Method and apparatus for constructing a multi-dimensional evaluation index system for a multi-energy system

Abstract

A method and device for constructing a multi-dimensional evaluation index system of a multi-energy system according to the present invention. The method includes the following steps: obtaining data of five dimensions of economy, environment, energy efficiency, reliability, and equipment of the multi-energy system as first-level indicators; constructing second-level indicators for the first-level indicators based on key features; constructing a multi-dimensional evaluation index system of the multi-energy system from five dimensions: constructing an index system hierarchy including an objective layer, a criterion layer, and a scheme layer based on the first-level and second-level indicators, calculating the weights of the second-level indicators using the network analytic hierarchy process and the entropy weight method respectively and combining the two to obtain the final weights of the second-level indicators, and then using the interval TOPSIS method to evaluate the first-level indicators. The multi-dimensional evaluation index system of the multi-energy system constructed by the present invention improves the comprehensiveness, scientificity, and practicability of the evaluation of the multi-energy system, promotes the construction and development of the multi-energy system project, and promotes the coordinated and sustainable development of various energy sources.

Description

Technical Field

[0001] The present invention relates to a method and device for constructing a multi - dimensional evaluation index system of a multi - energy system, belonging to the technical fields of integrated energy big data processing and multi - energy system construction. Background Technique

[0002] Traditional energy systems are fragmented from each other and have limited coordination, making it difficult to adapt to the rapid development of renewable energy. A multi - energy system can break the original mode of separate planning, design, and operation of traditional energy systems, and organically coordinate different types of energy systems to improve the safety, flexibility, and reliability of the distribution network and multi - type energy systems that adapt to the energy Internet.

[0003] To promote the construction and development of multi - energy systems, China has invested a great deal of time, energy, and financial resources in carrying out project research and demonstration applications related to multi - energy systems. However, the current engineering construction of multi - energy systems is still immature, and comprehensive evaluation is required before the construction of multi - energy system projects. At present, the evaluation index system for them is relatively scarce, and most studies only conduct one - sided evaluations or evaluations of only a few indicators. In terms of evaluation methods, only single - sided subjectivity or objectivity is achieved. In the process of the construction and development of multi - energy system projects, the perfection of the evaluation link is crucial for the effectiveness of the feedback link in the multi - energy system. If the feedback link cannot play a good role, it will affect the good development mechanism of the multi - energy system project construction. Therefore, it is particularly important to construct a comprehensive and effective evaluation index system that combines subjectivity and objectivity for the development of multi - energy systems.

[0004] In order to better guide the construction of multi - energy systems, reasonably measure the development of relevant technologies and the effectiveness of demonstration projects, and promote the construction and development of multi - energy system projects, it is necessary to systematically construct a multi - dimensional evaluation index system for multi - energy systems. Summary of the Invention

[0005] To solve the above problems, the present invention proposes a method and device for constructing a multi - dimensional evaluation index system of a multi - energy system, which can construct a multi - dimensional evaluation index system of a multi - energy system that combines subjectivity and objectivity, is comprehensive and effective, and better guides the construction and popularization of multi - energy systems.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0007] In a first aspect, a method for constructing a multi - dimensional evaluation index system of a multi - energy system provided by an embodiment of the present invention includes the following steps:

[0008] Obtain data of five dimensions of economy, environment, energy efficiency, reliability, and equipment of the multi - energy system and use them as primary indicators;

[0009] Construct the secondary indicators for the primary indicators based on their key features;

[0010] Construct a multi-dimensional evaluation index system for the multi-energy system from five dimensions: economy, environment, energy efficiency, reliability, and equipment. Build an index system hierarchy including the target layer, criterion layer, and scheme layer based on the primary and secondary indicators. Calculate the weights of the secondary indicators using the network analytic process (ANP) and the entropy weight method respectively, and combine the two to obtain the final weights of the secondary indicators. Then use the interval TOPSIS method to evaluate the primary indicators.

[0011] As a possible implementation of this embodiment, the calculation of the weights of the secondary indicators using the network analytic process (ANP) includes:

[0012] 1) Use the expert method to judge the relative importance of each indicator, and construct a judgment matrix according to the 9-level scale method:

[0013] Suppose there are element groups C1,…,C n in the scheme layer under the criterion layer in ANP, where C i has elements e ik (i = 1,…,N, k = 1,2,…,n j ). Indirectly compare and analyze the elements in the element group C i according to their influence on e ik to construct a judgment matrix;

[0014] 2) Establish a supermatrix:

[0015] Calculate the maximum eigenvalue and eigenvector of the judgment matrix. Obtain the ranking vector by the eigenvalue method and conduct a consistency test. If the consistency test is passed, the importance of element C i to C j gives the local weight vector matrix W ij :

[0016]

[0017] Combine the ranking vectors of all mutually influencing elements in the scheme layer to obtain the supermatrix W:

[0018]

[0019] Normalize the supermatrix W column-wise to obtain the weighted supermatrix:

[0020]

[0021]

[0022] where, aij is a weighting factor;

[0023] 3) Calculate the limit supermatrix:

[0024] Calculate the limit relative ranking vector for each supermatrix:

[0025]

[0026] The value of the corresponding row of the original matrix is the weight value of each evaluation index.

[0027] As a possible implementation of this embodiment, the calculation of the weights of the secondary indicators using the entropy weight method includes:

[0028] 1) Determine the target sequence, and perform normalization processing on the data of each index of the benefit-type index and cost-type index using the following formula:

[0029]

[0030] where s ij is the value of the j-th index of the evaluation scheme i, i = 1, 2,..., m, j = 1, 2,..., n, m is the number of evaluation schemes, and n is the number of evaluation indicators;

[0031] Perform standardization processing on the data of each index to obtain the normalized decision matrix E = (e ij ) m×n ;

[0032] 2) Calculate the weights:

[0033] Calculate the information entropy Y j of the j-th index:

[0034]

[0035] Calculate the index weights:

[0036]

[0037] As a possible implementation of this embodiment, the combination of the calculated weights of the secondary indicators to calculate the final weights of each secondary indicator includes:

[0038] Suppose the calculated weights of the secondary indicators are divided into w' = (w'1, w'2, K, w' n ) T and w” = (w”1, w”2, K, w” n ) T , then the final weight is:

[0039] w j = αw′ j + βw″j

[0040] Among them, α and β are the proportions of the weights calculated by the ANP method and the entropy weight method respectively in the final weight, α > 0, β > 0, and α + β = 1;

[0041] When 0 ≤ α + β ≤ 1, the interval weight is obtained:

[0042]

[0043] Among them, w j and are the upper and lower limits of [w j , respectively;

[0044] The evaluation result [Z i of the secondary index of the i-th evaluation scheme is obtained as:

[0045]

[0046] The evaluation result obtained at this time is an interval number.

[0047] As a possible implementation manner of this embodiment, the evaluation of the first-level index by using the interval TOPSIS method includes:

[0048] 1) Establish an interval evaluation matrix: According to the interval attribute values of different indicators of different schemes, construct an interval evaluation matrix [Z] = ([z ij ) m×n ;

[0049] 2) Perform standardization processing on the interval evaluation matrix:

[0050] The following formula is used to perform standardization processing on the interval evaluation matrix of benefit-type indicators and cost-type indicators:

[0051]

[0052] Among them, e ij and are the upper and lower limits of the interval number after standardization processing, z ij and are the upper and lower limits of the interval number before standardization processing;

[0053] 3) Calculate the Euclidean distance:

[0054] Calculate the positive and negative ideal interval numbers of the index weights and

[0055]

[0056]

[0057] where \(j = 1, 2, \ldots, n\);

[0058] Calculate the Euclidean distances between \([w j \) and and respectively:

[0059]

[0060]

[0061] 4) Calculate and normalize the relative approximation degree of the interval weights:

[0062] The calculation formula is:

[0063]

[0064]

[0065] where is the relative approximation degree of the interval weights, and \(w j is its normalization result;

[0066] 5) Calculate the weighted normalized decision matrix:

[0067] Let the elements in the weighted normalized decision matrix [E] be the set of \([e ij \), and \(w\) be the set of \(w j \), then the weighted

[0068] normalized decision matrix \([V]\) is:

[0069] [V]=w[E]

[0070] 6) Calculate the relative approximation degree of each alternative:

[0071] Calculate the Euclidean distances between the \(i\)-th alternative and the positive and negative ideal interval numbers and respectively and

[0072] Calculate the relative approximation degree \(c([v i )\) of the \(i\)-th alternative;

[0073] 7) Evaluate each alternative:

[0074] Sort the relative approximation degrees \(c([v i )\) from large to small. The larger the value, the better the corresponding alternative.

[0075] As a possible implementation of this embodiment, the first-level indicators include economic indicators (A), environmental indicators (B), energy efficiency indicators (C), reliability indicators (D), and equipment indicators (E).

[0076] As a possible implementation of this embodiment, in the process of constructing the second-level indicators for the first-level indicators based on key features,

[0077] The second-level indicators of the economic indicators (A) include operating income (A1), construction cost (A2), operating expenses (A3), net present value (A4), internal rate of return (A5), and dynamic investment payback period (A6);

[0078] The second-level indicators of the environmental indicators (B) include clean energy consumption rate (B1), environmental pollution emission level (B2), environmental pollution reduction level (B3), and environmental protection benefits (B4);

[0079] The second-level indicators of the energy efficiency indicators (C) include primary energy utilization rate (C1), primary energy savings rate (C2), comprehensive energy utilization efficiency (C3), and energy cost (C4);

[0080] The second-level indicators of the reliability indicators (D) include energy shortage supply rate (D1), average energy shortage supply time (D2), system energy shortage supply rate (D3), and power supply reliability benefits (D4);

[0081] The second-level indicators of the equipment indicators (E) include equipment utilization rate (E1), equipment operation efficiency (E2), network comprehensive loss (E3), and power supply quality (E4).

[0082] In the second aspect, an apparatus for constructing a multi-dimensional evaluation index system of a multi-energy system provided by an embodiment of the present invention includes:

[0083] A first-level indicator construction module, configured to obtain data in five dimensions of economy, environment, energy efficiency, reliability, and equipment of the multi-energy system, and use them as first-level indicators;

[0084] A second-level indicator construction module, configured to construct second-level indicators for the first-level indicators based on key features;

[0085] A system construction module, configured to construct a multi-dimensional evaluation index system of the multi-energy system from five dimensions of economy, environment, energy efficiency, reliability, and equipment.

[0086] As a possible implementation of this embodiment, the system construction module includes:

[0087] An index system hierarchy construction module, configured to construct an index system hierarchy including a target layer, a criterion layer, and a scheme layer based on the first-level indicators and the second-level indicators;

[0088] The secondary index weight calculation module is used to calculate respectively by using the network analytic hierarchy process and the entropy weight method and combine the two to obtain the final secondary index weight;

[0089] The relative approximation degree calculation module of the primary index is used to calculate the relative approximation degree of each primary index by using the secondary index weight;

[0090] The index evaluation module is used to evaluate the primary index by using the interval TOPSIS method.

[0091] Thirdly, a storage medium provided by an embodiment of the present invention stores a computer program, and when the computer program is run by a processor, it executes the method steps of constructing the multi - energy system multi - dimensional evaluation index system as described above.

[0092] The technical solution of the embodiment of the present invention may have the following beneficial effects:

[0093] The economic index constructed by the present invention should be able to well reflect the investment and construction level of the multi - energy system engineering project and intuitively reflect the development value of the multi - energy system; the environmental index should be able to reflect the environmental friendliness of the energy supply and use mode of the multi - energy system; the energy efficiency index should be able to reflect the utilization efficiency of different types of energy in the multi - energy system; the reliability index should be able to reflect the risk level of energy supply interruption in the multi - energy system; the equipment index should be able to reflect the utilization and loss conditions of complex and diverse equipment in the multi - energy system. The multi - energy system multi - dimensional evaluation index system constructed by the present invention improves the comprehensiveness, scientificity and practicability of the multi - energy system evaluation, promotes the construction and development of the multi - energy system project, and promotes the coordinated and sustainable development of various energy sources. Description of the Drawings

[0094] Figure 1 is a flowchart of a method for constructing a multi - energy system multi - dimensional evaluation index system shown according to an exemplary embodiment;

[0095] Figure 2 is a schematic structural diagram of a multi - energy system multi - dimensional evaluation index system shown according to an exemplary embodiment;

[0096] Figure 3 is a schematic structural diagram of a device for constructing a multi - energy system multi - dimensional evaluation index system shown according to an exemplary embodiment. Detailed Embodiments

[0097] The following further describes the present invention with reference to the drawings and embodiments:

[0098] To clearly illustrate the technical features of this solution, the present invention will be elaborated in detail below through specific embodiments and in conjunction with its accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, the components and settings of specific examples are described below. In addition, the present invention may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. The present invention omits the description of well-known components and processing technologies and processes to avoid unnecessarily limiting the present invention.

[0099] Figure 1 is a flowchart of a method for constructing a multi-dimensional evaluation index system of a multi-energy system shown according to an exemplary embodiment. As Figure 1 shown, a method for constructing a multi-dimensional evaluation index system of a multi-energy system provided by an embodiment of the present invention includes the following steps:

[0100] Obtain data on five dimensions of the multi-energy system, namely economy, environment, energy efficiency, reliability, and equipment, and use them as primary indicators;

[0101] Construct secondary indicators for the primary indicators based on key features;

[0102] Construct a multi-dimensional evaluation index system for the multi-energy system from five dimensions: economy, environment, energy efficiency, reliability, and equipment. Based on the primary indicators and secondary indicators, construct an index system hierarchy including an objective layer, a criterion layer, and a scheme layer. Calculate the weights of the secondary indicators using the network analytic hierarchy process and the entropy weight method respectively, and combine the two to obtain the final weights of the secondary indicators. Then, use the interval TOPSIS method to evaluate the primary indicators.

[0103] As a possible implementation manner of this embodiment, as Figure 2 shown, the primary indicators of the multi-dimensional evaluation index system of the multi-energy system include an economic indicator (A), an environmental indicator (B), an energy efficiency indicator (C), a reliability indicator (D), and an equipment indicator (E). The construction of the economic indicator should be able to well reflect the investment and construction level of the multi-energy system engineering project and intuitively reflect the development value of the multi-energy system; the construction of the environmental indicator should be able to reflect the environmental friendliness of the energy supply and use mode of the multi-energy system; the energy efficiency indicator should be able to reflect the utilization efficiency of different types of energy in the multi-energy system; the reliability indicator should be able to reflect the risk level of energy supply interruption in the multi-energy system; the equipment indicator should be able to reflect the utilization and loss conditions of the complex and diverse equipment in the multi-energy system.

[0104] As a possible implementation manner of this embodiment, as Figure 2As shown, the secondary indicators of the economic indicator (A) include operating income (A1), construction cost (A2), operating expenses (A3), net present value (A4), internal rate of return (A5), and dynamic investment payback period (A6);

[0105] The secondary indicators of the environmental indicator (B) include clean energy consumption rate (B1), environmental pollution emission level (B2), environmental pollution reduction level (B3), and environmental protection benefit (B4);

[0106] The secondary indicators of the energy efficiency indicator (C) include primary energy utilization rate (C1), primary energy savings rate (C2), comprehensive energy utilization efficiency (C3), and energy cost (C4);

[0107] The secondary indicators of the reliability indicator (D) include energy shortage supply rate (D1), average energy shortage supply time (D2), system energy shortage supply rate (D3), and energy supply reliability income (D4);

[0108] The secondary indicators of the equipment indicator (E) include equipment utilization rate (E1), equipment operation efficiency (E2), network comprehensive loss (E3), and energy supply quality (E4).

[0109] As a possible implementation manner of this embodiment, calculating the weights of the secondary indicators by using the network analytic hierarchy process includes:

[0110] 1) Using the expert method to judge the relative importance of each indicator, and constructing a judgment matrix according to the 9-level scale method:

[0111] Suppose there are element groups C1,…,C n in the scheme layer under the criterion layer in ANP, where C i has elements e ik (i = 1,…,N, k = 1,2,…,n j ), and indirectly comparing and analyzing the dominance of each element in the element group C i according to its influence on e ik to construct a judgment matrix;

[0112] 2) Establishing a supermatrix:

[0113] Calculating the maximum eigenvalue and eigenvector of the judgment matrix, obtaining the sorting vector by the eigenvalue method, and performing a consistency test. If the consistency test is passed, then according to the column vector representing the element C i the importance degree of C j can obtain the local weight vector matrix W ij :

[0114]

[0115] Combine the sorting vectors of all mutually influencing elements in the solution layer to obtain the supermatrix W:

[0116]

[0117] Perform column normalization on the supermatrix W to obtain the weighted supermatrix:

[0118]

[0119]

[0120] where a ij is the weighting factor;

[0121] 3) Calculate the limiting supermatrix:

[0122] Calculate the limiting relative sorting vector for each supermatrix:

[0123]

[0124] The value of the corresponding row of the original matrix is the weight value of each evaluation index.

[0125] As a possible implementation manner of this embodiment, the calculation of the weights of the secondary indicators by using the entropy weight method includes:

[0126] 1) Determine the target sequence, and use the following formula to normalize the data of each indicator for benefit-type indicators and cost-type indicators:

[0127]

[0128] where s ij is the value of the j-th indicator of the evaluation plan i, i = 1, 2,..., m, j = 1, 2,..., n, m is the number of evaluation plans, and n is the number of evaluation indicators;

[0129] Normalize the data of each indicator to obtain the normalized decision matrix E = (e ij ) m×n ;

[0130] 2) Calculate the weights:

[0131] Calculate the information entropy Y j of the j-th indicator:

[0132]

[0133] Calculate the indicator weights:

[0134]

[0135] As a possible implementation of this embodiment, the combination of the calculated secondary index weights to calculate the final weights of each secondary index includes:

[0136] Suppose the calculated secondary index weights are divided into w' = (w'1, w'2, …, w' n ) T and w” = (w”1, w”2, …, w” n ) T , then the final weight is:

[0137] w j = αw′ j + βw″ j

[0138] where α and β are the proportions of the weights calculated by the ANP method and the entropy weight method in the final weight, α > 0, β > 0, and α + β = 1;

[0139] When 0 ≤ α + β ≤ 1, the interval weight is obtained:

[0140]

[0141] where, w j and are the upper and lower limits of [w j ;

[0142] The evaluation result [Z i of the secondary index of the i-th evaluation scheme is:

[0143]

[0144] At this time, the obtained evaluation result is an interval number.

[0145] As a possible implementation of this embodiment, the evaluation of the primary index by using the interval TOPSIS method includes:

[0146] 1) Establish an interval evaluation matrix: According to the interval attribute values of different indicators of different schemes, construct an interval evaluation matrix [Z] = ([z ij ) m×n ;

[0147] 2) Standardize the interval evaluation matrix:

[0148] The following formula is used to standardize the interval evaluation matrix for benefit-type indicators and cost-type indicators:

[0149]

[0150] where, e ij and They are the upper and lower limits of the interval numbers after standardization, respectively. z ij and They are the upper and lower limits of the interval numbers before standardization, respectively.

[0151] 3) Calculate the Euclidean distance:

[0152] Calculate the positive and negative ideal interval numbers of the index weights and

[0153]

[0154]

[0155] where j = 1, 2, …, n;

[0156] Calculate the Euclidean distances between [w j and and respectively:

[0157]

[0158]

[0159] 4) Calculate the relative approximation degree of the interval weights and normalize it:

[0160] The calculation formula is:

[0161]

[0162]

[0163] where is the relative approximation degree of the interval weights, and w j is its normalization result;

[0164] 5) Calculate the weighted standardized decision matrix:

[0165] Let each element in the weighted standardized decision matrix [E] be the set of [e ij , and w be the set of w j , then the weighted

[0166] standardized decision matrix [V] is:

[0167] [V] = w[E]

[0168] 6) Calculate the relative approximation degree of each scheme:

[0169] Calculate the Euclidean distances between the i-th scheme and the positive and negative ideal interval numbers and respectively and

[0170] Calculate the relative approximation degree c([v i ) of the i-th solution;

[0171] 7) Evaluate each solution:

[0172] Sort the relative approximation degree c([v i ) from large to small. The larger the value, the better the solution.

[0173] The multi-dimensional evaluation index system of the multi-energy system constructed in this embodiment improves the comprehensiveness, scientificity and practicability of the multi-energy system evaluation, promotes the construction and development of the multi-energy system project, and promotes the coordinated and sustainable development of various energy sources.

[0174] As Figure 3 shown, an apparatus for constructing a multi-dimensional evaluation index system of a multi-energy system provided by an embodiment of the present invention includes:

[0175] A first-level index construction module, configured to obtain data of five dimensions of economy, environment, energy efficiency, reliability, and equipment of the multi-energy system and use them as first-level indexes;

[0176] A second-level index construction module, configured to construct second-level indexes for the first-level indexes according to key features;

[0177] A system construction module, configured to construct a multi-dimensional evaluation index system of the multi-energy system from five dimensions of economy, environment, energy efficiency, reliability, and equipment.

[0178] As a possible implementation manner of this embodiment, the system construction module includes:

[0179] An index system hierarchy construction module, configured to construct an index system hierarchy including an objective layer, a criterion layer, and a solution layer based on the first-level indexes and the second-level indexes;

[0180] A second-level index weight calculation module, configured to calculate and combine the two using the network analytic hierarchy process and the entropy weight method to obtain the final second-level index weight;

[0181] A first-level index relative approximation degree calculation module, configured to calculate the relative approximation degree of each first-level index using the second-level index weight;

[0182] An index evaluation module, configured to evaluate the first-level indexes using the interval TOPSIS method.

[0183] The process of constructing a multi-dimensional evaluation index system of a multi-energy system using the apparatus for constructing a multi-dimensional evaluation index system of a multi-energy system according to the present invention is as follows.

[0184] 1. Considering the development characteristics of the multi - energy system comprehensively, the first - level indicators (A, B, C, D, E) are determined from five dimensions: economy, environment, energy efficiency, reliability, and equipment. The construction of the economic indicator should be able to well reflect the investment and construction level of the multi - energy system engineering project and intuitively reflect the development value of the multi - energy system; the construction of the environmental indicator should be able to reflect the environmental friendliness of the energy supply and use mode of the multi - energy system; the energy - efficiency indicator should be able to reflect the utilization efficiency of different types of energy in the multi - energy system; the reliability indicator should be able to reflect the risk level of energy supply interruption in the multi - energy system; the equipment indicator should be able to reflect the utilization and loss situation of complex and diverse equipment in the multi - energy system.

[0185] 2. Under the first - level economic indicator, an economic secondary - indicator system is constructed based on their respective key characteristics. The secondary indicators under the economic indicator include operating income (A1), construction cost (A2), operating expenses (A3), net present value (A4), internal rate of return (A5), and dynamic investment payback period (A6). The definitions of each indicator are as follows:

[0186] Operating income (A1):

[0187] This indicator refers to the energy - supply income generated by the multi - energy system in one year, which can be divided into the power - supply income from clean - energy power generation, the energy - supply income of the combined cooling, heating and power (CCHP) unit, and the peak - shaving and valley - filling income of energy storage. The calculation formula is:

[0188] A1 = c e (W WT +W PV +W CCHP,E )+c ESS W ESS +c heat W CCHP,heat +c cool W CCHP,cool

[0189] Among them, c e 、c ESS 、c heat 、c cool are respectively the electricity - selling price inside the multi - energy system, the electricity - price difference of energy - storage peak - shaving and valley - filling, the heat - energy and cold - energy prices; W WT 、W PV 、W CCHP,e are respectively the power - supply amounts of wind power, photovoltaic power and CCHP in a whole year; W ESS is the electricity amount of energy - storage peak - shaving and valley - filling; W CCHP,heat and W CCHP,cool are respectively the heat - supply and cold - supply amounts of CCHP.

[0190] Construction cost (A2):

[0191] This indicator refers to the installation costs of various types of equipment invested in the initial stage of the construction of a multi - energy system. The calculation formula is:

[0192]

[0193] Among them, N D is the total number of equipment installations in the multi - energy system, c b,i is the unit installation cost of the i - th type of equipment, and W EP,i is the installed capacity of the i - th type of equipment.

[0194] Operating expenses (A3):

[0195] This indicator refers to the sum of the operation and maintenance costs, the electricity purchase cost from the external power grid, and the gas purchase cost from the external gas grid incurred by the multi - energy system each year during the operation period. The calculation formula is:

[0196] A3 = C om +C pc

[0197] Among them, C om is the operation and maintenance cost, and C pc is the energy purchase cost from the external energy network.

[0198] Net present value (A4):

[0199] This indicator refers to the difference between the sum of the discounted values of the annual net cash flows of the multi - energy system during the operation period and the construction cost. The calculation formula is:

[0200]

[0201] Among them, k dr is the discount rate, T op is the operation period of the multi - energy system, and B IES (t), C op (t) are the operation revenue and operation expenses of the multi - energy system in the t - th year respectively.

[0202] Internal rate of return (A5):

[0203] This indicator refers to the discount rate at which the total present value of the benefits and the total present value of the costs of the multi - energy system are equal during the operation period and the net present value is equal to 0. The calculation formula is:

[0204]

[0205] Among them, C IES (t) is the cost in the t - th year, and B IES (t)-C IES (t) represents the net cash flow of the multi - energy system in the t - th year.

[0206] Dynamic payback period (A6):

[0207] This indicator refers to the time when the cumulative present value of net cash flow is positive. The calculation formula is:

[0208]

[0209]

[0210] Among them, T pst is the number of years when the cumulative present value of net cash flow is positive, NPV(t) is the present value of the net present value flow of the multi - energy system in the t - th year, and NPV total (t - 1) is the cumulative present value of net cash flow of the multi - energy system in the (t - 1) - th year.

[0211] 3. Under the first - level environmental indicator, an environmental secondary - indicator system is constructed based on their respective key characteristics. The secondary indicators under the environmental indicator include the clean - energy consumption rate (B1), the level of environmental pollution emissions (B2), the level of environmental - pollution reduction (B3), and the environmental protection benefit (B4). The definitions of each indicator are as follows:

[0212] Clean - energy consumption rate (B1):

[0213] This indicator refers to the ratio of the clean - energy supply in the multi - energy system to the total load within a whole year. The calculation formula is:

[0214]

[0215] Among them, W e , W h , W c are the electricity, heat, and cooling load amounts in the multi - energy system within a whole year respectively.

[0216] Level of environmental pollution emissions (B2):

[0217] The pollutants of the multi - energy system are mainly the pollutants emitted during the operation of CCHP and the pollutants generated by coal - fired power generation when purchasing electricity from the external power grid. The pollutants include CO2, SO2, CO, and NOx, etc. The calculation formula is:

[0218] B2 = W p,em,CO2 +W p,em,SO2 +W p,em,CO +W p,em,NOx

[0219] Among them, W p,em,CO2 , W p,em,SO2 , W p,em,CO and W p,em,NOx are the emission amounts of CO2, SO2, CO, and NOx respectively.

[0220] Environmental pollution reduction level (B3):

[0221] This indicator refers to the pollutant emissions generated from the consumption of wind power and photovoltaic power generation equivalent to standard coal-fired power generation in a year. The calculation formula is:

[0222]

[0223] Among them, N p is the pollutant type, and α cg,i is the pollutant emissions generated from equivalent standard coal-fired power generation.

[0224] Environmental protection benefit (B4):

[0225] In a multi-energy system, the access of clean energy has significantly reduced pollutant emissions. The environmental protection benefit can be expressed by the pollutant reduction equivalent. The calculation formula is:

[0226]

[0227] Among them, v e,i is the environmental value of the i-th pollutant, and v p,i is the emission penalty of the i-th pollutant.

[0228] 4. Under the first-level energy efficiency indicator, an energy efficiency secondary indicator system is constructed based on their respective key characteristics. The secondary indicators under the energy efficiency indicator include the primary energy utilization rate (C1), the primary energy savings rate (C2), the comprehensive energy utilization efficiency (C3), and the energy cost (C4). The definitions of each indicator are as follows:

[0229] Primary energy utilization rate (C1):

[0230] This indicator refers to the proportion of various loads in a multi-energy system to the primary energy consumption. The calculation formula is:

[0231]

[0232] Among them, W E,Load , W H,Load , W C,Load are the total annual electricity, heat, and cooling loads of the multi-energy system respectively, p e , p g , p w , p s are the conversion coefficients of electricity, natural gas, wind energy, and solar energy respectively, W E,G is the gas purchase volume of the multi-energy system, and W E,WT , W E,PV are the wind energy received by the wind turbine and the solar energy received by the photovoltaic power generation unit respectively.

[0233] Primary energy saving rate (C2):

[0234] This indicator refers to the proportion of non-renewable energy saved by a multi-energy system compared to a traditional energy supply mode. The calculation formula is:

[0235]

[0236] Among them, COP H and COP C are the energy efficiency coefficients of electric heating and electric refrigeration respectively.

[0237] Overall energy utilization efficiency (C3), the calculation formula is:

[0238]

[0239] Among them, E i and E o are the values of the internal input energy and the consumed energy of the multi-energy system respectively.

[0240] Energy cost (C4):

[0241] Based on economic analysis methods, the electricity, heat, and cold output by the system are regarded as products of energy services. The calculation formula is:

[0242]

[0243] Among them: c e and c g and c p and c w are the unit costs of the input energy respectively, and Z is the equivalent cost flow of all equipment investment and operation and maintenance costs.

[0244] 5. Under the first-level reliability indicators, a second-level reliability indicator system is constructed based on their respective key characteristics. The second-level indicators under the reliability indicators include energy supply shortage rate (D1), average energy supply shortage time (D2), system energy supply shortage rate (D3), and energy supply reliability benefit (D4). The definitions of each indicator are as follows:

[0245] Energy supply shortage rate (D1):

[0246] This indicator refers to the proportion of the energy that cannot meet the user load demand inside the multi-energy system and needs to be purchased from outside in the user load demand. The calculation formula is:

[0247]

[0248] Average energy supply shortage time (D2):

[0249] This indicator refers to the average value of the energy shortage time during a certain time period, which is caused by failures of equipment, components, etc. and then the energy supply is restored. The calculation formula is:

[0250]

[0251] where N tf represents the average number of energy shortages in a whole year in a multi - energy system, and T f,i represents the time of the i - th energy shortage.

[0252] System energy shortage rate (D3):

[0253] This indicator refers to the ratio of the total energy shortage time caused by failures of equipment, components, etc. to the total operating time during a certain time period. The calculation formula is:

[0254]

[0255] Energy supply reliability benefit (D4):

[0256] This indicator refers to the ratio of the energy shortage time of important loads in a multi - energy system per year to the average shortage loss per hour of important loads, which is to avoid economic losses caused by energy shortages of important loads. The calculation formula is:

[0257]

[0258] where c AL is the average energy shortage loss per hour of important loads, N tf,c is the average number of energy shortages of important loads in a multi - energy system per year, and T fc,i is the time of the i - th energy shortage of important loads.

[0259] 6. Under the first - level equipment indicators, a second - level equipment indicator system is constructed based on their respective key characteristics. The second - level indicators under the equipment indicators include equipment utilization rate (E1), equipment operation efficiency (E2), energy loss (E3), and energy supply quality (E4). The definitions of each indicator are as follows:

[0260] Equipment utilization rate (E1):

[0261] This indicator refers to the ratio of the actual working time of a certain equipment per year to the planned working time. The calculation formula is:

[0262]

[0263] where T eur,i is the annual working time of the i - th equipment.

[0264] Equipment operation efficiency (E2):

[0265] The calculation formula is:

[0266]

[0267] Where, W i,i and W o,i respectively represent the input energy and output energy of the i-th equipment.

[0268] Energy loss (E3):

[0269] The calculation formula is:

[0270] E3 = W E,L + W G,L + W H,L + W C,L

[0271] Where, W E,L is the power grid transmission loss, W G,L is the natural gas transmission loss, W H,L is the heat energy transmission loss, W C,L is the cold energy transmission loss.

[0272] Energy supply quality (E4):

[0273] This index includes power quality, natural gas quality, heat energy quality and cold energy quality, and is used to evaluate the quality levels of electricity, gas, heat and cold energy in a multi-energy system.

[0274] 7. Construct a multi-dimensional evaluation index system for a multi-energy system from five dimensions: economy, environment, energy efficiency, reliability and equipment. In addition, use the subjective and objective comprehensive evaluation method based on interval TOPSIS to calculate the interval weights of the multi-dimensional evaluation index of the multi-energy system, so as to consider the objectivity, experience and uncertainty of the evaluation weights at the same time.

[0275] The specific steps to construct the multi-dimensional evaluation index system of the multi-energy system are as follows:

[0276] (1) According to the composition of the established multi-dimensional evaluation index system of the multi-energy system, construct an index system hierarchy including the target layer, criterion layer (primary index) and scheme layer (secondary index).

[0277] (2) Calculate the weights of the secondary indexes by using the Analytic Network Process (ANP) method and the entropy weight method respectively.

[0278] ANP method weight calculation:

[0279] 1) Establish a judgment matrix. Use the expert method to judge the relative importance of each index, and construct a judgment matrix according to the 9-level scale method. Suppose there are element groups C1, …, C n in the scheme layer under the criterion layer in ANP, where C i has elements e ik

[0280] (i = 1, …, N, k = 1, 2, …, n j ). Compare and analyze the indirect dominance of each element in the element group C i according to its influence on e ik , that is, construct a judgment matrix.

[0281] 2) Establish a supermatrix. Calculate the maximum eigenvalue and eigenvector of the judgment matrix, and obtain the sorting vector by the eigenvalue method. Then conduct a consistency test. If the consistency test is passed, the importance degree of C i to C j can be obtained according to the column vector representing the element C

[0282] to get the local weight vector matrix W ij :

[0283]

[0284] Combine the sorting vectors of all mutually influencing elements in the scheme layer to obtain the supermatrix W:

[0285]

[0286] Then normalize W by column to obtain the weighted supermatrix (a ij is the weighting factor).

[0287] 3) Calculate the limit supermatrix. Calculate the limit relative sorting vector of each supermatrix:

[0288]

[0289] The value of the corresponding row of the original matrix is the weight value of each evaluation index.

[0290] Calculation of entropy weight method weight:

[0291] 1) Determine the target sequence. Normalize the data of each index for benefit-type indicators and cost-type indicators: For benefit-type indicators:

[0292]

[0293] For cost-type indicators:

[0294]

[0295] Among them, s ij is the value of the j-th index of evaluation plan i (i = 1, 2, …, m, j = 1, 2, …, n), m is the number of evaluation plans, and n is the number of evaluation indicators.

[0296] Furthermore, standardize the data of each index to obtain the normalized decision matrix E = (e ij ) m×n .

[0297] 2) Calculate the weights. Calculate the information entropy Y j of the j-th index:

[0298]

[0299] Calculate the index weights:

[0300]

[0301] (3) Combine the weights of the secondary indicators calculated by the ANP method and the entropy weight method respectively to calculate the final weights of each secondary indicator.

[0302] Suppose the weights of the secondary indicators calculated by the ANP method and the entropy weight method are w' = (w'1, w'2, …, w' n ) T and w'' = (w''1, w''2, …, w'' n ) T , then the final weight can be obtained as:

[0303] w j = αw' j + βw'' j

[0304] Among them, α and β are the proportions of the weights calculated by the ANP method and the entropy weight method in the final weight respectively (α > 0, β > 0, α + β = 1). When 0 ≤ α + β ≤ 1, the interval weight is obtained:

[0305]

[0306] Among them, w j and are the upper and lower limits of [w j respectively.

[0307] Furthermore, the evaluation result [Z i of the secondary indicators of the i-th evaluation plan is obtained as:

[0308]

[0309] At this time, the obtained evaluation result is an interval number.

[0310] (4) Evaluate the first-level indicators using the interval TOPSIS method.

[0311] 1) Establish an interval evaluation matrix. According to the interval attribute values of different indicators for different schemes, construct the interval evaluation matrix [Z] = ([z ij ) m×n .

[0312] 2) Standardize the interval evaluation matrix. Respectively for benefit-type indicators and cost-type indicators, perform the standardization of the interval evaluation matrix:

[0313] For benefit-type indicators:

[0314]

[0315] For cost-type indicators:

[0316]

[0317] Among them, e ij and are respectively the upper and lower limits of the interval number after standardization, z ij and are respectively the upper and lower limits of the interval number before standardization.

[0318] 3) Calculate the Euclidean distance. First, calculate the positive and negative ideal interval numbers of the index weights and

[0319]

[0320]

[0321] (j = 1, 2, …, n)

[0322] Then, calculate the Euclidean distances between [w j and and :

[0323]

[0324]

[0325] 4) Calculate the relative approximation degree of the interval weight and normalize it. The calculation formula is:

[0326]

[0327]

[0328] Among them, is the relative approximation degree of interval weights, w j is its normalization result

[0329] 5) Calculate the weighted normalized decision matrix. Let each element in the weighted normalized decision matrix [E] be the set of [e ij , and w be the set of w j , then the weighted normalized decision matrix [V] is:

[0330] [V] = w[E]

[0331] 6) Calculate the relative approximation degree of each scheme. First, similar to step 3) in (4), calculate the Euclidean distances between the i-th scheme and the positive and negative ideal interval numbers and and Then, similar to step 4) in (4), calculate the relative approximation degree c([v i ) of the i-th scheme.

[0332] 7) Evaluate each scheme. Sort the c([v i ) obtained in step 6) of (4) from large to small. The larger its value, the better the scheme.

[0333] The present invention constructs a multi-dimensional evaluation index system for a multi-energy system from five dimensions: economy, environment, energy efficiency, reliability, and equipment. In addition, it is beneficial to improve the subjective and objective comprehensive evaluation method for intervals to calculate the interval weights of the multi-dimensional evaluation indexes of the multi-energy system, so as to take into account the uncertainty of the indexes in these five dimensions and the uncertainty of the calculation process, and quantitatively analyze the constructed secondary index system. First, according to the composition of the established multi-dimensional evaluation index system of the multi-energy system, construct an index system hierarchy including a target layer, a criterion layer (primary index), and a scheme layer (secondary index), and use the expert method to score the constructed index system hierarchy according to the "interval scale table"; then, construct an interval judgment matrix to obtain the interval weights of each index in the hierarchical structure model; secondly, refer to the secondary indexes of the multi-dimensional evaluation indexes of the multi-energy system to construct an index set, form an interval decision matrix and perform standardization processing; after that, calculate the interval entropy and interval entropy weight of the proposed indexes; finally, use the pre-selected weight factor to combine the interval analytic hierarchy process and the interval entropy weight method to obtain the comprehensive interval weights of the multi-dimensional evaluation indexes of the multi-energy system.

[0334] Taking a certain integrated energy system as the research object, the annual average radiation intensity in the area where it is located is 1500 kW·h / m 2 ​, the average wind speed is 5 m / s. The comprehensive energy system covers an area of 960 mu, with a building area of 575,000 square meters, a lighting area of approximately 20,000 square meters, an electricity load of about 3.70 MW, a heat load of about 2.90 MW, a cooling load of about 2.40 MW, and the annual electricity consumption of system users is about 4,271.95 MWh. The operation period of the comprehensive energy system is 10 years. The electricity purchase price from the large power grid is 0.64 yuan / (kWh), the network loss rate is 5%, and the energy efficiency ratio is 3. In the hybrid energy storage system, the capacity of lithium iron phosphate is 100 kW·2 h, the capacity of supercapacitor is 100 kW·10 s, and the capacity of lead-acid battery is 150 kW·1 h. Among them, the CCHP system mainly consists of a 600 kW gas internal combustion engine unit, a 300 kW gas turbine unit, and a 400 kW absorption refrigeration unit. The costs of each device of this energy system are shown in Table 1.

[0335] Table 1 Costs of Each Device of a Certain Comprehensive Energy System

[0336]

[0337] There are currently 3 schemes:

[0338] Scheme 1: CCHP system: power grid, 1.6 MW of CCHP system, and energy storage system;

[0339] Scheme 2: Photovoltaic-gas complementary system: power grid, 2.4 MW of photovoltaic units, 1.3 MW of CCHP system, and energy storage system;

[0340] Scheme 3: Wind-solar-photovoltaic-gas complementary system: power grid, 2.0 MW of photovoltaic units, 0.3 MW of wind turbine units, 1.3 MW of CCHP system, and energy storage system.

[0341] The index values of each scheme are shown in Tables 2 - 6.

[0342] Table 2 Economic Indicators

[0343]

[0344]

[0345] Table 3 Environmental Indicators

[0346]

[0347] Table 4 Energy Efficiency Indicators

[0348]

[0349] Table 5 Reliability Indicators

[0350]

[0351] Table 6 Equipment Indicators

[0352]

[0353] Calculate the weights of the secondary indicators using the ANP method and the entropy weight method respectively, and combine the results of the two to calculate the weights of each indicator. The evaluation results of the calculated secondary indicators are shown in Tables 7 and 8. It can be seen from this that Scheme 1 has the best results in the equipment secondary indicators, Scheme 2 performs best in the two secondary indicators of environment and energy efficiency, and Scheme 3 performs best in the two secondary indicators of economy and reliability.

[0354] Table 7 Secondary Indicator Values

[0355]

[0356] Table 8 Evaluation Results of Secondary Indicators

[0357]

[0358] Now set the interval weight values of the five first-level indicators of economy, environment, reliability, energy efficiency, and equipment to be: [w1] = [0.24, 0.26], [w2] = [0.26, 0.34], [w3] = [0.24, 0.26], [w5] = [0.09, 0.11]. Then, the positive ideal interval number of the interval weight value can be calculated as [0.34, 1], and the negative ideal interval number is [0, 0.09]. Then, the relative closeness degrees of the interval weights are calculated as c([w1]) = 0.2826, c([w2]) = 0.3517, c([w3]) = 0.2826, c([w4]) = 0.0907, c([w5]) = 0.0907 respectively. The normalized results are w1 = 0.2573, w2 = 0.3202, w3 = 0.2573, w4 = 0.0826, w5 = 0.0826. Finally, the relative approximation degrees of each scheme are obtained and sorted, as shown in Table 9.

[0359] Table 9 Relative Approximation Degrees of Each Scheme

[0360]

[0361] According to Table 9, Scheme 3 performs best, Scheme 2 comes second, and Scheme 1 is the worst.

[0362] Corresponding to the above method for starting an application program, an embodiment of the present invention also provides a storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the method for constructing a multi-dimensional evaluation index system for any multi-energy system as described above.

[0363] The startup device of the application provided by the embodiments of the present application can be specific hardware on the device, or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, its implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference can be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the foregoing described systems, devices, and units can all refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0364] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0365] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of devices or modules can be in electrical, mechanical, or other forms.

[0366] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they can be located in one place, or they can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0367] In addition, the various functional modules in the embodiments provided by the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0368] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in one or more flows and / or one or more blocks in the flow Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one or more blocks.

[0369] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction means that implements the functions specified in one or more flows and / or one or more blocks in the flow Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one or more blocks.

[0370] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or one or more blocks in the flow Figure 1 one or more flows and / or blocks Figure 1 or a means for implementing the functions specified in one or more blocks.

[0371] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific embodiments of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for constructing a multi-dimensional evaluation index system for a multi-energy system, characterized in that, It includes the following steps: Obtain data on five dimensions of the multi - energy system, namely economy, environment, energy efficiency, reliability, and equipment, and use them as primary indicators; Construct secondary indicators for the primary indicators based on key characteristics; Construct a multi - dimensional evaluation index system for the multi - energy system from five dimensions of economy, environment, energy efficiency, reliability, and equipment: Based on the primary indicators and secondary indicators, construct an index system hierarchy including the target layer, criterion layer, and scheme layer. Calculate the weights of the secondary indicators using the analytic network process and the entropy weight method respectively, and combine the two to obtain the final weights of the secondary indicators. Then use the interval TOPSIS method to evaluate the primary indicators; The calculation of the weights of the secondary indicators using the analytic network process includes: 1) Use the expert method to judge the relative importance of each indicator, and construct a judgment matrix according to the 9 - level scale method; Suppose there is an element group in the scheme layer under the criterion layer in ANP C 1, …,[ C m , where C i contains elements e ij , i = 1, …, m, j = 1, 2, …,[ n , m is the number of element groups, n is the element group C i the number of elements in; Arrange the elements in the element group C i according to their indirect dominance degree comparison analysis of the influence on e ij to construct a judgment matrix; 2) Establish a super - matrix; Calculate the maximum eigenvalue and eigenvector of the judgment matrix, obtain the sorting vector by the eigenvalue method, conduct a consistency test. If the consistency test is passed, then the elements are represented by the column vectors. C i For C j importance, the local weight vector matrix can be obtained W ij : Combine the ranking vectors of all the elements that influence each other in the scheme layer to obtain the supermatrix W : Normalize the supermatrix W to obtain a weighted supermatrix: Among them, a ij is a weighting factor; 3) Calculate the limiting super - matrix; Calculate the limiting relative ranking vector of each super - matrix; The value of the corresponding row of the original matrix is the weight value of each evaluation indicator; The combination of the calculated weights of the secondary indicators to calculate the final weights of each secondary indicator includes: Suppose the secondary index weights of the design calculation are divided into and , then the final weight is as follows: Among them, α , β are the proportions of the weights calculated by the ANP method and the entropy weight method respectively in the final weight, , , ; When , the interval weight is obtained as follows: Among them, and are the upper and lower limits of the interval weight w j , respectively. Obtain the i evaluation results of the secondary indicators of the Z i evaluation plan are: The evaluation result obtained at this time is an interval number; During the process of constructing secondary indicators for the primary indicators based on key characteristics, The secondary indicators of the economic indicator include operating income, construction cost, operation cost, net present value, internal rate of return, and dynamic investment payback period; The secondary indicators of the environmental indicator include clean energy consumption rate, environmental pollution emission level, environmental pollution reduction level, and environmental protection benefits; The secondary indicators of the energy - efficiency indicator include primary energy utilization rate, primary energy savings rate, comprehensive energy utilization efficiency, and energy cost; The secondary indicators of the reliability indicator include energy shortage supply rate, average energy shortage supply time, system energy shortage supply rate, and energy supply reliability benefit. The energy shortage supply rate refers to the proportion of the energy that cannot meet the user load demand within the multi - energy system and needs to be purchased from outside in the user load demand. The system energy shortage supply rate refers to the ratio of the total energy shortage supply time caused by equipment and component failures within a certain time period to the total operation time; The secondary indicators of the equipment indicator include equipment utilization rate, equipment operation efficiency, network comprehensive loss, and energy supply quality.

2. The method for constructing a multi-dimensional evaluation index system of a multi-energy system according to claim 1, characterized in that, The calculation of the weights of the secondary indicators using the entropy weight method includes: 1) Determine the target sequence, and use the following formula to normalize the data of each indicator for benefit - type indicators and cost - type indicators: Among them, s ij is the numerical value of the i th j index of the evaluation plan, i i = 1, 2, …, m and j = 1, 2, …, n , m where m is the number of evaluation plans, n and n is the number of evaluation indicators; Standardize the index data to obtain a normalized decision matrix ; 2) Calculate the weights: Calculate the j information entropy of the Y j th index: Calculate the indicator weights: 。 3. The method for constructing the multi-dimensional evaluation index system of the multi-energy system according to claim 1, characterized in that, The evaluation of the primary indicators using the interval TOPSIS method includes: 1) Establish an interval evaluation matrix: Based on the interval attribute values of different indicators for different schemes, construct the interval evaluation matrix [Z] = ( z ij ) m×n ; 2) Standardize the interval evaluation matrix: Use the following formula to standardize the interval evaluation matrix for benefit - type indicators and cost - type indicators: Among them, and are the upper and lower limits of the interval number after standardization respectively, and are the upper and lower limits of the interval number before standardization respectively; 3) Calculate the Euclidean distance; Positive and negative ideal interval numbers for calculating index weights and : Among them, j = 1, 2, …, n ; Calculate separately w j and and of the Euclidean distance: ； 4) Calculate the relative approximation degree of the interval weights and normalize them; The calculation formula is: Among them, is the relative approximation degree of the interval weight, is its normalization result; 5) Calculate the weighted standardized decision matrix; Let each element in the weighted normalized decision matrix , E be the set of [e ij , w be w j 's set, then the weighted normalized decision matrix V is: 6) Calculate the relative approximation degree of each scheme; The i th solution is calculated to have the Euclidean distance from the positive and negative ideal interval numbers and ; and ; The relative approximation of the i th solution is calculated ; 7) Evaluate each scheme: Sort the relative approximation degrees in descending order. The larger the value, the better the corresponding solution.

4. A device for constructing a multi-dimensional evaluation index system of a multi-energy system, characterized in that, It includes: A primary - indicator construction module, which is used to obtain data on five dimensions of the multi - energy system, namely economy, environment, energy efficiency, reliability, and equipment, and use them as primary indicators; The secondary index construction module is used to construct the secondary indexes of the primary index according to the key features; The system construction module is used to construct a multi-dimensional evaluation index system for the multi-energy system from five dimensions: economy, environment, energy efficiency, reliability, and equipment; The system construction module includes: The index system hierarchy construction module is used to construct an index system hierarchy including the target layer, criterion layer, and scheme layer based on the primary index and secondary index; The secondary index weight calculation module is used to calculate and combine the two using the analytic network process and entropy weight method respectively to obtain the final secondary index weight; The relative approximation degree calculation module of the primary index is used to calculate the relative approximation degree of each primary index using the secondary index weight; The index evaluation module is used to evaluate the primary index using the interval TOPSIS method; The calculation of the secondary index weight using the analytic network process includes: 1) Using the expert method to judge the relative importance of each index, and constructing a judgment matrix according to the 9-level scale method: Suppose that the element group in the scheme layer under the criterion layer in ANP has C 1, …, C m , where C i has elements e ij , i = 1, …, m , k = 1, 2, …, n . Compare and analyze the indirect dominance of each element in the element group C i according to their influence on e ij , so as to construct a judgment matrix; 2) Establishing a supermatrix: Calculate the maximum eigenvalue and eigenvector of the judgment matrix, obtain the sorting vector by the eigenvalue method, conduct a consistency test. If the consistency test is passed, then represent the elements by column vectors C i For C j the importance degree of, the local weight vector matrix can be obtained W ij : Combine the sorting vectors of all the elements that interact with each other in the scheme layer to obtain a supermatrix W : Normalize the supermatrix W to obtain a weighted supermatrix: wherein, a ij is a weighting factor; 3) Calculating the limiting supermatrix: Calculating the limiting relative ranking vector of each supermatrix: The value of the corresponding row of the original matrix is the weight value of each evaluation index; The combination of the calculated secondary index weights to calculate the final weights of each secondary index includes: Suppose the weights of the secondary indicators of the design calculation are divided into and , then the final weight is: Among them, α , β are the proportions of the weights calculated by the ANP method and the entropy weight method respectively in the final weight, , , ; When , the interval weights are obtained as follows: Among them, and are the upper and lower limits of the interval weight w j , respectively. Obtain the i evaluation results of the secondary indicators of the Z i th evaluation plan, which are: The evaluation result obtained at this time is an interval number; In the process of constructing the secondary index of the primary index according to the key features, The secondary indexes of the economic index include operating income, construction cost, operation cost, net present value, internal rate of return, and dynamic investment payback period; The secondary indexes of the environmental index include clean energy consumption rate, environmental pollution emission level, environmental pollution reduction level, and environmental protection benefits; The secondary indexes of the energy efficiency index include primary energy utilization rate, primary energy saving rate, comprehensive energy utilization efficiency, and energy cost; The secondary indexes of the reliability index include energy shortage supply rate, average energy shortage supply time, system energy shortage supply rate, and energy supply reliability benefit. The energy shortage supply rate refers to the proportion of the energy that cannot meet the user load demand inside the multi-energy system and needs to be purchased from the outside in the user load demand; the system energy shortage supply rate refers to the ratio of the total energy shortage supply time caused by equipment and component failures within a certain time period to the total operation time; The secondary indexes of the equipment index include equipment utilization rate, equipment operation efficiency, network comprehensive loss, and energy supply quality.

5. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is run by a processor, it executes the method steps of constructing the multi-dimensional evaluation index system of the multi-energy system as described in any one of claims 1-3.

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