Power grid static stability limit intelligent checking method and system based on full-scene coverage

Abstract

The application discloses a power grid static stability limit intelligent checking method and system based on full-scene coverage, constructs a node admittance matrix of a power grid system, constructs full-scene node power balance equations based on the corrected node admittance matrix, sets and samples multidimensionally based on a load fluctuation interval, a new energy output interval and a thermal power output interval to generate a full-scene set, identifies extreme operation modes in the full-scene set by using the full-scene node power balance equations, simulates fault scenes of each line and multiple line breakages in each scene in the full-scene set in sequence and obtains system power flow distribution, identifies full-scene static weak links, calculates full-scene section limits of the power grid system based on each extreme operation mode, and takes the full-scene section limits and the full-scene static weak links as full-scene power constraint expression formulas for safe operation of the power grid system in the fault scenes. The application can improve full-scene coverage capability, efficiency and accuracy of power grid static stability checking.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and control technology, and in particular relates to a method and system for intelligent verification of power grid static stability limits based on full scenario coverage. Background Technology

[0002] In power grid operation and management, the verification of static stability limits for cross sections is a core aspect of ensuring power grid security. Under the traditional manual verification model, dispatchers need to manually select key cross sections, configure calculation parameters, and rely on offline simulation tools to perform stability limit calculations one by one for different operating scenarios such as equipment maintenance and load transfer. However, significant technical shortcomings exist in practical applications: 1. In complex maintenance scenarios (such as multiple equipment shutdowns and multiple nested maintenance), manual screening and verification of objects is time-consuming and labor-intensive, and the amount of verification tasks increases exponentially, making it difficult to cover the entire scenario; 2. Human operation is prone to missing risk sections and requires high experience in operation, which not only reduces the efficiency of verification, but may also cause power grid safety hazards due to insufficient timeliness; 3. It relies heavily on offline simulation tools, has a cumbersome process, and cannot achieve intelligent analysis and automatic coverage of the entire scenario.

[0003] 4. It can only handle typical operating modes (such as fixed load and rated power output), and cannot cover actual operating scenarios such as load fluctuations, randomness of renewable energy output, and peak shaving of thermal power. When the system is under extreme conditions such as peak load, large-scale generation / outage of renewable energy, verification under a single operating mode is prone to leading to overly optimistic calculation of the section limit, which poses a risk of insufficient safety margin.

[0004] The core shortcomings of existing technologies lie in their low level of automation, numerous manual intervention steps, and lack of full-scenario coverage of load ranges, renewable energy output ranges, and thermal power output ranges, making it difficult to cope with the challenges brought about by the expansion of power grid scale and the increasing complexity of operation modes. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent verification method and system for power grid static stability limits based on full-scenario coverage. Through a standardized intelligent process, it enables rapid batch calculation of stability limits across multiple sections, improving the full-scenario coverage, efficiency, and accuracy of power grid static stability verification, and providing quantitative technical support for maintenance scheduling and dispatch decisions.

[0006] The present invention adopts the following technical solution.

[0007] This invention proposes an intelligent verification method for power grid static stability limits based on full scenario coverage, including: S1. Construct the node admittance matrix of the power grid system, and correct the node admittance matrix according to the non-standard transformer ratio parameters of the transformer branches; construct the node power balance equation for the whole scenario based on the corrected node admittance matrix. S2, set load fluctuation range, new energy output range and thermal power output range, perform multi-dimensional sampling on the load fluctuation range, new energy output range and thermal power output range to generate multiple sets of typical operating modes as a full scenario set; use the full scenario node power balance equation to identify the extreme operating modes in the full scenario set; S3, in each scenario in the full scenario set, simulate each line and multiple line breakage fault scenarios one by one, and obtain the system power flow distribution after line breakage in the fault scenario; S4. Based on the power flow distribution of the system, a power transfer distribution factor is introduced to quantify the sensitivity of the line to changes in system power and identify the static weak links in the whole scenario. S5. Based on each of the aforementioned extreme operating modes, the extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system; S6, output the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format to generate a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

[0008] More preferably, in S3, the method for identifying the extreme operating mode includes: Calculate the comprehensive feature parameters of each scene group in the full scene set. The specific calculation process is as follows: Calculate the difference between the actual load power and the rated load output of the system in the current scenario, and take the absolute value of the difference; normalize the absolute value using the allowable fluctuation range of the load power to obtain the deviation contribution value of the load dimension; Calculate the difference between the actual output of the new energy source and the rated output value of the new energy source under the current scenario. After taking the absolute value, normalize the absolute value using the maximum fluctuation range of the new energy output to obtain the deviation contribution value of the new energy dimension. Calculate the difference between the actual output of the thermal power unit and the rated output value of the thermal power unit under the current scenario. After taking the absolute value, normalize the absolute value using the adjustable range of the thermal power output to obtain the deviation contribution value of the thermal power dimension. The comprehensive characteristic parameters of the current scenario are obtained by summing the deviation contribution values ​​of the load dimension, the new energy dimension, and the thermal power dimension. ; The comprehensive feature parameters This indicates the overall degree to which the load, new energy sources, and thermal power output deviate from their rated states under the current scenario; Based on all comprehensive feature parameters in the full scene set Thus, the extreme operating mode is obtained.

[0009] More preferably, all comprehensive feature parameters in the entire scene set are included. Sort the data from largest to smallest, and select a percentage (a%) for the comprehensive feature parameters. The first a% scenario is considered as an extreme operating mode.

[0010] More preferably, in S4, based on the system power flow distribution, a power transfer distribution factor is introduced to quantify the sensitivity of the line to system power changes, and to identify static weak links in the entire scenario. Specific steps include: Based on the system power flow distribution in each scenario of the full scenario set, calculate the full scenario PTDF matrix for each line in the entire network, where PTDF refers to the power transmission allocation factor; The sum of the absolute values ​​of PTDF for each line in the entire scenario is calculated, and the sum of the absolute values ​​of PTDF for each line is used to represent the line sensitivity in the entire scenario. The line sensitivity in the entire scenario of all lines is then ranked. Based on the full-scenario line sensitivity of all lines, the static weak links of the power grid in the entire scenario are located.

[0011] More preferably, in S5, based on each of the extreme operating modes, an extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system. Specific steps include: For each of the aforementioned extreme operating modes, perform N-1 / N-2 fault simulations to obtain the corresponding cross-sectional limits. ,in This represents the cross-sectional limit under the Mth extreme operating mode; Compare the cross-sectional limits under various extreme operating conditions to determine the minimum cross-sectional limit value. With maximum limit value , as the limit for the entire scenario section.

[0012] More preferably, the method for determining the full-scenario power constraint expression is as follows: The full-scene cross-sectional limit obtained in step S5 is used as the total cross-sectional power constraint. Based on the total power constraint of the cross section, and combined with the power flow distribution of the system obtained in step S3, calculate the ratio of the power of each line to the total power of the cross section in each scenario, obtain the ratio range of each line, and take the union of the ratio ranges under all scenarios as the allocation coefficient interval of the line. The lower limit and upper limit of the total power constraint of the cross section are multiplied by the lower limit and upper limit of the allocation coefficient interval of each line respectively to obtain the power constraint interval of the line. Based on the inherent safety limits of each line, the power constraint range of the line is adjusted. The total power constraint of the cross section and the power constraint range of the corrected line are used as the power constraint expression for the whole scenario.

[0013] This invention also proposes an intelligent verification system for power grid static stability limits based on full-scenario coverage, including a full-scenario node power balance equation construction module, a full-scenario set construction module, a system power flow distribution calculation module, a full-scenario static weak link identification module, a full-scenario section limit calculation module, and a full-scenario power constraint construction module: The module for constructing the power balance equation for all scenarios constructs the node admittance matrix of the power grid system and corrects the node admittance matrix based on the non-standard transformer ratio parameters of the transformer branches; and constructs the power balance equation for all scenarios based on the corrected node admittance matrix. The full-scenario set construction module sets load fluctuation range, renewable energy output range and thermal power output range, performs multi-dimensional sampling on the load fluctuation range, renewable energy output range and thermal power output range, generates multiple sets of typical operating modes as the full-scenario set; and uses the full-scenario node power balance equation to identify the extreme operating modes in the full-scenario set. The system power flow distribution calculation module simulates each line and multiple line breakage fault scenarios in each scenario in the full scenario set, and obtains the system power flow distribution after line breakage in the fault scenario. The full-scenario static weak link identification module, based on the system power flow distribution, introduces a power transfer distribution factor to quantify the sensitivity of the line to system power changes, and identifies the full-scenario static weak link. The full-scenario cross-sectional limit calculation module calculates the full-scenario cross-sectional limit of the power grid system based on each of the aforementioned extreme operating modes using an extreme value search algorithm. The full-scenario power constraint construction module outputs the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format, generating a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

[0014] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0015] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention generates ≥100 typical operating modes through intelligent algorithms, covering load fluctuations of ±20%, new energy output range of 0-100%, and thermal power output range of 30%-100%. Compared with the traditional method that can only handle 5-10 limited scenarios, it significantly improves the coverage of extreme scenarios, reduces the conservatism of stability limits, and solves the safety hazards caused by insufficient scenario coverage in traditional methods.

[0017] 2. This invention compresses the traditional manual verification process, which takes several hours, to the minute level. It supports batch intelligent calculation of multiple sections in all scenarios, and solves the problems of traditional manual verification, which requires several hours to manually set the running mode and safety constraints one by one, is prone to errors and is inefficient.

[0018] 3. This invention requires no manual intervention and intelligently completes the entire process from weak link search to control strategy generation, solving the problems of reliance on experience and poor consistency in traditional methods, and realizing the transformation from "passive response" to "proactive prevention".

[0019] 4. This invention upgrades the stability limit verification from "offline typical mode experience type" to "automatic full-scenario quantitative type", providing quantitative support for the arrangement of power grid maintenance mode and real-time dispatch decision-making in all scenarios, and significantly improving the system's safety under all operating conditions. Attached Figure Description

[0020] Figure 1 This is a flowchart of the intelligent verification method for power grid static stability limits based on full scenario coverage according to the present invention. Figure 2 This is a flowchart of the method for intelligent verification of power grid static stability limits based on full scenario coverage, according to Embodiment 1 of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0022] like Figure 1 As shown, this invention proposes an intelligent verification method for power grid static stability limits based on full scenario coverage, including: S1. Construct the node admittance matrix of the power grid system, and correct the node admittance matrix according to the non-standard transformer ratio parameters of the transformer branches; construct the node power balance equation for the whole scenario based on the corrected node admittance matrix. S2, set load fluctuation range, new energy output range and thermal power output range, perform multi-dimensional sampling on the load fluctuation range, new energy output range and thermal power output range to generate multiple sets of typical operating modes as a full scenario set; use the full scenario node power balance equation to identify the extreme operating modes in the full scenario set; S3, in each scenario in the full scenario set, simulate each line and multiple line breakage fault scenarios one by one, and obtain the system power flow distribution after line breakage in the fault scenario; In S3, the method for identifying the extreme operating mode includes: Calculate the comprehensive feature parameters of each scene group in the full scene set. The specific calculation process is as follows: Calculate the difference between the actual load power and the rated load output of the system in the current scenario, and take the absolute value of the difference; normalize the absolute value using the allowable fluctuation range of the load power to obtain the deviation contribution value of the load dimension; Calculate the difference between the actual output of the new energy source and the rated output value of the new energy source under the current scenario. After taking the absolute value, normalize the absolute value using the maximum fluctuation range of the new energy output to obtain the deviation contribution value of the new energy dimension. Calculate the difference between the actual output of the thermal power unit and the rated output value of the thermal power unit under the current scenario. After taking the absolute value, normalize the absolute value using the adjustable range of the thermal power output to obtain the deviation contribution value of the thermal power dimension. The comprehensive characteristic parameters of the current scenario are obtained by summing the deviation contribution values ​​of the load dimension, the new energy dimension, and the thermal power dimension. ; The comprehensive feature parameters This indicates the overall degree to which the load, new energy sources, and thermal power output deviate from their rated states under the current scenario; Based on all comprehensive feature parameters in the full scene set Thus, the extreme operating mode is obtained.

[0023] All comprehensive feature parameters in the entire scene set Sort the data from largest to smallest, and select a percentage (a%) for the comprehensive feature parameters. The first a% scenario is considered as an extreme operating mode.

[0024] S4. Based on the power flow distribution of the system, a power transfer distribution factor is introduced to quantify the sensitivity of the line to changes in system power and identify the static weak links in the whole scenario. In S4, based on the system power flow distribution, a power transfer distribution factor is introduced to quantify the sensitivity of the line to system power changes, and to identify the static weak links in the entire scenario. Specific steps include: Based on the system power flow distribution in each scenario of the full scenario set, calculate the full scenario PTDF matrix for each line in the entire network, where PTDF refers to the power transmission allocation factor; The sum of the absolute values ​​of PTDF for each line in the entire scenario is calculated, and the sum of the absolute values ​​of PTDF for each line is used to represent the line sensitivity in the entire scenario. The line sensitivity in the entire scenario of all lines is then ranked. Based on the full-scenario line sensitivity of all lines, the static weak links of the power grid in the entire scenario are located.

[0025] S5. Based on each of the aforementioned extreme operating modes, the extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system; In S5, based on each of the aforementioned extreme operating modes, an extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system. Specific steps include: For each of the aforementioned extreme operating modes, perform N-1 / N-2 fault simulations to obtain the corresponding cross-sectional limits. ,in This represents the cross-sectional limit under the Mth extreme operating mode; Compare the cross-sectional limits under various extreme operating conditions to determine the minimum cross-sectional limit value. With maximum limit value , as the limit for the entire scenario section.

[0026] S6, output the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format to generate a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

[0027] The method for determining the full-scenario power constraint expression is as follows: The full-scene cross-sectional limit obtained in step S5 is used as the total cross-sectional power constraint. Based on the total power constraint of the cross section, and combined with the power flow distribution of the system obtained in step S3, calculate the ratio of the power of each line to the total power of the cross section in each scenario, obtain the ratio range of each line, and take the union of the ratio ranges under all scenarios as the allocation coefficient interval of the line. The lower limit and upper limit of the total power constraint of the cross section are multiplied by the lower limit and upper limit of the allocation coefficient interval of each line respectively to obtain the power constraint interval of the line. Based on the inherent safety limits of each line, the power constraint range of the line is adjusted. The total power constraint of the cross section and the power constraint range of the corrected line are used as the power constraint expression for the whole scenario.

[0028] This invention also proposes an intelligent verification system for power grid static stability limits based on full-scenario coverage, including a full-scenario node power balance equation construction module, a full-scenario set construction module, a system power flow distribution calculation module, a full-scenario static weak link identification module, a full-scenario section limit calculation module, and a full-scenario power constraint construction module: The module for constructing the power balance equation for all scenarios constructs the node admittance matrix of the power grid system and corrects the node admittance matrix based on the non-standard transformer ratio parameters of the transformer branches; and constructs the power balance equation for all scenarios based on the corrected node admittance matrix. The full-scenario set construction module sets load fluctuation range, renewable energy output range and thermal power output range, performs multi-dimensional sampling on the load fluctuation range, renewable energy output range and thermal power output range, generates multiple sets of typical operating modes as the full-scenario set; and uses the full-scenario node power balance equation to identify the extreme operating modes in the full-scenario set. The system power flow distribution calculation module simulates each line and multiple line breakage fault scenarios in each scenario in the full scenario set, and obtains the system power flow distribution after line breakage in the fault scenario. The full-scenario static weak link identification module, based on the system power flow distribution, introduces a power transfer distribution factor to quantify the sensitivity of the line to system power changes, and identifies the full-scenario static weak link. The full-scenario cross-sectional limit calculation module calculates the full-scenario cross-sectional limit of the power grid system based on each of the aforementioned extreme operating modes using an extreme value search algorithm. The full-scenario power constraint construction module outputs the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format, generating a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

[0029] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0030] The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is used to perform the steps of the above method according to the instructions.

[0031] Example 1 This invention proposes an intelligent verification method for power grid static stability limits based on full scenario coverage, such as... Figure 2 As shown, the specific steps are as follows.

[0032] S1. Construct the node admittance matrix of the power grid system and correct the node admittance matrix according to the non-standard transformer ratio; construct the node power balance equation for the whole scenario based on the corrected node admittance matrix.

[0033] The nodal admittance matrix can be constructed using topology methods, impedance matrix inversion methods, and generalized circuit theory methods, which will not be elaborated upon here. A full-scenario topology model of the power system is constructed based on the nodal admittance matrix, utilizing its sparsity, symmetry, and diagonal dominance to simplify the calculation process. Specifically, the nodal admittance matrix is ​​mathematically simplified using a DC power flow model. By neglecting transmission line resistance, assuming constant node voltage amplitudes and small phase angle differences, and eliminating reference nodes, the full-scenario nodal power balance equations are constructed. ; in, Inject active power into node i. For nodal susceptance matrix elements, Let be the voltage phase angle at node j. A fast solution for power flow distribution under all power grid operating scenarios is achieved using the nodal admittance matrix.

[0034] For the node admittance matrix, it is also necessary to handle the non-standard transformer ratio parameters of the transformer branches and correct the matrix elements to reflect the actual topology of the entire power grid scenario. For transformers with non-standard ratios... The transformer branch, its The equivalent circuit parameters are: , , ; in, Where k is the transformer impedance, and k is the non-standard turns ratio of the transformer. Let be the complex conjugate of k. , , These are the equivalent admittances between nodes ij, the equivalent admittance of node i to ground, and the equivalent admittance of node j to ground in the π-type equivalent circuit, respectively.

[0035] At this point, the elements of the nodal admittance matrix are corrected as follows: , , ; in, ( ) is the first i ( j The self-admittance element in the admittance matrix corresponding to the node; , For the first i Node and the jThe mutual admittance elements of nodes in the node admittance matrix.

[0036] S2 sets load fluctuation range, renewable energy output range and thermal power output range, and uses these ranges to generate multiple sets of typical operating modes as a full scenario set; using the full scenario node power balance equation, it identifies extreme operating modes in the full scenario set.

[0037] Set load fluctuation range New energy output range Thermal power output range Intelligent algorithms, including but not limited to genetic algorithms and particle swarm optimization algorithms, are introduced to perform multi-dimensional sampling of load fluctuation ranges, new energy output ranges, and thermal power output ranges to identify extreme operating modes.

[0038] In a preferred embodiment of the present invention, the load fluctuation range, the renewable energy output range, and the thermal power output range are encoded as 16-bit binary strings. The first 4 bits represent the load fluctuation percentage (0000=80%, 1111=120%), the middle 6 bits represent the renewable energy output percentage (000000=0%, 111111=100%), and the last 6 bits represent the thermal power output percentage (000000=20%, 111111=100%). The range of each range is set according to the actual situation.

[0039] Furthermore, the fitness function is designed as follows: Maximize through iterative optimization This enhances scene diversity and generates a complete scene collection. Among them, It is a very small positive number. For cross-sectional limits, The standard deviation of the cross-sectional limit for the scene set obtained after each iteration. Its mathematical definition is:

[0040] in, For the first The cross-sectional limit value for each scenario (such as the maximum allowable transmission power of a transmission line or cross section). The average of the limits for N scene sections is... N represents the total number of scenes.

[0041] The section limit is determined by comprehensively considering the power distribution relationship between the disconnected and non-disconnected branches in the entire scenario, combined with the power transfer ratio. When disconnecting branch A causes branch B to overload, the section limit formula for the combination of lines A and B when disconnecting line A is based on power conservation. as follows: ; PTD represents the power flow transfer ratio, which is determined by the power grid architecture; S limB This indicates the thermal stability limit for line B; and This is the power allocation factor, which reflects the impact of the initial power flow distribution on the quota. The calculation formula is as follows: , ; in, and To disconnect the power flow between branch A and branch B.

[0042] Specifically, in the iterative optimization of the genetic algorithm, the population size is set to 50, the crossover probability is 0.8, the mutation probability is 0.05, and the final full scene set is generated after 100 iterations.

[0043] Furthermore, the comprehensive feature parameters of each scene group in the entire scene set are calculated. : ; in, This represents the actual load power of the system in the current scenario. This is the rated (average) output value of the load; The allowable fluctuation range of load power (maximum value - minimum value); The actual output of new energy sources (wind power, photovoltaics, etc.) in the current scenario; This refers to the rated (average) output value of the new energy source; The maximum fluctuation range of power output for new energy sources; This represents the actual output of thermal power units in the current scenario; This refers to the rated (baseline) output value of thermal power plants; The adjustable range of thermal power output (maximum value - minimum value); In this embodiment, a larger K value indicates a greater probability that the output of load, renewable energy, and thermal power all deviate from their rated values ​​simultaneously in the scenario (such as a surge in load + a sudden drop in renewable energy + limited regulation of thermal power). Therefore, the extreme operating mode is determined by the magnitude of the K value. The scenarios with the highest K values ​​are selected as extreme operating modes, and adjustments are made according to the actual situation to ensure coverage of ≥90% of extreme operating conditions.

[0044] S3, for each scenario in the full scenario set, successively simulates each line and multiple line breakage fault scenarios to obtain the system power flow distribution after line breakage in the fault scenario.

[0045] Specifically, the system simulates the ground state and fault scenarios such as N-1 and N-2, updates the node admittance matrix, calculates the node voltage phase angle, and calculates the system power flow distribution for each scenario in the full scenario set to identify overloaded lines. The node admittance matrix can be updated using methods such as line removal; therefore, existing technologies for power flow calculation and node admittance matrix update will not be elaborated upon here.

[0046] S4, based on the system power flow distribution after line breakage in fault scenarios, introduces a power transfer distribution factor to quantify the sensitivity of the line to system power changes and identify static weak links in the entire scenario.

[0047] Specifically, based on the system power flow distribution in each scenario within the entire scenario set, the full-scenario PTDF matrix for each line in the entire network is calculated, where PTDF refers to the power transmission allocation factor: ; in, For a specific scene in the entire scene set, the route k For nodes j The power transmission allocation factor, and Let k be the power of line k and node j. and For the elements of the nodal impedance matrix, Let K be the reactance of line k.

[0048] The sum of the absolute values ​​of the Power Detection and Distribution Function (PTDF) for each line under all scenarios is calculated. This sum represents the line sensitivity across all scenarios for that line, and the line sensitivity of all lines is ranked. A larger sum of absolute PTDF values ​​indicates a higher likelihood of power overload during a fault, thus making that line a more likely statically weak link in the power grid. Based on the overall line sensitivity of all lines, the risk of fault cascading effects is predicted, thereby locating the statically weak link in the power grid across all scenarios. The process of using the PTDF matrix to predict the risk of fault cascading effects and thus locate the statically weak link in the power grid across all scenarios is a common task in power system analysis, and existing techniques will not be elaborated upon here.

[0049] S5 calculates the full-scene section limit based on each extreme operating mode in the full-scene set using an extreme value search algorithm.

[0050] Specifically, for each extreme operating mode, perform N-1 / N-2 fault simulations to obtain the corresponding cross-sectional limits. ,in This represents the cross-sectional limit under the Mth extreme operating mode. The minimum cross-sectional limit value is determined by comparing the cross-sectional limits under each extreme operating mode. With maximum limit value ; with minimum limit value As a rigid constraint for cross-sectional stability control across the entire scenario, with the maximum limit value As a reference for scheduling margin.

[0051] S6 uses rigid constraints and scheduling margin references as full-scenario section limits, outputs the full-scenario section limits and the analyzed full-scenario static weak links in a standardized text format, and generates corresponding full-scenario power constraint expressions to clarify the safety boundaries of the full-scenario power allocation of each line within the section. The results are written to an Excel file to provide intuitive data support for full-scenario scheduling and control, and ensure the safe operation of the system under full-fault scenarios such as N-1 and N-2.

[0052] Specifically, the steps for obtaining the power constraints across the entire scenario are as follows: The full-scene cross-sectional limit obtained in step S5 is used as the total cross-sectional power constraint. Based on the total power constraint of the cross section, and combined with the power flow distribution of the system obtained in step S3, calculate the ratio of the power of each line to the total power of the cross section in each scenario, obtain the ratio range of each line, and take the union of the ratio ranges under all scenarios as the allocation coefficient interval of the line. The lower limit and upper limit of the total power constraint of the cross section are multiplied by the lower limit and upper limit of the allocation coefficient interval of each line respectively to obtain the power constraint interval of the line. Based on the inherent safety limits of each line, the power constraint range of the line is adjusted. The total power constraint of the cross section and the power constraint range of the corrected line are used as the power constraint expression for the whole scenario.

[0053] The inherent safety limit of each line refers to the upper and lower power limits of each line during actual operation, which are set based on existing standards. The correction refers to constraining the power constraint range to the upper and lower power limits set based on existing standards.

[0054] Example 2 This invention also proposes an intelligent verification system for power grid static stability limits based on full-scenario coverage, including a full-scenario node power balance equation construction module, a full-scenario set construction module, a system power flow distribution calculation module, a full-scenario static weak link identification module, a full-scenario section limit calculation module, and a full-scenario power constraint construction module: The module for constructing the power balance equation for all scenarios constructs the node admittance matrix of the power grid system and corrects the node admittance matrix based on the non-standard transformer ratio parameters of the transformer branches; and constructs the power balance equation for all scenarios based on the corrected node admittance matrix. The full-scenario set construction module sets load fluctuation range, renewable energy output range and thermal power output range, performs multi-dimensional sampling on the load fluctuation range, renewable energy output range and thermal power output range, generates multiple sets of typical operating modes as the full-scenario set; and uses the full-scenario node power balance equation to identify the extreme operating modes in the full-scenario set. The system power flow distribution calculation module simulates each line and multiple line breakage fault scenarios in each scenario in the full scenario set, and obtains the system power flow distribution after line breakage in the fault scenario. The full-scenario static weak link identification module, based on the system power flow distribution, introduces a power transfer distribution factor to quantify the sensitivity of the line to system power changes, and identifies the full-scenario static weak link. The full-scenario cross-sectional limit calculation module calculates the full-scenario cross-sectional limit of the power grid system based on each of the aforementioned extreme operating modes using an extreme value search algorithm. The full-scenario power constraint construction module outputs the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format, generating a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

[0055] Example 3 The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in Embodiment 1.

[0056] Example 4 The present invention also proposes a terminal, including a processor and a storage medium: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to Embodiment 1.

[0057] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0058] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0059] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0060] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for intelligent verification of power grid static stability limits based on full scenario coverage, characterized in that, include: S1. Construct the node admittance matrix of the power grid system, and correct the node admittance matrix according to the non-standard transformer ratio parameters of the transformer branches; construct the node power balance equation for the whole scenario based on the corrected node admittance matrix. S2, set load fluctuation range, new energy output range and thermal power output range, perform multi-dimensional sampling on the load fluctuation range, new energy output range and thermal power output range to generate multiple sets of typical operating modes as a full scenario set; use the full scenario node power balance equation to identify the extreme operating modes in the full scenario set; S3, in each scenario in the full scenario set, simulate each line and multiple line breakage fault scenarios one by one, and obtain the system power flow distribution after line breakage in the fault scenario; S4. Based on the power flow distribution of the system, a power transfer distribution factor is introduced to quantify the sensitivity of the line to changes in system power and identify the static weak links in the whole scenario. S5. Based on each of the aforementioned extreme operating modes, the extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system; S6, output the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format to generate a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

2. The intelligent verification method for power grid static stability limits based on full scenario coverage as described in claim 1, characterized in that: In S3, the method for identifying the extreme operating mode includes: Calculate the comprehensive feature parameters of each scene group in the full scene set. The specific calculation process is as follows: Calculate the difference between the actual load power and the rated load output of the system in the current scenario, and take the absolute value of the difference; normalize the absolute value using the allowable fluctuation range of the load power to obtain the deviation contribution value of the load dimension; Calculate the difference between the actual output of the new energy source and the rated output value of the new energy source under the current scenario. After taking the absolute value, normalize the absolute value using the maximum fluctuation range of the new energy output to obtain the deviation contribution value of the new energy dimension. Calculate the difference between the actual output of the thermal power unit and the rated output value of the thermal power unit under the current scenario. After taking the absolute value, normalize the absolute value using the adjustable range of the thermal power output to obtain the deviation contribution value of the thermal power dimension. The comprehensive characteristic parameters of the current scenario are obtained by summing the deviation contribution values ​​of the load dimension, the new energy dimension, and the thermal power dimension. ; The comprehensive feature parameters This indicates the overall degree to which the load, new energy sources, and thermal power output deviate from their rated states under the current scenario; Based on all comprehensive feature parameters in the full scene set Thus, the extreme operating mode is obtained.

3. The intelligent verification method for power grid static stability limits based on full scenario coverage as described in claim 2, characterized in that: All comprehensive feature parameters in the entire scene set Sort the data from largest to smallest, and select a percentage (a%) for the comprehensive feature parameters. The first a% scenario is considered as an extreme operating mode.

4. The intelligent verification method for power grid static stability limits based on full scenario coverage as described in claim 1, characterized in that: In S4, based on the system power flow distribution, a power transfer distribution factor is introduced to quantify the sensitivity of the line to system power changes, and to identify the static weak links in the entire scenario. Specific steps include: Based on the system power flow distribution in each scenario of the full scenario set, calculate the full scenario PTDF matrix for each line in the entire network, where PTDF refers to the power transmission allocation factor; The sum of the absolute values ​​of PTDF for each line in the entire scenario is calculated, and the sum of the absolute values ​​of PTDF for each line is used to represent the line sensitivity in the entire scenario. The line sensitivity in the entire scenario of all lines is then ranked. Based on the full-scenario line sensitivity of all lines, the static weak links of the power grid in the entire scenario are located.

5. The intelligent verification method for power grid static stability limits based on full scenario coverage according to claim 1, characterized in that: In S5, based on each of the aforementioned extreme operating modes, an extreme value search algorithm is used to calculate the full-scenario cross-sectional limit of the power grid system. Specific steps include: For each of the aforementioned extreme operating modes, perform N-1 / N-2 fault simulations to obtain the corresponding cross-sectional limits. ,in This represents the cross-sectional limit under the Mth extreme operating mode; Compare the cross-sectional limits under various extreme operating conditions to determine the minimum cross-sectional limit value. With maximum limit value , as the limit for the entire scenario section.

6. The intelligent verification method for power grid static stability limits based on full scenario coverage according to claim 1, characterized in that: The method for determining the full-scenario power constraint expression is as follows: The full-scene cross-sectional limit obtained in step S5 is used as the total cross-sectional power constraint. Based on the total power constraint of the cross section, and combined with the power flow distribution of the system obtained in step S3, calculate the ratio of the power of each line to the total power of the cross section in each scenario, obtain the ratio range of each line, and take the union of the ratio ranges under all scenarios as the allocation coefficient interval of the line. The lower limit and upper limit of the total power constraint of the cross section are multiplied by the lower limit and upper limit of the allocation coefficient interval of each line respectively to obtain the power constraint interval of the line. Based on the inherent safety limits of each line, the power constraint range of the line is adjusted. The total power constraint of the cross section and the power constraint range of the corrected line are used as the power constraint expression for the whole scenario.

7. A smart verification system for power grid static stability limits based on full-scenario coverage, utilizing the method described in any one of claims 1-6, comprising a full-scenario node power balance equation construction module, a full-scenario set construction module, a system power flow distribution calculation module, a full-scenario static weak link identification module, a full-scenario section limit calculation module, and a full-scenario power constraint construction module, characterized in that: The module for constructing the power balance equation for all scenarios constructs the node admittance matrix of the power grid system and corrects the node admittance matrix based on the non-standard transformer ratio parameters of the transformer branches; and constructs the power balance equation for all scenarios based on the corrected node admittance matrix. The full-scenario set construction module sets load fluctuation range, renewable energy output range and thermal power output range, performs multi-dimensional sampling on the load fluctuation range, renewable energy output range and thermal power output range, generates multiple sets of typical operating modes as the full-scenario set; and uses the full-scenario node power balance equation to identify the extreme operating modes in the full-scenario set. The system power flow distribution calculation module simulates each line and multiple line breakage fault scenarios in each scenario in the full scenario set, and obtains the system power flow distribution after line breakage in the fault scenario. The full-scenario static weak link identification module, based on the system power flow distribution, introduces a power transfer distribution factor to quantify the sensitivity of the line to system power changes, and identifies the full-scenario static weak link. The full-scenario cross-sectional limit calculation module calculates the full-scenario cross-sectional limit of the power grid system based on each of the aforementioned extreme operating modes using an extreme value search algorithm. The full-scenario power constraint construction module outputs the full-scenario cross-sectional limit and the full-scenario static weak link in a standardized text format, generating a full-scenario power constraint expression that enables the power grid system to operate safely under fault scenarios.

8. The intelligent verification system for power grid static stability limits based on full scenario coverage according to claim 7, characterized in that: In the full-scenario cross-sectional limit calculation module, the method for determining the full-scenario power constraint expression is as follows: The full-scenario cross-sectional limit is used as the total cross-sectional power constraint; Based on the total power constraint of the cross section and combined with the power flow distribution of the system, the ratio of the power of each line to the total power of the cross section in each scenario is calculated to obtain the ratio range of each line. The union of the ratio ranges under all scenarios is taken as the allocation coefficient interval of the line. The lower limit and upper limit of the total power constraint of the cross section are multiplied by the lower limit and upper limit of the allocation coefficient interval of each line respectively to obtain the power constraint interval of the line. Based on the inherent safety limits of each line, the power constraint range of the line is adjusted. The total power constraint of the cross section and the power constraint range of the corrected line are used as the power constraint expression for the whole scenario.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-6.

10. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-6.

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