Flexible resource value assessment method and device applicable to new energy power system

Abstract

The present invention discloses a flexible resource value assessment method and device applicable to a new energy power system, the method comprising: obtaining a plurality of typical application scenarios of flexible resources in a power system, and based on the plurality of typical application scenarios, dividing the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer and a scheme layer from top to bottom; determining a first judgment matrix and a weight coefficient of the criterion layer for the target layer, determining a second judgment matrix and a weight coefficient of the indicator layer for the criterion layer, for each indicator of the indicator layer, determining a third judgment matrix and a weight coefficient of the scheme layer for the current indicator, and determining a relative score of the scheme layer for the indicator layer based on the third judgment matrix and a weight coefficient; comprehensively determining the relative score of the scheme layer for the indicator layer, the weight coefficient of the indicator layer for the criterion layer and the weight coefficient of the criterion layer for the target layer, finally determining the score of the scheme layer for the target layer, and judging the value of different flexible resources in the current application scenario according to the score.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a flexible resource value assessment method and device applicable to a new energy power system. Background Art

[0002] Under the new situation of national energy transformation and energy technology revolution, with the access of a high proportion of new energy to the power system, higher requirements are put forward for the flexibility of the power system. The traditional power system is mainly connected to conventional thermal power, hydropower or nuclear power, and the load shows a certain regularity. When a low proportion of new energy is connected, the power system needs to consider how to provide backup for the new energy system. With the further increase in the proportion of centralized and distributed new energy grid connection, the power system is required to have a higher flexible adjustment capability due to the randomness and volatility of its output. It is difficult to meet the future power system flexibility needs with high quality by relying solely on traditional resources such as thermal power units. The role of exploring and mobilizing new flexible resources in improving the flexibility of the power system, building a high proportion of new energy power system, and promoting energy transformation is becoming increasingly important.

[0003] With the rapid development of energy storage technology and distributed energy, the continuous innovation of Internet, communication and other technologies, and the advancement of the construction of ubiquitous power Internet of Things, new flexible resources such as electrochemical energy storage, demand-side response, and virtual power plants are increasingly involved in the interaction with the power system, which will provide the power system with more and more economical flexible adjustment capabilities. In recent years, my country's electrochemical energy storage has developed rapidly and its scale has continued to expand. As of the end of 2018, the cumulative installed capacity of electrochemical energy storage that has been put into operation was 1.01 million kilowatts, a year-on-year increase of 159%. It will play a role in improving the grid's acceptance of new energy, grid frequency regulation, peak shaving and valley filling, and improving power quality and power reliability. Demand-side resources change the normal power consumption pattern within a given time through demand-side response technology, reduce power load or transfer power load to other time periods. As a flexible resource, the dispatchable resources on the demand side are used as alternative resources on the supply side, thereby playing a role in smoothing the load curve, optimizing resource allocation, and improving system flexibility. As a new type of distributed power market operation mode, virtual power plants can scientifically predict user demand, system load conditions, power generation information, etc., and use the prediction results to formulate scientific and reasonable optimal power generation plans, thereby achieving reasonable control of power system operation, continuously optimizing the scheduling difficulty of photovoltaic power generation, and reducing the impact of power information use on the entire distribution network. At present, China has carried out preliminary explorations on the participation of electrochemical energy storage, demand-side response and virtual power plants in power system regulation. Foreign countries with mature power markets are also actively improving power market design to better play the role of these new flexible resources, such as the first 20MW virtual power plant that won the bid in the New England capacity market in the United States in February 2019.

[0004] However, the participation of new flexible resources in power system regulation is still in the exploratory stage, and there are many types of new flexible resources such as electrochemical energy storage, demand-side response, and virtual power plants, with different technical and economic characteristics and suitable application scenarios. In the future, with the gradual advancement of energy transformation and the continuous construction of ubiquitous power Internet of Things, the scale of new flexible resources such as demand-side response and virtual power plants will become larger and larger. Existing studies generally evaluate the feasibility of flexible resources from the perspective of technology or economic benefits, but there are few studies that comprehensively consider application, technology, and economy. It is urgent to study the value of new flexible resources such as electrochemical energy storage to provide support for promoting the application of new flexible resources in the power system, serving the transformation of the power system to adapt to a high proportion of new energy access, and accelerating the construction of a clean, low-carbon, safe and efficient energy system. Summary of the invention

[0005] The purpose of the present invention is to provide a flexible resource value assessment method and device applicable to a new energy power system, aiming to solve the above-mentioned problems in the prior art.

[0006] The present invention provides a flexible resource value assessment method applicable to a new energy power system, comprising:

[0007] Obtain multiple typical application scenarios of flexible resources in the power system, and based on the multiple typical application scenarios, divide the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer, and a solution layer from top to bottom;

[0008] Determine the first judgment matrix and weight coefficient of the criterion layer for the target layer, determine the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, and for each indicator of the indicator layer, determine the third judgment matrix and weight coefficient of the solution layer for the current indicator, and based on this, determine the relative score of the solution layer to the indicator layer;

[0009] The relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer are comprehensively considered to finally determine the score of the solution layer to the target layer. Based on the score, the value of different flexible resources in the current application scenario is judged.

[0010] The present invention provides a flexible resource value assessment device applicable to a new energy power system, comprising:

[0011] A division module is used to obtain multiple typical application scenarios of flexible resources in the power system, and based on the multiple typical application scenarios, divide the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer and a solution layer from top to bottom;

[0012] A calculation module is used to determine the first judgment matrix and weight coefficient of the criterion layer for the target layer, determine the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, determine the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer, and determine the relative score of the solution layer to the indicator layer based on the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer;

[0013] The evaluation module is used to comprehensively consider the relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer, and finally determine the score of the solution layer to the target layer, and judge the value of different flexible resources in the current application scenario based on the score.

[0014] The embodiment of the present invention solves the problem that the flexible resource value assessment in the prior art has little comprehensive consideration of multiple dimensions such as application, technology, and economy, and proposes a flexible resource value assessment method suitable for a high-proportion new energy power system, which provides support for comprehensively and objectively reflecting the application value of flexible resources in different application scenarios and promotes the large-scale application of flexible resources in the power system.

[0015] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a flexible resource value assessment method applicable to a new energy power system according to an embodiment of the present invention;

[0018] Figure 2 is a flowchart of detailed processing of a flexible resource value assessment method applicable to a new energy power system according to an embodiment of the present invention;

[0019] Figure 3 is a schematic diagram of energy storage applicability analysis for a peak load shaving scenario according to an embodiment of the present invention;

[0020] Figure 4 Schematic diagram of a flexible resource value assessment device for a new energy power system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to solve the problems in the prior art, an embodiment of the present invention proposes a flexible resource value assessment method suitable for a high-proportion new energy power system. The method combines the technical and economic characteristics of flexible resources, analyzes possible typical application scenarios, and uses the hierarchical analysis method to introduce subjective judgment and experience of the criterion layer into the model from multiple dimensions such as application, technology and economy. After quantification and normalization, the weight of each criterion or indicator is determined. At the same time, based on economic and technical parameters, the relative score of the flexible resources under a certain criterion is given. Finally, the total score of each flexible resource is calculated by combining the relative score of the flexible resource under a certain criterion and the weight of each criterion, and the application value of the flexible resource in a typical application scenario is judged by the score.

[0022] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0024] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] Method Embodiment

[0026] According to an embodiment of the present invention, a flexible resource value assessment method applicable to a new energy power system is provided. Figure 1 is a flow chart of a flexible resource value assessment method applicable to a new energy power system according to an embodiment of the present invention. Figure 1 As shown, the flexible resource value assessment method applicable to the new energy power system according to an embodiment of the present invention specifically includes:

[0027] Step 101, obtain multiple typical application scenarios of flexible resources in the power system, and based on the multiple typical application scenarios, divide the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer and a solution layer from top to bottom; wherein the multiple typical application scenarios specifically include: peak regulation scenarios, frequency regulation scenarios, voltage regulation scenarios and line congestion management scenarios; the target layer specifically includes: evaluation targets; the criterion layer specifically includes: multiple criteria affecting the evaluation of the evaluation targets; the indicator layer specifically includes: multiple evaluation indicators corresponding to each criterion; the solution layer specifically includes: multiple solutions to be evaluated.

[0028] Step 102, determining the first judgment matrix and weight coefficient of the criterion layer for the target layer, determining the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, determining the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer for the current indicator, and based on this, determining the relative score of the solution layer for the indicator layer; Step 102 specifically includes the following processing:

[0029] In combination with the flexibility requirements of typical application scenarios, the influence of multiple factors in the criterion layer on the target layer is determined, and a first judgment matrix of the criterion layer on the target layer is determined based on the influence, and the importance of different factors in the first judgment matrix is ​​compared by a nine-level scaling method, wherein the rows and columns of the first judgment matrix are the total number of multiple factors in the criterion layer;

[0030] Solving the characteristic roots and characteristic vectors for the constructed first judgment matrix, and when it is determined that the first judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to obtain a weight coefficient of the criterion layer to the target layer;

[0031] Combined with the flexibility requirements of typical application scenarios, determine the influence of multiple factors in the indicator layer on the criterion layer, determine a second judgment matrix of the indicator layer on the criterion layer based on the influence, and compare the importance of different factors in the second judgment matrix by a nine-level scaling method, wherein the rows and columns of the second judgment matrix are the total number of multiple factors in the indicator layer;

[0032] Solving the characteristic roots and characteristic vectors for the constructed second judgment matrix, and when it is determined that the second judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to determine the weight coefficient of the indicator layer to the criterion layer;

[0033] For each indicator of the indicator layer, according to the technical and economic characteristics of flexible resources, determine the third judgment matrix of the scheme layer for the current indicator, and compare the importance of different factors in the third judgment matrix through the nine-level scaling method, wherein the rows and columns of the third judgment matrix are the total number of schemes;

[0034] Solving the characteristic roots and characteristic vectors for the constructed third judgment matrix, and when it is determined that the third matrix satisfies the consistency check, using the characteristic vector as a weight vector to determine the weight coefficient of the solution layer for the current indicator;

[0035] After determining whether the scheme layer has determined the weight coefficients for each indicator of the indicator layer, the weight coefficients of the scheme layer for different indicators are combined to obtain the relative score of the scheme layer for the indicator layer.

[0036] Among them: determining the first judgment matrix and weight coefficient of the criterion layer for the target layer, determining the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, for each indicator of the indicator layer, determining the third judgment matrix and weight coefficient of the solution layer for the current indicator, and based on this, determining the relative score of the solution layer to the indicator layer specifically includes:

[0037] The first judgment matrix, the second judgment matrix and the third judgment matrix are determined according to formula 1-3:

[0038]

[0039] m ij =1 / m ji Formula 2;

[0040] m ii =1 Formula 3;

[0041] Where N is the total number of factors, and the element m in the matrix M is ij is the importance comparison value of factor i to factor j;

[0042] According to Formula 4, each element of the judgment matrix M is normalized according to each column vector, the normalized matrix is ​​summed row by row according to Formula 5, normalized again according to Formula 6, and the approximate value of the maximum characteristic root of the judgment matrix M is calculated according to Formula 7;

[0043]

[0044]

[0045]

[0046]

[0047] Among them, ω=[ω1,ω2,...,ω N ]T It is the approximate eigenvector of the judgment matrix M, and λ is the approximate value of the maximum eigenroot of the judgment matrix M.

[0048] The consistency check of the first judgment matrix, the second judgment matrix and the third judgment matrix specifically includes:

[0049] For a fixed N, a positive reciprocal matrix M′ is randomly constructed, and the element m′ of the positive reciprocal matrix M′ is ij is 1 to 9, randomly select a value from 1 / 1 to 1 / 9, and calculate the consistency index C of M′ I , so construct a certain number of M′, calculate all C I The average value is taken as the random consistency index R I ;

[0050] Perform consistency check according to Formula 8 and Formula 9:

[0051]

[0052]

[0053] Where: C I is the consistency index; if the sum of the N characteristic roots of the judgment matrix M is equal to N, then C I It is equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ;

[0054] In C I = 0, the judgment matrix M is a consistency matrix, where C I The larger the value, the more serious the inconsistency of the judgment matrix M.

[0055] Step 103, comprehensively consider the relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer, and finally determine the score of the solution layer to the target layer, and judge the value of different flexible resources in the current application scenario based on the score.

[0056] That is to say, the technical solution of the embodiment of the present invention specifically needs to be processed as follows:

[0057] 1) Steps to determine typical application scenarios: Select the four major demands of the power grid - peak regulation, frequency regulation, voltage regulation and line congestion management, to analyze the technical requirements for flexible resources.

[0058] 2) Determine the steps of the hierarchical system: Decompose the decision problem into 4 levels. The top level is the target level, i.e. the evaluation target A; the second level is the criterion level B, which is several types of criteria that affect the target evaluation; the third level is the indicator level C, which gives several important evaluation indicators for each criterion; the bottom level is the solution level D, which is several types of solutions to be evaluated.

[0059] 3) Steps to determine the judgment matrix of the criterion layer to the target layer: Combined with the flexibility requirements of typical application scenarios, analyze the impact of criterion layer factors on the target layer, and combine research and experience to give the judgment matrix of the criterion layer to the target layer. The rows and columns of the judgment matrix are the total number of factors, and the nine-level scaling method is used as a comparison scale for the importance of different factors.

[0060] 4) Steps to determine the weight coefficient of the criterion layer to the target layer: solve the characteristic roots and eigenvectors for the constructed judgment matrix. When the judgment matrix satisfies the consistency test, the eigenvector can be used as the weight vector, i.e., the weight. Since it is quite difficult to calculate the characteristic roots and eigenvectors of high-order matrices, and the pairwise comparison matrix is ​​a relatively rough quantitative result obtained through qualitative comparison, it is not necessary to calculate it accurately, and its characteristic roots and eigenvectors can be calculated by a simple approximate method.

[0061] 5) Steps to determine the judgment matrix of the indicator layer to the criterion layer: Combined with the flexibility requirements of typical application scenarios, analyze the impact of the indicator layer factors on the criterion layer, and combine research and experience to give the judgment matrix of the indicator layer to the criterion layer. The rows and columns of the judgment matrix are the total number of factors, and the nine-level scaling method is used as a comparison scale for the importance of different factors. Note that when comparing two factors, it is necessary to determine them according to the factors that are beneficial to the upper layer.

[0062] 6) The steps of determining the weight coefficient of the indicator layer to the criterion layer are as follows: solving the characteristic roots and characteristic vectors of the constructed judgment matrix. When the judgment matrix satisfies the consistency test, the characteristic vector can be used as the weight vector, i.e., the weight.

[0063] 7) For each indicator in the indicator layer, carry out the following steps in sequence.

[0064] 8) Steps to determine the judgment matrix of the current indicators at the solution level: Considering the technical and economic characteristics of flexible resources, combined with research and experience, the judgment matrix of the current indicators at the solution level is given. The rows and columns of the judgment matrix are the total number of solutions, and the nine-level scaling method is used as a comparison scale for the importance of different solutions.

[0065] 9) Steps to determine the weight coefficient of the solution layer for the current indicator: solve the characteristic roots and characteristic vectors for the constructed judgment matrix. When the judgment matrix satisfies the consistency test, the characteristic vector can be used as the weight vector, that is, the weight.

[0066] 10) The step of judging whether the solution layer has determined the weights of all the indicators in the indicator layer: if all the indicators have been traversed, proceed to the next step; otherwise, return to step 8).

[0067] 11) Steps to determine the relative score of the solution layer to the indicator layer: Combining the weights of the solution layer for different indicators is the relative score of the solution layer to the indicator layer.

[0068] 12) Steps to determine the value of flexible resources in different application scenarios: Based on the relative score of the solution layer to the indicator layer, the weight of the indicator layer to the criterion layer, and the weight of the criterion layer to the target layer, the score of the solution layer to the target layer can be finally determined. Based on the score, the value of different flexible resources in the current application scenario can be judged. End.

[0069] The above technical solution of the embodiment of the present invention is described below with reference to the accompanying drawings.

[0070] 1) Determine typical application scenarios:

[0071] The application of flexible resources such as electrochemical energy storage in the power system runs through all links of "generation, transmission, distribution and use", and has important application value in smoothing the output of new energy, promoting the grid connection of clean energy, reducing the cost of thermal power peak regulation, providing power supply quality and reliability, reducing the cost of electricity consumption on the user side, and participating in frequency and voltage regulation. Therefore, the four major demands of the power grid - peak regulation, frequency regulation, voltage regulation and line congestion management - are selected to analyze the technical requirements for flexible resources.

[0072] 2) Divide the decision-making problem into the goal layer, criterion layer, indicator layer and solution layer:

[0073] Here we take electrochemical energy storage as an example, in which the target layer is the application value of different types of energy storage in a certain power grid application scenario; the criterion layer includes the technical criteria, economic criteria and application criteria of various types of energy storage; in the indicator layer, the technical indicators include power level, number of cycles, discharge time, response speed, economic indicators include power cost, energy cost, system efficiency, self-discharge rate, practical indicators include safety, technology maturity, site selection flexibility, and environmental impact; the solution layer includes seven types of advanced energy storage technologies, including advanced compressed air energy storage, high-speed flywheel energy storage, lithium iron phosphate battery energy storage, ternary lithium battery energy storage, all-vanadium liquid flow battery energy storage, lead-carbon battery energy storage, and sodium-sulfur battery energy storage.

[0074] 3) Determine the judgment matrix:

[0075] The judgment matrix M is constructed as shown in the following formula (1).

[0076]

[0077] Where N is the total number of factors, and the element m in the matrix M is ijis the comparison value of the importance of factor i to factor j. Generally speaking, the nine-level scale method shown in the following table is used as the comparison scale of importance. Note that when comparing two factors, the factors that are beneficial to the upper layer are determined. Obviously, the importance comparison satisfies the following relationship.

[0078] m ij =1 / m ji (2)

[0079] m ii =1 (3)

[0080] Table 1 Comparison of factors’ importance

[0081]

[0082]

[0083] 4) Determine the weight coefficient:

[0084] Solve the characteristic roots and eigenvectors for the constructed judgment matrix M. When the judgment matrix M satisfies the consistency test, the eigenvector can be used as the weight vector in the above steps, that is, the weight. Since it is quite difficult to calculate the characteristic roots and eigenvectors of high-order matrices, and the pairwise comparison matrix is ​​a relatively rough quantitative result obtained by qualitative comparison, it is not necessary to calculate it accurately, and its characteristic roots and eigenvectors can be calculated by a simple approximate method. Here, the sum method is used to solve it, and its main steps are as follows.

[0085] a) Normalize each element of the judgment matrix M according to each column vector.

[0086]

[0087] b) Sum the rows of the normalized matrix.

[0088]

[0089] c) Normalize again.

[0090]

[0091] ω=[ω1,ω2,...,ω N ] T That is the approximate eigenvector of the judgment matrix M;

[0092] d) According to Mω=λω, the characteristic root is calculated as follows

[0093]

[0094] λ is the approximate value of the maximum eigenvalue of the judgment matrix M.

[0095] The sum method actually normalizes the column vectors of the judgment matrix M and takes the average value as the eigenvector of the judgment matrix M. Because when the judgment matrix M is a consistency matrix, each of its column vectors is an eigenvector. Therefore, if the inconsistency of the judgment matrix M is not serious, it is reasonable to take the average value of the column vectors (normalized) of the judgment matrix M as the approximate eigenvector.

[0096] e) Judgment matrix consistency test

[0097] The pairwise comparison matrix is ​​usually not a consistency matrix, but in order to use its eigenvector corresponding to the largest eigenroot as the weight vector of the compared factors, its inconsistency should be within the allowable range.

[0098] Define the consistency ratio C R :

[0099]

[0100]

[0101] Where: C I is the consistency index. I = 0, the judgment matrix M is the consistency matrix, C I The larger the value, the more serious the inconsistency of the judgment matrix M. Since the sum of the N characteristic roots of the judgment matrix M is exactly equal to N, C I Equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ; R I is a random consistency indicator. Calculate R I The process is: for a fixed N, randomly construct a positive reciprocal matrix M', whose element m' ij Randomly select values ​​from 1 to 9, 1 / 1 to 1 / 9, and calculate C of M' I As you can imagine, M′ is very inconsistent. I So construct quite a lot of M', using their C I The average value of R I Saaty calculated R for different N using 100-500 samples M′ I The values ​​are shown in the table below.

[0102] Table 2 Random consistency index corresponding to each order

[0103] N 1 2 3 4 5 6 7 8 9 10 11 <![CDATA[R I ]]> 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0104] When the consistency ratio C R When <0.1, it means that the judgment matrix is ​​constructed reasonably and its eigenvector can be used as the weight in the above steps; otherwise, the judgment matrix needs to be adjusted to satisfy the consistency test.

[0105] Based on the above discussion process, the weight coefficient can be obtained by establishing the judgment matrix M.

[0106] 5) Determine the score of the solution layer for the target layer:

[0107] Suppose the judgment matrix (pairwise comparison matrix) of criterion layer B on target layer A is M BA , which requires judging the various factors of importance comparison scale, that is, various criteria, according to M BA The calculated eigenvector V BA It is the weight of each indicator of the criterion layer in the energy storage applicability score of the target layer; similarly, let the judgment matrix of indicator layer C to criterion layer B be M CB , the characteristic vector V of the index layer to the criterion layer can be calculated CB ; In particular, the judgment matrix of D for the i-th criterion of the indicator layer C is M DCi , where the i-th criterion corresponding to the index of each type of energy storage that needs to be judged for the importance comparison scale is based on M DCi The calculated eigenvector V DCi That is, the relative score of the index corresponding to the ith criterion of each type of energy storage. The eigenvector V corresponding to each criterion DCi The following combination gives the relative scoring matrix V of each type of energy storage for each criterion: DC .

[0108] V DC =[V DC1 V DC2 … V DCN ] (10)

[0109] Where V DC，ji It is the relative score of the i-th indicator corresponding to the j-th energy storage.

[0110] Therefore, the score V of solution layer D on target layer A is DA , that is, the applicability score of each type of energy storage can be calculated as follows:

[0111] V DB =V DC V CB (11)

[0112] V DA =V DB V BA (12)

[0113] It should be noted here that if the economic and technical parameters of each type of energy storage remain unchanged, the constructed judgment matrix M DC Should be the same, the corresponding relative scores of each type of energy storage in each indicator V DC It should be unchanged. However, in the applicability scoring of different scenarios, the judgment matrix MCB and M BA The weight of each criterion on the suitability score should change accordingly. CB and V BA Therefore, considering different power grid application scenarios, the main modification criteria are to the applicability scoring matrix M CB and M BA

[0114] 6) Calculate the value of electrochemical energy storage:

[0115] Take the peak load scenario as an example.

[0116] First, according to the needs of the peak-shaving scenario, the applicability judgment matrix of each criterion and the index-criterion judgment matrix under the peak-shaving scenario are constructed, as shown in Tables 3 to 6, among which the continuous discharge time, energy cost and safety are given greater weights.

[0117] Table 3 Peak load criterion judgment matrix

[0118] Guidelines technical Economical Applicability technical 1.00 0.20 1.00 Economical 5.00 1.00 5.00 Applicability 1.00 0.20 1.00

[0119] Table 4 Technical indicator judgment matrix

[0120] Technical indicators Power level Cycle life Continuous discharge time Response speed Power level 1.00 1.00 0.20 5.00 Cycle life 1.00 1.00 0.20 5.00 Continuous discharge time 5.00 5.00 1.00 7.00 Response speed 0.20 0.20 0.14 1.00

[0121] Table 5 Economic index judgment matrix

[0122] Economic indicators Power cost Energy cost System efficiency Self-consumption rate Power cost 1.00 0.14 0.33 0.33 Energy cost 7.00 1.00 5.00 5.00 System efficiency 3.00 0.20 1.00 1.00 Self-consumption rate 3.00 0.20 1.00 1.00

[0123] Table 6 Applicability index judgment matrix

[0124] Applicable indicators Security Technology maturity Site flexibility Environmental impact Security 1.00 1.00 5.00 3.00 Technology maturity 1.00 1.00 5.00 3.00 Site flexibility 0.20 0.20 1.00 0.33 Environmental impact 0.33 0.33 3.00 1.00

[0125] The weight of each criterion on applicability is calculated based on Tables 3 to 6, as shown in Tables 7 to 10.

[0126] Table 7 Weights of each criterion

[0127] Guidelines technical Economical Applicability Weight 0.14 0.71 0.14

[0128] Table 8 Technical Indicator Weights

[0129] Technical indicators Power level Cycle life Continuous discharge time Response speed Weight 0.17 0.17 0.61 0.05

[0130] Table 9 Economic index weights

[0131] Economic indicators Power cost Energy cost System efficiency Self-consumption rate Weight 0.06 0.63 0.15 0.15

[0132] Table 10 Weights of Applicability Indicators

[0133] Applicable indicators Security Technology maturity Site flexibility Environmental impact Weight 0.39 0.39 0.07 0.15

[0134] The relative scores of various indicators of various types of energy storage are shown in Table 11.

[0135] Table 11 Relative scores of various indicators of various types of energy storage

[0136]

[0137] Finally, the weights of each indicator and the relative scores of each type of energy storage indicator are used to calculate the final energy storage applicability score, such as Figure 2 shown.

[0138] from Figure 2 It can be seen that lead-carbon battery energy storage is the most suitable for grid peak-shaving demand scenarios; in addition, the applicability of lithium iron phosphate battery energy storage, ternary lithium battery energy storage, advanced compressed air energy storage, all-vanadium liquid flow battery energy storage and sodium-sulfur battery energy storage decreases in turn; the applicability of high-speed flywheel energy storage is relatively low.

[0139] To summarize, the embodiment of the present invention proposes a flexible resource value assessment method suitable for a high proportion of new energy power systems based on the hierarchical analysis method, which solves the problem that previous flexible resource value assessments have rarely considered multiple dimensions such as application, technology, and economy. It provides support for comprehensively and objectively reflecting the application value of flexible resources in different application scenarios and promotes the large-scale application of flexible resources in power systems.

[0140] Device Embodiment

[0141] According to an embodiment of the present invention, a flexible resource value assessment device applicable to a new energy power system is provided. Figure 4 is a schematic diagram of a flexible resource value assessment device applicable to a new energy power system according to an embodiment of the present invention. Figure 4 As shown, the flexible resource value assessment device applicable to the new energy power system according to the embodiment of the present invention specifically includes:

[0142] The division module 40 is used to obtain multiple typical application scenarios of flexible resources in the power system, and based on the multiple typical application scenarios, divide the technical requirements for flexible resources from top to bottom into a target layer, a criterion layer, an indicator layer and a solution layer; wherein the multiple typical application scenarios specifically include: peak regulation scenarios, frequency regulation scenarios, voltage regulation scenarios and line congestion management scenarios; the target layer specifically includes: evaluation targets; the criterion layer specifically includes: multiple criteria affecting the evaluation of the evaluation targets; the indicator layer specifically includes: multiple evaluation indicators corresponding to each criterion; the solution layer specifically includes: multiple solutions to be evaluated.

[0143] The calculation module 42 is used to determine the first judgment matrix and weight coefficient of the criterion layer for the target layer, determine the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, determine the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer, and determine the relative score of the solution layer for the indicator layer based on the third judgment matrix and weight coefficient. The calculation module 42 is specifically used to:

[0144] In combination with the flexibility requirements of typical application scenarios, the influence of multiple factors in the criterion layer on the target layer is determined, and a first judgment matrix of the criterion layer on the target layer is determined based on the influence, and the importance of different factors in the first judgment matrix is ​​compared by a nine-level scaling method, wherein the rows and columns of the first judgment matrix are the total number of multiple factors in the criterion layer;

[0145] Solving the characteristic roots and characteristic vectors for the constructed first judgment matrix, and when it is determined that the first judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to obtain a weight coefficient of the criterion layer to the target layer;

[0146] Combined with the flexibility requirements of typical application scenarios, determine the influence of multiple factors in the indicator layer on the criterion layer, determine a second judgment matrix of the indicator layer on the criterion layer based on the influence, and compare the importance of different factors in the second judgment matrix by a nine-level scaling method, wherein the rows and columns of the second judgment matrix are the total number of multiple factors in the indicator layer;

[0147] Solving the characteristic roots and characteristic vectors for the constructed second judgment matrix, and when it is determined that the second judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to determine the weight coefficient of the indicator layer to the criterion layer;

[0148] For each indicator of the indicator layer, according to the technical and economic characteristics of flexible resources, determine the third judgment matrix of the scheme layer for the current indicator, and compare the importance of different factors in the third judgment matrix through the nine-level scaling method, wherein the rows and columns of the third judgment matrix are the total number of schemes;

[0149] Solving the characteristic roots and characteristic vectors for the constructed third judgment matrix, and when it is determined that the third matrix satisfies the consistency check, using the characteristic vector as a weight vector to determine the weight coefficient of the solution layer for the current indicator;

[0150] After determining whether the scheme layer has determined the weight coefficients for each indicator of the indicator layer, the weight coefficients of the scheme layer for different indicators are combined to obtain the relative score of the scheme layer for the indicator layer.

[0151] The first judgment matrix, the second judgment matrix and the third judgment matrix can be determined according to formulas 1-3:

[0152]

[0153] m ij =1 / m ji Formula 2;

[0154] m ii =1 Formula 3;

[0155] Where N is the total number of factors, and the element m in the matrix M is ij is the importance comparison value of factor i to factor j;

[0156] According to Formula 4, each element of the judgment matrix M is normalized according to each column vector, and the normalized matrix is ​​summed row by row according to Formula 5. It is normalized again according to Formula 6, and the approximate value of the maximum characteristic root of the judgment matrix M is calculated according to Formula 7:

[0157]

[0158]

[0159]

[0160]

[0161] Among them, ω=[ω1,ω2,...,ω N ] T It is the approximate eigenvector of the judgment matrix M, and λ is the approximate value of the maximum eigenroot of the judgment matrix M.

[0162] When performing consistency check, for a fixed N, a positive reciprocal matrix M′ is randomly constructed, and the element m′ of the positive reciprocal matrix M′ is ij is 1 to 9, randomly select a value from 1 / 1 to 1 / 9, and calculate the consistency index C of M′ I , so construct a certain number of M′, calculate all C I The average value is taken as the random consistency index R I ;

[0163] Perform consistency check according to Formula 8 and Formula 9:

[0164]

[0165]

[0166] Where: C I is the consistency index; if the sum of the N characteristic roots of the judgment matrix M is equal to N, then C I It is equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ;

[0167] In C l= 0, the judgment matrix M is a consistency matrix, where C I The larger the value, the more serious the inconsistency of the judgment matrix M.

[0168] The evaluation module 44 is used to comprehensively determine the relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer, and finally determine the score of the solution layer to the target layer, and judge the value of different flexible resources in the current application scenario based on the score.

[0169] The embodiment of the present invention is a device embodiment corresponding to the above-mentioned method embodiment. The specific operations of each module can be understood by referring to the description of the method embodiment, which will not be repeated here.

[0170] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0171] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0172] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0173] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0174] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0175] It should be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, system or computer program product. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0176] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0179] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0180] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0181] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0182] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0183] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0184] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0185] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A flexible resource value assessment method applicable to a new energy power system, characterized in that: include: Acquire multiple typical application scenarios of flexible resources in the power system, and divide the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer and a solution layer from top to bottom based on the multiple typical application scenarios; wherein the multiple typical application scenarios specifically include: peak regulation scenario, frequency regulation scenario, voltage regulation scenario and line congestion management scenario; Determine the first judgment matrix and weight coefficient of the criterion layer for the target layer, determine the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, determine the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer, and determine the relative score of the solution layer for the indicator layer based on this; specifically include: In combination with the flexibility requirements of typical application scenarios, the influence of multiple factors in the criterion layer on the target layer is determined, and a first judgment matrix of the criterion layer on the target layer is determined based on the influence, and the importance of different factors in the first judgment matrix is ​​compared by a nine-level scaling method, wherein the rows and columns of the first judgment matrix are the total number of multiple factors in the criterion layer; Solving the characteristic roots and characteristic vectors for the constructed first judgment matrix, and when it is determined that the first judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to obtain a weight coefficient of the criterion layer to the target layer; Combined with the flexibility requirements of typical application scenarios, determine the influence of multiple factors in the indicator layer on the criterion layer, determine a second judgment matrix of the indicator layer on the criterion layer based on the influence, and compare the importance of different factors in the second judgment matrix by a nine-level scaling method, wherein the rows and columns of the second judgment matrix are the total number of multiple factors in the indicator layer; Solving the characteristic roots and characteristic vectors for the constructed second judgment matrix, and when it is determined that the second judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to determine the weight coefficient of the indicator layer to the criterion layer; For each indicator of the indicator layer, according to the technical and economic characteristics of flexible resources, determine the third judgment matrix of the scheme layer for the current indicator, and compare the importance of different factors in the third judgment matrix through the nine-level scaling method, wherein the rows and columns of the third judgment matrix are the total number of schemes; Solving the characteristic root and characteristic vector of the constructed third judgment matrix, when it is determined that the third judgment matrix satisfies the consistency test, using the characteristic vector as the weight vector to determine the weight coefficient of the solution layer for the current indicator; After determining whether the scheme layer has determined the weight coefficients for each indicator of the indicator layer, the weight coefficients of different indicators of the scheme layer are combined to obtain the relative score of the scheme layer to the indicator layer; The first judgment matrix, the second judgment matrix and the third judgment matrix are determined according to formula 1-3: m ij =1 / m ji Formula 2; m ii =1 Formula 3; Where N is the total number of factors, and the element m in the matrix M is ij is the importance comparison value of factor i to factor j; According to Formula 4, each element of the judgment matrix M is normalized according to each column vector, the normalized matrix is ​​summed row by row according to Formula 5, normalized again according to Formula 6, and the approximate value of the maximum characteristic root of the judgment matrix M is calculated according to Formula 7; Among them, ω = [ω1, ω2,..., ω N ] T is the approximate eigenvector of the judgment matrix M, and λ is the approximate value of the maximum eigenroot of the judgment matrix M; The relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer are comprehensively considered to finally determine the score of the solution layer to the target layer. Based on the score, the value of different flexible resources in the current application scenario is judged.

2. The method according to claim 1, characterized in that The target layer specifically includes: evaluation targets; The criterion layer specifically includes: a plurality of criteria affecting the evaluation of the evaluation target; The indicator layer specifically includes: a plurality of evaluation indicators corresponding to each criterion; The solution layer specifically includes: multiple solutions to be evaluated.

3. The method according to claim 1, characterized in that: The consistency check of the first judgment matrix, the second judgment matrix and the third judgment matrix specifically includes: For a fixed N, a positive reciprocal matrix M′ is randomly constructed, and the element m′ of the positive reciprocal matrix M′ is ij is 1 to 9, randomly select a value from 1 / 1 to 1 / 9, and calculate the consistency index C of M′ I , so construct a certain number of M′, calculate all C I The average value is taken as the random consistency index R I ; Perform consistency check according to Formula 8 and Formula 9: Where: C I is the consistency index; if the sum of the N characteristic roots of the judgment matrix M is equal to N, then C I It is equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ; In C I = 0, the judgment matrix M is a consistency matrix, where C I The larger the value, the more serious the inconsistency of the judgment matrix M.

4. A flexible resource value assessment device applicable to a new energy power system, characterized in that: include: A division module is used to obtain multiple typical application scenarios of flexible resources in the power system, and based on the multiple typical application scenarios, divide the technical requirements for flexible resources into a target layer, a criterion layer, an indicator layer and a solution layer from top to bottom; the multiple typical application scenarios specifically include: peak regulation scenario, frequency regulation scenario, voltage regulation scenario and line congestion management scenario; The calculation module is used to determine the first judgment matrix and weight coefficient of the criterion layer for the target layer, determine the second judgment matrix and weight coefficient of the indicator layer for the criterion layer, determine the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer, and determine the relative score of the solution layer to the indicator layer based on the third judgment matrix and weight coefficient of the solution layer for each indicator of the indicator layer; the calculation module is specifically used to: In combination with the flexibility requirements of typical application scenarios, the influence of multiple factors in the criterion layer on the target layer is determined, and a first judgment matrix of the criterion layer on the target layer is determined based on the influence, and the importance of different factors in the first judgment matrix is ​​compared by a nine-level scaling method, wherein the rows and columns of the first judgment matrix are the total number of multiple factors in the criterion layer; Solving the characteristic roots and characteristic vectors for the constructed first judgment matrix, and when it is determined that the first judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to obtain a weight coefficient of the criterion layer to the target layer; Combined with the flexibility requirements of typical application scenarios, determine the influence of multiple factors in the indicator layer on the criterion layer, determine a second judgment matrix of the indicator layer on the criterion layer based on the influence, and compare the importance of different factors in the second judgment matrix by a nine-level scaling method, wherein the rows and columns of the second judgment matrix are the total number of multiple factors in the indicator layer; Solving the characteristic roots and characteristic vectors for the constructed second judgment matrix, and when it is determined that the second judgment matrix satisfies the consistency test, using the characteristic vector as a weight vector to determine the weight coefficient of the indicator layer to the criterion layer; For each indicator of the indicator layer, according to the technical and economic characteristics of flexible resources, determine the third judgment matrix of the scheme layer for the current indicator, and compare the importance of different factors in the third judgment matrix through the nine-level scaling method, wherein the rows and columns of the third judgment matrix are the total number of schemes; Solving the characteristic root and characteristic vector of the constructed third judgment matrix, when it is determined that the third judgment matrix satisfies the consistency test, using the characteristic vector as the weight vector to determine the weight coefficient of the solution layer for the current indicator; After determining whether the scheme layer has determined the weight coefficients for each indicator of the indicator layer, the weight coefficients of different indicators of the scheme layer are combined to obtain the relative score of the scheme layer to the indicator layer; The first judgment matrix, the second judgment matrix and the third judgment matrix are determined according to formula 1-3: m ij =1 / m ji Formula 2; m ii =1 Formula 3; Where N is the total number of factors, and the element m in the matrix M is ij is the importance comparison value of factor i to factor j; According to Formula 4, each element of the judgment matrix M is normalized according to each column vector, the normalized matrix is ​​summed row by row according to Formula 5, normalized again according to Formula 6, and the approximate value of the maximum characteristic root of the judgment matrix M is calculated according to Formula 7; Among them, ω=[ω1,ω2,...,ω N ] T is the approximate eigenvector of the judgment matrix M, and λ is the approximate value of the maximum eigenroot of the judgment matrix M; The evaluation module is used to comprehensively consider the relative score of the solution layer to the indicator layer, the weight coefficient of the indicator layer to the criterion layer, and the weight coefficient of the criterion layer to the target layer, and finally determine the score of the solution layer to the target layer, and judge the value of different flexible resources in the current application scenario based on the score.

5. The device according to claim 4, characterized in that The target layer specifically includes: evaluation targets; The criterion layer specifically includes: a plurality of criteria affecting the evaluation of the evaluation target; The indicator layer specifically includes: a plurality of evaluation indicators corresponding to each criterion; The solution layer specifically includes: multiple solutions to be evaluated.

6. The device according to claim 4, characterized in that The calculation module is specifically used for: For a fixed N, a positive reciprocal matrix M′ is randomly constructed, and the element m′ of the positive reciprocal matrix M′ is ij is 1 to 9, randomly select a value from 1 / 1 to 1 / 9, and calculate the consistency index C of M′ I , so construct a certain number of M′, calculate all C I The average value is taken as the random consistency index R I ; Perform consistency check according to Formula 8 and Formula 9: Where: C I is the consistency index; if the sum of the N characteristic roots of the judgment matrix M is equal to N, then C I It is equivalent to the absolute value of the average value of the remaining N-1 characteristic roots except λ; In C I = 0, the judgment matrix M is a consistency matrix, where C I The larger the value, the more serious the inconsistency of the judgment matrix M.

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