A method, system and electronic device for determining the operation and maintenance effect of a highway microgrid

Abstract

The present invention discloses a method, system and electronic device for determining the operation and maintenance effect of a highway microgrid, relating to the field of energy transportation. The present invention establishes a grid structure model of the highway microgrid to be determined, analyzes the operation scenarios and energy consumption status of the highway microgrid, constructs a set of operation and energy consumption scenarios of the highway microgrid, and obtains secondary indicators. Combining various scenarios of the highway microgrid, the improved combined weight, entropy weight - analytic hierarchy process is adopted, and the hesitant fuzzy set is introduced to calculate the weights of different indicators. Through fuzzy comprehensive evaluation, the difference in scores and the superiority and inferiority of each scenario indicator are determined, so as to obtain a comprehensive evaluation result, and further accurately obtain the operation and maintenance effect of the highway microgrid to be determined, comprehensively evaluate the operation level of the distribution network under various scenarios, and provide a reference for the evaluation and optimization of the distribution network scheme of the highway energy self - consistency system.

Description

Technical Field

[0001] The present invention relates to the field of energy transportation, and particularly to a method, a system and an electronic device for determining the operation and maintenance effect of a highway microgrid. Background Art

[0002] At present, the energy structure system is undergoing a transformation towards low-carbon energy. Under this background, it is of great significance to build a new type of transportation system with self-consistent energy. Building a new energy transportation system can significantly reduce carbon emissions in the transportation field, promote the conservation and sustainable utilization of energy resources, reduce dependence on fossil energy, drive the transformation of the energy structure, and promote the development of new energy technologies and infrastructure. For example, electric vehicles, intelligent transportation systems, and renewable energy will promote industrial upgrading and create job opportunities. The technological competitiveness in the field of new energy transportation will be enhanced, laying a solid foundation for sustainable development.

[0003] Highways are important transportation infrastructures. The highway new energy microgrid is an important component of the new energy transportation system. Highways have unique characteristics, such as long characteristic spans, wide coverage areas, and fixed grid structures. The power generation of the new energy microgrid is affected by the regional climate. With the increase in the penetration rate of electric vehicles, new operating environments and energy consumption scenarios are generated in the highway microgrid. For the highway microgrid in this scenario, building an evaluation system and method to evaluate its operation and maintenance effect can achieve a quantitative judgment of the new energy transportation system on the highway, find the key indicators affecting operation and maintenance, improve the operation and maintenance level, and promote the development of the energy transportation system.

[0004] Moreover, when building a new type of energy transportation system and the new energy microgrid supplies power to the highway, compared with the traditional highway distribution network, the highway microgrid generates electricity through distributed energy at the "source" end, and is affected by electric vehicle charging at the "load" end. Maintaining the balance between the source and the load can achieve the optimal regulation of energy through the charging and discharging of energy storage devices. Evaluating the effect of the highway microgrid can also ensure its safe and reliable operation, reduce the probability of faults, and maintain good economy, realizing the joint regulation of the source, the grid, the load, and the storage.

[0005] However, the prior art does not disclose a practical and effective evaluation system or method for the operation and maintenance effect of a highway microgrid, which cannot achieve a comprehensive evaluation of the highway microgrid and cannot provide guiding opinions for the development of energy transportation. Summary of the Invention

[0006] To solve the above problems existing in the prior art, the present invention provides a method, a system and an electronic device for determining the operation and maintenance effect of a highway microgrid.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for determining the operation and maintenance effect of a highway microgrid, comprising:

[0009] Establish a grid structure model of the highway microgrid to be determined, and obtain the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid based on the grid structure model;

[0010] Determine the basic index data based on the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid, and obtain the secondary indexes based on the basic index data;

[0011] Adopt a calculation method of improved combined weights to determine the score of each secondary index;

[0012] Adopt the entropy weight - analytic hierarchy process to determine the weights of the secondary indexes;

[0013] Fuse the weights of the secondary indexes and the scores of the secondary indexes to obtain the scores of the primary indexes;

[0014] Take the score of the primary index as the membership degree in the hesitant fuzzy set, and give the non - membership degree according to the importance degree of the operation scenarios of the high - speed microgrid to obtain the decision matrix;

[0015] Based on the decision matrix, determine the distance measure between each primary index through the PHFE distance measure to obtain the difference matrix;

[0016] Obtain the primary indexes and determine the weights of the primary indexes; the primary indexes include seven attributes: power supply index, power grid index, load index, energy storage index, economic index, and reliability index;

[0017] Adopt λ - Fuzzy measure to describe the relationship between the primary index combinations, and determine the weight values of the primary index combinations based on the weights of the primary indexes;

[0018] Use the Choquet integral to aggregate the weight values of the primary index combinations and the distance measure between the primary indexes to obtain the dominance matrix and the inferiority matrix;

[0019] Based on the dominance matrix and the inferiority matrix, determine the comprehensive dominance matrix, and take the elements in the comprehensive dominance matrix as the comprehensive evaluation results;

[0020] Based on the comprehensive evaluation results, determine the operation and maintenance effect of the highway microgrid to be determined.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0022] The present invention establishes a grid structure model of a highway microgrid to be determined, so as to analyze the operation scenarios and energy consumption status of the highway microgrid, construct an operation scenario set and an energy consumption scenario set of the highway microgrid, and then establish basic index data to obtain secondary indexes. Combining various scenarios of the obtained highway microgrid, an improved combined weight calculation method, entropy weight - analytic hierarchy process is adopted, and hesitant fuzzy sets are introduced to calculate the weights of different indexes. Finally, through fuzzy comprehensive evaluation, the difference size and superiority and inferiority degree of the scores of each scenario index are determined to obtain a comprehensive evaluation result, so as to accurately determine the operation and maintenance effect of the highway microgrid to be determined. Moreover, by analyzing the influencing factors of each index and evaluation weight of the microgrid under various scenarios, identifying the influence size of different indexes on the evaluation system, analyzing and obtaining the key indexes affecting operation and maintenance, and combining with the operation and maintenance effect, it plays a reference role in the operation and maintenance of the microgrid, can comprehensively evaluate the operation level under various scenarios, and provides a reference for the evaluation of the distribution network of the highway energy self - consistency system.

[0023] Furthermore, the present invention provides a system for determining the operation and maintenance effect of a highway microgrid. The system is used to implement the method for determining the operation and maintenance effect of the highway microgrid provided above; the system includes:

[0024] A scenario construction module, configured to establish a grid structure model of a highway microgrid to be determined, and obtain an operation scenario set of the highway microgrid and an energy consumption scenario set of the highway microgrid based on the grid structure model;

[0025] A secondary index determination module, configured to determine basic index data based on the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid, and obtain secondary indexes based on the basic index data;

[0026] A secondary index score determination module, configured to determine the score of each secondary index by using an improved combined weight calculation method;

[0027] A secondary index weight determination module, configured to determine the weights of secondary indexes by using entropy weight - analytic hierarchy process;

[0028] A primary index score determination module, configured to fuse the weights of the secondary indexes and the scores of the secondary indexes to obtain the scores of the primary indexes;

[0029] A decision matrix determination module, configured to use the scores of the primary indexes as membership degrees in the hesitant fuzzy set, and give non - membership degrees according to the importance degree of the operation scenarios of the high - speed microgrid to obtain a decision matrix;

[0030] A difference matrix determination module, configured to determine the distance measure between each primary index through PHFE distance measure based on the decision matrix to obtain a difference matrix;

[0031] The first-level index weight determination module is used to obtain first-level indexes and determine the weights of the first-level indexes; the first-level indexes include seven attributes: power supply index, power grid index, load index, energy storage index, economic index, and reliability index;

[0032] The first-level index combination weight determination module is used to use λ - fuzzy measure to describe the relationship between first-level index combinations, and determine the weight values of first-level index combinations based on the weights of first-level indexes;

[0033] The superiority-inferiority matrix determination module is used to use the Choquet integral to aggregate the weight values of the first-level index combinations and the distance measure between first-level indexes to obtain a superiority matrix and an inferiority matrix;

[0034] The comprehensive evaluation result determination module is used to determine a comprehensive superiority matrix based on the superiority matrix and the inferiority matrix, and use the elements in the comprehensive superiority matrix as the comprehensive evaluation result;

[0035] The operation and maintenance effect determination module is used to determine the operation and maintenance effect of the highway microgrid to be determined based on the comprehensive evaluation result.

[0036] Furthermore, the present invention also provides an electronic device, which includes:

[0037] A memory for storing a computer program;

[0038] A processor, connected to the memory, for retrieving and executing the computer program to implement the above-provided method for determining the operation and maintenance effect of a highway microgrid.

[0039] Optionally, the memory is a computer-readable storage medium.

[0040] Since the technical effects achieved by the system and the electronic device provided by the present invention are the same as those achieved by the method provided by the present invention above, they will not be elaborated here. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a flowchart of the method for determining the operation and maintenance effect of a highway microgrid provided by an embodiment of the present invention;

[0043] Figure 2It is the implementation flowchart of the method for determining the operation and maintenance effect of the highway microgrid provided by the embodiment of the present invention;

[0044] Figure 3 It is the structure diagram of the highway microgrid framework provided by the embodiment of the present invention;

[0045] Figure 4 It is the schematic diagram of the energy consumption scenario set of the highway microgrid provided by the embodiment of the present invention;

[0046] Figure 5 It is the schematic diagram of the comprehensive evaluation system of the highway microgrid provided by the embodiment of the present invention;

[0047] Figure 6 It is the flowchart of calculating the evaluation result provided by the embodiment of the present invention;

[0048] Figure 7 It is the schematic diagram of partial data of the calculation example of the highway microgrid provided by the embodiment of the present invention; among them, Figure 7 (a) is the schematic diagram of sunlight intensity in the calculation example of the highway microgrid, Figure 7 (b) is the schematic diagram of photovoltaic machine processing in the calculation example of the highway microgrid, Figure 7 (c) is the schematic diagram of daily wind speed distribution in the calculation example of the highway microgrid, Figure 7 (d) is the schematic diagram of fan output in the calculation example of the highway microgrid, Figure 7 (e) is the schematic diagram of the SOC change trend in the calculation example of the highway microgrid, Figure 7 (f) is the schematic diagram of the power supplied by the external power grid in the calculation example of the highway microgrid, Figure 7 (g) is the schematic diagram of the exchange power at the well point in the calculation example of the highway microgrid, Figure 7 ]>(h) is the schematic diagram of the total network loss in the calculation example of the highway microgrid, Figure 7 (I) is the schematic diagram of the voltage level in the calculation example of the highway microgrid. Detailed implementation mode

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] The purpose of the present invention is to provide a method, system and electronic device for determining the operation and maintenance effect of a highway microgrid, which can realize the comprehensive evaluation of the highway microgrid, determine the operation and maintenance effect of the highway microgrid, and thus provide effective guiding opinions for the development of energy transportation.

[0051] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] The rapid concept of the method for determining the operation and maintenance effect of the highway microgrid provided by the present invention is as follows: specifically model and analyze the highway microgrid, and propose the abnormal scenario set and the scenario set of the highway microgrid. Specific evaluation indicators and an evaluation method based on improved combined weights are proposed to establish an evaluation index system for the highway microgrid. Analyze the scoring results of the highway microgrid to identify the key indicators affecting the operation and maintenance of the microgrid. Based on this, the present invention provides the following embodiments to evaluate the operation and maintenance effect of the highway microgrid, so as to provide effective guiding opinions for selecting the optimal scheme of the highway microgrid and the development of energy transportation, and provide references for the evaluation and optimization of the distribution network scheme of the highway energy self-consistent system.

[0053] Embodiment 1

[0054] As Figure 1 shown, the method for determining the operation and maintenance effect of the highway microgrid provided in this embodiment includes:

[0055] Step 100: Establish a grid structure model of the highway microgrid to be determined, and obtain the operation scenario set and the energy consumption scenario set of the highway microgrid based on the grid structure model. The purpose of this step is mainly to determine the operation scenario and energy consumption scenario of the highway microgrid.

[0056] For example, after constructing the grid structure model as Figure 3 shown, there are a large number of abnormal operation times in the highway microgrid. Due to the closed nature of the highway, when a section in the middle of the highway enters an abnormal situation, the entire highway will be affected. In actual application, the abnormal situations can be classified into 4 categories: holidays, emergencies, special weather, and natural disasters. Accordingly, the operation scenario set of the highway is proposed to include: normal operation, holiday operation, operation under emergencies, operation under special weather, and natural disaster response.

[0057] Affected by factors such as wide coverage, crossing different regional terrains, and different transportation modes in different seasons, it is easy to cause different changes in the grid strength, load amount, and resource endowment of the highway microgrid. Based on this, the energy consumption scenario set of the highway as Figure 4 shown can be proposed.

[0058] Step 101: Determine the basic index data based on the operation scenario set and the energy consumption scenario set of the highway microgrid, and obtain secondary indicators based on the basic index data.

[0059] Combined with the obtained operation scenario set above, a comprehensive evaluation system for highway microgrids can be established. Specifically:

[0060] Analyze the evaluation requirements of highway microgrids and the degree to which the microgrids are affected under various scenarios, and establish a three-layer comprehensive evaluation system for highway microgrids (hereinafter referred to as the evaluation system for short). Highway microgrids have requirements for safe, efficient, and economic operation. For various scenarios, evaluation indicators are proposed from the perspectives of the composition and basic operation requirements of the microgrids, which can comprehensively evaluate highway microgrids. Select seven attributes, namely power supply index (C1), grid index (C2), load index (C3), energy storage index (C4), economic index (C5), reliability index (C6), and fault and recovery index (C7), for evaluation from the perspective of the composition of highway microgrids. Calculate the scores of each attribute according to the specific operation results of highway microgrids. The entire evaluation system is as Figure 5 shown. The secondary indicators are the next-level indicators of the seven evaluation attributes (as shown in the bottom box of Figure 5 ).

[0061] Step 102: Use an improved combined weight calculation method to determine the score of each secondary indicator.

[0062] In the actual application process, this step needs to first obtain the basic data corresponding to the secondary indicators generated during actual operation, perform dimensionless standardization processing on each type of secondary indicator, convert it into a secondary indicator score between (0-1) that is the higher the better, and perform subsequent calculations.

[0063] Among them, the score of the secondary indicator is expressed as:

[0064]

[0065] In the formula, x ij is the i-th secondary indicator under the j-th energy consumption scenario of the highway microgrid, x is the basic data corresponding to the secondary indicator, is the score of the secondary indicator, max represents taking the maximum value, and min represents taking the minimum value.

[0066] Fill the standardized data (i.e., the scores of the secondary indicators) into the matrix .

[0067] Step 103: Use the entropy weight - analytic hierarchy process to determine the weights of the secondary indicators. The implementation process of this step is as follows:

[0068] A. Use the entropy weight method to determine the weights:

[0069] 1) Determine the probability matrix of the secondary indicators. The probability matrix of the secondary indicators is p ij :

[0070]

[0071] 2) Determine the information entropy of the secondary indicators based on the probability matrix. The information entropy of the secondary indicators is S j :

[0072]

[0073] 3) Determine the weight of the first-level indicators based on the information entropy of the secondary indicators. The weight of the first-level indicators is α i :

[0074]

[0075] B. Use the analytic hierarchy process to determine the weight:

[0076] 1) Evaluate the importance comparison of any two indicators in the evaluation system according to the "1-9" scaling rule, and obtain the judgment matrix B = {b ij}. The "1-9" scaling rule is a rule set based on expert experience.

[0077] 2) Use the root method to determine the eigenvector of the judgment matrix B = {b ij}, and normalize the non-zero eigenvalue vectors in the eigenvector to obtain the weight of the second-level indicators. The weight of the second-level indicators is β i :

[0078]

[0079] In the formula, W i represents the i-th non-zero eigenvalue vector, b ij represents the element in the judgment matrix, and W j represents the j-th non-zero eigenvalue vector.

[0080] C. Combine the weight of the first-level indicators and the weight of the second-level indicators to obtain the weight of the secondary indicators, that is, the combined entropy weight method and the analytic hierarchy process to calculate the index weight, and obtain the comprehensive weight of the entropy weight analytic hierarchy process. This comprehensive weight of the entropy weight analytic hierarchy process is the weight of the secondary indicators, denoted as ω i :

[0081] ω i = k1α i + k2β i .

[0082] In the formula, k1 is the tendency degree of the evaluation system to the entropy weight method. α i is the weight of the first-level indicators. k2 is the tendency degree of the evaluation system to the analytic hierarchy process. β i is the weight of the second-level indicators.

[0083] Step 104: Integrate the weights of the secondary indicators and the scores of the secondary indicators to obtain the scores of the primary indicators. The obtained scores of the primary indicators (i.e., the evaluation results of the primary indicators) are as follows:

[0084]

[0085] In the formula, ζ is the score of the primary indicator, ω i is the weight of the i-th secondary indicator, x i is the score of the i-th secondary indicator, and n is the number of secondary indicators.

[0086] Step 105: Use the scores of the primary indicators as the membership degrees in the hesitant fuzzy set, and assign non-membership degrees according to the importance of the high-speed microgrid operation scenarios to obtain the decision matrix. Among them, the decision matrix is:

[0087] P = [p i′j M×N :

[0088]

[0089] In the formula, p i′j represents the evaluation value of the index j of the energy consumption scenario i′, and ν k represent the membership degrees and non-membership degrees under 5 normal or abnormal conditions, M is the number of energy consumption scenarios, and N is the number of indicators.

[0090] Step 106: Based on the decision matrix, determine the distance measure between each primary indicator through the PHFE distance measure to obtain the difference matrix. Among them, calculate the difference of the primary indicators in each scenario to obtain the relative difference matrix of each primary indicator. The relative difference matrix is expressed as Y c :

[0091]

[0092] In the formula, c represents the type of primary indicator corresponding to the relative difference matrix. i′ and k correspond to the two scenarios being compared. represents the element of the relative difference matrix, which is the relative difference value. d(p i′c , p kc ) represents the difference between the primary indicator p i′c in scenario i′ and the primary indicator p kc in scenario k, represents the score function of the primary indicator p i′c , represents the score function of the primary indicator p kc , μ A (x i ) represents the score of the i-th secondary indicator under the normal state A, M A ​Indicates the influence values of normal state A and abnormal state A, μ B (x i ) represents the score of the i-th secondary index under normal state B, M B Indicates the influence values of normal state B and abnormal state B, ν A (x i ) represents the score of the i-th secondary index under abnormal state A, Represents the score of the i-th secondary index under abnormal state B. N′ = A,B. μ N′ Is the abbreviation of μ N′ (x i ) ν N′ Is the abbreviation of ν N′ (x i )

[0093] Step 107: Obtain the first-level indicators and determine the weights of the first-level indicators. The implementation process of this step is as follows:

[0094] 1) According to the mutual relationship between the first-level indicators and the importance degree of the first-level indicators, give the comparison results between the indicators after Pythagorean fuzzy according to 7 scaling rules, and construct the direct relationship matrix of Pythagorean fuzzy. The elements in the direct relationship matrix of Pythagorean fuzzy are the score function results of Pythagorean fuzzy numbers.

[0095] Among them, let N = P(μ, v) be a Pythagorean fuzzy number, and the 7 scaling rules are: The score function of the Pythagorean fuzzy number is: In the formula, N p Represents the 7 scaling rules, absolutelyLow(AL) represents the scale of very low, Low(L) represents the scale of low, fairlyLow(FL) represents the scale of fairly low, mediumLow(ML) represents the scale of medium low, fairlyHigh(FH) represents the scale of very high, high(H) represents the scale of high, absolutelyHing(AH) represents the scale of definitely high, Represents the score function of the Pythagorean fuzzy number, μ p Represents the membership degree of the hesitant fuzzy number, v p Represents the non-membership degree of the hesitant fuzzy number, π p Represents the membership degree.

[0096] 2) Obtain the comprehensive relationship matrix based on the direct relationship matrix. Among them, the comprehensive relationship matrix is Z:

[0097]

[0098] In the formula, I is the identity matrix, C is the direct relationship matrix, cij is an element in the matrix.

[0099] 3) Determine the influence degree and the influenced degree of each first-level index according to the comprehensive relationship matrix. Among them, the influence degree is D:

[0100]

[0101] The influenced degree is R:

[0102]

[0103] In the formula, z ij represents the element of the comprehensive relationship matrix Z.

[0104] 4) Determine the weight of each first-level index based on the influence degree and the influenced degree. Among them, the weight of the first-level index is w i :

[0105]

[0106] In the formula, W i ′ is the importance degree of the index, D i is the influence degree of the i-th first-level index, R i is the influenced degree of the i-th first-level index, and W is a vector composed of W i ′.

[0107] Step 108: Describe the relationship between first-level index combinations using λ-fuzzy measure, and determine the weight value of the first-level index combination based on the weight of the first-level index. Let The calculation method of the λ value and the weight calculation formula of the index combination are:

[0108]

[0109] In the formula, and i≠j, -1≤λ≤∞, λ≠0, U represents the first-level index combination, κ(U) is the weight value of the first-level index combination, C j represents the i-th first-level index in the first-level index combination, κ(C j ) represents the weight of the j-th first-level index in the first-level index combination, P(C) represents the set of first-level index combinations, and λ represents the λ value in the λ-fuzzy measure.

[0110] Step 109: Use the Choquet integral to aggregate the weight value of the first-level index combination and the distance measure between the first-level indices to obtain the dominance matrix and the inferiority matrix. The implementation process of this step can be:

[0111] 1) The distance measure of each index of the energy consumption scenario x i to another scenario x k ​ Sorted from small to large, the sorting result is: Where σ(i) represents the i-th differential element in the ascending order sorting, and the corresponding attribute is C(σ(i)).

[0112] 2) Use the Choquet integral to aggregate the index set weights and index score results obtained from the λ-fuzzy measure, and calculate the superiority degree and inferiority degree matrix which is expressed as:

[0113]

[0114] In the formula: δ n is the loss factor, representing the expectation of avoiding losses. κ(U σ(i) ) represents the weight value of the first-level index combination containing the i-th differential element, σ(i) represents the i-th differential element, σ(i + 1) represents the (i + 1)-th differential element, κ(U σ(i+1) ) represents the weight value of the first-level index combination containing the (i + 1)-th differential element, N represents the number of differential elements, and δ n represents the loss factor.

[0115] Step 110: Determine the comprehensive superiority degree matrix based on the superiority degree matrix and the inferiority degree matrix, and use the elements in the comprehensive superiority degree matrix as the comprehensive evaluation results. The obtained comprehensive superiority degree matrix is:

[0116] ψ = ψ P - ψ N .

[0117] The comprehensive evaluation result is O A (x i ):

[0118]

[0119] In the formula, ψ represents the comprehensive superiority degree matrix, ψ P represents the superiority degree matrix, ψ N represents the inferiority degree matrix, represents the elements in the comprehensive superiority degree matrix.

[0120] Step 111: Determine the operation and maintenance effect of the expressway microgrid to be determined based on the comprehensive evaluation results.

[0121] Calculate the comprehensive evaluation result of each plan. Taking the intermediate scenario as the good state, normalize the obtained comprehensive evaluation results to obtain the final evaluation results of each plan.

[0122] After normalizing the evaluation scores of each scenario, calculate the impacts of each index and the subjective and objective weights on the evaluation index, find the key indicators affecting the score (i.e., the operation and maintenance effect of the microgrid), and it can play a reference and guiding role for the operation and maintenance plan of the high-speed microgrid.

[0123] In summary, for the multi-scenario characteristics of the highway microgrid, in step 105 of this embodiment, a hesitant fuzzy set improved evaluation method is introduced, and the final score is calculated according to the hesitant fuzzy set improved evaluation method. This specific calculation process is as Figure 6 shown.

[0124] Embodiment 2

[0125] As Figure 2 shown, the method for determining the operation and maintenance effect of the highway microgrid provided in this embodiment includes:

[0126] Step 1: Collect the information of the evaluated highway microgrid, establish the grid structure, analyze the operation status and energy consumption scenarios of the highway, establish the operation scenario set and energy consumption scenario set of the highway microgrid, and propose the evaluation index and system of the highway microgrid.

[0127] Step 2: Collect data and perform simulation calculations for various normal and abnormal situations to obtain the original operation data of the highway microgrid such as the new energy output curve and electric vehicle charging load of the highway microgrid, predict the electric vehicle charging load, obtain various specific data of the microgrid under each scenario, and perform standardization processing on the original data.

[0128] Step 3: According to the combined weight method, use the entropy weight - analytic hierarchy process to calculate the weights of the secondary indicators. Use the fuzzy DEMATEL to calculate the weights of the primary indicators. Combine the importance degree of the operation scenarios to obtain the decision matrix P under the hesitant fuzzy set.

[0129] Step 4: The P matrix calculates the difference matrix Y of each scenario according to the score distance measure; calculate the combined importance degree of the indicators by combining the weights of the primary indicators with the λ - fuzzy measure. The superiority and inferiority degrees of each scenario of the microgrid are calculated by Choquet integral according to the Y matrix based on the combined importance degree of the indicators, and the evaluation result of the microgrid is obtained after normalization.

[0130] Step 5: Analyze the evaluation result of the microgrid, compare the evaluation numerical results and reasons, find the weak links in the operation of the microgrid, find the key influencing indicators affecting the highway microgrid, study the influence degree of the evaluation indicators on the microgrid, and find the key influencing factors.

[0131] Based on the above description, this embodiment provides a calculation example, which is:

[0132] (1) Establish as Figure 3The shown highway microgrid network structure model. By collecting the operation data of the highway microgrid through literature and calculating the original operation data of the highway microgrid evaluation, and performing normalization, the data under some holiday scenarios are as shown in Table 1 below and Figure 7 as follows.

[0133] Table 1 Data table under some holiday scenarios

[0134]

[0135] For extremely large, medium-sized, and extremely small indicators, they are processed separately to obtain the normalized evaluation index scores. Then, according to the entropy weight - analytic hierarchy process, the comprehensive weight of each specific indicator under each evaluation angle is calculated, and the data is fused to obtain the results of each evaluation indicator.

[0136] (2) Calculate for each operating state. Taking the holiday scenario as an example, the calculation result of the entropy weight method: ω EWM ={0.417, 0.583; 0.174, 0.3, 0.526; 0.426, 0.3, 0.273; 0.539, 0.223, 0.237; 0.328, 0.447, 0.244; 0.541, 0.232, 0.227; 0.627, 0.373}

[0137] The calculation result of the analytic hierarchy process under the holiday scenario:

[0138] ω AHP ={0.667, 0.333; 0.4, 0.3, 0.3; 0.267, 0.4, 0.333; 0.231, 0.385, 0.485; 0.368, 0.368, 0.263; 0.438, 0.313, 0.25; 0.5, 0.5}

[0139] The comprehensive weight under the holiday scenario is:

[0140] ω AE ={0.604, 0.396; 0.344, 0.301, 0.356; 0.307, 0.375, 0.318; 0.308, 0.344, 0.358; 0.353, 0.388, 0.258;

[0141] 0.464, 0.292, 0.244; 0.532, 0.468}

[0142] The fusion result of the evaluation indicators under the holiday is shown in Table 2.

[0143] Table 2 Fusion result table of evaluation indicators under the holiday

[0144]

[0145] (3) Calculate the weights of each first-level index according to the Decision Lab method. The results of the expert evaluation of the mutual relationship between each evaluation index are shown in Table 3.

[0146] Table 3 Results of the mutual relationship between each evaluation index

[0147]

[0148] Convert the direct relationship matrix into a comprehensive relationship matrix, and then calculate the weight values of each first-level index according to the comprehensive relationship matrix: ω D = {0.114, 0.152, 0.153, 0.149, 0.143, 0.144, 0.145}.

[0149] (4) Calculate the difference matrix between different evaluation scenarios under each evaluation index. The C1 difference matrix is:

[0150]

[0151] (5) Calculate the dominance of each scenario, and the perceived dominance of each level of scenario is:

[0152]

[0153] (6) Calculate the comprehensive dominance as:

[0154] O = [-287.5 -63.9 -360.8 -162.1 -195.3 24.5 -249.1 -59.1].

[0155] (7) The evaluation results are shown in Table 4.

[0156] Table 4 Evaluation results

[0157]

[0158] (8) Impact analysis

[0159] After normalizing the evaluation scores of each scenario, calculate the impact of the score changes of each index value on the evaluation results. The average impact degree of each index on the evaluation results is shown in Table 5 below.

[0160] Table 5 Average impact degree of each index on the evaluation results

[0161]

[0162] Analysis shows that among the seven types of indicators, the load indicator has the greatest impact on the evaluation score of the microgrid, followed by the energy storage and fault indicators. The energy storage indicators are closely related to the load and power source indicators, and the fault indicators are greatly affected under abnormal conditions. The economic indicator has the least impact because the overall network loss of the microgrid remains at a low level and there is no obvious fluctuation in economy. When performing operation and maintenance, the content related to the load indicator can be taken as the key point. The energy storage indicator is closely related to other indicators and can be used as a reference to reflect the operation and maintenance level of the microgrid.

[0163] Embodiment 3

[0164] This embodiment provides a system for determining the operation and maintenance effect of a highway microgrid, which is used to implement the method for determining the operation and maintenance effect of the highway microgrid provided above. The system includes:

[0165] A scenario construction module, which is used to establish a grid structure model of the highway microgrid to be determined, and obtain the operation scenario set and energy consumption scenario set of the highway microgrid based on the grid structure model.

[0166] A secondary indicator determination module, which is used to determine the basic indicator data based on the operation scenario set and energy consumption scenario set of the highway microgrid, and obtain secondary indicators based on the basic indicator data.

[0167] A secondary indicator score determination module, which is used to determine the score of each secondary indicator by using an improved combined weight calculation method.

[0168] A secondary indicator weight determination module, which is used to determine the weight of secondary indicators by using the entropy weight - analytic hierarchy process.

[0169] A primary indicator score determination module, which is used to fuse the weight of secondary indicators and the score of secondary indicators to obtain the score of primary indicators.

[0170] A decision matrix determination module, which is used to take the score of primary indicators as the membership degree in the hesitant fuzzy set, and give the non - membership degree according to the importance degree of the operation scenarios of the highway microgrid to obtain the decision matrix.

[0171] A difference matrix determination module, which is used to determine the distance measure between each primary indicator through the PHFE distance measure based on the decision matrix to obtain the difference matrix.

[0172] A primary indicator weight determination module, which is used to obtain primary indicators and determine the weights of primary indicators. The primary indicators include seven attributes: power source indicator, grid indicator, load indicator, energy storage indicator, economic indicator, and reliability indicator.

[0173] A primary indicator combined weight determination module, which is used to describe the relationship between primary indicator combinations by using λ - fuzzy measure, and determine the weight value of primary indicator combinations based on the weights of primary indicators.

[0174] The superiority-inferiority degree matrix determination module is used to aggregate the weight values of the first-level index combinations and the distance measures between the first-level indexes by using the Choquet integral to obtain a superiority degree matrix and an inferiority degree matrix.

[0175] The comprehensive evaluation result determination module is used to determine a comprehensive superiority degree matrix based on the superiority degree matrix and the inferiority degree matrix, and use the elements in the comprehensive superiority degree matrix as the comprehensive evaluation results.

[0176] The operation and maintenance effect determination module is used to determine the operation and maintenance effect of the to-be-determined highway microgrid based on the comprehensive evaluation results.

[0177] Embodiment 4

[0178] This embodiment provides an electronic device, which includes:

[0179] A memory for storing a computer program.

[0180] A processor, connected to the memory, for retrieving and executing the computer program to implement the above-provided method for determining the operation and maintenance effect of a highway microgrid.

[0181] In addition, when the computer program in the above-mentioned memory is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks, or optical discs that can store program codes.

[0182] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0183] In this article, specific examples are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A method for determining the operation and maintenance effect of a highway microgrid, characterized in that Including: Establish a framework structure model of the highway microgrid to be determined, and obtain the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid based on the framework structure model; Determine the basic index data based on the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid, and obtain the secondary indexes based on the basic index data; Use the improved combined weight calculation method to determine the score of each secondary index; Use the entropy weight - analytic hierarchy process to determine the weight of the secondary indexes; Fuse the weight of the secondary indexes and the scores of the secondary indexes to obtain the score of the primary index; Take the score of the primary index as the membership degree in the hesitant fuzzy set, and give the non - membership degree according to the importance degree of the high - speed microgrid operation scenario to obtain the decision matrix; Based on the decision matrix, determine the distance measure between each primary index under each scenario through the PHFE distance measure to obtain the difference matrix; Obtain the primary indexes and determine the weights of the primary indexes; the primary indexes include seven attributes: power supply index, power grid index, load index, energy storage index, economic index, and reliability index; Obtain the primary indexes and determine the weights of the primary indexes, specifically including: giving the comparison results between the indexes after Pythagorean fuzzy according to the mutual relationship between the primary indexes and the importance degree of the primary indexes according to the scaling rule, constructing the Pythagorean fuzzy direct relationship matrix; obtaining the comprehensive relationship matrix based on the direct relationship matrix; determining the influence degree and the influenced degree of each primary index according to the comprehensive relationship matrix; determining the weight of each primary index based on the influence degree and the influenced degree; Use the λ - fuzzy measure to describe the relationship between the primary index combinations, and determine the weight value of the primary index combinations based on the weights of the primary indexes; Set The calculation method of the λ value and the calculation formula of the index combination weight are as follows: In the formula, and i≠j, -1≤λ≤∞, λ≠0, x i is the score of the i-th secondary index, n is the number of secondary indices, U represents the combination of primary indices, κ(U) is the weight value of the combination of primary indices, C j represents the j-th primary index in the combination of primary indices, κ(C j ) represents the weight value of the j-th primary index in the combination of primary indices, P(C) represents the set of combinations of primary indices, and λ represents the value of λ in the λ-fuzzy measure; Use the Choquet integral to aggregate the weight values of the primary index combinations and the distance measure between the primary indexes to obtain the dominance matrix and the inferiority matrix; the implementation process includes: Let x i The corresponding energy consumption scenario and x k The distance measure under each index of the corresponding energy consumption scenario Sort from small to large, and the sorting result is: Where σ(i) represents the i-th difference element in the ascending order, and the corresponding attribute is C(σ(i)); Represents the distance measure of the i-th difference element; The Choquet integral is used to aggregate the weight of the index set and the index score results obtained from the λ-fuzzy measure, and the dominance degree of the scheme is calculated respectively. and the inferiority degree matrix which is expressed as: where: δ n is the loss factor; κ(U σ(i) ) represents the weight value of the first-level index combination containing the i-th differential element, σ(i + 1) represents the (i + 1)-th differential element, κ(U σ(i+1) ) represents the weight value of the first-level index combination containing the (i + 1)-th differential element, N represents the number of differential elements, represents an element in the inferiority matrix, is an element in the superiority matrix; Determine the comprehensive dominance matrix based on the dominance matrix and the inferiority matrix, and take the elements in the comprehensive dominance matrix as the comprehensive evaluation results; the obtained comprehensive dominance matrix is: ψ = ψ P -ψ N ; The comprehensive evaluation result is O A (x i ): where ψ represents the comprehensive dominance matrix, and ψ P represents the dominance matrix, and ψ N represents the inferiority matrix, represents the element in the comprehensive dominance matrix, and M is the number of energy-using scenarios; Determine the operation and maintenance effect of the highway microgrid to be determined based on the comprehensive evaluation results.

2. The method for determining the operation and maintenance effect of the highway microgrid according to claim 1, wherein Use the improved combined weight calculation method to determine the score of each secondary index, specifically including: Perform dimensionless standardization processing on each index in the secondary indexes to obtain the scores of the secondary indexes; the scores of the secondary indexes are expressed as: where x ij is the i-th secondary index under the j-th energy consumption scenario of the highway microgrid, and x is the basic data corresponding to the secondary index, is the score of the secondary index, max represents taking the maximum value, and min represents taking the minimum value.

3. The method for determining the operation and maintenance effect of the highway microgrid according to claim 1, characterized in that Use the entropy weight - analytic hierarchy process to determine the weight of the secondary indexes, specifically including: Determine the probability matrix of the secondary indexes; Determine the information entropy of the secondary indexes based on the probability matrix; Determine the first index weight based on the information entropy of the secondary indexes; Compare and evaluate the importance degree of any two secondary indexes according to the "1 - 9" scaling rule to obtain the judgment matrix; the "1 - 9" scaling rule is a set rule; Use the square root method to determine the eigenvector of the judgment matrix, and perform normalization processing on the non - zero eigenvalue vector in the eigenvector to obtain the second index weight; Fuse the first index weight and the second index weight to obtain the weight of the secondary indexes.

4. The method for determining the operation and maintenance effect of the highway microgrid according to claim 1, characterized in that The score of the first-level index is: where ζ is the score of the first-level indicator, ω i is the weight of the i-th second-level indicator, and x i is the score of the i-th second-level indicator, and n is the number of second-level indicators.

5. The method for determining the operation and maintenance effect of the highway microgrid according to claim 1, wherein The 7 scaling rules are as follows: The score function of the Pythagorean fuzzy number is: In the formula, N p represents the 7 scaling rules, absolutelyLow (AL) represents, Low (L) represents, fairlyLow (FL) represents, mediumLow (ML) represents, fairlyHigh (FH) represents, high (H) represents, absolutelyHigh (AH) represents, represents the score function of the Pythagorean fuzzy number, μ p represents the membership degree, v p represents the non-membership degree, π p represents the hesitancy degree.

6. A system for determining the operation and maintenance effect of a highway microgrid, characterized in that, The system is used to implement the method for determining the operation and maintenance effect of the highway microgrid as described in any one of claims 1-5; the system includes: A scenario construction module, configured to establish a grid structure model of the highway microgrid to be determined, and obtain an operation scenario set of the highway microgrid and an energy consumption scenario set of the highway microgrid based on the grid structure model; A secondary index determination module, configured to determine basic index data based on the operation scenario set of the highway microgrid and the energy consumption scenario set of the highway microgrid, and obtain secondary indexes based on the basic index data; A secondary index score determination module, configured to use an improved combined weight calculation method to determine the score of each secondary index; A secondary index weight determination module, configured to use the entropy weight - analytic hierarchy process to determine the weight of the secondary index; A first-level index score determination module, configured to fuse the weight of the secondary index and the score of the secondary index to obtain the score of the first-level index; A decision matrix determination module, configured to use the score of the first-level index as the membership degree in the hesitant fuzzy set, and give the non-membership degree according to the importance degree of the highway microgrid operation scenario to obtain a decision matrix; A difference matrix determination module, configured to determine the distance measure between each first-level index based on the decision matrix through the PHFE distance measure to obtain a difference matrix; A first-level index weight determination module, configured to obtain the first-level index and determine the weight of the first-level index; the first-level index includes seven attributes: power supply index, power grid index, load index, energy storage index, economic index, and reliability index; A first-level index combined weight determination module, configured to use the λ-fuzzy measure to describe the relationship between the first-level index combinations, and determine the weight value of the first-level index combinations based on the weight of the first-level index; A superiority-inferiority matrix determination module, configured to use the Choquet integral to aggregate the weight value of the first-level index combinations and the distance measure between the first-level indexes to obtain a superiority matrix and an inferiority matrix; A comprehensive evaluation result determination module, configured to determine a comprehensive superiority matrix based on the superiority matrix and the inferiority matrix, and use the elements in the comprehensive superiority matrix as the comprehensive evaluation result; An operation and maintenance effect determination module, configured to determine the operation and maintenance effect of the highway microgrid to be determined based on the comprehensive evaluation result.

7. An electronic device, characterized in that, It includes: A memory, configured to store a computer program; A processor, connected to the memory, configured to retrieve and execute the computer program to implement the method for determining the operation and maintenance effect of the highway microgrid as described in any one of claims 1-5.

8. The electronic device according to claim 7, characterized in that, The memory is a computer-readable storage medium.

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