Adaptive evaluation method for distributed networked energy storage participating in transient voltage support

Abstract

The application relates to the technical field of power grid energy storage evaluation, in particular to a distributed network type energy storage participation transient voltage support adaptability evaluation method, which comprises the following steps: collecting basic data of an evaluation object, constructing an evaluation index system comprising five dimensions of power grid matching degree, support capacity margin, response coordination, operation reliability and economic rationality, and performing homodirectionalization and dimensionless processing on each index; a combination weighting method combining subjective weighting and objective weighting is used to determine the weight of each index, and adaptability comprehensive closeness is calculated based on the TOPSIS method; adaptability grades of the evaluation object are divided according to the adaptability comprehensive closeness, and a barrier degree model is used to identify key short-board indexes; adaptability comprehensive closeness is calculated respectively for different fault scenes and operation modes, weak scenes are found out by comparison, and targeted optimization and improvement strategies are output by combining short-board analysis. The application can improve the adaptability evaluation effect of transient voltage support.

Description

Technical Field

[0001] This application relates to the field of grid energy storage evaluation technology, specifically to an adaptive evaluation method for distributed grid-connected energy storage participating in transient voltage support. Background Technology

[0002] With the large-scale integration of distributed photovoltaic, wind power, and other new energy sources, the short-circuit capacity and inertia of distribution networks are decreasing, making transient voltage stability issues increasingly prominent. Grid-based energy storage can simulate the external characteristics of synchronous generators and actively provide transient voltage support, and is considered an effective means to improve the voltage stability of distribution networks.

[0003] However, the transient voltage support effect of grid-based energy storage is not the same under all grid conditions. Its support capability is affected by a combination of factors, including grid characteristics, energy storage configuration, and control strategies. For grid-based energy storage of the same capacity, the transient voltage support effect can vary greatly under different grid environments and operating scenarios. In some scenarios, the support effect is significant, while in others it may be insufficient or even trigger new oscillations.

[0004] Currently, research on grid-based energy storage largely focuses on the control strategies themselves, lacking a systematic approach to adaptive evaluation. Existing transient voltage support evaluation technologies for grid-based energy storage suffer from the following shortcomings: unsystematic evaluation index systems, highly subjective weight determination, lack of comprehensive quantitative methods, failure to indicate improvement directions, incomplete single-condition evaluation, and a disconnect between evaluation and optimization. Therefore, there is an urgent need to propose a new adaptive evaluation method that is multi-dimensional, combines subjective and objective approaches, is quantitatively comprehensive, can indicate improvement directions, and covers multiple scenarios. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides an adaptive evaluation method for distributed grid-connected energy storage to participate in transient voltage support, thereby resolving existing issues.

[0006] The adaptive evaluation method for distributed grid-connected energy storage participating in transient voltage support in this application adopts the following technical solution: One embodiment of this application provides an adaptive evaluation method for distributed grid-connected energy storage to participate in transient voltage support, comprising the following steps: Collect basic data on the evaluation objects, including equivalent characteristic parameters on the grid side and supporting capacity parameters on the energy storage side; An evaluation index system comprising five dimensions—grid matching degree, support capacity margin, response coordination, operational reliability, and economic rationality—is constructed, and each index is processed to be aligned and dimensionless. A combined weighting method that combines subjective and objective weighting is used to determine the weights of each indicator, and the overall adaptability is calculated based on the TOPSIS method. The evaluation objects are classified into adaptability levels based on their overall adaptability and the obstacle degree model is used to identify key weakness indicators. For different fault scenarios and operating modes, calculate the overall adaptability and fit, compare and identify weak scenarios, and output targeted optimization and improvement strategies based on the shortcomings analysis.

[0007] Preferably, the extraction of the equivalent characteristic parameters on the power grid side specifically includes: The short-circuit capacity at the point of common coupling is calculated as the square of the system rated voltage divided by the modulus of the grid equivalent impedance. The X / R ratio of the grid equivalent impedance, i.e., the ratio of equivalent reactance to equivalent resistance, is calculated. Voltage disturbance events at the point of common coupling over the past few years are statistically analyzed, including the number of sags, the number of rises, the average sag depth, and the average duration, to obtain the statistical parameters of disturbance characteristics. The ratio of the grid short-circuit capacity to the rated capacity of energy storage is calculated, i.e., the short-circuit capacity ratio.

[0008] Preferably, the calculation of the energy storage-side support capacity parameters specifically includes: Calculate the rated reactive power support capacity of the energy storage, i.e., the maximum reactive power that can be output under rated voltage; measure the transient voltage response time constant, i.e., the time required from the occurrence of voltage drop to the reactive power output reaching 90% of the rated value; determine the overload capacity parameters, including the maximum overload multiple and the overload duration; calculate the adjustable range of virtual inertia, i.e., the minimum and maximum values ​​of the virtual inertia coefficient, and the corresponding active power support capacity range; measure the control mode switching time, i.e., the transition time from normal mode to transient support mode.

[0009] Preferably, the construction of the evaluation index system specifically includes: The first dimension of grid matching degree includes: short-circuit capacity ratio, X / R ratio matching degree, voltage level matching coefficient, and disturbance frequency matching degree. The second dimension of support capacity margin includes: reactive power support margin, active power support margin, and overload capacity margin. The third dimension of response coordination includes: response speed matching, inertia damping coordination, and mode switching smoothness. The fourth dimension is operational reliability: equipment availability, control stability margin, and protection coordination. The fifth dimension is economic rationality: cost per unit capacity and operating loss rate.

[0010] Preferably, the combined weighting method for determining the weights of each indicator specifically includes: using the analytic hierarchy process (AHP) to calculate subjective weights and using the entropy weighting method to calculate objective weights; the weight of each indicator is the normalized result of multiplying the subjective weight and the objective weight of each indicator.

[0011] Preferably, the adaptive comprehensive proximity calculation based on the TOPSIS method specifically includes: Multiply each indicator value by its corresponding weight to construct a weighted standardized decision matrix; determine the positive ideal solution and the negative ideal solution: the positive ideal solution is a virtual solution in which all indicators take the optimal value, and the negative ideal solution is a virtual solution in which all indicators take the worst value; Calculate the Euclidean distance D from the evaluation object to the positive ideal solution. + The Euclidean distance D to the negative ideal solution - The formula for obtaining the adaptive comprehensive closeness C is: .

[0012] Preferably, the adaptive level classification specifically includes: dividing the evaluation object into multiple levels according to the overall adaptive closeness, and using Monte Carlo simulation to assess the confidence level of the evaluation results.

[0013] Preferably, the obstacle degree model identifies key weakness indicators, specifically including: Subtracting the standardized value of each indicator from the numerical value of 1 gives the deviation of each indicator, and the obstacle degree of each indicator is calculated based on this: ,in, Let represent the obstacle degree of indicator i, wi represent the weight of indicator i, and di represent the deviation degree of indicator i. All indicators are sorted from highest to lowest level of difficulty, and the top 100 indicators are selected as key weakness indicators.

[0014] Preferably, the acquisition of the weak point specifically includes: A set of typical evaluation scenarios is set, including four types of fault scenarios: three-phase short-circuit sag, single-phase ground sag, voltage swell, and large-capacity load switching, as well as three operating mode scenarios: peak load, off-peak load, and high photovoltaic output. Obtain the basic data of the evaluation object in each scenario, recalculate the values ​​of each indicator and the overall adaptability; compare the overall adaptability of each scenario, and identify the scenario with the lowest overall adaptability as the weak scenario.

[0015] Preferably, the generation of the optimization and improvement strategy specifically includes: Improvement directions are automatically matched based on the category of key bottleneck indicators: bottlenecks related to grid matching degree correspond to grid-side optimization suggestions, bottlenecks related to support capacity margin correspond to energy storage capacity configuration suggestions, and bottlenecks related to response coordination correspond to control strategy optimization suggestions. Provide scenario-based parameter adjustment suggestions for weak scenarios; sort the suggestions according to the expected improvement effect and implementation difficulty, and output a list of optimization and improvement strategies from high to low priority.

[0016] This application has at least the following beneficial effects: (1) Innovation of a systematic evaluation system with five dimensions and fifteen indicators. The adaptability of transient voltage support is a comprehensive concept with multiple dimensions, and a single indicator or a small number of indicators cannot fully reflect it. This application constructs a complete evaluation system with five dimensions and 15 indicators, including grid matching degree, support capacity margin, response coordination, operational reliability, and economic rationality. It covers all aspects of adaptability from the grid side to the energy storage side, from technical performance to economic cost, and from steady-state capability to transient response. The systematic indicator system ensures the comprehensiveness and depth of the evaluation and avoids the one-sidedness of a single indicator.

[0017] (2) Scientific Innovation of Combining Subjective and Objective Weighting. Determining weights is the core of the evaluation process. Purely subjective methods are highly arbitrary, while purely objective methods may deviate from reality. This application adopts a combined weighting scheme using the Analytic Hierarchy Process (AHP) and entropy weighting: the AHP reflects the relative importance of expert experience and indicators, while the entropy weighting reflects the dispersion and information content of the data itself. The two are combined through a multiplicative synthesis method. This combined weighting retains the expert's experience and judgment while incorporating the objective characteristics of the data, making it more scientific and reasonable than a single weighting method, and resulting in more credible evaluation results.

[0018] (3) Innovation in the Quantitative Method of TOPSIS Comprehensive Evaluation. The key challenge in evaluation is how to integrate multi-dimensional indicators into a single quantitative result. This application adopts the TOPSIS approximation ideal solution method, using the relative distance to the positive and negative ideal solutions as the basis for comprehensive evaluation. This scientifically addresses the comprehensive problem of multiple indicators, yielding a continuous quantitative value between 0 and 1. Compared to simple weighted summation, TOPSIS considers the overall distribution of indicators and ideal benchmarks, resulting in a more scientific and reasonable evaluation.

[0019] (4) Guiding innovation in obstacle degree and shortcoming identification. Traditional evaluations end with a score, leaving users unsure of where the shortcomings lie or how to improve. This application introduces an obstacle degree model to quantitatively calculate the degree of constraint of each indicator on overall adaptability, automatically ranking and identifying the top three key shortcomings. Shortcoming identification makes the evaluation results not only provide conclusions but also clear directions and priorities for improvement, upgrading the evaluation from scoring to diagnosis, and significantly enhancing its guiding value.

[0020] (5) Comprehensive innovation in multi-scenario comparative evaluation. Actual power grid operation modes are varied and fault types are diverse, so evaluation under a single operating condition cannot represent all scenarios. This application sets four typical fault scenarios and three operating mode scenarios, calculates and compares the comprehensive adaptability of each scenario, and automatically identifies the weakest scenario with the worst adaptability. Multi-scenario evaluation makes the evaluation results more comprehensive, can discover potential problems under extreme operating conditions, and provides a basis for targeted optimization.

[0021] (6) Innovative Closed-Loop Design from Evaluation to Optimization. Evaluation is not the goal; improvement is. This application establishes an automatic mapping mechanism from weakness analysis to optimization suggestions. It automatically matches improvement directions based on the category of weakness indicators, provides scenario-based parameter adjustment suggestions for weak scenarios, and outputs them in priority order. The evaluation results are directly transformed into actionable optimization strategies, opening up a closed loop from evaluation to improvement and greatly enhancing the practical value of the evaluation method.

[0022] (7) Innovative Reliability Assessment of Confidence. The input parameters themselves have a certain degree of uncertainty, and the reliability of the evaluation results needs to be assessed. This application introduces Monte Carlo simulation, applies random perturbations to the input parameters and repeats the calculation, and uses the stability of the statistical results to assess the confidence level. The confidence index allows users to know the reliability of the evaluation results, avoids misjudgments caused by parameter errors, and increases the robustness of the evaluation method. Attached Figure Description

[0023] Figure 1 A flowchart illustrating the steps of the adaptive evaluation method for distributed grid-connected energy storage participating in transient voltage support provided in this application. Detailed Implementation

[0024] The following section, in conjunction with the accompanying drawings, details the specific scheme of the adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support provided in this application.

[0025] This application provides an embodiment of an adaptive evaluation method for distributed grid-connected energy storage to participate in transient voltage support. For details, please refer to [link to relevant documentation]. Figure 1 This includes the following steps: This embodiment uses distributed grid-based energy storage in a 10kV distribution network of an industrial park as the evaluation object to verify the effectiveness of the proposed adaptability evaluation method. The total load of this distribution network is approximately 8MW, with a 2MW / 4MWh grid-based energy storage power station connected, mainly for improving transient voltage stability. The distributed photovoltaic capacity in this area is approximately 5MW, the grid short-circuit capacity is relatively weak, and transient voltage problems are quite prominent. The evaluation objective is to assess the adaptability level of this grid-based energy storage in supporting transient voltage, identify key shortcomings that restrict adaptability, pinpoint weak scenarios, and provide optimization and improvement suggestions.

[0026] Step S1: Basic data collection and feature parameter extraction.

[0027] This step serves as the input for the evaluation, collecting basic data from both the grid and energy storage sides and extracting characteristic parameters for the evaluation. Accurate basic parameters are a prerequisite for reliable evaluation results.

[0028] First, the extraction of equivalent characteristic parameters on the power grid side mainly includes the following four items: The first term is the short-circuit capacity Ssc. The short-circuit capacity at the energy storage connection point (i.e., the point of common coupling) is calculated based on the equivalent impedance of the power grid. The formula is: Where Un is the system rated voltage and Zeq is the equivalent impedance from the point of common coupling to the system side. Short-circuit capacity is a core indicator for measuring the strength of a power grid; the larger the short-circuit capacity, the stronger the power grid and the easier it is to support energy storage.

[0029] In this embodiment, the short-circuit capacity of the 10kV bus is approximately 80MVA.

[0030] The second item is the grid equivalent impedance X / R ratio. This ratio, the ratio of equivalent reactance to equivalent resistance, reflects the inductive and resistive properties of the grid. The X / R ratio has a significant impact on the control stability and support effect of grid-based energy storage; an excessively high or low X / R ratio can negatively affect control performance.

[0031] In this embodiment, the X / R ratio is approximately 7.2.

[0032] The third item is the statistical parameters of disturbance characteristics. For each point of common coupling (PCC), historical voltage disturbance data for the past three years are statistically analyzed, including: number of voltage sags, number of voltage rises, average sag depth, and average duration. Disturbance characteristics reflect the severity of transient voltage problems in the power grid and also determine the intensity of demand for energy storage support capabilities.

[0033] In this embodiment, there are approximately 12 sags per year, with an average sag depth of approximately 0.7 pu and an average duration of approximately 150 milliseconds.

[0034] The fourth item is the short-circuit capacity ratio. This is the ratio of the grid's short-circuit capacity to the rated capacity of the energy storage, i.e., Ssc / Sess. This ratio reflects the relative size of the energy storage capacity with respect to the grid. The smaller the ratio, the larger the energy storage is relative to the grid, the stronger its support capacity, but the more likely it is to cause stability problems.

[0035] In this embodiment, the short-circuit capacity is 80MVA, the rated energy storage capacity is 2MVA, and the short-circuit capacity ratio is 40.

[0036] Furthermore, the calculation of energy storage-side support capacity parameters mainly includes five items, as follows: The first item is rated reactive power support capability. This refers to the maximum reactive power that the energy storage converter can output under rated voltage. The reactive power support capability of grid-type energy storage is a core performance indicator for transient voltage support.

[0037] In this embodiment, the rated reactive power support capacity is approximately 2 Mvar.

[0038] The second term is the transient response time constant. This is the time required from the occurrence of a voltage drop to the reactive power output reaching 90% of its rated value. The faster the response speed, the better the transient support effect.

[0039] In this embodiment, the response time is approximately 15 milliseconds.

[0040] The third parameter is the overload capacity parameter, which includes the maximum overload multiple and the duration of overload. Overload capacity determines the peak support capacity under severe sags.

[0041] In this embodiment, the maximum overload factor is 1.5 times, and the duration is 500 milliseconds.

[0042] The fourth item is the adjustable range of virtual inertia. This includes the minimum and maximum values ​​of the virtual inertia coefficient, and the corresponding active power support capacity range. The active power support provided by virtual inertia is an important component of transient voltage support.

[0043] In this embodiment, the virtual inertia coefficient is adjustable from 2s to 8s.

[0044] The fifth item is the control mode switching time. This refers to the transition time from normal grid-connected mode to transient support mode. The faster and smoother the switching, the less disturbance it causes to the transient process.

[0045] In this embodiment, the mode switching time is approximately 20 milliseconds.

[0046] Step S2: Construction and standardization of a multi-dimensional evaluation index system.

[0047] This step involves constructing an evaluation indicator system and standardizing each indicator to ensure comparability. The scientific validity of the indicator system directly determines the quality of the evaluation. In this embodiment, an evaluation indicator system with five dimensions and a total of 15 indicators will be constructed, specifically as follows: The first dimension: grid compatibility (4 items). This dimension measures the degree of compatibility between energy storage and the power grid, that is, whether the grid characteristics are suitable for energy storage to play a supporting role. Specifically, it includes: Short-circuit capacity ratio: The best matching degree is achieved when the ratio of short-circuit capacity to energy storage capacity is within a reasonable range; too high or too low a ratio will reduce the matching degree. Using piecewise function standardization, a ratio between 20 and 50 is considered the optimal range. X / R ratio fit: The X / R ratio has a high fit within the optimal range (4 to 10) of the network control, and decreases when it deviates from the optimal range. Voltage level matching coefficient: The matching degree is highest when the rated voltage of energy storage matches the grid voltage level; Disturbance frequency suitability: Energy storage efficiency is highest when the disturbance frequency is moderate. Too few disturbances will leave energy storage idle, while too many disturbances will result in insufficient energy storage capacity, which will reduce the suitability.

[0048] The second dimension: Support capacity margin (3 items). This dimension measures the margin of the energy storage's transient support capacity relative to grid demand. Specifically, it includes: Reactive power support margin: The ratio of the rated reactive power capacity of energy storage to the reactive power required for the maximum sag of the power grid. The larger the ratio, the higher the margin. Active power support margin: The ratio of the active power support that energy storage can provide to the active power demand during transient processes; Overload capacity margin: The ratio of the maximum overload factor to the overload factor required in a severe fault scenario.

[0049] The third dimension: Response Coordination (3 items). This dimension measures the degree of coordination and matching between energy storage support response and grid transient processes. Specifically, it includes: Response speed matching degree: The ratio of energy storage response time to grid transient time constant; the faster the response speed, the higher the matching degree. Inertia-damping coordination: When the ratio of virtual inertia to damping coefficient is within a reasonable range, the coordination is high; if it is too large, it is prone to oscillation; if it is too small, the support is weak. Mode switching smoothness: The magnitude of current surge and voltage fluctuation during mode switching; the smaller the surge, the smoother the performance.

[0050] The fourth dimension: Operational reliability (3 items). This dimension measures the long-term operational reliability of energy storage support functions. Specifically, it includes: Equipment availability: The annual availability of energy storage converters reflects equipment reliability; Stability margin: The phase margin and gain margin of the mesh control; the larger the margin, the better the stability. Protection coordination degree: The degree of coordination between energy storage protection and power grid relay protection. Proper coordination will prevent malfunctions and failures to operate.

[0051] Fifth Dimension: Economic Rationality (2 items). This dimension measures the economic viability of transient voltage support. Specifically, it includes: Unit capacity support cost: The investment cost per unit capacity of energy storage; the lower the cost, the better the economic efficiency. Operating loss rate: The proportion of power loss during transient support process; the lower the loss, the more economical it is.

[0052] Because the various indicators have different dimensions and value ranges, they cannot be directly compared and weighted. Standardization is required to map them uniformly to the 0-1 range and classify them as positive indicators (the larger the better). For positive indicators (the larger the better), extreme value method is used for standardization. For negative indicators (the smaller the better), the reciprocal is taken first, or the reverse processing is performed before standardization. For interval-type indicators (optimal within a certain interval), a piecewise function is used, taking 1 within the interval and decreasing linearly when deviating from the interval.

[0053] After standardization, all 15 indicators range from 0 to 1, with higher values ​​indicating better adaptability, thus preparing for subsequent weighted aggregation.

[0054] Step S3: Combine weighting to determine the indicator weights and calculate the overall closeness of the adaptability.

[0055] This step is the core calculation process of the evaluation method. The weights of each indicator are scientifically determined through combined weighting, and then the overall adaptability is calculated using the TOPSIS method.

[0056] A combined weighting method that integrates subjective and objective weighting is adopted: Subjective weights were determined using the Analytic Hierarchy Process (AHP). Specifically, five experts in power systems and energy storage were invited to conduct pairwise comparisons and scores of the relative importance of each dimension and indicator, constructing a judgment matrix. The largest eigenvalue and its corresponding eigenvector of the judgment matrix were calculated, and after normalization, they became the subjective weights of each indicator. Simultaneously, a consistency check was performed, calculating the consistency ratio CR: CR = CI / RI; where CI is the consistency index and RI is the average random consistency index. When CR < 0.1, the judgment matrix was considered to have satisfactory consistency, and the weights were considered effective; otherwise, the judgment matrix needed adjustment.

[0057] In this embodiment, the five-dimensional subjective weights are approximately: grid matching degree 0.25, support capacity margin 0.30, response coordination 0.20, operational reliability 0.15, and economic rationality 0.10. The consistency ratio is approximately 0.07, which meets the requirements.

[0058] The objective weights employ the entropy weighting method. Specifically, 10 similar grid-type energy storage projects are collected as evaluation samples, and the 15 indicator values ​​of each sample constitute an evaluation matrix. Information entropy is calculated for each indicator; the smaller the entropy value, the greater the difference between the samples, the more information it provides, and the higher its weight should be. The objective weights are derived from the information entropy. The entropy weighting method assigns weights entirely based on the degree of difference in the data itself, avoiding the bias of subjective judgment. The calculation process of information entropy and the determination of objective weights from it is prior art known to those skilled in the art and will not be elaborated upon in this embodiment.

[0059] The combined weights employ a multiplicative synthesis method. Specifically, the weight of each indicator is the normalized result of multiplying the subjective weight and the objective weight of that indicator. Multiplicative synthesis can simultaneously reflect both subjective importance and objective information content; indicators with large subjective and objective weights will have large final weights, and indicators with small subjective and objective weights will have small final weights, thus achieving an organic combination of subjective and objective factors.

[0060] In this embodiment, the combined weight calculation results are as follows: the reactive power support margin weight is the highest at about 0.12, the short-circuit capacity ratio is about 0.10, the response speed matching degree is about 0.09, and the remaining indicators are between 0.04 and 0.08.

[0061] Furthermore, the fitness comprehensive closeness is calculated using TOPSIS (Topology of Solutions Approximating Ideal Solutions). The first step is to construct a weighted standardization matrix. Multiply the standardized value of each indicator by its corresponding weight to obtain the weighted standardization decision matrix. The second step is to determine the positive and negative ideal solutions. A positive ideal solution is a virtual solution where all indicators take the optimal value (each indicator is 1), and a negative ideal solution is a virtual solution where all indicators take the worst value (each indicator is 0). The third step is to calculate the distances. Calculate the Euclidean distance D from the evaluation object to the positive ideal solution. + The Euclidean distance D to the negative ideal solution - ; The fourth step is to calculate the overall adaptability and closeness. : ; The adaptive comprehensive closeness C ranges from 0 to 1. The closer it is to 1, the closer the evaluated object is to the positive ideal solution and the better the adaptability; the closer it is to 0, the closer it is to the negative ideal solution and the worse the adaptability.

[0062] In this embodiment, the overall adaptability closeness is approximately 0.72.

[0063] Step S4: Adaptability level classification and critical weakness identification.

[0064] This step determines the level of the comprehensive evaluation results and identifies the key shortcomings that restrict adaptability, so that the evaluation results have both conclusions and directions.

[0065] Based on the overall adaptability score C, the adaptability of the evaluated object is divided into five levels. Preferably, in this embodiment, the specific details are as follows: • Excellent: C≥0.85, very good adaptability, strong transient voltage support capability; • Good: 0.7≤C<0.85, good adaptability, basically meeting support requirements; • Generally: 0.5≤C<0.7, moderate adaptability, insufficient support for some scenarios; • Poor: 0.3≤C<0.5, poor adaptability, difficult to provide effective support; • Poor: C<0.3, very poor adaptability, basically unable to provide effective support.

[0066] In this embodiment, C=0.72, which is rated as good.

[0067] The reliability of the evaluation results was assessed using Monte Carlo simulation: each input parameter was subjected to a random perturbation of ±10%, and the calculation was repeated 1000 times. The proportion of the overall fitness fit falling within ±5% of the main result was the confidence level of the evaluation results.

[0068] In this embodiment, 928 out of 1000 simulations yielded results within the range of 0.684 to 0.756, with a confidence level of approximately 92.8%, which is greater than 90%, indicating that the evaluation results are reliable.

[0069] The degree of constraint of each indicator on adaptability is quantitatively analyzed using an obstacle degree model: The first step is to calculate the deviation. The deviation for each indicator... That is, 1 minus the standardized value, where, , These represent the deviation and standardized value of indicator i, respectively. The standardized value of each indicator is the normalized result of its values. The normalization method can be selected by the implementer, and this embodiment does not impose any restrictions. This embodiment uses the maximum-minimum normalization method. The larger the deviation, the further the indicator is from the optimal value, and the greater the room for improvement.

[0070] The second step is to calculate the obstacle degree. The formula for calculating the obstacle degree for each indicator is as follows: ,in, Let represent the obstacle degree of indicator i, wi be the weight of indicator i, and di be the deviation degree of indicator i. The numerator is the weight of the indicator multiplied by the deviation degree, and the denominator is the sum of the weights and deviation degrees of all indicators. The obstacle degree represents the percentage of the overall fitness constraint imposed by the indicator.

[0071] The third step is to sort and identify weaknesses. Sort the weaknesses from highest to lowest difficulty, and select the top three as key weakness indicators.

[0072] In this embodiment, the top three indicators of obstacle resistance are: overload capacity margin (approximately 18.5%), short-circuit capacity ratio (approximately 15.2%), and virtual inertia range (approximately 12.8%). These three are the most significant factors limiting the adaptability of the energy storage transient voltage support and should be prioritized for improvement.

[0073] Step S5: Multi-scenario comparison and evaluation and weak scenario identification.

[0074] This step expands the evaluation beyond the baseline assessment to evaluate multiple typical scenarios separately, identifying the weakest scenarios with the worst adaptability, thus making the evaluation more comprehensive.

[0075] This embodiment sets up seven typical evaluation scenarios in two categories: Fault scenarios (4 types): Three-phase short-circuit sag: the most serious symmetrical fault; Single-phase ground sag: the most common asymmetrical fault; Voltage sag: overvoltage scenario; High-capacity load switching: voltage fluctuations caused by it.

[0076] Operating modes (3 types): Peak load mode: grid load is at its maximum and voltage is low; Off-peak load mode: grid load is at its minimum and voltage is high; High photovoltaic output mode: distributed photovoltaic power generation is at full capacity and grid characteristics change.

[0077] For each scenario, adjust the corresponding grid-side parameters and energy storage operation parameters to obtain typical scenarios with different fault types and operating modes: Different fault types correspond to different slump depths, durations, and symmetrical / asymmetrical characteristics; Different operating modes correspond to different equivalent impedances of the power grid, voltage levels, and short-circuit capacities.

[0078] Recalculate the standardized values ​​of each indicator, and then calculate the overall adaptability of each scenario using the same weights and the TOPSIS method.

[0079] Furthermore, this embodiment will identify vulnerable scenarios. By comparing the overall adaptability of all scenarios, the scenario with the lowest overall adaptability is identified as a vulnerable scenario. Vulnerable scenarios are the operating conditions where transient voltage support in energy storage is most prone to problems, requiring focused attention and targeted optimization.

[0080] In this embodiment, the overall adaptability scores for each scenario are as follows: three-phase short-circuit sag: 0.61; single-phase ground fault sag: 0.75; voltage sag: 0.78; load switching: 0.82; peak load: 0.68; off-peak load: 0.76; high photovoltaic output: 0.70. The worst adaptability is in the three-phase short-circuit sag scenario, with an overall adaptability score of only 0.61, which is considered average and represents a weak point for this energy storage system. This indicates that under severe three-phase short-circuit conditions, the transient support capability of the energy storage system is relatively insufficient, and this is an area that needs to be prioritized for optimization.

[0081] Step S6: Automatic generation of optimization and improvement strategies.

[0082] This step, based on weakness analysis and vulnerability identification, automatically generates targeted optimization and improvement suggestions, creating a closed loop from evaluation to optimization.

[0083] Preferably, in this embodiment, the corresponding improvement direction is automatically matched according to the dimension to which the key weakness indicator belongs: • Shortcomings in grid matching → Grid-side optimization suggestions: such as adding dynamic reactive power compensation devices to improve short-circuit capacity, adjusting grid operation mode to improve X / R ratio, etc. • Shortcomings related to support capacity margin → Energy storage capacity configuration recommendations: such as increasing the capacity of energy storage converters, improving overload capacity, and expanding the range of virtual inertia, etc. • Suggestions for optimizing control strategies to address shortcomings in response coordination: such as optimizing control parameters, improving the virtual inertia adaptive law, and optimizing mode switching logic. • Operational reliability shortcomings → Reliability improvement suggestions: such as adding redundancy design, optimizing protection coordination schemes, etc.; • Shortcomings related to economic rationality → Suggestions for economic optimization: such as optimizing capacity allocation ratios and reducing operating losses; Furthermore, in this embodiment, specific parameter adjustment suggestions are provided for the identified vulnerable scenarios, preferably: • Three-phase short-circuit sag is weak → It is recommended to increase the transient overload factor and raise the upper limit of reactive current; • Weakness in unbalanced faults → It is recommended to add a negative sequence voltage control channel and optimize the unbalanced control strategy; • High photovoltaic output is weak → It is recommended to adjust the grid control parameters and increase damping to suppress high-frequency oscillations.

[0084] The output is prioritized, with suggestions that have a significant impact and are easy to implement receiving the highest priority and are recommended first. In this embodiment, the optimization suggestions are sorted by priority as follows: Increase the overload capacity of the energy storage converter by 2 times (high effect, medium difficulty) – addressing the shortcoming of overload capacity margin; optimize the virtual inertia parameters under three-phase short circuit scenarios (high effect, low difficulty) – addressing the weak three-phase short circuit scenario; expand the adjustable range of virtual inertia to 10s (medium effect, medium difficulty) – addressing the shortcoming of inertia range; dynamically adjust control parameters during peak hours (medium effect, low difficulty) – addressing peak load scenarios.

[0085] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An adaptive evaluation method for distributed grid-connected energy storage participating in transient voltage support, characterized in that, Includes the following steps: Collect basic data on the evaluation objects, including equivalent characteristic parameters on the grid side and supporting capacity parameters on the energy storage side; An evaluation index system comprising five dimensions—grid matching degree, support capacity margin, response coordination, operational reliability, and economic rationality—is constructed, and each index is processed to be aligned and dimensionless. A combined weighting method that combines subjective and objective weighting is used to determine the weights of each indicator, and the overall adaptability is calculated based on the TOPSIS method. The evaluation objects are classified into adaptability levels based on their overall adaptability and the obstacle degree model is used to identify key weakness indicators. For different fault scenarios and operating modes, calculate the overall adaptability and fit, compare and identify weak scenarios, and output targeted optimization and improvement strategies based on the shortcomings analysis.

2. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The extraction of equivalent characteristic parameters on the power grid side specifically includes: The short-circuit capacity at the point of common coupling is calculated as the square of the system rated voltage divided by the modulus of the grid equivalent impedance. The X / R ratio of the grid equivalent impedance, i.e., the ratio of equivalent reactance to equivalent resistance, is calculated. Voltage disturbance events at the point of common coupling over the past few years are statistically analyzed, including the number of sags, the number of rises, the average sag depth, and the average duration, to obtain the statistical parameters of disturbance characteristics. The ratio of the grid short-circuit capacity to the rated capacity of energy storage is calculated, i.e., the short-circuit capacity ratio.

3. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The calculation of the energy storage-side support capacity parameters specifically includes: Calculate the rated reactive power support capacity of the energy storage, i.e., the maximum reactive power that can be output under rated voltage; measure the transient voltage response time constant, i.e., the time required from the occurrence of voltage drop to the reactive power output reaching 90% of the rated value; determine the overload capacity parameters, including the maximum overload multiple and the overload duration; calculate the adjustable range of virtual inertia, i.e., the minimum and maximum values ​​of the virtual inertia coefficient, and the corresponding active power support capacity range; measure the control mode switching time, i.e., the transition time from normal mode to transient support mode.

4. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The construction of the evaluation index system specifically includes: The first dimension of grid matching degree includes: short-circuit capacity ratio, X / R ratio matching degree, voltage level matching coefficient, and disturbance frequency matching degree. The second dimension of support capacity margin includes: reactive power support margin, active power support margin, and overload capacity margin. The third dimension of response coordination includes: response speed matching, inertia damping coordination, and mode switching smoothness. The fourth dimension is operational reliability: equipment availability, control stability margin, and protection coordination. The fifth dimension is economic rationality: cost per unit capacity and operating loss rate.

5. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The combined weighting method for determining the weights of each indicator specifically includes: using the analytic hierarchy process (AHP) to calculate subjective weights and using the entropy weighting method to calculate objective weights; the weight of each indicator is the normalized result of multiplying the subjective weight and the objective weight of each indicator.

6. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The adaptive comprehensive proximity calculation based on the TOPSIS method specifically includes: Multiply each indicator value by its corresponding weight to construct a weighted standardized decision matrix; determine the positive ideal solution and the negative ideal solution: the positive ideal solution is a virtual solution in which all indicators take the optimal value, and the negative ideal solution is a virtual solution in which all indicators take the worst value; Calculate the Euclidean distance D from the evaluation object to the positive ideal solution. + The Euclidean distance D to the negative ideal solution - The formula for obtaining the adaptive comprehensive closeness C is: .

7. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The adaptive level classification specifically includes: dividing the evaluation object into multiple levels according to the overall adaptive closeness, and using Monte Carlo simulation to assess the confidence level of the evaluation results.

8. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The obstacle level model identifies key weakness indicators, specifically including: Subtracting the standardized value of each indicator from the numerical value of 1 gives the deviation of each indicator, and the obstacle degree of each indicator is calculated based on this: ,in, Let represent the obstacle degree of indicator i, wi represent the weight of indicator i, and di represent the deviation degree of indicator i. All indicators are sorted from highest to lowest level of difficulty, and the top 100 indicators are selected as key weakness indicators.

9. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The acquisition of the weak points specifically includes: A set of typical evaluation scenarios is set, including four types of fault scenarios: three-phase short-circuit sag, single-phase ground sag, voltage swell, and large-capacity load switching, as well as three operating mode scenarios: peak load, off-peak load, and high photovoltaic output. Obtain the basic data of the evaluation object in each scenario, recalculate the values ​​of each indicator and the overall adaptability; compare the overall adaptability of each scenario, and identify the scenario with the lowest overall adaptability as the weak scenario.

10. The adaptive evaluation method for distributed grid-type energy storage participating in transient voltage support as described in claim 1, characterized in that, The generation of the optimization and improvement strategy specifically includes: Improvement directions are automatically matched based on the category of key bottleneck indicators: bottlenecks related to grid matching degree correspond to grid-side optimization suggestions, bottlenecks related to support capacity margin correspond to energy storage capacity configuration suggestions, and bottlenecks related to response coordination correspond to control strategy optimization suggestions. Provide scenario-based parameter adjustment suggestions for weak scenarios; sort the suggestions according to the expected improvement effect and implementation difficulty, and output a list of optimization and improvement strategies from high to low priority.

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