Power grid toughness evaluation method under extreme weather

Abstract

The invention discloses a power grid toughness evaluation method in extreme weather, and relates to the related field of power grid monitoring, and the method comprises the steps: configuring a backtracking period, carrying out the backtracking of historical meteorological data, and building an extreme weather database; accessing a power grid system, constructing a topological structure of a power grid according to an access result, performing key node identification based on the topological structure and power grid data, and establishing a key node set by using digital twinning; constructing an extreme weather simulation scene based on the extreme weather database, performing a node response test on the key node set, and establishing a node test database; constructing a power grid toughness evaluation index, and performing response evaluation on the node test database according to the power grid toughness evaluation index; and outputting a power grid toughness evaluation result according to the response evaluation result, and performing power grid management according to the power grid toughness evaluation result. The technical problem that an existing power grid toughness evaluation method under extreme weather is insufficient in comprehensiveness and accuracy is solved, and the technical effect of improving evaluation accuracy and comprehensiveness is achieved.

Description

Technical Field

[0001] This application relates to the field of power grid monitoring, and particularly to a method for evaluating the resilience of power grids under extreme weather conditions. Background Art

[0002] With the intensification of global climate change, extreme weather events such as extreme heat, cold, strong storms, heavy rain, droughts, etc. occur frequently, posing a severe challenge to the stable operation of power grid systems. Therefore, how to effectively evaluate the resilience of power grids under extreme weather conditions, that is, the ability of power grids to maintain normal operation, quickly recover, and continue to provide services when facing extreme weather impacts, has become an important issue that needs to be solved urgently in the current power industry. Current evaluation methods mainly rely on historical data and expert experience, and qualitatively or quantitatively evaluate the structure, equipment performance, operation and maintenance strategies, etc. of the power grid to predict the operation status of the power grid under specific conditions. These methods often lack refined analysis for extreme weather, ignoring the dynamic changes and uncertain factors in the operation process of the power grid, resulting in inaccurate and incomplete evaluation results.

[0003] In the current related technologies, there are technical problems of insufficient comprehensiveness and accuracy in the method for evaluating the resilience of power grids under extreme weather conditions. Summary of the Invention

[0004] This application provides a method for evaluating the resilience of power grids under extreme weather conditions. By retrospectively analyzing historical meteorological data, establishing an extreme weather database, and combining with the topological structure of the power grid system and key node identification, an evaluation model is constructed. By simulating the node responses in extreme weather scenarios and using evaluation indicators such as reliability, redundancy, recoverability, and coordinated scheduling, the resilience of the power grid is comprehensively evaluated and other technical means are used to achieve the technical effect of improving the accuracy and comprehensiveness of the evaluation.

[0005] This application provides a method for evaluating the resilience of power grids under extreme weather conditions, including: Configuring a retrospective period, retrospectively analyzing historical meteorological data based on the retrospective period to establish an extreme weather database with weather patterns and frequency identifiers; after receiving the trust feedback of the power grid system, accessing the power grid system, constructing the topological structure of the power grid according to the access result, and identifying key nodes based on the topological structure and power grid data, and establishing a key node set using digital twins; constructing extreme weather simulation scenarios based on the extreme weather database, and conducting node response tests on the key node set based on the extreme weather simulation scenarios to establish a node test database; constructing power grid resilience evaluation indicators, and using the power grid resilience evaluation indicators to evaluate the responses of the node test database, where the power grid resilience evaluation indicators include reliability indicators, redundancy indicators, recovery indicators, and coordinated scheduling indicators; outputting the power grid resilience evaluation result according to the response evaluation result, and conducting power grid management based on the power grid resilience evaluation result.

[0006] In a possible implementation, the response evaluation of the node test database using the power grid resilience evaluation index performs the following processing: Perform hierarchical partitioning on the key node set, establish device-level nodes and connection-level nodes, and establish mapping weights based on the hierarchical partitioning results; establish a failure discrimination threshold for each node in the key node set, perform failure discrimination on the corresponding data in the node test database based on the failure discrimination threshold, and establish a failure discrimination result; construct a first reliability sub-index based on the failure frequency, failure duration, and mapping weights of the failure discrimination result; respectively perform data stability identification for the failure interval and the normal interval based on the node test database, and establish a second reliability sub-index based on the data stability identification result and the mapping weights; complete the reliability evaluation with the first reliability sub-index and the second reliability sub-index, and complete the response evaluation based on the reliability evaluation result.

[0007] In a possible implementation, the data stability identification for the failure interval and the normal interval based on the node test database performs the following processing: Based on the failure discrimination threshold, divide the node test database into a failure interval and a normal interval; respectively calculate the data stability values for the failure interval and the normal interval, and establish a failure stability value and a normal stability value; establish a first stability identification result based on the failure stability value and the normal stability value; perform fluctuation identification on the failure interval using the failure stability value, and establish a second stability identification result; perform fluctuation identification on the normal interval using the normal stability value, and establish a third stability identification result; complete the data stability identification with the first stability identification result, the second stability identification result, and the third stability identification result.

[0008] In a possible implementation, the response evaluation of the node test database using the power grid resilience evaluation index performs the following processing: Establish a node standby device mapping for the key node set based on the topological structure; establish a first redundancy evaluation result based on the number of node standby device mappings; perform an analysis of the response duration of redundant switching on the node test database, and establish a second redundancy evaluation result based on the response duration analysis result; perform a redundant energy efficiency score on the node test database, and establish a third redundancy evaluation result; complete the redundancy evaluation with the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result, and complete the response evaluation based on the redundancy evaluation result.

[0009] In a possible implementation, the response evaluation of the node test database using the power grid resilience evaluation index performs the following processing: Call the node test database to obtain the fault recovery duration and recovery effect; perform a recovery evaluation based on the fault recovery duration and the recovery effect, and complete the response evaluation based on the recovery evaluation result.

[0010] In a possible implementation, the node test database is evaluated in response to the grid resilience evaluation index, and the following processing is performed: Read the collaborative scheduling data in the node test database, and perform a scheduling rationality score based on the collaborative scheduling data, as follows: ; Wherein, represents the scheduling rationality score, is the load balance degree, , is the total number of critical nodes, represents any critical node, represents the critical node 's actual load, represents the average load of all critical nodes, represents the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, is the scheduling consistency, , wherein, represents the number of scheduling conflicts occurring in the th scheduling process, represents the total number of executions in the th scheduling process, represents any scheduling task, is the total number of scheduling tasks, , , , are the weights of load balance, scheduling response time, scheduling consistency, and scheduling conflict respectively; Generate a collaborative scheduling evaluation result based on the scheduling rationality score to complete the response evaluation.

[0011] In a possible implementation, the following processing is performed to establish a critical node set using digital twins: Call the device data of the grid device based on the access result, and create a digital twin model based on the device data; capture the real-time operation data of the grid device through sensors, and perform data synchronization of the digital twin model based on the real-time operation data capture result; call the digital twin model according to the critical node identification result to establish a critical node set.

[0012] In a possible implementation, the following processing is performed: Establish a pre-maintenance database based on the grid resilience evaluation result; perform weather prediction based on the real-time collected weather data, generate a maintenance plan based on the weather prediction result and the pre-maintenance database, and perform maintenance management based on the maintenance plan.

[0013] The power grid resilience assessment method under extreme weather proposed in this application first configures the retrospective period, performs historical meteorological data retrospective based on the retrospective period, and establishes an extreme weather database with weather patterns and frequency identifiers. Then, after receiving the trust feedback of the power grid system, it accesses the power grid system, constructs the topological structure of the power grid according to the access result, and identifies key nodes based on the topological structure and power grid data. A key node set is established using digital twins. Then, an extreme weather simulation scenario is constructed based on the extreme weather database, and node response tests are performed on the key node set based on the extreme weather simulation scenario to establish a node test database. Next, a power grid resilience assessment index is constructed, and the node test database is evaluated for response using the power grid resilience assessment index. The power grid resilience assessment index includes reliability index, redundancy index, recovery index, and coordinated scheduling index. Finally, the power grid resilience assessment result is output according to the response assessment result, and power grid management is carried out based on the power grid resilience assessment result, achieving the technical effect of improving the accuracy and comprehensiveness of the assessment. Brief Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments of the present invention will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of the present application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0015] Figure 1 It is a schematic flowchart of the power grid resilience assessment method under extreme weather provided by the embodiments of the present application.

[0016] Figure 2 It is a schematic flowchart of performing reliability assessment in the power grid resilience assessment method under extreme weather provided by the embodiments of the present application. Detailed Description of the Embodiments

[0017] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the detailed description of this application.

[0018] In order to make the purpose, technical solutions, and advantages of this application clearer, the present application will be further described in detail below in conjunction with the drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0020] The embodiments of this application provide a method for evaluating the resilience of the power grid under extreme weather conditions, as Figure 1 shown, the method includes: Step S100, configure the retrospective period, perform historical meteorological data retrospective based on the retrospective period, and establish an extreme weather database with weather pattern and frequency identifiers. Specifically, according to the evaluation requirements, determine a suitable time range as the retrospective period. The retrospective period is the time range used to collect and analyze historical meteorological data and should be long enough to include various types of extreme weather events. Use a meteorological data platform or database to retrospectively collect meteorological data within the retrospective period, including key meteorological indicators such as temperature, humidity, wind speed, and rainfall. Process and analyze the collected meteorological data to identify extreme weather events (such as typhoons, heavy rains, high temperatures, etc.), and organize these events and their related meteorological data into a database. At the same time, add weather pattern and frequency identifiers to each extreme weather event. The weather pattern is used to describe the type of extreme weather event, and the frequency identifier is a label for the occurrence frequency of the extreme weather event.

[0021] Step S200: After receiving the trust feedback from the power grid system, access the power grid system, construct the topological structure of the power grid according to the access result, identify the key nodes based on the topological structure and power grid data, and establish a key node set using digital twin. Specifically, before accessing the power grid system, ensure that the trust feedback from the power grid system is obtained to confirm the legitimacy and security of the assessment. According to the access result, connect the assessment system to the power grid system, and use the obtained power grid data to construct a topological structure diagram of the power grid, that is, a structure diagram describing the elements in the power grid and their connection relationships, including key elements such as substations, transmission lines, and distribution facilities and their connection relationships. Based on the topological structure and power grid data, identify the key nodes in the power grid. Key nodes are nodes that play an important role in the operation of the power grid and may cause large-scale power outages or failures in the power grid once they fail. Create digital twin models for the key nodes to enable simulation and evaluation in a virtual environment.

[0022] In a possible implementation, the step of establishing a key node set using digital twin in step S200 further includes step S210: Invoke the device data of the power grid equipment based on the access result, and create a digital twin model based on the device data. Specifically, after the power grid system gives trust feedback, successfully access the power grid system through technical means such as security authentication and interface docking. According to the access result, invoke the power grid equipment data stored in the power grid system, including detailed information such as the type, specification, location, and operating status of the equipment. Based on the obtained device data, use digital twin technology to create digital twin models that correspond one-to-one with the physical power grid equipment, and the models will truly reflect the physical characteristics and operating status of the power grid equipment. Step S220: Grab the real-time operating data of the power grid equipment through sensors, and synchronize the data of the digital twin model based on the result of the real-time operating data grab. Specifically, deploy sensors at key parts of the power grid equipment to monitor the operating status of the equipment in real time. Grab the operating data of the power grid equipment in real time through the sensors, such as voltage, current, temperature, etc. Synchronize the grabbed real-time operating data with the digital twin model to ensure that the digital twin model can reflect the actual operating status of the power grid equipment in real time. Step S230: Invoke the digital twin models according to the key node identification result, and establish a key node set. Specifically, according to the key node identification result, invoke the models corresponding to these key nodes from the digital twin models. Combine the invoked digital twin models together to form a key node set, and this set is used for subsequent node response tests and power grid resilience assessments. This implementation method can more specifically focus on and manage the key parts of the power grid by identifying key nodes and establishing a key node set, which helps to improve the accuracy of power grid resilience assessment under extreme weather conditions.

[0023] Step S300: Construct extreme weather simulation scenarios based on the extreme weather database, and conduct node response tests on the key node set based on the extreme weather simulation scenarios to establish a node test database. Specifically, according to the weather patterns and frequency identifications in the extreme weather database, construct multiple extreme weather simulation scenarios to simulate different types of extreme weather events. Apply the constructed extreme weather simulation scenarios to the key node set, observe and record the response of the key nodes under extreme weather, including voltage fluctuations, current changes, equipment failures, etc. Organize the data collected during the test into a node test database, which stores the response data of the key nodes under extreme weather simulation scenarios.

[0024] Step S400: Construct grid resilience evaluation indicators, and use the grid resilience evaluation indicators to evaluate the responses in the node test database. The grid resilience evaluation indicators include reliability indicators, redundancy indicators, recovery indicators, and coordinated scheduling indicators. Specifically, according to the characteristics and requirements of grid operation, construct a set of grid resilience evaluation indicator systems including reliability indicators, redundancy indicators, recovery indicators, and coordinated scheduling indicators. Among them, the grid resilience evaluation indicators are used to measure the ability of the grid to maintain normal operation and quickly recover under extreme weather; the reliability indicators are used to measure the ability of the grid to maintain power supply stability under extreme weather; the redundancy indicators are used to measure the ability of the grid to maintain power supply through standby equipment or paths in case of equipment failures or outages; the recovery indicators are used to measure the ability of the grid to quickly resume normal operation after failures or outages; the coordinated scheduling indicators are used to measure the ability of the grid to maintain stable operation by reasonably scheduling resources under extreme weather. Use the constructed grid resilience evaluation indicators to evaluate and analyze the data in the node test database to obtain the resilience performance of the grid under extreme weather.

[0025] Such as Figure 2As shown, in a possible implementation, the response evaluation of the node test database using the power grid resilience evaluation index, step S400 further includes step S410, hierarchically partitioning the key node set, establishing device layer nodes and connection layer nodes, and establishing mapping weights based on the hierarchical partitioning result. Specifically, a detailed hierarchical partitioning of the key node set is performed, including dividing the nodes into device layer nodes and connection layer nodes. Device layer nodes refer to specific devices in the power grid, such as transformers, generators, etc.; connection layer nodes refer to transmission lines, busbars, etc. that connect these devices. Based on the result of the hierarchical partitioning, a mapping weight is assigned to each node, and this weight reflects the importance and influence of the node in the power grid. Device layer nodes have a higher weight because they are directly involved in the conversion and distribution of electrical energy; connection layer nodes, although not directly involved in the conversion of electrical energy, play a key role in the connection and transmission of the power grid, so they also have corresponding weights. Step S420, establish a failure discrimination threshold for each node in the key node set, perform failure discrimination on the corresponding data in the node test database based on the failure discrimination threshold, and establish a failure discrimination result. Specifically, according to the operating standards and historical data of the power grid, a failure discrimination threshold is established for each key node, and this threshold is used to determine whether the node is in a failure state. Based on the established failure discrimination threshold, failure discrimination is performed on the data in the test database. If the operating data of the node exceeds its failure discrimination threshold, then the node is considered to be in a failure state. Step S430, construct a first reliability sub-index according to the failure frequency, failure duration, and mapping weight of the failure discrimination result. Specifically, according to the failure discrimination result, count the failure frequency of each node (the number of times the node fails per unit time) and the failure duration (the time each failure of the node lasts). Based on the statistically obtained failure data, combined with the mapping weight of the node, construct a first reliability sub-index, and this index reflects the reliability performance of the node in the power grid. Step S440, respectively perform data stability identification for the failure interval and the normal interval based on the node test database, and establish a second reliability sub-index based on the data stability identification result and the mapping weight. Specifically, based on the data in the node test database, perform data stability identification for the failure interval and the normal interval respectively, including calculating the stability value of the data, identifying the fluctuation situation of the data, etc. Based on the result of the data stability identification, combined with the mapping weight of the node, construct a second reliability sub-index, and this index reflects the stable operation ability of the node in the power grid. Step S450, complete the reliability evaluation with the first reliability sub-index and the second reliability sub-index, and complete the response evaluation based on the reliability evaluation result. Specifically, based on the first reliability sub-index and the second reliability sub-index, comprehensively evaluate the reliability of the node, including calculating the reliability score, ranking, etc. of the node. According to the result of the reliability evaluation, perform a response evaluation on the resilience of the power grid, including analyzing the operating conditions of the power grid under extreme weather, identifying potential risk points, etc.This implementation method comprehensively evaluates the reliability of nodes from multiple perspectives by constructing the first reliability sub-index and the second reliability sub-index, providing a more accurate and scientific decision-making basis for power grid managers and helping to improve the resilience of the power grid under extreme weather conditions.

[0026] In a possible implementation method, the data stability identification in the failure interval and the normal interval is respectively performed based on the node test database. Step S440 further includes step S441 of dividing the node test database into a failure interval and a normal interval based on a failure discrimination threshold. Specifically, each node data in the node test database is traversed, and the data is divided into two categories according to the failure discrimination threshold: one category is the data exceeding the threshold, which is marked as the failure interval; the other category is the data not exceeding the threshold, which is marked as the normal interval. Step S442 is to calculate the data stability values in the failure interval and the normal interval respectively and establish the failure stability value and the normal stability value. Specifically, for the data in the failure interval, statistical methods (such as the mean value, etc.) are used to calculate its stability value, that is, the failure stability value. For the data in the normal interval, the same statistical method is used to calculate its stability value, that is, the normal stability value. Step S443 is to establish the first stability identification result based on the failure stability value and the normal stability value. Specifically, the failure stability value is compared with the normal stability value to analyze the difference between the two. According to the comparison result, the first stability identification result is established, which describes the comparison of the data stability in the failure interval and the normal interval. Step S444 is to perform fluctuation identification on the failure interval using the failure stability value and establish the second stability identification result. Specifically, within the failure interval, the deviation between each data point and the failure stability value is calculated to describe the fluctuation of the data. According to the result of the fluctuation calculation, the second stability identification result is established, which describes the fluctuation of the data within the failure interval. Step S445 is to perform fluctuation identification on the normal interval using the normal stability value and establish the third stability identification result. Specifically, within the normal interval, the deviation between each data point and the normal stability value is also calculated. According to the result of the fluctuation calculation, the third stability identification result is established, which describes the fluctuation of the data within the normal interval. Step S446 is to complete the data stability identification with the first stability identification result, the second stability identification result, and the third stability identification result. Specifically, the first stability identification result, the second stability identification result, and the third stability identification result are integrated to form a complete data stability identification result. This implementation method more precisely evaluates the stability and reliability of key nodes under extreme weather conditions by dividing the node test database into a failure interval and a normal interval and performing calculations of stability values and fluctuation identifications, helping to more accurately identify potential risk points in the power grid.

[0027] In a possible implementation, the response evaluation of the node test database using the power grid resilience evaluation index, step S400 further includes step S460 of establishing a mapping of node standby devices for the critical node set based on the topological structure. Specifically, according to the power grid topological structure, clarify the connection relationships and hierarchical structures between each critical node. For each critical node, identify its possible standby devices, which can be alternative devices of the same type or alternative devices with similar functions. Based on the identification results, establish a mapping relationship between each critical node and its standby devices to form a node standby device mapping. Step S470, establish a first redundancy evaluation result based on the number of node standby device mappings. Specifically, traverse the node standby device mapping and count the number of standby devices corresponding to each critical node. According to the statistical results, establish a first redundancy evaluation result, which reflects the redundancy degree of critical nodes in the power grid. Step S480, analyze the response duration of redundant switching for the node test database and establish a second redundancy evaluation result based on the response duration analysis result. Specifically, based on the data in the node test database and the node standby device mapping, record the response duration from the failure of the critical node to the takeover of work by the standby device. According to the response duration analysis result, establish a second redundancy evaluation result, which reflects the efficiency and speed of redundant switching in the power grid. Step S490, perform a redundant energy efficiency score for the node test database and establish a third redundancy evaluation result. Specifically, based on the data in the node test database, analyze the energy efficiency performance of the standby device during operation, including indicators such as energy consumption and efficiency. According to the analysis results, establish an energy efficiency score for each standby device, which reflects the energy efficiency level of the standby device. Based on the energy efficiency score, establish a third redundancy evaluation result, which reflects the energy efficiency level of redundant devices in the power grid. Step S4100, complete the redundancy evaluation with the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result, and complete the response evaluation based on the redundancy evaluation result. Specifically, integrate the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result to form a complete redundancy evaluation result. Based on the redundancy evaluation result, perform a response evaluation on the redundant performance of the power grid, and this evaluation result reflects the recovery ability and redundancy level of the power grid when a critical node fails. This implementation method can more reasonably configure standby devices and redundant resources through detailed redundancy evaluation, avoid problems of resource waste and insufficient redundancy, and thus improve the recovery ability and resilience of the power grid when a critical node fails.

[0028] In a possible implementation manner, the response evaluation of the node test database is performed using the power grid resilience evaluation index. Step S400 further includes step S4110 of calling the node test database to obtain the fault recovery duration and recovery effect. Specifically, the fault recovery data of key nodes under simulated extreme weather conditions is extracted from the node test database, and the key data related to fault recovery is screened out, including the fault recovery duration and recovery effect. The fault recovery duration refers to the time required from the occurrence of a fault to the system's return to normal operation; the recovery effect refers to the performance of the system after recovery, such as voltage stability, current distribution, etc. Step S4120, perform a recovery evaluation based on the fault recovery duration and recovery effect, and complete the response evaluation based on the recovery evaluation result. Specifically, according to the requirements of power grid resilience evaluation, recovery evaluation indexes are constructed, including the threshold of the fault recovery duration, the scoring standard of the recovery effect, etc. The fault recovery duration and recovery effect obtained in step S4110 are compared and analyzed with the constructed evaluation indexes. By comparison, it is judged whether the recovery ability of key nodes under simulated extreme weather conditions meets the requirements of power grid resilience evaluation. Based on the results of data comparison and analysis, a recovery evaluation report or conclusion is generated, which is used to elaborate in detail the recovery ability of key nodes, including the compliance of the recovery duration, the scoring of the recovery effect, etc. The recovery evaluation result is used as part of the power grid resilience evaluation, combined with the results of reliability evaluation, redundancy evaluation, etc., to jointly complete the response evaluation stage of the power grid resilience evaluation. This implementation manner more comprehensively evaluates the resilience of the power grid under extreme weather conditions by introducing a recovery evaluation. Based on the recovery evaluation result, the recovery ability of key nodes can be more accurately understood, so as to formulate more reasonable power grid management strategies. For example, for nodes with weak recovery ability, measures such as strengthening maintenance and increasing standby equipment can be taken to improve their resilience.

[0029] In a possible implementation manner, the response evaluation of the node test database is performed using the power grid resilience evaluation index. Step S400 further includes step S4130 of reading the collaborative scheduling data in the node test database and performing a scheduling rationality scoring based on the collaborative scheduling data, as follows: ; Among them, represents the scheduling rationality score, is the load balance degree, , is the total number of key nodes, represents any one key node, represents the key node 's actual load, represents the average load of all key nodes, represents the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, is the scheduling consistency, , where, represents the number of scheduling conflicts that occur in the th scheduling process, represents the total number of executions in the th scheduling process, represents any scheduling task, is the total number of scheduling tasks, , , , They are the weights of load balance, scheduling response time, scheduling consistency, and scheduling conflicts respectively. Specifically, data related to collaborative scheduling is extracted from the slave node test database, including the load data of key nodes, scheduling response time, the number of conflicts during the scheduling process, and the total number of executions, etc. The load balance degree is an index to measure the load balance degree of key nodes in the power grid. The more balanced the load is, the more stable the power grid operation is. The scheduling response time is the time required from the issuance of the scheduling instruction to the completion of the execution. Calculate the average scheduling response time and compare it with the preset scheduling response time threshold. If the average response time exceeds the threshold, the score of the scheduling response time will be reduced. The scheduling consistency is an index to measure whether there are conflicts or inconsistencies during the scheduling process. Count the number of scheduling conflicts that occur in each scheduling process and compare it with the total number of executions. The fewer the number of conflicts, the higher the scheduling consistency. The resource utilization rate refers to the usage efficiency of resources during the scheduling process. Calculate the resource utilization rate according to the resource usage situation during the scheduling process. The higher the resource utilization rate, the higher the scheduling efficiency. Based on the above analysis, use the given formula to calculate the scheduling rationality score. This score synthesizes multiple factors such as load balance degree, scheduling response time, scheduling consistency, and scheduling conflicts, and assigns corresponding weights to each factor. Step S4140, generate a collaborative scheduling evaluation result based on the scheduling rationality score to complete the response evaluation. Specifically, according to the scheduling rationality score calculated in step 4130, analyze the rationality of the power grid collaborative scheduling. The higher the score, the more reasonable the collaborative scheduling is. Based on the result of the score analysis, generate a collaborative scheduling evaluation report or conclusion, which is used to elaborate on the rationality of the power grid collaborative scheduling, including the performance in terms of load balance degree, scheduling response time, scheduling consistency, and resource utilization rate, etc. Take the collaborative scheduling evaluation result as a part of the power grid resilience evaluation, combine it with the results of reliability evaluation, redundancy evaluation, and restoration evaluation, etc., to jointly complete the response evaluation stage of the power grid resilience evaluation. This implementation method comprehensively evaluates the resilience of the power grid under extreme weather conditions by introducing collaborative scheduling evaluation. The collaborative scheduling ability reflects the collaborative cooperation ability among key nodes of the power grid when dealing with extreme weather. Based on the collaborative scheduling evaluation result, it is possible to more accurately understand the performance of the power grid in collaborative scheduling, so as to formulate more reasonable power grid scheduling strategies. For example, for nodes with poor scheduling consistency or low resource utilization rate, measures such as optimizing the scheduling algorithm and increasing standby equipment can be taken to improve their collaborative scheduling ability.

[0030] Step S500: Output the power grid resilience assessment result according to the response assessment result, and conduct power grid management based on the power grid resilience assessment result. Specifically, output the response assessment result in the form of a report or a chart. According to the assessment result, formulate targeted power grid management strategies, including strengthening the protection of key nodes, increasing the redundancy of the power grid, optimizing the restoration strategy, etc., to improve the resilience of the power grid under extreme weather. In the embodiment of the present application, by retrospectively analyzing historical meteorological data, establishing an extreme weather database, and combining with the topological structure and key node identification of the power grid system, an assessment model is constructed. By simulating the node responses under extreme weather scenarios and using assessment indicators such as reliability, redundancy, restorability, and coordinated scheduling, a comprehensive assessment of the resilience of the power grid is achieved, thus achieving the technical effect of improving the accuracy and comprehensiveness of the assessment.

[0031] In a possible implementation manner, step S500 further includes step S510: Establish a pre-maintenance database based on the power grid resilience assessment result. Specifically, conduct a detailed analysis of the power grid resilience assessment result, including the scoring situations of reliability indicators, redundancy indicators, restoration indicators, and coordinated scheduling indicators, as well as the performance of each key node. Based on the assessment result, identify potential risk points that may exist in the power grid, which are key nodes with poor resilience or weak links in the system. For the identified potential risk points, establish a pre-maintenance database, which contains detailed information about the risk points, such as location, type, historical fault records, recommended maintenance measures, etc., for guiding subsequent maintenance management work. Step S520: Conduct meteorological prediction based on the real-time collected meteorological data, generate a maintenance plan based on the meteorological prediction result and the pre-maintenance database, and conduct maintenance management based on the maintenance plan. Specifically, collect the current meteorological data in real time through meteorological monitoring devices, including temperature, humidity, wind speed, wind direction, precipitation, etc. Use a meteorological prediction model to process and analyze the real-time collected meteorological data to predict the weather conditions in the next period of time, especially the possibility of extreme weather. According to the meteorological prediction result and the information in the pre-maintenance database, generate a targeted maintenance plan. Conduct maintenance management work according to the generated maintenance plan, including equipment inspection, preventive maintenance, emergency preparation, etc. At the same time, adjust and optimize the plan according to the actual situation during the maintenance process to ensure the normal operation of the power grid. This implementation manner can repair or replace equipment before it fails through preventive maintenance, thereby reducing power outage losses and maintenance costs caused by equipment failures, improving the emergency response ability of the power grid under extreme weather, and helping to improve the reliability and resilience of the power grid.

[0032] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps recited in the present application can be executed in a different order from that in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for evaluating the resilience of a power grid under extreme weather, characterized in that, The method includes: Configuring a backtracking period, backtracking historical meteorological data based on the backtracking period, and establishing an extreme weather database with weather patterns and frequency identifiers; After receiving the trust feedback of the power grid system, accessing the power grid system, constructing the topological structure of the power grid according to the access result, identifying key nodes based on the topological structure and power grid data, and establishing a key node set using digital twin; Constructing an extreme weather simulation scenario based on the extreme weather database, and performing node response tests on the key node set based on the extreme weather simulation scenario to establish a node test database; Constructing power grid resilience evaluation indicators, and performing response evaluation on the node test database with the power grid resilience evaluation indicators, where the power grid resilience evaluation indicators include reliability indicators, redundancy indicators, recovery indicators, and coordinated scheduling indicators; Outputting the power grid resilience evaluation result according to the response evaluation result, and performing power grid management with the power grid resilience evaluation result.

2. The method for evaluating the resilience of a power grid under extreme weather according to claim 1, wherein, The performing response evaluation on the node test database with the power grid resilience evaluation indicators further includes: Performing hierarchical partitioning on the key node set to establish device layer nodes and connection layer nodes, and establishing mapping weights based on the hierarchical partitioning result; Establishing a failure discrimination threshold for each node in the key node set, performing failure discrimination on the corresponding data in the node test database based on the failure discrimination threshold, and establishing a failure discrimination result; Constructing a first reliability sub-index according to the failure frequency, failure duration, and mapping weight of the failure discrimination result; Performing data stability identification on the failure interval and normal interval respectively based on the node test database, and establishing a second reliability sub-index based on the data stability identification result and mapping weight; Completing reliability evaluation with the first reliability sub-index and the second reliability sub-index, and completing response evaluation based on the reliability evaluation result.

3. The method for evaluating the resilience of a power grid under extreme weather according to claim 2, wherein, The performing data stability identification on the failure interval and normal interval respectively based on the node test database further includes: Dividing the node test database into a failure interval and a normal interval based on the failure discrimination threshold; Calculating the data stability values of the failure interval and the normal interval respectively to establish a failure stability value and a normal stability value; Establishing a first stability identification result based on the failure stability value and the normal stability value; Performing fluctuation identification on the failure interval using the failure stability value to establish a second stability identification result; Performing fluctuation identification on the normal interval using the normal stability value to establish a third stability identification result; Completing data stability identification with the first stability identification result, the second stability identification result, and the third stability identification result.

4. The method for evaluating the resilience of a power grid under extreme weather as described in claim 1, wherein, The performing response evaluation on the node test database with the power grid resilience evaluation indicators further includes: Establishing a node standby device mapping for the key node set based on the topological structure; Establishing a first redundancy evaluation result based on the number of node standby device mappings; Performing response duration analysis on the redundant switching of the node test database, and establishing a second redundancy evaluation result based on the response duration analysis result; Performing redundant energy efficiency scoring on the node test database to establish a third redundancy evaluation result; Completing redundancy evaluation with the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result, and completing response evaluation based on the redundancy evaluation result.

5. The method for evaluating the resilience of the power grid under extreme weather according to claim 1, wherein The response evaluation of the node test database using the power grid resilience evaluation index further includes: Invoking the node test database to obtain the fault recovery duration and recovery effect; Conducting a recovery evaluation based on the fault recovery duration and recovery effect, and completing the response evaluation based on the recovery evaluation results.

6. The method for evaluating the resilience of a power grid under extreme weather conditions according to claim 1, wherein The response evaluation of the node test database using the power grid resilience evaluation index further includes: Read the collaborative scheduling data in the node test database and perform a scheduling rationality score based on the collaborative scheduling data as follows: ; Among them, represents the scheduling rationality score, is the load balance degree, , is the total number of critical nodes, represents any one critical node, represents the critical node 's actual load, represents the average load of all critical nodes, represents the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, is the scheduling consistency, , among which, represents the number of scheduling conflicts that occur in the th scheduling process, represents the total number of executions in the th scheduling process, represents any one scheduling task, is the total number of scheduling tasks, , , , are the weights of load balance, scheduling response time, scheduling consistency, and scheduling conflict respectively; Generating a collaborative scheduling evaluation result with the scheduling rationality score to complete the response evaluation.

7. The method for evaluating the resilience of a power grid under extreme weather according to claim 1, wherein The establishment of the critical node set using digital twin further includes: Invoking the device data of the power grid equipment based on the access result, and creating a digital twin model based on the device data; Grabbing the real-time operation data of the power grid equipment through sensors, and synchronizing the data of the digital twin model based on the real-time operation data grabbing result; Invoking the digital twin model according to the critical node identification result to establish a critical node set.

8. The method for evaluating the resilience of the power grid under extreme weather according to claim 1, characterized in that, The method further includes: Establishing a pre-maintenance database based on the power grid resilience evaluation result; Conducting weather prediction based on the real-time collected weather data, generating a maintenance plan based on the weather prediction result and the pre-maintenance database, and performing maintenance management based on the maintenance plan.

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