Power distribution network bearing capacity comprehensive evaluation method and system based on AHP-CRITIC-MARCOS

Abstract

The invention relates to a power distribution network bearing capacity comprehensive evaluation method based on AHP-CRITIC-MARCOS. The method comprises the following steps: constructing a comprehensive evaluation index system comprising three levels of a power distribution network evaluation index, a traffic road network evaluation index and an electric vehicle charging user evaluation index; using an AHP method to calculate the subjective weight of the evaluation index, and performing consistency check by constructing a judgment matrix; by utilizing a CRITIC method, calculating a standard deviation and a correlation coefficient of the indexes, and calculating objective weights for measuring the contrast strength and conflict of the indexes; calculating a comprehensive weight through minimum identification information based on the subjective weight and the objective weight; and sorting and evaluating the bearing capacity of the power distribution network by using an MARCOS method. Compared with the prior art, the method fully considers the influence of multi-source factors on the bearing capacity of the power distribution network, and achieves the comprehensive comparison and evaluation of multiple alternative schemes.

Description

Technical Field

[0001] This invention relates to the field of distribution network operation assessment technology, and in particular to a comprehensive assessment method and system for distribution network carrying capacity based on AHP-CRITIC-MARCOS. Background Technology

[0002] With the increasing prevalence of electric vehicles (EVs), their penetration rate in power distribution networks is growing rapidly. Under large-scale EV grid integration, the impact of EVs on the operational safety, economy, and power quality of the power distribution network will become significant. Therefore, a comprehensive assessment of the power distribution network's carrying capacity is necessary to determine the maximum EV capacity it can accommodate under safe and economical operating conditions.

[0003] Existing methods for assessing the carrying capacity of distribution networks mostly employ single indicators or simple weighting methods, failing to comprehensively and objectively reflect the carrying capacity of distribution networks under complex scenarios. On the one hand, while subjective weighting methods (such as the traditional AHP) can analyze subjective weighting issues, their accuracy is limited due to relying solely on expert judgment. On the other hand, objective weighting methods (such as CRITIC) consider the correlation between indicators, but the weight differences are not significant enough to accurately reflect the importance of each indicator. Furthermore, traditional assessment methods (such as simple weighting and TOPSIS) suffer from problems such as failing to consider the relative importance of distance or computational complexity when ranking assessment results.

[0004] In summary, there is currently a lack of a comprehensive assessment method for the carrying capacity of power distribution networks to solve or partially solve the aforementioned problems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a comprehensive evaluation method and system for distribution network carrying capacity based on AHP-CRITIC-MARCOS, so as to realize the comprehensive comparison and evaluation of the distribution network carrying capacity of multiple alternative schemes.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] One aspect of the present invention provides a comprehensive assessment method for the carrying capacity of distribution networks based on AHP-CRITIC-MARCOS, comprising the following steps:

[0008] Construct a comprehensive evaluation index system that includes three levels: power distribution network evaluation index, transportation road network evaluation index, and electric vehicle charging user evaluation index;

[0009] The subjective weights of the evaluation indicators are calculated using the AHP method, and consistency is checked by constructing a judgment matrix.

[0010] Using the CRITIC method, the objective weights for measuring the comparative strength and conflict of indicators are calculated by calculating the standard deviation and correlation coefficient of the indicators.

[0011] Based on the subjective weight and the objective weight, a comprehensive weight is calculated using the minimum discriminative information.

[0012] The MARCOS method is used to rank and evaluate the carrying capacity of the distribution network.

[0013] As a preferred technical solution, the power distribution network evaluation indicators include voltage amplitude fluctuation, load fluctuation, line load variance, voltage deviation, and system active power loss; the traffic road network evaluation indicators include road travel time and road congestion; and the electric vehicle charging user evaluation indicators include charging satisfaction and charging waiting time.

[0014] As a preferred technical solution, the calculation process of the subjective weight includes the following steps:

[0015] Based on expert opinions, pairwise comparisons are made among the evaluation indicators to construct a judgment matrix;

[0016] Perform a consistency check on the judgment matrix;

[0017] The verified judgment matrix is ​​normalized, and the subjective weights of each indicator are calculated.

[0018] As a preferred technical solution, the calculation process of the objective weight includes the following steps:

[0019] Perform data standardization processing for each indicator;

[0020] Calculate the standard deviation and correlation coefficient of each indicator after standardization;

[0021] The objective weight of the index is calculated based on the standard deviation and the correlation coefficient.

[0022] As a preferred technical solution, the comprehensive weight is calculated using the following formula:

[0023]

[0024] Where, α i β i These are subjective weights and objective weights, respectively, ω i The overall weight is denoted by n, where n is the number of indicators.

[0025] As a preferred technical solution, the process of ranking and evaluating the carrying capacity of the distribution network using the MARCOS method includes the following steps:

[0026] Based on the comprehensive weight of each index value, the utility function value of each alternative scheme is calculated.

[0027] The alternatives were ranked using the MARCOS method.

[0028] The sorting results are converted into visual signals for evaluation.

[0029] As a preferred technical solution, the utility function value is calculated using the following formula:

[0030]

[0031] in, This represents the utility function associated with the ideal solution. Let f(K) represent the utility function associated with the negative ideal solution. i ) represents the utility function value of the i-th indicator.

[0032] Another aspect of the present invention provides a comprehensive assessment system for distribution network carrying capacity based on AHP-CRITIC-MARCOS, used to implement the aforementioned comprehensive assessment method for distribution network carrying capacity, the system comprising:

[0033] The indicator system construction module is used to construct a comprehensive evaluation indicator system that includes three levels: power distribution network evaluation indicators, transportation road network evaluation indicators, and electric vehicle charging user evaluation indicators.

[0034] The subjective weight calculation module is used to calculate the subjective weights of evaluation indicators using the AHP method and to perform consistency checks by constructing a judgment matrix.

[0035] The objective weight calculation module is used to calculate the objective weights that measure the comparative strength and conflict of indicators by using the CRITIC method, by calculating the standard deviation and correlation coefficient of the indicators.

[0036] The weight fusion module is used to calculate the comprehensive weight based on the subjective weight and the objective weight using the minimum discrimination information.

[0037] The sorting and visualization module is used to sort the carrying capacity of the distribution network using the MARCOS method and visualize it on the visualization terminal to achieve the assessment.

[0038] In another aspect, an electronic device is provided, comprising: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned AHP-CRITIC-MARCOS-based comprehensive assessment method for distribution network carrying capacity.

[0039] In another aspect, the present invention provides a computer-readable storage medium, characterized in that it includes one or more programs executable by one or more processors of an electronic device, said one or more programs including instructions for executing the aforementioned AHP-CRITIC-MARCOS-based comprehensive assessment method for distribution network carrying capacity.

[0040] Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] This invention fully considers the impact of multiple factors on the carrying capacity of the distribution network and achieves a comprehensive comparative evaluation of multiple alternative schemes: By comprehensively considering multiple indicators at three levels—the distribution network, the charging infrastructure network, and electric vehicle charging users—this invention can comprehensively evaluate the carrying capacity of the distribution network. Combining the AHP and CRITIC methods, it considers both the subjective opinions of experts and the objective characteristics of each indicator, thereby improving the objectivity and reliability of the evaluation results. Using the MARCOS method for comprehensive evaluation can effectively reflect the advantages and disadvantages of each alternative scheme and improve the accuracy of the evaluation results. Attached Figure Description

[0042] Figure 1 This is a flowchart of the comprehensive evaluation method for the carrying capacity of the distribution network in the embodiment;

[0043] Figure 2 This is a diagram illustrating the multi-level evaluation index system architecture in the embodiment.

[0044] Figure 3 This is a schematic diagram of the comprehensive assessment system for the carrying capacity of the power distribution network in the embodiment;

[0045] Figure 4 This is a schematic diagram of the electronic device in the embodiment. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0047] Example 1

[0048] To address the problems existing in the aforementioned technologies, this embodiment provides a comprehensive evaluation method for distribution network carrying capacity based on AHP-CRITIC-MARCOS. By establishing a multi-level evaluation index system and combining AHP (Analytic Hierarchy Process), CRITIC (weight determination method based on index correlation), and MARCOS (ranking method based on trade-offs), the carrying capacity of the distribution network is quantitatively evaluated. See also... Figure 1 The method specifically includes the following steps:

[0049] Step S1: Construct a comprehensive evaluation index system that includes three levels: power distribution network, transportation road network, and electric vehicle charging users.

[0050] Each level has a criteria layer and an indicator layer to quantitatively evaluate the operation and service effectiveness of the distribution network.

[0051] For details, see Figure 2 The multi-level evaluation indicator system includes:

[0052] Distribution network evaluation indicators: voltage amplitude fluctuation, load fluctuation, line load variance, voltage deviation, and system active power loss.

[0053] Traffic network assessment indicators: road travel time and road congestion.

[0054] Electric vehicle user evaluation metrics: charging satisfaction and charging waiting time.

[0055] Step S2: Use the AHP method to calculate the subjective weights of the evaluation indicators.

[0056] By collecting expert opinions, a judgment matrix is ​​constructed, and a consistency check is performed.

[0057] Specifically, step S2 includes steps S201-S203:

[0058] Step S201, construct the judgment matrix: Based on expert opinions, compare each evaluation indicator pairwise to construct the judgment matrix.

[0059] Step S202, Consistency check: Perform a consistency check on the judgment matrix to ensure the consistency of the matrix.

[0060] The formula for calculating the consistency test of the judgment matrix is:

[0061]

[0062] In the formula, λ max is the largest eigenvalue of the judgment matrix; n is the order of the judgment matrix. CI is the consistency index; RI is the random consistency index.

[0063] Calculate the consistency ratio CR value. If CR < 0.1, the judgment matrix is ​​considered to have passed the consistency test.

[0064] 1. Step S203, Calculate weights: Normalize the judgment matrix and calculate the subjective weights of each indicator.

[0065]

[0066] Step S3: Calculate the objective weights of each indicator using the CRITIC method.

[0067] This method measures the comparative strength and conflict of indicators by calculating the standard deviation and correlation coefficient of the indicators, thereby determining the weight of each indicator.

[0068] Specifically, step S3 includes steps S301-S303:

[0069] Step S301, Data standardization: Perform dimensionless processing on the data of each indicator to eliminate differences in dimensions and orders of magnitude.

[0070] Step S302, Calculate the standard deviation and correlation coefficient: Calculate the standard deviation ξ for each indicator. j and correlation coefficient r ij The strength and conflict of the comparative indicators are measured.

[0071]

[0072] r ij =cov(S′) i ,S′ j / (ξ i ,ξ j i,j=1,2,…,n

[0073] In the formula: ξ j Let r be the standard deviation of the j-th indicator; ij S′ is the correlation coefficient between the i-th indicator and the j-th indicator; i S′ j are the i-th and j-th columns of the standardized matrix S′, respectively.

[0074] Step S303, determine the weights: calculate the objective weights of each indicator based on the contrast intensity and conflict.

[0075]

[0076]

[0077] Step S4: Combine the subjective weights calculated by AHP with the objective weights calculated by CRITIC to obtain the comprehensive weights.

[0078] The least squares method is used to optimize the combination of subjective and objective weights to ensure that the overall weights are close to reality.

[0079] Specifically, the subjective weights calculated by AHP are combined with the objective weights calculated by CRITIC, and the comprehensive weights are obtained using the principle of minimum discriminative information. The objective function is:

[0080]

[0081] Solving this optimization model yields the following comprehensive weights:

[0082]

[0083] The overall weight vector is:

[0084] ω=[ω1,ω2,...ω n ] T

[0085] Step S5: Use the MARCOS method to rank and evaluate the carrying capacity of the distribution network.

[0086] This method ranks the alternatives based on the relationship between ideal and negative ideal alternatives, effectively reflecting the merits of each alternative. Specifically, step S5 includes steps S501-S509:

[0087] Step S501: Form an initial evaluation matrix, including a set of n criteria and m alternatives. This is obtained by collecting scores given by experts based on their evaluation of the alternatives according to the criteria.

[0088] Step S502 involves expanding the initial evaluation matrix by introducing the ideal solution (AI) and the negative ideal solution (AAI) to form an extended matrix.

[0089]

[0090] Step S503: The negative ideal solution (AAI) is the solution with the worst characteristics, while the ideal solution (AI) is the solution with the best characteristics. AAI and AI are defined by [missing information - likely a metric or a formula] based on the properties of different criteria.

[0091]

[0092]

[0093] In the formula: B represents a set of maximization criteria, that is, we want the score of the criterion to be as high as possible; C represents a set of minimization criteria, that is, we want the score of the criterion to be as low as possible.

[0094] Step S503: Normalize the expanded initial evaluation matrix.

[0095]

[0096] Where: n ij x represents the elements in the matrix obtained after normalization; ai and x ij This represents an element in the extended matrix.

[0097] Step S504, determine the weighting matrix V = [ν ij ] m×n The elements n of the normalized matrix ij Weighting coefficients w for each indicator j Multiplying them yields the elements in the weighted matrix, where the weight coefficients are the overall weights calculated above.

[0098] v ij =n ij ×w j

[0099] Step S505: Calculate alternative solution K i Utility relative to the ideal solution (AI) and the negative ideal solution (AAI).

[0100]

[0101] In the formula: S i S represents the sum of the elements in the i-th row of the weighted matrix V; ai S represents the sum of the elements in the ai-th row of the weighted matrix V; aai This represents the sum of the elements in the aai-th row of the weighted matrix V.

[0102] Step S506: Determine the utility function of the alternative solutions, calculated using the following formula:

[0103]

[0104] In the formula: This represents the utility function associated with the ideal solution; This represents the utility function associated with the negative ideal solution.

[0105] Step S507: Determine the utility functions related to the ideal solution (AI) and the negative ideal solution (AAI).

[0106]

[0107] Step S508: Sort the alternative solutions according to the value of the utility function.

[0108] Step S509, Visualization: Visualize the ranking results of each alternative solution on the visualization terminal to achieve evaluation.

[0109] Example 2

[0110] Based on Example 1, this example provides a comprehensive distribution network carrying capacity assessment system based on AHP-CRITIC-MARCOS to implement the aforementioned comprehensive distribution network carrying capacity assessment method. (See [link to example]). Figure 3 The system includes:

[0111] (1) Indicator system construction module, used to construct a comprehensive evaluation indicator system that includes three levels: power distribution network evaluation indicators, traffic road network evaluation indicators and electric vehicle charging user evaluation indicators;

[0112] (2) Subjective weight calculation module, which is used to calculate the subjective weight of the evaluation index using the AHP method and perform consistency verification by constructing a judgment matrix;

[0113] (3) Objective weight calculation module, which uses the CRITIC method to calculate the objective weight of the comparative strength and conflict of the indicators by calculating the standard deviation and correlation coefficient of the indicators.

[0114] (4) Weight fusion module, used to calculate comprehensive weight based on the subjective weight and the objective weight using minimum identification information;

[0115] (5) Sorting and visualization module, used to sort the carrying capacity of the distribution network using the MARCOS method and display it on the visualization terminal to achieve evaluation.

[0116] This invention comprehensively considers multiple indicators across three levels: distribution network, charging infrastructure network, and electric vehicle charging users, enabling a holistic assessment of the distribution network's carrying capacity. Combining the AHP and CRITIC methods, it considers both expert opinions and the objective characteristics of each indicator, improving the objectivity and reliability of the assessment results. Using the MARCOS method for comprehensive evaluation effectively reflects the advantages and disadvantages of each alternative, improving the accuracy of the assessment results. Power grid companies can comprehensively assess the distribution network's carrying capacity by determining the maximum capacity of electric vehicles that the distribution network can accommodate under safe and economical operating conditions. This invention is applicable to various operating scenarios and can provide a scientific basis for the planning, construction, and optimized operation of distribution networks.

[0117] Example 3

[0118] An electronic device includes: one or more processors and a memory, the memory storing one or more programs, the one or more programs including instructions for executing the comprehensive assessment method for distribution network carrying capacity based on AHP-CRITIC-MARCOS as described in Embodiment 1.

[0119] like Figure 4 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 1The method described herein. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0120] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0121] Example 4

[0122] This embodiment provides a computer-readable storage medium including one or more programs executable by one or more processors of an electronic device, the one or more programs including instructions for executing the comprehensive assessment method for distribution network carrying capacity based on AHP-CRITIC-MARCOS as described in Embodiment 1.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0124] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method, characterized in that, The method comprises the following steps: a comprehensive evaluation index system is constructed, which comprises power distribution network evaluation indexes, traffic road network evaluation indexes and electric vehicle charging user evaluation indexes; subjective weights of the evaluation indexes are calculated by using an AHP method, and consistency checking is performed by constructing a judgment matrix; objective weights of the evaluation indexes are calculated by using a CRITIC method, by calculating standard deviations and correlation coefficients of the evaluation indexes; comprehensive weights are calculated based on the subjective weights and the objective weights by using minimum discrimination information; a MARCOS method is used to sort and evaluate the carrying capacity of the power distribution network.

2. The power distribution network carrying capacity comprehensive evaluation method based on AHP-CRITIC-MARCOS according to claim 1, characterized in that, The power distribution network evaluation indexes comprise voltage amplitude fluctuation, load fluctuation, line load variance, voltage deviation and system active network loss; the traffic road network evaluation indexes comprise road driving time and road congestion degree; and the electric vehicle charging user evaluation indexes comprise charging satisfaction and charging waiting time.

3. The AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method according to claim 1, characterized in that, The calculation process of the subjective weights comprises the following steps: each evaluation index is compared with another based on expert opinions, and a judgment matrix is constructed; consistency checking is performed on the judgment matrix; the judgment matrix after checking is normalized, and the subjective weights of the evaluation indexes are calculated.

4. The AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method according to claim 1, characterized in that, The calculation process of the objective weights comprises the following steps: data standardization is performed on each evaluation index; standard deviations and correlation coefficients of the evaluation indexes after standardization are calculated; the objective weights of the evaluation indexes are calculated based on the standard deviations and the correlation coefficients.

5. The AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method according to claim 1, characterized in that, The comprehensive weights are calculated by using the following formula: Wherein, α i , β i are subjective weight, objective weight respectively, ω i is comprehensive weight, and n is index quantity.

6. The AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method according to claim 1, characterized in that, The process of using the MARCOS method to sort and evaluate the carrying capacity of the power distribution network comprises the following steps: utility function values of each alternative scheme are calculated based on the comprehensive weights and the values of the evaluation indexes; each alternative scheme is sorted by using the MARCOS method; the sorting result is converted into a visual signal, and evaluation is realized.

7. The AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation method according to claim 6, characterized in that, The utility function values are calculated by using the following formula: wherein, represents a utility function associated with the ideal solution, represents a utility function associated with the negative ideal solution, f(K i ) represents the utility function value of the i-th indicator.

8. An AHP-CRITIC-MARCOS-based power distribution network carrying capacity comprehensive evaluation system, characterized in that, The system is used to realize the comprehensive evaluation method of the carrying capacity of the power distribution network, and comprises: an index system construction module, which is used to construct a comprehensive evaluation index system comprising power distribution network evaluation indexes, traffic road network evaluation indexes and electric vehicle charging user evaluation indexes; a subjective weight calculation module, which is used to calculate subjective weights of the evaluation indexes by using an AHP method, and perform consistency checking by constructing a judgment matrix; an objective weight calculation module, which is used to calculate objective weights of the evaluation indexes by using a CRITIC method, by calculating standard deviations and correlation coefficients of the evaluation indexes; a weight fusion module, which is used to calculate comprehensive weights based on the subjective weights and the objective weights by using minimum discrimination information; a sorting and visualization module, which is used to sort the carrying capacity of the power distribution network by using a MARCOS method, and perform visual display on a visual terminal, and realize evaluation.

9. An electronic device, comprising: The method comprises the following steps: One or more processors and memory having stored therein one or more programs including instructions for performing the AHP-CRITIC-MARCOS based power distribution network carrying capacity comprehensive evaluation method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, One or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing the AHP-CRITIC-MARCOS based power distribution network carrying capacity comprehensive evaluation method of any one of claims 1-7.

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