A power grid resilience evaluation method and system based on urban lifeline cross-system interconnection

Abstract

The application relates to a kind of power grid resilience evaluation method and system based on urban lifeline cross-system interconnection, method includes: the connected network model of the to-be-evaluated power grid in target area is built;Through the preset cascading failure mechanism, the fault propagation of the to-be-evaluated power grid under the preset disaster situation is carried out, and the fault propagation result is obtained;Through the preset recovery mechanism, the recovery simulation of the to-be-evaluated power grid under the preset disaster situation is carried out, and the recovery simulation result is obtained;Based on the predefined structural resilience index, the fault propagation result and the recovery simulation result are analyzed, and the structural resilience of the to-be-evaluated power grid is obtained;Based on the predefined functional resilience index, the fault propagation result and the recovery simulation result are analyzed, and the functional resilience of the to-be-evaluated power grid is obtained;The structural resilience and functional resilience are fused to obtain comprehensive resilience, and the resilience evaluation of the to-be-evaluated power grid is completed.Compared with prior art, the comprehensiveness and accuracy of the power grid resilience evaluation are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of power system safety technology, and in particular to a method and system for assessing the resilience of a power grid based on the cross-system interconnection of urban lifelines. Background Technology

[0002] As a key component of national energy security, urban lifelines are becoming increasingly complex and large-scale with the diversification and intelligentization of development. Among them, the power grid, as the core of critical urban infrastructure, plays an important role in ensuring the electricity supply for social production and daily life.

[0003] With the increasing frequency of extreme weather events and the deepening coupling between the power grid and other lifeline systems (water, gas, communications, etc.), a single fault can easily trigger a cascading effect through inter-system dependencies, leading to widespread power outages. Traditional power grid resilience assessments often focus on physical layer topology or equipment reliability, neglecting the dynamic interaction between the information and physical layers under current new technologies, making it difficult to quantify the impact of new power grids. Furthermore, they lack consideration for the spatial heterogeneity of electricity demand, failing to reflect the actual social impact of demand gaps in different electricity demand zones, and their assessment indicators are too simplistic, failing to simultaneously reflect the differences in power grid structural integrity and functional effectiveness. To address these issues, patent application CN113962461A discloses a distribution network resilience enhancement strategy based on environmental data prediction. This method constructs a mapping model using power grid topology data and static power flow distribution data, and employs a neural network to fit dynamic response strategies for different fault stages, calculating structural resilience and functional resilience indicators in different time-series stages (resistance, survival, recovery) to assess power grid resilience. However, this method can only establish structure-function mapping relationships based on static data, and cannot characterize the bidirectional dynamic coupling and cascading failure process between the information layer and the physical layer under extreme events; it can only calculate the aggregated load value index of the entire network, and cannot reflect the spatial heterogeneity of electricity demand and the differences in socio-economic losses among different geographical regions and user types; it can only rely on historical fault scenario data to train neural networks, and cannot effectively cope with the problem of insufficient model generalization ability caused by the scarcity of extreme event data and the uncertainty of the behavior of new power grid components.

[0004] Therefore, current technologies have not yet fundamentally solved the problem of refined resilience assessment under the three-dimensional coupling of information, physical, and social factors. There is an urgent need for a new assessment method that can dynamically characterize system interactions, quantify the impact of spatial heterogeneity, and integrate multi-source uncertain data to meet the resilience management and decision support needs of the next-generation smart grid under extreme events. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a power grid resilience assessment method and system based on cross-system interconnection of urban lifelines, which significantly improves the comprehensiveness and accuracy of power grid resilience assessment.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for assessing the resilience of a power grid based on the inter-system interconnection of urban lifelines includes the following steps: Construct a connectivity network model of the power grid to be evaluated within the target area. The connectivity network model is a network model based on the cross-system coupling of urban lifelines. By using a pre-defined cascading fault mechanism, the fault propagation of the power grid to be evaluated under a pre-defined disaster scenario is carried out according to the connected network model, and the fault propagation results are obtained. By using a preset recovery mechanism, the power grid to be evaluated is simulated under a preset disaster scenario based on the connected network model, and the recovery simulation results are obtained. Based on predefined structural resilience indices, the fault propagation results and recovery simulation results of the power grid to be evaluated under preset disaster scenarios are analyzed to obtain the structural resilience of the power grid to be evaluated. Based on predefined functional resilience indicators, the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario are analyzed to obtain the functional resilience of the power grid to be evaluated. The structural resilience and functional resilience of the power grid to be evaluated are combined to obtain its comprehensive resilience, thus completing the resilience assessment of the power grid to be evaluated.

[0007] Furthermore, the connected network model is based on a cyber-physical-social architecture, comprising a social layer, a physical layer, and an information layer. The social layer divides the target area into power demand zones based on the building density, building functional zones and population density data of the target area, and determines the power supply and demand relationship between each power demand zone; The physical layer takes the power plants, substations and transmission lines of the power grid to be evaluated in the target area as network nodes, and establishes network node connection lines between the network nodes of the power grid to be evaluated and the network nodes of other systems in the urban lifeline according to the power supply and demand relationship between each power demand zone. The information layer is used to receive multi-source heterogeneous information from the physical layer and the social layer, and generate control commands through cascading fault mechanisms and recovery mechanisms to schedule and allocate power transmission of the power grid to be evaluated within the target area. The multi-source heterogeneous information includes real-time network operation data from the physical layer and real-time power supply and demand data from the social layer.

[0008] Furthermore, the electricity demand of the aforementioned electricity demand zone is as follows: In the formula, For the first Type indicator vector for each electricity demand partition For the first The area vector of each electricity demand zone. For the first Disaster fluctuation coefficient of each electricity demand zone under a preset disaster scenario For the first Load density vector for each electricity demand zone.

[0009] Furthermore, the maximum transmission energy of the network node connection line is: In the formula, This represents the maximum transmission energy of the connection line between network nodes, and characterizes the sum of the rated capacities of all parallel lines within the connection line between network nodes. This refers to the capacity margin factor for the connection lines between network nodes. The rated power flow of the lines connecting network nodes.

[0010] Furthermore, the specific steps for obtaining the fault propagation results by using a pre-defined cascading fault mechanism and based on the connected network model to propagate faults in the power grid under a pre-defined disaster scenario include: Calculate the initial failure probability of each network node under a preset disaster scenario, where the initial failure probability is: In the formula, For network nodes The initial failure probability, It follows a standard normal distribution. For disaster intensity, This represents the average disaster resilience of network nodes. The standard deviation of the disaster resilience of network nodes. For disaster state parameters, The impact path of the disaster; The node failure probability of each network node in the power grid under the preset disaster scenario is calculated based on the initial failure probability. The node failure probability is: In the formula, When simulating fault propagation and recovery of the power grid to be evaluated in the target area under a preset disaster scenario, the network nodes... The probability of node failure. The interlayer coupling strength; If the node failure probability of the network node exceeds a preset failure probability threshold, the network node is determined to be a faulty network node. After removing each faulty network node from its network node connection line, the power flow of the redundant line connection of each network node after the fault occurs is calculated by load redistribution to determine the effectiveness of each network node and obtain the fault propagation result.

[0011] Furthermore, the specific steps for determining the validity of each network node include: Based on the redundant line connections of each network node, the power flow after each network node fails under a preset disaster scenario is recalculated. If the power flow after a network node fails is greater than the maximum transmission energy of the line connecting the network node, then the network node fails.

[0012] Furthermore, the recovery mechanism includes self-healing recovery and manual repair of faulty network nodes. The self-healing repair is triggered by the information layer based on the cumulative recovery probability of the faulty network node within the fault duration. The cumulative recovery probability of the faulty network node within the fault duration is: In the formula, For cumulative recovery probability, The inherent recovery rate of network nodes. This refers to the duration of the fault. The manual repair involves dynamically generating a repair scheduling sequence based on the priority of social layer needs and the betweenness centrality of physical layer network nodes, and then repairing the faulty network nodes according to the repair scheduling sequence.

[0013] Furthermore, the structural resilience characterizes the ability of the power grid under evaluation to maintain physical connectivity, and the functional resilience characterizes the ability of the power grid under evaluation to meet societal needs.

[0014] Furthermore, the overall resilience is: In the formula, To enhance overall resilience, To assess the structural resilience of the power grid in the target area under a preset disaster scenario, This refers to the structural integrity of the power grid in the target area after a fault, when simulating fault propagation and recovery under a preset disaster scenario. This represents the initial structural integrity of the power grid to be evaluated in the target area during fault propagation and recovery simulations under preset disaster scenarios. For disaster intensity, Duration of the disaster To assess the functional resilience of the power grid in the target area under a preset disaster scenario, This refers to the demand satisfaction level after a fault in the target area's power grid under a preset disaster scenario, simulating fault propagation and recovery. This represents the initial demand satisfaction of the power grid to be evaluated in the target area when simulating fault propagation and recovery under a preset disaster scenario.

[0015] According to another aspect of the present invention, a power grid resilience assessment system based on inter-system interconnection of urban lifelines is provided, comprising: The connectivity network model construction module is used to construct a connectivity network model of the power grid to be evaluated within the target area. The connectivity network model is a network model based on the cross-system coupling of urban lifelines. The fault propagation result acquisition module is used to obtain the fault propagation result by performing fault propagation on the power grid to be evaluated under a preset disaster scenario through a preset cascading fault mechanism and according to the connected network model. The recovery simulation result acquisition module is used to perform recovery simulation on the power grid to be evaluated under a preset disaster scenario based on the connected network model through a preset recovery mechanism, and obtain the recovery simulation result; The structural resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined structural resilience indicators, and obtain the structural resilience of the power grid to be evaluated. The functional resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined functional resilience indicators, and obtain the functional resilience of the power grid to be evaluated. The resilience assessment module is used to integrate the structural resilience and functional resilience of the power grid to be assessed to obtain its comprehensive resilience, thereby completing the resilience assessment of the power grid to be assessed.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a connected network model with an information-physical-social architecture, dynamically coupling the social layer's power demand partitioning, the physical layer's power grid topology, and the information layer's real-time dispatch commands. Based on a cascading fault mechanism and a self-healing probability model, it simulates the interactive effects under disasters, achieving a comprehensive assessment of the structural integrity and functional effectiveness of urban power grids under disaster conditions. This solves the problems of traditional power grid resilience assessment methods being single-dimensional, neglecting dynamic interactions between systems, and addressing spatially heterogeneous social needs, significantly improving the comprehensiveness of power grid resilience assessment.

[0017] 2. This invention proposes a comprehensive quantitative assessment framework that integrates structural resilience and functional resilience indicators. By defining structural resilience indicators to analyze the failure rate of physical layer components, and combining them with functional resilience indicators to quantify the satisfaction of social electricity demand, the framework is dynamically integrated into a comprehensive resilience index. This solves the problem that traditional assessments rely on a single topology indicator and cannot simultaneously reflect the actual power supply capacity and social impact of the power grid. It achieves accurate characterization and dynamic tracking of power grid resilience from structure to function, significantly enhancing the accuracy of power grid resilience assessment. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a power grid resilience assessment method based on cross-system interconnection of urban lifelines proposed in this invention. Figure 2 This is a schematic diagram of the structure of a connected network model; Figure 3 This is a schematic diagram of the structure of a power grid resilience assessment system based on cross-system interconnection of urban lifelines proposed in this invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0020] Example 1 This embodiment provides a power grid resilience assessment method based on inter-system interconnection of urban lifelines, such as... Figure 1 As shown, it includes the following steps: S1. Construct a connectivity network model of the power grid to be evaluated within the target area. The connectivity network model is a cross-system coupled network model based on urban lifelines.

[0021] The connected network model is based on the information-physical-social architecture, which includes a social layer, a physical layer, and an information layer.

[0022] The social layer divides the target area into power demand zones based on building density, building functional zones and population density data, and determines the power supply and demand relationship between each power demand zone; The electricity demand in the electricity demand zones is as follows: In the formula, For the first Type indicator vector for each electricity demand partition For the first The area vector of each electricity demand zone. For the first Disaster fluctuation coefficient of each electricity demand zone under a preset disaster scenario For the first Load density vector for each electricity demand zone.

[0023] The following table shows an example of how the target area's electricity demand is zoned.

[0024] Table 1. Regional Division of Electricity Demand The physical layer models key components of the power grid and other systems in the urban lifeline to be evaluated. To characterize, among which, For network nodes, Line connections (including primary and backup lines) for network nodes defined based on power supply and demand relationships. The system to which the network nodes belong. Power plants, substations and transmission lines of the power grid to be evaluated within the target area are taken as network nodes, and network node connection lines between the network nodes of the power grid to be evaluated and the network nodes of other systems in the urban lifeline are established according to the power supply and demand relationship between each power demand zone.

[0025] The maximum transmission capacity of the network node connection line is: In the formula, This represents the maximum transmission energy of the connection line between network nodes, and characterizes the sum of the rated capacities of all parallel lines within the connection line between network nodes. This refers to the capacity margin factor for the connection lines between network nodes. The rated power flow of the lines connecting network nodes.

[0026] Examples of network nodes are shown in the table below.

[0027] Table 2 Node Category, Location, and Parameter Information By projecting the network nodes of the power grid to be evaluated and other systems along the city's lifeline onto the network based on their geographical location information and functional attributes, a connectivity network model of the target area's infrastructure system is obtained at the physical level. The relationships between different network nodes in the connectivity network model represent the power supply and demand relationships they maintain and the issuance of related dispatch instructions. For example, the power system provides necessary power guarantees for the operation of equipment in water supply and gas systems, while the information layer sends, executes, and responds to power dispatch by issuing relevant instructions.

[0028] The information layer receives multi-source heterogeneous information from the physical and social layers. It generates control commands through cascading fault and recovery mechanisms to schedule and allocate power transmission within the target area of ​​the power grid to be evaluated. This multi-source heterogeneous information includes real-time network operation data from the physical layer and real-time power supply and demand data from the social layer. The information layer also collects real-time data on network node devices in the physical layer, the execution of control strategies (such as self-healing), and calculates communication latency (e.g., randomly distributed from 10ms to 50ms). It then uses three different types of power dispatch information—industrial, residential, and commercial—to schedule and allocate power between the physical and social layers.

[0029] S2. Through a pre-set cascading fault mechanism, fault propagation is carried out on the power grid to be evaluated under a pre-set disaster scenario based on the connected network model, and the fault propagation results are obtained.

[0030] The specific steps for obtaining the fault propagation results by using a pre-defined cascading fault mechanism and based on a connected network model to propagate faults in the power grid under a pre-defined disaster scenario include: Calculate the initial failure probability of each network node under the preset disaster scenario. The initial failure probability is: In the formula, For network nodes The initial failure probability, It follows a standard normal distribution. For disaster intensity, This represents the average disaster resilience of network nodes. The standard deviation of the disaster resilience of network nodes. For disaster state parameters, The impact path of the disaster.

[0031] In specific application scenarios, typhoon disasters are used to simulate fault propagation and recovery in connected network models. The formulas for the movement path and intensity evolution of typhoon disasters in connected network models are as follows: In the formula, For typhoon disasters at coordinates The intensity of the disaster at the network node, This represents the maximum disaster intensity of a typhoon. coordinates The distance from the network node to the center of the typhoon disaster. This represents the maximum wind radius for typhoon disasters.

[0032] The node failure probability of each network node in the power grid under the preset disaster scenario is calculated based on the initial failure probability. The node failure probability is: In the formula, When simulating fault propagation and recovery of the power grid to be evaluated in the target area under a preset disaster scenario, the network nodes... The probability of node failure. The interlayer coupling strength; If the probability of a network node failure exceeds a preset failure probability threshold, the network node is determined to be a faulty network node. After removing each faulty network node from its network node connection lines, the power flow of the redundant line connections of each network node after the fault occurs is calculated through load redistribution to determine the effectiveness of each network node and obtain the fault propagation result.

[0033] The specific steps for determining the validity of each network node include: Based on the redundant line connections of each network node, the power flow after each network node fails under the preset disaster scenario is recalculated. If the power flow after a network node fails is greater than the maximum transmission energy of the network node's connection line, then the network node fails.

[0034] S3. Through a preset recovery mechanism, the power grid to be evaluated is simulated under a preset disaster scenario based on the connected network model to obtain the recovery simulation results.

[0035] The recovery mechanism includes self-healing recovery and manual repair of faulty network nodes. Self-healing repair is triggered by the information layer based on the cumulative recovery probability of the faulty network node within the fault duration. The cumulative recovery probability of the faulty network node within the fault duration is: In the formula, For cumulative recovery probability, The inherent recovery rate of network nodes. This refers to the duration of the fault.

[0036] The importance of network nodes is determined by a weighted ranking based on betweenness centrality and load importance. First, the betweenness centrality and load importance of all nodes in the network are calculated, and these two indicators are standardized to eliminate the influence of dimensions.

[0037] Betweenness centrality is specifically represented as follows: In the formula, Represents network nodes and network nodes The connection lines between them pass through network nodes And it is the number of paths that are the shortest paths. Indicates connection to network nodes and network nodes The number of shortest paths.

[0038] Calculate a comprehensive importance score for each node using the following formula: In the formula, To determine the overall importance score, The weighting coefficients for node betweenness centrality. For the standardized node betweenness centrality, The weighting coefficients for the importance of node load. The importance of node load after standardization.

[0039] Manual repair involves dynamically generating a repair scheduling sequence based on the priority of social layer needs and the betweenness centrality of physical layer network nodes, and then repairing faulty network nodes according to the repair scheduling sequence.

[0040] S4. Based on predefined structural resilience indicators, analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under preset disaster scenarios to obtain the structural resilience of the power grid to be evaluated.

[0041] Structural resilience is directly related to the physical layer in a connected network model, assessing the physical availability of network nodes (power plants, substations, etc.) and line connections (transmission lines), including initial structural integrity and post-fault structural integrity. Initial structural integrity refers to the scale of intact, operational physical components (nodes and lines) in the power grid within the potential impact range of a pre-defined disaster scenario before the disaster occurs; it can be quantified as the initial total number of normal nodes, total line length, or total capacity. Post-fault structural integrity refers to the scale of physical components in the power grid that remain in a faulty or failed state after fault propagation and recovery simulations following the disaster.

[0042] Structural toughness is used to characterize the ability of the power grid to maintain its physical connectivity, and its calculation formula is as follows: In the formula, To assess the structural resilience of the power grid in the target area under a preset disaster scenario, This refers to the structural integrity of the power grid in the target area after a fault, when simulating fault propagation and recovery under a preset disaster scenario. This represents the initial structural integrity of the power grid to be evaluated in the target area during fault propagation and recovery simulations under preset disaster scenarios. For disaster intensity, Duration of the disaster.

[0043] S5. Based on predefined functional resilience indicators, analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under preset disaster scenarios to obtain the functional resilience of the power grid to be evaluated.

[0044] The functional resilience index is used to measure the extent to which the power grid can meet the society's electricity demand after a disaster. It is directly related to the interaction results between the social layer, physical layer, and information layer in the connected network model, and evaluates the degree of matching between the actual power supply output of the power grid and social demand.

[0045] Functional resilience is used to characterize the ability of a power grid under evaluation to meet societal needs, and its calculation formula is as follows: In the formula, To assess the functional resilience of the power grid in the target area under a preset disaster scenario, This refers to the demand satisfaction level after a fault in the target area's power grid under a preset disaster scenario, simulating fault propagation and recovery. This represents the initial demand satisfaction of the power grid to be evaluated in the target area when simulating fault propagation and recovery under a preset disaster scenario.

[0046] S6. Integrate the structural resilience and functional resilience of the power grid to be evaluated to obtain its comprehensive resilience, and complete the resilience assessment of the power grid to be evaluated.

[0047] Overall resilience is: In the formula, To enhance overall resilience, To assess the structural resilience of the power grid in the target area under a preset disaster scenario, This refers to the functional resilience of the power grid in the target area under a preset disaster scenario.

[0048] Theoretically, the range of comprehensive resilience is between [0,1]. The higher the value of comprehensive resilience, the better the comprehensive resilience of the power grid under specific disaster scenarios and at specific times, that is, the stronger its overall disaster resistance and recovery capabilities.

[0049] In another preferred embodiment, the step further includes: S7. Identify key resilience points and formulate corresponding functional recovery paths.

[0050] By analyzing the time-series curves of structural resilience and functional resilience, key time points such as sharp performance decline, initial recovery, and recovery plateau period in the power grid and related systems corresponding to the connected network model are identified.

[0051] During periods of rapid performance degradation, resilience enhancement strategies are employed for disaster prevention and buffering. Specifically, at the information layer, a pre-disaster scheduling mode is activated, increasing the communication redundancy of core scheduling channels from a baseline of 70% to over 90%, and pre-setting critical control commands such as line load control and islanded operation. At the physical layer, preventative power flow adjustments are implemented, reducing the load rate of critical lines to below a safe threshold, reserving buffer capacity for impending disaster impacts. At the social layer, early warning information is sent to electricity-consuming units via smart meters and terminals, and demand-side response preparations are initiated.

[0052] The rapid recovery period is the golden window for emergency repair and recovery decisions after a failure. At the information layer, all backup communication links (including satellite communication channels) are activated, and the system status perception is dynamically updated based on real-time fault information. A fault impact assessment report is generated at intervals (e.g., every 5 minutes). At the physical layer, a weighted ranking based on node betweenness centrality and load importance is adopted to prioritize the repair of top-ranked (e.g., top 10%) critical nodes. At the same time, the capacity margin coefficient of critical lines is increased (e.g., from 1.2 to 1.5) to enhance system resilience. At the social layer, the load reduction plan is strictly implemented, and the power supply to core loads is guaranteed according to the pre-set emergency power supply priority list (medical > emergency > municipal > industrial > commercial > residential).

[0053] During the slow recovery phase, resilience enhancement strategies shifted from emergency repairs to optimized recovery. At the information layer, the focus of communication network recovery shifted from core links to full coverage, providing 24 / 7 status monitoring and scheduling support for on-site repair teams. At the physical layer, following the principle of repairing the main lines first and then the branch lines, remaining faulty network nodes were repaired, while equipment damage assessments and temporary reinforcements were carried out. At the social layer, a demand elasticity adjustment mechanism was established, coordinating large industrial users to adjust production shifts to nighttime periods of low electricity consumption, and adjusting the disaster scenario fluctuation coefficient from a fixed coefficient to dynamic fluctuation, while adjusting the intensity of electricity demand management on an hourly basis according to real-time power generation capacity.

[0054] Example 2 This embodiment provides a power grid resilience assessment system based on cross-system interconnection of urban lifelines, such as... Figure 3 As shown, it includes: The connected network model building module is used to construct a connected network model of the power grid to be evaluated within the target area. The connected network model is a cross-system coupled network model based on urban lifelines. The fault propagation result acquisition module is used to obtain the fault propagation result by propagating the power grid to be evaluated under a preset disaster scenario through a preset cascading fault mechanism and a connected network model. The recovery simulation result acquisition module is used to perform recovery simulation on the power grid to be evaluated under a preset disaster scenario based on a connected network model through a preset recovery mechanism, and obtain the recovery simulation results. The structural resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined structural resilience indicators, and obtain the structural resilience of the power grid to be evaluated. The functional resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined functional resilience indicators, and obtain the functional resilience of the power grid to be evaluated. The resilience assessment module is used to integrate the structural resilience and functional resilience of the power grid to be assessed to obtain its comprehensive resilience, thereby completing the resilience assessment of the power grid to be assessed.

[0055] The rest is the same as in Example 1.

[0056] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for assessing the resilience of a power grid based on cross-system interconnection of urban lifelines, characterized in that, Includes the following steps: Construct a connectivity network model of the power grid to be evaluated within the target area. The connectivity network model is a network model based on the cross-system coupling of urban lifelines. By using a pre-defined cascading fault mechanism, the fault propagation of the power grid to be evaluated under a pre-defined disaster scenario is carried out according to the connected network model, and the fault propagation results are obtained. By using a preset recovery mechanism, the power grid to be evaluated is simulated under a preset disaster scenario based on the connected network model, and the recovery simulation results are obtained. Based on predefined structural resilience indices, the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario are analyzed to obtain the structural resilience of the power grid to be evaluated. Based on predefined functional resilience indicators, the fault propagation results and recovery simulation results of the power grid to be evaluated under preset disaster scenarios are analyzed to obtain the functional resilience of the power grid to be evaluated. The structural resilience and functional resilience of the power grid to be evaluated are combined to obtain its comprehensive resilience, thus completing the resilience assessment of the power grid to be evaluated.

2. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 1, characterized in that, The connected network model is based on a cyber-physical-social architecture, comprising a social layer, a physical layer, and an information layer. The social layer divides the target area into power demand zones based on the building density, building functional zones and population density data of the target area, and determines the power supply and demand relationship between each power demand zone; The physical layer takes the power plants, substations and transmission lines of the power grid to be evaluated in the target area as network nodes, and establishes network node connection lines between the network nodes of the power grid to be evaluated and the network nodes of other systems in the urban lifeline according to the power supply and demand relationship between each power demand zone. The information layer is used to receive multi-source heterogeneous information from the physical layer and the social layer, and generate control commands through cascading fault mechanisms and recovery mechanisms to schedule and allocate power transmission of the power grid to be evaluated within the target area. The multi-source heterogeneous information includes real-time network operation data from the physical layer and real-time power supply and demand data from the social layer.

3. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 2, characterized in that, The electricity demand for the aforementioned electricity demand zones is as follows: In the formula, For the first Type indicator vector for each electricity demand partition For the first The area vector of each electricity demand zone. For the first Disaster fluctuation coefficient of each electricity demand zone under a preset disaster scenario For the first Load density vector for each electricity demand zone.

4. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 2, characterized in that, The maximum transmission capacity of the network node connection line is: In the formula, This represents the maximum transmission energy of the connection line between network nodes, and characterizes the sum of the rated capacities of all parallel lines within the connection line between network nodes. This refers to the capacity margin factor for the connection lines between network nodes. The rated power flow of the lines connecting network nodes.

5. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 1, characterized in that, The specific steps for obtaining the fault propagation results by using a pre-defined cascading fault mechanism and based on the connected network model to propagate faults in the power grid under a pre-defined disaster scenario include: Calculate the initial failure probability of each network node under a preset disaster scenario, where the initial failure probability is: In the formula, For network nodes The initial failure probability, It follows a standard normal distribution. For disaster intensity, This represents the average disaster resilience of network nodes. The standard deviation of the disaster resilience of network nodes. These are the state parameters of the disaster. The impact path of the disaster; Based on the initial fault probability, the node fault probability of each network node in the power grid under the preset disaster scenario is calculated, whereby the node fault probability is: In the formula, When simulating fault propagation and recovery of the power grid to be evaluated in the target area under a preset disaster scenario, the network nodes... The probability of node failure. The interlayer coupling strength; If the node failure probability of the network node exceeds a preset failure probability threshold, the network node is determined to be a faulty network node. After removing each faulty network node from its network node connection line, the power flow of the redundant line connection of each network node after the fault occurs is calculated by load redistribution to determine the effectiveness of each network node and obtain the fault propagation result.

6. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 5, characterized in that, The specific steps for determining the validity of each network node include: Based on the redundant line connections of each network node, the power flow after each network node fails under a preset disaster scenario is recalculated. If the power flow after a network node fails is greater than the maximum transmission energy of the line connecting the network node, then the network node fails.

7. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 2, characterized in that, The recovery mechanism includes self-healing recovery and manual repair of faulty network nodes. The self-healing repair is triggered by the information layer based on the cumulative recovery probability of the faulty network node within the fault duration. The cumulative recovery probability of the faulty network node within the fault duration is: In the formula, For cumulative recovery probability, The inherent recovery rate of network nodes. This refers to the duration of the fault. The manual repair involves dynamically generating a repair scheduling sequence based on the priority of social layer needs and the betweenness centrality of physical layer network nodes, and then repairing the faulty network nodes according to the repair scheduling sequence.

8. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 1, characterized in that, The structural resilience characterizes the ability of the power grid under evaluation to maintain physical connectivity, while the functional resilience characterizes the ability of the power grid under evaluation to meet societal needs.

9. The power grid resilience assessment method based on inter-system interconnection of urban lifelines according to claim 1, characterized in that, The overall resilience is: In the formula, To enhance overall resilience, To assess the structural resilience of the power grid in the target area under a preset disaster scenario, This refers to the structural integrity of the power grid in the target area after a fault, when simulating fault propagation and recovery under a preset disaster scenario. This represents the initial structural integrity of the power grid to be evaluated in the target area during fault propagation and recovery simulations under preset disaster scenarios. For disaster intensity, Duration of the disaster To assess the functional resilience of the power grid in the target area under a preset disaster scenario, This refers to the demand satisfaction level after a fault in the target area's power grid under a preset disaster scenario, simulating fault propagation and recovery. This represents the initial demand satisfaction of the power grid to be evaluated in the target area when simulating fault propagation and recovery under a preset disaster scenario.

10. A power grid resilience assessment system based on cross-system interconnection of urban lifelines, characterized in that, include: The connectivity network model construction module is used to construct a connectivity network model of the power grid to be evaluated within the target area. The connectivity network model is a network model based on the cross-system coupling of urban lifelines. The fault propagation result acquisition module is used to obtain the fault propagation result by performing fault propagation on the power grid to be evaluated under a preset disaster scenario through a preset cascading fault mechanism and according to the connected network model. The recovery simulation result acquisition module is used to perform recovery simulation on the power grid to be evaluated under a preset disaster scenario based on the connected network model through a preset recovery mechanism, and obtain the recovery simulation result; The structural resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined structural resilience indicators, and obtain the structural resilience of the power grid to be evaluated. The functional resilience acquisition module is used to analyze the fault propagation results and recovery simulation results of the power grid to be evaluated under a preset disaster scenario based on predefined functional resilience indicators, and obtain the functional resilience of the power grid to be evaluated. The resilience assessment module is used to integrate the structural resilience and functional resilience of the power grid to be assessed to obtain its comprehensive resilience, thereby completing the resilience assessment of the power grid to be assessed.

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