A method and device for dynamically evaluating response capability of a battery swap station for peak regulation and frequency regulation based on two-dimensional analysis, and a medium

Abstract

The present disclosure relates to a dynamic evaluation method and device for the peak regulation and frequency modulation response capability of a battery swap station based on two-dimensional analysis. The method comprises: constructing a multi-working condition evaluation index system for the peak regulation and frequency modulation response capability of the battery swap station, setting evaluation grades and score thresholds according to the grid operation working condition; using a subjective and objective fusion weight distribution method to calculate the comprehensive weights of static dimension indexes and dynamic dimension indexes respectively, and fusing by the product normalization method; calculating the closeness score of the static dimension of the battery swap station; constructing a dynamic response capability evaluation model based on the grey correlation degree method to calculate the grey correlation degree score of the dynamic dimension of the battery swap station; weighting and fusing the static score and the dynamic score to obtain the comprehensive score of the peak regulation and frequency modulation response capability of the battery swap station, and judging whether it meets the standard and participates in the priority according to the preset threshold. The present disclosure constructs a benchmark sequence consistent with the grid dispatching rules for different evaluation working conditions of the dynamic dimension, and improves the accuracy of the grey correlation degree method in the dynamic dimension evaluation.

Description

Technical Field

[0001] This disclosure relates to the field of energy storage technology, and more specifically, to a method, device, and medium for dynamic evaluation of the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis. Background Technology

[0002] Battery swapping stations are a new type of energy storage system with advantages such as flexible regulation and rapid response to grid dispatch. They can participate in grid peak shaving and frequency regulation as distributed energy storage units. However, in engineering applications, in addition to meeting the charging and swapping needs of electric vehicles, they also need to participate in grid peak shaving and frequency regulation auxiliary services. The operating conditions of core equipment are complex and variable, resulting in significant differences in peak shaving and frequency regulation response capabilities. Traditional evaluation methods are mostly single-condition adaptation, have one-sided weight allocation, and incomplete dimensional coverage, making it difficult to comprehensively, accurately, and dynamically evaluate the peak shaving and frequency regulation response capabilities of battery swapping stations. They also fail to provide benchmark data guidance for grid dispatch optimization and battery swapping station operation. These problems urgently need to be solved.

[0003] When electric vehicle battery swapping stations participate in grid peak shaving and frequency regulation, accurate assessment of their response capabilities is a prerequisite for ensuring the effectiveness of vehicle-grid interaction. Existing assessment methods, such as static assessment based on TOPSIS, can only reflect performance levels at a single moment, while dynamic assessment based on Grey Relational Analysis (GRA) can capture time-series characteristics. However, both are often simply combined, lacking a scientific integration mechanism that considers both subjective and objective weights. Furthermore, existing technologies suffer from the following shortcomings: the indicator models do not cover multiple operating conditions, including normal, heavy load, and extreme conditions, nor do they fully incorporate key dimensions such as adjustable capacity and duration; weight calculations are either purely objective (e.g., entropy weight method) and detached from actual needs, or purely subjective (e.g., analytic hierarchy process) and influenced by expert experience; there is a lack of organic integration between static levels and dynamic trends, and no adaptation design for peak shaving (emphasizing capacity boundaries) and frequency regulation (emphasizing time-series following) scenarios. Therefore, existing methods are insufficient for comprehensively and accurately assessing the overall response capabilities of battery swapping stations, necessitating an assessment method that considers multiple operating conditions, integrates subjective and objective factors, and coordinates static and dynamic approaches. Thus, one or more methods are needed to address these issues.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a method and device for dynamic evaluation of the peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis, thereby overcoming, to at least some extent, one or more problems caused by the limitations and defects of related technologies.

[0006] According to one aspect of this disclosure, a dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis is provided, including: A multi-condition evaluation index system for the peak-shaving and frequency regulation response capability of battery swapping stations is constructed. Evaluation levels and scoring thresholds are set according to the power grid operating conditions. Based on the evaluation levels and scoring thresholds, the evaluation indexes are divided into static dimension indexes and dynamic dimension indexes. A weighting method that combines subjective and objective factors is adopted to calculate the combined weights of the static and dynamic dimension indicators respectively. A static response capability assessment model is constructed based on the TOPSIS method. The national standard limit is used as the positive and negative ideal solutions, and the proximity score of the static dimension indicators of the battery swapping station is calculated by combining the comprehensive weights. A dynamic response capability assessment model is constructed based on the grey relational analysis method. The power grid dispatch instruction sequence is used as the baseline sequence, and the grey relational analysis score of the dynamic dimension index of the battery swapping station is calculated by combining the comprehensive weight. Based on the current operating conditions of the power grid, a fusion coefficient is set for static and dynamic dimension indicators. Based on the fusion coefficient, the proximity score of the static dimension indicators and the grey relational score of the dynamic dimension indicators are weighted and fused to obtain a comprehensive score for the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of battery swapping stations participating in peak shaving and frequency regulation is determined based on the overall score.

[0007] In one exemplary embodiment of this disclosure, the power grid operating conditions include normal operating conditions, medium- and low-voltage heavy-load operating conditions, and extreme operating conditions; The evaluation levels include: dynamic compliance and static compliance, dynamic compliance but static non-compliance, dynamic non-compliance but static compliance, and dynamic non-compliance and static non-compliance. The scoring thresholds set according to different working conditions are: comprehensive scoring threshold, static scoring threshold, and dynamic scoring threshold; The static dimension indicators include three criteria layers: capacity potential, steady-state accuracy, and adjustability, covering adjustable capacity, continuous charge and discharge time, battery SOC adjustable range, steady-state active power control error, peak shaving plan execution qualification rate, voltage deviation, rated charge and discharge power, and minimum active power regulation rate. The dynamic dimension indicators include three criterion layers: primary frequency modulation capability, secondary frequency modulation capability, and ramping capability. They cover primary frequency modulation response delay, primary frequency modulation rise time, primary frequency modulation maximum adjustment depth, AGC response start time, AGC response rise time, AGC adjustment accuracy, bidirectional ramping rate, ramping response delay, and ramping stability.

[0008] In one exemplary embodiment of this disclosure, the step of employing a subjective-objective fusion weight allocation method to calculate the comprehensive weights of the static dimension indicators and the dynamic dimension indicators includes: Using the analytic hierarchy process, a judgment matrix is ​​constructed for indicators at the same level under the same parent node. After consistency testing, the subjective weights of static and dynamic dimension indicators are synthesized. The entropy weight method is used to independently calculate the information entropy and difference coefficient for static and dynamic dimension indicators, and the objective weights of static and dynamic dimension indicators are obtained after normalization. The product normalization method is used to integrate subjective weights and objective weights, and a subjective weight ratio coefficient for working condition adaptation is introduced to obtain the comprehensive weights of static and dynamic dimension indicators.

[0009] In one exemplary embodiment of this disclosure, the subjective weighting is 0.6 under normal operating conditions, 0.7 under heavy-load operating conditions, and 0.8 under extreme operating conditions.

[0010] In one exemplary embodiment of this disclosure, the calculation of the grey relational score of the dynamic dimension index of the battery swapping station includes: The baseline sequence includes the AGC dispatch command sequence issued by the power grid, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence; The comparison sequence is the actual output time sequence of the battery swapping station; The dynamic trend similarity of each indicator is obtained by calculating the absolute value difference sequence, global extreme value and grey relational coefficient, and the grey relational score is obtained by weighted summation based on the comprehensive weight.

[0011] In one exemplary embodiment of this disclosure, the fusion coefficient of static dimension indicators and dynamic dimension indicators is set according to different working conditions. Under normal working conditions, the fusion coefficient of static dimension indicators is 0.7. Battery swapping stations with higher overall scores will be given priority in participating in grid peak shaving and frequency regulation services.

[0012] In one exemplary embodiment of this disclosure, the method further includes model validity verification: The Spearman-level correlation coefficient is used to verify the consistency between the evaluation ranking results and the actual power grid dispatch assessment results, requiring a correlation coefficient greater than 0.8; A sensitivity analysis of ±20% was performed on the fusion coefficient, requiring a Spearman correlation coefficient ≥0.9 for the sorting results before and after perturbation.

[0013] In one exemplary embodiment of this disclosure, the comprehensive weight and evaluation results are dynamically updated based on the rolling updated real-time operating data to achieve real-time dynamic evaluation of the peak-shaving and frequency regulation response capability of the battery swapping station.

[0014] In one aspect of this disclosure, a dynamic evaluation device for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis is provided, comprising: The indicator system construction module is used to construct a multi-condition evaluation indicator system for the peak-shaving and frequency regulation response capability of the battery swapping station. It sets evaluation levels and scoring thresholds according to the power grid operating conditions, and divides the evaluation indicators into static dimension indicators and dynamic dimension indicators based on the evaluation levels and scoring thresholds. The weight fusion calculation module is used to calculate the comprehensive weight of the static dimension index and the dynamic dimension index respectively by adopting a weight allocation method that combines subjective and objective factors. The static evaluation module is used to construct a static response capability evaluation model based on the TOPSIS method, using national standard limits as positive and negative ideal solutions, and combining the comprehensive weights to calculate the closeness score of the static dimension indicators of the battery swapping station. The dynamic evaluation module is used to construct a dynamic response capability evaluation model based on the grey relational analysis method. It uses the power grid dispatch instruction sequence as the baseline sequence and combines the comprehensive weight to calculate the grey relational score of the dynamic dimension indicators of the battery swapping station. The comprehensive evaluation module is used to set the fusion coefficient of static and dynamic dimension indicators according to the current operating conditions of the power grid. Based on the fusion coefficient of the static and dynamic dimension indicators, the proximity score of the static dimension indicators and the gray correlation score of the dynamic dimension indicators are weighted and fused to obtain a comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of the battery swapping station to participate in peak-shaving and frequency regulation is determined according to the comprehensive score.

[0015] In one exemplary embodiment of this disclosure, the indicator system construction module includes: Power grid operating conditions include normal operating conditions, medium and low voltage heavy load operating conditions, and extreme operating conditions; The evaluation levels include: meeting both dynamic and static standards, meeting dynamic standards but failing to meet static standards, failing to meet dynamic standards but meeting static standards, and failing to meet both dynamic and static standards. The scoring thresholds set according to different working conditions are: comprehensive scoring threshold, static scoring threshold, and dynamic scoring threshold; The static dimension indicators include three criteria layers: capacity potential, steady-state accuracy, and adjustability, covering adjustable capacity, continuous charge and discharge time, battery SOC adjustable range, steady-state active power control error, peak shaving plan execution qualification rate, voltage deviation, rated charge and discharge power, and minimum active power regulation rate. The dynamic dimension indicators include three criterion layers: primary frequency regulation capability, secondary frequency regulation capability, and ramping capability. They cover primary frequency regulation response delay, primary frequency regulation rise time, primary frequency regulation maximum adjustment depth, AGC response start time, AGC response rise time, AGC adjustment accuracy, bidirectional ramping rate, ramping response delay, and ramping stability.

[0016] In one exemplary embodiment of this disclosure, the weight fusion calculation module includes: The subjective weight calculation submodule is used to construct a judgment matrix for indicators of the same level under the same parent node using the analytic hierarchy process, and synthesize the subjective weights of static and dynamic dimension indicators after consistency verification. The objective weight calculation submodule is used to calculate the information entropy and difference coefficient independently for static and dynamic dimension indicators using the entropy weight method, and obtain the objective weights of static and dynamic dimension indicators after normalization. The fusion submodule is used to merge subjective weights and objective weights using the product normalization method, and introduces the subjective weight ratio coefficient of working condition adaptation to obtain the comprehensive weight of static and dynamic dimension indicators.

[0017] In one exemplary embodiment of this disclosure, the coefficient of the subjective weight ratio is set as follows: 0.6 under normal working conditions, 0.7 under heavy-load working conditions, and 0.8 under extreme working conditions.

[0018] In one exemplary embodiment of this disclosure, the dynamic evaluation module includes: The baseline sequence construction submodule is used to construct baseline sequences, including the AGC dispatch command sequence issued by the power grid, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence. The comparison sequence acquisition submodule is used to acquire the actual output time sequence of the battery swapping station as a comparison sequence. The dynamic score calculation submodule is used to obtain the dynamic trend similarity of each indicator by calculating the absolute value difference sequence, global extreme value and grey relation coefficient, and to obtain the grey relation score by weighted summation based on the comprehensive weight.

[0019] In an exemplary embodiment of this disclosure, the fusion coefficient of the static dimension index and the dynamic dimension index in the comprehensive evaluation module is set according to different working conditions. Under normal working conditions, the fusion coefficient of the static dimension index is 0.7. The comprehensive evaluation module is also used to determine the priority of battery swapping stations participating in peak shaving and frequency regulation according to the comprehensive score from high to low.

[0020] In one exemplary embodiment of this disclosure, the apparatus further includes a verification module, which is used to verify the consistency between the evaluation ranking results and the actual power grid dispatch assessment results using Spearman's level correlation coefficient, and requires the correlation coefficient to be greater than 0.8; The verification module performs a ±20% sensitivity analysis on the fusion coefficient and requires the Spearman correlation coefficient of the sorting results before and after the perturbation to be ≥0.9.

[0021] In one exemplary embodiment of this disclosure, the apparatus further includes a real-time update module, used to dynamically update the comprehensive weight and evaluation results based on the rolling updated real-time operating data, so as to realize the real-time dynamic evaluation of the peak-shaving and frequency regulation response capability of the battery swapping station.

[0022] In one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method according to any one of the preceding claims.

[0023] An exemplary embodiment of this disclosure discloses a dynamic evaluation method for the peak-shaving and frequency-regulating response capability of a battery swapping station based on dual-dimensional analysis. The method includes: constructing a multi-condition evaluation index system for the peak-shaving and frequency-regulating response capability of the battery swapping station, dividing the evaluation indexes into static and dynamic dimension indicators, and setting evaluation levels and scoring thresholds according to the power grid operating conditions; employing a weight allocation method that integrates subjective and objective factors to calculate the comprehensive weights of the static and dynamic dimension indicators, where subjective weights are determined based on the analytic hierarchy process (AHP), objective weights are determined based on the entropy weight method, and fused using the product normalization method; constructing a static response capability evaluation model based on the TOPSIS method, using national standard limits as positive and negative ideal solutions to calculate the proximity score of the static dimension of the battery swapping station; constructing a dynamic response capability evaluation model based on the grey relational analysis method, using the power grid dispatch instruction sequence as the benchmark sequence to calculate the grey relational score of the dynamic dimension of the battery swapping station; setting a fusion coefficient for the static and dynamic dimensions according to the current power grid operating conditions, weighting and fusing the static and dynamic scores to obtain a comprehensive score for the peak-shaving and frequency-regulating response capability of the battery swapping station, and determining whether it meets the standards and its priority based on a preset threshold.

[0024] This disclosure employs the Analytic Hierarchy Process (AHP) to determine subjective weights and the entropy weight method to determine objective weights, and then integrates them through product normalization. This approach retains the guiding role of expert experience in key indicators while utilizing the information entropy of the data itself to eliminate human interference, making the comprehensive weights more closely reflect the actual operating characteristics of the battery swapping station. This fundamentally solves the problem of inaccurate assessments caused by purely subjective or purely objective weighting. Based on the TOPSIS method, using national standard limits as positive and negative ideal solutions to calculate static proximity scores, it can accurately quantify the peak-shaving (capacity boundary) level of the battery swapping station at any given time. Based on the grey relational analysis method, using the power grid dispatch command sequence as the benchmark sequence to calculate dynamic grey relational scores, it can accurately reflect the response trend of the battery swapping station to frequency regulation (time-sequence following) commands. Both methods are independently modeled for the different requirements of peak shaving and frequency regulation, avoiding information aliasing from a single model. The fusion coefficients for static and dynamic dimensions are set according to the current operating conditions of the power grid. The static and dynamic scores are weighted and fused to obtain a comprehensive score, and compliance and participation priority are determined based on preset thresholds. This mechanism enables the assessment results to dynamically adapt to the different urgency requirements of the power grid for peak shaving or frequency regulation. While ensuring the accuracy of the assessment, it directly outputs quantifiable dispatching basis, achieving a precise match between the response capability of the battery swapping station and the actual needs of the power grid.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0026] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0027] Figure 1 A flowchart is shown for a dynamic evaluation method of peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis, according to an exemplary embodiment of the present disclosure. Figure 2 A schematic diagram of the evaluation index system for the peak-shaving and frequency regulation response capability of a battery swapping station, based on a two-dimensional analysis-based dynamic evaluation method according to an exemplary embodiment of the present disclosure, is shown. Figure 3 The illustration shows a dynamic and static classification weighting method based on subjective and objective fusion, which is a dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis, according to an exemplary embodiment of the present disclosure. Figure 4 A static evaluation flowchart of a dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis, according to an exemplary embodiment of the present disclosure, is shown. Figure 5 A dynamic evaluation flowchart of a method for dynamically evaluating the peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis, according to an exemplary embodiment of the present disclosure, is shown. Figure 6 A flowchart illustrating the evaluation process of the peak-shaving and frequency-regulation response capability of a battery swapping station based on a two-dimensional analysis dynamic evaluation method according to an exemplary embodiment of the present disclosure is provided. Figure 7 A schematic block diagram of a dynamic evaluation device for peak shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis is shown according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0029] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0030] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0031] In this example embodiment, a dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis is first provided; (Refer to...) Figure 1 As shown, the dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis may include the following steps: Step S110: Construct a multi-condition evaluation index system for the peak-shaving and frequency regulation response capability of the battery swapping station, set evaluation levels and scoring thresholds according to the power grid operating conditions, and divide the evaluation indexes into static dimension indicators and dynamic dimension indicators based on the evaluation levels and scoring thresholds. Step S120: Using a weight allocation method that combines subjective and objective factors, calculate the combined weights of the static and dynamic dimension indicators respectively. Step S130: Negative ideal solution, and in combination with the comprehensive weight, calculate the proximity score of the static dimension indicators of the battery swapping station; Step S140: Construct a dynamic response capability evaluation model based on the grey relational analysis method, using the power grid dispatch instruction sequence as the baseline sequence, and combine it with the comprehensive weight to calculate the grey relational score of the dynamic dimension index of the battery swapping station. Step S150: Set the fusion coefficient of static and dynamic dimension indicators according to the current operating conditions of the power grid. Based on the fusion coefficient of static and dynamic dimension indicators, perform weighted fusion of the proximity score of static dimension indicators and the gray relational score of dynamic dimension indicators to obtain a comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. Determine the priority of the battery swapping station to participate in peak-shaving and frequency regulation based on the comprehensive score.

[0032] An exemplary embodiment of this disclosure discloses a dynamic evaluation method for the peak-shaving and frequency-regulating response capability of a battery swapping station based on dual-dimensional analysis. The method includes: constructing a multi-condition evaluation index system for the peak-shaving and frequency-regulating response capability of the battery swapping station, dividing the evaluation indexes into static and dynamic dimension indicators, and setting evaluation levels and scoring thresholds according to the power grid operating conditions; employing a weight allocation method that integrates subjective and objective factors to calculate the comprehensive weights of the static and dynamic dimension indicators, where subjective weights are determined based on the analytic hierarchy process (AHP), objective weights are determined based on the entropy weight method, and fused using the product normalization method; constructing a static response capability evaluation model based on the TOPSIS method, using national standard limits as positive and negative ideal solutions to calculate the proximity score of the static dimension of the battery swapping station; constructing a dynamic response capability evaluation model based on the grey relational analysis method, using the power grid dispatch instruction sequence as the benchmark sequence to calculate the grey relational score of the dynamic dimension of the battery swapping station; setting a fusion coefficient for the static and dynamic dimensions according to the current power grid operating conditions, weighting and fusing the static and dynamic scores to obtain a comprehensive score for the peak-shaving and frequency-regulating response capability of the battery swapping station, and determining whether it meets the standards and its priority based on a preset threshold.

[0033] This disclosure employs the Analytic Hierarchy Process (AHP) to determine subjective weights and the entropy weight method to determine objective weights, and then integrates them through product normalization. This approach retains the guiding role of expert experience in key indicators while utilizing the information entropy of the data itself to eliminate human interference, making the comprehensive weights more closely reflect the actual operating characteristics of the battery swapping station. This fundamentally solves the problem of inaccurate assessments caused by purely subjective or purely objective weighting. Based on the TOPSIS method, using national standard limits as positive and negative ideal solutions to calculate static proximity scores, it can accurately quantify the peak-shaving (capacity boundary) level of the battery swapping station at any given time. Based on the grey relational analysis method, using the power grid dispatch command sequence as the benchmark sequence to calculate dynamic grey relational scores, it can accurately reflect the response trend of the battery swapping station to frequency regulation (time-sequence following) commands. Both methods are independently modeled for the different requirements of peak shaving and frequency regulation, avoiding information aliasing from a single model. The fusion coefficients for static and dynamic dimensions are set according to the current operating conditions of the power grid. The static and dynamic scores are weighted and fused to obtain a comprehensive score, and compliance and participation priority are determined based on preset thresholds. This mechanism enables the assessment results to dynamically adapt to the different urgency requirements of the power grid for peak shaving or frequency regulation. While ensuring the accuracy of the assessment, it directly outputs quantifiable dispatching basis, achieving a precise match between the response capability of the battery swapping station and the actual needs of the power grid.

[0034] The following will further explain a dynamic evaluation method for the peak-shaving and frequency regulation response capability of a battery swapping station based on two-dimensional analysis in this example embodiment.

[0035] Example 1: In step S110, a multi-condition evaluation index system for the peak-shaving and frequency regulation response capability of the battery swapping station can be constructed. The evaluation level and scoring threshold are set according to the power grid operating conditions, and the evaluation index is divided into static dimension index and dynamic dimension index based on the evaluation level and scoring threshold.

[0036] In this example embodiment, the method further includes: Power grid operating conditions include normal operating conditions, medium and low voltage heavy load operating conditions, and extreme operating conditions; The evaluation levels are divided into: dynamic and static compliance, dynamic compliance but static non-compliance, dynamic non-compliance but static compliance, and dynamic non-compliance and static non-compliance. The scoring thresholds set for different working conditions are: comprehensive scoring threshold, static scoring threshold, and dynamic scoring threshold.

[0037] The static dimension indicators include three criteria layers: capacity potential, steady-state accuracy, and adjustability, covering adjustable capacity, continuous charge and discharge time, battery SOC adjustable range, steady-state active power control error, peak shaving plan execution qualification rate, voltage deviation, rated charge and discharge power, and minimum active power regulation rate. The dynamic dimension indicators include three criterion layers: primary frequency regulation capability, secondary frequency regulation capability, and ramping capability. They cover primary frequency regulation response delay, primary frequency regulation rise time, primary frequency regulation maximum adjustment depth, AGC response start time, AGC response rise time, AGC adjustment accuracy, bidirectional ramping rate, ramping response delay, and ramping stability.

[0038] In step S120, a weight allocation method that combines subjective and objective factors can be used to calculate the combined weights of the static and dynamic dimension indicators respectively.

[0039] In this example embodiment, the use of a subjective-objective fusion weight allocation method to calculate the comprehensive weights of the static and dynamic dimension indicators includes: Subjective weights are calculated using the analytic hierarchy process (AHP). A judgment matrix is ​​constructed for indicators at the same level under the same parent node. After consistency testing, the subjective weights of static and dynamic dimension indicators are synthesized layer by layer. The objective weights are obtained by using the entropy weight method, which calculates the information entropy and difference coefficients independently for static and dynamic dimension indicators, and then normalizes them to obtain the objective weights of static and dynamic dimension indicators. The product normalization method is used to integrate subjective weights and objective weights, and a subjective weight ratio coefficient for working condition adaptation is introduced to obtain the comprehensive weights of static and dynamic dimension indicators.

[0040] In the embodiment of this example, the subjective weighting is 0.6 under normal operating conditions, 0.7 under heavy load conditions, and 0.8 under extreme operating conditions.

[0041] In step S130, a static response capability assessment model can be constructed based on the TOPSIS method, using national standard limits as positive and negative ideal solutions, and combined with the comprehensive weights to calculate the proximity score of the static dimension of the battery swapping station.

[0042] In step S140, a dynamic response capability assessment model can be constructed based on the grey relational analysis method. The power grid dispatch instruction sequence is used as the baseline sequence, and the grey relational score of the dynamic dimension of the battery swapping station is calculated in combination with the comprehensive weight.

[0043] In this example embodiment, calculating the grey relational score of the dynamic dimension index of the battery swapping station includes: The baseline sequence includes the AGC dispatch command sequence issued by the power grid, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence; The comparison sequence is the actual output time sequence of the battery swapping station; By calculating the absolute value difference sequence, global extreme value, and grey relational coefficient, the dynamic trend similarity of each indicator is obtained, and the grey relational score is obtained by weighted summation.

[0044] In step S150, the fusion coefficients of static and dynamic dimensions can be set according to the current operating conditions of the power grid. The proximity score of the static dimension and the gray relational score of the dynamic dimension are weighted and fused to obtain the comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of the battery swapping station to participate in peak-shaving and frequency regulation is determined according to the comprehensive score.

[0045] In this example embodiment, the method further includes: The fusion coefficient of static and dynamic dimension indicators is set according to different working conditions. Under normal working conditions, the fusion coefficient of static dimension indicators is 0.7. Battery swapping stations with higher overall scores will be given priority in participating in grid peak shaving and frequency regulation services.

[0046] In this example embodiment, the method further includes model validity verification: The Spearman-level correlation coefficient is used to verify the consistency between the evaluation ranking results and the actual power grid dispatch assessment results, requiring a correlation coefficient greater than 0.8; A sensitivity analysis of ±20% was performed on the fusion coefficient, requiring a Spearman correlation coefficient ≥0.9 for the sorting results before and after perturbation.

[0047] In this example embodiment, the method further includes: Based on rolling updates of real-time operational data, the comprehensive weight and evaluation results are dynamically updated to achieve real-time dynamic evaluation of the peak-shaving and frequency regulation response capabilities of battery swapping stations.

[0048] In the embodiments of this example, the key points of this disclosure are: a multi-condition evaluation index system for the peak-shaving and frequency regulation response capability of battery swapping stations; a dynamic comprehensive weight allocation method based on the integration of subjective and objective factors of battery swapping stations; a two-dimensional evaluation fusion mechanism of TOPSIS-grey relational degree method adapted to multiple operating conditions; and a method for constructing a dynamic trend benchmark sequence based on the ideal target value of peak-shaving and frequency regulation of battery swapping stations.

[0049] In this example embodiment, the points to be protected by this disclosure are: A multi-dimensional subjective and objective weight allocation method was designed to adapt to all operating conditions of peak shaving and frequency regulation of battery swapping stations. Combining the needs under normal, medium and low voltage load distribution and extreme operating conditions, the method utilizes the objectivity of the entropy weight method and the subjectivity of the analytic hierarchy process to perform weighted calculations on the static and dynamic dimensions respectively, avoiding mutual influence between the two dimensions and achieving scientific integration of weights.

[0050] A dynamic subjective and objective weight allocation method was designed to adapt to the implementation of power grid peak shaving and frequency regulation by battery swapping stations. The method combines real-time operational requirements to realize the dynamic allocation and transformation of weights.

[0051] A two-dimensional evaluation method for the peak-shaving and frequency-regulating response capability of battery swapping stations, consisting of the static dimension of TOPSIS and the dynamic dimension of grey relational analysis, is proposed. By transforming the coefficients, the static performance and dynamic trends of different scenarios can be integrated on demand.

[0052] A benchmark sequence construction method adapted to the dynamic evaluation of peak shaving and frequency regulation of battery swapping stations was designed. For different evaluation conditions in dynamic dimensions, a benchmark sequence consistent with the power grid dispatching rules was constructed, which improved the accuracy of the grey relational method in dynamic dimension evaluation.

[0053] Example 2: In this example embodiment, the technical problem this disclosure aims to solve is: Battery swapping stations participating in grid peak shaving and frequency regulation is a typical vehicle-pile-grid collaborative scenario, where battery swapping stations, electric vehicles, and the grid have complex nonlinear coupling relationships. Existing methods mostly focus on a single operating condition or a single dimension, lacking comprehensive evaluation of the entire process across multiple scenarios. Furthermore, they involve performance indicators such as adjustment rate, response speed, and adjustable capacity, failing to simultaneously consider the influence of subjective and objective factors, as well as the needs of static levels and dynamic trends. This disclosure can provide a comprehensive evaluation of the peak shaving and frequency regulation response capabilities of battery swapping stations. 1. To address the shortcomings of existing assessment methods that combine TOPSIS and grey relational analysis, where weight allocation is subjective or objective, resulting in deviations from actual needs or data patterns, and where only static capability assessments exist, a weight calculation scheme is proposed that combines real-time rolling data with a weighted calculation method that integrates subjective and objective factors, taking into account both expert experience and data objectivity.

[0054] 2. To address the problem that existing assessment schemes only perform static capability evaluations of peak-shaving and frequency regulation response capabilities at a single time point, failing to reflect the changing trends of the response capabilities of battery swapping stations over different time periods, this disclosure can simultaneously complete a comprehensive assessment of both static capability levels and dynamic trends based on continuously updated real-time operational data. This is more in line with the actual operational characteristics of battery swapping stations participating in grid peak-shaving and frequency regulation, and the assessment results are more valuable for reference.

[0055] 3. To address the issue that the existing evaluation indicator system does not cover normal, medium and low voltage distribution network heavy load, and extreme multi-condition working conditions, and lacks specificity for different scenarios, a customized indicator system for multiple working conditions is proposed to determine the target values ​​and thresholds of indicators under different working conditions, thereby improving the adaptability of evaluation results.

[0056] 4. To address the issue of confusion between peak shaving and frequency regulation in existing technologies under different operating conditions, this paper analyzes different operating conditions and divides the indicators into static and dynamic categories. Based on the needs under different operating conditions, it accurately selects the evaluation method that focuses on peak shaving or frequency regulation.

[0057] 5. To address the issue that the dynamic trend benchmark of the grey relational analysis method often uses empirical values ​​and lacks precise definition, a benchmark sequence is constructed using the ideal target value of peak shaving and frequency regulation of the battery swapping station. This improves the accuracy of dynamic dimension assessment and ultimately achieves a comprehensive and accurate assessment of the peak shaving and frequency regulation response capability of the battery swapping station.

[0058] In this example embodiment, the complete technical solution of this disclosure includes: Step 1: Determining and classifying the peak-shaving and frequency regulation response capability index system for battery swapping stations. Step 1.1: Establish a comprehensive evaluation factor set Based on the actual operating conditions of the power grid, it can be divided into three categories: normal operation, medium and low voltage heavy load operation, and extreme operating conditions. According to national standards, these three operating conditions can be further divided into three situations: 1) Normal operating conditions: power grid frequency deviation ≤ ±0.05Hz, voltage deviation ≤ ±5%, distribution network load rate ≤ 70%. At this time, the main demand of the power grid is peak regulation, and frequency regulation is a secondary demand; 2) Medium and low voltage heavy load: power grid frequency deviation ±0.05Hz~±0.2Hz, voltage deviation ±5%~±10%, distribution network load rate 70%~100%. At this time, the main demand of the power grid is frequency regulation, and frequency regulation is a secondary demand; 3) Extreme operation: power grid frequency deviation > ±0.2Hz, voltage deviation > ±10%, distribution network failure occurs, the core demand is emergency support for primary frequency regulation, and at this time, the main demand of the power grid is frequency regulation.

[0059] To address the three operating conditions encountered in actual power grid operation, a multi-dimensional evaluation index system for the peak-shaving and frequency regulation response capabilities of electric vehicle battery swapping stations is constructed. This system can be categorized into two main types: static response capability indicators and dynamic response capability indicators. Static response capability indicators can be further divided into three criteria: capacity potential, steady-state accuracy, and adjustability. The corresponding nine indicators are: adjustable capacity, continuous charge / discharge time, battery SOC adjustable range, steady-state active power control error, peak-shaving plan execution qualification rate, voltage deviation, rated charge / discharge power, and minimum active power regulation rate. Dynamic response capability indicators can be further divided into three criteria: primary frequency regulation capability, secondary frequency regulation capability, and ramping capability. The corresponding nine indicators are: primary frequency regulation response delay, primary frequency regulation rise time, primary frequency regulation maximum regulation depth, AGC response start time, AGC response rise time, AGC regulation accuracy, bidirectional ramping rate, ramping response delay, and ramping stability. These 17 indicators cover the requirements corresponding to three operating conditions under the power grid, and can comprehensively evaluate the response capability of the battery swapping station when participating in power grid peak shaving and frequency regulation.

[0060] like Figure 2As shown, the response capability of battery swapping stations when participating in power grid peak shaving and frequency regulation can be mainly divided into four layers: the first layer is the target layer, which is the comprehensive evaluation of the peak shaving and frequency regulation response capability of electric vehicle battery swapping stations; the second layer is the dimension layer, which includes a dual-layer system of static and dynamic dimensions; the third layer is the criterion layer, which includes seven criteria; and the fourth layer is the indicator layer, which includes 17 indicators.

[0061] Step 1.2: Establish a comprehensive evaluation factor set for the peak-shaving and frequency regulation response capabilities of battery swapping stations. The peak-shaving and frequency-regulating response capabilities of electric vehicle battery swapping stations are divided into four levels, forming an evaluation set K={k1,k2,k3,k4}, where elements k1-k4 represent dynamic response capability meeting standards, static response capability meeting standards, dynamic response capability failing standards but static response capability meeting standards, dynamic response capability meeting standards but static response capability failing standards, and dynamic response capability failing standards but static response capability failing standards. Typical urban power grid operation scenarios are selected: normal operation, medium- and low-voltage distribution network heavy load, and extreme operation. Comprehensive score thresholds are set for these three operating conditions: 0.5, 0.6, and 0.7; static score thresholds: 0.6, 0.5, and 0.4; and dynamic score thresholds: 0.5, 0.7, and 0.8. When the comprehensive score of the peak-shaving and frequency-regulating response capability of an electric vehicle battery swapping station is greater than the threshold, it meets the standard; conversely, when the comprehensive score is less than the threshold, it fails to meet the standard.

[0062] Step 2: Determine the weights of each factor affecting dynamic and static response capabilities based on the subject-object comprehensive weighting method. Step 2.1: Calculate the weights of static and dynamic indicators based on the Analytic Hierarchy Process (AHP). By collecting data in real time, the subjective weights are calculated using the Analytic Hierarchy Process (AHP). Only elements at the same level under the same parent node are compared pairwise using a 1-9 scale, with weights calculated layer by layer from top to bottom. This algorithm allows for quick adaptation to different operating conditions by simply changing the weights of the upper-level dimensions. For example, in normal operating conditions, peak shaving is the primary function, with frequency regulation as a secondary function. Increasing the static dimension weights in this case allows for a more accurate assessment of the peak shaving and frequency regulation response capabilities of the battery swapping station. Each layer undergoes a consistency check to ensure the feasibility of the method. The specific steps mainly consist of three steps: 1) Taking the normal operating conditions of the power grid as an example, judgment matrices are established for the first-level dimension layer, the second-level criterion layer, and the indicator layer, respectively. The importance of the evaluation indicators affecting the power grid frequency regulation and peak shaving capabilities of the swapping station under different operating conditions is compared pairwise. Based on expert opinions, the nine-scale method is used to score the above indicators pairwise. At the same time, an m×m judgment matrix A is constructed: one judgment matrix for the first-level dimension layer; two matrices for the second-level criterion layer; and three sub-dimensions for the indicator layer matrix.

[0063] Table 1. Matrix for Judging the Peak Shaving and Frequency Regulation Dimensional Response Capability of Battery Swapping Stations

[0064] Table 2. Response Capability Judgment Matrix for Peak Shaving and Frequency Regulation Criteria Layer of Battery Swapping Stations

[0065] (a) Static dimension

[0066] (b) Dynamic dimension

[0067] (a) Capacity potential

[0068] (b) Steady-state accuracy

[0069] (c) Adjustability Table 3. Matrix for Judging the Dynamic Response Capability of Battery Swapping Stations for Peak Shaving and Frequency Regulation

[0070] (a) Primary frequency modulation capability

[0071] (b) Secondary frequency modulation capability

[0072] (c) Climbing ability 2) Normalize and calculate the subjective weights for each layer separately. All judgment matrices are normalized using the arithmetic mean method, and the weight vectors for each dynamic and static dimension of the dimension layer are calculated. For both static and dynamic dimensions, the weight vectors for the corresponding second-level criteria are calculated. : (1) in, To determine the sum of the j-th column of a matrix, the final condition is: Simultaneously, the subjective weights of each layer under the two dimensions were obtained, and the requirements were met when performing normalization verification.

[0073] 3) Determine the consistency of all matrices. The nine judgment matrices are used to calculate the sorting weight vectors and perform consistency checks. First, the largest eigenvalue is calculated. Furthermore, the consistency index (CI) of the judgment matrix at different levels is calculated, and the average consistency index (RI) of the judgment matrix is ​​obtained by looking up a table. The consistency ratio (CR) is then calculated by combining CI and RI. (2) in: Then the judgment matrix passes the consistency test, and the judgment matrix A and the subjective weight vector are reasonable; if The judgment matrix needs to be reconstructed, experts need to re-evaluate, and the importance comparison of indicators needs to be adjusted until... The calculated CR values ​​for all four experts were less than 0.1, thus passing the first-pass test.

[0074] Finally, the total subjective weights are synthesized: multiplying layer by layer within the same dimension to obtain the total subjective weights of the static indicators. Total subjective weight of dynamic indicators The weights of the two dimensions are normalized respectively, satisfying ,in This refers to the number of static indicators. This refers to the number of dynamic indicators.

[0075] Step 2.2: Calculation of dynamic and static classification index weights based on the entropy weight method (1) Objective entropy weight method By collecting data in real time, the entropy weight method is used to standardize static and dynamic indicators separately, calculating objective weights independently without mixing cross-dimensional data. The specific steps for calculating weights using the entropy weight method are as follows: To avoid meaningless values ​​that may occur with the traditional entropy weight method, the following method is used to calculate the proportion zij of the indicator value of the i-th battery swapping station under the j-th indicator, thus preventing the occurrence of meaningless values: (3) Among them, y ij Let z be the dimensionless value of the j-th index of the i-th battery swapping station, satisfying 0 < z ij <1, to avoid the meaninglessness of logarithmic calculations.

[0076] Next, the information entropy of each indicator of the battery swapping station participating in the power grid peak shaving and frequency regulation evaluation indicators is calculated: (4) Difference coefficient vector Converting entropy values ​​into indicators of distinguishing ability:

[0077] Where n is the evaluation object. . The difference coefficient of the indicators is calculated by information entropy, and then normalized to obtain the objective weights in both static and dynamic dimensions. (5) in: These are the static and dynamic objective weights for peak shaving at battery swapping stations, respectively, to meet the requirements. The distribution is entirely determined by the data distribution, without any subjective bias. Simultaneously, the objective weights for each dimension are calculated and normalization verification is performed, confirming that the requirements are met.

[0078] Step 2.3: Calculation of weights for dynamic and static classification indicators based on the fusion of subjective and objective factors The subjective and objective weights are fused using a product-normalization method. Subjective and objective weights corresponding to the same dimensions are multiplied together, and the product result is then normalized to ensure that the final sum of the comprehensive weights is 1. The calculation formula is as follows: (6) in: The subjective and objective fusion weights under the static dimension. The weights represent the fusion of subjective and objective factors under a dynamic dimension. For adaptive subjective weight fusion coefficients under normal operating conditions =0.6, heavy load condition =0.7, extreme operating conditions =0.8, the more extreme the operating conditions, the higher the proportion of subjective weight, ensuring the core requirements of power grid security, such as Figure 3 As shown.

[0079] Step 3: Comprehensive Evaluation Model of Dual-Dimensional Peak Shaving and Frequency Modulation Response Capability Based on TOPSIS-GRA Step 3.1: Static Response Capability Assessment Model Based on TOPSIS like Figure 4 As shown, in order to evaluate the closeness of a battery swapping station to "ideal performance" from the perspective of "static distance," national standard limits are used as positive and negative ideal solutions to accurately quantify the steady-state access capability of the battery swapping station. First, the static comprehensive weight is... Integrating static indicator standardization matrix The weighted matrix is ​​obtained. : (7) The TOPSIS method ranks evaluation indicators by comparing their distances to the optimal and worst-case objectives. After standardization, a matrix Z is obtained, containing extremely large datasets. Simultaneously, the optimal and worst-case solutions can be extracted. The largest number in each column (for each indicator) is extracted to form the ideal optimal solution vector, which represents the national standard optimal value for each indicator. (8) Similarly, the smallest number in each column (each indicator) is used to form the ideal worst-case solution vector, which is the national standard lower limit value for each indicator: (9) And Euclidean distance is used to calculate the distance of each index to the positive and negative ideal solutions: (10) in, Let be the distance from the i-th battery swapping station to the ideal solution. Let be the distance from the i-th battery swapping station to the negative ideal solution.

[0080] The relative closeness of the current evaluation index is calculated and assigned a value. The calculation formula is as follows: (11) in, As an indicator The static capability evaluation score of a battery swapping station, among which The closer it is to 1, the stronger the steady-state peak-shaving capability.

[0081] Step 3.2: Evaluation Model of Dynamic Response Capability Based on Grey Relational Analysis The dynamic dimension only uses the fused dynamic weights. A grey relational analysis (GRA) assessment is conducted, using the real-time grid dispatch command sequence as a baseline sequence to directly quantify the actual ability of battery swapping stations to follow grid commands. like Figure 5 The overall dynamic evaluation process shown first involves constructing a dynamic sequence, including a baseline sequence. The sequence of AGC dispatch instructions, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence issued by the power grid, where T is the total number of time steps in the response cycle; comparison sequence The actual output time sequence of the i-th battery swapping station is completely synchronized with the reference time sequence. Transform the time series of index j for each sample i into a relative trend to eliminate differences in magnitude: (12) Correlation coefficient calculation: The grey relational coefficient reflects the degree of correlation between the indicator and the ideal sequence at a certain moment (resolution coefficient). (Typically, the value is 0.5). ① Calculate the absolute value difference sequence: (13) ②Global Extremum: (14) in: This represents the moment with the smallest deviation across all indicators and all times, i.e., the moment closest to the ideal sequence. This represents the largest deviation from the ideal sequence across all indicators and all moments.

[0082] ③ Correlation coefficient: The absolute difference between the comparison sequence and the reference sequence. Convert the values ​​to a range of 0 to 1 to measure the correlation between the two at that moment. : (15) in: To ensure the resolution coefficient is between 0 and 1, it is typically set to 0.5. For a certain indicator The average correlation coefficient across all time points reflects the dynamic trend similarity of a single indicator. Finally, a dynamic score is obtained by dynamically fusing weights and summing the results. Score range The closer it is to 1, the stronger the dynamic response capability. (16) Step 3.3 Comprehensive evaluation based on dynamic and static dimensions under different working conditions Two-dimensional evaluation process as follows Figure 6 As shown, the fusion coefficients for static and dynamic dimensions are set. Depending on the different operating conditions, different emphases may be set. Under normal operating conditions, the specific engineering application may be considered. Therefore, the first Overall score of each battery swapping station for: (17) At the same time, the eligibility criteria are determined based on the previously set dynamic and static score thresholds. Then, the eligibility to participate is determined by the comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. When the comprehensive score is greater than the threshold, the station can participate in peak-shaving and frequency regulation. The higher the score, the more suitable it is to participate in the grid peak-shaving and frequency regulation priority.

[0083] Step 3.4 Model Validation Consistency Verification: The Spearman rank correlation coefficient is used to verify the consistency between the ranking results of this method and the actual power grid dispatch assessment results. The formula is as follows: (18) In the formula: The difference in rank between the ranking determined by this method and the actual ranking determined by the power grid assessment is used as the verification standard. This proves that the model is effective.

[0084] Robustness verification: Sensitivity analysis of the core fusion coefficients α and β with a margin of ±20% was performed. The verification standard was that the Spearman correlation coefficient of the ranking results before and after the perturbation was ≥0.9, which proved that the model was robust.

[0085] In the embodiments of this example, the beneficial effects of the technical solution disclosed herein include: During the process of a battery swapping station participating in grid peak shaving and frequency regulation response, by defining three operating conditions—normal, medium- and low-voltage heavy load, and extreme—the different peak shaving or frequency regulation requirements of the grid for the battery swapping station under different operating conditions are clarified. Based on these requirements, the indicators are divided into two major dimensions: static and dynamic indicators, thereby more accurately assessing the response capabilities required by the grid for the battery swapping station under different conditions. By introducing rolling data and adopting a subjective-objective weighting method that combines objective data support from the entropy weight method with expert experience from the analytic hierarchy process, the problems of purely objective weights being disconnected from actual needs and purely subjective weights lacking data basis are avoided. Simultaneously, dynamic weights are reasonably allocated, making the weight allocation more real-time and reliable, and ensuring that the evaluation results are more in line with current actual needs. The TOPSIS method is used to quantify static performance, the grey relational analysis method is used to determine dynamic trends, and a fusion coefficient is combined for dual-dimensional on-demand integration, overcoming the one-sidedness of evaluation based solely on static dimensions. Meanwhile, the corresponding benchmark sequences for dynamic evaluation are the actual peak-shaving and ramp-up target instruction sequences issued by the power grid, the AGC real-time dispatch instruction sequences, and the primary frequency regulation standard response sequences stipulated by national standards. This fully aligns with the actual assessment rules of power grid dispatch, and the dynamic evaluation results can directly correspond to the assessment and settlement of power grid auxiliary services, thus solving the pain point of the disconnect between dynamic evaluation benchmarks and actual engineering practices.

[0086] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0087] Furthermore, this example embodiment also provides a dynamic evaluation device for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis. (Refer to...) Figure 7 As shown, the dynamic evaluation device 200 for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis may include: an index system construction module 210, a weight fusion calculation module 220, a static evaluation module 230, a dynamic evaluation module 240, and a comprehensive evaluation module 250. Wherein: The indicator system construction module 210 is used to construct a multi-condition evaluation indicator system for the peak-shaving and frequency regulation response capability of the power grid swapping station. It sets evaluation levels and scoring thresholds according to the power grid operating conditions, and divides the evaluation indicators into static dimension indicators and dynamic dimension indicators based on the evaluation levels and scoring thresholds. The weight fusion calculation module 220 is used to calculate the comprehensive weight of the static dimension index and the dynamic dimension index respectively by adopting a weight allocation method that combines subjective and objective factors. The static evaluation module 230 is used to construct a static response capability evaluation model based on the TOPSIS method, using national standard limits as positive and negative ideal solutions, and combining the comprehensive weights to calculate the closeness score of the static dimension indicators of the battery swapping station. The dynamic evaluation module 240 is used to construct a dynamic response capability evaluation model based on the grey relational method, using the power grid dispatch instruction sequence as the benchmark sequence and combining the comprehensive weight to calculate the grey relational score of the dynamic dimension index of the battery swapping station. The comprehensive evaluation module 250 is used to set the fusion coefficient of static and dynamic dimension indicators according to the current operating conditions of the power grid. Based on the fusion coefficient of the static and dynamic dimension indicators, the proximity score of the static dimension indicators and the gray relational score of the dynamic dimension indicators are weighted and fused to obtain a comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of the battery swapping station to participate in peak-shaving and frequency regulation is determined according to the comprehensive score.

[0088] In the device embodiment of this example, the power grid operating conditions in the indicator system construction module include normal operating conditions, medium and low voltage heavy load operating conditions, and extreme operating conditions; The evaluation levels include: meeting both dynamic and static standards, meeting dynamic standards but failing to meet static standards, failing to meet dynamic standards but meeting static standards, and failing to meet both dynamic and static standards. The scoring thresholds set according to different working conditions are: comprehensive scoring threshold, static scoring threshold, and dynamic scoring threshold; The static dimension indicators include three criteria layers: capacity potential, steady-state accuracy, and adjustability, covering adjustable capacity, continuous charge and discharge time, battery SOC adjustable range, steady-state active power control error, peak shaving plan execution qualification rate, voltage deviation, rated charge and discharge power, and minimum active power regulation rate. The dynamic dimension indicators include three criterion layers: primary frequency regulation capability, secondary frequency regulation capability, and ramping capability. They cover primary frequency regulation response delay, primary frequency regulation rise time, primary frequency regulation maximum adjustment depth, AGC response start time, AGC response rise time, AGC adjustment accuracy, bidirectional ramping rate, ramping response delay, and ramping stability.

[0089] In this example device embodiment, the weight fusion calculation module includes: The subjective weight calculation submodule is used to construct a judgment matrix for indicators of the same level under the same parent node using the analytic hierarchy process, and synthesize the subjective weights of static and dynamic dimension indicators after consistency verification. The objective weight calculation submodule is used to calculate the information entropy and difference coefficient independently for static and dynamic dimension indicators using the entropy weight method, and obtain the objective weights of static and dynamic dimension indicators after normalization. The fusion submodule is used to merge subjective weights and objective weights using the product normalization method, and introduces the subjective weight ratio coefficient of working condition adaptation to obtain the comprehensive weight of static and dynamic dimension indicators.

[0090] In the device embodiment of this example, the coefficient of the subjective weight ratio is set as follows: the subjective weight ratio is 0.6 under normal working conditions, 0.7 under heavy load working conditions, and 0.8 under extreme working conditions.

[0091] In this example device embodiment, the dynamic evaluation module includes: The baseline sequence construction submodule is used to construct baseline sequences, including the AGC dispatch command sequence issued by the power grid, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence. The comparison sequence acquisition submodule is used to acquire the actual output time sequence of the battery swapping station as a comparison sequence. The dynamic score calculation submodule is used to obtain the dynamic trend similarity of each indicator by calculating the absolute value difference sequence, global extreme value and grey relation coefficient, and to obtain the grey relation score by weighted summation based on the comprehensive weight.

[0092] In the device embodiment of this example, the fusion coefficient of static dimension indicators and dynamic dimension indicators in the comprehensive evaluation module is set according to different working conditions. Under normal working conditions, the fusion coefficient of static dimension indicators is 0.7. The comprehensive evaluation module is also used to determine the priority of battery swapping stations participating in peak shaving and frequency regulation according to the comprehensive score from high to low.

[0093] In this example device embodiment, the device further includes a verification module, which is used to verify the consistency between the evaluation ranking results and the actual power grid dispatch assessment results using Spearman's level correlation coefficient, and requires the correlation coefficient to be greater than 0.8; The verification module performs a ±20% sensitivity analysis on the fusion coefficient and requires the Spearman correlation coefficient of the sorting results before and after the perturbation to be ≥0.9.

[0094] In this example embodiment, the device further includes a real-time update module, which is used to dynamically update the comprehensive weight and evaluation results based on the rolling real-time operating data, so as to realize the real-time dynamic evaluation of the peak-shaving and frequency regulation response capability of the battery swapping station.

[0095] The specific details of each of the above-mentioned dynamic evaluation device modules for peak shaving and frequency regulation response capability of battery swapping stations based on dual-dimensional analysis have been described in detail in the corresponding dynamic evaluation method for peak shaving and frequency regulation response capability of battery swapping stations based on dual-dimensional analysis, so they will not be repeated here.

[0096] It should be noted that although several modules or units of a dynamic evaluation device 200 for peak-shaving and frequency-regulation response capability of a battery swapping station based on dual-dimensional analysis have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0097] In addition to the methods and apparatus described above, embodiments of this disclosure may also be computer program products comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods of various embodiments of this disclosure described in the foregoing portion of this specification.

[0098] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this disclosure. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on a user's computing device, partially on a user's computing device, as a standalone software package, partially on a user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0099] Furthermore, embodiments of this disclosure may also be computer-readable storage media storing computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods of various embodiments of this disclosure described in the foregoing portion of this specification.

[0100] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0101] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this disclosure and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0102] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0103] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A dynamic evaluation method for the peak-shaving and frequency regulation response capability of battery swapping stations based on dual-dimensional analysis, characterized in that, The method includes: A multi-condition evaluation index system for the peak-shaving and frequency regulation response capability of battery swapping stations is constructed. Evaluation levels and scoring thresholds are set according to the power grid operating conditions. Based on the evaluation levels and scoring thresholds, the evaluation indexes are divided into static dimension indexes and dynamic dimension indexes. A weighting method that combines subjective and objective factors is adopted to calculate the combined weights of the static and dynamic dimension indicators respectively. A static response capability assessment model is constructed based on the TOPSIS method. The national standard limit is used as the positive and negative ideal solutions, and the proximity score of the static dimension indicators of the battery swapping station is calculated by combining the comprehensive weights. A dynamic response capability assessment model is constructed based on the grey relational analysis method. The power grid dispatch instruction sequence is used as the baseline sequence, and the grey relational analysis score of the dynamic dimension index of the battery swapping station is calculated by combining the comprehensive weight. Based on the current operating conditions of the power grid, a fusion coefficient is set for static and dynamic dimension indicators. Based on the fusion coefficient, the proximity score of the static dimension indicators and the grey relational score of the dynamic dimension indicators are weighted and fused to obtain a comprehensive score for the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of battery swapping stations participating in peak shaving and frequency regulation is determined based on the overall score.

2. The method as described in claim 1, characterized in that, The power grid operating conditions include normal operating conditions, medium and low voltage heavy load operating conditions, and extreme operating conditions; The evaluation levels include: dynamic compliance and static compliance, dynamic compliance but static non-compliance, dynamic non-compliance but static compliance, and dynamic non-compliance and static non-compliance. The scoring thresholds set according to different working conditions are: comprehensive scoring threshold, static scoring threshold, and dynamic scoring threshold; The static dimension indicators include three criteria layers: capacity potential, steady-state accuracy, and adjustability, covering adjustable capacity, continuous charge and discharge time, battery SOC adjustable range, steady-state active power control error, peak shaving plan execution qualification rate, voltage deviation, rated charge and discharge power, and minimum active power regulation rate. The dynamic dimension indicators include three criterion layers: primary frequency modulation capability, secondary frequency modulation capability, and ramping capability. They cover primary frequency modulation response delay, primary frequency modulation rise time, primary frequency modulation maximum adjustment depth, AGC response start time, AGC response rise time, AGC adjustment accuracy, bidirectional ramping rate, ramping response delay, and ramping stability.

3. The method as described in claim 1, characterized in that, The weight allocation method, which integrates subjective and objective factors, calculates the combined weights of the static and dynamic dimension indicators, including: Using the analytic hierarchy process, a judgment matrix is ​​constructed for indicators at the same level under the same parent node. After consistency testing, the subjective weights of static and dynamic dimension indicators are synthesized. The entropy weight method is used to independently calculate the information entropy and difference coefficient for static and dynamic dimension indicators, and the objective weights of static and dynamic dimension indicators are obtained after normalization. The product normalization method is used to integrate subjective weights and objective weights, and a subjective weight ratio coefficient for working condition adaptation is introduced to obtain the comprehensive weights of static and dynamic dimension indicators.

4. The method as described in claim 3, characterized in that, Under normal operating conditions, the subjective weighting is 0.6; under heavy-load operating conditions, the subjective weighting is 0.7; and under extreme operating conditions, the subjective weighting is 0.

8.

5. The method as described in claim 1, characterized in that, The calculation of the grey relational score of the dynamic dimension indicators of the battery swapping station includes: The baseline sequence includes the AGC dispatch command sequence issued by the power grid, the primary frequency regulation standard response sequence, and the peak shaving and ramping response sequence; The comparison sequence is the actual output time sequence of the battery swapping station; The dynamic trend similarity of each indicator is obtained by calculating the absolute value difference sequence, global extreme value and grey relational coefficient, and the grey relational score is obtained by weighted summation based on the comprehensive weight.

6. The method as described in claim 1, characterized in that, In the method: The fusion coefficient of static and dynamic dimension indicators is set according to different working conditions. Under normal working conditions, the fusion coefficient of static dimension indicators is 0.

7. Battery swapping stations with higher overall scores will be given priority in participating in grid peak shaving and frequency regulation services.

7. The method as described in claim 1, characterized in that, The method also includes model validity verification: The Spearman-level correlation coefficient is used to verify the consistency between the evaluation ranking results and the actual power grid dispatch assessment results, requiring a correlation coefficient greater than 0.8; A sensitivity analysis of ±20% was performed on the fusion coefficient, requiring a Spearman correlation coefficient ≥0.9 for the sorting results before and after perturbation.

8. The method as described in claim 1, characterized in that, The method further includes: Based on rolling updates of real-time operational data, the comprehensive weight and evaluation results are dynamically updated to achieve real-time dynamic evaluation of the peak-shaving and frequency regulation response capabilities of battery swapping stations.

9. A dynamic evaluation device for the peak-shaving and frequency regulation response capability of a battery swapping station based on dual-dimensional analysis, characterized in that, The device includes: The indicator system construction module is used to construct a multi-condition evaluation indicator system for the peak-shaving and frequency regulation response capability of the battery swapping station. It sets evaluation levels and scoring thresholds according to the power grid operating conditions, and divides the evaluation indicators into static dimension indicators and dynamic dimension indicators based on the evaluation levels and scoring thresholds. The weight fusion calculation module is used to calculate the comprehensive weight of the static dimension index and the dynamic dimension index respectively by adopting a weight allocation method that combines subjective and objective factors. The static evaluation module is used to construct a static response capability evaluation model based on the TOPSIS method, using national standard limits as positive and negative ideal solutions, and combining the comprehensive weights to calculate the closeness score of the static dimension indicators of the battery swapping station. The dynamic evaluation module is used to construct a dynamic response capability evaluation model based on the grey relational analysis method. It uses the power grid dispatch instruction sequence as the baseline sequence and combines the comprehensive weight to calculate the grey relational score of the dynamic dimension indicators of the battery swapping station. The comprehensive evaluation module is used to set the fusion coefficient of static and dynamic dimension indicators according to the current operating conditions of the power grid. Based on the fusion coefficient of the static and dynamic dimension indicators, the proximity score of the static dimension indicators and the gray correlation score of the dynamic dimension indicators are weighted and fused to obtain a comprehensive score of the peak-shaving and frequency regulation response capability of the battery swapping station. The priority of the battery swapping station to participate in peak-shaving and frequency regulation is determined according to the comprehensive score.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1-8.

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