Method and system for evaluating electric energy quality improvement effect of large-scale electric vehicle charging station

Abstract

The invention relates to the technical field of electric vehicle and power grid interaction, in particular to a large electric vehicle charging station electric energy quality improvement effect evaluation method and system, and the method comprises the steps: obtaining and constructing an original data matrix, and carrying out the standardization preprocessing of heterogeneous index data; an improved three-gradient analytic hierarchy process is adopted, judgment information of experts on the importance of the electric energy quality indexes in the charging and discharging switching scene is analyzed, a judgment matrix is constructed, and subjective weights of the indexes are calculated; processing measured data of the charging station under different operation conditions by adopting an entropy weight method based on divergence maximization, and calculating objective weights of indexes; constructing a collaborative fusion model of subjective and objective weights by adopting a relative entropy distance, and calculating to obtain an optimal combination weight of a V2G scene decision guidance and mining data inherent rule target based on vehicle network interaction; a multi-criterion compromise solution sorting VIKOR algorithm which introduces weight and ideal solution dynamic adjustment is adopted to carry out final good and bad sorting on the improvement effects of different technical schemes.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle and power grid interaction technology, and in particular to a method and system for evaluating the power quality improvement effect of large electric vehicle charging stations. Background Technology

[0002] With the large-scale development of electric vehicles and the widespread application of vehicle-to-grid (V2G) interaction technologies, large-scale electric vehicle charging stations have become a new type of power load unit with bidirectional power regulation capabilities for charging and discharging. The random switching of charging and discharging states by a large number of electric vehicles within the charging station leads to power quality problems characterized by increased voltage deviation, enhanced frequency fluctuations, and complex harmonic spectra. The effective management and assessment of these problems directly affect the operational safety of charging stations and the power supply quality of the distribution network, and also create an urgent need to verify the effectiveness of power supply quality improvement technologies for charging stations that can adapt to the free switching of charging and discharging states.

[0003] In power quality assessment, existing technologies mainly revolve around two stages: weight determination and comprehensive evaluation. Subjective methods, such as the analytic hierarchy process (AHP), rely on expert experience and judgment. While they can reflect actual engineering needs, they suffer from strong subjectivity and difficulty in ensuring consistency. Objective weighting methods, such as the entropy weighting method, are based on statistical data analysis and have sufficient mathematical basis, but may overlook the actual physical importance of the indicators. While a combined subjective and objective weighting method can take into account the advantages of both, traditional fusion methods lack theoretical basis in determining weight ratios and do not consider the special characteristics of electric vehicle charging and discharging scenarios.

[0004] Regarding comprehensive evaluation models, existing methods such as fuzzy comprehensive evaluation and TOPSIS have significant shortcomings when dealing with charging station evaluation objects with multiple operating conditions and strong conflict indicators: the membership function determination of fuzzy comprehensive evaluation is subjective; although the TOPSIS method can determine positive and negative ideal solutions, it fails to fully consider the degree of closeness between the evaluation scheme and the ideal solution and the internal balance of the scheme; the traditional VIKOR method fails to fully consider the differences in importance of different indicators when determining the ideal solution, which weakens the decision-making role of key quality indicators.

[0005] Currently, existing technologies for evaluating the power quality improvement effect of large electric vehicle charging stations have the following shortcomings: First, a dedicated evaluation index system adapted to charging and discharging switching scenarios has not been established; second, the weight allocation method fails to reflect the relative importance of each power quality index under the V2G operation mode; and third, the evaluation model lacks the ability to make horizontal comparisons of different power quality improvement technology solutions.

[0006] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] This invention provides a method and system for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, thereby effectively solving the problems in the background art.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, comprising the following steps:

[0009] Acquire and construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance. Standardize and preprocess the heterogeneous index data.

[0010] An improved three-gradient hierarchical analysis method is used to analyze the judgment information of experts on the importance of power quality indicators in charge-discharge switching scenarios, construct a judgment matrix and calculate the subjective weight of the indicators;

[0011] An entropy weight method based on divergence maximization is used to process measured data of charging stations under different operating conditions and calculate the objective weights of indicators.

[0012] A collaborative fusion model of subjective and objective weights is constructed using relative entropy distance, and the optimal combination weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data are calculated.

[0013] The VIKOR algorithm, which introduces weights and dynamically adjusts the ideal solution, is used to sort the compromise solutions based on multiple criteria. Based on the optimal combination of weights, the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes are calculated, and the improvement effects of different technical schemes are finally ranked.

[0014] Furthermore, the standardization preprocessing of heterogeneous index data includes:

[0015] Suppose there are n observation points and m evaluation indicators, forming the original data matrix. To eliminate the influence of dimensions, a nonlinear standardization method is used to preprocess the data.

[0016] For benefit-type indicators, the standardized formula is:

[0017] ;

[0018] For cost-related indicators, the standardized formula is:

[0019] ;

[0020] In the formula, These are the standardized observations. To standardize the previous observations, , These are the upper and lower limits of the standardized interval, respectively.

[0021] Furthermore, the improved three-gradient hierarchical analysis method is used to analyze the experts' judgment information on the importance of power quality indicators in the charge-discharge switching scenario, construct a judgment matrix, and calculate the subjective weights of the indicators, including:

[0022] Construct a three-gradient judgment matrix:

[0023] ;

[0024] In the formula, =1 indicates that indicator i is more important than indicator j. =0 indicates that indicator i and indicator j are equally important. =-1 indicates that indicator i is less important than indicator j;

[0025] Calculate the relative advantage matrix B:

[0026] ;

[0027] In the formula, This is the hyperbolic tangent function, used to smooth differences;

[0028] Solve the matrix The eigenvector corresponding to the largest eigenvalue is obtained, and then normalized to obtain the subjective weight vector. .

[0029] Furthermore, the method employing the entropy weighting approach based on divergence maximization processes the measured data of charging stations under different operating conditions to calculate the objective weights of the indicators, including:

[0030] Calculate the first The first indicator Observations proportion :

[0031] ;

[0032] Calculate the first Entropy value of each indicator with divergence :

[0033] ;

[0034] ;

[0035] divergence Normalization is performed to obtain the objective weight vector. :

[0036] ;

[0037] In the formula, Let be the objective weight of the j-th indicator.

[0038] Furthermore, the collaborative fusion model for constructing subjective and objective weights using relative entropy distance includes:

[0039] Calculate subjective weights and objective weight Relative to its arithmetic average weight Relative entropy:

[0040] ;

[0041] Relative Entropy and The degree to which subjective and objective weights deviate from the average weights were measured separately, and the fusion coefficient was calculated based on this.

[0042] ;

[0043] Final combined weights for:

[0044] .

[0045] Furthermore, the VIKOR algorithm for ranking multi-criteria compromise solutions, which incorporates weights and dynamically adjusts the ideal solution, includes:

[0046] Determine the weighted normalization matrix:

[0047] ;

[0048] In the formula, Let the combined weight of the j-th indicator be , These are the standardized observations;

[0049] Introducing a weighting factor, the dynamic ideal solution is defined as follows:

[0050] Positive ideal solution:

[0051] ;

[0052] Negative ideal solution:

[0053] ;

[0054] In the formula, This is an adjustment parameter used to make the range of ideal solutions wider for indicators with larger weights, thereby enhancing the discriminative power of important indicators in decision-making.

[0055] Furthermore, the calculation of the group utility value, individual regret value, and trade-off evaluation index corresponding to the charging station operation status under different power quality improvement schemes based on the optimal combination weights, and the final ranking of the improvement effects of different technical schemes accordingly, includes:

[0056] Calculate group utility value and individual regret value :

[0057] ;

[0058] Calculate the trade-off evaluation index :

[0059] ;

[0060] In the formula, , , , , For decision-making mechanism coefficient;

[0061] according to Sort the values ​​from smallest to largest. The smaller the value, the better the overall power quality at that observation point.

[0062] This invention also includes a system for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, using the method described above, wherein the system comprises:

[0063] The data acquisition unit is used to acquire and construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance, and performs standardized preprocessing on the heterogeneous index data.

[0064] The subjective weighting unit is used to analyze the expert judgment information on the importance of power quality indicators in the charging and discharging switching scenario using the improved three-gradient hierarchical analysis method, construct the judgment matrix and calculate the subjective weight of the indicators.

[0065] The objective weighting unit is used to process the measured data of the charging station under different operating conditions and calculate the objective weight of the index using the entropy weighting method based on divergence maximization.

[0066] The combined weight unit is used to construct a collaborative fusion model of subjective and objective weights using relative entropy distance, and to calculate the optimal combined weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data.

[0067] The evaluation unit is used to employ the VIKOR algorithm, a multi-criteria compromise solution ranking algorithm that incorporates weights and dynamically adjusts the ideal solution. Based on the optimal combination of weights, it calculates the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes, and then ranks the improvement effects of different technical schemes accordingly.

[0068] The present invention also includes a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described above.

[0069] The present invention also includes a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0070] The beneficial effects of this invention are as follows: Addressing the frequent switching of charging and discharging states in large electric vehicle charging stations, a complete power quality improvement effect evaluation solution is proposed. By systematically integrating the improved three-gradient hierarchical analysis method, the divergence maximization entropy weight method, and the dynamic ideal solution VIKOR algorithm, a full-process evaluation system from data acquisition and weight allocation to scheme decision-making is established. This method fully considers the dynamic characteristics of power quality indicators in V2G scenarios, and can achieve comprehensive performance evaluation of different power quality improvement technology schemes under complex conditions of multiple operating conditions and multiple indicators, providing accurate and reliable technical basis for power quality management of charging stations.

[0071] The adaptive weight fusion model based on relative entropy distance effectively solves the problem of the lack of theoretical basis for the ratio of subjective and objective weights in traditional combined weighting methods. By calculating the relative entropy of subjective and objective weights relative to the average weight, the fusion coefficient is scientifically determined, so that the final combined weights fully reflect the experts' cognitive experience of key quality indicators in V2G scenarios and accurately reflect the statistical distribution characteristics of measured data from charging stations. This innovative mechanism significantly improves the scientificity and reliability of weight allocation, laying a solid foundation for accurately evaluating the effectiveness of power supply quality improvement technologies.

[0072] By employing the VIKOR algorithm for dynamic ideal solutions and introducing weighting factors to dynamically adjust the range of ideal solutions, the shortcomings of insufficient discrimination of key indicators in traditional multi-attribute decision-making methods are effectively overcome. This design strategically enhances the role of power quality indicators with higher weights in the decision-making process, thereby more accurately reflecting the performance differences of different power quality improvement schemes in key indicators. Simultaneously, combined with boundary buffering standardization methods, the interference of extreme measurement data on the evaluation results is effectively suppressed, ensuring the robustness of the evaluation system. The entire evaluation method has good engineering applicability and can be automated through programming, greatly improving its convenience and efficiency in practical applications at large charging stations. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a flowchart of the method in Example 1;

[0075] Figure 2 This is a schematic diagram of the system structure in Example 1;

[0076] Figure 3 This is a schematic diagram of the weighted fusion model of relative entropy distance in Example 2;

[0077] Figure 4 This is a schematic diagram of the VIKOR algorithm that introduces dynamic ideal solutions in Example 2;

[0078] Figure 5 This refers to the subjective and objective fusion weight allocation result in Example 2;

[0079] Figure 6 The comprehensive evaluation results of the dynamic VIKOR algorithm in Example 2;

[0080] Figure 7 This is a schematic diagram of the structure of the computer device of the present invention. Detailed Implementation

[0081] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0082] Example 1:

[0083] like Figure 1 As shown: A method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, including the following steps:

[0084] Acquire and construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance. Standardize and preprocess the heterogeneous index data.

[0085] An improved three-gradient hierarchical analysis method is used to analyze the judgment information of experts on the importance of power quality indicators in charge-discharge switching scenarios, construct a judgment matrix and calculate the subjective weight of the indicators;

[0086] An entropy weight method based on divergence maximization is used to process measured data of charging stations under different operating conditions and calculate the objective weights of indicators.

[0087] A collaborative fusion model of subjective and objective weights is constructed using relative entropy distance, and the optimal combination weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data are calculated.

[0088] The VIKOR algorithm, which introduces weights and dynamically adjusts the ideal solution, is adopted to sort the compromise solutions based on multiple criteria. Based on the optimal combination of weights, the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes are calculated, and the improvement effects of different technical schemes are finally ranked.

[0089] To address the frequent switching between charging and discharging states at large electric vehicle charging stations, a comprehensive power quality improvement evaluation solution is proposed. By systematically integrating an improved three-gradient analytic hierarchy process (AHP), the divergence-maximization entropy weighting method, and the dynamic ideal solution VIKOR algorithm, a full-process evaluation system is established, encompassing data acquisition, weight allocation, and solution decision-making. This method fully considers the dynamic characteristics of power quality indicators in V2G scenarios, enabling comprehensive performance evaluation of different power quality improvement technologies under complex conditions with multiple operating modes and indicators. This provides accurate and reliable technical support for power quality management at charging stations.

[0090] The adaptive weight fusion model based on relative entropy distance effectively solves the problem of the lack of theoretical basis for the ratio of subjective and objective weights in traditional combined weighting methods. By calculating the relative entropy of subjective and objective weights relative to the average weight, the fusion coefficient is scientifically determined, so that the final combined weights fully reflect the experts' cognitive experience of key quality indicators in V2G scenarios and accurately reflect the statistical distribution characteristics of measured data from charging stations. This innovative mechanism significantly improves the scientificity and reliability of weight allocation, laying a solid foundation for accurately evaluating the effectiveness of power supply quality improvement technologies.

[0091] By employing the VIKOR algorithm for dynamic ideal solutions and introducing weighting factors to dynamically adjust the range of ideal solutions, the shortcomings of insufficient discrimination of key indicators in traditional multi-attribute decision-making methods are effectively overcome. This design strategically enhances the role of power quality indicators with higher weights in the decision-making process, thereby more accurately reflecting the performance differences of different power quality improvement schemes in key indicators. Simultaneously, combined with boundary buffering standardization methods, the interference of extreme measurement data on the evaluation results is effectively suppressed, ensuring the robustness of the evaluation system. The entire evaluation method has good engineering applicability and can be automated through programming, greatly improving its convenience and efficiency in practical applications at large charging stations.

[0092] In this embodiment, the heterogeneous index data undergoes standardization preprocessing, including:

[0093] Suppose there are n observation points and m evaluation indicators, forming the original data matrix. To eliminate the influence of dimensions, a nonlinear standardization method is used to preprocess the data.

[0094] For benefit-type indicators, the standardized formula is:

[0095] ;

[0096] For cost-related indicators, the standardized formula is:

[0097] ;

[0098] In the formula, These are the standardized observations. To standardize the previous observations, , These are the upper and lower limits of the standardized interval, respectively.

[0099] Among them, an improved three-gradient hierarchical analysis method is used to analyze the experts' judgment information on the importance of power quality indicators in charge-discharge switching scenarios, construct a judgment matrix and calculate the subjective weights of the indicators, including:

[0100] Construct the three-gradient judgment matrix:

[0101] ;

[0102] In the formula, =1 indicates that indicator i is more important than indicator j. =0 indicates that indicator i and indicator j are equally important. =-1 indicates that indicator i is less important than indicator j;

[0103] Calculate the relative advantage matrix B:

[0104] ;

[0105] In the formula, This is the hyperbolic tangent function, used to smooth differences;

[0106] Solve the matrix The eigenvector corresponding to the largest eigenvalue is obtained, and then normalized to obtain the subjective weight vector. .

[0107] An entropy weight method based on divergence maximization is used to process measured data from charging stations under different operating conditions, and to calculate the objective weights of indicators, including:

[0108] Calculate the first The first indicator Observations proportion :

[0109] ;

[0110] Calculate the first Entropy value of each indicator with divergence :

[0111] ;

[0112] ;

[0113] divergence Normalization is performed to obtain the objective weight vector. :

[0114] ;

[0115] In the formula, Let be the objective weight of the j-th indicator.

[0116] In this embodiment, a collaborative fusion model of subjective and objective weights is constructed using relative entropy distance, including:

[0117] Calculate subjective weights and objective weight Relative to its arithmetic average weight Relative entropy:

[0118] ;

[0119] Relative Entropy and The degree to which subjective and objective weights deviate from the average weights were measured separately, and the fusion coefficient was calculated based on this.

[0120] ;

[0121] Final combined weights for:

[0122] .

[0123] The VIKOR algorithm, which employs a multi-criteria compromise solution ranking system and dynamically adjusts the ideal solution by introducing weights, includes:

[0124] Determine the weighted normalization matrix:

[0125] ;

[0126] In the formula, Let the combined weight of the j-th indicator be , These are the standardized observations;

[0127] Introducing a weighting factor, the dynamic ideal solution is defined as follows:

[0128] Positive ideal solution:

[0129] ;

[0130] Negative ideal solution:

[0131] ;

[0132] In the formula, This is an adjustment parameter used to make the range of ideal solutions wider for indicators with larger weights, thereby enhancing the discriminative power of important indicators in decision-making.

[0133] Among them, based on the optimal combination weight, the group utility value, individual regret value, and trade-off evaluation index corresponding to the charging station operation status under different power quality improvement schemes are calculated, and the improvement effects of different technical schemes are finally ranked according to their merits, including:

[0134] Calculate group utility value and individual regret value :

[0135] ;

[0136] Calculate the trade-off evaluation index :

[0137] ;

[0138] In the formula, , , , , For decision-making mechanism coefficient;

[0139] according to Sort the values ​​from smallest to largest. The smaller the value, the better the overall power quality at that observation point.

[0140] like Figure 2 As shown, this embodiment also includes a power quality improvement effect evaluation system for large-scale electric vehicle charging stations, using the method described above. The system includes:

[0141] The data acquisition unit is used to acquire and construct the raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance, and performs standardized preprocessing on the heterogeneous index data.

[0142] The subjective weighting unit is used to analyze the expert judgment information on the importance of power quality indicators in the charging and discharging switching scenario using the improved three-gradient hierarchical analysis method, construct the judgment matrix and calculate the subjective weight of the indicators.

[0143] The objective weighting unit is used to process the measured data of the charging station under different operating conditions and calculate the objective weight of the index using the entropy weighting method based on divergence maximization.

[0144] The combined weight unit is used to construct a collaborative fusion model of subjective and objective weights using relative entropy distance, and to calculate the optimal combined weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data.

[0145] The evaluation unit employs the VIKOR algorithm, a multi-criteria compromise solution ranking algorithm that incorporates weights and dynamically adjusts the ideal solution. Based on the optimal combination of weights, it calculates the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes, and then ranks the improvement effects of different technical schemes accordingly.

[0146] Example 2:

[0147] This specific implementation discloses a method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations based on a weighted approach combining subjective and objective factors and the VIKOR algorithm with a dynamic ideal solution. The method, designed for scenarios where electric vehicles freely switch between charging and discharging states, achieves a comprehensive performance evaluation of different power quality improvement technologies by establishing a dedicated evaluation system, optimizing the weighting calculation mechanism, and improving the decision-making model. The specific implementation steps include the following:

[0148] S1: Construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The data matrix should include at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance. Standardize the heterogeneous index data to eliminate differences in dimensions and tendencies.

[0149] S2: An improved three-gradient hierarchical analysis method is used to analyze the judgment information of experts on the importance of power quality indicators in the charging and discharging switching scenario, construct a judgment matrix and calculate the subjective weight of the indicators.

[0150] S3: The entropy weight method based on divergence maximization is used to process the measured data of charging stations under different operating conditions and calculate the objective weight of the indicators.

[0151] S4: Construct a collaborative fusion model of subjective and objective weights using relative entropy distance, and calculate the optimal combination weights that can both reflect the decision-making orientation of V2G scenarios and uncover the inherent laws of data.

[0152] S5: The VIKOR algorithm, which introduces weights and dynamically adjusts the ideal solution, is used to calculate the group utility value, individual regret value, and trade-off evaluation index corresponding to the charging station operation status under different power quality improvement schemes. Based on this, the improvement effects of different technical schemes are finally ranked.

[0153] The core of this invention lies in improving evaluation results through innovation at three technical levels: at the data preprocessing level, a dedicated index system and standardized methods adapted to charging and discharging scenarios are established; at the weight determination level, a weight optimization allocation mechanism that integrates subjective and objective factors is proposed; and at the scheme decision-making level, a dynamic ideal solution mechanism is innovatively introduced into the VIKOR algorithm. The synergistic effect of these three technical levels ensures that the evaluation results not only meet the specific requirements of V2G scenarios but also possess good engineering applicability.

[0154] In step S1, there are n observation points and m evaluation indicators, forming the original data matrix. To eliminate the influence of dimensions, a nonlinear standardization method is used to preprocess the data. For benefit-type indicators, the standardization formula is:

[0155]

[0156] For cost-related indicators, the standardized formula is:

[0157]

[0158] In step S2, the calculation process of the improved three-gradient analytic hierarchy process is as follows:

[0159] Construct the three-gradient judgment matrix:

[0160]

[0161] In equation (3), =1 indicates that indicator i is more important than indicator j. =0 indicates that indicator i and indicator j are equally important. =-1 indicates that indicator i is less important than indicator j.

[0162] Calculate the relative advantage matrix :

[0163]

[0164] In equation (4), It is a hyperbolic tangent function used to smooth differences and prevent the influence of individual extreme judgments.

[0165] By solving the matrix The eigenvector corresponding to the largest eigenvalue is obtained, and then normalized to obtain the subjective weight vector. .

[0166] In step S3, the calculation process based on the entropy weight method for divergence maximization is as follows:

[0167] Calculate the first The first indicator The proportion of each observation :

[0168]

[0169] Calculate the first Entropy value of each indicator with divergence :

[0170]

[0171]

[0172] divergence Normalization is performed to obtain the objective weight vector. :

[0173]

[0174] like Figure 3 As shown, in step S4, the weighted fusion model based on relative entropy distance is as follows:

[0175] Calculate subjective weights and objective weight Relative to its arithmetic average weight Relative entropy:

[0176]

[0177] Relative Entropy and The degree to which subjective and objective weights deviated from the average weights was measured separately. Based on this, the fusion coefficient was calculated:

[0178]

[0179] The further a certain weighting scheme deviates from the average level, the more "individualistic" it is considered. When merging, its weight should be appropriately reduced, and a scheme with a weight closer to the average level should be trusted more.

[0180] Final combined weights for:

[0181]

[0182] like Figure 4 As shown, in step S5, the specific process of introducing the VIKOR algorithm with dynamically adjusted weights and ideal solutions is as follows:

[0183] a) Determine the weighted normalization matrix:

[0184]

[0185] b) Determining the dynamic ideal solution: The traditional VIKOR method uses fixed positive ideal solutions (maximum values) and negative ideal solutions (minimum values). This embodiment introduces a weighting factor and defines the dynamic ideal solution as follows:

[0186] Positive ideal solution:

[0187]

[0188] Negative ideal solution:

[0189]

[0190] In the formula, With a small adjustment parameter, this design allows the range of ideal solutions for indicators with larger weights to be appropriately broadened, thereby enhancing the discriminative power of important indicators in decision-making.

[0191] c) Calculate group utility value and individual regret value :

[0192]

[0193] d) Calculate the trade-off evaluation index :

[0194]

[0195] In equation (16), , , , , This is the decision-making mechanism coefficient, usually taken as 0.5 to balance group utility and individual regret.

[0196] e) According to Sort the values ​​from smallest to largest. The smaller the value, the better the overall power quality at that observation point.

[0197] This embodiment uses the Wuxi Changjiang Road No. 3 Vehicle-to-Grid Interaction Verification Base, a demonstration project of the State Grid's "Research and Application of Power Supply Quality Improvement Technology Adapting to Free Switching of Charging and Discharging States," as an application object to verify the effectiveness of this method. This base is equipped with a large number of bidirectional charging piles, which can simulate typical operating conditions where electric vehicle clusters freely switch between various states such as charging, discharging, and idle. This invention aims to comprehensively evaluate and rank the operational effects of this base under different power quality management schemes.

[0198] In this embodiment, five different operating periods under different governance schemes at the base were selected as observation points, i.e., n=5. Five key power quality indicators strongly correlated with charge-discharge switching were selected, i.e., m=5, forming the original data matrix X as shown in Table 1.

[0199] Table 1 Original Data Matrix

[0200]

[0201] According to the specific implementation steps of this embodiment, the following can be obtained: Figure 5 The results of the subjective and objective fusion weight allocation shown are as follows: Figure 6 The dynamic VIKOR comprehensive evaluation results are shown.

[0202] Sort by Q value from smallest to largest. Figure 6 It can be seen that observation point 1 exhibits the best overall power quality performance, meaning that under this governance strategy, the power quality improvement effect is the best, while scheme 5 shows the worst overall performance. This assessment result can provide a direct and quantitative basis for power station operators to optimize their operation strategies and select the most effective power quality governance scheme.

[0203] Please see Figure 7 The diagram shows a structural schematic of a computer device provided in an embodiment of this application. An embodiment of this application provides a computer device 400, including a processor 410 and a memory 420. The memory 420 stores a computer program executable by the processor 410. When the computer program is executed by the processor 410, it performs the method described above.

[0204] This application embodiment also provides a storage medium 430, on which a computer program is stored, and the computer program is executed by a processor 410 to perform the above method.

[0205] The storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0206] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0207] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0208] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0209] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0210] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0211] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0212] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0213] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, characterized in that, Includes the following steps: Acquire and construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance. Standardize and preprocess the heterogeneous index data. An improved three-gradient hierarchical analysis method is used to analyze the judgment information of experts on the importance of power quality indicators in charge-discharge switching scenarios, construct a judgment matrix and calculate the subjective weight of the indicators; An entropy weight method based on divergence maximization is used to process measured data of charging stations under different operating conditions and calculate the objective weights of indicators. A collaborative fusion model of subjective and objective weights is constructed using relative entropy distance, and the optimal combination weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data are calculated. The VIKOR algorithm, which introduces weights and dynamically adjusts the ideal solution, is used to sort the compromise solutions based on multiple criteria. Based on the optimal combination of weights, the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes are calculated, and the improvement effects of different technical schemes are finally ranked.

2. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 1, characterized in that, The standardization preprocessing of heterogeneous index data includes: Suppose there are n observation points and m evaluation indicators, forming the original data matrix. To eliminate the influence of dimensions, a nonlinear standardization method is used to preprocess the data. For benefit-type indicators, the standardized formula is: ； For cost-related indicators, the standardized formula is: ； In the formula, These are the standardized observations. To standardize the previous observations, , These are the upper and lower limits of the standardized interval, respectively.

3. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 1, characterized in that, The improved three-gradient hierarchical analysis method is used to analyze the experts' judgment information on the importance of power quality indicators in charge-discharge switching scenarios, construct a judgment matrix, and calculate the subjective weights of the indicators, including: Construct a three-gradient judgment matrix: ； In the formula, =1 indicates that indicator i is more important than indicator j. =0 indicates that indicator i and indicator j are equally important. =-1 indicates that indicator i is less important than indicator j; Calculate the relative advantage matrix B: ； In the formula, This is the hyperbolic tangent function, used to smooth differences; Solve the matrix The eigenvector corresponding to the largest eigenvalue is obtained, and then normalized to obtain the subjective weight vector. .

4. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 3, characterized in that, The method employs an entropy weighting approach based on divergence maximization to process measured data from charging stations under different operating conditions and calculates objective weights for indicators, including: Calculate the first The first indicator Observations proportion : ； Calculate the first Entropy value of each indicator with divergence : ； ； divergence Normalization is performed to obtain the objective weight vector. : ； In the formula, Let be the objective weight of the j-th indicator.

5. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 4, characterized in that, The collaborative fusion model that uses relative entropy distance to construct subjective and objective weights includes: Calculate subjective weights and objective weight Relative to its arithmetic average weight Relative entropy: ； Relative Entropy and The degree to which subjective and objective weights deviate from the average weights were measured separately, and the fusion coefficient was calculated based on this. ； Final combined weights for: 。 6. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 1, characterized in that, The VIKOR algorithm, which employs a multi-criteria compromise solution sorting method that incorporates weights and dynamically adjusts the ideal solution, includes: Determine the weighted normalization matrix: ； In the formula, Let the combined weight of the j-th indicator be , These are the standardized observations; Introducing a weighting factor, the dynamic ideal solution is defined as follows: Positive ideal solution: ； Negative ideal solution: ； In the formula, This is an adjustment parameter used to make the range of ideal solutions wider for indicators with larger weights, thereby enhancing the discriminative power of important indicators in decision-making.

7. The method for evaluating the power quality improvement effect of large-scale electric vehicle charging stations according to claim 6, characterized in that, The calculation of the group utility value, individual regret value, and trade-off evaluation index corresponding to the charging station operation status under different power quality improvement schemes based on the optimal combination weights, and the final ranking of the improvement effects of different technical schemes based on these, includes: Calculate group utility value and individual regret value : ； Calculate the trade-off evaluation index : ； In the formula, , , , , For decision-making mechanism coefficient; according to Sort the values ​​in ascending order. The smaller the value, the better the overall power quality at that observation point.

8. A system for evaluating the power quality improvement effect of large-scale electric vehicle charging stations, characterized in that, Using the method of any one of claims 1 to 7, the system comprises: The data acquisition unit is used to acquire and construct a raw data matrix for power quality assessment of large electric vehicle charging stations. The raw data matrix includes at least voltage deviation, frequency deviation, harmonic distortion rate and three-phase imbalance, and performs standardized preprocessing on the heterogeneous index data. The subjective weighting unit is used to analyze the expert judgment information on the importance of power quality indicators in the charging and discharging switching scenario using the improved three-gradient hierarchical analysis method, construct the judgment matrix and calculate the subjective weight of the indicators. The objective weighting unit is used to process the measured data of the charging station under different operating conditions and calculate the objective weight of the index using the entropy weighting method based on divergence maximization. The combined weight unit is used to construct a collaborative fusion model of subjective and objective weights using relative entropy distance, and to calculate the optimal combined weights based on the decision-making orientation of the vehicle-to-grid (V2G) scenario and the goal of mining the inherent laws of data. The evaluation unit is used to employ the VIKOR algorithm, a multi-criteria compromise solution ranking algorithm that incorporates weights and dynamically adjusts the ideal solution. Based on the optimal combination of weights, it calculates the group utility value, individual regret value, and compromise evaluation index corresponding to the charging station operation status under different power quality improvement schemes, and then ranks the improvement effects of different technical schemes accordingly.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-7.

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