Adjustable resource response potential determination method and device, equipment and storage medium

Abstract

The invention discloses an adjustable resource response potential determination method, apparatus and device, and a storage medium. The method comprises the steps of obtaining b evaluation indexes of n power users related to adjustable resources in a virtual power plant on a evaluation level; based on the b evaluation indexes, determining a initial index matrixes corresponding to the a evaluation layers; based on the a initial index matrixes, determining a unified measurement quantitative index matrixes; determining a bull's-eye coefficient matrixes based on the a unified measure quantitative index matrixes; determining a index weight matrixes based on the a unified measure quantitative index matrixes; and on the basis of the a bullseye coefficient matrixes and the a index weight matrixes, determining response potentials of the n power consumers in each evaluation layer in the a evaluation layers, and obtaining e response potentials. The method is beneficial for improving the accuracy of response potential evaluation of the adjustable resources.

Description

Technical Field

[0001] The present application relates to the technical field of adjustable resource response potential assessment in power systems, and in particular to a method, device, equipment and storage medium for determining adjustable resource response potential. Background Art

[0002] Adjustable resources in the power system refer to power generation, power consumption or energy storage resources that can be flexibly adjusted according to the needs of the power grid, and are used to maintain the supply and demand balance and frequency stability of the power system. Among them, demand response is an important adjustable resource. After the power grid receives the peak shaving and valley filling signal, it can release response information to demand-side users, such as price incentive information, to enable power users to participate in the response, thereby balancing the supply and demand of the power grid and ensuring the normal operation of the power grid. Therefore, evaluating the response potential of adjustable resources is crucial for power system scheduling, renewable energy consumption and power grid stability.

[0003] Existing studies usually consider the load level when evaluating the response potential of adjustable resources, only considering the peak shaving and valley filling effect in the response potential, and using the peak-valley load difference as a quantitative indicator to evaluate the response potential.

[0004] However, the adjustable resource response potential is affected by multiple levels and multiple indicators. Evaluating the adjustable resource response potential only from the load level will lead to insufficient comprehensiveness of the evaluation and low accuracy of the determined adjustable resource response potential. Summary of the invention

[0005] In order to solve the above-mentioned problems existing in the prior art, the embodiments of the present application provide a method, device, equipment and storage medium for determining the response potential of adjustable resources. An initial indicator matrix is ​​established through multiple evaluation indicators of power users related to adjustable resources in a virtual power plant at multiple evaluation levels. A unified measurement quantified indicator matrix is ​​obtained through unified measurement changes of the initial indicator matrix. Then, a bull's eye coefficient matrix and an indicator weight matrix are constructed through the unified measurement quantified indicator matrix. Based on the bull's eye coefficient matrix and a indicator weight matrices, the response potential of power users at multiple evaluation levels is determined. In this way, the response potential of adjustable resources can be determined from multiple evaluation levels and multiple evaluation indicators. The evaluation method is more comprehensive, and the accuracy of the evaluation of the response potential of adjustable resources is improved.

[0006] In a first aspect, an embodiment of the present application provides a method for determining an adjustable resource response potential, comprising: Obtain b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level; each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; Based on the b evaluation indicators, determine a initial indicator matrix corresponding to the a evaluation levels; each evaluation level corresponds to an initial indicator matrix; Based on the a initial indicator matrices, determining a unified measurement quantization indicator matrices; the a initial indicator matrices correspond one-to-one to the a unified measurement quantization indicator matrices; Based on the a unified measurement quantization indicator matrix, a bull's eye coefficient matrix is ​​determined; the a unified measurement quantization indicator matrix corresponds to the a bull's eye coefficient matrix one by one; Based on the a unified measurement quantification indicator matrix, determining a indicator weight matrix; the a unified measurement quantification indicator matrix corresponds to the a indicator weight matrix one by one; Based on the a bullseye coefficient matrices and the a indicator weight matrices, the response potential of the n power users at each of the a evaluation levels is determined to obtain e response potentials; e=n a.

[0007] In a second aspect, an embodiment of the present application provides a device for determining adjustable resource response potential, including: An acquisition unit, used for acquiring b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level; each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; A processing unit, configured to determine a initial indicator matrix corresponding to the a evaluation levels based on the b evaluation indicators; each evaluation level corresponds to an initial indicator matrix; Based on the a initial indicator matrices, determining a unified measurement quantization indicator matrices; the a initial indicator matrices correspond one-to-one to the a unified measurement quantization indicator matrices; Based on the a unified measurement quantization indicator matrix, a bull's eye coefficient matrix is ​​determined; the a unified measurement quantization indicator matrix corresponds to the a bull's eye coefficient matrix one by one; Based on the a unified measurement quantification indicator matrix, determining a indicator weight matrix; the a unified measurement quantification indicator matrix corresponds to the a indicator weight matrix one by one; Based on the a bullseye coefficient matrices and the a indicator weight matrices, the response potential of the n power users at each of the a evaluation levels is determined to obtain e response potentials; e=n a.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the first aspect.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement the method described in the first aspect.

[0011] Implementing the embodiments of the present application has the following beneficial effects: In an embodiment of the present application, firstly, b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level are obtained. Then, based on the b evaluation indicators, a initial indicator matrix corresponding to a evaluation level is determined. Further, based on a initial indicator matrix, a unified measurement quantification indicator matrix is ​​determined. Based on a unified measurement quantification indicator matrix, a bull's eye coefficient matrix is ​​determined. Based on a unified measurement quantification indicator matrix, a indicator weight matrix is ​​determined. Finally, based on a bull's eye coefficient matrix and a indicator weight matrix, the response potential of n power users at each evaluation level in a evaluation level is determined to obtain e response potentials. Thus, by constructing an initial indicator matrix for a plurality of evaluation indicators corresponding to power users related to adjustable resources at a plurality of evaluation levels, and uniformly measuring and changing the initial indicator matrix at the same evaluation level, the evaluation indicators at the same evaluation level can be converted into the same dimension, so as to facilitate the analysis of the evaluation indicators and improve the efficiency of determining the response potential of adjustable resources. Based on the unified measurement quantitative indicator matrix obtained by the unified measurement change, the bull's eye coefficient matrix and the indicator weight matrix can be determined, and the response potential of power users at each assessment level can be determined based on the bull's eye coefficient matrix and the indicator weight matrix, so as to realize a comprehensive assessment of the response potential of power users at multiple assessment levels in combination with at least one assessment indicator corresponding to the power user at each assessment level, thereby solving the problem of low accuracy of the determined adjustable resource response potential caused by insufficient comprehensiveness of the adjustable resource response potential assessment, and improving the comprehensiveness and accuracy of the adjustable resource response potential assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 A schematic diagram of an application scenario of a method for determining adjustable resource response potential provided in an embodiment of the present application; Figure 2 A flow chart of a method for determining adjustable resource response potential provided in an embodiment of the present application; Figure 3 A schematic diagram of the division of an evaluation index provided in an embodiment of the present application; Figure 4 A response potential radar chart provided in an embodiment of the present application; Figure 5 A block diagram of the functional units of an adjustable resource response potential determination device provided in an embodiment of the present application; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0016] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] First, see Figure 1 , Figure 1 A schematic diagram of an application scenario of a method for determining the response potential of adjustable resources provided in an embodiment of the present application. The method is applied to the processing equipment of a virtual power plant (VPP), which is a "virtualized" power resource coordination and management system that participates in the power market and has the functions of a traditional power plant by aggregating and optimizing geographically dispersed distributed power generation resources, energy storage resources, user-side adjustable loads, and comprehensive energy resources through advanced information and communication technologies and intelligent control algorithms.

[0018] Among them, the processing device is the control and coordination center of the virtual power plant, which can monitor and control distributed power generation resources, energy storage resources, adjustable loads, integrated energy resources, etc. Among them, distributed power generation resources, energy storage resources, adjustable loads, integrated energy resources, etc. are all equipped with metering equipment for measuring the metering data of each resource. For example, a smart meter is set on the user side to measure the load data on the user side. The processing device can receive the metering data measured by the metering device of each resource through two-way communication technology, and can send adjustment information to each resource according to the metering data of each resource to adjust each resource. Optionally, the processing device may include core hardware for information storage, data processing and information communication, such as a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (Digital Signal Processing), etc., which is not specifically limited in this application. The processing device may also include electronic devices that integrate the above core hardware, such as smart phones, tablet computers, PDAs, laptops, mobile Internet devices MID (Mobile Internet Devices, MID for short), robots or wearable devices, etc., which are operable devices with display functions, and this application does not make specific restrictions on this. The processing device may also be an independent server, such as a cloud server, a virtual private server (VPS), an application server, etc., and this application does not make specific restrictions on this.

[0019] Among them, distributed generation resources, energy storage resources, adjustable loads on the user side, and comprehensive energy resources are all adjustable resources of virtual power plants. Adjustable resources, also known as flexibility resources, are resources in the power system that can be adjusted according to changes in power demand. By regulating and controlling adjustable resources, the power supply and demand of the power system can be balanced to ensure the safe and stable operation of the power grid. Among them, distributed generation resources can include renewable energy generation resources such as photovoltaic power stations and wind turbines, and can also include controllable distributed power sources such as micro gas turbines, biomass power generation, and diesel generators. It can also include cogeneration resources such as industrial waste heat power generation and regional heating supporting power generation. Energy storage resources can include: electrochemical energy storage, distributed energy storage, and electric vehicles. Adjustable loads can be divided into industrial adjustable loads, commercial adjustable loads, and residential adjustable loads according to the type of power users. Comprehensive energy resources can include: microgrids, hydrogen energy systems, thermal energy storage and other resources.

[0020] It should be noted that demand response is an important adjustable resource in the power system. The adjustable resource response potential is used to indicate the willingness of power users to participate in demand response. By evaluating the adjustable resource response potential, response information can be issued to users with high response potential in a targeted manner, so that power users can participate in the response and maintain the supply and demand balance of the power grid. Existing methods for evaluating the adjustable resource response potential usually evaluate from the load characteristics level. However, the adjustable resource response potential is also affected by multiple levels and multiple indicators, such as economic effects and the degree of power grid intelligence. Evaluating only from the load characteristics level will lead to insufficient comprehensiveness of the evaluation and low accuracy of the determined adjustable resource response potential.

[0021] To this end, applied to the above scenario, in the adjustable resource response potential determination method of the embodiment of the present application, the processing device obtains b evaluation indicators of n power users related to the adjustable resources in the virtual power plant at a evaluation level; each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; The processing device determines a initial indicator matrix corresponding to a evaluation level based on b evaluation indicators; each evaluation level corresponds to an initial indicator matrix; The processing device determines a unified measurement quantization indicator matrix based on a initial indicator matrix; the a initial indicator matrix corresponds to the a unified measurement quantization indicator matrix one by one; The processing device determines a bull's-eye coefficient matrices based on a unified measurement quantification indicator matrix; the a unified measurement quantification indicator matrix corresponds to the a bull's-eye coefficient matrix one by one; The processing device determines a indicator weight matrices based on a unified measurement quantification indicator matrix; the a unified measurement quantification indicator matrix corresponds to the a indicator weight matrices one by one; The processing device determines the response potential of n power users at each of a evaluation levels based on a bull's eye coefficient matrices and a indicator weight matrices, and obtains e response potentials; e=n a.

[0022] It can be seen that, applied to the above scenario, the processing device can construct an initial indicator matrix by performing a unified measurement change on the initial indicator matrix at the same evaluation level corresponding to multiple evaluation indicators of power users related to adjustable resources, and can convert the evaluation indicators at the same evaluation level into the same dimension, so as to facilitate the analysis of the evaluation indicators and improve the efficiency of determining the response potential of adjustable resources. Based on the unified measurement quantified indicator matrix obtained by the unified measurement change, the bull's eye coefficient matrix and the indicator weight matrix can be determined, and the response potential of power users at each evaluation level can be determined based on the bull's eye coefficient matrix and the indicator weight matrix, so as to realize a comprehensive evaluation of the response potential of power users at multiple evaluation levels by combining at least one evaluation indicator corresponding to the power user at each evaluation level, solving the problem of low accuracy of the determined adjustable resource response potential caused by insufficient comprehensiveness of the adjustable resource response potential evaluation, and improving the comprehensiveness and accuracy of the adjustable resource response potential evaluation.

[0023] The method of the embodiment of the present application is introduced below. Figure 2 , Figure 2 A flowchart of a method for determining adjustable resource response potential provided in an embodiment of the present application. The method is applied to a processing device in the above scenario, and the method includes but is not limited to the following steps: 201: Obtain b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level.

[0024] In the embodiment of the present application, each evaluation level corresponds to at least one evaluation indicator. n and a are positive integers, and b is an integer greater than or equal to a. The n power users may include at least one of the following users: industrial users, commercial users, and residential users. Figure 3 As shown, the a evaluation level for evaluating the responsiveness of adjustable resources includes at least the following evaluation levels: load characteristics, economic effects, and intelligence level.

[0025] Among them, the evaluation indicators corresponding to the load characteristics include at least one of the following evaluation indicators: interruptible load ratio, shiftable load ratio, reducible load ratio, load interruptible time, load shiftable time, and load reducible time. The interruptible load ratio is used to indicate the equipment capacity that the power user can interrupt. The shiftable load ratio is used to indicate the equipment capacity that the power user can shift to another time period. The reducible load ratio is used to indicate the equipment capacity that the power user can reduce the load. The load interruptible time is used to indicate the time period in a day when the power user can cut off the load. The load shiftable time is used to indicate the time period in a day when the power user can shift the load. The load reducible time is used to indicate the time period in a day when the power user can reduce the load. It should be noted that by evaluating at least one evaluation indicator corresponding to the load characteristics, the response potential of adjustable resources at the load characteristics level can be comprehensively analyzed.

[0026] Among them, the evaluation indicators corresponding to the economic effect include at least one of the following evaluation indicators: user economic level, proportion of electricity expenditure, grid dependence, and unit response benefit. The user economic level is used to indicate the economic proportion of the production and operation of the power user. The proportion of electricity expenditure is used to indicate the ratio of the electricity expenditure of the power user to the production economy. Grid dependence is used to indicate the proportion of the user's demand for the power supply capacity of the grid. Unit response benefit is used to indicate the benefits obtained by the user from participating in response regulation, which can be expressed by the difference between the compensation obtained for the unit response capacity and the unit response capacity cost. It should be noted that by evaluating at least one evaluation indicator corresponding to the economic effect, the willingness of power users to participate in response regulation at the economic benefit level can be analyzed.

[0027] Among them, the evaluation indicators corresponding to the degree of intelligence include at least one of the following evaluation indicators: smart meter coverage rate and communication network coverage rate. The smart meter coverage rate is used to indicate the proportion of smart meters in the area where the power users are located, which can reflect the user's ability to respond automatically. The communication network coverage rate is used to indicate the proportion of communication networks in the area where the power users are located, which can reflect the user's ability to receive demand response signals in a timely manner. It should be noted that by evaluating at least one evaluation indicator corresponding to the economic effect, the impact of the degree of intelligence in the area where the power users are located on the response potential of adjustable resources can be analyzed to reflect the willingness of the power users to participate in response regulation.

[0028] It can be understood that the b evaluation indicators of n power users at a evaluation level are pre-collected and calculated by the control center of the virtual power plant according to the corresponding actual operating parameters, and are stored in the database in the form of an evaluation indicator table. The processing device can directly obtain the evaluation indicator table from the database to obtain the b evaluation indicators of n power users at a evaluation level. The evaluation indicator table is shown in Table 1: Table 1 Evaluation index table

[0029] Among them, X1 represents the interruptible load ratio, X2 represents the load ratio that can be shifted, X3 represents the load ratio that can be reduced, X4 represents the load interruptible time, X5 represents the load shiftable time, and X6 represents the load reduction time. Y1 represents the user's economic level, Y2 represents the proportion of electricity expenditure, Y3 represents the grid dependence, and Y4 represents the unit response benefit. Z1 represents the smart meter coverage rate, and Z2 represents the communication network coverage rate. From Table 1, it is possible to determine the value of each evaluation index corresponding to each user of different user types. For example, the interruptible load ratio of power user 1, whose user type is an industrial user, is 13. It can be understood that the data in Table 1 are only exemplary, and each data in Table 1 is not described one by one here.

[0030] 202: Based on the b evaluation indicators, determine a initial indicator matrix corresponding to a evaluation level.

[0031] In the embodiment of the present application, each evaluation level corresponds to an initial indicator matrix. Each row in the initial indicator matrix corresponds to an electricity user, and each column corresponds to an evaluation indicator. For example, each initial indicator matrix is ​​D=(d ij )n×m, then the initial indicator matrix is ​​an n×m-order matrix, where n represents the number of electricity users, m represents the number of evaluation indicators corresponding to each evaluation level, and d ij It represents the element in the i-th row and j-th column in each initial indicator matrix, that is, the j-th evaluation indicator corresponding to the i-th power user.

[0032] In a specific implementation, the processing device can obtain all evaluation indicators corresponding to each of a evaluation levels, and according to the corresponding relationship between power users and evaluation indicators in each evaluation level, the evaluation indicators corresponding to each power user are used as corresponding elements of the initial indicator matrix in turn to obtain the initial indicator matrix corresponding to each evaluation level, thereby obtaining a initial indicator matrices.

[0033] In this way, by analyzing the initial indicator matrix corresponding to each assessment level, all assessment indicators of each electricity user at this assessment level can be comprehensively evaluated to determine the response potential of each electricity user at this assessment level, thereby improving the comprehensiveness and accuracy of the assessment of the response potential of adjustable resources.

[0034] 203: Based on a initial indicator matrix, determine a unified measurement quantization indicator matrix.

[0035] In an embodiment of the present application, a initial indicator matrices correspond one to one with a unified measurement quantization indicator matrices. The processing device can obtain corresponding a unified measurement quantization indicator matrices by performing a unified measurement change on each element in the a initial indicator matrices. Each row in the unified measurement quantization indicator matrix represents an electricity user, and each column represents an element of an evaluation indicator after the unified measurement change. For example, the i-th row and j-th column element in the unified measurement quantization indicator matrix corresponding to the load characteristic represents the element of the j-th evaluation indicator corresponding to the i-th electricity user at the load characteristic level after the unified measurement change.

[0036] Exemplarily, based on a initial indicator matrix, determining a unified measurement quantization indicator matrix may include: Determine the indicator type corresponding to each evaluation indicator in the b evaluation indicators, and obtain b indicator types; Based on b indicator types, determine a bullseye sequences corresponding to a initial indicator matrix; Based on a initial indicator matrices and a bull's eye sequences, a unified measurement change is performed on the a initial indicator matrices to obtain a unified measurement quantized indicator matrix.

[0037] Among them, each initial indicator matrix corresponds to a bull's eye sequence.

[0038] Specifically, the processing device first determines the indicator type corresponding to each evaluation indicator in the b evaluation indicators, and obtains b indicator types. Optionally, the indicator type can be any one of the following: benefit-type indicator and cost-type indicator. The benefit-type indicator indicates that the willingness of the power user to participate in the response based on the evaluation indicator is to maximize the benefit. The cost-type indicator indicates that the willingness of the power user to participate in the response based on the evaluation indicator is to minimize the cost. The indicator type corresponding to each evaluation indicator can be preset.

[0039] Based on the indicator type of the evaluation indicator corresponding to each evaluation level, the bull's eye sequence corresponding to each evaluation level can be determined, thereby determining a bull's eye sequences in turn. Each bull's eye sequence can correspond to a positive bull's eye sequence and a negative bull's eye sequence. Each positive bull's eye sequence includes the positive bull's eye of each evaluation indicator in the evaluation level corresponding to the positive bull's eye sequence, and each negative bull's eye sequence includes the negative bull's eye of each evaluation indicator in the evaluation level corresponding to the negative bull's eye sequence. Exemplarily, the bull's eye sequence can be determined by the following formula (1) and formula (2): Formula (1) Formula (2) Among them, formula (1) represents the calculation formula of the positive and negative bull's-eye corresponding to the benefit-type indicator, and formula (2) represents the calculation formula of the positive and negative bull's-eye corresponding to the cost-type indicator. It represents the positive bull's eye of the j-th evaluation indicator corresponding to each evaluation level, that is, the positive bull's eye corresponding to the j-th evaluation indicator of each positive bull's eye sequence. It represents the negative bull's eye of the j-th evaluation indicator corresponding to each evaluation level, that is, the negative bull's eye corresponding to the j-th evaluation indicator of each negative bull's eye sequence. represents the maximum value of the jth evaluation index of all power users corresponding to each evaluation level, It represents the minimum value of the jth evaluation index of all power users corresponding to each evaluation level. It should be noted that since the benefit-based index pursues the maximization of benefits, the positive bull's-eye corresponding to the benefit-based index is the maximum value of the benefit-based index, and the negative bull's-eye corresponding to the benefit-based index is the minimum value of the benefit-based index. Since the cost-based index pursues the minimization of costs, the positive bull's-eye corresponding to the cost-based index is the minimum value of the cost-based index, and the negative bull's-eye corresponding to the cost-based index is the maximum value of the cost-based index.

[0040] Based on formula (1) and formula (2), the positive target sequence d corresponding to each evaluation level can be determined in turn: 0 + =(d 01 + ,d 02 + ,…,d 0m + ) and negative bullseye sequence d 0 - =(d 01 - ,d 02 - ,…,d 0m - ).

[0041] Further, the processing device uses the sequence after the uniform measurement change of each positive bull's eye sequence as the positive bull's eye sequence of each unified measurement quantization indicator matrix, uses the sequence after the uniform measurement change of each negative bull's eye sequence as the negative bull's eye sequence of each unified measurement quantization indicator matrix, and performs a uniform measurement change on a initial indicator matrix to obtain a corresponding uniform measurement quantization indicator matrix. It can be understood that since the dimensions of each evaluation indicator are different, it is necessary to perform a uniform measurement change on each element in the initial indicator matrix to convert it into a uniform dimension, so that the processing device can process and analyze the evaluation indicators of each evaluation level, reduce the amount of calculation of the processing device's analysis and processing, and improve the calculation efficiency.

[0042] It can be seen that by determining the indicator type corresponding to each evaluation indicator, the positive bull's eye sequence and negative bull's eye sequence corresponding to the initial indicator matrix can be determined based on the indicator type corresponding to each evaluation indicator, so as to analyze the evaluation indicators of each power user through the positive bull's eye sequence and the negative bull's eye sequence, determine the response potential of each power user, and improve the accuracy of the evaluation of the response potential of adjustable resources. The initial indicator matrix is ​​uniformly measured and changed to obtain a unified measurement quantitative indicator matrix, so as to convert the evaluation indicators into a unified dimension, reduce the amount of calculation of the processing equipment for the analysis and processing of the evaluation indicators, and improve the calculation efficiency and accuracy, so that the adjustable resource response potential can be evaluated from multiple evaluation levels and multiple evaluation indicators through a unified measurement quantitative indicator matrix, and the comprehensiveness and accuracy of the evaluation can be improved.

[0043] Exemplarily, based on a initial indicator matrix and b target sequences, performing a unified measurement change on the a initial indicator matrix to obtain a unified measurement quantization indicator matrix may include: Each element in each initial indicator matrix is ​​uniformly measured and changed using the following formula (3): Formula (3) in, It represents the elements in the i-th row and j-th column of the unified measurement quantization indicator matrix obtained after the elements in the i-th row and j-th column of each initial indicator matrix are uniformly measured. | | represents the absolute value; It means to find the maximum value when the number of rows i changes and the number of columns j remains unchanged.

[0044] Based on formula (3), the elements r in each unified measurement quantification indicator matrix can be determined in turn: ij , and obtain each unified measurement quantization indicator matrix R n×m , where r ij Represents the element in the i-th row and j-th column of the unified measurement quantization indicator matrix, and the unified measurement quantization indicator matrix is ​​an n×m-order matrix.

[0045] Therefore, by making unified measurement changes to each initial indicator matrix, a unified measurement quantitative indicator matrix with unified dimension can be obtained, thereby simplifying the evaluation and analysis of each power user in each evaluation indicator through the unified measurement quantitative indicator matrix, and improving the efficiency and accuracy of the evaluation of the adjustable resource response potential.

[0046] 204: Based on a unified measurement quantification indicator matrix, determine a bull's eye coefficient matrix.

[0047] In an embodiment of the present application, a unified measurement quantitative indicator matrix corresponds to a bull's eye coefficient matrix one by one. Each bull's eye coefficient matrix can correspond to a positive bull's eye coefficient matrix and a negative bull's eye coefficient matrix. The bull's eye coefficient matrix is ​​an n×m-order matrix, and each element in the bull's eye coefficient matrix represents the positive bull's eye coefficient of the evaluation indicator of the power user corresponding to the element. The processing device can determine the positive bull's eye coefficient and the negative bull's eye coefficient corresponding to the elements in each unified measurement quantitative indicator matrix in turn according to the elements in each unified measurement quantitative indicator matrix, and obtain the bull's eye coefficient matrix corresponding to the unified measurement quantitative indicator matrix, thereby determining a bull's eye coefficient matrices in turn.

[0048] Exemplarily, determining a bullseye coefficient matrices based on a unified measurement quantification indicator matrix may include: Based on a unified measurement quantification indicator matrix, determine a standard bullseye sequence; Get the resolution factor; Based on a unified measurement quantization indicator matrix and a standard positive bullseye sequence, c first gray-level correlation differences are determined; Based on the resolution coefficient and c first grayscale correlation differences, a positive bullseye coefficient matrix is ​​determined; Based on a unified measurement quantization indicator matrix and a standard negative bullseye sequence, c second grayscale correlation differences are determined; Based on the resolution coefficient and the c second grayscale correlation differences, a negative bullseye coefficient matrices are determined.

[0049] In the embodiment of the present application, each standard bull's eye sequence corresponds to a standard positive bull's eye sequence and a standard negative bull's eye sequence. Each first grayscale correlation difference represents the absolute value of the difference between an element in a unified measurement quantization indicator matrix and the positive bull's eye corresponding to the element in a standard positive bull's eye sequence. c=n b. Each second grayscale correlation difference represents the absolute value of the difference between an element in a unified measurement quantization indicator matrix and the negative bull's eye corresponding to the element in a standard negative bull's eye sequence.

[0050] Specifically, the processing device first determines the standard bull's eye sequence corresponding to each unified measurement quantization indicator matrix according to the elements in each unified measurement quantization indicator matrix. Optionally, the standard bull's eye sequence can be determined by the above formula (3) to obtain the standard positive bull's eye sequence r 0 + =(1,1,…,1) and the quasi-negative bullseye sequence r 0 - =(0,0,…,0). Then, the resolution coefficient is obtained. The resolution coefficient is preset by the user. Optionally, the resolution coefficient can be 0.5.

[0051] Further, the processing device determines the absolute value of the difference between the element in each unified measurement quantization indicator matrix and the positive bullseye in the standard positive bullseye sequence corresponding to the element, and obtains the grayscale correlation difference between the element in each unified measurement quantization indicator matrix and the positive bullseye in the standard positive bullseye sequence corresponding to the element, thereby obtaining c first grayscale correlation differences in sequence. Based on the first grayscale correlation difference corresponding to all evaluation indicators of each evaluation level in the resolution coefficient and a evaluation levels, the positive bullseye coefficient matrix corresponding to each evaluation level can be determined, thereby obtaining a positive bullseye coefficient matrices.

[0052] In some feasible embodiments, each first grayscale correlation difference and each element in the positive bullseye coefficient matrix can be determined by formula (4): Formula (4) in, It represents the element in the i-th row and j-th column of each positive bull's-eye coefficient matrix, that is, the positive bull's-eye coefficient of the j-th evaluation indicator of the i-th power user corresponding to each positive bull's-eye coefficient matrix. Represents the grayscale correlation difference between the i-th row and j-th column element corresponding to each unified measurement quantization indicator matrix and the corresponding positive bull's eye. Represents the resolution coefficient. Represents the positive bullseye corresponding to the jth evaluation indicator in each standard positive bullseye sequence.

[0053] Based on this, each element in each positive bull's eye coefficient matrix can be determined in turn, thereby obtaining a positive bull's eye coefficient matrices. For example, the positive bull's eye coefficient matrix determined by the load characteristic corresponding evaluation index in the evaluation index table of Table 1 can correspond to the positive bull's eye coefficient matrix table of Table 2: Table 2 Positive bull's eye coefficient matrix

[0054] For example, the element in the first row and second column of the positive bullseye coefficient matrix indicates that the positive bullseye corresponding to the shiftable load ratio of power user 1 is 1.0000. It can be understood that the positive bullseye coefficient matrix shown in Table 2 is only exemplary, and all elements in Table 2 are not described here.

[0055] Similarly, the processing device can also determine the absolute value of the difference between the element in each unified measurement quantization indicator matrix and the negative bull's eye in the standard negative bull's eye sequence corresponding to the element, and obtain the grayscale correlation difference between the element in each unified measurement quantization indicator matrix and the negative bull's eye in the standard negative bull's eye sequence corresponding to the element, thereby obtaining c second grayscale correlation differences in sequence. Based on the second grayscale correlation difference corresponding to all evaluation indicators of each evaluation level in the resolution coefficient and a evaluation levels, the negative bull's eye coefficient matrix corresponding to each evaluation level can be determined, thereby obtaining a negative bull's eye coefficient matrices.

[0056] In some feasible embodiments, each second grayscale correlation difference and each element in the negative bull's eye coefficient matrix can be determined by formula (5): Formula (5) in, It represents the element in the i-th row and j-th column of each negative bull's-eye coefficient matrix, that is, the negative bull's-eye coefficient of the j-th evaluation indicator of the i-th power user corresponding to each negative bull's-eye coefficient matrix. Represents the grayscale correlation difference between the i-th row and j-th column element corresponding to each unified measurement quantization indicator matrix and the corresponding negative bull's eye. Indicates the negative bullseye corresponding to the jth evaluation indicator in each standard negative bullseye sequence. Based on this, each element in each negative bullseye coefficient matrix can be determined in turn, thereby obtaining a negative bullseye coefficient matrices. The negative bullseye coefficient matrix table corresponding to the negative bullseye coefficient matrix is ​​similar to the positive bullseye coefficient matrix table and is not shown here.

[0057] It can be seen that by determining the standard positive bullseye sequence and the standard negative bullseye sequence corresponding to each evaluation level, and based on the elements in the unified measurement quantification indicator matrix and the positive bullseye in the standard positive bullseye sequence, the first grayscale correlation difference corresponding to the evaluation indicator of each power user can be determined, thereby determining the positive bullseye coefficient matrix according to the first grayscale correlation difference corresponding to the evaluation indicator of each power user. Similarly, the second grayscale correlation difference corresponding to the evaluation indicator of each power user can be determined based on the elements in the unified measurement quantification indicator matrix and the negative bullseye in the standard negative bullseye sequence, thereby determining the negative bullseye coefficient matrix according to the second grayscale correlation difference corresponding to the evaluation indicator of each power user. In this way, by using the positive bullseye coefficient matrix and the negative bullseye coefficient matrix as reference matrices, the evaluation indicators corresponding to each evaluation level can be analyzed more accurately to determine a more accurate response potential and improve the accuracy of the determined adjustable resource response potential.

[0058] 205: Based on a unified measurement quantification indicator matrix, determine a indicator weight matrix.

[0059] In the embodiment of the present application, a unified measurement quantization indicator matrix corresponds to a indicator weight matrix. The indicator weight matrix is ​​a 1×m-order matrix, and each element in the indicator weight matrix represents the indicator weight of the evaluation indicator corresponding to the element.

[0060] Exemplarily, determining a indicator weight matrices based on a unified measurement quantification indicator matrix may include: Determine the standard deviation of each column in a unified measurement quantification indicator matrix, and use the standard deviation of each column as the comparison strength of the evaluation indicator corresponding to the column, and obtain b comparison strengths; Based on a unified measurement quantification indicator matrix and b comparison strengths, determine the correlation coefficient of any two evaluation indicators corresponding to each evaluation level in a evaluation levels; Based on the correlation coefficient of any two evaluation indicators corresponding to each of the a evaluation levels, determine the conflict value of any evaluation indicator corresponding to each of the a evaluation levels to obtain b conflict values; Based on b contrast strengths and b conflict values, b information carrying capacities are determined; Based on b information carrying amounts, determine the indicator weight matrix corresponding to each evaluation level in a evaluation levels, and obtain a indicator weight matrix.

[0061] In the embodiment of the present application, the b evaluation indicators correspond one-to-one to the b comparison intensities.

[0062] Specifically, the processing device first determines the average value of all elements in each column of each unified measurement quantitative indicator matrix, determines the standard deviation of all elements in each column according to the average value of all elements in each column, and uses the standard deviation of all elements in each column as the contrast strength of the evaluation indicator corresponding to the column, thereby obtaining the contrast strength of all corresponding evaluation indicators in each unified measurement quantitative indicator matrix, thereby obtaining b contrast strengths in turn.

[0063] Then, the processing device may determine the correlation coefficient of any two evaluation indicators corresponding to each evaluation level in the a evaluation levels according to the a unified measurement quantization indicator matrix and the b comparison strengths. Optionally, the correlation coefficient may be a Pearson correlation coefficient. Specifically, the processing device first determines each unified measurement quantization indicator matrix R n×m The corresponding matrix R consisting of all elements in the i-th column i And the matrix R consisting of all elements in the jth column j , and calculate R i With R j Then, obtain the contrast strength of the evaluation index corresponding to the elements of the ith column of each unified measurement quantization indicator matrix and the contrast strength of the evaluation index corresponding to the elements of the jth column, and determine the product of the contrast strength of the evaluation index corresponding to the elements of the ith column and the contrast strength of the evaluation index corresponding to the elements of the jth column. Finally, the ratio of the covariance to the product is used as the correlation coefficient of the evaluation index corresponding to the elements of the ith column and the evaluation index corresponding to the elements of the jth column. Based on this, the correlation coefficient of any two evaluation indicators corresponding to each evaluation level in the a evaluation levels can be obtained.

[0064] Furthermore, based on the correlation coefficient of any two evaluation indicators corresponding to each evaluation level, the conflict value of any evaluation indicator corresponding to each evaluation level can be determined, wherein the conflict value of each evaluation indicator can be determined by formula (6): Formula (6) in, Represents the conflict value of the j-th evaluation indicator corresponding to each evaluation level. Represents the correlation coefficient between the i-th evaluation indicator and the j-th evaluation indicator corresponding to each evaluation level.

[0065] Based on formula (6), the conflict value of any evaluation indicator corresponding to each evaluation level in the a evaluation levels can be determined in turn, thereby obtaining b conflict values ​​in turn.

[0066] Furthermore, the processing device takes the product of the conflict value of any evaluation indicator corresponding to each evaluation level and the contrast strength of the evaluation indicator as the information carrying capacity of the evaluation indicator, thereby obtaining the information carrying capacity of any evaluation indicator corresponding to each evaluation level, and then obtaining b information carrying capacities.

[0067] Finally, the processing device can determine the sum of the information carrying capacity of all evaluation indicators corresponding to each evaluation level, and obtain the total information carrying capacity corresponding to each evaluation level. The ratio of the information carrying capacity of any evaluation indicator corresponding to each evaluation level to the total information carrying capacity is used as the indicator weight of the evaluation indicator, thereby obtaining the indicator weight of any evaluation indicator corresponding to each evaluation level. By sequentially splicing all the indicator weights of all evaluation indicators corresponding to each evaluation level, the indicator weight matrix corresponding to the evaluation level can be obtained. Based on this, a indicator weight matrix can be obtained.

[0068] It can be seen that by determining the standard deviation of each column in each unified measurement quantitative indicator matrix and taking the standard deviation of each column as the comparison strength of the evaluation indicator corresponding to the column, the comparison strength of the evaluation indicators at each evaluation level can be obtained, and the correlation coefficient of any two evaluation indicators corresponding to each evaluation level can be determined based on the elements in each unified measurement quantitative indicator matrix and the corresponding comparison strength. Then, based on the correlation coefficient of any two evaluation indicators corresponding to each evaluation level, the conflict value of any evaluation indicator corresponding to each evaluation level can be determined, so as to determine the information carrying capacity of the evaluation indicators at each evaluation level based on the comparison strength and conflict value of the evaluation indicators at each evaluation level. Finally, according to the information carrying capacity of the evaluation indicators at each evaluation level, the indicator weight matrix corresponding to each evaluation level can be determined. In this way, the weight of the influence of each evaluation indicator on the adjustable resource response potential as a percentage of the influence of all evaluation indicators on the adjustable resource response potential can be accurately determined, so as to accurately determine the response potential corresponding to each power user and improve the accuracy of the determined adjustable resource response potential.

[0069] 206: Based on a bull's eye coefficient matrices and a indicator weight matrices, determine the response potential of n power users at each of the a evaluation levels to obtain e response potentials.

[0070] Where e=n a. The processing device can determine the benefit decision weight of each power user when it obtains benefits after responding and the loss decision weight when it bears losses after responding according to the indicator weight matrix, and determine the positive prospect value and negative prospect value of each power user when responding, so as to determine the response potential of each power user at each evaluation level according to the positive prospect value, negative prospect value, benefit decision weight and loss decision weight corresponding to each power user at each evaluation level.

[0071] Exemplarily, based on a bullseye coefficient matrices and a indicator weight matrices, determining the response potential of n power users at each of a evaluation levels to obtain e response potentials may include: Obtain the gain sensitivity, loss sensitivity and sensitivity coefficient; Based on a negative bullseye coefficient matrix and return sensitivity, determine c positive prospect values; Based on a positive bullseye coefficient matrix, loss sensitivity level and sensitivity coefficient, c negative prospect values ​​are determined; Obtain the degree of return preference and risk aversion; Based on a indicator weight matrix and the degree of benefit preference, determine b benefit decision weights; Based on a indicator weight matrix and risk aversion level, b loss decision weights are determined; Based on c positive prospect values, b benefit decision weights, c negative prospect values ​​and b loss decision weights, the response potential of n power users at each of a evaluation levels is determined to obtain e response potentials.

[0072] In the embodiment of the present application, the income sensitivity, loss sensitivity, sensitivity coefficient, income preference and risk aversion are all preset by the user. Optionally, the income sensitivity can be a random number greater than 0, and the loss sensitivity can be a random number less than 1. For example, both the income sensitivity and the loss sensitivity can be 0.88. Optionally, the sensitivity coefficient can be 2.25. The income preference can be 0.61. The risk aversion can be 0.69.

[0073] Specifically, the processing device first obtains the profit sensitivity, loss sensitivity, sensitivity coefficient, profit preference and risk aversion. Then, based on the profit sensitivity and the elements in each negative bull's-eye coefficient matrix, the positive prospect value of the evaluation indicator corresponding to the element relative to the power user corresponding to the element is determined. In this way, c positive prospect values ​​can be determined based on a negative bull's-eye coefficient matrices and the profit sensitivity. Based on the loss sensitivity, sensitivity coefficient and the elements in each positive bull's-eye coefficient matrix, the negative prospect value of the evaluation indicator corresponding to the element relative to the power user corresponding to the element is determined. In this way, c negative prospect values ​​can be determined based on a positive bull's-eye coefficient matrices and the loss sensitivity. Optionally, the positive prospect value and negative prospect value of each evaluation indicator relative to each power user are expressed by formula (7) and formula (8), respectively: Formula (7) Formula (8) in, It represents the positive prospect value of the j-th evaluation indicator at each evaluation level relative to the i-th electricity user. Indicates the degree of return sensitivity. It represents the negative prospect value of the j-th evaluation indicator at each evaluation level relative to the i-th electricity user. Indicates the degree of loss sensitivity. Represents the sensitivity coefficient.

[0074] Based on formula (7) and formula (8), the positive prospect value and negative prospect value of any evaluation indicator at each evaluation level relative to any power user can be determined respectively, and c positive prospect values ​​and c negative prospect values ​​can be obtained.

[0075] Furthermore, the processing device determines the profit decision weight corresponding to the evaluation indicator based on the profit preference degree and the indicator weight of any evaluation indicator corresponding to each indicator weight matrix. In this way, b profit decision weights can be determined based on a indicator weight matrices and the profit preference degree. Based on the risk aversion degree and the indicator weight of any evaluation indicator corresponding to each indicator weight matrix, the loss decision weight corresponding to the evaluation indicator is determined. In this way, b loss decision weights can be determined based on a indicator weight matrices and the risk aversion degree. Each profit decision weight and loss decision weight are determined by formula (9) and formula (10), respectively: Formula (9) Formula (10) in, Represents the benefit decision weight corresponding to the j-th evaluation indicator at each evaluation level. Represents the j-th indicator weight in the indicator weight matrix corresponding to each evaluation level. Indicates the degree of profit preference. Represents the loss decision weight corresponding to the j-th evaluation indicator at each evaluation level. Indicates the degree of risk aversion.

[0076] Based on formula (9), the processing device can determine the benefit decision weight corresponding to each evaluation indicator in turn, that is, obtain b benefit decision weights. Based on formula (10), the processing device can determine the loss decision weight corresponding to each evaluation indicator in turn, that is, obtain b loss decision weights.

[0077] Finally, the processing device determines the response potential of the n power users at each of the a evaluation levels based on the c positive prospect values, b benefit decision weights, c negative prospect values, and b loss decision weights, and obtains e response potentials. For example, each response potential can be determined by formula (11): Formula (11) in, represents the response potential of the i-th power user at each evaluation level. The processing device can determine the response potential of each power user at any evaluation level in turn based on formula (11) and c positive prospect values, b benefit decision weights, c negative prospect values ​​and b loss decision weights. In this way, e response potentials can be determined.

[0078] It should be noted that, since power users are usually more sensitive to losses, the determined response potential is usually less than 0. Optionally, the e response potentials can be added to a preset constant value to obtain the adjusted e response potentials, so that each response potential is greater than or equal to 0, thereby simplifying the analysis and processing of the response potential and improving the efficiency of the evaluation of the adjustable resource response potential. Exemplarily, the response potential table corresponding to the e response potentials is shown in Table 3: Table 3 Response potential table

[0079] For example, in Table 3, the response potential of power user 1 at the load characteristic level is 0.9809. It can be understood that the response potential table shown in Table 3 is only exemplary, and each data in Table 3 is not described one by one here.

[0080] It can be seen that based on a negative bull's-eye coefficient matrix and the degree of benefit sensitivity, c positive prospect values ​​can be determined, and based on a positive bull's-eye coefficient matrix, the degree of loss sensitivity and the sensitivity coefficient, c negative prospect values ​​can be determined. Then, based on a indicator weight matrix and the degree of benefit preference, b benefit decision weights can be determined, and based on a indicator weight matrix and the degree of risk aversion, b loss decision weights can be determined. Finally, based on c positive prospect values, b benefit decision weights, c negative prospect values ​​and b loss decision weights, the response potential of n power users at each of a evaluation levels can be determined, and e response potentials can be obtained. In this way, the benefits and losses of each power user participating in the response under different evaluation indicators can be analyzed, so as to determine the response potential of each power user at different evaluation levels, and improve the comprehensiveness and accuracy of the evaluation of the response potential of adjustable resources.

[0081] Exemplarily, the method may further include: Based on e response potentials, a radar chart of the response potentials of n power users for a evaluation level is obtained; Extracting a radar chart corresponding to each of the n power users from the response potential radar chart to obtain a response potential radar chart of the n users; Based on the n user response potential radar chart, determine the response potential evaluation result corresponding to each power user among the n power users, and obtain n response potential evaluation results; Based on the n response potential evaluation results, a control scheme corresponding to the adjustable resources of the virtual power plant is determined.

[0082] In an embodiment of the present application, the response potential radar chart is used to intuitively display the response potential of each power user at any evaluation level.

[0083] Specifically, the processing device first establishes a multidimensional coordinate system according to the number of evaluation levels. For example, at the three evaluation levels of load characteristics, economic effects, and intelligence, a three-dimensional coordinate system is established with load characteristics, economic effects, and intelligence as three coordinate axes. Then, according to the response potential of each power user at any evaluation level, the point of the power user on the coordinate axis of the evaluation level is determined, and the points of each power user on the coordinate axis of each evaluation level are connected in sequence to obtain a user response potential radar chart corresponding to each power user. In this way, a response potential radar chart corresponding to n power users can be obtained.

[0084] Then, the processing device can extract the radar map corresponding to each power user from the response potential radar map to obtain the user response potential radar map corresponding to each power user. Based on the user response potential radar map corresponding to each power user, the response potential corresponding to each power user is evaluated to obtain the response potential evaluation result corresponding to each power user.

[0085] Take the response potential of power users 2, 4 and 5 in Table 3 as an example. The response potential radar chart is as follows: Figure 4 As shown, from Figure 4 It can be seen that the response potential of power user 5 in terms of economic effect and intelligence is higher than that of power user 2 and power user 4. The response potential of power user 2 in terms of load characteristics is higher than that of power user 4 and power user 5. It can be understood that Figure 4 The response potential radar chart shown is for illustrative purposes only and is not intended to be used in any Figure 4 Describe each data in.

[0086] Finally, based on the response potential assessment results corresponding to each electricity user, the processing equipment can determine the control plan corresponding to the adjustable resources of the virtual power plant, and then control the adjustable resources according to the control plan corresponding to each adjustable resource to balance the supply and demand of the power system and ensure the stable operation of the power system.

[0087] Thus, based on e response potentials, a response potential radar chart can be obtained. By extracting the radar chart corresponding to each power user from the response potential radar chart, a user response potential radar chart corresponding to each power user can be obtained, so that the response potential of the power user at each evaluation level can be intuitively displayed to the user through the user response potential radar chart corresponding to each power user. Based on the response potential radar chart corresponding to each power user, the response potential evaluation result corresponding to each power user can be determined. Finally, based on the n response potential evaluation results corresponding to each power user, the control scheme corresponding to the adjustable resources of the virtual power plant is determined, so as to control the adjustable resources of the virtual power plant to ensure the stable operation of the power system.

[0088] In summary, in an embodiment of the present application, firstly, b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level are obtained. Then, based on the b evaluation indicators, a initial indicator matrix corresponding to a evaluation level is determined. Further, based on a initial indicator matrix, a unified measurement quantification indicator matrix is ​​determined. Based on a unified measurement quantification indicator matrix, a bull's eye coefficient matrix is ​​determined. Based on a unified measurement quantification indicator matrix, a indicator weight matrix is ​​determined. Finally, based on a bull's eye coefficient matrix and a indicator weight matrix, the response potential of n power users at each evaluation level in a evaluation level is determined to obtain e response potentials. Thus, by constructing an initial indicator matrix for a plurality of evaluation indicators corresponding to power users related to adjustable resources at a plurality of evaluation levels, and uniformly measuring and changing the initial indicator matrix at the same evaluation level, the evaluation indicators at the same evaluation level can be converted into the same dimension, so as to reduce the amount of calculation of the processing equipment for analyzing and processing the evaluation indicators, and improve the efficiency of determining the response potential of adjustable resources. Based on the unified measurement quantitative indicator matrix obtained by the unified measurement change, the bull's eye coefficient matrix and the indicator weight matrix can be determined, and the response potential of power users at each assessment level can be determined based on the bull's eye coefficient matrix and the indicator weight matrix, so as to realize a comprehensive assessment of the response potential of power users at multiple assessment levels in combination with at least one assessment indicator corresponding to the power user at each assessment level, thereby solving the problem of low accuracy of the determined adjustable resource response potential caused by insufficient comprehensiveness of the adjustable resource response potential assessment, and improving the comprehensiveness and accuracy of the adjustable resource response potential assessment.

[0089] See also Figure 5 , Figure 5 The following is a block diagram of the functional units of an adjustable resource response potential determination device provided in an embodiment of the present application. The adjustable resource response potential determination device 500 may include a processing device of any of the above embodiments. The adjustable resource response potential determination device 500 includes an acquisition unit 501 and a processing unit 502.

[0090] The acquisition unit 501 is used to acquire b evaluation indicators of n power users related to adjustable resources in the virtual power plant at a evaluation level; each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; A processing unit 502 is used to determine a initial indicator matrix corresponding to a evaluation level based on b evaluation indicators; each evaluation level corresponds to an initial indicator matrix; Based on a initial indicator matrix, a unified measurement quantization indicator matrix is ​​determined; the a initial indicator matrix corresponds to the a unified measurement quantization indicator matrix one by one; Based on a unified measurement quantification indicator matrix, a bull's-eye coefficient matrix is ​​determined; the a unified measurement quantification indicator matrix corresponds to the a bull's-eye coefficient matrix one by one; Based on a unified measurement quantification indicator matrix, a indicator weight matrix is ​​determined; a unified measurement quantification indicator matrix corresponds to a indicator weight matrix one by one; Based on a bull's eye coefficient matrices and a indicator weight matrices, the response potential of n power users at each of a evaluation levels is determined, and e response potentials are obtained; e=n a.

[0091] In some feasible embodiments, in determining a unified measurement quantization indicator matrix based on a initial indicator matrix, the processing unit 502 is specifically configured to: Determine the indicator type corresponding to each evaluation indicator in the b evaluation indicators, and obtain b indicator types; Based on b indicator types, determine a bull's eye sequences corresponding to a initial indicator matrix; each initial indicator matrix corresponds to a bull's eye sequence; Based on a initial indicator matrices and a bull's eye sequences, a unified measurement change is performed on the a initial indicator matrices to obtain a unified measurement quantized indicator matrix.

[0092] In some feasible embodiments, each bull's eye sequence corresponds to a positive bull's eye sequence; in terms of performing a unified measurement change on the a initial indicator matrices based on the a initial indicator matrices and the b bull's eye sequences to obtain a unified measurement quantization indicator matrix, the processing unit 502 is specifically used to: Each element in each initial indicator matrix is ​​uniformly measured and changed using the following formula:

[0093] in, represents the jth evaluation index corresponding to the i-th power user in each initial index matrix; It represents the element in the i-th row and j-th column of the unified measurement quantified indicator matrix obtained after the uniform measurement change is performed on the element in the i-th row and j-th column of each initial indicator matrix; represents the positive bullseye corresponding to the jth evaluation indicator of each positive bullseye sequence; || represents the absolute value; It means to find the maximum value when the number of rows i changes and the number of columns j remains unchanged.

[0094] In some feasible embodiments, each bull's-eye coefficient matrix corresponds to a positive bull's-eye coefficient matrix and a negative bull's-eye coefficient matrix; in determining a bull's-eye coefficient matrices based on a unified measurement quantization indicator matrix, the processing unit 502 is specifically used to: Based on a unified measurement quantification indicator matrix, a standard bull's eye sequence is determined; each standard bull's eye sequence corresponds to a standard positive bull's eye sequence and a standard negative bull's eye sequence; Get the resolution factor; Based on a unified measurement quantization indicator matrix and a standard positive bullseye sequence, c first grayscale correlation differences are determined; each first grayscale correlation difference represents the absolute value of the difference between an element in a unified measurement quantization indicator matrix and the positive bullseye corresponding to the element in a standard positive bullseye sequence; c=n b; Based on the resolution coefficient and c first grayscale correlation differences, a positive bullseye coefficient matrix is ​​determined; Based on a unified measurement quantization indicator matrix and a standard negative bullseye sequence, c second grayscale correlation differences are determined; each second grayscale correlation difference represents the absolute value of the difference between an element in a unified measurement quantization indicator matrix and the negative bullseye corresponding to the element in the a standard negative bullseye sequence; Based on the resolution coefficient and the c second grayscale correlation differences, a negative bullseye coefficient matrices are determined.

[0095] In some feasible embodiments, in determining a indicator weight matrices based on a unified measurement quantization indicator matrix, the processing unit 502 is specifically configured to: Determine the standard deviation of each column in a unified measurement quantization indicator matrix, and use the standard deviation of each column as the comparison strength of the evaluation indicator corresponding to the column, and obtain b comparison strengths; the b evaluation indicators correspond to the b comparison strengths one by one; Based on a unified measurement quantification indicator matrix and b comparison strengths, determine the correlation coefficient of any two evaluation indicators corresponding to each evaluation level in a evaluation levels; Based on the correlation coefficient of any two evaluation indicators corresponding to each of the a evaluation levels, determine the conflict value of any evaluation indicator corresponding to each of the a evaluation levels to obtain b conflict values; Based on b contrast strengths and b conflict values, b information carrying capacities are determined; Based on b information carrying amounts, determine the indicator weight matrix corresponding to each evaluation level in a evaluation levels, and obtain a indicator weight matrix.

[0096] In some feasible embodiments, in determining the response potential of n power users at each evaluation level in a evaluation levels based on a bull's eye coefficient matrices and a indicator weight matrices to obtain e response potentials, the processing unit 502 is specifically configured to: Obtain the gain sensitivity, loss sensitivity and sensitivity coefficient; Based on a negative bullseye coefficient matrix and return sensitivity, determine c positive prospect values; Based on a positive bullseye coefficient matrix, loss sensitivity level and sensitivity coefficient, c negative prospect values ​​are determined; Obtain the degree of return preference and risk aversion; Based on a indicator weight matrix and the degree of benefit preference, determine b benefit decision weights; Based on a indicator weight matrix and risk aversion level, b loss decision weights are determined; Based on c positive prospect values, b benefit decision weights, c negative prospect values ​​and b loss decision weights, the response potential of n power users at each of a evaluation levels is determined to obtain e response potentials.

[0097] In some feasible embodiments, the processing unit 502 is further configured to: Based on e response potentials, a radar chart of the response potentials of n power users for a evaluation level is obtained; Extracting a radar chart corresponding to each of the n power users from the response potential radar chart to obtain a response potential radar chart of the n users; Based on the n user response potential radar chart, determine the response potential evaluation result corresponding to each power user among the n power users, and obtain n response potential evaluation results; Based on the n response potential evaluation results, a control scheme corresponding to the adjustable resources of the virtual power plant is determined.

[0098] See also Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 600 includes a transceiver 601, a processor 602, and a memory 603. They are connected via a bus 604. The memory 603 is used to store computer programs and data, and can transmit the data stored in the memory 603 to the processor 602. The electronic device 600 may include the above-mentioned adjustable resource response potential determination device 500. The electronic device 600 may also include a processing device of any of the above-mentioned embodiments.

[0099] The processor 602 is used to read the computer program in the memory 603 and perform the following operations: Obtain b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level; each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; Based on b evaluation indicators, determine a initial indicator matrix corresponding to a evaluation level; Based on a initial indicator matrix, determine a unified measurement quantification indicator matrix; Based on a unified measurement quantification indicator matrix, determine a bullseye coefficient matrix; Based on a unified measurement quantification indicator matrix, determine a indicator weight matrix; Based on a bull's eye coefficient matrices and a indicator weight matrices, the response potential of n power users at each of a evaluation levels is determined to obtain e response potentials.

[0100] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0101] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any one of the methods described in the above method embodiments.

[0102] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is executed by a processor to implement part or all of the steps of any one of the methods recorded in the above method embodiments.

[0103] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0104] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0108] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0109] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0110] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining the response potential of adjustable resources, characterized in that: include: Obtain b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level; Each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; Based on the b evaluation indicators, determine a initial indicator matrix corresponding to the a evaluation levels; each evaluation level corresponds to an initial indicator matrix; Based on the a initial indicator matrices, determining a unified measurement quantization indicator matrices; the a initial indicator matrices correspond one-to-one to the a unified measurement quantization indicator matrices; Based on the a unified measurement quantization indicator matrix, a bull's eye coefficient matrix is ​​determined; the a unified measurement quantization indicator matrix corresponds to the a bull's eye coefficient matrix one by one; Based on the a unified measurement quantification indicator matrix, determining a indicator weight matrix; the a unified measurement quantification indicator matrix corresponds to the a indicator weight matrix one by one; Based on the a bullseye coefficient matrices and the a indicator weight matrices, the response potential of the n power users at each of the a evaluation levels is determined to obtain e response potentials; e=n a.

2. The method according to claim 1, characterized in that The step of determining a unified measurement quantization indicator matrix based on the a initial indicator matrix comprises: Determine the indicator type corresponding to each evaluation indicator in the b evaluation indicators to obtain b indicator types; Based on the b indicator types, determining a bull's eye sequences corresponding to the a initial indicator matrices; each initial indicator matrix corresponds to a bull's eye sequence; Based on the a initial indicator matrices and the a bull's eye sequences, the a initial indicator matrices are subjected to a unified measurement change to obtain the a unified measurement quantized indicator matrices.

3. The method according to claim 2, characterized in that Each of the bull's-eye sequences corresponds to a positive bull's-eye sequence; The step of performing a unified measurement change on the a initial indicator matrices based on the a initial indicator matrices and the b target sequences to obtain the a unified measurement quantization indicator matrices includes: Each element in each of the initial indicator matrices is uniformly measured and changed using the following formula: in, represents the jth evaluation index corresponding to the i-th power user in each of the initial index matrices; represents the element in the i-th row and j-th column of the unified measurement quantized indicator matrix obtained after the elements in the i-th row and j-th column of each of the initial indicator matrices are subjected to a unified measurement change; represents the positive bull's eye corresponding to the j-th evaluation indicator of each positive bull's eye sequence; | | represents the absolute value; It means to find the maximum value when the number of rows i changes and the number of columns j remains unchanged.

4. The method according to any one of claims 1 to 3, characterized in that: Each of the bull's-eye coefficient matrices corresponds to a positive bull's-eye coefficient matrix and a negative bull's-eye coefficient matrix; the determining of a bull's-eye coefficient matrices based on the a unified measurement quantification indicator matrices includes: Based on the a unified measurement quantization indicator matrix, a standard bull's eye sequence is determined; each of the standard bull's eye sequences corresponds to a standard positive bull's eye sequence and a standard negative bull's eye sequence; Get the resolution factor; Based on the a unified measurement quantization indicator matrix and the a standard positive bullseye sequences, c first grayscale correlation differences are determined; each of the first grayscale correlation differences represents the absolute value of the difference between an element in a unified measurement quantization indicator matrix and the positive bullseye corresponding to the element in the a standard positive bullseye sequences; c=n b; Determining a said positive bullseye coefficient matrices based on the said resolution coefficient and the said c first grayscale correlation differences; Based on the a unified measurement quantization indicator matrix and the a standard negative bullseye sequences, c second grayscale association differences are determined; each of the second grayscale association differences represents an absolute value of a difference between an element in a unified measurement quantization indicator matrix and a negative bullseye corresponding to the element in the a standard negative bullseye sequences; Based on the resolution coefficient and the c second grayscale association differences, a negative bull's eye coefficient matrices are determined.

5. The method according to any one of claims 1 to 3, characterized in that: The step of determining a indicator weight matrices based on the a unified measurement quantification indicator matrices comprises: Determine the standard deviation of each column in the a unified measurement quantization indicator matrix, and use the standard deviation of each column as the comparison strength of the evaluation indicator corresponding to the column, to obtain b comparison strengths; the b evaluation indicators correspond to the b comparison strengths one by one; Determine the correlation coefficient of any two evaluation indicators corresponding to each evaluation level in the a evaluation levels based on the a unified measurement quantification indicator matrix and the b comparison strengths; Based on the correlation coefficient of any two evaluation indicators corresponding to each of the a evaluation levels, determine the conflict value of any one of the evaluation indicators corresponding to each of the a evaluation levels to obtain b conflict values; Determining b information carrying capacities based on the b comparison strengths and the b conflict values; Based on the b information carrying capacities, determine the indicator weight matrix corresponding to each evaluation level in the a evaluation levels to obtain the a indicator weight matrices.

6. The method according to any one of claims 1 to 3, characterized in that: The step of determining the response potential of the n power users at each of the a evaluation levels based on the a bullseye coefficient matrices and the a indicator weight matrices to obtain e response potentials includes: Obtain the gain sensitivity, loss sensitivity and sensitivity coefficient; Based on a negative bullseye coefficient matrices and the return sensitivity level, c positive prospect values ​​are determined; Determining c negative prospect values ​​based on a positive bullseye coefficient matrices, the loss sensitivity level and the sensitivity coefficient; Obtain the degree of return preference and risk aversion; Determining b benefit decision weights based on the a indicator weight matrices and the benefit preference degree; Determining b loss decision weights based on the a indicator weight matrices and the risk aversion degree; Based on the c positive prospect values, the b benefit decision weights, the c negative prospect values ​​and the b loss decision weights, the response potential of the n electricity users at each of the a evaluation levels is determined to obtain the e response potentials.

7. The method according to claim 6, characterized in that The method further comprises: Based on the e response potentials, obtaining a radar chart of the response potentials of the n power users for the a evaluation levels; Extracting a radar chart corresponding to each of the n power users from the response potential radar chart to obtain n user response potential radar charts; Based on the n user response potential radar charts, determining a response potential evaluation result corresponding to each of the n power users, to obtain n response potential evaluation results; Based on the n response potential evaluation results, a control scheme corresponding to the adjustable resources of the virtual power plant is determined.

8. An adjustable resource response potential determination device, characterized in that: include: An acquisition unit, used for acquiring b evaluation indicators of n power users related to adjustable resources in a virtual power plant at a evaluation level; Each evaluation level corresponds to at least one evaluation indicator; n and a are positive integers, and b is an integer greater than or equal to a; A processing unit, configured to determine a initial indicator matrix corresponding to the a evaluation levels based on the b evaluation indicators; each evaluation level corresponds to an initial indicator matrix; Based on the a initial indicator matrices, determining a unified measurement quantization indicator matrices; the a initial indicator matrices correspond one-to-one to the a unified measurement quantization indicator matrices; Based on the a unified measurement quantization indicator matrix, a bull's eye coefficient matrix is ​​determined; the a unified measurement quantization indicator matrix corresponds to the a bull's eye coefficient matrix one by one; Based on the a unified measurement quantification indicator matrix, determining a indicator weight matrix; the a unified measurement quantification indicator matrix corresponds to the a indicator weight matrix one by one; Based on the a bullseye coefficient matrices and the a indicator weight matrices, the response potential of the n power users at each of the a evaluation levels is determined to obtain e response potentials; e=n a.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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