Electric power system risk assessment method in extreme weather scene based on hybrid simulation

Abstract

The embodiment of the invention provides an electric power system risk assessment method in an extreme weather scene based on hybrid simulation, and belongs to the field of electric power system risk assessment. The evaluation method comprises the following steps: acquiring power grid data and scene data of extreme weather; modeling according to the acquired scene data of the extreme weather; constructing a fault model of the line in the extreme weather scene to calculate the fault probability of the line; and calculating a risk index of the power system based on Monte Carlo simulation and the fault probability of the corresponding line. According to the evaluation method, various extreme weather factors can be integrated, and dynamic evolution characteristics of an initial fault and a cascading fault are described, so that planning and operation decisions of a power grid are supported.

Description

Technical Field

[0001] The present invention relates to the field of power system risk assessment, and in particular to a power system risk assessment method under extreme weather scenarios based on hybrid simulation. Background Art

[0002] In recent years, extreme weather events have brought huge challenges to the safe and stable operation of power systems. Under the influence of extreme weather such as strong winds and ice disasters, power system equipment such as lines are prone to physical damage, which in turn triggers chain grid failures and may lead to large-scale power outages.

[0003] Existing power system risk assessment methods mainly focus on the following aspects: 1. Equipment failure rate modeling: Many studies have established the impact mechanism of a single meteorological factor (such as wind speed or ice load) on power equipment based on statistical models and physical models. However, these methods rarely consider the combined effects of multiple meteorological factors and their combined impact on equipment failure rates.

[0004] 2. Analysis of cascading failure process: Traditional risk assessment research mainly analyzes the initial physical failure, but ignores the evolution process of cascading failures caused by power flow transfer. This neglect may lead to an incomplete understanding of the system failure propagation mechanism, which in turn affects the accuracy of risk assessment results.

[0005] To address the above issues, there is an urgent need to develop an evaluation method that can integrate multiple extreme weather factors and characterize the dynamic evolution characteristics of initial faults and cascading faults to support power grid planning and operation decisions. Summary of the invention

[0006] The purpose of an embodiment of the present invention is to provide a power system risk assessment method under extreme weather scenarios based on hybrid simulation, which can integrate multiple extreme weather factors and characterize the dynamic evolution characteristics of initial faults and cascading faults to support the planning and operation decisions of the power grid.

[0007] In order to achieve the above object, an embodiment of the present invention provides a method for risk assessment of a power system in an extreme weather scenario based on hybrid simulation, the method comprising: Obtain power grid data and extreme weather scenario data; Modeling is performed based on the acquired extreme weather scenario data; Construct a line failure model under extreme weather scenarios to calculate the line failure probability; Based on the Monte Carlo simulation and the corresponding line failure probability, the risk index of the power system is calculated.

[0008] Optionally, modeling is performed based on the acquired extreme weather scenario data, including: The basic model of extreme weather is constructed according to formula (6): , formula (6) in, In Cartesian coordinates Location Moment The intensity of weather, Indicates the intensity of the weather center, is a parameter that describes the impact range of severe weather. Indicates time The weather center location at that time.

[0009] Optionally, modeling is performed based on the acquired extreme weather scenario data, including: The storm scenario model is constructed according to formula (7): , formula (7) in, In Cartesian coordinates Location Moment The wind speed, and are wind speed amplitude and wind speed correction, respectively, in units of , and is a parameter that determines the impact range of a storm. For units; Used to describe the effect of wind force on the direction of the device.

[0010] Optionally, modeling is performed based on the acquired extreme weather scenario data, including: The model of ice disaster scenario is constructed according to formula (8): , formula (8) in, In Cartesian coordinates Location Moment The freezing rate, is the amplitude of ice accumulation velocity, in units of , is a parameter that determines the range of ice weather influence, and its unit is .

[0011] Optionally, a line failure model under extreme weather scenarios is constructed to calculate the failure probability of the line, including: According to formula (1), the failure rate of the line is obtained: , formula (1) in, Indicates the intensity of severe weather. represents the weather intensity relative to the design conditions, and are the shape parameter and scale parameter of the Weibull distribution, Indicates the failure rate of the line; Acquire line data of power grid data, and process the acquired lines in sections; The failure rate is calculated based on the weather intensity at the center point of each line section according to formula (2): , formula (2) in, is the line length, For length The line segment is Probability of failure within time; According to formula (3), the failure probability of the entire line is obtained: , formula (3) in, is the number of line segments, Indicates that the line is divided into After the paragraph, the serial number of each paragraph, For the Lines in Probability of failure within time.

[0012] Optionally, a line failure model under extreme weather scenarios is constructed to calculate the failure probability of the line, including: The probability of cascading failures is calculated according to formula (4): ,Formula (4) in, is the line overload outage probability, To protect the hidden fault probability, For line The active power, and Line Rated power and maximum transmission power.

[0013] Optionally, based on the Monte Carlo simulation and the corresponding line failure probability, a risk index of the power system is calculated, including: Obtain power grid data and extreme weather data, and initialize parameters, including the step length of the disaster failure , cascading failure step length , set the number of Monte Carlo simulations; Determine the current time point, and calculate the first failure probability of each line according to the current time point using formula (3); Randomly generate a first uniformly distributed variable, and determine whether the first uniformly distributed variable is greater than a first failure probability; When the first uniformly distributed variable is less than the first fault probability, it is determined that the line is faulty, and load shedding is performed according to a system derating operation response model; Update the time according to the current time point and the cascading failure step length, and calculate the second failure probability of the cascading failure probability of each line at the current time point according to formula (4); Randomly generate a second uniformly distributed variable, and determine whether the second uniformly distributed variable is greater than a second failure probability; When the second uniformly distributed variable is less than the second fault probability, it is determined that the line is faulty, and load shedding is performed according to a system derating operation response model; According to the current time point and the cascading failure step update time, return the second failure probability of the probability of cascading failure of each line at the current time point calculated according to formula (4) until the second uniform distribution variable is greater than the second failure probability; Update the time based on the current time point and the step length of the disaster failure; Determine whether the simulation of all time points is completed based on the updated time; If it is not completed, return to the step of determining the current time point, and calculate the first failure probability of each line according to the current time point by formula (3), until the simulation of all time points is completed; When the simulation is completed at all time points, the load loss of this simulation is calculated.

[0014] Optionally, based on the Monte Carlo simulation and the corresponding line failure probability, a risk index of the power system is calculated, including: When the first uniformly distributed variable is greater than the first fault probability, the line is judged to be normal, and the step of updating the time according to the current time point and the step length of the disaster fault is returned.

[0015] Optionally, based on the Monte Carlo simulation and the corresponding line failure probability, a risk index of the power system is calculated, including: The initial Monte Carlo simulation number is increased by one for update; Determine whether the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number; When the updated Monte Carlo simulation number is less than the set Monte Carlo simulation number, return to the step of determining the current time point, and calculating the first fault probability of each line according to the current time point by formula (6), until the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number; When the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number, the simulation is terminated and the final comprehensive risk assessment result is recorded.

[0016] Optionally, the system derating operation response model is obtained by formula (5): , formula (5) in, , Refers to the node number in the power system; is the total number of all nodes in the power system, including load nodes, generator nodes, and possible transmission nodes; is the number of load nodes, i.e., all load points connected to the power system; is the number of generators, Refers to the generator; When the power system cannot meet all load demands, in order to maintain system stability, it is necessary to The amount of electricity removed; Generator The amount of electricity generated; Refers to the node The amount of power required, i.e. the power demand of the node; for , Phase angle difference of nodes; For connection , Branch reactance of the node; For connection , Active power flow of the branch of the node; For connection , The maximum value of the line transmission power of the node; Refers to the total number of all transmission lines in the system; Refers to the minimum output power of the generator; Refers to the maximum output power of the generator.

[0017] Through the above technical solution, the method for risk assessment of power system in extreme weather scenarios based on hybrid simulation provided by the present invention obtains power grid data and extreme weather scenario data, and then can be modeled according to the acquired extreme weather scenario data, and the extreme weather may include strong winds and ice disasters. After the modeling is completed, a fault model of the line in extreme weather scenarios can be constructed, so that the failure probability of the line can be calculated. Based on the Monte Carlo simulation and the corresponding line failure probability, the risk index of the power system can be calculated, so as to support the planning and operation decision of the power grid.

[0018] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a method for risk assessment of a power system under extreme weather scenarios based on hybrid simulation according to an embodiment of the present invention; Figure 2 It is a flow chart of calculating the failure probability of a line of a power system risk assessment method under an extreme weather scenario based on hybrid simulation according to an embodiment of the present invention; Figure 3 is a flow chart of calculating risk indicators of a power system risk assessment method under extreme weather scenarios based on hybrid simulation according to an embodiment of the present invention; Figure 4 is a power system geographic information map according to one embodiment of the present invention; Figure 5 is a schematic diagram of line failure rate in a storm scenario according to an embodiment of the present invention; Figure 6 is a schematic diagram of line failure rate in an ice disaster scenario according to an embodiment of the present invention; Figure 7 is a load loss comparison diagram according to one embodiment of the present invention. DETAILED DESCRIPTION

[0020] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0022] Figure 1 : is a flow chart of a method for risk assessment of a power system under extreme weather scenarios based on hybrid simulation according to an embodiment of the present invention. In the present invention, the process of the assessment method may include: In step S1, power grid data and extreme weather scenario data are acquired.

[0023] In step S2, modeling is performed based on the acquired extreme weather scene data.

[0024] In step S3, a fault model of the line under extreme weather scenarios is constructed to calculate the failure probability of the line.

[0025] In step S4, based on the Monte Carlo simulation and the corresponding line failure probability, a risk index of the power system is calculated.

[0026] In the present invention, when performing an assessment, power grid data and extreme weather scenario data can be obtained. Extreme weather scenarios may include strong winds, ice disasters, and other weather. After obtaining the power grid data and extreme weather scenario data, modeling can be performed based on the acquired extreme weather scenario data. After modeling, a fault model of the line under the extreme weather scenario can be constructed, so that the failure probability of the line can be calculated. Based on the Monte Carlo simulation and the corresponding line failure probability, the risk index of the power system can be calculated, so as to support the planning and operation decision of the power grid.

[0027] In one embodiment of the present invention, when modeling based on the acquired extreme weather scene data, a basic model of extreme weather intensity can be constructed first. Extreme weather events usually have dynamic mobility, and their impact on the power system depends on key factors such as weather intensity, range, movement speed and movement direction. In order to describe the disturbance characteristics of extreme weather on the power system, this application constructs an extreme weather model based on physical parameters, covering typical scenarios such as storms and ice disasters. Extreme weather intensity usually reaches a peak near the center of the weather and gradually decays with increasing distance. According to formula (6), a basic model of extreme weather can be constructed: , formula (6) in, In Cartesian coordinates Location Moment The intensity of weather, Indicates the intensity of the weather center, is a parameter that describes the impact range of severe weather. Indicates time The position of the weather center in formula (6) can be obtained by formula (9): , formula (9) in, is the initial position; and are the moving speed and direction angle of the weather center respectively.

[0028] In the present invention, when constructing a scene data model of extreme weather, the extreme weather may include a storm scene. Therefore, a model of a storm scene may be constructed. The intensity of a storm is usually described by instantaneous wind speed, and the wind speed within its impact range is related to the distance from the weather center. The model of a storm scene may be constructed by formula (7): , formula (7) in, In Cartesian coordinates Location Moment The wind speed, and are wind speed amplitude and wind speed correction, respectively, in units of , and is a parameter that determines the impact range of a storm. For units; This application considers the situation where the wind has the greatest impact on the network line, that is, the wind is perpendicular to the equipment, that is, .

[0029] In one embodiment of the present invention, extreme weather may also include an ice disaster scenario. The conductor freezing rate in an ice disaster scenario accumulates with exposure time. Therefore, a model of an ice disaster scenario may be constructed using formula (8): , formula (8) in, In Cartesian coordinates Location Moment The freezing rate, is the amplitude of ice accumulation velocity, in units of , is a parameter that determines the range of ice weather influence, and its unit is .

[0030] In one embodiment of the present invention, Figure 2 As shown, the step of calculating the failure probability of the line may include: In step S5, the failure rate of the line is obtained according to formula (1): , formula (1) in, Indicates the intensity of severe weather. represents the weather intensity relative to the design conditions, and are the shape parameter and scale parameter of the Weibull distribution, Indicates the failure rate of the line; In step S6, line data of the power grid data is acquired, and the acquired lines are processed in sections.

[0031] In step S7, the failure rate is calculated according to formula (2) based on the weather intensity at the center point of each line segment.

[0032] , formula (2) in, is the line length, For length The line segment is Probability of failure within time.

[0033] In step S8, the failure probability of the entire line is obtained according to formula (3): , formula (3) in, is the number of line segments, Indicates that the line is divided into After the paragraph, the serial number of each paragraph, For the Lines in Probability of failure within time.

[0034] In the present invention, when calculating the failure probability of a line, the continuous impact of extreme weather causes the failure rate of the line to change dynamically, and the risks faced by lines in different geographical locations change with time and space. In order to describe the dynamic evolution of the failure rate, the present application can use the Weibull distribution function to model the failure rate of the line, and the failure rate of the line can be obtained according to formula (1). Using the statistical data of the existing power system, the parameter value under the storm scenario is , , the parameter value in the ice disaster scenario is , . represents the wind load or ice load of the line design. The design wind speed and ice thickness of the power grid are set to 25 m / s and 20 mm. In order to improve the calculation efficiency, the line can be processed in sections, and the failure rate of each section can be calculated according to the weather intensity at its center point. Assuming that the weather conditions remain unchanged in a short period of time, the failure events of the line approximately follow the Poisson distribution, so it can be calculated by formula (2) After obtaining the failure probability of each line segment, considering that the line is composed of a series of wire segments, the failure probability of the entire line can be obtained by formula (3).

[0035] In one embodiment of the present invention, line failures in extreme weather scenarios can cause significant changes in the state of the power system, which is mainly reflected in the redistribution of power flows. The transfer of power flows may cause line overloads, thereby causing cascading failures. To describe the probability of line overload and shutdown, the present application can use a piecewise function model to calculate the probability of cascading failures using formula (4): ,Formula (4) in, is the line overload outage probability, To protect the hidden fault probability, For line The active power, and Line Rated power and maximum transmission power.

[0036] In one embodiment of the present invention, Figure 3 As shown, the process of calculating the risk index of the power system may include: In step S9, the power grid data and extreme weather data are obtained, and the parameters are initialized, including the step length of the disaster failure. , cascading failure step length , set the number of Monte Carlo simulations.

[0037] In step S10, the current time point is determined, and the first failure probability of each line is calculated according to the current time point by formula (3).

[0038] In step S11, a first uniform distribution variable is randomly generated, and it is determined whether the first uniform distribution variable is greater than a first failure probability.

[0039] In step S12, when the first uniformly distributed variable is less than the first fault probability, it is determined that the line is faulty, and load shedding is performed according to the system derating operation response model.

[0040] In step S13, the time is updated according to the current time point and the cascading failure step length, and the second failure probability of the cascading failure probability of each line at the current time point is calculated according to formula (4).

[0041] In step S14, a second uniform distribution variable is randomly generated, and it is determined whether the second uniform distribution variable is greater than the second failure probability.

[0042] In step S15, when the second uniformly distributed variable is less than the second fault probability, it is determined that the line is faulty, and load shedding is performed according to the system derating operation response model.

[0043] In step S16, according to the current time point and the cascading failure step update time, the second failure probability of the probability of cascading failure occurring on each line at the current time point is calculated according to formula (4) until the second uniform distribution variable is greater than the second failure probability.

[0044] In step S17, the time is updated according to the current time point and the step length of the disaster failure.

[0045] In step S18, it is determined whether all time point simulations are completed based on the updated time.

[0046] If the simulation is not completed, return to the step of determining the current time point and calculating the first failure probability of each line according to the current time point by formula (3) until the simulation of all time points is completed.

[0047] In step S19, when the simulation at all time points is completed, the load loss of this simulation is calculated.

[0048] In the present invention, when calculating the risk index of the power system, a hybrid simulation can be performed, and the hybrid simulation can include: a disaster failure stage and a cascading failure stage. Disaster failure stage: a longer time step (hours) is used to simulate the dynamic evolution of meteorological factors; Cascading failure stage: switch to a short time step (seconds) to perform transient simulation of fault propagation; through a cyclic simulation mechanism, the coupling of meteorological processes and the dynamic behavior of the power system is achieved. When calculating the risk index of the power system, the power grid data and extreme weather data can be obtained first, and the parameters can be initialized, which can include the disaster failure step size. , cascading failure step length , set the number of Monte Carlo simulations. After initializing the parameters, the current time point can be determined, and the first fault probability of each line can be calculated according to the current time point by formula (3). After obtaining the first fault probability, a first uniform distribution variable can be randomly generated, and the first uniform distribution variable can be in the range of (0, 1). After obtaining the first uniform distribution variable, it can be determined whether the first uniform distribution variable is greater than the first fault probability. In the case where it is determined that the first uniform distribution variable is less than the first fault probability, a line fault can be determined. In the case of a line fault, the load can be cut according to the system derating operation response model, and then the current time point and the cascading failure step length can be calculated by formula ( Indicates the updated time. represents the time before the update) update time. After the time is updated, the second fault probability of the chain failure probability of each line at the current time point can be calculated according to formula (4). After obtaining the second fault probability, a second uniformly distributed variable can be randomly generated, and the second uniformly distributed variable can be in the range of (0, 1). After obtaining the second uniformly distributed variable, it can be determined whether the second uniformly distributed variable is greater than the second fault probability. When the second uniformly distributed variable is less than the second fault probability, the line fault can be determined, and the load can be cut according to the system derating operation response model. Then the time can be updated according to the current time point and the chain failure step length, and then the step S13 can be returned to continue calculating the chain failure probability and judging the chain failure until the system is stable, that is, until the second uniformly distributed variable is greater than the second fault probability, at which time the line can be determined to be normal. When the line is determined to be normal, the current time point and the disaster failure step length can be used to determine the line fault by using the formula Update time, then you can judge whether all time point simulations are completed according to the updated time. If not, you can return to step S10 to continue to determine the evaluation period, get the line failure probability, and then judge whether the line is faulty until all time point simulations are completed. This time point can be set according to extreme weather. Therefore, after all time point simulations are completed, the extreme weather is roughly over. When all time point simulations are not completed, the extreme weather is basically not over. Therefore, you can also judge whether the extreme weather is over to judge whether you need to continue to judge whether the line is faulty. When the simulation is completed, you can record the load loss of this simulation.

[0049] In one embodiment of the present invention, Figure 3 As shown, the process of calculating the risk index of the power system may also include: In step S20, when the first uniform distribution variable is greater than the first fault probability, it is determined that the line is normal, and the process returns to the step of updating the time according to the current time point and the step length of the fault due to the disaster.

[0050] In the present invention, when the first uniformly distributed variable is greater than the first fault probability, it can be determined that the line is normal, the next time step is entered, and the process returns to step S17.

[0051] In one embodiment of the present invention, Figure 3 As shown, the process of calculating the risk index of the power system may also include: In step S21, the initial Monte Carlo simulation times are increased by one for updating.

[0052] In step S22, it is determined whether the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number.

[0053] When the updated Monte Carlo simulation number is less than the set Monte Carlo simulation number, the process returns to the step of determining the current time point and calculating the first failure probability of each line according to the current time point by formula (3), until the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number.

[0054] In step S23, when the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number, the simulation is terminated and the final comprehensive risk assessment result is recorded.

[0055] In the present invention, in each simulation, the simulated load loss obtained by each calculation can be obtained, so it is necessary to determine whether simulation is still needed. During the simulation, the number of Monte Carlo simulations can be increased by one for update, and then it is determined whether it reaches the set number of Monte Carlo simulations. During each simulation, the simulated load loss can be recorded, and the comprehensive risk assessment result can be recorded after the set number of Monte Carlo simulations is reached.

[0056] Another method for calculating the risk index is that after all the simulations at the time points are completed, the simulation index risk of this time can be calculated according to the number of failures and the number of non-failures of each line obtained by judgment, and the total number can be obtained according to the number of failures and the number of non-failures, and then the simulation risk index can be determined by the ratio of the number of failures to the total number. The risk of the route can be determined by the simulation risk index. The comprehensive risk assessment index is obtained by averaging all the simulation risk indicators based on the obtained simulation risk index. The comprehensive risk assessment index can change continuously as the obtained simulation risk index increases. After the simulation is completed, the initial Monte Carlo simulation number can be increased by one for update. After the update is completed, it can be determined whether the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number. In the case where the updated Monte Carlo simulation number is less than the set Monte Carlo simulation number, it can return to step S10 until the updated Monte Carlo simulation number is greater than the set Monte Carlo number. In the case where the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number, the simulation can be terminated, and the last changed comprehensive risk assessment index can be obtained as the final comprehensive risk assessment index.

[0057] In one embodiment of the present invention, during the fault evolution process, the power transmission system needs to re-dispatch the generator or even urgently shed the load after multiple faults occur to meet the static safety conditions of the system. In the risk assessment of the power transmission system, the optimal power flow model based on DC power flow is often used to quickly calculate the group adjustment and load shedding amount. This application establishes the following system derating operation response model with the goal of minimizing the load shedding amount to simulate the load shedding response process of the system: , formula (8) in, , Refers to the node number in the power system; is the total number of all nodes in the power system, including load nodes, generator nodes, and possible transmission nodes; is the number of load nodes, i.e., all load points connected to the power system; is the number of generators, Refers to the generator; When the power system cannot meet all load demands, in order to maintain system stability, it is necessary to The amount of electricity removed; Generator The amount of electricity generated; Refers to the node The amount of power required, i.e. the power demand of the node; for , Phase angle difference of nodes; For connection , Branch reactance of the node; For connection , Active power flow of the branch of the node; For connection , The maximum value of the line transmission power of the node; Refers to the total number of all transmission lines in the system; Refers to the minimum output power of the generator; Refers to the maximum output power of the generator.

[0058] This application can be carried out based on the IEEE-39 node system to simulate the dynamic response and risk assessment of the power system under two extreme weather conditions: storm and ice disaster. The system includes 10 generators, 46 lines, and the load level is 6254 MW. In the chain failure analysis, in order to simplify the calculation, it is assumed that there is no hidden protection failure in the line ( ), the maximum transmission power of the line is set to 1.5 times its rated power ( ). To improve computational efficiency, the maximum search depth of the chain accident chain is is set to 2, and the threshold of line cascading failure probability is set to 0.01.

[0059] The network topology of the system is as follows Figure 4 As shown in the figure, a rectangular coordinate system is established in the figure to describe the spatial distribution and evolution of extreme weather. The initial positions of the storm and ice disaster are set to (137, 16) and (70, 18) respectively, and their movement trajectories are shown in Figure 4 shown.

[0060] In order to study the dynamic impact of extreme weather on the power system, it is assumed that the storm and ice disaster act on the IEEE-39 node system for 6 hours respectively, and the changes in line failure rate are analyzed every 30 minutes, for a total of 13 time periods. Figure 5 and Figure 6 The time-varying failure rates of some typical lines under storm and ice disaster conditions are shown respectively.

[0061] In the storm scenario, as the center of the storm gradually approaches line L30, its failure rate rises rapidly and reaches a peak at 1.5 hours (failure rate is 0.6). This period is a critical time to strengthen line reinforcement. As the center of the storm moves away, the failure rate begins to decrease, but because the storm's high wind speed radius acts on the line twice, the failure rate shows a "double peak" feature in the subsequent period. This phenomenon is consistent with the variation pattern of line failure rate under the influence of actual storms, verifying the accuracy of the model in describing the dynamic characteristics of line failures under storm conditions. Figure 6 In the ice disaster scene shown, as the center of the ice disaster moves from the lower right to the upper left, the line failure rate in the affected area gradually increases, especially for lines such as L30 and L7 close to the center of the ice disaster. Under the influence of the center of the ice disaster, the failure rate of these lines reaches 0.04, which is significantly higher than that of other lines. This phenomenon reflects the influence law that the intensity of the ice disaster gradually increases with the distance from the center. In addition, due to the slow melting rate of the ice load, the failure rate remains at a high and stable level throughout the ice disaster period, showing the cumulative destructive effect of the ice disaster on the line.

[0062] In order to study the dynamic evolution of dual-time dimension faults in power systems under extreme weather conditions and verify the rationality and applicability of the hybrid simulation model proposed in this application, this application calculates and compares system risk indicators through multiple rounds of Monte Carlo simulation. The impact of extreme weather on the power system is modeled under different scenarios, and the specific settings are as follows: Scenario 1: Only the direct impact of storms and ice disasters on power lines is considered, without considering the possibility of line cascading failures and the complex interactive relationship between extreme weather and power system fault propagation.

[0063] Scenario 2: Not only the direct impact of storms and ice disasters on the lines is considered, but also the chain failure effects caused by tidal flow transfer and surge flow are incorporated to simulate the dynamic evolution of system failures.

[0064] In the above two scenarios, 1,000 Monte Carlo simulations were performed on the lines within the range affected by extreme weather. The component status was judged time by time, and the system risk indicators were calculated and evaluated.

[0065] Figure 7The cumulative distribution function (CDF) of the power system load loss under two scenarios is shown. As can be seen from the figure, scenario 1 significantly underestimates the probability of large-scale power outages. Its CDF curve of load loss is mainly concentrated in a smaller range of load loss, and the scale of most power outages is limited to less than 20%. This shows that the model that does not consider the propagation effect of cascading faults does not adequately describe the dynamic characteristics of faults under extreme weather conditions and cannot fully characterize the evolution of system faults, especially the potential risks of multiple faults are not effectively reflected. In contrast, scenario 2 (shows a completely different trend, and its CDF curve (as shown by the dotted line) is significantly shifted to the right, covering a larger range of load loss scales, especially the probability of large-scale power outages is significantly increased. This change not only reflects the significant impact of cascading faults on the expansion of power outages, but also verifies the rationality of the hybrid simulation model proposed in this paper. By combining the initial fault under extreme weather conditions with the dynamics of cascading faults within the power grid, the model can capture the complex interactive relationship in the fault propagation process and truly reflect the system risk characteristics under extreme weather conditions.

[0066] Through the above technical solution, the method for risk assessment of power system in extreme weather scenarios based on hybrid simulation provided by the present invention obtains power grid data and extreme weather scenario data, and then can be modeled according to the acquired extreme weather scenario data, and the extreme weather may include strong winds and ice disasters. After the modeling is completed, a fault model of the line in extreme weather scenarios can be constructed, so that the failure probability of the line can be calculated. Based on the Monte Carlo simulation and the corresponding line failure probability, the risk index of the power system can be calculated, so as to support the planning and operation decision-making of the power grid.

[0067] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0071] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0072] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0073] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0074] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0075] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for risk assessment of power system under extreme weather scenarios based on hybrid simulation, characterized in that: The evaluation methods include: Obtain power grid data and extreme weather scenario data; Modeling is performed based on the acquired extreme weather scenario data; Construct a line failure model under extreme weather scenarios to calculate the failure probability of the line, including: According to formula (1), the failure rate of the line is obtained: , Formula (1) in, Indicates the intensity of severe weather. represents the weather intensity relative to the design conditions, and are the shape parameter and scale parameter of the Weibull distribution, Indicates the failure rate of the line; Acquire line data of power grid data, and process the acquired lines in sections; The failure rate is calculated based on the weather intensity at the center point of each line section according to formula (2): , Formula (2) in, is the line length, For length The line segment is Probability of failure within time; According to formula (3), the failure probability of the entire line is obtained: , Formula (3) in, is the number of line segments, Indicates that the line is divided into After the paragraph, the serial number of each paragraph, For the Lines in Probability of failure within time; Based on the Monte Carlo simulation and the corresponding line failure probability, the risk index of the power system is calculated.

2. The evaluation method according to claim 1, characterized in that: Construct a line failure model under extreme weather scenarios to calculate the failure probability of the line, including: The probability of cascading failures is calculated according to formula (4): ,Formula (4) in, is the line overload outage probability, To protect the hidden fault probability, For Line The active power, and Line Rated power and maximum transmission power.

3. The evaluation method according to claim 2, characterized in that: Based on Monte Carlo simulation and the corresponding line failure probability, the risk indicators of the power system are calculated, including: Obtain power grid data and extreme weather data, and initialize parameters, including the step length of the disaster failure , cascading failure step length , set the number of Monte Carlo simulations; Determine the current time point, and calculate the first failure probability of each line according to the current time point using formula (3); Randomly generate a first uniformly distributed variable, and determine whether the first uniformly distributed variable is greater than a first failure probability; When the first uniformly distributed variable is less than the first fault probability, it is determined that the line is faulty, and load shedding is performed according to a system derating operation response model; Update the time according to the current time point and the cascading failure step length, and calculate the second failure probability of the cascading failure probability of each line at the current time point according to formula (4); Randomly generate a second uniformly distributed variable, and determine whether the second uniformly distributed variable is greater than a second failure probability; When the second uniformly distributed variable is less than the second fault probability, it is determined that the line is faulty, and load shedding is performed according to a system derating operation response model; According to the current time point and the cascading failure step update time, return the second failure probability of the probability of cascading failure of each line at the current time point calculated according to formula (4) until the second uniform distribution variable is greater than the second failure probability; Update the time based on the current time point and the step length of the disaster failure; Determine whether the simulation of all time points is completed based on the updated time; If it is not completed, return to the step of determining the current time point, and calculate the first failure probability of each line according to the current time point by formula (3), until the simulation of all time points is completed; When the simulation is completed at all time points, the load loss of this simulation is calculated.

4. The evaluation method according to claim 3, characterized in that: Based on Monte Carlo simulation and the corresponding line failure probability, the risk indicators of the power system are calculated, including: When the first uniformly distributed variable is greater than the first fault probability, the line is judged to be normal, and the step of updating the time according to the current time point and the step length of the disaster fault is returned.

5. The evaluation method according to claim 4, characterized in that: Based on Monte Carlo simulation and the corresponding line failure probability, the risk indicators of the power system are calculated, including: The initial Monte Carlo simulation number is increased by one for update; Determine whether the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number; When the updated Monte Carlo simulation number is less than the set Monte Carlo simulation number, return to the step of determining the current time point, and calculating the first fault probability of each line according to the current time point by formula (3), until the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number; When the updated Monte Carlo simulation number is greater than the set Monte Carlo simulation number, the simulation is terminated and the final comprehensive risk assessment result is recorded.

6. The evaluation method according to claim 3, characterized in that: The system derating operation response model is obtained by formula (5): , Formula (5) in, , Refers to the node number in the power system; is the total number of all nodes in the power system, including load nodes, generator nodes, and possible transmission nodes; is the number of load nodes, i.e., all load points connected to the power system; is the number of generators, Refers to the generator; When the power system cannot meet all load demands, in order to maintain system stability, it is necessary to The amount of electricity removed; Generator The amount of electricity generated; Refers to the node The amount of power required, i.e. the power demand of the node; for , Phase angle difference of nodes; For connection , Branch reactance of the node; For connection , Active power flow of the branch of the node; For connection , The maximum value of the line transmission power of the node; Refers to the total number of all transmission lines in the system; Refers to the minimum output power of the generator; Refers to the maximum output power of the generator.

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