A method, device, equipment and medium for evaluating power supply capacity of a low-voltage distribution network

Abstract

This application relates to the field of computer technology and provides a method, apparatus, device, and medium for evaluating the power supply capacity of a low-voltage distribution network. The method includes: acquiring a target directed graph model when a fault is detected in the target low-voltage distribution network; acquiring a set of target load nodes for priority power restoration in non-fault areas based on the load data of each node in the target low-voltage distribution network and the target directed graph model; performing a path search from the node where the distributed device is located in the target directed graph model to obtain a target power supply restoration path based on the location and parameters of the distributed devices in the target directed graph model and the target load node set; and evaluating the power supply capacity of the target low-voltage distribution network based on the post-restoration operating state of the target low-voltage distribution network after executing the target power supply restoration path to obtain an evaluation value of the power supply capacity of the target low-voltage distribution network. The power supply capacity evaluation is achieved through path search using a directed graph model, improving power supply reliability and restoration efficiency.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method for evaluating the power supply capacity of a low-voltage distribution network, a device for evaluating the power supply capacity of a low-voltage distribution network, a corresponding electronic device, and a corresponding computer-readable storage medium. Background Technology

[0002] In the current power supply system, the low-voltage distribution network (hereinafter referred to as the low-voltage distribution network) is a key link directly facing users. Its stable operation is crucial to ensuring a reliable power supply. For example, in the scenario of a large plant being built, it is predicted that the load on the distribution transformers (hereinafter referred to as distribution transformers) near the new plant will increase significantly. At this time, there is a need to build new distribution transformers to cut off the load. However, for low-voltage distribution networks with complex structures, numerous devices, and wide distribution, regional faults are prone to occur during the construction of new distribution transformers. When a fault occurs in the distribution network, it is crucial to quickly restore power supply capacity to ensure normal power consumption for users and reduce economic losses and social impact.

[0003] In technologies related to the rapid restoration of power supply capacity after a distribution network fault, power supply capacity assessment can be performed based on traditional topology analysis methods or simple heuristic algorithms. Among them, the topology-based power supply capacity assessment scheme usually involves modeling and analyzing the distribution network topology to determine possible recovery paths after a fault; the power supply capacity assessment scheme based on simple heuristic algorithms usually involves providing recovery schemes for simple distribution network environments. However, due to the uncertainty of the low-voltage distribution network operating environment, both topology-based and simple heuristic algorithm-based power supply capacity assessment schemes are difficult to accurately assess power supply capacity and effectively achieve power restoration results. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and medium for assessing the power supply capacity of a low-voltage distribution network, which can improve the power supply reliability and recovery efficiency of the distribution network in low-voltage areas and effectively achieve power supply restoration.

[0005] In one aspect, embodiments of this application provide a method for assessing the power supply capacity of a low-voltage distribution network, the method comprising:

[0006] When a fault is detected in the target low-voltage distribution network, a target directed graph model is obtained; the target directed graph model is the directed graph model after removing the nodes and edges of the fault region from the initial directed graph model, and the target directed graph model is marked with the location and parameters of the distributed devices in the non-fault region;

[0007] Based on the load data of each node in the target low-voltage distribution network and the target directed graph model, obtain the set of target load nodes for priority power restoration in the non-faulty area;

[0008] Based on the location and parameters of the distributed devices in the target directed graph model, and the target load node set, a path search is performed starting from the node where the distributed devices are located in the target directed graph model to obtain the target power restoration path;

[0009] Based on the post-restoration operating status of the target low-voltage distribution network after executing the target power restoration path, the power supply capacity of the target low-voltage distribution network is evaluated to obtain the power supply capacity evaluation value of the target low-voltage distribution network; the power supply capacity evaluation value is used to indicate the ability to quickly restore power supply after a fault.

[0010] In another aspect, embodiments of this application also provide a power supply capacity assessment device for low-voltage distribution networks, the device comprising:

[0011] The directed graph model acquisition module is used to acquire a target directed graph model when a fault is detected in the target low-voltage distribution network. The target directed graph model is the directed graph model after removing the nodes and edges of the fault area from the initial directed graph model. The target directed graph model is marked with the location and parameters of the distributed devices in the non-fault area.

[0012] The load node acquisition module is used to acquire, based on the load data of each node in the target low-voltage distribution network and the target directed graph model, the set of target load nodes for priority power restoration in the non-faulty area;

[0013] The path search module is used to perform path search starting from the node where the distributed device is located in the target directed graph model, based on the location and parameters of the distributed device in the target directed graph model and the target load node set, to obtain the target power restoration path;

[0014] The power supply capacity assessment module is used to assess the power supply capacity of the target low-voltage distribution network based on the post-recovery operating status of the target low-voltage distribution network after the execution of the target power supply recovery path, and obtain the power supply capacity assessment value of the target low-voltage distribution network; the power supply capacity assessment value is used to indicate the ability to quickly restore power supply after a fault.

[0015] In another aspect, embodiments of this application also provide an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements any of the aforementioned low-voltage distribution network power supply capacity assessment methods.

[0016] In another aspect, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the methods for evaluating the power supply capacity of a low-voltage distribution network.

[0017] In another aspect, embodiments of this application also provide a computer program product containing instructions that, when run on a computer, cause the computer to execute the low-voltage distribution network power supply capacity assessment method described in the above aspects.

[0018] The low-voltage distribution network power supply capacity assessment method, apparatus, equipment, and storage medium provided in this application, when a fault is detected in the target low-voltage distribution network, adopts a target directed graph model that considers the location and parameters of distributed devices in non-faulty areas. Starting from the node where the distributed devices are located in the target directed graph model, a path search is performed on the target load node set to obtain a target power supply recovery path. This path is then used to assess the power supply capacity of the target low-voltage distribution network executing the power supply recovery path. This not only ensures that the target power supply recovery path can adapt to the dynamic changes of the power system based on the marked location and parameters reflecting the operating status of the distribution network, but also ensures the accuracy of the assessment of the rapid restoration of power supply capacity after the distribution network fault is restored, achieving accuracy and effectiveness of power supply restoration and improving the power supply reliability of the low-voltage distribution network; furthermore, by using the target directed graph model, the path search process only searches for paths with directed connections, eliminating the need to search all paths, thereby improving the recovery efficiency of the low-voltage distribution network. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of a method for assessing the power supply capacity of a low-voltage distribution network, as provided in an embodiment of this application.

[0020] Figure 2 This is a flowchart illustrating the steps of another low-voltage distribution network power supply capacity assessment method according to an embodiment of this application;

[0021] Figure 3 This is a structural block diagram of a low-voltage distribution network power supply capacity assessment device according to an embodiment of this application;

[0022] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application;

[0023] Figure 5 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] The reliable and stable operation of low-voltage distribution networks is conducive to ensuring a reliable power supply. Therefore, when a fault occurs in a low-voltage distribution network area, there is a need to quickly restore power supply to the faulty distribution network in order to improve the power supply reliability and restoration efficiency of the distribution network in the low-voltage area.

[0026] Among the technologies for rapidly restoring power supply capacity after a distribution network fault, one example is a topology-based power supply capacity assessment scheme. This scheme typically involves modeling and analyzing the distribution network topology to determine possible recovery paths after a fault. However, this scheme ignores the dynamic characteristics of the power system in actual operation, such as real-time load changes and the integration of distributed power sources. Furthermore, with the widespread application of distributed power sources, energy storage devices, and other power equipment in the distribution network, the complexity and uncertainty of the distribution network will be further increased, leading to insufficient accuracy and effectiveness of the recovery scheme. Another example is a power supply capacity assessment scheme based on simple heuristic algorithms. This scheme typically provides recovery solutions for simple distribution network environments. Although the aforementioned schemes can provide recovery solutions quickly to a certain extent, they lack the ability to dynamically adjust to the globally optimal solution, are prone to getting trapped in local optima, and cannot achieve the best power supply recovery effect in complex distribution network environments.

[0027] In summary, due to the uncertainty of the low-voltage distribution network operating environment, the aforementioned technologies are difficult to effectively achieve power restoration.

[0028] In this embodiment, when a fault is detected in the target low-voltage distribution network, a target directed graph model that considers the location and parameters of distributed devices in non-faulty areas is adopted. Starting from the node where the distributed devices are located in the target directed graph model, a path search is performed on the target load node set to obtain the target power supply restoration path. This path is then used to assess the power supply capacity of the target low-voltage distribution network executing the power supply restoration path. This not only ensures that the searched target power supply restoration path can adapt to the dynamic changes of the power system based on the marked location and parameters reflecting the operating status of the distribution network, but also ensures the accuracy of the assessment of the rapid power supply recovery capacity after the distribution network fault is restored, thus achieving the accuracy and effectiveness of power supply restoration and improving the power supply reliability of the low-voltage distribution network. Furthermore, the target directed graph model only searches for paths with directed connections during the path search process, eliminating the need to search all paths, thereby improving the recovery efficiency of the low-voltage distribution network. Furthermore, multiple feasible power restoration paths can be searched using the Depth-First Search (DFS) algorithm, and each power restoration path can be evaluated for multi-objective decision-making based on its path information to select the optimal final power restoration path. This enables global search in complex power distribution network environments, avoids getting trapped in local optima, and further achieves better power restoration results.

[0029] Reference Figure 1 This document illustrates a flowchart of a method for assessing the power supply capacity of a low-voltage distribution network according to an embodiment of this application. The method may specifically include the following steps:

[0030] Step S101: When a fault is detected in the target low-voltage distribution network, obtain the target directed graph model;

[0031] The low-voltage distribution network power supply capacity assessment method provided in this application embodiment can be applied to a power supply capacity assessment system. The power supply capacity assessment system can be a tool or platform for analyzing and assessing the power supply capacity of a power system or a specific power supply area. This application embodiment does not limit this.

[0032] Optionally, the target low-voltage distribution network can be any low-voltage distribution network in the power system. The low-voltage distribution network can include multiple nodes, and each node can be equipped with multiple types of sensors, such as voltage sensors, current sensors, temperature sensors, etc. The low-voltage distribution network can also be connected to distributed devices, which can include energy and energy storage devices. Distributed energy sources include solar photovoltaic energy, geothermal energy, natural gas distributed energy, etc., and energy storage devices include thermal storage devices, flywheel energy storage units, small compressed air energy storage devices, etc. The embodiments of this application do not limit this.

[0033] In some embodiments of this application, when a fault occurs in a low-voltage distribution network area, there is a need to quickly restore power to the faulty distribution network. In order to improve the power supply reliability and restoration efficiency of the distribution network in the low-voltage area, embodiments of this application can achieve this by using a directed graph model to search for paths with directed connections when the power supply capacity assessment system detects a fault in the target low-voltage distribution network.

[0034] In practical applications, the detection of distribution network faults can be achieved through a power supply capacity assessment system that uses electrical quantity change detection methods and fault indicator methods to detect whether a fault has occurred in the distribution network in real time. As an example, the electrical quantity change detection method monitors the current magnitude in real time using current transformers installed on each branch of the distribution network. When a fault such as a short circuit occurs, the current near the fault point will show a sudden and significant increase, usually far exceeding the current value during normal operation. Therefore, by setting a reasonable current threshold, when the monitored current exceeds the aforementioned threshold, a preliminary judgment can be made that a fault has occurred in the distribution network. As another example, in the fault indicator method, the fault indicator refers to the monitoring equipment installed on the distribution line, which can be divided into short-circuit fault indicators and ground fault indicators. When a short-circuit fault occurs, the short-circuit fault indicator judges and indicates the occurrence of the fault by detecting sudden changes in current, direction, and other characteristics.

[0035] Furthermore, when a fault is detected in the target low-voltage distribution network, the directed graph model obtained by the power supply capacity assessment system can be the target directed graph model. The target directed graph model is mainly the directed graph model after removing the nodes and edges of the fault area from the initial directed graph model, i.e., the post-fault directed graph model. This is to avoid searching for power restoration paths that pass through the nodes of the fault area when using the target directed graph model for path search in the future. By searching only the paths with directed connections through the directed graph model, the power restoration efficiency is improved, and the power restoration effect is effectively achieved.

[0036] The initial directed graph model can be constructed based on the network topology of the target low-voltage distribution network. The network topology data can include the connection relationship and line parameters of each node in the target low-voltage distribution network. For example, the power supply capacity assessment system can construct an initial directed graph model with each node as the vertex and each line as the directed edge. Each line can be used to indicate the connection relationship between nodes. The directed edges in the constructed initial directed graph model can be bidirectional edges. The specific construction process of the initial directed graph model is not limited in the embodiments of this application.

[0037] Optionally, the power supply capacity assessment system can also mark the locations and parameters of distributed devices in the initial directed graph model. These distributed devices can include distributed power sources and energy storage devices. Marking the locations of distributed devices in the directed graph model helps to clarify the specific locations of the nodes where the distributed devices are located, as well as the surrounding nodes. Marking the parameters of distributed devices in the directed graph model helps to clarify the performance configuration of each distributed device. In other words, the locations and parameters marked in the directed graph model can reflect the real-time operating status of the distribution network. It should be noted that the specific marking process for locations and parameters is not limited in the embodiments of this application.

[0038] In some embodiments of this application, when a fault is detected in the distribution network, the power supply capacity assessment system can determine the fault location information of the fault area in the target low-voltage distribution network. Then, based on the fault location information, the nodes and edges of the fault area can be removed from the initial directed graph model to obtain the target directed graph model, i.e., the post-fault directed graph model.

[0039] In the initial directed graph model, the locations and parameters of distributed devices are marked. In the target directed graph model obtained based on the initial directed graph model, the locations and parameters of distributed devices are also marked. However, the target directed graph model is a post-fault directed graph model. In the target directed graph model, the locations and parameters of distributed devices located in non-fault areas are mainly marked. This is to avoid searching for power restoration paths through nodes in the fault area in the future. It can also adapt to the dynamic changes of the power system, ensure the accuracy of power restoration paths, and improve the search speed of power restoration paths, thereby improving power restoration efficiency.

[0040] Step S102: Based on the load data of each node in the target low-voltage distribution network and the target directed graph model, obtain the set of target load nodes for priority power restoration in the non-faulty area;

[0041] When the power supply capacity assessment system detects a fault in the distribution network and quickly restores power to the faulty distribution network, after obtaining the target directed graph model, it can determine the set of target load nodes in the non-faulty area for priority power restoration, and then perform path search based on the target directed graph model and the target load node set.

[0042] In some embodiments of this application, the power supply capacity assessment system can perform load assessment on nodes in non-fault areas of the distribution network based on load data of each node in the distribution network and a post-fault directed graph model, and obtain a target load node set for priority power restoration.

[0043] For example, the load data of each node may include the real-time load size, load type and load interruption cost of each node. The load type includes residential load, commercial load and industrial load, wherein residential load has higher priority than commercial load, and commercial load has higher priority than industrial load.

[0044] In practical applications, firstly, the power supply capacity assessment system can determine the nodes in the non-faulty areas of the distribution network based on the post-fault directed graph model and all nodes in the distribution network. Then, it can assess the load of the nodes in the non-faulty areas of the distribution network based on the real-time load size, load type, and load interruption cost of each node. It should be noted that the specific load assessment process is not limited in the embodiments of this application.

[0045] Step S103: Based on the location and parameters of the distributed devices in the target directed graph model, and the target load node set, perform path search starting from the node where the distributed devices are located in the target directed graph model to obtain the target power supply restoration path;

[0046] In some embodiments of this application, the power supply capacity assessment system can perform path search in a post-fault directed graph model based on a depth-first search algorithm to obtain the target power supply recovery path.

[0047] Specifically, based on the location and parameters of the distributed devices in the target directed graph model, a path search is performed on the target load node set starting from the node where the distributed devices are located in the target directed graph model. Multiple feasible initial power restoration paths are found that can connect to the target load nodes in the target load node set, and the line capacity on the path must meet the preset load power supply requirements.

[0048] Furthermore, based on the path information of each initial power restoration path, a multi-objective decision-making evaluation can be performed on each initial power restoration path to obtain the target power restoration path, and then the optimal final power restoration path can be selected. This enables global search in complex distribution network environments, avoiding getting trapped in local optima and achieving better power restoration results. The path information can include path length, remaining line capacity, and power restoration time. The path length can be determined based on the number of nodes in each initial power restoration path. The remaining line capacity can be determined based on the path length, the rated capacity of each included edge, and the currently transmitted power. The power restoration time can be determined based on the path length, line transmission speed, and switching operation time. The specific multi-objective decision-making evaluation process is not limited in this embodiment.

[0049] Step S104: Based on the post-restoration operation status of the target low-voltage distribution network after executing the target power supply restoration path, evaluate the power supply capacity of the target low-voltage distribution network to obtain the power supply capacity evaluation value of the target low-voltage distribution network.

[0050] In some embodiments of this application, after selecting the optimal final power restoration path, the power supply capacity assessment system can execute the finally selected target power restoration path to restore power to the target low-voltage distribution network that has failed, obtain the operating status of the target low-voltage distribution network after power restoration, and assess the power supply capacity of the target low-voltage distribution network to quickly restore power after a fault based on the operating status after restoration.

[0051] Specifically, power restoration capability indicators can be determined based on the operational status of the distribution network after power restoration. Then, power supply capability assessment is performed based on these indicators to obtain the power supply capability assessment value for the target low-voltage distribution network. This assessment value indicates the ability to quickly restore power after a fault. It should be noted that the embodiments in this application do not limit the specific power supply capability assessment process.

[0052] In this embodiment, when a fault is detected in the target low-voltage distribution network, a target directed graph model that considers the location and parameters of distributed devices in non-faulty areas is adopted. Starting from the node where the distributed devices are located in the target directed graph model, a path search is performed on the target load node set to obtain the target power supply restoration path. This path is then used to assess the power supply capacity of the target low-voltage distribution network executing the power supply restoration path. This not only ensures that the searched target power supply restoration path can adapt to the dynamic changes of the power system based on the marked location and parameters reflecting the operating status of the distribution network, but also ensures the accuracy of the assessment of the rapid power supply recovery capacity after the distribution network fault is restored, thus achieving the accuracy and effectiveness of power supply restoration and improving the power supply reliability of the low-voltage distribution network. Furthermore, the target directed graph model only searches for paths with directed connections during the path search process, eliminating the need to search all paths, thereby improving the recovery efficiency of the low-voltage distribution network.

[0053] Reference Figure 2 This document illustrates a flowchart of another method for assessing the power supply capacity of a low-voltage distribution network, provided in an embodiment of this application. Applied to a power supply capacity assessment system, the method may specifically include the following steps:

[0054] Step S201: Based on the network topology of the target low-voltage distribution network, construct an initial directed graph model;

[0055] In this embodiment of the application, when a fault occurs in a low-voltage distribution network area, the power supply capacity assessment system can use a directed graph model to search for paths with directed connections to obtain a power supply restoration path, and then execute the power supply restoration path to restore power supply, thereby meeting the need for rapid power restoration of the faulty distribution network and improving the power supply reliability and restoration efficiency of the distribution network in the low-voltage area.

[0056] The application of directed graph models is mainly manifested in the use of post-fault directed graph models, which are obtained by processing the initial directed graph model. The power supply capacity assessment system can first construct the initial directed graph model of the target low-voltage distribution network. Furthermore, in order to reflect the operating status of the distribution network in real time and meet the adaptability to the dynamic changes of the power system, the location and parameters of distributed power sources and energy storage devices can be marked in the constructed initial directed graph model.

[0057] In some embodiments of this application, the power supply capacity assessment system can construct an initial directed graph model of the target low-voltage distribution network based on the network topology. The network topology data can include the connection relationships and line parameters of each node in the target low-voltage distribution network. For example, the power supply capacity assessment system can construct an initial directed graph model with each node as a vertex and each line as a directed edge. It should be noted that each line in the initial directed graph model can be used to indicate the connection relationships between nodes, and the directed edges in the constructed initial directed graph model can be bidirectional edges; this application does not impose any limitations on this.

[0058] In practical applications, the process of constructing an initial directed graph model can include establishing node numbers and mapping relationships, performing a depth-first search traversal, constructing edges and adding line parameters, and marking distributed power sources and energy storage devices.

[0059] The process of establishing node numbers and mapping relationships can be represented by the power supply capacity assessment system assigning unique numbering information to each node in the target low-voltage distribution network and establishing a mapping relationship between each node's numbering information and actual node information, such as name and type. For example, assume the node set is... Where m represents the number of nodes, which can be the i-th node n i Assignment Number Information ID i , i=1,2,...m.

[0060] The nodes in the target low-voltage distribution network can include power supply nodes. The depth-first search traversal process can be specifically represented as starting from the power supply node and using the depth-first search algorithm to traverse the entire target low-voltage distribution network. During the traversal, the set of child nodes can be obtained, the set of child nodes of each node can be recorded, and each child node can be assigned a number according to the numbering information of the parent node, that is, the numbering information of each node.

[0061] Optionally, the power supply capacity assessment system can employ a hierarchical numbering method, starting from the power node and using a depth-first search to obtain the set of child nodes for each node. Then, based on the numbering information of each node, each child node in the set of child nodes for each node is numbered sequentially to obtain the encoding information of each child node in the set of child nodes for each node. For example, assume the current parent node is n. c Its child node set is Where k represents the number of child nodes in the set of child nodes, then in the current parent node n c The numbering information of the i-th child node can be represented as: Where i = 1, 2, ..., k, c i For the elements in the aforementioned set of child nodes, ID c This refers to the current parent node n c The assigned number information, the assigned number information ID c It can be based on the ID information of the corresponding i-th node. i Sure.

[0062] The process of constructing edges and adding line parameters can be represented by traversing all node pairs, including the parent node and its child nodes, checking whether there is a connection between any two nodes, and constructing edges for node pairs with a connection. The constructed edges can be bidirectional edges. At this time, the corresponding line parameter information can also be added to the edges based on the resistance and reactance parameters between any two nodes.

[0063] Optionally, for all node pairs, including the parent node and its child nodes, the power supply capacity assessment system can traverse any two nodes. For any two nodes with a connection relationship, an edge is constructed between the two nodes, resulting in the edge set of the directed graph. For example, for a child node under a certain parent node, assume the following connection relationship data exists: , where, as an example, (n i1 ,n i2 ) represents the first child node n under the i-th parent node. i1 To the second child node n i2 There are connections, and at this point, the connection relationships (n) between the child nodes under the current parent node are... i1 ,n i2This can be converted into a directed edge (ID). i1 ID i2 As another example, (n) ik-1 ,n ik ) represents the (k-1)th child node n under the i-th parent node. ik-1 To the k-th child node n ik There exists a connection, for the connection relationship (n) of the child nodes under the i-th parent node. ik-1 ,n ik This can be converted into a directed edge (ID). ik-1 ID ik Optionally, the directed edges obtained from the aforementioned transformation can be added to the edge set. middle.

[0064] Optionally, the power supply capacity assessment system can add line parameter information to the edge between any two nodes based on the resistance and reactance parameters between them. For example, assume the edge (ID) i1 ID i2 The resistance of ) is R i1,i2 The reactance is X i1,i2 Then the parameters of the edge are expressed as .

[0065] The process of marking distributed power sources and energy storage devices can be represented as marking the location of the distributed power source based on the first target node information and adding parameter markings to its node based on the power capacity, and marking the location of the energy storage device based on the second target node information and adding parameter markings to its node based on the rated capacity, rated charging power and rated discharging power.

[0066] For example, suppose the first target node information of the distributed power source, i.e., the set of distributed power sources, is as follows: The second target node information of the energy storage device, that is, the set of energy storage devices. Where p represents the number of distributed power sources and q represents the number of energy storage devices, then for the i-th distributed power source... and the j-th energy storage device We can find the i-th distributed power source. Node ID information and the j-th energy storage device Node ID information Then, based on the node number information found above, the location can be marked. The marking rules can be as follows: Represents a distributed power node. This indicates the energy storage device node, completing the location marking of distributed power sources and energy storage devices.

[0067] After completing the location marking of distributed power sources and energy storage devices, parameter marking can be performed on the distributed power sources and energy storage devices. For example, assuming the distributed power source... The power capacity is For distributed power nodes For distributed power nodes The parameters of the tag can be represented as: Assuming an energy storage device The rated capacity is Rated charging power is Rated discharge power is For energy storage device nodes For energy storage device nodes The parameters of the tag can be represented as: .

[0068] In some embodiments of this application, the power supply capacity assessment system can integrate the above-mentioned node numbers, mapping relationships, edge and line parameters, and labeling information of distributed power sources and energy storage devices into a complete graph model, thereby constructing an initial directed graph model. Specifically, the integration process can be represented by the above-mentioned process, that is, after completing the process of establishing node numbers and mapping relationships, the process of depth-first search traversal, the process of constructing edges and adding line parameters, and the process of labeling distributed power sources and energy storage devices, it indicates that the process of constructing the initial directed graph model is completed.

[0069] It should be noted that the constructed mapping relationship can quickly locate the child nodes of the current node and assign them numbers. During traversal, the constructed mapping relationship can also be used to record the set of child nodes for each node, ensuring the completeness and correctness of the traversal. Furthermore, the constructed mapping relationship can also quickly find the numbers of any two nodes and determine whether a connection exists between them. When constructing edges, the mapping relationship can also help convert node numbers into actual node objects to add line parameters such as resistance and reactance to the edges. Additionally, the constructed mapping relationship can also quickly locate the nodes where distributed power sources and energy storage devices are located, and when marking parameters, the mapping relationship can help convert node numbers into actual node objects to add parameters such as power capacity and rated capacity. The specific application process of the constructed mapping relationship in the construction of the initial directed graph model is not limited in the embodiments of this application.

[0070] In some preferred embodiments of this application, the connectivity and rationality of the constructed initial directed graph can also be checked to ensure that there are no isolated nodes in the graph and that the parameters of the nodes and edges conform to actual physical laws.

[0071] For example, the connectivity checking method can be specifically manifested as using a depth-first search algorithm to traverse the directed graph and count the number of reachable nodes. For instance, suppose the set of reachable nodes is V. reachable Initially At this point, you can start from any node ID. s Start DFS traversal, ID s It can be the ID mentioned above. i and ID ck If any of the items are available, then the reachable node can be added to the reachable node set V. reachable In the middle, after the traversal is completed, if This indicates that the graph is connected. As mentioned above, m represents the number of nodes. The rationality check can specifically involve checking whether the transformer capacity and rated current of the switching equipment at each node meet the requirements of the line parameters. For example, suppose the k-th node... The transformer capacity is The rated current of the kth switching device is The maximum current of the line connected to the k-th node is The formula for judging reasonableness can be as follows:

[0072] ;

[0073] Among them, U k Indicates node ID k The voltage level. This application does not limit this to any particular voltage level.

[0074] Step S202: When a fault is detected in the target low-voltage distribution network, the nodes and edges of the fault area are removed from the initial directed graph model to obtain the target directed graph model.

[0075] In some embodiments of this application, when a fault is detected in the distribution network, the power supply capacity assessment system can determine the fault location information of the fault area in the target low-voltage distribution network. Then, based on the fault location information, the nodes and edges of the fault area can be removed from the initial directed graph model to obtain the target directed graph model, i.e., the post-fault directed graph model. This avoids searching for power restoration paths that pass through the nodes of the fault area when using the target directed graph model for subsequent path search. By searching only paths with directed connections through the directed graph model, the power restoration efficiency is improved, and the power restoration effect is effectively achieved.

[0076] For example, suppose the fault location information set is as follows: Among them, the i-th fault location information f infoi This information may include the suspected fault node number, related line identifiers, etc., which can be used to locate the i-th fault. infoiProcess it.

[0077] As an example, if the fault location information f infoi This includes multiple suspected node numbers. The most likely faulty node can be determined by comparing the electrical characteristics of the nodes (such as the degree of voltage and current anomalies). For example, node... The degree of voltage anomaly is ΔU i The degree of current anomaly is ΔI i The comprehensive anomaly index calculated from it can be: Where α and β are weighting coefficients, and α + β = 1, α > 0, β > 1, then a comprehensive anomaly index can be selected. The largest node is designated as the faulty node n. fault As another example, if the fault location information f infoi The system contains a line identifier. At this point, we can check whether the information of the nodes at both ends of the line is related to the filtered faulty nodes. If the information of the nodes at both ends of the line is related to the filtered faulty nodes, then we can determine that the line is a faulty line.

[0078] Furthermore, in order to remove the nodes and edges of the fault region from the initial directed graph model, the set of nodes and the set of edges of the fault region can be determined first based on the preprocessed fault location information.

[0079] For example, suppose the set of nodes in the fault region is N. fault The set of edges in the fault region is E. fault For the faulty node n fault One can consider its electrically associated nodes, for example, those with faulty node n. fault The set of directly connected nodes is N. direct Assume there are n nodes connected to the faulty node. direct ,at this time Assume that the set of nodes connected to the aforementioned faulty node whose voltage change rate exceeds a certain threshold is N. indirect Then the set of nodes in the fault region can be determined as follows:

[0080]

[0081] Then, for the node set N fault From the nodes in the graph, we can find the edges connecting nodes u and v. For example, if the initial edge set of the directed graph is E... initial Then the edge set of the fault region can be: .

[0082] After obtaining the set of nodes and edges in the faulty region, the nodes and edges in the faulty region can be removed from the initial directed graph model to obtain a temporary graph model. For example, suppose the node set of the initial directed graph is N. initial The edge set is E initial The set of temporary nodes after removal is N. temp The temporary edge set is E temp Then the following relationship exists: .

[0083] After removing nodes and edges from faulty regions, the temporary graph may become disconnected. In this case, the connectivity of the temporary graph can be checked and appropriate measures taken. For example, a breadth-first search (BFS) algorithm can be used to traverse the temporary graph, starting from the nodes... We begin with BFS, assuming the set of reachable nodes is N. reachable ,like If the temporary graph is disconnected, then to further restore connectivity, for the disconnected subgraph, we can find the boundary nodes of the two closest subgraphs. and boundary nodes Specifically, suppose the coordinates of node n in the subgraph are (x... n ,y n ), boundary node n b1 The coordinates are (x n1 ,y n1 ), boundary node n b2 The coordinates are (x n2 ,y n2 ), boundary node n b1 and boundary node n b2 The distance between these two nodes is At this point, you can select the two boundary nodes with the smallest distance, for example, by adding a virtual edge (x). b1 ,y b1 )arrive middle.

[0084] Furthermore, for the remaining nodes The voltage and current of the nodes will change, at which point the voltage and current of the remaining nodes can be updated.

[0085] For voltage and current updates, for example, assume node The original voltage was U n The current is I n The updated voltage and current can be recalculated based on Kirchhoff's laws and the network topology, for example, nodes. and There are N adjacent nodes connected together, and the set of adjacent nodes is N.adj The updated voltage can then be:

[0086]

[0087] Where node M is the set of adjacent nodes N adj any node, It is the association weight between node n and node M, which can be determined based on the line resistance R. nM and line reactance X nM The calculation yields the following result, and the specific calculation formula can be expressed as: .

[0088] And, for the remaining nodes Corresponding remaining edges Its current and power will change, at which point the current and power of the remaining side can be updated.

[0089] For updating current and power, for example, suppose the original current at edge (u,v) is... Power is The updated current and updated power It can be recalculated based on the node voltage and line parameters, and the specific formula is as follows:

[0090]

[0091] in, It represents the edge u Updated voltage, This represents the updated voltage at edge u. R represents the updated voltage at node n. uv X represents the line resistance of edge (u,v). uv This represents the line reactance of the edge (u,v). This represents the phase difference between the voltages at both ends of the edge (u,v).

[0092] After the above processing, the final directed graph model after the fault is obtained, whose node set can be: The edge set can be The nodes and edges all have updated parameters, completing the transformation from the initial directed graph model to the post-fault directed graph model, thus obtaining the target directed graph model subsequently applied to path search.

[0093] Step S203: Based on the load data of each node in the target low-voltage distribution network and the target directed graph model, obtain the set of target load nodes for priority power restoration in the non-faulty area;

[0094] In some embodiments of this application, the power supply capacity assessment system can perform load assessment on nodes in non-fault areas of the distribution network based on load data of each node in the distribution network and a post-fault directed graph model, to obtain a target load node set for priority power restoration, and then perform path search based on the target directed graph model and the target load node set.

[0095] In some embodiments of this application, the power supply capacity assessment system can first determine the nodes in the non-faulty areas of the distribution network, then determine the load data of the nodes in the non-faulty areas, determine the priority values ​​of the nodes in the non-faulty areas, and then determine the target load node set based on the determined priority values.

[0096] In practical applications, power supply capacity assessment systems can determine the nodes in non-fault areas of the distribution network based on the post-fault directed graph model and all nodes in the distribution network—that is, based on the node numbering information of each node in the distribution network and each node in the post-fault directed graph model. Specifically, this can be achieved by matching the node numbering information corresponding to the nodes in the non-fault areas of the distribution network through the post-fault directed graph model. Assume the node set of the post-fault directed graph model is N. final This indicates that the node set contains all nodes in the distribution network that are not affected by faults.

[0097] After identifying the nodes in the non-faulty areas of the distribution network, the power supply capacity assessment system can filter the load data of these nodes from the distribution network's load data. For example, the load data for each node can include the real-time load size, load type, and load interruption cost. Assuming the node set of the directed graph model after a fault is N... final The load data set of all nodes in the distribution network is ,in, Indicates the first Load data for each node, including real-time load size P. loadi Load type T loadi and load interruption cost C loadi At this time, the load data set of the non-faulty area The formula can be expressed as: .

[0098] In some embodiments of this application, the power supply capacity assessment system can assess the load of nodes in non-faulty areas of the distribution network based on the real-time load size, load type, and load interruption cost of each node. Specifically, this can be achieved by determining the target load node set based on the determined priority value.

[0099] Specifically, firstly, the power supply capacity assessment system can assign initial priority values ​​to nodes in non-faulty areas based on the priority relationship corresponding to the load type of the nodes. The load types include residential load, commercial load, and industrial load, with residential load having a higher priority than commercial load, and commercial load having a higher priority than industrial load.

[0100] For example, suppose the initial priority value of the residential load is The initial priority value for commercial load is The initial priority value of industrial load is For the load data set of non-faulty areas The i-th load data Its initial priority value The calculation formula can be:

[0101]

[0102] Optionally, load interruption cost reflects the economic losses or social impacts caused by power outages. Higher load interruption costs allow for priority power restoration. Specifically, the power supply capacity assessment system can adjust the initial priority values ​​corresponding to the load types of nodes in non-faulty areas based on load interruption costs, obtaining the adjusted priority value of the i-th node in the non-faulty area. The specific formula can be seen as follows:

[0103]

[0104] Among them, P basei This refers to the load data set of non-faulty areas. The initial priority value of the load data of the i-th node, C laodi This refers to the load data set of non-faulty areas. The load interruption cost of the load data of the i-th node. and These represent the load data sets for non-faulty areas. The minimum and maximum costs of all load interruptions.

[0105] Furthermore, the real-time load size also affects the priority of power restoration. Generally, larger loads contribute more to the stable operation of the system after power restoration. Specifically, the power supply capacity assessment system can adjust the priority values ​​of nodes in the non-faulty area based on the real-time load size to obtain the adjusted priority value of the i-th node in the non-faulty area. The specific formula can be seen as follows:

[0106]

[0107] Among them, P adi This refers to the load data set of non-faulty areas. The adjusted priority value of the i-th node, P laodi This refers to the load data set of non-faulty areas. The real-time load size of the i-th node in the load data. and These represent the load data sets for non-faulty areas. The minimum and maximum values ​​of all real-time load sizes.

[0108] Optionally, in the target directed graph model, i.e., the post-fault directed graph model, nodes closer to the power source nodes face relatively less difficulty and risk during power restoration and can be given higher priority. Specifically, the power supply capacity assessment system can determine the node distances between nodes in the non-fault area and the power source nodes based on the post-fault directed graph model, and optimize the corrected priority values ​​of the nodes in the non-fault area according to the node distances to obtain the final priority values ​​of the nodes in the non-fault area.

[0109] In practical applications, power supply capacity assessment systems can obtain the node distances between nodes in non-faulty areas and power supply nodes based on a post-fault directed graph model. For example, node distances can be calculated using a breadth-first search algorithm. Assuming that the node distances can be obtained through a breadth-first search algorithm... To power node The shortest path length is Final priority value P finali The specific calculation formula can be:

[0110]

[0111] Among them, P modi This refers to the load data set of non-faulty areas. The corrected priority value of the i-th node, d i This refers to the load data set of non-faulty areas. The shortest path length from the i-th node to the power node. Represents the load data set of non-faulty areas The maximum value of the shortest path length from all nodes to the power node.

[0112] Optionally, nodes with higher final priority values ​​are given priority for power restoration. The power supply capacity assessment system can determine the target load nodes based on the final priority values ​​of nodes in non-faulty areas. Specifically, this can be achieved by determining the final priority value. Load data set for non-faulty areas The load nodes in the system are sorted, and the nodes with the highest final priority values ​​are selected as the target load nodes for priority power restoration. For example, the set of target load nodes for priority power restoration is L. targer Select the final priority value Top The nodes are used as the target load node set. The value can be determined based on the actual situation, that is: .

[0113] The embodiments of this application can determine the target load node set for priority power restoration, so that the depth-first search algorithm combined with the target load node set can be used to evaluate multiple power restoration paths for multi-objective decision-making, select the optimal final power restoration path, adapt to the dynamic changes of the power system, ensure the accuracy of the power restoration path, improve the search speed of the power restoration path, and thus improve the power restoration efficiency.

[0114] Step S204: Based on the location and parameters of the distributed devices in the target directed graph model, and taking the node where the distributed devices are located in the target directed graph model as the starting node, perform path search on the target load node set to obtain the initial power supply restoration path;

[0115] In the initial directed graph model, the locations and parameters of distributed devices are marked. In the target directed graph model obtained based on the initial directed graph model, the locations and parameters of distributed devices are also marked. However, the target directed graph model is a post-fault directed graph model. In the target directed graph model, the locations and parameters of distributed devices located in non-fault areas are mainly marked. This is to avoid searching for power restoration paths through nodes in the fault area in the future. It can also adapt to the dynamic changes of the power system, ensure the accuracy of power restoration paths, and improve the search speed of power restoration paths, thereby improving power restoration efficiency.

[0116] In some instances of this application, the power supply capacity assessment system can use a depth-first search algorithm to perform path search starting from the node where the distributed power source or energy storage device is located in the directed graph model after the fault, based on the location and parameters of the distributed power source and energy storage device in the directed graph model after the fault, as well as the target load node for which power supply is to be restored first. The system can find multiple feasible initial power supply restoration paths that can connect to the target load node, and the line capacity on the path must meet the preset load power supply requirements.

[0117] Specifically, firstly, the power supply capacity assessment system can identify target nodes from the target load node set, and then determine the initial power supply restoration path based on the identified target nodes and starting nodes.

[0118] The process of determining the target node can be described as follows: obtaining the actual power demand of each node in the target load node set; then, based on the location of the distributed device in the target directed graph model, sequentially selecting starting nodes from the node set where the distributed device is located, and performing a depth-first search from each starting node to obtain the currently searched node; then, iterative optimization search can be performed, specifically by obtaining the set of adjacent nodes corresponding to the currently searched node, continuing the depth-first optimization search from each adjacent node in the adjacent node set, and updating the first remaining power supply capacity of the node where the distributed device is located to the second remaining power supply capacity during the search process, until the target node is a node in the target load node set, and the second remaining power supply capacity of the node where the distributed device is located is greater than or equal to the actual power demand of the target adjacent node. The target adjacent node that meets the above conditions is used as the target node for constructing the initial power restoration path.

[0119] In practical applications, the relevant data structures can be initialized before starting the path search. For example, suppose the node set of the directed graph model after a failure is... The edge set is The set of nodes where distributed power sources and energy storage devices are located is The set of target load nodes for priority power restoration is At this point, the power supply capacity assessment system can initialize the following attributes for every n nodes: access flag. Used to mark whether a node has been visited; parent node This is used to record the parent nodes of a node in the search path; it can also create an empty set of feasible paths. It is used to store the finally found feasible power restoration path.

[0120] The actual power demand of each target load node in the target load node set can be determined based on the real-time load data of each node. For example, assume the target load node set is... The i-th target load node in the target load node set is The real-time load size is For actual power demand The specific formula can be seen as follows:

[0121]

[0122] in, This represents the preset margin coefficient, with a value range of [value range missing]. .

[0123] Furthermore, the power supply capacity assessment system can assess the power supply capacity from the set of nodes where distributed power sources and energy storage devices are located. Select nodes in sequence Starting from each starting node, a depth-first search is performed. It should be noted that before starting the depth-first search, the j-th starting node can be... Access token and initialize its remaining power supply capacity. The rated power of the distributed power source or energy storage device In other words, the parameter markings of the nodes where distributed devices are located in the target directed graph model can be used to determine the first remaining power supply capacity of the nodes where distributed devices are located.

[0124] Optionally, the power supply capacity assessment system starts from the current starting node. After starting a depth-first search to obtain the currently found node, the set of neighboring nodes corresponding to the currently found node can be obtained. For each neighboring node in the set of neighboring nodes corresponding to the currently found node, based on the line capacity between the currently found node and the target neighboring node connected to the currently found node, as well as the actual power demand of the target neighboring node, a depth-first search is continued from the target neighboring node to obtain the target node.

[0125] For example, suppose the currently searched node is The currently searched nodes The set of adjacent nodes is s is a positive integer, representing the s-th neighboring node in the set of neighboring nodes of the currently searched node. The following operations can be performed: If If the neighboring nodes of the target have not yet been visited, then the connections between the currently searched nodes can be calculated. Its target neighboring nodes Line capacity Based on the calculated line capacity, a depth-first search is performed starting from the target's neighboring nodes.

[0126] As an example, if the target is adjacent to a node This means that the currently searched node belongs to the target load node set, and the power supply capacity assessment system can check the currently searched node. Current remaining power supply capacity Does the target adjacent node satisfy the condition? Actual power demand and line capacity Does it meet the actual power requirements? That is, determine whether the following inequalities are true:

[0127]

[0128] If the above equation holds true, it means that a target node that meets the power supply conditions and is currently in the target load node set has been found. In this context, the power supply condition can refer to the updated second remaining power supply capacity of the node where the distributed power source or energy storage device is located being greater than or equal to that of the target node. Actual power demand .

[0129] If the above inequality holds, the power supply capacity assessment system can update the target neighboring nodes. parent node and update the target's neighboring nodes. Remaining power supply capacity ,mark Continue from the target's adjacent nodes Start a depth-first search until the target node is found.

[0130] As another example, if the target is adjacent to the node This means that the currently searched node does not belong to the target load node set, assuming the current line capacity... Greater than the preset minimum transmission power Then the power supply capacity assessment system can update the target neighboring nodes. parent node And update the target's neighboring nodes. Remaining power supply capacity ,mark Continue from node Start a depth-first search until the target node is found.

[0131] In some embodiments of this application, after the target node is found, the power supply capacity assessment system can construct a power supply restoration path from the starting node to the target node by backtracking the parent node. It can also obtain the line capacity of each edge on the power supply restoration path and the cumulative demand power of all target load nodes passed from the starting node to the current edge. Based on the line capacity of each edge on the power supply restoration path and the cumulative demand power of at least one target load node passed from the starting node to the current edge, the initial power supply restoration path is determined.

[0132] For example, a power supply capacity assessment system can be constructed by tracing back to the parent node, starting from the initial node. To the target node The power restoration path, assuming the power restoration path is... For each edge on the power restoration path At this point, you can check its line capacity. Can the cumulative power demand of all target load nodes traversed from the starting node to the current edge always be met? Among them, cumulative power demand The formula for calculating can be shown below:

[0133]

[0134] If all edges on the power restoration path satisfy Then determine the path. It is feasible, and it can be added to the set of feasible paths. In this process, multiple feasible initial power restoration paths were obtained.

[0135] Step S205: Based on the path information of each initial power restoration path, evaluate each initial power restoration path to obtain the target power restoration path;

[0136] In some embodiments of this application, after searching for multiple feasible power restoration paths using a depth-first search algorithm, the power supply capacity assessment system can evaluate the multiple feasible power restoration paths and obtain the final selected target power restoration path. It can perform a global search in a complex power distribution network environment, avoid getting trapped in local optima, and achieve better power restoration results.

[0137] Optionally, the path information may include path length, remaining line capacity, and power restoration time. After finding multiple initial power restoration paths, the power supply capacity assessment system can comprehensively evaluate each initial power restoration path based on its path length, remaining line capacity, and power restoration time, and select the path with the best comprehensive evaluation as the set of power restoration paths, thereby obtaining the target power restoration path.

[0138] Specifically, firstly, the power supply capacity assessment system can determine the comprehensive evaluation index of each initial power supply restoration path based on the path length, remaining line capacity, and power supply restoration time of each initial restoration path. Then, the target power supply restoration path can be selected based on the determined comprehensive evaluation index.

[0139] The calculation process for the comprehensive evaluation index can be specifically described as follows: for each initial power restoration path, the path length is determined based on the number of nodes, and the remaining capacity of the line is determined based on the path length, the rated capacity of each edge, and the current transmitted power. Then, the power restoration time is determined based on the path length, the line transmission speed, and the switching operation time. Finally, the comprehensive evaluation index for each initial power restoration path is determined based on the path length, the remaining capacity of the line, and the power restoration time.

[0140] The path length can be determined based on the number of nodes in each initial power restoration path. For the i-th initial power restoration path... its path length Given the number of edges contained in the path, the power supply capacity assessment system can determine the path length based on the number of nodes in each initial power restoration path. For example, assume the initial power restoration path... The initial power restoration path Path length It can be: ,in, It is the initial power restoration path The number of nodes in the middle.

[0141] The remaining line capacity can be determined based on the path length, the rated capacity of each included edge, and the currently transmitted power. Specifically, the power supply capacity assessment system can obtain the initial power restoration path. The j-th edge Rated capacity and current transmitted power Then restore the path according to the initial power supply. The path length and the initial power restoration path The j-th edge Rated capacity and current transmitted power Determine the initial power restoration path Remaining line capacity For example, the rated capacity of the line is a known fixed value. The currently transmitted power can be calculated based on the previous load distribution and the power status of other nodes in the path. The specific formula for the remaining capacity of the line can be expressed as follows: .

[0142] Power restoration time is affected by a variety of factors, including path length. The system can determine the power supply recovery time by obtaining the total switching time and path transmission time of the initial power restoration path. This includes factors such as line transmission speed (different lines have different transmission speeds), switching operation time, and the number of switching operations in the path.

[0143] The total switching operation time of the initial power restoration path can be determined based on the number of switching operations and the path transmission time in the initial power restoration path.

[0144] For example, the power supply capacity assessment system can obtain the i-th initial power supply restoration path. line transmission speed According to the i-th initial power restoration path The length of the j-th edge and the transmission speed of the line Calculate path transmission time The specific formula can be: in, Represents the j-th edge The length of the path; then the number of switch operations in the path can be obtained. According to the initial power restoration path Number of switch operations in the path and switch operation time Calculate the total switching operation time. Then, based on the initial power restoration path Total switching operation time and path transmission time The power restoration time for the i-th initial power restoration path is determined as follows: .

[0145] Furthermore, the power supply capacity assessment system determines the comprehensive assessment index of the i-th initial power supply restoration path based on the path length, remaining line capacity, and power restoration time of each initial power supply restoration path. The specific formula is as follows:

[0146]

[0147] Among them, L tempi C refers to the path length of the i-th initial power restoration path. totali This refers to the remaining capacity of the i-th initial power restoration path. It is the maximum remaining capacity of the lines across all paths, T. i This refers to the power restoration time of the i-th initial power restoration path. It is the maximum power restoration time across all paths.

[0148] In some embodiments of this application, after obtaining the comprehensive evaluation indicators of each initial power restoration path, the power supply capacity assessment system can traverse all the comprehensive evaluation indicators of the initial power restoration paths, and calculate the voltage level and power distribution of each node after power restoration for each path in the power restoration path set, and determine whether the preset safe operation requirements are met.

[0149] In practical applications, the power supply capacity assessment system iterates through the comprehensive evaluation indicators of each initial power supply restoration path, sorts the initial power supply restoration paths according to a preset order, and obtains a path sequence. The preset order can be from largest to smallest; in this case, the initial power supply restoration path at the beginning of the path sequence can be determined as the first path, meaning the path with the highest comprehensive evaluation indicator can be selected. The corresponding initial power restoration path is determined to be the first path. and the first path Determine whether the preset safe operation requirements are met.

[0150] Optionally, the preset condition is that the voltage level is within a preset range, for example... And the power is less than a preset power threshold, for example... It should be noted that for the preset range... and preset power threshold The specific method can be determined based on the actual situation, and the embodiments in this application do not impose any limitations on it.

[0151] For the first path This allows us to calculate the voltage levels at each node after power is restored. Specifically, according to Kirchhoff's laws and Ohm's law, for nodes... Its voltage The following recursive formula can be used to calculate (the power node voltage is...). ),but ,in, It is through the edge The current, It is the edge The impedance; in terms of power distribution, for the edge Its transmission power can be: It should be noted that the edge For via node n k-1 With node n k-1 Connecting edges.

[0152] As an example, if the voltage levels of each node and the power of each edge of the first path meet the preset conditions after power is restored, that is, the first path... Voltage at each node after power is restored satisfy And the first path Transmission power of each edge after power is restored satisfy Then the first path can be identified as the target power restoration path.

[0153] As another example, if the voltage levels at each node and / or the power at each edge of the first path do not meet the preset conditions after power is restored, i.e., the first path... Voltage at each node after power is restored Not satisfied , and / or, the first path Transmission power of each edge after power is restored Not satisfied If the pre-defined path is not met, a suboptimal path can be selected. This suboptimal path is then evaluated to determine if it meets the preset safety operation requirements until those requirements are met, resulting in the final target power restoration path. Specifically, if the preset conditions are not met, the following steps are repeated continuously: A power restoration path is selected from the remaining paths (excluding the first path) in a preset order; that is, a path with comprehensive evaluation index E is selected from the remaining paths (excluding the first path). i The initial power restoration path with the second highest voltage is used as the new power restoration path. The voltage level of each node and the power of each edge of the new power restoration path are calculated after power restoration. It is also determined whether the voltage level of each node and the power of each edge of the new power restoration path meet the preset conditions. The new power restoration path is determined as the target power restoration path until the voltage level and power of the new power restoration path meet the preset conditions.

[0154] Step S206: Based on the post-restoration operation status of the target low-voltage distribution network after executing the target power supply restoration path, evaluate the power supply capacity of the target low-voltage distribution network to obtain the power supply capacity evaluation value of the target low-voltage distribution network.

[0155] In some embodiments of this application, after selecting the optimal final power restoration path, the power supply capacity assessment system can execute the finally selected target power restoration path to restore power to the target low-voltage distribution network that has failed, obtain the operating status of the target low-voltage distribution network after power restoration, and assess the power supply capacity of the target low-voltage distribution network to quickly restore power after a fault based on the operating status after restoration.

[0156] Specifically, the power supply recovery capability index of the target low-voltage distribution network can be determined based on the operational status of the target low-voltage distribution network after restoration. Then, the power supply capability of the target low-voltage distribution network can be evaluated based on the power supply recovery capability index to obtain the power supply capability evaluation value of the target low-voltage distribution network. The power supply capability evaluation value is used to indicate the ability to quickly restore power supply after a fault.

[0157] For example, power restoration capability indicators may include the load ratio of the target low-voltage distribution network to which power is restored, the power restoration time, and the voltage qualification rate after power restoration.

[0158] Optionally, the load proportion for the target low-voltage distribution network to restore power can be determined based on the restored power load obtained after executing the target power restoration path and the total power load of the non-faulted areas in the distribution network.

[0159] For example, suppose the set of all nodes in the directed graph model after a fault is . The set of nodes whose power supply has been restored is Among them, the set of power restoration nodes This refers to the set of nodes traversed in the final target power restoration path.

[0160] After executing the final power restoration path, the power capacity assessment system can obtain the set of restored power nodes. The node load of each restored power node is determined, and the restored power load is determined based on the node load of each restored power node; and, based on the set of all nodes in the directed graph model after the fault. The node load of each node in the distribution network is calculated, and the total grid load of the non-faulted areas in the distribution network is calculated. Then, based on the restored power load and the total grid load of the non-faulted areas in the distribution network, the proportion of load for power restoration is determined. The specific formula can be seen as follows:

[0161]

[0162] in, Indicates the restoration of power supply to the node. Indicates the node where power has been restored The node load, Represents the first directed graph model after a fault. 1 node Represents a node The node load.

[0163] Optionally, the power restoration time can be determined based on the first time of executing the final power restoration path and the second time of successfully restoring power to all power restoration nodes in the target power restoration path after the target power restoration path is executed.

[0164] For example, a power supply capacity assessment system can record the time from the start of the power restoration operation to the successful restoration of power to all power restoration nodes during the execution of the target power restoration path, i.e., the first time of executing the target power restoration path, and the second time of the successful restoration of power to all power restoration nodes in the final power restoration path after executing the target power restoration path. Then, based on the first time... Second time Determine the power restoration time. .

[0165] Optionally, the voltage qualification rate can be determined based on a first number of restored power supply nodes whose actual voltage is within a preset voltage qualification range after executing the target power supply restoration path, and a second number of nodes in the target power supply restoration path.

[0166] Specifically, the power supply capacity assessment system can first obtain the actual voltage of the power supply node after the target power supply restoration path is executed.

[0167] For example, a power supply capacity assessment system can obtain a set of power supply nodes that have been restored after executing a target power supply restoration path. Each power restoration node in The actual voltage for the set of restored power supply nodes. Each power restoration node in Based on circuit principles and the line parameters (resistance) in the final power restoration path, Reactance and power supply voltage The actual voltage is calculated using the following recursive formula. For example, from the power node to the power restoration node. The path traversed consists of a series of edges. The current of each edge can be calculated iteratively using Ohm's law and Kirchhoff's laws to ultimately obtain the node where power is restored. actual voltage It should be noted that the calculation process for the current of each side can refer to the recursive formula based on Kirchhoff's laws and Ohm's law mentioned above, and will not be repeated here in the embodiments of this application.

[0168] After obtaining the actual voltage of the power supply nodes restored after the execution of the target power supply restoration path, the power supply capacity assessment system can determine the first number of power supply nodes whose actual voltage is within the preset voltage qualification range, and the second number of power supply nodes restored in the final power supply restoration path. Based on the first number and the second number, the voltage qualification rate after power restoration is determined, i.e., voltage qualification rate = first number / second number.

[0169] In some embodiments of this application, the power supply capacity assessment process based on power supply recovery capability indicators can be expressed as follows: based on the load ratio, power supply recovery time, and voltage qualification rate, and compared with the ideal recovery load ratio, ideal power supply recovery time, and ideal recovery voltage qualification rate under ideal recovery conditions, the load ratio deviation, power supply recovery time deviation, and voltage qualification rate deviation are determined respectively; then, based on the load ratio deviation, power supply recovery time deviation, and voltage qualification rate deviation, a comprehensive deviation index is determined, and the power supply capacity assessment is performed based on the comprehensive deviation index to obtain the power supply capacity assessment value of the target low-voltage distribution network.

[0170] Optionally, the power supply capacity assessment system can determine the load ratio deviation based on the load ratio and the ideal recovery load ratio under ideal recovery conditions. The specific formula can be seen as follows:

[0171]

[0172] Among them, R load This indicates the proportion of actual recovery load under actual recovery conditions. This represents the proportion of ideal recovery load under ideal recovery conditions, typically... .

[0173] Furthermore, the power supply capacity assessment system can determine the deviation of the power supply restoration time based on the restoration time and the ideal restoration time under ideal restoration conditions. The specific formula can be seen as follows:

[0174]

[0175] Among them, T restored This indicates the actual power restoration time under the actual restoration status. This indicates the ideal power restoration time under ideal recovery conditions.

[0176] Furthermore, the power supply capacity assessment system can determine the deviation of the negative voltage qualification rate based on the voltage qualification rate and the ideal recovery voltage qualification rate under ideal recovery conditions. The specific formula can be seen as follows:

[0177]

[0178] in, Indicates the voltage qualification rate. This indicates the pass rate of the ideal recovery voltage under ideal recovery conditions.

[0179] Furthermore, the power supply capacity assessment system can determine a comprehensive deviation index based on load ratio deviation, power restoration time deviation, and voltage qualification rate deviation. The specific formula can be seen as follows:

[0180]

[0181] Furthermore, the power supply capacity assessment system can determine the power supply capacity assessment value based on the comprehensive deviation index. ,in, .

[0182] It should be noted that the power supply capacity assessment value The closer the value is to 1, the stronger the ability of the target low-voltage area distribution network to quickly restore power supply after a fault; power supply capacity assessment value The closer it is to 0, the weaker the ability to quickly restore power after a fault.

[0183] The embodiments of this application improve the accuracy of power supply recovery capability assessment by evaluating the ability of the distribution network to quickly restore power supply after a fault through load ratio, power restoration time, and voltage qualification rate.

[0184] In this embodiment, when a fault is detected in the target low-voltage distribution network, a target directed graph model that considers the location and parameters of distributed devices in non-faulty areas is adopted. Starting from the node where the distributed devices are located in the target directed graph model, a path search is performed on the target load node set to obtain the target power supply restoration path. This path is then used to assess the power supply capacity of the target low-voltage distribution network executing the power supply restoration path. This not only ensures that the searched target power supply restoration path can adapt to the dynamic changes of the power system based on the marked location and parameters reflecting the operating status of the distribution network, but also ensures the accuracy of the assessment of the rapid power supply recovery capacity after the distribution network fault is restored, thus achieving the accuracy and effectiveness of power supply restoration and improving the power supply reliability of the low-voltage distribution network. Furthermore, the target directed graph model only searches for paths with directed connections during the path search process, eliminating the need to search all paths, thereby improving the recovery efficiency of the low-voltage distribution network.

[0185] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0186] Reference Figure 3 This diagram illustrates a structural block diagram of a power supply capacity assessment device for a low-voltage distribution network, provided in an embodiment of this application. The device is applied to a power supply capacity assessment system and may specifically include the following modules:

[0187] The directed graph model acquisition module 301 is used to acquire the target directed graph model when a fault is detected in the target low-voltage distribution network. The target directed graph model is the directed graph model after removing the nodes and edges of the fault area from the initial directed graph model. The target directed graph model is marked with the location and parameters of the distributed devices in the non-fault area.

[0188] The load node acquisition module 302 is used to acquire the set of target load nodes for priority power restoration in non-fault areas based on the load data of each node in the target low-voltage distribution network and the target directed graph model.

[0189] The path search module 303 is used to perform path search starting from the node where the distributed device is located in the target directed graph model, based on the location and parameters of the distributed device in the target directed graph model and the target load node set, to obtain the target power supply restoration path;

[0190] The power supply capacity assessment module 304 is used to assess the power supply capacity of the target low-voltage distribution network based on the post-restoration operation status of the target low-voltage distribution network after the execution of the target power supply restoration path, and obtain the power supply capacity assessment value of the target low-voltage distribution network; the power supply capacity assessment value is used to indicate the ability to quickly restore power supply after a fault.

[0191] In some embodiments of this application, the path search module 303 may include the following sub-modules:

[0192] The power restoration path search submodule is used to perform path search on the target load node set based on the location and parameters of the distributed devices in the target directed graph model, starting from the node where the distributed devices are located in the target directed graph model, to obtain the initial power restoration path; based on the path information of each initial power restoration path, each initial power restoration path is evaluated to obtain the target power restoration path.

[0193] In some embodiments of this application, parameters are used to determine the first remaining power supply capacity of the node where the distributed device is located; the power supply recovery path search submodule may include the following units:

[0194] The initial power restoration path search unit is used to obtain the actual power demand of each node in the target load node set; based on the location of distributed devices in the target directed graph model, it sequentially selects starting nodes from the node set where the distributed devices are located, and performs a depth-first search from each starting node to obtain the currently searched node; it obtains the set of adjacent nodes corresponding to the currently searched node, and continues to perform a depth-optimized search from each adjacent node in the adjacent node set, updating the first remaining power supply capacity to the second remaining power supply capacity during the search process, until the target adjacent node is a node in the target load node set, and the second remaining power supply capacity is greater than or equal to the actual power demand of the target adjacent node, and the target adjacent node is taken as the target node; based on the target node and the starting node, the initial power restoration path is determined.

[0195] In some embodiments of this application, the initial power restoration path search unit may include the following sub-units:

[0196] The target node determination sub-unit is used to perform a depth-first search for each neighboring node in the set of neighboring nodes corresponding to the currently searched node, based on the line capacity between the currently searched node and the target neighboring node connected to the currently searched node, as well as the actual power demand of the target neighboring node.

[0197] In some embodiments of this application, the initial power restoration path search unit may include the following sub-units:

[0198] The initial power restoration path construction sub-unit is used to construct a power restoration path from the starting node to the target node based on the backtracking parent node method; the initial power restoration path is determined based on the line capacity of each edge on the power restoration path and the cumulative demand power of at least one target load node passed from the starting node to the current edge.

[0199] In some embodiments of this application, the path information includes path length, remaining line capacity, and power restoration time; the power restoration path search submodule may include the following units:

[0200] The target power restoration path search unit is used to determine the comprehensive evaluation index of each initial power restoration path based on the path length, remaining line capacity, and power restoration time of each initial power restoration path; iterates through the comprehensive evaluation index of each initial power restoration path, sorts the initial power restoration paths according to a preset order to obtain a path sequence; the initial power restoration path at the beginning of the path sequence is determined as the first path; if the voltage level of each node and the power of each edge of the first path meet preset conditions after power restoration, the first path is determined as the target power restoration path; wherein, the preset conditions are that the voltage level is within a preset range and the power is less than a preset power threshold; and / or, if the voltage level of each node and / or the power of each edge of the first path do not meet the preset conditions after power restoration, a new power restoration path is selected from the remaining paths other than the first path according to a preset order until the voltage level and power of the new power restoration path meet the preset conditions, and the new power restoration path is determined as the target power restoration path.

[0201] In some embodiments of this application, the power supply capability assessment module 304 may include the following sub-modules:

[0202] The power supply capacity assessment submodule is used to determine the power supply recovery capacity indicators of the target low-voltage distribution network based on its post-restoration operating status. These indicators include the load ratio of the target low-voltage distribution network after power restoration, the power restoration time, and the voltage qualification rate after power restoration. Based on the load ratio, power restoration time, and voltage qualification rate, and comparing them with the ideal restored load ratio, ideal restored power restoration time, and ideal restored voltage qualification rate under ideal restoration conditions, the module determines the load ratio deviation, power restoration time deviation, and voltage qualification rate deviation, respectively. Based on these deviations, a comprehensive deviation index is determined. Finally, the module performs a power supply capacity assessment based on the comprehensive deviation index to obtain the power supply capacity assessment value of the target low-voltage distribution network.

[0203] In this embodiment, when a fault is detected in the target low-voltage distribution network, a target directed graph model that considers the location and parameters of distributed devices in non-faulty areas is adopted. Starting from the node where the distributed devices are located in the target directed graph model, a path search is performed on the target load node set to obtain the target power supply restoration path. This path is then used to assess the power supply capacity of the target low-voltage distribution network executing the power supply restoration path. This not only ensures that the searched target power supply restoration path can adapt to the dynamic changes of the power system based on the marked location and parameters reflecting the operating status of the distribution network, but also ensures the accuracy of the assessment of the rapid power supply recovery capacity after the distribution network fault is restored, thus achieving the accuracy and effectiveness of power supply restoration and improving the power supply reliability of the low-voltage distribution network. Furthermore, the target directed graph model only searches for paths with directed connections during the path search process, eliminating the need to search all paths, thereby improving the recovery efficiency of the low-voltage distribution network.

[0204] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0205] This application also provides an electronic device, see embodiments thereof. Figure 4 The provided electronic device 400 includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and capable of running on the processor 420. When the computer program 411 is executed by the processor, it implements the various processes of the above-described low-voltage distribution network power supply capacity assessment method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0206] This application also provides a computer-readable storage medium, see embodiments thereof. Figure 5 The computer-readable storage medium 500 provided stores a computer program 411. When the computer program 411 is executed by the processor, it implements the various processes of the above-described low-voltage distribution network power supply capacity assessment method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0207] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0208] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of the embodiments of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division; in actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. Additionally, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms, none of which are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0209] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0210] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0211] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, apparatuses, or modules, and may be electrical, mechanical, or other forms.

[0212] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0214] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0215] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0216] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0217] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes; these computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0218] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0219] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0220] The technical solutions provided in the embodiments of this application have been described in detail above. Specific examples have been used in the embodiments of this application to illustrate the principles and implementation methods of the embodiments of this application. The description of the above embodiments is only for the purpose of helping to understand the methods and core ideas of the embodiments of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the embodiments of this application. Therefore, the content of this specification should not be construed as a limitation on the embodiments of this application.

Claims

1. A method for evaluating the power supply capacity of a low-voltage distribution network, characterized in that, The method includes: When a fault is detected in the target low-voltage distribution network, a target directed graph model is obtained; the target directed graph model is the directed graph model after removing the nodes and edges of the fault region from the initial directed graph model, and the target directed graph model is marked with the location and parameters of the distributed devices in the non-fault region; Based on the load data of each node in the target low-voltage distribution network and the target directed graph model, a set of target load nodes for priority power restoration in the non-faulty area is obtained. This set of target load nodes is determined based on the priority values ​​of the nodes in the non-faulty area. The specific steps for determining the priority values ​​include: assigning initial priority values ​​to the nodes in the non-faulty area according to the priority relationships corresponding to the load types of the nodes; and adjusting the initial priority values ​​corresponding to the load types of the nodes in the non-faulty area according to the load interruption cost to obtain the adjusted priority value of the i-th node in the non-faulty area. The specific formula is as follows: , where P basei This refers to the load data set of non-faulty areas. The initial priority value of the load data of the i-th node, C laodi This refers to the load data set of non-faulty areas. The load interruption cost of the load data of the i-th node. and These represent the load data sets for non-faulty areas. Find the minimum and maximum costs of all load interruptions; adjust the priority values ​​of nodes in the non-faulty area according to the real-time load size to obtain the adjusted priority value of the i-th node in the non-faulty area. The specific formula is as follows: , where P adi This refers to the load data set of non-faulty areas. The adjusted priority value of the i-th node, P loadi This refers to the load data set of non-faulty areas. The real-time load size of the i-th node in the load data. and These represent the load data sets for non-faulty areas. The minimum and maximum values ​​of all real-time loads are determined; the node distances between nodes in the non-faulty region and power supply nodes are determined based on the post-fault directed graph model, and the corrected priority values ​​of the nodes in the non-faulty region are optimized according to the node distances to obtain the final priority values ​​of the nodes in the non-faulty region, P. finali The calculation formula is as follows: , where P modi This refers to the load data set of non-faulty areas. The corrected priority value of the i-th node, d i This refers to the load data set of non-faulty areas. The shortest path length from the i-th node to the power node. Represents the load data set of non-faulty areas The maximum value of the shortest path length from all nodes to the power node; Based on the location and parameters of the distributed devices in the target directed graph model, and the target load node set, a path search is performed starting from the node where the distributed devices are located in the target directed graph model to obtain the target power restoration path; Based on the post-restoration operating status of the target low-voltage distribution network after executing the target power restoration path, the power supply capacity of the target low-voltage distribution network is evaluated to obtain the power supply capacity evaluation value of the target low-voltage distribution network; the power supply capacity evaluation value is used to indicate the ability to quickly restore power supply after a fault. Wherein, the set of nodes in the fault region is N fault for: , where n fault For the faulty node, N direct N is the set of nodes directly connected to the faulty node. indirect The set of nodes connected to the faulty node whose voltage change rate exceeds a certain threshold; the edge set E of the fault region. fault for: E initial Let N be the edge set of the initial directed graph, and u and v be the node set N. fault Nodes in; In the process of generating the target directed graph model, the temporary graph model obtained after removing the nodes and edges of the fault region from the initial directed graph model has the following relationship: , where N initial Let E be the node set of the initial directed graph. initial Let N be the edge set of the initial directed graph. temp E is the set of temporary nodes after removal. temp The set of temporary edges after removal; for disconnected subgraphs in the temporary graph, select the two boundary nodes of the two closest subgraphs to generate virtual edges, and add the virtual edges to the set of temporary edges after removal; for the remaining nodes The voltage is updated, and the updated voltage is: , where nodes and N adjacent nodes are connected, adj Let M be the set of adjacent nodes, and let N be the set of adjacent nodes. adj any node, U is the association weight between node n and node M. M The voltage at node M; and the voltage for the remaining nodes. Corresponding remaining edges The current and power are updated to obtain the target directed graph model.

2. The method according to claim 1, characterized in that, The step of performing a path search starting from the node where the distributed device is located in the target directed graph model, based on the location and parameters of the distributed devices in the target directed graph model and the target load node set, to obtain the target power restoration path includes: Based on the location and parameters of the distributed devices in the target directed graph model, and taking the node where the distributed devices are located in the target directed graph model as the starting node, a path search is performed on the target load node set to obtain the initial power restoration path; Based on the path information of each initial power restoration path, each initial power restoration path is evaluated to obtain the target power restoration path.

3. The method according to claim 2, characterized in that, The parameters are used to determine the first remaining power supply capacity of the node where the distributed device is located; the step of performing a path search on the target load node set based on the location and parameters of the distributed device in the target directed graph model, taking the node where the distributed device is located in the target directed graph model as the starting node, to obtain the initial power supply restoration path includes: Obtain the actual power demand of each node in the target load node set; Based on the location of the distributed devices in the target directed graph model, starting nodes are selected sequentially from the set of nodes where the distributed devices are located, and a depth-first search is performed starting from each starting node to obtain the currently searched node. Obtain the set of neighboring nodes corresponding to the currently searched node, and continue to perform deep optimization search starting from each neighboring node in the set of neighboring nodes. During the search process, update the first remaining power supply capacity to the second remaining power supply capacity until the target neighboring node is a node in the set of target load nodes and the second remaining power supply capacity is greater than or equal to the actual power demand of the target neighboring node. Then, take the target neighboring node as the target node. Based on the target node and the starting node, an initial power restoration path is determined.

4. The method according to claim 3, characterized in that, The step of continuing the depth-optimized search from each neighboring node in the set of neighboring nodes includes: For each neighboring node in the set of neighboring nodes corresponding to the currently searched node, a depth-first search is performed from the target neighboring node based on the line capacity between the currently searched node and the target neighboring node connected to the currently searched node, as well as the actual power demand of the target neighboring node.

5. The method according to claim 3, characterized in that, The target load node set includes at least one target load node; determining the initial power restoration path based on the target node and the starting node includes: Based on the parent node backtracking method, a power restoration path is constructed from the starting node to the target node; The initial power restoration path is determined based on the line capacity of each edge on the power restoration path and the cumulative demand power of at least one target load node traversed from the starting node to the current edge.

6. The method according to claim 2, characterized in that, The path information includes path length, remaining line capacity, and power restoration time; the process of evaluating each initial power restoration path based on its path information to obtain the target power restoration path includes: Based on the path length, remaining line capacity, and power restoration time of each initial power restoration path, a comprehensive evaluation index is determined for each initial power restoration path. Iterate through the comprehensive evaluation indicators of each initial power restoration path, sort the initial power restoration paths according to a preset order, and obtain the path sequence; The initial power restoration path that is at the beginning of the path sequence is determined as the first path; If the voltage level of each node and the power of each edge of the first path meet the preset conditions after power is restored, then the first path is determined as the target power restoration path; wherein, the preset conditions are that the voltage level is within a preset range and the power is less than a preset power threshold. And / or, if the voltage level of each node and / or the power of each edge of the first path does not meet the preset conditions after power is restored, a power restoration path is reselected from the remaining paths other than the first path according to the preset order until the voltage level and power of the new power restoration path meet the preset conditions, and the new power restoration path is determined as the target power restoration path.

7. The method according to claim 1, characterized in that, The step of assessing the power supply capacity of the target low-voltage distribution network based on its post-restoration operating status after executing the target power supply restoration path, and obtaining the power supply capacity assessment value of the target low-voltage distribution network, includes: Based on the restored operating status of the target low-voltage distribution network, the power supply restoration capability indicators of the target low-voltage distribution network are determined; the power supply restoration capability indicators include the load ratio of the target low-voltage distribution network to which power is restored, the power supply restoration time, and the voltage qualification rate after power supply restoration. Based on the load ratio, the power restoration time, and the voltage qualification rate, and compared with the ideal restored load ratio, ideal restored power restoration time, and ideal restored voltage qualification rate under ideal restoration conditions, the load ratio deviation, power restoration time deviation, and voltage qualification rate deviation are determined respectively. Based on the load ratio deviation, the power restoration time deviation, and the voltage qualification rate deviation, a comprehensive deviation index is determined; Based on the comprehensive deviation index, the power supply capacity is evaluated to obtain the power supply capacity evaluation value of the target low-voltage distribution network.

8. A power supply capacity assessment device for a low-voltage distribution network, characterized in that, The device includes: The directed graph model acquisition module is used to acquire a target directed graph model when a fault is detected in the target low-voltage distribution network. The target directed graph model is the directed graph model after removing the nodes and edges of the fault area from the initial directed graph model. The target directed graph model is marked with the location and parameters of the distributed devices in the non-fault area. The load node acquisition module is used to acquire a set of target load nodes for priority power restoration in the non-faulty area based on the load data of each node in the target low-voltage distribution network and the target directed graph model. The target load node set is determined based on the priority values ​​of the nodes in the non-faulty area. The specific steps for determining the priority values ​​include: assigning initial priority values ​​to the nodes in the non-faulty area according to the priority relationships corresponding to the load types of the nodes; and adjusting the initial priority values ​​corresponding to the load types of the nodes in the non-faulty area according to the load interruption cost to obtain the adjusted priority value of the i-th node in the non-faulty area. The specific formula is as follows: , where P basei This refers to the load data set of non-faulty areas. The initial priority value of the load data of the i-th node, C laodi This refers to the load data set of non-faulty areas. The load interruption cost of the load data of the i-th node. and These represent the load data sets for non-faulty areas. Find the minimum and maximum costs of all load interruptions; adjust the priority values ​​of nodes in the non-faulty area according to the real-time load size to obtain the adjusted priority value of the i-th node in the non-faulty area. The specific formula is as follows: , where P adi This refers to the load data set of non-faulty areas. The adjusted priority value of the i-th node, P laodi This refers to the load data set of non-faulty areas. The real-time load size of the i-th node in the load data. and These represent the load data sets for non-faulty areas. The minimum and maximum values ​​of all real-time loads are determined; the node distances between nodes in the non-faulty region and power supply nodes are determined based on the post-fault directed graph model, and the corrected priority values ​​of the nodes in the non-faulty region are optimized according to the node distances to obtain the final priority values ​​of the nodes in the non-faulty region, P. finali The calculation formula is: , where P modi This refers to the load data set of non-faulty areas. The corrected priority value of the i-th node, d i This refers to the load data set of non-faulty areas. The shortest path length from the i-th node to the power node. Represents the load data set of non-faulty areas The maximum value of the shortest path length from all nodes to the power node; The path search module is used to perform path search starting from the node where the distributed device is located in the target directed graph model, based on the location and parameters of the distributed device in the target directed graph model and the target load node set, to obtain the target power supply restoration path; the power supply capacity assessment module is used to assess the power supply capacity of the target low-voltage distribution network based on the post-restoration operating status of the target low-voltage distribution network after executing the target power supply restoration path, to obtain the power supply capacity assessment value of the target low-voltage distribution network; the power supply capacity assessment value is used to indicate the ability to quickly restore power supply after a fault; Wherein, the set of nodes in the fault region is N fault for: , where n fault For the faulty node, N direct N is the set of nodes directly connected to the faulty node. indirect The set of nodes connected to the faulty node whose voltage change rate exceeds a certain threshold; the edge set E of the fault region. fault for: E initial Let N be the edge set of the initial directed graph, and u and v be the node set N. fault Nodes in; In the process of generating the target directed graph model, the temporary graph model obtained after removing the nodes and edges of the fault region from the initial directed graph model has the following relationship: , where N initial Let E be the node set of the initial directed graph. initial Let N be the edge set of the initial directed graph. temp E is the set of temporary nodes after removal. temp The set of temporary edges after removal; for disconnected subgraphs in the temporary graph, select the two boundary nodes of the two closest subgraphs to generate virtual edges, and add the virtual edges to the set of temporary edges after removal; for the remaining nodes The voltage is updated, and the updated voltage is: , where nodes and N adjacent nodes are connected, adj Let M be the set of adjacent nodes, and let N be the set of adjacent nodes. adj any node, U is the association weight between node n and node M. M The voltage at node M; and the voltage for the remaining nodes. Corresponding remaining edges The current and power are updated to obtain the target directed graph model.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the power supply capacity assessment method for a low-voltage distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the power supply capacity assessment method for low-voltage distribution networks as described in any one of claims 1 to 7.

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