Power grid resilience assessment method under extreme weather conditions

Abstract

The present invention discloses a method for evaluating the resilience of power grids under extreme weather conditions, which relates to fields related to power grid monitoring. The method comprises: configuring a backtracking cycle, performing backtracking on historical meteorological data, and establishing an extreme weather database; accessing the power grid system, constructing the topology of the power grid based on the access results, and identifying key nodes based on the topology and power grid data, and establishing a key node set using digital twins; constructing extreme weather simulation scenarios based on the extreme weather database, performing node response tests on the key node set, and establishing a node test database; constructing power grid resilience assessment indicators, and performing response assessment on the node test database using the power grid resilience assessment indicators; outputting power grid resilience assessment results based on the response assessment results, and managing the power grid using the power grid resilience assessment results. The method solves the technical problems of insufficient comprehensiveness and accuracy of existing power grid resilience assessment methods under extreme weather conditions, and achieves the technical effect of improving the accuracy and comprehensiveness of the assessment.

Description

Technical Field

[0001] The present application relates to the field of power grid monitoring, and in particular to a method for evaluating power grid resilience under extreme weather conditions. Background Art

[0002] As global climate change intensifies, extreme weather events such as extreme high and low temperatures, severe storms, heavy rains, and droughts are becoming more frequent, posing a severe challenge to the stable operation of power grid systems. Therefore, effectively assessing the resilience of power grids in extreme weather—that is, their ability to maintain normal operation, recover quickly, and continue to provide services when faced with extreme weather shocks—has become a critical issue that the power industry urgently needs to address. Current assessment methods primarily rely on historical data and expert experience, using qualitative or quantitative assessments of the grid's structure, equipment performance, and operation and maintenance strategies to predict its operational status under specific conditions. These methods often lack detailed analysis of extreme weather events and overlook the dynamic changes and uncertainties in grid operation, resulting in inaccurate and incomplete assessment results.

[0003] Among the current related technologies, the grid resilience assessment methods under extreme weather conditions have technical problems such as lack of comprehensiveness and accuracy. Summary of the Invention

[0004] This application provides a method for assessing the resilience of power grids under extreme weather conditions. By tracing back historical meteorological data, establishing an extreme weather database, and combining the topology of the power grid system and identification of key nodes, an assessment model is constructed. By simulating node responses under extreme weather scenarios and utilizing assessment indicators such as reliability, redundancy, recovery, and coordinated scheduling, a comprehensive assessment of the resilience of the power grid is conducted, thereby achieving the technical effect of improving the accuracy and comprehensiveness of the assessment.

[0005] This application provides a method for assessing power grid resilience under extreme weather conditions, including:

[0006] Configure a backtracking cycle, perform backtracking on historical meteorological data based on the backtracking cycle, and establish an extreme weather database with weather pattern and frequency identifiers; after receiving trust feedback from the power grid system, access the power grid system, build the topology of the power grid based on the access result, identify key nodes based on the topology and power grid data, and use digital twins to establish a key node set; build extreme weather simulation scenarios based on the extreme weather database, and perform node response tests on the key node set based on the extreme weather simulation scenarios to establish a node test database; construct power grid resilience assessment indicators, and use power grid resilience assessment indicators to perform response assessment on the node test database, the power grid resilience assessment indicators include reliability indicators, redundancy indicators, recovery indicators, and collaborative scheduling indicators; output power grid resilience assessment results based on the response assessment results, and use the power grid resilience assessment results to manage the power grid.

[0007] In a possible implementation, the node test database is evaluated for response using the grid resilience evaluation index, and the following processing is performed:

[0008] The key node set is hierarchically divided, and device layer nodes and connection layer nodes are established, and mapping weights are established based on the hierarchical division results; a failure judgment threshold is established for each node in the key node set, and failure judgment of corresponding data in the node test database is performed based on the failure judgment threshold, and a failure judgment result is established; a first reliability sub-indicator is constructed according to the failure frequency, failure duration and mapping weight of the failure judgment result; data stability identification of failure intervals and normal intervals is performed respectively based on the node test database, and a second reliability sub-indicator is established based on the data stability identification result and the mapping weight; reliability evaluation is completed with the first reliability sub-indicator and the second reliability sub-indicator, and response evaluation is completed based on the reliability evaluation result.

[0009] In a possible implementation, the data stability identification of the failure interval and the normal interval is performed based on the node test database, and the following processing is performed:

[0010] The node test database is divided into a failure interval and a normal interval based on the failure discrimination threshold; the data stability values ​​of the failure interval and the normal interval are calculated respectively, and the failure stability value and the normal stability value are established; a first stable identification result is established based on the failure stability value and the normal stability value; the failure interval is subjected to fluctuation identification and a second stable identification result is established; the normal interval is subjected to fluctuation identification and a third stable identification result is established; and data stability identification is completed with the first stable identification result, the second stable identification result and the third stable identification result.

[0011] In a possible implementation, the node test database is evaluated for response using the grid resilience evaluation index, and the following processing is performed:

[0012] Establish node backup device mapping for key node sets based on the topological structure; establish a first redundancy evaluation result based on the number of node backup device mappings; perform response time analysis of redundant switching on the node test database, and establish a second redundancy evaluation result based on the response time analysis result; perform redundant energy efficiency scoring on the node test database, and establish a third redundancy evaluation result; complete redundancy evaluation based on the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result, and complete response evaluation based on the redundant evaluation result.

[0013] In a possible implementation, the node test database is evaluated for response using the grid resilience evaluation index, and the following processing is performed:

[0014] Call the node test database to obtain the fault recovery time and recovery effect; perform recovery evaluation based on the fault recovery time and recovery effect, and complete the response evaluation based on the recovery evaluation results.

[0015] In a possible implementation, the response evaluation of the node test database using the grid resilience evaluation index is performed by performing the following processing: reading the coordinated scheduling data in the node test database, and performing a scheduling rationality score based on the coordinated scheduling data, as follows: ;

[0016] in, Characterizes the scheduling rationality score, is the load balance degree, , is the total number of key nodes, Characterize any key node, Characterizing key nodes The actual load, Characterizes the average load of all key nodes, Characterizes the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, For scheduling consistency, ,in, Characterization The number of scheduling conflicts that occur during the scheduling process, Characterization The total number of executions in the scheduling process, Represents any scheduling task, is the total number of scheduled tasks, 、 、 、 are the weights of load balancing, scheduling response time, scheduling consistency, and scheduling conflict respectively;

[0017] Generate collaborative scheduling evaluation results based on scheduling rationality scores to complete response evaluation.

[0018] In a possible implementation, the digital twin is used to establish a key node set and perform the following processing:

[0019] Based on the access results, the device data of the power grid equipment is called, and a digital twin model is created based on the device data; the real-time operation data of the power grid equipment is captured through sensors, and the data of the digital twin model is synchronized based on the real-time operation data capture results; the digital twin model is called according to the key node identification results, and a key node set is established.

[0020] In a possible implementation, the following processing is performed:

[0021] A pre-maintenance database is established based on the results of the power grid resilience assessment; weather forecasts are performed based on real-time collected meteorological data; maintenance plans are generated based on the weather forecast results and the pre-maintenance database; and maintenance management is carried out based on the maintenance plans.

[0022] The method for assessing the resilience of a power grid under extreme weather conditions proposed in this application first configures a backtracking period, backtracks historical meteorological data based on the backtracking period, and establishes an extreme weather database with weather patterns and frequency identifiers. Then, after receiving trust feedback from the power grid system, the power grid system is connected. The topology of the power grid is constructed based on the access result, and key nodes are identified based on the topology 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 of the key node set are performed based on the extreme weather simulation scenario. A node test database is established, and then a power grid resilience assessment index is constructed. The node test database is response assessed using the power grid resilience assessment index. The power grid resilience assessment index includes reliability index, redundancy index, recovery index, and collaborative scheduling index. Finally, the power grid resilience assessment result is output based on the response assessment result, and the power grid is managed using the power grid resilience assessment result, thereby achieving the technical effect of improving the accuracy and comprehensiveness of the assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are 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 preceding or following operations are not necessarily performed in precise order. Instead, the various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0024] Figure 1 A flow chart of a method for assessing grid resilience under extreme weather conditions provided in an embodiment of the present application.

[0025] Figure 2 A schematic diagram of the process of performing reliability assessment in the method for assessing power grid resilience under extreme weather conditions provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that 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 art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The present invention provides a method for evaluating the resilience of power grids under extreme weather conditions. Figure 1 As shown, the method includes:

[0030] Step S100 configures a lookback period, performs a lookback on historical meteorological data based on the lookback period, and establishes an extreme weather database with weather pattern and frequency identifiers. Specifically, based on the assessment requirements, a suitable time range is determined as the lookback period. The lookback period is the time range used to collect and analyze historical meteorological data and should be long enough to include multiple types of extreme weather events. Utilize a meteorological data platform or database to look back and collect meteorological data within the lookback period, including key meteorological indicators such as temperature, humidity, wind speed, and rainfall. The collected meteorological data is processed and analyzed to identify extreme weather events (such as typhoons, heavy rains, high temperatures, etc.), and these events and their related meteorological data are organized into a database. At the same time, a weather pattern and frequency identifier are added 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 frequency of occurrence of the extreme weather event.

[0031] Step S200, after receiving the trust feedback from the power grid system, access the power grid system, build the topology of the power grid according to the access result, identify key nodes based on the topology and power grid data, and use digital twins to establish a key node set. Specifically, before accessing the power grid system, ensure that trust feedback from the power grid system is obtained to confirm the legitimacy and security of the assessment. According to the access result, the assessment system is connected to the power grid system, and the acquired power grid data is used to build a topology diagram of the power grid, that is, a structural 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 topology 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. Once they fail, they may cause large-scale power outages or failures in the power grid. Create digital twin models for key nodes so that they can be simulated and evaluated in a virtual environment.

[0032] In one possible implementation, the method of establishing a key node set using a digital twin further includes step S210, whereby device data of power grid devices is retrieved based on the access result, and a digital twin model is created based on the device data. Specifically, after the power grid system provides trust feedback, successful access to the power grid system is achieved through technical means such as security authentication and interface docking. Based on the access result, the power grid device data stored in the power grid system is retrieved, including detailed information such as device type, specifications, location, and operating status. Based on the acquired device data, a digital twin model is created using digital twin technology that corresponds exactly to the physical power grid device. The model accurately reflects the physical characteristics and operating status of the power grid device. Step S220, whereby real-time operating data of the power grid device is captured using sensors, and data synchronization of the digital twin model is performed based on the captured real-time operating data. Specifically, sensors are deployed at key locations of the power grid device to monitor the device's operating status in real time. The sensors capture real-time operating data of the power grid device, such as voltage, current, and temperature. This captured real-time operating data is synchronized with the digital twin model to ensure that the digital twin model reflects the actual operating status of the power grid device in real time. Step S230: Call the digital twin model based on the key node identification results and establish a key node set. Specifically, based on the key node identification results, call the models corresponding to these key nodes from the digital twin model. The called digital twin models are combined together to form a key node set, which is used for subsequent node response testing and grid resilience assessment. This implementation method, by identifying key nodes and establishing a key node set, can focus on and manage key parts of the power grid in a more targeted manner, helping to improve the accuracy of grid resilience assessments under extreme weather conditions.

[0033] Step S300: Construct an extreme weather simulation scenario based on the extreme weather database, perform node response tests on a set of key nodes based on the extreme weather simulation scenario, and establish a node test database. Specifically, based on the weather patterns and frequency identifiers 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 scenario to a set of key nodes, and observe and record the response of key nodes under extreme weather conditions, including voltage fluctuations, current changes, equipment failures, etc. The data collected during the test is organized into a node test database, which stores the response data of key nodes under extreme weather simulation scenarios.

[0034] Step S400, construct a power grid resilience evaluation index, and use the power grid resilience evaluation index to perform a response evaluation on the node test database. The power grid resilience evaluation index includes a reliability index, a redundancy index, a recovery index, and a coordinated scheduling index. Specifically, according to the characteristics and needs of power grid operation, a set of power grid resilience evaluation index systems including reliability indicators, redundancy indicators, recovery indicators, and coordinated scheduling indicators are constructed, wherein the power grid resilience evaluation index is an indicator used to measure the power grid's ability to maintain normal operation and rapid recovery under extreme weather conditions; the reliability index is an indicator that measures the power grid's ability to maintain power supply stability under extreme weather conditions; the redundancy index is an indicator that measures the power grid's ability to maintain power supply through backup equipment or paths when equipment fails or fails; the recovery index is an indicator that measures the power grid's ability to quickly restore normal operation after a failure or failure; and the coordinated scheduling index is an indicator that measures the power grid's ability to maintain stable operation through reasonable resource scheduling under extreme weather conditions. The constructed power grid resilience evaluation index is used to evaluate and analyze the data in the node test database to obtain the resilience performance of the power grid under extreme weather conditions.

[0035] like Figure 2As shown, in one possible implementation, the response evaluation of the node test database using the grid resilience assessment indicator is performed. Step S400 further includes step S410, hierarchically dividing the key node set into device-layer nodes and connection-layer nodes, and establishing mapping weights based on the hierarchical division results. Specifically, the key node set is divided into device-layer nodes and connection-layer nodes in a detailed hierarchical manner. Device-layer nodes refer to specific devices in the power grid, such as transformers and generators; connection-layer nodes refer to transmission lines and busbars that connect these devices. Based on the hierarchical division results, each node is assigned a mapping weight that reflects its importance and influence in the power grid. Device-layer nodes, because they directly participate in the conversion and distribution of electrical energy, have higher weights. Although connection-layer nodes do not directly participate in the conversion of electrical energy, they play a key role in the connection and transmission of the power grid and therefore also have corresponding weights. Step S420 establishes a failure determination threshold for each node in the key node set. Based on the failure determination threshold, failure determination is performed on the corresponding data in the node test database to generate a failure determination result. Specifically, based on the grid's operating standards and historical data, a failure threshold is established for each key node. This threshold is used to determine whether the node is in a failed state. Based on the established failure threshold, failure detection is performed on the data in the test database. If the node's operating data exceeds the threshold, the node is considered to be in a failed state. Step S430: A first reliability sub-indicator is constructed based on the failure frequency, failure duration, and mapping weights from the failure detection results. Specifically, based on the failure detection results, the failure frequency (the number of node failures per unit time) and failure duration (the duration of each node failure) of each node are calculated. Based on the statistical failure data and the node's mapping weight, a first reliability sub-indicator is constructed, which reflects the node's reliability performance in the grid. Step S440: Data stability identification is performed for both failed and normal intervals based on the node test database. A second reliability sub-indicator is then established based on the data stability identification results and the mapping weights. Specifically, data stability identification is performed for both failed and normal intervals based on the data in the node test database, including calculating data stability values ​​and identifying data fluctuations. Based on the results of data stability identification and combined with the mapping weight of the node, a second reliability sub-indicator is constructed. This indicator reflects the node's ability to operate stably in the power grid. Step S450: Complete the reliability assessment using the first reliability sub-indicator and the second reliability sub-indicator, and complete the response assessment based on the reliability assessment results. Specifically, based on the first reliability sub-indicator and the second reliability sub-indicator, a comprehensive assessment of the node's reliability is performed, including calculating the node's reliability score and ranking. Based on the results of the reliability assessment, a response assessment of the power grid's resilience is performed, including analyzing the power grid's operating conditions under extreme weather conditions and identifying potential risk points.This implementation method constructs the first reliability sub-indicator and the second reliability sub-indicator to comprehensively evaluate the reliability of nodes from multiple perspectives, providing grid managers with more accurate and scientific decision-making basis, and helping to improve the resilience of the grid in extreme weather.

[0036] In one possible implementation, the data stability identification for failure intervals and normal intervals is performed separately based on the node test database. Step S440 further includes step S441: dividing the node test database into failure intervals and normal intervals based on a failure discrimination threshold. Specifically, each node data in the node test database is traversed and, based on the failure discrimination threshold, the data is divided into two categories: one category, data exceeding the threshold, is marked as a failure interval; the other category, data not exceeding the threshold, is marked as a normal interval. Step S442: Data stability values ​​for the failure intervals and normal intervals are calculated separately to establish failure stability values ​​and normal stability values. Specifically, for the data in the failure intervals, a statistical method (such as the mean) is used to calculate its stability value, i.e., the failure stability value. For the data in the normal intervals, a similar statistical method is used to calculate its stability value, i.e., the normal stability value. Step S443: Establishing a 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. Based on the comparison results, a first stability identification result is established, which describes the comparative stability of the data in the failure intervals and the normal intervals. In step S444, the failure stability value is used to identify fluctuations in the failure interval, generating a 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 data fluctuations. Based on the results of the fluctuation calculation, a second stability identification result is generated, which describes the data fluctuations within the failure interval. In step S445, the normal stability value is used to identify fluctuations in the normal interval, generating a third stability identification result. Specifically, within the normal interval, the deviation between each data point and the normal stability value is similarly calculated. Based on the results of the fluctuation calculation, a third stability identification result is generated, which describes the data fluctuations within the normal interval. In step S446, data stability identification is completed using the first, second, and third stability identification results. Specifically, the first, second, and third stability identification results are integrated to form a complete data stability identification result. This implementation method, by segmenting the node test database into failure intervals and normal intervals and calculating stability values ​​and fluctuation identification, provides a more refined assessment of the stability and reliability of key nodes under extreme weather conditions, helping to more accurately identify potential risk points in the power grid.

[0037] In a possible implementation, the node test database is evaluated for response using the grid resilience evaluation index, and step S400 further includes step S460, which establishes a node backup device mapping for the key node set based on the topological structure. Specifically, according to the grid topology, the connection relationship and hierarchical structure between each key node are clarified, and for each key node, its possible backup device is identified. The backup device can be an alternative device of the same type or an alternative device with similar functions. Based on the identification result, a mapping relationship is established between each key node and its backup device to form a node backup device mapping. Step S470, a first redundancy evaluation result is established based on the number of node backup device mappings. Specifically, the node backup device mapping is traversed, and the number of backup devices corresponding to each key node is counted. Based on the statistical results, a first redundancy evaluation result is established, which reflects the redundancy level of the key nodes in the power grid. Step S480, a response time analysis of redundant switching is performed on the node test database, and a second redundancy evaluation result is established based on the response time analysis result. Specifically, based on the data in the node test database and the node backup device mapping, the response time from the failure of a critical node to the backup device taking over is recorded. Based on the response time analysis results, a second redundancy evaluation result is generated, which reflects the efficiency and speed of redundant switching in the power grid. Step S490: Redundancy energy efficiency scoring is performed using the node test database to generate a third redundancy evaluation result. Specifically, based on the data in the node test database, the energy efficiency performance of the backup devices during operation is analyzed, including indicators such as energy consumption and efficiency. Based on the analysis results, an energy efficiency score is generated for each backup device, which reflects the energy efficiency level of the backup device. Based on the energy efficiency score, a third redundancy evaluation result is generated, which reflects the energy efficiency level of the redundant devices in the power grid. Step S4100: A redundancy evaluation is completed using the first, second, and third redundancy evaluation results, and a response evaluation is performed based on the redundancy evaluation results. Specifically, the first, second, and third redundancy evaluation results are integrated to form a complete redundancy evaluation result. Based on the redundancy evaluation results, a response evaluation of the power grid's redundancy performance is performed, which reflects the power grid's resilience and redundancy level in the event of a critical node failure. This implementation method, through detailed redundancy assessment, can more reasonably configure backup equipment and redundant resources, avoid resource waste and insufficient redundancy, and thus improve the recovery capability and resilience of the power grid when key nodes fail.

[0038] In one possible implementation, the node test database is evaluated for response using grid resilience assessment indicators. Step S400 further includes step S4110, which calls the node test database to obtain fault recovery time and recovery effect. Specifically, fault recovery data of key nodes under simulated extreme weather conditions is extracted from the node test database, and key data related to fault recovery is screened out, including fault recovery time and recovery effect. Fault recovery time refers to the time required from the occurrence of a fault to the system's resumption of normal operation; recovery effect refers to the performance of the system after recovery, such as voltage stability and current distribution. Step S4120, a recovery assessment is performed based on the fault recovery time and recovery effect, and a response assessment is completed based on the recovery assessment results. Specifically, based on the requirements of the grid resilience assessment, a recovery assessment indicator is constructed, including a threshold for the fault recovery time, a scoring standard for the recovery effect, etc. The fault recovery time and recovery effect obtained in step S4110 are compared and analyzed with the constructed evaluation indicators. Through comparison, it is determined whether the recovery capabilities of the key nodes under simulated extreme weather conditions meet the requirements of the grid resilience assessment. Based on the results of data comparison and analysis, a recovery assessment report or conclusion is generated. This report or conclusion is used to elaborate on the recovery capabilities of key nodes, including whether the recovery time meets the standards and the recovery effect score. The recovery assessment results are used as part of the grid resilience assessment and combined with the results of reliability assessment, redundancy assessment, etc. to complete the response assessment phase of the grid resilience assessment. This implementation method introduces a recovery assessment to more comprehensively evaluate the resilience of the grid under extreme weather conditions. Based on the recovery assessment results, the recovery capabilities of key nodes can be more accurately understood, thereby formulating more reasonable grid management strategies. For example, for nodes with weaker recovery capabilities, measures such as strengthening maintenance and adding backup equipment can be taken to improve their resilience.

[0039] In one possible implementation, the response evaluation of the node test database is performed using the grid resilience evaluation index. Step S400 further includes step S4130 of reading the coordinated scheduling data in the node test database and performing a scheduling rationality score based on the coordinated scheduling data as follows: ;

[0040] in, Characterizes the scheduling rationality score, is the load balance degree, , is the total number of key nodes, Characterize any key node, Characterizing key nodes The actual load, Characterizes the average load of all key nodes, Characterizes the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, For scheduling consistency, ,in, Characterization The number of scheduling conflicts that occur during the scheduling process, Characterization The total number of executions in the scheduling process, Represents any scheduling task, is the total number of scheduled tasks, 、 、 、 The weights for load balance, dispatch response time, dispatch consistency, and dispatch conflict are respectively. Specifically, data related to coordinated scheduling is extracted from the node test database, including load data for key nodes, dispatch response time, number of conflicts during the dispatch process, and total number of executions. Load balance is a metric that measures the degree of load balance at key nodes in the power grid. The more balanced the load, the more stable the power grid operation. Dispatch response time is the time from the issuance of a dispatch instruction to its completion. The average dispatch response time is calculated and compared with a preset dispatch response time threshold. If the average response time exceeds the threshold, the dispatch response time score is reduced. Dispatch consistency is a metric that measures whether conflicts or inconsistencies occur during the dispatch process. The number of dispatch conflicts that occur in each dispatch process is counted and compared with the total number of executions. Fewer conflicts indicate higher dispatch consistency. Resource utilization refers to the efficiency of resource use during the dispatch process. Resource utilization is calculated based on resource usage during the dispatch process. Higher resource utilization indicates higher dispatch efficiency. Based on the above analysis, a dispatch rationality score is calculated using a given formula. This score integrates multiple factors, including load balance, dispatch response time, dispatch consistency, and dispatch conflict, and assigns a corresponding weight to each factor. Step S4140 generates a coordinated dispatch evaluation result based on the dispatch rationality score to complete the response assessment. Specifically, the rationality of the coordinated dispatch of the power grid is analyzed based on the dispatch rationality score calculated in step 4130. A higher score indicates more rational coordinated dispatch. Based on the results of the score analysis, a coordinated dispatch evaluation report or conclusion is generated. This report or conclusion describes the rationality of the coordinated dispatch of the power grid, including performance in terms of load balance, dispatch response time, dispatch consistency, and resource utilization. The coordinated dispatch evaluation results are used as part of the power grid resilience assessment and combined with the results of the reliability assessment, redundancy assessment, and recovery assessment to complete the response assessment phase of the power grid resilience assessment. This implementation method comprehensively assesses the resilience of the power grid under extreme weather conditions by introducing a coordinated dispatch evaluation. The coordinated dispatch capability reflects the ability of key nodes in the power grid to coordinate and cooperate when responding to extreme weather. Based on the coordinated dispatch evaluation results, the performance of the power grid in coordinated dispatch can be more accurately understood, thereby formulating more reasonable power grid dispatch strategies. For example, for nodes with poor dispatch consistency or low resource utilization, measures such as optimizing the dispatch algorithm and adding backup equipment can be adopted to improve their coordinated dispatch capability.

[0041] Step S500, output the grid resilience assessment result according to the response assessment result, and manage the grid based on the grid resilience assessment result. Specifically, the response assessment result is output in the form of a report or chart, and based on the assessment result, a targeted grid management strategy is formulated, including strengthening the protection of key nodes, improving the redundancy of the grid, optimizing the recovery strategy, etc., to improve the resilience of the grid under extreme weather conditions. The embodiment of the present application establishes an extreme weather database by looking back on historical meteorological data, and constructs an assessment model by combining the topology of the grid system and the identification of key nodes. By simulating the node response under extreme weather scenarios and using assessment indicators such as reliability, redundancy, recovery and coordinated scheduling, a comprehensive assessment of the resilience of the grid is conducted, thereby achieving the technical effect of improving the accuracy and comprehensiveness of the assessment.

[0042] In one possible implementation, step S500 further includes step S510, establishing a pre-maintenance database based on the grid resilience assessment results. Specifically, the grid resilience assessment results are analyzed in detail, including the scores for reliability, redundancy, recovery, and coordinated dispatch indicators, as well as the performance of each key node. Based on the assessment results, potential risk points in the grid are identified. These points are key nodes with poor resilience or system weaknesses. For each identified potential risk point, a pre-maintenance database is established. This database contains detailed information about the risk point, such as location, type, historical fault records, and recommended maintenance measures, to guide subsequent maintenance management. Step S520, a weather forecast is performed based on real-time meteorological data, a maintenance plan is generated based on the forecast results and the pre-maintenance database, and maintenance management is performed based on the maintenance plan. Specifically, current meteorological data, including temperature, humidity, wind speed, wind direction, and precipitation, is collected in real time using meteorological monitoring equipment. A meteorological forecast model is used to process and analyze the real-time meteorological data to predict weather conditions for a period of time, particularly the likelihood of extreme weather. A targeted maintenance plan is generated based on the forecast results and the information in the pre-maintenance database. Maintenance management is performed according to the generated maintenance plan, including equipment inspection, preventive maintenance, and emergency preparedness. Simultaneously, the plan is adjusted and optimized based on actual conditions during the maintenance process to ensure the normal operation of the power grid. This preventive maintenance approach allows equipment to be repaired or replaced before failure occurs, reducing power outage losses and repair costs caused by equipment failures. It also enhances the power grid's emergency response capabilities in extreme weather conditions, helping to improve its reliability and resilience.

[0043] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for assessing power grid resilience under extreme weather conditions, characterized in that: The method comprises: Configuring a backtracking period, backtracking historical meteorological data based on the backtracking period, and establishing an extreme weather database with weather pattern and frequency identifiers; After receiving trust feedback from the power grid system, it connects to the power grid system, builds the topology of the power grid based on the connection results, identifies key nodes based on the topology and power grid data, and establishes a set of key nodes using digital twins; Build extreme weather simulation scenarios based on the extreme weather database, conduct node response tests on key node sets based on the extreme weather simulation scenarios, and establish a node test database; Constructing a grid resilience evaluation index and evaluating the response of the node test database using the grid resilience evaluation index, wherein the grid resilience evaluation index includes a reliability index, a redundancy index, a recovery index, and a coordinated dispatching index; Output grid resilience assessment results based on response assessment results, and use the grid resilience assessment results to manage the grid; The response evaluation of the node test database using the grid resilience evaluation index also includes: Performing hierarchical division on the key node set, establishing device layer nodes and connection layer nodes, and establishing mapping weights based on the hierarchical division results; Establish a failure judgment threshold for each node in the key node set, perform failure judgment on the corresponding data in the node test database based on the failure judgment threshold, and establish a failure judgment result; The first reliability sub-index is constructed according to the failure frequency, failure duration and mapping weight of the failure discrimination result; Based on the node test database, data stability identification is performed for failure intervals and normal intervals respectively, and a second reliability sub-index is established based on the data stability identification results and mapping weights; Complete the reliability assessment using the first reliability sub-indicator and the second reliability sub-indicator, and complete the response assessment based on the reliability assessment results; The response evaluation of the node test database using the grid resilience evaluation index also includes: Read the collaborative scheduling data in the node test database and perform scheduling rationality scoring based on the collaborative scheduling data as follows: ;in, Characterizes the scheduling rationality score, is the load balance degree, , is the total number of key nodes, Characterize any key node, Characterizing key nodes The actual load, Characterizes the average load of all key nodes, Characterizes the average scheduling response time, is the preset scheduling response time threshold, is the resource utilization rate, For scheduling consistency, ,in, Characterization The number of scheduling conflicts that occur during the scheduling process, Characterization The total number of executions in the scheduling process, Represents any scheduling task, is the total number of scheduled tasks, 、 、 、 are the weights of load balancing, scheduling response time, scheduling consistency, and scheduling conflict respectively; Generate collaborative scheduling evaluation results based on scheduling rationality scores to complete response evaluation.

2. The method for evaluating power grid resilience under extreme weather conditions according to claim 1, wherein: The data stability identification of the failure interval and the normal interval based on the node test database also includes: The node test database is divided into failure interval and normal interval based on the failure judgment threshold; Calculate the data stability values ​​of the failure interval and the normal interval respectively, and establish the failure stability value and the normal stability value; Establishing a first stable identification result based on the failure stable value and the normal stable value; Using the failure stability value to identify the fluctuation of the failure interval, a second stable identification result is established; Using normal stable values ​​to identify fluctuations in normal intervals, a third stable identification result is established; The data stable recognition is completed with the first stable recognition result, the second stable recognition result, and the third stable recognition result.

3. The method for evaluating power grid resilience under extreme weather conditions according to claim 1, wherein: The response evaluation of the node test database using the grid resilience evaluation index also includes: Establish node backup device mapping of key node sets based on topology structure; Establishing a first redundancy evaluation result based on the number of node spare device mappings; Analyze the response time of redundant switching of the node test database, and establish a second redundancy evaluation result based on the response time analysis result; Use the node test database to perform redundancy energy efficiency scoring and establish the third redundancy evaluation results; The redundancy evaluation is completed using the first redundancy evaluation result, the second redundancy evaluation result, and the third redundancy evaluation result, and the response evaluation is completed based on the redundancy evaluation results.

4. The method for evaluating power grid resilience under extreme weather conditions according to claim 1, wherein: The response evaluation of the node test database using the grid resilience evaluation index also includes: Call the node test database to obtain the fault recovery time and recovery effect; Perform recovery assessment based on the fault recovery time and recovery effect, and complete response assessment based on the recovery assessment results.

5. The method for evaluating power grid resilience under extreme weather conditions according to claim 1, wherein: The use of digital twins to establish a key node set also includes: Based on the access results, the device data of the power grid equipment is called and a digital twin model is created based on the device data; Capture real-time operating data of power grid equipment through sensors, and synchronize data of the digital twin model based on the captured real-time operating data. The digital twin model is called based on the key node identification results to establish a key node set.

6. The method for evaluating power grid resilience under extreme weather conditions according to claim 1, wherein: The method further comprises: Establish a predictive maintenance database based on grid resilience assessment results; Perform weather forecasts based on real-time collected weather data, generate maintenance plans based on weather forecast results and the pre-maintenance database, and perform maintenance management based on the maintenance plans.

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