Power network dynamic toughness evaluation method and device, equipment and storage medium

Abstract

The invention provides a power network dynamic toughness evaluation method and device, equipment and a storage medium, and the method comprises the steps: splicing a rainstorm disaster Bayesian network model, a flood disaster Bayesian network model and a waterlogging disaster Bayesian network model, and obtaining a rainstorm-flood-waterlogging disaster spatio-temporal evolution Bayesian network model; obtaining a power network line damage evolution scene and a recovery scheme in the power network damage evolution scene according to the rainstorm-flood-waterlogging disaster spatio-temporal evolution Bayesian network model and the probability that the power facilities are damaged under the rainstorm disaster, the flood disaster and the waterlogging disaster; based on a power network line damage evolution scene and a recovery scheme under the power network damage evolution scene, power network dynamic toughness evaluation is performed, and theoretical support is provided for improving power network toughness and reducing disaster influence.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device, equipment, and storage medium for evaluating dynamic resilience of a power network. Background Art

[0002] As the artery of modern society, the importance of the power grid is self-evident. It carries multiple functions such as residential electricity consumption, enterprise production and operation, and urban infrastructure operation, and is a key force to support social and economic development. However, it should not be ignored that when heavy rain disasters strike, secondary disasters such as floods and waterlogging may occur. These disasters not only directly threaten the safety of life and property of the people, but also cause serious secondary impacts on the power grid. As the beginning of the disaster chain, the heavy rain not only directly impacts the power grid facilities, but also may cause subsequent flood disasters. Once the flood is formed, its huge impact force and inundation range will cause the power grid facilities to face devastating damage, especially the substations and transmission lines in low-lying areas. As the continuation of the disaster chain, the long-term accumulation of water in the waterlogging not only affects the normal operation of the equipment, but also may cause electrical failures, further exacerbating the damage to the power grid.

[0003] Power network resilience, as an indicator that characterizes the ability of power networks to maintain normal operation and recover quickly under extreme events, has become a hot research direction. However, in actual operation scenarios, the spatiotemporal evolution of the rainstorm disaster chain is complex, which makes the power grid often passive when responding, and it is difficult to effectively prevent and reduce the losses caused by disasters. Existing resilience assessment methods often only consider single disaster impact scenarios, and it is difficult to effectively characterize the power network resilience indicators under the "rainstorm-flood-waterlogging" disaster chain. There is an urgent need for an assessment framework that can characterize the spatiotemporal evolution relationship of the "rainstorm-flood-waterlogging" disaster chain and the dynamic resilience indicators of the power network, so as to improve the power network's ability to resist chain disaster impacts. Summary of the invention

[0004] In order to solve one of the above-mentioned technical defects, the present application provides a method, device, equipment and storage medium for dynamic resilience assessment of power network.

[0005] In a first aspect, the present application provides a method for evaluating dynamic resilience of a power network, the method comprising: Collect the topology of the power network and the geographical distribution of transmission lines; Based on the topological structure of the power network and the geographical distribution of transmission lines, the power network is divided into regions; Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster were spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster. Among them, the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is the 24-hour precipitation; the Bayesian network model of flood disaster takes the disaster-prone environmental factors of flood disaster as the input node, and the characteristic index of flood disaster as the output node. The disaster-prone environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters takes the disaster-prone environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-prone environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface; the characteristic indicator of waterlogging disasters is water depth; According to the Bayesian network model of rainstorm disaster, flood disaster and waterlogging disaster, the probability of damage to power facilities in rainstorm disaster, flood disaster and waterlogging disaster were evaluated respectively; According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained; among which, the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line; Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated.

[0006] In a second aspect of the present application, a device for evaluating dynamic resilience of a power network is provided, the device comprising: A collection module for collecting the topological structure of the power network and the geographical distribution of the transmission lines; A division module, used for dividing the power network into regions based on the topological structure of the power network and the geographical distribution of the transmission lines collected by the collection module; The first construction module is used to splice the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster based on the spatial factors between adjacent areas divided by the division module and the temporal factors within the area, so as to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster; among which, the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is the 24-hour precipitation; the Bayesian network model of flood disaster takes the disaster-pregnant environmental factors of flood disaster as the input node, the characteristic index of flood disaster as the input node, and the Bayesian network model of flood disaster as the input node. The indicators are output nodes, and the disaster-prone environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters takes the disaster-prone environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The disaster-prone environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface; the characteristic indicator of waterlogging disasters is water accumulation depth; The first evaluation module is used to evaluate the probability of damage to power facilities caused by rainstorm disasters, flood disasters, and waterlogging disasters, respectively, based on the Bayesian network model of rainstorm disasters, the Bayesian network model of flood disasters, and the Bayesian network model of waterlogging disasters; The second construction module is used to obtain the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario based on the Bayesian network model of the spatiotemporal evolution of the rainstorm-flood-waterlogging disaster constructed by the first construction module and the probability of damage to the power facilities under the rainstorm disaster, flood disaster and waterlogging disaster respectively evaluated by the first evaluation module; wherein the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line; The second evaluation module is used to evaluate the dynamic resilience of the power network based on the power network line damage evolution scenario constructed by the second construction module and the recovery plan under the power network damage evolution scenario.

[0007] In a third aspect of the present application, an electronic device is provided, including: Memory; Processor; and Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect above.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the method described in the first aspect above.

[0009] The present application provides a method, device, equipment, and storage medium for evaluating the dynamic resilience of a power network. The method includes: splicing a Bayesian network model of a rainstorm disaster, a Bayesian network model of a flood disaster, and a Bayesian network model of an urban waterlogging disaster to obtain a Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters; obtaining a power network line damage evolution scenario and a recovery plan under the power network damage evolution scenario based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters and the probability of power facilities being damaged by rainstorm disasters, flood disasters, and urban waterlogging disasters, respectively; and performing a dynamic resilience evaluation of the power network based on the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario, thereby providing theoretical support for improving the resilience of the power network and reducing the impact of disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A schematic diagram of a flow chart of a method for evaluating dynamic resilience of a power network provided in an embodiment of the present application; Figure 2 A schematic diagram of a power network provided in an embodiment of the present application; Figure 3 A schematic diagram of a power network area division scheme provided in an embodiment of the present application; Figure 4 A schematic diagram of a Bayesian network model for rainstorm disasters provided in an embodiment of the present application; Figure 5 A schematic diagram of a Bayesian network model for flood disasters provided in an embodiment of the present application; Figure 6 A schematic diagram of a Bayesian network model for waterlogging disasters provided in an embodiment of the present application; Figure 7 A schematic diagram of a Bayesian network model of a rainstorm-flood-waterlogging chain disaster provided in an embodiment of the present application; Figure 8 A schematic diagram of a time evolution model of a rainstorm-flood-waterlogging disaster provided in an embodiment of the present application; Fig. 9 A schematic diagram of a temporal and spatial evolution model of a rainstorm-flood-waterlogging disaster provided in an embodiment of the present application; Fig.10 A schematic diagram of a Bayesian network model of the spatiotemporal evolution of a rainstorm-flood-waterlogging disaster provided in an embodiment of the present application; Fig.11 A schematic diagram of a framework of a Bayesian network model for the spatiotemporal evolution of a rainstorm-flood-waterlogging disaster provided in an embodiment of the present application; Fig.12 A schematic diagram of the probability distribution of the evolution of the flood peak flow index in area A provided in an embodiment of the present application; Fig.13 A schematic diagram of the probability distribution of flood index evolution in region A provided in an embodiment of the present application; Fig.14 A schematic diagram of the probability distribution of the water depth index evolution in area A provided in an embodiment of the present application; Fig.15 A schematic diagram of the probability distribution of the evolution of the flood peak flow index in area B provided in an embodiment of the present application; Fig.16 A schematic diagram of the probability distribution of flood index evolution in region B provided in an embodiment of the present application; Fig.17 A schematic diagram of the probability distribution of the water depth index evolution in area B provided in an embodiment of the present application; Fig.18 A schematic diagram of the probability distribution of some line damage evolution provided in the embodiment of the present application; Fig.19 A schematic diagram of a dynamic resilience assessment model for a power network under a rainstorm-flood-waterlogging disaster provided in an embodiment of the present application; Fig. 20 A schematic diagram of a scenario of load supply rate evolution of a power network according to an embodiment of the present invention; Fig.21 A schematic diagram of the structure of a power network dynamic resilience assessment device provided in an embodiment of the present application; Fig. 22 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0012] In the process of realizing this application, the inventors found that power network resilience, as an indicator that characterizes the ability of power networks to maintain normal operation and recover quickly under extreme events, has become a hot research direction. However, in actual operation scenarios, the spatiotemporal evolution scenarios of rainstorm disaster chains are complex, which makes the power grid often passive when responding, and it is difficult to effectively prevent and reduce the losses caused by disasters. Existing resilience assessment methods often only consider single disaster impact scenarios, and it is difficult to effectively characterize the resilience indicators of power networks under the "rainstorm-flood-waterlogging" disaster chain. There is an urgent need for an assessment framework that can characterize the spatiotemporal evolution relationship of the "rainstorm-flood-waterlogging" disaster chain and the dynamic resilience indicators of power networks, so as to improve the ability of power networks to resist chain disaster impacts.

[0013] In response to the above problems, an embodiment of the present application provides a method, device, equipment, and storage medium for evaluating the dynamic resilience of an electric power network. The method includes: splicing a Bayesian network model of a rainstorm disaster, a Bayesian network model of a flood disaster, and a Bayesian network model of an urban waterlogging disaster to obtain a Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters; obtaining a power network line damage evolution scenario and a recovery plan under the power network damage evolution scenario based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters and the probability of power facilities being damaged by rainstorm disasters, flood disasters, and urban waterlogging disasters, respectively; performing a dynamic resilience assessment of the power network based on the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario, providing theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0014] See also Figure 1 This embodiment provides a method for evaluating the dynamic resilience of a power network. The implementation process of the method is as follows: 101, collect the topological structure of the power network and the geographical distribution of transmission lines.

[0015] In step 101, the topology and geographical location of the power network are collected to determine the geographical location and connection mode of the power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes, thereby obtaining the topology of the power network. The geographical distribution of the transmission and distribution lines is also determined.

[0016] Taking the improved IEEE33 node example, the power network Figure 2 As shown in the figure, node 0 is a power plant, and the remaining nodes are load nodes. It is assumed that the nodes will not be damaged during the evolution of the disaster, and the node parameters and line parameters are the same as the standard IEEE33 node model.

[0017] 102. Based on the topological structure of the power network and the geographical distribution of transmission lines, the power network is divided into regions.

[0018] In step 102, the area where the power network is located is divided into regions according to administrative divisions, such as Figure 3 As shown in Figure 2, it is divided into two regions. During the evolution of disasters, the disaster characteristic indicators in each region are the same.

[0019] 103. Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster.

[0020] The specific implementation process of step 103 is: 103-1, construct Bayesian network models for rainstorm disasters, flood disasters and urban waterlogging disasters.

[0021] When executing step 103-1, the disaster-prone environmental factors and disaster characteristic indicators of the three disasters of rainstorm, flood and waterlogging are extracted respectively, the Bayesian network node type and division method are determined, and a single disaster Bayesian network model is established, namely, a rainstorm disaster Bayesian network model, a flood disaster Bayesian network model and a waterlogging disaster Bayesian network model.

[0022] For example, the disaster-prone environmental factors and disaster characteristic indicators of the three disasters of heavy rain, flood and waterlogging shown in Table 1 are extracted.

[0023] Table 1

[0024] Among them, the 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm (non-heavy rain), 50.0~99.9 mm (heavy rain), 100.0~249.9 mm (heavy rain), and above 250.0 mm (extremely heavy rain).

[0025] The NDVI node is a discrete node with the following values: [-1, -0.35) (low coverage), [-0.35, 0.35) (medium coverage), and [0.35, 1] ​​(high coverage).

[0026] The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0027] The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland.

[0028] The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0029] The flood volume node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10 to 30 million cubic meters, 30 to 50 million cubic meters, and more than 50 million cubic meters.

[0030] The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

[0031] This embodiment uses precipitation to characterize rainstorm disasters. Since the existing weather forecasting technology is relatively mature, the precipitation data predicted by the Meteorological Bureau is considered as the evolution basis of the intensity of rainstorm disasters, that is, the Bayesian network of rainstorm disasters only contains the "precipitation" node. For flood and waterlogging disasters, their disaster-pregnant environmental factors and disaster characteristic indicators together constitute the nodes of their Bayesian network model.

[0032] 1. Determine the correlation between the environmental factors that cause rainstorm disasters and the characteristic indicators of disasters, and establish a Bayesian network model for rainstorm disasters. The Bayesian network model for rainstorm disasters uses the characteristic indicators of rainstorm disasters as output nodes, and the characteristic indicator of rainstorm disasters is 24-hour precipitation. Table 2 shows the node types and value ranges in the Bayesian network model for rainstorm disasters. Figure 4 The Bayesian network model of rainstorm disaster is shown.

[0033] Table 2

[0034] The flood disaster Bayesian network model uses flood disaster-prone environmental factors as input nodes and flood disaster characteristic indicators as output nodes. The flood disaster-prone environmental factors are 24-hour precipitation, normalized vegetation index, basin area ratio, land use type, and impervious surface ratio. The characteristic indicators of flood disasters are peak flow and flood volume. Table 3 shows the node types and value ranges in the flood disaster Bayesian network model. Figure 5 A Bayesian network model of flood disaster is shown.

[0035] Table 3

[0036] The Bayesian network model of waterlogging disasters uses the environmental factors that cause waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes. The environmental factors that cause waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized vegetation index, and impervious surface ratio. The characteristic indicator of waterlogging disasters is the depth of waterlogging. Table 4 shows the node types and value ranges in the Bayesian network model of waterlogging disasters. Figure 6 The Bayesian network model of waterlogging disaster is shown.

[0037] Table 4

[0038] 103-2, based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters.

[0039] The implementation process of step 103-2 is as follows: 1. By merging common variables, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​into a Bayesian network model of rainstorm-flood-waterlogging chain disaster. Figure 7 shown.

[0040] Among them, there are two types of common variables. The first type of common variables is: the output variable in a disaster model (such as Bayesian network A) is the same as the input variable of another disaster model (such as Bayesian network B); the second type of common variables is the same part of the input variables of different disaster models.

[0041] Through the common variable merging operation, the single disaster Bayesian network models can be merged to form a composite chain disaster Bayesian network model.

[0042] For the first type of public variables, the specific merging operation is as follows: deleting the public variables in Bayesian network A, and the parent node of the deleted public variables points to the public variables in Bayesian network B; for the second type of public variables, the merging operation is as follows: for the same variable, only one node is retained, and its connection relationship with all other nodes remains unchanged.

[0043] 2. Introduce the time factor within the divided area and construct a time evolution model of rainstorm-flood-waterlogging disasters.

[0044] Dynamic Bayesian network is used to characterize the evolution of the rainstorm-flood-waterlogging disaster chain on a time scale. The time factor within the divided area is introduced, and time slices are established for characterization at different moments. The impact relationship between different disasters is modeled within the slices, and the evolution of the same disaster is modeled between slices. For example, the evolution process satisfies the first-order Markov property, the intra-chip connection relationship and the inter-chip connection relationship do not change over time, and finally establish Figure 8 The time evolution model of heavy rain-flood-waterlogging disaster is shown.

[0045] 3. Introduce the spatial factors between the divided adjacent areas and construct a Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters.

[0046] Based on the temporal evolution model of rainstorm-flood-waterlogging disaster, the spatial factors between the divided adjacent regions are introduced to establish the temporal and spatial evolution model of rainstorm-flood-waterlogging disaster for different regions, such as Fig. 9 As shown in the figure, by determining the connection relationship between the sub-models of each region and characterizing the temporal evolution relationship in the same region and in multiple regions, the construction of the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters can be completed.

[0047] Among them, the intra-regional temporal evolution relationship describes the temporal evolution relationship of the internal disaster chain between regions A and B, and the inter-regional temporal evolution relationship describes the temporal mutual influence relationship between the occurrence of disasters in regions A and B.

[0048] Figure 8 The model shown describes the spatiotemporal evolution of the rainstorm-flood-waterlogging disaster chain between regions A and B. This embodiment considers the spatial impact relationship of the disaster chain as the spatial evolution of flood disasters in each region, and establishes a schematic diagram of the spatiotemporal evolution Bayesian network model of rainstorm-flood-waterlogging disasters by establishing the edge relationship between flood disaster output nodes in different regions, as shown in FIG. Fig.10 As shown in Figure 2, the architecture of the Bayesian network model for the spatiotemporal evolution of rainstorm-flood-waterlogging disasters is as follows: Fig.11 As shown in Figure 2, the same-region evolution relationship and cross-regional temporal evolution relationship of the Bayesian network model of the rainstorm-flood-waterlogging disaster spatiotemporal evolution are Fig.10 Same as shown in .

[0049] 104. Based on the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster, the probability of damage to power facilities caused by rainstorm disaster, flood disaster and waterlogging disaster respectively was evaluated.

[0050] In step 104, the mapping relationship between the rainstorm, flood, and waterlogging disaster indicators and the operating status of the transmission lines is analyzed based on the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster, and the Bayesian network model of waterlogging disaster, and the probability model of power facilities being damaged under rainstorm disaster, the probability model of power facilities being damaged under flood disaster, and the probability model of power facilities being damaged under waterlogging disaster are constructed, and then the probability model of power facilities being damaged under various disasters is obtained. That is, the probability of power facilities being damaged under rainstorm disaster is evaluated based on the probability model of power facilities being damaged under rainstorm disaster. , Evaluate the probability of power facilities being damaged by flood disasters based on the probability model of power facilities being damaged by flood disasters , Probability model of power facilities being damaged by waterlogging disasters Evaluate the probability of power facilities being damaged by waterlogging disasters .

[0051] 1. For rainstorm disasters The probability model of power facilities being damaged by heavy rain disasters is: , then the probability of damage to power facilities in rainstorm disasters is evaluated .

[0052] in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that power facilities can withstand.

[0053] Since the characteristic indicator of rainstorm disaster is 24-hour precipitation, is the 24-hour precipitation of the rainstorm disaster obtained according to the Bayesian network model of the rainstorm disaster, The maximum 24-hour precipitation amount in a rainstorm disaster that power facilities can withstand.

[0054] For example, if the power facility is a 10km long transmission line, then the probability of the 10km long transmission line being damaged in a rainstorm disaster is .

[0055] in, is the characteristic index value of the rainstorm disaster of the 10km long transmission line obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that the power facilities can withstand for a 10km long transmission line.

[0056] In the specific implementation, It can be 360 ​​mm (24-hour precipitation).

[0057] 2. For flood disasters The probability model of power facilities being damaged by flood disasters is: , then the probability of damage to power facilities in flood disasters is evaluated .

[0058] in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, .

[0059] , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0060] In the specific implementation, It can be 1500 cubic meters per second (peak flow), It can be 50 million cubic meters (flood volume), .

[0061] 3. For waterlogging disasters The probability model of damage caused by waterlogging disaster is: , then the probability of damage to power facilities caused by waterlogging is evaluated .

[0062] in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

[0063] Since the characteristic indicator of waterlogging disaster is the depth of waterlogging, is the water depth of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, The maximum depth of water accumulation in waterlogging disasters that power facilities can withstand.

[0064] For example, The depth of water is 60 cm.

[0065] 105. According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of power facilities being damaged by rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained.

[0066] The implementation process of step 105 is as follows: 105-1, based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the probability distribution of disaster evolution scenarios is determined.

[0067] For example, the initial operating state of the power network is that all lines and nodes are operating normally, and the disaster evolution analysis is carried out with 6 hours as the time unit. The relevant input node data of the two regions and the precipitation data in the next 60 hours are shown in Table 5.

[0068] Table 5

[0069] The values ​​"1-4" in "Precipitation" in Table 5 represent the corresponding state intervals in the Bayesian network corresponding to the actual data, which correspond to "less than 12.49 mm", "12.50-24.99 mm", "25.00-62.49 mm", and "more than 62.50 mm". Each numerical sequence represents the evolution of precipitation in the two regions in the next 60 hours.

[0070] The data in Table 5 are input into the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters, and the connection tree algorithm is used for reasoning to obtain the disaster evolution in each region, such as Figures 12 to 17 shown.

[0071] 105-2, based on the probability distribution of disaster evolution scenarios, obtain the power network line damage evolution scenario.

[0072] In step 105-2, based on the probability distribution of disaster evolution scenarios, the Monte Carlo sampling method is used to obtain several disaster chain spatiotemporal evolution scenarios. For each disaster chain spatiotemporal evolution scenario, combined with the probability model of damage to power facilities under various disasters, the Monte Carlo sampling method is used to obtain several power network damage evolution scenarios.

[0073] For example, Fig.18 The damage probability of some lines is shown. For different transmission line damage probability scenarios, the Monte Carlo sampling method is used to generate 100 groups of failure scenarios under different scenarios. One group of failure scenarios is shown in Table 6.

[0074] Among them, "1" indicates that the line is operating normally, and "0" indicates that the line fails.

[0075] Table 6

[0076] 105-3, formulate restoration plans under the scenarios of power network damage evolution.

[0077] Among them, the restoration plan under the scenario of power network damage evolution is determined according to the load supply importance of the transmission line. That is, the line importance is determined based on the load supply importance, the load supply capacity of each line in the power network is evaluated, the load supply importance of the transmission line is determined, and the repair is performed in descending order of load supply importance, and then the restoration plan under the scenario of power network damage evolution is formulated.

[0078] The load supply importance of a transmission line is the decrease in the load supply rate of the power network when only the line is damaged in the power network, that is, the load supply importance of any transmission line is , .

[0079] in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed.

[0080] For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal (i.e. normal operation) , transmission lines When the operating status of the , For transmission lines The operating status of the transmission line When the operating status is normal (i.e. the line is operating normally) , transmission lines When the operating status of the .

[0081] is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

[0082] For example, the load supply importance is shown in Table 7.

[0083] Table 7

[0084] Based on Table 7, the restoration scheme under the power network damage evolution scenario is as follows: repair the transmission lines in the order {1, 2, 3, 4, 5, 6, 22, 25, 7, 26, 23, 27, 28, 8, 29, 9, 10, 11, 12, 24, 30, 13, 18, 14, 19, 31, 15, 20, 16, 17, 21, 32}.

[0085] 106. Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated.

[0086] The implementation process of step 106 is as follows: 106-1, establish the optimal power flow model.

[0087] In specific implementation, the optimal power flow model can be established based on the power network line damage evolution scenario, with the goal of maximizing the load supply rate and the damage scenario as the model input.

[0088] The optimal power flow model can calculate the evolution of load supply rate of power network under disaster evolution scenario.

[0089] For example, the optimal power flow model is: .

[0090] st , .

[0091] , .

[0092] , .

[0093] , .

[0094] , .

[0095] , .

[0096] , .

[0097] , .

[0098] , .

[0099] , .

[0100] , .

[0101] , .

[0102] in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed.

[0103] It is a collection of transmission lines in the power network.

[0104] For transmission lines The active power transmitted on For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines The maximum reactive power transmitted. For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed. For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided. For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Provided reactive power.

[0105] For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node voltage. For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

[0106] The parameters of the optimal power flow model are shown in Table 8.

[0107] Table 8

[0108] The objective function of the optimal power flow model is: , which can maximize the total load supply rate of all nodes.

[0109] The constraints of the optimal power flow model are as follows: 1. Power transfer balance constraints , .

[0110] , .

[0111] 2. Output power and load power upper limit constraints , .

[0112] , .

[0113] , .

[0114] , .

[0115] The output power and load power upper limit constraints can ensure that the node output power and consumption power will not exceed their upper limits.

[0116] 3. Node voltage range constraints , .

[0117] Node voltage range constraints can ensure that the node voltage meets normal operating requirements.

[0118] 4. Line transmission power constraints , .

[0119] , .

[0120] The line transmission power constraint can ensure that the actual transmission power of the line does not exceed its upper limit. When the operating state of the transmission line is damaged (i.e. the line fails), When the line transmission power upper limit is 0.

[0121] 5. Line transmission power factor constraint , .

[0122] The power factor constraint of line transmission power can ensure that the actual transmission power meets the power factor requirement.

[0123] 6. Linear power flow constraints , .

[0124] , .

[0125] When the transmission line is in a damaged state (i.e., the line fails), , this constraint has no effect.

[0126] 106-2, input the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0127] 106-3, dynamic resilience assessment of power network based on power network load supply rate evolution scenario.

[0128] The specific implementation process of step 106-3 is: based on the load supply rate evolution scenario of the power network, determine the load supply rate of the power network at each time. Evaluating the dynamic resilience of the power network based on the load supply rate .

[0129] in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0, that is, the initial performance is 100%.

[0130] When evaluating the dynamic resilience of the power network, the load supply rate can be used as a power network performance indicator to establish a dynamic resilience evaluation model for the power network under heavy rain, flood and waterlogging disasters, such as Fig.19 shown.

[0131] See also Fig.19 , under the multiple impacts of "rainstorm-flood-waterlogging" disasters, the power grid first enters the defense process. With the gradual evolution and impact of the disaster, the performance of the power grid gradually decreases; after the evolution of the disaster ends, the power grid enters the recovery process and the performance gradually increases. Therefore, .

[0132] For example, the load supply rate evolution in a set of scenarios is as follows: Fig. 20 As shown in the figure, the resilience index in this scenario is 47.25%. Based on the dynamic resilience assessment model of power network under rainstorm-flood-waterlogging disasters, the resilience index of power network under each scenario is calculated, and the expected value can be taken to determine the resilience index of power network. It is 45.75%.

[0133] The power network dynamic resilience assessment method provided in this embodiment is a dynamic resilience assessment method that characterizes the power network's resistance and recovery capabilities under the chain disaster of "rainstorm-flood-waterlogging" by refining a mapping model between disaster characteristics and power network performance.

[0134] The method for dynamic resilience assessment of power networks provided in this embodiment has the following beneficial effects: the method of the present invention proposes a method for dynamic resilience assessment of power networks, which can realize the spatiotemporal evolution analysis of the "rainstorm-flood-waterlogging" disaster chain for rainstorm disaster scenarios, adopt the Monte Carlo sampling method to obtain the evolution scenario of power network damage, adopt the line importance method based on load supply to determine the restoration plan, and obtain the dynamic evolution scenario of power network load supply rate based on the optimal power flow model, realize the dynamic resilience assessment of the power network, and fully reflect the power network's response capability to "rainstorm-flood-waterlogging" disasters.

[0135] This embodiment provides a method for evaluating the dynamic resilience of a power network, which splices a Bayesian network model of a rainstorm disaster, a Bayesian network model of a flood disaster, and a Bayesian network model of an urban waterlogging disaster to obtain a Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters; according to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-urban waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and urban waterlogging disasters, respectively, the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario are obtained; based on the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated, which provides theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0136] Based on the same inventive concept of the power network dynamic resilience assessment method, this embodiment provides a power network dynamic resilience assessment device, see Fig.21 , the device comprises: The collection module 2101 is used to collect the topological structure of the power network and the geographical distribution of the transmission lines.

[0137] The division module 2102 is used to divide the power network into regions based on the topological structure of the power network and the geographical distribution of the transmission lines collected by the collection module 2101.

[0138] The first construction module 2103 is used to splice the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster based on the spatial factors between adjacent areas divided by the division module 2102 and the temporal factors within the area, so as to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster. Among them, the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is 24-hour precipitation. The Bayesian network model of flood disaster takes the disaster-pregnant environmental factors of flood disaster as the input node and the characteristic index of flood disaster as the output node, and the disaster-pregnant environmental factors of flood disaster are 24-hour precipitation, normalized vegetation index, proportion of watershed area, land use type, and proportion of impervious surface. The characteristic indicators of flood disaster are peak flow and flood volume. The Bayesian network model of waterlogging disaster takes the environmental factors that cause waterlogging disaster as input nodes and the characteristic indicators of waterlogging disaster as output nodes. The environmental factors that cause waterlogging disaster are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disaster is the depth of waterlogging.

[0139] The first evaluation module 2104 is used to evaluate the probability of power facilities being damaged by rainstorm disasters, flood disasters, and waterlogging disasters respectively based on the rainstorm disaster Bayesian network model, flood disaster Bayesian network model, and waterlogging disaster Bayesian network model.

[0140] The second construction module 2105 is used to obtain the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario based on the storm-flood-waterlogging disaster spatiotemporal evolution Bayesian network model constructed by the first construction module 2103 and the probability of damage to the power facilities under the storm disaster, flood disaster, and waterlogging disaster respectively evaluated by the first evaluation module 2104. The restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line.

[0141] The second evaluation module 2106 is used to evaluate the dynamic resilience of the power network based on the power network line damage evolution scenario constructed by the second construction module 2105 and the restoration plan under the power network damage evolution scenario.

[0142] Among them, the 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm, 50.0~99.9 mm, 100.0~249.9 mm, and above 250.0 mm.

[0143] The NDVI node is a discrete node, and its values ​​include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0144] The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0145] The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland.

[0146] The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0147] The flood volume node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10 to 30 million cubic meters, 30 to 50 million cubic meters, and more than 50 million cubic meters.

[0148] The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

[0149] The first evaluation module 2104 is used to evaluate the probability of damage to power facilities caused by heavy rain disasters. .in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that power facilities can withstand.

[0150] Assess the probability of damage to power facilities in flood disasters .in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0151] Assess the probability of damage to power facilities due to waterlogging disasters .in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

[0152] Among them, the load supply importance of any transmission line , .

[0153] in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged , For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

[0154] The second evaluation module 2106 is used to establish an optimal power flow model. The power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are input into the optimal power flow model to obtain the power network load supply rate evolution scenario. The power network dynamic resilience evaluation is performed based on the power network load supply rate evolution scenario.

[0155] The optimal power flow model is: .

[0156] st , .

[0157] , .

[0158] , .

[0159] , .

[0160] , .

[0161] , .

[0162] , .

[0163] , .

[0164] , .

[0165] , .

[0166] , .

[0167] , .

[0168] in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. It is a collection of transmission lines in the power network. For transmission lines The active power transmitted on For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines The maximum reactive power transmitted. For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed. For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided. For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Provided reactive power. For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node voltage. For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

[0169] Among them, the dynamic resilience assessment of the power network is carried out based on the evolution scenario of the power network load supply rate, including: Based on the load supply rate evolution scenario of the power network, the load supply rate of the power network at each moment is determined.

[0170] Assessing the dynamic resilience of power networks .

[0171] in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0.

[0172] The device provided in this embodiment obtains the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of power facilities being damaged by rainstorm disasters, flood disasters, and waterlogging disasters respectively; based on the power network line damage evolution scenario and the recovery plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated, which provides theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0173] Based on the same inventive concept of the method for evaluating the dynamic resilience of a power network, this embodiment provides an electronic device, such as Fig. 22 As shown, it includes: a memory 2201, a processor 2202, and a computer program.

[0174] The computer program is stored in the memory 2201 and is configured to be executed by the processor 2202 to implement the above-mentioned power network dynamic resilience assessment method.

[0175] Specifically, Collect the topological structure of the power network and the geographical distribution of transmission lines.

[0176] The power network is divided into regions based on the topological structure of the power network and the geographical distribution of transmission lines.

[0177] Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster. Among them, the Bayesian network model of rainstorm disaster takes the characteristic indicators of rainstorm disaster as the output node, and the characteristic indicator of rainstorm disaster is 24-hour precipitation. The Bayesian network model of flood disaster takes the disaster-prone environmental factors of flood disaster as the input node and the characteristic indicators of flood disaster as the output node. The disaster-prone environmental factors of flood disaster are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface. The characteristic indicators of flood disaster are peak flow and flood volume. The Bayesian network model of waterlogging disaster takes the disaster-prone environmental factors of waterlogging disaster as the input node and the characteristic indicators of waterlogging disaster as the output node. The disaster-prone environmental factors of waterlogging disaster are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disaster is the depth of water accumulation.

[0178] According to the Bayesian network model of rainstorm disaster, flood disaster and urban waterlogging disaster, the probability of power facilities being damaged by rainstorm disaster, flood disaster and urban waterlogging disaster respectively is evaluated.

[0179] According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained. Among them, the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line.

[0180] Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated.

[0181] Optionally, the 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm, 50.0~99.9 mm, 100.0~249.9 mm, and above 250.0 mm.

[0182] The NDVI node is a discrete node, and its values ​​include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0183] The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0184] The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland.

[0185] The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0186] The flood volume node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10 to 30 million cubic meters, 30 to 50 million cubic meters, and more than 50 million cubic meters.

[0187] The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

[0188] Optionally, based on the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster, the probability of damage to power facilities caused by rainstorm disaster, flood disaster and waterlogging disaster is evaluated, including: Assess the probability of damage to power facilities during heavy rain disasters .in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that power facilities can withstand.

[0189] Assess the probability of damage to power facilities in flood disasters .in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0190] Assess the probability of damage to power facilities due to waterlogging disasters .in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

[0191] Optionally, the load supply importance of any transmission line , .

[0192] in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged , For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

[0193] Optionally, based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, a power network dynamic resilience assessment is performed, including: Build an optimal power flow model.

[0194] The power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are input into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0195] The dynamic resilience of the power network is evaluated based on the load supply rate evolution scenario of the power network.

[0196] Optionally, the optimal power flow model is: .

[0197] st , .

[0198] , .

[0199] , .

[0200] , .

[0201] , .

[0202] , .

[0203] , .

[0204] , .

[0205] , .

[0206] , .

[0207] , .

[0208] , .

[0209] in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. It is a collection of transmission lines in the power network. For transmission lines The active power transmitted on For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines The maximum reactive power transmitted. For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed. For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided. For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Provided reactive power. For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node voltage. For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

[0210] Optionally, a power network dynamic resilience assessment is performed based on a power network load supply rate evolution scenario, including: Based on the load supply rate evolution scenario of the power network, the load supply rate of the power network at each moment is determined.

[0211] Assessing the dynamic resilience of power networks .

[0212] in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0.

[0213] The electronic device provided in this embodiment has a computer program executed by a processor to obtain power network line damage evolution scenarios and recovery plans under power network damage evolution scenarios based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, respectively; based on the power network line damage evolution scenarios and the recovery plans under the power network damage evolution scenarios, a dynamic resilience assessment of the power network is performed, providing theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0214] Based on the same inventive concept of the method for evaluating the dynamic resilience of a power network, this embodiment provides a computer-readable storage medium, and a computer program is stored thereon. The computer program is executed by a processor to implement the method for evaluating the dynamic resilience of a power network.

[0215] Specifically, Collect the topological structure of the power network and the geographical distribution of transmission lines.

[0216] The power network is divided into regions based on the topological structure of the power network and the geographical distribution of transmission lines.

[0217] Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster. Among them, the Bayesian network model of rainstorm disaster takes the characteristic indicators of rainstorm disaster as the output node, and the characteristic indicator of rainstorm disaster is 24-hour precipitation. The Bayesian network model of flood disaster takes the disaster-prone environmental factors of flood disaster as the input node and the characteristic indicators of flood disaster as the output node. The disaster-prone environmental factors of flood disaster are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface. The characteristic indicators of flood disaster are peak flow and flood volume. The Bayesian network model of waterlogging disaster takes the disaster-prone environmental factors of waterlogging disaster as the input node and the characteristic indicators of waterlogging disaster as the output node. The disaster-prone environmental factors of waterlogging disaster are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface. The characteristic indicator of waterlogging disaster is the depth of water accumulation.

[0218] According to the Bayesian network model of rainstorm disaster, flood disaster and urban waterlogging disaster, the probability of power facilities being damaged by rainstorm disaster, flood disaster and urban waterlogging disaster respectively is evaluated.

[0219] According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained. Among them, the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line.

[0220] Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated.

[0221] Optionally, the 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm, 50.0~99.9 mm, 100.0~249.9 mm, and above 250.0 mm.

[0222] The NDVI node is a discrete node, and its values ​​include: [-1, -0.35), [-0.35, 0.35), [0.35, 1].

[0223] The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0224] The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland.

[0225] The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, and 80%~100%.

[0226] The flood volume node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10 to 30 million cubic meters, 30 to 50 million cubic meters, and more than 50 million cubic meters.

[0227] The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

[0228] Optionally, based on the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster, the probability of damage to power facilities caused by rainstorm disaster, flood disaster and waterlogging disaster is evaluated, including: Assess the probability of damage to power facilities during heavy rain disasters .in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that power facilities can withstand.

[0229] Assess the probability of damage to power facilities in flood disasters .in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood volume index that the power facilities can withstand under flood disasters.

[0230] Assess the probability of damage to power facilities due to waterlogging disasters .in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

[0231] Optionally, the load supply importance of any transmission line , .

[0232] in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged , For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

[0233] Optionally, based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, a power network dynamic resilience assessment is performed, including: Build an optimal power flow model.

[0234] The power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are input into the optimal power flow model to obtain the power network load supply rate evolution scenario.

[0235] The dynamic resilience of the power network is evaluated based on the load supply rate evolution scenario of the power network.

[0236] Optionally, the optimal power flow model is: .

[0237] st , .

[0238] , .

[0239] , .

[0240] , .

[0241] , .

[0242] , .

[0243] , .

[0244] , .

[0245] , .

[0246] , .

[0247] , .

[0248] , .

[0249] in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed. It is a collection of transmission lines in the power network. For transmission lines The active power transmitted on For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines The maximum reactive power transmitted. For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed. For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided. For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Provided reactive power. For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node voltage. For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

[0250] Optionally, a power network dynamic resilience assessment is performed based on a power network load supply rate evolution scenario, including: Based on the load supply rate evolution scenario of the power network, the load supply rate of the power network at each moment is determined.

[0251] Assessing the dynamic resilience of power networks .

[0252] in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0.

[0253] The computer-readable storage medium provided in this embodiment has a computer program executed by a processor to obtain power network line damage evolution scenarios and recovery plans under power network damage evolution scenarios based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, respectively; based on the power network line damage evolution scenarios and the recovery plans under the power network damage evolution scenarios, a dynamic resilience assessment of the power network is performed, providing theoretical support for improving the resilience of the power network and reducing the impact of disasters.

[0254] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0255] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0256] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0258] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0259] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating dynamic resilience of a power network, characterized in that: The method comprises: Collect the topology of the power network and the geographical distribution of transmission lines; Based on the topological structure of the power network and the geographical distribution of the transmission lines, the power network is divided into regions; Based on the spatial factors between the divided adjacent regions and the temporal factors within the regions, the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster are spliced ​​to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters; among them, the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is the 24-hour precipitation; the Bayesian network model of flood disaster takes the disaster-prone environmental factors of flood disaster as the input node and the characteristic index of flood disaster as the output node. The disaster-prone environmental factors of flood disasters are 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters takes the disaster-prone environmental factors of waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes, and the disaster-prone environmental factors of waterlogging disasters are 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface; the characteristic indicator of waterlogging disasters is water accumulation depth; According to the Bayesian network model of rainstorm disaster, flood disaster and waterlogging disaster, the probability of damage to power facilities in rainstorm disaster, flood disaster and waterlogging disaster were evaluated respectively; According to the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters and the probability of damage to power facilities under rainstorm disasters, flood disasters, and waterlogging disasters, the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario are obtained; wherein the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line; Based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario, the dynamic resilience of the power network is evaluated.

2. The method according to claim 1, characterized in that The 24-hour precipitation node is a discrete node, and the values ​​include: below 49.9 mm, 50.0-99.9 mm, 100.0-249.9 mm, and above 250.0 mm; The NDVI node is a discrete node, and its values ​​include: [-1, -0.35), [-0.35, 0.35), [0.35, 1]; The basin area ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%; The land use type node is a discrete node, and its values ​​include: forest, shrub, construction land, grassland, permanent ice and snow, water body, farmland and wasteland; The impervious surface ratio node is a discrete node, and its values ​​include: 0~20%, 20%~40%, 40%~60%, 60%~80%, 80%~100%; The flood node is a discrete node, and the values ​​include: less than 10 million cubic meters, 10-30 million cubic meters, 30-50 million cubic meters, and more than 50 million cubic meters; The terrain humidity index node is a discrete node with values ​​of 0~10, 10~20, and 20~30.

3. The method according to claim 1, characterized in that The Bayesian network model for rainstorm disaster, the Bayesian network model for flood disaster and the Bayesian network model for waterlogging disaster are used to evaluate the probability of damage to power facilities under rainstorm disaster, flood disaster and waterlogging disaster, respectively, including: Assess the probability of damage to power facilities during heavy rain disasters ;in, is the characteristic index value of rainstorm disaster obtained according to the Bayesian network model of rainstorm disaster, It is the maximum value of the characteristic index of rainstorm disaster that the power facilities can withstand; Assess the probability of damage to power facilities in flood disasters ;in, is the probability of damage to power facilities corresponding to the peak flow under flood disasters, is the probability of damage to power facilities corresponding to the flood volume under flood disaster, is the weight coefficient of peak flow under flood disaster, ; , is the flood peak flow index value under flood disasters obtained according to the Bayesian network model of flood disasters, It is the maximum value of the peak flow index under flood disasters that the power facilities can withstand. is the flood index value under flood disaster obtained according to the Bayesian network model of flood disaster, It is the maximum value of the flood index under flood disaster that the power facilities can withstand; Assess the probability of damage to power facilities due to waterlogging disasters ;in, is the characteristic index value of waterlogging disaster obtained according to the Bayesian network model of waterlogging disaster, It is the maximum value of the characteristic index of urban flood disaster that the power facilities can withstand.

4. The method according to claim 1, characterized in that The importance of load supply of any transmission line , ; in, It is the collection of all power generation nodes, substation nodes, transmission nodes, distribution nodes and user nodes in the power network. , , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed; For transmission lines The importance of load supply, For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged , For transmission lines The operating status of the transmission line When the operating status is normal , transmission lines When the operating status is damaged ; is the set of transmission lines in the power network, For Node Active power consumed, For Node The active power required.

5. The method according to claim 1, characterized in that The power network dynamic resilience assessment based on the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario includes: Establish optimal power flow model; Inputting the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario into the optimal power flow model to obtain the power network load supply rate evolution scenario; The dynamic resilience of the power network is evaluated based on the power network load supply rate evolution scenario.

6. The method according to claim 5, characterized in that The optimal power flow model is: ; st ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； ， ； in, is the collection of all substation nodes and user load nodes in the power network, , and is the node identifier, For the node and The transmission lines formed, For the node and The transmission lines formed; It is a collection of transmission lines in the power network; For transmission lines is the active power transmitted, For transmission lines The active power transmitted on For transmission lines The reactive power transmitted on For transmission lines The reactive power transmitted on For transmission lines The maximum active transmission power, For transmission lines Maximum reactive transmission power; For Node Active power consumed, For Node Active power consumed, For Node Reactive power consumed, For Node Reactive power consumed; For Node The active power required, For Node The reactive power required, For Node The maximum active power provided, For Node Maximum reactive power provided; For Node The active power provided, For Node The active power provided, For Node The reactive power provided, For Node Reactive power provided; For Node The voltage lower limit, For Node The voltage, For Node The upper voltage limit, For Node Voltage; For transmission lines The operating status of For Node The power factor, For transmission lines The capacitance, For transmission lines The reactance, Any large number.

7. The method according to claim 5, characterized in that The performing of the power network dynamic resilience assessment based on the power network load supply rate evolution scenario includes: Determining the load supply rate of the power network at each moment based on the load supply rate evolution scenario of the power network; Assessing the dynamic resilience of power networks ; in, is the dynamic resilience value of the power network, To mark the moment, For the power network The load supply rate at the time, is the load supply rate of the power network at time 0.

8. A power network dynamic resilience assessment device, characterized in that: The device comprises: A collection module for collecting the topological structure of the power network and the geographical distribution of the transmission lines; A division module, used for dividing the power network into regions based on the topological structure of the power network and the geographical distribution of the transmission lines collected by the collection module; The first construction module is used to splice the Bayesian network model of rainstorm disaster, the Bayesian network model of flood disaster and the Bayesian network model of waterlogging disaster based on the spatial factors between adjacent areas divided by the division module and the temporal factors within the area, so as to obtain the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disaster; wherein the Bayesian network model of rainstorm disaster takes the characteristic index of rainstorm disaster as the output node, and the characteristic index of rainstorm disaster is the 24-hour precipitation; the Bayesian network model of flood disaster takes the disaster-pregnant environmental factors of flood disaster as the input node, the characteristic index of flood disaster The output nodes are the environmental factors that cause flood disasters, including 24-hour precipitation, normalized difference vegetation index, proportion of watershed area, land use type, and proportion of impervious surface; the characteristic indicators of flood disasters are peak flow and flood volume; the Bayesian network model of waterlogging disasters uses the environmental factors that cause waterlogging disasters as input nodes and the characteristic indicators of waterlogging disasters as output nodes, and the environmental factors that cause waterlogging disasters include 24-hour precipitation, flood volume, land use type, terrain humidity index, normalized difference vegetation index, and proportion of impervious surface; the characteristic indicator of waterlogging disasters is water depth; The first evaluation module is used to evaluate the probability of damage to power facilities caused by rainstorm disasters, flood disasters, and waterlogging disasters, respectively, based on the Bayesian network model of rainstorm disasters, the Bayesian network model of flood disasters, and the Bayesian network model of waterlogging disasters; The second construction module is used to obtain the power network line damage evolution scenario and the restoration plan under the power network damage evolution scenario based on the Bayesian network model of the spatiotemporal evolution of rainstorm-flood-waterlogging disasters constructed by the first construction module and the probability of damage to the power facilities under rainstorm disasters, flood disasters, and waterlogging disasters evaluated by the first evaluation module; wherein the restoration plan under the power network damage evolution scenario is determined according to the load supply importance of the transmission line; The second evaluation module is used to perform dynamic resilience evaluation of the power network based on the power network line damage evolution scenario constructed by the second construction module and the recovery plan under the power network damage evolution scenario.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

Images