user i in the kth period due to energy use change; is the transpose of x i,k ; respectively represent the response willingness of user i in the kth period considering the "anchoring effect" and the minimum acceptable incentive price of user i participating in IDR; is the transpose of ; s i,k the type of response amount provided by user i in the kth period; E3 is a 3x3 order unit matrix; θ i,k the response willingness of user i in the kth period; χ is a parameter b i,k , λ i,kthe proportionality coefficient of the price elasticity of demand, b i,k a parameter defined for simplifying mathematical expressions, the calculation formula of which is shown in equation (5); λ i,k the minimum incentive price acceptable to user i for participating in IDR in the kth period; the maximum consumption amount of user i in the kth period; the maximum curtailment amount of user i in the kth period; Ξ i the anchoring effect matrix, used to represent the influence of the “anchoring effect” of the user on the response behavior thereof, the mathematical expression of which is shown as follows:

[0076]

[0077] In the formula, Ξ i the anchoring effect matrix; s e,i,k the type of the amount of the electric energy response provided by user i in the kth period; the electric energy parameter defined for simplifying mathematical expressions; s IESP,e,k the type of the electric energy IDR issued by the IESP in the kth period; s h,i,k the type of the amount of the thermal energy response provided by user i in the kth period; the thermal energy parameter defined for simplifying mathematical expressions; s IESP,h,k the type of the thermal energy IDR issued by the IESP in the kth period; s g,i,k the type of the amount of the gas energy response provided by user i in the kth period; the gas energy parameter defined for simplifying mathematical expressions; s IESP,g,k the type of the gas energy IDR issued by the IESP in the kth period.

[0078] 2) the user response model considering the anchoring effect and the ratchet effect of the user;

[0079] In IDR, when the user participates in IDR and continuously obtains positive benefits, the user will form cognitive inertia, thereby increasing the willingness of the user to participate in IDR and reducing the minimum acceptable incentive price thereof; as can be known from equation (10), the minimum acceptable incentive price p λ,i,k is Based on the above theory, the minimum acceptable incentive price p

[0080]

[0081] In the formula, p λ,i,k is the minimum acceptable incentive price of user i for participating in IDR in the kth period; s i,k the type of the amount of the response provided by user i in the kth period; The formula for calculating the minimum acceptable incentive price of user i participating in IDR in the kth period considering the anchoring effect is shown in equation (10); s IESP,k IDR type published by IESP for the kth period; Ratchet effect matrix, used to represent the influence of "ratchet effect" on user response behavior, respectively represent the influence of "ratchet effect" on user electric energy response behavior, thermal energy response behavior, and gas energy response behavior.

[0082] When the user receives continuous positive incentives, the cognitive inertia of the user will evolve into behavioral inertia, which is related to the response behavior of the user in the previous period. If the user not only obtains positive benefits when participating in IDR, but also provides a decreasing trend of response amount, the response willingness of the user increases, and the response inertia increases. On the contrary, if the user obtains positive benefits when participating in IDR, but the response amount provided by the user increases, although the user will make a response behavior due to the behavior inertia, the response inertia of the user decreases, and the influence of the response inertia on the user response behavior also decreases. Due to the characteristics of one-way change of the response inertia, The values of each element in the matrix should be greater than 1. In addition, since the exponential model can accurately predict the consumption behavior of users in the market, the response inertia can be modeled as an exponential model. In summary, the ratchet effect matrix can be further modeled as shown in equation (13):

[0083]

[0084] In the formula, respectively represent the influence of "ratchet effect" on user electric energy response behavior, thermal energy response behavior, and gas energy response behavior. respectively represent the ratchet coefficients of electric energy, thermal energy, and gas, used to represent the influence degree of "ratchet effect" on the response amount of electric energy, thermal energy, and gas of user i in the kth period; Δx e,i,k-1 , Δx h,i,k-1 , Δx g,i,k-1 respectively represent the change amounts of electric energy, thermal energy, and gas response amounts of user k-2 period to k-1 period; s e,i,k-1 , s h,i,k-1 , s g,i,k-1 respectively represent the types of electric energy response amount, thermal energy response amount, and gas energy response amount provided by user i in the k-1th period. respectively represent the actual electric energy response amount, thermal energy response amount, and gas energy response amount provided by user i in the k-1th period; s e,i,k-2 , s h,i,k-2 , s g,i,k-2respectively, the type of the electric energy response amount, the type of the thermal energy response amount, the type of the gas energy response amount provided by the user i in the k-2 period; respectively, the actual electric energy response amount, the actual thermal energy response amount, the actual gas energy response amount provided by the user i in the k-2 period.

[0085] In combination with the formulas (10) to (13), the expected response amount provided by the user i in the k period can be rewritten as follows:

[0086]

[0087] In the formula, x i,k is the expected response amount provided by the user i in the k period predicted by the IESP; s IESP,k is the type of the IDR issued by the IESP in the k period; p i,k is the incentive price of the unit response amount issued by the IESP to the user i in the k period; s i,k is the type of the response amount provided by the user i in the k period; respectively, the price elasticity of the user i in the k period and the acceptable minimum incentive price for participating in the IDR after considering the “anchoring effect” and the “ratchet effect”, and the mathematical expression is shown in the formula (15):

[0088]

[0089] In the formula, x respectively, the price elasticity of the user i in the k period and the acceptable minimum incentive price for participating in the IDR after considering the “anchoring effect” and the “ratchet effect”; respectively, the price elasticity of the user i in the k period and the acceptable minimum incentive price for participating in the IDR after considering the “anchoring effect”; ε i,k is a parameter defined for simplifying the formula, respectively, x is the reciprocal of the parameter, respectively, the influence of the “ratchet effect” on the electric energy response behavior, the thermal energy response behavior, and the gas energy response behavior of the user.

[0090] Based on the formulas (10) and (12) to (15), the present application proposes a user response model considering the “anchoring effect” and the “ratchet effect”, and the user response model considering the anchoring effect and the ratchet effect of the user is represented by the following formula:

[0091]

[0092] In the formula, U″ i,k (x i,k ) is the benefit function of the user i in the k period considering the anchoring effect and the ratchet effect; pi,k denotes the incentive price of the unit response quantity released by the IESP to the user i in the kth period; T represents transposition; diag() represents generating a diagonal matrix, and the diagonal elements of the diagonal matrix are the element values of the matrix in the parentheses in sequence; s IESP,k denotes the IDR type released by the IESP in the kth period; x i,k denotes the expected response quantity of the user i provided by the IESP in the kth period; denotes the comfort loss function of the user i in the kth period, which represents the comfort loss of the user i in the kth period due to the energy use change; denotes the transposition of x i,k ; denotes the price elasticity and the acceptable minimum incentive price of the user i participating in the IDR in the kth period after considering the “anchoring effect” and “ratchet effect”; s i,k denotes the type of the response quantity provided by the user i in the kth period; E3 is a unit matrix of 3*3 order; ε i,k denotes a parameter defined for simplifying the formula, respectively represent taking the reciprocal of the parameter, respectively represent the influence of the “ratchet effect” on the electric energy response behavior, the thermal energy response behavior and the gas energy response behavior of the user; θ i,k denotes the response willingness of the user i in the kth period; E 3×1 denotes a matrix of 3*1 order and the elements are all 1; χ is the proportional coefficient of the parameters b i,k , λ i,k ; b i,k is a parameter defined for simplifying the mathematical expression, and the calculation formula is shown in formula (5); λ i,k denotes the acceptable minimum incentive price of the user i participating in the IDR in the kth period; denotes the maximum consumption quantity of the user i in the kth period; denotes the maximum reduction quantity of the user i in the kth period; Ξ i denotes an anchoring effect matrix, which is used to represent the influence of the “anchoring effect” of the user on the response behavior.

[0093] In the model shown in formula (16), once the IESP releases the incentive price in the kth period, the expected response quantity x i,k of the user i in the kth period is also determined; however, x i,k is the user response quantity predicted by the IESP, without considering the uncertainty of the user response, and thus there is a deviation from the actual response quantity provided by the user; the present application uses a random variable ξ i,k to represent the error between the expected response quantity and the actual response quantity of the user, as follows:

[0094]

[0095] wherein, is the actual response amount provided by user i in the kth time period, are the actual electric energy response amount, thermal energy response amount, and gas energy response amount provided by user i in the kth time period, respectively; x i,k is the expected response amount provided by user i in the kth time period predicted by the IESP; ξ i,k is a random variable vector representing the error between the expected response amount and the actual response amount of electric energy, thermal energy, and gas energy provided by user i in the kth time period, ξ i,k = [ξ e,i,k , ξ h,i,k , ξ g,i,k ] T , ξ e,i,k , ξ h,i,k , ξ g,i,k respectively represent the error between the expected response amount and the actual response amount of electric energy, thermal energy, and gas energy provided by user i in the kth time period, and the elements thereof respectively follow a normal distribution wherein μ e,i,k , μ h,i,k , μ g,i,k are the expectations of the errors, and σ e,i,k , σ h,i,k , σ g,i,k are the standard deviations of the errors. S220: considering the incentive cost and the penalty cost of the IESP, and establishing an IESP benefit model according to the minimum total cost;

[0096] (1) the objective function of the IESP benefit model;

[0097] When participating in the IDR, the IESP will dispatch the energy conversion equipment and the energy storage equipment with the goal of maximizing the benefit, and will issue an incentive subsidy to the user; the benefit of the IESP is composed of the income obtained from the market, the incentive cost issued to the user, and the penalty paid due to the uncompleted response task amount, wherein, since the income obtained by the IESP from the market after the market clearing is known, the objective function of the IESP benefit model is represented by the following formula:

[0098]

[0099] wherein U IESP is the response cost of the IESP in the kth time period; K is the total number of time periods of the comprehensive demand response; N is the number of users participating in the comprehensive demand response; p i,k represents the incentive price of the unit response amount issued by the IESP to user i in the kth time period; s IESP,kFor the IDR type published by IESP in the k-th time period; x i,k IESP predicts the expected response from user i in the k-th time period; β is the penalty factor for deviation in unit electrical, thermal, and gas response, β = [β e ,β h ,β g ] T ,β e β h β g These are the penalty coefficients for unit electrical, thermal, and gas response deviations, respectively; T is the transpose; || indicates taking the absolute value of the element within the parentheses; ζ k ζ represents the total deviation between the expected and actual responses of electricity, heat, and gas during time period k. k =[ζ e,k ,ζ h,k ,ζ g,k ] T , ζ e,k ζ h,k ζ g,k ζ represents the total deviation between the expected and actual responses of electricity, heat, and gas during time period k. e,k ζ h,k ζ g,k A positive value indicates that IESP did not complete the required response task in time period k, while a negative value indicates that IESP exceeded the required response task in time period k. According to equation (17), the total deviation of the response volume in time period k is equal to the sum of the deviations of the response volumes of each user in time period k, i.e.:

[0100]

[0101] Combining equation (17) and the properties of the normal distribution, we can know that ζ e,k ζ h,k ζ g,k They respectively follow a normal distribution

[0102] (2) Constraints of the IESP benefit model;

[0103] The constraints of the IESP benefit model are established based on the supply and demand balance requirements, the operating conditions of energy coupling equipment and energy storage equipment. The constraints of the IESP benefit model include the power balance constraint of IESP, the operating conditions constraint of IESP coupling equipment and the operating conditions constraint of IESP energy storage equipment.

[0104] 1) Power balance constraints of IESP;

[0105] The power balance between supply and demand of the integrated energy system is the premise of safe operation of the system and the purpose of IDR implementation. Therefore, the incentive strategy of IESP not only maximizes its own benefit, but also is subject to the power balance constraint. The power balance constraint of IESP is represented by the following formula:

[0106]

[0107] In the formula, G k is the baseline load of the k period, G k = [G e,k , G h,k , G g,k ] T , G e,k , G h,k , G g,k are the baseline loads of the k period of electric energy, thermal energy and gas energy respectively; each term on the right side of the formula represents the response behavior of IESP and all users in the k period respectively; C(v k ) is the energy coupling matrix; ΔL k is the response task amount of IESP in the k period, ΔL k = [ΔL e,k , ΔL h,k , ΔL g,k ] T , ΔL e,k , ΔL h,k , ΔL g,k are the response task amounts of electric energy, thermal energy and gas energy of IESP in the k period respectively; E k is the response amount provided by the energy storage device in the k period, E k = [E e,k , E h,k , E g,k ] T , E e,k , E h,k , E g,k are the response amounts of electric energy, thermal energy and gas energy provided by the energy storage device in the k period respectively, and E e,k , E h,k , E g,k are positive values when the energy storage device discharges energy and are negative values when the energy storage device charges energy; is the total actual response amount provided by all users in the k period;

[0108]

[0109] In the formula, respectively, are the total actual response of electric, thermal and gas energy provided by all users in the kth time period; N is the number of users participating in the integrated demand response; x i,k is the expected response provided by user i in the kth time period predicted by the IESP; || denotes the absolute value of the elements in the parentheses; ζ k is the total deviation of the expected and actual responses of electric, thermal and gas energy in the kth time period.

[0110] 2) Coupling device operating constraints of the IESP;

[0111] The energy is coupled through combined heating and power (CHP), electric boiler (EB) and gas turbine (GT), which can be integrated into an energy hub (EH) as shown in Figure 3 The EH can be modeled as follows:

[0112]

[0113] In the formula, C(υ k ) is the energy coupling matrix; υ k = [u1, u2, u3] T denotes the scheduling coefficient, u1, u2, u3 respectively represent the proportion of the input power of the EH and the input power of the EB, CHP and GT; η EB , η GF , respectively represent the working efficiency of the electric boiler, the working efficiency of the gas turbine, the working efficiency of the CHP for converting gas energy into thermal energy, and the working efficiency of the CHP for converting gas energy into electric energy.

[0114] The coupling devices in the EH should also satisfy the coupling device operating constraints of the IESP, which are represented by the following formula:

[0115]

[0116] In the formula, u1, u2, u3 respectively represent the proportion of the input power of the EH and the input power of the EB, CHP and GT; η EB , η GF , respectively represent the working efficiency of the electric boiler, the working efficiency of the gas turbine, the working efficiency of the CHP for converting gas energy into thermal energy, and the working efficiency of the CHP for converting gas energy into electric energy; G e,in , G g,in are the input power of the electric energy and the gas energy of the energy hub, respectively; are the rated output power of the electric boiler and the gas turbine, respectively; These are the rated output power for CHP gas energy conversion to heat energy and gas energy conversion to electrical energy, respectively.

[0117] 3) Operating condition constraints of IESP energy storage devices;

[0118] Energy storage devices are limited by rated power, rated capacity, and the continuity of charging and discharging. Therefore, energy storage devices should be subject to the IESP (Independent Energy Storage Program) operating condition constraints, which are expressed by the following formula:

[0119]

[0120] In the formula, S0 is the initial energy stored by the energy storage device before participating in IDR, S0 = [S e,0 ,S h,0 ,S g,0 ] T S e,0 S h,0 S g,0 These represent the initial electrical energy, initial thermal energy, and initial gas energy stored by the energy storage device before participating in IDR; ΔT is the duration of a single IDR participation by the energy storage device; t is the t-th time period, which is a parameter defined to distinguish the k-th time period; k is the k-th time period; K is the total number of time periods in the integrated demand response period; η ES η is the charging / discharging efficiency of energy storage devices. ES =[η e,ES ,η h,ES ,η g,ES ] T η e,es η h,ES η g,ES These are the electrical energy charging / discharging efficiency, thermal energy charging / discharging efficiency, and gas energy charging / discharging efficiency of energy storage devices, respectively; E t E represents the response provided by the energy storage device in time period t. t =[E e,t E h,t E g,t ] T E e,t E h,t E g,t These represent the electrical energy response, thermal energy response, and gas energy response provided by the energy storage device in time period t, respectively; S max E represents the upper limit of the capacity of energy storage devices. k E represents the response provided by the energy storage device in the k-th time period. k =[E e,k E h,k E g,k ] T E e,k Eh,k , E g,k , E e,k , E h,k , E g,k , E min , E max , E min = [E e,min , E h,min , E g,min ] T , E e,min , E h,min , E g,min , E max = [E e,max , E h,max , E g,max ] T , E e,max , E h,max , E g,max , E

[0121] Further, in one embodiment, the method further comprises estimating the ratchet coefficient in the user response model considering the anchoring effect and the ratchet effect based on the maximum likelihood theory and the Newton iteration method; calculating the expected response amount x i,k provided by the user i in the kth period based on the ratchet coefficient, and minimizing the deviation of the total actual response amount provided by all users in the kth period, so that the IESP can accurately predict the response amount provided by the users based on formula (14) and formulate the corresponding incentive strategy.

[0122] IESP calculates the mathematical relationship between the expected response amount and incentive price provided by user i in the k-th time period using formula (14). However, formula (14) contains unknown user characteristic parameters (i.e., the ratchet coefficient in the model parameters after considering the "ratchet effect"), which means that formula (14) still cannot accurately describe the mathematical relationship between the expected response amount and incentive price. It is necessary to estimate the value of the user characteristic parameters before formula (14) can accurately calculate the mathematical relationship between the expected response amount and incentive price provided by user i in the k-th time period, so as to subsequently apply the model constructed by this invention. In the established IDR two-level stochastic optimization model, the user's expected response quantity, which is the optimization variable, is replaced by the incentive price, thereby achieving the optimization of the incentive price. Therefore, this invention estimates the ratchet coefficient in the user response model based on the maximum likelihood theory and Newton's iteration method, so that IESP can accurately describe the relationship between the user's expected response quantity and the incentive price based on equation (14), so as to optimize the subsequent incentive strategy. The invention includes: establishing a nonlinear function of the ratchet coefficient to be estimated based on the maximum likelihood theory; and solving the nonlinear function of the ratchet coefficient to be estimated based on Newton's iteration method to obtain the value of the ratchet coefficient.

[0123] (1) Establish a nonlinear function for the ratchet coefficient to be estimated based on the maximum likelihood theory;

[0124] The ratchet coefficient to be estimated Unbiased estimation is performed, and equation (21) is processed based on maximum likelihood theory to obtain the log-likelihood function as shown below:

[0125]

[0126] In the formula, This is an estimated value for the ratchet coefficient. These are the estimated values ​​of the ratchet coefficients for electricity, heat, and gas for the user, respectively; f() represents the random variable ξ. i,k The probability density function, whose value is related to the ratchet coefficient to be estimated. Related; ξ i,t σ represents the error between the expected and actual response quantities of electricity, heat, and gas provided to user i in time period t; k is the kth time period; s is the set of energy types in the IDR; e, h, and g represent electricity, heat, and gas, respectively; σ represents the error between the expected and actual response quantities of electricity, heat, and gas provided to user i in time period t ... s,i Let ξ be a random variable that follows a normal distribution. i,k Standard deviation; x represents the actual response volume provided to user i in the t-th time period; s,i,t IESP predicts the expected response volume provided by user i in the t-th time period.

[0127] As can be seen from equation (26), the extrema of the likelihood function are only related to the expression containing... The terms are related to the term containing only the constant σ.s,i Since the terms are independent, the above maximization problem can be equivalent to the following expression:

[0128]

[0129] In the formula, J k Let be the equivalent function of the log-likelihood function shown in equation (26) for the k-th time period; k is the k-th time period; Let be the actual response volume provided to user i in the t-th time period, and its value is obtained from historical data. These represent the actual electrical energy response, thermal energy response, and gas energy response provided to user i during the t-th time period, respectively; σ s,i Let ξ be a random variable that follows a normal distribution. i,k Standard deviation; x i,t To predict the expected response amount provided by user i in the t-th time period after taking into account the "anchoring effect" and the "ratchet effect";

[0130] x i,t =[x e,i,t ,x h,i,t ,x g,i,t ] T

[0131]

[0132] In the formula, x e,i,t x h,i,t x g,i,t These are the expected electrical energy response, expected thermal energy response, and expected gas energy response provided by user i in the t-th time period, as predicted by IESP. This represents the price elasticity coefficient of user i in the t-th time period after considering the "anchoring effect" and the "ratchet effect"; s IESP,t For the IDR type published by IESP in the t-th time period; p i,t The incentive price that IESP issues to user i in the t-th time period; s i,g The type of response provided to user i in the t-th time period; This represents the lowest acceptable incentive price for user i in the t-th time period after considering the "anchoring effect" and the "ratchet effect".

[0133] To intuitively represent the parameters With x i,t The relationship shown in equation (15) ε i,k Substituting the parameters into equation (28), thus x i,t Transform it into the following form:

[0134]

[0135] where x i,t is the expected response quantity provided by user i at time period t considering the "anchoring effect" and "ratchet effect"; respectively represent the price elasticity coefficient and the acceptable minimum incentive price of user i at time period t considering the "anchoring effect"; s IESP,t is the IDR type released by IESP at time period t; p i,t is the incentive price per unit response quantity released by IESP to user i at time period t; e τ is a parameter defined for simplifying the expression, respectively are the ratchet coefficients of electricity, heat and gas to be estimated, Δx e,i,t-1 , Δx h,i,t-1 , Δx g,i,t-1 respectively are the change of electricity, heat and gas response quantities of user i from time period t-2 to t-1; s i,t is the type of response quantity provided by user i at time period t.

[0136] For the minimization problem shown in equation (27), the function J k with respect to is derived as follows:

[0137]

[0138] where J k is the equivalent function of the log-likelihood function shown in equation (26) at time period k; is the estimated value of the ratchet coefficient; is the actual response quantity provided by user i at time period t; x i,t is the expected response quantity provided by user i at time period t predicted by IESP; H t is x i,t with respect to is derived as follows:

[0139]

[0140] where, is the estimated value of the ratchet coefficient; x i,t is the expected response quantity provided by user i at time period t predicted by IESP;

[0141]

[0142] respectively are the electricity price elasticity coefficient, the heat price elasticity coefficient, the gas price elasticity coefficient of the user i in the tth period of time after considering the "anchoring effect", s IESP,e,t IESP,h,t IESP,g,t respectively are the electricity response type, the heat response type, the gas response type published by the IESP in the tth period of time, p e,i,t h,i,t g,i,t respectively are the unit electricity response incentive price, the unit heat response incentive price, the unit gas response incentive price published by the IESP to the user i in the tth period of time; e τ is a parameter defined for simplifying the expression, respectively are the to-be-estimated electricity ratchet coefficient, the heat ratchet coefficient, and the gas ratchet coefficient of the user, Δx e,i,t-1 h,i,t-1 g,i,t-1 respectively are the change amount of the electricity response amount, the heat response amount, and the gas response amount of the user i in the t-2th period of time to the t-1th period of time.

[0143] To obtain the optimal solution of , the function J k is set to zero with respect to the first-order partial derivative of , and a nonlinear function about the to-be-estimated parameter is obtained, and the nonlinear function of the to-be-estimated ratchet coefficient is represented by the following formula:

[0144]

[0145] In the formula, H is the nonlinear function of the to-be-estimated ratchet coefficient ; H t is the result of the first-order partial derivative of x i,t with respect to ; is the estimated value of the ratchet coefficient; is the actual response amount provided by the user i in the tth period of time; x i,t is the expected response amount provided by the user i in the tth period of time predicted by the IESP.

[0146] (2) A method for solving unknown characteristic parameters of a user is established based on the Newton iteration principle, that is, the nonlinear function of the to-be-estimated ratchet coefficient is solved based on the Newton iteration method, and the ratchet coefficient is obtained;

[0147] The formula (32) is solved based on the Newton iteration principle, and the iteration equation is shown in the following formula:

[0148]

[0149] In the formula, H ​​​​​​Electric energy parameter, thermal energy parameter, gas energy parameter defined for simplified formula respectively; IESP,e,k 、s IESP,h,k 、s IESP,g,k Electric energy response type, thermal energy response type, gas energy response type released by the kth period IESP respectively; Ratchet coefficient of the user to be estimated electrically, thermally and gasly respectively; Δx e,i,t-1 、Δx h,i,t-1 、Δx g,i,t-1 Variation of the electric, thermal and gas response amount of the user i from the t-2th period to the t-1th period; Actual electric, thermal and gas response amount provided by the user i in the tth period; x e,i,t 、x h,i,t 、x g,i,t Electric, thermal and gas expected response amount provided by the user i in the tth period predicted by the IESP; Hamiltonian operator; Estimated value of the ratchet coefficient.

[0150] The operation result of is shown in the following formula:

[0151]

[0152] In the formula, Indicates the symbol defined for the simplified formula, which is a constant and irrelevant to the estimated coefficient , ( Indicates the 1st, 2nd and 3rd elements in the matrix Θ * ) respectively) is shown in the following formula:

[0153]

[0154] In the formula, s e,i,t , s h,i,t , s g,i,t Electric, thermal and gas response amount type provided by the user i in the tth period; Electric, thermal and gas minimum incentive price acceptable after considering the anchoring effect in the tth period; e indicates the exponential function.

[0155] The present application establishes a parameter estimation method based on the maximum likelihood theory and the Newton iteration method, so that the IESP can accurately estimate the unknown characteristic parameters of the user which are nonlinear with the expected response amount of the user.

[0156] S230: constructing an IDR double-layer stochastic optimization model based on the user response model considering the user anchoring effect and the ratchet effect and the IESP benefit model;

[0157] constructing an IDR double-layer stochastic optimization model based on the user response model considering the user anchoring effect and the ratchet effect and the IESP benefit model;

[0158] Based on the user response model considering the user anchoring effect and the ratchet effect shown in formula (16) and the IESP benefit model shown in formula (18)-(25), an IDR double-layer stochastic optimization model taking IESP and users as subjects is proposed, wherein the upper-layer problem is an IESP cost minimization problem, and the lower-layer problem is a user benefit maximization problem, and the IDR double-layer stochastic optimization model is as follows:

[0159]

[0160] The IDR double-layer stochastic optimization model is inputted with the type of IDR released by the IESP at the current time, the incentive price released by the IESP to the user in the last period, the unit penalty coefficient of the uncompleted expected response amount, and the type of the response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user, and the expected input of the user to the incentive price, to obtain an optimized incentive price.

[0161] Since the objective function and the constraint condition of the IESP contain a random variable ζ k , and formula (39) contains the optimization model of the user and the IESP, the optimization problem established in the application is a double-layer stochastic optimization problem, and the double-layer optimization problem is converted into a single-layer deterministic optimization problem for efficient solving to obtain an optimized incentive price, which is the optimal incentive price of the unit response amount released by the IESP to the user.

[0162] Firstly, the random variable ζ k in formula (18) and formula (21) is converted into a determined expected value E(|ζ k |), so as to convert the random optimization problem into a deterministic optimization problem; the calculation result of E(|ζ k |) is as follows:

[0163]

[0164] In the formula, E() represents the expected value of the element in the parentheses; ζ k is the total deviation of the expected response amount and the actual response amount of electricity, heat and gas in the k period; and ξ i,kError between the expected response amount and the actual response amount provided by the IESP to the user i in the kth time period; σ i Standard deviation of the random variable ξ i,k that obeys the normal distribution.

[0165] Secondly, according to the formula (39), the decision variable of the lower layer problem is x i,k The decision variable of the upper layer problem is p i,k The present application establishes the mathematical relationship between x i,k and p i,k , and equivalently replaces p i,k in the upper layer problem with the function about x i,k , so as to convert the double-layer optimization problem into a single-layer optimization problem for solving.

[0166] Through solving the lower layer problem, p i,k can be expressed as the function about x i,k , as follows:

[0167]

[0168] In addition, the KKT condition of the lower layer problem is as follows:

[0169]

[0170] In the formula, x i,k is the expected response amount of the IESP predicted for the user i in the kth time period; p i,k is the incentive price of the unit response amount issued by the IESP to the user i in the kth time period; s IESP,k is the IDR type issued by the IESP in the kth time period; is the price elasticity coefficient of the user i in the kth time period after considering the "anchoring effect" and "ratchet effect"; s i,k is the type of the response amount provided by the user i in the kth time period; is the acceptable minimum incentive price of the user i in the kth time period after considering the "anchoring effect" and "ratchet effect"; is the maximum consumption amount of the user i in the kth time period; is the maximum reduction amount of the user i in the kth time period.

[0171] Then, the decision variable p i,k of the upper layer problem is replaced by the formula (42), and the objective function of the upper layer problem can be rewritten as follows:

[0172]

[0173] In the formula, U IESp is the equivalent benefit function of the IESP; x i,k T; is the price elasticity coefficient of user i in the kth period after considering the anchoring effect and the ratchet effect; is the acceptable minimum incentive price of user i in the kth period after considering the anchoring effect and the ratchet effect; T is the transpose; s i,k is the type of response amount provided by user i in the kth period; β is the penalty coefficient of unit electric, heat, and gas response amount deviation; E() represents the expected value of the element in the parentheses; ζ k is the total deviation of the expected response amount and the actual response amount of electric, heat, and gas in the kth period.

[0174] In summary, the original double-layer stochastic optimization problem is converted into a single-layer deterministic optimization problem with x i,k , E k , and υ k as decision variables to obtain the incentive strategy; the single-layer deterministic optimization problem is as follows:

[0175] The objective function is:

[0176]

[0177] In the formula, U IESP is the equivalent benefit function of the IESP; x i,k T; is the price elasticity coefficient of user i in the kth period after considering the anchoring effect and the ratchet effect; is the acceptable minimum incentive price of user i in the kth period after considering the anchoring effect and the ratchet effect; T is the transpose; s i,k is the type of response amount provided by user i in the kth period; β is the penalty coefficient of unit electric, heat, and gas response amount deviation; E() represents the expected value of the element in the parentheses; ζ k is the total deviation of the expected response amount and the actual response amount of electric, heat, and gas in the kth period.

[0178] The constraint conditions include formula (4) to formula (5), formula (11), formula (13), formula (19), and formula (20) to formula (25).

[0179] The present application establishes a user response model to obtain the relationship between the expected response amount and the IESP incentive price; since there are two optimization variables, i.e., the expected response amount and the incentive price, in the IESP model, the response amount variable in the IESP is substituted according to the relationship between the response amount and the price obtained from the user model; then, the optimization of the incentive price is realized by solving the objective of minimum incentive cost and numerous constraints.

[0180] S300: Adjust the incentive price of the unit response amount of the user published by the current IESP based on the optimized incentive price, so as to guide the user to use energy in the peak and valley to achieve the expected response amount.

[0181] To verify the effectiveness and superiority of the optimization modeling proposed in the application, simulation comparison experiments are performed on the model of the application and existing models. The main characteristics of each model are shown in Table 1.

[0182] Table 1 Comparison of main characteristics of four models

[0183]

[0184] The relevant parameters involved in the interaction between IESP and multi-energy market, energy conversion equipment and energy storage equipment parameters, and part of the response characteristic parameters of the user are all known parameters in the simulation; the values of the anchoring coefficient and the ratchet coefficient will be set according to the specific experimental purpose in the future.

[0185] (1) Analysis of the influence of anchoring effect on IDR;

[0186] To analyze the influence of the "anchoring effect" of the user on the implementation effect of IDR, the application performs simulation comparison experiments on model ② and model ③; among them, the anchoring coefficient of all users in model ③ is randomly set in the interval [0.9, 1]; it is verified that the actual response amount and response deviation of the user in model ③ are significantly different from those in model ②; model ③ can significantly improve the benefit of user participation in IDR compared with model ②; therefore, the user response model considering the "anchoring effect" can obviously improve the actual response amount of the user in each period and each type of energy, and reduce the deviation between the actual response amount and the expected response amount; thus, after considering the "anchoring effect" of the user, IESP can more accurately predict the response behavior of the user under different incentive prices in each period, so as to develop more effective incentive strategies to tap the response potential of the user and ensure that the actual response amount of the user is as close as possible to the expected response amount.

[0187] (2) Analysis of the influence of ratchet effect on IDR;

[0188] To analyze the influence of the "ratchet effect" on the implementation effect of IDR, the simulation comparative experiments of model ③ and model ④ are carried out; wherein, the anchor coefficients of all users in model ④ are set as the same as model ③, and the ratchet coefficients are randomly set in the range of [0, 0.1]; it is verified that the response deviation in model ④ is reduced compared with model ③, the punishment cost and the total cost of model ④ are far less than those of model ③ although the incentive cost of model ④ is slightly higher, and the benefits of most users in model ④ are higher than those in model ③; therefore, by fine modeling of the "ratchet effect" of users, IESP can more accurately simulate the correlation of response behaviors of users in different time periods, more accurately predict the response behavior of users in the current period, so as to formulate more effective incentive strategies, avoid over-response or under-response of users, avoid that users cause great comfort loss by over-changing energy-using behaviors in a single period, on the other hand, appropriately increasing the incentive price guarantees the economic benefits of users and avoids the higher punishment cost of IESP due to the difficulty in mobilizing the response enthusiasm of users, realizes the win-win of both, and verifies that the model proposed in the application can improve the effectiveness and economy of the incentive strategy of IESP.

[0189] (3) Model effectiveness analysis;

[0190] In order to comprehensively verify the superiority of the model, the model 1, the model 2, the model 3 and the model 4 are simulated and compared and analyzed; it is verified that the user response quantity and the deviation in the model 1 are the largest, the model 2 is the second, the response deviation of the model 3 is smaller, and the response deviation of the model 4 is almost 0; the IESP in the model 1 has the lowest incentive cost and the highest total cost in each period, the costs of the IESP in the model 2 are higher in each period and have the highest total cost, the IESP in the model 3 has higher incentive cost and total cost in part of the period, although the incentive cost of the IESP in the model 4 is slightly high, the penalty cost and the total cost in each period are the lowest; this is because the model 1 does not consider the differentiated user characteristics and issues a unified incentive strategy, so that although the incentive cost is the lowest, the enthusiasm of the user to participate in the response is not high, the response deviation is larger, and high penalty cost is generated; the model 2 issues different incentive prices for different users, but only considers the objective economic benefits of the user response, ignores the subjective psychological feelings of the user, and therefore still cannot fully tap the user response potential; in the model 3, the IESP formulates an economic incentive strategy after considering the 'anchoring effect', so that the response deviation and the cost are reduced, but since the 'ratchet effect' of the user is not concerned, the IESP cannot formulate a targeted incentive strategy according to the user response inertia, so that the response deviation and the penalty cost are higher; the model 4 considers the 'anchoring effect' and the 'ratchet effect' of the user, can more accurately predict the user response behavior and formulate a more effective incentive strategy, can follow the response inertia of the user to improve the response enthusiasm of the user, can avoid the decrease of the marginal utility of the incentive due to excessive flatness, can reduce the response deviation and the total cost through appropriate investment of the incentive cost, and verifies the effectiveness and the superiority of the model.

[0191] The model 4 can significantly improve the benefit of the user, so that the total benefit of the user in the whole period is the highest; this is because after considering the 'anchoring effect' and the 'ratchet effect' of the user, the IESP can not only pay attention to the comparison psychology of the user to the incentive prices in different periods, but also pay attention to the correlation of the user energy use behaviors in different periods, the formulated strategy can meet the psychological expectation of different users and conform to the individualized energy use rules of the user, and the benefit of the user is effectively improved; in addition, there are a few users with lower benefits in the model 4. This is because the IESP issues lower incentive prices to part of the users who are difficult to mobilize, so as to reduce the incentive cost, guarantee the economic benefits of most users and reduce the total cost, and the superiority of the model is verified again.

[0192] In summary, the user 'anchoring effect' and 'ratchet effect' have great influence on the IESP incentive strategy, the model can accurately predict the user response behavior and formulate more effective incentive strategy, reduce the cost of IESP while improving the user's benefit, and realize the win-win of IESP and user.

[0193] Embodiment two

[0194] An embodiment of the present application provides a system for considering anchoring-ratchet effect to stimulate user energy consumption, comprising a collection module, an IDR double-layer random optimization model construction module and an optimization module.

[0195] The collection module is used for collecting the IDR type published by the IESP at the current moment, the incentive price published by the IESP to the user in the last period, the unit penalty coefficient of the uncompleted expected response amount and the type of the response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user and the expectation of the user to the incentive price.

[0196] The IDR double-layer random optimization model construction module is used for constructing an IDR double-layer random optimization model based on the user response model considering the user anchoring effect and ratchet effect and the IESP benefit model, and inputting the IDR type published by the IESP at the current moment, the incentive price published by the IESP to the user in the last period, the unit penalty coefficient of the uncompleted expected response amount and the type of the response amount provided by the user, the price elasticity coefficient of the user, the minimum acceptable incentive price of the user and the expectation of the user to the incentive price into the IDR double-layer random optimization model, and obtaining an optimized incentive price.

[0197] The optimization module is used for adjusting the unit response amount incentive price published by the current IESP to the user based on the optimized incentive price, so as to guide the user to use energy in the peak and valley, so as to achieve the expected response amount.

[0198] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the embodiments of the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software mode depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0199] In the embodiments of the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described embodiments of the apparatus are merely schematic, and the division of the units is merely logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0200] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0201] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit.

[0202] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various storage media that can store program codes.

[0203] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed by the present application can be easily thought of by those skilled in the art, and should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for incentivizing user energy consumption considering the anchoring-ratchet effect, characterized in that, Includes the following steps: S100: Collects the IDR type issued by IESP at the current moment, the incentive price issued by IESP to users in the previous period, the unit penalty coefficient for not meeting the expected response volume, the type of response volume provided by users, the user's price elasticity coefficient, the user's minimum acceptable incentive price, and the user's expectation of the incentive price. S200: Based on the user response model and IESP benefit model that consider user anchoring effect and ratchet effect, an IDR two-level stochastic optimization model is constructed; and the IDR type issued by IESP at the current time, the incentive price issued by IESP to users in the previous period, the unit penalty coefficient for not meeting the expected response volume, the type of response volume provided by the user, the user's price elasticity coefficient, the user's minimum acceptable incentive price, and the user's expectation of the incentive price are input into the IDR two-level stochastic optimization model to obtain the optimized incentive price; S300: Based on the optimized incentive price, adjust the incentive price per unit response volume issued by IESP to users to guide users' energy demand during peak and off-peak periods to achieve the expected response volume; The user response model that considers the user anchoring effect and the ratchet effect is expressed by the following formula: In the formula, U″ i,k (x i,k Let p be the benefit function of user i in the k-th time period after considering the anchoring effect and the ratchet effect; i,k This represents the incentive price per unit response issued by IESP to user i in the k-th time period; T is the transpose; diag() generates a diagonal matrix whose diagonal elements are the values ​​of the matrix within the parentheses; s IESP,k For the IDR type published by IESP in the k-th time period; x i,k For IESP, predict the expected response volume provided by user i in the k-th time period; Let be the comfort loss function for user i in the k-th time period; Let s represent the price elasticity coefficient and the acceptable minimum incentive price for user i in the k-th time period after considering the anchoring effect and ratchet effect, respectively; i,k The type of response provided to user i during time period k; E3 is a 3×3 identity matrix; ε i,k The parameter θ is defined to simplify the formula. i,k E represents user i's willingness to respond in the k-th time period; 3×1 It is a 3×1 matrix with all elements equal to 1; χ is the parameter b. i,k , λ i,k The proportionality coefficient, b i,k A parameter defined to simplify mathematical expressions; λ i,k The lowest acceptable incentive price for user i to participate in IDR during the k-th time period; This represents the maximum amount of data consumed by user i during time period k. The maximum reduction for user i during time period k; Ξ i This is the anchoring effect matrix.

2. The method for incentivizing users to use energy according to claim 1, characterized in that, Step S200 includes: the IDR two-level stochastic optimization model transforms the two-level stochastic optimization problem into a single-level deterministic optimization problem for solution, thereby obtaining the optimized incentive price.

3. The method for incentivizing users to use energy according to claim 1, characterized in that, Step S200 includes the following sub-steps: S210: Establish a user response model that considers user dissatisfaction and economic benefits; and based on the user response model, establish a user response model that considers user anchoring effect and ratchet effect; S220: Consider the incentive and penalty costs of IESP, and establish an IESP benefit model based on minimizing total cost; S230: Based on the user response model that considers the user anchoring effect and ratchet effect and the IESP benefit model, construct the IDR two-level stochastic optimization model.

4. The method for incentivizing users to use energy according to claim 3, characterized in that, The user response model that considers user dissatisfaction and economic benefits is expressed by the following formula: In the formula, U i,k (x i,k Let p be the benefit function for user i in the k-th time period; i,k This represents the incentive price per unit response issued by IESP to user i in the k-th time period; T is the transpose; diag() generates a diagonal matrix whose diagonal elements are the values ​​of the matrix within the parentheses; s IESP,k For the IDR type published by IESP in the k-th time period; x i,k For IESP, predict the expected response volume provided by user i in the k-th time period; Let θ be the comfort loss function for user i in the k-th time period; i,k Let λ represent user i's willingness to respond in the k-th time period; i,k The lowest acceptable incentive price for user i to participate in IDR in the k-th time period; s i,k The type of response quantity provided to user i during time period k; This represents the maximum amount of data consumed by user i during time period k. This represents the maximum reduction amount for user i during time period k.

5. The method for incentivizing users to use energy according to claim 3, characterized in that, Step S210 includes: The user response model is optimized by considering the user's anchoring effect to obtain a user response model that considers the user's anchoring effect; and the user response model that considers the user's ratchet effect is optimized by considering the user's ratchet effect to obtain a user response model that considers both the user's anchoring effect and the ratchet effect.

6. The method for incentivizing users to use energy according to claim 1, characterized in that, The IESP prediction of the expected response amount provided by user i in the k-th time period is calculated in the following way: In the formula, x i,k IESP predicts the expected response from user i in the k-th time period; s IESP,k For the IDR type published by IESP in the k-th time period; p i,k s is the incentive price per unit response volume that IESP publishes to user i in the k-th time period; i,k The type of response quantity provided to user i during time period k; To determine the price elasticity coefficient and acceptable minimum incentive price for user i in the k-th time period after considering the anchoring and ratchet effects, the ratchet coefficient in the modified model parameters after considering the anchoring and ratchet effects is estimated using maximum likelihood theory and Newton's iteration method.

7. The method for incentivizing users to use energy according to claim 1, characterized in that, In step S200, the objective function of the IESP benefit model is expressed by the following formula: In the formula, U IESP p represents the response cost for the k-th time period of IESP; K represents the total number of time periods in the Integrated Demand Response Period; N represents the number of users participating in the Integrated Demand Response Period; i,k s represents the incentive price per unit response quantity that IESP publishes to user i in the k-th time period; IESP,k For the IDR type published by IESP in the k-th time period; x i,k For IESP, predict the expected response volume provided by user i in the k-th time period; β is the penalty factor for deviation in unit electrical, thermal, and gaseous response; T is the transpose; ζ k The total deviation between the expected and actual responses of electricity, heat, and gas during time period k; || represents taking the absolute value.

8. The method for incentivizing users to use energy according to claim 7, characterized in that, The constraints of the IESP benefit model include the power balance constraint of the IESP, the operating condition constraint of the IESP coupling equipment, and the operating condition constraint of the IESP energy storage equipment.

9. A system for incentivizing user energy consumption based on the method of any one of claims 1-8, considering the anchor-ratchet effect, characterized in that, It includes a data acquisition module, an IDR two-level stochastic optimization model construction module, and an optimization module; The data acquisition module is used to collect the IDR type issued by IESP at the current moment, the incentive price issued by IESP to users in the previous period, the unit penalty coefficient for failure to meet the expected response volume, the type of response volume provided by users, the user's price elasticity coefficient, the user's minimum acceptable incentive price, and the user's expectation of the incentive price. The IDR two-layer stochastic optimization model construction module is used to construct an IDR two-layer stochastic optimization model based on a user response model that considers user anchoring effect and ratchet effect and an IESP benefit model; and inputs the IDR type issued by IESP at the current time, the incentive price issued by IESP to users in the previous time period, the unit penalty coefficient for not meeting the expected response volume, the type of response volume provided by the user, the user's price elasticity coefficient, the user's minimum acceptable incentive price, and the user's expectation of the incentive price into the IDR two-layer stochastic optimization model to obtain the optimized incentive price; The optimization module is used to adjust the incentive price per unit response volume issued by IESP to users based on the optimized incentive price, thereby guiding users' energy demand during peak and off-peak periods to achieve the expected response volume.

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