Disaster prevention strategy determination method and device for main-distribution network, equipment and medium

Abstract

The invention discloses a disaster prevention strategy determination method and device of a main-distribution network, equipment and a medium, and relates to the technical field of power system elastic evaluation, and the method comprises the steps: determining a target topology model of a system based on a preset island balance strategy and topology information of a main network and a distribution network in a main-distribution network cooperative power system; generating a random fault scene based on a preset improved Monte Carlo algorithm, and performing load flow analysis and cascade fault simulation analysis of the fault scene based on a fault generation result and the target topology model; analyzing a fault coupling condition of the main-distribution network based on a fault scene analysis result, a system collapse threshold and a seepage theory, and determining a critical threshold curve in a two-dimensional failure space; and determining a criticality index corresponding to each component in the main-distribution network and a comprehensive toughness evaluation index of the system based on the critical threshold curve so as to determine a target disaster prevention strategy. According to the invention, critical conditions of main-distribution network cooperative failure can be identified, weak links of the system can be identified, and the resistance of the system can be enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system resilience assessment technology, and in particular to a method, apparatus, equipment and medium for determining disaster prevention strategies for the main-distribution network. Background Technology

[0002] The coordinated operation capability of the main grid and distribution network is directly related to the overall security and power supply reliability of the power grid. In a power system with coordinated main and distribution networks, the failure of critical equipment such as lines and transformers may trigger a chain reaction, causing widespread power outages. Traditional reliability assessments of power systems (such as the N-1 criterion) have significant limitations in extreme disaster scenarios and are difficult to cope with the complex situation of simultaneous failure of multiple components in the system.

[0003] However, existing solutions mostly focus on the resilience analysis of a single transmission system, neglecting the fault coupling effect between the main grid and the distribution network. This leads to discrepancies between the resulting assessments and the actual resilience requirements of the system. Furthermore, these solutions are often tailored to specific fault scenarios and lack universality analysis for random disturbance events, making it difficult to provide comprehensive guidance for system disaster prevention planning. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, equipment, and medium for determining disaster prevention strategies for the main-distribution network, which can identify the critical conditions for coordinated failure of the main-distribution network and identify weak links in the system, thereby providing a reliable basis for power system disaster prevention planning and enhancing the system's resilience. The specific solution is as follows:

[0005] Firstly, this application provides a method for determining disaster prevention strategies for a primary-distribution network, including:

[0006] Based on the preset islanding balancing strategy and the topology information of the main grid and distribution network in the power system of the main grid and distribution network coordination, a corresponding topology model is constructed to determine the target topology model corresponding to the power system.

[0007] Random fault scenarios are generated based on a pre-defined improved Monte Carlo algorithm. Based on the corresponding fault generation results and the target topology model, power flow analysis and cascade fault simulation analysis are performed on each fault scenario to determine the fault scenario analysis results.

[0008] Based on the fault scenario analysis results, system collapse threshold, and seepage theory, the fault coupling of the main-distribution network is analyzed to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space; the critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region.

[0009] Based on the preset criticality analysis strategy, preset system resilience assessment rules, and the critical threshold curve, the criticality index corresponding to each component in the main-distribution network and the comprehensive resilience assessment index of the power system are determined, and the target disaster prevention strategy is determined according to the criticality index and the comprehensive resilience assessment index.

[0010] Optionally, the construction of a corresponding topology model based on the topology information of the main grid and distribution network in the power system based on a preset islanding balancing strategy and main-distribution network coordination includes:

[0011] Power flow modeling is performed based on the first topology information corresponding to the main grid in a power system with main-distribution grid coordination to determine the main grid topology structure model; the first topology information includes power plants, substations and transmission lines in the main grid;

[0012] Modeling is performed based on a preset modeling algorithm and the second topology information corresponding to the distribution network in the power system to determine the distribution network topology structure model; the second topology information includes distribution transformers, feeders and distributed power sources in the distribution network.

[0013] The power balance formula for the power system in islanded operation mode is determined based on the power generation output information of the main grid, the power generation and load demand information of the distributed power sources in the distribution network.

[0014] Steady-state analysis of the power system is performed based on the DC power flow model to determine the power flow distribution expression inside the island;

[0015] The target topology model is determined based on the main grid topology model, the distribution network topology model, the power balance formula, the power flow distribution expression, and the islanding operation constraints. The islanding operation constraints include transmission line capacity limits, generator output limits, load supply constraints, and voltage stability constraints.

[0016] Optionally, before performing power flow analysis and cascading fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model, the method further includes:

[0017] Analyze historical extreme disaster data, and construct fault probability models corresponding to each component in the main-distribution network under different disaster types based on the analysis results;

[0018] The failure probability model is integrated into the target topology model to obtain the integrated target topology model.

[0019] Optionally, the generation of random fault scenarios based on a preset improved Monte Carlo algorithm includes:

[0020] Random sampling is performed based on a preset probability distribution function corresponding to the power system to determine the number of faulty lines;

[0021] Based on the number of faulty lines, target faulty lines are selected from the power system to determine the corresponding fault scenario information;

[0022] The process then jumps back to the step of random sampling based on the preset probability distribution function corresponding to the power system until the preset termination condition is met, and the fault generation result is determined based on the generated fault scenario information.

[0023] Optionally, the step of performing power flow analysis and cascading fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model includes:

[0024] For the various fault scenario information in the fault generation results, power flow analysis and cascade fault simulation analysis of the main-distribution network are performed based on the target topology model, and the final state information of the power system is recorded to determine the fault scenario analysis results.

[0025] Optionally, the analysis of the fault coupling between the main and distribution networks based on the fault scenario analysis results, system collapse threshold, and seepage theory includes:

[0026] A two-dimensional failure space is constructed based on the number of faulty lines and the seepage theory.

[0027] In the two-dimensional failure space, the fault critical points of the main network and the distribution network are determined based on the fault scenario analysis results and the system collapse threshold, and the fault critical points are fitted to determine the critical threshold curve; the system collapse threshold includes the load loss ratio threshold, the network connectivity threshold, and the voltage stability threshold.

[0028] Optionally, the step of determining the criticality indicators corresponding to each component in the main-distribution network and the comprehensive resilience assessment indicators of the power system based on a preset criticality analysis strategy, preset system resilience assessment rules, and the critical threshold curve includes:

[0029] By simulating failure scenarios of different types and locations of components in the main-distribution network under static topology and dynamic fault propagation processes, the disturbance effects of these components on the boundaries of the resilient region are analyzed, and the evolution trajectory of the system state is recorded. Based on the disturbance analysis results and the evolution trajectory, corresponding criticality indicators are determined. The criticality indicators include the resilient region area reduction rate and the change in critical fault probability. The components include power plants, substations, and transmission lines in the main network and distribution transformers, feeders, and distributed power sources in the distribution network.

[0030] The corresponding elastic region area is determined based on the critical threshold curve to determine the comprehensive resilience assessment index of the power system.

[0031] Secondly, this application provides a disaster prevention strategy determination device for a main-distribution network, comprising:

[0032] The model building module is used to construct a corresponding topology model based on the topology information of the main grid and distribution network in a power system with preset islanding balancing strategy and main-distribution network coordination, so as to determine the target topology model corresponding to the power system.

[0033] The fault analysis module is used to generate random fault scenarios based on a preset improved Monte Carlo algorithm, and to perform power flow analysis and cascade fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model, so as to determine the fault scenario analysis results.

[0034] The threshold curve determination module is used to analyze the fault coupling of the main-distribution network based on the fault scenario analysis results, system collapse threshold, and seepage theory, so as to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space; the critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region.

[0035] The strategy determination module is used to determine the criticality indicators corresponding to each component in the main-distribution network and the comprehensive resilience assessment indicators of the power system based on the preset criticality analysis strategy, the preset system resilience assessment rules and the critical threshold curve, and to determine the target disaster prevention strategy based on the criticality indicators and the comprehensive resilience assessment indicators.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the steps of the aforementioned method for determining the disaster prevention strategy of the main-distribution network.

[0039] Fourthly, this application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method for determining the disaster prevention strategy of the main-distribution network.

[0040] As can be seen, in this application, a corresponding topology model is constructed based on the topology information of the main grid and distribution network in a power system with a preset islanding balancing strategy and main-distribution network coordination, to determine the target topology model corresponding to the power system; random fault scenarios are generated based on a preset improved Monte Carlo algorithm, and power flow analysis and cascade fault simulation analysis of each fault scenario are performed based on the corresponding fault generation results and the target topology model to determine the fault scenario analysis results; the fault coupling of the main-distribution network is analyzed based on the fault scenario analysis results, system collapse threshold, and seepage theory to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space; the critical threshold curve is used to divide the two-dimensional failure space into a resilient region and a collapse region; based on a preset criticality analysis strategy, a preset system resilience assessment rule, and the critical threshold curve, the criticality index corresponding to each component in the main-distribution network and the comprehensive resilience assessment index of the power system are determined, and the target disaster prevention strategy is determined based on the criticality index and the comprehensive resilience assessment index. In other words, this application first constructs a target topology model of a power system with coordinated main and distribution networks. Then, it randomly generates fault scenarios and performs power flow analysis and cascading fault simulation analysis for each fault scenario based on the target topology model. Next, it uses the fault scenario analysis results, system collapse thresholds, and seepage theory to analyze the fault coupling of the main and distribution networks, determining the critical threshold curve in the two-dimensional failure space. Then, based on this critical threshold curve, it analyzes the criticality indicators of the components and the comprehensive resilience assessment indicators of the power system to determine the target disaster prevention strategy. This approach identifies the critical conditions for coordinated failure of the main and distribution networks and identifies weak links in the system, thus providing a reliable basis for power system disaster prevention planning and enhancing the system's resilience. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 A flowchart illustrating a method for determining disaster prevention strategies for a main-distribution network, as provided in this application;

[0043] Figure 2 This application provides a schematic diagram of a critical threshold curve in a two-dimensional failure space.

[0044] Figure 3 A schematic diagram illustrating a primary-distribution network cascading fault provided in this application;

[0045] Figure 4 A schematic diagram of a system fault propagation table provided in this application;

[0046] Figure 5 A schematic diagram of the elastic region curve of the T6D2 system provided in this application;

[0047] Figure 6 A schematic diagram of the criticality of each line provided in this application;

[0048] Figure 7 A schematic diagram of the elastic region curve of the improved T6D2 system provided in this application;

[0049] Figure 8 A schematic diagram of a disaster prevention strategy determination device for a main-distribution network provided in this application;

[0050] Figure 9 This application provides a structural diagram of an electronic device. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Existing solutions mostly focus on the resilience analysis of a single transmission system, neglecting the fault coupling effect between the main grid and the distribution network. This leads to discrepancies between the resulting assessments and the actual resilience requirements of the system. Furthermore, these solutions are often tailored to specific fault scenarios, lacking universality analysis for random disturbances, and thus failing to provide comprehensive guidance for system disaster prevention planning. Therefore, this application provides a disaster prevention strategy determination scheme for the main grid and distribution network. This scheme can identify the critical conditions for coordinated failure of the main grid and distribution network and identify weak links in the system, thereby providing a reliable basis for power system disaster prevention planning and enhancing the system's resilience.

[0053] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for determining a disaster prevention strategy for a main-distribution network, including:

[0054] Step S11: Based on the preset islanding balancing strategy and the topology information of the main grid and distribution network in the power system of main grid-distribution network coordination, construct the corresponding topology model to determine the target topology model corresponding to the power system.

[0055] In this embodiment, the first step is to construct detailed topology models of the main power grid and distribution network, which form the foundation of the entire scheme. During the modeling process, the actual structural characteristics of the power system are fully considered, and the main power grid and distribution network are modeled as two interconnected but distinct subsystems. The main power grid model needs to include components such as power plants, substations, and transmission lines, with a focus on the structural characteristics of high-voltage transmission networks. The distribution network model, on the other hand, needs to depict in detail components such as distribution transformers, feeders, and distributed generation sources, reflecting the topological characteristics of medium- and low-voltage distribution networks. The two subsystems are interconnected through specific coupling nodes to form a complete power network model. That is, firstly, power flow modeling is performed based on the first topology information corresponding to the main grid in the power system with main-distribution grid coordination to determine the main grid topology model; the first topology information includes power plants, substations, and transmission lines in the main grid; secondly, modeling is performed based on a preset modeling algorithm and the second topology information corresponding to the distribution network in the power system to determine the distribution network topology model; the second topology information includes distribution transformers, feeders, and distributed generation in the distribution network; thirdly, the power balance formula of the power system in islanded operation mode is determined based on the power output information of the main grid, the power generation and load demand information of the distributed generation in the distribution network; fourthly, steady-state analysis of the power system is performed based on the DC power flow model to determine the power flow distribution expression inside the island; fifthly, the target topology model is determined based on the main grid topology model, the distribution network topology model, the power balance formula, the power flow distribution expression, and islanded operation constraints; the islanded operation constraints include transmission line capacity limits, generator output limits, load supply constraints, and voltage stability constraints. Specifically, appropriate modeling methods are adopted for the main network and distribution network based on their different characteristics: the main network focuses on power flow transmission capacity modeling, using DC power flow or fast AC power flow algorithms; while the distribution network pays more attention to the radial network structure and the impact of distributed power sources, using calculation methods suitable for the characteristics of the distribution network, such as forward-backward substitution. This differentiated modeling strategy ensures both the accuracy of the model and the computational efficiency.

[0056] Regarding power balance in the main-distribution grid islanding mode, after islanding is formed, the generation, load demand, and distribution network supply capacity within the island must satisfy the power conservation relationship. In islanding mode, the load supply in the distribution network is highly dependent on the capacity of local distributed power sources and the stock of their energy storage systems. When the main grid loses some of its power supply capacity, the distribution network may need to adopt load shedding strategies to avoid power supply imbalance within the island. The large-scale generation provided by the main grid is often the primary power source within the island, while the role of distributed power sources in the distribution network in islanding mode depends on their sustainability. The power balance equation for islanding, i.e., the power balance formula, is as follows:

[0057] .

[0058] In the formula, This represents the output of the i-th power generation unit in the main grid at time t, i.e., the power generation output information of the main grid; This represents the power generation of the i-th distributed power source (such as photovoltaic or wind power) in the distribution network at time t; This represents the load demand of the i-th user at time t, i.e., load demand information, which may include industrial, commercial, and residential electricity consumption; the summation term on the right. This represents the power flowing into transmission line l at time t. Let l represent the power flowing out through transmission line l at time t, l+ represents the power flowing into the transmission line, and l- represents the power flowing out of the transmission line.

[0059] Regarding the power flow distribution expression within an island, in the main-distribution network islanding operation mode, the power flow distribution is affected by various factors, such as the availability of main grid power sources, the output of distributed generation sources in the distribution network, and load regulation capabilities. When there are few available distributed generation sources in the distribution network, the island may become more dependent on the main grid's power supply. If a cascading fault occurs in the main grid, the island's power supply capacity will further decrease. To calculate the power flow distribution within the island, a DC power flow method is typically used for approximate calculation. This method is suitable for steady-state analysis of large-scale power systems and can effectively simplify computational complexity. The power flow distribution expression in the island is shown below:

[0060] .

[0061] In the formula, This indicates the operating status of transmission line l at time t (1 for operating, 0 for out of service). and Let represent the voltage angles at the two nodes of transmission line l at time t; Let M be the reactance of transmission line l, where M represents a maximum value, for example, 10000 in this case.

[0062] Regarding the constraints of islanded operation of the main-distribution network, a series of constraints need to be imposed to ensure the safe operation of the power system within the island, including transmission line capacity limits, generator output limits, and voltage stability constraints. Among these, the power flow constraints for transmission lines are as follows:

[0063] .

[0064] In the formula, This represents the maximum power capacity of transmission line l.

[0065] The power output constraints of the main grid and distributed generation are as follows:

[0066] .

[0067] In the formula, It is a binary variable, representing the operating status of the i-th generating unit in the main grid at time t. Its value of 0 indicates that the generator is out of service, and its value of 1 indicates that the generator is operating normally. This represents the lower limit of the power generation of the i-th power generation unit in the main grid; This represents the maximum power generation capacity of the i-th power generation unit in the main grid; It is a binary variable representing the operating status of the i-th distributed power source in the distribution network at time t. When its value is 0, it means that the distributed generator is out of service, and when it is 1, it means that the distributed generator is operating normally. This represents the lower limit of the power generation capacity of the i-th distributed power source in the distribution network; This represents the upper limit of the power generation of the i-th distributed generation unit in the distribution network. The above two formulas limit the output range of the main grid and distributed generation units, preventing excessive dispatch from causing system instability.

[0068] The stability constraints for the voltage angle of transmission lines are as follows:

[0069] .

[0070] In the formula, Let represent the voltage phase angle of the i-th node in the system at time t; This represents the lower limit of the voltage phase angle at the i-th node in the system; This represents the upper limit of the voltage phase angle at the i-th node in the system. This formula ensures that the voltage angle within the islanded system remains within a stable range, preventing voltage instability from escalating into a fault.

[0071] The load supply constraints are as follows:

[0072] .

[0073] In the formula, Let be the load demand response quantity corresponding to the i-th type of user load demand at time t; Let be the rated load demand corresponding to the i-th type of user load demand at time t. This constraint is used to ensure that, in islanded mode, the load reduction strategy will not affect the normal power consumption of critical users.

[0074] The frequency constraints are as follows:

[0075] ;

[0076] .

[0077] In the formula, This means finding the infimum (i.e., the minimum probability in the worst case) in the set of probability distributions. Represents the true probability distribution; This represents the active power output vector of a distributed generator. This represents the boundary active power vector between the transmission system and the distribution system. It represents the theoretical probability distribution; the f() function is the frequency calculation function, used to evaluate the system frequency deviation; This represents the boundary active power between the current TS and the i-th DS; This represents the initial value of the boundary active power; Indicates nodes in the power distribution system; Represents the set of all power distribution systems; This represents the current active power output of the i-th distributed generator; This represents the initial value of the active power output of the i-th distributed generator; Represents a distributed generator node; This represents the set of all distributed generators; Indicates the rated capacity of the generator; This represents the frequency response rate of the i-th generator; Represents the set of generator nodes; This represents the set of all generators. This formula defines the frequency deviation of the system under the most unfavorable scheduling strategy as not exceeding a frequency threshold. The robustness condition requires the failure probability to be lower than This embodies the core idea of ​​the critical threshold in seepage theory. The above formula quantifies that the difference between the net power imbalance adjustment by traditional power generation and the fluctuation of renewable energy directly determines the direction of frequency deviation, and its dynamic coupling mechanism is the key to the interdependence and resilience analysis of the main and distribution networks.

[0078] In this way, under the context of coordinated operation of the primary and distribution networks, the islanding balancing strategy can effectively improve the system's power supply capacity under fault conditions. Through power balance equations, power flow calculations, and operational constraints, power allocation in islanded mode can be optimized, load shedding reduced, and the risk of fault propagation lowered. These mathematical models provide theoretical support for assessing the resilience of the primary and distribution networks and can be used to analyze system recovery strategies under different fault scenarios, thereby providing a basis for decision-making to improve the reliability of the power grid.

[0079] Furthermore, after completing the topology modeling, it is necessary to clearly define the criteria for determining system failure. This embodiment adopts a multi-index comprehensive criterion, which mainly includes three dimensions: load loss ratio, network connectivity, and voltage stability. Among them, the load loss ratio is the most critical criterion; when the load loss caused by a fault exceeds a preset threshold, the system is judged to have failed. Simultaneously, auxiliary criteria such as the scale of islands formed by network splits and the degree of voltage drop at key nodes are considered. The setting of these criteria fully considers the safety constraints in the actual operation of the power system, ensuring that the evaluation results have engineering guidance value and providing a basic network architecture and evaluation basis for subsequent analysis.

[0080] Understandably, to improve the accuracy of the model, this embodiment specifically considers the equipment failure characteristics under extreme disaster conditions during the modeling process. That is, historical extreme disaster data is analyzed, and based on the analysis results, failure probability models corresponding to each component in the main-distribution network under different disaster types are constructed; these failure probability models are then integrated into the target topology model to obtain the integrated target topology model.

[0081] Step S12: Generate random fault scenarios based on the preset improved Monte Carlo algorithm, and perform power flow analysis and cascade fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model, so as to determine the fault scenario analysis results.

[0082] In this embodiment, the evaluation system focuses on analyzing the relationship between the initial faulty line and the final state of the system after fault propagation. First, a method for generating initial fault scenarios needs to be constructed. An improved Monte Carlo method is used to generate random fault scenarios in two stages: first, initial fault orders are sampled according to a probability distribution; then, specific faulty lines are randomly selected. Through large-scale repeated sampling, diverse fault combinations are covered, ensuring the statistical significance and comprehensiveness of the evaluation results. That is, first, random sampling is performed based on a preset probability distribution function corresponding to the power system to determine the number of faulty lines; then, target faulty lines are selected from the power system based on the number of faulty lines to determine the corresponding fault scenario information; the process then jumps back to the step of random sampling based on the preset probability distribution function corresponding to the power system until a preset termination condition is reached. The fault generation result is determined based on the generated fault scenario information. The preset termination condition can be a pre-set number of iterations.

[0083] Regarding the specific process of generating random fault scenarios, the fault order is first determined: In the first stage, the number of initial faulty lines is determined through random sampling. Let the fault order, i.e., the number of faulty lines, be... For a containing A system with [number] lines satisfies the following probability distribution :

[0084] .

[0085] In the formula, The parameter input is the set probability distribution function, and can be adjusted according to historical data or research needs; N is the total number of lines.

[0086] Next, fault line selection is performed: the second stage involves selecting the fault level that has been determined. Based on this, randomly select from N lines The initial faulty lines are denoted as set . This sampling process can be used... Combinatorial mathematical representation:

[0087] .

[0088] Through the two stages described above, this embodiment constructs a systematic and widely applicable initial fault scenario generation mechanism. Throughout the simulation process, the above process is repeated multiple times (the number of repetitions can be set to a specific value). This generates a large number of initial fault scenarios, which are used for fault propagation analysis and system final state research in subsequent resilience assessments.

[0089] Furthermore, after generating random fault scenarios, power flow calculations and cascaded fault simulations are performed on each set of fault scenarios, and the final state of the system is recorded. That is, for the various fault scenario information in the fault generation results, power flow analysis and cascaded fault simulation analysis of the main-distribution network are performed based on the target topology model, and the final state information of the power system is recorded to determine the fault scenario analysis results.

[0090] Step S13: Based on the fault scenario analysis results, system collapse threshold, and seepage theory, analyze the fault coupling of the main-distribution network to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space; the critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region.

[0091] In this embodiment, after completing the fault analysis, a two-dimensional failure space is constructed based on the number of faulty lines and seepage theory. Within this two-dimensional failure space, the fault critical points of the main grid and the distribution network are determined based on the fault scenario analysis results and the system collapse threshold. These critical points are then fitted to determine a critical threshold curve. The system collapse threshold includes a load loss ratio threshold, a network connectivity threshold, and a voltage stability threshold. Specifically, the two-dimensional failure space is constructed based on the initial number of faults in the main and distribution networks. The network connectivity phase transition characteristics are analyzed using seepage theory to quantify the coupling effect of faults in the main and distribution networks, providing theoretical support for defining the boundary of the resilient region. During the process, the critical points are fitted in conjunction with the fault analysis results to form a threshold curve, which divides the failure space into a resilient region and a collapse region.

[0092] It is important to understand that the application of seepage theory in the context of two-dimensional failure space within the main distribution network system is of great significance. Under extreme weather conditions, the main grid may suffer severe shocks, with critical components such as generators and power lines potentially experiencing significant damage, leading to generator shutdowns and line interruptions, thus triggering widespread power outages. While a certain number of distributed generators exist within the distribution network, their limited and highly volatile generating capacity means that the main energy supply for the distribution network still relies heavily on the main grid. Therefore, a failure in the main grid will significantly impact the distribution network. Similarly, when extreme weather causes a failure in the distribution network, the main grid needs to supply more power to the distribution network to meet its load demand as much as possible, which undoubtedly increases the power flow pressure on the main grid. The main and distribution networks exhibit significant mutual influence under extreme weather conditions, and this complex coupling relationship makes it difficult for traditional analysis methods to accurately assess the overall resilience of the system. Seepage theory, however, can effectively quantify the resilience boundary of multi-energy networks, providing a new perspective for assessing the resilience of the main distribution network system. By constructing a two-dimensional failure space to describe the joint failure effects of main and distribution network components, its critical threshold curve reveals the conditions for system collapse. The cascading failure model based on flow heterogeneity further extends the classical seepage method by introducing a dynamic flow redistribution mechanism to capture real physical constraints such as power line overload tripping. The identification of key components relies on the sensitivity analysis of seepage probability to network connectivity; failure of key nodes or high-load channels will significantly alter the value of this probability.

[0093] In this way, this application can focus on the overall impact of large-scale events on system resilience. By considering different types and numbers of initial faults and their propagation processes, it assesses the system's operational status under various potential fault scenarios, thus providing a comprehensive evaluation of the resilience of the main distribution network system from a macroscopic perspective, rather than being limited to the analysis of specific faulty lines. Since there is a strong energy interdependence between the main and distribution networks, network faults in one network will affect the other; therefore, considering the degree of impact of individual network faults on other networks is also an important part of the resilience evaluation system. The application of seepage theory enables the quantification and analysis of this complex interaction, thereby providing theoretical support and decision-making basis for improving the resilience of the main distribution network system.

[0094] In this embodiment, based on the macroscopic resilience evaluation system described above, this embodiment first proposes the concept of a single network resilience region, which is defined as the number of initially faulty network elements (i.e., components) and the system state, such as... Figure 2 As shown, its specific physical meaning is to obtain the minimum initial component failure ratio corresponding to the current system state. For another system, a similar curve exists, as shown... Figure 2 As shown. These two systems can refer to the main network system and the distribution network system, respectively.

[0095] The resilient region aggregates disturbance scenarios that cause minor losses to system function, while the boundary of the resilient region represents critical scenarios that disrupt the system. As the number of initially faulty lines increases, the overall system state gradually declines. This is a one-dimensional threshold curve, meaning that for the main and distribution networks, only the impact of faults on their respective lines on the network's state is studied. However, for the main-distribution power system, due to the close energy coupling between the main and distribution networks, a fault in one network will affect the load supply capacity of the other system. Therefore, it is necessary to develop the one-dimensional threshold curve in resilience assessment to two dimensions, such as... Figure 2 As shown. In Figure 2 In the diagram, the horizontal axis represents the proportion of initially faulty lines in the main grid, and the vertical axis represents the proportion of initially faulty lines in the distribution network. Taking the blue main grid threshold curve as an example, under the initial fault conditions corresponding to the points on this line, the main grid will eventually reach a state of collapse after fault propagation. Below the area under this curve, that is, after reducing the number of faulty lines in the main and distribution networks, the main grid will not collapse. The physical meaning of power system collapse here refers to a certain proportion of system load loss, as shown in the following formula:

[0096] .

[0097] In the formula, This represents the final state indicator of the system. A value of 0 indicates that the system has reached a state of collapse after fault propagation. Indicates system fault propagation to The load level supplied by the system at each stage; The threshold representing the eventual collapse of the system; T represents the fault propagation time; Indicates the initial line fault percentage; Represents the set of nodes in a power system; Represents a node The initial load.

[0098] Throughout the entire resilience assessment process, to ensure the convergence of the resilience curve, the minimum failure ratio in the assessment state is used. That is, when the initial fault line in the main network is 2, both fault order 1 and 2 in the distribution network will cause the system to collapse in the final state. In this case, to ensure the robustness of the system resilience curve, fault order 1 in the distribution network is selected as the point on the resilience curve, as shown in the following formula.

[0099] .

[0100] In the formula, This represents the lower limit of the initial proportion of mainnet fault lines that would cause the mainnet to crash. This represents the minimum percentage of faulty distribution network lines required to cause the main network to crash. This represents the maximum percentage of the minimum main network failure lines required to cause the distribution network to crash. This represents the minimum percentage of faulty lines in the distribution network that would cause the distribution network to crash. Indicates the initial fault ratio of the main power grid; Indicates the initial fault ratio of the distribution network; Indicates the status indicators of the power transmission network system; These represent the status indicators of the power distribution network system.

[0101] Step S14: Based on the preset criticality analysis strategy, preset system resilience assessment rules and the critical threshold curve, determine the criticality index corresponding to each component in the main-distribution network and the comprehensive resilience assessment index of the power system, and determine the target disaster prevention strategy according to the criticality index and the comprehensive resilience assessment index.

[0102] In this embodiment, after defining the system's resilient region, key components are further identified, and the overall resilience of the system is assessed. Specifically, by simulating failure scenarios of different types and locations of components in the main-distribution network under static topology and dynamic fault propagation processes, the perturbation effect of these components on the boundary of the resilient region is analyzed, and the evolution trajectory of the system state is recorded. Based on the perturbation analysis results and the evolution trajectory, corresponding criticality indicators are determined. These criticality indicators include the resilient region area reduction rate and the change in critical fault probability. The components include power plants, substations, and transmission lines in the main network, and distribution transformers, feeders, and distributed generation in the distribution network. The corresponding resilient region area is determined based on the critical threshold curve to establish the comprehensive resilience assessment index of the power system. Thus, based on threshold curve sensitivity analysis, the impact of each line on system resilience is quantified. Criticality indicators reflect the contribution of component failure to the resilient region boundary, identifying key nodes and high-risk lines, providing a basis for targeted reinforcement.

[0103] Specifically, regarding criticality analysis, based on the sensitivity analysis principle in permeation theory, a complete component criticality assessment system is established by systematically simulating failure scenarios for each component and calculating its perturbation effect on the elastic region boundary. In actual calculations, it is necessary to comprehensively consider the different mechanisms of action of components in static topology and dynamic fault propagation processes to ensure that the assessment results reflect both network structure characteristics and actual physical processes. The core of criticality assessment lies in establishing the mapping relationship between component failure and system performance degradation. Considering the characteristics of the main distribution network coordinated system, this embodiment adopts multi-dimensional evaluation indicators, including parameters such as the elastic region area reduction rate caused by component failure and the change in critical fault probability. By designing a hierarchical simulation experiment scheme, failure scenarios of different types and locations of components are simulated sequentially, recording the evolution trajectory of system state variables, and ultimately forming a complete component criticality spectrum. This method based on full-scenario simulation can effectively capture the nonlinear effects and coupling relationships existing in the system. In this way, by quantifying the impact of each network component on the overall system resilience, accurate decision support can be provided for power grid hardening.

[0104] Regarding the overall resilience assessment of the system, the abstract resilience region is transformed into analytical resilience values ​​throughout the entire resilience assessment process. Therefore, it is necessary to formulate relevant resilience indicators. Based on the penetration threshold curve, it can be seen that the final state of the system reflects its resilience level as it faces more initial failures. Therefore, the area under the threshold curve can be selected as the resilience indicator in the assessment process, as shown in the following formula:

[0105] .

[0106] In the formula, R() function represents the main-distribution network resilience coefficient; The function represents the distribution network resilience coefficient; The function represents the main grid resilience coefficient; Indicates the mainnet elastic region; Indicates the flexible distribution network area; Indicates the main-distribution network elasticity zone; This represents the main network elasticity curve; This represents the distribution network resilience curve.

[0107] In this way, resilience indicators can reflect a system's ability to cope with all possible random disturbance events. The larger the resilience index, the stronger the system's resilience against disturbance events. The proposed resilience zone has two main application scenarios. First, the proposed resilience zone and corresponding resilience indicators can help system operators assess the system's ability to withstand potential disasters. Specifically, operators can quickly assess the system's functional losses and the number of failed components. By comparing the degree of disturbance caused by a disaster with the resilience zone, operators can understand the distance between system operating points and the extent of collapse. Second, the assessment results can guide system operators to develop preventative and emergency measures to enhance system resilience and avoid collapse. Specifically, by analyzing the impact of component failure on resilience indicators, key components that contribute to the formation of a resilience zone can be identified. Based on this, preventative measures are given to protect key components from downtime after disturbances. In addition, emergency measures can be taken to ensure the energy supply to critical loads. Through these measures, the resilience zone can be expanded, thereby improving the system's ability to withstand certain extreme disturbance events.

[0108] In this way, the area of ​​the elastic region is used as a toughness index to comprehensively reflect the system's ability to withstand random failures. Combined with the results of the criticality analysis, the effectiveness of the reinforcement measures is evaluated, forming a closed-loop toughness improvement strategy of "evaluation-optimization-verification".

[0109] In summary, this embodiment provides a main-distribution network resilience assessment scheme based on seepage theory for power system resilience analysis and optimization under extreme disaster scenarios. By constructing a two-dimensional failure space for the main-distribution network coordinated system, it achieves a quantitative assessment of the coupling failure effects across voltage level grids, overcoming the limitations of traditional single-network assessment methods. The proposed resilience region boundary curve and resilience index provide intuitive and quantifiable decision-making basis for grid disaster prevention planning, enabling operators to accurately predict the system's collapse critical point under extreme disasters. This approach better aligns with actual grid operation characteristics, identifies systemic risks that are difficult to detect using traditional methods, and offers computational efficiency sufficient for online analysis, supporting rapid assessment of large-scale grids. The proposed hardening strategy is economical and efficient, guiding the optimal allocation of limited disaster prevention resources and effectively identifying the critical conditions for main-distribution network coordinated failure, providing a scientific basis for power system disaster prevention planning. Furthermore, the hardening of key components significantly enhances the system's ability to withstand extreme disasters, offering a new approach to smart grid resilience optimization. Moreover, this scheme is not only applicable to the planning and design phase of power systems but can also be used for real-time risk assessment and emergency decision support during operation. Furthermore, it can be combined with artificial intelligence technology to achieve dynamic prediction and adaptive hardening of resilience boundaries, providing key technical support for building a new generation of resilient power grids. In addition, it can be extended to resilience assessment of other critical infrastructure, showing broad application prospects.

[0110] Therefore, this application first constructs a target topology model of a power system with coordinated main and distribution networks. Then, it randomly generates fault scenarios and performs power flow analysis and cascading fault simulation analysis for each fault scenario based on the target topology model. Next, it uses the fault scenario analysis results, system collapse threshold, and seepage theory to analyze the fault coupling of the main and distribution networks, determining the critical threshold curve in the two-dimensional failure space. Then, based on this critical threshold curve, it analyzes the criticality indicators of the components and the comprehensive resilience assessment indicators of the power system to determine the target disaster prevention strategy. This approach identifies the critical conditions for coordinated failure of the main and distribution networks and identifies weak links in the system, thus providing a reliable basis for power system disaster prevention planning and enhancing the system's resilience.

[0111] The following is combined Figures 3 to 7 The timing diagrams disclosed herein provide a detailed explanation of the technical solutions of the embodiments of this application.

[0112] In this embodiment, the T6D2 system (i.e., a power system consisting of a six-node main network and two distribution networks) is used as the test network for case analysis to verify the scheme proposed in the aforementioned embodiments. This test network consists of a main network and distribution networks. The main network contains six nodes and several power lines, while the two distribution networks are respectively connected to specific nodes of the main network to form an overall main-distribution network system structure.

[0113] In the T6D2 system, the main grid comprises multiple generation units and load nodes, whose power supply needs to be transmitted to various distribution networks through the main grid to maintain the stable operation of the overall system. The power flow between main grid nodes follows the DC power flow equation and is affected by constraints such as generator capacity, grid transmission capacity, and node power balance. In addition, as an extension of the main grid, the power supply capacity of the distribution network depends on the operating status of the main grid and is affected by line power flow constraints and fault propagation characteristics.

[0114] To study the resilience of the main-distribution network, this embodiment combines... Figure 3 The illustrated cascading failure model of the main grid and distribution network analyzes the impact of the damage to the main grid power lines and the number of faulty lines in the distribution network on the final load supply of the main grid and distribution network under different initial fault scenarios. During fault propagation, as critical lines are damaged, the main grid may gradually split into multiple isolated areas. To maintain the stable operation of these islands, this embodiment adopts an island balancing strategy. Based on the principle of minimum load reduction, power reallocation is used to adjust the distribution of generation and load, so that the supply and demand within the islands reach a new equilibrium. The power flow calculation after reallocation uses the DC power flow method and further evaluates the overload situation of each line to determine the evolution path of fault propagation. The fault propagation situation of the system is as follows: Figure 4 As shown.

[0115] Combination Figure 4 As shown, the initial disturbance triggers fault propagation between the main grid and the distribution network, leading to a gradually expanding cascading failure effect. This process involves load transfer and imbalance between the distribution network and the main grid, manifested as a continuous load decrease. As the system fault expands, further faults in the distribution network lines cause a decrease in the main grid load, while the distribution network load is affected more rapidly. This phenomenon reveals the vulnerability and interdependence of the main-distribution network system when encountering cascading failures.

[0116] at the same time, Figure 5 Furthermore, one-dimensional seepage threshold points for both transmission and distribution network systems can be observed. Specifically, the intersection of the seepage threshold curve with the x-axis represents the seepage threshold point when considering only main grid fault disturbances, while the intersection with the y-axis corresponds to the seepage threshold point when considering only distribution network fault disturbances. For example, in... Figure 5 In the data, when only main grid line faults are considered, the seepage thresholds for the power grid and distribution network are 0.55 and 0.9, respectively. This indicates that the number of initial main grid fault lines required to cause the distribution network to collapse is greater than the number required to cause the power grid to collapse. This is because, in the case of only main grid line faults, the core functions of the distribution network can still be maintained, and the distribution load will not be immediately affected until the proportion of main grid line faults reaches a high level. Similarly, when no main grid lines are faulty, the seepage thresholds for the power network are higher than those for the distribution network, at 0.55 and 0.35, respectively. This means that, when only distribution network faults are considered, more distribution network lines need to be attacked to cause the main grid to collapse, while the distribution network itself has relatively weak fault tolerance.

[0117] Therefore, the defined resilience region method can extend the one-dimensional threshold point in traditional resilience analysis methods into a two-dimensional threshold curve, enabling a more comprehensive characterization of the collapse conditions of the main-distribution network system. In other words, traditional seepage theory-based methods can only reveal the minimum failure proportion of a single type of component that leads to system collapse, while the resilience region method proposed in this embodiment can identify the set of critical failure proportions that simultaneously lead to the collapse of both the main network and the distribution network system, thereby more accurately characterizing the criticality of fault propagation in the main-distribution network.

[0118] Simultaneously, to assess the impact of each component on the resilience zone, the criticality metrics of transmission and distribution network lines need to be calculated. Taking the above model as an example, the criticality distribution of transmission and distribution network lines is obtained through... Figure 6As shown in the diagram, the line criticality map obtained through this resilience assessment method can intuitively reflect the weak links in the main-distribution coordinated power supply network. Specifically, since line criticality reflects the probability of a line failure leading to system collapse during the resilience assessment process, the higher the probability of the system ultimately collapsing if that line fails. Therefore, a higher line criticality means a greater probability of system collapse when it fails, indicating a stronger correlation between that line and system stability, meaning that the line can be considered a weak link in the system. Due to network heterogeneity, i.e., different lines are located at different power supply load locations, the criticality of different links in the system varies. Based on the ranking of criticality, we can identify which links in the system are weak. For lines with high criticality, such as line 1, we can see that under different initial fault scenarios, this line is often more susceptible to damage. Conversely, for lines with lower criticality, because they are located in the lower part of the power supply chain, they are often less susceptible to damage under different initial fault scenarios.

[0119] After obtaining the criticality values ​​of different lines, these critical lines can be reinforced. In this embodiment, specific reinforcement measures include increasing the power threshold of the line. After reinforcing the four lines with the highest criticality values ​​in the system, to illustrate the accuracy of weak link identification and the effectiveness of the reinforcement measures, a two-dimensional toughness experiment of this embodiment is performed again. For comparison, four lines are randomly selected for reinforcement and then subjected to the two-dimensional toughness experiment again. The results of both experiments are as follows: Figure 7 As shown, the toughness index after reinforcement can be calculated using the formula, and a significant improvement is found, which indicates the accuracy of weak link identification and the effectiveness of reinforcement operations in the system.

[0120] See Figure 8 As shown in the embodiments of this application, a disaster prevention strategy determination device for a main-distribution network is also disclosed, comprising:

[0121] Model building module 11 is used to build a corresponding topology model based on the topology information of the main grid and distribution network in a power system with preset islanding balancing strategy and main-distribution network coordination, so as to determine the target topology model corresponding to the power system.

[0122] The fault analysis module 12 is used to generate random fault scenarios based on a preset improved Monte Carlo algorithm, and to perform power flow analysis and cascade fault simulation analysis of each fault scenario based on the corresponding fault generation results and the target topology model, so as to determine the fault scenario analysis results.

[0123] The threshold curve determination module 13 is used to analyze the fault coupling of the main-distribution network based on the fault scenario analysis results, system collapse threshold and seepage theory, so as to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space; the critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region.

[0124] The strategy determination module 14 is used to determine the criticality index corresponding to each component in the main-distribution network and the comprehensive resilience assessment index of the power system based on the preset criticality analysis strategy, the preset system resilience assessment rules and the critical threshold curve, and to determine the target disaster prevention strategy according to the criticality index and the comprehensive resilience assessment index.

[0125] Therefore, this application first constructs a target topology model of a power system with coordinated main and distribution networks. Then, it randomly generates fault scenarios and performs power flow analysis and cascading fault simulation analysis for each fault scenario based on the target topology model. Next, it uses the fault scenario analysis results, system collapse threshold, and seepage theory to analyze the fault coupling of the main and distribution networks, determining the critical threshold curve in the two-dimensional failure space. Then, based on this critical threshold curve, it analyzes the criticality indicators of the components and the comprehensive resilience assessment indicators of the power system to determine the target disaster prevention strategy. This approach identifies the critical conditions for coordinated failure of the main and distribution networks and identifies weak links in the system, thus providing a reliable basis for power system disaster prevention planning and enhancing the system's resilience.

[0126] In some specific embodiments, the model building module 11 may specifically include:

[0127] The first model building unit is used to perform power flow modeling based on the first topology information corresponding to the main grid in the power system of main-distribution network coordination, so as to determine the main grid topology structure model; the first topology information includes power plants, substations and transmission lines in the main grid;

[0128] The second model building unit is used to perform modeling based on a preset modeling algorithm and the second topology information corresponding to the distribution network in the power system, so as to determine the distribution network topology model; the second topology information includes distribution transformers, feeders and distributed power sources in the distribution network.

[0129] A power balance unit is used to determine the power balance formula of the power system in islanded operation mode based on the power generation output information of the main grid, the power generation and load demand information of the distributed power sources in the distribution network;

[0130] The power flow distribution determination unit is used to perform steady-state analysis of the power system based on the DC power flow model in order to determine the power flow distribution expression inside the island.

[0131] The model determination unit is used to determine the target topology model based on the main network topology model, the distribution network topology model, the power balance formula, the power flow distribution expression, and islanding operation constraints; the islanding operation constraints include transmission line capacity limits, generator output limits, load supply constraints, and voltage stability constraints.

[0132] In some specific embodiments, the disaster prevention strategy determination device for the main-distribution network may further include:

[0133] The fault probability model determination unit is used to analyze historical extreme disaster data and construct fault probability models corresponding to each component in the main-distribution network under different disaster types based on the analysis results.

[0134] The model integration unit is used to integrate the fault probability model into the target topology model to obtain the integrated target topology model.

[0135] In some specific embodiments, the fault analysis module 12 may specifically include:

[0136] The quantity determination unit is used to perform random sampling based on a preset probability distribution function corresponding to the power system to determine the number of faulty lines.

[0137] A line selection unit is used to select a target faulty line from the power system based on the number of faulty lines, so as to determine the corresponding fault scenario information;

[0138] The step jump unit is used to jump back to the step of random sampling based on the preset probability distribution function corresponding to the power system until the preset termination condition is reached, and to determine the fault generation result based on the generated fault scenario information.

[0139] In some specific embodiments, the fault analysis module 12 may specifically include:

[0140] The analysis unit is used to perform power flow analysis and cascaded fault simulation analysis of the main-distribution network based on the target topology model for various fault scenario information in the fault generation results, and to record the final state information of the power system to determine the fault scenario analysis results.

[0141] In some specific embodiments, the threshold curve determination module 13 may specifically include:

[0142] A spatial construction unit is used to construct a two-dimensional failure space based on the number of faulty lines and seepage theory.

[0143] The curve determination unit is used to determine the fault critical point of the main network and the distribution network in the two-dimensional failure space based on the fault scenario analysis results and the system collapse threshold, and to fit the fault critical point to determine the critical threshold curve; the system collapse threshold includes the load loss ratio threshold, the network connectivity threshold, and the voltage stability threshold.

[0144] In some specific embodiments, the strategy determination module 14 may specifically include:

[0145] The criticality index determination unit is used to analyze the disturbance effect of different types and locations of components in the main-distribution network on the boundary of the elastic region by simulating failure scenarios in static topology and dynamic fault propagation process, and to record the evolution trajectory of the system state, so as to determine the corresponding criticality index based on the disturbance analysis results and the evolution trajectory; the criticality index includes the elastic region area reduction rate and the change in critical fault probability; the components include power plants, substations and transmission lines in the main network and distribution transformers, feeders and distributed power sources in the distribution network;

[0146] The comprehensive resilience assessment unit is used to determine the area of ​​the corresponding elastic region based on the critical threshold curve, so as to determine the comprehensive resilience assessment index of the power system.

[0147] Furthermore, embodiments of this application also disclose an electronic device, Figure 9 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0148] Figure 9 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the disaster prevention strategy determination method for the main-distribution network disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be a computer.

[0149] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0150] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0151] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the main-distribution network disaster prevention strategy determination method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0152] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for determining the disaster prevention strategy of the main-distribution network. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0153] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0154] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0156] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0157] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining disaster prevention strategies for a main-distribution network, characterized in that, include: Based on the preset islanding balancing strategy and the topology information of the main grid and distribution network in the power system of the main grid and distribution network coordination, a corresponding topology model is constructed to determine the target topology model corresponding to the power system. Random fault scenarios are generated based on a pre-defined improved Monte Carlo algorithm. Based on the corresponding fault generation results and the target topology model, power flow analysis and cascade fault simulation analysis are performed on each fault scenario to determine the fault scenario analysis results. Based on the fault scenario analysis results, system collapse threshold, and seepage theory, the fault coupling of the main-distribution network is analyzed to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space. The critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region. Based on the preset criticality analysis strategy, preset system resilience assessment rules, and the critical threshold curve, the criticality index corresponding to each component in the main-distribution network and the comprehensive resilience assessment index of the power system are determined, and the target disaster prevention strategy is determined according to the criticality index and the comprehensive resilience assessment index.

2. The method for determining disaster prevention strategies for the main-distribution network according to claim 1, characterized in that, The construction of a corresponding topology model based on the topology information of the main grid and distribution network in the power system based on the preset islanding balancing strategy and main-distribution network coordination includes: Power flow modeling is performed based on the first topology information corresponding to the main grid in a power system with main-distribution grid coordination to determine the main grid topology structure model; the first topology information includes power plants, substations and transmission lines in the main grid; Modeling is performed based on a preset modeling algorithm and the second topology information corresponding to the distribution network in the power system to determine the distribution network topology structure model; the second topology information includes distribution transformers, feeders and distributed power sources in the distribution network. The power balance formula for the power system in islanded operation mode is determined based on the power generation output information of the main grid, the power generation and load demand information of the distributed power sources in the distribution network. Steady-state analysis of the power system is performed based on the DC power flow model to determine the power flow distribution expression inside the island; The target topology model is determined based on the main grid topology model, the distribution network topology model, the power balance formula, the power flow distribution expression, and the islanding operation constraints. The islanding operation constraints include transmission line capacity limits, generator output limits, load supply constraints, and voltage stability constraints.

3. The method for determining disaster prevention strategies for the main-distribution network according to claim 1, characterized in that, Before performing power flow analysis and cascading fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model, the following steps are also included: Analyze historical extreme disaster data, and construct fault probability models corresponding to each component in the main-distribution network under different disaster types based on the analysis results; The failure probability model is integrated into the target topology model to obtain the integrated target topology model.

4. The method for determining the disaster prevention strategy of the main-distribution network according to any one of claims 1 to 3, characterized in that, The generation of random fault scenarios based on the preset improved Monte Carlo algorithm includes: Random sampling is performed based on a preset probability distribution function corresponding to the power system to determine the number of faulty lines; Based on the number of faulty lines, target faulty lines are selected from the power system to determine the corresponding fault scenario information; The process then jumps back to the step of random sampling based on the preset probability distribution function corresponding to the power system until the preset termination condition is met, and the fault generation result is determined based on the generated fault scenario information.

5. The method for determining disaster prevention strategies for the main-distribution network according to claim 4, characterized in that, The power flow analysis and cascading fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model include: For the various fault scenario information in the fault generation results, power flow analysis and cascade fault simulation analysis of the main-distribution network are performed based on the target topology model, and the final state information of the power system is recorded to determine the fault scenario analysis results.

6. The method for determining disaster prevention strategies for the main-distribution network according to claim 5, characterized in that, The analysis of the fault coupling between the main and distribution networks based on the fault scenario analysis results, system collapse threshold, and seepage theory includes: A two-dimensional failure space is constructed based on the number of faulty lines and the seepage theory. In the two-dimensional failure space, the fault critical points of the main network and the distribution network are determined based on the fault scenario analysis results and the system collapse threshold, and the fault critical points are fitted to determine the critical threshold curve; the system collapse threshold includes the load loss ratio threshold, the network connectivity threshold, and the voltage stability threshold.

7. The method for determining disaster prevention strategies for the main-distribution network according to claim 1, characterized in that, The determination of criticality indicators corresponding to each component in the main-distribution network and the comprehensive resilience assessment indicators of the power system, based on a preset criticality analysis strategy, preset system resilience assessment rules, and the critical threshold curve, includes: By simulating failure scenarios of different types and locations of components in the main-distribution network under static topology and dynamic fault propagation processes, the disturbance effects of these components on the boundaries of the resilient region are analyzed, and the evolution trajectory of the system state is recorded. Based on the disturbance analysis results and the evolution trajectory, corresponding criticality indicators are determined. The criticality indicators include the resilient region area reduction rate and the change in critical fault probability. The components include power plants, substations, and transmission lines in the main network and distribution transformers, feeders, and distributed power sources in the distribution network. The corresponding elastic region area is determined based on the critical threshold curve to determine the comprehensive resilience assessment index of the power system.

8. A disaster prevention strategy determination device for a main-distribution network, characterized in that, include: The model building module is used to construct a corresponding topology model based on the topology information of the main grid and distribution network in a power system with preset islanding balancing strategy and main-distribution network coordination, so as to determine the target topology model corresponding to the power system. The fault analysis module is used to generate random fault scenarios based on a preset improved Monte Carlo algorithm, and to perform power flow analysis and cascade fault simulation analysis for each fault scenario based on the corresponding fault generation results and the target topology model, so as to determine the fault scenario analysis results. The threshold curve determination module is used to analyze the fault coupling of the main-distribution network based on the fault scenario analysis results, system collapse threshold and seepage theory, so as to determine the two-dimensional failure space and the critical threshold curve in the two-dimensional failure space. The critical threshold curve is used to divide the two-dimensional failure space into an elastic region and a collapse region. The strategy determination module is used to determine the criticality indicators corresponding to each component in the main-distribution network and the comprehensive resilience assessment indicators of the power system based on the preset criticality analysis strategy, the preset system resilience assessment rules and the critical threshold curve, and to determine the target disaster prevention strategy based on the criticality indicators and the comprehensive resilience assessment indicators.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the method for determining the disaster prevention strategy of the main-distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer programs, which, when executed by a processor, implement the method for determining the disaster prevention strategy of the main-distribution network as described in any one of claims 1 to 7.

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