Power grid disaster damage assessment method and system based on natural disaster chain

Abstract

The invention provides a power grid disaster damage assessment method and system based on a natural disaster chain, and relates to the technical field of power distribution network disaster damage assessment. A static security domain of a power distribution network of a target power grid in a disaster scene is determined according to historical operation data, and static stability identification is performed on the static security domain; obtaining a static security node of the target power grid in the disaster scene; performing balance adjustment on the operation risk index of each power grid station in the target power grid disaster scene based on the transient loss index to obtain the operation instability load of the target power grid when the target power grid suffers from the disaster; determining the load attenuation deviation of the target power grid after the disaster according to the loss load index and the static safety node; and performing collaborative quantitative evaluation on disaster damage information of the target power grid according to the load attenuation deviation and the unstable load amount, and generating a power grid disaster damage evaluation report. According to the invention, intelligent scheduling monitoring and multi-dimensional disaster damage assessment can be carried out on the power grid in a complex dynamic environment after the power grid suffers from a disaster, so that the decision optimization capability of the disaster damage assessment of the power grid is improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution network disaster loss assessment, and more specifically, to a power distribution network disaster loss assessment method and system based on a natural disaster chain. Background Art

[0002] Distribution network damage assessment refers to the quantitative assessment of the impact of natural disasters on distribution networks, analyzing the load loss, equipment damage, unstable operation and other issues of the power grid after the disaster, and providing a basis for post-disaster recovery and optimized scheduling. This assessment method combines all links of the disaster chain, from the power grid operation status before, during and after the disaster, to comprehensively simulate and analyze the damage of the power grid under different disaster scenarios.

[0003] However, the existing power grid damage assessment methods based on natural disaster chains usually lack comprehensive consideration of the dynamic changes of power grids after disasters and complex post-disaster scenarios, which makes it difficult to accurately reflect the overall loss status of the post-disaster power grid during the post-disaster load recovery, equipment damage, and power grid stability analysis. This makes it impossible for traditional methods to effectively identify the potential risks and recovery difficulties of the power grid under different disaster scenarios, resulting in inaccurate damage assessment results and inability to provide accurate decision support for power grid dispatching, thus affecting the efficiency and effectiveness of post-disaster power grid recovery. Therefore, how to conduct intelligent dispatch monitoring and multi-dimensional damage assessment of the power grid in the complex dynamic environment after the power grid is affected by the disaster in order to improve the decision-making optimization ability of power grid damage assessment is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a power grid damage assessment method and system based on natural disaster chains, which can perform intelligent dispatching monitoring and multi-dimensional damage assessment on the power grid in a complex dynamic environment after the power grid is hit by a disaster, so as to improve the decision-making optimization capability of power grid damage assessment.

[0005] In a first aspect, the present application provides a method for evaluating power grid damage based on a natural disaster chain, the method comprising the following steps:

[0006] Conduct intelligent dispatching monitoring of the target power grid and obtain historical operation data of the target power grid in the natural disaster chain;

[0007] Determine the static security domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data, perform static stability identification on the static security domain, and obtain the static security node of the target power grid in the disaster scenario;

[0008] Obtain the load loss index of the target power grid after the disaster and access the transient loss index of the distribution network in each operation scenario in the disaster scenario, balance and adjust the operation risk index of each power grid site in the disaster scenario of the target power grid based on the transient loss index, and obtain the unstable load of the target power grid when it is disaster-stricken;

[0009] Constructing the load adaptation level of the target power grid in each disaster-affected scenario based on the natural disaster chain, determining the loss load index of the target power grid in each disaster-affected scenario according to the load adaptation level, and then determining the load attenuation deviation of the target power grid after the disaster according to the loss load index and the static safety node;

[0010] A coordinated quantitative assessment of the target power grid's damage information is performed based on the load attenuation deviation and the unstable load amount, and a power grid damage assessment report is generated.

[0011] In this embodiment, determining the static safety domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data specifically includes:

[0012] Determining the static information entropy of the distribution network of the target power grid in the disaster scenario;

[0013] Determining safe operation indicators of the distribution network of the target power grid in a disaster scenario based on the historical operation data;

[0014] The static safety domain of the distribution network of the target power grid in the disaster scenario is determined according to the static information entropy and the safe operation index.

[0015] In this embodiment, static stability identification is performed on the static security domain to obtain the static security nodes of the target power grid in the disaster scenario, specifically including:

[0016] Determining the load type of the target power grid in the disaster scenario according to the static security domain;

[0017] determining a static load characteristic based on the load type;

[0018] The static safety nodes of the target power grid in the disaster scenario are determined through the static load characteristics.

[0019] In this embodiment, obtaining the load loss index of the target power grid after the disaster and accessing the transient loss index of the distribution network in each operation scenario in the disaster scenario specifically includes:

[0020] Determine the load loss index of the target power grid after the disaster;

[0021] Access the load-exceeding nodes of the distribution network in each operation scenario in the disaster-affected scenario;

[0022] The load loss index of the target power grid after the disaster is determined according to the load over-limit node, and the transient loss index of the distribution network in each operation scenario in the disaster scenario is connected.

[0023] In this embodiment, the operation risk index of each power grid site in the target power grid disaster scenario is balanced and adjusted based on the transient loss index, and the unstable load amount of the target power grid when the disaster occurs is obtained, which specifically includes:

[0024] Determine the operational risk index of each power grid site in the target power grid disaster scenario;

[0025] Determine the instability regulation amount when the power grid is affected by a disaster according to the operation risk index;

[0026] By comparing the instability adjustment amount with the transient loss index, the instability load amount of the target power grid operating when it is hit by a disaster is obtained.

[0027] In this embodiment, determining the load attenuation deviation of the target power grid after the disaster based on the loss load index and the static safety node specifically includes:

[0028] Determining the load bias of the target power grid after the disaster according to the loss load index;

[0029] Determine the attenuation compatibility of the target power grid after the disaster according to the static safety node;

[0030] The load attenuation deviation of the target power grid after the disaster is determined by the load bias amount and the attenuation compatibility amount.

[0031] In this embodiment, the load adaptation level refers to the ability of the power grid to adapt to load fluctuations, operate stably, and restore load in different disaster scenarios under the influence of emergencies such as natural disasters.

[0032] In this embodiment, the unstable load refers to the unstable load portion in the post-disaster power grid caused by the disaster and regulation measures.

[0033] In this embodiment, the transient loss index refers to the degree of dynamic loss of the power grid caused by load fluctuations, current and voltage changes, and equipment failures in a disaster scenario.

[0034] In a second aspect, the present application provides a power grid disaster loss assessment system based on a natural disaster chain, which is used to execute a power grid disaster loss assessment method based on a natural disaster chain, and the assessment system includes:

[0035] A data acquisition module is used to perform intelligent dispatching monitoring of the target power grid and obtain historical operation data of the target power grid in the natural disaster chain;

[0036] A security identification module, used to determine the static security domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data, perform static stability identification on the static security domain, and obtain the static security node of the target power grid in the disaster scenario;

[0037] A balancing and adjustment module is used to obtain the load loss index of the target power grid after the disaster, access the transient loss index of the distribution network in each operation scenario in the disaster scenario, and balance and adjust the operation risk index of each power grid site in the disaster scenario of the target power grid based on the transient loss index to obtain the unstable load of the target power grid when it is affected by the disaster;

[0038] A load loss module is used to construct a load adaptation level of the target power grid in each disaster-affected scenario based on the natural disaster chain, determine the loss load index of the target power grid in each disaster-affected scenario according to the load adaptation level, and then determine the load attenuation deviation of the target power grid after the disaster based on the loss load index and the static safety node;

[0039] The quantitative assessment module is used to perform a collaborative quantitative assessment on the disaster loss information of the target power grid according to the load attenuation deviation and the unstable load amount, and generate a power grid disaster loss assessment report.

[0040] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0041] By performing intelligent grid dispatching monitoring on the target power grid, the historical operation data of the target power grid in the natural disaster chain is obtained; the static safety domain of the distribution network of the target power grid in the disaster scene is determined based on the historical operation data, and the static stability identification of the static safety domain is performed to obtain the static safety node of the target power grid in the disaster scene; the load loss index of the target power grid after the disaster is obtained and connected to the transient loss index of the distribution network in each operation scenario in the disaster scene, and the operation risk index of each power grid site in the disaster scene of the target power grid is balanced and adjusted based on the transient loss index to obtain the unstable load amount of the target power grid when the disaster occurs; the load adaptation level of the target power grid in each disaster scene is constructed based on the natural disaster chain, and the loss load index of the target power grid in each disaster scene is determined according to the load adaptation level, and then the load attenuation deviation of the target power grid after the disaster is determined by the loss load index and the static safety node; the disaster loss information of the target power grid is collaboratively quantitatively evaluated according to the load attenuation deviation and the unstable load, and a power grid disaster loss assessment report is generated.

[0042] It can be seen that in this application, first of all, by acquiring and analyzing the historical operation data of the power grid, it is possible to fully understand the operating status and load changes of the power grid before and after the natural disaster, and provide accurate data support for post-disaster recovery and emergency dispatch. By defining the static safety domain, the key stable areas of the power grid in post-disaster operation can be identified to ensure the safety of the power grid. By combining the load loss index and the transient loss index, the load recovery capability and equipment operation risk of the post-disaster power grid can be comprehensively evaluated to ensure that short-term fluctuations and equipment failures that may occur during the operation of the power grid are identified and adjusted in a timely manner. By establishing a load adaptation level, the ability to match the load distribution of the power grid with post-disaster recovery can be provided for power grid load recovery under different disaster scenarios. By collaboratively quantifying the evaluation of the loss of the post-disaster power grid, the impact of various factors on the power grid can be comprehensively considered, and an accurate disaster loss assessment report can be generated to provide a detailed decision-making basis for power grid recovery.

[0043] To sum up, the technical solution adopted in this application can perform intelligent dispatching monitoring and multi-dimensional disaster loss assessment of the power grid in a complex dynamic environment after the power grid is hit by a disaster, so as to improve the decision-making optimization capability of power grid disaster loss assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0045] Figure 1 It is a flow chart of a power grid disaster loss assessment method based on a natural disaster chain provided by the present application;

[0046] Figure 2 is an exemplary flow chart for determining the amount of unstable load running on a target power grid when it is hit by a disaster according to the present application;

[0047] Figure 3 is an exemplary flow chart for determining the loss load index of the target power grid in various disaster scenarios provided by the present application;

[0048] Figure 4 It is a module structure diagram of a power grid disaster loss assessment system based on a natural disaster chain provided by this application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0050] Embodiment 1: In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a power grid damage assessment method based on a natural disaster chain according to this embodiment of the present application, and the assessment method includes the following steps:

[0051] In step S1, the target power grid is monitored for intelligent dispatching to obtain historical operation data of the target power grid in the natural disaster chain.

[0052] In the specific implementation, first, the monitoring system of the target power grid (such as the SCADA system) needs to continuously collect real-time data of the power grid during the disaster, including the load, voltage, current, equipment status, and abnormal operation of the power grid. By connecting with the power grid management platform, the data of historical disaster events are collected and stored to ensure the time series integrity and accuracy of the data. Next, since there may be noise and outliers in the data collection process, data cleaning techniques (such as outlier removal, interpolation method to fill missing data, etc.) are used to improve data quality. In addition, the power grid operation data under special circumstances after the disaster, such as load surge and equipment failure, need special attention and annotation, so as to conduct in-depth analysis of the impact of the disaster. After the data processing is completed, the different historical operation data in the disaster chain are classified by data analysis methods (such as statistical analysis, cluster analysis, etc.) to identify the performance of the power grid under various natural disasters. The post-disaster recovery model is trained using machine learning technology, so that the system can predict the operation status of the power grid when the disaster occurs, that is, to obtain the historical operation data of the target power grid in the natural disaster chain.

[0053] It should be noted that, in the present application, historical operation data refers to various data about the operation status of the power grid recorded in the past period of time, especially during different natural disasters or other emergencies.

[0054] In step S2, a static safety domain of the distribution network of the target power grid in the disaster scenario is determined according to the historical operation data, and static stability identification is performed on the static safety domain to obtain a static safety node of the target power grid in the disaster scenario.

[0055] In this embodiment, the static safety domain of the distribution network of the target power grid in the disaster scenario is determined according to the historical operation data in the following manner, namely:

[0056] Determining the static information entropy of the distribution network of the target power grid in the disaster scenario;

[0057] Determining safe operation indicators of the distribution network of the target power grid in a disaster scenario based on the historical operation data;

[0058] The static safety domain of the distribution network of the target power grid in the disaster scenario is determined according to the static information entropy and the safe operation index.

[0059] In specific implementation, the overall stability of the power grid can be evaluated by quantifying the state information of voltage, current, load, etc. of each node of the power grid, and using entropy calculation methods (such as Shannon information entropy formula). In disaster scenarios, due to load fluctuations and equipment damage, the static information entropy of the power grid usually increases, which means that the operation of the power grid is more complex and uncertain. Safe operation indicators include parameters such as voltage, current, and power flow direction of each node of the power grid to ensure that the power grid will not fail due to load overload, voltage instability, or equipment damage. By analyzing historical data and combining disaster factors (such as extreme weather, equipment failure rate, etc.), a set of safe operation standards can be established to identify the operating conditions that the power grid should maintain in various post-disaster scenarios. For example, some nodes may need to be maintained within a certain voltage range, and some lines need to control the flow of load to ensure that they are not overloaded. Static information entropy is used as a measure of the risk of system instability, combined with safe operation indicators to define which areas of the power grid can operate safely and stably, and which areas may be unstable or fail. By comparing historical data and post-disaster scenario simulations, the static safety domain is further refined. For example, areas with high static information entropy may be considered out of the safety domain because these areas have poor stability. According to the constraints of safe operation indicators, the safety area of ​​the power grid is adjusted to ensure that the power grid can maintain basic stability after a disaster.

[0060] It should be noted that, in this application, static information entropy refers to the description of the uncertainty or chaos degree of the power grid system in a static state; safe operation index refers to the description of the stable operating parameters that the power grid should achieve under specific operating conditions, usually including voltage, current, load, etc., which are used to ensure the safety and reliability of power grid operation; static safety domain refers to the area that can ensure the stability of the power grid and no faults under the static operating conditions of the power grid.

[0061] In this embodiment, static stability identification is performed on the static security domain to obtain the static security node of the target power grid in the disaster scene, which can be specifically implemented in the following manner:

[0062] Determining the load type of the target power grid in the disaster scenario according to the static security domain;

[0063] determining a static load characteristic based on the load type;

[0064] The static safety nodes of the target power grid in the disaster scenario are determined through the static load characteristics.

[0065] In the specific implementation, first, it is necessary to determine the load change pattern through the historical load data of the power grid and the post-disaster prediction model. For example, the load in some areas may recover rapidly after the disaster, while the load in other areas will drop sharply due to the damage of facilities. By analyzing these load change characteristics, the load type of the power grid can be divided, such as stable load, fluctuating load, unstable load, etc. For each load type (such as stable load, fluctuating load, etc.), the relevant static load characteristics are extracted according to the load, equipment carrying capacity and voltage parameters of each node of the power grid. For example, for stable load, the load fluctuation of each node of the power grid is small, and the load change is relatively stable; for fluctuating load, the characteristics of its fluctuation frequency, amplitude, etc. are analyzed to determine the static load characteristics. Finally, by analyzing the static load characteristics of each node of the power grid, nodes with high load stability and less impact on system failures can be identified. These nodes usually have high voltage stability, load carrying capacity and less current fluctuations. In the static safety domain, after analyzing the static load characteristics, these static safety nodes are determined to ensure that these nodes can play a key role in the post-disaster load recovery process and ensure the stability of the power grid, that is, static safety nodes are obtained.

[0066] It should be noted that, in this application, load type refers to the changing characteristics of the load of the power grid in a specific scenario, usually including the load size, volatility, change rate and time distribution; static load characteristics refer to the static properties that describe each load type of the power grid; static safety nodes refer to nodes that can maintain stable operation and contribute to the overall safety of the power grid under specific static load conditions.

[0067] In step S3, the load loss index of the target power grid after the disaster is obtained, and the transient loss index of the distribution network in each operation scenario in the disaster-stricken scenario is accessed. Based on the transient loss index, the operation risk index of each power grid site in the disaster-stricken scenario of the target power grid is balanced and adjusted to obtain the unstable load amount of the target power grid when it is affected by the disaster.

[0068] In this embodiment, the following steps can be used to obtain the transient loss index of the distribution network in each operation scenario in the disaster-affected scenario after the target power grid is damaged, namely:

[0069] Determine the load loss index of the target power grid after the disaster;

[0070] Access the load-exceeding nodes of the distribution network in each operation scenario in the disaster-affected scenario;

[0071] The load loss index of the target power grid after the disaster is determined according to the load over-limit node, and the transient loss index of the distribution network in each operation scenario in the disaster scenario is connected.

[0072] In the specific implementation, first, collect post-disaster load data through the power grid monitoring system, and compare the load changes in various areas of the power grid before and after the disaster. Pay special attention to the severely affected areas and the affected distribution network nodes, extract the load change data of these nodes, and calculate the load loss value of each node by comparing the historical load and the post-disaster load. The calculation method based on time series (such as root mean square error, load ratio loss, etc.) can be used to determine the load loss percentage of each node. The load loss of each node is summarized to obtain the load loss index of the entire power grid, and the load loss degree of the power grid in post-disaster recovery is evaluated. That is, the load loss index is determined. Then, the load of each node of the distribution network is monitored in real time, especially during the high load period after the disaster, and the load data is analyzed to identify those nodes that exceed the maximum allowable load. This can be done by setting a load threshold (such as the rated load of the equipment) to determine which nodes are in an overload state, and comparing the load of each distribution network node with the threshold. If the load of a node exceeds its carrying capacity, it is considered that the node has "overloaded". These nodes are marked as "load overload nodes". Finally, transient data of each node of the power grid is collected, including rapid changes in load, current and voltage fluctuations, etc. Combined with post-disaster load data, the short-term overload or equipment abnormality that may be caused by load-overlimit nodes during the dynamic changes after the disaster is evaluated. Through transient simulation models (such as power flow calculation, time domain analysis, etc.), dynamic simulation of overlimit nodes is performed to analyze the stability of these nodes during post-disaster load fluctuations. In the power flow calculation, the topological changes of the post-disaster power grid (such as line disconnection or load transfer caused by damage to some equipment) are taken into account, and the load distribution and power flow of the power grid are recalculated. The current and voltage fluctuations of the load-overlimit nodes will be calculated and evaluated to predict the transient response of the nodes. For key equipment, the transient simulation is used to check whether the equipment is subjected to loads exceeding the design load, such as overcurrent or voltage fluctuations. By comprehensively evaluating factors such as the degree of overload, temperature change, and aging of the equipment, the probability of equipment damage or the possible maintenance cost is calculated. Combined with the load loss and equipment damage assessment, an overall transient loss index is formed, that is, the transient loss index is obtained.

[0073] It should be noted that in disaster scenarios, due to equipment damage or a sharp increase in load caused by the disaster, the load on certain nodes in the power grid may exceed its maximum carrying capacity, causing a load "over-limit" phenomenon.

[0074] It should be noted that in this application, the load loss index refers to the index of actual load loss caused by equipment failure, load overload, line damage, etc. in the power grid after a disaster; the load over-limit node refers to the node in the power grid where the load exceeds the maximum carrying capacity of the equipment or line; the transient loss index refers to the dynamic loss degree caused by load fluctuations, current and voltage changes and equipment failure in the power grid in a disaster scenario.

[0075] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart of determining the unstable load amount of the target power grid when it is hit by a disaster in an embodiment of the present application. In this embodiment, the operation risk index of each power grid site in the target power grid disaster scene is balanced and adjusted based on the transient loss index, and the unstable load amount of the target power grid when it is hit by a disaster can be obtained by the following steps:

[0076] First, in step S31, the operation risk index of each power grid site in the target power grid disaster scenario is determined;

[0077] Then, in step S32, the instability adjustment amount when the power grid is affected by a disaster is determined according to the operation risk index;

[0078] Finally, in step S33, the unstable load amount of the target power grid when it is hit by a disaster is obtained by comparing the unstable adjustment amount with the transient loss index.

[0079] In the specific implementation, first, according to the post-disaster load changes, calculate whether the load of each power grid site exceeds its maximum carrying capacity. The power flow calculation method can be used to simulate the post-disaster power grid operation to obtain the load changes of the nodes and their comparison with the rated load. For each node, its stability is evaluated in combination with its load status, equipment health status and network topology. For example, the response of voltage and current changes during load fluctuations is simulated by time domain analysis to identify the risk of possible faults such as overcurrent and overvoltage. Comprehensively considering factors such as overload, equipment failure probability, and power grid topology changes, an operation risk index is calculated for each node. The calculation of the operation risk index can be weighted and normalized. The higher the index value, the greater the risk of the node encountering a fault or instability. Then, according to the size of the operation risk index, the nodes of the power grid are divided into different risk levels. For example, a threshold can be set to classify high-risk and low-risk nodes. For high-risk nodes, appropriate load reduction measures need to be taken to prevent these nodes from becoming unstable due to excessive load. For low-risk nodes, too much intervention may not be required. By controlling the load of nodes with higher risk levels, their load pressure is reduced. According to the risk index of each node and its load situation, the required instability regulation amount is calculated. This is usually achieved through measures such as load reduction and equipment switch adjustment. For example, for high-risk nodes, the instability regulation amount may be the load amount that needs to be reduced at the node. Finally, compare the instability regulation amount and the transient loss index to determine the load amount that may need to be adjusted after the disaster. For example, if the instability regulation amount is greater than the transient loss, it means that the grid regulation measures are excessive and may cause imbalance in other areas of the grid; if the instability regulation amount is less than the transient loss, it may be necessary to further adjust the grid strategy to reduce post-disaster instability, where the instability load of the grid is calculated by comparing the instability regulation amount with the transient loss (the percentage can be calculated as the instability load).

[0080] It should be noted that in this application, the operation risk index is a quantification of the risk of failure or instability at each grid site after a disaster; the instability adjustment amount refers to the amount of load adjustment or equipment adjustment required to maintain grid stability during post-disaster operation; the instability load amount refers to the load instability part of the post-disaster grid caused by disasters and adjustment measures.

[0081] In step S4, the load adaptation level of the target power grid in each disaster-affected scenario is constructed based on the natural disaster chain, and the loss load index of the target power grid in each disaster-affected scenario is determined according to the load adaptation level. Then, the load attenuation deviation of the target power grid after the disaster is determined by the loss load index and the static safety node.

[0082] In specific implementation, the load adaptation level of the target power grid in each disaster-affected scenario based on the natural disaster chain can be implemented in the following ways: First, simulate the change of the power grid load after the disaster according to different natural disaster types (such as typhoons, earthquakes, floods, etc.). Through the load data of the post-disaster scenario, evaluate the load fluctuations in each region and determine which regions may suffer serious load losses or overloads. The post-disaster load changes can be predicted by historical data analysis, weather forecasts, and geological exploration. Next, evaluate the recovery capacity of the power grid in different post-disaster scenarios. This includes the repair capacity of power grid equipment, the line recovery speed, and the activation of backup power supplies. According to the recovery capacity, determine the maximum load range that the power grid can support and the stable load level after recovery. Finally, according to the load fluctuation amplitude, recovery capacity, and post-disaster load stability, the load adaptation capacity of the power grid in each disaster-affected scenario is divided into multiple levels. For example, a high adaptation level means that the area can withstand large load fluctuations and recover quickly after the disaster, while a low adaptation level means that the load recovery is difficult, which may lead to long-term instability, that is, the load adaptation level is obtained.

[0083] It should be noted that in this application, the load adaptation level refers to the ability of the power grid to adapt to load fluctuations, operate stably and restore load in different disaster scenarios under the influence of emergencies such as natural disasters.

[0084] Preferably, in this embodiment, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart of determining the loss load index of the target power grid in each disaster-affected scenario in an embodiment of the present application. In this embodiment, determining the loss load index of the target power grid in each disaster-affected scenario according to the load adaptation level can be implemented by the following steps:

[0085] First, in step S41, the transient risk of the target power grid in each disaster scenario is determined according to the load adaptation level;

[0086] Then, in step S42, the load loss factor of the target power grid in each disaster scenario is determined;

[0087] Finally, in step S43, the loss load index of the target power grid in each disaster scenario is determined according to the transient risk degree and the load loss factor.

[0088] In the specific implementation, first, the load adaptation level of the power grid is associated with the post-disaster load fluctuation. For example, in areas with low adaptation levels, the load recovery is slow and the fluctuation amplitude is large, which is easy to cause overload or power grid failure. Then, based on the adaptation level of the power grid, combined with dynamic factors such as post-disaster voltage and current changes, the transient risk that each area may face after the disaster is calculated. Usually, technologies such as power flow calculation and time domain simulation are used to simulate load fluctuations, and further evaluate the possible power grid stability and transient risks after the disaster, that is, to obtain the transient risk degree. Then, according to the degree of equipment damage and recovery of the power grid after the disaster, the load loss of each power grid site is evaluated. The loss factor is usually related to factors such as equipment failure rate, failure time, and line damage degree. Among them, the post-disaster load recovery model is used to simulate the load loss of each node during the power grid recovery process under different disaster conditions. According to the load recovery rate and equipment repair time, the load loss factor is calculated, and the load loss factor can be output through the post-disaster load recovery model. Finally, the transient failures and stability problems encountered by the power grid after the disaster are evaluated based on the transient risk degree, and combined with the load loss factor, the load loss in different regions in the disaster scenario is analyzed. According to the weighted relationship between the transient risk degree and the load loss factor, the loss load index of each region is calculated, where the loss load index = transient risk degree * load loss factor.

[0089] It should be noted that in this application, transient risk refers to the degree of dynamic risk that may occur in the power grid under the influence of post-disaster load fluctuations, equipment failures, etc. The lower the load adaptation level, the worse the adaptability of the power grid to post-disaster load changes and the higher the risk; the load loss factor refers to the influence coefficient of load loss caused by equipment failure, load overload or other problems in the disaster scene; the loss load index refers to the degree of load loss caused by equipment failure, load overload, line damage and other factors in the power grid after the disaster.

[0090] In this embodiment, the load attenuation deviation of the target power grid after the disaster is determined by the loss load index and the static safety node in the following manner, namely:

[0091] Determining the load bias of the target power grid after the disaster according to the loss load index;

[0092] Determine the attenuation compatibility of the target power grid after the disaster according to the static safety node;

[0093] The load attenuation deviation of the target power grid after the disaster is determined by the load bias and the attenuation compatibility.

[0094] In the specific implementation, first, the reduction in the actual load of the power grid after the disaster is calculated through the loss load index, which is mainly based on factors such as equipment damage and load loss. It is evaluated through the relationship between the load loss and the recovery capacity. For example, if the loss load index is X, it means that the load lost after the disaster is X, and the actual load of the power grid is the load before the disaster minus X, so as to obtain the load offset. Then, based on the determination of the static safety domain and the static safety node, the key nodes that can operate stably in the power grid after the disaster are identified. Static safety nodes refer to those nodes that can maintain the safety of the power grid even in the case of load fluctuations. Calculate the degree of reduction in the load carrying capacity of these nodes, that is, the attenuation compatibility. The attenuation compatibility can be calculated by the difference between the load carrying capacity of the node and the post-disaster load. Finally, it is calculated by the weighted average of the load offset and the attenuation compatibility. Evaluate the relationship between the actual load of the power grid after the disaster (that is, the load offset) and the attenuation capacity (attenuation compatibility). Then, use these two factors to calculate the deviation that may occur during the actual load recovery process. That is: load attenuation deviation = load offset - attenuation compatibility. The difference between the load bias and the attenuation compatibility is taken as the absolute value.

[0095] It should be noted that, in this application, load bias refers to the difference between the actual load and the ideal load of the power grid after a disaster, which usually indicates the gap in the load recovery process of the power grid after a disaster; attenuation compatibility refers to the degree of decline in the load carrying capacity of the power grid after a disaster due to load fluctuations, equipment failures and other factors; load attenuation deviation refers to the load recovery deviation caused by load bias and attenuation capacity during the load recovery process of the power grid after a disaster.

[0096] In step S5, a coordinated quantitative assessment is performed on the target power grid's damage information based on the load reduction deviation and the unstable load amount, and a power grid damage assessment report is generated.

[0097] In the specific implementation, first, the load attenuation deviation and unstable load are used as key indicators to integrate the data of the post-disaster operation status of the power grid. The load attenuation deviation reflects the load gap that may exist in the power grid recovery process, while the unstable load indicates the instability risk of the power grid after the disaster due to load fluctuations, equipment damage, etc. The combination of the two can comprehensively evaluate the post-disaster loss of the power grid. Then, the load attenuation deviation and the unstable load are weighted and analyzed using the collaborative evaluation model. By considering the mutual influence of these two indicators, the overall post-disaster loss of the power grid is evaluated. The model can quantify the degree of post-disaster loss by combining factors such as load changes, equipment status, and node stability of the power grid. For example, the comprehensive loss of the power grid under different conditions can be calculated by simulating load fluctuations and equipment damage rates under different disaster scenarios. Finally, a power grid disaster loss assessment report is generated based on the results of the collaborative quantitative assessment. The report will list in detail the load loss, equipment damage degree, recovery capability, and potential stability issues in each area. At the same time, recovery strategies and priorities are proposed based on the evaluation results to help power grid dispatchers make scientific decisions. The report also includes post-disaster risk assessment, recovery resource allocation, and possible dispatching plans.

[0098] In this embodiment, in S5, a collaborative quantitative assessment of the disaster loss information of the target power grid is performed based on the load attenuation deviation and the unstable load, and the resources of the power grid are optimized based on the assessment results, wherein a resource allocation optimization model is constructed based on a comprehensive loss index and an emergency resource information set in the assessment results, and the comprehensive loss index is determined based on a partitioned risk assessment model and power grid load data.

[0099] In an optional embodiment of the present application, the process of constructing the partition risk assessment model includes:

[0100] Determine key statistical features in each preset time period, wherein the key statistical features may include statistical features such as variance, average value, difference between maximum and minimum values ​​of various historical power parameter values ​​in the time period, health status of each power grid device, various statistical features of various environmental parameters in the time period, and quantitative values ​​of all geographic information features, such as altitude, slope, land use type, terrain classification index, digital elevation and other information;

[0101] A Bayesian network model is used as a partition risk assessment model, wherein the partition risk assessment model takes key statistical features of each time period as input;

[0102] Use structural learning algorithms to build the structure of the Bayesian network to represent the conditional dependencies between key statistical features and manually add known physical or engineering constraints;

[0103] The partition risk assessment model outputs an estimated probability of failure occurring in each time period according to the key statistical characteristics of the time period; it iteratively optimizes the parameters of each conditional probability table in the Bayesian network by using the maximum likelihood estimation or Bayesian estimation method for each time period;

[0104] In an optional embodiment of the present application, the resource demand ratio Bi is calculated according to the following formula:

[0105]

[0106] Wherein, i represents the serial number mark of each power grid partition; Ri represents the estimated resource demand mark in the i-th power grid partition, and the estimated resource demand Ri=a1×Li+a2×Ni+a3×Ri; wherein a1, a2 and a3 are all preset proportional coefficients; wherein Li represents the real-time power load mark of the i-th power grid partition; Ni represents the number mark of the key power facilities in the i-th power grid partition; FPi represents the failure probability mark of the i-th power grid partition estimated by the partition risk assessment model; Bi represents the resource demand ratio mark in the i-th power grid partition.

[0107] The grid loss index Wherein, a4 and a5 are preset proportional coefficients, and Ltotal is the total load of all grid partitions.

[0108] In an optional embodiment of the present application, the minimization target optimization function M is used as the optimization target, and the first constraint condition and the second constraint condition are used as the constraint condition set to form a resource allocation optimization model:

[0109] The expression of the objective optimization function M of the resource allocation optimization model is: M = ∑ i (Si×(1-xi)+Pi+xi); where Pi is the cost of allocating emergency resources to the i-th grid partition; xi represents the allocation ratio variable set for each grid partition; the total amount of emergency resources is 1.

[0110] It can be seen that in this application, first of all, by acquiring and analyzing the historical operation data of the power grid, it is possible to fully understand the operating status and load changes of the power grid before and after the natural disaster, and provide accurate data support for post-disaster recovery and emergency dispatch. By defining the static safety domain, the key stable areas of the power grid in post-disaster operation can be identified to ensure the safety of the power grid. By combining the load loss index and the transient loss index, the load recovery capability and equipment operation risk of the post-disaster power grid can be comprehensively evaluated to ensure that short-term fluctuations and equipment failures that may occur during the operation of the power grid are identified and adjusted in a timely manner. By establishing a load adaptation level, the ability to match the load distribution of the power grid with post-disaster recovery can be provided for power grid load recovery under different disaster scenarios. By collaboratively quantifying the evaluation of the loss of the post-disaster power grid, the impact of various factors on the power grid can be comprehensively considered, and an accurate disaster loss assessment report can be generated to provide a detailed decision-making basis for power grid recovery.

[0111] To sum up, the technical solution adopted in this application can perform intelligent dispatching monitoring and multi-dimensional disaster loss assessment of the power grid in a complex dynamic environment after the power grid is hit by a disaster, so as to improve the decision-making optimization capability of power grid disaster loss assessment.

[0112] Embodiment 2: This application provides a power grid damage assessment system based on natural disaster chains, referring to Figure 4 As shown, this figure is a schematic diagram of an evaluation system according to this embodiment of the present application, and the evaluation system includes:

[0113] The data acquisition module 100 is used to perform intelligent dispatching monitoring of the target power grid and obtain historical operation data of the target power grid in the natural disaster chain;

[0114] A safety identification module 200 is used to determine the static safety domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data, perform static stability identification on the static safety domain, and obtain the static safety node of the target power grid in the disaster scenario;

[0115] The balancing and regulating module 300 is used to obtain the load loss index of the target power grid after the disaster, and to access the transient loss index of the distribution network in each operation scenario in the disaster scenario. Based on the transient loss index, the operation risk index of each power grid site in the disaster scenario of the target power grid is balanced and regulated to obtain the unstable load of the target power grid when it is disaster-stricken;

[0116] The load loss module 400 is used to construct the load adaptation level of the target power grid in each disaster-affected scene based on the natural disaster chain, determine the loss load index of the target power grid in each disaster-affected scene according to the load adaptation level, and then determine the load attenuation deviation of the target power grid after the disaster according to the loss load index and the static safety node;

[0117] The quantitative assessment module 500 is used to perform a collaborative quantitative assessment on the disaster loss information of the target power grid according to the load attenuation deviation and the unstable load amount, and generate a power grid disaster loss assessment report.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power grid damage assessment method based on natural disaster chains, characterized in that: The evaluation method comprises the following steps: Conduct intelligent dispatching monitoring of the target power grid and obtain historical operation data of the target power grid in the natural disaster chain; Determine the static security domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data, perform static stability identification on the static security domain, and obtain the static security node of the target power grid in the disaster scenario; Obtain the load loss index of the target power grid after the disaster and access the transient loss index of the distribution network in each operation scenario in the disaster scenario, balance and adjust the operation risk index of each power grid site in the disaster scenario of the target power grid based on the transient loss index, and obtain the unstable load of the target power grid when it is disaster-stricken; Constructing the load adaptation level of the target power grid in each disaster-affected scenario based on the natural disaster chain, determining the loss load index of the target power grid in each disaster-affected scenario according to the load adaptation level, and then determining the load attenuation deviation of the target power grid after the disaster according to the loss load index and the static safety node; A coordinated quantitative assessment of the target power grid's damage information is performed based on the load attenuation deviation and the unstable load amount, and a power grid damage assessment report is generated.

2. A method for evaluating power grid damage based on a natural disaster chain as claimed in claim 1, characterized in that: Determining the static safety domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data specifically includes: Determining the static information entropy of the distribution network of the target power grid in the disaster scenario; Determining safe operation indicators of the distribution network of the target power grid in a disaster scenario based on the historical operation data; The static safety domain of the distribution network of the target power grid in the disaster scenario is determined according to the static information entropy and the safe operation index.

3. A method for evaluating power grid damage based on natural disaster chains as claimed in claim 1, characterized in that: The static security domain is statically identified to obtain the static security nodes of the target power grid in the disaster scenario, specifically including: Determining the load type of the target power grid in the disaster scenario according to the static security domain; determining a static load characteristic based on the load type; The static safety nodes of the target power grid in the disaster scenario are determined through the static load characteristics.

4. A method for evaluating power grid damage based on a natural disaster chain as claimed in claim 1, characterized in that: Obtaining the load loss index of the target power grid after the disaster. The transient loss index of the distribution network in each operation scenario in the disaster scene specifically includes: Determine the load loss index of the target power grid after the disaster; Access the load-exceeding nodes of the distribution network in each operation scenario in the disaster-affected scenario; The load loss index of the target power grid after the disaster is determined according to the load over-limit node, and the transient loss index of the distribution network in each operation scenario in the disaster scenario is connected.

5. A method for evaluating power grid damage based on natural disaster chains as claimed in claim 1, characterized in that: Based on the transient loss index, the operation risk index of each power grid site in the target power grid disaster scenario is balanced and adjusted to obtain the unstable load of the target power grid when it is disaster-stricken, which specifically includes: Determine the operational risk index of each power grid site in the target power grid disaster scenario; Determine the instability regulation amount when the power grid is affected by a disaster according to the operation risk index; By comparing the instability adjustment amount with the transient loss index, the instability load amount of the target power grid operating when it is hit by a disaster is obtained.

6. A method for evaluating power grid damage based on natural disaster chains as claimed in claim 1, characterized in that: Determining the load attenuation deviation of the target power grid after the disaster based on the loss load index and the static safety node specifically includes: Determining the load bias of the target power grid after the disaster according to the loss load index; Determine the attenuation compatibility of the target power grid after the disaster according to the static safety node; The load attenuation deviation of the target power grid after the disaster is determined by the load bias and the attenuation compatibility.

7. A method for evaluating power grid damage based on natural disaster chains as claimed in claim 1, characterized in that: The load adaptation level refers to the ability of the power grid to adapt to load fluctuations, operate stably and restore load in different disaster scenarios under the influence of emergencies such as natural disasters.

8. A method for evaluating power grid damage based on a natural disaster chain as claimed in claim 1, characterized in that: The unstable load refers to the unstable load portion in the post-disaster power grid caused by the disaster and regulation measures.

9. A method for evaluating power grid damage based on a natural disaster chain as claimed in claim 1, characterized in that: The transient loss index refers to the degree of dynamic loss of the power grid caused by load fluctuations, current and voltage changes, and equipment failures in disaster scenarios.

10. A power grid damage assessment system based on natural disaster chains, used to execute a power grid damage assessment method based on natural disaster chains as claimed in any one of claims 1 to 9, characterized in that: The evaluation system comprises: A data acquisition module is used to perform intelligent dispatching monitoring of the target power grid and obtain historical operation data of the target power grid in the natural disaster chain; A security identification module, used to determine the static security domain of the distribution network of the target power grid in the disaster scenario according to the historical operation data, perform static stability identification on the static security domain, and obtain the static security node of the target power grid in the disaster scenario; A balancing and adjustment module is used to obtain the load loss index of the target power grid after the disaster, access the transient loss index of the distribution network in each operation scenario in the disaster scenario, and balance and adjust the operation risk index of each power grid site in the disaster scenario of the target power grid based on the transient loss index to obtain the unstable load of the target power grid when it is affected by the disaster; A load loss module is used to construct a load adaptation level of the target power grid in each disaster-affected scenario based on the natural disaster chain, determine the loss load index of the target power grid in each disaster-affected scenario according to the load adaptation level, and then determine the load attenuation deviation of the target power grid after the disaster based on the loss load index and the static safety node; The quantitative assessment module is used to perform a collaborative quantitative assessment on the disaster loss information of the target power grid according to the load attenuation deviation and the unstable load amount, and generate a power grid disaster loss assessment report.

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