Power system multi-scene key section identification method and system considering flexible resource transmission limitation

Abstract

The invention discloses a power system multi-scene key section identification method and system considering flexible resource transmission limitation, and relates to the technical field of power system fault detection. Calculating power grid power flow after flexible resources respond to new energy fluctuation through an alternating current power flow method; generating a key section identification index based on a power flow expected value, a power flow overload probability and a fault influence index of a power grid power flow calculation branch; and sorting all branches according to the key section identification index, and selecting the branch with the top sorting as the key section of the power grid. The method is used for solving the problem that the key section recognition accuracy is insufficient because the uncertainty of new energy and the flexible resource effect are not considered in the existing method.

Description

Technical Field

[0001] This invention relates to the field of power system fault detection technology, and more specifically, to a method and system for identifying critical sections of power systems in multiple scenarios, taking into account the constraints of flexible resource transmission. Background Art

[0002] The power flow distribution of power grids exhibits strong uncertainty and volatility. To ensure the safe and stable operation of the power grid under changing environments, dispatching and planning departments need to identify and monitor sections of the power grid that may become bottlenecks. However, existing methods for identifying critical sections still have significant shortcomings.

[0003] On the one hand, traditional methods often rely on power flow calculations under a single operating mode or typical scenario for cross-section selection, lacking a systematic characterization of new energy output prediction errors and random fluctuations. When the actual output deviates significantly from the prediction, existing cross-section identification results often become invalid, failing to accurately reflect the true risks of the power grid. On the other hand, while some methods consider multiple operating scenarios, they neglect the transmission limitations and adjustment boundaries of flexible resources, relying solely on idealized models in power flow calculations and cross-section assessments, leading to significant biases in the identification results and a lack of engineering applicability.

[0004] Furthermore, existing methods for constructing key section indicators often rely on single indicators, lacking a comprehensive evaluation of multi-dimensional risk factors and failing to fully reflect the operational risks and fault impacts of branches under various scenarios. This not only reduces the reliability of section identification results but may also lead to the omission of some high-risk sections, increasing potential risks in power grid dispatching and operation. Especially against the backdrop of continuously increasing inter-regional power transmission ratios and rapid growth in renewable energy installed capacity, the problem of traditional methods failing to adequately consider both uncertainty and flexibility in identifying key sections is becoming increasingly prominent. There is an urgent need to propose a key section identification method that can integrate multi-source operational data, consider flexible resource transmission constraints, and adapt to the uncertainties of new energy sources. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for identifying critical sections of power systems in multiple scenarios that takes into account the constraints of flexible resource transmission. By introducing new energy prediction and flexible resource transmission constraints, power flow calculation and section evaluation are performed in multiple scenarios to solve the problem that existing methods fail to take into account both the uncertainty of new energy sources and the role of flexible resources, resulting in insufficient accuracy in identifying critical sections.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for identifying critical sections in power systems under flexible resource transmission constraints in multiple scenarios includes the following steps: Based on the power system's operating mode and renewable energy forecast data, the power flow of the grid after the flexible resource response to renewable energy fluctuations is calculated using an AC power flow method; Based on the power flow, the expected power flow value, power flow overload probability, and fault impact index of the branches are calculated to generate critical section identification indicators; Based on the critical section identification indicators, all branches are ranked, and the branches with the highest ranking are selected as critical sections of the power grid.

[0007] In a preferred embodiment, the calculation of the power grid flow after flexible resource response to new energy fluctuations using the AC power flow method includes: performing power flow calculation under preset operating conditions and forecast conditions to obtain the initial power flow distribution of the power grid; establishing an output uncertainty model and generating a scenario set based on new energy forecast data and historical statistical data; and calculating the adjusted power grid flow under each scenario by taking the scenario set and the initial power flow benchmark of the power grid as input, combined with flexible resource response and transmission sensitivity.

[0008] In a preferred embodiment, the scenario set acquisition steps are as follows: based on new energy forecasts and historical statistical data, an output uncertainty model is established; based on the output uncertainty model, a random scenario generation method is used to obtain an uncertain scenario set.

[0009] In a preferred embodiment, 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; calculate the flexible resource response capacity of each generator node based on the scenario set; and adjust 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.

[0010] In a preferred embodiment, determining the response scheme includes: setting upper and lower limits for the output of flexible resources, ramping constraints, and inter-regional transmission capacity constraints, and allocating the response amount of each node accordingly.

[0011] In a preferred embodiment, the key section identification index generation steps are as follows: performing 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; calculating the branch fault impact index based on the branch interruption distribution factor; and performing a weighted summation of the expected power flow value, power flow overload probability, and fault impact index to obtain the key section identification index.

[0012] In a preferred embodiment, the probability statistical analysis is performed using a weighted method based on the probability of scenario occurrence, where each scenario has the same weight when all scenarios are generated with equal probability.

[0013] In a preferred embodiment, the weighted summation of the expected power flow value, the probability of power flow overload, and the failure impact index includes the following steps: dimensionlessizing the expected power flow value, the overload risk, and the failure impact to obtain a standardized index set; calculating the discriminative power of each index based on the standardized index set to obtain initial weight values; and normalizing the initial weight values ​​to obtain the weights used for the weighted summation.

[0014] This invention provides a multi-scenario critical section identification system for power systems that considers flexible resource transmission constraints, comprising: a power flow calculation module, used to calculate the power flow of the grid after flexible resource response to new energy fluctuations based on the power system's operating mode and new energy forecast data, using an AC power flow method; a section index generation module, used to generate critical section identification indicators based on the power flow calculation of branch expected values, power flow overload probabilities, and fault impact indicators; and a critical section screening module, used to sort all branches according to the critical section identification indicators and select the branches with the highest ranking as critical sections of the power grid.

[0015] A power system multi-scenario critical section identification device considering flexible resource transmission constraints includes a memory and a processor: the memory is used to store a program; the processor is used to execute the program to implement the various steps of the power system multi-scenario critical section identification method considering flexible resource transmission constraints.

[0016] The technical effects and advantages of this invention's method for identifying critical sections in multiple scenarios of power systems, considering flexible resource transmission constraints, are as follows: This invention, by introducing flexible resource transmission constraints in multiple scenarios for power grid flow calculation, can comprehensively reflect the impact of uncertainties in renewable energy output on power grid operation, improve the accuracy and reliability of key section identification, and thus effectively support the safe and stable operation of the power grid under conditions of high proportion of renewable energy access. Attached Figure Description

[0017] Figure 1 A schematic diagram of the process for identifying key sections of power systems in multiple scenarios, taking into account flexible resource transmission constraints, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the intraday predicted power output curve of wind power provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the photovoltaic intraday predicted power output curve provided in an embodiment of the present invention; Figure 4 A schematic diagram comparing branch power flow expectation and overload risk provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the system topology and key cross-sections provided in an embodiment of the present invention; Figure 6A block diagram of a power system multi-scenario critical section identification system considering flexible resource transmission constraints provided in an embodiment of the present invention; Figure 7 This is a structural block diagram of an exemplary electronic device provided for implementing embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1, Figure 1 This invention presents a method for identifying critical sections of power systems in multiple scenarios, taking into account flexible resource transmission constraints. The method includes the following steps: S1, based on the power system's operating mode and new energy forecast data, calculates the power grid flow after flexible resource response to new energy fluctuations using the AC power flow method; S2, based on the expected value of power flow, power flow overload probability and fault impact index of the power flow calculation branch, generates key section identification index; S3. Based on the key section identification index, all branches are sorted, and the branches with the highest ranking are selected as the key sections of the power grid.

[0020] This embodiment calculates power flow in multiple scenarios, fully considering the uncertainty of new energy output and the flexible resource adjustment characteristics, and truly reflects the changing patterns of power grid operation. By comprehensively evaluating branch power flow expectations, overload risks, and fault impacts, it establishes unified and quantitative key section identification indicators to ensure the comparability and accuracy of the results. Furthermore, by ranking and screening key sections based on the indicators, it achieves efficient identification of key branches affecting power grid security and stability, thereby providing reliable technical support for operation scheduling and risk prevention and control.

[0021] S1, based on the power system's operating mode and new energy forecast data, calculates the power grid flow after new energy fluctuations in response to flexible resource changes using the AC power flow method.

[0022] It should be noted that the data comes from power system operation mode data provided by the dispatching agency, including node topology, branch parameters, and unit output boundaries; wind speed and solar radiation forecast data provided by meteorological departments and new energy power plants; and historical operation data, including load time series curves and renewable energy output curves. Among them, the operation mode data is used to establish the basic model for power grid flow calculation, and the new energy forecast data is used to generate uncertainty scenario sets.

[0023] In this embodiment, the calculation of the power grid flow after flexible resource response to new energy fluctuations using the AC power flow method includes: S11, Perform power flow calculation under preset operating mode and forecast conditions to obtain the initial power flow distribution of the power grid; specifically: based on the given power system new energy forecast data, load forecast data, and unit output, use the AC power flow method to calculate the initial power flow distribution of the power grid under the given power system operating mode; The method of calculating the initial power flow distribution of the power grid under a given power system operation mode using the AC power flow method of formulas (1)-(4) uses formulas (3) and (4) as constraints, and the specific formulas are as follows: (1) (2) (3) (4) In the above formula, P ij and Q ij For the active and reactive power flows of branch ij. i The voltage magnitude of bus i, θ ij Let B be the voltage phase angle difference between bus i and bus j. ij For the susceptance of branch ij, G ij Let B be the conductance of branch ij. ii and G ii Let be the self-susceptance and self-conductance of node i, respectively. for The set of generators connected to the node. for The set of new energy sources connected to the node for The set of branches connected to a node For nodes The set of connected loads. Let g be the active power generated by generator g. Let g be the reactive power generated by the generator. The active power generated by new energy sources. This represents the reactive power generated by generator r. , Represents a node The parallel conductance susceptance parameter, P d Q is the active power of load node d; d It is the active load power of load node d.

[0024] S12, Based on new energy forecast data and historical statistical data, establish an output uncertainty model and generate a set of scenarios; The establishment of the output uncertainty model specifically involves: Based on the predicted data and historical statistics of various new energy power plants in the power system, a multivariate normal distribution probability model for new energy is constructed as shown in Equation (5), and the correlation coefficient matrix is ​​calculated as shown in Equation (6): (5) (6) In the formula: To characterize the multivariate random variable distribution function of the power output of each new energy power plant as a model for the uncertainty of power system output, The number of new energy power plants, The vector of random variables representing the power output of each new energy power station. The predicted power output of each new energy power station and All are of dimension Column vectors; for The correlation coefficient matrix of the dimension represents the covariance relationship among the power outputs of various new energy sources, describing the correlation and variance of the uncertainty of new energy power output, ρ lk This is the correlation coefficient between the outputs l and k of the new energy power plant.

[0025] The generated scenario set is specifically generated by: generating a multivariate normal distribution of the power output of each new energy power station through a power output uncertainty model, and generating U scenarios based on the multivariate normal distribution using Monte Carlo sampling. .

[0026] S13, taking the scenario set and the initial power flow distribution of the power grid as input, calculates the adjusted power flow for each scenario, specifically: First, using equations (7) and (8), based on the power system grid structure parameters, the power transfer distribution factor of the power grid is calculated to characterize the change in branch power flow caused by changes in node power transmission: (7) (8) In the formula: The power distribution factor matrix of the power system grid has a dimension of . , The number of branches of the space frame, The total number of generator nodes includes both new energy units and conventional units. Let be the power distribution factor of branch l corresponding to generator node k. and These are the node numbers at the beginning and end of branch l, respectively; The actual reactance value of branch l, The inverse matrix of the grid susceptance matrix is ​​the first... i (l) Line number k Column elements.

[0027] Secondly, for scenarios with uncertain new energy output, the flexible resource response capacity of each generator node is calculated using equations (9) and (10). u The flexible resource adjustment vector corresponding to each uncertain new energy scenario is represented as follows: : (9) (10) In the formula: This is a vector representing the adjustable coefficient of each generator node.

[0028] Finally, the power flow after the power deviation caused by the fluctuation of new energy sources due to flexible resource response is calculated using equation (11): (11) In the formula: For meritorious trends The initial power flow vector of the power grid.

[0029] 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.

[0030] 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.

[0031] In this embodiment, the steps for generating the key cross-section identification index are as follows: 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.

[0032] (12) (13) (14) 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.

[0033] 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: (15) (16) 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 Let K be the reactance values ​​of line k and line l; For the system network reactance matrix, the first Line number Element; The branch fault impact index is for branch l.

[0034] S23, using equation (17) to perform a weighted summation of the expected power flow value, power flow overload probability, and fault impact index, the key section identification index is obtained: (17) In the formula: branch road l Key cross-section identification indicators , and These are the weighting coefficients for each indicator.

[0035] The weighted summation of the expected power flow value, power flow overload probability, and fault impact index includes the following weighting calculation: The expected value of power flow, the probability of power flow overload, and the indicators of fault impact are dimensionless to obtain a standardized indicator set; specifically, let the set of monitored branches participating in the ranking be... The number of items is For any index vector (can be matched) , and One method is to use the interval scaling method for dimensionless transformation, with the following formula: (18) in To prevent small amounts with a denominator of zero, it is advisable to take the following in practice: ; Obtain a standardized indicator set .

[0036] The initial weights are obtained by calculating the discriminative power of each indicator based on a standardized indicator set; specifically, the initial weights are calculated using the information entropy-discriminative power method. For any indicator... First calculate the normalization ratio (If the denominator is zero, then take all branches of the index) The information entropy is: (19) Discrimination level is:

[0037] The initial weights are normalized to obtain the weights used for the weighted summation. The formula is as follows: (20) Thus obtain Substitute into equation (17) The weighted summation formula.

[0038] This step, within a unified framework of multi-scenario weighted statistics and sensitivity analysis of the impact of interruption, comprehensively characterizes the average load level, over-limit risk, and cascading effects of faults in the branch, forming measurable, comparable, and scale-uniform key section identification indicators. This provides a quantitative basis and reliability guarantee for subsequent selection of key sections by ranking according to the indicators.

[0039] S3. Based on the key section identification index, all branches are sorted, and the branches with the highest ranking are selected as the key sections of the power grid.

[0040] It should be noted that the critical section refers to the set of branches that have a significant impact on the safety and stability of the power grid under multiple operating conditions, and its selection is based on the critical section identification index calculated in step S2.

[0041] In this embodiment, the key section selection step is as follows: Collect the key section identification indicators of each branch obtained in step S2 , forming a set ,in The set of monitored branches participating in the evaluation, the total number of branches is .

[0042] Sort the index values ​​of each branch in the set in descending order, and denote the sorted sequence as:

[0043] in This indicates the branch number after sorting.

[0044] Finally, set the ratio threshold. Select the ones that appear earlier in the sorting list. The branch road is designated as the critical section. The formula is:

[0045] in This is the set of key cross-sections. Proportional threshold. The value can be set according to scheduling or security analysis requirements, for example... This indicates that the top 10% of the branch roads are selected as the key sections.

[0046] This step, by ranking the key indicators of branches and extracting the top few high-risk branches, can accurately identify key sections affecting power grid safety while ensuring computational efficiency, providing a reliable basis for subsequent safety assessments and operational decisions.

[0047] Example 2: To verify the applicability and effectiveness of the method of the present invention, a reduced-scale example system of a provincial power grid was selected as the research object, and the method described in Example 1 was used for analysis. The system includes 39 nodes, 46 branches, 10 conventional units, 6 wind farms and 4 photovoltaic power stations, and is also equipped with 2 pumped storage power stations and 1 battery energy storage power station as flexible resources.

[0048] 1) Description of the case study scenario In the example, the operation mode data provided by the dispatching agency includes node topology, branch parameters and unit output boundaries. The predicted output of the new energy power station is given by the meteorological department based on intraday forecasts, and the error statistics are derived from the operation history of the past year.

[0049] Load curve: The typical load sequence of the summer peak day is selected, with a peak load of 25GW.

[0050] Wind power forecast: 5GW installed capacity, forecast curve as shown Figure 2 As shown; the prediction error follows a zero-mean normal distribution with a standard deviation of 10%.

[0051] Photovoltaic forecast: 3GW of installed capacity, forecast curve as shown Figure 3 As shown; the prediction error follows a Beta distribution Beta(2,5).

[0052] Flexible resource parameters: see Table 1.

[0053] Table 1

[0054] The scene set is generated using the Monte Carlo method, with a set number of scenes. Based on probability models for wind power and solar power, a set of new energy output scenarios was obtained through sampling. Table 2 shows 10 scenarios, including 6 wind farms (W1–W6, total installed capacity 5GW) and 4 photovoltaic farms (PV1–PV4, total installed capacity 3GW). All values ​​are in MW, all scenarios are equally probable, and Pk is set to 0.002.

[0055] Table 2

[0056] 2) Power flow calculation and critical section generation Following step S11 of Example 1, AC power flow calculation is performed on the baseline operating mode to obtain the initial power flow distribution of the power grid.

[0057] Using the scene generation method in step S12, the net injection deviation of nodes in 500 scenes is obtained, and the total injection deviation in each scene is constructed by combining the flexible resource mapping matrix.

[0058] In step S13, the branch power flow change caused by the node power transmission change 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 for each scenario.

[0059] Subsequently, following step S2 of Example 1, weighted statistics are performed on the power flow results of each scenario to obtain the expected power flow and overload risk of each branch; the branch interruption distribution factor is used to perform interruption impact analysis to obtain the branch fault impact index; finally, the information entropy method is used to calculate the weights and perform weighted summation to generate the key section identification index.

[0060] In step S3, the indicators are sorted in descending order, and a proportional threshold is set. The top 10% of the branch roads were selected as the key sections.

[0061] 3) Results Comparison and Analysis Figure 4 The expected power flow and overload risk of some branch lines are compared. It can be seen that the load level of some inter-regional interconnection lines increases significantly and the overload risk increases significantly under the scenario of high proportion of new energy access.

[0062] Table 3 shows the identification results of the top 5 key cross sections.

[0063] Table 3

[0064] As shown in Table 3, this method can effectively identify branches with high power flow levels, high overload risk, and strong fault impact in multiple scenarios. Compared with traditional single-scenario power flow analysis, it more comprehensively reflects the impact of uncertainty in renewable energy output and flexible resource adjustment on power grid operation.

[0065] Figure 5 A 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.

[0066] Example 3, Figure 6 A multi-scenario critical section identification system for power systems considering flexible resource transmission constraints is presented, including: 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 by assessing the power flow expectation, overload risk and fault impact of the branch through power grid power flow evaluation. 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.

[0067] Example 4, 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.

[0068] 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.

[0069] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0070] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0071] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0074] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints, characterized in that, The following steps are involved: 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.

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 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; By weighting and summing the expected value of the power flow, the probability of power flow overload, and the failure impact index, the key section identification index is obtained.

6. The method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints as described in claim 5, 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.

7. The method for identifying critical sections of power systems in multiple scenarios considering flexible resource transmission constraints as described in claim 6, characterized in that, The weighted summation of the expected power flow value, power flow overload probability, and fault impact index includes the following weighting calculation: 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.

8. 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 1-7, 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.

9. 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 each step of the method for identifying key sections of power systems in multiple scenarios, taking into account the flexible resource transmission constraints, as described in any one of claims 1-8.

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