Method and device for determining adjustable potential of power utilization object and computer equipment

Abstract

The application discloses a kind of adjustable potential determination method, device and computer equipment of electric object, wherein the method comprises: determining multiple adjustable potential indexes, wherein multiple adjustable potential indexes include: load management capability, adjustment capacity size, historical credit level;Obtain the index value of electric object under multiple adjustable potential indexes;Based on the index value under multiple adjustable potential indexes, using adjustable potential model, determine the adjustable potential of electric object, wherein the adjustable potential model is constructed based on multiple adjustable potential indexes.This application solves the technical problem of inaccuracy in determining the adjustable potential of electric object in related technologies.

Description

Technical Field

[0001] This invention relates to the field of power technology, and more specifically, to a method, apparatus, and computer equipment for determining the adjustable potential of an electricity user. Background Technology

[0002] Retail-side flexible users (electricity consumers) are characterized by numerous locations, small volumes, and wide distribution, making it difficult for them to meet the access conditions for the electricity market and power system dispatch. When regulating these flexible users, it is necessary to understand their adjustable potential. However, in related technologies, due to the diversity of evaluation indicators, this is a multi-attribute decision problem, thus exhibiting strong randomness and fuzziness.

[0003] Therefore, in related technologies, there is an issue of inaccuracy in determining the adjustable potential of electricity users.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and computer device for determining the adjustable potential of an electricity user, in order to at least solve the technical problem of inaccurate determination of the adjustable potential of an electricity user in related technologies.

[0006] According to one aspect of the present invention, a method for determining the adjustable potential of an electricity user is provided, comprising: determining a plurality of adjustable potential indicators, wherein the plurality of adjustable potential indicators include: load management capability, regulation capacity, and historical credit level; obtaining indicator values ​​of the electricity user under the plurality of adjustable potential indicators; and determining the adjustable potential of the electricity user by employing an adjustable potential model based on the indicator values ​​under the plurality of adjustable potential indicators, wherein the adjustable potential model is constructed based on the plurality of adjustable potential indicators.

[0007] Optionally, before determining the adjustable potential of the electricity user by using an adjustable potential model based on the values ​​of the multiple adjustable potential indicators, the method further includes: determining the weights of the multiple adjustable potential indicators; and constructing the adjustable potential model based on the multiple adjustable potential indicators and their weights.

[0008] Optionally, determining the weights of the plurality of adjustable potential indicators includes: determining the relative importance of the plurality of adjustable potential indicators by comparing them pairwise to obtain a judgment matrix; and determining the weights of the plurality of adjustable potential indicators based on the judgment matrix.

[0009] Optionally, determining the weights of the plurality of adjustable potential indicators based on the judgment matrix includes: determining the initial weights of the plurality of adjustable potential indicators based on the judgment matrix; normalizing the judgment matrix to obtain a normalized judgment matrix; determining the entropy values ​​of the plurality of adjustable potential indicators based on the normalized judgment matrix; determining the entropy weights of the plurality of adjustable potential indicators based on the entropy values ​​of the plurality of adjustable potential indicators; and correcting the initial weights of the plurality of adjustable potential indicators based on the entropy weights of the plurality of adjustable potential indicators to obtain the weights of the plurality of adjustable potential indicators.

[0010] Optionally, constructing the adjustable potential model based on the plurality of adjustable potential indicators and their weights includes: obtaining a plurality of sample potential assessment models; constructing a target matrix based on the plurality of sample potential assessment models and the plurality of adjustable potential indicators; solving the target matrix to obtain a positive ideal solution and a negative ideal solution; determining the Euclidean distances of the plurality of sample potential assessment models to the positive ideal solution and the negative ideal solution, respectively; determining the grey relational degree between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution, respectively; determining the relative proximity of the plurality of sample potential assessment models based on the Euclidean distances and the grey relational degree; and determining the adjustable potential model from the plurality of sample potential assessment models based on the relative proximity.

[0011] Optionally, the step of constructing a target matrix based on the plurality of sample potential assessment models and the plurality of adjustable potential indicators includes: dimensionless transformation of the plurality of adjustable potential indicators to obtain a plurality of dimensionless adjustable potential indicators; constructing a dimensionless matrix based on the plurality of sample potential assessment models and the plurality of dimensionless adjustable potential indicators; and weighting the dimensionless matrix to obtain the target matrix.

[0012] Optionally, determining the grey relational degree between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution includes: determining the grey relational coefficient matrix between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution, respectively; and determining the grey relational degree between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution based on the grey relational coefficient matrix.

[0013] Optionally, determining the relative closeness of the plurality of sample potential assessment models based on the Euclidean distance and the grey relational degree includes: determining the closeness between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution based on the Euclidean distance and the grey relational degree; and determining the relative closeness of the plurality of sample potential assessment models based on the closeness.

[0014] According to another aspect of the present invention, an apparatus for determining the adjustable potential of an electricity user is provided, comprising: a first determining module for determining a plurality of adjustable potential indicators, wherein the plurality of adjustable potential indicators include: load management capability, regulation capacity, and historical credit level; an acquiring module for acquiring the indicator values ​​of the electricity user under the plurality of adjustable potential indicators; and a second determining module for determining the adjustable potential of the electricity user based on the indicator values ​​under the plurality of adjustable potential indicators and employing an adjustable potential model, wherein the adjustable potential model is constructed based on the plurality of adjustable potential indicators.

[0015] According to another aspect of the present invention, a computer device is provided, comprising: a memory and a processor, the memory storing a computer program; the processor being configured to execute the computer program stored in the memory, wherein the computer program, when executed, causes the processor to perform the adjustable potential determination method for an electricity consumer as described in any of the preceding claims.

[0016] In this embodiment of the invention, the adjustable potential of the electricity user is determined by using an adjustable potential model based on the values ​​of the multiple adjustable potential indicators. By constructing an adjustable potential model based on multiple adjustable potential indicators, the purpose of determining the adjustable potential in a targeted manner based on the adjustable potential indicators is achieved. This realizes the technical effect of accurately determining the adjustable potential of the electricity user, and solves the technical problem of inaccurate determination of the adjustable potential of the electricity user in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a method for determining the adjustable potential of an electricity user according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of the aggregation flexibility user method provided in the embodiments of the present invention;

[0020] Figure 3 This is a flowchart of another aggregation flexibility user method provided by an optional embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the flexible user-adjustable potential index system provided by an optional embodiment of the present invention;

[0022] Figure 5 This is a structural block diagram of an adjustable potential determination device for an electricity user according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] According to an embodiment of the present invention, a method embodiment for determining the adjustable potential of an electrical object is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] Figure 1 This is a flowchart of a method for determining the adjustable potential of an electricity user according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0027] Step S102: Determine multiple adjustable potential indicators, including: load management capacity, adjustment capacity size, and historical credit level.

[0028] Step S104: Obtain the index values ​​of the electricity user under multiple adjustable potential indicators;

[0029] Step S106: Based on the index values ​​under multiple adjustable potential indicators, an adjustable potential model is used to determine the adjustable potential of the electricity user. The adjustable potential model is constructed based on multiple adjustable potential indicators.

[0030] Through the above steps, by adopting index values ​​based on multiple adjustable potential indicators and using an adjustable potential model, the adjustable potential of the electricity user is determined. By constructing an adjustable potential model based on multiple adjustable potential indicators, the purpose of determining the adjustable potential in a targeted manner based on the adjustable potential indicators is achieved. This realizes the technical effect of accurately determining the adjustable potential of the electricity user and solves the technical problem of inaccurate determination of the adjustable potential of the electricity user in related technologies.

[0031] As an optional implementation, different adjustable potential indicators have varying degrees of importance. Therefore, to make the subsequent assessment of the adjustable potential of the electricity user more accurate, weights can be assigned to the adjustable potential indicators. That is, different weights can be assigned to adjustable potential indicators of different importance. An adjustable potential model can be constructed based on the adjustable potential indicators and their corresponding weights, making the constructed model more accurate. For example, before determining the adjustable potential of the electricity user based on the indicator values ​​under multiple adjustable potential indicators and using the adjustable potential model, the following steps can be taken: determining the weights of multiple adjustable potential indicators; and constructing an adjustable potential model based on the multiple adjustable potential indicators and their weights.

[0032] As an optional implementation, the weights of multiple adjustable potential indicators can be determined in various ways. For example, the importance of the indicators can be directly compared, or the relative importance of the multiple adjustable potential indicators can be determined by comparing them pairwise to obtain a judgment matrix. Based on the judgment matrix, the weights of the multiple adjustable potential indicators can be determined.

[0033] As an optional implementation, when determining the weights of multiple adjustable potential indicators based on the judgment matrix, the weights of the multiple adjustable potential indicators can be determined based on information entropy. For example, the following processing method can be adopted: determine the initial weights of multiple adjustable potential indicators based on the judgment matrix; normalize the judgment matrix to obtain a normalized judgment matrix; determine the entropy values ​​of multiple adjustable potential indicators based on the normalized judgment matrix; determine the entropy weights of multiple adjustable potential indicators based on the entropy values ​​of multiple adjustable potential indicators; and correct the initial weights of multiple adjustable potential indicators based on the entropy weights of multiple adjustable potential indicators to obtain the weights of multiple adjustable potential indicators.

[0034] As an optional implementation, when constructing an adjustable potential model based on multiple adjustable potential indicators and their weights, the adjustable potential model can be constructed in various ways. For example, the following grey relational ideal point method can be used to construct the adjustable potential model. For instance, the following processing method can be adopted: obtain multiple sample potential assessment models; construct a target matrix based on the multiple sample potential assessment models and multiple adjustable potential indicators; solve the target matrix to obtain positive and negative ideal solutions; determine the Euclidean distances from the multiple sample potential assessment models to the positive and negative ideal solutions, respectively; determine the grey relational degree between the multiple sample potential assessment models and the positive and negative ideal solutions; determine the relative proximity of the multiple sample potential assessment models based on the Euclidean distance and grey relational degree; and determine the adjustable potential model from the multiple sample potential assessment models based on the relative proximity.

[0035] As an optional implementation, to achieve uniformity among the various indicators, they can be dimensionless to avoid the differences introduced by the indicators and to prevent inaccuracies in the adjustable potential model. For example, based on multiple sample potential assessment models and multiple adjustable potential indicators, a target matrix can be constructed, including: dimensionlessizing multiple adjustable potential indicators to obtain multiple dimensionless adjustable potential indicators; constructing a dimensionless matrix based on multiple sample potential assessment models and multiple dimensionless adjustable potential indicators; and weighting the dimensionless matrix to obtain the target matrix.

[0036] As an optional embodiment, when determining the grey relational degree between multiple sample potential assessment models and the positive and negative ideal solutions, the following processing method can be adopted: first, determine the grey relational coefficient matrix between the multiple sample potential assessment models and the positive and negative ideal solutions respectively; then, based on the grey relational coefficient matrix, determine the grey relational degree between the multiple sample potential assessment models and the positive and negative ideal solutions.

[0037] As an optional embodiment, when determining the relative closeness of multiple sample potential assessment models based on Euclidean distance and grey relational degree, the following processing method can be adopted: based on Euclidean distance and grey relational degree, determine the closeness between multiple sample potential assessment models and positive ideal solutions and negative ideal solutions respectively; based on the closeness, determine the relative closeness of multiple sample potential assessment models.

[0038] In response to the new situation of dual carbon targets and considering the characteristics of flexible users, this invention proposes an aggregated flexible user optimization method in optional embodiments, aiming to tap the adjustment potential of retail-side flexible users and ensure the safe, stable, economical, and low-carbon operation of the power grid.

[0039] The optional embodiments of this invention provide a preferred method for aggregating flexible users. This method is used to assess the adjustable potential of retail-side flexible users, improve the efficiency of aggregating users' participation in ancillary services and demand response markets, and enhance the flexibility adjustment capabilities of the power system. The method includes four steps. First, an assessment index system for the adjustable potential of flexible users is established from three dimensions: load management capability, adjustable potential size, and historical credit level. Second, a subjective and objective combined weighting method is used to calculate the weights of each index. Third, an assessment model based on the grey relational ideal point method is constructed. Finally, based on the calculated weights of each index, an assessment model for the adjustable potential of flexible users is constructed using the approximation of the ideal point method. Finally, the adjustable potential of flexible users is ranked according to the assessment results, helping power sales companies to better select and aggregate users.

[0040] Figure 2 This is a flowchart of the aggregation flexibility user method provided in the embodiments of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0041] S1, Establishment of a flexible user-adjustable potential assessment index system

[0042] This paper constructs an evaluation index system for the adjustable potential of flexible users from three dimensions: load management capability, adjustable potential, and historical credit level. Load management capability includes three indicators: online power monitoring capability, data communication capability, and equipment failure rate. Adjustable potential mainly includes four indicators: adjustment rate, adjustment duration, maximum adjustable capacity, and load shifting capability. Historical credit level mainly includes two indicators: number of credit assessments and contract default rate.

[0043] S2, Calculation of indicator weights based on the subjective and objective combined weighting method

[0044] First, the indicator system constructed in step S1 is weighted using a subjective method. To reduce information loss in the indicator system and make the weights more objective, the entropy weight method is used for correction based on the subjective weighting.

[0045] S3, Construction of an evaluation model based on the grey relational ideal point method

[0046] First, based on the weights of each indicator calculated in step S2, a flexible user adjustability potential assessment model based on the approximation of the ideal point method is constructed. Second, considering the limited sample data for each indicator, the assessment model is modified using grey relational analysis. With limited information, this model reflects the gap between the flexible user adjustability potential and the ideal value, as well as the changing relationships between various factors, thus making the assessment results more reasonable.

[0047] S4, Flexible User-Adjustable Potential Sorting and Optimization

[0048] Based on the S3 evaluation model, the adjustable potential of flexible users is calculated and ranked according to size. The electricity sales company then selects flexible users within its agency scope based on the evaluation results, and aggregates qualified flexible users to participate in the electricity ancillary services market and demand response market, thereby obtaining the optimal returns.

[0049] Figure 3 This is a flowchart of another aggregation flexibility user method provided by an optional embodiment of the present invention, such as... Figure 3 As shown, this aggregation flexibility user method includes the following steps:

[0050] S1. Establishment of a Flexibility and User Adjustability Potential Assessment Index System

[0051] Figure 4 This is a schematic diagram of the flexible user-adjustable potential indicator system provided by an optional embodiment of the present invention, such as... Figure 4 As shown, this flexible user-adjustable potential indicator system includes:

[0052] (1) Load management capability.

[0053] Load management capability reflects a user's ability to monitor and control their own load, including three indicators: online power monitoring capability, data communication capability, and equipment failure rate.

[0054] 1) Load monitoring capability. Load monitoring capability refers to whether the user has configured its own load management device or system to realize the measurement and monitoring of electricity consumption.

[0055] 2) Data communication capability. Data communication capability refers to whether users can accurately and promptly obtain information and instructions issued by the electricity sales company.

[0056] 3) Equipment failure rate. The equipment failure rate measures the stability and reliability of user equipment. The lower the equipment failure rate, the higher its adjustment potential.

[0057] (2) Adjustable potential

[0058] Adjustable potential mainly includes four indicators: adjustment rate, adjustment duration, maximum adjustable capacity, and load shifting capability.

[0059] 1) Regulation rate. Regulation rate refers to the time from when a user receives a dispatch signal to when it begins to respond. It reflects the user's ability to receive grid dispatch signals. The shorter the time, the faster the regulation rate.

[0060] 2) Adjustment duration. Adjustment duration refers to the total time that users can accept grid dispatch, that is, the total duration for which users can accept load reduction. The longer the duration, the higher its value.

[0061] 3) Maximum adjustable capacity. Maximum adjustable capacity refers to the maximum capacity that users can provide by participating in grid regulation, that is, the amount of load that users can reduce. The greater the reduction, the higher its value.

[0062] 4) Load shifting capacity. Load shifting capacity refers to the amount of load that a user can transfer when participating in grid regulation. It can be measured by the ratio of the user's increased electricity consumption during off-peak hours to the user's decreased electricity consumption during peak hours.

[0063] (3) Historical credit level

[0064] Historical creditworthiness mainly includes two indicators: the number of credit assessments and the contract default rate.

[0065] 1) Number of credit assessments. The number of times a user is assessed for failing to participate in grid regulation response on time. The more assessments a user receives, the lower their credit level.

[0066] 2) Contract Default Rate. The contract default rate is the ratio between the average reduction amount by the user and the contract subscription reduction amount during the contract response period.

[0067] S2. Calculation of indicator weights based on the subjective and objective combined weighting method

[0068] Based on the flexible user adjustability potential assessment index system established in step S1, calculate the weight of each index.

[0069] S2-1. Calculation of index weights based on subjective analysis method

[0070] (1) Construct the judgment matrix

[0071] The comprehensive value assessment system for electricity demand-side resources is divided into three layers: the target layer, the value layer, and the indicator layer. First, the judgment matrix for each layer's indicators is constructed. Let the indicator set for each layer be (B1, B2, ..., B...). n The relative importance of each indicator is determined by pairwise comparison, denoted as b. ij b ij This indicates the relative importance of indicator i compared to indicator j. Let matrix B = (b... ij ) is the judgment matrix, and the calculation formula is shown in (1).

[0072]

[0073] Determine the element b in matrix B ij The assignment method is determined using the "1-9" scale, and each element satisfies: b ij >0; b ij =1 / b ji b ii =1, see Table 1 for details.

[0074] Table 1 “1-9” Scale Method

[0075]

[0076] (2) Weight Calculation

[0077] This invention patent uses the square root method to calculate the weight of each indicator, and the calculation formula is shown in (2).

[0078]

[0079] In the formula: ω 0i Indicator B i The weight.

[0080] (3) Check the consistency of the judgment matrix.

[0081] To prevent biased judgments from causing errors in the results, a consistency check is performed on the judgment matrix, and the calculation formula is shown in (3):

[0082]

[0083]

[0084] In the formula, λ max Let represent the largest eigenvalue of the characteristic equation, and n represent the order of the judgment matrix B.

[0085] Analysis of the consistency test results shows that the smaller the CI, the better the consistency of the judgment matrix.

[0086] S2-2. Weight Correction Calculation Based on Information Entropy

[0087] The judgment matrix B is normalized, and the calculation formula is shown in (5).

[0088]

[0089] Let the entropy value of the i-th index be e. i The calculation formula is shown in (6).

[0090]

[0091] In the formula, k > 0, and usually k = 1 / ln, n = 1, 2, 3...n.

[0092] Let the entropy weight of the i-th index be τ. i The calculation formula is shown in (7).

[0093]

[0094] Using τi The initial weights ω of the indicators obtained by the modified subjective analysis method 0i And the corrected weight is denoted as ω. 1i The calculation formula is shown in (8).

[0095]

[0096] In the formula, ω 1i ∈(0,1), and ∑ω 1i =1.

[0097] S3. Construction of an evaluation model based on the grey relational ideal point method

[0098] S3-1. Modeling using the method of approximating ideal points

[0099] (1) Initial processing of data

[0100] Assume the sample size is m, the number of evaluation indicators is n, and the value of each evaluation indicator is X. ij (i = 1, 2, ..., m; j = 1, 2, ..., n). Considering the different dimensions of the values ​​of each indicator, the values ​​of each indicator are dimensionless. According to the type of indicator, they can be divided into three types: benefit-type, cost-type, and interval-type. Benefit-type indicators mean that the larger the value, the better; cost-type indicators mean that the smaller the value, the better.

[0101] Let X ij Let y be the original value of the j-th index of the i-th scheme. ij This is the dimensionless value of the j-th index in the i-th scheme. The specific processing method is shown in formulas (9) and (10).

[0102] For efficiency-type indicators:

[0103]

[0104] For cost-related indicators:

[0105]

[0106] The dimensionless matrix is ​​weighted to obtain the weighted matrix Z = (z ij ) m×n .

[0107] (2) Calculate the Euclidean distance between the positive and negative ideal solutions.

[0108] Calculate the ideal solution Z based on the weighting matrix. + and negative ideal solution Z - The calculation formulas are shown in (11) and (12) below:

[0109]

[0110]

[0111] in,

[0112] Calculate the Euclidean distance from each solution to the positive and negative ideal solutions. and The calculation formulas are shown in (13), (14), (15), and (16):

[0113]

[0114]

[0115] Dimensionless processing:

[0116]

[0117]

[0118] S3-2. Correction of Grey Relational Analysis

[0119] (1) Calculate the grey relational degree between the evaluation object and the positive and negative ideal solutions.

[0120] Calculate the grey relational coefficient matrix R between each scheme and the positive and negative ideal solutions. + and R - The calculation formulas are shown in (17) and (18) below:

[0121]

[0122]

[0123] Let R be the grey relational coefficient matrix. + and R - They are respectively Where θ∈(0,1) is the resolution coefficient. Usually, the correlation has the greatest information content when θ takes the value of 0.5.

[0124] Calculate the grey relational relationship r between each scheme and the positive and negative ideal solutions respectively. i + and r i - The calculation formulas are shown in (19), (20), (21), and (22):

[0125]

[0126]

[0127] Dimensionless output force:

[0128]

[0129]

[0130] (2) Calculate the value assessment relative to the schedule

[0131] Based on the Euclidean distance and grey relational degree after dimensionless quantization, the proximity of the evaluation scheme to the positive and negative ideal solutions is calculated, and the calculation formulas are shown in (23) and (24).

[0132]

[0133]

[0134] In the formula, α and β are usually taken as 0.5.

[0135] Based on the proximity of the evaluation scheme to the positive and negative ideal solutions and Calculate the overall progress of the evaluation scheme. The calculation formula is shown in (25).

[0136]

[0137] In the formula, Q i This indicates the relative closeness, reflecting how close the evaluated solution is to the ideal solution. It supports the ranking of user-adjustable potential in step S4.

[0138] S4. Flexibility: User-adjustable potential ranking and optimization

[0139] Based on the S3 evaluation model, calculate the relative proximity Q for each user. i When Q i The larger the value of Q, the closer the proposed solution is to the ideal. i The smaller the value, the lower the closeness of the solution to the negative ideal solution. The user-adjustable potential for flexibility is ranked according to the relative closeness to the target solution; the greater the closeness, the better the potential, and vice versa.

[0140] Based on adjustable potential ranking, flexible users within the agency scope are screened, and qualified flexible users are aggregated to participate in the power ancillary services market and demand response market, thereby obtaining optimal returns.

[0141] In this embodiment of the invention, a device for determining the adjustable potential of an electricity user is also provided. Figure 5 This is a structural block diagram of the adjustable potential determination device for an electricity user according to an embodiment of the present invention, such as... Figure 5As shown, the device includes: a first determining module 52, an acquiring module 54, and a second determining module 56. The device will be described below.

[0142] The first determining module 52 is used to determine multiple adjustable potential indicators, including load management capability, regulation capacity, and historical credit level; the acquiring module 54 is connected to the first determining module 52 and is used to acquire the indicator values ​​of the electricity user under the multiple adjustable potential indicators; the second determining module 56 is connected to the acquiring module 54 and is used to determine the adjustable potential of the electricity user based on the indicator values ​​under the multiple adjustable potential indicators and using an adjustable potential model, wherein the adjustable potential model is constructed based on the multiple adjustable potential indicators.

[0143] According to an embodiment of the present invention, a computer device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program; and the processor is configured to execute the computer program stored in the memory, wherein the computer program, when running, causes the processor to execute the adjustable potential determination method for any of the above-described electrical objects.

[0144] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, the device where the computer-readable storage medium is located executes the adjustable potential determination method of any of the above-mentioned methods.

[0145] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0146] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0147] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0148] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0149] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] If the integrated unit is implemented as 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 solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0151] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the adjustable potential of an electricity user, characterized in that, include: Multiple adjustable potential indicators are identified, including: load management capability, adjustment capacity, and historical credit level; the load management capability includes: online power monitoring capability, data communication capability, and equipment failure rate; the adjustment capacity includes: adjustment rate, adjustment duration, maximum adjustable capacity, and load shifting capability; and the historical credit level includes: number of credit assessments and contract default rate. Obtain the index values ​​of the electricity user under the multiple adjustable potential indicators; Based on the index values ​​under the multiple adjustable potential indicators, an adjustable potential model is used to determine the adjustable potential of the electricity user, wherein the adjustable potential model is constructed based on the multiple adjustable potential indicators; Before determining the adjustable potential of the electricity user based on the values ​​of the multiple adjustable potential indicators and using the adjustable potential model, the method further includes: determining the weights of the multiple adjustable potential indicators based on a subjective and objective weighting method; and constructing the adjustable potential model based on the multiple adjustable potential indicators and their weights. The step of constructing the adjustable potential model based on the plurality of adjustable potential indicators and their weights includes: obtaining a plurality of sample potential assessment models; constructing a target matrix based on the plurality of sample potential assessment models and the plurality of adjustable potential indicators; solving the target matrix to obtain a positive ideal solution and a negative ideal solution; determining the Euclidean distances of the plurality of sample potential assessment models to the positive ideal solution and the negative ideal solution, respectively; determining the grey relational degree between the plurality of sample potential assessment models and the positive ideal solution and the negative ideal solution, respectively; determining the relative proximity of the plurality of sample potential assessment models based on the Euclidean distances and the grey relational degree; and determining the adjustable potential model from the plurality of sample potential assessment models based on the relative proximity.

2. The method according to claim 1, characterized in that, Determining the weights of the plurality of adjustable potential indicators includes: The relative importance of the multiple adjustable potential indicators is determined by comparing them pairwise, thus obtaining a judgment matrix. Based on the judgment matrix, the weights of the plurality of adjustable potential indicators are determined.

3. The method according to claim 2, characterized in that, The determination of the weights of the plurality of adjustable potential indicators based on the judgment matrix includes: Based on the judgment matrix, the initial weights of the plurality of adjustable potential indicators are determined; The judgment matrix is ​​normalized to obtain the normalized judgment matrix; Based on the normalized judgment matrix, the entropy values ​​of the plurality of adjustable potential indicators are determined. Based on the entropy values ​​of the plurality of adjustable potential indicators, the entropy weights of the plurality of adjustable potential indicators are determined; Based on the entropy weights of the multiple adjustable potential indicators, the initial weights of the multiple adjustable potential indicators are corrected to obtain the weights of the multiple adjustable potential indicators.

4. The method according to claim 1, characterized in that, The construction of the target matrix based on the multiple sample potential assessment models and the multiple adjustable potential indicators includes: The multiple adjustable potential indicators are dimensionless to obtain multiple dimensionless adjustable potential indicators. Based on the multiple sample potential assessment models and the multiple dimensionless adjustable potential indicators, a dimensionless matrix is ​​constructed. The dimensionless matrix is ​​weighted to obtain the target matrix.

5. The method according to claim 1, characterized in that, Determining the grey relational degree between the multiple sample potential assessment models and the positive ideal solution and the negative ideal solution includes: Determine the grey relational coefficient matrix between the multiple sample potential assessment models and the positive ideal solution and the negative ideal solution, respectively; Based on the grey relational coefficient matrix, the grey relational degree between the multiple sample potential assessment models and the positive ideal solution and the negative ideal solution is determined.

6. The method according to claim 1, characterized in that, The determination of the relative closeness of the multiple sample potential assessment models based on the Euclidean distance and the grey relational degree includes: Based on the Euclidean distance and the grey relational degree, the proximity between the multiple sample potential assessment models and the positive ideal solution and the negative ideal solution are determined respectively. Based on the proximity, the relative closeness of the potential assessment models for the multiple samples is determined.

7. A device for determining the adjustable potential of an electrical object, characterized in that, include: The first determining module is used to determine multiple adjustable potential indicators, wherein the multiple adjustable potential indicators include: load management capability, adjustment capacity size, and historical credit level; the load management capability includes: online power monitoring capability, data communication capability, and equipment failure rate; the adjustment capacity size includes adjustment rate, adjustment duration, maximum adjustable capacity, and load shifting capability; and the historical credit level includes the number of credit assessments and contract default rate. The acquisition module is used to acquire the index values ​​of the electricity user under the multiple adjustable potential indicators; The second determining module is used to determine the adjustable potential of the electricity user based on the index values ​​under the plurality of adjustable potential indicators and using an adjustable potential model, wherein the adjustable potential model is constructed based on the plurality of adjustable potential indicators; The device is further configured to, before determining the adjustable potential of the electricity user based on the adjustable potential model using the indicator values ​​under the plurality of adjustable potential indicators, determine the weights of the plurality of adjustable potential indicators based on the subjective and objective combined weighting method; and construct the adjustable potential model based on the plurality of adjustable potential indicators and the weights of the plurality of adjustable potential indicators. The device is further configured to: acquire multiple sample potential assessment models; construct a target matrix based on the multiple sample potential assessment models and the multiple adjustable potential indicators; solve the target matrix to obtain positive ideal solutions and negative ideal solutions; determine the Euclidean distances of the multiple sample potential assessment models to the positive ideal solutions and the negative ideal solutions, respectively; determine the grey relational degree between the multiple sample potential assessment models and the positive ideal solutions and the negative ideal solutions, respectively; determine the relative proximity of the multiple sample potential assessment models based on the Euclidean distances and the grey relational degree; and determine the adjustable potential model from the multiple sample potential assessment models based on the relative proximity.

8. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the adjustable potential determination method for an electricity user as described in any one of claims 1 to 6.

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