A resource aggregation optimization method, apparatus, and storage medium for power demand response.

Abstract

This invention discloses a resource aggregation optimization method, apparatus, and storage medium for power demand response. The method includes: constructing an initial objective function for a demand response aggregation optimization model and setting physical constraints for the initial objective function; acquiring invited capacity data, actual response capacity data, and deviation data from users and load aggregators, respectively, and inputting them into an effective response tier processing model to calculate the effective response capacity with different deviation relationships and establish corresponding tiers; outputting the corresponding effective response capacity tiers for users and load aggregators; setting tier state variables and tier intermediate variables for each tier, and setting corresponding logical constraints; updating the objective function of the demand response aggregation optimization model; inputting the updated objective function into the demand response aggregation optimization model; outputting decision result data; and obtaining a resource aggregation optimization application scheme for load aggregators to conduct scheduling transactions under the day-ahead invitation mode.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a resource aggregation optimization method, apparatus and storage medium for power demand response. Background Technology

[0002] Existing methods for aggregated analysis of adjustable resources primarily represent the power regulation range and corresponding dispatch costs of the aggregated group from the perspective of various resource constraints in directly controlled virtual power plants. They incorporate equivalent constraints into the main grid's dispatch optimization model, thereby achieving the connection between the aggregated objects and the power system's economic dispatch plan. However, in practice, the volume of resources fully capable of direct control response is limited in the context of load-side resource participation in dispatch transactions. To better leverage the responsiveness of load-side resources, in addition to using a direct control mode to call upon load-side resources, an invitation mode is typically employed to encourage various users to respond according to time periods.

[0003] Day-ahead invitation response refers to a response invitation issued by the power grid or government agencies during the day-ahead planning phase when a short-term power shortage or insufficient reserve is anticipated the following day. Large users or load aggregators that meet the eligibility criteria respond to the invitation and execute the response during the operating day. Finally, the response benefits or deviation assessment fees are obtained based on the evaluation of the actual response effectiveness. Day-ahead invitation response has become one of the main modes of electricity demand response in some provinces and regions of my country. Considering the uncertainty of user response, different levels of response quality evaluation standards are usually set, comparing the actual response effect of load-side resources with the expected declared capacity to complete the tiered assessment and compensation.

[0004] However, in the demand response implementation process, load aggregators need to further explore demand response capabilities and improve the overall efficiency of demand-side resource utilization by integrating dispersed user resources with different response qualities and cost characteristics. Load aggregators will deduct agency user expenses from the revenue of the demand response market and obtain a portion of the profit. Under the day-ahead demand response model, the optimal aggregation decision for the aggregation object needs to consider not only the physical constraints of the resource characteristics themselves, but also the impact of the demand response organizer's tiered certification standards for effective response capacity, the benefits and cost sharing methods between integrators and users, etc., and make aggregation decisions from the perspective of maximizing expected benefits. Therefore, existing aggregation analysis methods for adjustable resources characterized by cost fitting of aggregation objects and power adjustment range are difficult to apply to the aggregation decisions of load aggregators under the day-ahead demand response model. Summary of the Invention

[0005] This invention provides a resource aggregation optimization method, apparatus, and storage medium for power demand response, which can be applied to the aggregation decision-making of load aggregators under the day-ahead invitation mode. This enhances the load aggregator's ability to adjust the user-side resources it has already represented, improves the accuracy of the load aggregator's participation in the day-ahead invitation response decision-making, and generates more efficient and accurate demand response aggregation results.

[0006] To achieve the above effects, this embodiment of the invention provides a resource aggregation optimization method for power demand response, including constructing an initial objective function for a demand response aggregation optimization model based on the effective response capacity of the load aggregator, deviation assessment fees, demand response unit compensation standards, and expenditure fees of the users represented by the load aggregator, with the goal of maximizing expected revenue, and setting physical constraints for the initial objective function;

[0007] The system acquires invited capacity data and actual response capacity data of users and load aggregators within a preset time period, respectively, and inputs them into an effective response capacity tier processing model. This model calculates a first effective response capacity based on the invited capacity data and the actual response capacity data, and establishes corresponding effective response capacity tiers based on the deviation relationship between the invited capacity data and the actual response capacity data. The model then outputs the corresponding effective response capacity tiers for users and load aggregators, along with their corresponding deviation relationships; each effective response capacity tier corresponds to one deviation relationship.

[0008] Set the level state variables and corresponding logical constraints for each effective response capacity level. Based on all level state variables, update the relationship of the first effective response capacity to obtain the relationship of the second effective response capacity for users and load aggregators respectively. Based on the relationship of the second effective response capacity for users and the relationship of the second effective response capacity for load aggregators, update the initial objective function of the demand response aggregation optimization model to obtain the first objective function. Set level intermediate variables and corresponding linear logical constraints for the first objective function to obtain the second objective function.

[0009] The second objective function is input into the demand response aggregation optimization model so that the demand response aggregation optimization model outputs decision result data to obtain a resource aggregation optimization application scheme, which can be used by load aggregators to conduct scheduling transactions in the day-ahead invitation mode.

[0010] As a preferred embodiment, the resource aggregation optimization method for power demand response of the present invention, for the day-ahead invitation mode, determines the effective response capacity and establishes corresponding tiers based on the response characteristics of user resources already represented by load aggregators, the benefit and cost sharing method between load aggregators and users, and the tiered evaluation criteria for effective response capacity, by analyzing the deviation between the actual response capacity of load aggregators and users and the capacity requested in response. Based on the tiering of effective response capacity of load aggregators and users, and from the perspective of load aggregators, a demand response aggregation optimization model is constructed with the goal of maximizing expected revenue.

[0011] Compared to existing resource aggregation models that only consider the physical constraints of resource characteristics, the effective response capacity tiering evaluation method set by the demand response aggregation optimization model of this invention is compatible with the uncertainty of user resource response, takes into account the differences in resource characteristics and agency settlement relationships of numerous users, as well as the requirements of the demand response organizer for effective response capacity tiering certification. By using decision optimization methods to configure the response capacity available to already-agented users, more accurate aggregation declaration information is formed. This improves the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected benefits when load aggregators participate in day-ahead invitation responses; enhances the load aggregator's ability to adjust the resources of already-agented users under the day-ahead invitation mode, and improves the accuracy of the load aggregator's decision-making in participating in day-ahead invitation responses.

[0012] The demand response aggregation optimization model of this invention, due to its effective response capacity tiered evaluation method, exhibits significant nonlinear relationships among the optimal aggregation decision variables. To address this nonlinear coupling, by introducing tiered state variables and intermediate tier variables, along with corresponding logical constraints, a linearization process is proposed to transform the aggregation model into an equivalent mixed-integer programming model. This ensures the demand response aggregation optimization model is easy and efficient to solve, thereby improving the efficiency of optimal demand response aggregation decisions and enabling the model to produce more efficient and accurate demand response aggregation results.

[0013] As the preferred option, with the goal of maximizing expected returns, the initial objective function of the demand response aggregation optimization model is constructed as follows:

[0014] With the goal of maximizing expected returns, the initial objective function of the demand response aggregation optimization model is set as follows:

[0015]

[0016] Among them, P t R represents the integrator's effective response capacity during time period t; t L represents the deviation assessment fee for the integrator during time period t; tThis represents the unit compensation standard for the load aggregator's demand response during time period t; U1 and U2 represent the sets of users with linked compensation and fixed compensation represented by the aggregator, respectively; C 1j and C 2j These represent the expenses incurred by users receiving joint compensation and those receiving fixed compensation, respectively, through the integrator's agency; μ j This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement; p j,t Indicates the user's effective response capacity; λ j R represents the agreed-upon user-borne assessment cost sharing ratio coefficient; 1j,t This refers to the assessment fee for user response deviations, evaluated according to the assessment criteria of the invitation response organizer; L fix This indicates the fixed settlement fee stipulated in the contract;

[0017] The constraints of the initial objective function include: user effective response capacity constraints, load aggregator effective response capacity constraints, user resource application capacity range constraints, actual response capacity adjustment rate constraints, load aggregator deviation assessment cost constraints, invited application capacity and estimated actual response capacity constraints, aggregator maximum and minimum application capacity constraints, and minimum continuous response time constraints.

[0018] Among them, the effective response capacity constraint for load aggregators is:

[0019]

[0020] Among them, P t This represents the effective response capacity used by the integrator to calculate demand response revenue during time period t; D t and A t These represent the integrator's requested capacity and actual response capacity confirmed during time period t, respectively; R dz R dis R nor Let R be the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for the effective response capacity, respectively. dz <R dis <R nor N dis Effective response capacity is a discount factor relative to the actual response capacity.

[0021] The effective response capacity constraint for users is:

[0022]

[0023] The users include the linked compensation users and the fixed compensation users, d j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,tThis represents the actual response capacity of agent user j during time period t.

[0024] As a preferred embodiment, the demand response aggregation optimization model of this invention is designed for the implementation mode of day-ahead demand response and the settlement agency relationship between load aggregators and users subject to linkage compensation and fixed compensation. The agency settlement method is determined through negotiation between the user and the aggregator. After submitting the aggregation agency contract to the organizer, the user can participate in day-ahead demand response. Taking into account the response characteristics of the user resources already represented by the aggregator, the benefit and cost sharing method between the aggregator and the user, and the tiered evaluation criteria for effective response capacity, this model, from the perspective of the load aggregator, constructs an aggregation optimization model for power demand response with the goal of maximizing expected revenue. This improves the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected revenue; enhances the load aggregator's ability to regulate user-side resources already represented under the day-ahead demand mode; and improves the accuracy of the load aggregator's participation in day-ahead demand response decisions.

[0025] As a preferred approach, based on the deviation between the invited capacity declaration data and the actual response capacity data, a corresponding effective response capacity level is established, specifically as follows:

[0026] Calculate the effective response capacity for different deviation relationships:

[0027]

[0028] Where, d j,t This indicates the capacity requested by user or load aggregator j during time period t; a j,t R represents the actual response capacity of user or load aggregator j during time period t; dz R dis R nor These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for the effective response capacity, respectively, and R... dz <R dis <R nor N dis This is the discount factor between the effective response capacity and the actual response capacity.

[0029] Based on the deviation between the invited capacity declaration data and the actual response capacity data, four corresponding effective response capacity levels are established, wherein the deviation relationship includes: a j,t <R dz d j,t R dz d j,t ≤a j,t <R dis d j,t R dis d j,t ≤a j,t <Rnor d j,t and a j,t ≥R nor d j,t These correspond to the first, second, third, and fourth effective response capacity levels, respectively.

[0030] As a preferred embodiment, the resource aggregation optimization method for power demand response of the present invention, for the day-ahead invitation mode, determines the effective response capacity and establishes corresponding tiers based on the response characteristics of user resources already represented by the load aggregator, the benefit and cost sharing method between the load aggregator and users, and the tiered evaluation criteria for effective response capacity, by analyzing the deviation between the actual response capacity and the requested capacity of the load aggregator and users. When the load aggregator participates in day-ahead invitation response, the effective response capacity is tiered according to the tiered certification criteria for effective response capacity of the demand response organization. This takes into account the resource characteristics of numerous users, the differences in agency settlement relationships, and the requirements of the demand response organization for tiered certification of effective response capacity. The method uses decision optimization to configure the available response capacity for represented users, forming more accurate aggregated declaration information, improving the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected benefits; enhancing the load aggregator's ability to adjust user-side resources under the day-ahead invitation mode, and improving the accuracy of the load aggregator's decision-making in participating in day-ahead invitation response.

[0031] As a preferred approach, a gear state variable and corresponding logical constraint are set for each gear position. Based on all gear state variables, the relationship for the first effective response capacity is updated, specifically as follows:

[0032] Set the gear state variable y for each gear. i,1,t y i,2,t y i,3,t and y i,4,t ; where y i,1,t y i,2,t y i,3,t and y i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0033] The logical constraints for setting the gear position state variable are as follows:

[0034]

[0035] Where M represents a constant used to relax the constraints;

[0036] The updated effective response capacity relationship is as follows:

[0037] p j,t =Ndis y i,2,t a j,t +y i,3,t a j,t +R nor y i,4,t d j,t .

[0038] As a preferred embodiment, the existence of deviation relationships in the effective response capacity levels of the present invention leads to obvious nonlinear characteristics in the demand response aggregation optimization model. In each effective response capacity level established according to the evaluation criteria of effective response capacity, the level state variables and corresponding logical constraints of the response levels of users and load integrators for each time period are set to realize the linearization processing of the demand response aggregation optimization model, which can ensure the simplicity and efficiency of the solution, thereby improving the efficiency of the optimal aggregation decision of demand response, so that the demand response aggregation optimization model can form a more efficient and accurate demand response aggregation result.

[0039] As a preferred approach, based on the relationship between the user's second effective response capacity and the load aggregator's second effective response capacity, the objective function of the demand response aggregation optimization model is updated to obtain the first objective function, which is as follows:

[0040] The relationship between the user's second effective response capacity and the load integrator's second effective response capacity are as follows:

[0041]

[0042] Among them, D t and A t Y represents the declared capacity and actual response capacity confirmed by the load aggregator during time period t, respectively. 2,t and Y 4,t Let d represent the state variables of the second load aggregator position and the fourth load aggregator position, respectively. j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,t This represents the actual response capacity of proxy user j during time period t. s y i,2,t y i,3,t and y i,4,t Let N represent the gear state variables for the second, third, and fourth user gears, respectively. dis It is the discount factor between the effective response capacity and the actual response capacity;

[0043] Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows:

[0044]

[0045] Among them, R dz R dis R nor These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively; L t U1 represents the unit compensation standard for the load aggregator's demand response during time period t; U2 and U1 represent the sets of linked compensation users and fixed compensation users represented by the load aggregator, respectively.

[0046] As a preferred embodiment, this invention sets up user and load aggregator status variables and corresponding logical constraints in each effective response capacity tier established according to the effective response capacity tier evaluation criteria, updates the objective function of the demand response aggregation optimization model, eliminates the nonlinear relationship of effective response capacity in the model, and ensures that the demand response aggregation optimization model has the simplicity and efficiency of solution, thereby improving the efficiency of optimal demand response aggregation decision-making, so that the demand response aggregation optimization model can form a more efficient and accurate demand response aggregation result.

[0047] As a preferred approach, intermediate variables and corresponding linear logic constraints are set for the first objective function, specifically as follows:

[0048] Set the intermediate variable w for gear position i,1,t w i,2,t w i,3,t and w i,4,t ;

[0049]

[0050] Among them, w i,1,t w i,2,t w i,3,t and w i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0051] The logical constraints for the intermediate gear variable are set as follows:

[0052]

[0053]

[0054]

[0055]

[0056] As a preferred embodiment, after eliminating the nonlinear relationship of effective response capacity in the demand response aggregation optimization model, the present invention still includes nonlinear terms between the tiered state variables and the invited / reported capacity and actual response capacity. By setting intermediate variables for users and load aggregators and corresponding linear logic constraints in each effective response capacity tier established according to the tiered evaluation criteria, the objective function of the demand response aggregation optimization model is updated again to eliminate the nonlinear terms between the tiered state variables and the invited / reported capacity and actual response capacity in the model. This ensures the simplicity and efficiency of solving the demand response aggregation optimization model, thereby improving the efficiency of optimal demand response aggregation decision-making and enabling the demand response aggregation optimization model to generate more efficient and accurate demand response aggregation results.

[0057] As the preferred option, the demand response aggregation optimization model outputs decision result data, specifically:

[0058] The demand response aggregation optimization model solves for the second objective function and outputs the invited capacity d of users and integrators during the invitation period t. j,t and D t The expected actual response capacity a j,t and A t And deviation assessment fees R for integrators and users of linkage compensation categories. t and R 1j,t ;

[0059] The expected costs that integrators need to pay to users Where, μ j This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement; p j,t Indicates the user's effective response capacity; λ j R represents the agreed-upon user-borne assessment cost sharing ratio coefficient; 1j,t This refers to the assessment fee for user response deviations, evaluated according to the assessment criteria of the invitation response organizer; L fix This indicates the fixed settlement fee stipulated in the contract;

[0060] The integrator's aggregated bid price for time period t:

[0061] As a preferred embodiment, after linearizing the demand response aggregation optimization model, this invention can directly solve for the following parameters within the invitation period t: the invited application capacity of users and integrators, the expected actual response capacity, the deviation assessment costs for integrators and users requiring linkage compensation, the expected costs that integrators need to pay to users, and the aggregation application price of integrators in period t. Finally, the decision result data is output. The demand response aggregation optimization model yields more efficient, accurate, and precise demand response aggregation results.

[0062] Accordingly, the present invention also provides a resource aggregation optimization device for power demand response, comprising: a model building module, a tier processing module, a linear processing module, and a resource aggregation optimization module;

[0063] The model building module is used to construct an initial objective function for the demand response aggregation optimization model based on the load aggregator's effective response capacity, deviation assessment fees, demand response unit compensation standards, and the expenditure fees of the users represented by the load aggregator, with the goal of maximizing expected revenue, and to set the physical constraints of the initial objective function.

[0064] The tier processing module is used to acquire the invited capacity declaration data and actual response capacity data of users and load aggregators within a preset time period, respectively, and input them into the effective response capacity tier processing model. The effective response capacity tier processing model calculates the first effective response capacity based on the invited capacity declaration data and the actual response capacity data, and establishes corresponding effective response capacity tiers based on the deviation relationship between the invited capacity declaration data and the actual response capacity data. It then outputs the corresponding effective response capacity tiers for users and load aggregators and their corresponding deviation relationships; wherein each effective response capacity tier corresponds to one deviation relationship.

[0065] The linear processing module is used to set the level state variables and corresponding logical constraints for each effective response capacity level; based on all level state variables, it updates the relationship of the first effective response capacity to obtain the relationship of the second effective response capacity for users and load aggregators respectively; based on the relationship of the second effective response capacity for users and the relationship of the second effective response capacity for load aggregators, it updates the initial objective function of the demand response aggregation optimization model to obtain the first objective function; and sets the level intermediate variables and corresponding linear logical constraints for the first objective function to obtain the second objective function.

[0066] The resource aggregation optimization module is used to input the second objective function into the demand response aggregation optimization model, so that the demand response aggregation optimization model outputs decision result data and obtains a resource aggregation optimization application scheme, which can be used by load aggregators to carry out scheduling transactions in the day-ahead invitation mode.

[0067] As a preferred embodiment, the model construction module and tier processing module of the power demand response resource aggregation optimization device of the present invention are designed for the day-ahead invitation mode. Based on the response characteristics of user resources already represented by the load integrator, the benefit and cost sharing method between the load integrator and users, and the tiering evaluation criteria for effective response capacity, the effective response capacity is determined by analyzing the deviation between the actual response capacity of the load integrator and users and the capacity requested in the invitation, and corresponding tiers are established. Based on the tiering of the effective response capacity of the load integrator and users, a demand response aggregation optimization model is constructed from the perspective of the load integrator, with the goal of maximizing expected revenue.

[0068] Compared to existing resource aggregation models that only consider the physical constraints of resource characteristics, the model building module of this invention constructs a demand response aggregation optimization model. The effective response capacity tiering evaluation method in this model is compatible with the uncertainty of user resource responses, takes into account the differences in resource characteristics and agent settlement relationships among numerous users, and considers the requirements of the demand response organizer for effective response capacity tiering certification. By using decision optimization methods to configure the available response capacity for already agented users, more accurate aggregation declaration information is generated. This improves the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected revenues when load integrators participate in day-ahead invitation responses. It also enhances the load integrator's ability to adjust the resources of already agented users under the day-ahead invitation mode, improving the accuracy of the load integrator's decision-making in day-ahead invitation responses.

[0069] The demand response aggregation optimization model of this invention, due to its effective response capacity tiered evaluation method, exhibits significant nonlinear relationships among the optimal aggregation decision variables. To address the nonlinear coupling relationships among these aggregation decision variables, the linearization module introduces tiered state variables and intermediate tier variables, along with corresponding logical constraints, to linearize the aggregation model, transforming it into an equivalent mixed-integer programming model. This ensures the demand response aggregation optimization model is easy and efficient to solve, thereby improving the efficiency of optimal demand response aggregation decisions and enabling the model to produce more efficient and accurate demand response aggregation results.

[0070] As a preferred embodiment, the linear processing module includes: a gear position state variable setting unit and a gear position intermediate variable setting unit;

[0071] The gear position state variable setting unit is used to set the gear position state variable y for each gear. i,1,t y i,2,t y i,3,t and y i,4,t ; where y i,1,t y i,2,t y i,3,tand y i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0072] The logical constraints for setting the gear position state variable are as follows:

[0073]

[0074] Where M represents a constant used to relax the constraints;

[0075] The updated effective response capacity relationship is as follows:

[0076] p j,t =N dis y i,2,t a j,t +y i,3,t a j,t +R nor y i,4,t d j,t ;

[0077] The formulas for updating the second effective response capacity of users and the formulas for updating the second effective response capacity of load integrators are as follows:

[0078]

[0079] Among them, D t and A t Y represents the declared capacity and actual response capacity confirmed by the load aggregator during time period t, respectively. 2,t and Y 4,t Let d represent the state variables of the second load aggregator position and the fourth load aggregator position, respectively. j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,t This represents the actual response capacity of proxy user j during time period t. s y i,2,t y i,3,t and y i,4,t Let N represent the gear state variables for the second, third, and fourth user gears, respectively. dis It is the discount factor between the effective response capacity and the actual response capacity;

[0080] Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows:

[0081]

[0082] Among them, R dz R dis R norThese are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively; L t This represents the unit compensation standard for the load aggregator's demand response during time period t; U1 and U2 represent the sets of users subject to linkage compensation and fixed compensation, respectively, represented by the load aggregator.

[0083] The gear intermediate variable setting unit is used to set the gear intermediate variable w. i,1,t w i,2,t w i,3,t and w i,4,t ;

[0084]

[0085] Among them, w i,1,t w i,2,t w i,3,t and w i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0086] The logical constraints for the intermediate gear variable are set as follows:

[0087]

[0088]

[0089]

[0090]

[0091] As a preferred embodiment, the existence of deviation relationships in the effective response capacity levels of the present invention leads to significant nonlinear characteristics in the demand response aggregation optimization model. The linear processing module sets the level state variables and corresponding logical constraints for each time period of the user and load aggregator in each effective response capacity level established according to the effective response capacity grading evaluation criteria. The linear processing module also sets the level state variables and corresponding logical constraints for the user and load aggregator in each effective response capacity level established according to the effective response capacity grading evaluation criteria, updating the objective function of the demand response aggregation optimization model to eliminate the nonlinear relationship of effective response capacity in the model. This achieves linearization of the demand response aggregation optimization model, ensuring the simplicity and efficiency of the solution, thereby improving the efficiency of optimal demand response aggregation decision-making and enabling the demand response aggregation optimization model to generate more efficient and accurate demand response aggregation results.

[0092] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program; wherein, when the computer program is executed, it controls the device in which the computer-readable storage medium is located to perform a resource aggregation optimization method for power demand response as described in the present invention. Attached Figure Description

[0093] Figure 1 This is a flowchart illustrating an embodiment of the resource aggregation optimization method for power demand response provided by the present invention.

[0094] Figure 2 This is a schematic diagram of an embodiment of the resource aggregation and optimization device for power demand response provided by the present invention. Detailed Implementation

[0095] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0096] Example 1

[0097] Please refer to Figure 1 A method provided in an embodiment of the present invention includes steps S101-S104:

[0098] Step S101: Based on the load aggregator's effective response capacity, deviation assessment cost, demand response unit compensation standard, and the expenditure costs of the users represented by the load aggregator, construct an initial objective function for the demand response aggregation optimization model with the goal of maximizing expected revenue, and set physical constraints for the initial objective function.

[0099] In this embodiment, with the goal of maximizing expected returns, the initial objective function of the demand response aggregation optimization model is constructed as follows:

[0100] With the goal of maximizing expected returns, the initial objective function of the demand response aggregation optimization model is set as follows:

[0101]

[0102] Among them, P t R represents the integrator's effective response capacity during time period t; t L represents the deviation assessment fee for the integrator during time period t; tThis represents the unit compensation standard for the load aggregator's demand response during time period t; U1 and U2 represent the sets of users with linked compensation and fixed compensation represented by the aggregator, respectively; C 1j and C 2j These represent the expenses incurred by users receiving joint compensation and those receiving fixed compensation, respectively, through the integrator's agency; μ j This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement; p j,t Indicates the user's effective response capacity; λ j R represents the agreed-upon user-borne assessment cost sharing ratio coefficient; 1j,t This refers to the assessment fee for user response deviations, evaluated according to the assessment criteria of the invitation response organizer; L fix This indicates the fixed settlement fee stipulated in the contract;

[0103] The constraints of the initial objective function include: user effective response capacity constraints, load aggregator effective response capacity constraints, user resource application capacity range constraints, actual response capacity adjustment rate constraints, load aggregator deviation assessment cost constraints, invited application capacity and estimated actual response capacity constraints, aggregator maximum and minimum application capacity constraints, and minimum continuous response time constraints.

[0104] Among them, the effective response capacity constraint for load aggregators is:

[0105]

[0106] Among them, P t This represents the effective response capacity used by the integrator to calculate demand response revenue during time period t; D t and A t These represent the integrator's requested capacity and actual response capacity confirmed during time period t, respectively; R dz R dis R nor Let R be the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for the effective response capacity, respectively. dz <R dis <R nor N dis Effective response capacity is a discount factor relative to the actual response capacity.

[0107] The effective response capacity constraint for users is:

[0108]

[0109] The users include the linked compensation users and the fixed compensation users, d j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,tThis represents the actual response capacity of agent user j during time period t.

[0110] In this embodiment, the expenses incurred by users receiving joint compensation and fixed compensation through the integrator's agency are specifically as follows:

[0111] Expenses incurred by users eligible for joint compensation:

[0112] Where, μ j This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement; L t p represents the unit compensation price for demand response during time period t; j,t Indicates the user's effective response capacity; λ j R represents the agreed-upon user-borne assessment cost sharing ratio coefficient; 1j,t This refers to the assessment fee for user response deviations, evaluated according to the assessment criteria of the invitation response organizer.

[0113] Costs for users with fixed compensation:

[0114] In the formula, L fix This refers to the fixed settlement fee stipulated in the contract.

[0115] In this embodiment, the constraints of the initial objective function further include: user resource application capacity range constraints, actual response capacity adjustment rate constraints, load aggregator deviation assessment cost constraints, invited application capacity and estimated actual response capacity constraints, integrator maximum and minimum application capacity constraints, and minimum continuous response time constraints, specifically:

[0116] User resource application capacity constraints:

[0117] d jmin <d j,t <d jmax ;

[0118] In the formula: d j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; d jmin and d jmax These represent the lower and upper limits of the capacity that user j can delegate for application, respectively.

[0119] Adjustment rate constraint of actual response capacity:

[0120]

[0121] In the formula: and These represent the power limits for user j within adjacent time intervals, representing the upward and downward adjustments respectively; a j,tThis represents the estimated actual response capacity of agent user j during time period t;

[0122] a j,t =ε j d j,t ;

[0123] Where: ε j This represents the response rate parameter estimated based on the actual response capacity and invited response capacity of user j in historical response scenarios.

[0124] Deviation assessment cost constraints for load integrators:

[0125]

[0126] In the formula: R t L represents the deviation assessment fee for the integrator during time period t; t K represents the unit compensation price for demand response during time period t; t This represents the response deviation assessment standard for time period t.

[0127]

[0128] In the formula: To minimize penalty costs, L t Let t be the compensation price for the period t, and s be the deviation assessment coefficient.

[0129] Invited application capacity and estimated actual response capacity constraints:

[0130]

[0131] Integrator's maximum and minimum application capacity constraints:

[0132] I t D min ≤D t ≤I t D max ;

[0133] In the formula: D min and D max These represent the maximum and minimum declared capacity of the aggregator, respectively; I t This represents a 0-1 variable indicating the integrator's status upon receiving an invitation during time period t.

[0134] Minimum sustained response time constraint:

[0135]

[0136] In the formula: T min This indicates the integrator's minimum continuous response time.

[0137] Step S102: Obtain the invited capacity declaration data and actual response capacity data of users and load aggregators within a preset time period, respectively, and input them into the effective response capacity tier processing model. The effective response capacity tier processing model calculates the first effective response capacity based on the invited capacity declaration data and the actual response capacity data, and establishes the corresponding effective response capacity tier based on the deviation relationship between the invited capacity declaration data and the actual response capacity data. The model outputs the corresponding effective response capacity tiers and corresponding deviation relationships for users and load aggregators, respectively. Each effective response capacity tier corresponds to one deviation relationship.

[0138] In this embodiment, based on the deviation between the invited capacity data and the actual response capacity data, a corresponding effective response capacity level is established, specifically as follows:

[0139] Calculate the effective response capacity for different deviation relationships:

[0140]

[0141] Where, d j,t This indicates the capacity requested by user or load aggregator j during time period t; a j,t R represents the actual response capacity of user or load aggregator j during time period t; dz R dis R nor These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for the effective response capacity, respectively, and R... dz <R dis <R nor N dis This is the discount factor between the effective response capacity and the actual response capacity.

[0142] Based on the deviation between the invited capacity declaration data and the actual response capacity data, four corresponding effective response capacity levels are established, wherein the deviation relationship includes: a j,t <R dz d j,t R dz d j,t ≤a j,t <R dis d j,t R dis d j,t ≤a j,t <R nor d j,t and a j,t ≥R nor d j,t These correspond to the first, second, third, and fourth effective response capacity levels, respectively.

[0143] In this embodiment, a gear state variable and corresponding logical constraint are set for each gear position. Based on all gear state variables, the relationship of the first effective response capacity is updated, specifically as follows:

[0144] Set the gear state variable y for each gear. i,1,t y i,2,t y i,3,t and y i,4,t ; where y i,1,t y i,2,t y i,3,t and y i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0145]

[0146]

[0147] The logical constraints for setting the gear position state variable are as follows:

[0148]

[0149] Where M represents a constant used to relax the constraints;

[0150] The updated effective response capacity relationship is as follows:

[0151] p j,t =N dis y i,2,t a j,t +y i,3,t a j,t +R nor y i,4,t d j,t .

[0152] In this embodiment, the objective function of the demand response aggregation optimization model is updated based on the relationship between the user's second effective response capacity and the load aggregator's second effective response capacity to obtain the first objective function, specifically:

[0153] The relationship between the user's second effective response capacity and the load integrator's second effective response capacity are as follows:

[0154]

[0155] Among them, D t and A t Y represents the declared capacity and actual response capacity confirmed by the load aggregator during time period t, respectively. 2,t and Y 4,tLet d represent the state variables of the second load aggregator position and the fourth load aggregator position, respectively. j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,t This represents the actual response capacity of proxy user j during time period t. s y i,2,t y i,3,t and y i,4,t Let N represent the gear state variables for the second, third, and fourth user gears, respectively. dis It is the discount factor between the effective response capacity and the actual response capacity;

[0156] Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows:

[0157]

[0158] Among them, R dz R dis R nor These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively; L t U1 represents the unit compensation standard for the load aggregator's demand response during time period t; U2 and U1 represent the sets of linked compensation users and fixed compensation users represented by the load aggregator, respectively.

[0159] Step S103: Set the level state variables and corresponding logical constraints for each effective response capacity level; update the relationship of the first effective response capacity based on all level state variables to obtain the relationship of the second effective response capacity for users and load aggregators respectively; update the initial objective function of the demand response aggregation optimization model based on the relationship of the second effective response capacity of users and the relationship of the second effective response capacity of load aggregators to obtain the first objective function; set level intermediate variables and corresponding linear logical constraints for the first objective function to obtain the second objective function.

[0160] In this embodiment, intermediate variables and corresponding linear logic constraints are set for the first objective function, specifically as follows:

[0161] Set the intermediate variable w for gear position i,1,t w i,2,t w i,3,t and w i,4,t ;

[0162]

[0163] Among them, w i,1,t w i,2,t wi,3,t and w i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0164] The logical constraints for the intermediate gear variable are set as follows:

[0165]

[0166]

[0167]

[0168]

[0169] Step S104: Input the second objective function into the demand response aggregation optimization model so that the demand response aggregation optimization model outputs decision result data to obtain a resource aggregation optimization application scheme for load aggregators to conduct scheduling transactions in the day-ahead invitation mode.

[0170] In this embodiment, the demand response aggregation optimization model outputs decision result data, specifically:

[0171] The demand response aggregation optimization model solves for the second objective function and outputs the invited capacity d of users and integrators during the invitation period t. j,t and D t The expected actual response capacity a j,t and A t And deviation assessment fees R for integrators and users of linkage compensation categories. t and R 1j,t ;

[0172] The expected costs that integrators need to pay to users Where, μ j This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement; p j,t Indicates the user's effective response capacity; λ j R represents the agreed-upon user-borne assessment cost sharing ratio coefficient; 1j,t This refers to the assessment fee for user response deviations, evaluated according to the assessment criteria of the invitation response organizer; L fix This indicates the fixed settlement fee stipulated in the contract;

[0173] The integrator's aggregated bid price for time period t:

[0174] Implementing the embodiments of the present invention has the following effects:

[0175] The resource aggregation optimization method for power demand response of this invention is designed for the day-ahead invitation mode. Based on the response characteristics of user resources already represented by load aggregators, the benefit and cost sharing methods between load aggregators and users, and the tiered evaluation criteria for effective response capacity, the method determines the effective response capacity and establishes corresponding tiers by analyzing the deviation between the actual response capacity and the requested capacity of load aggregators and users. Building upon the tiered effective response capacity of load aggregators and users, and from the perspective of load aggregators, a demand response aggregation optimization model is constructed with the goal of maximizing expected revenue.

[0176] Compared to existing resource aggregation models that only consider the physical constraints of resource characteristics, the effective response capacity tiering evaluation method set by the demand response aggregation optimization model of this invention is compatible with the uncertainty of user resource response, takes into account the differences in resource characteristics and agency settlement relationships of numerous users, as well as the requirements of the demand response organizer for effective response capacity tiering certification. By using decision optimization methods to configure the response capacity available to already-agented users, more accurate aggregation declaration information is formed. This improves the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected benefits when load aggregators participate in day-ahead invitation responses; enhances the load aggregator's ability to adjust the resources of already-agented users under the day-ahead invitation mode, and improves the accuracy of the load aggregator's decision-making in participating in day-ahead invitation responses.

[0177] The demand response aggregation optimization model of this invention, due to its effective response capacity tiered evaluation method, exhibits significant nonlinear relationships among the optimal aggregation decision variables. To address this nonlinear coupling, by introducing tiered state variables and intermediate tier variables, along with corresponding logical constraints, a linearization process is proposed to transform the aggregation model into an equivalent mixed-integer programming model. This ensures the demand response aggregation optimization model is easy and efficient to solve, thereby improving the efficiency of optimal demand response aggregation decisions and enabling the model to produce more efficient and accurate demand response aggregation results.

[0178] Example 2

[0179] Please refer to Figure 2 An apparatus provided in an embodiment of the present invention includes: a model building module 201, a gear processing module 202, a linear processing module 203, and a resource aggregation optimization module 204;

[0180] The model building module is used to construct an initial objective function for the demand response aggregation optimization model based on the load aggregator's effective response capacity, deviation assessment fees, demand response unit compensation standards, and the expenditure fees of the users represented by the load aggregator, with the goal of maximizing expected revenue, and to set the physical constraints of the initial objective function.

[0181] The tier processing module is used to acquire the invited capacity declaration data and actual response capacity data of users and load aggregators within a preset time period, respectively, and input them into the effective response capacity tier processing model. The effective response capacity tier processing model calculates the first effective response capacity based on the invited capacity declaration data and the actual response capacity data, and establishes corresponding effective response capacity tiers based on the deviation relationship between the invited capacity declaration data and the actual response capacity data. It then outputs the corresponding effective response capacity tiers for users and load aggregators and their corresponding deviation relationships; wherein each effective response capacity tier corresponds to one deviation relationship.

[0182] The linear processing module is used to set the level state variables and corresponding logical constraints for each effective response capacity level; based on all level state variables, it updates the relationship of the first effective response capacity to obtain the relationship of the second effective response capacity for users and load aggregators respectively; based on the relationship of the second effective response capacity for users and the relationship of the second effective response capacity for load aggregators, it updates the initial objective function of the demand response aggregation optimization model to obtain the first objective function; and sets the level intermediate variables and corresponding linear logical constraints for the first objective function to obtain the second objective function.

[0183] The resource aggregation optimization module is used to input the second objective function into the demand response aggregation optimization model, so that the demand response aggregation optimization model outputs decision result data and obtains a resource aggregation optimization application scheme, which can be used by load aggregators to carry out scheduling transactions in the day-ahead invitation mode.

[0184] The linear processing module includes: a gear position state variable setting unit and a gear position intermediate variable setting unit;

[0185] The gear position state variable setting unit is used to set the gear position state variable y for each gear. i,1,t y i,2,t y i,3,t and y i,4,t ; where y i,1,t y i,2,t y i,3,t and y i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0186] The logical constraints for setting the gear position state variable are as follows:

[0187]

[0188] Where M represents a constant used to relax the constraints;

[0189] The updated effective response capacity relationship is as follows:

[0190] p j,t =N dis y i,2,t a j,t +y i,3,t a j,t +R nor y i,4,t d j,t ;

[0191] The formulas for updating the second effective response capacity of users and the formulas for updating the second effective response capacity of load integrators are as follows:

[0192]

[0193] Among them, D t and A t Y represents the declared capacity and actual response capacity confirmed by the load aggregator during time period t, respectively. 2,t and Y 4,t Let d represent the state variables of the second load aggregator position and the fourth load aggregator position, respectively. j,t This indicates the declared capacity of user j, represented by the load aggregator, during time period t; a j,t This represents the actual response capacity of proxy user j during time period t. s y i,2,t y i,3,t and y i,4,t Let N represent the gear state variables for the second, third, and fourth user gears, respectively. dis It is the discount factor between the effective response capacity and the actual response capacity;

[0194] Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows:

[0195]

[0196] Among them, R dz R dis R nor These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively; L t This represents the unit compensation standard for the load aggregator's demand response during time period t; U1 and U2 represent the sets of users subject to linkage compensation and fixed compensation, respectively, represented by the load aggregator.

[0197] The gear intermediate variable setting unit is used to set the gear intermediate variable w. i,1,t w i,2,t wi,3,t and w i,4,t ;

[0198]

[0199] Among them, w i,1,t w i,2,t w i,3,t and w i,4,t These correspond to the first gear, second gear, third gear, and fourth gear, respectively.

[0200] The logical constraints for the intermediate gear variable are set as follows:

[0201]

[0202]

[0203]

[0204]

[0205] The aforementioned resource aggregation optimization apparatus for power demand response can implement the resource aggregation optimization method for power demand response described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application's embodiments can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0206] Implementing the embodiments of the present invention has the following effects:

[0207] The model building module and tier processing module of the power demand response resource aggregation optimization device of this invention are designed for the day-ahead invitation mode. Based on the response characteristics of user resources already represented by load integrators, the benefit and cost sharing method between load integrators and users, and the tiering evaluation criteria for effective response capacity, the effective response capacity is determined by analyzing the deviation between the actual response capacity of load integrators and users and the capacity requested in the invitation, and corresponding tiers are established. Based on the tiering of the effective response capacity of load integrators and users, and from the perspective of load integrators, a demand response aggregation optimization model is constructed with the goal of maximizing expected revenue.

[0208] Compared to existing resource aggregation models that only consider the physical constraints of resource characteristics, the model building module of this invention constructs a demand response aggregation optimization model. The effective response capacity tiering evaluation method in this model is compatible with the uncertainty of user resource responses, takes into account the differences in resource characteristics and agent settlement relationships among numerous users, and considers the requirements of the demand response organizer for effective response capacity tiering certification. By using decision optimization methods to configure the available response capacity for already agented users, more accurate aggregation declaration information is generated. This improves the accuracy of the demand response aggregation optimization model in calculating expected response effects and expected revenues when load integrators participate in day-ahead invitation responses. It also enhances the load integrator's ability to adjust the resources of already agented users under the day-ahead invitation mode, improving the accuracy of the load integrator's decision-making in day-ahead invitation responses.

[0209] The demand response aggregation optimization model of this invention, due to its effective response capacity tiered evaluation method, exhibits significant nonlinear relationships among the optimal aggregation decision variables. To address the nonlinear coupling relationships among these aggregation decision variables, the linearization module introduces tiered state variables and intermediate tier variables, along with corresponding logical constraints, to linearize the aggregation model, transforming it into an equivalent mixed-integer programming model. This ensures the demand response aggregation optimization model is easy and efficient to solve, thereby improving the efficiency of optimal demand response aggregation decisions and enabling the model to produce more efficient and accurate demand response aggregation results.

[0210] Example 3

[0211] Accordingly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the resource aggregation optimization method for power demand response as described in any of the above embodiments.

[0212] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0213] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0214] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0215] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0216] Wherein, if the modules / units integrated in the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0217] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A resource aggregation optimization method for electricity demand response, characterized in that, include: Based on the load aggregator's effective response capacity, deviation assessment fees, demand response unit compensation standards, and the expenditure fees of the users represented by the load aggregator, with the goal of maximizing expected revenue, an initial objective function for the demand response aggregation optimization model is constructed, and physical constraints are set for the initial objective function. The system acquires invited capacity data and actual response capacity data of users and load aggregators within a preset time period, respectively, and inputs them into an effective response capacity tier processing model. This model calculates a first effective response capacity based on the invited capacity data and the actual response capacity data, and establishes corresponding effective response capacity tiers based on the deviation relationship between the invited capacity data and the actual response capacity data. The model then outputs the corresponding effective response capacity tiers for users and load aggregators, along with their corresponding deviation relationships; each effective response capacity tier corresponds to one deviation relationship. Set the status variables and corresponding logical constraints for each effective response capacity level, and update the relationship of the first effective response capacity based on all the status variables to obtain the relationship of the second effective response capacity for users and load aggregators respectively; wherein, setting the status variables and corresponding logical constraints for each level, and updating the relationship of the first effective response capacity based on all the status variables, specifically involves: Set the gear state variable for each gear. , , and ;in, , , and These correspond to the first gear, second gear, third gear, and fourth gear, respectively. The logical constraints for setting the gear position state variable are as follows: ； in, This represents a constant used to relax the constraints; The updated effective response capacity relationship is as follows: ； Based on the relationship between the user's second effective response capacity and the load aggregator's second effective response capacity, the initial objective function of the demand response aggregation optimization model is updated to obtain the first objective function, which is as follows: The relationship between the user's second effective response capacity and the load integrator's second effective response capacity are as follows: ； in, and These represent the requested capacity and the actual response capacity confirmed by the load aggregator during time period t, respectively. and These represent the status variables of the second load aggregator level and the fourth load aggregator level, respectively. This indicates the capacity requested by user j, who is represented by the load aggregator, during time period t; This represents the actual response capacity of proxy user j during time period t. , and These represent the gear position status variables for the second, third, and fourth user gears, respectively. It is the discount factor between the effective response capacity and the actual response capacity; Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows: ； in, , , These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively. This indicates the unit compensation standard for the load aggregator's demand response during time period t; and These represent the sets of users subject to linkage compensation and fixed compensation, respectively, represented by the load aggregator. express t Response deviation assessment criteria for different time periods Y 1,t For the third load integrator's position, the position status variable is... This represents the percentage of revenue sharing from user requests as stipulated in the agency agreement. This represents the agreed-upon ratio of the assessment costs to be shared by the users. This indicates the fixed settlement fee stipulated in the contract; By setting intermediate variables and corresponding linear logic constraints for the first objective function, the second objective function is obtained, as follows: Set the intermediate gear variable , , and ; ； in, , , and These correspond to the first gear, second gear, third gear, and fourth gear, respectively. The linear logic constraint for the intermediate gear variable is set as follows: ； ； ； ;in, This represents a constant used to relax the constraints; The second objective function is input into the demand response aggregation optimization model so that the demand response aggregation optimization model outputs decision result data to obtain a resource aggregation optimization application scheme, which can be used by load aggregators to conduct scheduling transactions in the day-ahead invitation mode.

2. The resource aggregation optimization method for electricity demand response as described in claim 1, characterized in that, The initial objective function for constructing the demand response aggregation optimization model, with the goal of maximizing expected returns, is as follows: With the goal of maximizing expected returns, the initial objective function of the demand response aggregation optimization model is set as follows: ； in, This indicates the integrator's effective response capacity during time period t; This represents the deviation assessment fee for the integrator during time period t; This indicates the unit compensation standard for the load aggregator's demand response during time period t; and These represent the sets of users receiving joint compensation and those receiving fixed compensation, respectively, represented by the integrator. and These represent the expenses incurred by users receiving joint compensation and users receiving fixed compensation, respectively, through the integrator's agency. This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement. Indicates the user's effective response capacity; This represents the agreed-upon user-shared assessment cost ratio coefficient. This refers to the assessment fee for user response deviations, calculated based on the evaluation criteria of the invitation response organizer. This indicates the fixed settlement fee stipulated in the contract; The constraints of the initial objective function include: user effective response capacity constraints, load aggregator effective response capacity constraints, user resource application capacity range constraints, actual response capacity adjustment rate constraints, load aggregator deviation assessment cost constraints, invited application capacity and estimated actual response capacity constraints, aggregator maximum and minimum application capacity constraints, and minimum continuous response time constraints. Among them, the effective response capacity constraint for load aggregators is: ； in, This indicates the effective response capacity used by the integrator to calculate demand response revenue during time period t; and These represent the integrator's requested capacity and actual response capacity confirmed during time period t, respectively. , , These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively. ; Effective response capacity is a discount factor relative to the actual response capacity. The effective response capacity constraint for users is: ； The users include the users receiving the linked compensation and the users receiving the fixed compensation. This indicates the capacity requested by user j, who is represented by the load aggregator, during time period t; This represents the actual response capacity of agent user j during time period t.

3. The resource aggregation optimization method for electricity demand response as described in claim 1, characterized in that, The step of establishing corresponding effective response capacity levels based on the deviation between the invited capacity declaration data and the actual response capacity data is as follows: Calculate the effective response capacity for different deviation relationships: ； in, This indicates the capacity requested by user or load aggregator j during time period t; This represents the actual response capacity of user or load aggregator j during time period t; , , These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for the effective response capacity, respectively. ; This is the discount factor between the effective response capacity and the actual response capacity. Based on the deviation between the invited capacity declaration data and the actual response capacity data, four corresponding effective response capacity levels are established, wherein the deviation relationship includes: , , and These correspond to the first, second, third, and fourth effective response capacity levels, respectively.

4. The resource aggregation optimization method for electricity demand response as described in claim 1, characterized in that, The demand response aggregation optimization model outputs decision result data, specifically: The demand response aggregation optimization model solves for the second objective function and outputs the invited capacity reported by users and integrators during the invitation period t. and Expected actual response capacity and And deviation assessment fees for integrators and users of linkage compensation. and ; The expected costs that integrators need to pay to users : ;in, This represents the percentage of revenue sharing from demand response agreed upon by the user in the agency agreement. Indicates the user's effective response capacity; This represents the agreed-upon user-shared assessment cost ratio coefficient. This refers to the assessment fee for user response deviations, calculated based on the evaluation criteria of the invitation response organizer. This refers to the fixed settlement fee stipulated in the contract. Indicates the load integrator's t Compensation standards for demand response units during specific time periods; The integrator's aggregated bid price for time period t: .

5. A resource aggregation and optimization device for electricity demand response, characterized in that, include: The module includes a model building module, a gear processing module, a linear processing module, and a resource aggregation and optimization module. The model building module is used to construct an initial objective function for the demand response aggregation optimization model based on the load aggregator's effective response capacity, deviation assessment fees, demand response unit compensation standards, and the expenditure fees of the users represented by the load aggregator, with the goal of maximizing expected revenue, and to set the physical constraints of the initial objective function. The tier processing module is used to acquire the invited capacity declaration data and actual response capacity data of users and load aggregators within a preset time period, respectively, and input them into the effective response capacity tier processing model. The effective response capacity tier processing model calculates the first effective response capacity based on the invited capacity declaration data and the actual response capacity data, and establishes corresponding effective response capacity tiers based on the deviation relationship between the invited capacity declaration data and the actual response capacity data. It then outputs the corresponding effective response capacity tiers for users and load aggregators and their corresponding deviation relationships; wherein each effective response capacity tier corresponds to one deviation relationship. The linear processing module is used to set the level state variables and corresponding logical constraints for each effective response capacity level; based on all level state variables, it updates the relationship of the first effective response capacity to obtain the relationship of the second effective response capacity for users and load aggregators respectively; based on the relationship of the second effective response capacity for users and the relationship of the second effective response capacity for load aggregators, it updates the initial objective function of the demand response aggregation optimization model to obtain the first objective function; and sets the level intermediate variables and corresponding linear logical constraints for the first objective function to obtain the second objective function. The gear position status variable setting unit is used to set the gear position status variable for each gear. , , and ;in, , , and These correspond to the first gear, second gear, third gear, and fourth gear, respectively. The logical constraints for setting the gear position state variable are as follows: ； in, This represents a constant used to relax the constraints; Update the initial objective function of the demand response aggregation optimization model to obtain the first objective function as follows: ； in, , , These are the dead zone authentication coefficient, discount zone authentication coefficient, and saturation zone authentication coefficient for effective response capacity, respectively. This indicates the unit compensation standard for the load aggregator's demand response during time period t; and These represent the sets of users subject to linkage compensation and fixed compensation, respectively, represented by the load aggregator. Y 3,t For the third load integrator's position, the position status variable is... express t Response deviation assessment criteria for different time periods Y 1,t For the third load integrator's position, the position status variable is... This represents the percentage of revenue sharing from user requests as stipulated in the agency agreement. This represents the agreed-upon ratio of the assessment costs to be shared by the users. This indicates the fixed settlement fee stipulated in the contract; The gear intermediate variable setting unit is used to set the gear intermediate variable. , , and ; ； in, , , and These correspond to the first gear, second gear, third gear, and fourth gear, respectively. The logical constraints for the intermediate gear variable are set as follows: ； ； ； ； The resource aggregation optimization module is used to input the second objective function into the demand response aggregation optimization model, so that the demand response aggregation optimization model outputs decision result data and obtains a resource aggregation optimization application scheme, which can be used by load aggregators to carry out scheduling transactions in the day-ahead invitation mode.

6. The resource aggregation and optimization device for power demand response as described in claim 5, characterized in that, The linear processing module includes: a gear position state variable setting unit and a gear position intermediate variable setting unit; The updated effective response capacity relationship is as follows: ； The formulas for updating the second effective response capacity of users and the formulas for updating the second effective response capacity of load integrators are as follows: ； in, and These represent the requested capacity and the actual response capacity confirmed by the load aggregator during time period t, respectively. and These represent the status variables of the second load aggregator level and the fourth load aggregator level, respectively. This indicates the capacity requested by user j, who is represented by the load aggregator, during time period t; This represents the actual response capacity of proxy user j during time period t. , and These represent the gear position status variables for the second, third, and fourth user gears, respectively. It is the discount factor between the effective response capacity and the actual response capacity.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program; wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a resource aggregation optimization method for power demand response as described in any one of claims 1 to 4.

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