Wind power credible capacity evaluation method and device based on cascading failure

Abstract

The invention discloses a cascading failure-based wind power credible capacity evaluation method and device, and the method comprises the steps: obtaining the topological structure information of a power transmission network frame of a target power system, the conventional attribute information of a conventional unit, and the wind power attribute information of a wind turbine unit, and obtaining the actual power grid operation information of a target region within a preset time period; on the basis of the information, representative scenes of the target power system before, during and after the cascading failure occur are constructed; fault evolution operation simulation is carried out on the target power system in the representative scene before the fault, the representative scene in the fault and the representative scene after the fault, and a power grid state characteristic matrix of the target power system in each representative scene is determined based on simulation results; and based on the power grid state characteristic matrix, determining the wind power credible capacity through alternate iteration by using the upper layer model and the lower layer model.

Description

Technical Field

[0001] This invention relates to the field of power grid security technology, and in particular to a method and apparatus for assessing the reliable capacity of wind power based on cascading failures. Background Technology

[0002] Credible capacity assessment is one of the typical methods for evaluating the contribution of wind power in current research. Because renewable energy generation is volatile and affected by resource supply, it is necessary to estimate its credible capacity or capacity credibility.

[0003] Currently, the reliable capacity of wind power is typically assessed under normal conditions, and the assessment result is used as the final reliable capacity. However, the environment in which the power system operates changes in real time. This approach, which only uses the reliable capacity predicted under normal conditions as the final reliable capacity of wind power, results in low accuracy in the assessment of reliable wind power capacity. Summary of the Invention

[0004] This invention provides a method and apparatus for assessing the reliable capacity of wind power based on cascading failures, which mainly improves the accuracy of the assessment of the reliable capacity of wind power.

[0005] According to a first aspect of the present invention, a method for assessing the reliable capacity of wind power based on cascading failures is provided, comprising: Acquire information on the transmission network topology of the target power system, the conventional attribute information of conventional generating units, and the wind power attribute information of wind turbine units, and acquire actual grid operation information of the target area within a preset time period; Based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information, representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure are constructed. The representative scenarios include the pre-failure representative scenario, the during-failure representative scenario, and the post-failure representative scenario. Fault evolution operation simulations were performed on the target power system under representative scenarios before the fault, during the fault, and after the fault, respectively. Based on the simulation results, the power grid state feature matrix of the target power system under the representative scenarios before the fault, during the fault, and after the fault was determined. A reliable wind power capacity assessment model based on two-layer optimization is obtained, wherein the reliable wind power capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state feature matrix, the reliable wind power capacity of the target power system is determined by alternating iterations using the upper-layer model and the lower-layer model.

[0006] According to a second aspect of the present invention, a wind power reliable capacity assessment device based on cascading failures is provided, comprising: The acquisition unit is used to acquire information on the transmission network topology of the target power system, the conventional attribute information of conventional generating units, the wind power attribute information of wind turbine units, and the actual power grid operation information of the target area within a preset time period. The construction unit is used to construct representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure, based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information. The representative scenarios include the pre-failure representative scenario, the during-failure representative scenario, and the post-failure representative scenario. The simulation unit is used to simulate the fault evolution operation of the target power system under representative scenarios before the fault, representative scenarios during the fault, and representative scenarios after the fault, respectively, and to determine the power grid state feature matrix of the target power system under representative scenarios before the fault, representative scenarios during the fault, and representative scenarios after the fault based on the simulation results. A determination unit is used to obtain a wind power reliable capacity assessment model based on two-layer optimization, wherein the wind power reliable capacity assessment model includes an upper-layer model and a lower-layer model, and the wind power reliable capacity of the target power system is determined by alternating iteration using the upper-layer model and the lower-layer model based on the power grid state feature matrix.

[0007] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for assessing the reliable capacity of wind power based on cascading failures.

[0008] According to a fourth aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described wind power reliable capacity assessment method based on cascading failures.

[0009] According to the present invention, a method and apparatus for assessing the reliable capacity of wind power based on cascading failures is provided. Compared with the current method of assessing the reliable capacity of wind power under normal scenarios and taking the assessment result as the final reliable capacity, the present invention constructs representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure based on the transmission network topology information, the conventional attribute information of the conventional units, the wind power attribute information of the wind turbines, and the actual grid operation information. The present invention then performs fault evolution operation simulations on the target power system under the representative scenarios before, during, and after the failures, respectively. Based on the simulation results, the present invention determines the representative scenarios of the target power system under the representative scenarios before, during, and after the failures. The power grid state characteristic matrix under the aforementioned representative scenarios after a fault, by constructing representative scenarios before, during, and after a fault, can comprehensively cover the entire life cycle of a cascading fault. Before a fault, it can understand the state of the system during normal operation and potential weak links; during a fault, it can monitor the dynamic changes of the system in the fault evolution process in real time; and after a fault, it can assess the damage and recovery capability of the system. It can grasp the characteristics of the system at different stages from a global perspective, thereby improving the accuracy of wind power reliable capacity assessment. Through a two-layer optimized wind power reliable capacity assessment model, the wind power reliable capacity of the target power system is determined by alternating iteration. Through alternating iteration, the wind power reliable capacity can be assessed more comprehensively and accurately, fully considering the complex characteristics of the system under different operating states, thereby further improving the accuracy of wind power reliable capacity assessment. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 The flowchart of a wind power reliable capacity assessment method based on cascading failures provided by an embodiment of the present invention is shown. Figure 2 This invention provides a flowchart of another wind power reliable capacity assessment method based on cascading failures, according to an embodiment of the present invention. Figure 3 This diagram illustrates how the total reliable capacity and capacity reliability of wind power generation vary with the penetration rate of wind power generation, according to an embodiment of the present invention. Figure 4 A schematic diagram of the structure of a wind power reliable capacity assessment device based on cascading failures provided in an embodiment of the present invention is shown. Figure 5 This invention provides a schematic diagram of another wind power reliable capacity assessment device based on cascading failures, according to an embodiment of the present invention. Figure 6 A schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention is shown. Detailed Implementation

[0011] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the present application can be combined with each other.

[0012] Currently, the method of assessing the reliable capacity of wind power under normal conditions and using the assessment results as the final reliable capacity is flawed because the environment in which the power system operates changes in real time. This method, which only uses the reliable capacity predicted under normal conditions as the final reliable capacity of wind power, results in low accuracy in the assessment of the reliable capacity of wind power.

[0013] To address the aforementioned problems, embodiments of the present invention provide a wind power reliable capacity assessment method based on cascading failures, such as... Figure 1 As shown, the method includes: 101. Obtain the topology information of the transmission network of the target power system, the conventional attribute information of conventional units, and the wind power attribute information of wind turbine units, and obtain the actual power grid operation information of the target area within a preset time period.

[0014] The transmission network topology information includes information such as the line capacity and line connection relationships of the transmission network corresponding to the target power system; the conventional attribute information includes information such as the number of conventional generating units, installed capacity, and total peak load, where conventional generating units are the generating units in the power system excluding wind turbines; the wind power attribute information includes the number of wind turbines, installed capacity, and total peak load; and the actual grid operation information includes data such as the actual wind power output, load, and temperature curves of the target area within a preset time period, such as simulating the operation status of the IEEE 39-node and IEEE 118-node test systems. This embodiment of the invention determines the reliable wind power capacity by comprehensively analyzing the transmission network topology information, the conventional attribute information of conventional generating units, the wind power attribute information of wind turbines, and the actual grid operation information of the target area within a preset time period, thereby improving the accuracy of determining the reliable wind power capacity.

[0015] 102. Based on the transmission network topology information, conventional attribute information of conventional generating units, wind power attribute information of wind turbine units, and actual power grid operation information, construct representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure. The representative scenarios include the pre-failure representative scenario, the during-failure representative scenario, and the post-failure representative scenario.

[0016] In this embodiment of the invention, a representative scenario incorporating multiple uncertainties is constructed to generate the scenario support required for subsequent disruption risk assessment. Using wind energy, load demand, and high temperature as feature dimensions, a source-load-temperature scenario is extracted based on clustering technology. After obtaining the source-load-temperature scenario under the initial system state, a cascading failure model is used to simulate topology and power flow state switching to simulate cascading failures. Based on this, clustering technology is used to construct a final scenario that comprehensively represents the system behavior before, during, and after cascading failures, including a pre-failure representative scenario, a mid-failure representative scenario, and a post-failure representative scenario.

[0017] Specifically, representative scenarios before a fault are constructed based on collected data, considering factors such as typical operating modes, load levels, and power generation distribution of the target power system under normal operating conditions. For example, during peak load periods, conventional generating units allocate power according to economic dispatch principles, and wind farms predict power output based on the prevailing wind speed, thus constructing a representative normal operating scenario. Representative scenarios during a fault are analyzed by examining historical cascading fault cases and potential fault modes of the system to determine the initial faulting components and fault types that may lead to cascading faults. For example, a three-phase short-circuit fault on a critical transmission line is selected as the initial fault, and representative scenarios for different fault development stages are constructed, considering the fault propagation and evolution process. Multiple representative scenarios during a fault can be set according to the severity and scope of the fault, such as the system state shortly after the fault occurs and the system state after the fault triggers subsequent failures of other components. Representative scenarios after a fault are constructed by considering the system recovery process and final state after the cascading fault ends. For example, assuming a fault causes a power outage in a part of the area, after the fault is cleared, power is gradually restored through automatic system recovery and manual intervention, constructing representative scenarios of different recovery stages until the system returns to normal operation or stable load-limited operation.

[0018] 103. Conduct fault evolution operation simulations for the target power system under representative scenarios before, during, and after the fault, respectively. Based on the simulation results, determine the power grid state characteristic matrix of the target power system under representative scenarios before, during, and after the fault.

[0019] For embodiments of the present invention, the simulation of representative scenarios before the fault is as follows: Power system simulation software (such as PSS / E, BPA, etc.) is used to input data from representative scenarios before the fault into the software for steady-state operation simulation. During the simulation, the software calculates various parameters of the system under normal operating conditions, such as node voltages, line power flow, and generator output, based on the system topology, component parameters, and operating conditions. These simulation results are recorded as the basic data (grid state) under the representative scenarios before the fault. The simulation of representative scenarios during the fault is as follows: For each representative scenario during the fault, the corresponding initial fault and fault development process are set in the simulation software. For example, for a three-phase short-circuit fault scenario on a transmission line, the time, location, and duration of the fault are set to simulate the dynamic response process of the system after the fault occurs. The software calculates the changes in system parameters such as voltage, frequency, and power during the fault process, as well as possible phenomena such as equipment overload and voltage instability. The changes in key parameters at different fault stages are recorded to form the simulation results of representative scenarios during the fault. The simulation of representative scenarios after the fault is as follows: The system recovery process after the fault is set in the simulation software, including the actions of automatic devices and manual intervention. The simulation system gradually recovers from a fault state to a stable operating state, calculating parameters such as voltage recovery at each node, load recovery sequence and amount, and generator start-up and output adjustment. Simulation results of representative post-fault scenarios are recorded to reflect the system's recovery capability and state changes. Finally, in each simulation, the target power system outputs operating status data (such as node voltage, line power flow, and generator output), and a power grid state characteristic matrix is ​​constructed based on this data. This embodiment of the invention, by constructing representative scenarios before, during, and after a fault, comprehensively covers the entire lifecycle of a cascading fault. Before a fault, the system's normal operating state and potential weaknesses can be understood; during a fault, the dynamic changes of the system during the fault evolution process can be monitored in real time; and after a fault, the system's damage and recovery capability can be assessed. This allows for a global understanding of the system's characteristics at different stages, thereby improving the accuracy of wind power reliable capacity assessment.

[0020] 104. Obtain a wind power reliable capacity assessment model based on two-layer optimization. The wind power reliable capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state feature matrix, the wind power reliable capacity of the target power system is determined by alternating iterations of the upper-layer model and the lower-layer model.

[0021] In this embodiment of the invention, a wind power reliable capacity assessment model based on two-layer optimization is constructed. An upper-layer model is established with the objective of minimizing the difference between the interruption risk after wind power replacement (replacing the wind power system in the target power system with equivalent units) and the interruption risk before replacement, constrained by both power source and grid constraints. A lower-layer model is established with the objective of minimizing the interruption risk after accepting the maximum output limit of the equivalent units, constrained by both power source and grid constraints. The aim is to evaluate the relationship between the reliable capacity of wind power generation and cascading failures. The two layers alternately update the feasible solution domain of reliable capacity through iterative feedback, ultimately obtaining the wind power reliable capacity of the target power system that meets the requirements. The model solution process is as follows: In each iteration, the upper-layer model integrates feedback information regarding the equivalent unit capacity reduction constraint. Then, under the condition that the interruption risk remains unchanged, it generates a feasible upper limit for the equivalent unit capacity for the lower-layer model. Subsequently, the lower-layer model is solved, and under the current equivalent unit capacity constraint, the error between the minimum interruption risk and a preset threshold (the preset threshold is set according to actual needs) is verified to be within a certain range. If the error is within a certain range, it indicates that the minimum interruption risk load requirement is met; otherwise, it indicates that the minimum interruption risk does not meet the requirement. If the minimum interruption risk does not meet the requirement, it indicates that the equivalent unit capacity is still exaggerated, and a new reduction requirement is proposed and fed back to the upper-level model. In each iteration of the two-layer model, the equivalent unit capacity is updated twice. Through multiple iterations, the equivalent unit capacity (wind power reliable capacity) with the minimum interruption risk equal to the set value is finally obtained. Here, interruption risk refers to the possibility and potential loss degree that power transmission or supply will be interrupted due to failures or abnormalities in some key components of the power system (such as transmission lines, transformers, generators, etc.), resulting in some or all loads being unable to receive power supply. This embodiment of the invention uses a two-layer optimized wind power reliable capacity assessment model to determine the wind power reliable capacity of the target power system through alternating iterations. Through alternating iterations, the wind power reliable capacity can be assessed more comprehensively and accurately, fully considering the complex characteristics of the system under different operating conditions, thereby further improving the accuracy of wind power reliable capacity assessment.

[0022] According to the present invention, a wind power reliable capacity assessment method based on cascading failures, compared with the current method of assessing wind power reliable capacity under normal scenarios and using the assessment result as the final reliable capacity, the present invention constructs representative scenarios representing the grid characteristics of the target power system before, during, and after a cascading failure based on the transmission network topology information, the conventional attribute information of the conventional units, the wind power attribute information of the wind turbines, and the actual grid operation information. Furthermore, it performs fault evolution operation simulations on the target power system under the representative scenarios before, during, and after the failure, and based on the simulation results, determines the representative scenarios of the target power system before, during, and after the failure. The power grid state characteristic matrix under the representative scenarios after the fault, by constructing representative scenarios before, during, and after the fault, can comprehensively cover the entire life cycle of cascading faults. Before the fault, it can understand the state of the system during normal operation and potential weak links. During the fault, it can grasp the dynamic changes of the system in the fault evolution process in real time. After the fault, it can assess the damage and recovery capability of the system. It can grasp the characteristics of the system at different stages from a global perspective, thereby improving the accuracy of wind power reliable capacity assessment. The wind power reliable capacity assessment model with two-layer optimization is used to determine the wind power reliable capacity of the target power system through alternating iteration. Through alternating iteration, the wind power reliable capacity can be assessed more comprehensively and accurately, fully considering the complex characteristics of the system under different operating states, and thus further improving the accuracy of wind power reliable capacity assessment.

[0023] Furthermore, to better illustrate the above process of evaluating the reliable capacity of wind power based on cascading failures, as a refinement and extension of the above embodiments, this invention provides another method for evaluating the reliable capacity of wind power based on cascading failures, such as... Figure 2 As shown, the method includes: 201. Obtain the transmission network topology information of the target power system, the conventional attribute information of conventional units, and the wind power attribute information of wind turbine units, and obtain the actual power grid operation information of the target area within a preset time period.

[0024] Specifically, the system obtains the transmission network topology information of the target power system, the conventional attribute information of conventional generating units, and the wind power attribute information of wind turbine units from the power grid database, and obtains the actual power grid operation information of the target area within a preset time period.

[0025] 202. Based on the transmission network topology information, conventional attribute information of conventional generating units, wind power attribute information of wind turbine units, and actual power grid operation information, construct representative scenarios representing the characteristics of the target power system before, during, and after cascading failures. The representative scenarios include the pre-fault representative scenario before the cascading failure, the during-fault representative scenario during the cascading failure, and the post-fault representative scenario after the cascading failure.

[0026] For this embodiment of the invention, a simulation model of cascading failures induced by high temperatures in the target power system is constructed. On one hand, high temperatures affect the power generation and load demand of wind turbine generators, exacerbating the evolution of cascading failures. Specifically, during extreme high-temperature events, air density decreases and wind speed weakens, leading to a decline in wind turbine generator power generation. Simultaneously, cooling loads in residential and industrial sectors surge. Fluctuations in load demand and wind power output can cause uneven power flow distribution, thereby exposing weak links in the power grid and triggering cascading failures. Furthermore, there is a strong coupling and correlation between load demand, wind power output, and ambient temperature, as shown below:

[0027] in, S It is a real sample dataset under high temperature conditions; s t For sample data in the dataset; a t , w t , d t They represent time respectively t The ambient temperature of the power system, the output power of wind power generation, and the load demand; a t , w t , d t The sizes are 1×1 and 1× N W , 1× N B ,in N W and N B These represent the number of wind farms and busbars in the power grid, respectively. T This is the time period simulating a cascading failure; It is the lower limit of the high temperature range.

[0028] Based on the existing outage probability model of the target power system, the improved model is as follows:

[0029]

[0030]

[0031]

[0032] in, p ij ( F ij ) represents the probability of a line fault; F ij It is a line ij Power flow; p 0 represents the current carrying capacity during long-term operation. The probability of line outage within the specified range; This refers to the long-term allowable current carrying capacity and transmission limit of the line. The probability of failure during operation; p ij，0 It is the probability that a power outage will occur on a transmission line because the load exceeds the transmission capacity limit of 0. p ij，1 It is the probability that a power outage will occur on a transmission line because the load exceeds the transmission capacity limit by 1. and ξ represents the allowable long-term current carrying capacity and power transmission limit at room temperature, respectively; ξ is the comprehensive correction factor for the power transmission limit of the line under different ambient temperatures.

[0033] line ij physical damage probability q ij ( L ij That is, its annual average failure probability, which can be calculated using an exponential model according to the following formula, where L ij For line length. Overall probability during high-temperature periods. ψ ( F ij It can be derived from the following formula:

[0034]

[0035] in, λ Failure probability in that year; Δ t It represents the time step; T represents the total time.

[0036] Furthermore, it is necessary to construct representative scenarios of the target power system before, during, and after cascading failures, representing the characteristics of the power grid. Based on this, step 202 specifically includes: determining the initial historical state scenario of the target power system before the cascading failure, determining the initial historical state data in the initial historical state scenario, and constructing a historical scenario matrix from the initial historical state data. ,in, , The ambient temperature data of the target power system at time t in the initial historical state data. The wind power output of the target power system at time t in the initial historical state data. For the target power system load demand at time t in the initial historical state data, The number of data points in the initial historical state data; based on the historical scene matrix. The initial historical state scenarios are clustered, and based on the clustering results, a representative initial scenario matrix is ​​determined. ,in, , For the first Ambient temperature data in a representative initial scenario, For the first Wind power output in a representative initial scenario For the first The load demand in each representative initial scenario, where m is the total number of representative initial scenarios; based on the representative initial scenario matrix... The system uses the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information to determine the load factor and load loss representing the grid characteristics of the target power system before, during, and after a cascading failure. Based on the load factor and load loss, an initial cascading failure evolution matrix is ​​constructed. ,in, , In the i-th representative initial scenario, the load factor of the target power system line after Nk faults is... Let n be the load loss of the c-th fault chain in the i-th representative initial scenario, and n be the fault chain number; and let n be the initial cascading fault evolution matrix based on each representative initial scenario. Constructing a cascading failure evolution matrix ,in, , Represents the evolution matrix of the i-th initial cascading failure. After extracting representative cascading failure scenarios, the line load rate after the Nk-th fault link, For the evolution matrix of the i-th initial cascading failure The load loss after the f-th fault link after extracting representative cascading failure scenarios, and e is the number of fault chains after extracting representative cascading failure scenarios; based on the historical scenario matrix Representative initial scene matrix Initial cascading failure evolution matrix Construct the final scene matrix ,in, Based on the final scene matrix The goal is to determine representative scenarios of the target power system before, during, and after a cascading failure, representing the characteristics of the power grid.

[0037] Specifically, the initial historical state scenario refers to the wind power-load demand-high temperature scenario. Initial historical state data includes ambient temperature data of the target power system, wind power output, load demand, and other information. A historical scenario matrix is ​​constructed. To balance the difficulty of solving the subsequent optimization model with the accuracy of the solution, techniques such as k-means++ clustering are used to generate representative wind power-load demand-high temperature scenarios. These scenarios can determine the initial network and power flow distribution state of the power system. During cascading failures, the power system undergoes topology and power flow state switching to construct the initial cascading failure evolution matrix. We construct scenarios for the power system state before, during, and after cascading faults. Since the sample cluster centers constructed by the k-medoids clustering algorithm represent real-world scenarios and can reverse the situation where line power flow is zero after an Nk fault, we use this algorithm to extract representative cascading fault scenarios for implementing blocking strategies. For each representative initial scenario, we define the cascading fault evolution matrix. This allows for the construction of representative cascading failure scenarios. Ultimately, a matrix is ​​constructed describing the system's state characteristics before, during, and after the occurrence of cascading failures; this is the final scenario matrix. .in Each line represents a scenario, reflecting the characteristics of the system state.

[0038] Furthermore, the above-mentioned process of clustering the initial historical state scenario includes: initializing the centroid vectors corresponding to different clusters and determining the initial scenario feature vectors corresponding to the initial historical state scenario; calculating the distance between the initial scenario feature vectors and the centroid vectors corresponding to different clusters, and classifying the initial historical state scenario into different clusters based on the distance; obtaining the updated centroid vectors corresponding to the initial historical state scenario in different clusters based on the initial scenario feature vectors; reclassifying the initial historical state scenario into different clusters based on the updated centroid vectors until the updated centroid vectors do not change, and finally classifying the initial historical state scenario into different clusters as the initial historical state scenario under different clustering categories, wherein the initial historical state scenario under different clustering categories all include the wind power-load demand-high temperature scenario.

[0039] Representative scenarios include scenarios under different load levels, power generation output distribution scenarios, grid topology stability scenarios, equipment aging early warning scenarios, meteorological risk early warning scenarios, equipment failure scenarios, power flow transfer leading to overload scenarios, and partial power outage recovery scenarios.

[0040] Specifically, the centroid vectors corresponding to the initial centroids of K clusters are selected. For the initial scene feature vectors corresponding to multiple initial historical state scenarios, the distances from each initial scene feature vector to the K centroid vectors are calculated, and each initial scene feature vector is assigned to the cluster corresponding to the nearest centroid vector. Then, for each cluster, the centroid and its corresponding centroid vector are recalculated, and the initial historical state scenarios are reclassified into different clusters. This process is repeated until the position of the centroid does not change, i.e., the centroid vector does not change. Finally, the initial historical state scenarios classified into different clusters are determined as the initial historical state scenarios under different cluster categories. Then, a representative category is selected from different cluster categories as the representative initial scenarios.

[0041] 203. Conduct fault evolution operation simulations for the target power system under representative scenarios before, during, and after the fault, respectively. Based on the simulation results, determine the power grid state characteristic matrix of the target power system under representative scenarios before, during, and after the fault.

[0042] In this embodiment of the invention, after constructing representative scenarios before, during, and after a fault, the cascading fault simulation model of the target power system simulates the fault evolution operation under these scenarios. During the simulation, the operating state parameters and output parameters of the target power system are recorded. The operating state parameters can include voltage state, current state, frequency state, transmission line state, topology changes, etc.; the output parameters include information such as output power. Finally, a power grid state feature matrix is ​​constructed from these parameters.

[0043] 204. Obtain a wind power reliable capacity assessment model based on two-layer optimization. The wind power reliable capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state feature matrix, the reliable capacity of wind power in the target power system is determined by alternating iterations of the upper-layer model and the lower-layer model.

[0044] In this embodiment of the invention, to improve the prediction accuracy of the wind power reliable capacity assessment model, it is first necessary to construct the wind power reliable capacity assessment model. Based on this, the method includes: replacing the wind power system in the target power system with an equivalent unit, and constructing an objective function that minimizes the difference between the interruption risk after the wind power system replacement and the interruption risk before the replacement. ,in, , To mitigate the risk of power outages in the target power system before wind power system replacement, This refers to the load reduction at the τth equivalent unit capacity iteration of power system node h under the c-th representative cascading failure scenario in the i-th representative initial scenario after wind power system replacement. For the state data of the target power system in the i-th representative initial scenario, For the state data of the target power system under the c-th representative cascading failure scenario, A collection of representative initial scenes, A collection of representative cascading failure scenarios, Define the target power system node set; determine the upper-level model constraints based on the objective function. Based on the constraints of the aforementioned upper-level model, the upper-level model is constructed, wherein the constraints of the upper-level model include power supply-side constraints and grid-side constraints; the objective function is constructed with the goal of minimizing the blocking risk after accepting the maximum output limit of the equivalent unit. , Determine the constraints of the lower-level model based on the objective function. The lower-level model is constructed based on the constraints of the lower-level model, wherein the constraints of the lower-level model include power supply side constraints and grid side constraints.

[0045] Specifically, the upper-level model construction aims to minimize the difference in disruption risk between the wind power replacement process and the original wind power replacement process. Minimize, as shown in the following formula:

[0046] The constraints of the upper-level model are as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in, Indicates the first τ In the next iteration, the generator g In the i The first representative initial scenario c Output under a representative cascading failure scenario; Indicates the first τ In the next iteration, the line hl ( hl (The nodes at both ends of the line) in the first i The first representative initial scenario cTrends under representative chain failure scenarios; Indicates the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario h Load at the location; Let h be the set of nodes in the power system; h represents the nodes in the power system. For the line hl susceptance; For the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario h The voltage phase angle; For the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario l The voltage phase angle; For the first τ During the nth iteration, the 1st i The first representative initial scenario c A representative cascading failure scenario for the circuit hl The running status, =1 indicates that the line is in operation. =0 indicates that the line is open; For line nodes; For the line hl Long-term allowable current carrying capacity; For the first τ In the next iteration, the generator g The gradient of the slope; For the first τ During the nth iteration, the 1st i The first representative initial scenario c Generator in a representative cascading failure scenario g The running status, =1 indicates that the unit is running. =0 indicates that the unit is out of service; For the first τ During the nth iteration, the 1st i The first representative initial scenario c Equivalent unit under a representative cascading failure scenario e contribution; For the first τ During the nth iteration, the 1st i The first representative initial scenario c A representative cascading failure scenario for the circuit hlThe running status, =1 indicates that the line is in operation. =0 indicates that the line is open; For nodes l； Indicates the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario h Load at the location; Indicates the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario h Initial load at the location; For nodes h The lower limit of the voltage phase angle; Indicates the first τ During the nth iteration, the 1st i The first representative initial scenario c Nodes in a representative cascading failure scenario h Voltage phase angle at the point; busbar h Upper limit of voltage phase angle; Indicates the first τ During the nth iteration, the 1st i The first representative initial scenario c Voltage phase angle of the balance node in a representative cascading failure scenario; This represents the initial voltage phase angle at the saturation point; and These are the upper and lower output limits of the equivalent unit e, respectively; It is the change in output of the equivalent unit e; and These are the upper and lower limits of variation for the equivalent unit e, respectively; This refers to the operating state of the equivalent unit e; It is the maximum output value of the equivalent unit derived from the low-level model under all representative cascading failure scenarios and all representative initial scenarios; It is a collection of equivalent units; and Let represent the output of the conventional generator before and after blocking the cascading failure in the c-th representative cascading failure scenario under the i-th representative initial scenario; and These represent the wind farm output before and after the cascading failure; and These represent the independent operating states of conventional generator set g and wind farm w, respectively. and These represent the output changes of a conventional generator (g) and a wind farm (w), respectively. and These are the upper and lower output limits of the conventional generator g, respectively. This is the lower limit output value of wind farm w; and These are the upper and lower limits of variation for the conventional generator g, respectively. and These are the upper and lower limits of the wind speed for wind farm w, respectively; and They represent independent conventional power plants and wind farms, respectively; M is a sufficiently large positive number.

[0063] Lower-level model construction: The objective function is shown below:

[0064] In addition to the constraints of the upper-level model, the lower-level model also has the following constraints:

[0065] in, This represents the maximum output value of the equivalent unit output by the upper-level model under all representative cascading failure scenarios in all representative initial scenarios.

[0066] Therefore, by using the above objective function and constraints, we can construct an upper-level model and a lower-level model, which together form a wind power reliable capacity assessment model based on two-level optimization.

[0067] Furthermore, after constructing the upper-level and lower-level models, it is necessary to predict the reliability of wind power based on the upper-level and lower-level models. Therefore, step 204 specifically includes: generating a feasible upper limit for the equivalent unit capacity using the upper-level model based on the power grid state feature matrix; using the feasible upper limit for the equivalent unit capacity as a constraint, verifying whether the minimum interruption risk of the target power system is less than a preset threshold based on the power grid state feature matrix using the lower-level model; if so, determining the reliable wind power capacity of the target power system based on the feasible upper limit for the equivalent unit capacity; otherwise, generating reduction demand information for the equivalent unit capacity; and then... Information is transmitted to the upper-layer model. Based on the power grid state feature matrix and the reduction demand information, the upper-layer model generates a new feasible upper limit for the equivalent unit capacity. Using the new feasible upper limit for the equivalent unit capacity as a constraint, the lower-layer model verifies whether the new minimum interruption risk of the target power system is less than a preset threshold based on the power grid state feature matrix. If so, the reliable wind power capacity of the target power system is determined based on the new feasible upper limit for the equivalent unit capacity. Otherwise, new reduction demand information for the equivalent unit capacity is generated and transmitted to the upper-layer model to continuously iterate and determine the reliable wind power capacity of the target power system.

[0068] The preset threshold is set according to actual needs. Specifically, in each iteration, the upper-level model integrates feedback information on the equivalent unit capacity reduction constraints, and then generates a feasible upper limit for the equivalent unit capacity for the lower-level model under the condition that the blocking risk remains unchanged. The lower-level model is then solved to verify whether the minimum blocking risk is equal to (or less than) the preset threshold under the current equivalent unit capacity constraints. If the minimum blocking risk does not meet the above requirements, it indicates that the equivalent unit capacity is still overstated. New reduction requirements are then proposed and fed back to the upper-level model, which redetermines the feasible upper limit for the equivalent unit capacity. This newly determined feasible upper limit is then transmitted to the lower-level model for minimum blocking risk verification. Based on the verification results, it is determined whether the reliable wind power capacity can be directly generated. In each iteration of the two-layer model, the equivalent unit capacity is updated twice. Through multiple iterations, the equivalent unit capacity with a minimum blocking risk less than or equal to the preset threshold is finally determined and used as the reliable wind power capacity. The model is modeled as a mixed integer linear programming (MILP) problem, which can be solved efficiently using the Yalmip toolbox and the Gurobi solver.

[0069] 205. Determine the wind power installed capacity in the target power system, and determine the wind power reliability of the target power system based on the reliable wind power capacity and the wind power installed capacity.

[0070] In this embodiment of the invention, after determining the reliable wind power capacity, it is also necessary to determine the reliability of the wind power. To determine the reliability of the wind power, it is first necessary to determine the installed wind power capacity. Based on this, step 205 specifically includes: determining the conventional unit capacity of the target power system. Load level Based on the aforementioned conventional unit capacity Load level Reliable wind power capacity Determine the ability of the target power system to withstand cascading failures. Based on the target power system's ability to withstand cascading failures. The load level The capacity of the conventional unit Determine the wind power installed capacity of the target power system. ,in, .

[0071] Specifically, in mitigating cascading failures, it is necessary to establish a conversion relationship between wind power and conventional power sources to enable a feasible comparison between the two. The definition of reliable wind power capacity is adopted: when a wind farm is removed from the original system and replaced by fully reliable virtual conventional units (equivalent units) with a specific capacity, the power system's ability to withstand cascading failures remains unchanged. The capacity of these virtual conventional units is defined as the reliable wind power capacity, which can be calculated using the following formula:

[0072] Where R{*, D} represents the power system's ability to withstand cascading failures when the unit capacity and load level are at "*" and D, respectively; C c Indicates the capacity of a conventional unit; C w It refers to the installed capacity of wind power; C e This represents the capacity of the virtual conventional turbines after wind power replacement, i.e., the reliable wind power capacity. The above formula can be used to calculate the installed wind power capacity in the target power system. Credibility of wind power It can be defined as the ratio of reliable capacity to installed wind power capacity, as shown in the following formula:

[0073] For example, taking the IEEE-118 node system as an example, this study investigates the reliable capacity and capacity reliability of wind power generation in mitigating cascading failures. By evaluating the capacity in steps of 20% of the maximum possible capacity, the total reliable capacity and capacity reliability of wind power generation are derived, as shown in the attached figure. Figure 3As shown (where Proportion is the penetration rate of wind power, Total available capacity is the total reliable capacity, and capacity availability is the reliability of wind power capacity), although the penetration rate of wind power continues to increase, the total reliable capacity of wind power shows a trend towards saturation. At the same time, the reliability of wind power capacity is declining, decreasing from 27.8% to 20.1%.

[0074] According to another wind power reliable capacity assessment method based on cascading failures provided by the present invention, compared with the current method of assessing wind power reliable capacity under normal scenarios and taking the assessment result as the final reliable capacity, the present invention constructs representative scenarios representing the grid characteristics of the target power system before, during, and after cascading failures based on the transmission network topology information, the conventional attribute information of the conventional units, the wind power attribute information of the wind turbines, and the actual grid operation information. Then, it performs fault evolution operation simulations on the target power system under the representative scenarios before, during, and after the faults, respectively. Based on the simulation results, it determines the representative scenarios of the target power system before and during the faults. The power grid state characteristic matrix under the representative scenarios after the fault, by constructing representative scenarios before, during, and after the fault, can comprehensively cover the entire life cycle of cascading faults. Before the fault, it can understand the state of the system during normal operation and potential weak links. During the fault, it can grasp the dynamic changes of the system in the fault evolution process in real time. After the fault, it can assess the damage and recovery capability of the system. It can grasp the characteristics of the system at different stages from a global perspective, thereby improving the accuracy of wind power reliable capacity assessment. The wind power reliable capacity assessment model with two-layer optimization is used to determine the wind power reliable capacity of the target power system through alternating iteration. Through alternating iteration, the wind power reliable capacity can be assessed more comprehensively and accurately, fully considering the complex characteristics of the system under different operating states, thereby further improving the accuracy of wind power reliable capacity assessment.

[0075] Furthermore, as Figure 1 In specific implementation, embodiments of the present invention provide a wind power reliable capacity assessment device based on cascading failures, such as... Figure 4 As shown, the device includes: an acquisition unit 31, a construction unit 32, a simulation unit 33, and a determination unit 34.

[0076] The acquisition unit 31 can be used to acquire the transmission network topology information of the target power system, the conventional attribute information of conventional units, the wind power attribute information of wind turbine units, and the actual power grid operation information of the target area within a preset time period.

[0077] The construction unit 32 can be used to construct representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure, based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information. The representative scenarios include the pre-failure representative scenario, the during-failure representative scenario, and the post-failure representative scenario.

[0078] The simulation unit 33 can be used to simulate the fault evolution operation of the target power system under the representative scenarios before the fault, the representative scenarios during the fault, and the representative scenarios after the fault, respectively. Based on the simulation results, the power grid state feature matrix of the target power system under the representative scenarios before the fault, the representative scenarios during the fault, and the representative scenarios after the fault is determined.

[0079] The determining unit 34 can be used to obtain a wind power reliable capacity assessment model based on two-layer optimization. The wind power reliable capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state feature matrix, the wind power reliable capacity of the target power system is determined by alternating iterations using the upper-layer model and the lower-layer model.

[0080] In specific application scenarios, in order to construct representative scenarios of the target power system before, during, and after cascading failures, representing the characteristics of the power grid, such as... Figure 5 As shown, the construction unit 32 includes a determination module 321 and a construction module 322.

[0081] The determining module 321 can be used to determine the initial historical state scenario of the target power system before the occurrence of a cascading failure, determine the initial historical state data in the initial historical state scenario, and construct a historical scenario matrix from the initial historical state data. ,in, , The ambient temperature data of the target power system at time t in the initial historical state data. The wind power output of the target power system at time t in the initial historical state data. For the target power system load demand at time t in the initial historical state data, This represents the number of data points in the initial historical state data.

[0082] The determining module 321 can also be used based on the historical scene matrix. The initial historical state scenarios are clustered, and based on the clustering results, a representative initial scenario matrix is ​​determined. ,in, , For the first Ambient temperature data in a representative initial scenario, For the first Wind power output in a representative initial scenario For the first The load demand in a representative initial scenario, where m is the total number of representative initial scenarios.

[0083] The construction module 322 can be used to construct based on the representative initial scene matrix. The system uses the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information to determine the load factor and load loss representing the grid characteristics of the target power system before, during, and after a cascading failure. Based on the load factor and load loss, an initial cascading failure evolution matrix is ​​constructed. ,in, , In the i-th representative initial scenario, the load factor of the target power system line after Nk faults is... Let n be the load loss of the c-th fault chain in the i-th representative initial scenario, and n be the fault chain number.

[0084] The building module 322 can also be used for the initial cascading failure evolution matrix based on each representative initial scenario. Constructing a cascading failure evolution matrix ,in, , Represents the evolution matrix of the i-th initial cascading failure. After extracting representative cascading failure scenarios, the line load rate after the Nk-th fault link, For the evolution matrix of the i-th initial cascading failure The load loss after the f-th fault link after extracting representative cascading failure scenarios is given by e, where e is the number of fault chains after extracting representative cascading failure scenarios.

[0085] The construction module 322 can also be used based on the historical scene matrix. Representative initial scene matrix Initial cascading failure evolution matrix Construct the final scene matrix ,in, .

[0086] The determining module 31 can also be used to determine the final scene matrix. The goal is to determine representative scenarios of the target power system before, during, and after a cascading failure, representing the characteristics of the power grid.

[0087] In specific application scenarios, in order to construct a reliable wind power capacity assessment model based on two-layer optimization, the device also includes a model building unit 35.

[0088] The model building unit 35 can be used to replace the wind power system in the target power system with an equivalent unit, and construct an objective function that minimizes the difference between the interruption risk after the wind power system replacement and the interruption risk before the replacement. ,in, , To mitigate the risk of power outages in the target power system before wind power system replacement, This refers to the load reduction at the τth equivalent unit capacity iteration of power system node h under the c-th representative cascading failure scenario in the i-th representative initial scenario after wind power system replacement. For the state data of the target power system in the i-th representative initial scenario, For the state data of the target power system under the c-th representative cascading failure scenario, A collection of representative initial scenes, A collection of representative cascading failure scenarios, Define the target power system node set; determine the upper-level model constraints based on the objective function. Based on the constraints of the aforementioned upper-level model, the upper-level model is constructed, wherein the constraints of the upper-level model include power supply-side constraints and grid-side constraints; the objective function is constructed with the goal of minimizing the blocking risk after accepting the maximum output limit of the equivalent unit. , Determine the constraints of the lower-level model based on the objective function. The lower-level model is constructed based on the constraints of the lower-level model, wherein the constraints of the lower-level model include power supply side constraints and grid side constraints.

[0089] In specific application scenarios, in order to determine the reliable wind power capacity of the target power system, the determining unit 34 includes a generation module 341, a verification module 342, and an iterative determining module 343.

[0090] The generation module 341 can be used to generate a feasible upper limit of the equivalent unit capacity based on the power grid state feature matrix and the upper-level model.

[0091] The verification module 342 can be used to verify, based on the power grid state feature matrix and the lower-level model, whether the minimum interruption risk of the target power system is less than a preset threshold, with the feasible upper limit of the equivalent unit capacity as a constraint. If so, the reliable wind power capacity of the target power system is determined based on the feasible upper limit of the equivalent unit capacity; otherwise, the reduction demand information of the equivalent unit capacity is generated.

[0092] The generation module 341 can also be used to transmit the demand reduction information to the upper-level model, and generate a new feasible upper limit for the equivalent unit capacity based on the power grid state feature matrix and the demand reduction information.

[0093] The iterative determination module 343 can be used to verify, based on the power grid state feature matrix and the lower-level model, whether the new minimum interruption risk of the target power system is less than a preset threshold, using the feasible upper limit of the new equivalent unit capacity as a constraint. If so, the reliable wind power capacity of the target power system is determined based on the feasible upper limit of the new equivalent unit capacity. Otherwise, new equivalent unit capacity reduction demand information is generated and transmitted to the upper-level model to continuously iteratively determine the reliable wind power capacity of the target power system.

[0094] In a specific application scenario, in order to cluster the initial historical state scenarios, the determining module 321 can be used to initialize the centroid vectors corresponding to different clusters and determine the initial scene feature vectors corresponding to the initial historical state scenarios; calculate the distance between the initial scene feature vectors and the centroid vectors corresponding to different clusters, and based on the distance, divide the initial historical state scenarios into different clusters; obtain the updated centroid vectors corresponding to the initial historical state scenarios in different clusters based on the initial scene feature vectors; and re-divide the initial historical state scenarios into the different clusters based on the updated centroid vectors until the updated centroid vectors do not change. The initial historical state scenarios finally divided into the different clusters are determined as the initial historical state scenarios under different cluster categories, wherein the initial historical state scenarios under different cluster categories all include the wind power-load demand-high temperature scenario.

[0095] In specific application scenarios, in order to determine the reliability of wind power, the device also includes a reliability determination unit 36.

[0096] The credibility determination unit 36 ​​can be used to determine the wind power installed capacity in the target power system; and to determine the wind power credibility of the target power system based on the wind power credibility capacity and the wind power installed capacity.

[0097] In specific application scenarios, in order to determine the installed capacity of wind power, the reliability determination unit 36 ​​can be used to determine the capacity of conventional units in the target power system. Load level Based on the aforementioned conventional unit capacity Load level Reliable wind power capacity Determine the ability of the target power system to withstand cascading failures. Based on the target power system's ability to withstand cascading failures. The load level The capacity of the conventional unit Determine the wind power installed capacity of the target power system. ,in, .

[0098] It should be noted that other corresponding descriptions of the functional modules involved in the wind power reliable capacity assessment device based on cascading failures provided in this embodiment of the invention can be found in [reference needed]. Figure 1 The corresponding description of the method shown will not be repeated here.

[0099] Based on the above, Figure 1 Correspondingly, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the following steps: acquiring the transmission network topology information, conventional attribute information of conventional generating units, and wind power attribute information of wind turbines of the target power system, and acquiring the actual power grid operation information of the target area within a preset time period; based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbines, and the actual power grid operation information, constructing representative scenarios representing the power grid characteristics of the target power system before, during, and after a cascading failure, respectively, wherein the representative scenarios include the pre-failure scenario before the cascading failure. Representative scenarios, representative scenarios during the cascading failure, and representative scenarios after the cascading failure; fault evolution operation simulations are performed on the target power system under the representative scenarios before the fault, the representative scenarios during the fault, and the representative scenarios after the fault, respectively. Based on the simulation results, the power grid state characteristic matrix of the target power system under the representative scenarios before the fault, the representative scenarios during the fault, and the representative scenarios after the fault is determined; a wind power reliable capacity assessment model based on two-layer optimization is obtained, wherein the wind power reliable capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state characteristic matrix, the reliable capacity of the wind power of the target power system is determined by alternating iterations using the upper-layer model and the lower-layer model.

[0100] Based on the above, Figure 1 The method shown and as Figure 4 The embodiment of the device shown in the invention also provides a physical structure diagram of a computer device, such as... Figure 6 As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor. Both the memory 42 and the processor 41 are mounted on a bus 43. When the processor 41 executes the program, it performs the following steps: acquiring the transmission network topology information of the target power system, the conventional attribute information of conventional generating units, and the wind power attribute information of wind turbines; and acquiring the actual power grid operation information of the target area within a preset time period. Based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbines, and the actual power grid operation information, it constructs representative scenarios representing the power grid characteristics of the target power system before, during, and after a cascading failure. The scenarios include a representative scenario before the cascading failure, a representative scenario during the cascading failure, and a representative scenario after the cascading failure. Fault evolution operation simulations are performed on the target power system under these scenarios. Based on the simulation results, the grid state characteristic matrix of the target power system under these scenarios is determined. A wind power reliable capacity assessment model based on two-layer optimization is obtained, comprising an upper-layer model and a lower-layer model. Based on the grid state characteristic matrix, the reliable wind power capacity of the target power system is determined through alternating iterations using the upper-layer model and the lower-layer model.

[0101] Through the technical solution of this invention, based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbines, and the actual grid operation information, representative scenarios representing the grid characteristics of the target power system before, during, and after a cascading fault are constructed for each scenario. Fault evolution operation simulations are then performed on the target power system under these representative scenarios. Based on the simulation results, a grid state characteristic matrix for the target power system under these three scenarios is determined. Representative scenarios during and after a fault can comprehensively cover the entire lifecycle of a cascading failure. Before a fault, it allows understanding of the system's normal operating status and potential weaknesses. During a fault, it enables real-time monitoring of the system's dynamic changes during the fault evolution process. After a fault, it allows assessment of system damage and recovery capabilities. It can grasp the characteristics of the system at different stages from a global perspective, thereby improving the accuracy of wind power reliable capacity assessment. By using a two-layer optimized wind power reliable capacity assessment model to determine the wind power reliable capacity of the target power system through alternating iterations, it can more comprehensively and accurately assess wind power reliable capacity, fully considering the complex characteristics of the system under different operating conditions, and further improving the accuracy of wind power reliable capacity assessment.

[0102] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0103] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating wind power credible capacity based on cascading failure, characterized in that, include: Acquire information on the transmission network topology of the target power system, the conventional attribute information of conventional generating units, and the wind power attribute information of wind turbine units, and acquire actual grid operation information of the target area within a preset time period; Based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbine units, and the actual power grid operation information, representative scenarios representing the characteristics of the target power system before, during, and after a cascading failure are constructed. The representative scenarios include the pre-failure representative scenario, the during-failure representative scenario, and the post-failure representative scenario. Fault evolution operation simulations were performed on the target power system under representative scenarios before the fault, during the fault, and after the fault, respectively. Based on the simulation results, the power grid state feature matrix of the target power system under the representative scenarios before the fault, during the fault, and after the fault was determined. A reliable wind power capacity assessment model based on two-layer optimization is obtained, wherein the reliable wind power capacity assessment model includes an upper-layer model and a lower-layer model. Based on the power grid state feature matrix, the reliable wind power capacity of the target power system is determined by alternating iterations using the upper-layer model and the lower-layer model.

2. The method of claim 1, wherein, Based on the transmission network topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind turbines, and the actual grid operation information, representative scenarios representing the grid characteristics of the target power system before, during, and after cascading failures are constructed, including: determining an initial historical state scenario of the target power system before the cascading failure occurs, determining initial historical state data in the initial historical state scenario, and constructing a historical scenario matrix from the initial historical state data wherein, , is environmental temperature data of the target power system at time t in the initial historical state data, is wind power output of the target power system at time t in the initial historical state data, is load demand of the target power system at time t in the initial historical state data, is the number of data in the initial historical state data; based on the historical scenario matrix , the initial historical state scenarios are clustered, and based on the clustering result, a representative initial scenario matrix of the initial historical state scenarios is determined , wherein, , is the ambient temperature data in the mth representative initial scenario, is the wind power output in the mth representative initial scenario, is the load demand in the mth representative initial scenario, m is the total number of scenarios of the representative initial scenarios;​​ based on the representative initial scene matrix , the power grid topology information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind power generating units, the actual power grid operation information, determining the load rate and load loss representing the characteristics of the power grid before, during and after the occurrence of the cascading failure of the target power system, and based on the load rate and load loss, constructing an initial cascading failure evolution matrix , wherein, , is the load rate of the line of the target power system after N-k failures in the i-th representative initial scene, is the load loss of the c-th failure chain in the i-th representative initial scene, and n is the failure chain number. initial cascading failure evolution matrix based on each representative initial scenario , construct the cascading failure evolution matrix wherein, , represents the line load rate after the N-kth failure link after extracting the representative cascading failure scenario in the ith initial cascading failure evolution matrix is the load loss after the fth failure link after extracting the representative cascading failure scenario in the ith initial cascading failure evolution matrix e is the number of failure links after extracting the representative cascading failure scenario;​ based on the historical scenario matrix , a representative initial scenario matrix , an initial cascading failure evolution matrix , constructing a final scenario matrix wherein ; determining, based on the final scenario matrix representative scenarios indicative of power grid characteristics before, during, and after a cascading failure for the target power system.

3. The method of claim 1, wherein, Before obtaining the wind power reliable capacity assessment model based on two-layer optimization, the method further includes: The wind power system in the target power system is replaced by an equivalent unit, and a target function is constructed, which aims to minimize the difference between the blocking risk after the wind power system is replaced and the blocking risk before the wind power system is replaced wherein, , is the blocking risk of the target power system before the wind power system is replaced, is the load shedding amount of the power system node h at the τth equivalent unit capacity iteration in the cth representative cascading failure scenario under the ith representative initial scenario after the wind power system is replaced, is the state data of the target power system under the ith representative initial scenario, is the state data of the target power system under the cth representative cascading failure scenario, is a set of representative initial scenarios, is a set of representative cascading failure scenarios, is a set of target power system nodes; determining upper model constraints based on the target function and the upper model constraints, constructing the upper model, wherein the upper model constraints include power supply side constraints and power grid side constraints; Objective function is constructed with the target of minimum risk of blocking after accepting the upper limit of maximum output of equivalent units , ; determining lower model constraints based on the objective function and the lower model constraints, constructing the lower model, wherein the lower model constraints include power source side constraints and power grid side constraints.

4. The method of claim 1, wherein, The step of determining the reliable wind power capacity of the target power system based on the power grid state feature matrix, using the upper-level model and the lower-level model through alternating iterations, includes: Based on the power grid state feature matrix, a feasible upper limit for the equivalent unit capacity is generated using the upper-level model; Using the feasible upper limit of the equivalent unit capacity as a constraint, based on the power grid state feature matrix, the lower-level model is used to verify whether the minimum interruption risk of the target power system is less than a preset threshold. If so, the reliable wind power capacity of the target power system is determined based on the feasible upper limit of the equivalent unit capacity; otherwise, the reduction demand information of the equivalent unit capacity is generated. The demand reduction information is transmitted to the upper-level model. Based on the power grid state feature matrix and the demand reduction information, the upper-level model is used to generate a new feasible upper limit for the equivalent unit capacity. The method further comprises:

5. The method of claim 2, wherein, determining wind power installed capacity in the target power system; determining wind power reliability of the target power system based on the wind power credible capacity and the wind power installed capacity. The determining the wind power installed capacity in the target power system comprises: The method further comprises: obtaining unit, configured to obtain power grid network topology structure information of a target power system, conventional attribute information of conventional generating units, wind power attribute information of wind power generating units, and actual power grid operation information of a target region in a preset time period; 6. The method of claim 1, wherein, construction unit, configured to construct representative scenarios representing power grid characteristics of the target power system before, during and after cascading failures based on the power grid network topology structure information, the conventional attribute information of the conventional generating units, the wind power attribute information of the wind power generating units and the actual power grid operation information, wherein the representative scenarios include a pre-fault representative scenario before the cascading failures, a fault representative scenario during the cascading failures and a post-fault representative scenario after the cascading failures; simulation unit, configured to respectively perform fault evolution operation simulation of the target power system in the pre-fault representative scenario, the fault representative scenario and the post-fault representative scenario, and determine power grid state characteristic matrices of the target power system in the pre-fault representative scenario, the fault representative scenario and the post-fault representative scenario based on simulation results; ​ 7. The method of claim 6, wherein, ​ determining a conventional generating unit capacity of the target power system , a load level ; based on the regular unit capacity , load level , wind power credible capacity , determine the ability of the target power system to resist cascading failures ; based on the ability of the target power system to withstand cascading failures , the load level , the conventional generating unit capacity , determining a wind power installed capacity in the target power system wherein .

8. A device for evaluating wind power credible capacity based on cascading failure, characterized in that, ​ ​ ​ ​ A determining unit is configured to obtain a wind power credible capacity evaluation model based on double-layer optimization, wherein the wind power credible capacity evaluation model comprises an upper-layer model and a lower-layer model, and the wind power credible capacity of the target power system is determined by alternating iteration based on the power grid state feature matrix, the upper-layer model and the lower-layer model.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.

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