Power distribution network bearing capacity evaluation system construction method and device, computer equipment, storage medium and computer program product

Abstract

The invention relates to a power distribution network bearing capacity evaluation system construction method and device, computer equipment, a storage medium and a computer program product. Relates to the technical field of data processing. The method comprises the following steps: defining a plurality of evaluation objects corresponding to a power distribution network evaluation system, and respectively obtaining a plurality of first-level evaluation indexes corresponding to each evaluation object; obtaining a plurality of second-level evaluation indexes corresponding to each first-level evaluation index; establishing an evaluation object set, an evaluation grade set and an index weight set, and determining index weights of a plurality of evaluation objects, a plurality of first-level evaluation indexes and a plurality of second-level evaluation indexes based on the evaluation object set, the evaluation grade set and the index weight set, and constructing a power distribution network bearing capacity evaluation system comprising a plurality of evaluation objects, a plurality of first-level evaluation indexes and a plurality of second-level evaluation indexes based on the index weights. By adopting the method, the bearing capacity evaluation efficiency of the to-be-evaluated power distribution network can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, computer equipment, storage medium and computer program product for constructing a distribution network carrying capacity assessment system. Background Art

[0002] As electric vehicles become more and more popular, the load caused by charging them may cause complex problems in the operation of the power grid, such as local equipment overload, branch current overload and power quality degradation, which will pose a threat to the safety of the power grid. Therefore, studying the architecture and regulation methods of new distribution systems has become an urgent problem to be solved.

[0003] However, the current research on electric vehicle charging demand and its impact on distribution networks is usually conducted from a single perspective, such as mathematical analysis, simulation and algorithm, sensitivity-based evaluation, comprehensive evaluation, and static safety constraint methods. However, the above-mentioned existing technologies cannot unify the scope and complexity of the distribution network to be evaluated, and thus cannot comprehensively and objectively evaluate the carrying capacity of the distribution network to be evaluated, affecting the evaluation efficiency. Therefore, the current single evaluation system has the problem of low efficiency in evaluating the carrying capacity of the distribution network to be evaluated. Summary of the invention

[0004] Based on this, it is necessary to provide a distribution network carrying capacity assessment system construction method, device, computer equipment, computer-readable storage medium and computer program product to address the technical problem that the current single assessment system has low efficiency in assessing the carrying capacity of the distribution network to be assessed.

[0005] In a first aspect, the present application provides a method for constructing a distribution network carrying capacity assessment system, comprising:

[0006] Define multiple evaluation objects corresponding to the distribution network evaluation system, and obtain multiple primary evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network, and the charging demand users;

[0007] Respectively obtain a plurality of second-level evaluation indicators corresponding to each of the first-level evaluation indicators;

[0008] Establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0009] In one embodiment, the obtaining of multiple first-level evaluation indicators corresponding to each of the evaluation objects includes: obtaining safety, reliability, efficiency and quality as multiple first-level evaluation indicators of the distribution network; obtaining operation efficiency and coordination balance as multiple first-level evaluation indicators of the charging network; obtaining service reliability and service efficiency as multiple first-level evaluation indicators of the charging demand users.

[0010] In one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the distribution network include: line overload rate, distribution transformer overload rate, load fluctuation rate, system load rate, voltage offset rate and system average network loss; after respectively obtaining the multiple secondary evaluation indicators corresponding to each of the first-level evaluation indicators, it includes: obtaining the first numerical change rate of the multiple secondary evaluation indicators of the distribution network; determining the first carrying capacity of the distribution network according to the first numerical change rate.

[0011] In one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the charging network include: average charging time, utilization balance and charging station capacity-load ratio; after respectively obtaining the multiple secondary evaluation indicators corresponding to each of the first-level evaluation indicators, it also includes: obtaining the second numerical change rate of the multiple second-level evaluation indicators of the charging network; determining the second carrying capacity of the charging network according to the second numerical change rate.

[0012] In one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the charging demand user include: charging satisfaction rate and charging waiting time; after respectively obtaining the multiple secondary evaluation indicators corresponding to each of the first-level evaluation indicators, it also includes: obtaining the third numerical change rate of the multiple secondary evaluation indicators of the charging demand user; determining the third carrying capacity of the charging demand user for the distribution network according to the third numerical change rate.

[0013] In one of the embodiments, before establishing an evaluation object set, an evaluation level set and an indicator weight set, and determining the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, it includes: converting the actual values ​​of each of the second-level evaluation indicators into corresponding relative values; obtaining the relative indicator weight of each of the evaluation objects corresponding to each of the second-level evaluation indicators based on the multiple relative values; obtaining the information entropy value of each of the evaluation objects according to the relative indicator weight, and determining the information entropy redundancy and indicator weight of each of the evaluation objects based on the information entropy value, so as to obtain the comprehensive indicator score of each of the evaluation objects.

[0014] In a second aspect, the present application also provides a distribution network carrying capacity assessment system construction device, including:

[0015] A first-level evaluation index acquisition module is used to define multiple evaluation objects corresponding to the distribution network evaluation system, and respectively obtain multiple first-level evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network and the charging demand users;

[0016] A secondary evaluation indicator acquisition module is used to respectively acquire a plurality of secondary evaluation indicators corresponding to each of the primary evaluation indicators;

[0017] An evaluation system establishment module is used to establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, evaluation level set and indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0018] In a third aspect, the present application further provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0019] Define multiple evaluation objects corresponding to the distribution network evaluation system, and obtain multiple primary evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network, and the charging demand users;

[0020] Respectively obtain a plurality of second-level evaluation indicators corresponding to each of the first-level evaluation indicators;

[0021] Establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0022] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0023] Define multiple evaluation objects corresponding to the distribution network evaluation system, and obtain multiple primary evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network, and the charging demand users;

[0024] Respectively obtain a plurality of second-level evaluation indicators corresponding to each of the first-level evaluation indicators;

[0025] Establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0026] In a fifth aspect, the present application further provides a computer program product, the computer program product comprising a computer program, which implements the following steps when executed by a processor:

[0027] Define multiple evaluation objects corresponding to the distribution network evaluation system, and obtain multiple primary evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network, and the charging demand users;

[0028] Respectively obtain a plurality of second-level evaluation indicators corresponding to each of the first-level evaluation indicators;

[0029] Establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0030] The above-mentioned distribution network carrying capacity assessment system construction method, device, computer equipment, storage medium and computer program product have the following beneficial effects in the process of constructing the distribution network carrying capacity assessment system: first, define multiple assessment objects corresponding to the distribution network assessment system, and obtain multiple first-level assessment indicators corresponding to each assessment object respectively; wherein the assessment objects include distribution networks, charging networks and charging demand users; then obtain multiple second-level assessment indicators corresponding to each first-level assessment indicator respectively; finally, establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple assessment objects, multiple first-level assessment indicators and multiple second-level assessment indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity assessment system including multiple assessment objects, multiple first-level assessment indicators and multiple second-level assessment indicators based on the indicator weights. The above process defines multiple evaluation objects and obtains the first-level evaluation indicators of the evaluation objects, and obtains more detailed second-level evaluation indicators based on the first-level evaluation indicators, and then digitally calculates the weights of different evaluation indicators of different evaluation objects, and finally establishes a complete distribution network carrying capacity evaluation system. The use of this evaluation system can comprehensively measure the carrying capacity of the distribution network, and can also rely on this evaluation system for effective processing in large-scale data. Therefore, the distribution network carrying capacity evaluation system established by the above process improves the efficiency of the carrying capacity evaluation of the distribution network to be evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 It is a flowchart of a method for constructing a distribution network carrying capacity evaluation system in one embodiment;

[0033] Figure 2 A schematic diagram of a flow chart of steps for constructing a distribution network carrying capacity assessment system in one embodiment;

[0034] Figure 3 is a schematic diagram of a distribution network carrying capacity assessment system in one embodiment;

[0035] Figure 4 A schematic diagram of a distribution network carrying capacity assessment process in an embodiment;

[0036] Figure 5 A schematic diagram of a network topology of a certain urban area transportation road network in an embodiment;

[0037] Figure 6 A schematic diagram of a traffic-electricity coupling model in an embodiment;

[0038] Figure 7 A schematic diagram of normalized data of comprehensive evaluation indicators of a distribution network in one embodiment;

[0039] Figure 8 It is a structural block diagram of a device for constructing a distribution network carrying capacity evaluation system in one embodiment;

[0040] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0042] In one embodiment, Figure 1 As shown, a method for constructing a distribution network carrying capacity assessment system is provided. This embodiment uses the method applied to a terminal as an example. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps S102 to S106. Among them:

[0043] Step S102, defining multiple evaluation objects corresponding to the distribution network evaluation system, and obtaining multiple first-level evaluation indicators corresponding to each evaluation object; wherein the evaluation objects include the distribution network, the charging network, and the users with charging needs.

[0044] Among them, the distribution network evaluation system represents a comprehensive framework that can be used to evaluate the performance, efficiency and carrying capacity of the distribution network; the first-level evaluation indicators are determined according to the demand characteristics of different evaluation objects; the distribution network is the power grid system responsible for distributing electricity from the high-voltage transmission system to end users; the charging network is a network that provides power for charging equipment such as electric vehicles, usually composed of charging stations and related facilities; charging demand users are user groups that need charging services, which can include electric car owners and large charging sites.

[0045] Optionally, the first-level evaluation indicator can be a direct measure of the overall performance of the evaluation object, such as reliability, availability, efficiency, etc.; the evaluation object can be added or deleted according to needs.

[0046] Step S104, respectively obtaining a plurality of second-level evaluation indicators corresponding to each first-level evaluation indicator.

[0047] Among them, the secondary evaluation indicators are indicators used for quantitative evaluation, which are the refinement and concretization of the primary evaluation indicators, such as power supply quality, scarcity of charging piles, etc., and each primary evaluation indicator can correspond to multiple different or the same secondary evaluation indicators.

[0048] Step S106, establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0049] Among them, the evaluation object set is the set of all evaluation objects, which includes distribution networks, charging networks, and users with charging needs; the evaluation level set is a graded representation of the evaluation results, which is used to clearly indicate the performance or impact of different indicators, such as excellent, good or ordinary; the indicator weight set represents the impact of each evaluation indicator, and the accuracy of the evaluation results can be improved by changing the indicator weight; the distribution network carrying capacity assessment system is a comprehensive assessment system used to judge the carrying capacity of the distribution network under current and future load conditions.

[0050] Specifically, the distribution network carrying capacity evaluation system can be a system including four levels of evaluation indicators, which can include the target layer, object layer, criterion layer and indicator layer. The distribution network carrying capacity evaluation system finally established is as follows: Figure 2 shown.

[0051] In the above-mentioned distribution network carrying capacity evaluation system construction method, firstly, multiple evaluation objects corresponding to the distribution network evaluation system are defined, and multiple first-level evaluation indicators corresponding to each evaluation object are obtained respectively; wherein the evaluation objects include distribution network, charging network and charging demand users; then multiple second-level evaluation indicators corresponding to each first-level evaluation indicator are obtained respectively; finally, an evaluation object set, an evaluation level set and an indicator weight set are established, and the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators are determined based on the evaluation object set, the evaluation level set and the indicator weight set, and a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators is constructed based on the indicator weights. The above process defines multiple evaluation objects and obtains the first-level evaluation indicators of the evaluation objects, and obtains more detailed second-level evaluation indicators according to the first-level evaluation indicators, and then digitally calculates the weights of different evaluation indicators of different evaluation objects, and finally establishes a complete distribution network carrying capacity evaluation system, which can comprehensively measure the carrying capacity of the distribution network, and can also rely on the evaluation system for effective processing in the case of large-scale data. Therefore, the distribution network carrying capacity evaluation system established by the above process improves the efficiency of the carrying capacity evaluation of the distribution network to be evaluated.

[0052] In an exemplary embodiment, obtaining multiple first-level evaluation indicators corresponding to each evaluation object includes: obtaining safety, reliability, efficiency, and quality as multiple first-level evaluation indicators of the distribution network; obtaining operation efficiency and coordination balance as multiple first-level evaluation indicators of the charging network; obtaining service reliability and service efficiency as multiple first-level evaluation indicators of users with charging needs.

[0053] Among them, the three first-level indicators corresponding to the distribution network: safety and reliability can refer to the ability of the distribution network to prevent failures and accidents during operation, which can ensure uninterrupted power supply and the safety of electrical equipment; high efficiency refers to the distribution network's ability to achieve maximum power output with minimal loss during power transmission and management; quality refers to the distribution network's guarantee of power quality, which can include voltage stability, frequency stability and power supply stability.

[0054] Furthermore, among the two first-level indicators corresponding to the charging network: operational efficiency can indicate the efficiency of the charging network in providing charging services, such as the utilization rate of charging piles, charging speed and power conversion rate; coordination balance is used to indicate that the charging load provided by the charging network at different times and in different regions is balanced, which can avoid excessive concentration of power supply or insufficient power supply.

[0055] More importantly, among the two first-level indicators of users with charging needs: service reliability can indicate the stability of the charging service experienced by users with charging needs, that is, users can obtain charging services in a timely manner when they need them; service efficiency refers to the charging response speed and fault handling speed that can meet the requirements of users with charging needs.

[0056] In this embodiment, by obtaining the first-level evaluation indicators corresponding to the distribution network, charging network and charging demand users, the user's electricity consumption experience can be obtained through the first-level evaluation indicators; and the resource utilization rate is improved under low operating cost conditions; it also ensures the scientific nature of management strategies and planning, and realizes the long-term impact of comprehensive analysis.

[0057] In one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the distribution network include: line overload rate, distribution transformer overload rate, load fluctuation rate, system load rate, voltage offset rate and system average network loss; after respectively obtaining the multiple secondary evaluation indicators corresponding to each first-level evaluation indicator, it includes: obtaining the first numerical change rate of the multiple secondary evaluation indicators of the distribution network; determining the first carrying capacity of the distribution network according to the first numerical change rate.

[0058] Among them, the line overload rate can be expressed by A 11 It is used to measure the impact of branch power flow changes on the distribution network. By comparing the line load changes before and after the charging load is connected, it reflects the system's carrying capacity and adaptability when facing the new load. For example, when an electric vehicle is charging, the power load it requires will be connected to the distribution network, which will affect the power flow distribution of the distribution network and may cause the current or power of some branches to increase, thereby changing the overload rate of the above branches. 11 The specific calculation method is: 11 =RB1-RB.

[0059] in: Moreover, RB and RB1 are the average overload probabilities of the power flow in the distribution network branches before and after the electric vehicle is connected; B is the set of distribution network branches; NB is the total number of distribution network branches; Is branch b a heavy-load line at time t? I is the probability of branch power flow overload, and the critical value is 85% to determine whether the branch is overloaded; b,t is the active power flow value of branch b at time t; I b,rated is the rated transmission power of the line.

[0060] By comparing the changes in the overload rate before and after the electric vehicle charging load is connected, the carrying capacity of the distribution network can be effectively evaluated. If the overload rate increases significantly, it means that the operating safety and reliability of some branches may be affected. The capacity of the distribution network can be increased or the charging strategy can be optimized. Correspondingly, if the change is small, it means that the distribution network can meet the carrying capacity requirements of the electric vehicle charging load.

[0061] Furthermore, the distribution transformer overload rate, load fluctuation rate, system load rate, voltage deviation rate and system average network loss can be expressed as A 12 , A 21 , A 22 , A 31 and A 32 The functions are respectively used to evaluate the impact of electric vehicle charging load on the load rate of the power supply transformer after access, to evaluate the change of the peak-to-valley difference of the distribution network load before and after the access of electric vehicle charging load, and thus reflect its ability to cope with charging load fluctuations, to evaluate the change of the load capacity of the distribution system after the access of electric vehicle charging load, to quantify the impact of electric vehicle charging on the node voltage of the distribution network to evaluate its carrying capacity, and to evaluate the impact of large-scale electric vehicle access on the total loss of the distribution network; the corresponding calculation method is: 12 =RT1-RT

[0062] in: Moreover, RT and RT1 are the average overload probabilities of the distribution transformer before and after the electric vehicle is connected; N T is the total number of distribution transformers in the system; is the load rate of the qth distribution transformer at time t; S q,t is the load rate of the qth distribution transformer at time t; S q,rated is the rated load rate of the qth transformer, which is 0.8; A 21 =RP1-RP Where: Moreover, RP and RP1 are the daily average load fluctuation rates of the system before and after the electric vehicle load is connected; and are the peak and valley values ​​of system load in a day; P t load A is the total system load at each time period during the day; 22 =RL1-RL, where: Moreover, RL and RL1 are the system daily average load rates before and after the electric vehicle load is connected; A 31 =RU1-RU, where: In addition, RU and RU1 are the system node voltage deviation rates before and after the electric vehicle load is connected; N m is the total number of nodes; U m,t is the voltage value of the mth node at time t; U m,puis the voltage per unit value of the mth node, the default value is 1.0pu; in, And, P i,t , P j,t are the injected active powers of the balancing node i and other nodes outside the balancing node at time t respectively.

[0063] Accordingly, by 12 , A 21 , A 22 , A 31 and A 32 The acquisition of can reflect the carrying capacity, regulation capacity and adaptability of the distribution network.

[0064] In this embodiment, the first value change rate of multiple secondary evaluation indicators, namely A 11 , A 12 , A 21 , A 22 , A 31 and A 32 and then determining the first carrying capacity of the distribution network according to the first numerical change rate, that is, when the numerical value changes greatly, it indicates that the operation and adaptability of the distribution network decreases, and when the change is small, it indicates that the distribution network can maintain stable operation.

[0065] Furthermore, in one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the charging network include: average charging time, utilization balance and charging station capacity-load ratio; after respectively obtaining the multiple secondary evaluation indicators corresponding to each first-level evaluation indicator, it also includes: obtaining the second numerical change rate of the multiple secondary evaluation indicators of the charging network; and determining the second carrying capacity of the charging network according to the second numerical change rate.

[0066] Among them, the average charging time, utilization balance and charging station capacity ratio can be calculated using B 11 , B 21 and B 22 Indicates that the corresponding calculation method is: Where Nc is the total number of charging facility nodes; is the charging time of the electric vehicle at the nth charging station during period t; and, The charging time of the electric vehicle at the nth charging station; in, Among them, R L , are the capacity-load ratio of each charging facility and its average value; Sn is the total equipment capacity of the nth charging station; The maximum value of the electric vehicle charging load at the nth charging station.

[0067] Specifically, B 11 Measuring the average usage time of electric vehicle charging facilities in a day, reflecting the utilization efficiency of the facilities; B 21 Reflects the distribution of electric vehicle charging time, that is, the utilization rate and distribution balance of facilities in each time period; B 22 It reflects the distribution of electric vehicles in the charging network, that is, the carrying capacity and load coordination of facilities in different regions.

[0068] In this embodiment, by obtaining the second value change rate of multiple secondary evaluation indicators of the charging network, that is, B 11 , B 21 and B 22 The rate of change of the second numerical value is used to determine the second carrying capacity of the charging network, that is, when the value is large, it means that the stability of the charging network is reduced, and when it is small, it means that the charging network can maintain stable operation.

[0069] In one embodiment, the multiple secondary evaluation indicators corresponding to the multiple first-level evaluation indicators of the charging demand user include: charging satisfaction rate and charging waiting time; after respectively obtaining the multiple second-level evaluation indicators corresponding to each first-level evaluation indicator, it also includes: obtaining the third numerical change rate of the multiple second-level evaluation indicators of the charging demand user; determining the third carrying capacity of the charging demand user for the distribution network according to the third numerical change rate.

[0070] Among them, the charging satisfaction rate and charging waiting time can be expressed by C 11 and C 21 , the corresponding calculation method is:

[0071] And, N LEV is the total number of electric vehicles that need charging; i is the satisfaction of the charging demand of the i-th electric vehicle; V is the set of users who meet the target charging power; and, are the waiting time in queue and the actual charging time of the i-th electric vehicle respectively.

[0072] Specifically, C 11 As a key yardstick for evaluating charging service quality and distribution network carrying capacity, its core lies in measuring the proportion of users who obtain the target charging power from the perspective of reliability; C 21 It reflects the sum of the waiting time in queue and the actual charging time of electric vehicles at the charging station, revealing the total time required for users to complete charging from the time they arrive at the charging station. It also reflects the response speed and carrying capacity of the distribution network in dealing with the charging load.

[0073] In this embodiment, the third value change rate of multiple secondary evaluation indicators of the charging demand user is obtained, that is, B 11 , B 21and B 22 The rate of change of the third value; determining the third carrying capacity of the user with charging demand according to the third numerical change rate, thereby correspondingly improving the efficiency of the charging service and optimizing the layout of the charging facilities.

[0074] More specifically, in one embodiment, before establishing an evaluation object set, an evaluation level set, and an indicator weight set, and determining the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators, and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set, and the indicator weight set, it includes: converting the actual values ​​of each second-level evaluation indicator into corresponding relative values; obtaining the relative indicator weights of each evaluation object corresponding to each second-level evaluation indicator based on multiple relative values; obtaining the information entropy value of each evaluation object according to the relative indicator weight, and determining the information entropy redundancy and indicator weight of each evaluation object based on the information entropy value, so as to obtain the comprehensive indicator score of each evaluation object.

[0075] For example, the specific process is as follows: Standardization: A vector x is set, which contains the actual values ​​of multiple underlying indicators, specifically expressed as x={x 11 , x 12 , …, x nj}, in order to ensure the accuracy of the comprehensive weight calculation, the actual index value is normalized before calculation, and the actual value is converted into a relative value to eliminate the influence of the dimension and calculation accuracy difference. The extreme difference forward processing can be selected as the specific method of normalization, and x ij =|x ij |, the specific method is as follows: According to the normalized standard index value, the index proportion of the i-th evaluation object under the j-th index can be calculated, which is recorded as P ij The specific calculation method is to add the normalized index value of the i-th evaluation object to the normalized index values ​​of all evaluation objects under this index to obtain the relative weight of the object under this index. The calculation method is: According to the proportion of the index value, the information entropy value Ej of the j-th index can be calculated, and the proportion of each index P ij Substitute into the entropy formula: Indicator information entropy redundancy and weight: Information redundancy g of the jth indicator j is: g j =1-E j Then calculate the weight of the jth indicator for: Finally, we get the comprehensive score of the index, that is, the comprehensive score of the i-th evaluation object ξ i for:

[0076] In addition, the indicator weights can be determined to establish an evaluation object set U = {U1, U2, ...U m}, each object Ui in the set represents a bottom-level evaluation index, and m represents the total number of bottom-level indicators in the evaluation index system; establish an evaluation level set V = {V1, V2, ... V k}, contains k comment levels, with "excellent", "good" and "poor" as indicator evaluation levels. Among them, "excellent" means that the distribution network indicators are in an excellent state and no improvement is needed; "good" means that the indicators perform well and can cope with normal operation and general faults, and no adjustment is required in the short term; "poor" means that the indicators can only meet normal operation and need to be moderately improved in the short term. According to the characteristics of the indicators, they are divided into maximum benefit type and minimum cost type, and the corresponding membership fuzzy function is defined for each type of indicator. On this basis, the corresponding fuzzy score set M = {M1, M2, M3} is constructed, where each element represents the score when the membership of the level is 1. The specific fuzzy score calculation method is as follows:

[0077] In the formula, is the membership value of index j; M k is the score corresponding to the comment set, k=1, 2, 3 represent the evaluation levels of “excellent”, “good” and “poor” respectively.

[0078] Through the above embodiment, the entropy weight method is used to determine the elements of the weight set A, which is expressed as A = {A1, A2, ...A m}, the entropy weight method quantifies the relative importance of different indicators in the comprehensive evaluation by analyzing the entropy value of the indicators. Specifically, the smaller the entropy value of the indicator, the more concentrated its information, indicating that it occupies a more important position in the evaluation system; while the larger the entropy value of the indicator, the less information it provides, and thus the lower the weight. Therefore, combining fuzzy evaluation and information entropy theory, using a combination of subjective and objective evaluation methods can more accurately reflect the actual effects of relevant indicators, thereby improving the accuracy and reliability of the evaluation results.

[0079] Furthermore, in one embodiment, a comprehensive scoring method is proposed, which combines the entropy weight method with the fuzzy comprehensive theory to construct a comprehensive weight matrix W of each evaluation object at the criterion level. A1 -W C1 and the corresponding fuzzy rating matrix M A1 -M C1 , calculate the comprehensive fuzzy score of each evaluation object.

[0080] Specifically, taking the operation of the distribution network as an example, the calculation method of its comprehensive score is: In order to comprehensively evaluate the comprehensive carrying capacity of the distribution network for electric vehicles, a comprehensive evaluation index system covering three dimensions of distribution network, charging facility network and electricity demand users is constructed. By introducing a flexible variable decision coefficient δ, a quantitative evaluation of the carrying capacity of the distribution network under different operating entities is achieved, so that it can better adapt to diverse application scenarios and user needs. The specific calculation method is: In the formula, δ A , δ B , δ C It is a variable weight coefficient, which is determined by the focus of the operational decision maker.

[0081] In an exemplary embodiment, the node voltage, branch power flow and system loss can be used as evaluation indicators to analyze the impact of charging load on the distribution network under different penetration rates. The maximum deviation rate of the node voltage is expressed as: Where, U1 is the rated voltage of the node; U i,t is the actual node voltage; T D is the maximum time limit for voltage measurement; N m is the set of distribution network nodes; the branch flow overload is expressed as: I limit =λ ij P′ ij ×100%, In the formula, I limit is the critical value of branch power flow overload; ij is the branch power flow load rate; P ij is the rated transmission capacity of the branch; the average system loss is expressed as: In the formula, are the injected powers of the balancing node and other nodes outside the balancing node respectively.

[0082] In the above embodiment, by selecting the maximum node voltage deviation rate and the branch power flow overload rate as evaluation indicators, the carrying capacity of the distribution network under large-scale electric vehicle charging load can be quantitatively analyzed. First, the charging load data of the electric vehicle is collected, and the voltage value of each node is calculated based on these data, and then the maximum deviation rate of the node voltage is determined; then, by analyzing the current changes of the branch under different charging load scenarios, the branch power flow overload rate can be calculated; finally, these calculated indicators are compared and analyzed with the design specifications of the distribution network to facilitate the acquisition of the weak links of the distribution network under different load levels.

[0083] This application provides a method for constructing a distribution network carrying capacity assessment system. In order to better understand the process of constructing the above distribution network carrying capacity assessment system, combined with Figure 3 As shown, the specific process of the method for constructing the distribution network carrying capacity evaluation system of the present application is described in detail below, including the following steps:

[0084] Step S302, obtaining key evaluation indicators of the distribution network's carrying capacity for electric vehicles and establishing a distribution network carrying capacity evaluation process.

[0085] Specifically, Figure 4 As shown in the figure, the distribution network carrying capacity assessment process is represented. First, the urban distribution network topology, system parameters, equipment parameters, system load, and initial electric vehicle penetration rate are input. Then, the electric vehicle charging load is predicted, the distribution network assessment unit is divided, the operation scenario is set under different EV penetration rates, the optimal power flow of the distribution network is iteratively calculated, and the distribution network operation safety and economic indicators are analyzed. Then, it is determined whether the node voltage exceeds the limit. If not, it continues to determine whether the branch power flow exceeds the limit. If it does not, the electric vehicle penetration rate can be dynamically increased. On the contrary, if the branch power flow exceeds the limit, the result is output, that is, the actual critical penetration rate of electric vehicles, and the distribution network’s quantitative assessment process for electric vehicle carrying capacity ends.

[0086] Step S304: Obtain a first evaluation index and a second evaluation index of the distribution network carrying capacity.

[0087] Step S306: determine the indicator weights of different second evaluation indicators.

[0088] Step S308: Establish a distribution network carrying capacity assessment system.

[0089] Specifically, we can take a certain urban area (15×15km) as an example. The traffic road network in this area is constructed based on the functional zoning of urban land. The road network topology and functional area division of this area are as follows: Figure 5 As shown, the traffic convenience and functional area distribution are fully considered. According to the urban planning, the area is divided into 19 residential areas (corresponding to Figure 5 H1-H19), 17 work business areas (corresponding Figure 5 W1-W17), 10 (corresponding to Figure 5 E1-E10) public entertainment areas and 6 other places (corresponding to Figure 5 O1-O6), this layout makes residents' life, work and entertainment activities more convenient. There are a total of 127 roads and 75 nodes in the area, forming a complex traffic network. The design of each road takes into account the flow, function and surrounding environment to ensure the smoothness and safety of traffic, providing important basic data for subsequent traffic analysis and planning; and the length of the road is calculated by plane coordinate distance, and according to the speed limit of urban roads, the roads in the area are divided into two levels: Level 1 roads usually refer to major traffic arteries; and Level 2 roads are secondary arteries. The node road network topology structure is combined with the IEEE33 node distribution network model to form a Figure 6 The schematic diagram of the traffic-electricity coupling model is shown in Figure 1. In addition, the road classification standards are detailed in Table 1:

[0090] Table 1: Urban road classification standards

[0091]

[0092] The power distribution system in this area adopts a 10kV radial network. The transformer capacity of all nodes is 6300kVA and connected to the 110kV grid. The rated capacity of the upper transformer is 63000kVA / 110kV, and a 10% margin is set to cope with the maximum demand of the load. The data comes from the IEEE33 node system, the voltage reference is 12.66kV, and the power reference is 100MVA, except for the PQ node. The road network area in this area is about 225km 2 , the total number of motor vehicles reaches 500,000, and the number of electric vehicles is adjusted according to the penetration rate. In order to further study the impact of electric vehicle grid connection on the distribution network, we set three electric vehicle penetration rates of 15%, 30% and 50% as simulation scenarios. Each penetration rate scenario was systematically simulated, and the results under different penetration rates were compared. The specific data are shown in Table 2. After obtaining the simulation data, a comprehensive fuzzy evaluation was conducted on the distribution network after large-scale electric vehicles were connected to the grid. The evaluation process covered the scenarios of 15%, 30% and 50% electric vehicle penetration rates, aiming to fully reveal the impact of electric vehicle grid connection on the comprehensive carrying capacity of the distribution network and identify the key factors.

[0093] Table 2: Comparison of simulation results for different scenarios

[0094]

[0095] Under different electric vehicle penetration scenarios, the time series flow calculation of the distribution network was carried out to obtain the actual operating parameters of the power grid. The original simulation data of the comprehensive evaluation index of the distribution network is shown in Table 3. The normalized data obtained by forward normalization of the actual index values ​​is shown in Figure 7 As shown, through further calculation, the entropy weights of the corresponding indicators x1, x2, ..., x11 under the evaluation criterion layer and the weights of the criterion layer are shown in Tables 4 and 5. In addition, according to the evaluation indicators of the distribution network, charging facilities and charging users, the bottom-level secondary indicators are divided into three categories: cost indicators include A 11 , A 12 , A 21 , A 31 , A 32 , B 22 and C 21 ; Efficiency indicators include A 22 and C 11 ; The intermediate index is B 11 and B 21Different membership functions are used for these indicators to clearly define the fuzzy relationship between them and the three evaluation levels of “excellent, good, and poor”, where “excellent” corresponds to 100 points, “good” corresponds to 85 points, and “poor” corresponds to 65 points. After completing the information entropy weighting and fuzzy evaluation, the comprehensive fuzzy scores of each criterion layer are given as shown in Table 6.

[0096] Moreover, when the penetration rate of electric vehicles approaches the critical point of the distribution network's carrying capacity, the scores of various indicators drop significantly, especially in terms of safety, reliability, operating efficiency and economy. Therefore, the charging satisfaction and average waiting time of electric vehicle users are closely related to the ratio of vehicles to charging piles in the distribution network. In the specific plan, the vehicle-to-pile ratio of the distribution network is lower than 1:1, indicating that the charging needs of most users can only be met when the scale of electric vehicle grid connection is limited. With the gradual increase in the number of electric vehicles, it is particularly important to increase the vehicle-to-pile ratio in the urban distribution network to ensure that the charging needs of users can be met to the greatest extent.

[0097] Table 3: Original simulation data of comprehensive evaluation indicators of distribution network

[0098]

[0099] Table 4: Information entropy weights of underlying indicators

[0100] Evaluation object x1 x2 x3 x4 x5 x6 Weight 0.084 0.070 0.074 0.087 0.071 0.070 Evaluation object x7 x8 x9 x10 x11 Weight 0.187 0.072 0.072 0.070 0.146

[0101] Table 5: Criteria indicator weights

[0102]

[0103] Table 6: Fuzzy scores at each criterion level

[0104] Evaluation indicators A1 A2 A3 A B1 B2 B C1 C2 C 15% EV penetration 92.6 93.8 92.7 93.0 100 100 100 87.4 83.4 85.4 30% EV penetration 92.5 87.6 88.6 89.6 100 96.3 98.2 76.2 83.4 79.8 50% EV penetration 76.4 74.6 74.2 75.0 100 77.2 88.6 75.4 83.4 79.4

[0105] By combining the weight matrix with the fuzzy score matrix and the variable coefficient δ n , a comprehensive fuzzy score is given to the carrying capacity of the distribution network under different electric vehicle penetration scenarios, and the final comprehensive score is shown in Table 7.

[0106] Table 7: Comprehensive scoring results of distribution network carrying capacity in multiple scenarios

[0107]

[0108] When setting the variable coefficient δ n=1 / 3, the results of the evaluation from the perspective of the balance of distribution network, charging facility network and charging users show that the overall carrying capacity of the distribution network is at an upper-middle level. However, when the penetration rate of electric vehicles reaches the critical value of 50%, the comprehensive score of the distribution network will drop below 85 points. If the penetration rate continues to rise, the security and stability of the power grid will face greater pressure. Therefore, in actual operational decisions, the operating status of the distribution network must be strictly monitored. According to different decision-making priorities, a variety of quantitative evaluation results can be obtained. The above evaluation results can not only provide strong data support for the construction and development of the power grid, but also provide important references for future planning to ensure that the access of electric vehicles will not affect the normal operation of the distribution network. In addition, it is also crucial to flexibly adjust the layout and management strategies of charging facilities to adapt to the changing number and usage patterns of electric vehicles, and ultimately achieve a benign interaction between the power grid and electric vehicles.

[0109] Through the above embodiments, a hierarchical evaluation index system for the electric vehicle carrying capacity of the distribution network is established, covering the distribution network, the charging network and the charging demand users, and a quantitative evaluation is performed through the criterion layer and the index layer. Moreover, through the multi-level evaluation index system, the impact of large-scale access of electric vehicles on various aspects of the distribution network can be analyzed in detail and accurately quantified, and a comprehensive evaluation can be achieved; a comprehensive evaluation method combining information entropy and fuzzy theory is adopted to deal with the uncertainty in a complex environment, and a comprehensive fuzzy score is calculated. The method combining information entropy and fuzzy theory enhances the ability to deal with the uncertainty in the complex environment of the distribution network, improves the reliability of the evaluation results, and can effectively deal with the uncertainty; based on the analysis of the coupling relationship between urban power and transportation networks, four The evaluation index system is designed, and the weights of the underlying indicators are determined using methods such as the standard deviation method and the entropy weight method. The evaluation system takes into account the impact of large-scale access of electric vehicles on the long-term operating cost, stability and reliability of the distribution network, ensuring the scientific nature of management strategies and plans, and achieving comprehensive analysis of long-term impacts. An evaluation index set, an evaluation result set and an indicator weight set are established to quantitatively evaluate the operation and carrying capacity of the distribution network after the access of electric vehicles, taking into account the rationality, reliability and economy of the system operation. The proposed method has the ability to dynamically model complex systems. By deeply understanding the complex relationship between electric vehicle charging behavior and distribution network variables, the ability to predict the long-term evolution of the system and response strategies is improved, achieving the purpose of dynamic modeling.

[0110] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0111] Based on the same inventive concept, the embodiment of the present application also provides a distribution network carrying capacity assessment system construction device for implementing the distribution network carrying capacity assessment system construction method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more distribution network carrying capacity assessment system construction device embodiments provided below can refer to the limitations of the distribution network carrying capacity assessment system construction method above, and will not be repeated here.

[0112] In an exemplary embodiment, Figure 8 As shown, a distribution network carrying capacity evaluation system construction device is provided, including: a primary evaluation index acquisition module 801, a secondary evaluation index acquisition module 802 and an evaluation system establishment module 803, wherein:

[0113] The first-level evaluation index acquisition module 801 is used to define multiple evaluation objects corresponding to the distribution network evaluation system, and respectively obtain multiple first-level evaluation indicators corresponding to each evaluation object; wherein the evaluation objects include the distribution network, the charging network and the charging demand users;

[0114] A secondary evaluation indicator acquisition module 802 is used to respectively acquire a plurality of secondary evaluation indicators corresponding to each primary evaluation indicator;

[0115] The evaluation system establishment module 803 is used to establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

[0116] Furthermore, in one embodiment, the first-level evaluation index acquisition module 801 is also used to obtain safety, reliability, efficiency and quality as multiple first-level evaluation indicators of the distribution network; obtain operation efficiency and coordination balance as multiple first-level evaluation indicators of the charging network; obtain service reliability and service efficiency as multiple first-level evaluation indicators of charging demand users.

[0117] Furthermore, in one embodiment, the secondary evaluation index acquisition module 802 is also used to, after respectively acquiring multiple secondary evaluation indicators corresponding to each first-level evaluation indicator, include: acquiring a first numerical change rate of multiple secondary evaluation indicators of the distribution network; and determining a first carrying capacity of the distribution network based on the first numerical change rate.

[0118] Furthermore, in one embodiment, the secondary evaluation index acquisition module 802 is further used to acquire a second value change rate of a plurality of secondary evaluation indicators of the charging network; and determine a second carrying capacity of the charging network according to the second value change rate.

[0119] Furthermore, in one embodiment, the secondary evaluation index acquisition module 802 is also used to obtain the third numerical change rate of multiple secondary evaluation indicators of the charging demand user; and determine the third carrying capacity of the charging demand user for the distribution network based on the third numerical change rate.

[0120] Furthermore, in one embodiment, the evaluation system establishment module 803 is also used to convert the actual values ​​of each secondary evaluation indicator into corresponding relative values; obtain the relative indicator weights of each evaluation object corresponding to each secondary evaluation indicator based on multiple relative values; obtain the information entropy value of each evaluation object according to the relative indicator weight, and determine the information entropy redundancy and indicator weight of each evaluation object based on the information entropy value, so as to obtain the comprehensive indicator score of each evaluation object.

[0121] Each module in the above-mentioned distribution network carrying capacity assessment system construction device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0122] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store distribution network carrying capacity assessment system construction data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution network carrying capacity assessment system construction method is implemented.

[0123] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0124] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0125] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0126] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0127] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0128] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processors involved in each embodiment provided in this application may be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logic devices based on quantum computing, etc., but are not limited thereto.

[0129] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0130] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a distribution network carrying capacity assessment system, characterized in that: The method comprises: Define multiple evaluation objects corresponding to the distribution network evaluation system, and obtain multiple primary evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network, and the charging demand users; Respectively obtain a plurality of second-level evaluation indicators corresponding to each of the first-level evaluation indicators; Establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

2. The method according to claim 1, characterized in that The step of respectively obtaining a plurality of first-level evaluation indicators corresponding to each of the evaluation objects comprises: Obtaining safety, reliability, efficiency and quality as a plurality of the first-level evaluation indicators of the distribution network; Acquire operation efficiency and coordination balance as a plurality of the first-level evaluation indicators of the charging network; Service reliability and service efficiency are obtained as the plurality of primary evaluation indicators of the charging demand user.

3. The method according to claim 2, characterized in that The multiple secondary evaluation indicators corresponding to the multiple primary evaluation indicators of the distribution network include: line overload rate, distribution transformer overload rate, load fluctuation rate, system load rate, voltage deviation rate and system average network loss; After respectively obtaining a plurality of secondary evaluation indicators corresponding to each of the primary evaluation indicators, the method further comprises: Obtaining a first value change rate of a plurality of the secondary evaluation indicators of the distribution network; A first carrying capacity of the distribution network is determined according to the first numerical change rate.

4. The method according to claim 3, characterized in that The multiple secondary evaluation indicators corresponding to the multiple primary evaluation indicators of the charging network include: average charging time, utilization balance and charging station capacity-load ratio; After respectively obtaining a plurality of secondary evaluation indicators corresponding to each of the primary evaluation indicators, the method further includes: Obtaining a second value change rate of a plurality of the secondary evaluation indicators of the charging network; A second carrying capacity of the charging network is determined according to the second numerical change rate.

5. The method according to claim 4, characterized in that The multiple secondary evaluation indicators corresponding to the multiple primary evaluation indicators of the charging demand user include: charging satisfaction rate and charging waiting time; After respectively obtaining a plurality of secondary evaluation indicators corresponding to each of the primary evaluation indicators, the method further includes: Obtaining a third value change rate of the plurality of secondary evaluation indicators of the charging demand user; A third carrying capacity of the charging demand user for the distribution network is determined according to the third numerical value change rate.

6. The method according to claim 1, characterized in that Before establishing the evaluation object set, the evaluation level set and the indicator weight set, and determining the indicator weights of the plurality of evaluation objects, the plurality of first-level evaluation indicators and the plurality of second-level evaluation indicators based on the evaluation object set, the evaluation level set and the indicator weight set, the method comprises: Convert the actual value of each of the secondary evaluation indicators into a corresponding relative value; Obtaining the relative indicator weight of each evaluation object corresponding to each secondary evaluation indicator based on the plurality of relative values; The information entropy value of each evaluation object is obtained according to the relative indicator weight, and the information entropy redundancy and indicator weight of each evaluation object are determined based on the information entropy value, so as to obtain the comprehensive indicator score of each evaluation object.

7. A distribution network carrying capacity assessment system construction device, characterized in that: The device comprises: A first-level evaluation index acquisition module is used to define multiple evaluation objects corresponding to the distribution network evaluation system, and respectively obtain multiple first-level evaluation indicators corresponding to each of the evaluation objects; wherein the evaluation objects include the distribution network, the charging network and the charging demand users; A secondary evaluation indicator acquisition module is used to respectively acquire a plurality of secondary evaluation indicators corresponding to each of the primary evaluation indicators; An evaluation system establishment module is used to establish an evaluation object set, an evaluation level set and an indicator weight set, determine the indicator weights of multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the evaluation object set, evaluation level set and indicator weight set, and construct a distribution network carrying capacity evaluation system including multiple evaluation objects, multiple first-level evaluation indicators and multiple second-level evaluation indicators based on the indicator weights.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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