A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network

Abstract

The present invention discloses a comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network, comprising the following steps: S1, establishing a power grid operation index system divided into a target layer, a criterion layer, and an indicator layer; determining the number m of schemes to be evaluated, the number s of criteria, and the number n of indicators under criterion j; j and the index value x ijk ; S2, calculate the fuzzy evaluation result r of the index layer ijk ; S3, using the entropy weight method, according to the index values ​​corresponding to the m charging facility access points, calculate the index layer weight w of the criterion c,ij ; S4, use the hierarchical analysis method to calculate the criterion layer weight w b,j S5. Calculate fuzzy evaluation results for the criterion and target layers. To address the inaccuracy of the AHP due to the excessive number of indicators, this invention uses the entropy weight method to determine indicator weights for the indicator layer. To address the lack of quantitative data in the AHP, this invention uses a fuzzy evaluation method to handle fuzzy evaluation objects. This method effectively improves the accuracy of the impact assessment of electric vehicle charging facilities connected to the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a comprehensive evaluation method for the impact of electric vehicle charging facilities being connected to a power distribution network. Background Art

[0002] With the increasing energy crisis and environmental pollution, the development of electric vehicles has garnered widespread attention. As supporting infrastructure, electric vehicle charging facilities are a crucial component of future distribution networks. The quality of their construction and operation directly impacts the safe and economical operation of distribution networks. As the number of electric vehicles continues to increase, the large-scale access of electric vehicles through charging facilities will cause fluctuations in the power grid, significantly impacting grid operation. Therefore, selecting appropriate distribution network access points for existing charging facilities is crucial. To compare the advantages and disadvantages of different access options, it is necessary to establish corresponding impact evaluation indicators and conduct quantitative analysis using specific methods.

[0003] Existing evaluation methods often set up several categories of evaluation indicators. Using the Analytic Hierarchy Process (AHP), these indicators are then weighted and evaluated for each level. Based on this index system and methodology, each evaluation indicator for charging facilities connected to the distribution network is scored individually. The final evaluation score is calculated based on the weights of each indicator. Repeating these steps yields the evaluation value of each EV charging and swapping facility connection to the distribution network. Higher evaluation values ​​indicate a higher overall level of the solution.

[0004] However, the single-use AHP approach also has its drawbacks. First, when there are too many indicators, the data statistics are large, and weights are difficult to determine. Increasing the number of indicators requires constructing a judgment matrix with deeper levels, a larger number of indicators, and a larger scale. Because the weights calculated from the constructed judgment matrix are not necessarily reasonable, it is difficult to determine which element in the matrix is ​​faulty when it fails consistency tests. Second, the limited quantitative data and the preponderance of qualitative elements make it difficult to be convincing. Today, the evaluation of scientific methods generally requires rigorous mathematical reasoning and comprehensive quantitative methods. The AHP approach cannot handle ambiguous evaluation objects through precise numerical means, nor can it provide a more scientific, reasonable, and realistic quantitative evaluation of data containing ambiguous information. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a highly accurate comprehensive evaluation method for the impact of electric vehicle charging facilities being connected to a distribution network.

[0006] To achieve the above objectives, the technical solutions provided by the present invention are:

[0007] A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to a distribution network includes the following steps:

[0008] S1. Establish a power grid operation indicator system divided into target layer, criterion layer, and indicator layer; determine the number of schemes to be evaluated m, the number of criteria s, and the number of indicators under criterion j n j and the index value x ijk ;

[0009] S2, build membership function, and according to membership function, index value x ijk The corresponding standardized value and scoring matrix F are used to calculate the fuzzy evaluation result r of the indicator layer ijk ;

[0010] S3. Using the entropy weight method, calculate the indicator layer weight w of the criterion based on the indicator values ​​corresponding to the m charging facility access points c,ij ;

[0011] S4. Calculate the criterion layer weight w using the hierarchical analysis method b,j ;

[0012] S5, based on the fuzzy evaluation results of the indicator layer r ijk , the indicator layer weight w of the criterion c,ij , criterion layer weight w b,j , calculate the fuzzy evaluation results of the criterion layer and the target layer.

[0013] Furthermore, in the power grid operation indicator system established in step S1, the criteria of the criterion layer include safety and reliability, high efficiency, economy, and quality; the indicators of the indicator layer include load rate, N-1 pass rate, capacity load ratio, network loss, equipment life, voltage deviation, harmonics, and three-phase imbalance;

[0014] Among them, load rate and N-1 pass rate correspond to safety and reliability; capacity-load ratio corresponds to high efficiency; network loss and equipment life correspond to economy; voltage deviation, harmonics, and three-phase imbalance correspond to quality.

[0015] Furthermore, the step S2 specifically includes:

[0016] The value x of index i under criterion j in access scheme k is converted into ijk Normalized to y ijk , whose value belongs to [0,1];

[0017] Assuming that the review set V has 5 evaluation levels, the rating matrix is ​​a 5-dimensional row vector F;

[0018]

[0019] The membership function of each evaluation level is constructed by combining the semi-trapezoidal and triangular methods. According to the membership function and the standardized value of the index, the index level fuzzy evaluation matrix RANK is determined. ijk ;

[0020]

[0021] Therefore, the fuzzy evaluation result of the index layer is determined to be r ijk :

[0022] r ijk =F·RANK ijk (3).

[0023] Furthermore, the specific process of step S3 includes:

[0024] S3-1. Determine the information entropy E of index i under criterion j based on the index values ​​corresponding to the m access points of the charging facility. ij :

[0025]

[0026] (4) In the formula, m is the number of charging facility access points. ijk =0, take y ijk lny ijk =0;

[0027] S3-2. Use the entropy weight method to determine the indicator layer weight w of indicator i under criterion j c,ij :

[0028]

[0029] (5) In the formula, n j is the number of indicators in criterion j.

[0030] Furthermore, the step S4 uses the analytic hierarchy process to calculate the criterion layer weight w b,j The specific process includes:

[0031] S4-1, construct judgment matrix D;

[0032] S4-2, perform consistency check on the judgment matrix D to determine whether the judgment matrix D as a whole satisfies logical consistency. If so, proceed to step S4-3; otherwise, return to step S4-1 to reconstruct the judgment matrix D;

[0033] S4-3. Calculate the criterion layer weight w b,j .

[0034] Furthermore, the step S4-1 uses the paired comparison method and the comparison scale method to construct the judgment matrix D, which is as follows:

[0035] Compare the s indicators in the criterion layer pairwise to form an s×s order judgment matrix D:

[0036]

[0037] (6) Where n is the number of evaluation indicators, and the element d of D is pq It represents the comparison result of index p and index q, which is obtained according to the importance of evaluation index and comparison scale method, where 1, 3, 5, 7, and 9 represent equally important, slightly important, obviously important, strongly important, and extremely important respectively; when p = q, d pq =1; when p≠q,

[0038] Furthermore, in step S4-2, the specific process of performing consistency check on the judgment matrix D includes:

[0039] Define the consistency ratio C of the judgment matrix D R for:

[0040]

[0041]

[0042] In formulas (7) and (8), C I is the inconsistency index of the judgment matrix D, λ max is the maximum eigenvalue of the judgment matrix D, R I is the average random consistency index, which is determined by the order n of the judgment matrix D;

[0043] When the consistency ratio C R Less than the set threshold C of the consistency ratio R0 When , it indicates that the judgment matrix D meets the consistency requirement, otherwise the judgment matrix D does not meet the consistency requirement.

[0044] Furthermore, the step S4-3 uses the geometric mean method to determine the criterion layer weights, where the weight w of the jth criterion is b,j The calculation method is as follows:

[0045]

[0046] (9) In the formula, s is the dimension, d is the pq Indicates the comparison result of index p and index q.

[0047] Furthermore, the step S5 specifically includes:

[0048] Determine the score for the criterion layer:

[0049]

[0050] (10) In ijk is the fuzzy evaluation result of the indicator layer, w c,ij is the indicator layer weight of criterion j;

[0051] The score of solution k in the target layer can be calculated as follows:

[0052]

[0053] (11) In the formula, w b,j is the criterion layer weight w b,j .

[0054] Compared with the existing technology, the principles and advantages of this technical solution are as follows:

[0055] To address the inaccuracy of the AHP due to the excessive number of indicators, this technical solution uses the entropy weight method to determine the indicator weights at the indicator level. To address the lack of quantitative data in the AHP, this technical solution uses a fuzzy evaluation method to handle fuzzy evaluation objects. These methods effectively improve the accuracy of the impact assessment of electric vehicle charging facilities connected to the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for use in the embodiments or the prior art descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0057] Figure 1 This is a principle flow chart of a comprehensive evaluation method for the impact of electric vehicle charging facilities connected to a distribution network according to the present invention;

[0058] Figure 2 A schematic diagram of the indicator system. DETAILED DESCRIPTION

[0059] The present invention will be further described below in conjunction with specific embodiments:

[0060] like Figure 1 As shown, the comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network described in this embodiment includes the following steps:

[0061] S1. Establish a power grid operation indicator system divided into target layer, criterion layer, and indicator layer; determine the number of schemes to be evaluated m, the number of criteria s, and the number of indicators under criterion j n j and the index value x ijk ;

[0062] like Figure 2As shown in the figure, in the established power grid operation indicator system, the criteria of the criterion layer include safety and reliability, high efficiency, economy, and quality; the indicators of the indicator layer include load rate, N-1 pass rate, capacity load ratio, network loss, equipment life, voltage deviation, harmonics, and three-phase imbalance;

[0063] Among them, load rate and N-1 pass rate correspond to safety and reliability; capacity-load ratio corresponds to high efficiency; network loss and equipment life correspond to economy; voltage deviation, harmonics, and three-phase imbalance correspond to quality.

[0064] S2, build membership function, and according to membership function, index value x ijk The corresponding standardized value and scoring matrix F are used to calculate the fuzzy evaluation result r of the indicator layer ijk ;

[0065] The specific process of this step is as follows:

[0066] The value x of index i under criterion j in access scheme k is converted into ijk Normalized to y ijk , whose value belongs to [0,1];

[0067] Assuming that the review set V has 5 evaluation levels, the rating matrix is ​​a 5-dimensional row vector F;

[0068]

[0069] The membership function of each evaluation level is constructed by combining the semi-trapezoidal and triangular methods. According to the membership function and the standardized value of the index, the index level fuzzy evaluation matrix RANK is determined. ijk ;

[0070]

[0071] Therefore, the fuzzy evaluation result of the index layer is determined to be r ijk :

[0072] r ijk =F·RANK ijk (3).

[0073] S3. Using the entropy weight method, calculate the indicator layer weight w of the criterion according to the indicator values ​​corresponding to the m charging facility access points c,ij ;

[0074] The specific process of this step is as follows:

[0075] S3-1. Determine the information entropy E of index i under criterion j based on the index values ​​corresponding to the m access points of the charging facility. ij :

[0076]

[0077] (4) In the formula, m is the number of charging facility access points. ijk =0, take y ijk lny ijk =0;

[0078] S3-2. Use the entropy weight method to determine the indicator layer weight w of indicator i under criterion j c,ij :

[0079]

[0080] (5) In the formula, n j is the number of indicators in criterion j.

[0081] S4. Calculate the criterion layer weight w using the hierarchical analysis method b,j The specific process is as follows:

[0082] S4-1. Use the paired comparison method and the comparative scale method to construct the judgment matrix D, as follows:

[0083] Compare the s indicators in the criterion layer pairwise to form an s×s order judgment matrix D:

[0084]

[0085] (6) Where n is the number of evaluation indicators, and the element d of D is pq It represents the comparison result of index p and index q, which is obtained according to the importance of evaluation index and comparison scale method, where 1, 3, 5, 7, and 9 represent equally important, slightly important, obviously important, strongly important, and extremely important respectively; when p = q, d pq =1; when p≠q,

[0086] S4-2. Perform consistency check on the judgment matrix D to determine whether the judgment matrix D as a whole satisfies logical consistency. The specific process of performing consistency check on the judgment matrix D includes:

[0087] Define the consistency ratio C of the judgment matrix D R for:

[0088]

[0089]

[0090] In formulas (7) and (8), C I is the inconsistency index of the judgment matrix D, λ max is the maximum eigenvalue of the judgment matrix D, R I is the average random consistency index, which is determined by the order n of the judgment matrix D; n and R IThe relationship is shown in Table 1 below:

[0091]

[0092] Table 1

[0093] Set the threshold C of the consistency ratio R0 =0.50, when C R <C R0 When =0.50, it indicates that the judgment matrix D meets the consistency requirement, otherwise the judgment matrix D does not meet the consistency requirement.

[0094] If satisfied, proceed to step S4-3, otherwise return to step S4-1 to reconstruct the judgment matrix D;

[0095] S4-3, use the geometric mean method to determine the weight of its criterion layer, where the weight of the jth criterion w b,j The calculation method is as follows:

[0096]

[0097] (9) In the formula, s is the dimension, d is the pq Indicates the comparison result of index p and index q.

[0098] S5, based on the fuzzy evaluation results of the indicator layer r ijk , the indicator layer weight w of the criterion c,ij , criterion layer weight w b,j , calculate the fuzzy evaluation results of the criterion layer and the target layer.

[0099] The specific process of this step is as follows:

[0100] Determine the score for the criterion layer:

[0101]

[0102] (10) In ijk is the fuzzy evaluation result of the indicator layer, w c,ij is the indicator layer weight of criterion j;

[0103] The score of solution k in the target layer can be calculated as follows:

[0104]

[0105] (11) In the formula, w b,j is the criterion layer weight w b,j .

[0106] To address the inaccuracy of the AHP due to the excessive number of indicators, this embodiment uses the entropy weight method to determine the indicator weights for the indicator layer. To address the lack of quantitative data in the AHP, this embodiment uses a fuzzy evaluation method to handle fuzzy evaluation objects. Through these methods, this embodiment effectively improves the accuracy of the impact assessment of electric vehicle charging facilities connected to the distribution network.

[0107] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, any changes made based on the shape and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network, characterized in that: The following steps are involved: S1. Establish a power grid operation indicator system divided into target layer, criterion layer, and indicator layer; determine the number of schemes to be evaluated m, the number of criteria s, and the number of indicators under criterion j n j and the index value x ijk ; S2, build membership function, and according to membership function, index value x ijk The corresponding standardized value and scoring matrix F are used to calculate the fuzzy evaluation result r of the indicator layer ijk ; S3. Using the entropy weight method, calculate the indicator layer weight w of the criterion according to the indicator values ​​corresponding to the m charging facility access points c,ij ; S4. Calculate the criterion layer weight w using the hierarchical analysis method b,j ; S5, based on the fuzzy evaluation results of the indicator layer r ijk , the indicator layer weight w of the criterion c,ij , criterion layer weight w b,j , calculate the fuzzy evaluation results of the criterion layer and the target layer; The step S2 specifically includes: The value x of index i under criterion j in access scheme k is converted into ijk Normalized to y ijk , whose value belongs to [0,1]; Assuming that the review set V has 5 evaluation levels, the rating matrix is ​​a 5-dimensional row vector F; The membership function of each evaluation level is constructed by combining the semi-trapezoidal and triangular methods. According to the membership function and the standardized value of the index, the index level fuzzy evaluation matrix RANK is determined. ijk ; Therefore, the fuzzy evaluation result of the index layer is determined to be r ijk : r ijk =F·RANK ijk (3)。 2. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 1, characterized in that: In the power grid operation indicator system established in step S1, the criteria of the criterion layer include safety and reliability, high efficiency, economy, and quality; the indicators of the indicator layer include load rate, N-1 pass rate, capacity load ratio, network loss, equipment life, voltage deviation, harmonics, and three-phase imbalance; Among them, load rate and N-1 pass rate correspond to safety and reliability; capacity-load ratio corresponds to high efficiency; network loss and equipment life correspond to economy; voltage deviation, harmonics, and three-phase imbalance correspond to quality.

3. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 1, characterized in that: The specific process of step S3 includes: S3-1. Determine the information entropy E of index i under criterion j based on the index values ​​corresponding to the m access points of the charging facility. ij : (4) In the formula, m is the number of charging facility access points. ijk =0, take y ijk lny ijk =0; S3-2. Use the entropy weight method to determine the indicator layer weight w of indicator i under criterion j c,ij : (5) In the formula, n j is the number of indicators in criterion j.

4. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 1, characterized in that: The step S4 uses the hierarchical analysis method to calculate the criterion layer weight w b,j The specific process includes: S4-1, construct judgment matrix D; S4-2, perform consistency check on the judgment matrix D to determine whether the judgment matrix D as a whole satisfies logical consistency. If so, proceed to step S4-3; otherwise, return to step S4-1 to reconstruct the judgment matrix D; S4-3. Calculate the criterion layer weight w b,j .

5. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 4, characterized in that: The step S4-1 uses the paired comparison method and the comparison scale method to construct the judgment matrix D, which is as follows: Compare the s indicators in the criterion layer pairwise to form an s×s order judgment matrix D: (6) Where n is the number of evaluation indicators, and the element d of D is pq It represents the comparison result of index p and index q, which is obtained according to the importance of evaluation index and comparison scale method, where 1, 3, 5, 7, and 9 represent equally important, slightly important, obviously important, strongly important, and extremely important respectively; when p = q, d pq =1; when p≠q, 6. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 4, characterized in that: In step S4-2, the specific process of performing consistency check on the judgment matrix D includes: Define the consistency ratio C of the judgment matrix D R for: In formulas (7) and (8), C I is the inconsistency index of the judgment matrix D, λ max is the maximum eigenvalue of the judgment matrix D, R I is the average random consistency index, which is determined by the order n of the judgment matrix D; When the consistency ratio C R Less than the set threshold C of the consistency ratio R0 When , it indicates that the judgment matrix D meets the consistency requirement, otherwise the judgment matrix D does not meet the consistency requirement.

7. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 4, characterized in that: The step S4-3 uses the geometric mean method to determine the criterion layer weights, where the weight w of the jth criterion is b,j The calculation method is as follows: (9) In the formula, s is the dimension, d is the pq Indicates the comparison result of index p and index q.

8. A comprehensive evaluation method for the impact of electric vehicle charging facilities connected to the distribution network according to claim 1, characterized in that: The step S5 specifically includes: Determine the score for the criterion layer: (10) In ijk is the fuzzy evaluation result of the indicator layer, w c,ij is the indicator layer weight of criterion j; The score of solution k in the target layer can be calculated as follows: (11) In the formula, w b,j is the criterion layer weight w b,j .

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