Comprehensive benefit evaluation method and system of hydrogen energy storage system and medium

Abstract

The invention relates to a comprehensive benefit evaluation method and system of a hydrogen energy storage system, and a medium. The method comprises the following steps: constructing a comprehensive benefit evaluation index system of the hydrogen energy storage system; obtaining related evaluation information of the hydrogen energy storage system, processing uncertain information by adopting a triangular fuzzy number in combination with the comprehensive benefit evaluation index system, and performing defuzzification processing by adopting a CFCS method to obtain a defuzzification direct influence matrix; a DEMATEL method is adopted to quantify the mutual influence relation between the evaluation indexes, and input-output indexes are obtained; based on the input-output indexes, performance analysis is carried out by adopting a non-parametric method, the input and output efficiency of each evaluation index is calculated, and an initial performance evaluation sorting result is determined; according to the initial efficiency evaluation sorting result, an entropy weight-TOPSIS method is adopted for evaluation, and a final efficiency evaluation sorting result is obtained and serves as a comprehensive benefit evaluation result. Compared with the prior art, the method has the advantages of high accuracy and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen energy storage assessment, and particularly to a comprehensive benefit evaluation method, system and medium for a hydrogen energy storage system. Background Art

[0002] As a green and efficient secondary energy source, hydrogen energy has the unique ability to convert various energy forms such as electricity, liquid fuel, and heat energy, breaking the boundaries between traditional energy systems. In the process of promoting the transformation of the power system to clean and low-carbon, hydrogen energy plays a crucial role. The process of hydrogen production by electrolyzing water promotes the utilization of renewable energy and reduces the dependence on fossil energy. Due to its large-scale and long-term energy storage characteristics, it provides extensive peak shaving capabilities for the power grid, enhancing the resource allocation ability of the power grid and the flexibility of the renewable energy power system.

[0003] Currently, there is no patent research on the comprehensive evaluation of hydrogen energy storage, and there is relatively little comprehensive evaluation in the field of hydrogen energy storage in scientific research; in terms of evaluation technology, there are also defects in interdisciplinary evaluation methods. Current innovations in data envelopment analysis (DEA) for input-output include improvements in combination with other evaluation methods or direct use, etc. For example: a patent for a logistics performance evaluation method based on improved DEA-AHP, which constructs an index system for the entire logistics process, and constructs a judgment matrix by integrating the DEA method on the basis of AHP, and determines the score of the index by determining the weight through pure data to measure the operating efficiency of the entire logistics system; a patent for an employee workload evaluation method and system based on ANP-DEA, which screens the workload evaluation indicators based on the network analysis method after determining the employee workload evaluation indicators; finally, based on the above-mentioned screened workload evaluation indicators, the data envelopment analysis method is used to obtain the employee workload evaluation result; a patent for a method for identifying the effectiveness of rumor refutation information release based on DEA-GBDT, which constructs an index system for influencing factors of the effectiveness of rumor refutation information release, and uses machine learning algorithms to measure the indicators; constructs a data envelopment analysis model (DEA) to comprehensively evaluate a single decision-making unit, that is, classify the efficiency of a single piece of rumor refutation information; uses four methods, namely Spearman correlation coefficient, distance correlation coefficient, ridge regression model, and random forest model, for integrated feature selection to select the most representative and predictive features. The existing interdisciplinary evaluation technologies focus on improving the steps or direct application, without considering the defects of the evaluation itself.

[0004] The above-mentioned interdisciplinary evaluation method focuses on step improvement or direct application, without considering the defects of the evaluation itself. The comprehensive evaluation of hydrogen energy storage is a wide-ranging and complex issue. Current academic research on the evaluation of hydrogen energy storage often only focuses on its economic or environmental benefits. This single-perspective evaluation method ignores other important factors such as technical benefits and social benefits. The literature [Zhang Li, Ye Bin, Yin Chenxu, et al. Analysis of the Economy and Development Prospect of Wind Power to Hydrogen [J]. Northeast Electric Power Technology, 2020, 41(7): 5–9, 37.] used the levelized cost analysis method and the internal rate of return method to analyze the cost-benefit changes in all links such as hydrogen production, storage, and transportation, proposed an economic analysis model for wind power full-power hydrogen production considering industrial chain conduction, and evaluated the development prospect of wind power to hydrogen based on this. The literature [Li Jiarong, Lin Jin, Xing Xuetao, et al. Module Selection and Optimization Planning of Power-to-Hydrogen (P2H) in Active Distribution Networks Based on a Unified Operation Model [J]. Proceedings of the CSEE, 2021, 41(12): 4021–4033.] applied key economic indicators such as equipment investment, power purchase cost, and hydrogen sales revenue to study the economy of power-to-hydrogen in active distribution networks. The literature [Zhao G, Kraglund M R, Frandsen H L, et al. Life cycle assessment of H2O electrolysis technologies [J]. International Journal of Hydrogen Energy, 2020, 45(43): 23765-23781.] compared the economies of different types of electrolytic hydrogen production technologies from the perspectives of lifespan, hydrogen production efficiency, etc. The literature [Alamri F S, Saeed M H, Saeed M. A hybrid entropy-based economic evaluation of hydrogen generation techniques using Multi-Criteria Decision Making [J]. International Journal of Hydrogen Energy, 2024, 49: 711-723.] used the multi-criteria decision-making (MCDM) method based on hybrid entropy to analyze the economy of eight different hydrogen production methods, calculated the weights of each alternative using weighted entropy, and then used them as inputs for three different MCDM techniques, namely TOPSIS, VIKOR, and MultiMOORA. The multi-functional method used provided a robust priority analysis. Single-benefit evaluation leads to a lack of comprehensiveness in the evaluation results, being too one-sided in considering problems, and lacking a comprehensive benefit evaluation of the hydrogen energy storage system.

[0005] The potential of hydrogen energy is huge. Currently, the methods used for the evaluation of hydrogen energy storage mainly include the technique for order preference by similarity to an ideal solution (TOPSIS), the analytic hierarchy process, the entropy weight method and its improvements, etc. The literature [Wu, M., Wu, J., Zhu, R., Chen, Y., Yin, J., & Xue, Y. (2024, May). Benefit Assessment of Integrated Energy Systems Considering Hydrogen Utilization in the Context of Dual Carbon Goals. In 2024 IEEE 2nd International Conference on Power Science and Technology (ICPST) (pp. 1765-1772). IEEE.] proposed a comprehensive evaluation model combining the best-worst method (BWM) and the improved entropy weight method, and through the introduction of the coefficient of variation, the benefit evaluation of the integrated energy system including the hydrogen production process was carried out. Literature E., & Kahraman, C. (2024). Integrated AHP&TOPSIS methodology using intuitionistic Z-numbers: An application on hydrogen storage technology selection. Expert Systems with Applications, 239, 122382. proposed a Z-AHP and Z-TOPSIS decision-making model combining intuitionistic fuzzy numbers and α-cut method to evaluate and rank alternatives, and verified its effectiveness through a case study of hydrogen storage technology selection problem. The literature [Zhang, Y., Zhao, X., Zhao, Y., Cui, X., Zhang, Y., & Zhao, H. (2024). Pre-selection scheme evaluation of hybrid energy storage for distribution network based on utility combination method. Journal of Energy Storage, 88, 111497.] introduced a comprehensive evaluation method combining Analytic Hierarchy Process (AHP), Entropy Weight Method (EWM) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) for the optimization of hybrid energy storage system (HES) configuration, and verified it through power supply reliability simulation of IEEE RBTS-BUS6 distribution network to improve the power supply reliability of distribution network. The literature [Li, J., Liang, Z., Zhao, W., Li, G., & Li, M. Selection and evaluation method of hybrid electrolyzer hydrogen production system [J]. High Voltage Engineering, 2024, 50(06): 2653-2662.] proposed a selection and evaluation method of hybrid electrolyzer based on Analytic Hierarchy Process-Entropy Weight Method (AHP-EWM), and obtained the optimal type of hydrogen production electrolyzer through the calculation of weights. However, the above comprehensive evaluation methods all inevitably require the calculation of index weights, and there is no clear standard for the calculation of index weights at present, and the evaluation results obtained by different weight calculation methods often vary.

[0006] Data envelopment analysis (DEA) is a multivariate analysis tool used to evaluate the relative efficiency of multiple homogeneous decision-making units (DMUs). When there are multiple simultaneously efficient decision-making units, the super efficiency-data envelopment analysis (SE-DEA) model can be used to further rank the efficient decision-making units. In other fields, the literature [Zhao, L. B., Wang, L., Wan, C., Wu, M. J., Yuan, K., & Song, Y. (2022). Sub-sector energy efficiency evaluation of urban integrated energy systems based on data envelopment analysis. Automation of Electric Power Systems (17), 132-141.] proposed an energy efficiency evaluation method based on the associated network DEA model, which optimizes the weights to identify the weak links within the system and the key links to improve the system energy efficiency; the literature [Lin, C. H., Zhang, Y., Shao, Z. G., Liu, Y. Y., & Feng, D. D. (2021). Comprehensive assessment of long-term power quality based on fuzzy DEA. High Voltage Engineering (05), 1751-1761.] determined the weights of various power quality evaluation indicators based on the data envelopment analysis (DEA) theory and constructed a comprehensive evaluation model of power quality by combining the fuzzy evaluation method. The DEA model improves the objectivity and simplicity of evaluation by eliminating the need for subjective weighting and dimension unification, but may ignore the logical relationship between indicators due to overemphasis on individual differences, sometimes resulting in evaluation results that do not match the actual situation. Therefore, establishing a scientific input-output index system is the basic premise for the application of the DEA model.

[0007] In the current evaluation system, the common practice is to divide the indicators into output indicators and input indicators: for output indicators, the larger the value, the better the system performance, while for input indicators, on the contrary, the smaller the value, the better the system performance. Although this method is simple and direct, it does not consider the possible complex interactions and internal relationships between the indicators. When the evaluation system contains a large number of indicators, intricate relationship networks may form among these indicators, and there may even be a situation of mutual causation, which makes the method of determining indicators based on intuitive judgment lack a scientific basis. To solve this problem, the decision making trial and evaluation laboratory (DEMATEL) method can be adopted. DEMATEL is a system analysis method based on matrix and graph theory, which reveals the interactions between elements within the system by analyzing the influence relationships between them. Using DEMATEL to scientifically analyze the input-output logical relationships existing among the indicators and organically combining it with DEA can effectively solve the problem of the integration of the internal logic between the evaluation index system and the evaluation model, and obtain scientific measurement results. To solve the problem that the traditional DEMATEL method is limited to the real number domain, in other fields, the literature [Feng, X., Li, E., Li, J., & Wei, C. (2024). Critical influencing factors of employees’ green behavior: Three-stage hybrid fuzzy DEMATEL–ISM–MICMAC approach. Environment, development and sustainability, 26(7), 17783-17811.] has extended it to the fuzzy domain, achieving an accurate analysis of the evaluation system with complex and fuzzy relationships.

[0008] In summary, it is particularly important to conduct a comprehensive evaluation of hydrogen energy storage. At the same time, the comprehensive evaluation of hydrogen energy storage is also the key to improving the efficiency of the industrial chain and promoting the overall development of related research fields. The development of hydrogen energy storage technology is in its infancy, and there are few published patents on existing comprehensive evaluation technologies for hydrogen energy storage. Although academic research is underway, the research focuses on single benefits, and there are drawbacks in the evaluation methods. Due to the vast territory, there are significant differences in the technical, environmental, and social benefits in different regions. An evaluation that only considers single benefits is incomplete, and focusing on single-benefit research cannot fully reflect the comprehensive situation. Drawbacks in the evaluation methods will affect the accuracy of the research. Summary of the Invention

[0009] The purpose of the present invention is to provide a comprehensive benefit evaluation method, system, and medium for a hydrogen energy storage system that can improve the accuracy of the evaluation results of the comprehensive benefits of the hydrogen energy storage system.

[0010] The object of the present invention can be achieved by the following technical solutions:

[0011] A comprehensive benefit evaluation method for a hydrogen energy storage system, comprising the following steps:

[0012] Construct a comprehensive benefit evaluation index system for the hydrogen energy storage system;

[0013] Obtain relevant evaluation information of the hydrogen energy storage system, combine the comprehensive benefit evaluation index system, use triangular fuzzy numbers to process uncertain information, and use the CFCS method for defuzzification to obtain a defuzzified direct influence matrix;

[0014] Based on the defuzzified direct influence matrix, use the DEMATEL method to quantify the mutual influence relationship between evaluation indicators and obtain input-output indicators;

[0015] Based on the input-output indicators, use non-parametric methods for efficiency analysis to determine the initial efficiency evaluation ranking result;

[0016] According to the initial efficiency evaluation ranking result, use the entropy weight-TOPSIS method for evaluation to obtain the final efficiency evaluation ranking result as the comprehensive benefit evaluation result.

[0017] Further, the comprehensive benefit evaluation index system includes an objective layer, an attribute layer, and an index layer. The attribute layer includes multiple dimensions of evaluation indicators, including an economic benefit dimension, a technical benefit dimension, an environmental benefit dimension, and a social benefit dimension. The index layer includes multiple evaluation indicators under each dimension. Among them, the evaluation indicators under the economic benefit dimension include: total cost, energy purchase cost, and marginal electricity price for hydrogen production.

[0018] The evaluation indicators under the technical benefit dimension include: electrolyzer fluctuating power, average release depth of energy storage, and abandoned wind rate;

[0019] The evaluation indicators under the environmental benefit dimension include: carbon emissions, and energy conversion efficiency;

[0020] The evaluation indicators under the social benefit dimension include: employment guarantee, public recognition, and industrial chain promotion.

[0021] Further, the step of using triangular fuzzy numbers to process uncertain information includes:

[0022] Based on the relevant evaluation information, perform the conversion of triangular fuzzy numbers through the triangular fuzzy number importance degree and qualitative index performance degree conversion table to form a triangular fuzzy direct influence matrix. The triangular fuzzy number importance degree and qualitative index performance degree conversion table includes four columns, namely language evaluation, influence value, symbol representation, and triangular fuzzy number.

[0023] When the language evaluation is considered to have no impact and the impact value is 0, the compliance is expressed as NO, and the triangular fuzzy number is (0, 0, 0.25);

[0024] When the language evaluation is considered to have a very weak impact and the impact value is 1, the compliance is expressed as VL, and the triangular fuzzy number is (0, 0.25, 0.5);

[0025] When the language evaluation is considered to have a very weak impact and the impact value is 2, the compliance is expressed as L, and the triangular fuzzy number is (0.25, 0.5, 0.75);

[0026] When the language evaluation is considered to have a very weak impact and the impact value is 3, the compliance is expressed as H, and the triangular fuzzy number is (0.5, 0.75, 1.0);

[0027] When the language evaluation is considered to have a very weak impact and the impact value is 4, the compliance is expressed as VH, and the triangular fuzzy number is (0.75, 1.0, 1.0).

[0028] Further, the steps of obtaining the defuzzified direct impact matrix include:

[0029] Normalize the triangular fuzzy numbers in the triangular fuzzy direct impact matrix, where the expression for normalization is:

[0030]

[0031] According to the normalization result, calculate the normalized value, where the calculation expression for the normalized value is:

[0032]

[0033] Calculate the total normalized value according to the normalized value, where the calculation expression for the total normalized value is:

[0034]

[0035] Calculate the defuzzified value of the relevant evaluation information according to the total normalized value, where the calculation expression for the defuzzified value is:

[0036]

[0037] According to the defuzzified value, obtain the defuzzified direct impact matrix, where the expression for each element in the defuzzified direct impact matrix is:

[0038]

[0039] In the formula, x is the key variable in the normalization and defuzzification processes, is the left endpoint value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the maximum value of the largest difference among all fuzzy numbers; represents the middle value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the right endpoint value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the standardized value of the left and right endpoints of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the total standardized value calculated based on the standardized value; is the defuzzified value obtained based on the total standardized value; z ij is an element in the defuzzified direct influence matrix;

[0040] Furthermore, the steps of obtaining the input-output indicators include:

[0041] Based on the defuzzified direct influence matrix, the canonical influence matrix is obtained by using the row maximum method, where the calculation expression of the canonical influence matrix is:

[0042]

[0043] In the formula, B is the canonical influence matrix, x ij is an element in the defuzzified direct influence matrix; n is the total number of evaluation indicators;

[0044] Based on the canonical influence matrix, a comprehensive influence matrix is established, and the calculation expression of the comprehensive influence matrix is:

[0045]

[0046] In the formula, T is the comprehensive influence matrix, B k is the k-th power of the canonical influence matrix B;

[0047] Calculate the influence degree and the influenced degree of each element in the comprehensive influence matrix, and the calculation expressions are respectively:

[0048]

[0049] In the formula, D i is the influence degree, C i is the influenced degree;

[0050] Based on the influence degree and the influenced degree of each element, calculate the centrality and the reason degree of each element, and the calculation expressions are respectively:

[0051] M i = D i + C i

[0052] R i = D i - C i

[0053] In the formula, M i is the centrality, and R i is the causality;

[0054] Taking the centrality as the abscissa and the causality as the ordinate, a causal relationship graph is plotted as the input-output index.

[0055] Furthermore, the non-parametric method is the DEA method.

[0056] Furthermore, the steps for determining the initial performance evaluation ranking result include:

[0057] Based on the input-output index, it is assumed that there are n DMUs in total, and each DMU has m inputs and r outputs. An optimization model is established with the goal of the optimal relative efficiency of the hydrogen energy storage system, which is expressed as:

[0058]

[0059] In the formula, X j = {x 1j , x 2j , …, x mj}, Y j = {y 1j , y 2j , …, y rj}, α = [α1, …, α r T is the output weight, α r is the weight of the r-th output index, β = [β1, …, β m T is the input weight, β m is the weight of the m-th input index, α s is the weight of the s-th output index, x j , y j are the input and output data of the DMU, y sj is the s-th output index of the DMU j , β i is the weight of the i-th input index, x ij is the i-th input index of the DMU j ;

[0060] The Charnes-Cooper method is used to convert the optimization model into a linear model, and the expression of the linear model containing o DMUs o is: ​​

[0061]

[0062] In the formula, ρ is the optimal solution, and λ j is the weight coefficient, and S + , S - are the slack variables of input and output, X o is the input, and Y o is the output;

[0063] Based on the above linear model, we have:

[0064] If ρ = 1 and S - = 0, S + = 0, then DMU o is strongly efficient, indicating that when the input is X o , the hydrogen energy storage system has reached the optimal output Y o ;

[0065] If ρ = 1, but S - or S + is not 0, then DMU o is weakly efficient;

[0066] If ρ < 1, then DMU o is invalid;

[0067] Furthermore, rank according to the efficiency of DMU o to obtain the initial efficiency evaluation ranking result.

[0068] Furthermore, the steps of using the entropy weight - TOPSIS method for evaluation include:

[0069] Based on the strongly efficient solutions of DMU o and their corresponding index matrices, arrange them into an n×m matrix, and the expression is:

[0070]

[0071] In the formula, X is the matrix, n is the number of objects, and m is the number of indicators;

[0072] Use the range method to normalize and standardize the n×m matrix to obtain the standardized matrix:

[0073]

[0074] In the formula, Z is the standardized matrix;

[0075] Based on the standardized matrix, calculate the proportion of indicators, and the calculation expression of the proportion is:

[0076]

[0077] Wherein, P ij is the proportion of the value of the i-th object under the j-th index in this index, and z ij is an element in the standardized matrix;

[0078] Based on the proportion of the index, calculate the index entropy value, and the calculation expression of the index entropy value is:

[0079]

[0080] Wherein, E j is the entropy value of the j-th index, and when P ij = 0, P ij lnP ij = 0;

[0081] Based on the index entropy value, calculate the difference coefficient of the index, and the calculation expression of the difference coefficient is:

[0082] G j = 1 - E j

[0083] Wherein, G j is the difference coefficient of the j-th index;

[0084] Based on the difference coefficient of the index, calculate the weight of the index, and the calculation expression of the weight of the index is:

[0085]

[0086] Wherein, W j is the weight of the j-th index;

[0087] Multiply each column of the standardized matrix by the weight of the index to obtain a weighted matrix:

[0088]

[0089] Wherein, Z is the weighted matrix, and ω m is the weight of the m-th index;

[0090] Standardize the weighted matrix to obtain a weighted standardized matrix, and further obtain the positive and negative ideal solutions, wherein the weighted standardized matrix is expressed as:

[0091]

[0092] The positive ideal solution is expressed as:

[0093]

[0094] The negative ideal solution is expressed as:

[0095]

[0096] In the formula, Z is the weighted normalization matrix, and Z + is the positive ideal solution, and Z - is the negative ideal solution;

[0097] Calculate the distances between the object and the positive and negative ideal solutions according to the Euclidean distance, and the calculation expression of the distance is:

[0098]

[0099] In the formula, is the distance between the object and the positive and negative ideal solutions;

[0100] Calculate the relative closeness degree based on the distances between the positive and negative ideal solutions and sort them to obtain the final efficiency evaluation ranking result as the comprehensive benefit evaluation result. The calculation expression of the relative closeness degree is:

[0101]

[0102] In the formula, S i is the relative closeness degree.

[0103] The present invention also provides a comprehensive benefit evaluation system for a hydrogen energy storage system, including:

[0104] System construction module: used to construct a comprehensive benefit evaluation index system for the hydrogen energy storage system;

[0105] Fuzzy-CFCS processing module: used to obtain relevant evaluation information of the hydrogen energy storage system, combine with the comprehensive benefit evaluation index system, process uncertain information using triangular fuzzy numbers, and perform defuzzification processing using the CFCS method to obtain a defuzzified direct influence matrix;

[0106] Quantification module: used to quantify the mutual influence relationship between each evaluation index based on the defuzzified direct influence matrix using the DEMATEL method to obtain input-output indexes;

[0107] Intermediate module: used to perform efficiency analysis using a non-parametric method based on the input-output indexes, calculate the input-output efficiency of each evaluation index, and determine the initial efficiency evaluation ranking result;

[0108] Compensation module: used to perform evaluation using the entropy weight-TOPSIS method according to the initial efficiency evaluation ranking result to obtain the final efficiency evaluation ranking result as the comprehensive benefit evaluation result.

[0109] The present invention also provides a computer-readable storage medium, including one or more programs for execution by one or more processors of an electronic device, and the one or more programs include instructions for executing the comprehensive benefit evaluation method of the hydrogen energy storage system as described above.

[0110] Compared with the prior art, the present invention has the following beneficial effects:

[0111] (1) Based on the traditional efficiency analysis, the present invention constructs a three-layer comprehensive evaluation method coupling the FCD-efficiency analysis method and the entropy weight-TOPSIS method. By preprocessing triangular fuzzy numbers, the CFCS method, and the DEMATEL method, performing intermediate processing on the traditional efficiency analysis method, and performing final compensation processing with the entropy weight-TOPSIS method, the deficiencies of directly specifying input-output indicators in the traditional efficiency analysis are made up through preprocessing and intermediate processing, and the entropy weight-TOPSIS method solves the problem of inability to rank when the traditional efficiency analysis method is strongly effective, thereby improving the accuracy of the comprehensive benefit evaluation results of the hydrogen energy storage system.

[0112] (2) Aiming at the problem that the DEA method cannot rank when the traditional DEA is strongly effective in the efficiency evaluation results of each scheme, the present invention uses the entropy weight-TOPSIS method to make up for the defects of DEA, providing a new perspective and helping to improve the accuracy of evaluation.

[0113] (3) The method of the present invention can not only improve the accuracy of hydrogen energy storage evaluation, but also be extended to other fields requiring efficiency evaluation. Since it can provide more objective and comprehensive evaluation results, it has potential application value in many fields such as energy management, resource allocation, and environmental impact assessment. In the power system, this method can be used to evaluate the efficiency of different energy conversion and storage technologies and optimize the operation and management of the power grid; in the environmental field, this method can be used to evaluate the efficiency of different emission reduction measures and provide a scientific basis for policy making; it can be used for reference in logistics or other field evaluations. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 is a schematic flow chart of the method of the present invention;

[0115] Figure 2 is a diagram of the input-output index system of the FCD preprocessing module of the present invention;

[0116] Figure 3 is a comparison diagram of the method of the present invention and DEA. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0117] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and provides a detailed implementation manner and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0118] This embodiment provides a comprehensive benefit evaluation method for a hydrogen energy storage system. As Figure 1 shown, the method includes the following steps:

[0119] S110. Construct a comprehensive benefit evaluation index system for the hydrogen energy storage system.

[0120] For the sustainable development of the hydrogen energy storage system in production, when considering the economic operation of the system, the impacts on technology, environment, and society should also be considered: in terms of the economy of the system, while considering the total cost of the system, the energy purchase cost and the cost of the marginal electricity price for hydrogen production during system operation should also be considered; in terms of technology, the equipment situation and the wind curtailment rate are also very important, so the fluctuating power of the electrolyzer, the average release depth of the energy storage, and the wind curtailment rate should all be considered; in terms of the ecological environment, attention should be paid to carbon emissions and energy conversion efficiency to reduce greenhouse gas emissions; in terms of society, attention should be paid to the connection with society, and the impacts of the development of hydrogen energy storage on employment security, public recognition, and industrial chain promotion should be concerned. On the basis of meeting feasibility, scientificity, independence, and comprehensiveness, a multi-faceted system evaluation of the hydrogen energy storage system is established, and the established evaluation system is shown in Table 1.

[0121] Table 1 Comprehensive benefit evaluation index system

[0122]

[0123] S120. Construct a method combining triangular fuzzy numbers, the CFCS method, and the DEMATEL method as a pre-module to obtain input-output indicators.

[0124] The constructed FCD pre-module includes a fuzzy-CFCS processing module and a quantification module. The fuzzy-CFCS processing module is processed by combining triangular fuzzy numbers and the CFCS method, and the quantification module is processed by the DEMATEL method. Triangular fuzzy numbers provide an intuitive method for dealing with uncertainty and ambiguity problems by constructing a weight and evaluation matrix with a membership function; the defuzzification of the CFCS method reduces information loss and improves the accuracy of decision-making by precisifying fuzzy data; the DEMATEL method systematically analyzes the mutual relationships and influences among various factors by constructing a direct influence matrix and a total influence matrix. The combined use of these methods can provide comprehensive and scientific inputs for DEA. The steps of the FCD pre-module are as follows:

[0125] (1) Use the hesitant fuzzy linguistic term set to collect the evaluation information of decision-makers. Through the triangular fuzzy number importance degree conversion table as shown in the following table, convert the linguistic evaluation into an impact value, so as to realize the conversion of triangular fuzzy numbers;

[0126] Table 2 Triangular Fuzzy Number Importance Degree Conversion Table

[0127] Language evaluation Influence value Symbol representation Triangular fuzzy number No influence 0 NO (0，0，0.25) Weak influence 1 VL (0，0.25，0.5) Relatively weak influence 2 L (0.25，0.5，0.75) Relatively strong influence 3 H (0.5，0.75，1.0) Very strong influence 4 VH (0.75，1.0，1.0)

[0128] (2) Convert it into a triangular fuzzy direct influence matrix according to the triangular fuzzy number importance degree and the qualitative index performance degree conversion table;

[0129] (3) Standardize the triangular fuzzy numbers of each expert's score. The calculation formula for standardization is:

[0130]

[0131] Among them,

[0132] (4) Calculate the standardized values on the left and right. The calculation formulas for the standardized values on the left and right are:

[0133]

[0134] (5) Calculate the total standardized value. The calculation formula for the total standardized value is:

[0135]

[0136] (6) Calculate the defuzzified value of the evaluations of k experts. The calculation formula for the defuzzified value is:

[0137]

[0138] (7) Integrate the evaluations of k experts to obtain the defuzzified direct influence matrix. The calculation formula for the direct influence matrix is:

[0139]

[0140] (8) Use the defuzzified direct influence matrix Z to perform the calculation of DEAMTEL. Sum each row of the matrix Z and take the maximum value. Divide all elements in the matrix Z by the maximum value to determine the normalized influence matrix B. The calculation formula for the normalized influence matrix B is:

[0141]

[0142] (9) Establish the comprehensive influence matrix. The calculation formula for the influence matrix is:

[0143]

[0144] Among them, I is the identity matrix;

[0145] (10) Calculate the influence degree D of each element i , the influenced degree C i , the centrality M i , the reason degree R i and the weight;

[0146] Furthermore, the calculation process of the influence degree D i includes:

[0147]

[0148] Furthermore, the calculation process of the influenced degree C i includes:

[0149]

[0150] Furthermore, the calculation process of the centrality M i includes:

[0151] M i = D i + C i ;

[0152] Furthermore, the calculation process of the reason degree R i includes:

[0153] R i = D i - C i ;

[0154] (11) Taking the centrality as the abscissa and the reason degree as the ordinate, draw a causal relationship diagram. Above the X-axis in the causal relationship diagram are the input indicators, and below are the output indicators, that is, the identification process of the input-output indicators.

[0155] S130. Input the above input-output indicators and use the traditional DEA method as an intermediate module for evaluation.

[0156] DEA is a non-parametric method for evaluating the relative efficiency of decision-making units. It measures efficiency by constructing a production frontier surface and has advantages such as handling multiple inputs and outputs, dimensionlessness, no need to preset a functional form, and avoiding subjective factors. By taking the input-output indicators output by the pre-module as the DEA input, it is more scientific and rational than directly specifying in the traditional sense.

[0157] The steps of the DEA intermediate module are:

[0158] (1) Assume that there are n DMUs in total, and each DMU has m types of inputs and r types of outputs. The optimization model with the goal of the optimal relative efficiency of the system is as follows:

[0159]

[0160] Among them, X j ={x 1j , x 2j , …, x mj}, where x ij is the i-th input index of the DMU j ; Y j ={y 1j , y 2j , …, y rj}, where y sj is the s-th output index of the DMU j ; the output weight and the input weight are α = [α1, …, α r T , β = [β1, …, β m T .

[0161] (2) Using Charnes-Cooper to convert the above fraction into a linear model, taking o decision-making units DMU o as an example (the input is X o , and the output is Y o ), the calculation formula is:

[0162]

[0163] Among them, λ j is the weight coefficient; S + , S - are the slack variables of the input and output respectively, and the optimal solution of the model is ρ;

[0164] (3) The meanings of S - , S + , and ρ in the above model include:

[0165] When ρ = 1, S - = 0, and S + = 0, the DMU o is called DEA strongly effective, that is, when the input is X o , the system has reached the optimal output Y o ;

[0166] When ρ = 1, but S - or S + is not 0, the DMU o is called DEA weakly effective;

[0167] When ρ < 1, the DMU o is called DEA ineffective.

[0168] ​​S140. To address the problem that the DEA efficiency evaluation results cannot be ranked, an entropy weight method-TOPSIS compensation module is constructed to evaluate the comprehensive benefits of the hydrogen energy storage system.

[0169] The entropy weight method is an objective weighting method that determines weights by calculating the entropy values of each index. It can objectively reflect the discrimination ability of the indexes and reduce the influence of subjective factors. However, it is highly sensitive to data and may be greatly affected by minor changes in the data, resulting in instability of the weights. The TOPSIS method is a commonly used multi-attribute decision-making method that determines the quality of evaluation objects by calculating the distances between evaluation objects and the ideal solution and the worst solution. The results are intuitive and easy to understand, and it can comprehensively consider the influence of multiple attribute factors at the same time. However, the TOPSIS method is sensitive to the determination of weights. If the weights are set unreasonably, it may affect the accuracy of the results. The entropy weight method-TOPSIS combines the objective weighting of the entropy weight method and the distance evaluation of the ideal and worst solutions of the TOPSIS method, which can evaluate and rank more scientifically and reasonably. It can use the weights objectively determined by the entropy weight method, combine the intuitive evaluation process of the TOPSIS method, improve the accuracy and reliability of the evaluation, and reduce the limitations of a single method, making the evaluation results more stable and comprehensive.

[0170] The steps of the entropy weight method-TOPSIS as a compensation module are as follows:

[0171] (1) Arrange the DEA-efficient matrix into an n×m matrix, where n is the number of objects and m is the number of indexes;

[0172]

[0173] (2) Use the range method to normalize and standardize the original matrix;

[0174] (3) Obtain the standardized matrix Z, and the formula for the standardized matrix Z is:

[0175]

[0176] (4) Calculate the proportion P of the value of the i-th object under the j-th index in this index ij ,

[0177]

[0178] (5) Calculate the entropy value E of the j-th index j , and the entropy value E of the index j The calculation formula is:

[0179]

[0180] where, when P ij = 0, P ij lnP ij = 0;

[0181] (6) Calculate the coefficient of variation G of the j-th index j , the coefficient of variation G j The calculation formula is:

[0182] G j = 1 - E j ;

[0183] (7) Calculate the weight W of the j-th index j , the weight W j The calculation formula is:

[0184]

[0185] (8) Multiply each column of the standardized matrix Z by the corresponding weight to determine the weighted matrix Z

[0186]

[0187] (9) Determine the positive and negative ideal solutions. The weighted standardized matrix Z at this time is:

[0188]

[0189] Among them, the positive ideal solution formula is:

[0190]

[0191] Among them, the negative ideal solution formula is:

[0192]

[0193] (10) Calculate the distances between the object and the positive and negative ideal solutions according to the Euclidean distance:

[0194]

[0195] (11) Calculate the relative closeness degree through the negative ideal solution distance and sort. The formula is:

[0196]

[0197] Obtain the comprehensive benefit evaluation result of hydrogen energy storage

[0198] The present invention constructs an evaluation index system, which includes economic benefit indicators, technical benefit indicators, environmental benefit indicators, and social benefit indicators. Based on the constructed comprehensive benefit evaluation indicators, a method combining triangular fuzzy numbers, the CFCS method, and the DEMATEL method is constructed as the FCD pre-module to obtain input-output indicators. The above input-output indicators are input into the traditional DEA method to analyze the effectiveness of each scheme, calculate the input-output efficiency of the evaluation indicators, and determine the effectiveness evaluation ranking. To address the problem that the traditional DEA cannot rank when it is strongly effective for the above evaluation results, the entropy weight-TOPSIS method is further used as a supplementary module for evaluation to obtain the comprehensive benefit evaluation of the hydrogen energy storage system. This method makes up for the defects of the traditional DEA and provides a new perspective. By evaluating examples and comparing the evaluation results with published papers, it is in line with them, verifying the effectiveness of this method and the beneficial effects brought, and it can be extended to cross-field evaluation disciplines.

[0199] Examples are as follows:

[0200] Comprehensive evaluation calculation of the numerical example:

[0201] 1) The numerical example data is based on building the structure of a green hydrogen and blue hydrogen energy system with equipment such as electrolyzed green hydrogen, steam methane reforming blue hydrogen, hydrogen-diesel engines, and hydrogen storage tanks. Then, under the carbon trading mechanism, a low-carbon economic dispatch model with the minimum sum of carbon trading costs, wind power operation and curtailment penalty costs, energy purchase costs, and blue hydrogen purification costs as the optimization goal is established. For different carbon emission factors and hydrogen blending ratios, the indicators of 27 scenarios in three scenarios: the operation scenario using green hydrogen, the operation scenario using only blue hydrogen, and the coordinated operation of green hydrogen and blue hydrogen are simulated. The results of the first 8 indicators are obtained through simulation, and the results are based on the references. The last 3 indicators are obtained by inviting 8 experts to score and take the average. The indicator values of each scheme are shown in Table 3 below.

[0202] Table 3 Summary of each indicator data

[0203]

[0204] 2) FCD pre-module

[0205] According to the steps of the FCD pre-module, 8 experts in related fields are invited to construct a semantic direct influence matrix through pairwise comparison, assuming that the evaluation weight values of each expert are the same. Combining with the table, it is converted into a triangular intuitionistic fuzzy direct influence matrix. The CFCS method is used to defuzzify the triangular fuzzy numbers, and the comprehensive influence matrix is obtained through DEMATEL calculation, including influence degree, being influenced degree, centrality, and reason degree. The calculated results are shown in Table 4 below:

[0206] Table 4 Influence degree, etc., ranking and factor attributes of each factor

[0207]

[0208] Plot the calculation results of the reasonability and centrality of all the above indicators into a centrality-reasonability scatter plot, as Figure 2 shown. Figure 2 Among them, the indicators falling in the first, second, third, and fourth quadrants represent high importance of cause factors, low importance of cause factors, low importance of result factors, and high importance of result factors respectively. The input indicators and output indicators of the comprehensive benefits of the hydrogen energy storage system attributed to the collaborative optimization of blue and green hydrogen, namely the total cost, energy purchase cost, electrolyzer fluctuation power, and average energy storage release depth, constitute the input indicator system; the marginal electricity price of hydrogen production, wind curtailment rate, carbon emissions, energy conversion efficiency, employment guarantee, public recognition, and industrial chain promotion constitute the output indicator system;

[0209] 3) DEA intermediate module

[0210] After determining the input-output indicator system based on the FCD front-end module, perform the processing and calculation of DEA to comprehensively evaluate 27 scenarios under different carbon emission factors and hydrogen blending ratios. Among them, scenarios 1-9 are operation scenarios using only green hydrogen; scenarios 10-18 are operation scenarios using only blue hydrogen; scenarios 19-27 are operation scenarios with the collaboration of green hydrogen and blue hydrogen. The evaluation results of each system are shown in Table 5.

[0211] Table 5 DEA effectiveness analysis

[0212]

[0213]

[0214] It can be seen from the comprehensive evaluation results that there are more non-DEA-effective cases for green hydrogen, and scenarios 2 to 6 are non-DEA-effective. Among blue hydrogen, scenarios 13 and 14 are weakly DEA-effective. The comprehensive efficiency values of most of the green hydrogen and blue hydrogen are close to 1, only scenario 27 is non-DEA-effective, and there is no input redundancy and output shortage in the remaining scenarios, so they are DEA-effective. That is, in the case study, it is considered that there is a large room for improvement in green hydrogen, and there is good development space for the collaboration of green hydrogen and blue hydrogen.

[0215] 4) Entropy weight method - TOPSIS as a compensation module

[0216] In view of the fact that it is impossible to determine the advantages and disadvantages when DEA is strongly effective in Table 5 above, add the proposed compensation function and calculate according to the formula. The scenario ranking results are shown in Table 6. According to the results, the scenario ranking is: scenario 26 > scenario 1 > scenario 8 > scenario 19 > scenario 20 > scenario 9 > scenario 25 > scenario 22 > scenario 21 > scenario 24 > scenario 23 > scenario 10 > scenario 17 > scenario 18 > scenario 11 > scenario 12 > scenario 16 > scenario 15.

[0217] Table 6 Ranking of the compensation module for the strong DEA scenario

[0218]

[0219]

[0220] In the measurement results of each scheme of traditional DEA, the ranking of the scheme by traditional DEA is repeated, and it is impossible to measure the scenario where the efficiency is 1, that is, when it is strongly DEA and cannot be ranked. Table 7 shows the ranking comparison between DEA and the three-layer comprehensive evaluation under different scenarios.

[0221] Table 7 Ranking comparison between DEA and the three-layer comprehensive evaluation

[0222]

[0223] The visualization graph of the ranking comparison between DEA and the three-layer comprehensive evaluation is as Figure 3 shown. The three-layer comprehensive evaluation method proposed in the present invention shows that the comprehensive benefit evaluation result of the blue-green hydrogen collaborative optimization hydrogen energy storage system is the best when the carbon emission factor is 0.581 and the hydrogen blending ratio is 20%, followed by the case when the carbon emission factor is 0.581 and the hydrogen blending ratio is 10%. When the carbon emission factor and the hydrogen blending ratio are measured as 0.581 and 10% respectively, the comprehensive benefit is poor and there is a large room for improvement. The above evaluation results are consistent with the results of the published papers, which proves the effectiveness of the proposed evaluation method. The three-layer comprehensive evaluation method constructed in this paper has integrity and logic.

[0224] In summary, the method of this embodiment constructs a three-layer comprehensive evaluation method based on the coupling of the FCD-DEA method and the entropy weight-TOPSIS method on the basis of the traditional DEA evaluation method. By using the triangular fuzzy number, the CFCS method and the DEMATEL method as the FCD pre-module, the traditional DEA method as the middle module, and the entropy weight-TOPSIS method as the compensation module, the comprehensive benefit of the hydrogen energy storage system is evaluated. The FCD pre-module constructed in the present invention makes up for the deficiency of the traditional DEA module in directly specifying the input-output indicators, and the constructed entropy weight-TOPSIS method compensation module solves the problem that the traditional DEA cannot be ranked when it is strongly effective. The three-layer comprehensive evaluation model is innovative and complete. To verify the correctness of the algorithm, based on the analysis of the index simulation data, the evaluation results are compared with the published papers. The comparison shows that the evaluation has a certain degree of accuracy, which is beneficial to the construction and development of future system projects.

[0225] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0226] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program codes. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0227] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0228] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in this computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0229] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 in one block or a plurality of blocks.

[0230] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0231] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An integrated benefit evaluation method for a hydrogen energy storage system, characterized in that, It includes the following steps: Construct a comprehensive benefit evaluation index system for the hydrogen energy storage system; Obtain relevant evaluation information of the hydrogen energy storage system, combine the comprehensive benefit evaluation index system, use triangular fuzzy numbers to process uncertain information, and use the CFCS method for defuzzification processing to obtain a defuzzified direct impact matrix; Based on the defuzzified direct impact matrix, use the DEMATEL method to quantify the mutual influence relationship between evaluation indicators and obtain input-output indicators; Based on the input-output indicators, use non-parametric methods for efficiency analysis to determine the initial efficiency evaluation ranking results; According to the initial efficiency evaluation ranking results, use the entropy weight-TOPSIS method for evaluation to obtain the final efficiency evaluation ranking results as the comprehensive benefit evaluation results.

2. The comprehensive benefit evaluation method of a hydrogen energy storage system according to claim 1, characterized in that The comprehensive benefit evaluation index system includes a target layer, an attribute layer, and an index layer. The attribute layer includes multiple dimensions of evaluation indicators, including an economic benefit dimension, a technical benefit dimension, an environmental benefit dimension, and a social benefit dimension. The index layer includes multiple evaluation indicators under each dimension. Among them, the evaluation indicators under the economic benefit dimension include: total cost, energy purchase cost, and marginal electricity price for hydrogen production; The evaluation indicators under the technical benefit dimension include: electrolyzer fluctuation power, average energy storage release depth, and wind curtailment rate; The evaluation indicators under the environmental benefit dimension include: carbon emissions, energy conversion efficiency; The evaluation indicators under the social benefit dimension include: employment security, public recognition, and industrial chain promotion.

3. The comprehensive benefit evaluation method of a hydrogen energy storage system according to claim 1, wherein, The steps of using triangular fuzzy numbers to process uncertain information include: Based on the relevant evaluation information, perform the conversion of triangular fuzzy numbers through the triangular fuzzy number importance degree and qualitative index performance degree conversion table to form a triangular fuzzy direct impact matrix. The triangular fuzzy number importance degree and qualitative index performance degree conversion table includes four columns, namely language evaluation, impact value, symbol representation, and triangular fuzzy number; When the language evaluation is no impact and the impact value is 0, the symbol representation is NO, and the triangular fuzzy number is (0, 0, 0.25); When the language evaluation is very weak impact and the impact value is 1, the symbol representation is VL, and the triangular fuzzy number is (0, 0.25, 0.5); When the language evaluation is very weak impact and the impact value is 2, the symbol representation is L, and the triangular fuzzy number is (0.25, 0.5, 0.75); When the language evaluation is very weak impact and the impact value is 3, the symbol representation is H, and the triangular fuzzy number is (0.5, 0.75, 1.0); When the language evaluation is very weak impact and the impact value is 4, the symbol representation is VH, and the triangular fuzzy number is (0.75, 1.0, 1.0).

4. The comprehensive benefit evaluation method of a hydrogen energy storage system according to claim 3, characterized in that The steps of obtaining the defuzzified direct impact matrix include: Standardize the triangular fuzzy numbers in the triangular fuzzy direct impact matrix, where the expression for standardization is: According to the standardization processing results, calculate the standardized value, where the calculation expression for the standardized value is: Calculate the total standardized value according to the standardized value, where the calculation expression for the total standardized value is: According to the total standardization value, calculate the defuzzification value of the relevant evaluation information, where the calculation expression of the defuzzification value is: According to the defuzzification value, obtain the defuzzification direct influence matrix, where the expression of each element in the defuzzification direct influence matrix is: where x is the key variable in the standardization and defuzzification processes, is the left endpoint value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the maximum value of the maximum differences among all fuzzy numbers; represents the middle value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; represents the right endpoint value of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; is the standardized value of the left and right endpoints of the k-th fuzzy number of the i-th evaluation object under the j-th evaluation index; x k ij is the total standardized value calculated based on the standardized value; z k ij is the defuzzified value obtained based on the total standardized value; z ij is an element in the defuzzification direct influence matrix.

5. The comprehensive benefit evaluation method of a hydrogen energy storage system according to claim 1, characterized in that The steps of obtaining the input-output indicators include: Based on the defuzzification direct influence matrix, use the row maximum method to obtain the normalized influence matrix, where the calculation expression of the normalized influence matrix is: where B is the canonical influence matrix, and x ij is an element in the defuzzification direct influence matrix; n is the total number of evaluation indexes; Based on the normalized influence matrix, establish the comprehensive influence matrix, and the calculation expression of the comprehensive influence matrix is: where T is the comprehensive influence matrix, and B k is the k-th power of the standardized influence matrix B; Calculate the influence degree and the influenced degree of each element in the comprehensive influence matrix, and the calculation expressions are respectively: where D i is the influence degree, and C i is the affected degree; Based on the influence degree and the influenced degree of each element, calculate the centrality and the reason degree of each element, and the calculation expressions are respectively: M i = D i + C i R i = D i - C i where M i is the centrality, and R i is the causality; Taking the centrality as the abscissa and the reason degree as the ordinate, draw a causal relationship diagram as the input-output indicator.

6. The comprehensive benefit evaluation method for a hydrogen energy storage system according to claim 1, wherein, The non-parametric method is the DEA method.

7. The comprehensive benefit evaluation method of a hydrogen energy storage system according to claim 6, wherein The steps of determining the initial efficiency evaluation ranking result include: Based on the input-output indicator, assume that there are n DMUs in total, each DMU has m inputs and r outputs, and establish an optimization model with the goal of the optimal relative efficiency of the hydrogen energy storage system, expressed as: where X j ={x 1j , x 2j , …, x mj}, Y j ={y 1j , y 2j , …, y rj}, α = [α1, …, α r T is the output weight, α r is the weight of the r-th output index, β = [β1, …, β m T is the input weight, β m is the weight of the m-th input index, α s is the weight of the s-th output index, x j , y j are the input and output data of the DMU, y sj are the s output indices of the DMU j , β i is the weight of the i-th input index, ij are the i input indices of the DMU j ;​​ The Charnes-Cooper method is used to transform the optimization model into a linear model, which contains o DMUs o The expression of the linear model is as follows: where ρ is the optimal solution, and λ j is the weight coefficient, S + , S - are the slack variables of input and output, X o is the input, and Y o is the output; Based on the linear model, there is: If ρ = 1 and S - = 0, S + = 0, then the DMU o is considered strongly efficient, indicating that when the input is X o , the hydrogen energy storage system has achieved the optimal output Y o ; If ρ = 1, but S - or S + is not 0, then the DMU o is considered weakly efficient; If ρ < 1, then the DMU is considered o ineffective; Further sort according to the performance of DMU o to obtain the initial performance evaluation sorting result.

8. The comprehensive benefit evaluation method for a hydrogen energy storage system according to claim 7, wherein The steps of using the entropy weight-TOPSIS method for evaluation include: Based on DMU o A strongly effective solution and its corresponding index matrix, arranged as an n×m matrix, with the expression: In the formula, X is a matrix, n is the number of objects, and m is the number of indicators; Use the range method to normalize and standardize the n×m matrix to obtain the standardized matrix: In the formula, Z is the standardized matrix; Based on the standardized matrix, calculate the proportion of the indicators, where the calculation expression of the proportion is: where P ij is the proportion of the value of the i-th object under the j-th index in the index, and z ij is an element in the standardized matrix; Based on the proportion of the indicators, calculate the indicator entropy value, where the calculation expression of the indicator entropy value is: where E j is the entropy value of the j-th index, and when P ij = 0, P ij lnP ij = 0; Based on the indicator entropy value, calculate the difference coefficient of the indicators, where the calculation expression of the difference coefficient is: G j = 1 - E j where G j is the difference coefficient of the j-th index; Based on the difference coefficient of the indicators, calculate the weight of the indicators, where the calculation expression of the weight of the indicators is: where W j is the weight of the j-th index; Multiply each column of the standardized matrix by the weight of the indicators to obtain the weighted matrix: where Z is the weighting matrix and ω m is the weight of the m-th index; Standardize the weighted matrix to obtain the weighted standardized matrix, and further obtain the positive and negative ideal solutions, where the weighted standardized matrix is expressed as: The positive ideal solution is expressed as: The negative ideal solution is expressed as: where Z is the weighted normalization matrix, Z + is the positive ideal solution, and Z - is the negative ideal solution; Calculate the distances between the objects and the positive and negative ideal solutions according to the Euclidean distance, where the calculation expression of the distance is: In the formula, is the distance between the object and the positive ideal solution and the negative ideal solution; Based on the distances between the positive and negative ideal solutions, calculate the relative closeness degree and sort to obtain the final efficiency evaluation ranking result as the comprehensive benefit evaluation result, where the calculation expression of the relative closeness degree is: where S i is the relative closeness degree.

9. An integrated benefit evaluation system for a hydrogen energy storage system, characterized in that, Include: System construction module: used to construct the comprehensive benefit evaluation index system of the hydrogen energy storage system; Fuzzy-CFCS processing module: used to obtain the relevant evaluation information of the hydrogen energy storage system, combine with the comprehensive benefit evaluation index system, use triangular fuzzy numbers to process uncertain information, and use the CFCS method for defuzzification processing to obtain the defuzzification direct influence matrix; Quantification module: configured to quantify the mutual influence relationship between each evaluation index by using the DEMATEL method based on the defuzzified direct influence matrix, and obtain input-output indexes; Intermediate module: configured to perform effectiveness analysis by using a non-parametric method based on the input-output indexes, calculate the input-output efficiency of each evaluation index, and determine the initial effectiveness evaluation ranking result; Compensation module: configured to perform evaluation by using the entropy weight-TOPSIS method according to the initial effectiveness evaluation ranking result, obtain the final effectiveness evaluation ranking result, and use it as the comprehensive benefit evaluation result.

10. A computer-readable storage medium, characterized in that, Comprising one or more programs executed by one or more processors of the power supply device, the one or more programs comprising instructions for executing the comprehensive benefit evaluation method of the hydrogen energy storage system according to any one of claims 1-8.

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