A method and system for simulating power-off application management

Abstract

The application provides a kind of analog power transmission and reception application management method and system, belong to power system technical field, specifically include: with power transmission and reception influence node data, and the load variation data in different power transmission and reception influence node, in energy storage adjustment node, according to target strategy, the power outage management of energy storage adjustment node is carried out, and it is used as target adjustment node, with the composition data of the target adjustment node of energy storage adjustment node, and the load variation type of different target adjustment node, determine the need to carry out energy storage adjustment node analog power transmission and reception processing, with the energy storage adjustment data and power outage data of power transmission and reception influence node in different time period, the determination of energy storage adjustment node analog power transmission and reception management method is carried out, improve the energy stability degree of power user during power transmission and reception.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and in particular relates to a simulated power outage and restoration application management method and system. Background Technology

[0002] Power outage and restoration operations are among the riskiest types of work in power systems. Incorrect operating sequences can lead to serious electrical misoperations such as closing circuits with ground wires or pulling disconnect switches under load, causing equipment damage, power outages, and even personal injury. Traditionally, the transmission of power outage and restoration skills relies primarily on a mentor-apprentice model. New employees gradually master the skills through on-the-job observation and assisted operation, resulting in long training cycles and insufficient practice opportunities due to the scarcity of actual power outage and restoration opportunities on-site. Furthermore, the formulation and verification of maintenance outage plans depend on the experience and judgment of engineers, lacking the conditions for verification on actual equipment, making it difficult to identify potential operational risks in advance. Statistics show that a significant proportion of misoperation accidents are related to insufficient operator experience and inadequately verified operating plans.

[0003] To address the aforementioned issues, various power outage and restoration simulation training and solution verification systems have been developed. These systems utilize paper or electronic primary wiring diagrams, with trainers annotating the operational steps on the diagrams and trainees simulating the process by pointing and verbally describing the steps. Some companies have developed computer-based two-dimensional operation ticket simulation systems, where trainees click on equipment icons according to the operation ticket sequence, and the system performs a simple correctness check on the order. However, these systems suffer from the following technical problems: When power outages and restorations are carried out, the power outage of the energy storage regulation node will affect the energy consumption stability of other nodes in the line. Therefore, how to determine the appropriate energy storage space for the energy storage device of the energy storage regulation node based on the simulated power outage and restoration process, and thus ensure the energy consumption stability of different nodes in the line as well as the stability of the power transmission switching control of the energy storage regulation node, has become an urgent technical problem to be solved.

[0004] Specifically, this application provides a method and system for simulating power outage and restoration application management. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a simulated power outage and restoration application management method, which includes: S1 takes the distribution network node with energy storage device as the energy storage regulation node, uses the regulation data of the energy storage device in the energy storage regulation node to determine the regulation and use of the energy storage device in different distribution network nodes, and determines the identification method of the power outage and restoration impact node in the distribution network node based on the regulation and use of the energy storage device in different distribution network nodes of different energy storage regulation nodes in the line. S2 uses the power outage and power restoration impact node data, as well as the load change data in different power outage and power restoration impact nodes, to identify the energy storage regulation node that performs power outage management according to the target strategy among the energy storage regulation nodes, and uses it as the target regulation node. S3 uses the composition data of the target regulating node of the energy storage regulating node and the load change type of different target regulating nodes to determine the simulated power outage and power supply management method of the energy storage regulating node when it is necessary to perform simulated power outage and power supply processing of the energy storage regulating node. It uses the energy storage regulating data and power outage data of the nodes affected by power outage and power supply in different time periods to determine the simulated power outage and power supply management method of the energy storage regulating node.

[0006] The beneficial effects of this invention are as follows: Based on the regulation and usage of energy storage regulation nodes, the frequency of their regulation and usage is determined. Based on the frequency of regulation and usage of different energy storage regulation nodes, the impact of power outages on different distribution network nodes is determined. Based on the impact level, a method for identifying nodes affected by power outages and restorations in the distribution network is determined. This lays the foundation for determining simulation regulation and management methods for different energy storage regulation nodes based on nodes affected by power outages and restorations, and ultimately for improving energy consumption stability during power outages and restorations.

[0007] By constructing time period groups based on temperature and humidity ranges at different times, the frequency of energy storage regulation and handling of nodes affected by power outages in different time period groups and the number of power outages caused by node voltage exceeding limits are determined. The need for different energy storage regulation and handling of nodes affected by power outages after a power outage is determined in different time period groups. Based on the need for energy storage regulation and handling of nodes affected by power outages in different time period groups, the simulated power outage and supply management method for energy storage regulation nodes is determined, i.e., in which time period groups simulated power outage and supply handling of energy storage regulation nodes is performed, thus laying the foundation for ensuring energy stability.

[0008] Furthermore, the regulation data of the energy storage device in the energy storage regulation node is determined based on the regulation time period of the energy storage device in different distribution network nodes.

[0009] Furthermore, the regulation and usage of the energy storage device in different distribution network nodes includes the number of regulation and usage periods of the energy storage device in different distribution network nodes.

[0010] Furthermore, the method for determining the identification method of the nodes affected by power outages in the power distribution network nodes is as follows: S11 Based on the aforementioned regulation usage, determine the average daily usage duration of the energy storage device in the energy storage regulation node during the regulation usage period in the distribution network nodes excluding the energy storage regulation node; S12 determines the usage frequency type of the energy storage regulation node based on the average daily usage time; S13 determines the identification method for nodes affected by power outages and restorations in the distribution network based on the frequency of use of different energy storage regulation nodes.

[0011] Furthermore, the method for determining the target adjustment node is as follows: S21 determines the number of nodes affected by the power outage based on the data of nodes affected by the power outage; S22 uses load change data from different power outage and restoration affected nodes to determine the load change type of different power outage and restoration affected nodes; S23 determines the target regulating node among the energy storage regulating nodes based on the number of nodes affected by the power outage and the different load change types of the nodes affected by the power outage.

[0012] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described simulated power outage and restoration application management method when running the computer program.

[0013] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a simulated power outage and restoration application management method; Figure 2 This is a flowchart of a method for determining the identification of nodes affected by power outages in a distribution network. Figure 3 This is a flowchart illustrating the method for determining the target adjustment node. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0018] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0019] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a simulated power outage and restoration application management method is provided, specifically including: S1 takes the distribution network node with energy storage device as the energy storage regulation node, uses the regulation data of the energy storage device in the energy storage regulation node to determine the regulation and use of the energy storage device in different distribution network nodes, and determines the identification method of the power outage and restoration impact node in the distribution network node based on the regulation and use of the energy storage device in different distribution network nodes of different energy storage regulation nodes in the line. S2 uses the power outage and power restoration impact node data, as well as the load change data in different power outage and power restoration impact nodes, to identify the energy storage regulation node that performs power outage management according to the target strategy among the energy storage regulation nodes, and uses it as the target regulation node. S3 uses the composition data of the target regulating node of the energy storage regulating node and the load change type of different target regulating nodes to determine the simulated power outage and power supply management method of the energy storage regulating node when it is necessary to perform simulated power outage and power supply processing of the energy storage regulating node. It uses the energy storage regulating data and power outage data of the nodes affected by power outage and power supply in different time periods to determine the simulated power outage and power supply management method of the energy storage regulating node.

[0020] Furthermore, the regulation data of the energy storage device in the energy storage regulation node is determined based on the regulation time period of the energy storage device in different distribution network nodes.

[0021] The energy storage regulation node refers to a node in the distribution network that is equipped with energy storage devices (such as battery energy storage systems, flywheel energy storage, etc.), which can output or absorb electrical energy to other distribution network nodes during line regulation; the regulation data refers to the time period records of the energy storage regulation node's regulation operations to various distribution network nodes within the historical statistical period, including regulation start and end times, regulation direction, and regulation amount; the regulation usage refers to the number and distribution of time periods in which the energy storage device of the energy storage regulation node actually performs regulation operations in various distribution network nodes other than its own node.

[0022] Suppose there are multiple energy storage regulation nodes in a certain distribution network line. The energy storage devices of each node have provided regulation support to different distribution network nodes in the past several statistical periods. By summarizing the regulation period records of each node, we can initially grasp the breadth and frequency distribution of regulation coverage of each energy storage regulation node to other distribution network nodes.

[0023] This step establishes a quantitative representation of the regulation usage of each node by systematically integrating historical regulation data of energy storage regulation nodes. Its significance lies in providing a reliable data foundation for the accurate identification of the impact range of subsequent power outages and restorations, avoiding subjective judgments on affected nodes in the absence of historical data support, thereby improving the objectivity and accuracy of identifying the impact nodes of power outages and restorations.

[0024] Furthermore, the regulation and usage of the energy storage device in different distribution network nodes includes the number of regulation and usage periods of the energy storage device in different distribution network nodes.

[0025] Specifically, such as Figure 2 As shown, the method for determining the identification of nodes affected by power outages in the distribution network is as follows: In this embodiment, the frequency of energy storage regulation node use is determined based on the regulation usage of the energy storage regulation node. Based on the frequency of different energy storage regulation node uses, the impact of power outages on different distribution network nodes is determined. Based on the impact level, a method for identifying nodes affected by power outages and restorations in the distribution network is determined. This lays the foundation for determining a simulation regulation management method for different energy storage regulation nodes based on nodes affected by power outages and restorations, thereby improving energy consumption stability during power outages and restorations.

[0026] S11 Based on the aforementioned regulation usage, determine the average daily usage duration of the energy storage device in the energy storage regulation node during the regulation usage period in distribution network nodes other than the energy storage regulation node; The average daily usage time refers to the cumulative duration of actual daily regulation operations performed by the energy storage device of the energy storage regulation node in the target distribution network node within the statistical period, reflecting the regulation support strength of the energy storage regulation node for a specific distribution network node.

[0027] Assuming the statistical period is multiple complete natural months, the energy storage device of a certain energy storage regulation node has performed multiple regulation operations in multiple distribution network nodes within this period. By dividing the total duration of the regulation operations received by the energy storage device in each distribution network node by the number of statistical days, the average daily usage time of the energy storage regulation node in each distribution network node can be obtained.

[0028] This step transforms the regulation and usage of energy storage devices into a quantifiable and comparable daily average duration index. Its significance lies in eliminating the interference of differences in statistical period length on the judgment of frequency, and enabling the regulation intensity between different energy storage regulation nodes to have a unified dimension that can be compared horizontally, thereby laying the foundation for the accurate classification of subsequent frequency types.

[0029] S12 Based on the average daily usage time, determine the usage frequency type of the energy storage regulation node; The term "frequent use type" refers to classifying each energy storage regulation node into three frequent types based on the regulation intensity level corresponding to its average daily usage time. Among these, the frequency of the first type is higher than that of the second type, and the frequency of the second type is higher than that of the third type. In other words, the longer the average daily usage time, the higher the frequency type.

[0030] Assuming there are multiple energy storage regulation nodes in the distribution network, based on the average daily usage time of the energy storage devices at each node, they are classified into Category I, Category II, or Category III frequent types according to a preset classification threshold range. Nodes with longer average daily usage time are classified into Category I frequent type, those with moderate usage time into Category II, and those with shorter usage time into Category III.

[0031] This step maps continuous daily average duration values ​​to discrete frequent type labels. Its significance lies in providing a unified category input for the differentiated determination of subsequent power outage and power restoration impact node identification methods, avoiding the complexity of judgment rules caused by directly using continuous values, and facilitating the introduction of weight coefficients for comprehensive evaluation in situation judgment.

[0032] S13 Based on the usage frequency type of different energy storage regulation nodes, determine the identification method of the nodes affected by power outages and restorations in the distribution network nodes.

[0033] The method for identifying nodes affected by power outages and restorations refers to the rules for determining the range of distribution network nodes that need to be focused on in power outage and restoration scenarios based on the overall usage frequency of energy storage regulation nodes in the line. Specifically, it includes three situations.

[0034] It should be noted that if the number of energy storage regulation nodes in the line is less than the preset threshold for the number of energy storage regulation nodes, then when there is a power outage at the energy storage regulation node, the energy storage regulation node used for energy storage regulation response in the distribution network node will change. Therefore, in order to ensure the reliability of regulation when the energy storage regulation node changes, the method for identifying the nodes affected by power outages in the distribution network node is to take all distribution network nodes (i.e., nodes other than energy storage regulation nodes) whose daily average regulation time of the energy storage device using the energy storage regulation node is greater than the preset regulation time number as nodes affected by power outages. The preset threshold for the number of energy storage regulation nodes refers to the boundary value used to distinguish whether the scale of energy storage regulation nodes in the line is large enough; the number of daily average regulation periods refers to the average number of time periods during which a distribution network node receives energy storage regulation operations each day within the statistical period.

[0035] If the number of energy storage regulation nodes in the line does not exceed the preset threshold, it indicates that the energy storage resources of the line are not abundant. Any power outage at any node may cause changes in the regulation response links of multiple other nodes. Therefore, a broader identification method is adopted to include all distribution network nodes whose daily average number of regulation periods reaches the preset level in the scope of nodes affected by power outages and restorations.

[0036] The identification method in this case expands the scope of affected nodes. Its significance lies in ensuring that no important distribution network nodes that may be affected are overlooked when the number of energy storage regulation nodes is small, thereby providing a sufficient analytical basis for the subsequent screening of target regulation nodes.

[0037] Additionally, it should be noted that if the number of energy storage regulation nodes in the line is greater than the preset threshold for the number of energy storage regulation nodes, the usage frequency weight value of the energy storage regulation nodes is determined based on the usage frequency type of different energy storage regulation nodes. If the average usage frequency weight value of different energy storage regulation nodes is greater than the preset frequency weight threshold, then the method for identifying the nodes affected by power outages and restorations in the distribution network nodes is to classify all distribution network nodes (i.e., nodes other than energy storage regulation nodes) whose daily average regulation time period of the energy storage devices utilizing the energy storage regulation nodes is greater than the preset regulation time period as nodes affected by power outages and restorations. The usage frequency weight value refers to the weight value assigned according to the usage frequency type of the energy storage regulation node. The first type of frequent use corresponds to the highest weight, and the three types of frequent use correspond to the lowest weight. The preset frequent use weight threshold refers to the boundary value of the total weight used to determine whether the overall regulation frequency of the line has reached the high load level.

[0038] If the number of energy storage regulation nodes in the line exceeds a preset threshold, the average value of the usage frequency weight of all energy storage regulation nodes is calculated. If the average value of this weight exceeds the preset frequency weight threshold, it indicates that although the number of nodes in the entire line is large, the overall regulation load is heavy, and a broad identification method is still adopted.

[0039] It also includes the following: if the average value of the usage frequency weight values ​​of different energy storage regulation nodes is not greater than the preset frequency weight threshold, then the method for identifying the nodes affected by power outages in the distribution network nodes is to take the distribution network nodes (i.e., nodes other than energy storage regulation nodes) whose daily average number of regulation periods using energy storage devices using energy storage regulation nodes is greater than the preset number of regulation periods and whose daily average duration of regulation periods is greater than the preset duration threshold as nodes affected by power outages. The preset duration threshold refers to the judgment boundary value for further filtering distribution network nodes with a longer average duration of a single adjustment period, provided that the number of daily adjustment periods meets the condition. This dual condition setting makes the identification of affected nodes more accurate, and only distribution network nodes that meet both the adjustment frequency and adjustment duration conditions are included in the scope of influence.

[0040] If the number of energy storage regulation nodes in the line exceeds the threshold and the average weight of the whole is low, it indicates that the overall regulation load of the line is light. In this case, strict dual conditions are used for identification, and only distribution network nodes whose average daily regulation period and average daily regulation duration both reach the preset level are included in the nodes affected by power outages and restorations.

[0041] By introducing dual screening conditions, this approach aims to achieve accurate identification in scenarios with low overall regulation load, reduce the redundancy of subsequent analysis, and improve the accuracy of identifying power outage and restoration impact nodes and the efficiency of subsequent management resource utilization.

[0042] Specifically, such as Figure 3 As shown, the method for determining the target adjustment node is as follows: In this embodiment, based on the power outage and restoration node data and the load change data in different power outage and restoration affected nodes, the degree of impact of the energy storage regulation node being in a power outage state on the power outage and restoration affected nodes is determined. That is, the more power outage and restoration nodes there are, and the more severe the load change in different power outage and restoration affected nodes, the higher the degree of impact of the energy storage regulation node being in a power outage state on the power outage and restoration affected nodes. The target regulation node is determined by using the degree of impact of the energy storage regulation node being in a power outage state on the power outage and restoration affected nodes. This not only avoids the situation of poor regulation flexibility caused by managing the energy storage space of all energy storage regulation nodes during power outages, but also ensures the reliability of using the target regulation node to regulate the power outage and restoration affected nodes during load changes.

[0043] S21 Determine the number of nodes affected by the power outage based on the data on nodes affected by the power outage; The number of nodes affected by power outages and restorations refers to the total number of all nodes affected by power outages and restorations as determined by the S1 identification method. This number is the core input indicator for subsequent determination of whether to proceed to load change type analysis.

[0044] Assuming that multiple nodes affected by power outages are identified in the distribution network line by S1, the total number of these nodes is counted and compared with a preset threshold for the number of affected nodes to determine the subsequent processing path.

[0045] This step provides a quantitative basis for subsequent branch judgments. Its significance lies in making an initial judgment on the breadth of the power outage impact by the absolute number of nodes affected by the power outage and restoration. For cases with a small impact range (number below the threshold), the lightweight target adjustment node can be directly determined, avoiding unnecessary complex analysis and calculation, and improving management efficiency.

[0046] It should be noted that if the number of nodes affected by the power outage is less than the preset threshold for the number of affected nodes, the target regulating node is determined to be a variable type of energy storage regulating node. That is, its energy storage device is set to its rated capacity before the power outage. This satisfies the self-regulation requirements of the variable type of energy storage regulating node when it is off-grid, and can also respond to the regulation requirements of the nodes affected by the power outage in a timely manner.

[0047] If the number of nodes affected by the power outage is not less than a preset threshold for the number of affected nodes, then proceed to step S22.

[0048] S22 uses load change data from different power outage / restoration affected nodes to determine the load change type of different power outage / restoration affected nodes; The load change type refers to the change intensity level determined by the proportion of the number of load change dates affecting the nodes affected by power outages and restorations. It includes three types of change: Type 1, Type 2, and Type 3, where Type 1 is greater than Type 2, and Type 2 is greater than Type 3. The load change date refers to the date on which the proportion of the time period whose deviation rate from the average load of different time periods on that date is greater than a preset deviation rate is greater than a preset time period proportion threshold.

[0049] Assuming the statistical period is several months in the past, for each node affected by power outages and restorations, the deviation rate of its load in each time period relative to the average load of that day is calculated daily. If the proportion of time periods with a deviation rate exceeding the preset deviation rate on a certain day exceeds the preset time proportion threshold, then that day is a load change date for that node. Finally, the load change type of each node is determined by the proportion of load change dates to the total number of statistical days.

[0050] This step, by constructing a composite indicator of the percentage of days with load changes, is significant in that it quantifies the degree of load instability of each node affected by power outages and restorations from a time perspective. This provides a refined classification basis for the differentiated selection of target adjustment nodes, ensuring that the selection of target adjustment nodes matches the actual adjustment demand intensity of each affected node.

[0051] Specifically, in the steps above: S221 Determine the change weight value of the power outage and power restoration affected nodes based on the load change type of different power outage and power restoration affected nodes, and determine whether the average value of the change weight value of different power outage and power restoration affected nodes is greater than the preset weight threshold. If so, determine that the target regulating node, in addition to the energy storage regulating nodes of type I and type II change, also includes the energy storage regulating node with the highest number of load change dates among the energy storage regulating nodes that do not belong to type I and type II change. If not, proceed to step S222. The variable weight value refers to the quantitative weight assigned to the load change type of the node affected by the power outage and restoration. Type I change type corresponds to the highest weight, and Type III change type corresponds to the lowest weight. The target quantity ratio refers to the proportion of the number of energy storage regulation nodes that need to be included in the target regulation node selection in addition to Type I and Type II change types, relative to all non-Type I and non-Type II energy storage regulation nodes.

[0052] Assuming that the load change type classification has been completed for each power outage and restoration affected node, calculate the average value of the change weight value of all power outage and restoration affected nodes. If the average value exceeds the preset weight threshold, it indicates that the overall load change is relatively serious. The target adjustment node needs to cover a wider range of energy storage adjustment nodes. In addition to the first and second type of change type energy storage adjustment nodes, the three types of nodes with the highest load change date proportions are additionally included in the target quantity proportion.

[0053] This step uses the weighted average to determine the severity of the overall impact. Its significance lies in expanding the range of target adjustment nodes when the overall load change of the affected nodes is significant, ensuring that a sufficient number of energy storage adjustment nodes participate in the response. At the same time, through precise screening based on the "highest percentage of load change dates", the three types of nodes that have historically required the most frequent adjustment support are prioritized, thereby improving the targeting of the selection of target adjustment nodes.

[0054] S222 Determine the load change risk coefficient based on the number of nodes affected by power outages and the change weight values ​​of different nodes affected by power outages and restorations. Determine whether the load change risk coefficient is greater than a preset risk coefficient threshold. If yes, proceed to step S23. If no, determine that the target regulating node is, in addition to the energy storage regulating nodes of the first type of change, also includes the energy storage regulating node that does not belong to the first type of change and has the highest percentage of the number of load change dates among the second target quantity percentages (less than the target quantity percentage). The load variation risk coefficient refers to a comprehensive coefficient that assesses the overall load variation risk level of the line by taking into account the number of nodes affected by power outages and the variation weight value of each node. Its calculation is based on the product of the proportion of the number of nodes affected by power outages and the average value of the variation weight values ​​of all nodes affected by power outages and the number of ...

[0055] Assuming that the number of nodes affected by power outages and restorations is multiple, and the change weight values ​​of each node have been determined, the number of nodes and their weights are substituted into the corresponding formula to calculate the load change risk coefficient. If the coefficient does not exceed the preset risk coefficient threshold, it indicates that the overall risk level is controllable. Only a small number of high-change nodes are added to the energy storage regulation nodes of the first type of change according to the proportion of the second target number, so as to avoid excessive occupation of energy storage regulation resources.

[0056] This step achieves a quantitative assessment of overall risk by constructing a load variation risk coefficient. Its significance lies in the fact that when the overall variation of the affected nodes is moderate, by simplifying the target adjustment node scale, more flexible adjustment resources are reserved for the line, so as to ensure the basic adjustment needs of the affected nodes during power outages and restorations while maintaining the overall adjustment flexibility of the line.

[0057] S23. Based on the number of nodes affected by the power outage and the different load variation types of the nodes affected by the power outage, determine the target regulating node among the energy storage regulating nodes.

[0058] The determination of the target regulation node in stage S23 is further based on the proportion of nodes affected by power outages of type I change. Specifically, it includes: judging whether the proportion of nodes affected by power outages of type I change is above the first-class proportion threshold based on the load change type of different power outages and restorations. If so, the target regulation node is determined to include not only energy storage regulation nodes of type I change and type II change, but also the energy storage regulation node with the highest proportion of load change dates among the energy storage regulation nodes that do not belong to type I change and type II change. If not, the target regulation node is determined to include not only energy storage regulation nodes of type I change, but also the energy storage regulation node with the highest proportion of load change dates among the energy storage regulation nodes that do not belong to type I change.

[0059] If the proportion of Category I change type nodes among the nodes affected by power outages and restorations is relatively high (exceeding the preset Category I proportion threshold), it indicates that the load fluctuations of most affected nodes are severe. The target adjustment nodes need to cover both Category I and Category II change type energy storage adjustment nodes as well as some Category III nodes. If the proportion of Category I nodes is low, it indicates that there are fewer nodes with severe fluctuations. In this case, it is only necessary to add some high-variability non-Category I nodes to the existing Category I change type energy storage adjustment nodes.

[0060] This step achieves a refined matching of the target regulation node range by judging the concentration of nodes with high volatility. Its significance lies in ensuring that the composition of the target regulation nodes is highly consistent with the actual change pattern of nodes affected by power outages and restorations. This avoids both over-expanding the target regulation node range, which would lead to a waste of regulation resources, and under-expanding the range, which would prevent some nodes with high volatility from receiving sufficient regulation support.

[0061] Furthermore, it was determined that simulated power outage and restoration management of energy storage regulation nodes is required, specifically including: In this embodiment, based on the composition data of the target regulating node and the load change type of the target regulating node, the impact of putting the target regulating node into use after a power outage on the energy consumption stability of the node affected by power outage and restoration is determined. Based on the impact of putting the target regulating node into use after a power outage on the energy consumption stability of the node affected by power outage and restoration, it is determined whether simulated power outage and restoration management of the energy storage regulating node is required, thereby laying the foundation for further improving energy consumption stability.

[0062] S31 Determine the composition ratio of the target regulating nodes in the energy storage regulating node based on the composition data of the target regulating nodes of the energy storage regulating node. The composition ratio of the target regulation nodes refers to the ratio of the number of target regulation nodes to the total number of all energy storage regulation nodes. This ratio reflects the share of energy storage nodes participating in target strategy control in power outage management in the total energy storage resources of the entire line.

[0063] Assuming there are multiple energy storage regulation nodes in the line, and some of the target regulation nodes are selected through S2 screening, the ratio of the number of target regulation nodes to the total number of energy storage regulation nodes is calculated to obtain the composition ratio of the target regulation nodes.

[0064] This step quantifies the size of the target regulating nodes into a compositional proportion. Its significance lies in assessing the coverage of the target regulating nodes through relative proportions rather than absolute numbers, so that the judgment result is not affected by the size of the line, and provides a standardized indicator for subsequent judgment on whether power outage and restoration management needs to be simulated.

[0065] Specifically, the above steps include the following: Determine whether the proportion of the target regulation node in the energy storage regulation node is less than the preset proportion threshold. If so, when there are a large number of energy storage regulation nodes that are out of power, the target regulation node may not be able to effectively respond to the energy storage regulation demand. Therefore, it is determined that simulated power outage and restoration management of the energy storage regulation node is required. If not, proceed to step S32. The preset composition ratio threshold is a critical ratio value used to distinguish whether the scale of the target regulation nodes is sufficient. When the composition ratio is lower than the threshold, it means that the number of target regulation nodes may be insufficient relative to the overall power outage scale, and the energy storage configuration needs to be optimized in advance through simulated power outage and restoration management.

[0066] Assuming the target adjustment node composition ratio is 40% and the preset composition ratio threshold is 50%, then 40% < 50%, the composition ratio is insufficient, and it is directly determined that simulated power outage and restoration management is required; if the composition ratio is 65%, exceeding the threshold, then proceed to S32 for further judgment.

[0067] This judgment quickly identifies scenarios with insufficient resource allocation by the composition ratio. Its significance lies in intervening in advance when the scale of the target adjustment node is significantly too small, so as to avoid unstable energy supply due to insufficient adjustment resources when actual power outages and restorations occur, and to provide advance protection for energy safety.

[0068] S32 determines the number of target control nodes under different load change types based on the different load change types of the target control nodes; The number of target adjustment nodes under different load change types refers to the number of target adjustment nodes counted according to the load change type (Class I, Class II, and Class III change types) of their corresponding coverage areas.

[0069] Assuming there are a total of several target regulation nodes, the number of target regulation nodes of type I, type II, and type III change types are counted according to the load change type of the nodes affected by power outages and restorations covered by each node, in order to analyze the concentration of nodes with high change types among the target regulation nodes.

[0070] This step, by statistically analyzing the distribution of the number of target adjustment nodes according to the type of change, is significant in that it provides refined classification data for subsequent assessment of the overall adjustment pressure of the target adjustment nodes, enabling subsequent management decisions to make targeted judgments based on the actual proportion of high-load nodes among the target adjustment nodes.

[0071] Based on the number of target regulating nodes under different load change types, determine whether the proportion of target regulating nodes of a certain type of change is greater than the preset target node proportion threshold. If so, determine that simulated power outage and power supply management of energy storage regulating nodes is required. If not, proceed to step S33. The threshold for the proportion of target adjustment nodes of a certain type of change refers to the critical proportion used to determine whether nodes with high change types are concentrated among the target adjustment nodes. If the proportion of a certain type is high, it indicates that the overall adjustment pressure is large, and the adjustment strategy needs to be optimized in advance through simulated power outage and restoration management.

[0072] Assume there are 8 target adjustment nodes, of which 5 are of type 1 change, accounting for 5 ÷ 8 = 62.5%. If the preset target node percentage threshold is 60%, then 62.5% > 60%, indicating that simulated power outage management is required; if the percentage of type 1 change nodes is 50%, which is below the threshold, then proceed to step S33.

[0073] This judgment quickly identifies high-pressure scenarios by focusing on high-volume nodes. Its significance lies in the timely triggering of simulated power outage and restoration management when most of the target regulation nodes are in a state of high load fluctuation, ensuring the optimal configuration of energy storage range and timing of deployment before actual power outage and restoration occur.

[0074] S33 determines whether simulated power outage management of the energy storage regulation nodes is required based on the composition ratio of the target regulation nodes in the energy storage regulation nodes and the number of energy storage regulation nodes under different load change types.

[0075] Specifically, the above steps include the following: S331 Based on the load change type of the target regulating node, determine the load stability weight coefficient of the target regulating node, and determine whether the average value of the load stability weight coefficient of different target regulating nodes is above the preset stability weight coefficient threshold. If so, determine that there is no need to perform simulated power outage management of the energy storage regulating node; otherwise, proceed to step S332. The load stability weighting coefficient refers to a coefficient reflecting the degree of load stability assigned based on the load variation type of the target regulating node. Its value ranges from 0 to 1. The more severe the load variation type, the smaller the coefficient. The coefficient is largest for three types of variation and smallest for one type of variation. Specifically, it is determined according to the load stability weighting coefficient corresponding to the load variation type of the target regulating node.

[0076] Assuming that the load stability weight coefficient for a type 1 variable node is 0.3, for a type 2 variable node it is 0.6, and for a type 3 variable node it is 0.9, calculate the average load stability weight coefficient of all target regulation nodes. If the average value is not lower than the preset stability weight coefficient threshold, it indicates that the overall load of the target regulation nodes is relatively stable and there is no need to simulate power outage management.

[0077] This step comprehensively evaluates the overall stability of the target regulating nodes by using the average load stability weighting coefficient. Its significance lies in the fact that, in scenarios where the composition ratio of the target regulating nodes and the proportion of Class I nodes both pass the threshold test, the comprehensive evaluation of the stability weighting coefficient further avoids misjudgment and ensures that simulated power outage management is only triggered when the overall load instability is indeed high, thereby reducing unnecessary management intervention.

[0078] S332 determines the adjustment adaptation value based on the average value of the load stability weight coefficient of different target adjustment nodes and the composition ratio of the target adjustment nodes in the energy storage adjustment nodes. It then determines whether the adjustment adaptation value is greater than the preset adaptation threshold. If so, it is determined that no simulated power outage management of the energy storage adjustment nodes is required. If not, it is determined that simulated power outage management of the energy storage adjustment nodes is required. This enables the determination of the energy storage space and power supply timing of different energy storage adjustment nodes during power outages, thereby ensuring the power supply stability of power users in the line. The adjustment and adaptation value refers to the comprehensive adaptation index, which is the ratio of the average value of the comprehensive load stability weight coefficient to the target adjustment node. It is determined by the product or average of the two and reflects the comprehensive adaptation degree of the current target adjustment node in response to the adjustment needs of the node affected by power outages and restorations. The preset adaptation threshold refers to the comprehensive adaptation critical value used to finally determine whether power outage and restoration management needs to be simulated.

[0079] Assuming the average load stability weighting coefficient of the target regulating node is α and the proportion of the target regulating node is β, then the regulating adaptation value = α × β. If this value is greater than the preset adaptation threshold, it means that the target regulating node can fully cope with the energy storage regulation needs of different nodes during power outages and restorations in terms of both stability and scale. That is, when there is an energy storage regulation need, the target regulating node in the power outage state can be put back into operation to achieve energy storage regulation. At this time, the regulation reliability is high and there is no need to perform simulated power outage and restoration management. If it is lower than the threshold, then it is determined that simulated power outage and restoration management is required.

[0080] This step involves constructing a comprehensive adaptation index that takes into account both node quality (stability weight coefficient) and quantity (composition ratio). Its significance lies in avoiding the one-sidedness of judgment based on a single dimension. It ensures that only when the target regulation node has significant deficiencies in both stability and scale will it be determined that power outage and restoration management simulation is necessary, thereby achieving precise and on-demand allocation of power outage and restoration management resources.

[0081] This embodiment achieves refined decision-making regarding the need for simulated power outage and restoration management by constructing a four-stage progressive judgment mechanism: "composition ratio → category proportion → stable weighted average → comprehensive adaptation value". Its core value lies in two aspects: first, by progressively filtering out scenarios that do not require simulated management through multi-stage judgments, resource waste is avoided; second, by introducing a comprehensive adaptation index that considers both node quality and scale, the comprehensiveness and accuracy of the final judgment result are ensured, providing a reliable triggering basis for determining subsequent simulated power outage and restoration management methods.

[0082] Furthermore, the method for determining the simulated power outage and restoration management method for the energy storage regulation node is as follows: In this embodiment, time period groups are constructed based on the temperature and humidity ranges of different time periods. The frequency of energy storage regulation processing for nodes affected by power outages in different time period groups and the number of power outages caused by node voltage exceeding limits are determined. The degree of need for energy storage regulation processing for nodes affected by power outages after a power outage is determined in different time period groups. Based on the degree of need for energy storage regulation processing for nodes affected by power outages in different time period groups, the simulated power outage management method for energy storage regulation nodes is determined, that is, in which time period groups the simulated power outage processing for energy storage regulation nodes is carried out, thereby laying the foundation for ensuring energy stability.

[0083] S41. By utilizing the temperature and humidity ranges of different time periods, the time periods are divided into different time period groups. Based on the energy storage adjustment data of the power outage and power supply affected nodes in different time period groups, the average number of energy storage adjustment times of the power outage and power supply affected nodes in different time period groups is determined. The average number of energy storage adjustment times of the power outage and power supply affected nodes in different time period groups is used as the base adjustment times of the power outage and power supply affected nodes in the time period group. The temperature and humidity range refers to the environmental condition range jointly defined by the temperature range and humidity range. Time periods with the same temperature and humidity range characteristics are grouped into the same time period group. Their power load distribution patterns are similar, so their demand for energy storage regulation and the demand for power outages due to node voltage exceeding limits are consistent. The number of energy storage regulation operations of the nodes affected by power outages and restorations refers to the number of times the nodes affected by power outages and restorations obtain regulation support from the energy storage regulation nodes within a specific time period. The basic number of regulation operations refers to the representative statistical value of the average number of energy storage regulation operations of each node affected by power outages and restorations in all time periods within the same time period group.

[0084] Assuming that temperature and humidity data for each time period within the statistical period have been recorded, the temperature and humidity are divided into multiple intervals, and different intervals are combined to form multiple time period groups. Each time period is assigned to the corresponding group according to its temperature and humidity. Then, the average number of times the power outage and power supply affect the energy storage adjustment of the nodes in each time period group is calculated as the base number of adjustments for that group.

[0085] This step constructs time period groups by using temperature and humidity ranges. Its significance lies in linking external environmental factors with users' electricity load, thereby establishing a connection with power outages and restorations and energy storage regulation needs. This enables subsequent simulated power outage and restoration management methods to adaptively adjust according to external environmental conditions, achieving precise control of energy storage regulation nodes under different seasons or climate conditions.

[0086] S411 Based on the basic adjustment count of the power outage and power supply impact nodes in different time period groups, determine the power outage and power supply nodes whose basic adjustment count is greater than a preset adjustment count threshold. These power outage and power supply nodes in the time period group are designated as matching nodes in the time period group. Time period groups with a number of matching nodes greater than a preset matching node number threshold are designated as matching time period groups. It is then determined whether the number of matching time period groups is greater than a preset matching time period group number threshold. If so, the simulated power outage and power supply management method for the energy storage regulation node is determined to be: as long as a matching node exists in the time period group, simulated power outage and power supply processing of the energy storage regulation node is performed. This determines the optimal energy storage range of the energy storage regulation node during a power outage and when it should be put into use, reducing the number of power outage and power supply processing operations for the energy storage regulation node and ensuring the stability of energy supply. If not, proceed to step S412. The matching node refers to a node affected by power outages or re-energization in a certain time period group whose basic adjustment count exceeds a preset adjustment count threshold, indicating that the node has frequent adjustment needs under the temperature and humidity conditions; the matching time period group refers to a time period group containing a number of matching nodes that reaches a preset number threshold, indicating that the overall adjustment demand is high under the temperature and humidity conditions; the preset matching time period group number threshold refers to a critical number used to determine whether a time period group with high adjustment demand is widespread.

[0087] Assuming that multiple temperature and humidity time period groups are formed within the statistical period of the distribution network, the nodes affected by power outages and restorations in each group that exceed the preset threshold for the number of basic adjustments are screened one by one. The number of matching nodes in each group is counted. Groups with the number of matching nodes reaching the preset threshold for the number of matching nodes are marked as matching time period groups. If the number of matching time period groups exceeds the preset threshold for the number of matching time period groups, it indicates that time period groups with high adjustment demand are widespread, and the broadest simulated power outage and restoration strategy is adopted.

[0088] This step involves a three-level screening mechanism: matching nodes → matching time period groups → number of matching time period groups. Its significance lies in quickly identifying line conditions with generally high regulation demands. In cases of highly concentrated regulation demands, the most conservative simulated power outage and restoration strategy is adopted (triggered as long as a matching node exists), ensuring maximum guarantee of power supply stability.

[0089] Specifically, based on the electricity consumption data of different nodes in the time period group, the optimal energy storage range and deployment strategy of the energy storage regulation node during power outages and restorations are determined, with the goal of minimizing the number of power outages and restorations handled by the energy storage regulation node, thus achieving the determination of the optimal power outage and restoration control strategy in the time period group.

[0090] S42 Based on the power outage data of the time periods in the time period group, determine the number of energy storage regulation nodes that are out of power in different time periods in the time period group, and take the average number of energy storage regulation nodes that are out of power in different time periods as the number of power outage nodes in the time period group. The energy storage regulation node that experiences a power outage refers to an energy storage regulation node that is in a power outage state for a certain period of time due to reasons such as node voltage exceeding the limit; the number of power outage nodes in the time period group refers to the average number of power outage energy storage regulation nodes in each time period included in the time period group, reflecting the average severity of the power outage event under the temperature and humidity conditions.

[0091] Assuming that the historical power outage records for each time period group have been compiled, the number of power outage energy storage regulation nodes for each time period within each time period group is counted one by one, and the average value is calculated as the number of power outage nodes for that group, which is used to determine whether the group needs to carry out simulated power outage and restoration management.

[0092] This step involves statistically analyzing the average power outage scale under different temperature and humidity conditions. Its significance lies in establishing a quantitative correlation between the external environment and the frequency of power outages, providing objective historical data support for whether to trigger simulated power outage and restoration management under specific temperature and humidity conditions. This enables the simulated power outage and restoration management method to have an adaptive response capability to external environmental conditions.

[0093] S421 Determine whether there is a matching node in the time period group. If yes, proceed to step S422. If no, determine that the time period group does not need to perform simulated power outage and power supply management of the energy storage regulation node. The existence of the matching node is a prerequisite for determining whether a certain time period group has basic regulation needs. If there are no nodes in a certain time period group that are affected by power outages and restorations exceeding the basic regulation number, it means that the overall regulation needs of the time period group are low and there is no need to conduct simulated power outage and restoration management.

[0094] If the basic adjustment count of all nodes affected by power outages in a certain temperature and humidity period group (such as a low temperature and low humidity winter night group) is lower than the preset adjustment count threshold, then there are no matching nodes in this group, and it is directly determined that the group does not need to perform simulated power outage management; otherwise, if there are matching nodes, then proceed to S422 to continue the judgment.

[0095] This step, as a pre-filtering step in the S4 process, is significant in that it quickly eliminates time periods with obviously insufficient adjustment needs, reduces unnecessary complex judgments and calculations in the future, and allows the overall analysis process to focus on time periods with genuine adjustment needs, thereby improving management efficiency.

[0096] S422 Determine whether the number of power outage nodes in the time period group is greater than the preset threshold for the number of power outage nodes. If yes, determine that the simulated power outage and power supply management method for the energy storage regulation node is to perform simulated power outage and power supply processing on the energy storage regulation node in the time period group, that is, to determine the optimal energy storage range of the energy storage regulation node during a power outage and when it should be put into use, reduce the number of power outage and power supply processing on the energy storage regulation node, and ensure the stability of energy supply. If not, proceed to step S43. The preset threshold for the number of outage nodes refers to the critical average value used to determine whether the scale of outages in a certain time period group is relatively high. If the average number of outage nodes in the time period group exceeds the threshold, it indicates that outage events are relatively frequent and large in scale under the temperature and humidity conditions. Simulated power outage and restoration processing should be carried out to determine the optimal regulation strategy of the energy storage regulation node in advance.

[0097] Assuming the average number of power outage nodes in a certain time period is 3.5 per time period, and the preset threshold for the number of power outage nodes is 3, then 3.5 > 3, which meets the condition, and it is determined that the group in this time period needs to undergo simulated power outage and restoration processing; if the average is 2.0 per time period, which does not exceed the threshold, then proceed to S43 for further comprehensive judgment.

[0098] This step enables rapid identification of high-risk time periods by using the average scale of power outages. Its significance lies in prioritizing the triggering of simulated power outage and restoration management under temperature and humidity conditions that result in frequent and large-scale power outages, ensuring sufficient pre-set adjustment capabilities in scenarios where historical power outage problems are prominent.

[0099] S43. Based on the basic adjustment times and the number of outage nodes for different power outage and restoration impact nodes in different time period groups, determine the simulated power outage and restoration management method for the energy storage regulation node.

[0100] The above steps include the following: The time period groups with matching nodes are used as the filtering time period groups. Based on the number of the filtering time period groups in the time period groups and the proportion of the number of filtering time period groups that undergo simulated power outage and restoration processing, a simulation adaptation coefficient is determined. It is then determined whether the simulation adaptation coefficient is greater than a preset adaptation coefficient threshold. If so, the simulated power outage and restoration management method for the energy storage regulation node is determined as follows: If the number of occurrences of the remaining filtering time period groups in the most recent preset time period is greater than a preset number threshold, then simulated power outage and restoration processing is performed. If not, the simulated power outage and restoration management method for the energy storage regulation node is determined as follows: If the number of occurrences of all remaining filtering time period groups in the most recent preset time period is greater than a preset number threshold, then simulated power outage and restoration processing is performed on the filtering time period group with the highest number of occurrences of the target quantity in the most recent preset time period.

[0101] The selected time period group refers to a subset of time period groups in which there are matching nodes (i.e., nodes affected by power outages and restorations whose basic adjustment counts exceed a preset threshold) in all time period groups; the simulation adaptation coefficient refers to the product of the total number of selected time period groups and the proportion of selected time period groups that have been determined to undergo simulated power outage and restoration processing, reflecting the degree of adaptation of the overall selected time period groups that have been covered by simulated power outage and restoration management; the remaining selected time period groups refer to selected time period groups that have not yet determined management methods, except for the time period groups that have been determined to undergo simulated power outage and restoration processing in S411 and S422.

[0102] Assuming the line forms multiple temperature and humidity time period groups, there are several screening time period groups with matching nodes. Some of these groups have already been determined to undergo simulated power outage and restoration processing in stage S411 or S422. The simulation adaptation coefficient is obtained by multiplying the total number of screening time period groups by the proportion of those that have undergone simulated power outage and restoration processing. Based on the comparison between this coefficient and the preset adaptation coefficient threshold, the management strategy for the remaining screening time period groups is determined.

[0103] This step comprehensively evaluates the coverage of simulated power outage and restoration management ratios through simulation adaptation coefficients. Its significance lies in adopting differentiated triggering strategies for the remaining screening time groups for which management methods are not yet clearly defined, based on the overall coverage level: when the coverage level is high, a more lenient single triggering condition is adopted (triggering occurs when the recent occurrence frequency of any remaining screening time group exceeds a threshold), while when the coverage level is low, a stricter collective triggering condition is adopted (triggering is then applied to the target number group that occurs most frequently when all remaining screening time groups have recently occurred at high frequencies), thereby achieving precise and on-demand allocation of simulated power outage and restoration management resources.

[0104] This embodiment establishes a mechanism for determining the simulated power outage and restoration management method, using temperature and humidity time period groups as units and matching nodes and power outage scale as dual-dimensional triggering conditions. This mechanism enables adaptive optimization of energy storage regulation node management strategies under different external environmental conditions. The core value lies in two aspects: First, by classifying time period groups by temperature and humidity, the simulated power outage and restoration management can sense changes in the external environment and adaptively adjust strategies, avoiding resource waste caused by "unified management throughout all time periods." Second, through a three-tiered triage judgment (S411 broad coverage → S422 power outage scale screening → S43 comprehensive adaptation and refinement), it achieves gradient settings for the triggering conditions of simulated power outage and restoration management under various scenarios, maximizing management flexibility and economy while ensuring energy supply stability.

[0105] When performing simulated power outage and restoration optimization, a mathematical model is first established based on the historical electricity consumption data of each node in the time period group. The optimization objective is to minimize the number of nodes in a power outage state that need to be powered back during the power outage and restoration process. The model uses the electricity consumption curves of all nodes in the time period group, the capacity range of energy storage regulation nodes, and their charging and discharging efficiencies as input variables. It iterates through different energy storage deployment strategies in historical power outage and restoration records (such as charging energy storage nodes to a certain range before a power outage, prioritizing which energy storage node in a power outage state should supply power during the power outage and restoration process), calculating the number of nodes that need to be powered back during the power outage process under each strategy. The optimal energy storage range for an energy storage regulation node is determined by analyzing the node's capacity during a power outage. If the existing operating energy storage regulation nodes cannot meet the energy storage regulation needs, the number of nodes in a power outage state that need to be re-activated is minimized, thus covering the power supply needs of as many neighboring nodes as possible and reducing the number of nodes requiring power restoration during a power outage state to a minimum.

[0106] Based on this, and combining verified control schemes from historical power outage and restoration data, an adaptive evaluation of each selected strategy is conducted. The strategy with the fewest power restoration nodes and the highest energy storage utilization rate under similar time-group conditions during power outages is selected as the optimal control strategy, and a regulation record is generated accordingly. The regulation record records the power outage and restoration arrangements for each node in the current time-group, the charging and discharging interval settings of the energy storage regulation nodes, and the time windows for their commissioning, forming a traceable and reusable control strategy archive, providing direct reference for subsequent power outage and restoration scheduling in similar scenarios.

[0107] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described simulated power outage and restoration application management method when running the computer program.

[0108] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0109] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0110] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for managing simulated power outages and restorations, characterized in that, Specifically, it includes: The distribution network nodes with energy storage devices are designated as energy storage regulation nodes. The regulation data of the energy storage devices in the energy storage regulation nodes are used to determine the regulation and usage of the energy storage devices in different distribution network nodes. Based on the regulation and usage of the energy storage devices in different distribution network nodes of different energy storage regulation nodes in the line, a method for identifying the nodes affected by power outages and restorations in the distribution network nodes is determined. Using the power outage and power restoration impact node data, and the load change data in different power outage and power restoration impact nodes, identify the energy storage regulation node that performs power outage management according to the target strategy, and use it as the target regulation node. Based on the composition data of the target regulating nodes of the energy storage regulating nodes and the load variation types of different target regulating nodes, when it is determined that simulated power outage and power restoration processing of the energy storage regulating nodes is required, the energy storage regulating data and power outage data of the nodes affected by power outage and power restoration in different time periods are used to determine the simulated power outage and power restoration management method of the energy storage regulating nodes.

2. The simulated power outage and restoration application management method as described in claim 1, characterized in that, The regulation data of the energy storage device in the energy storage regulation node is determined based on the regulation period of the energy storage device in different distribution network nodes.

3. The simulated power outage and restoration application management method as described in claim 1, characterized in that, The regulation and use of the energy storage device in different distribution network nodes includes the number of time periods during which the energy storage device is regulated and used in different distribution network nodes.

4. The simulated power outage and restoration application management method as described in claim 1, characterized in that, The method for determining the identification method of the nodes affected by power outages in the power distribution network nodes is as follows: Based on the aforementioned regulation usage, the average daily usage duration of the energy storage device in the energy storage regulation node during the regulation usage period in the distribution network nodes other than the energy storage regulation node is determined. Based on the average daily usage time, the usage frequency type of the energy storage regulation node is determined; Based on the frequency of use of different energy storage regulation nodes, a method for identifying nodes affected by power outages and restorations in the distribution network is determined.

5. The simulated power outage and restoration application management method as described in claim 4, characterized in that, The usage frequency type of the energy storage regulation node is determined based on the average daily usage time of the energy storage devices in the energy storage regulation node, specifically based on the usage frequency type corresponding to the average daily usage time.

6. The simulated power outage and restoration application management method as described in claim 5, characterized in that, The frequently used types include three types: Type I, Type II, and Type III, with Type I being more frequent than Type II, and Type II being more frequent than Type III.

7. The simulated power outage and restoration application management method as described in claim 4, characterized in that, Based on the usage frequency of different energy storage regulation nodes, a method for identifying nodes affected by power outages and restorations in the distribution network is determined, specifically including: If the number of energy storage regulation nodes in the line is greater than the preset threshold for the number of energy storage regulation nodes, then the method for identifying the nodes affected by power outages and restorations in the distribution network nodes is to classify all distribution network nodes whose average daily regulation time period using energy storage devices exceeds the preset regulation time period as nodes affected by power outages and restorations.

8. The simulated power outage and restoration application management method as described in claim 1, characterized in that, The method for determining the target adjustment node is as follows: Based on the data on nodes affected by the power outage, determine the number of nodes affected by the power outage; By using load change data from different power outage and restoration affected nodes, the load change types of different power outage and restoration affected nodes are determined. Based on the number of nodes affected by power outages and the different load variation types of the nodes affected by power outages, the target regulating node among the energy storage regulating nodes is determined.

9. The simulated power outage and restoration application management method as described in claim 8, characterized in that, The load change type of the node affected by the power outage or restoration is determined based on the proportion of the number of load change dates of the node affected by the power outage or restoration. The load change type includes three types: type 1, type 2, and type 3, with type 1 being greater than type 2, and type 2 being greater than type 3.

10. A computer system, comprising: A memory and processor connected by communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a simulated power outage and restoration application management method according to any one of claims 1-9.

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