A power grid weak link identification method, system, device and medium based on regulation capability margin dynamic evolution

Abstract

The present application relates to the technical field of power system risk assessment, and particularly relates to a power grid weak link identification method, system, device and medium based on regulation capacity margin dynamic evolution, comprising obtaining power grid basic physical parameters, dividing regulation partitions based on the basic physical parameters, constructing a partition initial regulation capacity quantitative model, and calculating the initial regulation margin of each regulation partition; obtaining source and load uncertainty disturbance time sequence and disturbance time sequence cumulative effect, simulating the dynamic process of local and cross-zone regulation resources to suppress power gap, obtaining the regulation margin time sequence attenuation path and multi-dimensional physical characteristic parameters of each regulation partition; based on the multi-dimensional physical characteristic parameters, constructing an improved evaluation model, and classifying and identifying the weak causes of each regulation partition; extracting multi-dimensional operation characteristics from the regulation margin time sequence attenuation path and the weak causes, constructing a weak degree dynamic evolution comprehensive identification model, and performing weak degree sorting on each regulation partition of the whole network and extracting key bottleneck characteristics.

Description

Technical Field

[0001] This invention relates to the technical field of power system risk assessment, and in particular to a method, system, device, and medium for identifying weak links in the power grid based on the dynamic evolution of regulation capacity margin. Background Technology

[0002] The large-scale integration of high-proportion renewable energy sources and high-proportion power electronic equipment into the power grid has fundamentally transformed the system's energy structure. Due to the significant volatility, intermittency, and randomness of renewable energy output, the power grid faces increasingly severe uncertain power shocks, posing a serious challenge to the safe and stable operation of the system under extreme weather conditions or large-scale power shortages. Traditional power grid security assessment methods mainly rely on static security checks or static sensitivity evaluations of typical operating scenarios. While these methods have good applicability in deterministic operating environments, they reveal shortcomings such as insufficient identification accuracy and incomplete mechanism characterization when addressing the risks brought about by the strong randomness on both the source and load sides.

[0003] The coordinated identification of the dynamic response capability of regulation resources and the transmission capacity of the physical grid is crucial for improving grid resilience. Traditional methods for identifying weak links often focus on instantaneous static indicators at a specific cross-section, making it difficult to quantify the dynamic evolution of regulation resources from abundance to depletion under sustained shocks. Furthermore, existing technologies often overlook the interception effect of the grid's physical limits on regulation resources when analyzing cross-regional support capabilities. This leads to situations where, even with theoretically sufficient regulation margins in external regions, physical blockages prevent effective support to receiving areas when a cross-section is saturated. Moreover, as system fluctuation rates increase, the response delay characteristics of regulation resources have a more significant impact on the success or failure of supply assurance. Ignoring the time lag characteristics of the physical execution layer can lead to an inability to accurately identify hidden imbalance risks, resulting in blind spots in regulation response.

[0004] In the context of the current energy transition, identifying weak links in the power grid requires integrating multiple strategies, including dynamic evolution analysis, evaluation of cross-regional support bottlenecks, and correction of resource response characteristics. A key challenge to address is how to accurately decouple and quantitatively score the causes of weaknesses by simulating the real-world loss trajectory of adjustment margins under complex spatiotemporal coupling constraints. This is a critical issue that urgently needs to be addressed to improve the resilience of power grid operations and optimize the allocation of flexible resources. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for identifying weak links in a power grid based on the dynamic evolution of its regulation capacity margin, which can improve the operational resilience of the power transmission network and effectively track and accurately locate the process of exhaustion of the regulation capacity of the power transmission network throughout its entire evolution cycle. This invention can effectively solve the problems in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin, comprising the following steps: Obtain the basic physical parameters of the power grid, divide the regulation zones based on the basic physical parameters, construct a quantitative model of the initial regulation capacity of each zone, and calculate the initial regulation margin of each regulation zone. Obtain the source load uncertainty disturbance time series and disturbance time series cumulative effect, simulate the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtain the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation zone; Based on multi-dimensional physical characteristic parameters, an improved evaluation model is constructed to classify and identify the weak causes of each regulation zone. Multidimensional operational features are extracted from the time-series decay path of the adjustment margin and the causes of weakness, and a comprehensive identification model for the dynamic evolution of weakness is constructed. The weakness of each adjustment partition in the entire network is sorted and key bottleneck features are extracted.

[0007] Furthermore, the basic physical parameters include the power grid physical topology, power grid cross-section constraints, regulation resource capacity boundaries, and inter-regional tie line transmission limit constraints; the regulation resource capacity boundaries include the spinning reserve capacity of synchronous units, controllable loads, and energy storage sets.

[0008] Furthermore, the quantitative model expression for the initial adjustment capability of the partition is as follows: ; In the formula, For the first Each partition The upward adjustment margin at any given moment; This refers to the set of units within this partition; and For the unit Maximum output and current output; The upward spinning reserve capacity that generator sets within the zone can provide to cope with instantaneous power shortages; For energy storage collection, This refers to the maximum discharge regulation capability of energy storage resources under the constraints of state of charge. This represents the allowance for cross-regional support under the thermal stability limit of the connecting line.

[0009] Furthermore, the received cross-regional support margin of the connecting line under the thermal stability limit is obtained by quantification using a preset effective cross-regional support margin model. The expression of the effective cross-regional support margin model is as follows: ; In the formula, Indicates partition exist The effective cross-regional support margin obtained at any time, that is, the cross-regional support margin received by the tie line under the thermal stability limit; This indicates a direct connection to the partition. The collection of all inter-regional connecting lines; Indicates the contact line Thermal stability transmission limit power; Indicates the contact line exist Real-time operating power flow; Indicates via communication line With partitions A set of connected external adjacent partitions; Indicates adjacent partitions exist Remaining adjustable capacity that can be delivered at any time; This represents the total regulation resources that all adjacent zones can theoretically provide through this tie line on the power supply side.

[0010] Furthermore, the step of obtaining the adjustment margin timing decay path for each of the adjustment partitions includes: A multivariate Copula function is used to capture the spatial nonlinear correlation of source load disturbances in each of the aforementioned adjustment zones, a source load disturbance correlation model is constructed, and a time series of source load uncertainty disturbances is generated; The timing evolution of the adjustment margin loss is performed. Taking the initial adjustment margin of the partition as the evolution baseline state, and combining the timing accumulation effect of the disturbance, the adjustment margin loss of each time period is accumulated through continuous integration. Local adjustment resources and cross-regional support resources are called together according to preset weights to smooth out the power gap, so as to obtain the timing decay path of the adjustment margin of each adjustment partition.

[0011] Furthermore, the expression for the source-load disturbance correlation model is as follows: ; In the formula: express The joint probability distribution function of the net load gap in each adjustment zone; Indicates the first The net load gap random variable of each adjustment zone; Indicates the first Marginal distribution function of net load gap in each zone; This represents a connection function that can connect the edge distribution functions of each partition; This represents the spatial correlation parameter of the Copula function.

[0012] Furthermore, the timing evolution expression for the execution margin loss is as follows: ; In the formula: Indicates partition exist The remaining adjustment margin at any given time; Indicates partition In the present The adjustment margin state value at any given time; Indicates the observation time step Continuous integral operators within; express Time partitioning Net load gap strength; This represents the local resource response coefficient of the partition; express Real-time cross-regional support power obtained through the communication line.

[0013] Furthermore, the multi-dimensional physical characteristic parameters include resource consumption rate, tie-line power transmission distribution factor, and physical execution response time delay.

[0014] Furthermore, the improved evaluation model outputs the cross-regional support effectiveness coefficient and the blocking pressure coefficient. The quantitative results of the cross-regional support effectiveness coefficient and the blocking pressure coefficient are combined to match the corresponding failure attributes and classify and identify the weak causes of each regulation zone. The expression for the cross-regional support effectiveness coefficient is as follows: ; In the formula: In order to be in Time, partition For partitions The cross-regional support effectiveness coefficient is set. When the effectiveness coefficient is lower than a preset threshold, it is determined that there is a transmission blind spot in the path. To support partitioning weak zones of the target The set of physical transmission paths between them; For connecting lines exist Real-time operating power flow; Connecting lines Thermal stability transmission limit power; For partitioning Injected power to the tie line Power transfer distribution factor affected by power flow; The expression for the blocking pressure coefficient is as follows: ; In the formula: For partitioning The blocking pressure coefficient of the control response; The total duration sequence for simulation identification; In order to be in After undergoing constant regulation and scheduling, the zones The residual power gap still exists; For partitioning exist The total depth of theoretical regulatory capacity that is always available; Adjust the average response time of resources for this partition; This is a response time delay correction factor based on the exponential decay characteristic.

[0015] Furthermore, the multidimensional operating characteristics include margin loss rate, margin remaining level, and regulatory response pressure.

[0016] Furthermore, the expression for the dynamic evolution comprehensive identification model of weakness is as follows: ; In the formula: For partitioning The overall vulnerability score is used to characterize the vulnerability of the node in the process of dynamic consumption of system defense resources; The average loss rate for adjusting the margin of this partition; This represents the minimum remaining margin percentage, where This represents the minimum residual margin within the simulation period. This is the initial margin; The factor representing the maximum degree of loss; The average blocking pressure of the control response within the simulation cycle; , , These are the weighting coefficients for the various indicators, namely, the loss rate, the maximum loss factor, and the average blocking pressure of the response.

[0017] The present invention also provides a power grid weak link identification system based on dynamic evolution of regulation capability margin, comprising: The initial regulation capacity quantification module acquires the basic physical parameters of the power grid, divides the regulation zones based on the basic physical parameters, constructs the initial regulation capacity quantification model of the zones, and calculates the initial regulation margin of each regulation zone. The margin dynamic evolution simulation module obtains the source load uncertainty disturbance time series sequence and disturbance time series cumulative effect, simulates the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtains the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation partition. The weakness cause classification and identification module, based on multi-dimensional physical feature parameters, constructs an improved evaluation model to classify and identify the weakness causes of each of the aforementioned adjustment zones; The vulnerability identification module extracts multi-dimensional operational features from the adjustment margin time-series decay path and the vulnerability causes, constructs a vulnerability dynamic evolution comprehensive identification model, sorts the vulnerability of each adjustment partition in the entire network, and extracts key bottleneck features.

[0018] The present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power grid weak link identification method based on dynamic evolution of regulation capability margin as described in any of the preceding claims.

[0019] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the power grid weak link identification method based on dynamic evolution of regulation capability margin as described in any of the preceding claims.

[0020] The technical solution of this invention can achieve the following technical effects: Considering the dynamic evolution of regulation capacity margin and network topology constraints, while quantifying the original defense depth of each region in response to uncertain shocks, the physical causes of regulation failure are decoupled to improve the operational resilience of the transmission network; the collaborative supply guarantee potential of flexibility resources on both the source, grid and load sides is identified to achieve effective tracking and precise locking of the process of exhausting the regulation capacity of the transmission network throughout its entire evolution cycle. Attached Figure Description

[0021] 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 some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the power grid weak link identification method based on dynamic evolution of regulation capability margin in this invention; Figure 2 A time-series dynamic loss trajectory of regulation capacity margin caused by the use of flexibility resources in each regulation zone; Figure 3 A bottleneck arrangement in descending order of comprehensive indicators of the vulnerability of critical and vulnerable nodes across the entire network, and a decoupling diagnostic diagram of stacked multi-dimensional physical causes. Figure 4 This is a structural diagram of the power grid weak link identification system based on dynamic evolution of regulation capability margin in this invention; Figure 5 This is a schematic diagram of the structure of the computer electronic device in this invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0024] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[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 invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] like Figures 1 to 3 The method for identifying weak links in a power grid based on the dynamic evolution of regulation capacity margin, as shown, includes the following steps: Obtain the basic physical parameters of the power grid, divide the regulation zones based on the basic physical parameters, construct a quantitative model of the initial regulation capacity of each zone, and calculate the initial regulation margin of each regulation zone. Obtain the source load uncertainty disturbance time series and disturbance time series cumulative effect, simulate the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtain the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation zone; Based on multi-dimensional physical characteristic parameters, an improved evaluation model is constructed to classify and identify the weak causes of each regulation zone. Multidimensional operational features are extracted from the time-series decay path of the adjustment margin and the causes of weakness, and a comprehensive identification model for the dynamic evolution of weakness is constructed. The weakness of each adjustment partition in the entire network is sorted and key bottleneck features are extracted.

[0027] In this embodiment, considering the dynamic evolution of regulation capacity margin and network topology constraints, while quantifying the original defense depth of each zone in response to uncertain shocks, the physical causes of regulation failure are decoupled to improve the operational resilience of the transmission network; the collaborative supply potential of flexibility resources on both the source, grid, and load sides is identified to effectively track and accurately lock the process of exhausting the regulation capacity of the transmission network throughout its entire evolution cycle.

[0028] Based on the above embodiments, the basic physical parameters include the power grid physical topology, power grid cross-section constraints, regulation resource capacity boundaries, and inter-regional tie-line transmission limit constraints; the regulation resource capacity boundaries include synchronous unit spinning reserve capacity, controllable load, and energy storage set. The quantitative model expression for the initial adjustment capability of the partition is as follows: ; In the formula, For the first Each partition The upward adjustment margin at any given moment; This refers to the set of units within this partition; and For the unit Maximum output and current output; The upward spinning reserve capacity that generator sets within the zone can provide to cope with instantaneous power shortages; For energy storage collection, This refers to the maximum discharge regulation capability of energy storage resources under the constraints of state of charge. This is the allowance for cross-regional support of the connecting line under the thermal stability limit; The received cross-region support margin of the connecting line under the thermal stability limit is obtained by quantification using a preset effective cross-region support margin model. The expression of the effective cross-region support margin model is as follows: ; In the formula, Indicates partition exist The effective cross-regional support margin obtained at any time, that is, the cross-regional support margin received by the tie line under the thermal stability limit; This indicates a direct connection to the partition. The collection of all inter-regional connecting lines; Indicates the contact line Thermal stability transmission limit power; Indicates the contact line exist Real-time operating power flow; Indicates via communication line With partitions A set of connected external adjacent partitions; Indicates adjacent partitions exist Remaining adjustable capacity that can be delivered at any time; This represents the total regulation resources that all adjacent zones can theoretically provide through this tie line on the power supply side.

[0029] In this embodiment, a quantitative model of the initial regulation capacity of each region is constructed by combining the physical topology of the power grid, the cross-sectional constraints of the power grid, the boundary of the regulation resource capacity, and the transmission limit constraints of the inter-regional tie lines. This model integrates the regulation capacity of the spinning reserve capacity of synchronous units, controllable loads, and energy storage sets, and can calculate the upward regulation margin of each region, quantifying the initial regulation capacity level of each region. At the same time, through the effective inter-regional support margin model, the incoming inter-regional support margin of the tie lines under the thermal stability limit is quantified, fully considering the transmission constraints of the inter-regional tie lines and the remaining regulation capacity that can be transmitted from adjacent regions, ensuring that the calculation of the initial regulation margin conforms to the actual operating constraints of the power grid.

[0030] Based on the above embodiments, the step of obtaining the adjustment margin timing decay path for each adjustment partition includes: A multivariate Copula function is used to capture the spatial nonlinear correlation of source load disturbances in each of the aforementioned adjustment zones, a source load disturbance correlation model is constructed, and a time series of source load uncertainty disturbances is generated; The timing evolution of the adjustment margin loss is performed. Taking the initial adjustment margin of the partition as the evolution baseline state, and combining the timing accumulation effect of the disturbance, the adjustment margin loss of each time period is accumulated through continuous integration. Local adjustment resources and cross-regional support resources are called together according to preset weights to smooth out the power gap, so as to obtain the timing decay path of the adjustment margin of each adjustment partition.

[0031] In this embodiment, a multivariate Copula function is used to capture the spatial nonlinear correlation of source-load disturbances in each regulation zone, and a source-load disturbance correlation model is constructed. The generated source-load uncertainty disturbance time series can accurately reflect the correlation characteristics of disturbances between regions and fit the actual supply and demand fluctuations. When performing margin loss time series evolution, the initial regulation margin of the zone is used as the evolution benchmark state. Combined with the time series cumulative effect of disturbances, the regulation margin loss of each time period is accumulated through continuous integration. The process of local regulation resources and cross-regional support resources jointly smoothing the power gap is simulated according to preset weights. The obtained regulation margin time series decay path can fully present the dynamic loss process of regulation capacity.

[0032] Based on the above embodiments, the source-load disturbance correlation model expression is as follows: ; In the formula: express The joint probability distribution function of the net load gap of each adjustment zone is used to uniformly describe the concurrent probability of power imbalance risk in multiple regions across the entire network. Indicates the first The net load gap random variable of each regulation zone is determined by the fluctuation of new energy output and the load forecast deviation within that zone. Indicates the first The marginal distribution function of the net load gap in each partition is used to describe the statistical characteristics of power imbalance within a single partition; This represents a connection function that can connect the marginal distribution functions of each partition, thereby constructing a joint distribution model that can reflect the nonlinear correlation between regions; This represents the spatial correlation parameter of the Copula function, used to quantify the correlation strength of source and load disturbances between different geographical locations.

[0033] In this embodiment, the source-load disturbance correlation model constructs a joint distribution model that reflects the nonlinear correlation between regions by combining the Copula function with the marginal distribution function of the net load gap in each region. This model can uniformly describe the concurrent probability of power imbalance risk in multiple regions across the entire network. The random variable of net load gap in the model is jointly determined by the fluctuation of new energy output and the load forecast deviation within the region. The marginal distribution function can accurately describe the statistical characteristics of power imbalance within a single region. The spatial correlation parameter quantifies the correlation strength of source and load disturbances between regions with different geographical locations, making the generated source-load uncertainty disturbance time series more consistent with the actual disturbance characteristics of the power grid.

[0034] Based on the above embodiments, the timing evolution expression for margin loss is as follows: ; In the formula: Indicates partition exist The remaining adjustment margin at a given time is used to characterize the system's remaining supply capacity after a period of losses. Indicates partition In the present The adjustment margin state value at any given time; Indicates the observation time step The continuous integral operator within is used to accumulate and calculate the real-time loss of the regulating capacity during this period; express Time partitioning The net load shortfall intensity is the main disturbance component leading to the loss of adjustment margin; This represents the local resource response coefficient of the partition, used to balance the action weights of local adjustment resources and cross-regional support resources when mitigating the gap. express The effectiveness of cross-regional support power obtained through the communication line at all times reflects the contribution of external regions to compensating for the gaps in this region.

[0035] In this embodiment, by executing the margin loss time-series evolution expression, the remaining adjustment margin changes of each partition in a continuous period can be dynamically tracked, and the attenuation process of the system's supply guarantee capacity after a period of loss can be quantified. Among them, the continuous integral operator effectively accumulates the real-time loss of adjustment capacity caused by the main disturbance component of net load gap intensity. At the same time, the local resource response coefficient and cross-regional support power effectiveness are introduced to distinguish the actual contribution weight of local adjustment resources and external support in smoothing the gap, thereby accurately reflecting the comprehensive effect of disturbance and compensation on the adjustment margin. This expression incorporates the time accumulation effect, disturbance intensity and multi-type resource coordination into a unified framework, which can improve the predictability and attribution accuracy of the adjustment margin evolution trend, provide a quantitative basis for predicting the risk of supply guarantee capacity degradation and optimizing the timing of resource allocation, and support the formulation of targeted reinforcement strategies for weak partitions.

[0036] Based on the above embodiments, the multi-dimensional physical characteristic parameters include resource loss rate, tie-line power transmission distribution factor, and physical execution response time delay; Based on the above embodiments, the improved evaluation model outputs the cross-regional support effectiveness coefficient and the blocking pressure coefficient, and matches the corresponding failure attributes by combining the quantitative results of the cross-regional support effectiveness coefficient and the blocking pressure coefficient, and classifies and identifies the weak causes of each of the adjustment zones. The expression for the cross-regional support effectiveness coefficient is as follows: ; In the formula: In order to be in Time, partition For partitions The cross-regional support effectiveness coefficient is set. When the effectiveness coefficient is lower than a preset threshold, it is determined that there is a transmission blind spot in the path. To support partitioning weak zones of the target The set of physical transmission paths between them; For connecting lines exist Real-time operating power flow; Connecting lines Thermal stability transmission limit power; For partitioning Injected power to the tie line Power transfer distribution factor affected by power flow; The expression for the blocking pressure coefficient is as follows: ; In the formula: For partitioning The blocking pressure coefficient of the control response is used to quantify the intensity of the bottleneck in the effectiveness of supply guarantee faced by the system during dynamic evolution. The total duration sequence for simulation identification; In order to be in After undergoing constant regulation and scheduling, the zones The residual power gap still exists; For partitioning exist The total depth of theoretical regulatory capacity that is always available; Adjust the average response latency of the resources for this partition to characterize the time delay between the triggering of the command and the actual output of the resource. This is a response lag correction factor based on exponential decay characteristics, which quantifies the physical inhibition effect of response speed on regulation capability.

[0037] In this embodiment, by improving the evaluation model to output the cross-regional support effectiveness coefficient and the congestion pressure coefficient, it is possible to quantitatively determine whether there are blind spots in the transmission path and dynamically characterize the bottleneck strength of the system's supply guarantee effectiveness. Combining the quantitative results of the two coefficients, it is possible to accurately match the corresponding failure attributes and then classify and identify the weak causes of each regulation zone. Among them, the cross-regional support effectiveness coefficient uses real-time power flow, thermal stability limit, and power transmission distribution factor to identify the physical transmission limitation problem between the support zone and the target zone; the congestion pressure coefficient integrates the residual power gap, theoretical regulation capability depth, and response time delay correction factor to reflect the timeliness constraint of the actual role of regulation resources. This process coordinates the analysis of transmission capability and regulation response, which can improve the pertinence of weak link location and the comprehensiveness of cause identification.

[0038] Based on the above embodiments, the multidimensional operating characteristics include margin loss rate, margin remaining level, and control response pressure; The expression for the dynamic evolution comprehensive identification model of weakness is as follows: ; In the formula: For partitioning The overall vulnerability score is used to characterize the vulnerability of the node in the process of dynamic consumption of system defense resources; The average loss rate of the regulation margin in this partition can quantify the urgency and temporal evolution intensity of the system regulation resource depletion when dealing with extreme power uncertainty shocks; This represents the minimum remaining margin percentage, where This represents the minimum residual margin within the simulation period. This is the initial margin, reflecting the extreme depth of adjustment for resource depletion; This is the maximum loss factor; the closer this value is to 1, the more exhausted the adjustment margin of this partition becomes. The average blocking pressure of the control response during the simulation period comprehensively reflects the bottleneck in the supply guarantee effect caused by the response time delay and section blockage of the node. , , These are the weighting coefficients of various indicators, namely the loss rate, the maximum loss degree factor, and the average blocking pressure of the response, which are used to balance the influence weights of different risk dimensions on vulnerability identification. They can be dynamically adjusted according to the system supply priority to achieve a dynamic balance of risk measurement among the three dimensions of loss severity, depletion depth, and response delay.

[0039] In this embodiment, a comprehensive vulnerability dynamic evolution identification model is constructed, incorporating multi-dimensional operational characteristics into a unified quantitative framework to output a comprehensive vulnerability score. This score characterizes the vulnerability of each partition during the dynamic depletion of system defense resources. The average loss rate quantifies the urgency and temporal evolution intensity of system regulation resource depletion in response to extreme power uncertainty shocks. The minimum remaining margin ratio reflects the extreme depth of regulation resource depletion. The closer the maximum loss factor is to 1, the more depleted the regulation margin tends to be. The average blocking pressure comprehensively reflects the bottleneck in supply effectiveness caused by response delay and cross-sectional blocking. Simultaneously, the weighting coefficients can be dynamically adjusted according to the system supply priority, achieving a dynamic balance in risk measurement among the three dimensions of loss severity, depletion depth, and response delay. This model comprehensively identifies the causes of vulnerabilities, improving the comprehensiveness and adaptability of vulnerability localization.

[0040] like Figure 4 As shown, the present invention also provides a power grid weak link identification system based on dynamic evolution of regulation capability margin, which specifically includes the following modules; The initial regulation capacity quantification module acquires the basic physical parameters of the power grid, divides the regulation zones based on the basic physical parameters, constructs the initial regulation capacity quantification model of the zones, and calculates the initial regulation margin of each regulation zone. The margin dynamic evolution simulation module obtains the source load uncertainty disturbance time series sequence and disturbance time series cumulative effect, simulates the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtains the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation partition. The weakness cause classification and identification module, based on multi-dimensional physical feature parameters, constructs an improved evaluation model to classify and identify the weakness causes of each of the aforementioned adjustment zones; The vulnerability identification module extracts multi-dimensional operational features from the adjustment margin time-series decay path and the vulnerability causes, constructs a vulnerability dynamic evolution comprehensive identification model, sorts the vulnerability of each adjustment partition in the entire network, and extracts key bottleneck features.

[0041] like Figure 5As shown, the present invention also provides an electronic device, characterized in that it includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power grid weak link identification method based on the dynamic evolution of regulation capacity margin as described above, including the following steps: acquiring basic physical parameters of the power grid; dividing regulation zones based on the basic physical parameters; constructing a quantitative model of the initial regulation capacity of each zone; calculating the initial regulation margin of each regulation zone; acquiring the time series sequence of source-load uncertainty disturbances and the cumulative effect of disturbance time series; simulating the dynamic process of local and cross-regional regulation resources smoothing out power gaps; obtaining the time series decay path of regulation margin and multi-dimensional physical characteristic parameters of each regulation zone; constructing an improved evaluation model based on the multi-dimensional physical characteristic parameters; classifying and identifying the weak causes of each regulation zone; extracting multi-dimensional operating features from the time series decay path of regulation margin and the weak causes; constructing a comprehensive identification model of weakness dynamic evolution; ranking the weakness of each regulation zone in the entire network and extracting key bottleneck features.

[0042] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the power grid weak link identification method based on the dynamic evolution of regulation capacity margin as described above, including the following steps: acquiring basic physical parameters of the power grid; dividing regulation zones based on the basic physical parameters; constructing a quantitative model of the initial regulation capacity of each zone; calculating the initial regulation margin of each regulation zone; acquiring the time series sequence of source-load uncertainty disturbances and the cumulative effect of disturbance time series; simulating the dynamic process of local and cross-regional regulation resources smoothing out power gaps; obtaining the time series decay path of regulation margin and multi-dimensional physical characteristic parameters of each regulation zone; constructing an improved evaluation model based on the multi-dimensional physical characteristic parameters; classifying and identifying the weak causes of each regulation zone; extracting multi-dimensional operating features from the time series decay path of regulation margin and the weak causes; constructing a comprehensive identification model of weakness dynamic evolution; ranking the weakness of each regulation zone in the entire network and extracting key bottleneck features.

[0043] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. "A plurality of" means two or more, unless otherwise explicitly specified.

[0044] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., 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. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0046] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0047] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0048] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0049] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0050] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for identifying weak links in a power grid based on the dynamic evolution of regulation capacity margin, characterized in that, Includes the following steps: Obtain the basic physical parameters of the power grid, divide the regulation zones based on the basic physical parameters, construct a quantitative model of the initial regulation capacity of each zone, and calculate the initial regulation margin of each regulation zone. Obtain the source load uncertainty disturbance time series and disturbance time series cumulative effect, simulate the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtain the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation zone; Based on multi-dimensional physical characteristic parameters, an improved evaluation model is constructed to classify and identify the weak causes of each regulation zone. Multidimensional operational features are extracted from the time-series decay path of the adjustment margin and the causes of weakness, and a comprehensive identification model for the dynamic evolution of weakness is constructed. The weakness of each adjustment partition in the entire network is sorted and key bottleneck features are extracted.

2. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 1, characterized in that, The basic physical parameters include the power grid physical topology, power grid cross-section constraints, regulation resource capacity boundaries, and inter-regional tie line transmission limit constraints; the regulation resource capacity boundaries include the spinning reserve capacity of synchronous units, controllable loads, and energy storage sets.

3. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 2, characterized in that, The quantitative model expression for the initial adjustment capability of the partition is as follows: ； In the formula, For the first Each partition The upward adjustment margin at any given moment; For the first A collection of units within a zone; and For the unit Maximum output and current output; The upward spinning reserve capacity that generator sets within the zone can provide to cope with instantaneous power shortages; For energy storage collection, This refers to the maximum discharge regulation capability of energy storage resources under the constraints of state of charge. This represents the allowance for cross-regional support under the thermal stability limit of the connecting line.

4. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 3, characterized in that, The received cross-region support margin of the connecting line under the thermal stability limit is obtained by quantification using a preset effective cross-region support margin model. The expression of the effective cross-region support margin model is as follows: ； In the formula, Indicates partition exist The effective cross-regional support margin obtained at any time, that is, the cross-regional support margin received by the tie line under the thermal stability limit; This indicates a direct connection to the partition. The collection of all inter-regional connecting lines; Indicates the contact line Thermal stability transmission limit power; Indicates the contact line exist Real-time operating power flow; Indicates via communication line With partitions A set of connected external adjacent partitions; Indicates adjacent partitions exist Remaining adjustable capacity that can be delivered at any time; This represents the total regulation resources that all adjacent zones can theoretically provide through tie lines on the power supply side.

5. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 1, characterized in that, The steps for obtaining the adjustment margin timing decay path for each of the aforementioned adjustment partitions include: A multivariate Copula function is used to capture the spatial nonlinear correlation of source load disturbances in each of the aforementioned adjustment zones, a source load disturbance correlation model is constructed, and a time series of source load uncertainty disturbances is generated; The timing evolution of the adjustment margin loss is performed. Taking the initial adjustment margin of the partition as the evolution baseline state, and combining the timing accumulation effect of the disturbance, the adjustment margin loss of each time period is accumulated through continuous integration. Local adjustment resources and cross-regional support resources are called together according to preset weights to smooth out the power gap, so as to obtain the timing decay path of the adjustment margin of each adjustment partition.

6. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 5, characterized in that, The expression for the source-load disturbance correlation model is as follows: ； In the formula: express The joint probability distribution function of the net load gap in each adjustment zone; Indicates the first The net load gap random variable of each adjustment zone; Indicates the first Marginal distribution function of net load gap in each zone; This represents a connection function that can connect the edge distribution functions of each partition; This represents the spatial correlation parameter of the Copula function.

7. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 5, characterized in that, The timing evolution expression for the execution margin loss is as follows: ； In the formula: Indicates partition exist The remaining adjustment margin at any given time; Indicates partition In the present The adjustment margin state value at any given time; Indicates the observation time step Continuous integral operators within; express Time partitioning Net load gap strength; This represents the local resource response coefficient of the partition; express Real-time cross-regional support power obtained through the communication line.

8. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 1, characterized in that, The multi-dimensional physical characteristic parameters include resource loss rate, tie-line power transmission distribution factor, and physical execution response time delay.

9. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 8, characterized in that, The improved evaluation model outputs the cross-regional support effectiveness coefficient and the blockage pressure coefficient. The quantitative results of the cross-regional support effectiveness coefficient and the blockage pressure coefficient are combined to match the corresponding failure attributes and classify and identify the weak causes of each regulation zone. The expression for the cross-regional support effectiveness coefficient is as follows: ； In the formula: In order to be in Time, partition For partitions The cross-regional support effectiveness coefficient; To support partitioning weak zones of the target The set of physical transmission paths between them; For connecting lines exist Real-time operating power flow; Connecting lines Thermal stability transmission limit power; For partitioning Injected power to the tie line Power transfer distribution factor affected by power flow; The expression for the blocking pressure coefficient is as follows: ； In the formula: For partitioning The blocking pressure coefficient of the control response; The total duration sequence for simulation identification; In order to be in After undergoing constant regulation and scheduling, the zones The residual power gap still exists; For partitioning exist The total depth of theoretical regulatory capacity that is always available; For partitioning Adjust the average response time lag of resources; This is a response time delay correction factor based on the exponential decay characteristic.

10. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 1, characterized in that, The multidimensional operating characteristics include margin loss rate, margin remaining level, and regulatory response pressure.

11. The method for identifying weak links in a power grid based on the dynamic evolution of regulation capability margin according to claim 10, characterized in that, The expression for the dynamic evolution comprehensive identification model of weakness is as follows: ； In the formula: For partitioning The overall score for the degree of weakness; For partitioning The average loss rate of the adjustment margin; This represents the minimum remaining margin percentage, where This represents the minimum residual margin within the simulation period. This is the initial margin; The factor representing the maximum degree of loss; The average blocking pressure of the control response within the simulation cycle; , , These are the weighting coefficients for the various indicators, namely, the loss rate, the maximum loss factor, and the average blocking pressure of the response.

12. A power grid weak link identification system based on dynamic evolution of regulation capability margin, characterized in that, include: The initial regulation capacity quantification module acquires the basic physical parameters of the power grid, divides the regulation zones based on the basic physical parameters, constructs the initial regulation capacity quantification model of the zones, and calculates the initial regulation margin of each regulation zone. The margin dynamic evolution simulation module obtains the source load uncertainty disturbance time series sequence and disturbance time series cumulative effect, simulates the dynamic process of local and cross-regional regulation resources to smooth the power gap, and obtains the regulation margin time series decay path and multi-dimensional physical characteristic parameters of each regulation partition. The weakness cause classification and identification module, based on multi-dimensional physical feature parameters, constructs an improved evaluation model to classify and identify the weakness causes of each of the aforementioned adjustment zones; The vulnerability identification module extracts multi-dimensional operational features from the adjustment margin time-series decay path and the vulnerability causes, constructs a vulnerability dynamic evolution comprehensive identification model, sorts the vulnerability of each adjustment partition in the entire network, and extracts key bottleneck features.

13. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power grid weak link identification method based on the dynamic evolution of regulation capability margin as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the power grid weak link identification method based on the dynamic evolution of regulation capability margin as described in any one of claims 1 to 11.

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