Method, device, computer device, readable storage medium and program product for determining yield

Abstract

The application relates to a method and device for determining income, a computer device, a computer readable storage medium and a computer program product. The method comprises: obtaining response indexes of a plurality of building objects, the response indexes representing response capabilities of the building objects to grid instructions, and the response indexes comprising at least one of the following: a response timeliness index, a response effectiveness index, a historical response average time index, and a historical response average effectiveness index; determining comprehensive scores of the building objects according to the response indexes of the building objects; and determining target incomes of the building objects according to the comprehensive scores of the building objects. The method can more accurately distinguish the response performances of different building objects, and can more fairly, accurately and incentively allocate incomes to the building objects according to the response performances of the different building objects.

Description

Technical Field

[0001] This application relates to the field of power distribution network technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining revenue. Background Technology

[0002] Guided by dual carbon targets, the power grid is accelerating its transformation into a new type of power system with multi-dimensional interaction between power generation, grid, load, and storage. As flexible and adjustable loads, large-scale buildings led by building aggregators directly affect the safety, stability, and economic operation of the power system by participating in the interaction between power grid supply and demand.

[0003] However, the current revenue-sharing mechanism in the industry suffers from insufficient fairness in revenue distribution, making it difficult to effectively incentivize construction projects to proactively optimize their response capabilities. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can more accurately distinguish the response performance of different building objects and allocate revenue to building objects more fairly, accurately, and incentivizingly based on the response performance of different building objects.

[0005] In a first aspect, this application provides a method for determining revenue, comprising: acquiring response indicators of multiple building objects, wherein the response indicators represent the responsiveness of the building objects to power grid commands, and the response indicators include at least one of the following:

[0006] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators; based on the response indicators of the building object, determine the comprehensive score of the building object;

[0007] The target benefits of a building are determined based on its overall score.

[0008] In one embodiment, a comprehensive score for a building object is determined based on its response metrics, including:

[0009] Based on the response indicators of the building object, determine the weights, first sequence, and second sequence of the response indicators. The first sequence includes the maximum value of each response indicator, and the second sequence includes the minimum value of each response indicator.

[0010] Based on the response index of the building object, the weight of the response index, and the first sequence, the first Euclidean distance and the first similarity are determined. The first Euclidean distance represents the distance between the response index of the building object and the first sequence, and the first similarity represents the similarity between the response index of the building object and the first sequence.

[0011] Based on the response index of the building object, the weight of the response index, and the second sequence, the second Euclidean distance and the second similarity are determined. The second Euclidean distance represents the distance between the response index of the building object and the second sequence, and the second similarity represents the similarity between the response index of the building object and the second sequence.

[0012] The comprehensive score of the building object is determined based on the first Euclidean distance, the second Euclidean distance, the first similarity, the second similarity, the first weight, and the second weight. The first weight is the weight corresponding to the first Euclidean distance and the second Euclidean distance, and the second weight is the weight corresponding to the first similarity and the second similarity.

[0013] In one embodiment, determining the weights of the response metrics based on the building object's response metrics includes:

[0014] The third weight of the response indicators is determined based on their importance.

[0015] The fourth weight of the response index is determined based on the degree of dispersion of the response index;

[0016] The overall weight of the response indicators is determined based on the third and fourth weights.

[0017] In one embodiment, a third weight for the response metrics is determined based on their importance, including:

[0018] Based on the importance of the response indicators, a judgment matrix is ​​constructed, and the element a of the judgment matrix is... ij This indicates the importance of response indicator i relative to response indicator j, where i and j iterate from 1 to m, and m is the number of response indicators.

[0019] Determine the eigenvector corresponding to the largest eigenvalue of the judgment matrix;

[0020] The third weight is determined based on the eigenvector corresponding to the largest eigenvalue.

[0021] In one embodiment, the method further includes:

[0022] Before determining the third weight based on the eigenvector corresponding to the largest eigenvalue, a consistency check is performed on the judgment matrix;

[0023] Based on the eigenvector corresponding to the largest eigenvalue, the third weight is determined, including:

[0024] If the judgment matrix passes the consistency test, the third weight is determined based on the eigenvector corresponding to the largest eigenvalue.

[0025] In one embodiment, a consistency check is performed on the judgment matrix, including:

[0026] The consistency of the judgment matrix is ​​checked based on the number of the largest eigenvalues ​​and the number of response indicators.

[0027] In one embodiment, the response timeliness indicator Determined by the following formula:

[0028]

[0029]

[0030] in, This is the actual response start time. The instruction requires a start time. This is the difference between the actual response start time and the command-required start time, where if... If , it indicates a delayed response. This indicates an early response. In order to improve response time, The maximum allowable early response threshold, and The penalty coefficients are all greater than 0, among which Compared to A larger value is used to strengthen the penalty for latency. As a factor of excessively premature punishment, .

[0031] Based on the above methods for determining response timeliness indicators, through Depicting timeliness, and through Higher penalties are imposed for delayed responses; in addition, a threshold for allowing early responses is set. An excessively early response penalty factor is introduced when the early response exceeds a threshold. This allows for the penalty correction of delayed and overly premature responses and a clear distinction between the timeliness contributions of different building objects.

[0032] In one embodiment, the response validity metric Determined by the following formula:

[0033]

[0034]

[0035] in, This is the actual response end time; The actual response power at time t. Let be the target response power at time t; Time weighting, where peak hours can be... Assign a higher value; The fluctuation penalty coefficient, ≥0, the value can be in the range of 0.1 to 1.0. A larger value indicates a stronger penalty for power fluctuations; Let be the variance of the power deviation, and N be the number of sampling points within the response time period. This represents the average power deviation.

[0036] Based on the above method for determining response effectiveness indicators, energy deviation and power fluctuation are incorporated into the effectiveness evaluation on the basis of the consistency between the actual response quantity and the target response quantity. Higher time weight is given to the deviation during peak periods and a fluctuation penalty coefficient is set, so that the evaluation can reflect both deviation and fluctuation and be more sensitive to key periods, thereby widening the difference in effectiveness between different building objects. The actual response quantity is the integral of the actual response power over time, and the target response quantity is the integral of the target response power over time.

[0037] In one embodiment, the historical response average time metric Determined by the following formula:

[0038]

[0039] in, The time deviation of the k-th historical response. This represents the total number of historical responses. Time decay factor ( Recent responses have a higher weighting. The optimal time deviation among all building objects' historical responses. The worst time deviation in the historical response of all building objects; This is a normalized historical average time indicator.

[0040] The aforementioned recent responses can be understood as historical responses that are closer to the present.

[0041] The optimal time deviation mentioned above can be understood as the time deviation corresponding to achieving the minimum delay or a reasonably early response.

[0042] Based on the above method for determining the historical average time index, a weighted average is used to summarize the time deviation of multiple historical responses and a time decay factor is introduced to give higher weight to recent responses. At the same time, the best and worst historical time deviations are combined for normalization, thereby obtaining a long-term response speed stability index that focuses more on recent performance and is comparable across building objects.

[0043] In one embodiment, the historical response average efficiency index It can be determined by the following formula:

[0044]

[0045] In the formula: This serves as an indicator of the effectiveness of the k-th historical response. This is the industry average response efficiency benchmark. This is the normalized historical average efficiency index. This represents the total number of historical responses.

[0046] Based on the above method for determining the historical average response efficiency index, a weighted average is used to summarize the historical response efficiency multiple times and is corrected by combining it with the industry average response efficiency benchmark value. This forms a historical average efficiency index that can be used to characterize the long-term response stability of building objects and improve comparability, thereby supporting incentives for building objects with long-term high-quality responses.

[0047] Secondly, this application also provides an apparatus for determining revenue, comprising:

[0048] The acquisition module is used to acquire response metrics for multiple building objects. These response metrics represent the building objects' responsiveness to power grid commands, and include at least one of the following:

[0049] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators.

[0050] The processing module is used to determine the overall score of a building object based on its response indicators.

[0051] The processing module is also used to determine the target benefits of a building object based on its comprehensive score.

[0052] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0053] Obtain response metrics for multiple building objects. These metrics represent the building objects' responsiveness to power grid commands. Each response metric includes at least one of the following:

[0054] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators; based on the response indicators of the building object, determine the comprehensive score of the building object;

[0055] The target benefits of a building are determined based on its overall score.

[0056] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0057] Obtain response metrics for multiple building objects. These metrics represent the building objects' responsiveness to power grid commands. Each response metric includes at least one of the following:

[0058] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators; based on the response indicators of the building object, determine the comprehensive score of the building object;

[0059] The target benefits of a building are determined based on its overall score.

[0060] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0061] Obtain response metrics for multiple building objects. These metrics represent the building objects' responsiveness to power grid commands. Each response metric includes at least one of the following:

[0062] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators; based on the response indicators of the building object, determine the comprehensive score of the building object;

[0063] The target benefits of a building are determined based on its overall score.

[0064] The aforementioned methods, apparatus, computer equipment, computer-readable storage media, and computer program products for determining revenue, when determining the revenue of a building object, quantify the comprehensive score of the building object from multiple dimensions, including response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators. Since response timeliness indicators and response effectiveness indicators can reflect the actual performance of the building object in the current response, and historical average response time indicators and historical average response efficiency indicators can reflect the stability of the building object in historical performance, the comprehensive score of the building object takes into account both the actual performance of the building object in the current response and the stability of the building object in historical performance. This forms a more comprehensive evaluation system for the response behavior of building objects, thereby enabling more accurate differentiation of the response performance of different building objects, and more fair, accurate, and incentivizing allocation of revenue to building objects based on their response performance. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating a method for determining revenue in one embodiment;

[0067] Figure 2 This is a flowchart illustrating step 102 in one embodiment;

[0068] Figure 3 This is a flowchart illustrating step 201 in one embodiment;

[0069] Figure 4 This is a flowchart illustrating step 301 in one embodiment;

[0070] Figure 5 This is a schematic diagram illustrating the weights of each response metric in one embodiment;

[0071] Figure 6 This is a schematic diagram illustrating the overall score of each building object in one embodiment;

[0072] Figure 7 A structural block diagram of a device for determining revenue in one embodiment;

[0073] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0075] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0076] Before introducing the embodiments of this application, the technical terms involved will be introduced first:

[0077] Building aggregator: refers to the entity that organizes and manages the participation of multiple building loads in grid interaction, and is responsible for aggregating the adjustable capacity on the building side and carrying out unified evaluation and revenue sharing.

[0078] Power grid supply and demand interaction: refers to the two-way coordination process between the power grid and the building-side load, in which the building responds to the power grid's instructions by adjusting its energy consumption and obtains corresponding benefits.

[0079] The Analytic Hierarchy Process (AHP) is a method that uses expert comparisons to construct a judgment matrix and calculates eigenvectors to obtain subjective weights.

[0080] Entropy weighting method: a method that uses information entropy to reflect the degree of data dispersion and determines objective weights accordingly. The more obvious the data differences, the more prominent the indicator effect usually is.

[0081] Information entropy: a measure used to characterize the uncertainty or amount of information in indicator data, and is used to calculate weights in the entropy weight method.

[0082] Grey Relational Analysis (GRA) is a decision-making method that ranks building objects based on the similarity between their response index sequences and the optimal and worst sequences.

[0083] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a decision-making method that uses various response indicators to form optimal and worst sequences and calculates the distance to the ideal solution to compare their performance.

[0084] Euclidean distance (Euclid): A metric used to calculate the distance between each response metric and the best or worst reference point.

[0085] In one exemplary embodiment, such as Figure 1 As shown, a method for determining revenue is provided, including the following steps 102 to 103.

[0086] Step 101: Obtain response metrics for multiple building objects. Response metrics represent the building objects' ability to respond to power grid commands.

[0087] The response indicators include at least one of the following: response timeliness indicator, response effectiveness indicator, historical average response time indicator, and historical average response efficiency indicator.

[0088] Specifically, when the power grid system issues a power grid command to each building object, the building object will respond to the power grid command. In the process of responding to the power grid command, the building aggregator can obtain the aforementioned response indicators of each building object.

[0089] Among them, the response timeliness index is used to measure the time deviation between the building receiving the power grid command and the actual start of the response; the response effectiveness index is used to measure the degree of consistency between the actual response amount and the target response amount; the historical response average time index is used to reflect the stability of the building's long-term response speed; and the historical response average efficiency index is used to measure the stability of the building's long-term response effect.

[0090] Step 102: Determine the overall score of the building object based on its response indicators.

[0091] Specifically, after obtaining the aforementioned response indicators for each building object, the building aggregator can combine these indicators to determine a comprehensive score for each building object.

[0092] Understandably, the stronger a building's responsiveness to power grid commands, the higher its overall score.

[0093] Step 103: Determine the target revenue corresponding to the building object based on the comprehensive score of the building object.

[0094] Specifically, after determining the comprehensive score of each building object, the building aggregator determines the target revenue corresponding to each building object based on the comprehensive score. That is, it allocates the target revenue matching its comprehensive score to the building object from the total revenue of the building group participating in the power grid supply and demand interaction.

[0095] Revenue sharing is a core element connecting the evaluation and incentive mechanisms of building object responsiveness. Its design must simultaneously satisfy the fairness of matching contribution with benefits, the incentive to encourage building objects to improve their responsiveness, and the sustainability of preventing low-performing building objects from exiting the market.

[0096] Based on this, the revenue sharing mechanism can consist of two parts: contribution allocation and basic guarantee, which can be determined by the following formula:

[0097] (1)

[0098] In the formula: For the final benefit of the i-th building object, Let n represent the total revenue generated by the building complex participating in the power grid supply and demand interaction, and n be the total number of building objects participating in this revenue sharing. The overall score for the i-th building object. This is the proportional coefficient for apportionment based on rating, representing the portion of the total revenue allocated according to the contribution of the building object. This indicates the basic guaranteed income.

[0099] Understandably, the higher the overall score of a building object, the more target benefits will be allocated to it.

[0100] Based on the above technical solution, when determining the benefits of a building object, a comprehensive score is quantified from multiple dimensions, including response timeliness, response effectiveness, historical average response time, and historical average response efficiency. Since response timeliness and response effectiveness reflect the building object's true performance in the current response, and historical average response time and historical average response efficiency reflect the stability of the building object's historical performance, the comprehensive score of a building object takes into account both its true performance in the current response and its stability in historical performance. This forms a more comprehensive evaluation system for the building object's response behavior, enabling more accurate differentiation of the response performance of different building objects. Based on the response performance of different building objects, benefits can be allocated to building objects more fairly, accurately, and in a more incentivizing manner.

[0101] Alternatively, as an implementation method, the response timeliness indicator It can be determined by the following formula:

[0102] (2)

[0103] (3)

[0104] in, This is the actual response start time. The instruction requires a start time. This is the difference between the actual response start time and the command-required start time, where if... If , it indicates a delayed response. This indicates an early response. In order to improve response time, The maximum allowable early response threshold, and The penalty coefficients are all greater than 0, among which Compared to A larger value is used to strengthen the penalty for latency. As a factor of excessively premature punishment, .

[0105] Based on the above methods for determining response timeliness indicators, through Depicting timeliness, and through Higher penalties are imposed for delayed responses; in addition, a threshold for allowing early responses is set. An excessively early response penalty factor is introduced when the early response exceeds a threshold. This allows for the penalty correction of delayed and overly premature responses and a clear distinction between the timeliness contributions of different building objects.

[0106] Alternatively, as an implementation method, response effectiveness metrics It can be determined by the following formula:

[0107] (4)

[0108] (5)

[0109] in, This is the actual response end time; The actual response power at time t. Let be the target response power at time t; Time weighting, where peak hours can be... Assign a higher value; The fluctuation penalty coefficient, ≥0, the value can be in the range of 0.1 to 1.0. A larger value indicates a stronger penalty for power fluctuations; Let be the variance of the power deviation, and N be the number of sampling points within the response time period. This represents the average power deviation.

[0110] Based on the above method for determining response effectiveness indicators, energy deviation and power fluctuation are incorporated into the effectiveness evaluation on the basis of the consistency between the actual response quantity and the target response quantity. Higher time weight is given to the deviation during peak periods and a fluctuation penalty coefficient is set, so that the evaluation can reflect both deviation and fluctuation and be more sensitive to key periods, thereby widening the difference in effectiveness between different building objects. The actual response quantity is the integral of the actual response power over time, and the target response quantity is the integral of the target response power over time.

[0111] Alternatively, as one implementation method, the historical response average time metric It can be determined by the following formula:

[0112] (6)

[0113] in, The time deviation of the k-th historical response. This represents the total number of historical responses. Time decay factor ( Recent responses have a higher weighting. The optimal time deviation among all building objects' historical responses. The worst time deviation in the historical response of all building objects; This is a normalized historical average time indicator.

[0114] The aforementioned recent responses can be understood as historical responses that are closer to the present.

[0115] The optimal time deviation mentioned above can be understood as the time deviation corresponding to achieving the minimum delay or a reasonably early response.

[0116] Based on the above method for determining the historical average time index, a weighted average is used to summarize the time deviation of multiple historical responses and a time decay factor is introduced to give higher weight to recent responses. At the same time, the best and worst historical time deviations are combined for normalization, thereby obtaining a long-term response speed stability index that focuses more on recent performance and is comparable across building objects.

[0117] Alternatively, as an implementation method, the historical average response efficiency index It can be determined by the following formula:

[0118] (7)

[0119] In the formula: This serves as an indicator of the effectiveness of the k-th historical response. This is the industry average response efficiency benchmark. This is the normalized historical average efficiency index. This represents the total number of historical responses.

[0120] Based on the above method for determining the historical average response efficiency index, a weighted average is used to summarize the historical response efficiency multiple times and is corrected by combining it with the industry average response efficiency benchmark value. This forms a historical average efficiency index that can be used to characterize the long-term response stability of building objects and improve comparability, thereby supporting incentives for building objects with long-term high-quality responses.

[0121] Alternatively, as one implementation method, step 102 above can be achieved through the following steps 201 to 204:

[0122] Step 201: Based on the response indicators of the building object, determine the weights, first sequence, and second sequence of the response indicators. The first sequence includes the maximum value of each response indicator, and the second sequence includes the minimum value of each response indicator.

[0123] Specifically, a building aggregator can determine the maximum and minimum values ​​of each response metric based on all response metrics of multiple building objects. For example, for response metric j, n building objects correspond to n response metrics j, and the building aggregator can determine the maximum value from the n response metrics j. and minimum value Ultimately, there are m maximum values. Composed of sequences For the first sequence, correspondingly, there are m minimum values. Composed of sequences This is the second sequence.

[0124] For example, the first sequence mentioned above can be determined by the following relation:

[0125] (8)

[0126] The second sequence mentioned above can be determined by the following relation:

[0127] (9)

[0128] in, Where n represents the number of building objects, and m represents the number of response metrics for each building object. Representing the The first building object One response indicator, Represents n response metrics corresponding to n building objects The maximum value in, Represents n response metrics corresponding to n building objects The minimum value in.

[0129] It is worth mentioning that, for an introduction to determining the weight of response indicators based on the response indicators of the building object, please see the relevant description below.

[0130] Step 202: Determine the first Euclidean distance based on the response index of the building object, the weight of the response index, and the first sequence; determine the second Euclidean distance based on the response index of the building object, the weight of the response index, and the second sequence. The first Euclidean distance represents the distance between the response index of the building object and the first sequence, and the second Euclidean distance represents the distance between the response index of the building object and the second sequence.

[0131] Specifically, after determining the first sequence and the second sequence, the building aggregator can determine the first Euclidean distance based on the approximation ideal solution, according to the response index of the building object, the weight of the response index, and the first sequence, and can determine the second Euclidean distance according to the response index of the building object, the weight of the response index, and the second sequence.

[0132] For example, for building object i, its corresponding first Euclidean distance Second Euclidean distance It can be determined by the following formula, where, This represents the Euclidean distance between the j response metrics corresponding to building object i and the first sequence mentioned above. This represents the Euclidean distance between the j response metrics corresponding to building object i and the second sequence mentioned above:

[0133] (10)

[0134] in, The weight representing the j-th response metric, regarding , , For further explanation, please refer to the aforementioned descriptions. For the sake of brevity, these details will not be repeated here.

[0135] Step 203: Determine the first similarity based on the response index of the building object, the weight of the response index, and the first sequence; determine the second similarity based on the response index of the building object, the weight of the response index, and the second sequence. The first similarity represents the similarity between the response index of the building object and the first sequence, and the second similarity represents the similarity between the response index of the building object and the second sequence.

[0136] Specifically, after determining the first sequence and the second sequence, the building aggregator can determine the first similarity based on the grey relational analysis method, according to the response index of the building object, the weight of the response index, and the first sequence, and can determine the second similarity based on the response index of the building object, the weight of the response index, and the second sequence.

[0137] For example, for building object i, its corresponding first similarity Second similarity It can be determined by the following formula, where, This represents the similarity between the j response indicators corresponding to building object i and the first sequence mentioned above. The similarity between the j response metrics corresponding to building object i and the second sequence mentioned above is expressed as follows:

[0138] (11)

[0139] in, The weight representing the j-th response metric. This is an adjustment coefficient, with a value range of (0, 1]. , , For further explanation, please refer to the aforementioned descriptions. For the sake of brevity, these details will not be repeated here.

[0140] Step 204: Determine the comprehensive score of the building object based on the first Euclidean distance, the second Euclidean distance, the first similarity, the second similarity, the first weight, and the second weight. The first weight is the weight corresponding to the first Euclidean distance and the second Euclidean distance, and the second weight is the weight corresponding to the first similarity and the second similarity.

[0141] Specifically, after determining the first Euclidean distance, second Euclidean distance, first similarity, and second similarity for each building object, the building aggregator can determine the first Euclidean distance for the i-th building object. Second Euclidean distance First similarity Second similarity And the first weight and the second weight are used to determine the comprehensive score of the i-th building object. .

[0142] For example, a building aggregator can determine the overall score of the i-th building object using the following formula. :

[0143] (12)

[0144] in, It is a positive composite quantity. It is a negative composite quantity. As the first weight, it can be understood as the contribution weight of approximating the ideal solution. The second weight can be understood as the contribution weight of the grey relational analysis method.

[0145] The following section describes how to determine the weights of response indicators by combining their importance and dispersion:

[0146] Alternatively, as an implementation method, the step of determining the weight of the response index based on the response index of the building object can be determined by the following steps 301, 302, and 303:

[0147] Step 301: Determine the third weight of the response indicators based on their importance.

[0148] Specifically, the third weight can be the subjective weight determined by authoritative experts in the field based on their own level of knowledge for each response. For example, experts can make full use of their knowledge and professional experience to distinguish the importance of each response indicator, assigning higher weights to response indicators that they consider more important and lower weights to response indicators that they consider less important.

[0149] Alternatively, as an implementation method, the above-mentioned step of determining the third weight of the response indicator based on the importance of the response indicator can also be implemented through the following steps 401, 402, and 403.

[0150] Step 401: Construct a judgment matrix based on the importance of the response indicators. The element a of the judgment matrix... ij This indicates the importance of response indicator i relative to response indicator j. i and j are iterated from 1 to m, where m is the number of response indicators.

[0151] Specifically, the third weight can be determined using the analytic hierarchy process. For example, leading experts in the field can fully utilize their knowledge and experience to differentiate the importance of each response indicator and construct a judgment matrix based on the importance of each response indicator.

[0152] For example, response indicators include response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators. Experts can compare the relative importance of these four response indicators at the same level pairwise and fill the comparison results into the judgment matrix using a scale of 1-9. Table 1 lists the definition of the 1-9 scale method.

[0153] Table 1

[0154]

[0155] The judgment matrix can be as follows:

[0156] (13)

[0157] Here, the response indicators that are compared pairwise can be denoted as b. i b j The element a in the above matrix ij This indicates the response indicator b. i Relative to response index b j The importance of element a ji This indicates the response index b. j Relative to response index b i The degree of importance, if experts believe b i Compared to b j Important, i.e., b j No b i If it is important, then 'a' in the above matrix ij Greater than 1, and a ji For a ij The reciprocal of.

[0158] For example, let's denote the response timeliness indicator, response effectiveness indicator, historical average response time indicator, and historical average response efficiency indicator as b1, b2, b3, and b4, respectively. Assume that experts believe that for a single response, the response timeliness indicator and the response effectiveness indicator are almost equally important, but the response effectiveness indicator is slightly more important, i.e., b2 is more important than b1. In this case, a 21 The value of a can be 2. According to the reciprocal rule, a 12 The value of is 1 / 2.

[0159] For example, because the power grid is more concerned with the real-time quality of the current response, experts may consider the current response timeliness performance more important than the historical average performance; that is, b1 is more important than b3. Furthermore, experts may consider the importance of b1 relative to b3 to be between significantly important and strongly important. In this case, a 13 The value of can be a scale value between obvious importance and strong importance, for example, a 13 The value of a is 6. According to the reciprocal rule, a 31 The value of is 1 / 6.

[0160] It is understandable that the order of the judgment matrix is ​​determined by the number of response indicators. For example, if the number of response indicators is 4, then the matrix should be a 4×4 square matrix with the diagonal elements set to 1, indicating that each response indicator is of equal importance to itself.

[0161] Step 402: Determine the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0162] Specifically, after determining the judgment matrix corresponding to the above response indicators, the eigenvector corresponding to the largest eigenvalue of the judgment matrix can be determined based on the geometric mean method. For example, the eigenvector corresponding to the largest eigenvalue of the judgment matrix can be determined by the following formula:

[0163] (14)

[0164] Where m represents the number of response indicators.

[0165] Step 403: Determine the third weight of the response index based on the eigenvector corresponding to the largest eigenvalue of the judgment matrix.

[0166] Specifically, after determining the eigenvector corresponding to the largest eigenvalue of the judgment matrix, the eigenvector can be normalized using the following formula:

[0167] (15)

[0168] The final result This is the third weight mentioned above.

[0169] Optionally, as an implementation method, a consistency check can be performed on the judgment matrix before determining the third weight based on the above judgment matrix.

[0170] Specifically, experts conducted response indicator b i b j When making comparisons, inconsistencies in the judgment matrices may arise due to conflicting value orientations, inconsistent rating methods, or failure to satisfy the proportionality requirement when assigning importance.

[0171] Therefore, to avoid the above situation, a consistency check can be performed on the judgment matrix before determining the third weight based on the judgment matrix. For example, the consistency check can be performed on the judgment matrix based on the number of the largest eigenvalues ​​and the number of response indicators.

[0172] For example, when performing a consistency check on the judgment matrix based on the number of the largest eigenvalues ​​and the number of response indicators, the following formula can be used to check whether the consistency of the judgment matrix meets the requirements:

[0173] (16)

[0174] in, The largest eigenvalue is calculated based on the judgment matrix, where m is the order of the judgment matrix, i.e., the number of response indicators. The smaller the CI value, the lower the degree of inconsistency of the judgment matrix. The consistency ratio needs to be quantified by the random consistency index RI according to the following formula.

[0175] (17)

[0176] Wherein, CR is the final test coefficient, and the RI value can be obtained by querying the order m. When CR < 0.1, it means that the consistency of the judgment matrix is ​​acceptable. When CR >= 0.1, the judgment matrix needs to be adjusted to adapt to the consistency test. Finally, the third weight is determined based on the judgment matrix that has been tested.

[0177] Step 302: Determine the fourth weight of the response index based on the dispersion of the response index.

[0178] Specifically, building aggregators can determine the weight of response indicators based on the degree of dispersion of the response indicators. For example, if the difference between the response effectiveness indicators of different building objects is small, it can be considered that the role played by the response effectiveness indicators is not significant. Based on this, the response effectiveness indicators can be assigned a small weight. Conversely, if the difference between the response effectiveness indicators is large, it can be considered that the role played by the response effectiveness indicators is prominent. Based on this, the response timeliness indicators can be assigned a high weight.

[0179] Alternatively, as one implementation method, step 302 can be implemented using the entropy weight method. For example, the fourth weight can be determined by the following formula:

[0180] (18)

[0181] in, Let j be the percentage of the j-th indicator on the i-th building object. For the information entropy of each indicator, Information utility value, It is the fourth weight.

[0182] Step 303: Determine the comprehensive weight of the response index based on the third and fourth weights.

[0183] Specifically, since the third weight is determined by expert experience, knowledge reserves, and industry understanding, it has a certain degree of subjectivity and randomness, while the fourth weight is calculated from the response indicator data, it has a certain degree of objectivity. Based on this, a linear weighting method can be used on the third and fourth weights to ultimately obtain the weight of the response indicator that integrates subjective and objective factors.

[0184] For example, the weights of response indicators can be determined using the following formula:

[0185] (19)

[0186] In the formula, The weights for the final output response metrics. This is the percentage coefficient for the third weight, and its value can be [0,1].

[0187] It is worth mentioning that the third weight can also be called the subjective weight, and the fourth weight can also be called the objective weight.

[0188] Furthermore, in specific implementations, the weight of the response indicator can be determined based on the importance of the response indicator, or based on the dispersion of the response indicator, or based on both the importance and dispersion of the response indicator. This application does not limit this.

[0189] It should be noted that the response metrics mentioned above can be the original response metrics, or they can be composed of original response metrics and original response metrics that need to be preprocessed. For example, if there are extremely small response metrics among the m original response metrics, in this case, the extremely small response metrics can be positively processed to transform them into extremely large response metrics. Finally, the extremely large response metrics obtained after positive processing and the original response metrics that do not need to be positively processed together constitute the response metrics mentioned above. The original response metrics that do not need to be positively processed can be understood as being extremely large response metrics themselves.

[0190] For example, the following formula can be used to positively process the minimal response index:

[0191] (20)

[0192] In the formula, x represents a very small response metric that is currently undergoing positive processing, such as a response timeliness metric. This represents the original response timeliness index of the i-th building object. The response timeliness index is the original response timeliness index of the i-th building object after positive transformation. This represents the maximum value in the dataset of all building objects corresponding to the minimal metric. For example, the minimal response metric is a response timeliness metric. This represents the maximum value among the n original response timeliness indicators corresponding to n building objects.

[0193] Optionally, after completing the positive transformation of the extremely small response index, the obtained response index can be standardized to eliminate the influence of dimensions, and the standardized response index can be used as the response index mentioned above.

[0194] For example, the response metrics can be standardized using the following calculations:

[0195] (twenty one)

[0196] in, Represents the first before standardization. The j-th response index of a building object, For the standardized version The j-th response index of a building object, .

[0197] By constructing indicators such as response timeliness and response effectiveness, and penalizing or correcting factors such as delayed response, excessively premature response, deviation, and fluctuation, the differences in timeliness and effectiveness among different building objects can be clearly distinguished and a contribution score can be formed, thus more accurately reflecting the true contribution of building objects. By introducing historical dimension indicators such as historical average response time and historical average response efficiency, and combining weighted averages and time decay factors to comprehensively reflect historical response performance, the evaluation results can reflect the long-term stability and continuous reliability of building objects' responses, thereby strengthening the incentive for building objects with consistently high-quality responses.

[0198] Furthermore, the weights of response indicators are determined by integrating the analytic hierarchy process (AHP) and the entropy weight method, and a contribution ranking is formed by combining the grey relational analysis method and the approximation of the ideal solution method. Based on this, a revenue sharing structure with contribution allocation and basic guarantee is adopted to make the revenue more matched with the contribution and to guarantee the minimum revenue level, thereby improving the fairness of distribution and the effectiveness of incentives and reducing the risk of exit.

[0199] It is worth mentioning that, in the embodiments of this application, by The matrix formed It can be called a standardized matrix.

[0200] The effectiveness of the method for determining revenue provided in the embodiments of this application is verified using the following five building objects as examples.

[0201] For example, to verify the effectiveness of the revenue sharing method provided in the embodiments of this application, five buildings involved in the power grid supply and demand interaction were selected as research objects to simulate the response index evaluation and revenue distribution process under a single response scenario.

[0202] Based on the differences in response speed and stability of building objects, the characteristics of each building object are preset as follows: Building object 1 has a fast response speed and very small power fluctuation; Building object 2 has a slow response but small fluctuation; Building object 3 has an extremely slow response and small fluctuation; Building object 4 has a very fast response but large power fluctuation; Building object 5 has a medium response speed and small fluctuation.

[0203] The response scores of each building object were calculated using four indicators: response timeliness, response effectiveness, historical average response time, and historical average response efficiency. The results are shown in Table 2.

[0204] Table 2. Scores of each item in the architectural object response evaluation.

[0205]

[0206] Based on the scores of the building object response evaluation sub-items, the weights of the response indicators described above are used to calculate the following: Figure 5The weights of the various response indicators shown are used to calculate the overall score for each building object, as follows: Figure 6 As shown.

[0207] The overall score clearly reflects the differences in response behavior among different building objects. Building object 1, with its superior performance, demonstrates a significant advantage in both response timeliness and effectiveness due to its fast response speed and minimal power fluctuations, resulting in a leading overall score. Building object 5, with a moderate response speed and low fluctuations, performs at a moderate level in its core indicators, placing its overall score in the middle. Building object 4, with a fast response but large power fluctuations, shows some strength in historical response speed stability, but the high volatility of individual responses leads to poor response effectiveness, lowering its overall score. Building object 2, with a slow response, has a relatively low overall score due to insufficient timeliness and mediocre response effectiveness. Building object 3, with an extremely slow response, ranks last in all indicators, resulting in the lowest overall score.

[0208] Overall, the comprehensive scores of each building object are highly consistent with the preset response characteristics of each building object. The core indicators (response effectiveness and response timeliness) have a dominant influence on the scores, while historical indicators play a supplementary and adjusting role. This fully verifies that the evaluation system can accurately distinguish the response performance of different building objects and has good rationality and discrimination.

[0209] The final benefit of each building object is calculated based on a comprehensive score, and a score allocation ratio coefficient is set. The distribution can be 0.9, meaning 90% of the total revenue is allocated according to contribution, with the remaining 10% as a basic guarantee. The revenue allocation results are shown in Table 3.

[0210] Table 3. Income Allocation for Each Building

[0211]

[0212] As shown in Table 3, the revenue sharing results strictly follow the logic of evaluation indicators and comprehensive scores. Fairness is fully maintained, with the building object 1, which has the highest comprehensive score, receiving the highest revenue of 8.64 yuan, and the building object 3, which has the lowest score, receiving the lowest revenue of 3.30 yuan. The revenue is strictly positively correlated with the comprehensive score, fully reflecting the core logic that contribution determines reward. Incentives are precisely matched. Although the total revenue is relatively small, the highest and lowest revenues still differ by about 2.6 times. This differentiated revenue effectively guides building objects to optimize their response behavior. For example, building object 4 needs to reduce power fluctuations to improve its effectiveness score. Sustainability is strongly guaranteed. All building objects receive a basic guarantee of 0.57 yuan. Even the worst-performing building object 3 receives positive incentives, preventing it from exiting due to low revenue. This well adapts to the cluster stability requirements in small-scale scenarios.

[0213] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0214] Based on the same inventive concept, this application also provides an apparatus for determining revenue in order to implement the method for determining revenue described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the apparatus for determining revenue provided below can be found in the limitations of the method for determining revenue described above, and will not be repeated here.

[0215] In one exemplary embodiment, such as Figure 7 As shown, an apparatus for determining revenue is provided, comprising: an acquisition module 701 and a processing module 702, wherein:

[0216] The acquisition module 701 is used to acquire response indicators of multiple building objects. The response indicators represent the building objects' responsiveness to power grid commands, and the response indicators include at least one of the following:

[0217] Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators.

[0218] Processing module 702 is used to determine the comprehensive score of the building object based on the building object's response indicators;

[0219] The processing module 702 is also used to determine the target benefit of the building object based on the comprehensive score of the building object.

[0220] Optionally, in some embodiments, the processing module 702 is specifically used for:

[0221] Based on the response indicators of the building object, determine the weights, first sequence, and second sequence of the response indicators. The first sequence includes the maximum value of each response indicator, and the second sequence includes the minimum value of each response indicator.

[0222] Based on the response index of the building object, the weight of the response index, and the first sequence, the first Euclidean distance is determined. Based on the response index of the building object, the weight of the response index, and the second sequence, the second Euclidean distance is determined. The first Euclidean distance represents the distance between the response index of the building object and the first sequence, and the second Euclidean distance represents the distance between the response index of the building object and the second sequence.

[0223] The first similarity is determined based on the response index of the building object, the weight of the response index, and the first sequence. The second similarity is determined based on the response index of the building object, the weight of the response index, and the second sequence. The first similarity represents the similarity between the response index of the building object and the first sequence, and the second similarity represents the similarity between the response index of the building object and the second sequence.

[0224] The comprehensive score of the building object is determined based on the first Euclidean distance, the second Euclidean distance, the first similarity, the second similarity, the first weight, and the second weight. The first weight is the weight corresponding to the first Euclidean distance and the second Euclidean distance, and the second weight is the weight corresponding to the first similarity and the second similarity.

[0225] Optionally, in some embodiments, the processing module 702 is specifically used for:

[0226] Based on the importance of the response indicators, a third weight is determined; based on the dispersion of the response indicators, a fourth weight is determined; and based on the third and fourth weights, a comprehensive weight for the response indicators is determined.

[0227] Optionally, in some embodiments, the processing module 702 is specifically used for:

[0228] Based on the importance of the response indicators, a judgment matrix is ​​constructed, and the element a of the judgment matrix is... ij This indicates the importance of response indicator i relative to response indicator j, where i and j iterate from 1 to m, and m is the number of response indicators; the eigenvector corresponding to the largest eigenvalue of the judgment matrix is ​​determined based on the eigenvector corresponding to the largest eigenvalue, and the third weight is determined accordingly.

[0229] Optionally, in some embodiments, the processing module 702 is further configured to:

[0230] Before determining the third weight based on the eigenvector corresponding to the largest eigenvalue, a consistency check is performed on the judgment matrix; the processing module 702 is specifically used to: determine the third weight based on the eigenvector corresponding to the largest eigenvalue if the judgment matrix passes the consistency check.

[0231] The processing module 702 is specifically used to: perform a consistency check on the judgment matrix based on the number of the largest eigenvalues ​​and the number of response indicators.

[0232] Each module in the aforementioned revenue-determining device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the operations corresponding to each module.

[0233] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interfaces, and a communication interface. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the input / output interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for determining revenue.

[0234] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0235] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above embodiments.

[0236] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the steps described in the above embodiments.

[0237] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above embodiments.

[0238] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based deterministic logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0239] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0240] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining revenue, characterized in that, Applied to power grid systems, the method includes: Obtain response metrics for multiple building objects, whereby the response metrics represent the building objects' responsiveness to power grid commands, and the response metrics include at least one of the following: Response timeliness index, response effectiveness index, historical average response time index, and historical average response efficiency index; based on the response index of the building object, determine the comprehensive score of the building object; The target benefit of the building is determined based on its comprehensive score.

2. The method according to claim 1, characterized in that, The step of determining the comprehensive score of the building object based on its response indicators includes: Based on the response indicators of the building object, determine the weights, a first sequence, and a second sequence of the response indicators, wherein the first sequence includes the maximum value of each response indicator, and the second sequence includes the minimum value of each response indicator. A first Euclidean distance is determined based on the response index of the building object, the weight of the response index, and the first sequence. A second Euclidean distance is determined based on the response index of the building object, the weight of the response index, and the second sequence. The first Euclidean distance represents the distance between the response index of the building object and the first sequence, and the second Euclidean distance represents the distance between the response index of the building object and the second sequence. A first similarity is determined based on the response index of the building object, the weight of the response index, and the first sequence. A second similarity is determined based on the response index of the building object, the weight of the response index, and the second sequence. The first similarity represents the similarity between the response index of the building object and the first sequence, and the second similarity represents the similarity between the response index of the building object and the second sequence. The comprehensive score of the building object is determined based on the first Euclidean distance, the second Euclidean distance, the first similarity, the second similarity, the first weight, and the second weight. The first weight is the weight corresponding to the first Euclidean distance and the second Euclidean distance, and the second weight is the weight corresponding to the first similarity and the second similarity.

3. The method according to claim 2, characterized in that, The step of determining the weight of the response index based on the response index of the building object includes: Based on the importance of the response indicators, a third weight is determined for each response indicator; Based on the degree of dispersion of the response index, a fourth weight of the response index is determined; The comprehensive weight of the response index is determined based on the third weight and the fourth weight.

4. The method according to claim 3, characterized in that, The step of determining the third weight of the response indicator based on its importance includes: Based on the importance of the response indicators, a judgment matrix is ​​constructed, wherein the element a of the judgment matrix is... ij This indicates the importance of response indicator i relative to response indicator j, where i and j iterate from 1 to m, and m is the number of response indicators. Determine the eigenvector corresponding to the largest eigenvalue of the judgment matrix; The third weight is determined based on the eigenvector corresponding to the largest eigenvalue.

5. The method according to claim 4, characterized in that, The method further includes: Before determining the third weight based on the eigenvector corresponding to the largest eigenvalue, a consistency check is performed on the judgment matrix; The step of determining the third weight based on the feature vector corresponding to the largest eigenvalue includes: If the judgment matrix passes the consistency test, the third weight is determined based on the eigenvector corresponding to the largest eigenvalue.

6. The method according to claim 5, characterized in that, The consistency check of the judgment matrix includes: The consistency of the judgment matrix is ​​checked based on the number of the largest eigenvalues ​​and the number of response indicators.

7. An apparatus for determining revenue, characterized in that, The device includes: An acquisition module is used to acquire response indicators of multiple building objects, wherein the response indicators represent the building objects' responsiveness to power grid commands, and the response indicators include at least one of the following: Response timeliness indicators, response effectiveness indicators, historical average response time indicators, and historical average response efficiency indicators. The processing module is used to determine the overall score of the building object based on its response indicators; The processing module is also used to determine the target benefit of the building object based on the comprehensive score of the building object.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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