Method, system and device for assessing operational risk of power system considering facility damage

Abstract

The application discloses a power system operation risk assessment method, system and equipment considering facility damage, and belongs to the risk assessment field. The method is as follows: a first power grid topology of a power system, each unit output prediction value and each node load demand prediction value are acquired; a damaged facility is removed from the first power grid topology structure, a second power grid topology structure is constructed, and then an optimal load shedding model is constructed; the output prediction value and the demand prediction value are input into the model, a benchmark state vector and an active load shedding amount are solved; a random disturbance variable is determined according to the benchmark state vector, first and second each-order semi-invariant variables are obtained by linear mapping of the disturbance variable, and a node voltage probability density function and a line power flow probability density function are fitted according to the first and second each-order semi-invariant variables; and an operation risk value of the power system is determined according to the active load shedding amount, the voltage probability density function and the line power flow probability density function. Therefore, the application can improve the accuracy of risk assessment.

Description

Technical Field

[0001] This invention relates to the field of risk assessment, and more particularly to methods, systems and equipment for assessing the operational risks of power systems that take into account facility damage. Background Technology

[0002] In extreme scenarios such as frequent extreme weather events, major natural disasters, and wars, transmission lines, substations, and power generation facilities in the power system may suffer physical damage or be forced to shut down, leading to drastic changes in the power grid topology. After facility damage occurs, power transmission channels are restricted, significantly reducing system operational safety and power supply reliability. Simultaneously, the output of renewable energy sources such as wind and solar power is inherently volatile due to meteorological conditions, and load demand is also significantly uncertain due to user behavior and forecasting errors. The superposition of facility damage and operational uncertainties further complicates the system's operational status. Even after emergency dispatch measures are implemented, the system may still face serious risks such as voltage exceeding limits, line power flow exceeding limits, or even large-scale power outages, directly threatening the continuous power supply capacity of critical loads and the normal operation of society.

[0003] Currently, existing technologies primarily conduct power system safety assessments along two paths. The first path is based on resilience assessment methods using complex network theory, which characterize the impact of facility damage on the overall system performance by analyzing changes in system topology (such as node degree, connectivity, and clustering coefficients). The second path is based on operational safety analysis methods using power flow calculations. These typically involve deterministic or stochastic power flow simulations of a pre-defined N-1 fault scenario (i.e., a single component outage) to verify whether line power flow and node voltages meet safety constraints, thereby determining the system's operational safety level. However, these existing technologies have significant shortcomings in terms of assessment accuracy. First, complex network-based methods focus on topological characteristics and cannot reflect the power balance and supply capacity of the system under actual operational constraints after facility damage, resulting in a significant discrepancy between their assessment results and actual operational risks. Secondly, most power flow calculation-based methods are limited to N-1 fault scenarios, making it difficult to cover situations where multiple facilities are damaged simultaneously (Nk faults) under war or extreme disasters. More importantly, these methods typically assume that the system always maintains a normal supply-demand balance, rarely considering emergency control actions such as load shedding that the dispatch center is forced to take after severe facility damage. They fail to incorporate emergency operating conditions into the evaluation framework, resulting in evaluation results that cannot truly reflect the degree of loss of system power supply capacity and the actual level of operational risk under extreme scenarios, thus having significant limitations in engineering applications. Summary of the Invention

[0004] This invention provides a method, system, and equipment for assessing the operational risks of power systems that take into account facility damage, thereby improving the accuracy of operational risk assessment under the dual impact of "multiple severe damages" and "unstable power generation and consumption".

[0005] This invention provides a method for assessing the operational risk of a power system that takes into account facility damage, comprising: When equipment failure occurs in the power system, the first power grid topology, the output forecast of each generator set, and the demand forecast of each node load are obtained. Remove the damaged equipment from the first power grid topology to construct the second power grid topology; Based on the second power grid topology, an optimal load shedding model is constructed. The predicted output values ​​and the predicted demand values ​​are input into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under the scenario of facility damage and the load shedding amount of each node. Based on the emergency operation baseline state vector and the prediction error probability distribution of the injected power at each node, random disturbance variables are determined. The random disturbance variables are linearly mapped using the Jacobian matrix to obtain the first semi-invariants of the node voltage change and the second semi-invariants of the line power flow change. The node voltage probability density function is obtained by fitting the first semi-invariants, and the line power flow probability density function is obtained by fitting the second semi-invariants. The operational risk value of the power system is determined based on the load shedding amount, node voltage probability density function, and line power flow probability density function.

[0006] This invention addresses the fundamental modeling problem of traditional N-1 scenarios failing to cover severe Nk faults by removing damaged equipment from the original topology and reconstructing a second power grid topology. This accurately maps the physical structural abrupt changes under simultaneous multi-point damage, providing a power grid model consistent with actual damage conditions for subsequent calculations. Secondly, an optimal load shedding model is constructed based on this damaged topology, and output and load forecasts are used as inputs for solution. This step simulates the emergency control behavior of the dispatch center under severe power supply shortages. The obtained emergency operating baseline state vector is not an idealized power flow solution, but a real stable section that realistically considers the cost of load shedding and actual power supply capacity. This provides a physically feasible benchmark for subsequent uncertainty analysis, avoiding significant deviations in risk assessment from actual operating conditions due to neglecting emergency measures. Furthermore, regarding the fluctuation of wind and solar loads, using the emergency baseline state as an anchor point, the random disturbances of nodal injected power are linearly mapped into semi-invariants of state variable changes using the Jacobian matrix, and a probability density function is fitted. This process, employing the semi-invariant method, efficiently and accurately quantifies the propagation characteristics of operational uncertainties after severe topological weakening, avoiding the computational errors and efficiency bottlenecks caused by the large sampling of traditional Monte Carlo methods. It can accurately capture the probability distribution characteristics of voltage and power flow exceeding limits under fluctuations, thus overcoming the inherent defect that deterministic power flow cannot assess fluctuation risks. Finally, the comprehensive risk value is calculated by integrating the active power load shedding amount with the voltage and power flow probability density functions, incorporating deterministic emergency consequences and probabilistic fluctuation risks into a unified quantitative framework. The organic combination of these three factors ensures that the final risk value comprehensively reflects the consequences of structural damage and the impact of operational fluctuations. Therefore, this method achieves a more comprehensive and accurate quantitative assessment of system supply capacity and operational safety under the dual impact of multi-point damage and operational uncertainty.

[0007] Further, the step of inputting each of the predicted output values ​​and each of the predicted demand values ​​into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under the facility damage scenario and the load shedding amount of each node includes: Input the predicted output values ​​and the predicted demand values ​​into the optimal load shedding model; Under the premise of satisfying all the constraints of the optimal load shedding model, the optimal load shedding model is solved by an optimization solution algorithm to obtain the optimal solution that minimizes the total weighted active power load reduction of all nodes. The voltage amplitude and voltage phase angle of each node under the second power grid topology are extracted from the optimal solution and combined to form the emergency operation reference state vector; The actual active load value of each node is extracted from the optimal solution, and the active load shedding amount of each node is determined based on the difference between the original active load demand and the actual active load value of each node.

[0008] By using the predicted values ​​as a benchmark and solving the optimal load shedding model, a physically feasible emergency operating state can be obtained, providing an accurate benchmark for subsequent fluctuation assessment.

[0009] Furthermore, the construction of the optimal load shedding model based on the second power grid topology includes: Obtain the original active load demand and original reactive load demand of each node under the second power grid topology, the upper and lower limits of active power output, the upper and lower limits of reactive power output, the upper and lower limits of safe operating voltage of each node, and the maximum allowable phase angle difference between the nodes at both ends of each transmission line. Based on the preset load importance weighting coefficient and the original active load demand, an optimization objective function is established with the goal of minimizing the total weighted active load reduction of all nodes. Based on the node admittance parameters of the second power grid topology, the upper and lower limits of active power output, the upper and lower limits of reactive power output, the maximum allowable phase angle difference, the original active load demand, the original reactive load demand, and the power factor of the load at each node, the constraints of the optimal load shedding model are established. The constraints include node power balance equation constraints, generator output constraints, node voltage safety constraints, branch phase angle difference constraints, load shedding range constraints, and load power factor constraints.

[0010] By establishing an optimization model with complete constraints such as power factor, we can ensure that the emergency dispatch plan strictly meets multiple physical and safety limitations, thus guaranteeing the authenticity of the assessment basis.

[0011] Further, determining the random disturbance variable based on the prediction error probability distribution of the emergency operation baseline state vector and the injected power of each node includes: Obtain the first historical prediction error statistics of the wind power generation units connected to each node under the second power grid topology, the second historical prediction error statistics of the photovoltaic power generation units, and the third historical prediction error statistics of the load. Based on the first historical prediction error statistics, the second historical prediction error statistics, and the third historical prediction error statistics, the first prediction error probability distribution of the output of each wind power generation unit, the second prediction error probability distribution of the output of each photovoltaic power generation unit, and the third prediction error probability distribution of the load of each node are calculated respectively. Based on the first prediction error probability distribution, the second prediction error probability distribution, and the third prediction error probability distribution, the random variation of active power injection and reactive power injection at each node are determined respectively. The random variation of active power injection and reactive power injection at all nodes are combined to form the random disturbance variable.

[0012] By statistically analyzing the historical prediction errors of wind power, photovoltaics, and load, the distribution of dual uncertainties between source and load can be accurately characterized, providing a reliable input for quantifying volatility risk.

[0013] Furthermore, the step of using the Jacobian matrix to linearly map the random disturbance variables to obtain the first order semi-invariants of node voltage changes and the second order semi-invariants of line power flow changes includes: Using the emergency operation reference state vector as the reference operating point, the node power balance equation and branch power flow equation are linearized at the reference operating point to obtain the first linear mapping relationship between the random disturbance variable and the node voltage change, and the second linear mapping relationship between the random disturbance variable and the line power flow change. The Jacobian matrix is ​​used to linearly transform the semi-invariants of the random disturbance variables according to the first linear mapping relationship to obtain the first semi-invariants of the node voltage change. The Jacobian matrix is ​​then used to linearly transform the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second semi-invariants of the line power flow change.

[0014] By linearizing random disturbances at a reference point, the probability distribution of voltage and power flow can be obtained efficiently and accurately, thus precisely capturing the characteristics of wave propagation.

[0015] Further, the step of linearly transforming the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second semi-invariants of the line power flow change includes: Obtain the Jacobian matrix at the reference running point, and determine the coefficient matrix in the second linear mapping relationship based on the inverse of the Jacobian matrix; For the kth order semi-invariant, each element in the coefficient matrix is ​​raised to the kth power, and all elements after raising to the kth power are rearranged according to the row and column positions of the original coefficient matrix to construct the power coefficient matrix corresponding to the kth order. The target vector composed of the kth order semi-invariants of the random disturbance variables is obtained by performing matrix multiplication with the power coefficient matrix corresponding to the kth order. By iterating through each order of semi-invariant, performing the steps of obtaining the power, constructing the power coefficient matrix, and matrix multiplication as described above, and summing up the target vectors obtained from each order, the second order of semi-invariants of the power flow change of the line are obtained.

[0016] By mapping semi-invariants of various orders through power operations on the coefficient matrix, the higher-order statistical relationship between disturbances and state variables is accurately quantified, ensuring accurate fitting of the probability distribution.

[0017] Further, determining the power system's operational risk value based on the load shedding amount, node voltage probability density function, and line power flow probability density function includes: Based on the voltage probability density function of each node, the preset lower and upper voltage operating limits of each node, the voltage over-limit risk value of each node is determined, and the voltage over-limit risk values ​​of all nodes are summed to obtain the total voltage over-limit risk value of the system. Based on the power flow probability density function of each line and the preset upper limit of transmission power for each line, the power flow limit risk value of each line is determined, and the power flow limit risk values ​​of all lines are summed to obtain the total power flow limit risk value of the system. The total load shedding risk of the system is determined based on the load shedding amount of each node and the preset load importance weight coefficient of each node. The total risk of system voltage exceeding limits, the total risk of system power flow exceeding limits, and the total risk of system load shedding are weighted and summed to obtain the operating risk value of the power system.

[0018] By weighted summing of the three risks—voltage, power flow, and load shedding—a unified quantitative assessment of the consequences of structural damage and the impact of operational fluctuations can be achieved, comprehensively reflecting the overall risk of the system under dual impacts.

[0019] Another embodiment of the present invention provides a power system operation risk assessment system that takes into account facility damage, comprising: the system being used to perform the steps of the power system operation risk assessment method that takes into account facility damage as described in the present invention.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the power system operation risk assessment method taking into account facility damage as described in the present invention.

[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the power system operation risk assessment method considering facility damage as described in the present invention. Attached Figure Description

[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating an embodiment of the power system operation risk assessment method that takes into account facility damage provided in this application; Figure 2 This is a flowchart illustrating one embodiment of steps S201 to S204 provided in this application; Figure 3 This is a flowchart illustrating one embodiment of steps S301 to S304 provided in this application. Detailed Implementation

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

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0031] See Figure 1 To improve the accuracy of operational risk assessment, an embodiment of the present invention provides a method for assessing the operational risk of a power system that takes into account facility damage, comprising steps S101 to S105: Step S101: When equipment damage occurs in the power system, obtain the first power grid topology of the power system, the output forecast of each generator set, and the demand forecast of each node load. In some embodiments, when equipment damage is detected in the power system, complete static basic data of the power system to be evaluated is obtained from the dispatch automation system or energy management system. This data specifically includes the original topological connections of the power grid, i.e., the first power grid topology. The static parameters include the impedance and admittance parameters of each transmission line, the turns ratio and equivalent parameters of the transformers, the rated capacity and technical output range of each generator set, and the baseline distribution information of the loads carried by each node. After the above static parameters are ready, it is also necessary to obtain the operational forecast information corresponding to the time of damage. Specifically, the predicted active and reactive power output values ​​of each generator set are generally obtained based on short-term or ultra-short-term weather forecasts, wind and solar resource forecasts, and dispatch plans, while the load forecast value is determined comprehensively based on historical electricity consumption patterns, real-time load monitoring data, and factors such as temperature. It should be noted that equipment damage must be based on a pre-defined scenario such as an extreme disaster or war attack, and the specific type of facility damage must be clearly defined. This type may include any one or more combinations of power transmission line outages or damages, power transformer outages or damages, and generator set outages or damages.

[0032] Step S102: Remove the damaged equipment from the first power grid topology to construct the second power grid topology; In some embodiments, based on externally input extreme disaster or attack assumptions, a specific list of facility damage is determined. This list includes, but is not limited to, any one or more combinations of transmission line outages or damage, transformer equipment outages or damage, and generator unit outages or damage. This is within the first power grid topology, i.e., the original power grid model. Assuming complete data acquisition, for each damaged device in the list, the corresponding device status is changed from "Running" to "Out of Service" in the power system network database. The network topology node-branch association matrix is ​​updated synchronously, removing the branch numbers of damaged lines or transformers, and treating the connected nodes of the faulty units as passive nodes. Based on this, connectivity is checked on the updated topology to verify whether the system has formed islands after the damage. If islands exist, the matching relationship between loads and power sources within the islands needs to be further marked. After completing the above device status update and topology association matrix correction, the second power grid topology after facility damage can be obtained. .

[0033] It should be noted that the second power grid topology All electrical parameters and connections of the undamaged parts were retained, while the impact of faulty components on the system was eliminated, thus forming a complete post-damage power grid model that can be used for subsequent power flow calculations, providing an accurate structural foundation for the establishment of the subsequent optimal load shedding model.

[0034] Step S103: Based on the second power grid topology, construct an optimal load shedding model, and input each of the predicted output values ​​and each of the predicted demand values ​​into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under the facility damage scenario and the load shedding amount of each node. In some embodiments, constructing an optimal load shedding model based on the second power grid topology includes: obtaining the original active and reactive load demands, upper and lower limits of active and reactive power output, upper and lower limits of safe operating voltage for each node under the second power grid topology, and the maximum allowable phase angle difference between the nodes at both ends of each transmission line; establishing an optimization objective function with the goal of minimizing the total weighted active load reduction of all nodes based on preset load importance weighting coefficients and the original active load demands; and establishing constraints for the optimal load shedding model based on the node admittance parameters of the second power grid topology, the upper and lower limits of active and reactive power output, the maximum allowable phase angle difference, the original active load demands, the original reactive load demands, and the power factor of the load at each node, wherein the constraints include node power balance equation constraints, generator output constraints, node voltage safety constraints, branch phase angle difference constraints, load shedding range constraints, and load power factor constraints.

[0035] In some embodiments, during actual modeling, it is necessary to start from the reconstructed second power grid topology. The model extracts all the original parameters required by the model item by item. First, the second power grid topology. Original active power load requirements of each node Compared with the original reactive load demand The upper and lower limits of active power output corresponding to each generator node and And the upper and lower limits of no-work output and The safe operating voltage upper and lower limits for each node are determined based on the technical nameplate parameters of each unit. and According to the voltage allowable range specified in the power grid dispatching regulations, the maximum allowable phase angle difference between the two ends of each transmission line is set. The value is predetermined as a constant value based on the setting value of the line protection device or the requirements for stable system operation.

[0036] After extracting the above parameters, it is also necessary to preset the load importance weighting coefficient according to the actual importance of the load. This coefficient is used to distinguish the power supply priority of loads on different nodes. Based on this, an objective function is established with the goal of minimizing the total weighted active power load reduction of all nodes. In this formula This represents the active power load reduction at node i. Subsequently, based on the node admittance matrix parameters of the damaged power grid and combined with the extracted boundary parameters, a constraint system covering multiple types of security constraints is established sequentially: Nodal power balance equation constraints: and In the formula, , Here, represents the voltage magnitude and voltage phase angle at node i, respectively. , These are the adjacent nodes directly connected to node i. Voltage amplitude and phase angle; branch road Admittance angle; and These are the real and imaginary parts of the self-admittance of node i in the node admittance matrix, respectively.

[0037] Generator output constraints include: Generator active power output constraints: And the reactive power output constraint of the generator set: In the formula, This represents the total active power output of the generator unit at node i. This represents the total reactive power output of the generator set at node i. Node voltage safety constraints: In the formula, Let be the voltage amplitude at node i; Branch phase angle difference constraint: In the formula, , Let be the voltage phase angles at nodes i and α, respectively; branch road The maximum tolerable phase angle difference is usually a pre-defined constant value; Load cutoff range constraints: and In the formula, , These represent the actual active load and actual reactive load of node i, respectively. , Let be the original active load demand and the original reactive load demand of node i, respectively, and be the predicted load of node i after damage, which are known constants. and These are the lower limits of active and reactive loads that node i is allowed to cut off, respectively, and are usually taken as 0.

[0038] The load power factor constraint is: ,in Let be a known constant, representing the power factor of the load at node i. This assumption can effectively reduce the complexity of solving the model.

[0039] It should be noted that all the above constraints together constitute the complete feasible region of the optimal load shedding model, ensuring the physical executability of the emergency operating state obtained by the solution.

[0040] By establishing an optimization model with complete constraints such as power factor, we can ensure that the emergency dispatch plan strictly meets multiple physical and safety limitations, thus guaranteeing the authenticity of the assessment basis.

[0041] In some embodiments, the step of inputting the predicted output values ​​and the predicted demand values ​​into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under the scenario of facility damage and the load shedding amount of each node includes: inputting the predicted output values ​​and the predicted demand values ​​into the optimal load shedding model; under the premise of satisfying all the constraints of the optimal load shedding model, solving the optimal load shedding model by an optimization solution algorithm to obtain the optimal solution that minimizes the sum of the weighted active power load reduction of all nodes; extracting the voltage amplitude and voltage phase angle of each node under the second power grid topology from the optimal solution and combining them to form the emergency operation baseline state vector; extracting the actual active power load value of each node from the optimal solution, and determining the active power load shedding amount of each node based on the difference between the original active power load demand and the actual active power load value of each node.

[0042] In some embodiments, the previously obtained predicted output values ​​of each generator unit are used as a benchmark reference for the node injection power, and the predicted demand values ​​of each node load are used as the original load demand parameters. and These values ​​are then substituted into the already constructed optimal load shedding model. Under the common constraints of all conditions, a nonlinear programming algorithm (such as the interior-point method or sequential quadratic programming) is used to numerically solve the above model, obtaining the optimal solution that minimizes the sum of weighted active power load reductions at all nodes. This optimal solution includes the voltage amplitude of the power grid at each node after the damage. Phase angle with voltage Active power output of each generator unit With no effort Actual active load of each node Compared with actual reactive load The optimal values ​​of all state variables are obtained. Based on this, the voltage amplitude of each node under the second power grid topology is extracted from the optimal solution. and voltage phase angle Combined to form an emergency operation baseline state vector Simultaneously, extract the actual active power load value of each node. And based on the original active power load requirements of each node. Compared with actual active load value The difference The active power load shedding amount of each node is calculated and determined on a node-by-node basis, providing a data basis for the load shedding risk item in the subsequent risk index calculation.

[0043] By using the predicted values ​​as a benchmark and solving the optimal load shedding model, a physically feasible emergency operating state can be obtained, providing an accurate benchmark for subsequent fluctuation assessment.

[0044] Step S104: Based on the emergency operation reference state vector and the prediction error probability distribution of the injected power at each node, determine the random disturbance variables, use the Jacobian matrix to perform a linear mapping on the random disturbance variables, and obtain the first semi-invariants of the node voltage change and the second semi-invariants of the line power flow change. Fit the first semi-invariants to obtain the node voltage probability density function, and fit the second semi-invariants to obtain the line power flow probability density function. Please refer to Figure 2 In some embodiments, determining the random disturbance variable based on the prediction error probability distribution of the emergency operation baseline state vector and the injected power of each node includes steps S201 to S204: Step S201: Obtain the first historical prediction error statistics of the wind power generation unit connected to each node in the second power grid topology, the second historical prediction error statistics of the photovoltaic power generation unit, and the third historical prediction error statistics of the load. In some embodiments, the deviation records between the predicted and actual output values ​​of wind power generation units connected to each node under the second grid topology are extracted from the historical operation database of the power system as first historical prediction error statistics. Similarly, historical prediction error records of each photovoltaic power generation unit are extracted as second historical prediction error statistics. At the same time, historical prediction error records of the load of each node are extracted as third historical prediction error statistics. The time span of these historical statistics typically covers a complete operating cycle of more than one year to fully cover the fluctuation characteristics under different seasons and meteorological conditions.

[0045] Step S202: Based on the first historical prediction error statistics, the second historical prediction error statistics, and the third historical prediction error statistics, respectively calculate the first prediction error probability distribution of the output of each wind power generation unit, the second prediction error probability distribution of the output of each photovoltaic power generation unit, and the third prediction error probability distribution of the load of each node. In some embodiments, based on the three types of historical statistical data mentioned above, mathematical statistical methods are used to fit the data and estimate parameters. For each wind power generation unit, a corresponding first prediction error probability distribution is determined based on the sample distribution of its historical prediction errors. This distribution can take the form of a normal distribution or a Gaussian mixture distribution, depending on the goodness-of-fit test results of the actual data. Similarly, a second prediction error probability distribution is obtained by statistically fitting the historical prediction error samples of each photovoltaic power generation unit; a third prediction error probability distribution is obtained by statistically fitting the historical prediction error samples of each node load. All of the above probability distributions can be estimated from historical output or load error data.

[0046] Step S203: Based on the first prediction error probability distribution, the second prediction error probability distribution, and the third prediction error probability distribution, determine the random variation of active power injection and reactive power injection for each node, respectively. In some embodiments, after obtaining the prediction error probability distribution of each random variable, the random variation of the injected power at each node is determined according to these distribution functions. The actual output of wind power and photovoltaic power can be expressed as the sum of the predicted value and the prediction error, and the actual value of load demand can also be expressed as the sum of the predicted value and the prediction error. That is, the output of all generator units and the load power can be written as... In the form of, For predicted values, This represents the prediction error. Based on this, according to the first, second, and third prediction error probability distributions, the random variation of the active power injection at each node is determined through random sampling or analytical calculation. and random variation of reactive power injection .

[0047] Step S204: Combine the random changes in active power injection and reactive power injection of all nodes to form the random disturbance variable.

[0048] In some embodiments, the random variations in active power injection and reactive power injection at all nodes in the system are combined in node number order to form a complete random disturbance vector. Among them, the node injection power vector is uniformly summarized as The form is given by , where S represents the magnitude of the active and reactive power injected into each node, and is expressed as the sum of the expected value and the change. , here That is, by all nodes and The random perturbation variables that together form the input for subsequent linear mapping using the Jacobian matrix.

[0049] It should be noted that the random variation in active power injection at each node is determined. and random variation of reactive power injection Combined into random disturbance variables Further calculations are then required. The semi-invariants of each order are used for subsequent linear mappings. For the probability density function is Continuous random variables, order central moments Solve using the following formula: ,in Let the first moment of the random variable be the expected value: Next, the semi-invariants of each order are calculated based on the relationship between the central moments and the semi-invariants. Considering both computational speed and accuracy, the first six orders of semi-invariants for all random variables are taken, with the specific relationships as follows: ,in, Let ν be the moment of the random variable, i.e. The second moment is represented; thus, the random disturbance variable is obtained. The semi-invariants of each order provide input for subsequent linear transformations using the Jacobian matrix.

[0050] By statistically analyzing the historical prediction errors of wind power, photovoltaics, and load, the distribution of dual uncertainties between source and load can be accurately characterized, providing a reliable input for quantifying volatility risk.

[0051] In some embodiments, the step of using the Jacobi matrix to linearly map the random disturbance variables to obtain the first order semi-invariants of node voltage changes and the second order semi-invariants of line power flow changes includes: using the emergency operation reference state vector as a reference operating point, linearizing the node power balance equation and the branch power flow equation at the reference operating point to obtain the first linear mapping relationship between the random disturbance variables and node voltage changes, and the second linear mapping relationship between the random disturbance variables and line power flow changes; using the Jacobi matrix to linearly transform the semi-invariants of the random disturbance variables according to the first linear mapping relationship to obtain the first order semi-invariants of node voltage changes; and using the Jacobi matrix to linearly transform the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second order semi-invariants of line power flow changes.

[0052] In some embodiments, the emergency operation reference state vector is used as the reference operation point, at which the node power balance equation is linearized. The functional relationship between node injected power and node state variables is as follows: In the formula, , These represent the active power and reactive power injected into node i, respectively. For node admittance matrix, node i and node... The mutual admittance between them; the above system of equations can be summarized as follows: ,in To inject the active and reactive power vectors into each node, The state variable vector consists of the voltage magnitude and phase angle of each node.

[0053] At the reference operating point General Expanding by Taylor series, we get After ignoring higher-order terms, we can obtain , and then export In the formula That is, the Jacobian matrix at the baseline operating point. It can be decomposed by the Newton-Raphson method. Therefore, the random disturbance variable was established. With node voltage change The first linear mapping relationship between them means that the change in state variables can be represented by a linear combination of the changes in injected power at each node.

[0054] At the same time, the branch power flow equations also need to be linearized at the reference operating point. The formulas for the active power flow and reactive power flow of branch m−n are: In the formula: , These represent the active and reactive power flows of branch m and n, respectively. , Let m be the real and imaginary parts of the m-th row and n-th column elements of the nodal admittance matrix; Let mn be the transformation ratio of the branch; Assuming the ground admittance of branch mn is half, the above formula can be summarized as follows: Where Z is the branch power flow vector. At the reference operating point General Expanding by Taylor series, we get After ignoring higher-order terms, we can obtain The aforementioned Substitute the input to export. ,make Then there is This led to the establishment of random disturbance variables. With line power flow change The second linear mapping relationship between them also shows that the change in line power flow can be represented by a linear combination of the changes in injected power at each node.

[0055] In some embodiments, the Jacobian matrix is ​​used to linearly transform the semi-invariants of the random disturbance variables according to the first linear mapping relationship to obtain the first semi-invariants of the node voltage change. Specifically, when the first linear mapping relationship is obtained... Subsequently, because the semi-invariants of random variables possess two important properties—that the semi-invariants of each order of independent random variables can be added together—random variables... times A k-th order semi-invariant is equal to the k-th order semi-invariant of that variable. Based on the above properties, times. The semi-invariants of each order can be obtained from The semi-invariants of each order are expressed as ,in and They are respectively and The kth order semi-invariant vector, It is by The matrix is ​​obtained by raising each element to the kth power. The calculation is then performed by iterating through each order of semi-invariants according to this relation. Taking the kth order as an example, the power coefficient matrix corresponding to that order is first obtained. The method of construction is to take out each element of the Jacobian inverse matrix one by one and calculate its k-th power, and then rearrange all the power results according to the row and column positions of the original matrix; then the random perturbation variable The k-th order semi-invariant vector Performing a matrix multiplication operation with the power coefficient matrix yields the node voltage change. The k-th order semi-invariant vector By performing the above operations sequentially on all orders, the first semi-invariants of the node voltage change can be obtained, including the first-order semi-invariants (corresponding to the expected value) up to the sixth-order semi-invariants.

[0056] It should be noted that, The matrix is ​​2b×2N (where b is the number of branches and N is the number of nodes). Its elements are obtained as follows: For branch m−n, the partial derivatives of its active and reactive power flows with respect to the node voltage phase angle and voltage magnitude are calculated using the following formula: Where H, N, and K are all Jacobian matrices, and their elements are obtained according to the following formula: ,in, , , , All are elements of the Jacobian matrix; , , These represent the voltage amplitudes at nodes m, n, and k, respectively.

[0057] In some embodiments, the step of linearly transforming the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second semi-invariants of the line power flow change includes: obtaining the Jacobian matrix at the reference operating point, and determining the coefficient matrix in the second linear mapping relationship based on the inverse of the Jacobian matrix; for the k-th order semi-invariant, calculating the k-th power for each element in the coefficient matrix, and rearranging all elements after calculating the k-th power according to the row and column positions of the original coefficient matrix to construct the power coefficient matrix corresponding to the k-th order; performing matrix multiplication on the vector formed by the k-th order semi-invariants of the random disturbance variables and the power coefficient matrix corresponding to the k-th order to obtain the target vector formed by the k-th order semi-invariants of the line power flow change; traversing each order semi-invariant, performing the above steps of calculating the power, constructing the power coefficient matrix, and performing matrix multiplication, and summing the target vectors obtained from each order to obtain the second semi-invariants of the line power flow change.

[0058] In some embodiments, the Newton-Raphson power flow calculation is first performed at a baseline operating point to obtain the Jacobian matrix at that operating point. Based on this, Perform LU decomposition or direct inversion to obtain its inverse matrix. Subsequently, based on the Taylor expansion of the branch power flow equations and neglecting higher-order terms, the following was obtained: The aforementioned Substitute the input to export. At the same time, Then there is and explain This is a 2b×2N dimensional matrix, which is the coefficient matrix in the second linear mapping relationship. Its function is to represent the random perturbation of the node injection power. Linear mapping represents the random variation of power flow in a line. .

[0059] Among them, the semi-invariants of random variables have two important properties: First, as long as random variables satisfy a mutual independence relationship, the semi-invariants of different orders of random variables can be added together; Second, random variables... times A k-th order semi-invariant is equal to the k-th order semi-invariant of that variable. According to the second property, for the k-th order semi-invariant, it is necessary to adjust the coefficient matrix. Each element in the matrix is ​​raised to the kth power. In practice, the original coefficient matrix is... Each element Take them out one by one and calculate their k-th power. Then, these exponentiation results are rearranged according to the row and column positions of the element in the original coefficient matrix to construct a matrix similar to... A new matrix of the same dimension is the power coefficient matrix corresponding to the k-th order. .

[0060] Then, random disturbance variables The kth order semi-invariants constitute a vector This vector is then combined with the power coefficient matrix constructed in the second step. Perform matrix multiplication. Based on the aforementioned semi-invariant linear transformation relationship, The semi-invariants of each order can be obtained from The semi-invariants of each order are expressed as follows: In the formula: , They are respectively , k-th order semi-invariants; , It is by The matrix obtained by raising each element of T0 to the kth power. Therefore, and Performing a standard matrix-vector multiplication, the product yields the change in power flow along the line. The target vector is formed by the kth order semi-invariants.

[0061] Finally, the process of exponentiation of coefficient matrix elements, construction of exponentiation coefficient matrices, and matrix multiplication is performed on all orders of semi-invariants. If only the first 6 orders of semi-invariants are considered, the above calculations are performed at each order k=1, 2, 3, 4, 5, 6. The target vectors calculated at each order are then summarized in order to obtain the complete power flow variation. The second order of semi-invariants includes first-order semi-invariants (corresponding to expected value), second-order semi-invariants (corresponding to variance), and so on up to sixth-order semi-invariants. These semi-invariants will then be used to fit the probability density function of the power flow through Gram-Charlier series expansion and other methods, and thus participate in the subsequent power flow exceedance risk assessment.

[0062] It should be noted that since the random perturbation variables of the injected power at each node satisfy the condition of mutual independence, the semi-invariants of each order of random variables can be directly added together, which provides a mathematical guarantee for the linear transformation in the form of matrix multiplication mentioned above.

[0063] By mapping semi-invariants of various orders through power operations on the coefficient matrix, the higher-order statistical relationship between disturbances and state variables is accurately quantified, ensuring accurate fitting of the probability distribution.

[0064] In some embodiments, the node voltage probability density function is obtained by fitting the first-order semi-invariants, and the line power flow probability density function is obtained by fitting the second-order semi-invariants. Specifically, the obtained first-order and second-order semi-invariants cannot be directly used as probability density functions; instead, the semi-invariants must first be converted into central moments of each order based on the mathematical relationship between the central moments and the semi-invariants. For the random variable to be determined, its first-order central moment... Second-order central moments (i.e., variance), third central moments Fourth-order central moments Fifth-order central moments Sixth-order central moments ,in This represents the semi-invariants of each order (i.e., the first and second order semi-invariants). This represents the standard deviation. After obtaining the central moments of each order, we further solve for the constant coefficients of each order in the Gram-Charlier series expansion. The correspondence between each order coefficient and the central moment is as follows: zeroth order coefficient... First-order coefficients Second-order coefficients Third-order coefficients Fourth-order coefficients Fifth-order coefficients Sixth-order coefficients The above relationships apply to both nodal voltages and line power flow variables, the only difference being the source of the semi-invariants substituted—the first-order semi-invariants correspond to nodal voltages, and the second-order semi-invariants correspond to line power flow. Based on this, substituting the constant coefficients into the Gram-Charlier expansion yields the probability density functions of each random variable to be determined, expressed as follows: ,in The probability density function of the standard normal distribution (mean is 0, variance is 1). , , These are their respective derivatives. Based on this, by substituting the coefficients corresponding to the first-order semi-invariants, the probability density function of the voltage at each node can be fitted. Substituting the coefficients corresponding to the second-order semi-invariants into the equations, the probability density function of the power flow for each line can be fitted. .

[0065] It should be noted that the calculation formulas used for the first and second order semi-invariants in the subsequent fitting process are exactly the same; the only difference lies in the object being processed: the first order semi-invariants originate from the node voltage change. The statistical characteristics are used to fit the results, which are then used to describe the probability distribution of voltage at each node; the second order of semi-invariants originates from the changes in line power flow. The statistical characteristics are used to fit the results and describe the probability distribution of power flow in each line.

[0066] By linearizing random disturbances at a reference point, the probability distribution of voltage and power flow can be obtained efficiently and accurately, thus precisely capturing the characteristics of wave propagation.

[0067] Step S105: Determine the operating risk value of the power system based on the load shedding amount, node voltage probability density function, and line power flow probability density function.

[0068] Please refer to Figure 3 In some embodiments, step S105 includes steps S301 to S304: Step S301: Based on the voltage probability density function of each node, the preset lower voltage operating limit and upper voltage operating limit of each node, determine the voltage over-limit risk value of each node, and sum the voltage over-limit risk values ​​of all nodes to obtain the total voltage over-limit risk value of the system. In some embodiments, the voltage exceedance risk value of each node is calculated based on the voltage probability density function of each node and preset lower and upper limits for each node's voltage operation. The voltage exceedance risk of node i includes two components: one is the risk of voltage falling below the lower limit, i.e., from 0 to... Within the interval, with As a penalty term, using the node voltage probability density function The first is the integral of the weights; the second is the risk of the voltage exceeding the upper limit, i.e., from... To the positive infinity interval, with As a penalty item, also with Let be the integral of the weights. This can be expressed as the formula: The node voltage probability density function here. This is obtained by fitting the first order semi-invariants from the previous step using Gram-Charlier series expansion. Based on this, the voltage exceedance risk values ​​for all nodes are summed, i.e. Where N is the number of nodes in the system, the total risk of system voltage exceeding limits can be obtained.

[0069] Step S302: Based on the power flow probability density function of each line and the preset upper limit of transmission power for each line, determine the power flow limit exceedance risk value of each line, and sum the power flow limit exceedance risk values ​​of all lines to obtain the total power flow limit exceedance risk value of the system. In some embodiments, the power flow exceedance risk value of each line is calculated based on the line power flow probability density function of each line and the preset upper limit value of the transmission power of each line. Similar to voltage exceedance, the power flow exceedance risk of line i also includes two components: one is the risk that the transmission power is lower than the upper limit in the negative direction (i.e., reverse exceedance), from... to the interval of negative infinity (from negative infinity to) (points), in As a penalty term, using the line power flow probability density function The first is the integral of the weights; the second is the risk of transmission power exceeding the upper limit in the positive direction. To the positive infinity interval, with As a penalty item, also with Let be the integral of the weights. This can be expressed as the formula: The power flow probability density function here. This is obtained by fitting the second order semi-invariants from the previous step using Gram-Charlier series expansion. Based on this, the power flow exceedance risk values ​​for all lines are summed, i.e. This gives the total risk value of system power flow exceeding limits.

[0070] Step S303: Determine the total system load shedding risk value based on the load shedding amount of each node and the preset load importance weighting coefficient of each node; In some embodiments, the formula for calculating load shedding risk is as follows: Specifically, for each node i, first determine the load importance weighting coefficient for that node. Multiply by the active power load shedding amount of that node First, obtain the weighted load shedding amount for that node; then, sum the weighted load shedding amounts for all nodes, using this sum as the numerator. Accordingly, the weighted original active power load demand of all nodes is summed, and the result is used as the denominator. The ratio of the numerator to the denominator is the total risk of system load shedding. This indicator reflects the weighted power loss ratio caused by the dispatch center taking optimal load shedding measures in the event of facility damage.

[0071] Step S304: The total value of the system voltage over-limit risk, the total value of the system power flow over-limit risk, and the total value of the system load shedding risk are weighted and summed to obtain the operating risk value of the power system.

[0072] In some embodiments, the above three risk indicators are weighted and summed to obtain the operating risk value of the power system. The calculation formula is as follows: In the formula , , These are the weighting coefficients for the system voltage over-limit risk index, power flow over-limit risk index, and load shedding risk index, respectively, reflecting the relative importance of each index.

[0073] It should be noted that these three weighting coefficients can be selected in advance by dispatching and operation experts based on the actual power grid safety operation requirements, combined with historical operating experience and the actual situation of the current scenario.

[0074] It should be noted that the comprehensive operational risk value The larger the value, the worse the system's power supply capacity and operational safety are under the scenario of facility damage, and the higher the risk level; conversely, the smaller the value, the better the system still has a good safety margin and power supply reliability under the scenario.

[0075] By weighted summing of the three risks—voltage, power flow, and load shedding—a unified quantitative assessment of the consequences of structural damage and the impact of operational fluctuations can be achieved, comprehensively reflecting the overall risk of the system under dual impacts.

[0076] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: Existing technologies are mostly designed for risk assessments in routine operation or N-1 fault scenarios, making them difficult to apply to situations where multiple facilities are simultaneously damaged (Nk faults), such as war or extreme disasters. This invention constructs an emergency dispatch model after multiple facilities are damaged, simulating the coordinated operation of the dispatch center under damaged facility conditions. This model can assess the operational risks of the power system under extreme damage scenarios, thus expanding the applicability of risk assessment methods.

[0077] Existing risk assessment methods typically focus only on voltage and line power flow exceeding limits, failing to adequately consider emergency control measures such as load shedding that can be taken by the dispatch center. This invention introduces optimal load shedding decisions into the risk assessment process, incorporating the load shedding ratio into the risk indicator system. This allows the assessment results to reflect the system's actual power supply capacity under extreme conditions, thus possessing greater practical engineering significance.

[0078] This invention addresses the fundamental modeling problem of traditional N-1 scenarios failing to cover severe Nk faults by removing damaged equipment from the original topology and reconstructing a second power grid topology. This accurately maps the physical structural abrupt changes under simultaneous multi-point damage, providing a power grid model consistent with actual damage conditions for subsequent calculations. Secondly, an optimal load shedding model is constructed based on this damaged topology, and output and load forecasts are used as inputs for solution. This step simulates the emergency control behavior of the dispatch center under severe power supply shortages. The obtained emergency operating baseline state vector is not an idealized power flow solution, but a real stable section that realistically considers the cost of load shedding and actual power supply capacity. This provides a physically feasible benchmark for subsequent uncertainty analysis, avoiding significant deviations in risk assessment from actual operating conditions due to neglecting emergency measures. Furthermore, regarding the fluctuation of wind and solar loads, using the emergency baseline state as an anchor point, the random disturbances of nodal injected power are linearly mapped into semi-invariants of state variable changes using the Jacobian matrix, and a probability density function is fitted. This process, employing the semi-invariant method, efficiently and accurately quantifies the propagation characteristics of operational uncertainties after severe topological weakening, avoiding the computational errors and efficiency bottlenecks caused by the large sampling of traditional Monte Carlo methods. It can accurately capture the probability distribution characteristics of voltage and power flow exceeding limits under fluctuations, thus overcoming the inherent defect that deterministic power flow cannot assess fluctuation risks. Finally, the comprehensive risk value is calculated by integrating the active power load shedding amount with the voltage and power flow probability density functions, incorporating deterministic emergency consequences and probabilistic fluctuation risks into a unified quantitative framework. The organic combination of these three factors ensures that the final risk value comprehensively reflects the consequences of structural damage and the impact of operational fluctuations. Therefore, this method achieves a more comprehensive and accurate quantitative assessment of system supply capacity and operational safety under the dual impact of multi-point damage and operational uncertainty.

[0079] Based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a power system operation risk assessment system that takes into account facility damage, comprising: the system being used to perform the steps of the power system operation risk assessment method that takes into account facility damage as described in the present invention.

[0080] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power system operation risk assessment method considering facility damage provided by any of the above-described method embodiments of the present invention.

[0081] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0082] Based on the above embodiments of the power system operation risk assessment method taking into account facility damage, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power system operation risk assessment method taking into account facility damage of any embodiment of the present invention.

[0083] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0084] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0085] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0086] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power system operation risk assessment method considering facility damage as described in any of the above-described method embodiments of the present invention.

[0087] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0088] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing the operational risk of a power system taking into account facility damage, characterized in that, include: When equipment failure occurs in the power system, the first power grid topology, the output forecast of each generator set, and the demand forecast of each node load are obtained. Remove the damaged equipment from the first power grid topology to construct the second power grid topology; Based on the second power grid topology, an optimal load shedding model is constructed. The predicted output values ​​and the predicted demand values ​​are input into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under the scenario of facility damage and the load shedding amount of each node. Based on the emergency operation baseline state vector and the prediction error probability distribution of the injected power at each node, random disturbance variables are determined. The random disturbance variables are linearly mapped using the Jacobian matrix to obtain the first semi-invariants of the node voltage change and the second semi-invariants of the line power flow change. The node voltage probability density function is obtained by fitting the first semi-invariants, and the line power flow probability density function is obtained by fitting the second semi-invariants. The operational risk value of the power system is determined based on the load shedding amount, node voltage probability density function, and line power flow probability density function.

2. The power system operation risk assessment method considering facility damage according to claim 1, characterized in that, The step of inputting the predicted output values ​​and the predicted demand values ​​into the optimal load shedding model to solve for the emergency operation baseline state vector of the power system under facility damage scenarios and the load shedding amount of each node includes: Input the predicted output values ​​and the predicted demand values ​​into the optimal load shedding model; Under the premise of satisfying all the constraints of the optimal load shedding model, the optimal load shedding model is solved by an optimization solution algorithm to obtain the optimal solution that minimizes the total weighted active power load reduction of all nodes. The voltage amplitude and voltage phase angle of each node under the second power grid topology are extracted from the optimal solution and combined to form the emergency operation reference state vector; The actual active load value of each node is extracted from the optimal solution, and the active load shedding amount of each node is determined based on the difference between the original active load demand and the actual active load value of each node.

3. The power system operation risk assessment method considering facility damage according to claim 2, characterized in that, The construction of the optimal load shedding model based on the second power grid topology includes: Obtain the original active load demand and original reactive load demand of each node under the second power grid topology, the upper and lower limits of active power output, the upper and lower limits of reactive power output, the upper and lower limits of safe operating voltage of each node, and the maximum allowable phase angle difference between the nodes at both ends of each transmission line. Based on the preset load importance weighting coefficient and the original active load demand, an optimization objective function is established with the goal of minimizing the total weighted active load reduction of all nodes. Based on the node admittance parameters of the second power grid topology, the upper and lower limits of active power output, the upper and lower limits of reactive power output, the maximum allowable phase angle difference, the original active load demand, the original reactive load demand, and the power factor of the load at each node, the constraints of the optimal load shedding model are established. The constraints include node power balance equation constraints, generator output constraints, node voltage safety constraints, branch phase angle difference constraints, load shedding range constraints, and load power factor constraints.

4. The power system operation risk assessment method considering facility damage according to claim 1, characterized in that, The step of determining random disturbance variables based on the prediction error probability distribution of the emergency operation baseline state vector and the injected power of each node includes: Obtain the first historical prediction error statistics of the wind power generation units connected to each node under the second power grid topology, the second historical prediction error statistics of the photovoltaic power generation units, and the third historical prediction error statistics of the load. Based on the first historical prediction error statistics, the second historical prediction error statistics, and the third historical prediction error statistics, the first prediction error probability distribution of the output of each wind power generation unit, the second prediction error probability distribution of the output of each photovoltaic power generation unit, and the third prediction error probability distribution of the load of each node are calculated respectively. Based on the first prediction error probability distribution, the second prediction error probability distribution, and the third prediction error probability distribution, the random variation of active power injection and reactive power injection at each node are determined respectively. The random variation of active power injection and reactive power injection at all nodes are combined to form the random disturbance variable.

5. The power system operation risk assessment method considering facility damage according to claim 1, characterized in that, The method of linearly mapping the random disturbance variables using the Jacobian matrix to obtain the first order semi-invariants of node voltage changes and the second order semi-invariants of line power flow changes includes: Using the emergency operation reference state vector as the reference operating point, the node power balance equation and branch power flow equation are linearized at the reference operating point to obtain the first linear mapping relationship between the random disturbance variable and the node voltage change, and the second linear mapping relationship between the random disturbance variable and the line power flow change. The Jacobian matrix is ​​used to linearly transform the semi-invariants of the random disturbance variables according to the first linear mapping relationship to obtain the first semi-invariants of the node voltage change. The Jacobian matrix is ​​then used to linearly transform the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second semi-invariants of the line power flow change.

6. The power system operation risk assessment method considering facility damage according to claim 5, characterized in that, The step of linearly transforming the semi-invariants of the random disturbance variables according to the second linear mapping relationship to obtain the second semi-invariants of the power flow change includes: Obtain the Jacobian matrix at the reference running point, and determine the coefficient matrix in the second linear mapping relationship based on the inverse of the Jacobian matrix; For the kth order semi-invariant, each element in the coefficient matrix is ​​raised to the power of k, and all elements after raising to the power of k are rearranged according to the row and column positions of the original coefficient matrix to construct the power coefficient matrix corresponding to the kth order. The target vector composed of the kth order semi-invariants of the random disturbance variables is obtained by performing matrix multiplication with the power coefficient matrix corresponding to the kth order. By iterating through each order of semi-invariant, performing the steps of obtaining the power, constructing the power coefficient matrix, and matrix multiplication as described above, and summing up the target vectors obtained from each order, the second order of semi-invariants of the power flow change of the line are obtained.

7. The power system operation risk assessment method considering facility damage according to claim 1, characterized in that, The determination of the power system's operational risk value based on the load shedding amount, node voltage probability density function, and line power flow probability density function includes: Based on the voltage probability density function of each node, the preset lower and upper voltage operating limits of each node, the voltage over-limit risk value of each node is determined, and the voltage over-limit risk values ​​of all nodes are summed to obtain the total voltage over-limit risk value of the system. Based on the power flow probability density function of each line and the preset upper limit of transmission power for each line, the power flow limit risk value of each line is determined, and the power flow limit risk values ​​of all lines are summed to obtain the total power flow limit risk value of the system. The total load shedding risk of the system is determined based on the load shedding amount of each node and the preset load importance weighting coefficient of each node; The total risk of system voltage exceeding limits, the total risk of system power flow exceeding limits, and the total risk of system load shedding are weighted and summed to obtain the operating risk value of the power system.

8. A power system operation risk assessment system that takes into account facility damage, characterized in that, include: The system is used to perform the power system operation risk assessment method that takes into account facility damage as described in any one of claims 1-7.

9. A terminal device, characterized in that, include: One or more processors; A memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the steps of the power system operation risk assessment method taking into account facility damage as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, the device containing the computer-readable storage medium is controlled to perform the steps of the power system operation risk assessment method taking into account facility damage as described in any one of claims 1-7.

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