Power system multi-scenario key section identification method and system considering flexible resource transmission restriction

Abstract

The application discloses a power system multi-scene key section identification method and system considering flexible resource transmission restriction, relates to the technical field of power system fault detection, and comprises the following steps: based on the operation mode of the power system and new energy prediction data, the power grid power flow after the flexible resource responds to the new energy fluctuation is calculated through an alternating current flow method; the power flow expectation value, power flow overload probability and fault influence index of the branch are calculated based on the power grid power flow, and the key section identification index is generated; according to the key section identification index, all branches are sorted, and the branches at the front of the sorting are selected as the key sections of the power grid. The application is used to solve the problem that the existing method fails to consider the new energy uncertainty and the role of the flexible resource, thereby leading to insufficient accuracy of key section identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system fault detection, and more particularly, to a power system multi-scenario key section identification method and system considering flexible resource transmission constraints. BACKGROUND

[0002] The power flow distribution of the power grid presents strong uncertainty and volatility. In order to ensure the safety and stability of the power grid in a variable environment, the dispatching and planning departments need to identify and monitor the sections that may become bottlenecks in the power grid. However, the existing key section identification methods still have obvious deficiencies.

[0003] On the one hand, traditional methods are mostly based on single operating mode or power flow calculation under typical scenarios for section screening, lacking system characterization of new energy output prediction error and random fluctuations. When the actual output deviates greatly from the prediction, the existing section identification results often fail to accurately reflect the real risk of the power grid. On the other hand, although some methods consider multiple scenarios, they ignore the transmission constraints and adjustment boundaries of flexible resources, and only use idealized models instead in power flow calculation and section evaluation, resulting in large deviations in the identification results and lack of engineering applicability.

[0004] In addition, the existing key section index construction methods are mostly based on a single index, lacking comprehensive evaluation of multi-dimensional risk factors, and difficult to fully reflect the operating risk and fault impact of the branch under multiple scenarios. This not only reduces the reliability of the section identification results, but also may cause some high-risk sections to be missed, increasing the potential risk in power grid dispatching and operation. Especially under the background of increasing cross-regional power transmission and rapid growth of renewable energy installations, the traditional methods have insufficient consideration of uncertainty and flexibility in identifying key sections, and there is an urgent need to propose a key section identification method that can integrate multi-source operating data, consider flexible resource transmission constraints and adapt to new energy uncertainty. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a power system multi-scenario key section identification method considering flexible resource transmission constraints, which introduces new energy prediction and flexible resource transmission constraints to perform power flow calculation and section evaluation under multiple scenarios, to solve the problem that the existing methods fail to consider new energy uncertainty and flexible resource effects, resulting in insufficient accuracy of key section identification.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] The method for identifying the key section of the power system in multiple scenarios considering the flexible resource transmission limit comprises the following steps: based on the operation mode of the power system and the new energy prediction data, the power flow of the power grid after the flexible resource responds to the new energy fluctuation is calculated by an alternating current flow method; the power flow expectation value, the power flow overload probability and the fault influence index of the branch are calculated based on the power flow calculation of the power grid, and the key section identification index is generated; according to the key section identification index, all branches are sorted, and the branches at the front of the sorting are selected as the key sections of the power grid.

[0008] In a preferred embodiment, the calculation of the power flow of the power grid after the flexible resource responds to the new energy fluctuation by the alternating current flow method comprises: performing power flow calculation under the preset operation mode and the predicted condition to obtain the initial power flow distribution of the power grid; based on the new energy prediction data and the historical statistical data, an output uncertainty model is established and a scenario set is generated; the adjusted power flow under each scenario is calculated by taking the scenario set and the initial power flow benchmark of the power grid as inputs, and combining the flexible resource response and the transmission sensitivity.

[0009] In a preferred embodiment, the scenario set acquisition step is as follows: based on the new energy prediction and the historical statistical data, an output uncertainty model is established; and based on the output uncertainty model, a random scenario generation method is used to obtain the uncertainty scenario set.

[0010] In a preferred embodiment, the calculation of the adjusted power flow under each scenario comprises: calculating the power transfer distribution factor of the power grid according to the grid structure parameters of the power system; calculating the flexible resource response capacity of each generator node based on the scenario set; and adjusting the initial power flow distribution of the power grid by combining the power transfer distribution factor and the flexible resource response capacity to obtain the power flow of the power grid.

[0011] In a preferred embodiment, the determination of the response scheme comprises: setting the upper and lower limits of the output of the flexible resource, the ramping constraint and the cross-zone transmission capacity constraint, and allocating the response amount of each node according to the constraints.

[0012] In a preferred embodiment, the key section identification index generation step is as follows: performing probability statistical analysis on the power flow of the power grid under the scenario set to obtain the power flow expectation value and the power flow overload probability of each branch; calculating the fault influence index of the branch based on the branch opening distribution factor; and performing weighted summation on the power flow expectation value, the power flow overload probability and the fault influence index to obtain the key section identification index.

[0013] In a preferred embodiment, the probability statistical analysis is performed in a weighted manner based on the scenario occurrence probability, and when each scenario is generated with equal probability, the weights of the scenarios are the same.

[0014] In a preferred embodiment, the power flow expectation value, power flow overload probability and fault impact index are weighted and summed, and the calculation of the weight value comprises: dimensionless of the power flow expectation, overload risk and fault impact to obtain a set of standardized indexes; calculating the discrimination of each index based on the set of standardized indexes to obtain the weight initial value; normalizing the weight initial value to obtain the weight value used for weighted sum.

[0015] The application provides a power system multi-scenario key section identification system considering flexible resource transmission constraints, comprising: a power flow calculation module, which is used for calculating the power flow of a power grid after flexible resources respond to new energy fluctuations based on the operation mode of the power system and new energy prediction data through an alternating current power flow method; a section index generation module, which is used for generating key section identification indexes by calculating the power flow expectation value, power flow overload probability and fault impact index of branches based on the power flow calculation of the power grid; and a key section screening module, which is used for sorting all branches according to the key section identification indexes and selecting branches at the top of the sorting as the key sections of the power grid.

[0016] A power system multi-scenario key section identification device considering flexible resource transmission constraints, comprising a memory and a processor: the memory is used for storing programs; the processor is used for executing the programs to realize each step of the power system multi-scenario key section identification method considering flexible resource transmission constraints.

[0017] The power system multi-scenario key section identification method considering flexible resource transmission constraints has the following technical effects and advantages:

[0018] The application can comprehensively reflect the influence of new energy output uncertainty on power grid operation by introducing the transmission constraints of flexible resources in power flow calculation under multiple scenarios, improve the accuracy and reliability of key section identification, and effectively support the safe and stable operation of the power grid under the condition of high proportion of new energy access. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The power system multi-scenario key section identification method considering flexible resource transmission constraints provided by the embodiments of the application is shown in the flowchart;

[0020] Figure 2 The wind power daily prediction output curve provided by the embodiments of the application is shown in the schematic diagram;

[0021] Figure 3 The photovoltaic daily prediction output curve provided by the embodiments of the application is shown in the schematic diagram;

[0022] Figure 4 The branch power flow expectation and overload risk comparison schematic diagram provided by the embodiments of the application is shown in the schematic diagram;

[0023] Figure 5A system topology and a key section schematic diagram provided for an embodiment of the present application are shown in the following figure.

[0024] Figure 6 A composition block diagram of a power system multi-scenario key section identification system considering flexible resource transmission restrictions provided for an embodiment of the present application is shown in the following figure.

[0025] Figure 7 A structural block diagram of an exemplary electronic device that can be used to implement an embodiment of the present disclosure is shown in the following figure. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative labor fall within the scope of the present application.

[0027] Embodiment 1, Figure 1 A power system multi-scenario key section identification method considering flexible resource transmission restrictions is provided in the present application, including the following steps:

[0028] S1, based on the operating mode of the power system and the new energy prediction data, the power grid power flow after the flexible resource responds to the new energy fluctuation is calculated through the alternating current flow method.

[0029] S2, based on the power grid power flow, the branch power flow expectation value, power flow overload probability and fault impact index are calculated to generate the key section identification index.

[0030] S3, according to the key section identification index, all branches are sorted, and the branches at the front of the sorting are selected as the key sections of the power grid.

[0031] In this embodiment, the power grid power flow is calculated under multiple scenarios, which can fully consider the new energy output uncertainty and the flexible resource regulation characteristics, and truly reflect the change law of the power grid operating state. Through the comprehensive evaluation of the branch power flow expectation, overload risk and fault impact, a unified and quantitative key section identification index is established to ensure the comparability and accuracy of the results. Further, by sorting and screening the key sections, efficient identification of the key branches affecting the safety and stability of the power grid is realized, thereby providing reliable technical support for operation and dispatching and risk prevention and control.

[0032] S1, based on the operating mode of the power system and the new energy prediction data, the power grid power flow after the flexible resource responds to the new energy fluctuation is calculated through the alternating current flow method.

[0033] It should be noted that the data is derived from the dispatching mechanism to provide power system operation mode data, including node topology, branch parameters, unit output boundary; meteorological department and new energy power station provide wind speed, illumination prediction data, and historical operation data, including load time curve, renewable energy output curve; wherein the operation mode data is used to establish the basic model of power grid power flow calculation, and the new energy prediction data is used to generate an uncertainty scene set.

[0034] In the embodiment, the power grid power flow after the flexible resource responds to the new energy fluctuation is calculated by an alternating current power flow method, including:

[0035] S11, under the preset operation mode and the predicted condition, power flow calculation is performed to obtain the initial power flow distribution of the power grid; specifically, according to the given power system new energy prediction data, load prediction data, and unit output condition, the alternating current power flow method is used to calculate the initial power flow distribution of the power grid under the given power system operation mode;

[0036] The alternating current power flow method of formula (1)-(4) is used to calculate the initial power flow distribution of the power grid under the given power system operation mode, and formula (3) and formula (4) are constraint conditions, and the specific formula is as follows:

[0037] (1)

[0038] (2)

[0039] (3)

[0040] (4)

[0041] In the above formula, P ij and Q ij are the active and reactive power flows of branch ij. v i is the voltage amplitude of bus i, θ ij is the voltage phase angle difference of bus i and bus j. B ij is the susceptance of branch ij, and G ij is the conductance of branch ij. B ii and G ii are the self-susceptance and self-conductance of node i, respectively. is the set of generators connected to node i, is the set of new energies connected to node i, is the set of branches connected to node i, is the set of loads connected to node . is the active power output of generator g, The reactive power emitted by the generator g. The active power emitted by the new energy r, The reactive power emitted by the generator r. , The node The parallel conductance and susceptance parameters P d is the active load power of the load node d; Q d is the active load power of the load node d.

[0042] S12, based on new energy prediction data and historical statistical data, an output uncertainty model is established and a scene set is generated;

[0043] The establishment of the output uncertainty model is specifically:

[0044] According to the prediction data and historical statistical data of each new energy power station of the power system, a new energy multivariate normal distribution probability model is constructed as shown in formula (5), and a correlation coefficient matrix is calculated as shown in formula (6):

[0045] (5)

[0046] (6)

[0047] In the formula: is a multivariate random variable distribution function representing the output of each new energy power station as an output uncertainty model of the power system, is the number of new energy power stations, is a random variable vector of the output of each new energy power station, is the predicted value of the output of each new energy power station, and are column vectors with a dimension of . is a correlation coefficient matrix with a dimension of , indicating the covariance relationship between the outputs of each new energy, describing the correlation and variance of the output uncertainty of the new energy, and ρ lk is the correlation coefficient between the outputs of new energy power stations l and k.

[0048] The generation of the scene set is specifically: through the output uncertainty model, the multivariate normal distribution of the output of each new energy power station is generated, and based on the multivariate normal distribution, U scenes are generated by Monte Carlo sampling .

[0049] S13, taking the scene set and the initial power flow distribution of the power grid as inputs, the adjusted power flow of the power grid under each scene is calculated, specifically:

[0050] Firstly, the power transfer distribution factors of the power grid are calculated according to the grid structure parameters by formula (7) and formula (8), which represent the branch power flow change caused by the change of node power transmission:

[0051] (7)

[0052] (8)

[0053] In the formula: is the power distribution factor matrix of the power grid structure, the dimension is , is the number of branches of the grid structure, is the total number of generator nodes, including new energy units and conventional units. is the power distribution factor of branch l corresponding to generator node k, and are the node numbers at the beginning and end of branch l respectively; is the actual reactance value of branch l, is the inverse matrix of the grid admittance matrix i (l) element in the k row and column.

[0054] Secondly, the flexible resource response capacity of each generator node is calculated by formula (9) and formula (10) for the new energy output uncertainty scenario, and the flexible resource adjustment vector corresponding to the u th new energy uncertainty scenario is represented as :

[0055] (9)

[0056] (10)

[0057] In the formula: is the adjustment coefficient vector representing the adjustment freedom of each generator node.

[0058] Finally, the power flow of the power grid after the power deviation caused by the response of the flexible resource to the fluctuation of the new energy is calculated by formula (11):

[0059] (11)

[0060] In the formula: is the active power flow constituted by the initial power flow vector of the power grid.

[0061] This step introduces the uncertainty of new energy prediction and flexible resource constraints on the basis of the power grid's benchmark operation mode to establish a multi-scenario power flow calculation model, which can accurately reflect the changing patterns of power flow distribution in different operating scenarios, thereby providing comprehensive and reliable data support for subsequent identification of key sections.

[0062] S2 generates key section identification indicators based on the expected power flow value, power flow overload probability, and fault impact index of the power grid power flow calculation branch.

[0063] In this embodiment, the steps for generating the key cross-section identification index are as follows:

[0064] S21, using equations (12)-(14) to perform probabilistic statistical analysis on power flow in the scenario set, to obtain the expected power flow value and power flow overload probability of each branch; the probabilistic statistical analysis is performed using a weighted method based on the probability of scenario occurrence, and when each scenario is generated with equal probability, the weight of each scenario is the same.

[0065] (12)

[0066] (13)

[0067] (14)

[0068] In the formula: For an uncertain scenario u, branch l Apparent power; branch road l Power grid flow; branch road l The initial reactive current; branch road l The rated transmission capacity; This is a function that evaluates to 1 if a condition is met, and 0 otherwise. and Branch roads l Trend expectation and trend overload probability.

[0069] S22, using equations (15) and (16) based on the branch interruption distribution factor, calculate the branch fault impact index to characterize the degree of impact of other branch faults on its power flow:

[0070] (15)

[0071] (16)

[0072] In the formula: The branch distribution interruption factor characterizes the fault in branch k, due to the influence of power flow transfer on branch l; and the reactance value of line k and line l; the element of the system network reactance matrix in the row and the column; the branch fault influence index of branch l.

[0073] S23, weighting and summing the power flow expectation value, the power flow overload probability and the fault influence index by using formula (17) to obtain the key section identification index:

[0074] (17)

[0075] In the formula: the key section identification index of branch l , , and are weight coefficients of each index respectively.

[0076] The weighting and summing of the power flow expectation value, the power flow overload probability and the fault influence index, the calculation of the weight value includes:

[0077] The power flow expectation value, the power flow overload probability and the fault influence index are dimensionless, and a standardized index set is obtained; specifically, the monitored branch set participating in ranking is , and the number of rows is . For any index vector (corresponding to , and ), interval scaling method is used for dimensionless, and the formula is:

[0078] (18)

[0079] Wherein is a small amount to prevent the denominator from being zero, and may be taken in implementation; and the standardized index set is obtained.

[0080] The discrimination degree of each index is calculated based on the standardized index set, and the weight initial value is obtained; specifically, the information entropy-discrimination degree method is used to calculate the weight initial value. For any index , the normalized proportion is calculated (if the denominator is zero, the value of is taken for all branches of the index), and the information entropy is:

[0081] (19)

[0082] The discrimination degree is:

[0083]

[0084] The weight initial value is normalized to obtain the weight used in the weighted summation. The formula is as follows:

[0085] (20)

[0086] Thus, the weighted summation formula of is obtained.

[0087] This step comprehensively describes the average load level, over-limit risk and fault cascading impact of the branch by unifying the multi-scenario weighted statistics and the breaking impact sensitivity analysis, forms a key section identification index that is measurable, comparable and unified in scale, and provides quantitative basis and reliability guarantee for subsequent sorting and selection of key sections according to the index.

[0088] S3, according to the key section identification index, all branches are sorted, and the branches at the top of the sorting are selected as the key sections of the power grid.

[0089] It should be noted that the key section refers to a branch set that has a significant impact on the safety and stability of the power grid under multi-scenario operating conditions, and is selected based on the key section identification index calculated in step S2.

[0090] In this embodiment, the key section selection step is as follows:

[0091] The key section identification index of each branch obtained in step S2 is collected to form a set , wherein is the set of monitored branches participating in the evaluation, and the total number of branches is .

[0092] The index values of each branch in the set are sorted in descending order, and the sorted sequence is recorded as:

[0093]

[0094] , wherein represents the number of the branch after sorting.

[0095] Finally, a proportion threshold is set, and the top branches in the sorting are selected as the key sections. The formula is as follows:

[0096]

[0097] , wherein is the set of key sections. The proportion threshold ​​The value of the branch criticality index can be set according to scheduling or safety analysis requirements, for example indicates that the top 10% of branches are selected as the key section.

[0098] This step can accurately identify the key section affecting the safety of the power grid while ensuring the calculation efficiency by sorting the branch criticality index and extracting the top several high-risk branches, thereby providing a reliable basis for subsequent safety evaluation and operation decision.

[0099] In order to verify the applicability and effectiveness of the method, a reduced example system of a provincial power grid is selected as the research object, and the method described in Embodiment 1 is used for analysis. The system includes 39 nodes, 46 branches, 10 conventional units, 6 wind power stations and 4 photovoltaic power stations, and additionally configures 2 pumped storage power stations and 1 battery energy storage power station as flexible resources.

[0100] 1) Example scenario description

[0101] In the example, the operation mode data provided by the scheduling institution includes node topology, branch parameters and unit output boundary, the predicted output of the new energy station is given by the meteorological department based on the intra-day prediction, and the error statistical data is derived from the operation history in the past year.

[0102] Load curve: the typical load time sequence of the summer peak day is selected, and the peak load is 25 GW.

[0103] Wind power prediction: installed capacity 5 GW, prediction curve as shown in Figure 2 ; the prediction error obeys the normal distribution with zero mean, and the standard deviation is 10%.

[0104] Photovoltaic prediction: installed capacity 3 GW, prediction curve as shown in Figure 3 ; the prediction error obeys the Beta distribution Beta(2,5).

[0105] Flexible resource parameters: see Table 1.

[0106] Table 1

[0107]

[0108] The generation of the scenario set adopts the Monte Carlo method, sets the number of scenarios , samples according to the probability model of wind power and photovoltaic power to obtain the new energy output scenario set . Table 2 shows 10 scenarios, the system includes 6 wind power stations (W1-W6, total installed capacity 5 GW) and 4 photovoltaic power stations (PV1-PV4, total installed capacity 3 GW). The numerical unit is MW, all scenarios are equally probable, and Pk is set to 0.002.

[0109] Table 2

[0110]

[0111] 2) Generation of key sections

[0112] According to the step S11 of the embodiment 1, the AC power flow calculation is performed on the reference operation mode to obtain the initial power flow distribution of the power grid.

[0113] The node net injection deviation under 500 scenarios is obtained by using the scenario generation method of the step S12, and the total injection deviation under each scenario is constructed in combination with the flexible resource mapping matrix.

[0114] In the step S13, the branch power flow variation caused by the node power transmission variation is obtained, and the initial power flow distribution of the power grid is adjusted in combination with the total injection deviation under each scenario to obtain the final power flow result of each scenario.

[0115] Subsequently, according to the step S2 of the embodiment 1, the weighted statistics is performed on the power flow results of each scenario to obtain the branch power flow expectation and overload risk; the branch outage distribution factor is used for outage influence analysis to obtain the branch fault influence index; finally, the information entropy method is used to calculate the weight and weighted sum to generate the key section identification index.

[0116] In the step S3, the index is sorted in descending order, and the proportion threshold is set to select the top 10% of the branches as the key sections.

[0117] 3) Result comparison and analysis

[0118] Figure 4 The power flow expectation value and overload risk of part of the branches are compared, and it can be seen that the load level of part of the cross-regional tie lines is obviously increased under the high proportion of new energy access scenarios, and the overload risk is significantly increased.

[0119] Table 3 gives the top 5 key section identification results.

[0120] Table 3

[0121]

[0122] As can be seen from Table 3, the method can effectively identify the branches with high power flow level, large overload risk and strong fault influence under multiple scenarios. Compared with the traditional single-scenario power flow analysis, the method more comprehensively reflects the influence of new energy output uncertainty and flexible resource adjustment on the power grid operation.

[0123] Figure 5A topology diagram of the simulation system is provided, where the dots represent the power grid buses (i.e., nodes), and the numbers correspond one-to-one with the bus numbers in the simulation network data; the lines represent transmission branches between buses, and the thicker the line, the more critical the branch is identified as a critical section in step S3. As can be seen from the diagram, critical sections are mainly concentrated in inter-regional corridors (such as buses 3-8, 4-9, and 5-10) and main corridors (such as buses 3-4 and 4-5). These areas bear significant power flow transfers and are highly sensitive to fluctuations in renewable energy output; once a fault or output deviation occurs, it can easily cause a cascading overload. Figure 5 The distribution can be visually verified. The method of this invention can effectively identify channels in the power grid with tight carrying capacity and prominent operational risks, providing a visual reference for scheduling and planning.

[0124] Example 3, Figure 6 A multi-scenario critical section identification system for power systems considering flexible resource transmission constraints is presented, including:

[0125] The power flow calculation module is used to calculate the power grid power flow after the flexible resource response to new energy fluctuations based on the power system's operating mode and new energy forecast data, using AC power flow methods.

[0126] The cross-section index generation module is used to generate key cross-section identification indicators by assessing the power flow expectation, overload risk and fault impact of the branch through power grid power flow evaluation.

[0127] The critical section screening module is used to sort all branches according to the critical section identification index and select the branches with the highest ranking as the critical sections of the power grid.

[0128] Example 4,

[0129] A power system critical section identification device considering flexible resource transmission constraints, such as Figure 7 As shown, it includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement any of the embodiments in Example 1.

[0130] Since the power system multi-scenario critical section identification device considering flexible resource transmission constraints described in this embodiment is the device used to implement the method in Embodiment 1 of this invention, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in Embodiment 1 of this application. Therefore, how the electronic device implements the method in the embodiments of this application will not be described in detail here. Any device used by those skilled in the art to implement the method in the embodiments of this application falls within the scope of protection of this application.

[0131] The above formulas are all de-dimensioned to calculate the numerical values, the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters in the formulas are set by a person skilled in the art according to the actual situation.

[0132] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product.

[0133] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0134] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0135] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0136] Finally, the above is only the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints, characterized in that, Includes the following steps: Based on the power system's operating mode and new energy forecast data, the power grid flow after the flexible resource response to new energy fluctuations is calculated using the AC power flow method. Based on the expected power flow value, power flow overload probability and fault impact index of the power grid power flow calculation branch, key section identification index is generated. Based on the key section identification indicators, all branches are ranked, and the top-ranked branches are selected as the key sections of the power grid. The steps for generating the key cross-section identification indicators are as follows: Probabilistic statistical analysis of power flow in the scenario set is performed to obtain the expected power flow value and power flow overload probability of each branch. Based on the branch failure distribution factor, calculate the branch fault impact index; The key section identification index is obtained by weighted summation of the expected value of power flow, the probability of power flow overload and the failure impact index. The weight calculation for the weighted summation includes: The expected value of power flow, the probability of power flow overload, and the indicators of fault impact are dimensionless to obtain a standardized set of indicators. The discrimination of each indicator is calculated based on the standardized indicator set to obtain the initial weight values; Normalize the initial weights to obtain the weights used for weighted summation.

2. The method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints as described in claim 1, characterized in that, The calculation of power flow after flexible resource response to new energy fluctuations using the AC power flow method includes: Power flow calculations are performed under preset operating conditions and predicted conditions to obtain the initial power flow distribution of the power grid. Based on new energy forecast data and historical statistical data, an output uncertainty model is established and a set of scenarios is generated; Using the scenario set and the initial power flow distribution of the power grid as input, the adjusted power flow of the power grid under each scenario is calculated.

3. The method for identifying key sections of power systems in multiple scenarios considering flexible resource transmission constraints according to claim 2, characterized in that, The steps for obtaining the scene set are as follows: Based on new energy forecast data and historical statistical data, an output uncertainty model is established; Based on the power output uncertainty model, a random scenario generation method is used to obtain a set of uncertain scenarios.

4. The method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints as described in claim 3, characterized in that, The steps for calculating the adjusted power flow under each scenario are as follows: Calculate the power transfer distribution factor of the power grid based on the power system grid structure parameters; Based on the scenario set, calculate the flexible resource response capacity of each generator node; By combining the power transfer distribution factor and the flexible resource response capacity to adjust the initial power flow distribution of the power grid, the power flow of the power grid is obtained.

5. The method for identifying key sections of power systems in multiple scenarios considering flexible resource transmission constraints according to claim 4, characterized in that, The probability statistical analysis is performed using a weighted method based on the probability of scenario occurrence. When each scenario is generated with equal probability, the weight of each scenario is the same.

6. An apparatus for identifying critical sections of a power system in multiple scenarios, considering flexible resource transmission constraints, as described in any one of claims 1-5, characterized in that, include: The power flow calculation module is used to calculate the power grid power flow after the flexible resource response to new energy fluctuations based on the power system's operating mode and new energy forecast data, using AC power flow methods. The cross-section index generation module is used to generate key cross-section identification indicators based on the power flow expectation value, power flow overload probability and fault impact index of the branch based on the power flow calculation. The critical section screening module is used to sort all branches according to the critical section identification index and select the branches with the highest ranking as the critical sections of the power grid.

7. A power system multi-scenario critical section identification device considering flexible resource transmission constraints, characterized in that, Including memory and processor: The memory is used to store programs; The processor is used to execute the program to implement the method for identifying key sections of power systems in multiple scenarios, taking into account the constraints of flexible resource transmission, as described in any one of claims 1-5.

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