A method and system for evaluating the priority of power grid node recovery in an extreme environment

Abstract

The application discloses a kind of extreme environment under power grid node recovery priority evaluation method and system, the method includes: according to the failure probability of line in each node, the disaster influence factor of each node is determined;According to the disaster influence factor and disaster information of each node, determine the unit after disaster influence in node load and node unit output;According to the unit after disaster influence in node load and the unit recovery income of unit in node, determine the modified expected recovery income of unit in node;According to the unit after disaster influence in node load and the unit recovery income of load in node, determine the modified expected recovery income of load in node;The modified expected recovery income of unit in each node and the modified expected recovery income of load in each node are added, and the expected recovery income of each node is obtained;According to the expected recovery income of each node, determine the recovery priority of each node in power grid. Realize the accurate determination of each node recovery priority in power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power grid recovery technology, and particularly relates to a method and system for evaluating the priority of power grid node recovery under extreme environment. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Natural disasters are the main inducement of blackouts and increase the risk of power grid recovery control. How to reasonably consider the disaster factors in the recovery control decision and avoid their adverse effects is a problem to be solved. Therefore, it is necessary to quantitatively analyze the disaster impact on the recovery state of the power grid, establish a quantitative analysis index and evaluate the change of node generation and load recovery benefits under different disaster impact.

[0004] The academic circle has carried out a large amount of research on the dispatching defense technology of power grid against disasters. The literature "Power outage defense system concept in extreme external disasters (I) New challenges and reflections" further proposes to build an online defense system against disasters and its key links on the basis of the research on traditional online preventive control, emergency control, correction control and their coordinated optimization, and proposes the defense idea of "active splitting and zoned power supply" under extreme disaster conditions. The literature "Review of dispatching defense technology of interconnected power grid against extreme external disasters" points out that the research on candidate measure set adjustment technology that adapts to environmental changes and the research on multi-level dispatching collaborative control should be strengthened, including the research on online recovery control decision of multi-level dispatching collaboration. However, there is no research on how to consider the impact of disasters on the importance of node recovery in the recovery control decision, so it is difficult to ensure the correctness of the recovery control decision result under disaster conditions. SUMMARY

[0005] In order to solve the above problems, the present application provides a method and system for evaluating the priority of power grid node recovery under extreme environment, which realizes the accurate determination of the recovery priority of each node in the power grid.

[0006] To achieve the above purpose, the present application adopts the following technical solutions:

[0007] In the first aspect, a method for evaluating the priority of power grid node recovery under extreme environment is provided, comprising:

[0008] Obtaining disaster information of each node in the power grid, and fault probability of lines in each node, unit recovery benefit of units and unit recovery benefit of loads;

[0009] According to the fault probability of the lines in each node, the disaster impact factor of each node is calculated and determined;

[0010] Determine the unit restoration benefit of the generator in the node according to the disaster information and the disaster influence factor of the node;

[0011] Determine the corrected expected restoration benefit of the generator in the node according to the unit restoration benefit of the generator in the node after the disaster and the unit restoration benefit of the generator in the node;

[0012] Determine the corrected expected restoration benefit of the load in the node according to the unit restoration benefit of the load in the node after the disaster and the unit restoration benefit of the load in the node;

[0013] Add the corrected expected restoration benefit of the generator in the node and the corrected expected restoration benefit of the load in the node to obtain the expected restoration benefit of the node;

[0014] Determine the restoration priority of each node in the power grid according to the expected restoration benefit of each node.

[0015] Further, obtain the power grid operation data, equipment outage state, maintenance plan, equipment geographic information, and equipment design parameters and abnormal and alarm data;

[0016] Determine the failure probability of the line in the node according to the power grid operation data, equipment outage state, maintenance plan, equipment geographic information, disaster information, and equipment design parameters and abnormal and alarm data.

[0017] Further, add the failure probabilities of the power transmission lines in the node whose failure probabilities are greater than the set probability threshold to obtain the total failure probability of the power transmission lines in the node;

[0018] Calculate the proportion of the power transmission lines in the node whose failure probabilities are greater than the set probability threshold in all power transmission lines in the node to obtain the node failure line proportion;

[0019] Weighted sum the total failure probability of the power transmission lines in the node and the node failure line proportion to obtain the initial disaster influence factor of each node;

[0020] Normalize all the initial disaster influence factors of the nodes to obtain the disaster influence factor of each node.

[0021] Further, determine the generator output in the node after the disaster according to the disaster information, disaster influence factor, and generator restoration benefit dynamic adjustment function of the node;

[0022] Determine the load in the node after the disaster according to the disaster information, disaster influence factor, and load restoration benefit dynamic adjustment function of the node;

[0023] The generator restoration benefit dynamic adjustment function of the node is obtained by fitting the known disaster influence factors corresponding to different disaster information of the node and the generator output in the node after the disaster;

[0024] The load recovery benefit dynamic adjustment function of the node is obtained by fitting the disaster impact factor corresponding to the different disaster information of the node and the post-disaster node load.

[0025] Further, the corrected expected recovery benefit of the unit in the node is obtained by multiplying the unit recovery benefit of the unit in the node by the unit output of the unit in the node after being affected by the disaster.

[0026] The corrected expected recovery benefit of the load in the node is obtained by multiplying the unit recovery benefit of the load in the node by the load in the node after being affected by the disaster.

[0027] Further, the recovery priority of each node is determined according to the size of the expected recovery benefit of each node.

[0028] The larger the expected recovery benefit of the node is, the higher the recovery priority of the node is, and the earlier the node is recovered.

[0029] In a second aspect, an evaluation system for the recovery priority of a power grid node in an extreme environment is provided, comprising:

[0030] An information acquisition module is configured to acquire disaster information of each node in the power grid, and the failure probability of each line in each node, the unit recovery benefit of each unit, and the unit recovery benefit of each load.

[0031] An impact factor evaluation module is configured to calculate and determine the disaster impact factor of each node according to the failure probability of each line in each node.

[0032] An expected recovery benefit adjustment module is configured to determine the unit output of each unit in each node and the load in each node after being affected by the disaster according to the disaster impact factor and the disaster information of each node; determine the corrected expected recovery benefit of each unit in each node according to the unit output of each unit in each node after being affected by the disaster and the unit recovery benefit of each unit in each node; and determine the corrected expected recovery benefit of each load in each node according to the load in each node after being affected by the disaster and the unit recovery benefit of each load in each node.

[0033] A node benefit evaluation module is configured to add the corrected expected recovery benefit of each unit in each node and the corrected expected recovery benefit of each load in each node to obtain the expected recovery benefit of each node.

[0034] A node recovery priority determination module is configured to determine the recovery priority of each node in the power grid according to the expected recovery benefit of each node.

[0035] In a third aspect, a computer device is provided, comprising:

[0036] A processor is adapted to execute a computer program.

[0037] A computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and the computer program, when executed by the processor, implements the method for evaluating the restoration priority of the power grid node in an extreme environment according to the first aspect.

[0038] In a fourth aspect, a computer readable storage medium is provided, wherein a computer program is stored in the computer readable storage medium, and the computer program is adapted to be loaded and executed by a processor to implement the method for evaluating the restoration priority of the power grid node in an extreme environment according to the first aspect.

[0039] In a fifth aspect, a computer program product is provided, wherein the computer program product comprises a computer program, and the computer program, when executed by a processor, implements the method for evaluating the restoration priority of the power grid node in an extreme environment according to the first aspect.

[0040] Compared with the prior art, the method has the following beneficial effects:

[0041] The method for evaluating the restoration priority of the power grid node in an extreme environment and the system can dynamically evaluate the line fault probability, determine the disaster influence factor of each node on this basis, determine the unit output and the node load in the node affected by the disaster through the determined disaster influence factor and the disaster information, and then determine the corrected expected restoration income of the unit in the node and the corrected expected restoration income of the load according to the unit output and the node load in the node affected by the disaster, correct the expected restoration income of the unit and the load in each node, and finally accurately obtain the expected restoration income of the node; and the restoration priority of the node is determined according to the expected restoration income of each node. The method can adapt to the change of the external environment, accurately determine the expected restoration income of the node, accurately determine the restoration priority of the node, improve the quantitative evaluation ability of the disaster-affected degree of the power grid in the restoration state, and ensure the feasibility, economy and practicability of the power grid node restoration control decision.

[0042] The advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0043] The drawings accompanying the specification of this application form a part thereof, serve to provide further understanding of the application, and together with the description of the exemplary embodiments of the application given below, serve to explain the application, and do not constitute an improper limitation on the application.

[0044] Figure 1 A flowchart of the method for evaluating the restoration priority of the power grid node in an extreme environment disclosed in the embodiments;

[0045] Figure 2A system block diagram for implementing the power grid node recovery priority evaluation system under extreme environment is disclosed. DETAILED DESCRIPTION

[0046] The application is further described below in conjunction with the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed description is illustrative only, and is intended to provide further description in order to provide a fuller enabling teaching of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0048] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0049] In the case of no conflict, the embodiments in the application and the features in the embodiments can be combined with each other.

[0050] Embodiment 1

[0051] Natural disasters are the main inducement of blackouts and increase the risk of power grid recovery control. How to reasonably consider the disaster factors in the recovery control decision and avoid their adverse effects is a problem to be solved. Therefore, it is necessary to quantitatively analyze the disaster impact on the recovery state of the power grid, establish quantitative analysis indicators and evaluate the changes in node power generation and load recovery benefits under different disaster impact.

[0052] The academic circle has carried out a lot of research on the dispatching defense technology of power grid against disasters. The literature "Power outage defense system concept in extreme external disasters (I) New challenges and reflections" further proposes to build an online defense system against disasters and its key links on the basis of traditional online preventive control, emergency control, correction control and their coordinated optimization research, and proposes the defense idea of "active splitting and partition power supply" under extreme disaster conditions. The literature "Review of dispatching defense technology of interconnected power grid against extreme external disasters" points out that the research on candidate measure set adjustment technology that adapts to environmental changes and the research on multi-level dispatching collaborative control should be strengthened, including the research on online recovery control decision of multi-level dispatching collaboration. However, there is no research on how to consider the impact of disasters on the importance of node recovery in the recovery control decision, so it is difficult to ensure the correctness of the recovery control decision result under disaster conditions.

[0053] Furthermore, under the influence of disasters, the expected recovery benefits and recovery importance of units and loads at each node will change. For example, under typhoon disasters, the risk of wind power plants being cut off due to the typhoon's impact increases, and the recovery benefits of wind turbine starting power should be reduced based on the typhoon disaster impact factor of the node. Since typhoons reduce the recovery reliability of load feeders, the load recovery benefits of affected nodes should be reduced based on the disaster impact factor of the node. As another example, under extreme high or low temperature weather, the recovery benefits of residential loads can be fitted and adjusted based on historical data. Current power grid recovery control methods do not consider the impact of different disaster conditions on the expected recovery benefits of nodes, thus preventing the final determined power grid recovery sequence from achieving optimal results.

[0054] Therefore, to further improve the online decision-making technology for power grid restoration control, this embodiment, based on existing research results, especially on the quantitative assessment of the probability of line faults caused by disasters, focuses on how to achieve online automatic assessment of the nodal disaster impact factors for power grid restoration control under the premise of realizing automatic collection of environmental information and dynamic assessment of the probability of line faults based on environmental changes. This enables further online calculation and adjustment of nodal restoration priorities, allowing the restoration control decision results to adapt to changes in the external environment and ensuring the feasibility, economy, and practicality of the control decisions.

[0055] This embodiment provides a detailed description of a method for prioritizing the recovery of power grid nodes under extreme conditions.

[0056] like Figure 1 As shown in this embodiment, a method for prioritizing the recovery of power grid nodes under extreme conditions is disclosed, including:

[0057] S1: Obtain disaster information for each node in the power grid, as well as the fault probability of the lines, the unit recovery revenue of the generating units, and the unit recovery revenue of the load in each node.

[0058] This embodiment acquires power grid operation data, equipment outage status, maintenance plans, equipment geographic information, equipment design parameters, and anomaly and alarm data.

[0059] Based on power grid operation data, equipment outage status, maintenance plans, equipment geographic information, disaster information, equipment design parameters, and anomaly and alarm data, the probability of line failure in each node is determined.

[0060] In practice, the recoverability of the line is determined based on power grid operation data, equipment outage status, maintenance plans, and abnormal and alarm data.

[0061] When the recoverable state of a line is determined to be unrecoverable, the probability of failure for that line is 1.

[0062] When the line is determined to be a recoverable state line, the fault probability of the line is determined according to power grid operation data, disaster information, device geographic information and device design parameters.

[0063] S2: According to the fault probability of the line in the node, the disaster influence factor of each node is calculated.

[0064] The process of determining the disaster influence factor of each node in the embodiment includes:

[0065] The fault probabilities of the power transmission lines in the node whose fault probability is greater than the set probability threshold are added to obtain the total fault probability of the power transmission lines in the node;

[0066] The proportion of the power transmission lines in the node whose fault probability is greater than the set probability threshold in all power transmission lines in the node is calculated to obtain the fault line proportion of the node;

[0067] The total fault probability of the power transmission lines in the node and the fault line proportion of the node are weighted and summed to obtain the initial disaster influence factor of each node;

[0068] The initial disaster influence factors of all nodes are normalized to obtain the disaster influence factor of each node.

[0069] Wherein, the total fault probability of the power transmission lines in the node is α d,m :

[0070]

[0071] In the formula, α m,k is the fault probability of the kth power transmission line in the line connected with the node m, N L,m is the total number of power transmission lines connected with the node m, N D,m is the total number of power transmission lines connected with the node m, and N L,m is the total number of power transmission lines in the node m whose disaster fault probability is greater than the set probability threshold.

[0072] The proportion of N D,m in N L,m is used to correct α d,m to obtain the initial disaster influence factor β m ; ω d,1 and ω d,2 are weight values, generally ω d,1 ≥ ω d,2 , and ω d,1 + ω d,2 = 1.

[0073] β m = ω d,1 α d,m + ω d,2 (N D,m / N L,m ) (2)

[0074] The process of normalizing the initial disaster influence factor of all nodes is:

[0075]

[0076] In the formula, β' m is the disaster influence factor of node m, β f is the maximum value of the initial disaster influence factor of all nodes. The node is a power plant or a substation in a full stop state.

[0077] S3: According to the disaster influence factor and disaster information of each node, determine the unit of the unit after the disaster influence of the node unit output and node load;

[0078] According to the unit recovery benefit of the unit in the node after the disaster influence and the unit recovery benefit of the unit in the node, determine the corrected expected recovery benefit of the unit in the node;

[0079] According to the node load after the disaster influence and the unit recovery benefit of the load in the node, determine the corrected expected recovery benefit of the load in the node.

[0080] In the specific implementation, according to the disaster information of the node, the disaster influence factor and the unit recovery benefit dynamic adjustment function of the unit, the unit output of the node after the disaster influence is determined;

[0081] According to the disaster information of the node, the disaster influence factor and the load recovery benefit dynamic adjustment function, the node load after the disaster influence is determined.

[0082] The unit recovery benefit dynamic adjustment function of the node is obtained by fitting the known disaster influence factor and the unit output of the node after the disaster corresponding to different disaster information of the node; In the formula, D m is the disaster information of node m.

[0083] The load recovery benefit dynamic adjustment function of the node is obtained by fitting the known disaster influence factor and the node load after the disaster corresponding to different disaster information of the node.

[0084] In this embodiment, after obtaining the unit output g G (D m ,β' m ) and the node load g L (D m ,β' m ) after the disaster influence, the unit output g G (D m ,β' m ) and the unit recovery benefit R G,m,iMultiply to obtain the corrected expected recovery revenue R' of the unit in the node. G,m,i .

[0085] R' G,m,i =g G (D m ,β' m )R G,m,i (4)

[0086] In the formula, i is the generator unit number in node m, i = 1, 2, ..., N. G,m N G,m It is the total number of generator sets in node m.

[0087] The node load g after the disaster L (D m ,β' m The unit recovery revenue R of the load in the node L,m,j Multiply to obtain the expected recovery gain R' after adjusting the load in the node. L,m,j .

[0088] R' L,m,j =g L (D m ,β' m )R L,m,j (5)

[0089] In the formula, j is the load number in node m, j = 1, 2, ..., N. L,m N L,m It is the total number of loads in node m.

[0090] S4: Sum the adjusted expected recovery revenue of the units in each node and the adjusted expected recovery revenue of the load to obtain the expected recovery revenue V of each node. m .

[0091]

[0092] S5: Determine the recovery priority of each node in the power grid based on the expected recovery benefits of each node.

[0093] In this embodiment, the recovery priority of each node is determined according to the expected recovery benefit of each node; wherein, the greater the expected recovery benefit of a node, the higher the recovery priority of the node, and the earlier the node is restored.

[0094] In practice, the expected recovery benefits of each node are sorted in descending order, and the nodes ranked higher are given priority for recovery.

[0095] The extreme environment power grid node recovery priority evaluation method disclosed by the embodiment can realize dynamic evaluation of line fault probability, and determine disaster influence factors of nodes on this basis, determine unit output and node load of nodes affected by disasters through the determined disaster influence factors and disaster information, then determine the corrected expected recovery benefits of the unit and the corrected expected recovery benefits of the load of the nodes according to the unit output and the node load of the nodes affected by disasters, correct the expected recovery benefits of the unit and the load of the nodes, and finally accurately obtain the expected recovery benefits of the nodes; the recovery priority of the nodes is determined according to the expected recovery benefits of the nodes. The expected recovery benefits of the nodes can be accurately determined according to the changes of the external environment, and the recovery priority of the nodes is accurately determined, the quantification evaluation ability of the disaster-affected degree of the recovery state power grid is improved, and the feasibility, economy and practicability of the power grid node recovery control decision are ensured.

[0096] Embodiment 2

[0097] In this embodiment, a power grid node recovery priority evaluation system under an extreme environment is disclosed, as shown in Figure 2 , comprising:

[0098] An information acquisition module is configured to acquire disaster information of each node in a power grid, and fault probability of a line in each node, unit recovery benefits of a unit and a load;

[0099] An influence factor evaluation module is configured to calculate and determine disaster influence factors of each node according to the fault probability of the line in each node;

[0100] An expected recovery benefit adjustment module is configured to determine unit output and node load of each node affected by disasters according to the disaster influence factors and the disaster information of each node; determine the corrected expected recovery benefits of the unit of each node according to the unit output of each node affected by disasters and the unit recovery benefits of the unit of each node; and determine the corrected expected recovery benefits of the load of each node according to the node load of each node affected by disasters and the unit recovery benefits of the load of each node;

[0101] A node benefit evaluation module is configured to add the corrected expected recovery benefits of the unit and the corrected expected recovery benefits of the load of each node to obtain the expected recovery benefits of each node;

[0102] A node recovery priority determination module is configured to determine the recovery priority of each node in the power grid according to the expected recovery benefits of each node.

[0103] The expected recovery benefit adjustment module comprises a power generation benefit adjustment module and a load benefit adjustment module.

[0104] The power generation benefit adjustment module is configured to determine the unit restoration benefit of the unit in the node according to the unit output of the unit in the node after the disaster and the unit restoration benefit of the unit in the node.

[0105] In a specific implementation, the load benefit adjustment module is configured to determine the unit restoration benefit of the load in the node according to the load in the node after the disaster and the unit restoration benefit of the load in the node.

[0106] The expected restoration benefit adjustment module is configured to determine the unit output of the unit in the node after the disaster according to the disaster information of the node, the disaster influence factor and the unit restoration benefit dynamic adjustment function of the unit.

[0107] The expected restoration benefit adjustment module is configured to determine the unit output of the unit in the node after the disaster according to the disaster information of the node, the disaster influence factor and the unit restoration benefit dynamic adjustment function of the unit.

[0108] The unit restoration benefit dynamic adjustment function of the node is obtained by fitting the known disaster influence factors corresponding to different disaster information of the node and the unit output in the node after the disaster.

[0109] The unit restoration benefit dynamic adjustment function of the node is obtained by fitting the known disaster influence factors corresponding to different disaster information of the node and the unit output in the node after the disaster.

[0110] The load benefit adjustment module is configured to multiply the unit output of the unit in the node after the disaster by the unit restoration benefit of the unit in the node to obtain the modified expected restoration benefit of the unit in the node.

[0111] The load benefit adjustment module is configured to multiply the unit output of the unit in the node after the disaster by the unit restoration benefit of the unit in the node to obtain the modified expected restoration benefit of the unit in the node.

[0112] The application also discloses a computer device, which comprises:

[0113] A processor is adapted to execute a computer program.

[0114] A computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the power grid node restoration priority evaluation method in an extreme environment disclosed in embodiment 1.

[0115] The application also discloses a computer readable storage medium which stores a computer program, and the computer program is adapted to be loaded and executed by the processor to implement the power grid node restoration priority evaluation method in an extreme environment disclosed in embodiment 1.

[0116] The application further discloses a computer program product, which comprises a computer program, and the computer program, when executed by a processor, realizes the power grid node recovery priority evaluation method in an extreme environment disclosed in Embodiment 1.

[0117] The method disclosed in Embodiment 1 can be directly embodied by a hardware processor to be executed, or be executed by a combination of hardware and software modules in the processor. The software modules can be located in a random memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, and other mature storage media in the art. The storage media is located in a memory, and the processor reads information in the memory to combine the hardware to complete the steps of the above method. To avoid repetition, no longer detailed description is made herein.

[0118] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solutions. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0119] Although the specific embodiments of the application are described above in combination with the drawings, the description is not a limitation on the protection scope of the application, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the application without creative labor are still within the protection scope of the application.

Claims

1. A method for prioritizing the recovery of power grid nodes under extreme environments, characterized in that, include: Obtain disaster information for each node in the power grid, as well as the fault probability of lines, unit recovery revenue of generating units, and unit recovery revenue of loads at each node; Based on the failure probability of the lines in each node, the disaster impact factor of each node is calculated and determined. Based on the disaster impact factors and disaster information of each node, determine the unit output and node load of the node after the disaster. Based on the unit output and unit recovery revenue of the units in the node after the disaster, determine the corrected expected recovery revenue of the units in the node; Based on the node load after the disaster and the unit recovery revenue of the load in the node, determine the corrected expected recovery revenue of the load in the node; The expected recovery revenue of each node is obtained by adding the corrected expected recovery revenue of the units in each node and the corrected expected recovery revenue of the load. The recovery priority of each node in the power grid is determined based on the expected recovery benefits of each node.

2. The method for prioritizing power grid node recovery under extreme environments as described in claim 1, characterized in that, Acquire power grid operation data, equipment outage status, maintenance plans, equipment geographic information, equipment design parameters, and anomaly and alarm data; Based on power grid operation data, equipment outage status, maintenance plans, equipment geographic information, disaster information, equipment design parameters, and anomaly and alarm data, the probability of line failure in each node is determined.

3. The method for prioritizing power grid node recovery under extreme environments as described in claim 1, characterized in that, The total fault probability of the transmission lines in a node is obtained by summing the fault probabilities of the transmission lines in the node that have a fault probability greater than a set probability threshold. The proportion of faulty lines in a node is obtained by calculating the proportion of transmission lines with a fault probability greater than a set probability threshold among all transmission lines in the node. The initial disaster impact factor for each node is obtained by weighting and summing the total failure probability of the transmission lines at the node with the proportion of faulty lines at the node. The initial disaster impact factors of all nodes are normalized to obtain the disaster impact factors of each node.

4. The method for prioritizing power grid node recovery under extreme environments as described in claim 1, characterized in that, Based on the disaster information of the nodes, the disaster impact factors, and the dynamic adjustment function of the unit recovery benefits, the unit output of the nodes after the disaster is determined; The load on a node after being affected by a disaster is determined based on the node's disaster information, disaster impact factors, and dynamic adjustment function for load recovery benefits. Among them, the dynamic adjustment function of unit recovery revenue of a node is obtained by fitting the disaster impact factors corresponding to different disaster information of the node and the unit output of the node after the disaster. The dynamic adjustment function for the load recovery benefit of a node is obtained by fitting the disaster impact factors corresponding to different disaster information of the node and the node load after the disaster.

5. The method for prioritizing power grid node recovery under extreme environments as described in claim 1, characterized in that, Multiply the unit output of the node affected by the disaster by the unit recovery revenue of the node to obtain the corrected expected recovery revenue of the node. Multiply the node load affected by the disaster by the unit recovery revenue of the load in the node to obtain the corrected expected recovery revenue of the load in the node.

6. The method for prioritizing power grid node recovery under extreme environments as described in claim 1, characterized in that, Determine the recovery priority of each node based on the expected recovery benefit of each node; The greater the expected recovery benefit of a node, the higher its recovery priority, and the earlier it will recover.

7. A power grid node recovery priority assessment system under extreme environments, characterized in that, include: The information acquisition module is used to acquire disaster information of each node in the power grid, as well as the fault probability of the lines, the unit recovery revenue of the generating units, and the unit recovery revenue of the load in each node; The impact factor assessment module is used to calculate and determine the disaster impact factor of each node based on the failure probability of the lines in each node. The expected recovery benefit adjustment module is used to determine the unit output and node load of nodes affected by disasters based on the disaster impact factors and disaster information of each node; to determine the corrected expected recovery benefit of the units in the nodes based on the unit output and unit recovery benefit of the units in the nodes affected by disasters; and to determine the corrected expected recovery benefit of the load in the nodes based on the node load and unit recovery benefit of the load in the nodes affected by disasters. The node revenue assessment module is used to add the corrected expected recovery revenue of the units in each node and the corrected expected recovery revenue of the load to obtain the expected recovery revenue of each node. The node recovery priority determination module is used to determine the recovery priority of each node in the power grid based on the expected recovery benefits of each node.

8. An electronic device, characterized in that, The device includes: A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the method for prioritizing power grid node recovery under extreme conditions as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed by a processor as described in any one of claims 1-6, a method for prioritizing the recovery of power grid nodes under extreme conditions.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for prioritizing power grid node recovery under extreme conditions as described in any one of claims 1-6.

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