Power grid equipment investment comprehensive benefit evaluation method and system considering functions and cost

Abstract

The invention discloses a power grid equipment investment comprehensive benefit evaluation method and system considering functions and cost. The method comprises the following steps: establishing a province-city-county power grid equipment investment comprehensive benefit evaluation index system library; determining each index data, and generating a comprehensive benefit evaluation index database; calculating subjective and objective weights of power grid equipment investment comprehensive benefit evaluation by adopting an analytic hierarchy process and an entropy weight method, and generating a comprehensive benefit evaluation index weight library; calculating a comprehensive weight of power grid equipment investment comprehensive benefit evaluation based on a least square method, and adding the comprehensive weight to a comprehensive benefit evaluation index weight library; calculating the power grid equipment investment comprehensive benefit correlation degree by adopting a grey correlation degree method, and generating a correlation degree sorting library; constructing a comprehensive benefit index trace tracking diagnosis model, and generating a comprehensive benefit evaluation index deviation contribution library; and according to the correlation degree sorting library and the comprehensive benefit evaluation index deviation contribution library, completing the comprehensive benefit evaluation of the power grid equipment investment. A method and a platform are provided for power grid enterprises to evaluate equipment benefits, and the fund use efficiency is improved.

Description

Technical Field

[0001] The present invention relates to a benefit evaluation method, and in particular to a comprehensive benefit evaluation method and system for power grid equipment investment taking into account function and cost. Background Art

[0002] At present, the comprehensive benefit evaluation methods for power grid equipment investment are mainly divided into objective evaluation methods and subjective evaluation methods. Data envelopment analysis method, approximate ideal solution sorting method, entropy method, principal component analysis method, mean square error method, etc. belong to objective evaluation methods; subjective evaluation methods generally include Delphi method, hierarchical analysis method, fuzzy comprehensive evaluation method, grey comprehensive evaluation method, etc. In addition, subjective evaluation and objective data can be combined to formulate relevant evaluation methods. However, according to the needs of society and the development trend of the power grid, the comprehensive benefit evaluation method and system research of power grid equipment investment need to be further explored. At present, there is no comprehensive benefit evaluation method and system research on the functional attributes and responsibilities of the power grid. The evaluation angle is mostly aimed at the economic benefits and operating efficiency of power grid equipment. There is one-sidedness. There is little research on the political, economic, and social responsibilities undertaken by the power grid and the multiple functional attributes such as safe supply, energy transformation, and high-quality services. It is impossible to achieve a reasonable measurement of investment results, and there is no consideration of tracking and diagnosing the weak links that lead to poor evaluation results, and it is impossible to take targeted countermeasures. Summary of the invention

[0003] The present invention aims to propose a method and system for evaluating the comprehensive benefits of power grid equipment investment taking into account function and cost.

[0004] To achieve the above purpose, the technical solution of the present invention is as follows:

[0005] A method and system for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs, comprising the following steps:

[0006] Step 1: Establish a comprehensive benefit evaluation index system database for power grid equipment investment at the provincial, municipal and county levels;

[0007] Step 2: Input the data of each indicator according to the evaluation indicator system library in step 1 to generate a comprehensive benefit evaluation indicator database;

[0008] Step 3: Based on the comprehensive benefit evaluation index database in step 2, the subjective and objective weights of the comprehensive benefit evaluation of power grid equipment investment are calculated using the analytic hierarchy process and the entropy weight method to generate a comprehensive benefit evaluation index weight library;

[0009] Step 4: Calculate the comprehensive weight of the comprehensive benefit evaluation of power grid equipment investment based on the least squares method and add it to the comprehensive benefit evaluation index weight library;

[0010] Step 5: Based on the evaluation index weight library in step 4, the grey correlation method is used to calculate the correlation of the comprehensive benefits of power grid equipment investment, and a correlation ranking library is generated;

[0011] Step 6: Construct a comprehensive benefit indicator trace tracking diagnosis module based on the correlation ranking library in step 5 to generate a comprehensive benefit evaluation indicator deviation contribution library;

[0012] Step 7: Based on the correlation ranking library in step 5 and the comprehensive benefit evaluation index deviation contribution library in step 6, complete the comprehensive benefit evaluation of power grid equipment investment and output the comprehensive benefit evaluation results.

[0013] Beneficial Effects

[0014] The present invention first establishes a comprehensive benefit evaluation index system library for power grid equipment investment in provinces, cities and counties, and uses the analytic hierarchy process, entropy weight method and least square method to construct a comprehensive benefit evaluation index weight library for power grid equipment investment. The comprehensive benefit evaluation results of power grid equipment investment are calculated by the grey correlation method and the index trace tracking module. Power grid enterprises can more accurately identify and evaluate the benefits of different power grid equipment investment projects, thereby optimizing investment decisions and improving the efficiency of capital use. At the same time, the comprehensive benefit index trace tracking diagnosis module can accurately locate the weak links that lead to poor comprehensive benefit evaluation results, calculate the deviation contribution rate of each index to the evaluation results, deeply explore the value of equipment investment benefit data, and determine the indicators that need to be studied in depth, so as to effectively prevent and control risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flow chart of comprehensive benefit evaluation of power grid equipment investment according to the present invention. DETAILED DESCRIPTION

[0016] A method and system for evaluating the comprehensive benefits of power grid equipment investment considering power grid functions and costs, comprising the following steps:

[0017] Step 1: Establish a comprehensive benefit evaluation index system library for power grid equipment investment at the provincial, municipal and county levels; the provincial power grid equipment investment comprehensive benefit evaluation index system library includes three levels of indicators:

[0018] The first-level indicator is the target layer;

[0019] The secondary indicators are the criteria layer, including safe supply, energy transformation, quality service, efficiency and benefits;

[0020] The third-level indicators, i.e. the ultimate indicators of this evaluation index system, are the solution layer, including the "N-1" pass rate, source-grid-load coordinated development index, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicators; the annual new energy access matching degree, distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicators; the voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low-voltage substation ratio, and frequent outage substation ratio, which are subordinate to the quality service indicators; the capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicators, which are subordinate to the efficiency and benefit indicators;

[0021] Among them, the “N-1” pass rate = the number of 10 kV lines with interconnection and load transfer capability / the total number of 10 kV lines;

[0022] Source-grid-load coordinated development index = source-load matching index + power supply capacity index = conventional power supply installed capacity growth rate / load growth rate + 750-110 or 66 kV transformer capacity growth rate / various power supply installed capacity growth rate + 750-110 or 66 kV transformer capacity growth rate / load growth rate;

[0023] Equipment health level = proportion of old and high-damage equipment; where: proportion of old and high-damage equipment = total number of old and high-damage equipment / total number of equipment;

[0024] 10 kV interconnection rate = number of 10 kV lines with interconnection / total number of 10 kV lines;

[0025] Annual new energy access matching degree = new energy utilization rate growth level + new energy access progress index = new energy utilization rate accumulated from this year to this period / new energy utilization rate in the same period last year + current accumulated new energy access scale / annual new energy access scale;

[0026] Distributed generation capacity = 1-the number of public distribution transformers that are heavily overloaded due to the access to distributed generation / the total number of public distribution transformers;

[0027] The carrying capacity of electric vehicle charging and swapping facilities = 1-the number of heavy overloads and low voltages in public distribution transformers connected to charging facilities / the total number of public distribution transformers connected to charging facilities;

[0028] Effective coverage rate of power distribution automation = number of DTU and FTU terminals with two or three remote controls / number of switches on transport poles, ring network cabinets, switch stations, and ring network distribution rooms;

[0029] Voltage qualification rate = (accumulated operation time of the power grid voltage deviation within the limit range) / total operation statistical time;

[0030] Power supply reliability rate = 1-average power outage time of power grid users / total operation statistical time;

[0031] Average distribution and transformation capacity per household in rural power grid = 10 or 20 kV distribution and transformation capacity of rural power grid / number of low-voltage users;

[0032] The proportion of low-voltage areas = the total number of low-voltage areas / the total number of areas;

[0033] The proportion of areas with frequent outages = the total number of areas with frequent outages / the total number of areas;

[0034] Capacity-load ratio = total capacity of substation equipment / maximum power supply load of the power grid at this voltage level;

[0035] Equipment life cycle operation rationality rate = 0.8 × number of old and retired equipment in the year / number of retired equipment in the year + 0.2 × average retirement age of non-old equipment / 30;

[0036] Load rate = (actual output power / rated capacity) × 100%;

[0037] The city and county power grids are optimized based on the provincial power grid indicators in light of their own production and operation realities; the city and county power grid evaluation indicator system library includes three levels of indicators:

[0038] The first-level indicator is the target layer;

[0039] The secondary indicators are the criteria layer, including safe supply, energy transformation, quality service, efficiency and benefits;

[0040] The third-level indicators, i.e. the ultimate indicators of this evaluation index system, are the solution layer, including the "N-1" pass rate, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicators; the distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicators; the voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low-voltage substation ratio, and frequent outage substation ratio, which are subordinate to the quality service indicators; the capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicators, which are subordinate to the efficiency and benefit indicators;

[0041] Step 2: Input the data of each indicator according to the evaluation indicator system library in step 1 to generate a comprehensive benefit evaluation indicator database;

[0042] According to step 1, input the data of each indicator into the evaluation indicator system library to generate a comprehensive benefit evaluation indicator database. The comprehensive benefit evaluation indicator database of power grid equipment investment includes: the "N-1" pass rate, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicator; the annual new energy access matching degree, distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicator; the voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low voltage area ratio, and frequent outage area ratio, which are subordinate to the quality service indicator; the capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicator, which are subordinate to the efficiency and benefit indicator.

[0043] Step 3: Based on the comprehensive benefit evaluation index database in step 2, the subjective and objective weights of the comprehensive benefit evaluation of power grid equipment investment are calculated using the analytic hierarchy process and the entropy weight method to generate a comprehensive benefit evaluation index weight library;

[0044] According to the comprehensive benefit evaluation index database in step 2, the subjective and objective weights of the comprehensive benefit evaluation of power grid equipment investment are calculated by using the analytic hierarchy process and the entropy weight method to generate a comprehensive benefit evaluation index weight library;

[0045] c1. Construct a comprehensive benefit evaluation index system for power grid equipment investment from multiple dimensions including safety and supply, energy transformation, quality service, efficiency and benefits;

[0046] c2. Use the analytic hierarchy process to calculate the subjective weight of the comprehensive benefit evaluation index of power grid equipment investment. The analytic hierarchy process is a combination of qualitative and quantitative analysis, simulating the decision-making thinking process of people. It has the characteristics of clear thinking, simple methods, and strong systematicity. It is a complex large system that analyzes multiple objectives, multiple factors, and multiple criteria. According to the analytic hierarchy process, an evaluation index system is established to determine the subjective weight of the ultimate index. The ultimate index refers to the last level index of the evaluation index system. It specifically includes the following steps:

[0047] c21. Establish a hierarchical model. In order to evaluate the comprehensive benefits of power grid equipment investment, it is necessary to build an index system library of factors affecting the comprehensive benefits of power grid equipment investment. Based on an in-depth analysis of actual problems, the relevant indicators affecting the comprehensive benefits of power grid equipment investment are decomposed into several levels. The indicators at the same level are subordinate to the indicators at the previous level or have an impact on the indicators at the previous level, and at the same time dominate the indicators at the next level or are affected by the indicators at the next level.

[0048] Taking into account the impact of the function and cost of the power grid on the investment in power grid equipment, as well as the significance of power grid equipment investment to the operation of power grid enterprises, the present invention constructs a comprehensive benefit evaluation index system library for power grid equipment investment from the four dimensions of safe supply, energy transformation, high-quality service, and efficiency and benefit.

[0049] c22. Construct a judgment matrix. Starting from the second-level indicator of the evaluation index system constructed in step c21, for n indicators of the same level belonging to the same upper-level indicator, use the pairwise comparison method to construct a judgment matrix A. ij , judgment matrix A ij One or more, until the ultimate indicator is built.

[0050]

[0051] Among them, i and j are the i-th and j-th indicators among n indicators of the same level, i≤n, j≤n;

[0052] The importance of each indicator in the pairwise comparison method is to collect the scores of experts in different fields on the importance of the indicators, and use the average of each score as the final score result. The results of the pairwise comparison method are expressed by the scales in Tables 1 and 2:

[0053] Table 1 Results of pairwise comparison method

[0054] Scale definition 1 Factor i is as important as factor j 3 Factor i is slightly more important than factor j 5 Factor i is more important than factor j 7 Factor i is more important than factor j 9 Factor i is definitely more important than factor j

[0055] Table 2 Appendix of pairwise comparison method

[0056]

[0057]

[0058] c23. Calculate the weight vector and perform a consistency test. For each judgment matrix, calculate the maximum eigenvalue and its corresponding eigenvector, and use the consistency index, random consistency index, and consistency ratio to perform a consistency test. If the test passes, the eigenvector (normalized) is the weight vector; if it fails, consider reconstructing the judgment matrix. The approximate value of the eigenvector is usually obtained by the summation method or the root method.

[0059] The consistency check specifically includes the following steps:

[0060] c231. Calculate the consistency test index.

[0061]

[0062] Where: max Represents the judgment matrix A ij The largest characteristic root of ij The order of

[0063] c232. Find the corresponding average random consistency index RI.

[0064] Table 3 Average random consistency index

[0065] N 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.24 1.36 1.41 1.45

[0066] c233. Calculate the consistency ratio CR.

[0067] CR=CI / RI

[0068] One or more judgment matrices A for each level of indicators ij A consistency test is performed. When CR < 0.1, the consistency of the judgment matrix is ​​considered acceptable; when CR > 0.1, the judgment matrix should be appropriately modified.

[0069] c24. Use the summation method to calculate the eigenvectors and eigenvalues.

[0070] The specific steps include:

[0071] c241, sum the data in each column, Get the sum vector B j =[b1,b2,...,b m ]. c242. Calculate the normalized vector C ij .in We can get:

[0072]

[0073] c243, calculate the weight vector w jAHP The calculation formula is as follows:

[0074]

[0075] c3. The entropy weight method is used to calculate the objective weight of the comprehensive benefit evaluation index of power grid equipment investment. The entropy value method is to measure the information amount of the data by calculating the information entropy of the index, that is, to determine the weight of the index according to the impact of the relative change degree of the index on the whole. It is an objective weighting method. Entropy is a measure of uncertainty. When the difference provided by the index is large, the smaller the uncertainty, the greater the amount of useful information, the smaller the entropy value, and the greater the entropy weight; otherwise, the opposite is true. The advantage of the entropy weight method is that it completely defines the value and weight of the data from the degree of discreteness of the data itself.

[0076] In the comprehensive evaluation process, the contribution and role of each factor are different. Different weights should be assigned according to the size of each factor, so that the data of each factor can play a full role and the evaluation results are more objective and reliable. As an important part of the objective weighting method, the entropy weight method is based on the variability of the index to determine the objective weight. The greater the information entropy, the higher the degree of variability, the greater the amount of information contained, and the higher the weight, and vice versa. This method is simple to calculate and highly objective, which makes up for the shortcomings of the subjective weighting method and has a wide range of applications.

[0077] The specific calculation steps of the entropy weight method are as follows:

[0078] c31. Construct matrix P based on the original data of the indicator mn .

[0079]

[0080] Among them, P mn Represents the value of the nth indicator in the mth region.

[0081] c32, standardize the original data to obtain the standardized matrix P m ' n The standardization process of extremely large indicators such as unit asset power sales revenue and extremely small indicators such as annual operation and maintenance costs is as follows:

[0082]

[0083] c33. Calculate the entropy value e of the nth indicator n ,for:

[0084]

[0085] c34. Calculate the coefficient of difference g of the nth indicator i :

[0086] g i =1-e j

[0087] c35. Calculate the weight w of the nth indicator jentropy , generate the objective weights of comprehensive benefit evaluation indicators:

[0088]

[0089] Step 4: Calculate the comprehensive weight of the comprehensive benefit evaluation of power grid equipment investment based on the least squares method and add it to the comprehensive benefit evaluation index weight library;

[0090] Calculate the comprehensive weight of comprehensive benefit evaluation of power grid equipment investment based on the least squares method and add it to the comprehensive benefit evaluation index weight library;

[0091] The "combined evaluation method" is to combine various methods to achieve the effect of taking advantage of each other's strengths and making up for each other's weaknesses. For the combination of single evaluation methods, the weights of single evaluation methods can be combined, or the evaluation ranking results of single evaluation methods can be combined. In other words, the combined evaluation method can be divided into "combination of weight coefficients" and "combination of evaluation results".

[0092] In order to overcome the subjective judgment experience in the hierarchical analysis method and the over-emphasis on data in the entropy weight method, and to improve the scientificity and accuracy of the evaluation results, the idea of ​​combined weighting is used to design the comprehensive weight, which can fully reflect the importance of the information contained in each indicator. In order to reduce the degree of deviation between subjective weights and objective weights, the least squares optimization idea is used to determine the proportional coefficient of the weight and generate the comprehensive weight of the indicator:

[0093]

[0094] Where: α represents the weight coefficient; w n,i represents the subjective weight of the nth indicator; w n,j It represents the objective weight of the nth indicator; F represents the minimum target value of the sum of variances solved by the comprehensive weight.

[0095] Step 5: Based on the evaluation index weight library in step 4, the grey correlation method is used to calculate the correlation of the comprehensive benefits of power grid equipment investment, and a correlation ranking library is generated;

[0096] According to the evaluation index weight library in step 4, the grey correlation method is used to calculate the correlation of the comprehensive benefits of power grid equipment investment, and a correlation ranking library is generated;

[0097] The grey correlation comprehensive evaluation method is to judge the correlation between each power grid equipment investment according to the similarity of the index data images of the power grid equipment investment projects. The grey correlation analysis method can be based on the grey process of the grey system. The more similar the image of the comparison series of the power grid equipment investment projects is to the image of the reference series of the power grid equipment investment projects, the greater the correlation. That is, by obtaining the correlation between each power grid equipment investment project and the reference series, the final scheme ranking is obtained according to the size of the correlation between each project.

[0098] The basic steps of the grey relational method are as follows:

[0099] e1. Construct a comparison series.

[0100] All index values ​​of each evaluation object - power grid equipment investment project are the comparison series of the project, as shown in the following formula:

[0101] C i ′(j)={C i (1),C i (2),...,C i (m)}

[0102] Where i=1,2,···,n represents the number of power grid equipment investment projects evaluated and studied; j=1,2,···,m represents the number of indicators of power grid efficiency level.

[0103] e2. Determine the reference number series.

[0104] The maximum value of each power grid project indicator is taken as the reference sequence C0(j).

[0105] C0(j)={C0(1),C0(2),...,C0(m)}

[0106] e3. Dimensionless processing of comprehensive benefit level index value of power grid equipment investment.

[0107] In order to avoid the interference of different dimensional orders of magnitude on the evaluation, the indicator data is dimensionless.

[0108]

[0109] e4. Determine the weights of comprehensive benefit evaluation factors for power grid equipment investment.

[0110] Based on the comparison sequence C i With the reference sequence C0, obtain the index C i (j) is the correlation coefficient ξi(j).

[0111]

[0112] e5. Obtain the relevance of each power grid equipment investment project and generate a relevance ranking library.

[0113] The correlation coefficients of various indicators of the evaluated power grid equipment investment projects are weighted averaged to obtain the correlation degree γ i , whose expression is:

[0114]

[0115] The calculated association degree forms a association degree sorting library. i The larger the value is, the better the benefit level of the corresponding power grid equipment investment project will be.

[0116] Step 6: Construct a comprehensive benefit indicator trace tracking diagnosis module based on the correlation ranking library in step 5 to generate a comprehensive benefit evaluation indicator deviation contribution library;

[0117] According to the correlation ranking library in step 5, a comprehensive benefit indicator trace tracking diagnosis module is constructed to generate a comprehensive benefit evaluation indicator deviation contribution library;

[0118] Only the comprehensive evaluation results of power grid equipment were obtained, but no matter at the regional level or the equipment level, the weak links that led to poor evaluation results could be located in the evaluation index system, and corresponding measures could not be taken in a targeted manner. Based on the correlation ranking, this section constructs a trace tracking model, calculates the deviation contribution rate of each indicator to the evaluation results, generates an indicator deviation contribution library, deeply mines the value of equipment investment benefit data, and determines the indicators that need to be studied in depth, so as to effectively prevent and control risks.

[0119] The trace tracking diagnosis model performs reverse analysis on the results of the multi-attribute combination evaluation model, and uses the evaluation results to infer the problematic attributes and specific indicators. The specific process is as follows:

[0120] f1. Comparison of evaluation results. The evaluation objects are sorted by the grey correlation method to obtain the attribute evaluation value matrix e and comprehensive evaluation value δ of each evaluation object in a certain year. j (j=1,2,...,n), as shown below:

[0121]

[0122] Compare the comprehensive evaluation values ​​and find the object with the best evaluation value

[0123]

[0124] f2. Calculate the basic deviation rate of the secondary index. Compare the attribute evaluation value of the region with the best evaluation value with the attribute evaluation values ​​of other regions to obtain the basic deviation rate ε between each region and the best region. ij (j≠k, j=1,2,...,n) and the deviation rate matrix ε.

[0125]

[0126] f3. Calculate the deviation contribution and deviation contribution rate. According to the weight μ of each attribute in the comprehensive evaluation method i (i=1,2,...,m) is determined, and combined with the basic deviation rate, the deviation contribution η of each attribute of each evaluation object is calculated ij (j≠k,j=1,2,...,n).

[0127] η ij =ε ij μ i

[0128] Calculate the attribute deviation rate λ of each evaluation object separately i (i=1,2,...,m)

[0129]

[0130] f4. Determine the attributes that need to be analyzed in depth. According to the obtained attribute deviation rate of each evaluation object, determine the attribute with the largest deviation rate of each evaluation object. and conduct in-depth analysis.

[0131]

[0132] In this way, the problematic attributes of each evaluation object can be determined, and then the deviation rate of specific indicators can be analyzed based on the attribute.

[0133] f5. Calculate the basic indicator deviation rate. Determine the specific deviation attribute through the previous step, perform deviation analysis on the indicators of the evaluation object and the optimal evaluation object under this attribute, and calculate the basic indicator deviation rate:

[0134]

[0135] f6. Calculate the basic index deviation contribution and contribution rate. Combined with the comprehensive weight of each index under this attribute γ l (l=1,2,...,L) and the index deviation rate obtained in the previous step, calculate the basic index deviation contribution and contribution rate θ lj (j≠k).

[0136]

[0137] f7. Sort the index deviation contribution and generate the index deviation contribution library for comprehensive benefit evaluation of power grid equipment investment. Sort the indexes according to the deviation contribution rate of each basic index and generate the index deviation contribution library for comprehensive benefit evaluation of power grid equipment investment. The index with the largest deviation rate is the main factor causing the difference between the evaluation object and the optimal evaluation object. Similarly, analyze the reasons for the difference between the evaluation object and the optimal evaluation object and provide relevant explanations.

[0138] Step 7: Based on the correlation ranking library in step 5 and the comprehensive benefit evaluation index deviation contribution library in step 6, complete the comprehensive benefit evaluation of power grid equipment investment and output the comprehensive benefit evaluation results.

[0139] According to the correlation ranking library in step 5 and the comprehensive benefit evaluation index deviation contribution library in step 6, the comprehensive benefit evaluation of power grid equipment investment is completed and the comprehensive benefit evaluation results are output.

[0140] The idea of ​​the present invention for comprehensive benefit evaluation of power grid equipment investment is to establish a comprehensive benefit evaluation index system library of power grid equipment investment in provinces, cities and counties by considering the function and cost of the power grid, and to generate a weight library of comprehensive benefit evaluation indexes of power grid investment equipment by using the analytic hierarchy process, entropy weight method and least squares method. The grey correlation method is used to obtain a correlation ranking library of each evaluation object, and a comprehensive evaluation index trace tracking model is used to generate a comprehensive evaluation index deviation contribution library. The comprehensive benefit evaluation result of power grid equipment investment is obtained through the correlation ranking library and the index deviation contribution library.

[0141] The specific steps are explained as follows:

[0142] Step 1: Input expert scoring results and indicator databases.

[0143] Step 2: Use the analytic hierarchy process to solve the subjective weights of the comprehensive benefit evaluation indicators of power grid equipment investment. Use the entropy weight method to solve the objective weights of the comprehensive benefit evaluation indicators of power grid equipment investment. Add the obtained subjective and objective weights to the weight library of the comprehensive benefit evaluation indicators of power grid equipment investment.

[0144] Step 3: Use the least squares method to solve the comprehensive weight of the comprehensive benefit evaluation index of power grid equipment investment, and add the obtained comprehensive weight to the comprehensive benefit evaluation index weight library of power grid equipment investment. According to the least squares principle, the optimal allocation coefficient of subjective weight and objective weight is determined by the simultaneous least squares optimization function, thereby obtaining the comprehensive weight of the evaluation index.

[0145] Step 4: Use the grey correlation analysis method to solve the comprehensive benefit level of power grid equipment investment projects and generate a correlation ranking library. Perform consistency processing on the indicator data, calculate the grey correlation coefficient and the correlation of the comprehensive evaluation, and obtain the correlation and comprehensive ranking of each power grid equipment investment project.

[0146] Step 5: Use the comprehensive benefit indicator trace tracking diagnosis model to generate a comprehensive benefit evaluation indicator deviation contribution library. Calculate the deviation contribution rate of each indicator to the evaluation result and obtain the indicators that need to be further explored and studied.

[0147] Step 6: Combine the correlation ranking database of step 4 and the comprehensive evaluation index deviation contribution database of step 5 to obtain the comprehensive benefit evaluation result of power grid equipment investment.

[0148] The present invention is further described below in conjunction with some specific embodiments.

[0149] Example

[0150] A comprehensive benefit evaluation method and system for power grid equipment investment considering function and cost, power grid equipment overview: 5 grids in a certain area, including the following steps, such as Figure 1 As shown:

[0151] Step 1: Establish a comprehensive benefit evaluation index system library for power grid equipment investment at the provincial, municipal and county levels; Considering the three major political, economic and social responsibilities undertaken by the company and the multiple functional attributes of the power grid, starting from the four dimensions of safe supply, energy transformation, high-quality service and efficiency and benefits, extract conventional traditional indicators with accurate representation and strong applicability from the "Guidelines for Economic Evaluation of Transmission and Transformation Projects" (DL / T 5438-2009), "Implementation Plan for the Promotion of the Asset Life Cycle Management System of State Grid Corporation of China" and national and industry-related infrastructure, financial and taxation policies and systems, and then, guided by the strategic goals and development direction of State Grid in the new era, refine and design new supplementary indicators, collect and sort out power grid equipment investment benefit indicators, and form a comprehensive benefit evaluation index library for power grid equipment investment.

[0152] Step 2: Input the data of each indicator according to the evaluation indicator system library in step 1 to generate a comprehensive benefit evaluation indicator database; the data include: "N-1" pass rate, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicator; annual new energy access matching degree, distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicator; voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low voltage area ratio, and frequent outage area ratio, which are subordinate to the quality service indicator; capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicator, which are subordinate to the efficiency and benefit indicator. The comprehensive benefit evaluation indicator data of power grid equipment investment are shown in Table 4.

[0153] Table 4 Comprehensive benefit evaluation index data of power grid equipment investment

[0154]

[0155]

[0156] Step 3: Based on the comprehensive benefit evaluation index database in step 2, the analytic hierarchy process and entropy weight method are used to calculate the subjective and objective weights of the comprehensive benefit evaluation of power grid equipment investment, and generate a comprehensive benefit evaluation index weight library.

[0157] Firstly, the analytic hierarchy process is used to divide the comprehensive benefit evaluation indicators of power grid equipment investment into the target layer, the criterion layer, and the scheme layer. According to the correlation between different indicators and the investment benefits of power grid equipment, the criterion layer can be divided into four links, namely, safe supply B1, energy transformation B2, quality service B3, and efficiency and benefit B4. Safe supply includes four indicators: "N-1" pass rate C1, source-grid-load coordinated development index C2, equipment health level C3, and 10 kV effective interconnection rate C4; energy transformation includes four indicators: annual new energy access matching degree C5, distributed power supply carrying capacity C6, electric vehicle charging and swapping facility carrying capacity C7, and distribution automation terminal coverage rate C8; quality service includes five items: voltage qualification rate C9, power supply reliability rate C10, rural grid household average distribution and transformation capacity C11, low voltage area proportion C12, and frequent outage area proportion C13; efficiency and benefit include capacity-load ratio C14, equipment life cycle operation rationality rate C15, and load rate C16.

[0158] Table 5 Comprehensive benefit evaluation index system for power grid equipment investment

[0159]

[0160]

[0161] According to the AHP structural model, a questionnaire was generated for review and completion by experts in the fields of power grid planning, operation, and evaluation. The average values ​​of the experts were used to construct a judgment matrix, and the eigenvector, i.e., the subjective weight of the comprehensive benefit index of power grid equipment investment, was calculated. First, the weight of the criterion layer under the target layer was calculated, and then the weight of the scheme layer under the criterion layer was calculated, and the consistency of each judgment matrix was tested. If it is less than 0.1, it means that the consistency is relatively satisfactory. Otherwise, it is necessary to re-assign the calculation according to the actual situation of power grid equipment investment until the judgment matrix can pass the consistency test.

[0162] Table 6 Judgment matrix of criterion layer under target layer

[0163] Target layer A B1 B2 B3 B4 Weight Safe supply B1 1 3 3 2 0.4463 Energy Transition B2 1 / 3 1 1 1 / 2 0.1405 Quality Service B3 1 / 3 1 1 1 / 2 0.1405 Efficiency and Benefits B4 1 / 2 2 2 1 0.2727

[0164] After the consistency test, the consistency ratio of the judgment matrix of the criterion layer under the target layer is 0.0045<0.1. Then, the eigenvector is calculated according to the judgment matrix of the solution layer (C1-C4) under the criterion layer (safety guarantee B1), as shown in Table 7.

[0165] Table 7 Judgment matrix of solution layer under criterion layer B1

[0166] Safe supply B1 C1 C2 C3 C4 Weight "N-1" pass rate C1 1 2 4 3 0.4461 Source-grid-load coordinated development index C2 1 / 2 1 3 2 0.2900 Equipment health level C3 1 / 4 1 / 3 1 1 / 2 0.0929 10 kV effective interconnection rate C4 1 / 3 1 / 2 2 1 0.1710

[0167] The consistency result of the judgment matrix is ​​0.015<0.1, which determines that the weights of C1-C4 relative to the target layer are 0.4461, 0.2900, 0.0929, and 0.1710. The characteristic vectors of the judgment matrix calculation of the solution layer (C5-C8) under the criterion layer (energy transformation B2) are shown in Table 8.

[0168] Table 8 Judgment matrix of the scheme layer under the criterion layer (energy transformation B2)

[0169] Energy Transition B2 C5 C6 C7 C8 Weight Annual new energy access matching degree C5 1 2 4 3 0.4461 Distributed power generation capacity C6 1 / 2 1 3 2 0.2900 Carrying capacity of electric vehicle charging and swapping facilities C7 1 / 4 1 / 3 1 1 / 2 0.0929 Effective coverage of distribution automation C8 1 / 3 1 / 2 2 1 0.1710

[0170] The consistency result of the judgment matrix is ​​0.015<0.1, which determines that the weights of C5-C8 relative to the target layer are 0.4461, 0.2900, 0.0929, and 0.1710. The characteristic vectors of the judgment matrix calculation of the solution layer (C9-C13) under the criterion layer (quality service B3) are shown in Table 9.

[0171] Table 9 Judgment matrix of the scheme layer under the criterion layer (quality service B3)

[0172] Quality Service B3 C9 C10 C11 C12 C13 Weight Voltage pass rate C9 1 1 / 2 3 2 4 0.2713 Power supply reliability C10 2 1 4 3 5 0.3876 Average capacity of rural power grid transformers per household C11 1 / 3 1 / 4 1 1 / 2 2 0.1055 Low voltage area ratio C12 1 / 2 1 / 3 2 1 3 0.1766 Percentage of areas with frequent outages C13 1 / 4 1 / 5 1 / 2 1 / 3 1 0.0590

[0173] The consistency result of the judgment matrix is ​​0.0202<0.1, which determines that the weights of C9-C13 relative to the target layer are 0.2713, 0.3876, 0.1055, 0.1766, and 0.0590. The characteristic vectors of the judgment matrix calculation of the solution layer (C14-C16) under the criterion layer (efficiency benefit B4) are shown in Table 10.

[0174] Table 10 Judgment matrix of the scheme layer under the criterion layer (efficiency benefit B4)

[0175] Efficiency and Benefit B4 C14 C15 C16 Weight Load ratio C14 1 2 3 0.5294 Equipment life cycle operation rationality rate C15 1 / 2 1 2 0.3088 Load factor C16 1 / 3 1 / 2 1 0.1618

[0176] The consistency result of the judgment matrix is ​​0.0341<0.1, which determines that the weights of C14-C16 relative to the target layer are 0.5294, 0.3088, and 0.1618.

[0177] After calculating the weights of other scheme layers relative to the criterion layer in turn, the importance of each factor in the scheme layer relative to the target layer is finally determined by merging them to obtain the subjective weight of the comprehensive benefit evaluation index of power grid equipment investment. The calculation results are shown in Table 11.

[0178] Table 11 Subjective weights of comprehensive benefit evaluation indicators for power grid equipment investment

[0179]

[0180] Then, the entropy weight method is used to determine the objective weight of each value assessment indicator. First, the extremely large, extremely small, and moderate indicators are standardized, and then the information entropy of each indicator is calculated. Finally, the objective weight of the indicator is determined, as shown in Table 12.

[0181] Table 12 Evaluation index information entropy and objective weight

[0182]

[0183]

[0184] Step 4: Calculate the comprehensive weight of the comprehensive benefit evaluation of power grid equipment investment based on the least squares method and add it to the comprehensive benefit evaluation index weight library; according to the least squares principle, determine the optimal allocation coefficient of the subjective weight and the objective weight by jointly establishing the least squares optimization function, thereby determining the comprehensive weight of the evaluation index, as shown in Table 13.

[0185] Table 13 Weights of comprehensive benefit evaluation indicators for power grid equipment investment

[0186]

[0187] Step 5: Based on the evaluation index weight library in step 4, the grey correlation method is used to calculate the correlation of the comprehensive benefits of power grid equipment investment and generate a correlation ranking library.

[0188] First, determine the reference series. Perform consistency processing on the basic index data to obtain the consistent index data. Because the consistent indexes are all positive indexes, the maximum value of each index is taken as the evaluation standard to obtain the reference series, as shown in Table 14.

[0189] Table 14 Reference series of comprehensive benefit evaluation indicators for power grid equipment investment

[0190]

[0191] Secondly, the grey correlation coefficient is calculated. The resolution coefficient is taken as 0.5, and the data matrix is ​​calculated to obtain the grey correlation coefficient of the power grid equipment investment benefit evaluation index, as shown in Table 15.

[0192] Table 15 Grey correlation coefficients of comprehensive benefit evaluation indicators of power grid equipment investment

[0193]

[0194]

[0195] Finally, the correlation of the comprehensive evaluation is calculated. The correlation coefficient of the indicators is calculated according to the gray correlation calculation formula and the comprehensive weight obtained in step 4, and the correlation and comprehensive ranking of each power grid equipment are obtained, as shown in Table 16.

[0196] Table 16 Correlation and ranking of comprehensive benefits of power grid equipment investment

[0197] Evaluation object Relevance Ranking A-Grid 0.4104 3 B Grid 0.4446 2 C-Grid 0.6157 1 D-Grid 0.1745 4 E-Grid 0.1517 5

[0198] According to the results, the C grid has the highest correlation, with a value of 0.6157, ranking 1st among the five grids; the B grid is second, the A grid is third, the D grid is fourth, and the E grid is last.

[0199] Step 6: Construct a comprehensive benefit indicator trace tracking diagnosis module based on the correlation ranking library in step 5 to generate a comprehensive benefit evaluation indicator deviation contribution library. Take the optimal values ​​of the basic indicators of the five grids as the ideal object, calculate the deviation of the basic indicators of each grid from the ideal object, identify the problems of grid power investment and the key indicators affecting the comprehensive benefits, and calculate the basic indicator deviation contribution rate of the five grids. Set the indicator deviation contribution rate exceeding 10% as the weak link of the grid.

[0200] Table 17 Deviation contribution rate of grid investment comprehensive benefit evaluation index

[0201]

[0202]

[0203] It can be seen from Table 17 that the indicators of the maximum attribute deviation contribution rate of grid A exceeding 10% are: "N-1" pass rate, 10 kV effective interconnection rate, proportion of frequently shut down stations and load rate; the indicators of the maximum attribute deviation contribution rate of grid B exceeding 10% are: distributed power supply carrying capacity, proportion of frequently shut down stations; the indicators of the maximum attribute deviation contribution rate of grid C exceeding 10% are: "N-1" pass rate, annual new energy access matching degree, distributed power supply carrying capacity, and effective coverage rate of distribution automation; the indicators of the maximum attribute deviation contribution rate of grid D exceeding 10% are: equipment health level, annual new energy access matching degree, and load rate; the indicators of the maximum attribute deviation contribution rate of grid E exceeding 10% are: "N-1" pass rate, proportion of low voltage stations, and load rate.

[0204] Step 7: According to the correlation ranking library in step 5 and the comprehensive benefit evaluation index deviation contribution library in step 6, complete the comprehensive benefit evaluation of power grid equipment investment and output the comprehensive benefit evaluation results. As shown in Tables 16 and 17, the investment comprehensive benefit of C grid is the best, and its weak links are "N-1" pass rate, annual new energy access matching degree, distributed power supply carrying capacity, and effective coverage rate of distribution automation. Therefore, the investment recommendations for it are: optimize the grid structure and improve the level of energy transformation.

[0205] A comprehensive benefit evaluation system for power grid equipment investment considering functions and costs, including:

[0206] A module for forming comprehensive benefit evaluation indicators of power grid equipment investment, used to construct a comprehensive benefit evaluation indicator library;

[0207] The module for forming comprehensive benefit evaluation index data of power grid equipment investment is used to construct a comprehensive benefit evaluation index database and generate a comprehensive benefit evaluation index data list;

[0208] The module for forming the subjective and objective weights of comprehensive benefit evaluation indicators of power grid equipment investment is used to construct a weight library of comprehensive benefit evaluation indicators;

[0209] A comprehensive weight forming module for comprehensive benefit evaluation index of power grid equipment investment is used to form the comprehensive weight of the comprehensive benefit evaluation index and add the comprehensive weight to the comprehensive benefit evaluation index weight library;

[0210] A module for forming the relevance of power grid equipment investment projects, used to construct a relevance ranking library;

[0211] The power grid equipment investment comprehensive benefit indicator trace tracking and diagnosis formation module is used to form the power grid equipment investment comprehensive benefit indicator trace tracking and diagnosis results, and build a power grid equipment investment comprehensive benefit evaluation indicator deviation contribution library.

[0212] The comprehensive benefit evaluation module of power grid equipment investment completes the comprehensive benefit evaluation of power grid equipment investment based on the correlation ranking library and the comprehensive benefit evaluation index deviation contribution library.

[0213] The system executes the comprehensive benefit evaluation method for power grid equipment investment considering function and cost as described in any one of claims 1-7.

[0214] The present invention constructs a comprehensive benefit evaluation system for power grid equipment investment from the perspective of power grid function and cost, identifies which equipment investment is most critical to improving power grid performance, thereby improving the efficiency of capital use, and better helps power grid companies understand and adapt to the volatility of new energy, optimize supply and demand balance strategies, and at the same time evaluate the risks associated with new energy penetration, identify potential risk points, such as the instability of new energy grid connection, changes in market demand, etc., so that companies can prepare in advance and formulate response strategies. Construct a comprehensive benefit indicator trace tracking diagnostic model, deeply explore the value of equipment investment benefit data, determine the indicators that need to be studied in depth, and effectively prevent and control risks.

[0215] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method and system for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs, characterized by: Step 1: Establish a comprehensive benefit evaluation index system library for power grid equipment investment at the provincial, municipal and county levels; the provincial power grid equipment investment comprehensive benefit evaluation index system library includes three levels of indicators: The first-level indicator is the target layer; The secondary indicators are the criteria layer, including safe supply, energy transformation, quality service, efficiency and benefits; The third-level indicators, i.e. the ultimate indicators of this evaluation index system, are the solution layer, including the "N-1" pass rate, source-grid-load coordinated development index, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicators; the annual new energy access matching degree, distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicators; the voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low-voltage substation ratio, and frequent outage substation ratio, which are subordinate to the quality service indicators; the capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicators, which are subordinate to the efficiency and benefit indicators; Among them, "N-1" pass rate = the number of 10 kV lines with interconnection and load transfer capability / the total number of 10 kV lines; Source-grid-load coordinated development index = source-load matching index + power supply capacity index = conventional power supply installed capacity growth rate / load growth rate + 750-110 or 66 kV transformer capacity growth rate / various power supply installed capacity growth rate + 750-110 or 66 kV transformer capacity growth rate / load growth rate; Equipment health level = proportion of old and high-damage equipment; where: proportion of old and high-damage equipment = total number of old and high-damage equipment / total number of equipment; 10 kV interconnection rate = number of 10 kV lines with interconnection / total number of 10 kV lines; Annual new energy access matching degree = new energy utilization rate growth level + new energy access progress index = new energy utilization rate accumulated from this year to this period / new energy utilization rate in the same period last year + current accumulated new energy access scale / annual new energy access scale; Distributed generation capacity = 1-the number of public distribution transformers that are heavily overloaded due to the access to distributed generation / the total number of public distribution transformers; The carrying capacity of electric vehicle charging and swapping facilities = 1-the number of heavy overloads and low voltages in public distribution transformers connected to charging facilities / the total number of public distribution transformers connected to charging facilities; Effective coverage rate of power distribution automation = number of DTU and FTU terminals with two or three remote controls / number of switches on transport poles, ring network cabinets, switch stations, and ring network distribution rooms; Voltage qualification rate = (accumulated operation time of the power grid voltage deviation within the limit range) / total operation statistical time; Power supply reliability rate = 1-average power outage time of power grid users / total operation statistical time; Average distribution and transformation capacity per household in rural power grid = 10 or 20 kV distribution and transformation capacity of rural power grid / number of low-voltage users; The proportion of low-voltage areas = the total number of low-voltage areas / the total number of areas; The proportion of areas with frequent outages = the total number of areas with frequent outages / the total number of areas; Capacity-load ratio = total capacity of substation equipment / maximum power supply load of the power grid at this voltage level; Equipment life cycle operation rationality rate = 0.8 × number of old and retired equipment in the year / number of retired equipment in the year + 0.2 × average retirement age of non-old equipment / 30; Load factor = (actual output power / rated capacity) × 100%.

2. The method for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs according to claim 1 is characterized by: The city and county power grids are optimized based on the provincial power grid indicators in light of their own production and operation realities; the city and county power grid evaluation indicator system library includes three levels of indicators: The first-level indicator is the target layer; The secondary indicators are the criteria layer, including safe supply, energy transformation, quality service, efficiency and benefits; The third-level indicators, i.e. the ultimate indicators of this evaluation index system, are the solution layer, including the "N-1" pass rate, equipment health level, and 10 kV effective interconnection rate, which are subordinate to the safety supply indicators; the distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to the energy transformation indicators; the voltage qualification rate, power supply reliability rate, rural grid household distribution and transformation capacity, low-voltage substation ratio, and frequent outage substation ratio, which are subordinate to the quality service indicators; the capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicators, which are subordinate to the efficiency and benefit indicators; Step 2: Input the data of each indicator according to the evaluation indicator system library in step 1 to generate a comprehensive benefit evaluation indicator database; Step 3: Based on the comprehensive benefit evaluation index database in step 2, the subjective and objective weights of the comprehensive benefit evaluation of power grid equipment investment are calculated using the analytic hierarchy process and the entropy weight method to generate a comprehensive benefit evaluation index weight library; Step 4: Calculate the comprehensive weight of the comprehensive benefit evaluation of power grid equipment investment based on the least squares method and add it to the comprehensive benefit evaluation index weight library; Step 5: Based on the evaluation index weight library in step 4, the grey correlation method is used to calculate the correlation of the comprehensive benefits of power grid equipment investment, and a correlation ranking library is generated; Step 6: Construct a comprehensive benefit indicator trace tracking diagnosis module based on the correlation ranking library in step 5 to generate a comprehensive benefit evaluation indicator deviation contribution library; Step 7: Based on the correlation ranking library in step 5 and the comprehensive benefit evaluation index deviation contribution library in step 6, complete the comprehensive benefit evaluation of power grid equipment investment and output the comprehensive benefit evaluation results.

3. The method for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs according to claim 1 is characterized by: The database of comprehensive benefit evaluation indicators of power grid equipment investment in step 2 includes data such as: "N-1" pass rate, equipment health level, and 10 kV effective interconnection rate, which are subordinate to safety and supply indicators; annual new energy access matching degree, distributed power supply carrying capacity, electric vehicle charging and swapping facility carrying capacity, and distribution automation effective coverage rate, which are subordinate to energy transformation indicators; voltage qualification rate, power supply reliability rate, per capita distribution and transformation capacity of rural power grid households, proportion of low-voltage substations, and proportion of frequently shut-down substations, which are subordinate to quality service indicators; capacity-load ratio, equipment life cycle operation rationality rate, and load rate indicators, which are subordinate to efficiency and benefit indicators.

4. The method for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs according to claim 1 is characterized by: The step 3 further includes the following contents: c1. Construct a comprehensive benefit evaluation index system for power grid equipment investment from multiple dimensions including safety and supply, energy transformation, quality service, efficiency and benefits; c2. The subjective weights of comprehensive benefit evaluation indicators of power grid equipment investment are calculated using the analytic hierarchy process. The specific calculation steps of the analytic hierarchy process are as follows: c21. Construct a judgment matrix: Starting from the second-level indicator of the evaluation index system constructed in step c1, for n indicators of the same level belonging to the same upper-level indicator, use the pairwise comparison method to construct a judgment matrix A. ij , judgment matrix A ij One or more, until the ultimate indicator is reached: Among them, i and j are the i-th and j-th indicators among n indicators of the same level, i≤n, j≤n; a ij is the importance score of the comparison between the ith indicator and the jth indicator; a nn is the score value of the nth indicator; The importance of each indicator in the pairwise comparison method is to collect the scores of experts in different fields on the importance of the indicators, and use the average of each score as the final score result; c22. Calculate the weight vector and perform consistency test to generate the subjective weight of comprehensive benefit evaluation index: For each judgment matrix, calculate the maximum characteristic root and its corresponding characteristic vector, and use consistency index, random consistency index and consistency ratio to perform consistency test; if the test passes, the characteristic vector is the weight vector; if it fails, consider reconstructing the judgment matrix; the approximate value of the characteristic vector is usually obtained by summation method or root method; the weight vector that passes the test forms an indicator subjective weight library; c23. Use the summation method to calculate the eigenvector and eigenvalue; c3. The entropy weight method is used to calculate the objective weights of the comprehensive benefit evaluation indicators of power grid equipment investment. The specific calculation steps of the entropy weight method are as follows: c31. Construct matrix P based on the original data of the indicator mn : Among them, m represents the number of regions; n represents the number of indicators; p mn represents the value of the nth indicator in the mth region; p ij represents the jth index of the i-th region; c32, standardize the original data to obtain the standardized matrix P m ' n , where the standardization process of extremely large indicators such as unit asset sales revenue and extremely small indicators such as annual operation and maintenance costs is as follows: in, It represents the minimum value of the nth indicator in the mth area; It represents the maximum value of the nth indicator in the mth area; c33. Calculate the entropy value e of the jth indicator j First, to find the proportion y of each indicator ij , calculate the evaluation decision matrix: Wherein, i=1,2,...,m represents the evaluation area; j=1,2,...,n represents the evaluation index; c34. Calculate the coefficient of difference g of the jth indicator j : g j =1-e j c35. Calculate the weight of the jth indicator Generate objective weights of comprehensive benefit evaluation indicators:

5. The method for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs according to claim 1 is characterized by: The step 4 further includes the following contents: applying the least squares optimization idea to determine the proportional coefficient of the weight and generate the comprehensive weight of the indicator: Where: α represents the weight coefficient; w j represents the comprehensive weight of the jth indicator; It represents the subjective weight of the nth indicator; represents the objective weight of the jth indicator; min F represents the minimum target value of the sum of variances of the comprehensive weight solution.

6. The method for comprehensive benefit evaluation of power grid equipment investment considering function and cost according to claim 1 is characterized in that: said step 5 further comprises the following contents: The basic steps of the grey correlation method are as follows: e1. Construct a comparison sequence: All index values ​​of each evaluation object - power grid equipment investment project are the comparison series of the project, as shown in the following formula: C i ′(j)={C i (1),C i (2),...,C i (m)} Where i = 1, 2, ···, n represents the number of power grid equipment investment projects under evaluation; j = 1, 2, ···, m represents the number of indicators of power grid benefit level; C i (m) represents the index value of the mth index; e2. Determine the reference sequence: Take the maximum value of each microgrid project indicator as the reference sequence C0(j): C0(j)={C0(1),C0(2),...,C0(m)} in, j=1,2,···,m represents the number of indicators of power grid efficiency level; C0(m) represents the maximum value of the mth indicator; e3. Dimensionless processing of comprehensive benefit level index value of power grid equipment investment: In order to avoid the interference of different dimensional orders of magnitude on the evaluation, the indicator data is dimensionless. Among them, C i (j) is the indicator data after dimensionless processing; e4. Determine the weights of comprehensive benefit evaluation factors for power grid equipment investment: Based on the comparison sequence C i With the reference sequence C0, obtain the index C i The correlation coefficient ξi(j) on (j), Among them, ρ is the resolution coefficient, which can numerically amplify the difference between the correlation coefficients, and ρ=0.5; e5. Obtain the relevance of each power grid equipment investment project and generate a relevance ranking library: The correlation coefficients of various indicators of the evaluated power grid equipment investment projects are weighted averaged to obtain the correlation degree γ i , whose expression is: Among them, j = 1, 2, ···, n represents the number of indicators of power grid efficiency level; The calculated association degree forms a association degree sorting library; where γ i The larger the value is, the better the benefit level of the corresponding power grid equipment investment project will be.

7. The method for evaluating the comprehensive benefits of power grid equipment investment considering functions and costs according to claim 1 is characterized by: The step 6 further includes the following contents: f1. Comparison of evaluation results: Sort each evaluation object by the grey correlation method to obtain the attribute evaluation value matrix e and comprehensive evaluation value δ of each evaluation object in a certain year j (j=1,2,...,n), as shown below: Comparison of comprehensive evaluation value δ j , find the object with the best evaluation value f2. Calculate the basic deviation rate of the secondary index, compare the attribute evaluation value of the region with the best evaluation value with the attribute evaluation value of other regions, and obtain the basic deviation rate ε between each region and the best region ij (j≠k,j=1,2,...,n) and the deviation rate matrix ε; Among them, e ik Indicates the attribute evaluation value of the area with the best evaluation value; e ij Represents the attribute evaluation value of other regions; ε mn Indicates m Indicator No. n Deviation rate of regions; f3, calculate the deviation contribution and deviation contribution rate; according to the weight μ of each attribute in the comprehensive evaluation method i (i=1,2,...,m) is determined, and combined with the basic deviation rate, the deviation contribution η of each attribute of each evaluation object is calculated ij (j≠k,j=1,2,...,n); or ij =e ij m i Calculate the attribute deviation rate λ of each evaluation object separately i (i=1,2,...,m) f4. Determine the attributes that need in-depth analysis: According to the obtained attribute deviation rate of each evaluation object, determine the attribute with the largest deviation rate of each evaluation object. and conduct in-depth analysis; Thus, the attributes of each evaluation object with problems can be determined, and then the deviation rate of specific indicators is analyzed based on the attributes; f5. Calculate the basic indicator deviation rate; determine the specific deviation attribute through the previous step, perform deviation analysis on the indicators of the evaluation object and the optimal evaluation object under this attribute, and calculate the basic indicator deviation rate v lj : Where l = 1, 2, ..., L represents the attribute that needs to be analyzed in depth; y lk Indicates the index of the best evaluation object under this attribute; lj Indicates the index of the evaluation object under this attribute; f6. Calculate the basic indicator deviation contribution and contribution rate; Combine the comprehensive weight of each indicator under this attribute γ l (l=1,2,...,L) and the basic indicator deviation rate obtained in the previous step, calculate the basic indicator deviation contribution rate θ lj (j≠k); f7. Sort the indicator deviation contribution to generate the deviation contribution library of power grid equipment investment comprehensive benefit evaluation indicators; Sort the indicators according to the deviation contribution rate of each basic indicator to generate the deviation contribution library of power grid equipment investment comprehensive benefit evaluation indicators; The indicator with the largest deviation rate is the main factor causing the difference between the evaluation object and the optimal evaluation object, and so on, analyze the reasons for the difference between the evaluation object and the optimal evaluation object, and give relevant explanations.

8. A comprehensive benefit evaluation system for power grid equipment investment considering function and cost, characterized by: The system includes the following modules: A module for forming comprehensive benefit evaluation indicators of power grid equipment investment, used to construct a comprehensive benefit evaluation indicator library; The module for forming comprehensive benefit evaluation index data of power grid equipment investment is used to construct a comprehensive benefit evaluation index database and generate a comprehensive benefit evaluation index data list; The module for forming the subjective and objective weights of comprehensive benefit evaluation indicators of power grid equipment investment is used to construct a weight library of comprehensive benefit evaluation indicators; A comprehensive weight forming module for comprehensive benefit evaluation index of power grid equipment investment is used to form the comprehensive weight of the comprehensive benefit evaluation index and add the comprehensive weight to the comprehensive benefit evaluation index weight library; A module for forming the relevance of power grid equipment investment projects, used to construct a relevance ranking library; A module for forming a trace tracking and diagnosis of comprehensive benefit indicators of power grid equipment investment is used to form a trace tracking and diagnosis result of comprehensive benefit indicators of power grid equipment investment and to build a deviation contribution library of comprehensive benefit evaluation indicators of power grid equipment investment; The comprehensive benefit evaluation module of power grid equipment investment completes the comprehensive benefit evaluation of power grid equipment investment based on the correlation ranking library and the comprehensive benefit evaluation index deviation contribution library; The system executes the comprehensive benefit evaluation method for power grid equipment investment considering function and cost as described in any one of claims 1-7.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It comprises a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions execute the method described in any one of claims 1 to 7 when executed.

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