A method for assessing the resilience of distribution networks based on a multi-dimensional resilience index system

Abstract

This application relates to a distribution network resilience assessment method based on a multi-dimensional resilience index system, belonging to the field of power industry technology. The method includes: obtaining the target functional curve of the target distribution network and the actual functional curves under multiple test scenarios; obtaining the missing area index, the extreme value index of the missing region, and the system recovery duration index of the functional curve under each test scenario based on the target functional curve and the actual functional curve; and obtaining the resilience assessment result of the target distribution network based on the missing area index, the extreme value index of the missing region, and the system recovery duration index of the functional curve under each test scenario. This invention solves the problems of traditional distribution network resilience assessment methods having fewer indicators, not considering the access of inverter power sources, and being unable to cope with threats posed by uncertainties such as extreme weather, resulting in incomplete assessments and an inability to meet the resilience assessment needs of distribution networks with a high proportion of inverter power sources.

Description

Technical Field

[0001] This application relates to the field of power industry, and in particular to a method for assessing the resilience of distribution networks based on a multi-dimensional resilience index system. Background Technology

[0002] With the acceleration of global energy transition and the construction of new power systems, distribution networks face uncertainties such as increased extreme weather and growing user demand, posing a serious threat to their safe operation. Resilience assessment has become crucial to ensuring system stability.

[0003] Currently, traditional distribution network resilience assessment methods use only one indicator and do not include complex scenarios such as the integration of inverter power sources. As a result, the assessment results are difficult to reflect the true resilience performance of distribution networks with a high proportion of inverter power sources. They are unable to fully identify weak links in the system, support the planning and optimization of new distribution networks, and meet the needs of the current power system development. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides a distribution network resilience assessment method based on a multi-dimensional resilience index system. This method improves the adaptability to complex scenarios in distribution networks with a high proportion of inverter power sources and solves the problems of traditional methods having fewer indicators, failing to consider uncertainties such as inverter power source access and extreme weather, resulting in incomplete assessments and difficulty in meeting current needs.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] This application provides a method for assessing the resilience of a distribution network based on a multi-dimensional resilience index system. The method includes:

[0007] Obtain the target functional curve of the target distribution network and the actual functional curve under multiple test scenarios;

[0008] Based on the actual function graphs and the target function graphs in each test scenario, the missing area index, the missing region extreme value index, and the system recovery duration index of the function graphs in each test scenario are obtained.

[0009] Based on the missing area index, missing region extreme value index, and system recovery duration index of the functional curves under each test scenario, the resilience assessment results of the target distribution network are obtained.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application proposes a distribution network resilience assessment method based on a multi-dimensional resilience index system. It constructs a target function curve by determining key performance indicators and their weights, obtains actual function curves through multi-scenario interference tests, and extracts core parameters such as the missing area of ​​the function curves through comparative analysis. After normalization, a three-dimensional index is obtained, and a resilience assessment coordinate system is established based on this index to calculate a comprehensive resilience index, achieving a precise quantitative assessment of distribution network resilience. First, key performance indicators such as power supply capacity, voltage level, and user outage ratio are selected. The weights of each indicator are determined using the analytic hierarchy process (AHP) to construct the target function curve. Then, actual function curves for each scenario are generated through interference tests such as thunderstorms and line short circuits. Next, the target function curve and the actual function curve are compared to extract the missing area, extreme values ​​of the missing region, and the duration of grid recovery. Subsequently, a three-dimensional index is obtained through normalization, and the Euclidean distance from the origin is calculated as the comprehensive resilience index. Based on this, the resilience of each scenario is assessed, forming a comprehensive resilience assessment result.

[0012] The technical solution of this application solves the problem of inaccurate resilience quantification caused by single indicators and insufficient scenario coverage in traditional assessments by integrating multiple steps such as weight determination of the analytic hierarchy process, graph construction of multi-scenario tests, normalization processing of three-dimensional indices, and comprehensive evaluation of coordinate system visualization. It achieves a comprehensive assessment of the resilience of distribution networks with a high proportion of inverter power sources. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart illustrating a distribution network resilience assessment method based on a multi-dimensional resilience index system provided in this application embodiment;

[0015] Figure 2 A logical diagram illustrating the resilience assessment method for distribution networks with high proportion of inverter power sources based on a multi-dimensional resilience index system provided in this application embodiment;

[0016] Figure 3 This is a schematic diagram of the process for obtaining actual functional graphs through multi-scenario interference testing provided in an embodiment of this application.

[0017] Figure 4 This is a geometric calculation diagram of the missing area of ​​functional lines in a power distribution network interference scenario provided in an embodiment of this application.

[0018] Figure 5This is a geometric calculation diagram of the extreme values ​​of the missing region and the system recovery duration under the power distribution network interference scenario provided in the embodiments of this application;

[0019] Figure 6 This is a schematic diagram of the coordinate system for visualizing the resilience assessment of the multi-dimensional resilience index of the power distribution network provided in this application embodiment. Detailed Implementation

[0020] This application provides a distribution network resilience assessment method based on a multi-dimensional resilience index system to address the technical problems of existing traditional distribution network resilience assessment methods, which have few indicators, do not consider complex scenarios such as inverter power supply access, and are difficult to cope with uncertainties such as extreme weather and user demand growth, thus failing to meet current needs.

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0023] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0024] Examples, as shown in the appendix Figure 1 and attached Figure 2 As shown, this application provides a method for assessing the resilience of a distribution network based on a multi-dimensional resilience index system. The method includes the following steps:

[0025] S110: Obtain the target functional curve of the target distribution network and the actual functional curves under multiple test scenarios. The functional curve refers to the curve that reflects the change of the functional state of the distribution network over time.

[0026] In this embodiment of the application, in the scenario of distribution network resilience assessment with a high proportion of inverter power sources, in order to comprehensively reflect the grid performance and quantify the impact of each indicator, it is necessary to determine the key indicators and their weights through the analytic hierarchy process.

[0027] Specifically, the power grid supply capacity, voltage level, and user power outage ratio are selected as core performance indicators. These indicators cover the power grid supply capacity, operational stability, and the degree of impact on users.

[0028] Meanwhile, experts in the field were invited to conduct pairwise comparisons and scoring of the three indicators, using a 1-9 scale to reflect the differences in importance between the indicators, and thus forming the indicator comparison and scoring results.

[0029] Furthermore, a judgment matrix is ​​constructed based on the scoring results, the matrix order and the maximum eigenvalue are calculated, and a consistency index is determined based on the matrix order and the maximum eigenvalue. Consistency verification is then performed by combining the average random consistency index of the corresponding order.

[0030] When the judgment matrix passes the verification, the normalized vector corresponding to the largest eigenvalue is used as the weight of each index to ensure that the weight allocation is reasonable.

[0031] This step, through the analytic hierarchy process (AHP) approach, provides a scientific basis for the weighting of indicators for subsequent functional curve construction and resilience assessment, ensuring the comprehensiveness and accuracy of the assessment.

[0032] Step S110 in the method provided in this application embodiment includes:

[0033] Multiple power grid performance indicators of the target distribution network are obtained, and the weight of each power grid performance indicator is determined. The target distribution network contains a high proportion of inverter-type power sources.

[0034] Based on the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, a target function curve of the target distribution network is constructed. The target function curve is the function curve of the target distribution network during normal operation.

[0035] Multiple test scenarios of the target distribution network are obtained. Interference tests are performed on the target distribution network under each test scenario. Based on the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, the actual functional curves under each test scenario are obtained.

[0036] In this embodiment of the application, in order to scientifically quantify the influence of each indicator in the resilience assessment of distribution networks with a high proportion of inverter power sources, it is necessary to determine the weight of each indicator through the analytic hierarchy process (AHP) to ensure the objectivity and rationality of the assessment.

[0037] The method provided in this application embodiment, "obtaining multiple power grid performance indicators of the target distribution network and determining the index weight of each power grid performance indicator" includes:

[0038] Acquire multiple power grid performance indicators, including power grid supply capacity indicators, voltage level indicators, and user power outage ratio indicators;

[0039] The weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index are determined by using the analytic hierarchy process (AHP).

[0040] In this embodiment of the application, to accurately capture the multi-dimensional operating characteristics of a distribution network with a high proportion of inverter power sources, it is necessary to select core performance indicators that can comprehensively reflect the resilience of the grid and determine their weights through scientific methods, so as to provide a reliable basis for the subsequent construction of functional diagrams and resilience assessment.

[0041] Specifically, the power supply capacity index F is first obtained by collecting operational data, monitoring records, and user feedback from the target distribution network. 系统 Voltage level index F 电压 User power outage ratio index F 用户 The basic data.

[0042] Among them, the power grid supply capacity index F 系统 Through the formula "F" 系统 The power grid's power supply capacity index F is calculated as "=1−∑Outage Capacity / Total Power Supply Capacity×100%". 系统 A larger value indicates a smaller proportion of outage capacity to total power supply capacity, and stronger grid continuity and resilience in the "power supply capacity dimension"; conversely, a smaller value indicates a larger proportion of outage capacity, weaker grid power supply capacity, and more prominent resilience shortcomings.

[0043] In addition, voltage level index F 电压 Through the formula "F" 电压 =1 / n×∑(U i / U N The value is determined by "U × 100%" to reflect operational stability. i表示 The actual operating voltage U of the i-th node (or monitoring point) in the distribution network N This indicates the rated voltage of a distribution network node; the voltage level index F is... 电压A larger value indicates a smaller average deviation between the actual voltage and the rated voltage at each node, and a more stable voltage operation in the distribution network; conversely, a smaller value indicates a larger average voltage deviation, poorer operational stability, and weaker resilience.

[0044] In addition, the user power outage ratio indicator F 用户 According to the formula "F 用户 =1−∑Number of users experiencing power outages / Total number of users×100%” is used to correlate the degree of impact on users. Here, the power outage ratio F for that user is the indicator. 用户 A higher value indicates fewer users affected by interference or faults, resulting in a better user experience; conversely, a lower value indicates a wider and deeper impact of faults or interferences on the user side, with users experiencing a more significant power outage impact.

[0045] These indicators are used as the core evaluation criteria to comprehensively cover the key characteristics of the distribution network in terms of power supply capacity, operating status and user impact, ensuring coverage of the key performance characteristics of the distribution network.

[0046] Furthermore, the weights of each indicator are determined using the analytic hierarchy process (AHP) to ensure that the weight allocation is scientific and reasonable.

[0047] The method provided in this application embodiment includes the step of "using the analytic hierarchy process (AHP) to determine the weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index," which comprises:

[0048] The power grid supply capacity index, the voltage level index, and the user power outage ratio index are compared and scored pairwise to obtain the index comparison and scoring results.

[0049] Based on the scoring results of the aforementioned indicators, a judgment matrix is ​​constructed, and the matrix order and the maximum eigenvalue are obtained from the judgment matrix.

[0050] The consistency index is calculated based on the matrix order and the largest eigenvalue, and the average random consistency index is obtained based on the matrix order.

[0051] The judgment matrix is ​​subjected to consistency verification based on the consistency index and the average random consistency index. When the judgment matrix passes the consistency verification, the index weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index are determined based on the normalized vector corresponding to the largest eigenvalue.

[0052] In this embodiment of the application, in order to scientifically quantify the influence weight of each performance index in a distribution network with a high proportion of inverter power sources, it is necessary to use the analytic hierarchy process (AHP) to achieve the orderly sorting and quantitative assignment of the importance of the indexes, so as to ensure that the weight allocation meets the actual evaluation requirements.

[0053] Specifically, firstly, a hierarchical model is established, with the system function F (a core indicator for comprehensively evaluating the resilience of the distribution network) as the target layer, and the corresponding F... 系统 (System power supply capacity index) is the power grid power supply capacity index, corresponding to F. 电压 (Voltage Level Index) voltage level indicator, corresponding F 用户 The user power outage ratio index serves as the criterion layer, while different distribution network schemes or assessment objects serve as the scheme layer, clarifying the hierarchical relationship between each layer.

[0054] Furthermore, pairwise comparisons and scoring were conducted for the indicators. A team of experts in power system planning, operation, and resilience assessment was organized to compare the power grid supply capacity, voltage level, and user outage ratio indicators, and the 1-9 scale method was used to determine the relative importance scores between the indicators.

[0055] In this system, 1 indicates equal importance, 3 indicates slightly important, 5 indicates significantly important, 7 indicates strongly important, and 9 indicates extremely important. The reverse is used to determine the relative importance score.

[0056] For example, if experts consider the power supply capacity indicator to be significantly more important than the voltage level indicator and assign it a score of 5, then the voltage level indicator has a score of 1 / 5 relative to the power supply capacity indicator; if the voltage level indicator and the user power outage ratio indicator are equally important, they are each assigned a score of 1, thus forming a complete indicator comparison score result.

[0057] Furthermore, a judgment matrix is ​​constructed based on the scoring results. Let the three indicators be denoted as A (power supply capacity, corresponding to F...). 系统 B (voltage level, corresponding to F) 电压 C (user power outage ratio, corresponding to F) 用户 If the expert scores are 3 points for A relative to B, 5 points for A relative to C, and 2 points for B relative to C, then the corresponding judgment matrix M is:

[0058] ;

[0059] Here, the matrix order m is 3, representing the number of indicators involved in the comparison.

[0060] Furthermore, the largest eigenvalue λ is calculated through matrix eigenvalue decomposition. max The principal eigenvalues, i.e., the largest eigenvalues, of the judgment matrix are obtained using the power method iterative solution. For example, the matrix above can be calculated to yield λ. max ≈3.05.

[0061] Furthermore, through the formula "Conformity Index CI = (λ)", maxThe consistency index is calculated as CI = (3.05-3) / (3-1) = 0.025. Then, based on the matrix order m=3, the average random consistency index RI is obtained from the table as 0.52.

[0062] Among them, the industry-standard values ​​are as follows: RI=0 for 1st and 2nd order matrices, RI=0.52 for 3rd order matrices, RI=0.89 for 4th order matrices, RI=1.12 for 5th order matrices, RI=1.26 for 6th order matrices, RI=1.36 for 7th order matrices, RI=1.41 for 8th order matrices, and RI=1.46 for 9th order matrices.

[0063] Furthermore, the judgment matrix is ​​validated for consistency based on the consistency index and the average random consistency index. The specific calculation formula is "consistency ratio CR = CI / RI". After substituting the data, we obtain CR = 0.025 / 0.52 ≈ 0.048. Since CR < 0.1, the judgment matrix is ​​deemed to have passed the consistency check, indicating that the expert scoring logic is consistent and the result is reliable.

[0064] Conversely, if CR ≥ 0.1, feedback is needed to the experts to readjust the score, and the above steps are repeated until the matrix passes the verification.

[0065] Finally, after the judgment matrix passes the consistency check, the eigenvector corresponding to the largest eigenvalue is normalized (each element is divided by the sum) to obtain the weight of each indicator.

[0066] For example, the above matrix λ max The corresponding eigenvectors are [0.63, 0.25, 0.12]. After normalization, the power supply capacity index (F) is determined. 系统 Weight w1=0.63, voltage level index (F) 电压 Weight w2=0.25, user power outage ratio index (F) 用户 The weight w3 = 0.12.

[0067] Furthermore, the obtained weight parameters w1, w2, and w3 are substituted into the formula "F = w1 × F". 系统 +w2×F 电压 +w3×F 用户 This achieves a weighted fusion of three single-dimensional indicators: power supply capacity, voltage level, and user power outage ratio, thereby accurately quantifying the comprehensive system function F of the distribution network.

[0068] By analyzing the weights of power grid supply capacity, voltage level, and user outage ratio using the analytic hierarchy process (AHP), we combined expert experience with subjective judgments on the importance of these indicators with mathematical verification to ensure consistency in these judgments. This provides a scientific and reliable basis for the integration of multiple indicators in subsequent distribution network resilience assessments.

[0069] Furthermore, in order to accurately reflect the normal operation status of distribution networks with a high proportion of inverter power sources, it is necessary to construct a target function curve by combining key performance indicators and their weights, so as to provide a benchmark reference for subsequent resilience assessment.

[0070] Among them, the functional curve refers to the curve with time as the horizontal axis and the comprehensive functional evaluation value of the distribution network as the vertical axis, which reflects the dynamic change of the functional status of the distribution network at different times. It can intuitively present the complete process of the distribution network from normal operation to disturbance and then to recovery.

[0071] Specifically, based on the normal operating threshold range of the obtained power grid supply capacity indicators, voltage level indicators, and user power outage ratio indicators, the baseline values ​​of each indicator under ideal conditions are determined, and the target function curve is constructed based on this.

[0072] For example, the benchmark value of the power supply capacity index is taken as 90% of the maximum power supply load of the distribution network design, corresponding to the system power supply capacity index F. 系统 Ideally, this reflects a lossless power supply capability; the voltage level index benchmark is set at the midpoint of the ±5% range of the rated voltage, corresponding to the voltage level index F. 电压 The ideal state reflects zero voltage operation deviation; the benchmark value for the user power outage ratio index is 0, corresponding to the user power outage ratio index F. 用户 Ideally, this means there should be no power outages affecting the user side.

[0073] At the same time, the baseline values ​​of each indicator are assigned weights w1, w2, and w3 according to the analytic hierarchy process.

[0074] (Saving w1+w2+w3=1) We perform weighted integration to establish a function model with time as the horizontal axis and comprehensive performance evaluation value as the vertical axis.

[0075] The comprehensive performance evaluation value is expressed by the formula: "Comprehensive performance evaluation value = w1 × F". 系统 +w2×F 电压 +w3×F 用户 "Calculations show that when all indicators are within the normal threshold, the comprehensive performance evaluation value is 1, which represents the optimal power grid operation status."

[0076] Furthermore, by combining typical daily operating data of the target distribution network (such as load, voltage, and user power outage monitoring data for 24 consecutive hours), the comprehensive performance evaluation value is calculated for each time period and its changing trend is fitted to form a continuous target function curve.

[0077] For example, during off-peak hours (such as 2-5 a.m.), the load is stable, the voltage deviation is small, and the comprehensive performance evaluation value is stable at around 0.95; during peak hours (such as 6-9 p.m.), due to load fluctuations, the voltage and power supply capacity deviate slightly, the evaluation value decreases slightly but remains above 0.85, and the overall graph shows a stable fluctuation around 1.

[0078] This target function curve fully reflects the normal functional performance of the distribution network under no-interference conditions, providing a clear benchmark for subsequent comparative analysis of the performance degradation after interference, and ensuring the accuracy and relevance of the resilience assessment.

[0079] As attached Figure 3 As shown, the method provided in this application embodiment includes the following steps: "obtaining multiple test scenarios of the target distribution network, performing interference tests on the target distribution network under each test scenario, and obtaining the actual functional curve under each test scenario based on the multiple power grid performance indicators and the indicator weights of each power grid performance indicator".

[0080] Determine the first test scenario from the plurality of test scenarios;

[0081] Based on the first test scenario, an interference test is performed on the target distribution network to obtain the first test result;

[0082] Based on the first test results, and combining the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, a first actual function graph under the first test scenario is constructed.

[0083] Following the method of constructing the first actual function graph under the first test scenario, the actual function graphs of the remaining test scenarios are constructed to obtain the actual function graphs under each test scenario.

[0084] In this embodiment of the application, in order to accurately capture the impact of different interference scenarios on the power grid function, it is necessary to conduct scenario-based interference tests and graph construction to form an actual function graph that can reflect the actual operating status, so as to provide a basis for subsequent resilience index calculation.

[0085] Specifically, the first test scenario is determined from multiple test scenarios, and this scenario must be representative. For example, "thunderstorm weather causing line short circuits accompanied by 30% of grid-connected inverters disconnecting from the grid" is selected as the first test scenario, which includes both natural interference and reflects the impact of the low inertia and weak support characteristics of inverter power supplies on the power grid.

[0086] Furthermore, based on the interference parameters of the first test scenario (such as the line failure rate corresponding to thunderstorm wind speed, inverter disconnection threshold, short circuit fault duration, etc.), simulated interference is applied to the target distribution network, and dynamic data of grid power supply capacity, voltage level, and user power outage ratio are collected in real time during and after the interference to form the first test result containing time series.

[0087] The data acquisition frequency is set to once per minute to ensure that instantaneous fluctuation characteristics can be captured.

[0088] Furthermore, based on the results of the first test, and combined with the already determined power grid supply capacity index F 系统 Voltage level index F 电压 User power outage ratio index F 用户 The corresponding weights (w1, w2, w3) are used to calculate the comprehensive performance evaluation value at each time point, and then combined with interpolation fitting to form a continuous first actual function curve F(t).

[0089] The calculation method for the comprehensive performance evaluation value is consistent with that of the target function curve, namely, using the formula "Comprehensive performance evaluation value F(t) = w1 × F". 系统 (t)+w2×F 电压 (t)+w3×F 用户 (t) is obtained.

[0090] For example, 5 minutes after the interference occurred, F 系统 =0.6 (Power supply capacity reduced to 60% of the baseline), F 电压 =0.8 (voltage level fluctuates to 80% of the reference), F 用户 =0.85 (15% of users experienced power outages). Combining the weights w1=0.63, w2=0.25, and w3=0.12, the comprehensive performance evaluation value F(t) is calculated as 0.63×0.6+0.25×0.8+0.12×0.85=0.65. The corresponding graph shows a clear downward inflection point at this node.

[0091] Similarly, following the method of constructing the first actual function curve, interference tests, data collection, comprehensive performance evaluation value calculation, and curve fitting are carried out sequentially for other test scenarios such as "line short circuit fault" and "load sudden increase of 20%", and finally the actual function curves under each test scenario are obtained.

[0092] These actual functional graphs fully illustrate the attenuation and recovery process of distribution network functions under different types and intensities of interference. For example, the graph for a line short-circuit fault scenario may show a sudden drop followed by a rapid recovery, while the graph for a sudden load increase scenario may show a slow decline followed by gradual stabilization.

[0093] The actual functional curves obtained through the above steps lay a solid data foundation for subsequent comparative analysis with the target functional curves and for quantifying resilience indicators, ensuring that the resilience performance of the distribution network under various disturbances can be fully reflected.

[0094] S120: Based on the actual function graphs and the target function graphs in each test scenario, obtain the missing area index, the missing region extreme value index, and the system recovery duration index for each test scenario.

[0095] In this embodiment of the application, in the scenario of distribution network resilience assessment with a high proportion of inverter power sources, in order to quantify the impact of different interference scenarios on the grid function and recovery capability, it is necessary to extract key resilience indicators by comparing the actual function curve with the target function curve, so as to provide a basis for comprehensive assessment.

[0096] Specifically, the first test scenario is selected from each test scenario, and its corresponding first actual function graph is obtained. At the same time, the target function graph that has been constructed is retrieved, and the index calculation is carried out based on the two.

[0097] Furthermore, a functional curve coordinate system is established, and the first actual functional curve and the target functional curve are input into this coordinate system to generate a comparison chart of the first functional curve. Based on this comparison chart, the missing area of ​​the functional curve, the extreme value of the missing region, and the system recovery duration are determined in the first test scenario.

[0098] Similarly, in the same way as determining the relevant parameters of the first test scenario, the missing area of ​​the functional graph, the extreme value of the missing area, and the duration of power grid recovery are determined for the remaining test scenarios.

[0099] Finally, the parameters of all scenarios are normalized to obtain the missing area index, missing region extreme value index, and system recovery duration index of the functional graph under each test scenario, providing specific quantitative indicators for distribution network resilience assessment.

[0100] This step compares and analyzes the actual and target function curves under various test scenarios, and combines parameter extraction and normalization to transform the degree of damage and recovery capability of the power grid into a quantifiable index, providing a standardized indicator basis for subsequent comprehensive evaluation of the resilience of distribution networks with a high proportion of inverter power sources.

[0101] Step S120 in the method provided in this application embodiment includes:

[0102] Obtain the first actual functional graph under the first test scenario;

[0103] Based on the first actual functional curve and the target functional curve, determine the missing area, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario;

[0104] Following the method of determining the missing area of ​​the function graph, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario, the missing area of ​​the function graph, the extreme value of the missing region, and the duration of power grid recovery in the other test scenarios are determined, resulting in multiple missing areas of function graphs, multiple extreme values ​​of missing regions, and multiple durations of power grid recovery.

[0105] Based on the missing area of ​​the multiple functional lines, the extreme values ​​of the multiple missing regions, and the duration of the multiple power grid recovery, the missing area index, the extreme value index of the missing region, and the system recovery duration index are determined for each of the test scenarios.

[0106] In this embodiment of the application, in order to quantify the resilience performance of a distribution network with a high proportion of inverter power sources under different interference scenarios, it is necessary to compare the actual function curve with the target function curve, and obtain a standardized resilience assessment index through parameter extraction and normalization, so as to provide a quantitative basis for comprehensive evaluation.

[0107] Specifically, firstly, the first actual function graph F(t) under the first test scenario is obtained, and then the constructed target function graph F is retrieved. T (t), and the analysis is based on both.

[0108] The method provided in this application embodiment includes the step of "determining the missing area, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario based on the first actual functional curve and the target functional curve":

[0109] Establish a coordinate system for the functional graph;

[0110] Input the first actual function curve and the target function curve into the function curve coordinate system to obtain a comparison chart of the first function curve;

[0111] Based on the first functional curve comparison chart, the missing area of ​​the functional curve in the first test scenario is obtained. The missing area of ​​the functional curve is the area enclosed by the difference between the target functional curve and the first actual functional curve.

[0112] Based on the comparison chart of the first functional curve, the extreme value of the missing region and the duration of power grid recovery under the first test scenario are determined. The extreme value of the missing region is the maximum difference between the first actual functional curve and the target functional curve, and the duration of power grid recovery is the time from the start of the interference to the recovery to the target functional curve.

[0113] In this embodiment of the application, in order to accurately quantify the degree of damage and recovery status of the power distribution network function under the first test scenario, it is necessary to transform the abstract functional changes into specific quantitative indicators by comparing the actual functional curve and the target functional curve and extracting parameters.

[0114] Specifically, first, a functional graph coordinate system is established, with time (t) as the horizontal axis (unit: minutes) and the comprehensive performance evaluation value (F) as the vertical axis (value range 0-1, corresponding to 0%~100% system function). The coordinate scale and data precision are clearly defined to ensure the accuracy of the graph display.

[0115] Furthermore, the first actual function curve F(t) and the target function curve F T (t) After aligning the coordinates according to the time dimension, input the coordinate system to generate a comparison chart of the first functional graph. By overlaying the graphs, the deviation and recovery process of the power grid function before and after the interference can be intuitively presented.

[0116] Wherein, the first actual function curve F(t) represents the functional change under the interference scenario, and the target function curve F T (t) represents the functional baseline during normal operation, which is usually set to 100%, i.e., F. T (t)=1.

[0117] For example, if the first test scenario is a "line short circuit fault", the target function curve F can be seen in the first function curve comparison chart. T (t) During normal operation, it remains stable at 100% (i.e., F) T (t)=1), which appears as a horizontal straight line in the function curve comparison chart; while the first actual function curve F(t) drops rapidly after the disturbance occurs at the 10-minute mark, reaching its lowest value at the 15-minute mark, and then gradually recovers, returning to the level of the target curve F at the 40-minute mark. T (t) overlap.

[0118] The missing area ΔA of the function graph line is obtained by calculating the area of ​​the enclosed region of the two graph lines on the time axis. The difference within the time interval can be accumulated by calculating the definite integral.

[0119] Specifically, the formula for calculating the missing area ΔA of the function chart line is:

[0120] ;

[0121] In the formula, F T F(t) represents the target function curve during normal operation, F(t) represents the actual function curve, and T0 represents the time required for the function to recover to 100%. T The positions of F(t), T0, and ΔA in the graph are shown in the attached figure. Figure 4 As shown.

[0122] For example, in the above scenario, the integral result of the area enclosed by the two graph lines is 280 (minutes / evaluation value), that is, the missing area ΔA of the function graph lines in this scenario is 280.

[0123] Furthermore, based on the comparison chart of the first functional graph, combined with the extreme values ​​F of the missing region... E Based on the definition of system recovery duration T0, key resilience parameters are extracted.

[0124] Specifically, for the extreme value F in the missing region E By traversing the actual function curve F(t) and the target function curve F... T The difference data of (t) is used to filter out F. T The minimum value of (t) is the maximum difference between the two graphs. Where F... E The location for obtaining the values ​​is shown in the attached figure. Figure 5 As shown.

[0125] For example, also in the above "line short circuit fault" scenario, if F(t) = 0.35 at the 15th minute (target curve F... T (t)=1), then F E =1-0.35=0.65, which is consistent with the attached... Figure 5 China F E Corresponding to the vertical span, the larger the value, the weaker the system's defense, which means the more severe the functional damage.

[0126] Furthermore, for the system recovery duration T0, starting from the start of the disturbance (the 10th minute), the actual function curve F(t) recovers to the target curve F. T Taking the time (t) as the endpoint (40 minutes), calculate the time difference T0 = 40 - 10 = 30 minutes, and compare it with the attached... Figure 5 The horizontal span of T0 corresponds to the value of the system. The larger the value, the weaker the system's resilience, that is, the slower the recovery speed.

[0127] Among them, the missing area of ​​the functional line reflects the total cumulative loss of power grid function, the extreme value of the missing area reflects the most severe degree of functional damage, and the duration of power grid recovery represents the time it takes for the power grid to return to normal after being disturbed. The three together constitute the key parameters for evaluating the resilience of the power grid under the first test scenario.

[0128] Similarly, for the remaining test scenarios, the same method was used to determine the missing area, extreme values ​​of the missing region, and grid recovery duration of the functional curve in the first test scenario. This involves first constructing a comparison chart of the functional curves for each scenario, and then comparing the actual functional curve F(t) with the target curve F. T (t) Superimpose; then calculate the missing area ΔA of the function curve, which is the region enclosed by the definite integral, and extract the extreme value F of the missing region.E The minimum difference in the graph is used to calculate the system recovery time T0, which is the recovery time difference. Finally, the ΔA and F values ​​for all scenarios are summarized. E T0 forms a set of resilience parameters for multiple scenarios, thereby covering the resilience characteristics of different types of interference.

[0129] Furthermore, based on a set of resilience parameters for multiple scenarios, the standardized resilience index for each scenario is determined by extreme value normalization, enabling comparable analysis between different scenarios.

[0130] The method provided in this application embodiment includes the step of "determining the missing area index, the missing region extreme value index, and the system recovery duration index for each test scenario based on the missing area of ​​the multiple function graphs, the extreme values ​​of the multiple missing regions, and the duration of the multiple power grid recovery".

[0131] The maximum and minimum missing areas of the function lines are determined based on the missing areas of the multiple function lines.

[0132] Determine the maximum and minimum extreme values ​​of the missing regions based on the multiple extreme values ​​of the missing regions;

[0133] The maximum and minimum power grid recovery durations are determined based on the multiple power grid recovery durations.

[0134] Obtain the missing area, extreme value of the missing region, and duration of power grid recovery in the first test scenario. Combine the maximum value of the missing area, the minimum value of the missing area, the maximum value of the extreme value of the missing region, the minimum value of the extreme value of the missing region, the maximum value of the duration of power grid recovery, and the minimum value of the duration of power grid recovery to obtain the missing area index, the extreme value index of the missing region, and the system recovery duration index in the first test scenario.

[0135] Following the method of obtaining the missing area index, missing region extreme value index, and system recovery duration index of the function graph in the first test scenario, the missing area index, missing region extreme value index, and system recovery duration index of the function graph in the other test scenarios are obtained, thus obtaining the missing area index, missing region extreme value index, and system recovery duration index of the function graph in each test scenario.

[0136] In this embodiment of the application, in order to achieve standardized comparison of distribution network resilience indicators under different test scenarios, it is necessary to transform the original parameters of each scenario into a unified scale evaluation index through the determination of extreme values ​​and index calculation of the entire test scenario, so as to ensure the scientificity and comparability of the evaluation results.

[0137] Specifically, we first conducted a statistical analysis on the missing areas of multiple functional graphs, and then selected the maximum and minimum values ​​as the reference benchmarks for the entire test scenario of this indicator.

[0138] Similarly, the maximum and minimum values ​​are extracted from the extreme values ​​of multiple missing regions, and the maximum and minimum values ​​are determined from the duration of multiple power grid recovery periods, forming three sets of extreme value data for the entire test scenario.

[0139] For example, assume that in all test scenarios, the maximum value of the missing area of ​​the function graph is 500 (minutes / evaluation value) and the minimum value is 80 (minutes / evaluation value); the maximum value of the extreme value of the missing area is 0.85 (evaluation value) and the minimum value is 0.2 (evaluation value); and the maximum value of the power grid restoration duration is 200 minutes and the minimum value is 40 minutes.

[0140] Among them, these extreme value data of the entire test scenario cover the parameter fluctuation range of all scenarios, providing a unified normalization scale for subsequent index calculation, so that the evaluation results of different scenarios can be directly compared.

[0141] Furthermore, for the first test scenario, various indices are calculated by combining the missing area of ​​its functional graph, the extreme value of the missing area, the duration of power grid restoration, and the corresponding extreme value of the entire test scenario.

[0142] The method provided in this application embodiment includes the following steps: "obtaining the missing area, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario; combining the maximum value of the missing area, the minimum value of the missing area, the maximum value of the extreme value of the missing region, the minimum value of the extreme value of the missing region, the maximum value of the duration of power grid recovery, and the minimum value of the duration of power grid recovery to obtain the missing area index, the extreme value index of the missing region, and the system recovery duration index in the first test scenario."

[0143] Based on the missing area of ​​the function graph in the first test scenario, the maximum value of the missing area of ​​the function graph, and the minimum value of the missing area of ​​the function graph, calculate the missing area index of the function graph in the first test scenario;

[0144] Calculate the missing region extremum index in the first test scenario based on the missing region extreme value, the maximum value of the missing region extreme value, and the minimum value of the missing region extreme value in the first test scenario.

[0145] The system recovery duration index under the first test scenario is calculated based on the power grid recovery duration, the maximum power grid recovery duration, and the minimum power grid recovery duration under the first test scenario.

[0146] In this embodiment of the application, in order to transform the original parameters of the first test scenario into a standardized index that can directly reflect the toughness level, it is necessary to calculate the index value based on the extreme values ​​of the entire test scenario through a formula so that the index value is positively correlated with the toughness performance, so as to facilitate intuitive evaluation.

[0147] Specifically, the missing area index of the function graph is first calculated. Based on the missing area ΔA of the function graph in the first test scenario, and combined with the maximum missing area ΔA... max and minimum value ΔA min Calculate the missing area index ΔA′ of the function graph.

[0148] Specifically, the formula for calculating the missing area index ΔA′ of the function chart is as follows:

[0149] ;

[0150] For example, if ΔA = 280 (minutes - evaluation value) for the first test scenario, ΔA max =500 (minutes / evaluation value), ΔA min =80 (minutes - evaluation value), then ΔA′=1-(280-80) / (500-80)=1-200 / 420≈0.52, that is, the missing area index of the functional line of this scene is 0.52.

[0151] Among them, the missing area index of the functional line reflects the relative resilience of the power grid's cumulative loss of function under the first test scenario. The larger the value, the smaller the cumulative loss and the stronger the resilience; conversely, the smaller the value, the larger the cumulative loss and the weaker the resilience.

[0152] Furthermore, the extremum index of the missing region is calculated. Based on the extremum F of the missing region in the first test scenario... E Combined with the maximum value F of the missing region Emax and minimum value F Emin Calculate the extreme value exponent F of the missing region. E ′.

[0153] Specifically, the missing region extreme value index F E The formula for calculating ′ is:

[0154] ;

[0155] Wherein, Δη is the preference coefficient, which can be introduced into the formula according to the actual evaluation needs. Δη∈[0,1] is used to adjust the extreme value weight.

[0156] For example, if F in the first test scenario E =0.65 (evaluation value), F Emax =0.85 (evaluation value), F Emin=0.2 (evaluation value), assuming Δη is 0.5, then F E =1-2×0.5×(0.65-0.2) / (0.85-0.2)=1-0.45 / 0.65≈0.31, that is, the extreme value index of the missing region in this scene is 0.31.

[0157] Similarly, the extreme value index of the missing region reflects the relative resilience of the power grid function under the first test scenario, with a larger value indicating a smaller maximum damage and stronger resilience; conversely, a smaller value indicates a larger maximum damage and weaker resilience.

[0158] Simultaneously, the system recovery duration exponent is calculated. Based on the grid recovery duration T0 under the first test scenario, combined with the maximum grid recovery duration T... 0max and minimum value T 0min Calculate the system recovery duration exponent T0′.

[0159] Specifically, the formula for calculating the system recovery duration exponent T0′ is as follows:

[0160] ;

[0161] Wherein, Δη is also a preference coefficient, which can be introduced into the formula according to the actual evaluation needs.

[0162] For example, if T0 = 30 minutes in the first test scenario, T 0max =200 minutes, T 0min =40 minutes, Δη is also taken as 0.5, since T0 < T 0min Substituting into the formula, we get T0′=1-(2−2×0.5)×(30-40) / (200-40)=1-(-10) / 160≈1.06. At this point, we take the upper limit of the exponent as 1 (to ensure that the exponent is in the range of [0,1], reflecting that the recovery speed is better than the optimal scenario).

[0163] Among them, the system recovery duration index reflects the relative resilience of the power grid recovery efficiency under the first test scenario. The larger the value, the faster the recovery speed and the stronger the resilience; conversely, the smaller the value, the slower the recovery speed and the weaker the resilience.

[0164] The three indices obtained through the above calculation steps quantify the resilience performance of the distribution network in the first test scenario from different dimensions, and all of them are positively correlated with resilience, providing a clear judgment standard for subsequent resilience comparisons between scenarios.

[0165] Similarly, the above calculation process is performed sequentially for the remaining test scenarios to obtain the missing area index ΔA′ of the function graph for each scenario. n The extreme value index F of the missing region E ′ nand system recovery duration exponent T0′ n .

[0166] This step transforms the original parameters of all test scenarios into standardized indices in the range [0, 1]. This preserves the differences in resilience characteristics among different scenarios and enables direct comparability of evaluation results between different scenarios, laying a unified quantitative foundation for subsequent comprehensive resilience assessment.

[0167] S130: Based on the missing area index, missing region extreme value index, and system recovery duration index of the functional curves under each test scenario, the resilience assessment results of the target distribution network are obtained.

[0168] In this embodiment of the application, in the scenario of distribution network resilience assessment with a high proportion of inverter power sources, in order to comprehensively measure the overall resilience performance of the power grid under different interference scenarios, it is necessary to establish a three-dimensional assessment coordinate system and perform quantitative calculations to integrate multi-dimensional indices into an intuitive resilience assessment result.

[0169] Specifically, based on multiple power grid performance indicators such as power grid supply capacity, voltage level, and user outage ratio, a three-dimensional resilience assessment coordinate system is constructed with the missing area index of the functional curve, the extreme value index of the missing region, and the system recovery duration index as coordinate axes.

[0170] Furthermore, the first test scenario is determined from each test scenario, and the missing area index ΔA′ and the missing region extreme value index F of the function graph under this scenario are extracted. E The system recovery duration index T0′ and these three indices are used as three-dimensional coordinate parameters and input into the resilience assessment coordinate system to obtain the first scene coordinates (ΔA′1, F). E ′1,T0′1).

[0171] Furthermore, based on the coordinates of the first scene, the Euclidean distance D between it and the origin of the coordinate system (0, 0, 0) is calculated, and this distance is used as the comprehensive resilience index (RCI) in the first test scene.

[0172] Furthermore, following the same method used to calculate the resilience assessment result of the first test scenario, the RCI values ​​of the remaining test scenarios are calculated sequentially to obtain the resilience assessment results for each scenario.

[0173] Finally, the resilience assessment results of all test scenarios are summarized to form a comprehensive resilience assessment report of the target distribution network under different disturbance conditions, which fully presents the distribution of its resilience strength and weaknesses.

[0174] Step S130 in the method provided in this application embodiment includes:

[0175] A resilience assessment coordinate system is established based on the aforementioned multiple power grid performance indicators;

[0176] The first test scenario is determined from each of the test scenarios, and the missing area index, the missing region extreme value index, and the system recovery duration index of the first test scenario are extracted.

[0177] Input the missing area index, missing region extreme value index, and system recovery duration index of the first test scenario into the resilience assessment coordinate system to obtain the coordinates of the first scenario;

[0178] Based on the coordinates of the first scene, the resilience assessment result under the first test scene is obtained;

[0179] Following the same method used to calculate the resilience assessment results under the first test scenario, the resilience assessment results under the remaining test scenarios are obtained, and then summarized to form the resilience assessment results of the target distribution network.

[0180] In this embodiment of the application, in order to comprehensively evaluate the overall resilience level of a distribution network with a high proportion of inverter power sources under different interference scenarios, it is necessary to establish a three-dimensional evaluation coordinate system to integrate multi-dimensional indices into intuitive quantitative results, thereby providing a basis for decision-making for grid resilience optimization.

[0181] Specifically, based on multiple power grid performance indicators such as power grid supply capacity, voltage level, and user power outage ratio, a system is constructed with the missing area index (ΔA′) of the functional curve as the X-axis and the extreme value index (F′) of the missing region as the X-axis. E A three-dimensional toughness assessment coordinate system is used, with the Y-axis being T0′ and the system recovery duration exponent (T0′) being Z-axis (as shown in the attached figure). Figure 6 (As shown).

[0182] The coordinate axes all take values ​​in the range of [0, 1], the origin of the coordinate system is (0, 0, 0), which represents the worst toughness state, and the coordinates (1, 1, 1) represent the best toughness state.

[0183] Furthermore, a first test scenario (such as "extreme thunderstorm weather causing inverter cluster to go offline") is selected from the various test scenarios, and the missing area index ΔA′1 and the missing region extreme value index F of the functional graph under this scenario are extracted. E The system recovery duration exponent T0′1 and the system recovery duration exponent T0′1 are used as coordinate parameters to input into the resilience assessment coordinate system to obtain the first scenario coordinates (ΔA′1, F). E ′1,T0′1).

[0184] For example, if ΔA′1=0.52 and F in the first test scenario... E If T0′1=0.31 and T0′1=1, then the corresponding coordinates of the first scene are (0.52, 0.31, 1).

[0185] Furthermore, based on the coordinates of the first scene, the resilience assessment results under the first test scene are obtained.

[0186] The method provided in this application embodiment includes the step of "obtaining the resilience assessment result under the first test scenario based on the first scene coordinates" as follows:

[0187] Obtain the coordinates of the origin of the toughness assessment coordinate system;

[0188] Calculate the Euclidean distance between the first scene coordinates and the origin coordinates, and use it as the resilience assessment result under the first test scene.

[0189] In this embodiment of the application, in order to transform the three-dimensional index data under the first test scenario into a single comprehensive resilience index, it is necessary to integrate multi-dimensional information through coordinate distance calculation to ensure that the evaluation results are intuitive and quantitatively comparable.

[0190] Specifically, the origin of the resilience assessment coordinate system is first defined as (0, 0, 0). This origin represents the worst state of the distribution network resilience, i.e., the missing area index ΔA′ and the extreme value index F of the missing region. E The extreme case where both the system recovery duration exponent T0′ and the system recovery duration exponent T0′ are 0.

[0191] Furthermore, based on the already determined first scene coordinates (ΔA′1, F... E The Euclidean distance D between the coordinates (′1, T0′1) and the origin is calculated using the Euclidean distance formula. The specific formula can be expressed as:

[0192]

[0193] Furthermore, since the Euclidean distance D integrates the resilience characteristics of three dimensions—the functional curve missing area index, the missing region extreme value index, and the system recovery duration index—and its value is positively correlated with the distribution network resilience level, the Euclidean distance D is the comprehensive resilience quantification index (ResilienceComposite Index, RCI) under the first test scenario.

[0194] Specifically, the formula for calculating the comprehensive resilience quantification index RCI is as follows:

[0195] ;

[0196] Among them, the larger the RCI value, the stronger the overall ability of the distribution network to resist interference, withstand functional loss and restore normal operation under the test scenario; conversely, the smaller the RCI value, the greater the cumulative functional loss, the more severe the damage and the lower the recovery efficiency when the distribution network copes with this type of interference, and the weaker its overall resilience.

[0197] For example, if the coordinates of the first scene are (0.52, 0.31, 1), the comprehensive resilience quantification index under the first test scene can be calculated by substituting the coordinates into the formula. The larger this value, the stronger the resilience of the power distribution network in this scenario.

[0198] Furthermore, following the same method used to calculate the resilience assessment results for the first test scenario, resilience assessments are performed sequentially for the remaining test scenarios.

[0199] Specifically, for each scenario, the missing area index ΔA′ of its functional graph is first extracted. n The extreme value index F of the missing region E ′ n and system recovery duration exponent T0′ n These three indices are input as coordinate parameters into the resilience assessment coordinate system to obtain the three-dimensional coordinates of the corresponding scenario. Then, the Euclidean distance D between the coordinates and the origin of the coordinate system is calculated, and the Euclidean distance D is directly used as the comprehensive resilience quantification index (RCI) of the scenario, thus serving as the resilience assessment result for each scenario.

[0200] For example, in the "line short circuit fault" scenario, the missing area index of the functional diagram is 0.65, the extreme value index of the missing region is 0.42, and the system recovery duration index is 0.8. Its three-dimensional coordinates are (0.65, 0.42, 0.8). After calculation... The three-dimensional coordinates of the scenario "Industrial load increases by 20%" are (0.8, 0.75, 0.6). The three-dimensional coordinates of the "inverter cluster offline" scenario are (0.4, 0.3, 0.5). Through this type of calculation, the Euclidean distance D for all test scenarios can be obtained, and then the corresponding RCI value can be obtained, forming the resilience assessment results for each scenario.

[0201] Finally, the resilience assessment results of all test scenarios were summarized, and the strength gradient of the distribution network resilience performance under different disturbance conditions was clearly presented by statistically sorting the RCI values ​​of each scenario.

[0202] For example, as can be seen from the above examples, the RCI value is the highest (1.28) in the scenario of "industrial load suddenly increases by 20%", indicating that the distribution network is the most resilient in this scenario, while the RCI value is the lowest (0.76) in the scenario of "inverter cluster disconnection", reflecting its weaker resilience and that it is a weak link in the distribution network when dealing with interference.

[0203] Furthermore, by combining in-depth analysis of the three-dimensional coordinate parameters of each scenario, the causes of toughness differences can be further clarified.

[0204] For example, the scenario of "20% increase in industrial load" has high functional line missing area index (0.8) and missing area extreme value index (0.75), indicating that the cumulative functional loss is small and the degree of damage is light, thus supporting high overall resilience. In contrast, the three indices of the scenario of "inverter cluster disconnection" are all at low levels, especially the system recovery duration index (0.5), which is low, indicating insufficient recovery efficiency and resulting in weak overall resilience.

[0205] By summarizing and analyzing the above steps, we can not only make a horizontal comparison of the resilience of the distribution network under different interference scenarios, but also further analyze the reasons for the resilience differences based on the three-dimensional coordinate parameters of each scenario, providing a clear direction for the subsequent targeted development of resilience improvement strategies.

[0206] The embodiments of this application, through the specific implementation methods described above, achieve the following technical effects:

[0207] This application proposes a distribution network resilience assessment method based on a multi-dimensional resilience index system. First, performance indicators such as power supply capacity, voltage level, and user outage ratio are acquired. A judgment matrix is ​​constructed through pairwise comparisons and scoring. After consistency verification, the weights are determined using the analytic hierarchy process (AHP), and a target functional curve is constructed in conjunction with benchmark values. Next, test scenarios such as thunderstorms and line short circuits are set up. Simulated interference is applied to the distribution network based on the interference parameters of each scenario, and dynamic data is collected in real time. A comprehensive performance evaluation value is calculated, and actual functional curves for each scenario are generated. Then, the target and actual functional curves are compared. The missing area of ​​the functional curve is calculated using definite integrals, and the extreme values ​​of the missing regions are determined by traversing the difference data. The duration of grid recovery is calculated from the start of the interference to the moment the curves overlap. After normalization, the missing area index, the extreme value index of the missing region, and the system recovery duration index are obtained. These are input into a three-dimensional resilience assessment coordinate system, and the Euclidean distance from the origin is calculated as a comprehensive resilience index. Based on this, the resilience of each scenario is assessed, and a comprehensive assessment result is formed.

[0208] The method provided in this application, through the technical solution of "indicator weight determination - construction of target function curve and actual function curve - three-dimensional parameter extraction - comprehensive resilience assessment", solves the problem of inaccurate quantification caused by single indicators and insufficient scenario coverage in traditional assessments. It achieves accurate quantification from indicator selection to resilience assessment, and provides reliable technical support for the resilience optimization of distribution networks with a high proportion of inverter power sources.

[0209] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0210] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0211] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for assessing the resilience of distribution networks based on a multi-dimensional resilience index system, characterized in that, The method includes: Obtain the target functional curve of the target distribution network and the actual functional curves under multiple test scenarios. The functional curve refers to the curve that reflects the change of the functional state of the distribution network over time. Based on the actual function graphs and the target function graphs in each test scenario, the missing area index, the missing region extreme value index, and the system recovery duration index for each test scenario are obtained, including: Obtain the first actual functional graph under the first test scenario; Based on the first actual functional curve and the target functional curve, determine the missing area, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario; Following the method of determining the missing area of ​​the function graph, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario, the missing area of ​​the function graph, the extreme value of the missing region, and the duration of power grid recovery in the other test scenarios are determined, resulting in multiple missing areas of function graphs, multiple extreme values ​​of missing regions, and multiple durations of power grid recovery. Based on the missing areas of the multiple function graphs, the extreme values ​​of the multiple missing regions, and the duration of the multiple power grid recovery, the missing area index, the extreme value index of the missing region, and the system recovery duration index are determined for each of the test scenarios, including: The maximum and minimum missing areas of the function lines are determined based on the missing areas of the multiple function lines. Determine the maximum and minimum extreme values ​​of the missing regions based on the multiple extreme values ​​of the missing regions; The maximum and minimum power grid recovery durations are determined based on the multiple power grid recovery durations. Obtain the missing area, extreme value of the missing region, and power grid recovery duration under the first test scenario. Combine the maximum and minimum missing area of ​​the functional graph, the maximum and minimum extreme values ​​of the missing region, the maximum and minimum power grid recovery durations, and the maximum and minimum power grid recovery durations to obtain the missing area index, the extreme value index of the missing region, and the system recovery duration index under the first test scenario, including: Based on the missing area of ​​the function graph in the first test scenario, the maximum value of the missing area of ​​the function graph, and the minimum value of the missing area of ​​the function graph, calculate the missing area index of the function graph in the first test scenario; Calculate the missing region extremum index in the first test scenario based on the missing region extreme value, the maximum value of the missing region extreme value, and the minimum value of the missing region extreme value in the first test scenario. Calculate the system recovery duration index under the first test scenario based on the power grid recovery duration, the maximum power grid recovery duration, and the minimum power grid recovery duration under the first test scenario; Following the method of obtaining the missing area index, missing region extreme value index and system recovery duration index of the function graph in the first test scenario, the missing area index, missing region extreme value index and system recovery duration index of the function graph in the other test scenarios are obtained, thus obtaining the missing area index, missing region extreme value index and system recovery duration index of the function graph in each test scenario. Based on the missing area index, missing region extreme value index, and system recovery duration index of the functional graphs under each test scenario, the resilience assessment results of the target distribution network are obtained.

2. The method according to claim 1, characterized in that, Obtain the target function curve of the target distribution network and the actual function curves under multiple test scenarios, including: Multiple power grid performance indicators of the target distribution network are obtained, and the weight of each power grid performance indicator is determined. The target distribution network contains a high proportion of inverter-type power sources. Based on the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, a target function curve of the target distribution network is constructed. The target function curve is the function curve of the target distribution network during normal operation. Multiple test scenarios of the target distribution network are obtained. Interference tests are performed on the target distribution network under each test scenario. Based on the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, the actual functional curves under each test scenario are obtained.

3. The method according to claim 2, characterized in that, Obtain multiple power grid performance indicators of the target distribution network, and determine the weight of each power grid performance indicator, including: Acquire multiple power grid performance indicators, including power grid supply capacity indicators, voltage level indicators, and user power outage ratio indicators; The weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index are determined by using the analytic hierarchy process (AHP).

4. The method according to claim 3, characterized in that, The weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index are determined using the analytic hierarchy process (AHP), including: The power grid supply capacity index, the voltage level index, and the user power outage ratio index are compared and scored pairwise to obtain the index comparison and scoring results. Based on the scoring results of the aforementioned indicators, a judgment matrix is ​​constructed, and the matrix order and the maximum eigenvalue are obtained from the judgment matrix. The consistency index is calculated based on the matrix order and the largest eigenvalue, and the average random consistency index is obtained based on the matrix order. The judgment matrix is ​​subjected to consistency verification based on the consistency index and the average random consistency index. When the judgment matrix passes the consistency verification, the index weights of the power grid supply capacity index, the voltage level index, and the user power outage ratio index are determined based on the normalized vector corresponding to the largest eigenvalue.

5. The method according to claim 2, characterized in that, Multiple test scenarios are obtained for the target distribution network. Interference tests are performed on the target distribution network under each test scenario. Based on the multiple power grid performance indicators and their respective weights, the actual functional curves for each test scenario are obtained, including: Determine the first test scenario from the plurality of test scenarios; Based on the first test scenario, an interference test is performed on the target distribution network to obtain the first test result; Based on the first test results, and combining the multiple power grid performance indicators and the indicator weights of each power grid performance indicator, a first actual function graph under the first test scenario is constructed. Following the method of constructing the first actual function graph under the first test scenario, the actual function graphs of the remaining test scenarios are constructed to obtain the actual function graphs under each test scenario.

6. The method according to claim 1, characterized in that, Based on the first actual functional curve and the target functional curve, determine the missing area, the extreme value of the missing region, and the duration of power grid recovery in the first test scenario, including: Establish a coordinate system for the functional graph; Input the first actual function curve and the target function curve into the function curve coordinate system to obtain a comparison chart of the first function curve; Based on the first functional curve comparison chart, the missing area of ​​the functional curve in the first test scenario is obtained. The missing area of ​​the functional curve is the area enclosed by the difference between the target functional curve and the first actual functional curve. Based on the comparison chart of the first functional curve, the extreme value of the missing region and the duration of power grid recovery under the first test scenario are determined. The extreme value of the missing region is the maximum difference between the first actual functional curve and the target functional curve, and the duration of power grid recovery is the time from the start of the interference to the recovery to the target functional curve.

7. The method according to claim 3, characterized in that, Based on the missing area index, missing region extreme value index, and system recovery duration index of the functional curves under each test scenario, the resilience assessment results of the target distribution network are obtained, including: A resilience assessment coordinate system is established based on the aforementioned multiple power grid performance indicators; The first test scenario is determined from each of the test scenarios, and the missing area index, the missing region extreme value index, and the system recovery duration index of the first test scenario are extracted. Input the missing area index, missing region extreme value index, and system recovery duration index of the first test scenario into the resilience assessment coordinate system to obtain the coordinates of the first scenario; Based on the coordinates of the first scene, the resilience assessment result under the first test scene is obtained; Following the same method used to calculate the resilience assessment results under the first test scenario, the resilience assessment results under the remaining test scenarios are obtained, and then summarized to form the resilience assessment results of the target distribution network.

8. The method according to claim 7, characterized in that, Based on the coordinates of the first scene, the resilience assessment results under the first test scene are obtained, including: Obtain the coordinates of the origin of the toughness assessment coordinate system; Calculate the Euclidean distance between the first scene coordinates and the origin coordinates, and use it as the resilience assessment result under the first test scene.

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