Energy storage capacity configuration evaluation system, method and equipment and storage medium

Abstract

The invention relates to an energy storage capacity configuration evaluation system, method and device and a storage medium, and the system constructs an evaluation index module for constructing a first-level evaluation index and a second-level evaluation index according to a plurality of energy storage power station parameters. And the initial index weight determination module is used for performing assignment on each primary evaluation index and each secondary evaluation index by constructing a judgment matrix according to an analytic hierarchy process, and determining initial weights of the primary evaluation indexes and the secondary evaluation indexes. And the target index weight obtaining module is used for correcting the initial weights of the first-level evaluation index and the second-level evaluation index. And the fuzzy comprehensive evaluation module is used for constructing a fuzzy comprehensive evaluation model based on the target weight. And the evaluation result determination module is used for determining an energy storage capacity configuration evaluation result based on the fuzzy comprehensive evaluation model. The problem of low reliability of energy storage power station capacity configuration evaluation in the prior art when facing a multi-factor and multi-level complex problem is solved.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage capacity configuration evaluation, and particularly to an integrated evaluation system, method, device and storage medium for energy storage capacity configuration. Background Art

[0002] With the growing global demand for clean energy and the increasing proportion of renewable energy (such as wind energy and solar energy) in the power system, one of the main functions of energy storage power stations is to release electrical energy during peak power demand to supplement the power supply capacity of the power grid; at the same time, store excess electrical energy during low power periods to improve the flexibility and reliability of the power system. However, the effective operation of energy storage power stations highly depends on reasonable energy storage capacity configuration.

[0003] As an important means to balance the power grid load, if the energy storage capacity configuration of the energy storage power station is too low, it will not be able to effectively cope with the fluctuations of new energy, which may lead to unstable power grid frequency and even cause power accidents. If the capacity configuration is too high while the actual demand is insufficient, this will not only increase the initial investment cost and later maintenance cost of the power station, resulting in a large amount of equipment and capital waste, but also make the operation efficiency of the power station low, the return period long, and even may face the risk of long-term losses.

[0004] Currently, when evaluating the energy storage capacity configuration of energy storage power stations in the face of multi-factor and multi-level complex problems, there is a problem of low reliability. Summary of the Invention

[0005] Embodiments of the present application provide an energy storage capacity configuration evaluation system, method, device and storage medium to solve the problem of low reliability in the evaluation of energy storage capacity configuration of energy storage power stations in the related art when facing multi-factor and multi-level complex problems.

[0006] In the first aspect, embodiments of the present application provide an energy storage capacity configuration evaluation system, including:

[0007] A construction evaluation index module, configured to construct a primary evaluation index and a secondary evaluation index according to a plurality of energy storage power station parameters; wherein, the secondary evaluation index belongs to the primary evaluation index;

[0008] An initial index weight determination module, configured to assign values to each of the primary evaluation index and the secondary evaluation index by constructing a judgment matrix according to the analytic hierarchy process, and determine the initial weights of the primary evaluation index and the secondary evaluation index;

[0009] A target index weight acquisition module, configured to correct the initial weights of the primary evaluation index and the secondary evaluation index according to the entropy method, and obtain the target weights of the primary evaluation index and the secondary evaluation index;

[0010] Construct a fuzzy comprehensive evaluation module for constructing a fuzzy comprehensive evaluation model based on the target weights, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indicators to the primary evaluation indicators.

[0011] A module for determining the evaluation result is used to determine the evaluation result of the energy storage capacity configuration based on the current parameters of several energy storage power stations and the fuzzy comprehensive evaluation model.

[0012] In one embodiment, when the module for determining the initial index weights determines the initial index weights of the primary evaluation indicators and the secondary evaluation indicators by constructing a judgment matrix for each of the primary evaluation indicators and the secondary evaluation indicators according to the analytic hierarchy process, it is used for:

[0013] Construct judgment matrices for the primary evaluation indicators and the secondary evaluation indicators respectively by the Saaty scale method, and based on the judgment matrices, assign values according to the importance degree among the primary evaluation indicators and according to the importance degree among the secondary evaluation indicators belonging to each primary evaluation indicator, so as to obtain the assignment results of each primary evaluation indicator and the assignment results of each secondary evaluation indicator;

[0014] Based on the assignment results of the primary evaluation indicators, calculate the eigenvectors of each primary evaluation indicator, and obtain the initial weights of each primary evaluation indicator through the initial weight formula;

[0015] Based on the assignment results of the secondary evaluation indicators, calculate the eigenvectors of each secondary evaluation indicator, and obtain the initial weights of each secondary evaluation indicator through the initial weight formula.

[0016] In one embodiment, when the module for determining the initial index weights calculates the eigenvectors of each primary evaluation indicator based on the assignment results of the primary evaluation indicators and obtains the initial weights of each primary evaluation indicator through the initial weight formula, it is used for:

[0017] Based on the assignment results of the primary evaluation indicators, through the formula

[0018] Calculate the eigenvectors of each primary evaluation indicator, and through the initial weight formula Obtain the initial weights of each primary evaluation indicator;

[0019] Where, represents the eigenvector of the i-th indicator, and a ij represents the score of the indicator a i for the indicator a j in the analytic hierarchy process judgment matrix.

[0020] In one embodiment, the system further includes a decision matrix construction module, which is used for:

[0021] Normalize the positive and negative indicators in the original data to obtain the normalized data;

[0022] Based on the normalized data, construct a decision matrix according to the first-level evaluation indicators and the second-level evaluation indicators.

[0023] In one embodiment, when the target index weight acquisition module corrects the initial weights of the first-level evaluation indicators and the second-level evaluation indicators according to the entropy method to obtain the target weights of the first-level evaluation indicators and the second-level evaluation indicators, it is used for:

[0024] Calculate the proportion of each first-level evaluation indicator and each second-level evaluation indicator according to the decision matrix;

[0025] Based on the proportion of each first-level evaluation indicator and each second-level evaluation indicator, calculate the entropy value of each first-level evaluation indicator and each second-level evaluation indicator;

[0026] Based on the entropy value of each first-level evaluation indicator and each second-level evaluation indicator, calculate the difference coefficient of each first-level evaluation indicator and each second-level evaluation indicator;

[0027] Based on the difference coefficient of each first-level evaluation indicator and each second-level evaluation indicator, modify the initial weights of the first-level evaluation indicators and the second-level evaluation indicators to obtain the modified weights of the first-level evaluation indicators and the second-level evaluation indicators;

[0028] Normalize the modified weights of the first-level evaluation indicators and the second-level evaluation indicators to obtain the target weights of the first-level evaluation indicators and the second-level evaluation indicators.

[0029] In one embodiment, when the target index weight acquisition module modifies the initial weights of the first-level evaluation indicators and the second-level evaluation indicators based on the difference coefficient of each first-level evaluation indicator and each second-level evaluation indicator to obtain the modified weights of the first-level evaluation indicators and the second-level evaluation indicators, it is used for:

[0030] Based on the difference coefficient of each first-level evaluation indicator and each second-level evaluation indicator, through the formula Z j =w i ×d j (j = 1, 2…n) modify the initial weights of the first-level evaluation indicators and the second-level evaluation indicators to obtain the modified weights of the first-level evaluation indicators and the second-level evaluation indicators;

[0031] Wherein, Z j represents the target weight, w i represents the initial weight, d j represents the difference coefficient.

[0032] In one embodiment, a fuzzy comprehensive evaluation module is constructed. When constructing a fuzzy comprehensive evaluation model based on the target weight, it is used for:

[0033] Taking the primary evaluation indicators and the secondary evaluation indicators as the factor set, and taking the preset evaluation grades as the evaluation set;

[0034] Constructing a membership degree fuzzy relationship matrix between the factor set and the evaluation set;

[0035] Based on the membership degree fuzzy relationship matrix, constructing a fuzzy comprehensive evaluation model according to the target weight.

[0036] In a second aspect, an embodiment of the present application provides a method for evaluating energy storage capacity configuration. The method includes:

[0037] Constructing primary evaluation indicators and secondary evaluation indicators according to several energy storage power station parameters; wherein, the secondary evaluation indicators belong to the primary evaluation indicators;

[0038] According to the analytic hierarchy process, assigning values to each of the primary evaluation indicators and the secondary evaluation indicators by constructing a judgment matrix, and determining the initial weights of the primary evaluation indicators and the secondary evaluation indicators;

[0039] According to the entropy value method, correcting the initial weights of the primary evaluation indicators and the secondary evaluation indicators to obtain the target weights of the primary evaluation indicators and the secondary evaluation indicators;

[0040] Based on the target weights, constructing a fuzzy comprehensive evaluation model, wherein the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indicators to the primary evaluation indicators,

[0041] According to the current several energy storage power station parameters, determining the evaluation result of the energy storage capacity configuration based on the fuzzy comprehensive evaluation model.

[0042] In a third aspect, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements an energy storage capacity configuration evaluation system as described in the first aspect above.

[0043] Fourthly, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements an energy storage capacity configuration evaluation system as described in the first aspect above.

[0044] The energy storage capacity configuration evaluation system, method, device and storage medium provided by the embodiments of the present application at least have the following technical effects.

[0045] By constructing an evaluation index module for constructing a primary evaluation index and a secondary evaluation index according to several energy storage power station parameters, where the primary evaluation index belongs to the secondary evaluation index; determining an initial index weight module for assigning values to each primary evaluation index and secondary evaluation index by constructing a judgment matrix according to the analytic hierarchy process to determine the initial weights of the primary evaluation index and the secondary evaluation index; obtaining a target index weight module for correcting the initial weights of the primary evaluation index and the secondary evaluation index according to the entropy method to obtain the target weights of the primary evaluation index and the secondary evaluation index; constructing a fuzzy comprehensive evaluation module for constructing a fuzzy comprehensive evaluation model based on the target weights, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation index to the primary evaluation index; determining an evaluation result module for determining the energy storage capacity configuration evaluation result based on the fuzzy comprehensive evaluation model according to the current several energy storage power station parameters. The accurate evaluation of the energy storage capacity configuration is realized, the reliability of the energy storage capacity configuration evaluation is improved, and the problem of low reliability in the energy storage power station capacity configuration evaluation in the related technology when facing complex problems with multiple factors and multiple levels is solved.

[0046] The details of one or more embodiments of the present application are set forth in the following drawings and description to make the other features, objects, and advantages of the present application more comprehensible. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0048] Figure 1 is a system structure block diagram of an energy storage capacity configuration evaluation system shown according to an exemplary embodiment;

[0049] Figure 2 is a schematic structural diagram of the hierarchical relationship between various indicators shown according to an exemplary embodiment;

[0050] Figure 3 is a flowchart of an energy storage capacity configuration evaluation method shown according to an exemplary embodiment;

[0051] Figure 4It is a structural block diagram of an electronic device shown according to an exemplary embodiment. Detailed implementation manners

[0052] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts belong to the scope of protection of the present application.

[0053] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, the present application can also be applied to other similar scenarios based on these drawings without creative efforts. In addition, it can also be understood that although the efforts made in such a development process may be complex and time-consuming, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0054] Referring to "embodiments" in the present application means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0055] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the ordinary meanings understood by those with ordinary skills in the technical field to which this application belongs. The words such as "a", "an", "one kind", "the" and the like involved in this application do not indicate a limitation in quantity and may represent a singular or plural number. The terms "include", "comprise", "have" and any variations thereof involved in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The words such as "connect", "be connected", "couple" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in this application means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the front and rear associated objects. The terms "first", "second", "third" and the like involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0056] In this article, it should be understood that the terms involved may be technical means for implementing a part of the present invention or other summary technical terms. For example, the terms may include:

[0057] Entropy method: By calculating the information entropy of each index to determine its weight. The larger the information entropy, the more information the index contains and the greater the weight. This method is based on data and has high objectivity and reliability.

[0058] Analytical Hierarchy Process (AHP): By hierarchically decomposing the problem, a hierarchical structure model is constructed, and the importance is compared at each level to determine the weight of the index. This method ensures the accuracy of the weight through mathematical operations.

[0059] In a first aspect, an embodiment of the present application provides an energy storage capacity configuration evaluation system, which is applied to the evaluation of the energy storage capacity configuration of a thermal power-energy storage combined peak shaving project. Figure 1 is a system structure block diagram of an energy storage capacity configuration evaluation system shown according to an exemplary embodiment, as Figure 1 shown, the system includes:

[0060] Construct an evaluation index module for constructing primary evaluation indexes and secondary evaluation indexes according to a number of energy storage power station parameters; wherein, the secondary evaluation indexes belong to the primary evaluation indexes.

[0061] Determine the initial index weight module for assigning values to each of the primary evaluation indexes and the secondary evaluation indexes by constructing a judgment matrix according to the analytic hierarchy process, and determining the initial weights of the primary evaluation indexes and the secondary evaluation indexes.

[0062] Obtain the target index weight module for correcting the initial weights of the primary evaluation indexes and the secondary evaluation indexes according to the entropy method, and obtaining the target weights of the primary evaluation indexes and the secondary evaluation indexes.

[0063] Construct a fuzzy comprehensive evaluation module for constructing a fuzzy comprehensive evaluation model based on the target weights, wherein the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indexes to the primary evaluation indexes.

[0064] Determine the evaluation result module for determining the evaluation result of the energy storage capacity configuration based on the current number of energy storage power station parameters and the fuzzy comprehensive evaluation model.

[0065] In summary, the embodiment of the present application provides an energy storage capacity configuration evaluation system, which realizes the accurate evaluation of the energy storage capacity configuration, improves the reliability of the energy storage capacity configuration evaluation, and solves the problem of low reliability in the evaluation of the energy storage power station capacity configuration in the face of multi-factor and multi-level complex problems through the mutual cooperation among the constructed evaluation index module, the determined initial index weight module, the obtained target index weight module, the constructed fuzzy comprehensive evaluation module, and the determined evaluation result module.

[0066] In an embodiment, the constructed evaluation index module is used to construct primary evaluation indexes and secondary evaluation indexes according to a number of energy storage power station parameters; wherein, the secondary evaluation indexes belong to the primary evaluation indexes. Specifically, it includes:

[0067] Optionally, Table 1 is the corresponding table of secondary evaluation indexes belonging to primary evaluation indexes. As shown in Table 1, the constructed primary evaluation indexes include energy storage basic indexes, energy efficiency indexes, power station and battery indexes, economic indexes, energy storage development indexes, and environmental protection indexes.

[0068] The secondary evaluation indexes of energy storage method, cell specification, battery type, battery cooling system, and capacity ratio belong to the primary energy storage basic indexes;

[0069] The secondary evaluation indexes of power station comprehensive efficiency, energy storage loss rate, charge and discharge conversion efficiency, power station distribution loss rate, and power station power consumption rate belong to the primary energy efficiency indexes;

[0070] The secondary evaluation criteria of power station outage factor, battery cluster failure rate, power station dispatching response success rate, power station availability factor, and power station utilization factor belong to the first-level power station and battery indicators;

[0071] The secondary evaluation indicators of unit investment cost, power station revenue model, and investment payback period belong to the first-level economic indicators;

[0072] The secondary evaluation indicators of energy storage power station development policies, government support policies, and power industry development needs belong to the first-level energy storage development indicators;

[0073] The secondary evaluation indicators of floor area per unit capacity, pollutant emission reduction benefits, and battery recovery rate belong to the first-level environmental protection indicators.

[0074] Table 1: Correspondence Table of Secondary Evaluation Indicators Belonging to Primary Evaluation Indicators

[0075]

[0076]

[0077] Effect analysis of constructing the evaluation index module: The primary indicators comprehensively cover multiple aspects such as technical performance, economic benefits, and environmental impacts, ensuring that the comprehensive benefits of energy storage capacity configuration can be comprehensively evaluated, making the evaluation results closer to the true situation of the evaluation object. It helps decision-makers comprehensively understand the comprehensive performance of the energy storage system in the project, so as to make more reasonable decisions. In addition, the primary and secondary indicators have clear guiding directions, which can guide the optimization and improvement directions of energy storage technologies. It helps to guide the better application of energy storage technologies in subsequent projects and improve the benefits of thermal power peak shaving. In addition, each indicator is operable to improve the evaluation efficiency and evaluation reliability. At the same time, each indicator also has environmental protection characteristics, reflecting the requirements in the directions of environmental protection and resource utilization. For the primary and secondary indicators, by soliciting opinions from multiple experts in this technical field, the importance of the selected technical indicators for the current evaluation is reflected from a statistical perspective.

[0078] In one embodiment, an initial index weight module is determined, which is used to assign values to each of the primary evaluation indicators and the secondary evaluation indicators by constructing a judgment matrix according to the analytic hierarchy process, and determine the initial weights of the primary evaluation indicators and the secondary evaluation indicators. Specifically, it includes:

[0079] By using the Saaty scale method, construct judgment matrices for the primary evaluation indicators and the secondary evaluation indicators respectively, and based on the judgment matrices, assign values according to the importance levels among the primary evaluation indicators, and assign values according to the importance levels among the secondary evaluation indicators belonging to the primary evaluation indicators, to obtain the assignment results of each primary evaluation indicator and the assignment results of each secondary evaluation indicator;

[0080] Based on the assignment results of the first-level evaluation indicators, calculate the eigenvectors of each of the first-level evaluation indicators, and obtain the initial weights of each of the first-level evaluation indicators through the initial weight formula;

[0081] Based on the assignment results of the second-level evaluation indicators, calculate the eigenvectors of each of the second-level evaluation indicators, and obtain the initial weights of each of the second-level evaluation indicators through the initial weight formula.

[0082] Optionally, first, Figure 2 is a schematic structural diagram of the hierarchical relationship between indicators shown in an exemplary embodiment. As Figure 2 shown, it is necessary to clarify the hierarchical relationship between indicators before constructing the judgment matrix. For the evaluation indicators of this application, "Comprehensive Evaluation of Energy Storage Capacity Configuration for the Thermal Power - Energy Storage Combined Peak Shaving Project of Company C" is the target layer, 6 first-level indicators can be regarded as the criterion layer, and 24 second-level indicators can be regarded as the sub-criterion layer. Constructing the judgment matrix means making pairwise judgments on the indicators in the criterion layer and the sub-criterion layer. When constructing the judgment matrix, the score of indicator Ai for indicator Aj is aij, and the judgment matrix is shown in Table 2.

[0083] Table 2: Judgment Matrix

[0084]

[0085] The data in the matrix also needs to meet the following conditions:

[0086] a ij ×a ji = a ii = a jj = 1 (i, j = 1, 2…n)

[0087] When assigning values, it is necessary to make pairwise comparisons of each indicator, and use Saaty's 1 - 9 scale method and its reciprocal for assignment and comparison evaluation, as shown in Table 3 specifically.

[0088] Table 3: Saaty Scale Method

[0089]

[0090] In this embodiment, the judgment matrix of the first-level indicators is shown in Table 4,

[0091] Table 4: Judgment Matrix of First-Level Indicators

[0092]

[0093] As can be seen from Table 4, the energy storage basic indicators are the most important among the first-level indicators, followed by the environmental protection indicators, then the energy efficiency indicators, the importance of the economic indicators and the power station and battery indicators is weaker, and the importance of the energy storage development indicators is the lowest.

[0094] Table 5: Judgment Matrix of Energy Storage Basic Indexes

[0095]

[0096] As can be seen from Table 5, among the secondary indexes of the energy storage basic indexes, the importance degrees from large to small are: capacity ratio, battery cooling system, energy storage method, cell specification, battery type.

[0097] Table 6: Judgment Matrix of Energy Efficiency Indexes

[0098]

[0099] As can be seen from Table 6, among the energy efficiency indexes, the importance degrees from large to small are: energy storage loss rate, power station distribution loss rate, power station distribution loss rate, power station comprehensive efficiency, power station power consumption rate.

[0100] Table 7: Judgment Matrix of Power Station and Battery Indexes

[0101]

[0102] As can be seen from Table 7, among the power station and battery indexes, the importance degrees from large to small are: battery cluster failure rate, power station utilization factor, power station availability factor, power station outage factor, and power station dispatching response success rate.

[0103] Table 8: Judgment Matrix of Economic Indexes

[0104]

[0105] As can be seen from Table 8, among the economic indexes, the importance degrees from large to small are: investment payback period, unit investment cost, energy storage income policy.

[0106] Table 9: Judgment Matrix of Energy Storage Development Indexes

[0107]

[0108] As can be seen from Table 9, among the energy storage development indexes, the importance degrees from large to small are: energy storage power station development policy, government support policy, power industry development needs.

[0109] Table 10: Judgment Matrix of Environmental Protection Indexes

[0110]

[0111] As can be seen from Table 10, among the environmental protection indexes, the importance degrees from large to small are: pollutant emission reduction benefit, battery recovery rate, floor area per unit capacity.

[0112] Secondly, determine the index weights. The geometric mean method (root method) is selected to calculate the weights, and the steps are as follows:

[0113] (1) Calculate the geometric mean of each row of data in the judgment matrix to obtain an n-dimensional vector.

[0114]

[0115] Where represents the eigenvector of the i-th index, and a ij represents the index a in the judgment matrix of the analytic hierarchy process i for the index a j score.

[0116] (2) Normalize each vector to obtain the initial weight coefficient.

[0117]

[0118] Where represents the eigenvector of the i-th index, and a ij represents the index a in the judgment matrix of the analytic hierarchy process i for the index a j score.

[0119] According to the judgment matrix of the first-level index and the formula, the eigenvector can be obtained:

[0120] Where represents the eigenvector of the energy storage basic index, and a 1j represents the index a in the judgment matrix of the analytic hierarchy process 1 for the index a j score.

[0121] Similarly, the eigenvectors of each first-level evaluation index can be obtained,

[0122] and then the weights can be calculated as:

[0123]

[0124] Similarly, the initial weights of each first-level evaluation index, w 2 = 0.1231, w 3 = 0.1057, w 4 = 0.1025, w 5 = 0.0813, w 6 = 0.1449.

[0125] From this, the maximum eigenvalue λmax can be calculated as 6.367, and the consistency ratio CR is 0.058, which meets the consistency test. The results of the first-level index analytic hierarchy process are shown in Table 11.

[0126] Table 11: Analytical Hierarchy Process Results of First-Level Indicators

[0127]

[0128] As can be seen from Table 11, among the first-level indicators, the initial weight of the energy storage basic indicators is 0.4425, the initial weight of the energy efficiency indicators is 0.1231, the initial weight of the power station and battery indicators is 0.1057, the initial weight of the economic indicators is 0.1025, the initial weight of the energy storage development indicators is 0.0813, and the initial weight of the environmental protection indicators is 0.1449.

[0129] According to the calculation method of the first-level indicators, calculate the initial weights of the second-level indicators. The weight analysis of the second-level indicators of the energy storage basic indicators is shown in Table 12.

[0130] Table 12: Weight Analysis Results of Second-Level Indicators of Energy Storage Basic Indicators

[0131]

[0132]

[0133] As can be seen from Table 12, among the second-level indicators of the energy storage basic indicators, the initial weight of the energy storage method is 0.2363, the initial weight of the cell specification is 0.1408, the initial weight of the battery type is 0.0984, the initial weight of the battery cooling system is 0.472, and the initial weight of the capacity ratio is 0.4773.

[0134] The weight analysis of the second-level energy efficiency indicators is shown in Table 13.

[0135] Table 13: Weight Analysis Results of Second-Level Energy Efficiency Indicators

[0136]

[0137] As can be seen from Table 13, among the second-level energy efficiency indicators, the initial weight of the overall power station efficiency is 0.0798, the initial weight of the energy storage loss rate is 0.4104, the initial weight of the power station power consumption rate is 0.0583, the initial weight of the charge-discharge conversion efficiency is 0.2128, and the initial weight of the power station distribution loss rate is 0.2387.

[0138] The weight analysis of the second-level reliability indicators is shown in Table 14.

[0139] Table 14: Weight Analysis Results of Second-Level Reliability Indicators

[0140]

[0141]

[0142] As can be seen from Table 14, among the secondary indicators of reliability, the initial weight of the power station outage coefficient is 0.0616, the initial weight of the battery cluster failure rate is 0.3727, the initial weight of the power station dispatching response success rate is 0.0603, the initial weight of the power station availability factor is 0.1741, and the initial weight of the power station utilization factor is 0.3313.

[0143] The weight analysis of the secondary indicators of economy is shown in Table 15.

[0144] Table 15: Results of the weight analysis of the secondary indicators of economy

[0145]

[0146] As can be seen from Table 15, among the secondary indicators of economy, the initial weight of the unit investment cost is 0.309, the initial weight of the power station revenue model is 0.1095, and the initial weight of the investment payback period is 0.5815.

[0147] The weight analysis of the secondary indicators of the energy storage development index is shown in Table 16.

[0148] Table 16: Results of the weight analysis of the secondary indicators of the energy storage development index

[0149]

[0150] As can be seen from Table 16, among the secondary indicators of the energy storage development index, the initial weight of the energy storage power station development policy is 0.625, the initial weight of the government support policy is 0.2385, and the initial weight of the need for the development of the power industry is 0.1365.

[0151] The weight analysis of the environmental protection secondary indicators is shown in Table 17.

[0152] Table 17: Results of the weight analysis of the environmental protection secondary indicators

[0153]

[0154]

[0155] According to Table 17, among the secondary indicators of environmental protection, the initial weight of the pollutant emission reduction benefit is the largest, which is 0.5396, the initial weight of the battery recovery rate is 0.297, and the initial weight of the floor area per unit capacity is 0.1634.

[0156] Through the above calculations and analyses, the weights of each level of indicators are obtained, and the weights of each level of indicators are summarized. The results are shown in Table 18.

[0157] Table 18: Initial weight values of each level of indicators

[0158]

[0159] Effective effect analysis of the initial index weight module: By constructing a judgment matrix through the Analytic Hierarchy Process (AHP) and assigning values to the relative importance between indicators at all levels, the qualitative evaluation criteria can be transformed into quantitative data. This step ensures that the importance of each evaluation indicator is scientifically and reasonably quantified, avoiding subjective arbitrariness and improving the authority and credibility of the evaluation results. The determination of the initial weight provides a basis for subsequent weight correction and comprehensive evaluation. It reflects the relative importance of each evaluation indicator in the energy storage capacity configuration and provides a preliminary decision-making basis for decision-makers.

[0160] In one embodiment, the system further includes a decision matrix construction module for:

[0161] Normalize the positive and negative indicators in the original data to obtain the normalized data;

[0162] Based on the normalized data, construct a decision matrix according to the first-level evaluation indicators and the second-level evaluation indicators.

[0163] Optionally, assuming that there are m samples and n evaluation indicators in the original data, the following decision matrix can be established:

[0164]

[0165] For the obtained initial data, due to different dimensions or the existence of extreme values, it may affect subsequent processing and analysis. Therefore, it is necessary to normalize the original data. The normalization method for indicators is as follows:

[0166] Normalization of positive indicators:

[0167]

[0168] In the formula, X ij represents the score of the i-th sample for the j-th evaluation indicator.

[0169] Normalization of negative indicators:

[0170]

[0171] In the formula, X ij represents the score of the i-th sample for the j-th evaluation indicator.

[0172] The new matrix obtained after normalization is as follows:

[0173]

[0174] Analysis of the beneficial effects of the decision matrix construction module: Through normalization, the influence of different dimensions is eliminated, enabling all evaluation indicators to be compared on the same scale. It avoids unreasonable weight distribution caused by overly large or small values of certain indicators, improving the accuracy and reliability of subsequent analysis. It helps to more accurately calculate the proportions, entropy values, and difference coefficients of each indicator, ultimately enhancing the scientificity and rationality of the entire evaluation system.

[0175] In one embodiment, a fuzzy comprehensive evaluation module is constructed: It is used to construct a fuzzy comprehensive evaluation model based on the target weights. Among them, the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from secondary evaluation indicators to primary evaluation indicators. Specifically, it includes:

[0176] According to the decision matrix, calculate the proportion of each primary evaluation indicator and each secondary evaluation indicator;

[0177] Based on the proportions of each primary evaluation indicator and each secondary evaluation indicator, calculate the entropy value of each primary evaluation indicator and each secondary evaluation indicator;

[0178] Based on the entropy values of each primary evaluation indicator and each secondary evaluation indicator, calculate the difference coefficient of each primary evaluation indicator and each secondary evaluation indicator;

[0179] Based on the difference coefficients of each primary evaluation indicator and each secondary evaluation indicator, modify the initial weights of the primary evaluation indicators and secondary evaluation indicators to obtain the modified weights of the primary evaluation indicators and secondary evaluation indicators;

[0180] Perform normalization on the modified weights of the primary evaluation indicators and secondary evaluation indicators to obtain the target weights of the primary evaluation indicators and secondary evaluation indicators.

[0181] Optionally, before calculating the weights, first calculate the proportion of the indicators. The proportion of the i-th data of the j-th indicator is:

[0182]

[0183] In the formula, x′ ij represents the normalized score of the i-th sample for the j-th evaluation indicator.

[0184] Based on the proportion, the entropy value of the j-th indicator can be further obtained:

[0185]

[0186] In the formula, P ij represents the proportion of the i-th data of the j-th indicator, m represents the number of samples, and n represents the number of evaluation indicators.

[0187] The coefficient of variation is related to the entropy value. The larger the entropy value, the smaller the degree of dispersion of the index, and thus the smaller the coefficient of variation. The coefficient of variation of the j-th index can be calculated from the entropy value and is defined as:

[0188] d j =1 - e j (j = 1, 2…n)

[0189] In the formula, e j represents the entropy of the j-th index.

[0190] The initial weights of the indices can be corrected using the coefficient of variation:

[0191] Z j =w i ×d j (j = 1, 2…n)

[0192] In the formula, Z j is the corrected weight, w j is the weight of the j-th index obtained by the analytic hierarchy process, and d j is the coefficient of variation of the j-th index obtained by the entropy method.

[0193] Performing normalization on the corrected weight Z j can obtain the final weight.

[0194]

[0195] In the formula, Z j is the corrected weight, and ω j is the final normalized weight.

[0196] Assign values to the first-level indices. The value range can be between 1 and 5. From 1 to 5, the degree of importance increases in sequence. The assignment of the first-level indices is shown in Table 19.

[0197] Table 19: Statistics of the number of people scoring the first-level indices

[0198]

[0199] Use SPSS software to analyze the scoring data, solve the entropy value and coefficient of variation of each index, and correct the initial weights accordingly. The analysis results of the first-level indices are shown in Table 20.

[0200] Table 20: Correction results of the first-level indices

[0201]

[0202] The summary of the scoring of the second-level indices is shown in Table 21.

[0203] Table 21: Statistics of the number of people scoring the second-level indices

[0204]

[0205]

[0206] Use SPSS software to analyze the scoring data, solve the entropy values and variation coefficients of each secondary index, correct the initial weights, and obtain the final weights of the secondary indices through normalization. Statistically classify the final weights of each level of indices as shown in Table 22.

[0207] Table 22: Final weights of each level of indices

[0208]

[0209] As can be seen from Table 22, after correcting the weights using the entropy method, among the first-level indices, the weight of the energy storage basic index (A1) still remains the largest and slightly increases, with a final weight of 0.4709. The weights of the energy efficiency index (A2) and the power station and battery index (A3) slightly decrease, the weights of the economic index (A4) and the energy storage development index (A5) slightly increase, and the environmental protection index (A6) remains basically unchanged.

[0210] Beneficial effect analysis of the target index weight acquisition module: Combine the analytic hierarchy process (AHP) with the entropy method. The AHP can give full play to the advantages of subjective judgment. By constructing a hierarchical structure model, complex problems are decomposed into multiple levels and factors, and the relative importance of each factor is judged to determine the weights. This method has strong pertinence and flexibility when facing multi-factor and multi-level complex problems. The entropy method is based on the concept of information entropy, and determines the weights by calculating the information entropy of the indices, which can objectively reflect the variation degree and information volume of each index in the system, avoiding the interference of subjective factors. By integrating the advantages of the AHP and the entropy method, a more realistic and reliable weight distribution result is obtained.

[0211] In one embodiment, construct a fuzzy comprehensive evaluation module: used to construct a fuzzy comprehensive evaluation model based on the target weights, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indices to the first-level evaluation indices. Specifically, it includes:

[0212] Take the first-level evaluation indices and the secondary evaluation indices as the factor set, and take the preset evaluation grades as the evaluation set;

[0213] Construct a membership degree fuzzy relationship matrix between the factor set and the evaluation set;

[0214] Based on the membership degree fuzzy relationship matrix, construct a fuzzy comprehensive evaluation model according to the target weights.

[0215] Optionally, first, determine the factor set and the evaluation set. The first-level indicators of energy storage basic indicators, energy efficiency indicators, power station and battery indicators, economic indicators, energy storage development indicators, and environmental protection indicators are respectively represented as F1, F2, F3, F4, F5, F6. Therefore, the factor set F of the first-level indicators = {F1, F2, F3, F4, F5, F6}. Under the first-level indicator of energy storage basic indicators, there are 5 second-level indicators, namely energy storage method, cell specification, battery type, battery cooling system, and capacity ratio, which can be respectively represented as F11, F12, F13, F14, F15. Therefore, the factor set F1 under the energy storage basic indicators = {F11, F12, F13, F14, F15}. Similarly, the factor set F2 under the energy efficiency indicator = {F21, F22, F23, F24, F25}, the factor set F3 under the power station and battery indicator = {F31, F32, F33, F34, F35}, the factor set F4 under the economic indicator = {F11, F42, F43}, the factor set F5 under the energy storage development indicator = {F51, F52, F53}, and the factor set F6 under the environmental protection indicator = {F61, F62, F63}. In order to make the evaluation as detailed as possible, five levels of evaluation grades can be set, namely excellent, good, fair, qualified, and unqualified. These five levels can be respectively represented as 95 points, 85 points, 75 points, 65 points, and 55 points. Therefore, the evaluation set E = {excellent, good, fair, qualified, unqualified}.

[0216] Secondly, construct the fuzzy relation matrix. Before constructing the fuzzy relation matrix, first, find experts to score each indicator to determine the membership degree between each indicator and the evaluation set. At the same time, the indicator system of this application embodiment includes two levels. Therefore, it is necessary to establish a matrix from the second-level indicators to the first-level indicators. For the second-level indicators of the energy storage basic indicators, the fuzzy relation matrix can be expressed as:

[0217]

[0218] where rij represents the membership degree relationship between the factor set F1 and the evaluation set E1.

[0219] The scoring grades of each indicator include five grades: excellent, good, fair, qualified, and unqualified, corresponding to 95 points, 85 points, 75 points, 65 points, and 55 points of the evaluation set respectively.

[0220] After normalizing the scores of each second-level indicator, the membership degree between each indicator and the evaluation set can be obtained, so as to construct the fuzzy relation matrix corresponding to the indicator.

[0221] Finally, construct the fuzzy comprehensive evaluation model. For the second-level indicators of the energy storage system, the weights of each indicator can be expressed as Q1 = (q11, q12, q13, q14, q15). Thus, the comprehensive evaluation result of the energy storage basic indicators can be obtained:

[0222] B 1 = Q 1 * R 1 = (b 11 , b 12 , b 13 , b 14 , b 15 )

[0223] Similarly, the fuzzy evaluation results of the energy efficiency index, power station and battery index, economic index, energy storage development index, and environmental protection index are as follows:

[0224] B 2 = Q 2 * R 2 = (b 21 , b 22 , b 23 , b 24 , b 25 )

[0225] B 3 = Q 3 * R 3 = (b 31 , b 32 , b 33 , b 34 , b 35 )

[0226] B 4 = Q 4 * R 4 = (b 41 , b 42 , b 43 , b 44 , b 45 )

[0227] B 5 = Q 5 * R 5 = (b 51 , b 52 , b 53 , b 54 , b 55 )

[0228] B 6 = Q 6 * R 6 = (b 61 , b 62 , b 63 , b 64 , b 65 )

[0229] After obtaining the fuzzy evaluation results of the secondary indicators, the fuzzy relation matrix of the comprehensive evaluation model for the energy storage capacity configuration of the thermal power - energy storage combined peak - shaving project of Company C can be established:

[0230]

[0231] Analysis of the beneficial effects of constructing the fuzzy comprehensive evaluation module: Among the evaluation indicators, there are both quantitative and qualitative indicators. In view of the diversity and complexity of the evaluation indicators, in the embodiments of the present application, a model is established through the fuzzy comprehensive evaluation method, which can combine these qualitative and quantitative factors, effectively handle the fuzziness and uncertainty of the data, conduct a comprehensive and systematic evaluation, and thus more accurately reflect the actual situation of the project, providing a scientific and reasonable basis for the evaluation of the project.

[0232] In one embodiment, the evaluation result determination module: is used to determine the evaluation result of the energy storage capacity configuration based on the fuzzy comprehensive evaluation model according to the current parameters of several energy storage power stations. Specifically, it includes:

[0233] Optionally, using the vector Q to represent the weights of each first - level indicator, combined with the above - mentioned fuzzy matrix, the evaluation result of the comprehensive evaluation model for the energy storage capacity configuration of the thermal power - energy storage combined peak - shaving project of Company C can be obtained:

[0234] B = Q * R=(b 1 ,b 2 ,b 3 ,b 4 ,b 5 )

[0235] In order to make the result of the comprehensive evaluation more intuitive and clear, calculate the final score of the project according to the scores corresponding to the evaluation levels:

[0236] S=(95,85,75,65,55)*B T

[0237] According to the interval (evaluation set) to which the final score belongs, a scientific and reasonable evaluation of the quality of the project can be made.

[0238] Analysis of the beneficial effects of the evaluation result determination module: By combining the target weight vector Q and the fuzzy relation matrix R, and using the principles of fuzzy mathematics to calculate the comprehensive score S, the scientificity and rationality of the evaluation result are ensured. It not only integrates multi - dimensional evaluation information but also considers the importance of each indicator, improving the accuracy and reliability of the evaluation result.

[0239] In summary, the system structure block diagram of an energy storage capacity configuration evaluation system provided by an embodiment of the present application constructs an evaluation index module for constructing primary evaluation indexes and secondary evaluation indexes according to several energy storage power station parameters; among them, the primary evaluation indexes belong to the secondary evaluation indexes. A module for determining the initial index weights is used to assign values to each primary evaluation index and secondary evaluation index by constructing a judgment matrix according to the analytic hierarchy process to determine the initial weights of the primary evaluation indexes and secondary evaluation indexes. A module for obtaining the target index weights is used to correct the initial weights of the primary evaluation indexes and secondary evaluation indexes according to the entropy method to obtain the target weights of the primary evaluation indexes and secondary evaluation indexes. A fuzzy comprehensive evaluation module is constructed for constructing a fuzzy comprehensive evaluation model based on the target weights, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indexes to the primary evaluation indexes. A module for determining the evaluation result is used to determine the evaluation result of the energy storage capacity configuration based on the fuzzy comprehensive evaluation model according to the current several energy storage power station parameters. The accurate evaluation of the energy storage capacity configuration is realized, the reliability of the energy storage capacity configuration evaluation is improved, and the problem of low reliability in the capacity configuration evaluation of energy storage power stations in the related technology when facing complex problems with multiple factors and multiple levels is solved.

[0240] In a second aspect, an embodiment of the present application provides an energy storage capacity configuration evaluation method. Figure 3 It is a flowchart of an energy storage capacity configuration evaluation method shown according to an exemplary embodiment. As Figure 3 shown, the method includes:

[0241] Step S101: Construct primary evaluation indexes and secondary evaluation indexes according to several energy storage power station parameters; among them, the secondary evaluation indexes belong to the primary evaluation indexes.

[0242] Step S102: According to the analytic hierarchy process, assign values to each primary evaluation index and secondary evaluation index by constructing a judgment matrix to determine the initial weights of the primary evaluation indexes and secondary evaluation indexes.

[0243] Step S103: According to the entropy method, correct the initial weights of the primary evaluation indexes and secondary evaluation indexes to obtain the target weights of the primary evaluation indexes and secondary evaluation indexes.

[0244] Step S104: Based on the target weights, construct a fuzzy comprehensive evaluation model, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indexes to the primary evaluation indexes.

[0245] Step S105: According to the current several energy storage power station parameters, determine the evaluation result of the energy storage capacity configuration based on the fuzzy comprehensive evaluation model.

[0246] In summary, for the energy storage capacity configuration evaluation method provided in this application, first-level evaluation indicators and second-level evaluation indicators are constructed based on several energy storage power station parameters, where the second-level evaluation indicators belong to the first-level evaluation indicators. According to the analytic hierarchy process, each first-level evaluation indicator and second-level evaluation indicator is assigned a value by constructing a judgment matrix to determine the initial weights of the first-level evaluation indicators and second-level evaluation indicators. According to the entropy method, the initial weights of the first-level evaluation indicators and second-level evaluation indicators are corrected to obtain the target weights of the first-level evaluation indicators and second-level evaluation indicators. Based on the target weights, a fuzzy comprehensive evaluation model is constructed, where the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the second-level evaluation indicators to the first-level evaluation indicators. According to the current several energy storage power station parameters, based on the fuzzy comprehensive evaluation model, the energy storage capacity configuration evaluation result is determined. The accurate evaluation of the energy storage capacity configuration is realized, the reliability of the energy storage capacity configuration evaluation is improved, and the problem of low reliability in the capacity configuration evaluation of energy storage power stations in the related art when facing complex problems with multiple factors and multiple levels is solved.

[0247] It should be noted that the energy storage capacity configuration evaluation system provided in this embodiment is used to implement the above implementation manners, and those that have been described will not be repeated. As used above, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the above embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0248] In a third aspect, an embodiment of the present application provides an electronic device, Figure 4 which is a block diagram of an electronic device shown according to an exemplary embodiment. As Figure 4 shown, the electronic device may include a processor 41 and a memory 42 storing computer program instructions.

[0249] Specifically, the above-mentioned processor 41 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0250] Among them, the memory 42 may include a mass storage for data or instructions. By way of example and not limitation, the memory 42 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In suitable cases, the memory 42 may include removable or non-removable (or fixed) media. In suitable cases, the memory 42 may be internal or external to the data processing device. In a particular embodiment, the memory 42 is non-volatile memory. In a particular embodiment, the memory 42 includes a read-only memory (ROM) and a random access memory (RAM). In suitable cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable read-only memory (EAROM), or a flash memory, or a combination of two or more of these. In suitable cases, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0251] The memory 42 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 41.

[0252] The processor 41 reads and executes the computer program instructions stored in the memory 42 to implement any one of the energy storage capacity configuration evaluation systems in the above embodiments.

[0253] In one embodiment, an energy storage capacity configuration evaluation device may further include a communication interface 43 and a bus 40. Among them, as Figure 4 shown, the processor 41, the memory 42, and the communication interface 43 are connected through the bus 40 and complete communication with each other.

[0254] The communication interface 43 is used to implement communication between the modules, devices, units, and / or devices in the embodiments of the present application. The communication port 43 can also implement data communication with other components, such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0255] The bus 40 includes hardware, software, or both, and couples components of an energy storage capacity configuration evaluation device to each other. The bus 40 includes at least one of the following, including but not limited to: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, the bus 40 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, the bus 40 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0256] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, it implements an energy storage capacity configuration evaluation system provided in the first aspect.

[0257] Among them, more specifically, the readable storage medium may include but is not limited to: portable disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0258] In a possible implementation manner, the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps of implementing an energy storage capacity configuration evaluation method provided in the second aspect.

[0259] Among them, the program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, executed as an independent software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0260] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0261] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A system for evaluating energy storage capacity configuration, characterized in that: include: Constructing an evaluation index module, which is used to construct a primary evaluation index and a secondary evaluation index according to a number of energy storage power station parameters; wherein the secondary evaluation index belongs to the primary evaluation index; An initial indicator weight determination module is used to assign values ​​to each of the first-level evaluation indicators and the second-level evaluation indicators by constructing a judgment matrix according to the hierarchical analysis method to determine the initial weights of the first-level evaluation indicators and the second-level evaluation indicators; A module for obtaining target indicator weights is used to modify the initial weights of the first-level evaluation indicator and the second-level evaluation indicator according to an entropy method to obtain target weights of the first-level evaluation indicator and the second-level evaluation indicator; Constructing a fuzzy comprehensive evaluation module, which is used to construct a fuzzy comprehensive evaluation model based on the target weight, wherein the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation index to the primary evaluation index; The evaluation result determination module is used to determine the energy storage capacity configuration evaluation result based on the fuzzy comprehensive evaluation model according to the current parameters of several energy storage power stations.

2. The energy storage capacity configuration evaluation system according to claim 1, characterized in that: The module for obtaining the target indicator weight is used to: According to the decision matrix, calculating the weight of each of the first-level evaluation indicators and each of the second-level evaluation indicators; Based on the weight of each of the first-level evaluation indicators and each of the second-level evaluation indicators, calculating the entropy value of each of the first-level evaluation indicators and each of the second-level evaluation indicators; Based on the entropy values ​​of each of the first-level evaluation indicators and each of the second-level evaluation indicators, calculating the difference coefficient between each of the first-level evaluation indicators and each of the second-level evaluation indicators; Based on the difference coefficient between each of the first-level evaluation indicators and each of the second-level evaluation indicators, modify the initial weights of the first-level evaluation indicators and the second-level evaluation indicators to obtain modified weights of the first-level evaluation indicators and the second-level evaluation indicators; The corrected weights of the first-level evaluation index and the second-level evaluation index are normalized to obtain target weights of the first-level evaluation index and the second-level evaluation index.

3. The energy storage capacity configuration evaluation system according to claim 2, characterized in that: The module for obtaining the weight of the target indicator is used to modify the initial weights of the first-level evaluation indicator and the second-level evaluation indicator based on the difference coefficient between each of the first-level evaluation indicators and each of the second-level evaluation indicators to obtain the modified weights of the first-level evaluation indicator and the second-level evaluation indicator: Based on the difference coefficient of each of the first-level evaluation indicators and each of the second-level evaluation indicators, the formula Z j =w i ×d j (j=1, 2…n) modifying the initial weights of the first-level evaluation index and the second-level evaluation index to obtain the modified weights of the first-level evaluation index and the second-level evaluation index; In the formula, Z j represents the target weight, w i represents the initial weight, d j represents the coefficient of variation.

4. The energy storage capacity configuration evaluation system according to claim 1, characterized in that: The module for determining the initial indicator weights is used to: assign values ​​to each of the first-level evaluation indicators and the second-level evaluation indicators by constructing a judgment matrix according to the analytic hierarchy process to determine the initial indicator weights of the first-level evaluation indicators and the second-level indicators. By using the Saaty scaling method, constructing a judgment matrix for the first-level evaluation indicators and the second-level evaluation indicators respectively, and assigning values ​​according to the importance of each of the first-level evaluation indicators and the importance of each of the second-level evaluation indicators to which the first-level evaluation indicators belong based on the judgment matrix, obtaining the assignment results of each of the first-level evaluation indicators and the assignment results of each of the second-level evaluation indicators; Based on the assignment results of the first-level evaluation indicators, the characteristic vectors of the first-level evaluation indicators are calculated, and the initial weights of the first-level evaluation indicators are obtained through the initial weight formula; Based on the assignment results of the secondary evaluation indicators, the characteristic vectors of the secondary evaluation indicators are calculated, and the initial weights of the secondary evaluation indicators are obtained through an initial weight formula.

5. The energy storage capacity configuration evaluation system according to claim 4, characterized in that: The module for determining the initial indicator weights is used to calculate the characteristic vectors of each of the first-level evaluation indicators based on the assignment results of the first-level evaluation indicators, and obtain the initial weights of each of the first-level evaluation indicators through the initial weight formula: Based on the assignment results of the first-level evaluation indicators, the formula Calculate the eigenvectors of each of the first-level evaluation indicators and use the initial weight formula Obtaining the initial weight of each of the first-level evaluation indicators; in, represents the eigenvector of the i-th indicator, a ij Represents the indicator a in the AHP judgment matrix i For indicator a j 's scoring.

6. The energy storage capacity configuration evaluation system according to claim 1, characterized in that: The system also includes a decision matrix building module for: Normalizing the positive indicators and negative indicators in the original data to obtain normalized data; Based on the normalized data, a decision matrix is ​​constructed according to the primary evaluation index and the secondary evaluation index.

7. The energy storage capacity configuration evaluation system according to claim 1, characterized in that: Constructing a fuzzy comprehensive evaluation module, when constructing a fuzzy comprehensive evaluation model based on the target weight, is used to: Taking the first-level evaluation index and the second-level evaluation index as a factor set, and taking the preset evaluation level as an evaluation level; Constructing a fuzzy relationship matrix of membership between the factor set and the evaluation set; Based on the membership fuzzy relationship matrix and according to the target weight, a fuzzy comprehensive evaluation model is constructed.

8. A method for evaluating energy storage capacity configuration, characterized in that: The method comprises: According to a number of energy storage power station parameters, construct a first-level evaluation index and a second-level evaluation index; wherein the second-level evaluation index belongs to the first-level evaluation index; According to the hierarchical analysis method, each of the first-level evaluation indicators and the second-level evaluation indicators is assigned a value by constructing a judgment matrix to determine the initial weights of the first-level evaluation indicators and the second-level evaluation indicators; According to the entropy method, the initial weights of the first-level evaluation index and the second-level evaluation index are corrected to obtain the target weights of the first-level evaluation index and the second-level evaluation index; Based on the target weights, a fuzzy comprehensive evaluation model is constructed, wherein the fuzzy comprehensive evaluation model includes establishing a fuzzy relationship matrix from the secondary evaluation indicators to the primary evaluation indicators; According to the current parameters of several energy storage power stations, based on the fuzzy comprehensive evaluation model, the energy storage capacity configuration evaluation result is determined.

9. An electronic device, characterized in that: The invention comprises a memory and a processor, a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, an energy storage capacity configuration evaluation system as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an energy storage capacity configuration evaluation system as described in any one of claims 1 to 7 is implemented.

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