Multi-index comprehensive evaluation method of hydrogen storage technology in island power grid peak regulation

Abstract

The invention relates to a multi-index comprehensive evaluation method for a hydrogen storage technology in island power grid peak regulation, and the method comprises the following steps: constructing a comprehensive evaluation index system for the participation of the hydrogen storage technology in island power grid peak regulation, and the island power grid comprises hydrogen energy storage; based on the comprehensive evaluation index system, an AHP method and a CRITIC method are adopted to calculate the subjective weight and the objective weight of each evaluation index; based on the subjective weight and the objective weight of each evaluation index, calculating a combined weight of each evaluation index by adopting a game theory; calculating an average membership degree of each evaluation index according to a cloud model theory, and calculating a fusion weight of each evaluation index in combination with the combined weight; and based on an improved TOPSIS method, carrying out coupling calculation on the fusion weight of each evaluation index, and outputting a sorting result of different hydrogen storage technologies in island power grid peak regulation as a final evaluation result. Compared with the prior art, the method has the advantages of accurate evaluation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen energy storage and power system evaluation, and in particular to a multi-index comprehensive evaluation method for hydrogen storage technology in peak regulation of an isolated power grid. Background Art

[0002] As an energy storage technology with great potential, hydrogen energy storage has broad application prospects in power systems, especially in solving the volatility of renewable energy and improving the stability of the power grid. Hydrogen energy storage converts electrical energy into hydrogen and stores it. It has significant application potential in power grid peak regulation, energy storage, load balancing, etc., and can effectively improve the flexibility and regulation capabilities of the power grid. However, despite the broad application prospects of hydrogen energy storage, the current evaluation system for hydrogen energy storage is still in the development stage. In particular, in terms of how to systematically and comprehensively evaluate the comprehensive performance of hydrogen energy storage, existing research and technology still have certain deficiencies.

[0003] At present, the evaluation methods for hydrogen energy storage are relatively simple, mostly focusing on the benefit evaluation in a specific field, such as technical benefits, economic benefits, environmental benefits, etc., and lacking a comprehensive evaluation of multi-dimensional comprehensive benefits. Some existing studies mainly evaluate hydrogen energy storage as a whole based on a single indicator or a simple multi-criteria decision-making method (such as AHP, DEA, etc.), but these methods are mostly limited to certain single benefits or fixed application scenarios, and lack a dynamic evaluation of the comprehensive performance of different hydrogen storage technologies under different grid peak load scenarios.

[0004] For example, although some existing patented technical solutions have proposed methods based on data envelopment analysis (DEA) or improved AHP to evaluate the efficiency of logistics systems, or combined ANP with DEA to evaluate employee workload, these methods mostly focus on the efficiency evaluation of a certain aspect, lacking comprehensive consideration of multi-dimensional benefits and adaptability to specific application scenarios. In addition, although the application of interdisciplinary evaluation methods has made initial progress in some fields, in the evaluation of hydrogen storage technology, how to effectively combine multiple dimensions such as technology, economy, environment and society to build a scientific and reasonable evaluation system is still a challenge that needs to be solved.

[0005] At present, the evaluation methods for hydrogen storage technology in the peak load regulation of isolated power grids are still imperfect. Most studies and existing technology patents focus on evaluating the benefits of a single dimension or a specific scenario, ignoring the impact of regional differences and multi-dimensional comprehensive benefits. The power systems in different regions and the peak load regulation needs, technology development stages, environmental and social benefits they face vary greatly. A single-dimensional evaluation cannot fully reflect the comprehensive performance of hydrogen energy storage in the peak load regulation of different isolated power grids. Therefore, the existing evaluation methods cannot effectively meet the needs for comprehensive evaluation of different hydrogen storage technologies under the changing peak load regulation needs of power grids.

[0006] Existing studies mainly analyzed the application of hydrogen storage technology in power system peak shaving from a single dimension of economy or technology. The literature [Li Na, Li Zhiyuan, Wang Nan, et al. Exploration and Research on the Development Path of Hydrogen Energy Storage Peak Shaving Station [J]. Energy of China, 2021, 43(1): 55-59, 67.] adopted the hydrogen storage method of high-pressure gaseous hydrogen storage tanks and mainly analyzed the technical and economic feasibility of building a hydrogen energy storage peak shaving station in the high-purity hydrogen demand center. The literature [Zhang Kaipeng, Yang Xuemei, Zhang Hongtian, et al. Optimal Configuration of Hydrogen Energy Storage in Distribution Network Considering the Participation of "Photovoltaic-Energy Storage" Coupling in Peak Shaving [J]. Power Grid and Clean Energy, 2023, 39(10): 95-103, 112.] also adopted high-pressure gaseous hydrogen storage tanks and established a configuration model for photovoltaic-hydrogen energy storage assisted peak shaving, systematically analyzed the economic dimension analysis with operation and maintenance costs and carbon emission costs as the optimization objectives, and explored how to reduce the costs of energy storage during peak shaving in the distribution network and improve its operation economy and stability. However, the above articles all focused on the technical and economic analysis of the entire hydrogen energy storage system and ignored the key factor of hydrogen storage technology. The literature [Davies, E., Ehrmann, A., & Schwenzfeier-Hellkamp, E. (2024). Safety of hydrogen storage technologies. Processes, 12(10), 2182.] focused on different hydrogen storage technologies and conducted a safety risk assessment of the entire hydrogen energy storage system, and carried out risk assessment through typical sensors and explosion-proof measures. The literature [Liang Qianchao, Zhao Jianfeng, Liang Yifan, et al. Development Status of Hydrogen Storage Technology [J]. Journal of Naval University of Engineering, 2022, 34(3): 92-101.] reviewed the hydrogen storage principles, constituent materials and development status of different hydrogen storage technologies, providing a reference for studying and analyzing the current technical status and availability that various hydrogen storage technologies can achieve. Currently, the methods adopted for the evaluation of hydrogen energy storage mainly include the analytic hierarchy process, entropy weight method and TOPSIS method, etc. However, the above comprehensive evaluation methods inevitably require the calculation of index weights, and there is currently no clear standard for the calculation of index weights, and the evaluation results obtained by different weight calculation methods often vary.

[0007] Ignored the uncertainty and ambiguity in the evaluation process: Many existing technologies failed to fully consider the uncertainty and ambiguity in the evaluation process. For example, the actual performance of hydrogen storage technology is affected by various factors, such as technological progress, policy changes, environmental factors, etc., and these factors have a high degree of uncertainty. Although some methods tried to introduce fuzzy mathematics and statistical methods for processing, they often lacked intuitive and systematic processing methods, resulting in the inability to effectively quantify and evaluate the impact of this uncertainty and ambiguity on the evaluation results.

[0008] Inaccuracy of weight determination methods: In some evaluation methods, the determination of weights usually relies on subjective judgments of experts or data-driven analysis methods such as AHP (Analytic Hierarchy Process) or DEA (Data Envelopment Analysis). The limitations of these methods are that the assignment of their weights may be affected by biases in expert experience, or predictions based on historical data may not accurately reflect future change trends. In addition, traditional weight allocation methods fail to fully consider the mutual relationships and dynamic changes of weights among various evaluation indicators, which may lead to errors or instability in evaluation results.

[0009] In summary, the existing evaluation methods for hydrogen storage technologies still have obvious deficiencies in multi-dimensional comprehensive evaluation and have not formed a complete evaluation system. Therefore, it is necessary to fill the gap in the multi-index comprehensive evaluation of hydrogen energy storage in the peak shaving of isolated island power grids based on in-depth analysis of existing technologies and provide effective support for further technology optimization and decision-making. Summary of the Invention

[0010] The purpose of the present invention is to provide a multi-index comprehensive evaluation method for hydrogen storage technologies in the peak shaving of isolated island power grids to improve evaluation accuracy.

[0011] The purpose of the present invention can be achieved through the following technical solutions:

[0012] A multi-index comprehensive evaluation method for hydrogen storage technologies in the peak shaving of isolated island power grids includes the following steps:

[0013] Construct a comprehensive evaluation index system for hydrogen storage technologies participating in the peak shaving of isolated island power grids, where hydrogen energy storage is included in the isolated island power grid;

[0014] Based on the comprehensive evaluation index system, calculate the subjective weights and objective weights of each evaluation index by using the AHP method and the CRITIC method respectively;

[0015] Based on the subjective weights and objective weights of each evaluation index, calculate the combined weights of each evaluation index by using game theory;

[0016] Calculate the average membership degree of each evaluation index according to the cloud model theory, and calculate the fusion weights of each evaluation index in combination with the combined weights;

[0017] Based on the improved TOPSIS method, perform coupling calculation on the fusion weights of each evaluation index, and output the ranking results of different hydrogen storage technologies in the peak shaving of isolated island power grids as the final evaluation results.

[0018] Furthermore, the comprehensive evaluation index system includes an objective layer, an attribute layer, an index layer, and a solution layer. The attribute layer includes multiple dimensions of evaluation indicators, including a technology dimension, an economic dimension, an environmental dimension, and a social benefit dimension. The index layer includes multiple evaluation indicators under each dimension, and the solution layer includes different hydrogen storage technologies, including gaseous hydrogen storage, liquid hydrogen storage, solid hydrogen storage, and chemical hydrogen storage.

[0019] Furthermore, the evaluation indicators under the technology dimension include conversion efficiency, start-up response time, stability, and reliability.

[0020] The evaluation indicators under the economic dimension include initial investment cost, operation and maintenance cost, and life cycle cost.

[0021] The evaluation indicators under the environmental dimension include carbon emissions and resource consumption.

[0022] The evaluation indicators under the social benefit dimension include employment security, policy support level, and social acceptance.

[0023] Furthermore, the steps of calculating the subjective weights of each evaluation indicator by the AHP method include:

[0024] Based on the comprehensive evaluation index system, construct a judgment matrix A = (a ij ) n×n , and calculate the weight vector of each evaluation indicator by the expert scoring method, where a ij is the importance of indicator i relative to indicator j, and n is the dimension;

[0025] Calculate the maximum eigenvalue in the judgment matrix, and perform a consistency test according to the weight vector of each indicator;

[0026] Judge whether the consistency test result is less than the set threshold. If so, it indicates that the consistency check is satisfied. If not, it indicates that the consistency test is not satisfied, and recalculate the weight vector of each evaluation indicator;

[0027] For the judgment matrix that satisfies the consistency test, calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix, and normalize the eigenvector to obtain the subjective weights of each evaluation indicator.

[0028] Furthermore, the expression of the consistency test is:

[0029]

[0030] In the formula, CR is the consistency test result, CI is the consistency index, used to measure the consistency of the judgment matrix, RI is the average random consistency index of the judgment matrix, and λ is the maximum eigenvalue.

[0031] Furthermore, the steps of calculating the objective weights of each evaluation index by using the CRITIC method include:

[0032] Perform dimensionless processing on the evaluation indexes in the comprehensive evaluation index system to obtain dimensionless index data, where the dimensionless processing includes the following:

[0033] Positive normalization processing:

[0034]

[0035] Inverse normalization processing:

[0036]

[0037] Based on the dimensionless index data, calculate the standard deviation of each evaluation index, where the calculation expression of the standard deviation is:

[0038]

[0039] Based on the dimensionless index data, calculate the correlation of each evaluation index by using the Pearson correlation coefficient method, where the calculation expression of the correlation is:

[0040]

[0041] Based on the standard deviation and correlation of each evaluation index, calculate the comprehensive information amount of each evaluation index, where the calculation expression of the comprehensive information amount is:

[0042]

[0043] Based on the comprehensive information amount of each evaluation index, calculate the objective weight of each evaluation index, where the calculation expression of the objective weight is:

[0044]

[0045] In the formula, x′ ij is the value after dimensionless processing, x ij is the original data of the i-th evaluation object on the j-th index, x max and x min are the maximum and minimum values of the j-th evaluation index respectively, s j is the standard deviation of the j-th evaluation index, is the average value of the j-th evaluation index, r jk is the correlation coefficient between the j-th evaluation index and the k-th evaluation index, x′ ik is the dimensionless value of the i-th evaluation object on the k-th index, is the standardized mean value of the k-th evaluation index, C jis the information quantity of the j-th evaluation index, s j is the standard deviation of the j-th evaluation index, m is the total number of indexes, w j is the objective weight of the j-th evaluation index.

[0046] Furthermore, the calculation steps of the combined weights of the evaluation indexes include:

[0047] According to the subjective weights and objective weights of the evaluation indexes, calculate the weight differences of the evaluation indexes, and the calculation expression of the weight differences is:

[0048] Δw j =|w Aj -w Cj |

[0049] Based on the weight differences of the evaluation indexes, construct an optimal objective function to solve the balance parameter α, and the expression of the optimal objective function is:

[0050]

[0051] Based on the balance parameter α, construct a game theory objective function, and fuse the subjective weights and objective weights of the evaluation indexes by linear combination to obtain the combined weights of the evaluation indexes. The calculation expression of the combined weights of the evaluation indexes is:

[0052] w j =αw Aj +(1-α)w Cj

[0053] In the formula, Δw j is the difference between the subjective weight and the objective weight of the j-th index, w Aj , w Cj are the subjective and objective weight values of the j-th index, w j is the combined weight of the j-th index, α is the balance parameter, 0≤α≤1, and m is the total number of indexes.

[0054] Furthermore, the calculation steps of the average membership degree of the evaluation indexes include:

[0055] Divide each evaluation index in the comprehensive evaluation index system into multiple grade intervals, and set the expected value E x , entropy E n and hyperentropy H e ;

[0056] According to the entropy E n and hyperentropy H e of each evaluation index, generate a random entropy E′ n through normal distribution, and the random entropy E′n The expression of

[0057] E′ n = N(E n , H e )

[0058] According to the random entropy E′ n and the expected value E x , generate cloud droplets x i , the expression of the cloud droplet x i is:

[0059] x i = N(E x , E′ n )

[0060] According to the generated cloud droplets x i calculate the membership degree of the cloud droplets further, as the average membership degree, and the calculation expression of the membership degree of the cloud droplets is:

[0061]

[0062] In the formula, E′ n is the random entropy, N is the total number of cloud droplets, x i is the value of the i-th cloud droplet, μ(x) is the membership degree, and x is the cloud droplet value.

[0063] Furthermore, the calculation expression of the fusion weight of each evaluation index is:

[0064] W = w1μ1 + w2μ2 + … + w n μ n

[0065] In the formula, W is the fusion weight of each index, w1, w2…w n are the weights of each index, and μ1, μ2…μ n are the average membership degrees of each index calculated by the cloud model.

[0066] Furthermore, the steps of performing the coupling calculation include:

[0067] Construct a decision matrix X m×n :

[0068]

[0069] Normalize the decision matrix X n×n , and the normalization expression is:

[0070] Positive index:

[0071]

[0072] Inverse index:

[0073]

[0074] Calculations are performed based on the fusion weights and normalization results of the evaluation indicators to obtain a weighted normalization matrix V, where the calculation expression for each element in the weighted normalization matrix V is:

[0075] v ij = W j ·r ij

[0076] Based on the weighted normalization matrix V, calculate the distance between each solution and the ideal solution. The calculation expression is:

[0077] Distance between each solution and the positive ideal solution

[0078]

[0079] Distance between each solution and the negative ideal solution

[0080]

[0081] Based on the distance between each solution and the ideal solution, calculate the relative closeness C of each solution i , and use the relative closeness C of each solution i as the sorting result of the hydrogen storage technology in the peak shaving of the island power grid. Among them, the calculation expression of the relative closeness C of each solution i is:

[0082]

[0083] In the formula, m is the number of solutions, n is the number of indicators, r ij is the normalization result, x ij is the evaluation value of the i-th solution for the j-th indicator, min(x j ) is the minimum value of the j-th indicator, v ij is the weighted normalization value, W j is the weight of the indicator j, is the distance between each solution and the positive ideal solution, is the distance between each solution and the negative ideal solution, is the positive ideal value of the j-th indicator, that is, the optimal target value, is the negative ideal value of the j-th indicator, that is, the worst target value, C i is the relative closeness of each solution, which is between [0, 1], and the larger the value, the higher the ranking.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] (1) Based on the traditional AHP-CRITIC method, the present invention combines game theory for combined weighting, thereby reducing the problem of weight imbalance caused by absolute subjectivity and objectivity. Then, by innovatively introducing the cloud model, it can effectively handle the randomness and fuzziness in evaluation indicators, and fuse with the combined weights determined by game theory to obtain more accurate combined weights. Finally, by improving the TOPSIS method, that is, performing weighted normalization processing with the combined weights, and calculating to obtain a more accurate final evaluation ranking.

[0086] (2) The method of the present invention combines the objective information entropy weight and subjective factors, and uses the cloud model to capture uncertainty, making the evaluation more comprehensive and detailed. This method not only considers the actual performance of each hydrogen storage technology, but also takes into account the uncertain factors in the evaluation process, making the evaluation process more comprehensive and complete.

[0087] (3) The present invention establishes a comprehensive and diversified evaluation framework, comprehensively considering four dimensions of technology, economy, environment and social benefits, and can systematically and scientifically evaluate the performance of different hydrogen storage technologies in power grid peak shaving. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 is a schematic diagram of the method flow of the present invention;

[0089] Figure 2 is a comprehensive evaluation index system diagram of different hydrogen storage technologies of the present invention in the peak shaving of an island power grid;

[0090] Figure 3 is a comparison diagram of the subjective and objective weights and game theory weights of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0091] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and gives the detailed implementation manner and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0092] This embodiment provides a multi-index comprehensive evaluation method for hydrogen storage technology in the peak shaving of isolated power grids. The aim is to establish a comprehensive and diversified evaluation framework, comprehensively considering the four dimensions of technology, economy, environment, and society, and conduct a systematic and scientific evaluation of the performance of different hydrogen storage technologies in power grid peak shaving. Based on the traditional AHP-CRITIC method, this invention combines weights by adding game theory, thereby reducing the weight imbalance problem caused by absolute subjectivity and objectivity. Then, by innovatively introducing the cloud model, it can effectively handle the randomness and fuzziness in evaluation indicators, and fuse with the combined weights determined by game theory to obtain more accurate combined weights. Finally, by improving the TOPSIS method, that is, performing weighted normalization processing with the combined weights and calculating the final evaluation ranking. Through simulation verification and comparison with the results of published public papers, the verification results are consistent, ensuring the accuracy of the method and facilitating the wide application of hydrogen energy storage technology and the development of power grid intelligence. Specifically, as Figure 1 shown, the method includes the following steps:

[0093] Step 1: Establishment of comprehensive evaluation indicators for hydrogen storage technology in the peak shaving of isolated power grids:

[0094] To establish an evaluation index system applicable to peak shaving scenarios, it is necessary to comprehensively measure from multiple dimensions to ensure the scientificity and objectivity of the evaluation results. Construct an evaluation index system from the four dimensions of technology, economy, environment, and social benefits, conduct systematic analysis on the performance of different hydrogen storage technologies in power grid peak shaving, and determine the evaluation method in combination with the evaluation content;

[0095] The technology dimension mainly focuses on the performance of hydrogen storage technology in actual applications, reflecting its ability to meet system requirements during power grid peak shaving; the economic dimension focuses on evaluating the cost-benefit performance of the hydrogen energy storage system to ensure the return on investment and economic feasibility of the project; the application of hydrogen storage technology in power grid peak shaving should meet the requirements of low-carbon environmental protection, so environmental impact becomes an important dimension of evaluation; in addition to the indicators in terms of technology, economy, and environment, the potential social benefits of hydrogen storage technology should also be evaluated. On the basis of meeting comparability, independence, and comprehensiveness, the following evaluation index system is established:

[0096] Table 1 Comprehensive evaluation index system

[0097]

[0098] Step 2: Calculate the subjective weight values of indicators according to the AHP method:

[0099] Calculate the subjective weights based on the established evaluation index system according to AHP:

[0100] Construct the judgment matrix A=(a ij )n×n , calculate the weight vector by the expert scoring method;

[0101] Calculate the maximum eigenvalue λ of the judgment matrix, and conduct a consistency test according to the weights of each index:

[0102]

[0103] Among them, the calculation formula of CI is as follows:

[0104]

[0105] RI — is the average random consistency index of the judgment matrix. The specific value is related to the order of the judgment matrix. The reference values are shown in the following table; if the calculation result CR < 0.1, it means that the consistency of the judgment matrix is relatively high. If CR ≥ 0.1, it means that the consistency of the judgment matrix is poor and needs to be re-assigned;

[0106] Table 2 Average random consistency index values

[0107] n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51

[0108] After passing the consistency test, calculate the eigenvector corresponding to the maximum eigenvalue λ of the judgment matrix, and then normalize the eigenvector to obtain the subjective weight value W of each evaluation index A .

[0109] Step 3. Calculate the objective weight value of the index according to the CRITIC method:

[0110] First, dimensionless the index data: Dimensionless the original data of different indexes to ensure the comparability of data with different units. The formula is as follows:

[0111] Positive processing:

[0112]

[0113] Negative processing:

[0114]

[0115] Among them, x ij is the original data of the i-th evaluation object on the j-th index, x' ij is the value after dimensionless, x max and x min are the maximum and minimum values of the j-th index respectively;

[0116] Then, calculate the standard deviation of each index. The standard deviation is an index to measure the difference of the index. The larger the standard deviation, the greater the discrimination of the index for different evaluation objects. The calculation formula is as follows:

[0117]

[0118] Among them, s j is the standard deviation of the j-th index; is the average value of the j-th index, and the calculation formula is as follows:

[0119]

[0120] Then, calculate the correlation between each index. In order to consider the redundancy between indexes, the CRITIC method also needs to calculate the correlation between each index. The Pearson correlation coefficient is used to measure the correlation between indexes, and the calculation formula of the correlation coefficient is:

[0121]

[0122] Among them, r jk is the correlation coefficient between the j-th index and the k-th index;

[0123] Then, calculate the comprehensive information volume of each index, and it is necessary to consider both the standard deviation of this index and its correlation with other indexes. The information volume can be calculated by the following formula:

[0124]

[0125] Among them, C j is the information volume of the j-th index; s j is the standard deviation of the j-th index; r jk is the correlation between the j-th index and the k-th other index; m is the total number of indexes;

[0126] Calculate the weight according to the information volume of each index, and the weight calculation formula is as follows:

[0127]

[0128] Finally, obtain the objective weight W C .

[0129] Step 4: Calculate the combined weight according to game theory:

[0130] First, define the weight difference of each index:

[0131] Δw j =|w Aj -w Cj |

[0132] Among them, Δw j is the difference between the subjective weight and the objective weight of the j-th index, w Aj , w CjThey are the subjective and objective weight values of each index calculated previously respectively;

[0133] Then, construct the game theory objective function and fuse the weights of the two by means of linear combination, that is:

[0134] w j =αw Aj +(1 - α)w Cj

[0135] where w j is the final fused weight of the j-th index; α is the balance parameter, 0 ≤ α ≤ 1, which is used to adjust the weight proportion of the AHP and CRITIC methods;

[0136] Then, construct the optimal objective function to solve for α, and the objective function is constructed as:

[0137]

[0138] The objective function is to minimize the weight error of each index, and the optimal α value can be obtained by solving this objective function;

[0139] After obtaining the optimal α value by solving through the game theory model, substitute it into the linear combination formula to obtain the combined weight.

[0140] Step 5: Calculate the average membership degree of the index according to the cloud model theory, and calculate the final fused weight of the index with the weight combination determined by game theory:

[0141] Combined with the actual operation data, literature survey and expert interviews of the existing hydrogen storage projects, each evaluation index is divided into 5 grade intervals. The process of determining the evaluation index grades includes:

[0142] Literature data: Select high-quality journal literatures related to hydrogen storage technology in the past five years, and extract key index data from them;

[0143] Expert scoring: Invite engineers and researchers in the field of hydrogen energy storage to score the subjective indexes (such as social acceptance and policy support) of hydrogen storage technology, and use the average score as the data reference;

[0144] Historical case data: Combine the actual operation data of the existing hydrogen storage projects (such as operation and maintenance costs, carbon emissions), and process the data by taking the mean value. Each index is divided into grades by combining historical data and expert scoring.

[0145] Set the expected value E x 、entropy E n and hyperentropy H e for each index according to the grade interval;

[0146] Generate random entropy E′ through normal distribution n , simulating the ambiguity fluctuations in reality:

[0147] E′ n = N(E n , H e )

[0148] Based on the expected value and random entropy E′ n , generate cloud droplets x i , and these cloud droplets represent the possible values of this fuzzy index:

[0149] x i = N(E x , E′ n )

[0150] Calculate the membership degree of cloud droplets, and the calculation formula is as follows:

[0151]

[0152] Among them, μ(x) represents the membership degree; x is the cloud droplet value;

[0153] Finally, combine the average membership degree of the index with the combined weight determined by game theory to calculate the fusion weight of each index, and the calculation formula is as follows:

[0154] W = w1μ1 + w2μ2 + … + w n μ n

[0155] Among them, w1, w2 … w n are the weights of each index, μ1, μ2 … μ n are the average membership degrees of each index calculated through the cloud model; W is the fusion weight of each index;

[0156] Step 6: Conduct evaluation and ranking according to the improved TOPSIS method:

[0157] The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) is also known as the method of distance from the best and worst solutions. This method ranks the alternatives by calculating the Euclidean distances between each alternative and the ideal solution and the negative ideal solution, so as to obtain the optimal choice. The improved TOPSIS method is combined with the fusion weight obtained by the combination of the cloud model and the game theory combined weighting method, and the calculation steps are as follows:

[0158] First, construct the decision matrix X m×n

[0159]

[0160] Among them, m is the number of solutions, n is the number of indicators, and x ij is the evaluation value of the i-th solution for the j-th indicator;

[0161] Use normalization to unify the dimensions of indicators with different units. The normalization formula is as follows:

[0162] Positive indicator:

[0163]

[0164] Negative indicator:

[0165]

[0166] Among them, min(x j ) is the minimum value of the j-th indicator;

[0167] Calculate the weighted normalization matrix V based on the weights W of each indicator obtained by the game theory combination weighting method and the cloud model:

[0168] v ij = W j ·r ij

[0169] Then calculate the distance between each solution and the ideal solution:

[0170] The distance between each solution and the positive ideal solution

[0171]

[0172] The distance between each solution and the negative ideal solution

[0173]

[0174] Calculate the relative closeness C of each solution i :

[0175]

[0176] Among them, C i is between [0, 1]. The larger the value, the higher the ranking. Finally, obtain the ranking result.

[0177] This method systematically analyzes the performance of different hydrogen storage technologies in power grid peak shaving, comprehensively measures from multiple dimensions, including conversion efficiency, start-up response time, stability and reliability, initial investment cost, operation and maintenance cost, life cycle cost, carbon emissions, resource consumption, policy support, and social acceptance, and determines the evaluation method in combination with the evaluation content; uses a combination of game theory combined weighting method and cloud model theory to determine the combined weights of various evaluation indicators, avoiding the uncertainty when determining weights by a single method, so as to obtain more reasonable and effective index weights; uses the improved TOPSIS method, uses normalization to unify the dimensions of indicators with different units, so as to utilize data more objectively, avoiding the differences caused by inconsistent data units, and coupling and calculating with the combined weights, thus ensuring the accuracy of the final evaluation ranking results. The evaluation flow chart is as shown in Figure 1 shown. By evaluating the cases, verifying through simulation, and comparing with the results of published public papers, the effectiveness of the method is verified.

[0178] Examples are as follows:

[0179] This embodiment considers a certain isolated island power grid peak shaving system configured with a hydrogen energy storage system. The index data used mainly comes from published journal literature, industry reports, historical case data, and partial expert scoring. Combining with the actual operation data of the existing hydrogen storage projects, and performing mean processing on the data. The various indicators combined with historical data are shown in Table 3.

[0180] Based on the constructed evaluation index system, calculate the subjective weights according to AHP. The comprehensive evaluation index system diagram is as shown in the appendix Figure 2 shown. Five practitioners in the field of hydrogen energy storage were invited. After obtaining the judgment matrices of each layer by expert scoring and passing the consistency test, the subjective weights of each evaluation index were determined, as shown in Table 4.

[0181] Table 3 Summary of each index data

[0182]

[0183] Table 4 Subjective weight values

[0184]

[0185] Calculate the subjective weights according to the CRITIC method. First, perform dimensionless processing on the original data of each index. The dimensionless index data is shown in Table 5.

[0186] Furthermore, calculate the standard deviation, correlation, and objective weights of each index. The calculation results are shown in Table 6.

[0187] Further calculate the combined weights according to game theory. The calculation results are shown in Table 7.

[0188] From the above table and the appendix Figure 3 it can be seen that the weight differences of the four-dimensional indicators of hydrogen storage technology in power grid peak shaving are not significant; the difference between the maximum value and the minimum value is 18.4%. Among them, the weights of the technical dimension and the economic dimension indicators rank first and second respectively, indicating that the flexible adjustment ability of hydrogen energy storage to the volatility of renewable energy is the primary factor in the power grid peak shaving and valley filling links, which is consistent with the inherent characteristics of randomness, volatility, and intermittency of renewable energy systems. The low weight of the environmental dimension indicates that hydrogen energy storage has a low impact on the environment in power grid peak shaving.

[0189] Table 5 Data after dimensionless processing

[0190]

[0191] Table 6 Objective weight values

[0192]

[0193] Table 7 Combined weight values

[0194]

[0195] The combined weight calculation fusion weight is determined through the cloud model theory and game theory, and the final fusion weight is calculated and determined. The settlement results are shown in Table 8.

[0196] Table 8 Index fusion weights

[0197]

[0198] Finally, the benefit ranking is calculated and determined according to the improved TOPSIS method. First, a decision matrix X m×n is constructed according to the evaluation index system, and the decision matrix is shown in Table 9.

[0199] Table 9 Decision matrix

[0200]

[0201] Furthermore, the normalized matrix is obtained through formula calculation, as shown in Table 10:

[0202] Table 10 Normalized matrix

[0203]

[0204] The weighted normalized matrix with the fusion weight W is obtained through formula calculation, as shown in Table 11:

[0205] Table 11 Weighted normalized matrix

[0206]

[0207] Furthermore, the positive ideal solution, negative ideal solution, and Euclidean distance ranking are further calculated, as shown in Table 12:

[0208]

[0209] The Euclidean distance ranking is shown in Table 13:

[0210]

[0211] The present invention proposes a multi-index evaluation method that comprehensively couples the game theory combined weighting method based on the AHP-CRITIC method and the cloud model-improved TOPSIS method. It can be seen from the results that the top three of the four energy storage technologies are chemical hydrogen storage, gaseous hydrogen storage, and solid hydrogen storage, and the ranking scores of the three are not much different. The last one in the ranking is liquid hydrogen storage, and the reason may be the challenges in aspects such as large-scale application, cost-effectiveness, or technical limitations. This ranking combines the objective information entropy weight and subjective factors, and uses the cloud model to capture the uncertainty, making the evaluation more comprehensive and detailed. This method not only considers the actual performance of each hydrogen storage technology, but also takes into account the uncertain factors in the evaluation process, making the evaluation process more comprehensive and complete. The above evaluation results match the research results of the published literature, proving the effectiveness of the evaluation method.

[0212] In summary, the core of the above method lies in constructing a multi-index evaluation system that comprehensively considers the four dimensions of technology, economy, environment, and society, and combines the subjective and objective weighting methods, game theory, cloud model, and improved TOPSIS method to scientifically and systematically evaluate the comprehensive performance of different hydrogen storage technologies in the peak shaving of the island power grid. This method can overcome the single benefit and limitations in the existing evaluation technologies, comprehensively reflect the actual application performance of hydrogen storage technologies in complex power grid peak shaving scenarios, and provide a scientific basis for power grid peak shaving decisions.

[0213] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an 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 can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. And the aforementioned storage medium includes: various media 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 that can store program codes.

[0214] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely 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 memory, CD-ROM, optical memory, etc.) that contain computer-usable program code. The solutions in the embodiments of the present invention can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript, etc.

[0215] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the 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 means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0216] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0217] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0218] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0219] 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. A multi-index comprehensive evaluation method for a hydrogen storage technology in peak shaving of an isolated power grid, characterized in that, It includes the following steps: Construct a comprehensive evaluation index system for the peak shaving of the isolated power grid with the participation of hydrogen storage technologies, where the isolated power grid includes hydrogen energy storage; Based on the comprehensive evaluation index system, calculate the subjective weight and objective weight of each evaluation index by using the AHP method and the CRITIC method respectively; Based on the subjective weight and objective weight of each evaluation index, calculate the combined weight of each evaluation index by using game theory; Calculate the average membership degree of each evaluation index according to the cloud model theory, and calculate the fusion weight of each evaluation index in combination with the combined weight; Based on the improved TOPSIS method, perform coupled calculation on the fusion weights of the evaluation indexes, and output the ranking results of different hydrogen storage technologies in the peak shaving of the isolated power grid as the final evaluation result.

2. The multi-index comprehensive evaluation method of a hydrogen storage technology for peak shaving in an island power grid according to claim 1, wherein The comprehensive evaluation index system includes a target layer, an attribute layer, an index layer, and a scheme layer. The attribute layer includes multiple dimensions of evaluation indexes, including technical dimension, economic dimension, environmental dimension, and social benefit dimension. The index layer includes multiple evaluation indexes under each dimension, and the scheme layer includes different hydrogen storage technologies, including gaseous hydrogen storage, liquid hydrogen storage, solid hydrogen storage, and chemical hydrogen storage.

3. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an isolated power grid according to claim 2, characterized in that The evaluation indexes under the technical dimension include conversion efficiency, start-up response time, stability, and reliability; The evaluation indexes under the economic dimension include initial investment cost, operation and maintenance cost, and life cycle cost; The evaluation indexes under the environmental dimension include carbon emissions and resource consumption; The evaluation indexes under the social benefit dimension include employment security, policy support degree, and social acceptance degree.

4. A multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that The steps of calculating the subjective weight of each evaluation index by using the AHP method include: Based on the comprehensive evaluation index system, construct a judgment matrix \(A=(a ij ) n×n , and calculate the weight vector of each evaluation index through the expert scoring method, where \(a ij \) is the importance of index \(i\) relative to index \(j\), and \(n\) is the dimension; Calculate the maximum eigenvalue in the judgment matrix, and perform consistency test according to the weight vector of each index; Judge whether the consistency test result is less than the set threshold. If so, it indicates that the consistency check is satisfied. If not, it indicates that the consistency test is not satisfied, and recalculate the weight vector of each evaluation index; For the judgment matrix that satisfies the consistency test, calculate the eigenvector corresponding to the maximum eigenvalue of the judgment matrix, and normalize the eigenvector to obtain the subjective weight of each evaluation index.

5. The multi-index comprehensive evaluation method of a hydrogen storage technology for peak shaving in an isolated power grid according to claim 4, characterized in that The expression of the consistency test is: In the formula, CR is the consistency test result, CI is the consistency index used to measure the consistency of the judgment matrix, RI is the average random consistency index of the judgment matrix, and λ is the maximum eigenvalue.

6. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that The steps of calculating the objective weight of each evaluation index by using the CRITIC method include: Perform dimensionless processing on the evaluation indexes in the comprehensive evaluation index system to obtain dimensionless index data, where the dimensionless processing includes the following: Positive processing: Reverse processing: Based on the dimensionless index data, calculate the standard deviation of each evaluation index, where the calculation expression of the standard deviation is: Based on the dimensionless index data, calculate the correlation of each evaluation index by using the Pearson correlation coefficient method, where the calculation expression of the correlation is: Based on the standard deviation and correlation of each evaluation index, calculate the comprehensive information amount of each evaluation index, where the calculation expression of the comprehensive information amount is: Based on the comprehensive information quantity of each evaluation index, calculate the objective weight of each evaluation index, where the calculation expression of the objective weight is: where x' ij is the dimensionless value, x ij is the original data of the i-th evaluation object on the j-th index, x max and x min are the maximum and minimum values of the j-th evaluation index respectively, s j is the standard deviation of the j-th evaluation index, is the average value of the j-th evaluation index, r jk is the correlation coefficient between the j-th evaluation index and the k-th evaluation index, x' ik is the dimensionless value of the i-th evaluation object on the k-th index, is the standardized mean of the k-th evaluation index, C j is the information content of the j-th evaluation index, s j is the standard deviation of the j-th evaluation index, m is the total number of indices, w j is the objective weight of the j-th evaluation index.

7. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that, The calculation steps of the combined weight of each evaluation index include: According to the subjective weight and objective weight of each evaluation index, calculate the weight difference of each evaluation index, where the calculation expression of the weight difference is: Δw j = |w Aj - w Cj | Based on the weight difference of each evaluation index, construct an optimal objective function to solve the balance parameter α, where the expression of the optimal objective function is: Based on the balance parameter α, construct a game theory objective function, and fuse the subjective weight and objective weight of each evaluation index in a linear combination manner to obtain the combined weight of each evaluation index, where the calculation expression of the combined weight of each evaluation index is: w j = αw Aj + (1 - α)w Cj where Δw j is the difference between the subjective weight and the objective weight of the j-th index, w Aj , w Cj are the subjective and objective weight values of the j-th index, w j is the combined weight of the j-th index, α is the balance parameter, 0 ≤ α ≤ 1, and m is the total number of indices.

8. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that The calculation steps of the average membership degree of each evaluation index include: Divide each evaluation index in the comprehensive evaluation index system into multiple grade intervals, and set the expected value E of each evaluation index according to the grade intervals x , entropy E n , and hyper entropy H e ; According to the entropy E of each evaluation index n and the hyperentropy H e , a random entropy E′ is generated through a normal distribution n , and the expression of the random entropy E′ n is as follows: E′ n = N(E n , H e ) According to the random entropy E′ n and the expected value E x , generate a cloud droplet x i , and the expression of the cloud droplet x i is: x i = N(E x , E' n ) According to the generated cloud droplets x i Further calculate the membership degree of cloud droplets as the average membership degree. The calculation expression of the membership degree of cloud droplets is as follows: where E′ n is the random entropy, N is the total number of cloud droplets, x i is the value of the i-th cloud droplet, μ(x) is the membership degree, and x is the cloud droplet value.

9. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that The calculation expression of the fusion weight of each evaluation index is: W = w1μ1 + w2μ2 + … + w n μ n Where W is the fusion weight of each index, and w1, w2…w n are the weights of each index, and μ1, μ2…μ n are the average membership degrees of each index calculated through the cloud model.

10. The multi-index comprehensive evaluation method of a hydrogen storage technology in peak shaving of an island power grid according to claim 1, characterized in that The steps for performing coupling calculation include: Construct decision matrix X m×n : For the decision matrix X m×n perform normalization, and the normalization expression is: Positive index: Negative index: According to the fusion weight and normalization result of each evaluation index, calculate to obtain a weighted normalization matrix V, where the calculation expression of each element in the weighted normalization matrix V is: v ij = W j ·r ij According to the weighted normalization matrix V, calculate the distance between each scheme and the ideal solution, and the calculation expression is: Distance between each solution and the positive ideal solution Distance between each solution and the negative ideal solution Calculate the relative closeness C of each solution according to the distance between each solution and the ideal solution i , and use the relative closeness C of each solution i as the sorting result of the hydrogen storage technology in the peak shaving of the island power grid, where the calculation expression of the relative closeness C of each solution i is as follows: where m is the number of solutions, n is the number of indicators, and r ij is the normalization result, x ij is the evaluation value of the i-th solution for the j-th indicator, min(x j ) is the minimum value of the j-th indicator, v ij is the weighted normalization value, W j is the weight of indicator j, is the distance between each solution and the positive ideal solution, is the distance between each solution and the negative ideal solution, is the positive ideal value of the j-th indicator, i.e., the optimal target value, is the negative ideal value of the j-th indicator, i.e., the worst target value, C i is the relative closeness of each solution, which is between [0, 1], and the larger the value, the higher the ranking.

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