Electro-hydrogen comprehensive energy system evaluation method and system

Abstract

The invention provides a method for evaluating an electricity-hydrogen comprehensive energy system. The method comprises the following steps of: constructing an evaluation standard according to a multi-dimensional comprehensive index; converting the multi-dimensional comprehensive index into a triangular intuitionistic fuzzy number, and performing normalization processing on the triangular intuitionistic fuzzy number; the subjective weight of the triangular intuitionistic fuzzy number is calculated through an optimal and worst method; the objective weight of the triangular intuitionistic fuzzy number is calculated through a CRITIC method; fusing the subjective weight and the objective weight through a game theory model to obtain a final weight; and based on the final weight, sorting the alternative schemes through a multi-attribute boundary approximation comparison method to obtain an evaluation result. According to the method, a comprehensive evaluation system based on fuzzy comprehensive evaluation is constructed, the evaluation index subjective and objective combination weighting method is constructed by introducing the game theory idea, the rationality and reliability of weight quantification are improved, and a reliable basis is provided for planning and decision making of a comprehensive energy system.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy technology, and in particular to an evaluation method and system for an electric-hydrogen integrated energy system. Background Art

[0002] Energy is a key factor in driving and supporting social development. The world is currently undergoing a profound transformation from fossil fuels to sustainable, low-carbon energy sources. In this global transition to a new energy system, hydrogen energy has attracted considerable attention as a prominent solution.

[0003] However, existing evaluation methods for electric-hydrogen integrated energy systems are mostly limited to a single dimension, lacking coordinated consideration of technical, economic, environmental, and social benefits, and are unable to effectively handle the uncertainty of subjective and objective data. Traditional methods often rely on subjective experience or a single objective indicator in weight allocation, resulting in one-sided evaluation results that cannot accurately reflect the complexity of the system; at the same time, the lack of fuzzy information representation tools makes it difficult to quantify and integrate qualitative indicators, restricting the scientific nature and adaptability of comprehensive evaluation. In addition, existing research has significant gaps in integrating game theory and fuzzy mathematics tools to balance subjective and objective weights and optimize multi-attribute decision-making, making it difficult to meet the dynamic evaluation needs of emerging energy systems. Therefore, it is very necessary to design an evaluation method and system for electric-hydrogen integrated energy systems. Summary of the Invention

[0004] The purpose of the present invention is to provide an evaluation method and system for an electric-hydrogen integrated energy system, by constructing a comprehensive evaluation system based on fuzzy comprehensive evaluation and introducing game theory ideas to improve the rationality and reliability of weight quantification.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] An evaluation method for an electric-hydrogen integrated energy system comprises the following steps:

[0007] Construct evaluation criteria based on multi-dimensional comprehensive indicators; multi-dimensional comprehensive indicators include: primary indicators and secondary indicators;

[0008] The multi-dimensional comprehensive index is converted into triangular intuitionistic fuzzy numbers, and the triangular intuitionistic fuzzy numbers are normalized;

[0009] The subjective weight of triangular intuitionistic fuzzy numbers is calculated by the best-worst method;

[0010] The objective weight of triangular intuitionistic fuzzy numbers is calculated by CRITIC method;

[0011] The final weight is obtained by integrating subjective weight and objective weight through the game theory model;

[0012] Based on the final weights, the alternatives are ranked using a multi-attribute boundary approximation comparison method to obtain the evaluation results.

[0013] Optionally, the first-level indicators include: technical indicators, economic indicators, environmental indicators and social indicators; technical indicators include 5 second-level indicators, namely: energy self-sufficiency rate, comprehensive efficiency, renewable energy penetration rate, project scalability and hydrogen energy productivity; economic indicators include 4 second-level indicators, namely: net present value, internal rate of return, investment payback period and operation and maintenance costs; environmental indicators include 4 second-level indicators, namely: carbon dioxide emissions, emissions of other gas pollutants, noise impact and renewable energy rate; social indicators include 5 second-level indicators, namely: public satisfaction, employment benefits, residents' satisfaction, industry comprehensive benefit improvement and policy adaptability.

[0014] Optionally, the multi-dimensional comprehensive index is converted into a triangular intuitionistic fuzzy number, and the triangular intuitionistic fuzzy number is normalized, including:

[0015] Construct intuitionistic fuzzy sets;

[0016] Determine triangular intuitionistic fuzzy numbers based on the membership and non-membership of intuitionistic fuzzy sets;

[0017] The multi-dimensional comprehensive index is converted into triangular intuitionistic fuzzy numbers, and the triangular intuitionistic fuzzy numbers are normalized through different conversion formulas.

[0018] The normalized triangular intuitionistic fuzzy numbers are defuzzified.

[0019] Optionally, the multi-dimensional comprehensive index is converted into a triangular intuitionistic fuzzy number, and the triangular intuitionistic fuzzy number is normalized by different conversion formulas, including:

[0020] Divide the multi-dimensional comprehensive indicators into benefit indicators and cost indicators;

[0021] By formula Normalize the triangular intuitionistic fuzzy numbers of the benefit indicators;

[0022] By formula The triangular intuitionistic fuzzy numbers of cost indicators are normalized.

[0023] Optionally, the subjective weight of the triangular intuitionistic fuzzy number is calculated by the best-worst method, including:

[0024] Select the best and worst indicators among triangular intuitionistic fuzzy numbers;

[0025] Construct preference comparison vectors of the best indicator, the worst indicator and the rest of the indicators respectively;

[0026] A mathematical programming model is constructed and solved according to the preference comparison vector to obtain the subjective weight.

[0027] Optionally, the objective weight of the triangular intuitionistic fuzzy number is calculated by the CRITIC method, including:

[0028] Construct the original data matrix based on triangular intuitionistic fuzzy numbers;

[0029] Normalize the original data matrix according to different multi-dimensional comprehensive indicators;

[0030] Calculate the correlation coefficient of the normalized original data matrix;

[0031] Based on the correlation coefficient, the objective weight is calculated using the information formula.

[0032] Optionally, the original data matrix is ​​normalized according to different multi-dimensional comprehensive indicators, including:

[0033] By formula Normalize the benefit indicators in the original data matrix; where s ij is the evaluation information of the jth indicator of the ith scheme among the m schemes with n indicators. is the minimum value of all n indicator evaluation information for the i-th scheme, is the minimum value of the indicator evaluation information for the i-th scheme;

[0034] By formula Normalize the cost indicators in the original data matrix.

[0035] Optionally, the subjective weights and objective weights are fused through a game theory model to obtain the final weights, including:

[0036] Construct game theory models based on subjective and objective weights;

[0037] By minimizing the deviation of the game theory model, a linear equation system is generated and solved to obtain the optimal weight coefficient;

[0038] The optimal weight coefficient is normalized to obtain the final weight.

[0039] Optionally, based on the final weights, the alternatives are ranked using a multi-attribute boundary approximation comparison method to obtain evaluation results, including:

[0040] Construct a decision matrix based on triangular intuitionistic fuzzy numbers;

[0041] Perform weighted operation on the decision matrix according to the final weight to obtain a weighted decision matrix;

[0042] Construct an approximate boundary region matrix;

[0043] Calculate the difference between the weighted decision matrix and the approximate boundary region matrix;

[0044] The total distance of the alternatives is calculated based on the difference, and the total distance is sorted to obtain the evaluation result.

[0045] An electric-hydrogen integrated energy system evaluation system, comprising:

[0046] Standard construction module, used to construct evaluation standards based on multi-dimensional comprehensive indicators; multi-dimensional comprehensive indicators include: primary indicators and secondary indicators;

[0047] The fuzzy conversion module is used to convert the multi-dimensional comprehensive indicators into triangular intuitionistic fuzzy numbers and perform normalization on the triangular intuitionistic fuzzy numbers;

[0048] The subjective quantification module is used to calculate the subjective weight of triangular intuitionistic fuzzy numbers through the best-worst method;

[0049] The objective quantification module is used to calculate the objective weight of triangular intuitionistic fuzzy numbers using the CRITIC method;

[0050] The subjective and objective fusion module is used to fuse subjective weights and objective weights through a game theory model to obtain the final weight;

[0051] The ranking evaluation module is used to rank the alternatives based on the final weights through the multi-attribute boundary approximation comparison method to obtain the evaluation results.

[0052] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the electric-hydrogen integrated energy system evaluation method and system provided by the present invention, the method includes: constructing an evaluation standard based on multi-dimensional comprehensive indicators; the multi-dimensional comprehensive indicators include: primary indicators and secondary indicators; converting the multi-dimensional comprehensive indicators into triangular intuitionistic fuzzy numbers, and normalizing the triangular intuitionistic fuzzy numbers; calculating the subjective weight of the triangular intuitionistic fuzzy numbers by the best-worst method; calculating the objective weight of the triangular intuitionistic fuzzy numbers by the CRITIC method; fusing the subjective weight and the objective weight through a game theory model to obtain the final weight; based on the final weight, sorting the alternatives through a multi-attribute boundary approximation comparison method to obtain the evaluation result. This method constructs a comprehensive evaluation system based on fuzzy comprehensive evaluation, and constructs a subjective and objective combined weighting method of evaluation indicators by introducing the idea of ​​game theory, thereby improving the rationality and reliability of weight quantification and providing a reliable basis for planning and decision-making of integrated energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a flow chart of the method for evaluating the electric-hydrogen integrated energy system of the present invention;

[0055] Figure 2 This is a flow chart of calculating subjective weights using the best-worst method of the present invention;

[0056] Figure 3 This is a flow chart of calculating objective weights using the CRITIC method of the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] like Figure 1 As shown, the present invention provides a method for evaluating an electric-hydrogen integrated energy system, comprising the following steps:

[0060] Step 100: Constructing evaluation criteria based on multi-dimensional comprehensive indicators; the multi-dimensional comprehensive indicators include: primary indicators and secondary indicators;

[0061] Specifically, the first-level indicators include: technical indicators, economic indicators, environmental indicators and social indicators. Technical indicators mainly describe energy utilization and system operation status, including 5 second-level indicators, namely: energy self-sufficiency rate, comprehensive efficiency, renewable energy penetration rate, project scalability and hydrogen energy productivity. Economic indicators reflect the economic operation of the energy system, including 4 second-level indicators, namely: net present value, internal rate of return, investment payback period and operation and maintenance costs. Environmental indicators take into account the impact of the energy system on the ecological environment and natural resource utilization, including 4 second-level indicators, namely: carbon dioxide emissions, emissions of other gas pollutants, noise impact and renewable energy rate. Social indicators mainly refer to the impact of the energy system on urban development, daily life, health and social development, including 5 second-level indicators, namely: public satisfaction, employment benefits, resident satisfaction, industry comprehensive benefit improvement and policy adaptability. The specific calculation formulas for each indicator are as follows:

[0062] 1) Energy self-sufficiency rate reflects the ability of a region, community or project to produce sufficient energy using local resources, including renewable and non-renewable resources. It represents the security and resilience of the energy system in terms of energy supply, and also reflects its stability under changes in the external market. The calculation formula is:

[0063]

[0064] Among them, ESR is the energy self-sufficiency rate, U is the total input energy of the system, in ten thousand kWh, R is the energy value of new energy, in ten thousand kWh, and N is the energy value of non-renewable energy, in ten thousand kWh.

[0065] 2) Overall efficiency refers to the efficiency of various energy forms within the system, which is used to evaluate the overall effectiveness and energy saving potential. The calculation formula is:

[0066]

[0067] Among them, CE is comprehensive efficiency, W IN is the total input energy of the system, in ten thousand kWh, W E is the electrical load of the system, in ten thousand kWh; Q E is the heat load of the system, unit is 10,000 kWh.

[0068] 3) Renewable energy penetration is the degree to which renewable energy can be integrated and utilized within the existing energy infrastructure. This indicator reflects the contribution of renewable energy sources such as solar, wind, and hydropower to the overall energy mix. The calculation formula is:

[0069]

[0070] Among them, W SThe electrical energy converted from solar energy, unit is 10,000 kWh; W W The electric energy converted from wind energy, unit is 10,000 kWh; W buy is the electricity purchased from the external grid, in ten thousand kWh; γ is the proportion of renewable energy in the purchased electricity.

[0071] 4) Project scalability demonstrates the flexibility of the construction and operation models within the industrial park, as well as its potential for sustainable development. By assessing project scalability, decision makers can gauge the project's resilience and its ability to incorporate new technologies, processes, or practices that align with emerging trends and sustainable development goals.

[0072] 5) Hydrogen productivity is the ratio between the economic benefits obtained in the process of producing industrial by-product hydrogen and the input costs. The calculation formula is:

[0073]

[0074] Among them, HYR is hydrogen productivity; is the price of hydrogen, in yuan; is the hydrogen production, unit is kg; c cog is the coke oven gas consumption, unit: nm 3 ;P cog is the price of coke oven gas, in yuan; c electrolysis P is the power consumption of hydrogen production by water electrolysis, in ten thousand kWh; elecricity is the system output electricity price, in RMB; c oper,cog is the operation and maintenance cost of hydrogen purification facilities, unit: RMB; c oper,electrolysis is the operation and maintenance cost of water electrolysis facilities, in RMB; IC cog is the investment cost of hydrogen purification facilities, unit: RMB; IC electrolysis is the investment cost of water electrolysis facilities, in yuan.

[0075] 6) Net present value is the difference between the present value of cash inflows and the present value of cash outflows over a specific period. If the net present value is positive, it means that the expected benefits exceed the expected costs, indicating that the investment is likely to be profitable. The calculation formula is:

[0076]

[0077] Wherein, NFC(t) is the net cash flow in year t, in RMB; i is the discount rate, which is 8% in this embodiment; I is the initial investment amount, in RMB; and n is the expected useful life of the project, in years.

[0078] 7) The internal rate of return is the ratio of the present value of cash inflows to the value of cash outflows. It is used to assess the attractiveness of an investment and reflects the actual rate of return on a project. The calculation formula is:

[0079]

[0080] Among them, IRR is the internal rate of return.

[0081] 8) The payback period is used to assess the time required for an investment project to recoup its initial investment. It takes into account the time value of the project's future cash flows and is also used to assess the project's risk and return. The calculation formula is:

[0082]

[0083] Where N is the investment payback period.

[0084] 9) Operation and maintenance expenses include labor, routine inspections, repairs, spare parts, and other resource costs required to ensure smooth and efficient operations.

[0085] 10) Carbon dioxide emissions are defined as the ratio of the total annual heating, cooling, and water pump energy savings to the building area after adding new equipment such as air source heat pump units and adjusting the equipment's operating hours. The calculation formula is:

[0086]

[0087] Among them, Q CO2 is carbon dioxide emissions, in tons; c electricity is the total amount of electricity purchased from the grid, in ten thousand kWh; c gas is the total amount of natural gas used, in cubic meters; grid is the grid emission coefficient, unit is kg / ton; λ grid is the natural gas emission coefficient, unit is kg / cubic meter.

[0088] 11) Other gaseous pollutant emissions refer to the annual production of sulfur dioxide (SO2), nitrogen oxides (NO x ) and the total annual emissions of particulate matter. The calculation formula is:

[0089]

[0090] Among them, Q pollution is the emission of other gaseous pollutants; and The sulfur dioxide (SO2) and nitrogen oxides (NO x ) and particulate matter emissions, in kg / cubic meter; and The sulfur dioxide (SO2), nitrogen oxides (NO x ) and particulate matter emissions, in kg / kWh.

[0091] 12) The renewable energy ratio indicates the proportion of renewable energy used in system operation. The size of the renewable energy ratio indicates the degree of reliance on clean energy. The calculation formula is:

[0092]

[0093] Among them, RR is the renewable energy ratio; U is the total input energy of the system, in ten thousand kWh; R is the energy value of the new energy, in ten thousand kWh.

[0094] 13) Public satisfaction is a standard for measuring the public's satisfaction or recognition of the integrated energy system solution. It is set according to actual conditions and is not limited in this embodiment.

[0095] 14) Employment benefit is expressed as the positive impact of the project on job creation. It measures the number of new jobs created due to the total investment in the project, highlighting the project's contribution to the local economy. The calculation formula is:

[0096]

[0097] Among them, EB is employment benefit; Δ jobs It represents the number of jobs created, in units of people; TI represents the total investment in the project, in units of yuan.

[0098] 15) Resident satisfaction refers to residents' views on the energy system, which is set according to actual conditions and is not limited in this embodiment.

[0099] Step 200: converting the multi-dimensional comprehensive index into a triangular intuitionistic fuzzy number and normalizing the triangular intuitionistic fuzzy number;

[0100] Specifically, we first construct an intuitionistic fuzzy set, which is expressed as:

[0101] A={x,μ A (x),ν A (x)|x∈X};

[0102] Among them, μ A (x) is the membership of x in set A, ν A (x) is the non-membership degree of x in set A, 0≤μ A (x)≤1,0≤ν A (x)≤1.π A (x) is the hesitation degree of x in set A, and its calculation formula is: π A (x) = 1 - μ A (x)-ν A (x). Then, the triangular intuitionistic fuzzy number TIFNs is determined based on the membership and non-membership of the intuitionistic fuzzy set, and the expression is:

[0103]

[0104] Among them, a represents the lower bound of the possible values ​​of the fuzzy number, a represents the most likely value of the fuzzy number, Indicates the upper bound of the possible values ​​of the fuzzy number, the membership degree ω of the triangular intuitionistic fuzzy number a' and non-membership μ a The expressions are:

[0105]

[0106] Among them, ω a' (x) and μ a Satisfying 0≤ω a' (x)≤1,0≤μ a ≤1, and 0≤ω a' (x)+μ a ≤1. Then the multi-dimensional comprehensive indicators are divided into benefit indicators and cost indicators, and the benefit indicators and cost indicators are converted into triangular intuitionistic fuzzy numbers through different conversion formulas, and then normalized into a dimensionless state to ensure that each indicator value is on the same scale. Converted into dimensionless triangular intuitionistic fuzzy numbers In the process, the benefit index is calculated by the formula Normalization is performed; the cost index is calculated by the formula Perform normalization processing, where Finally, the formula The normalized triangular intuitionistic fuzzy number is defuzzified. The triangular intuitionistic fuzzy number of this embodiment also has a conversion relationship with the linguistic variables evaluated by the decision maker, as shown in Table 1:

[0107] Table 1 Correspondence table of triangular intuitionistic fuzzy numbers

[0108]

[0109] It should be noted that triangular intuitionistic fuzzy numbers can accurately represent information that is difficult to describe with precise values, allowing decision makers to have a more comprehensive understanding of the research object. They have the advantages of simple calculation, minimal information loss, and easy integration with other methods.

[0110] Step 300: Calculate the subjective weight of triangular intuitionistic fuzzy number by the best-worst method; the specific steps are as follows: Figure 2 Shown, including:

[0111] Step 301: Select the best index and the worst index in the triangular intuitionistic fuzzy number;

[0112] Specifically, the triangular intuitionistic fuzzy numbers of all indicators to be evaluated are combined into a decision indicator set {a1, a2, …, an}, and an optimal indicator and a worst indicator are selected from them.

[0113] Step 302: construct preference comparison vectors of the best indicator and the worst indicator with the remaining indicators respectively;

[0114] Specifically, the preference for the optimal indicator over all other indicators is scored using a scale from 1 to 9, where 1 indicates equal importance and 9 indicates extreme importance. The comparison vector for the optimal indicator is A B =(a B1 ,a B2 ,…,a Bn ), scale value a Bi Represents the optimal index a B Preference relative to indicator i. At the same time, the preference for the worst indicator is also scored, and the vector of the preference of other indicators relative to the worst indicator is expressed as A W =(a 1W ,a 2W …,a nW ), where the scale value a iW Indicates the worst indicator a W The preference for indicator i. The meaning of the scale values ​​of the best indicator and the worst indicator are shown in Table 2 and Table 3:

[0115] Table 2 Optimal indicator correspondence table

[0116]

[0117] Table 3 Worst index correspondence table

[0118]

[0119] Step 303: Construct a mathematical programming model based on the preference comparison vector and solve it to obtain the subjective weight.

[0120] Specifically, the weights of each indicator in the decision indicator set are defined as ω1, ω2, ..., ω n , the weight of the optimal indicator is defined as ω B , the weight of the worst indicator is defined as ω W , and construct a mathematical programming model, the expression is:

[0121] minξ

[0122] st

[0123]

[0124] w j ≥0,for all j

[0125] Where ξ is the error between the actual weight and the calculated weight. Solving this mathematical programming expression yields the subjective weight. This embodiment also performs a consistency test on the obtained subjective weight, using the consistency ratio to reflect the degree of consistency in the decision maker's comparison. The calculation formula for the consistency ratio CR is:

[0126]

[0127] Among them, the consistency index CI value is shown in Table 4. If the CR obtained by substituting the data is <0.1, it means that the consistency is met. The closer the CR is to 0, the better the consistency is. When it is 0, it is completely consistent.

[0128] Table 4 CI value table

[0129] <![CDATA[a BW ]]> 1 2 3 4 5 6 7 8 9 CI 0.00 0.44 1.00 1.63 2.30 3.00 3.73 4.46 5.23

[0130] It should be noted that the best-worst method can achieve consistent results with less information. First, all indicators involved in the weight calculation are sorted out and determined to form a complete indicator system and clearly define the analysis boundaries. Next, the most critical optimal indicator and the least influential worst indicator are selected from the indicator system as reference benchmarks for subsequent comparisons. The remaining indicators are then compared in importance with the optimal and worst indicators, and a 1-9 scale is used to quantify the scores, with 1 representing equal importance and 9 representing extremely high importance. Higher numbers indicate more significant differences in importance. Finally, a judgment model is constructed based on these comparisons. Through specialized mathematical calculations, the weights of each indicator relative to the optimal and worst indicators are determined, and the subjective weights are then integrated to determine the weights.

[0131] Step 400: Calculate the objective weight of the triangular intuitionistic fuzzy number by CRITIC method; the specific steps are as follows: Figure 3 Shown, including:

[0132] Step 401: constructing an original data matrix based on triangular intuitionistic fuzzy numbers;

[0133] Specifically, the triangular intuitionistic fuzzy numbers of m objects to be evaluated and n evaluation indicators are combined into the original data matrix where s ij Represents the jth evaluation metric value of the i-th object.

[0134] Step 402: normalizing the original data matrix according to different multi-dimensional comprehensive indicators;

[0135] Specifically, through the formula Normalize the benefit indicators in the original data matrix; use the formula Normalize the cost index in the original data matrix, where x ij represents the normalized value, s ij is the evaluation information of the jth indicator of the ith scheme among the m schemes with n indicators. is the minimum value of all n indicator evaluation information for the i-th scheme, is the minimum value of the evaluation index information for the i-th scheme.

[0136] Step 403: Calculate the correlation coefficient of the normalized original data matrix;

[0137] Specifically, the calculation formula of the correlation coefficient is:

[0138]

[0139] in is the mean of the jth indicator, r jk is the correlation coefficient between the jth indicator and the kth indicator.

[0140] Step 404: Based on the correlation coefficient, the objective weight is calculated using the information quantity formula.

[0141] Specifically, the information quantity formula is:

[0142]

[0143] where σ j represents the jth standard deviation, C j is the amount of information. Then through the formula Calculate the attribute weight W of each indicator j , and use it as the objective weight.

[0144] It's important to note that the CRITIC method determines attribute weights by assessing the correlations between them. Unlike methods that assume that attributes are independent, the CRITIC method considers the relationships between attributes and assigns weights based on their impact on the decision outcome. This ensures a comprehensive assessment and improves the reliability and objectivity of decision-making processes in various applications.

[0145] Step 500: The subjective weight and the objective weight are integrated through a game theory model to obtain the final weight;

[0146] Specifically, a weight vector is first constructed based on subjective weights and objective weights, and then a game theory model is constructed based on the weight vector; then, a set of linear equations is generated and solved by minimizing the deviation of the game theory model to obtain the optimal weight coefficient; finally, the optimal weight coefficient is normalized to obtain the final weight.

[0147] It should be noted that when m methods are used to calculate the weights of n indicators, the kth weight vector is represented as w k={w k1 ,w k2 ,…,w km}, (k=1,2…,m), the game theory model composed of is expressed as: where c k is the weight coefficient. In order to minimize the deviation between the combined weight calculated by game theory and the subjective weight and objective weight calculated, the objective function is used Optimize the weight relationship and obtain the equivalent linear equations under the optimal derivative conditions After solving, we get the optimal weight coefficient c m By formula Normalize the optimal weight coefficient and then use the formula Calculate the final weight.

[0148] It's understandable that the introduction of game theory scientifically integrates subjective and objective weights. Subjective weights reflect the decision-maker's judgment based on their experience and expertise, while objective weights are determined based on the inherent characteristics of the data and objective laws. Within the framework of game theory, the two aren't simply added together; rather, a balance is found through a series of complex and rigorous calculations and analyses. In this process, game theory enables subjective preferences and objective data to complement each other. The resulting final weights fully respect the decision-maker's strategic intent while ensuring that the weight assignments closely align with the real-world data. This improves the rationality of indicator weights, overcomes the limitations of relying solely on a single type of weight, and takes into account the inherent relationships between criteria.

[0149] Step 600: Based on the final weights, the alternatives are ranked using a multi-attribute boundary approximation comparison method to obtain an evaluation result.

[0150] Specifically, the decision matrix is ​​constructed according to the triangular intuitionistic fuzzy number using the same method as step 401. Then according to the formula y ij =w j (x ij +1) to perform weighted operations to obtain a weighted decision matrix, where y ij Represents the weight of the j-th indicator, and the weighted decision matrix Y is: Then construct the approximate boundary region matrix, which is expressed as; G=[g1,g2,…,g n ], where g j is the boundary approximation area value. Then calculate the difference between the elements in the weighted decision matrix and the approximate boundary area matrix G, and the calculation formula is: where d i =y 1i -g iFinally, the total distance D of each alternative to the approximate boundary area is calculated based on the difference i , and sort the total distance, D i The higher the value, the better the alternative is, thus obtaining the evaluation result, D i The calculation formula is:

[0151] It should be noted that the multi-attribute boundary approximation comparison method comprehensively considers the performance of each plan under different indicators, and quantitatively analyzes and ranks the attributes of each plan, making the evaluation results clear and intuitive, allowing decision makers to quickly understand the advantages and disadvantages of each plan, and providing a valuable reference for subsequent decision-making.

[0152] The present invention also provides an electric-hydrogen integrated energy system evaluation system, comprising:

[0153] Standard construction module, used to construct evaluation standards based on multi-dimensional comprehensive indicators; multi-dimensional comprehensive indicators include: primary indicators and secondary indicators;

[0154] The fuzzy conversion module is used to convert the multi-dimensional comprehensive indicators into triangular intuitionistic fuzzy numbers and perform normalization on the triangular intuitionistic fuzzy numbers;

[0155] The subjective quantification module is used to calculate the subjective weight of triangular intuitionistic fuzzy numbers through the best-worst method;

[0156] The objective quantification module is used to calculate the objective weight of triangular intuitionistic fuzzy numbers using the CRITIC method;

[0157] The subjective and objective fusion module is used to fuse subjective weights and objective weights through a game theory model to obtain the final weight;

[0158] The ranking evaluation module is used to rank the alternatives based on the final weights through the multi-attribute boundary approximation comparison method to obtain the evaluation results.

[0159] In some other embodiments, different industrial park energy systems are designed and analyzed to evaluate their overall performance in terms of energy supply, economic feasibility, environmental protection, and social impact. Example A1 serves as a baseline for comparison with other solutions. In Example A1, solar and wind energy provide energy for photovoltaic power generation equipment and wind turbines. In addition to generating electricity to supply the electrical load, part of the electricity is stored in gravity energy storage and electrochemical energy storage equipment, and another part of the electricity is used to produce hydrogen in the water electrolysis system. Industrial by-product gas is transported to the hydrogen purification system to produce high-purity hydrogen, and then transported together with the hydrogen produced by the water electrolysis system to the hydrogen fuel cell to generate electricity and supply it to the system. After absorbing heat through the heat recovery unit, part of the heat is supplied to the system's thermal load, and the rest is stored in the thermal energy storage device. Similarly, after the gas boiler uses natural gas as the main fuel to power the system, the recovered heat is distributed between the thermal load of the supply system and the storage in the thermal energy storage device. The power grid generates electricity to supply the electrical load. Therefore, the system can utilize multiple energy inputs to meet the needs of thermal load and electrical load, improving its stability. In addition, the thermal energy storage and electric energy storage facilities are operated by the system according to actual conditions.

[0160] Example A2 does not produce hydrogen, but uses industrial by-product hydrogen for hydrogen-based combined heat and power generation (CHP); Example A3 relies entirely on wind energy and photovoltaic (PV) electricity to produce hydrogen, making it the only source of hydrogen for the electric hydrogen energy system; Example A4 does not include photovoltaic and wind power generation equipment; Example A5 combines hydrogen with natural gas co-firing to reduce carbon emissions. The systems of these embodiments include wind turbines, gas boilers, hydrogen fuel cells, photovoltaic power generation equipment, and pressure swing adsorption (PSA) hydrogen production and purification systems. In addition, these systems are equipped with electrochemical energy storage and thermal energy storage facilities, and receive energy inputs from the public power grid, natural gas, solar energy, wind energy, and hydrogen energy. In all embodiments, the electrical load and thermal load outputs of the system are the same, and the equipment installation is shown in Table 5:

[0161] Table 5 Equipment installation status of different embodiments

[0162]

[0163] To combine qualitative and quantitative criteria, all quantitative criteria were converted into triangular fuzzy numbers with a membership degree of 1 and a non-membership degree of 0. Subsequently, the triangular fuzzy numbers from the five expert-rated questionnaires were normalized based on the cost and benefit criteria and then defuzzified. Finally, assuming that the five experts' ratings were equally weighted, a decision matrix was obtained, as shown in Table 6:

[0164] Table 6 Standardization decision matrix

[0165]

[0166]

[0167] After obtaining the decision matrix, the next step is to calculate the weights of the evaluation indicators. The data for different indicators are not uniform in terms of scale. Subjective weights are determined using the best-worst (BWM) method, while objective weights are calculated using the CRITIC method. Furthermore, when using the BWM method to determine subjective weights, expert discussions determined that the optimal indicator is the proportion of renewable energy, and the worst indicator is the improvement in overall industry benefits. The specific weighting results are shown in Table 7:

[0168] Table 7 Weight results

[0169]

[0170]

[0171] Overall, there is little difference between the subjective and objective weights of indicators. The subjective weights of technical and economic indicators are slightly higher than the objective weights, while the objective weights of environmental and social indicators are higher than the subjective weights. This discrepancy between the subjective and objective weights of secondary indicators reflects their varying contributions to primary indicators. For example, among environmental indicators, the objective weight of air pollutant emissions is 0.249, significantly higher than the subjective weight of 0.1467. In contrast, the subjective weight of renewable energy share is 0.5067, almost double its objective weight of 0.2276. Therefore, from the perspective of decision makers, the contribution of renewable energy to the environment is greater than that of air pollutant emissions. The discrepancy between subjective and objective weights highlights the difference between subjective perceptions of the importance of certain factors and objective measures of their actual impact. This discrepancy emphasizes the need to balance these two perspectives in decision-making to ensure a comprehensive and effective assessment.

[0172] After obtaining objective and subjective weights, game theory was used to calculate the combined weights of the different weighted data dimensions. The results showed that the objective weight accounted for 51.67%, while the subjective weight accounted for 48.32%. The technical dimension had the highest weight, reaching 32.9%. The economic and environmental weights were relatively close, at 24.52% and 23.84%, respectively. The social weight was 23.82%, while the lowest was 17.12%. Specifically, there were significant differences between the weights. The three highest weights were hydrogen yield, maintenance costs, and overall efficiency, with weights of 10.41%, 10.04%, and 8.83%, respectively. The three lowest weights were improved industry overall benefits, public satisfaction, and project scalability, with weights of 2.31%, 2.39%, and 2.78%, respectively.

[0173] After calculating the indicator weights, the multi-attribute boundary approximation comparison method (MABAC) was used to rank and select the alternatives. The final ranking of the alternatives is shown in Table 8.

[0174] Table 8 Distance results

[0175]

[0176] A comprehensive analysis of the research options reveals that A3 and A4, which exclude wind and solar energy and industrial by-product hydrogen as energy inputs, respectively, are significantly inferior to the other three implementations in terms of technology, economics, environment, and society. Although A2 forgoes wind and solar energy for hydrogen production, and the two implementations have varying technical indicators, A2 is significantly inferior to A1 in terms of economics, and there are also some gaps in social and economic indicators. A5, which combines hydrogen with natural gas for combustion, closely matches A1 in several indicators, but still has significant gaps in economics, such as net present value and internal rate of return.

[0177] The beneficial effects of the present invention are as follows:

[0178] 1) This method constructs a comprehensive indicator system from four dimensions: technology, economy, environment, and society, covering all aspects of energy activities;

[0179] 2) By using the three dimensions of membership, non-membership, and hesitation of triangular intuitionistic fuzzy numbers, a more three-dimensional fuzzy information representation framework was constructed. This not only quantifies the fuzziness of the data itself, but also accurately captures the cognitive uncertainty of experts during the evaluation process, significantly improving the reliability and decision-making guidance value of the comprehensive evaluation of the electric-hydrogen system.

[0180] 3) Through the game theory method, a subtle fusion of subjective weights and objective weights is achieved. While fully considering the subjective preferences of decision makers based on experience, knowledge and specific goals, it also deeply explores the objective information contained in the data, significantly improving the rationality and reliability of the method.

[0181] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0182] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for evaluating an electric-hydrogen integrated energy system, characterized in that: The steps include: Constructing evaluation criteria based on multi-dimensional comprehensive indicators; the multi-dimensional comprehensive indicators include: primary indicators and secondary indicators; Converting the multi-dimensional comprehensive index into a triangular intuitionistic fuzzy number and normalizing the triangular intuitionistic fuzzy number; The subjective weight of the triangular intuitionistic fuzzy number is obtained by calculating the best-worst method; The objective weight of the triangular intuitionistic fuzzy number is calculated by the CRITIC method; The subjective weight and the objective weight are integrated through a game theory model to obtain a final weight; Based on the final weights, the alternatives are ranked using a multi-attribute boundary approximation comparison method to obtain an evaluation result.

2. The method for evaluating an electric-hydrogen integrated energy system according to claim 1, characterized in that: The first-level indicators include: technical indicators, economic indicators, environmental indicators and social indicators; the technical indicators include 5 second-level indicators, namely: energy self-sufficiency rate, comprehensive efficiency, renewable energy penetration rate, project scalability and hydrogen energy productivity; the economic indicators include 4 second-level indicators, namely: net present value, internal rate of return, investment payback period and operation and maintenance costs; the environmental indicators include 4 second-level indicators, namely: carbon dioxide emissions, emissions of other gas pollutants, noise impact and renewable energy rate; the social indicators include 5 second-level indicators, namely: public satisfaction, employment benefits, residents' satisfaction, industry comprehensive benefit improvement and policy adaptability.

3. The method for evaluating an electric-hydrogen integrated energy system according to claim 1, wherein: The multi-dimensional comprehensive index is converted into a triangular intuitionistic fuzzy number, and the triangular intuitionistic fuzzy number is normalized, including: Construct intuitionistic fuzzy sets; Determining the triangular intuitionistic fuzzy number according to the membership and non-membership of the intuitionistic fuzzy set; Converting the multi-dimensional comprehensive index into the triangular intuitionistic fuzzy number, and normalizing the triangular intuitionistic fuzzy number through different conversion formulas; The normalized triangular intuitionistic fuzzy number is defuzzified.

4. The method for evaluating an electric-hydrogen integrated energy system according to claim 3, wherein: The multi-dimensional comprehensive index is converted into the triangular intuitionistic fuzzy number, and the triangular intuitionistic fuzzy number is normalized by different conversion formulas, including: Dividing the multi-dimensional comprehensive indicators into benefit indicators and cost indicators; By formula The triangular intuitionistic fuzzy number of the benefit index is normalized; wherein the expression of the triangular intuitionistic fuzzy number is: By formula The triangular intuitionistic fuzzy number of the cost indicator is normalized.

5. The method for evaluating an electric-hydrogen integrated energy system according to claim 1, wherein: The subjective weight of the triangular intuitionistic fuzzy number is calculated by the best-worst method, including: Selecting the best index and the worst index among the triangular intuitionistic fuzzy numbers; Constructing preference comparison vectors of the optimal indicator, the worst indicator and the remaining indicators respectively; A mathematical programming model is constructed and solved according to the preference comparison vector to obtain the subjective weight.

6. The method for evaluating an electric-hydrogen integrated energy system according to claim 4, characterized in that: The objective weight of the triangular intuitionistic fuzzy number is calculated by the CRITIC method, including: Constructing an original data matrix according to the triangular intuitionistic fuzzy numbers; Normalizing the original data matrix according to different multi-dimensional comprehensive indicators; Calculating the correlation coefficient of the normalized original data matrix; Based on the correlation coefficient, the objective weight is calculated using an information quantity formula.

7. The method for evaluating an electric-hydrogen integrated energy system according to claim 6, characterized in that: Normalizing the original data matrix according to different multi-dimensional comprehensive indicators includes: By formula Normalize the benefit indicators in the original data matrix; where s ij is the evaluation information of the jth indicator of the ith scheme among the m schemes with n indicators. is the minimum value of all n indicator evaluation information for the i-th scheme, is the minimum value of the indicator evaluation information for the i-th scheme; By formula Normalizing the cost indicators in the original data matrix.

8. The method for evaluating an electric-hydrogen integrated energy system according to claim 1, wherein: The subjective weight and the objective weight are integrated through a game theory model to obtain the final weight, including: constructing the game theory model based on the subjective weight and the objective weight; Generate a linear equation system by minimizing the deviation of the game theory model and solve it to obtain the optimal weight coefficient; The optimal weight coefficient is normalized to obtain the final weight.

9. The method for evaluating an electric-hydrogen integrated energy system according to claim 1, wherein: Based on the final weights, the alternatives are ranked using a multi-attribute boundary approximation comparison method to obtain evaluation results, including: Constructing a decision matrix according to the triangular intuitionistic fuzzy number; Performing a weighted operation on the decision matrix according to the final weight to obtain a weighted decision matrix; Construct an approximate boundary region matrix; Calculating a difference between the weighted decision matrix and the approximate boundary region matrix; The total distances of the alternative solutions are calculated according to the differences, and the total distances are sorted to obtain the evaluation result.

10. An electric-hydrogen integrated energy system evaluation system, characterized in that: include: Standard construction module, used to construct evaluation standards based on multi-dimensional comprehensive indicators; The multi-dimensional comprehensive indicators include: primary indicators and secondary indicators; A fuzzy conversion module, configured to convert the multi-dimensional comprehensive index into a triangular intuitionistic fuzzy number and perform normalization processing on the triangular intuitionistic fuzzy number; A subjective quantification module, configured to calculate the subjective weight of the triangular intuitionistic fuzzy number by using the best-worst method; An objective quantification module, configured to calculate the objective weight of the triangular intuitionistic fuzzy number by using the CRITIC method; A subjective and objective fusion module, configured to fuse the subjective weight and the objective weight through a game theory model to obtain a final weight; The ranking evaluation module is used to rank the alternative plans based on the final weights by a multi-attribute boundary approximation comparison method to obtain an evaluation result.

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