Virtual power plant peak regulation benefit distribution method, equipment and medium

Abstract

The invention relates to a virtual power plant peak regulation benefit distribution method and device, and a medium. The method comprises the following steps: constructing a comprehensive contribution index system of virtual power plant peak regulation benefits; a CRITIC weighting method is used to distribute weights for each index in the comprehensive contribution index system of the peak regulation benefit; normalizing the indexes, combining the normalized indexes with the weights to form a corresponding income distribution scheme, and constructing a weighting matrix formed by the normalized indexes; according to a CRITIC-GRA-TOPSIS model, the relative closeness degree of each income distribution scheme and a weighting matrix is calculated; and according to the relative close degree and the protocol electricity price of the virtual power plant allocation mechanism, peak regulation benefit distribution is carried out. According to the method, the comprehensive contribution index system of the peak regulation benefit of the virtual power plant is constructed, so that a clear classification and quantification basis is provided for each evaluation index.

Description

Technical Field

[0001] The invention relates to a method, a device and a medium for allocating peak-shaving benefits of a virtual power plant, and belongs to the technical field of peak-shaving of virtual power plants. Background Art

[0002] With the continuous growth of energy demand and the increasing prominence of environmental problems, traditional energy systems are facing huge challenges. In order to meet energy demand while reducing environmental pollution, renewable energy (such as solar energy and wind energy) has gradually been integrated into the power system. However, the intermittent and instability of renewable energy has brought new difficulties to the stable operation of the power system. As an emerging energy management model, Virtual Power Plant (VPP) realizes the efficient utilization and management of decentralized energy by integrating multiple distributed energy resources (such as wind power, photovoltaics, energy storage, electric vehicles, etc.).

[0003] A virtual power plant is a comprehensive energy management system that integrates multiple distributed energy resources (DER), loads, and energy storage systems through communication technology. Its core goal is to achieve unified management and dispatch of distributed resources through optimized dispatch and coordinated control, thereby improving the reliability, economy, and environmental protection of the power system. Virtual power plants can not only provide additional power support during peak grid demand, but also perform peak load regulation during low demand periods, thereby balancing power supply and demand and reducing system operating costs.

[0004] In the operation of virtual power plants, the allocation of peak load benefits is a key issue. Peak load benefits refer to the benefits achieved in the power system through peak load measures (such as load transfer, energy storage charging and discharging, etc.). How to fairly and reasonably allocate these benefits involves the weighing of multiple factors, such as the contribution of each participant, resource type, geographical location, market mechanism, etc. Traditional allocation methods usually rely on a single evaluation indicator, which makes it difficult to fully reflect the actual contribution and benefits of each participant.

[0005] The CRITIC-GRA-TOPSIS model is a comprehensive evaluation method that combines the advantages of the CRITIC method, grey relational analysis (GRA) and TOPSIS method, and is used to comprehensively evaluate complex problems with multiple indicators and multiple objects.

[0006] Prior art, such as the Chinese patent application with patent number "CN116862551A", discloses a new energy consumption electricity price decision-making method that takes user privacy protection into consideration.

[0007] The problem with the above existing technologies is that they mainly focus on the evaluation of demand response potential and the training of pricing models based on multi-objective optimization, but when building an evaluation system, there may be a lack of clear methods and basis for the weight allocation of various indicators. Reinforcement learning algorithms require a lot of interaction and trial and error to train the model, which means that a lot of data support is required. However, in practical applications, obtaining a sufficient amount of high-quality data can be a challenge. Summary of the invention

[0008] In order to solve the above problems existing in the prior art, the present invention proposes a method, device and medium for allocating peak load benefits of a virtual power plant.

[0009] The technical income distribution plan of the present invention is as follows:

[0010] In one aspect, the present invention provides a method for allocating peak load benefits of a virtual power plant, comprising the following steps:

[0011] Construct a comprehensive contribution index system for the peak load regulation benefits of virtual power plants;

[0012] The CRITIC-GRA-TOPSIS model is used to assign weights to each indicator in the comprehensive contribution index system of peak load regulation benefits;

[0013] Normalize each indicator and construct a weighted matrix consisting of each normalized indicator and each profit distribution plan with the preset profit distribution plan;

[0014] Calculate the relative closeness of each of the profit distribution plans to the weighted matrix according to the CRITIC-GRA-TOPSIS model;

[0015] The peak load regulation benefits are distributed according to the relative closeness and the negotiated electricity price of the virtual power plant sharing mechanism.

[0016] As a preferred implementation, the comprehensive contribution index includes:

[0017] The preparation time, duration, daily charging and discharging times, maximum limit of regulation capacity, minimum limit of regulation capacity, load fluctuation rate, peak-valley coefficient, power regulation rate, and peak-shaving electricity contribution rate of the virtual power plant.

[0018] As a preferred implementation, the method for assigning weights to indicators is:

[0019] Get information about the indicator:

[0020]

[0021] Among them, C j represents the total amount of information contained in the jth indicator, δ j represents the standard deviation of the jth indicator, Indicates the conflict between the jth indicator and the ith indicator, r ij It represents the evaluation coefficient of the jth index and the ith index;

[0022] Assign weights to indicators:

[0023]

[0024] Among them, W j represents the weight of the jth indicator.

[0025] As a preferred implementation, the method for calculating the relative closeness between each of the profit distribution schemes and the weighted matrix is:

[0026] Calculate the Euclidean distance between the positive ideal solution Z+ and the negative ideal solution Z- of the weighted matrix Z and each income distribution scheme;

[0027] Calculate the positive ideal grey correlation between the positive ideal solution Z+ of the weighted matrix Z and each benefit distribution scheme, and the negative ideal grey correlation between the negative ideal solution Z- and each benefit distribution scheme;

[0028] According to the Euclidean distance, the positive ideal grey correlation degree and the negative ideal grey correlation degree, the relative closeness between the weighted matrix Z and each income distribution scheme is calculated.

[0029] As a preferred implementation, the method for calculating the Euclidean distance is:

[0030] The positive ideal solution Z+ is expressed as:

[0031]

[0032] The negative ideal solution Z- is expressed as:

[0033]

[0034] in, represents the optimal value of the positive ideal solution Z+ Represents the worst value of the negative ideal solution Z- z ij represents the element in the i-th row and j-th column of the weighted matrix Z, max() represents the maximum function, and min() represents the minimum function;

[0035] Calculate the Euclidean distance between the positive ideal solution Z+ and the i-th income distribution plan

[0036] Calculate the Euclidean distance between the negative ideal solution Z- and the i-th income distribution plan

[0037] Euclidean distance Euclidean distance Perform dimensionless processing:

[0038]

[0039] in, Represents the dimensionless Euclidean distance Represents the dimensionless Euclidean distance

[0040] As a preferred implementation, the method for calculating the positive ideal grey relational degree and the negative ideal grey relational degree is:

[0041] Calculate the positive ideal grey correlation coefficient matrix between the positive ideal solution Z+ and the i-th income distribution scheme;

[0042] The element of the positive ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as:

[0043]

[0044] Calculate the negative ideal grey correlation coefficient matrix between the negative ideal solution Z- and the i-th income distribution scheme;

[0045] The element of the negative ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as:

[0046]

[0047] in, It means taking i as the index to find the minimum function. It means taking j as the index to find the minimum function. It means to take the maximum value function for index i. represents the maximum value function with j as index, θ represents the resolution coefficient, θ∈(0,1);

[0048] Calculate the positive grey correlation degree of the i-th income distribution scheme according to the positive ideal grey correlation coefficient matrix

[0049]

[0050] Calculate the negative grey correlation degree of the i-th income distribution scheme according to the negative ideal grey correlation coefficient matrix

[0051]

[0052] Grey relational degree Negative grey relation Perform dimensionless processing:

[0053]

[0054]

[0055] in, Represents the positive grey relational degree after dimensionless processing Represents the negative grey relational degree after dimensionless processing

[0056] As a preferred implementation, the method for calculating the relative closeness is:

[0057] According to the dimensionless positive grey correlation, negative grey correlation and Euclidean distance, the closeness between the i-th income distribution scheme and the positive ideal solution is calculated. Closeness of negative ideal solution

[0058]

[0059] Among them, α and β represent adjustment coefficients;

[0060] Based on proximity Proximity Calculate the relative progress of the i-th income distribution plan and the weighted matrix Z:

[0061]

[0062] In fact, Q i Represents the relative progress of the i-th revenue distribution plan and the weighted matrix Z.

[0063] TOPSIS method is a commonly used comprehensive evaluation method within a group, which can make full use of the information of the original data, and its results can accurately reflect the gap between the evaluation profit distribution schemes. Its basic idea is to define the ideal solution and negative ideal solution of the decision problem, and then find a profit distribution scheme among the feasible profit distribution schemes so that it is closest to the ideal solution and farthest from the negative ideal solution.

[0064] As a preferred implementation, the method for allocating the peak load benefit is:

[0065]

[0066] Among them, E M represents the total amount of the virtual power plant's peak load sharing, E VPP Indicates the proposed allocation amount of the overall peak load-shaving revenue of the virtual power plant, represents the total distributed resource peak load contribution amount, q srepresents the electricity price of the sth distributed resource peak-shaving protocol, ρ s represents the peak load contribution of the sth distributed resource, E OP represents the investment and operation and maintenance expenditure of the virtual power plant, Ny represents the preset operation cycle, r represents the discount rate, and E INV represents the investment cost, E S represents the allocation amount of the sth resource, Q S Represents the relative progress of the sth revenue distribution plan and the weighted matrix Z.

[0067] On the other hand, the present invention further provides an electronic device having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for allocating peak-shaving benefits of a virtual power plant as described in any embodiment of the present invention is implemented.

[0068] On the other hand, the present invention also provides a computer-readable storage medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the virtual power plant peak-shaving benefit allocation method as described in any embodiment of the present invention.

[0069] The present invention has the following beneficial effects:

[0070] According to the characteristics and functions of the virtual power plant and the actual situation of peak-shaving benefits, the present invention constructs a comprehensive contribution index system from multiple dimensions. This index system can comprehensively reflect the key indicators of the virtual power plant in the peak-shaving process, and provides a scientific basis for subsequent weight allocation and benefit distribution. The CRITIC-GRA-TOPSIS model combines the advantages of the CRITIC weighting method and gray relational analysis (GRA), and can objectively reflect the correlation and difference between the indicators, thereby avoiding the deviation caused by subjective weighting. The objective weight of each indicator is calculated by the CRITIC method, and then the correlation between the indicators is considered in combination with the GRA method, and finally a more accurate weight allocation result is obtained. This method can ensure the reasonable reflection of each indicator in the comprehensive contribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flowchart for implementing the method. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technology benefit distribution scheme in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0074] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0075] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0076] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0077] Embodiment 1:

[0078] See also Figure 1 The present invention provides a method for allocating peak load benefits of a virtual power plant, comprising the following steps:

[0079] Construct a comprehensive contribution index system for the peak load regulation benefits of virtual power plants;

[0080] The CRITIC-GRA-TOPSIS model is used to assign weights to each indicator in the comprehensive contribution index system of peak load regulation benefits;

[0081] Normalize each indicator and construct a weighted matrix consisting of each normalized indicator and each profit distribution plan with the preset profit distribution plan;

[0082] Calculate the relative closeness of each of the profit distribution plans to the weighted matrix according to the CRITIC-GRA-TOPSIS model;

[0083] The peak load regulation benefits are distributed according to the relative closeness and the negotiated electricity price of the virtual power plant sharing mechanism.

[0084] As a preferred implementation, the comprehensive contribution index includes:

[0085] The preparation time for virtual power plant peak load regulation is the minimum time from when the power demand side resources receive the regulation instruction to when they start to achieve the established regulation effect, which reflects the timeliness of the power demand side resources participating in the regulation. The preparation time includes day-ahead, hour-ahead and near real-time (minutes, seconds).

[0086] The duration of virtual power plant peak regulation is the length of time that the virtual power plant resources maintain the established adjustment effect of power and electricity in a single event.

[0087] The daily charging and discharging times of the virtual power plant for peak regulation are the times when the resources are regulated to implement effective charging and discharging power every day.

[0088] The maximum limit of the regulation capacity of the virtual power plant for peak regulation is the maximum limit of the total adjustable capacity of the demand-side resources.

[0089] The minimum limit of the regulation capacity of the virtual power plant for peak regulation is the minimum limit of the total adjustable capacity of the demand-side resources.

[0090] The load fluctuation rate of the virtual power plant peak load regulation is the load dispersion degree when the unit average load is expressed. It is the ratio of the load standard deviation to the mean, reflecting the relative size of the load dispersion degree:

[0091]

[0092] Where, f is the load fluctuation rate; s load is the load standard deviation; is the load mean.

[0093] The peak-to-valley coefficient of the virtual power plant's peak load regulation is the valley-to-peak coefficient, which indicates the ratio of the load valley-to-peak value:

[0094]

[0095] In the formula, α is the valley-peak coefficient; P min is the minimum load valley peak value, P max is the peak value of the maximum load valley.

[0096] The power regulation rate of the virtual power plant peak regulation is the ratio of the maximum adjustable power to the regulation capacity per unit time (per minute), usually expressed as a percentage of the rated capacity and a minute, that is, % PN / min (PN = rated power); the calculation formula for the unidirectional regulation rate is:

[0097]

[0098] Where: S is the unidirectional regulation rate (unit: percentage of rated capacity / minute), L is the actual unidirectional regulation amount of demand-side resources, P Nis the rated capacity of the demand-side resource, and Y is the unidirectional test time.

[0099] The arithmetic mean of the regulation rates of the demand-side resources during the two unidirectional tests is taken as the regulation rate of the demand-side resources.

[0100] The peak-shaving electricity contribution rate of the virtual power plant's peak-shaving is the ratio of the electricity contributed by the regulation resources within the virtual power plant in a single peak-shaving operation.

[0101] As a preferred implementation, the method for assigning weights to indicators is:

[0102] Get information about the indicator:

[0103]

[0104] Among them, C j represents the total amount of information contained in the jth indicator, δ j represents the standard deviation of the jth indicator, Indicates the conflict between the jth indicator and the ith indicator, r ij It represents the evaluation coefficient of the jth index and the ith index;

[0105] Assign weights to indicators:

[0106]

[0107] Among them, W j represents the weight of the jth indicator.

[0108] As a preferred implementation, the method for calculating the relative closeness between each of the profit distribution schemes and the weighted matrix is:

[0109] Calculate the Euclidean distance between the positive ideal solution Z+ and the negative ideal solution Z- of the weighted matrix Z and each income distribution scheme;

[0110] Calculate the positive ideal grey correlation between the positive ideal solution Z+ of the weighted matrix Z and each benefit distribution scheme, and the negative ideal grey correlation between the negative ideal solution Z- and each benefit distribution scheme;

[0111] According to the Euclidean distance, the positive ideal grey correlation degree and the negative ideal grey correlation degree, the relative closeness between the weighted matrix Z and each income distribution scheme is calculated.

[0112] As a preferred implementation, the method for calculating the Euclidean distance is:

[0113] The positive ideal solution Z+ is expressed as:

[0114]

[0115] The negative ideal solution Z- is expressed as:

[0116]

[0117] in, represents the optimal value of the positive ideal solution Z+ Represents the worst value of the negative ideal solution Z- z ij represents the element in the i-th row and j-th column of the weighted matrix Z, max() represents the maximum function, and min() represents the minimum function;

[0118] Calculate the Euclidean distance between the positive ideal solution Z+ and the i-th income distribution plan

[0119] Calculate the Euclidean distance between the negative ideal solution Z- and the i-th income distribution plan

[0120] Euclidean distance Euclidean distance Perform dimensionless processing:

[0121]

[0122] in, Represents the dimensionless Euclidean distance Represents the dimensionless Euclidean distance

[0123] As a preferred implementation, the method for calculating the positive ideal grey relational degree and the negative ideal grey relational degree is:

[0124] Calculate the positive ideal grey correlation coefficient matrix between the positive ideal solution Z+ and the i-th income distribution scheme;

[0125] The element of the positive ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as:

[0126]

[0127] Calculate the negative ideal grey correlation coefficient matrix between the negative ideal solution Z- and the i-th income distribution scheme;

[0128] The element of the negative ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as:

[0129]

[0130] in, It means taking i as the index to find the minimum function. It means taking the minimum value function with j as index, It means to take the maximum value function for index i. represents the maximum value function with j as index, θ represents the resolution coefficient, θ∈(0,1);

[0131] Calculate the positive grey correlation degree of the i-th income distribution scheme according to the positive ideal grey correlation coefficient matrix

[0132]

[0133] Calculate the negative grey correlation degree of the i-th income distribution scheme according to the negative ideal grey correlation coefficient matrix

[0134]

[0135] Grey relational degree Negative grey relation Perform dimensionless processing:

[0136]

[0137] in, Represents the positive grey relational degree after dimensionless processing Represents the negative grey relational degree after dimensionless processing

[0138] As a preferred implementation, the method for calculating the relative closeness is:

[0139] According to the dimensionless positive grey correlation, negative grey correlation and Euclidean distance, the closeness between the i-th income distribution scheme and the positive ideal solution is calculated. Closeness of negative ideal solution

[0140]

[0141] Among them, α and β represent adjustment coefficients;

[0142] Based on proximity Proximity Calculate the relative progress of the i-th income distribution plan and the weighted matrix Z:

[0143]

[0144] In fact, Q i Represents the relative progress of the i-th revenue distribution plan and the weighted matrix Z.

[0145] As a preferred implementation, the method for allocating the peak load benefit is:

[0146]

[0147] Among them, E M represents the total amount of the virtual power plant's peak load sharing, E VPP Indicates the proposed allocation amount of the overall peak load-shaving revenue of the virtual power plant, represents the total distributed resource peak load contribution amount, q s represents the electricity price of the sth distributed resource peak-shaving protocol, ρ s represents the peak load contribution of the sth distributed resource, E OP represents the investment and operation and maintenance expenditure of the virtual power plant, Ny represents the preset operation cycle, r represents the discount rate, and E INV represents the investment cost, E S represents the allocation amount of the sth resource, Q S Represents the relative progress of the sth revenue distribution plan and the weighted matrix Z.

[0148] Embodiment 2:

[0149] This embodiment provides an electronic device having a computer program stored thereon, and when the computer program is executed by a processor, the method for allocating peak-shaving benefits of a virtual power plant as described in any embodiment of the present invention is implemented.

[0150] Embodiment three:

[0151] This embodiment provides a computer-readable storage medium for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the virtual power plant peak-shaving benefit allocation method as described in any embodiment of the present invention.

[0152] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0153] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technology benefit distribution scheme. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0154] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0155] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical benefit distribution scheme of the present application is essentially or the part that contributes to the prior art or the part of the technical benefit distribution scheme can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk and other media that can store program codes.

[0156] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for allocating peak load benefits of a virtual power plant, characterized in that: The following steps are involved: Construct a comprehensive contribution index system for the peak load regulation benefits of virtual power plants; The CRITIC-GRA-TOPSIS model is used to assign weights to each indicator in the comprehensive contribution index system of peak load regulation benefits; Normalize each indicator and construct a weighted matrix consisting of each normalized indicator and each profit distribution plan with the preset profit distribution plan; Calculate the relative closeness of each of the profit distribution plans to the weighted matrix according to the CRITIC-GRA-TOPSIS model; The peak load regulation benefits are distributed according to the relative closeness and the negotiated electricity price of the virtual power plant sharing mechanism.

2. The method for allocating peak load benefits of a virtual power plant according to claim 1, characterized in that: The comprehensive contribution index includes: The preparation time, duration, daily charging and discharging times, maximum limit of regulation capacity, minimum limit of regulation capacity, load fluctuation rate, peak-valley coefficient, power regulation rate, and peak-shaving electricity contribution rate of the virtual power plant.

3. The method for allocating peak load benefits of a virtual power plant according to claim 1, characterized in that: The method of assigning weights to indicators is: Get information about the indicator: Among them, C j represents the total amount of information contained in the jth indicator, δ j represents the standard deviation of the jth indicator, Indicates the conflict between the jth indicator and the ith indicator, r ij It represents the evaluation coefficient of the jth index and the ith index; Assign weights to indicators: Among them, W j represents the weight of the jth indicator.

4. The method for allocating peak load benefits of a virtual power plant according to claim 1, characterized in that: The method for calculating the relative closeness between each of the income distribution schemes and the weighted matrix is: Calculate the Euclidean distance between the positive ideal solution Z+ and the negative ideal solution Z- of the weighted matrix Z and each income distribution scheme; Calculate the positive ideal grey correlation between the positive ideal solution Z+ of the weighted matrix Z and each benefit distribution scheme, and the negative ideal grey correlation between the negative ideal solution Z- and each benefit distribution scheme; According to the Euclidean distance, the positive ideal grey correlation degree and the negative ideal grey correlation degree, the relative closeness between the weighted matrix Z and each income distribution scheme is calculated.

5. The method for allocating peak load benefits of a virtual power plant according to claim 4, characterized in that: The method for calculating the Euclidean distance is: The positive ideal solution Z+ is expressed as: The negative ideal solution Z- is expressed as: in, represents the optimal value of the positive ideal solution Z+ Represents the worst value of the negative ideal solution Z- z ij represents the element in the i-th row and j-th column of the weighted matrix Z, max() represents the maximum function, and min() represents the minimum function; Calculate the Euclidean distance between the positive ideal solution Z+ and the i-th income distribution plan Calculate the Euclidean distance between the negative ideal solution Z- and the i-th income distribution plan Euclidean distance Euclidean distance Perform dimensionless processing: in, Represents the dimensionless Euclidean distance Represents the dimensionless Euclidean distance 6. The method for allocating peak load benefits of a virtual power plant according to claim 5, characterized in that: The method for calculating the positive ideal grey relational degree and the negative ideal grey relational degree is: Calculate the positive ideal grey correlation coefficient matrix between the positive ideal solution Z+ and the i-th income distribution scheme; The element of the positive ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as: Calculate the negative ideal grey correlation coefficient matrix between the negative ideal solution Z- and the i-th income distribution scheme; The element of the negative ideal grey correlation coefficient matrix in the i-th row and j-th column is It is expressed as: in, It means taking i as the index to find the minimum function. It means taking the minimum value function with j as index, It means to take the maximum value function for index i. represents the maximum value function with j as index, θ represents the resolution coefficient, θ∈(0,1); Calculate the positive grey correlation degree of the i-th income distribution scheme according to the positive ideal grey correlation coefficient matrix Calculate the negative grey correlation degree of the i-th income distribution scheme according to the negative ideal grey correlation coefficient matrix Grey relational degree Negative grey relation Perform dimensionless processing: in, Represents the positive grey relational degree after dimensionless processing Represents the negative grey relational degree after dimensionless processing 7. The method for allocating peak load benefits of a virtual power plant according to claim 6, characterized in that: The method for calculating the relative closeness is: According to the dimensionless positive grey correlation, negative grey correlation and Euclidean distance, the closeness between the i-th income distribution scheme and the positive ideal solution is calculated. Closeness of negative ideal solution Among them, α and β represent adjustment coefficients; Based on proximity Proximity Calculate the relative progress of the i-th income distribution plan and the weighted matrix Z: In fact, Q i Represents the relative progress of the i-th revenue distribution plan and the weighted matrix Z.

8. The method for allocating peak load benefits of a virtual power plant according to claim 1, characterized in that: The distribution method of the peak load regulation benefit is as follows: Among them, E M represents the total amount of the virtual power plant's peak load sharing, E VPP Indicates the proposed allocation amount of the overall peak load-shaving revenue of the virtual power plant, represents the total distributed resource peak load contribution amount, q s represents the electricity price of the sth distributed resource peak-shaving protocol, ρ s represents the peak load contribution of the sth distributed resource, E OP represents the investment and operation and maintenance expenditure of the virtual power plant, Ny represents the preset operation cycle, r represents the discount rate, and E INV represents the investment cost, E S represents the allocation amount of the sth resource, Q S Represents the relative progress of the sth revenue distribution plan and the weighted matrix Z.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the virtual power plant peak-shaving benefit allocation method as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the virtual power plant peak-shaving benefit allocation method as described in any one of claims 1 to 8 is implemented.

Images