A method and terminal for allocating input resources of distribution network

Abstract

The present invention discloses a method for allocating input resources of a distribution network, which establishes an input allocation index system and an input-output index system, and obtains the index values ​​of each three-level index in the input allocation index system and the input-output index system of each funded unit; for each funded unit, the comprehensive weight of each three-level index is determined by combining the hierarchical analysis method and the variation coefficient method, and the input-output coefficient and the input allocation coefficient are calculated by combining the index value and the comprehensive weight of each three-level index; according to the input-output coefficient and the input allocation coefficient of each funded unit, input resources are allocated; two sets of index systems are constructed, which are more accurate, improve the problems of a single index system and ignoring the development trend of indicators, improve the scientificity and authenticity of the input resource allocation model, make the allocation result have higher accuracy, and adopt a comprehensive weighting method based on the hierarchical analysis method and the variation coefficient method to determine the weight of each indicator, which is more reasonable and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent construction of distribution networks, and in particular to a method and terminal for allocating input resources of distribution networks. Background Art

[0002] With the advancement of intelligent distribution network construction, the construction of smart grids, and the increasing impact of distributed power generation on distribution networks, resource investment strategies for distribution networks are receiving increasing attention. However, the allocation of distribution network investment resources is a project with numerous attributes and complex influencing factors. How to allocate investment resources scientifically and rationally remains an urgent problem to be solved.

[0003] In order to further improve the theoretical system of resource input decision-making in distribution networks, domestic and foreign scholars have carried out a lot of work in this regard. However, there are still problems such as unbalanced resource input structure and insufficient resource allocation means in the construction of indicator system and resource input allocation model.

[0004] There are certain limitations in the construction of the indicator system. It either only analyzes a certain indicator or the starting point of considering the indicator system is not comprehensive enough. How to establish a more comprehensive indicator system is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and terminal for allocating input resources of a distribution network, so as to establish a more comprehensive and accurate indicator system for considering the decision-making of input resource allocation of the distribution network.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for allocating input resources of a distribution network, comprising the steps of:

[0008] S1. Establish an input allocation indicator system and an input-output indicator system, and obtain the indicator values ​​of each third-level indicator in the input allocation indicator system and the input-output indicator system for each funded unit;

[0009] The three-level indicators of the input allocation index system and the input-output index system include the proportion of lightly loaded 10kV lines, the average value of the maximum load rate of 10kV lines, the proportion of lightly loaded 10kV distribution transformers, the average value of the maximum load rate of 10kV distribution transformers, the proportion of heavily loaded 10kV lines, the proportion of heavily loaded 10kV distribution transformers, the power supply reliability rate, the comprehensive voltage compliance rate and the comprehensive line loss rate;

[0010] S2. For each funded unit, determine the comprehensive weight of each third-level indicator by combining the analytic hierarchy process and the coefficient of variation method, and calculate the input-output coefficient and input allocation coefficient by combining the indicator value and the comprehensive weight of each third-level indicator;

[0011] S3. Allocate input resources according to the input-output coefficient and input allocation coefficient of each funded unit.

[0012] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0013] A terminal for allocating resources invested in a distribution network comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for allocating resources invested in a distribution network are implemented.

[0014] The beneficial effects of the present invention are as follows: a method and terminal for allocating input resources for a distribution network of the present invention construct two sets of indicator systems, namely, input allocation indicators and input-output indicators for the distribution network, which are more accurately considered, improve the problems of a single indicator system and neglect of indicator development trends, improve the scientificity and authenticity of the input resource allocation model, and make the results of input resource allocation have higher accuracy. In addition, the advantages and disadvantages of subjective and objective weighting methods are comprehensively considered, and the comprehensive weighting method based on the hierarchical analysis method and the coefficient of variation method is adopted to determine the weight of each indicator, which is more reasonable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0016] Figure 2 This is a structural diagram of a terminal for allocating resources input to a distribution network according to an embodiment of the present invention;

[0017] Figure 3 A schematic diagram of comprehensive weight calculation for a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0018] Figure 4 A schematic diagram of a membership function of a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0019] Figure 5 A schematic diagram of a reward coefficient calculation process for a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0020] Figure 6 This is a flow chart of an input-output model of a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0021] Figure 7 This is a flow chart of an input allocation model of a method for allocating input resources in a distribution network according to an embodiment of the present invention;

[0022] Figure 8This is a schematic diagram of the overall process of a method for allocating input resources to a distribution network according to an embodiment of the present invention;

[0023] Description of labels:

[0024] 1. A terminal for allocating input resources of a distribution network; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0025] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0026] Please refer to Figure 1 as well as Figures 3 to 8 , a method for allocating input resources of a distribution network, comprising the steps of:

[0027] S1. Establish an input allocation indicator system and an input-output indicator system, and obtain the indicator values ​​of each third-level indicator in the input allocation indicator system and the input-output indicator system for each funded unit;

[0028] The three-level indicators of the input allocation index system and the input-output index system include the proportion of lightly loaded 10kV lines, the average value of the maximum load rate of 10kV lines, the proportion of lightly loaded 10kV distribution transformers, the average value of the maximum load rate of 10kV distribution transformers, the proportion of heavily loaded 10kV lines, the proportion of heavily loaded 10kV distribution transformers, the power supply reliability rate, the comprehensive voltage compliance rate and the comprehensive line loss rate;

[0029] S2. For each funded unit, determine the comprehensive weight of each third-level indicator by combining the analytic hierarchy process and the coefficient of variation method, and calculate the input-output coefficient and input allocation coefficient by combining the indicator value and the comprehensive weight of each third-level indicator;

[0030] S3. Allocate input resources according to the input-output coefficient and input allocation coefficient of each funded unit.

[0031] From the above description, it can be seen that the beneficial effects of the present invention are: a method and terminal for allocating input resources of a distribution network of the present invention construct two sets of indicator systems of input allocation indicators and input-output indicators of the distribution network, which are more accurate, improve the problems of a single indicator system and ignoring the development trend of indicators, improve the scientificity and authenticity of the allocation model of input resources, make the results of input resource allocation have higher accuracy, and comprehensively consider the advantages and disadvantages of subjective and objective weighting methods, and adopt a comprehensive weighting method based on hierarchical analysis method and coefficient of variation method to determine the weight of each indicator, which is more reasonable and accurate.

[0032] Furthermore, the calculation of the comprehensive weight specifically includes the following steps:

[0033] For each funded unit, perform the following steps:

[0034] S21. Determine the subjective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system through the analytic hierarchy process;

[0035] S22. Determine the objective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system by using the coefficient of variation method;

[0036] S23. Calculate the comprehensive weight of each of the three-level indicators based on the subjective weight and the objective weight;

[0037] S24. Determine the indicator weights of the input allocation indicator system and the secondary indicators in the input-output indicator system by using a subjective weight method.

[0038] From the above description, it can be seen that the present invention determines the subjective weight through the hierarchical analysis method, determines the objective weight through the coefficient of variation method, and determines the comprehensive weight by combining the subjective weight and the objective weight, comprehensively considering the advantages and disadvantages of the subjective and objective weighting methods, which is more reasonable and accurate. Since the objective coefficient of variation method requires indicator data support, and the secondary indicators do not have specific indicator data, the weights of the secondary indicators are only calculated by the subjective hierarchical analysis method.

[0039] Furthermore, the step S21 includes the steps of:

[0040] S211. Obtain the expert's offline scoring information for each level 3 indicator based on the preset scale and construct a comparison matrix:

[0041]

[0042] Among them, the diagonal elements are all 1, a ij is the scale of the i-th indicator for the j-th indicator, and n is the number of three-level indicators;

[0043] S212, normalize each column element of the comparison matrix:

[0044]

[0045] S213, add up the normalized comparison matrix of each column by row to obtain the initial subjective weight w i :

[0046]

[0047] S214: Normalize the subjective weights to obtain a subjective weight matrix:

[0048] w'=(w1',w2',...,w n ') T .

[0049] As can be seen from the above description, the present invention calculates the subjective weights of the three-level indicators in the above manner.

[0050] Furthermore, the step S22 includes the steps of:

[0051] S221. Establish an original matrix containing M funded units and N third-level indicator values:

[0052] X=[a ij ] M×N ;

[0053] S222. For the power supply reliability index and the comprehensive voltage qualification rate index, the standard index data is weakened by adjusting the parameters by the frontier distance method:

[0054] QY n =100%-a n ;

[0055] Get the normalized matrix:

[0056] K=[b ij ] M×N ;

[0057] Among them, QY n is the frontier distance of the nth indicator, a n It is the power supply reliability index or the comprehensive voltage qualification rate index;

[0058] S223. For each third-level indicator, calculate the standard deviation based on each funded unit:

[0059]

[0060] Among them, M is the total number of funded units, b ij is the standardized value of the jth third-level indicator of the i-th funded unit, is the average value of the standardized value of the jth third-level indicator of each unit, D j is the standard deviation of the jth third-level indicator;

[0061] S224. Calculate the coefficient of variation CV of each third-level indicator j :

[0062]

[0063] Calculate the objective weight matrix:

[0064] θ=[θ1,θ2,…,θ N ] T ;

[0065] in:

[0066]

[0067] As can be seen from the above description, the present invention calculates the objective weights of the three-level indicators in the above manner.

[0068] Furthermore, the step S23 is specifically as follows:

[0069] According to the subjective weight w' and the objective weight θ, the comprehensive weight λ of each three-level indicator is calculated:

[0070]

[0071] Among them, α1 and α2 are the preset weighting coefficients of the hierarchical analysis method and the preset weighting coefficient of the coefficient of variation method, respectively.

[0072] From the above description, it can be seen that the subjective weight and the objective weight are weighted by the preset weighting coefficient to obtain the comprehensive weight of each three-level indicator.

[0073] Furthermore, the calculation of the input-output coefficient in step S2 specifically includes the following steps:

[0074] S25. For each funded unit, calculate the indicator scores of each third-level indicator in the input-output indicator system using the triangular fuzzy membership algorithm, calculate the technical efficiency based on the DEA algorithm, and compare and analyze the indicator scores and technical efficiency in the historical period;

[0075] S26. For each third-level indicator, if both the technical efficiency and the indicator score increase within the preset period, a first reward coefficient is assigned to the third indicator. If both the technical efficiency and the indicator score decrease within the preset period, the third indicator is assigned a second reward coefficient. Otherwise, a third reward index is assigned to the third indicator. The reward coefficients for each third-level indicator are obtained, where the third reward coefficient is 1, the first reward coefficient is greater than 1, and the second reward coefficient is less than 1.

[0076] S27. According to the ranking of the total reward coefficients of the funded units, for each funded unit ranked within the top preset range, the input-output coefficient is set to a first value, otherwise it is set to a second value, the first value is greater than 1, and the second value is 1.

[0077] As can be seen from the above description, since the input-output indicator system contains more than two levels of indicators, the scoring algorithm based on the interval triangular membership fuzzy theory is used to calculate the indicator scores. The use of fuzzy theory based on the interval triangular membership can overcome the problem of similar scores due to small differences in indicator data, and achieve the optimal interval score. In order to better judge the quality and efficiency improvement of each unit and make the allocation decision of input resources more realistic and effective, the present invention sets a "reward mechanism" to modify the indicators. The reward coefficient is determined based on the development trend of the indicator technical efficiency and score. It is used to connect the subsequent input allocation model and the input-output model, providing support for the allocation decision of input resources from the input-output efficiency level.

[0078] Furthermore, the input allocation indicator system includes first-level indicators: evaluation index indicators;

[0079] The input-output system includes the first-level indicators: distribution network output;

[0080] The evaluation index indicators and the distribution network output both include secondary indicators: development quality and grid security;

[0081] The development quality includes power supply reliability, comprehensive voltage qualification rate, comprehensive line loss rate, proportion of lightly loaded 10kV lines, average value of maximum load rate of 10kV lines, proportion of lightly loaded 10kV distribution transformers and average value of maximum load rate of 10kV distribution transformers;

[0082] The grid safety includes the proportion of heavy-loaded 10kV lines, the proportion of heavy-loaded 10kV distribution transformers and the N-1 pass rate;

[0083] The N-1 passing rate refers to the passing rate of the single-failure safety criterion.

[0084] From the above description, it can be seen that the input allocation index system and the input-output index system are both hierarchical index systems, which include grid equipment usage indicators such as power supply reliability, comprehensive line loss rate, and light-load 10kV line ratio, which can better reflect the grid status of the funded unit.

[0085] Furthermore, the calculation of technical efficiency based on the DEA algorithm includes the following steps:

[0086] Based on the BCC model of the DEA algorithm, the technical efficiency of each three-level indicator is calculated using a linear programming model:

[0087] maxV P =μ T Y0+μ0

[0088]

[0089] Among them, V P Represents the technical efficiency of each third-level indicator, ω T、μ T They represent the weight parameters of the three-level input indicator X and the three-level output indicator Y, μ0 represents the correction coefficient of the DEA algorithm, and n is the number of three-level indicators;

[0090] Since the current platform is limited to solving the minimum value for linear programming models, dual programming is performed on the above formula:

[0091]

[0092] Among them, V D is the technical efficiency after dual planning, S - 、S + They represent the slack variables of the three-level input indicator X and the three-level output indicator Y, and λ represents the intensity variable of the three-level input-output indicator.

[0093] From the above description, it can be seen that the present invention calculates the technical efficiency of each third-level indicator of the input-output index system in the above manner. Technical efficiency can be discussed from the perspective of output. If the technical efficiency value of the third-level indicator of a region is 1, it means that the input-output situation of the region in that year on this indicator is relatively optimal.

[0094] Furthermore, the calculation of the input allocation coefficient specifically includes the steps of:

[0095] For each funded unit:

[0096] S28. In the input allocation indicator system, the indicator scores of the three-level indicators under the development index indicator are calculated based on the indicator values, and the indicator scores of the three-level indicators under the evaluation index indicator are calculated using the triangular fuzzy membership algorithm;

[0097] S29. Perform weighted calculation based on the indicator scores and comprehensive weights of each third-level indicator to obtain the indicator scores of the development index indicator and the indicator scores of the evaluation index indicator, and perform product calculation on the indicator scores of the development index indicator and the indicator scores of the evaluation index indicator to obtain the input allocation coefficient.

[0098] From the above description, it can be seen that since the development index indicator in the input allocation indicator system has only one level, the score can be calculated based on the indicator value; and the evaluation index indicator contains more than two levels of indicators, so the indicator score is calculated based on the scoring algorithm based on the interval triangular membership fuzzy theory, and the indicator score of the development index indicator and the indicator score of the evaluation index indicator are multiplied to obtain the input allocation coefficient.

[0099] Furthermore, the step S3 is specifically as follows:

[0100] The allocation of input resources is carried out according to the input-output coefficient and input allocation coefficient of each funded unit:

[0101]

[0102] ZH m =TZ m ×TR m ;

[0103] Among them, RD represents rigid input demand, Q m is the input resources of the mth funded unit, Q is the total input resources of the year, M is the total number of funded units, ZH m is the comprehensive score of the total score indicators of the mth funded unit, TZ m is the input allocation coefficient of the mth unit, TR m is the input-output coefficient of the mth unit.

[0104] From the above description, it can be seen that the present invention allocates input resources based on the two indicator systems of input-output and input allocation, while also taking into account the rigid input demand, improving the scientificity and authenticity of the input resource allocation model, and making the results of input resource allocation have higher accuracy.

[0105] Please refer to Figure 2 A terminal for allocating resources invested in a distribution network includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method for allocating resources invested in a distribution network are implemented.

[0106] A method and terminal for allocating input resources of a distribution network of the present invention are suitable for allocating and calculating input resources of a distribution network.

[0107] Please refer to Figure 1 as well as Figures 3 to 8 , embodiment 1 of the present invention is:

[0108] A method for allocating input resources of a distribution network, comprising the steps of:

[0109] S1. Establish an input allocation indicator system and an input-output indicator system, and obtain the indicator values ​​of each third-level indicator in the input allocation indicator system and the input-output indicator system for each funded unit;

[0110] The three-level indicators of the input allocation index system and the input-output index system include the proportion of light-loaded 10kV lines, the average maximum load rate of 10kV lines, the proportion of light-loaded 10kV distribution transformers, the average maximum load rate of 10kV distribution transformers, the proportion of heavy-loaded 10kV lines, the proportion of heavy-loaded 10kV distribution transformers, power supply reliability, comprehensive voltage qualification rate and comprehensive line loss rate.

[0111] In this embodiment, a multi-index system needs to be constructed. The construction of a multi-index system is the core and foundation for allocating input resources for the distribution network. The scientific nature and applicability of the index system directly affect the rationality of the input resource allocation results. Therefore, based on the idea of ​​focusing on power grid development, highlighting investment results, and clarifying data sources, this invention forms 22 basic evaluation indicators for distribution network input resources through various methods such as distribution network indicator correlation analysis, indicator observability analysis, and the Delphi method. It then establishes an input allocation index system and an input-output index system according to different dimensions.

[0112] (1) Input allocation indicator system

[0113] The indicator structure is divided into development index indicators and evaluation index indicators according to the nature of the indicators. The evaluation index part is analyzed from four dimensions: development quality, power grid security, input-output, and lean management.

[0114] From the perspective of resource investment, we determine whether indicators are positive or negative. A higher indicator value indicates a greater need for resource investment, which is considered positive; a lower indicator value indicates a negative one. We identified 13 positive indicators and 7 negative indicators. The specific indicator system is shown in Table 1.

[0115] Table 1

[0116]

[0117]

[0118] (2) Input-output indicator system

[0119] The input-output evaluation indicators are divided into distribution network input indicators and distribution network output indicators according to the nature of the indicators. The distribution network output part is analyzed from three dimensions: development quality, power grid security, and efficiency and benefit.

[0120] From an input-output perspective, we determine whether indicators are positive or negative. If the indicator value is higher, the input-output ratio is better, and the indicator is positive; otherwise, it is negative. We identified 14 positive indicators and 8 negative indicators. The specific indicator system is shown in Table 2.

[0121] Table 2

[0122]

[0123]

[0124] S2. For each funded unit, the comprehensive weight of each third-level indicator is determined by combining the hierarchical analysis method and the coefficient of variation method, and the input-output coefficient and the input allocation coefficient are calculated by combining the indicator value and the comprehensive weight of each third-level indicator.

[0125] In this embodiment, a city (county) company is taken as a funded unit, and the funded unit will also be referred to as a city (county) company hereinafter.

[0126] The calculation of the comprehensive weight in step S2 specifically includes the following steps:

[0127] For each funded unit, perform the following steps:

[0128] S21. Determine the subjective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system through the analytic hierarchy process;

[0129] The step S21 includes the following steps:

[0130] S211. Obtain the expert's offline scoring information for each level 3 indicator based on the preset scale and construct a comparison matrix:

[0131]

[0132] Among them, the diagonal elements are all 1, a ij is the scale of the i-th indicator for the j-th indicator, and n is the number of three-level indicators;

[0133] S212, normalize each column element of the comparison matrix:

[0134]

[0135] S213, add up the normalized comparison matrix of each column by row to obtain the initial subjective weight w i :

[0136]

[0137] S214: Normalize the subjective weights to obtain a subjective weight matrix:

[0138] w'=(w1',w2',...,w n ') T .

[0139] In this embodiment, subjective weights are determined using the Analytic Hierarchy Process (AHP). The Analytic Hierarchy Process (AHP) is a subjective weighting method commonly used to solve complex problems with multiple criteria and multiple objectives. In this embodiment, experts were organized to conduct offline discussions and use a 1-9 scale to compare and score each feature in the indicator layer, as shown in Table 3. The expert scoring sheet was then filled in to determine the importance of each indicator. The scoring information was then used to construct a comparison matrix.

[0140] Table 3

[0141] <![CDATA[Scale a ij > meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with the two elements, element i is slightly more important than element j 5 Indicates that compared with the two elements, element i is significantly more important than element j 7 Indicates that compared with the two elements, element i is more important than element j 9 Indicates that compared with the two elements, element i is extremely more important than element j 2，4，6，8 The middle value of the above adjacent judgment reciprocal <![CDATA[If the judgment value obtained by comparing factor j with factor i is a ji = 1 / a ij >

[0142] Then the subjective weight of each third-level indicator is calculated using the above method.

[0143] In this embodiment, after the subjective weight is calculated, a consistency check is required for the calculation result.

[0144] S22. Determine the objective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system by using the coefficient of variation method.

[0145] Since the AHP method is largely based on expert experience and is highly subjective, the coefficient of variation method uses mathematical theoretical methods to determine weights and is more objective.

[0146] The step S22 includes the following steps:

[0147] S221. Establish an original matrix containing M funded units and N third-level indicator values:

[0148] X=[a ij ] M×N .

[0149] In this embodiment, considering that the values ​​of the power supply reliability rate and the comprehensive voltage qualification rate are too close, the parameters are adjusted based on the frontier distance method to weaken the standard indicator data. That is, reaching the set value is considered to be full score, and the standardized matrix is ​​obtained:

[0150] S222. For the power supply reliability index and the comprehensive voltage qualification rate index, the standard index data is weakened by adjusting the parameters by the frontier distance method:

[0151] QY n =100%-a n ;

[0152] Get the normalized matrix:

[0153] K=[b ij ] M×N ;

[0154] Among them, QY n is the frontier distance of the nth indicator, a n It is a power supply reliability index or a comprehensive voltage qualification rate index.

[0155] In this embodiment, only two indicators, "power supply reliability rate" and "comprehensive voltage qualification rate", are considered. If they reach the target value, they are not included in the calculation, that is, the impact of their differences is not considered.

[0156] S223. For each third-level indicator, calculate the standard deviation based on each funded unit:

[0157]

[0158] Among them, M is the total number of funded units, b ij is the standardized value of the jth third-level indicator of the i-th funded unit, is the average value of the standardized value of the jth third-level indicator of each unit, D j is the standard deviation of the jth third-level indicator;

[0159] S224. Calculate the coefficient of variation CV of each third-level indicator j :

[0160]

[0161] Calculate the objective weight matrix:

[0162] θ=[θ1,θ2,…,θ N ] T ;

[0163] in:

[0164]

[0165] S23. Calculate the comprehensive weight of each third-level indicator based on the subjective weight and the objective weight.

[0166] Since the AHP method lacks objective basis and the coefficient of variation law cannot reflect the importance of indicators to actual problems, the present invention combines the two methods to calculate the comprehensive weight vector of each three-level indicator.

[0167] The step S23 is specifically as follows:

[0168] According to the subjective weight w' and the objective weight θ, the comprehensive weight λ of each three-level indicator is calculated:

[0169]

[0170] Among them, α1 and α2 are the weight coefficients of the preset hierarchical analysis method and the preset weight coefficient of the coefficient of variation method respectively. The overall process of calculating the comprehensive weight can be referred to Figure 3 .

[0171] In this embodiment, referring to the results of the survey in other provinces and the results of the measurement and analysis, α1 and α2 are set to 0.3 and 0.7 respectively.

[0172] S24. Since the objective coefficient of variation method requires indicator data support, and the secondary indicators do not have specific indicator data, the weights of the secondary indicators are calculated only by the subjective hierarchical analysis method. The specific steps are the same as the method for determining the subjective weights of the third-level indicators mentioned above.

[0173] The calculation of the input-output coefficient in step S2 specifically includes the following steps:

[0174] S25. For each funded unit, the index score of each third-level index in the input-output index system is calculated using the triangular fuzzy membership algorithm, and the technical efficiency is calculated based on the DEA algorithm, and compared and analyzed with the index score and the technical efficiency in the historical years.

[0175] In this embodiment, since the input-output index system contains more than two levels of indicators, the index scores are calculated based on the scoring algorithm of interval triangular membership fuzzy theory. The use of fuzzy theory based on interval triangular membership can overcome the problem of close scores due to small differences in indicator data and achieve interval optimal scoring. According to the interval distribution characteristics of each indicator data, the threshold value of each evaluation set is determined based on the fuzzy theory of interval triangular membership. The specific indicator scoring process is as follows:

[0176] (1) Determine the indicator score threshold. For the calculation of each indicator value, the indicator values ​​of each city (county) company are horizontally ranked based on the individual indicator, and the three thresholds of the maximum value, the middle value, and the minimum value of the indicator are determined using the tertiary method, which correspond to X1, X2, and X3 in Tables 4 and 5, respectively.

[0177] Table 4. Triangular membership function of positive index interval

[0178] Membership interval <![CDATA[μ 100 (x)]]> <![CDATA[μ 80 (x)]]> <![CDATA[μ 60 (x)]]> <![CDATA[[X1,+∞]]]> 1 0 0 <![CDATA[[X2,X1]]]> <![CDATA[(x-X2) / (X1-X2)]]> <![CDATA[(X1-x) / (X1-X2)]]> 0 <![CDATA[[X3,X2]]]> 0 <![CDATA[(x-X3) / (X2-X3)]]> <![CDATA[(X2-x) / (X2-X3)]]> <![CDATA[[-∞,X3]]]> 0 0 1

[0179] Table 5 Triangular membership function of negative indicator interval

[0180] Membership interval <![CDATA[μ 100 (x)]]> <![CDATA[μ 80 (x)]]> <![CDATA[μ 60 (x)]]> <![CDATA[[X1,+∞]]]> 0 0 1 <![CDATA[[X2,X1]]]> 0 <![CDATA[(X1-x) / (X1-X2)]]> <![CDATA[(x-X2) / (X1-X2)]]> <![CDATA[[X3,X2]]]> <![CDATA[(X2-x) / (X2-X3)]]> <![CDATA[(x-X3) / (X2-X3)]]> 0 <![CDATA[[-∞,X3]]]> 1 0 0

[0181] (2) Calculate the membership of each index value. According to the index value of each city (county) company, calculate the membership of the city (county) company index value to the maximum value, the middle value and the minimum value respectively. The membership function distribution is as follows: Figure 4 shown.

[0182] (3) Calculate the three-level indicator score of an individual to be evaluated:

[0183]

[0184] Where, lsd k,i is the score of the i-th indicator of the k-th individual to be evaluated, μ j (x k,i ) is the membership value of the i-th indicator of the k-th individual to be evaluated in the j-th evaluation set, q j is the jth evaluation set score, and the present invention sets μ 60 (x), μ 80 (x), μ 100(x) corresponds to scores of 60, 80, and 100, respectively.

[0185] In this embodiment, the calculation of technical efficiency based on the DEA algorithm includes the following steps:

[0186] Based on the BCC model of the DEA algorithm, the technical efficiency of each three-level indicator is calculated using a linear programming model:

[0187]

[0188] Among them, V P Represents the technical efficiency of each third-level indicator, ω T 、μ T They represent the weight parameters of the three-level input indicator X and the three-level output indicator Y respectively, μ0 represents the correction coefficient of the DEA algorithm, and n is the number of three-level indicators.

[0189] The BCC model is the basic model in the DEA algorithm. It explores efficiency from the perspective of output. That is, under the same input level, it compares the output resource achievement model, which is called the "input-oriented model." Its corresponding result is "technical efficiency." DEA = 1 is called "technical efficiency."

[0190] In this embodiment, in the input-output model, the output of each company with the same input is evaluated based on technical efficiency, reflecting each company's ability to achieve the best economic benefits without violating certain resource constraints. With similar inputs, the higher the output of each city (county) company, the higher the technical efficiency. The BCC model based on the DEA algorithm calculates the technical efficiency of the three-level indicators. The linear programming model is a tool for finding the optimal solution under constraints. Therefore, the present invention uses the linear programming model to calculate technical efficiency.

[0191] Since the current platform is limited to solving the minimum value for linear programming models, dual programming is performed on the above formula:

[0192]

[0193] Among them, V D is the technical efficiency after dual planning, S - 、S + They represent the slack variables of the three-level input indicator X and the three-level output indicator Y, and λ represents the intensity variable of the three-level input-output indicator.

[0194] Technical efficiency can be discussed from the perspective of output. If the technical efficiency value of the third-level indicator of a region is 1, it means that the input-output situation of the region in terms of this indicator is relatively optimal in that year.

[0195] S26. For each third-level indicator, if the technical efficiency and the indicator score both increase within the preset period, the first reward coefficient will be given to the third indicator; if the technical efficiency and the indicator score both decrease within the preset period, the third indicator will be based on the second reward coefficient; otherwise, the third indicator will be given a third reward index to obtain the reward coefficients of each third-level indicator, where the third reward coefficient is 1, the first reward coefficient is greater than 1, and the second reward coefficient is less than 1.

[0196] In this embodiment, in order to better judge the quality improvement and efficiency enhancement of each unit and make the allocation decision of input resources more real and effective, the present invention sets up a "reward mechanism" to correct the indicators, and determines the reward coefficient based on the development trend of the indicator technical efficiency and score, which is used to connect the input allocation model and the input-output model, and provide support for the allocation decision of input resources from the input-output benefit level.

[0197] In this embodiment, let the reward coefficient be K. If the technical efficiency and indicator score of a certain indicator are both improved within two years, then K is set to be greater than 1; if they are both reduced within two years, then K is less than 1; in other cases, K is set to 1.

[0198] S27. According to the ranking of the total reward coefficients of the funded units, for each funded unit ranked within the top preset range, the input-output coefficient is set to a first value, otherwise it is set to a second value, the first value is greater than 1, and the second value is 1.

[0199] In this embodiment, after obtaining the reward coefficients of the three-level indicators, the reward coefficients of the companies in each city and county are obtained through weighted calculation. Specifically, the reward coefficients of the first-level indicators are obtained by step-by-step weighting using the following formula and then sorted:

[0200]

[0201] Among them, K' k is the reward coefficient of the kth superior indicator, K k,i is the reward coefficient of the i-th subordinate indicator under the k-th superior indicator, λ i is the comprehensive weight of the i-th subordinate indicator.

[0202] In this embodiment, the preset range is 30%, and the top 30% of units are finally awarded:

[0203]

[0204] Where TR m is the input-output coefficient of the mth funded unit. The specific process can also be referred to Figure 5 shown.

[0205] The calculation of the input allocation coefficient specifically includes the following steps:

[0206] For each funded unit:

[0207] S28. In the input allocation index system, the index scores of the three-level indicators under the development index index are calculated based on the index values, and the index scores of each three-level indicator under the evaluation index index are calculated using the triangular fuzzy membership algorithm.

[0208] Since the development index indicator in the input allocation indicator system has only one level, the score can be calculated based on the indicator value; while the evaluation index indicator contains more than two levels of indicators, the indicator score is calculated using a scoring algorithm based on the interval triangular membership fuzzy theory.

[0209] The scoring algorithm of interval triangular membership fuzzy theory can be referred to above.

[0210] The indicator value of the development index indicator is calculated as follows: based on the indicator value, the indicator scores of the planned investment proportion, the comprehensive factor of electricity sales and the proportion of total distribution network assets are all the indicator value * 100.

[0211] S29. Perform weighted calculation based on the indicator scores and comprehensive weights of each third-level indicator to obtain the indicator scores of the development index indicator and the indicator scores of the evaluation index indicator, and perform product calculation on the indicator scores of the development index indicator and the indicator scores of the evaluation index indicator to obtain the input allocation coefficient.

[0212] In this embodiment, the first-level indicator score is obtained by weighting the third-level indicator scores calculated from the input allocation model using the following formula:

[0213]

[0214] Among them, Hk is the score value of the k-th superior indicator, lsd k,i is the score of the ith subordinate indicator under the kth superior indicator, λ i is the comprehensive weight of the ith subordinate indicator, and n is the number of indicators.

[0215] The input allocation coefficient is obtained by multiplying the development index score of the first-level indicator by the evaluation index score:

[0216] TZ m =∏H m,k ;

[0217] TZ m is the input allocation coefficient of the mth unit, H m,k It is the score of the kth first-level indicator of the mth unit, that is, the product of the development index score and the evaluation index score.

[0218] In this embodiment, as can be seen from the above, the present invention establishes an input allocation index system and an input-output index system based on the basic evaluation index of distribution network resource input, and constructs an input allocation model and an input-output model respectively. Among them, the input-output model is used to "look back" and guide resource input. First, the input-output status of each company is evaluated based on historical data; second, the reward coefficient is calculated based on the company's indicator scores in previous years and applied to future resource input allocation. The specific process is as follows: Figure 6 The input allocation model is based on business system data, guided by a set of basic indicator systems, and combined with the reward coefficient provided by the input-output model to establish an input resource allocation knowledge system, realize the calculation of input allocation coefficients, and support the allocation of input resources from provinces to cities and cities to counties. The specific process is as follows: Figure 7 To better connect the input allocation model with the input-output model, the present invention introduces a reward coefficient to modify the indicators through a "reward mechanism," thereby constructing a hierarchical "province to city, city to county" two-tier distribution network investment resource decision-making model.

[0219] S3. Allocate input resources based on the input-output coefficient and input allocation coefficient of each funded unit;

[0220] The step S3 is specifically as follows:

[0221] The allocation of input resources is carried out according to the input-output coefficient and input allocation coefficient of each funded unit:

[0222]

[0223] ZH m =TZ m ×TR m ;

[0224] Among them, RD represents rigid input demand, Q m is the input resources of the mth funded unit, Q is the total input resources of the year, M is the total number of funded units, ZH m is the comprehensive score of the total score indicators of the mth funded unit, TZ m is the input allocation coefficient of the mth unit, TR m is the input-output coefficient of the mth unit.

[0225] In this embodiment, as described above, based on the comprehensive score of the city (county), combined with the total input resources and rigid input demand, the city (county) input resources are allocated. Among them, the rigid input demand (Rigid Demand, RD) refers to the demand that is less affected by the price in the supply and demand relationship of the commodity, and does not participate in the allocation of input resources. The rigid input demand of the distribution network reflects that the regional distribution network is a hard indicator, construction requirement or special event and strategy, such as special resources that are set up with priority for disaster prevention and resistance. The present invention sets the rigid input demand of the distribution network mainly to include important events or meetings RD1, disaster emergency RD2 and strategic investment RD3. The calculation of the rigid input demand RD is shown in the formula:

[0226] RD=RD1+RD2+RD3.

[0227] In general, the allocation process of input resources can refer to Figure 8 shown.

[0228] Please refer to Figure 2 , the second embodiment of the present invention is:

[0229] A terminal 1 for allocating resources invested in a distribution network comprises a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps of the method for allocating resources invested in a distribution network in the first embodiment above are implemented.

[0230] In summary, the method and terminal for allocating input resources in a distribution network provided by the present invention construct two sets of indicator systems, namely, input allocation indicators and input-output indicators of the distribution network, which are more accurate, improve the problems of a single indicator system and ignoring the development trend of indicators, improve the scientificity and authenticity of the input resource allocation model, and make the results of input resource allocation have higher accuracy. It also comprehensively considers the advantages and disadvantages of subjective and objective weighting methods, and adopts a comprehensive weighting method based on hierarchical analysis method and coefficient of variation method to determine the weight of each indicator, which is more reasonable and accurate.

[0231] It can realize the investment resource allocation plan of the two-tier distribution network with clear hierarchy and responsibilities of "province to city, city to county", while improving the problems of single indicator system and ignoring the development trend of indicators, improving the scientificity and authenticity of the allocation model of investment resources, and making the allocation results of investment resources have higher accuracy.

[0232] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for allocating input resources of a distribution network, characterized in that: Including steps: S1. Establish an input allocation indicator system and an input-output indicator system, and obtain the indicator values ​​of each third-level indicator in the input allocation indicator system and the input-output indicator system for each funded unit; The three-level indicators of the input allocation index system and the input-output index system include the proportion of lightly loaded 10kV lines, the average value of the maximum load rate of 10kV lines, the proportion of lightly loaded 10kV distribution transformers, the average value of the maximum load rate of 10kV distribution transformers, the proportion of heavily loaded 10kV lines, the proportion of heavily loaded 10kV distribution transformers, the power supply reliability rate, the comprehensive voltage compliance rate and the comprehensive line loss rate; S2. For each funded unit, determine the comprehensive weight of each third-level indicator by combining the analytic hierarchy process and the coefficient of variation method, and calculate the input-output coefficient and input allocation coefficient by combining the indicator value and the comprehensive weight of each third-level indicator; The calculation of the input-output coefficient in step S2 specifically includes the following steps: S25. For each funded unit, calculate the indicator scores of each third-level indicator in the input-output indicator system using the triangular fuzzy membership algorithm, calculate the technical efficiency based on the DEA algorithm, and compare and analyze the indicator scores and technical efficiency in the historical period; S26. For each third-level indicator, if both the technical efficiency and the indicator score increase within the preset period, a first reward coefficient is assigned to the third-level indicator. If both the technical efficiency and the indicator score decrease within the preset period, a second reward coefficient is assigned to the third-level indicator. Otherwise, a third reward coefficient is assigned to the third-level indicator. The reward coefficients for each third-level indicator are obtained, where the third reward coefficient is 1, the first reward coefficient is greater than 1, and the second reward coefficient is less than 1. S27. Based on the ranking of the total reward coefficients of the funded units, for each funded unit ranked within a preset range, set the input-output coefficient to a first value, otherwise set it to a second value, where the first value is greater than 1 and the second value is 1; The calculation of the input allocation coefficient specifically includes the following steps: For each funded unit: S28. In the input allocation indicator system, the indicator scores of the three-level indicators under the development index indicator are calculated based on the indicator values, and the indicator scores of the three-level indicators under the evaluation index indicator are calculated using the triangular fuzzy membership algorithm; The input allocation indicator system includes first-level indicators: evaluation index indicators and development index indicators; S29. Perform a weighted calculation based on the indicator scores and comprehensive weights of the three-level indicators to obtain the indicator scores of the development index indicator and the indicator scores of the evaluation index indicator, and perform a product calculation on the indicator scores of the development index indicator and the evaluation index indicator to obtain the input allocation coefficient; S3. Allocate input resources based on the input-output coefficient and input allocation coefficient of each funded unit; The step S3 is specifically as follows: The allocation of input resources is carried out according to the input-output coefficient and input allocation coefficient of each funded unit: Among them, RD represents rigid input demand, Q m is the input resources of the mth funded unit, Q is the total input resources of the year, M is the total number of funded units, ZH m is the comprehensive score index of the mth funded unit, TZ m is the input allocation coefficient of the mth unit, TR m is the input-output coefficient of the mth unit.

2. A method for allocating input resources of a distribution network according to claim 1, characterized in that: The calculation of the comprehensive weight in step S2 specifically includes the following steps: For each funded unit, perform the following steps: S21. Determine the subjective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system through the analytic hierarchy process; S22. Determine the objective weights of the three-level indicators in the input allocation indicator system and the input-output indicator system by using the coefficient of variation method; S23. Calculate the comprehensive weight of each of the three-level indicators based on the subjective weight and the objective weight; S24. Determine the indicator weights of the input allocation indicator system and the secondary indicators in the input-output indicator system by using a subjective weight method.

3. A method for allocating input resources of a distribution network according to claim 2, characterized in that: The step S21 includes the following steps: S211. Obtain the expert's offline scoring information for each level 3 indicator based on the preset scale and construct a comparison matrix: Among them, the diagonal elements are all 1, a ij is the scale of the i-th indicator for the j-th indicator, and n is the number of three-level indicators; S212, normalize each column element of the comparison matrix: S213, add up the normalized comparison matrix of each column by row to obtain the initial subjective weight w i : S214: Normalize the initial subjective weights to obtain a subjective weight matrix: w'=(w1',w2',...,w n ') T 。 4. A method for allocating input resources of a distribution network according to claim 3, characterized in that: The step S22 includes the following steps: S221. Establish an original matrix containing M funded units and N third-level indicator values: X=[a ij ] M×N ; S222. For the power supply reliability index and the comprehensive voltage qualification rate index, the standard index data is weakened by adjusting the parameters by the frontier distance method: QY n =100%-a n ; Get the normalized matrix: K=[b ij ] M×N ; Among them, QY n is the frontier distance of the nth indicator, a n is the power supply reliability index or the comprehensive voltage qualification rate index, b ij is the standardized value of the jth third-level indicator of the i-th funded unit, and K is the standardized matrix of the third-level indicator values; S223. For each third-level indicator, calculate the standard deviation based on each funded unit: Among them, M is the total number of funded units, is the average value of the standardized value of the jth third-level indicator of each unit, D j is the standard deviation of the jth third-level indicator; S224. Calculate the coefficient of variation CV of each third-level indicator j : Calculate the objective weight matrix: θ=[θ1,θ2,…,θ N ] T ; The calculation of the jth element in the objective weight matrix is:

5. A method for allocating input resources of a distribution network according to claim 4, characterized in that: The step S23 is specifically as follows: According to the subjective weight matrix w' and the objective weight matrix θ, the comprehensive weight λ of each three-level indicator is calculated: Among them, α1 and α2 are the preset weighting coefficients of the hierarchical analysis method and the preset weighting coefficient of the coefficient of variation method, respectively.

6. A method for allocating input resources of a distribution network according to claim 1, characterized in that: The input-output indicator system includes the first-level indicators: distribution network output; The evaluation index indicators and the distribution network output both include secondary indicators: development quality and grid security; The development quality includes power supply reliability, comprehensive voltage qualification rate, comprehensive line loss rate, proportion of lightly loaded 10kV lines, average value of maximum load rate of 10kV lines, proportion of lightly loaded 10kV distribution transformers and average value of maximum load rate of 10kV distribution transformers; The grid safety includes the proportion of heavy-loaded 10kV lines, the proportion of heavy-loaded 10kV distribution transformers and the N-1 pass rate; The N-1 passing rate refers to the passing rate of the single-failure safety criterion.

7. A terminal for allocating resources input to a distribution network, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for allocating resources input to a distribution network according to any one of claims 1 to 6 are implemented.

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