Section safety margin dynamic evaluation method and device

Abstract

The invention provides a section safety margin dynamic evaluation method and device, and relates to the technical field of new energy. The method comprises the following steps: acquiring power operation data in a power transmission section; determining a new energy prediction scene and a current operation mode set according to the power operation data; discretizing the new energy prediction scene to obtain a plurality of new energy uncertain scene set data; obtaining a section probabilistic power flow set according to the multiple new energy uncertain scene set data; and according to the section probabilistic power flow set, optimizing a corresponding section limit value in the current operation mode set to obtain a safety margin evaluation result of the power transmission section. The method does not depend on an uninterpretable non-convex nonlinear model or a complex uncertainty quantization technology. Based on definite mathematical calculation and logic processes, the decision transparency and reliability can be improved, the influence of artificial intelligence reasoning errors is reduced, and the final evaluation result is more accurate and reliable.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a method and apparatus for dynamic evaluation of cross-sectional safety margin. Background Technology

[0002] With the integration of a high proportion of renewable energy into modern power systems, the complexity and randomness of system stability boundaries have increased significantly. Independent System Operators (ISOs) find it difficult to fully incorporate complex stability constraints into their operational tasks, such as unit allocation, economic dispatch, and electricity market clearing. Therefore, they typically project the stability boundary onto a low-dimensional cross-sectional power transmission space and use cross-sectional limits as a stability indicator.

[0003] This method facilitates dispatchers in tracking inter-regional power flows and ensuring power system safety by controlling them below limits. However, calculating power transmission limits is time-consuming and requires dynamic stability verification against numerous potential incidents. Therefore, transmission limits are typically set as conservative and static values / rules a year or quarter in advance, determined by recalculating sectional transmission power or total transfer capacities (TTC) under a few extreme operating scenarios and selecting the minimum value. However, in power systems rich in renewable energy, this method faces the following challenges:

[0004] First, the randomness of renewable energy increases the search space for extreme operating conditions, complicating stability constraints and making them difficult to accurately identify. Second, the increasing diversity of operating and stability modes also expands the variance of transmission capacity (TTC) among these modes. Setting sectional quotas based on worst-case TTC would non-technically limit transmission potential.

[0005] While data-driven approaches offer a potential solution, they still face numerous challenges in dynamic TTC calculations for specific operating conditions. First, ISOs demand transparency in data-driven decision-making, but leading models are often uninterpretable non-convex nonlinear systems, and linear alternatives frequently prove unreliable. Second, the inherently flawed reasoning nature of artificial intelligence erodes its industry credibility. Although modeling can be achieved using uncertainty quantification techniques such as Bayesian neural networks and deep evidence learning, these techniques suffer from high complexity and poor scalability in operational optimization. Finally, current AI-based TTC point estimation methods rely on accurate new energy forecasts, failing to consider the impact of forecast errors on model learning, resulting in poor final accuracy.

[0006] To address the above issues, a novel dynamic assessment method for security margin is urgently needed to resolve the problems existing in the current dynamic calculation of total transmission capacity. Summary of the Invention

[0007] This invention provides a method and apparatus for dynamic evaluation of cross-sectional safety margin to address the limitations of current dynamic calculation of total transmission capacity.

[0008] In a first aspect, embodiments of the present invention provide a method for dynamic evaluation of cross-sectional safety margin, including:

[0009] Acquire power operation data within the transmission section;

[0010] Based on power operation data, determine the new energy forecasting scenarios and the current set of operating modes;

[0011] Discretize the new energy prediction scenarios to obtain multiple sets of data for uncertain new energy scenarios;

[0012] Based on multiple sets of uncertain new energy scenarios, a cross-sectional probabilistic power flow set is obtained; the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint values ​​under each operating mode;

[0013] Based on the cross-sectional probabilistic power flow set, the cross-sectional limit values ​​corresponding to the current operating mode set are optimized to obtain the safety margin assessment results of the transmission cross-section.

[0014] In one possible implementation, a cross-sectional probabilistic power flow set is obtained based on data from multiple uncertain renewable energy scenario sets, including:

[0015] Based on data from multiple uncertain scenarios of new energy sources, the line admittance matrix is ​​obtained;

[0016] The power flow transfer factor matrix is ​​obtained by transforming the line admittance matrix; the power flow transfer factor matrix includes the linear power flow transfer factor.

[0017] Calculate the probabilistic power flow set of the cross section based on the power flow transfer factor matrix.

[0018] In one possible implementation, the probabilistic power flow set of the cross section is calculated based on the power flow transfer factor matrix, including:

[0019] Calculate the probabilistic power flow set of the cross section based on the power flow transfer factor matrix and the first formula;

[0020] The first formula is:

[0021]

[0022] Among them, P F For cross-sectional probabilistic power flow sets; is the baseline value of the tidal current at the cross section; PTDF(·) represents the function for calculating the tidal current at the cross section based on the linear tidal current transfer factor; For the active power output of synchronous machines within the power transmission section under new energy forecasting scenarios; For the active power output of loads within the transmission section under the scenario of new energy prediction; For the set of uncertain new energy scenarios obtained based on discretization; P G P is the active power output of the synchronous machine within the transmission section. L For the active power output of the load within the transmission section; P re For the active power output of new energy sources within the power transmission section; For the set of routes; This is the power flow transfer factor matrix.

[0023] In one possible implementation, the cross-sectional limit values ​​corresponding to the current operating mode set are optimized based on the cross-sectional probabilistic power flow set to obtain the safety margin assessment results of the transmission cross-sections, including:

[0024] The matching results are obtained by matching multiple operating modes in the cross-sectional probability power flow set with the current operating mode set;

[0025] Based on the matching results, the cross-section limit values ​​corresponding to the current operating mode set are optimized to obtain the safety margin assessment results of the transmission cross-section.

[0026] In one possible implementation, multiple operating modes in the cross-sectional probabilistic power flow set are matched with the current set of operating modes to obtain matching results, including:

[0027] Calculate the confidence level between multiple operating modes in the cross-sectional probabilistic power flow set and the current operating modes included in the current operating mode set;

[0028] For each current operating mode included in the current operating mode set, multiple operating modes in the cross-sectional probability power flow set with confidence levels greater than a preset confidence threshold are used as matching operating modes to obtain matching results.

[0029] In one possible implementation, based on the matching results, the cross-sectional limit values ​​corresponding to the current operating mode set are optimized to obtain the safety margin assessment results of the transmission cross-sections, including:

[0030] For any current operating mode included in the current operating mode set, iterate through the section limit constraint value of each matching operating mode corresponding to it.

[0031] If any cross-sectional limit constraint value is greater than the cross-sectional limit value corresponding to the current operating mode, then the cross-sectional limit value corresponding to the current operating mode is replaced with the cross-sectional limit constraint value, and the operating mode is adjusted to obtain the optimized cross-sectional limit value corresponding to the current operating mode.

[0032] The current transmission power within the transmission section is compared with the optimized limit value of the section to obtain the safety margin assessment result of the transmission section.

[0033] In one possible implementation, the current operating mode set includes the current operating modes and the cross-sectional limit value corresponding to each current operating mode; the current operating mode set is determined based on power operation data, including:

[0034] The power operation data is clustered to obtain multiple clusters, and each cluster is used as a current operation mode.

[0035] The data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode.

[0036] In one possible implementation, the data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode, including:

[0037] Using the second formula, the data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode;

[0038] The second formula is:

[0039]

[0040] Among them, Γ f,j This is the section limit value corresponding to the j-th current operating mode; P s,is,j This is the lower limit of the cross-section quota corresponding to the j-th current operating mode; This represents the maximum quota value for the section corresponding to the j-th current operating mode; For the purpose of anticipating accidents; For the set of cross-sections; C j This represents the cluster set corresponding to the j-th current running mode; It is the stability label after stability verification for the i-th operating mode under the c-th anticipated accident, where 0 indicates unsafe and 1 indicates safe. This represents the power transfer at the f-th section in the i-th operation.

[0041] In one possible implementation, the power operation data is clustered to obtain multiple clusters, and each cluster is used as a current operating mode, including:

[0042] Treat each data point in the power operation data as an independent cluster;

[0043] Calculate the single-link distance between each cluster in the cluster set and construct a distance matrix to determine the nearest cluster for each individual cluster;

[0044] For any given independent cluster, merge it with its nearest cluster to obtain a new cluster;

[0045] Treat each new cluster as an independent cluster, and return to the step of calculating the single link distance between clusters in the cluster set and constructing a distance matrix to determine the nearest cluster for each independent cluster, until the number of independent clusters obtained meets the target number of cluster sets;

[0046] Each individual cluster obtained at this point is taken as a current operating mode.

[0047] Secondly, embodiments of the present invention provide a dynamic assessment device for cross-sectional safety margin, comprising:

[0048] The acquisition module is used to acquire power operation data within the transmission section;

[0049] The determination module is used to determine the new energy forecast scenarios and the current set of operating modes based on power operation data;

[0050] The discrete module is used to discretize the new energy prediction scenarios to obtain multiple sets of new energy uncertainty scenario data;

[0051] The analysis module is used to obtain the cross-sectional probabilistic power flow set based on multiple new energy uncertain scenario sets of data; the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint value under each operating mode;

[0052] The optimization module is used to optimize the cross-sectional limit values ​​corresponding to the current operating mode set based on the cross-sectional probabilistic power flow set, so as to obtain the safety margin assessment results of the transmission cross-section.

[0053] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0054] In this embodiment of the invention, firstly, an evaluation is conducted by combining real-time and predicted information on new energy sources. Discretizing the new energy prediction scenarios yields multiple sets of uncertain scenario data, fully considering the randomness of new energy sources. This makes the evaluation more closely reflect the complex and ever-changing actual operating conditions, avoiding evaluation bias caused by ignoring randomness, and is more accurate than the traditional method of statically setting limits. Secondly, by determining the cross-sectional probabilistic power flow sets and corresponding limit constraints under various operating modes, different operating conditions are comprehensively covered. Optimizing the cross-sectional limit values ​​of the current operating mode set allows for flexible adjustment of limits according to actual conditions, avoiding non-technical limitations on transmission potential such as setting limits based on the worst-case scenario, thus improving the transmission efficiency and resource utilization efficiency of the power system. Finally, this embodiment of the invention can acquire data in real time, analyze and optimize limits, providing dispatchers with real-time and accurate assessment results of the transmission section safety margin. In summary, compared with existing data-driven methods, the method provided by this embodiment of the invention does not rely on uninterpretable non-convex nonlinear models or complex uncertainty quantification techniques. Based on explicit mathematical calculations and logical processes, it can improve decision transparency and reliability, reduce the impact of artificial intelligence reasoning errors, and make the final evaluation results more accurate and reliable. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating the implementation of the dynamic assessment method for cross-sectional safety margin provided in this embodiment of the invention.

[0056] Figure 2 This is a schematic diagram of the structure of the dynamic evaluation device for cross-sectional safety margin provided in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0058] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0059] Figure 1 This is a flowchart illustrating the implementation of the dynamic assessment method for cross-sectional safety margin provided in this embodiment of the invention. Figure 1 As shown, the method may include:

[0060] Step 110: Obtain power operation data within the transmission section.

[0061] In this embodiment, the power operation data may include active power output data of the goose synchro within the transmission section, active power output data of the load, and power output data of new energy sources.

[0062] Step 120: Based on power operation data, determine the new energy forecast scenarios and the current operation mode set.

[0063] In this embodiment, power operation data can be analyzed, and based on the current operating characteristics of the power operation data, various operating modes corresponding to power operation can be determined to obtain the current operating mode set.

[0064] In this embodiment, considering that the stable operation of the power grid will be affected after the integration of new energy sources, traditional dynamic assessment methods for the safety margin of transmission sections use the corresponding section limit value for assessment for a period of time after the integration of new energy sources. However, in practical applications, the integration time of new energy sources is often relatively short, possibly only instantaneous, and maintaining the same safety margin for a long period of time will not accurately assess the safety margin of the transmission section.

[0065] Therefore, this embodiment fully considers the randomness of new energy sources, avoids evaluation bias caused by ignoring randomness, and predicts the scenario of the next time period in advance based on the current power operation data, so as to provide a basis for subsequently redetermining the section limit for each operation mode.

[0066] Step 130: Discretize the new energy prediction scenarios to obtain multiple sets of new energy uncertainty scenario data.

[0067] In this embodiment, considering that the new energy prediction scenarios conform to the characteristics of a normal distribution, discretizing the new energy prediction scenarios can fully account for the randomness of new energy, making the assessment more consistent with the complex and ever-changing actual operating conditions and avoiding assessment bias caused by ignoring randomness. Each uncertain new energy scenario can reflect the changes in data flow in the transmission lines before and after new energy access.

[0068] In this embodiment, the process can be represented as:

[0069]

[0070] in, Represents a normal distribution; Indicates the expected value; This indicates the active power output of renewable energy within the transmission section under the renewable energy forecasting scenario; init This represents the initial operating mode after optimal power flow calculation under the new energy prediction scenario, with a cross-sectional power flow baseline value of P. Finit ; For the active power output of synchronous machines within the power transmission section under new energy forecasting scenarios; For the active power output of the load within the transmission section under the scenario of new energy forecasting; sc This represents the initial operating mode after optimal power flow calculation under uncertain new energy scenarios; P L For the active power output of loads within the transmission section under uncertain new energy scenarios; Let ρ be the set of uncertain new energy scenarios obtained based on discretization; ρ is the probability set. This is a set of uncertain scenarios for new energy.

[0071] Step 140: Based on multiple sets of uncertain new energy scenarios, obtain the cross-sectional probabilistic power flow set; wherein, the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint values ​​under each operating mode.

[0072] In this embodiment, after discretizing the new energy prediction scenarios, each new energy uncertainty scenario includes corresponding power operation data. Based on the power operation data included in each new energy uncertainty scenario, the corresponding cross-sectional probabilistic power flow data is calculated, that is, the corresponding predicted operation mode and the corresponding cross-sectional quota constraint value under the predicted operation mode are calculated. Based on the cross-sectional probabilistic power flow data corresponding to each new energy uncertainty scenario, a cross-sectional probabilistic power flow set is obtained.

[0073] Step 150: Based on the cross-sectional probabilistic power flow set, optimize the cross-sectional limit values ​​corresponding to the current operating mode set to obtain the safety margin assessment results of the transmission cross-section.

[0074] In this embodiment, the cross-sectional probabilistic power flow set includes the cross-sectional power flow values ​​of the transmission line under various typical uncertain scenarios of new energy sources obtained based on dynamic analysis after the intervention of new energy sources. In order to improve accuracy, the cross-sectional power flow values ​​can be used as cross-sectional power flow constraint values, i.e., upper and lower limits.

[0075] By pooling the probabilistic power flow across various operating scenarios and the power flow constraint values ​​under each operating scenario, the corresponding cross-sectional limit values ​​in the current operating mode set are dynamically adjusted. This avoids non-technical limitations on transmission potential, such as setting limits based on the worst-case scenario in the traditional approach, and improves the transmission efficiency and resource utilization efficiency of the power system.

[0076] In summary, this invention obtains multiple sets of uncertain scenario data by discretizing new energy prediction scenarios, fully considering the randomness of new energy sources. This makes the assessment more closely reflect the complex and ever-changing actual operating conditions, avoiding assessment bias caused by ignoring randomness, and is more accurate than the traditional static limit setting method. Secondly, by determining the cross-sectional probabilistic power flow set and corresponding limit constraint values ​​under various operating modes, it comprehensively covers different operating conditions. Optimizing the cross-sectional limit values ​​of the current operating mode set allows for flexible adjustment of limits according to actual conditions, avoiding non-technical limitations on transmission potential such as setting limits based on the worst-case scenario in traditional methods, thereby improving the transmission efficiency and resource utilization efficiency of the power system. Finally, this invention can acquire data, analyze and optimize limits in real time, providing dispatchers with real-time and accurate transmission section safety margin assessment results.

[0077] In an optional embodiment, the current operating mode set includes the current operating modes and the cross-sectional limit value corresponding to each current operating mode; step 120, determining the current operating mode set based on power operation data, may include:

[0078] Step 121: Cluster the power operation data to obtain multiple clusters, and use each cluster as a current operation mode.

[0079] Step 122: Calculate the data within each cluster to obtain the cross-sectional quota value corresponding to each current operating mode.

[0080] In this embodiment, to achieve rapid calculation of cross-sectional quota values ​​and address the problem of overly conservative single-section quota values, a multi-level rapid quota evaluation method is constructed. This method generates offline multi-level quota linear rules to achieve online rapid evaluation and approximate the calculation accuracy of TTCs. Specifically, power operation data is clustered to identify different typical operating modes, obtaining the current operating mode, which then serves as the data support for each level of the multi-level quota evaluation. This can be achieved by first using circuit operation data as the dataset, then vectorizing the power operation data collected at each acquisition time of the transmission section into feature vectors, and finally clustering these feature vectors.

[0081] The dataset can be represented as: D = {x1, ..., x} i ,…,x n}

[0082] x i Let be the feature vector at the i-th acquisition time, which is represented as:

[0083] Among them, P G P is the active power output of the synchronous machine within the transmission section. L For the active power output of the load within the transmission section; P re It provides active power output for new energy sources within the power transmission section.

[0084] In this embodiment, the cross-sectional quota value includes an upper limit and a lower limit; the cross-sectional quota value corresponding to each current operating mode is calculated using the following formula based on the data obtained for each pair within each cluster:

[0085]

[0086] Among them, Γ f,j This is the section limit value corresponding to the j-th current operating mode; P s,is,j This is the lower limit of the cross-section quota corresponding to the j-th current operating mode; This represents the maximum quota value for the section corresponding to the j-th current operating mode; For the purpose of anticipating accidents; For the set of cross-sections; C j This represents the cluster set corresponding to the j-th current running mode; It is the stability label after stability verification for the i-th operating mode under the c-th anticipated accident, where 0 indicates unsafe and 1 indicates safe. This represents the power transfer at the f-th section in the i-th operation.

[0087] In an optional embodiment, step 121, which involves clustering the power operation data to obtain multiple clusters and treating each cluster as a current operating mode, may include:

[0088] Each data point in the power operation data is treated as an independent cluster.

[0089] Calculate the single-link distances between clusters in the cluster set and construct a distance matrix to determine the closest cluster for each individual cluster.

[0090] For any given independent cluster, merge it with its nearest cluster to obtain a new cluster.

[0091] Treat each new cluster as an independent cluster, and return to calculate the single-link distance between clusters in the cluster set, and construct a distance matrix to determine the nearest cluster for each independent cluster, until the number of independent clusters obtained meets the target cluster set number.

[0092] Each individual cluster obtained at this point is taken as a current operating mode.

[0093] In this embodiment, each data point in the power operation data is treated as an independent cluster, that is:

[0094] C i ={x i},

[0095] Computational clusters Each cluster C i The single-link distances between them are calculated, and a distance matrix is ​​constructed to determine the distances between each independent cluster C. i The closest cluster, i.e.:

[0096]

[0097] Among them, (C) a C b ) indicates that for cluster C a The closest cluster is C b ;d(C i C j ) represents cluster C i C j The multidimensional Euclidean distance between them.

[0098] For any independent cluster C a It is compared with its nearest cluster C. b Merging them yields a new cluster C. new That is: C new =C a ∪C b .

[0099] Since the clusters in the cluster set have changed at this time, the cluster set can be updated. In the middle, C a C b Remove and the new cluster C new Add to cluster middle.

[0100] At the same time, the distance matrix is ​​updated for each new cluster as an independent cluster; that is, for each new cluster C... new and any cluster C in the current cluster set. i Recalculate the inter-cluster distance d(C) new C i The update process continues until the number of independent clusters obtained meets the target cluster set number, at which point each independent cluster obtained is taken as a current running mode.

[0101] In an optional embodiment, step 140, which involves obtaining a cross-sectional probabilistic power flow set based on multiple new energy uncertain scenario sets of data, may include:

[0102] Based on data from multiple uncertain scenarios of new energy sources, the line admittance matrix is ​​obtained.

[0103] The power flow transfer factor matrix is ​​obtained by transforming the line admittance matrix; the power flow transfer factor matrix includes the linear power flow transfer factor.

[0104] Calculate the probabilistic power flow set of the cross section based on the power flow transfer factor matrix.

[0105] In this embodiment, the Linear Power Flow Transfer Factor (LPFTF) is an important concept in power system analysis, used to describe the impact of changes in power flow on a specific point or line within the power system. It has significant application value in the planning, operation, and control of power systems.

[0106] In this embodiment, considering that the predicted power output scenarios of new energy sources conform to the characteristics of a normal distribution, after discretization, they are divided into multiple sets of uncertain new energy scenarios. Then, the probabilistic power flow set of the cross-section can be calculated using the linear power flow transfer factor. Specifically, the linear power flow transfer factor first obtains the line admittance matrix using data from multiple sets of uncertain new energy scenarios, and then transforms the line admittance matrix to obtain the final result.

[0107] In this embodiment, the cross-sectional probabilistic power flow set can be calculated using the following formula:

[0108]

[0109] Among them, P F For cross-sectional probabilistic power flow sets; is the baseline value of the tidal current at the cross section; PTDF(·) represents the function for calculating the tidal current at the cross section based on the linear tidal current transfer factor; For the active power output of synchronous machines within the power transmission section under new energy forecasting scenarios; For the active power output of loads within the transmission section under the scenario of new energy prediction; For the set of uncertain new energy scenarios obtained based on discretization; P G P is the active power output of the synchronous machine within the transmission section. L For the active power output of the load within the transmission section; P re For the active power output of new energy sources within the power transmission section; For the set of routes; This is the power flow transfer factor matrix.

[0110] In one possible implementation, step 150 optimizes the cross-sectional limit values ​​corresponding to the current operating mode set based on the cross-sectional probabilistic power flow set to obtain the safety margin assessment results of the transmission cross-sections, which may include:

[0111] Step 151: Match the various operating modes in the cross-sectional probabilistic power flow set with the current operating mode set to obtain the matching results;

[0112] Step 152: Based on the matching results, optimize the cross-section limit values ​​corresponding to the current operating mode set to obtain the safety margin assessment results of the transmission cross-section.

[0113] In this embodiment, multiple operating modes in the cross-sectional probability power flow set can be matched with each current operating mode in the current operating mode set as a basis, so that a current operating mode corresponds to at least one operating mode in the cross-sectional probability power flow set, that is, a predicted operating mode.

[0114] Then, based on the predicted operating mode matched by each current operating mode, the cross-sectional limit value corresponding to each current operating mode is dynamically optimized to obtain the safety margin assessment result of the transmission cross-section.

[0115] In an optional embodiment, step 151, which matches multiple operating modes in the cross-sectional probabilistic power flow set with the current operating mode set to obtain a matching result, may include:

[0116] Calculate the confidence level between multiple operating modes in the cross-sectional probabilistic power flow set and the current operating modes included in the current operating mode set.

[0117] For each current operating mode included in the current operating mode set, multiple operating modes in the cross-sectional probability power flow set with confidence levels greater than a preset confidence threshold are used as matching operating modes to obtain matching results.

[0118] In this embodiment, the confidence level between each predicted operating mode in the cross-sectional probabilistic power flow set and the current operating modes included in the current operating mode set can be calculated using the following formula:

[0119]

[0120] Where j represents the j-th operating mode; o represents the operating mode of the cross-sectional probability power flow concentration; This represents the average of all current operating modes in the current operating mode set. It is expressed as: γ represents the confidence level; o sc (γ) represents the probability ρ of passing through the scene. sc Extract the set of current operating modes that satisfy the confidence level γ; P F (γ) represents the probability ρ of passing through the scene. sc The probabilistic power flow set of cross sections satisfying the confidence level γ is extracted.

[0121] Using the above formula, multi-level limits are matched to each current operating mode in the current operating mode set that meets the confidence level, so as to obtain the matching operating mode corresponding to each current operating mode and thus determine the matching result.

[0122] In this embodiment, whether the confidence level is met can be calculated using the following formula:

[0123]

[0124] in, This represents the set of scene indexes that meet the confidence level.

[0125] In an optional embodiment, step 152, based on the matching results, optimizes the cross-section limit values ​​corresponding to the current operating mode set to obtain the safety margin assessment results of the transmission cross-section, which may include:

[0126] For any current operating mode included in the current operating mode set, iterate through the section limit constraint values ​​of each matching operating mode.

[0127] If any cross-sectional quota constraint value is greater than the cross-sectional quota value corresponding to the current operating mode, then the cross-sectional quota value corresponding to the current operating mode is replaced with the cross-sectional quota constraint value, and the operating mode is adjusted to obtain the optimized cross-sectional quota value corresponding to the current operating mode.

[0128] The current transmission power within the transmission section is compared with the optimized limit value of the section to obtain the safety margin assessment result of the transmission section.

[0129] In this embodiment, for any current operating mode that meets the requirements, the types of its corresponding matching operating modes are counted. Starting with the matching operating mode with the most types, the process is traversed, iterating through the cross-sectional quota constraint value of each corresponding matching operating mode. If, during the traversal, the cross-sectional quota constraint value of any matching operating mode is greater than the cross-sectional quota value corresponding to the current operating mode, then the cross-sectional quota value corresponding to the current operating mode is replaced with the cross-sectional quota constraint value, and the operating mode is adjusted to obtain the optimized cross-sectional quota value corresponding to the current operating mode.

[0130] Finally, based on the acquired power operation data, the current transmission power within the transmission section is determined. This current transmission power is then compared with the optimized limit value for the section to determine whether it falls within the optimized limit value, thus establishing the safety margin assessment result for the transmission section.

[0131] In summary, this invention constructs a multi-level partitioning method for cross-sectional quotas using a hierarchical clustering algorithm and determines the quotas at each level based on the principle of conservatism. By incorporating the prior distribution of new energy predictions into the dynamic assessment input for cross-sectional safety, robust cross-sectional quotas are obtained. Furthermore, by combining the probabilistic power flow set calculated using the linear power flow transfer factor, a confident dynamic safety margin assessment result is obtained.

[0132] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0133] Figure 2 A schematic diagram of the structure of the dynamic evaluation device for cross-sectional safety margin provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0134] like Figure 2 As shown, the cross-sectional safety margin dynamic evaluation device 2 includes:

[0135] Module 21 is used to acquire power operation data within the transmission section;

[0136] The determination module 22 is used to determine the new energy prediction scenarios and the current operating mode set based on power operation data;

[0137] Discrete module 23 is used to discretize the new energy prediction scenario to obtain multiple new energy uncertainty scenario set data;

[0138] Analysis module 24 is used to obtain the cross-sectional probabilistic power flow set based on multiple new energy uncertain scenario sets of data; wherein, the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint value under each operating mode;

[0139] The optimization module 25 is used to optimize the cross-section limit value corresponding to the current operating mode set based on the cross-section probabilistic power flow set, so as to obtain the safety margin assessment result of the transmission cross-section.

[0140] In one possible implementation, the analysis module 24 is specifically used for:

[0141] Based on data from multiple uncertain scenarios of new energy sources, the line admittance matrix is ​​obtained;

[0142] The power flow transfer factor matrix is ​​obtained by transforming the line admittance matrix; the power flow transfer factor matrix includes the linear power flow transfer factor.

[0143] Calculate the probabilistic power flow set of the cross section based on the power flow transfer factor matrix.

[0144] In one possible implementation, the analysis module 24 is specifically used for:

[0145] Calculate the probabilistic power flow set of the cross section based on the power flow transfer factor matrix and the first formula;

[0146] The first formula is:

[0147]

[0148] Among them, P F For cross-sectional probabilistic power flow sets; is the baseline value of the tidal current at the cross section; PTDF(·) represents the function for calculating the tidal current at the cross section based on the linear tidal current transfer factor; For the active power output of synchronous machines within the power transmission section under new energy forecasting scenarios; For the active power output of loads within the transmission section under the scenario of new energy prediction; For the set of uncertain new energy scenarios obtained based on discretization; P G P is the active power output of the synchronous machine within the transmission section. L For the active power output of the load within the transmission section; P re For the active power output of new energy sources within the power transmission section; For the set of routes; This is the power flow transfer factor matrix.

[0149] In one possible implementation, optimization module 25 is specifically used for:

[0150] The matching results are obtained by matching multiple operating modes in the cross-sectional probability power flow set with the current operating mode set;

[0151] Based on the matching results, the cross-section limit values ​​corresponding to the current operating mode set are optimized to obtain the safety margin assessment results of the transmission cross-section.

[0152] In one possible implementation, optimization module 25 is specifically used for:

[0153] Calculate the confidence level between multiple operating modes in the cross-sectional probabilistic power flow set and the current operating modes included in the current operating mode set;

[0154] For each current operating mode included in the current operating mode set, multiple operating modes in the cross-sectional probability power flow set with confidence levels greater than a preset confidence threshold are used as matching operating modes to obtain matching results.

[0155] In one possible implementation, optimization module 25 is specifically used for:

[0156] For any current operating mode included in the current operating mode set, iterate through the section limit constraint value of each matching operating mode corresponding to it.

[0157] If any cross-sectional limit constraint value is greater than the cross-sectional limit value corresponding to the current operating mode, then the cross-sectional limit value corresponding to the current operating mode is replaced with the cross-sectional limit constraint value, and the operating mode is adjusted to obtain the optimized cross-sectional limit value corresponding to the current operating mode.

[0158] The current transmission power within the transmission section is compared with the optimized limit value of the section to obtain the safety margin assessment result of the transmission section.

[0159] In one possible implementation, the current operating mode set includes the current operating modes and the cross-sectional quota value corresponding to each current operating mode; the determining module 22 is specifically used for:

[0160] The power operation data is clustered to obtain multiple clusters, and each cluster is used as a current operation mode.

[0161] The data within each cluster is calculated to obtain the cross-sectional limit value corresponding to each current operating mode.

[0162] In one possible implementation, module 22 is specifically used for:

[0163] Using the second formula, the data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode;

[0164] The second formula is:

[0165]

[0166] Among them, Γ f,j This is the section limit value corresponding to the j-th current operating mode; P s,is,j This is the lower limit of the cross-section quota corresponding to the j-th current operating mode; This represents the maximum quota value for the section corresponding to the j-th current operating mode; For the purpose of anticipating accidents; For the set of cross-sections; C j This represents the cluster set corresponding to the j-th current running mode; It is the stability label after stability verification for the i-th operating mode under the c-th anticipated accident, where 0 indicates unsafe and 1 indicates safe. This represents the power transfer at the f-th section in the i-th operation.

[0167] In one possible implementation, module 22 is specifically used for:

[0168] Treat each data point in the power operation data as an independent cluster;

[0169] Calculate the single-link distance between each cluster in the cluster set and construct a distance matrix to determine the nearest cluster for each individual cluster;

[0170] For any given independent cluster, merge it with its nearest cluster to obtain a new cluster;

[0171] Treat each new cluster as an independent cluster, and return to the step of calculating the single link distance between clusters in the cluster set and constructing a distance matrix to determine the nearest cluster for each independent cluster, until the number of independent clusters obtained meets the target number of cluster sets;

[0172] Each individual cluster obtained at this point is taken as a current operating mode.

[0173] Figure 3This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0174] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0175] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0176] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0177] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0178] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for dynamic evaluation of cross-sectional safety margin, characterized in that, include: Acquire power operation data within the transmission section; Based on the power operation data, new energy prediction scenarios and the current set of operating modes are determined; The new energy prediction scenarios are discretized to obtain multiple sets of new energy uncertainty scenario data; Based on the multiple new energy uncertain scenario sets of data, a cross-sectional probabilistic power flow set is obtained; wherein, the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint value under each operating mode; Based on the probabilistic power flow set of the transmission section, the corresponding section limit value in the current operating mode set is optimized to obtain the safety margin assessment result of the transmission section.

2. The method for dynamic evaluation of cross-sectional safety margin according to claim 1, characterized in that, The step of obtaining the cross-sectional probabilistic power flow set based on the multiple new energy uncertain scenario sets data includes: Based on the data from the multiple uncertain scenarios of new energy sources, the line admittance matrix is ​​obtained; The line admittance matrix is ​​transformed to obtain the power flow transfer factor matrix; wherein the power flow transfer factor matrix includes a linear power flow transfer factor. Calculate the cross-sectional probabilistic power flow set based on the power flow transfer factor matrix.

3. The method for dynamic evaluation of cross-sectional safety margin according to claim 2, characterized in that, The step of calculating the cross-sectional probabilistic power flow set based on the power flow transfer factor matrix includes: Calculate the cross-sectional probabilistic power flow set based on the power flow transfer factor matrix and the first formula; The first formula is: Among them, P F For cross-sectional probabilistic power flow sets; is the baseline value of the tidal current at the cross section; PTDF(·) represents the function for calculating the tidal current at the cross section based on the linear tidal current transfer factor; For the active power output of synchronous machines within the power transmission section under new energy forecasting scenarios; For the active power output of loads within the transmission section under the scenario of new energy prediction; For the set of uncertain new energy scenarios obtained based on discretization; P G P is the active power output of the synchronous machine within the transmission section. L For the active power output of the load within the transmission section; P re For the active power output of new energy sources within the power transmission section; For the set of routes; This is the power flow transfer factor matrix.

4. The method for dynamic evaluation of cross-sectional safety margin according to claim 1, characterized in that, The step of optimizing the cross-sectional limit value corresponding to the current operating mode set based on the cross-sectional probabilistic power flow set to obtain the safety margin assessment result of the transmission cross-section includes: The various operating modes in the cross-sectional probability power flow set are matched with the current operating mode set to obtain the matching result; Based on the matching results, the cross-sectional limit values ​​corresponding to the current operating mode set are optimized to obtain the safety margin assessment results of the transmission cross-section.

5. The method for dynamic evaluation of cross-sectional safety margin according to claim 4, characterized in that, The step of matching multiple operating modes in the cross-sectional probabilistic power flow set with the current operating mode set to obtain matching results includes: Calculate the confidence level between the various operating modes in the cross-sectional probabilistic power flow set and the current operating modes included in the current operating mode set; For each current operating mode included in the current operating mode set, multiple operating modes in the cross-sectional probability power flow set with a confidence level greater than a preset confidence threshold are used as matching operating modes to obtain matching results.

6. The method for dynamic evaluation of cross-sectional safety margin according to claim 5, characterized in that, The step of optimizing the cross-sectional limit value corresponding to the current operating mode set based on the matching result to obtain the safety margin assessment result of the transmission cross-section includes: For any current operating mode included in the current operating mode set, iterate through the section limit constraint value of each matching operating mode corresponding to it. If any cross-sectional limit constraint value is greater than the cross-sectional limit value corresponding to the current operating mode, then the cross-sectional limit value corresponding to the current operating mode is replaced with the cross-sectional limit constraint value, and the operating mode is adjusted to obtain the optimized cross-sectional limit value corresponding to the current operating mode. The current transmission power within the transmission section is compared with the optimized limit value of the section to obtain the safety margin assessment result of the transmission section.

7. The method for dynamic evaluation of cross-sectional safety margin according to claim 1, characterized in that, The current operating mode set includes the current operating modes and the cross-sectional limit value corresponding to each current operating mode; The step of determining the current operating mode set based on the power operation data includes: The power operation data is clustered to obtain multiple clusters, and each cluster is used as a current operation mode. The data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode.

8. The method for dynamic evaluation of cross-sectional safety margin according to claim 7, characterized in that, The calculation of the cross-sectional limit value corresponding to each current operating mode for the data within each cluster includes: Using the second formula, the data within each cluster is calculated to obtain the cross-sectional quota value corresponding to each current operating mode; The second formula is: Among them, Γ f,j This is the section limit value corresponding to the j-th current operating mode; This is the lower limit of the cross-section quota corresponding to the j-th current operating mode; This represents the maximum quota value for the section corresponding to the j-th current operating mode; For the purpose of anticipating accidents; For the set of cross-sections; C j This represents the cluster set corresponding to the j-th current running mode; It is the stability label after stability verification for the i-th operating mode under the c-th anticipated accident, where 0 indicates unsafe and 1 indicates safe. This represents the power transfer at the f-th section in the i-th operation.

9. The method for dynamic evaluation of cross-sectional safety margin according to claim 7, characterized in that, The process of clustering the power operation data to obtain multiple clusters, and using each cluster as a current operating mode, includes: Each data point in the power operation data is treated as an independent cluster; Calculate the single-link distance between each cluster in the cluster set and construct a distance matrix to determine the nearest cluster for each individual cluster; For any given independent cluster, merge it with its nearest cluster to obtain a new cluster; Treat each new cluster as an independent cluster, and return to the step of calculating the single link distance between clusters in the cluster set and constructing a distance matrix to determine the nearest cluster for each independent cluster, until the number of independent clusters obtained meets the target number of cluster sets; Each individual cluster obtained at this point is taken as a current operating mode.

10. A dynamic assessment device for cross-sectional safety margin, characterized in that, include: The acquisition module is used to acquire power operation data within the transmission section; The determination module is used to determine the new energy prediction scenario and the current operating mode set based on the power operation data; The discrete module is used to discretize the new energy prediction scenario to obtain multiple sets of new energy uncertainty scenario data; The analysis module is used to obtain the cross-sectional probabilistic power flow set based on the multiple new energy uncertain scenario set data; wherein, the cross-sectional probabilistic power flow set includes multiple operating modes and the corresponding cross-sectional quota constraint value under each operating mode; The optimization module is used to optimize the cross-sectional limit value corresponding to the current operating mode set based on the cross-sectional probabilistic power flow set, so as to obtain the safety margin assessment result of the transmission cross-section.

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