Power system risk assessment method and system for multiple types of extreme weather

Abstract

The invention relates to the field of power grids, and particularly discloses a multi-type extreme weather oriented power system risk assessment method and device. Comprising the steps of defining risk scene elements, determining a risk scene judgment confidence value based on a threshold value under a power grid normal operation condition, constructing a full-scene comprehensive evaluation framework, establishing an extreme weather occurrence probability model, performing generation and screening by using an approximate daily search method and an approximate distance method, and obtaining a source load power probability model. A fault probability model under the influence of different factors is constructed, a series model is utilized, a comprehensive time-varying fault probability model is determined, a power grid fault state occurrence probability model is obtained, comprehensive analysis is carried out, a risk scene probability model is obtained, and a risk consequence index system is established according to a power grid system operation potential risk. And inputting the target power grid data into the risk scene probability model, and performing evaluation by using the risk consequence index system to obtain a risk evaluation result. And the disaster response capability of various aspects of a future power system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of power grids, and in particular to a method and device for risk assessment of a power system facing multiple types of extreme weather. Background Art

[0002] The traditional power system is gradually transformed into a new power system with the characteristics of "three highs and two peaks". At the same time, the frequent occurrence of extreme weather such as heavy rain, high temperature, and blizzards has led to changes in source-load characteristics and increased probability of equipment failure. The system operation safety risk is prominent and shows strong coupling characteristics with extreme weather. However, the current power system only uses 8760h simulation and "N-1" verification under conventional scenarios to verify the power supply reliability and operation safety of the planning scheme, and does not consider the impact of extreme weather in the evaluation process, resulting in insufficient awareness of extreme weather risks. Therefore, how to scientifically evaluate the system risks under future low-probability weather in the planning stage is of great practical significance for improving the feasibility and disaster response capabilities of the new power system planning scheme.

[0003] For the risk assessment of traditional power systems, existing literature has conducted a lot of research from four steps: uncertainty modeling, risk indicator modeling, state sampling, and consequence calculation. Relevant research on power systems facing extreme weather has also achieved rich results. The research focuses on quantifying the impact of meteorological disasters on the source, grid, and load sides of the system, establishing system simulation models, and conducting risk assessment. On the source side, considering the risk of insufficient power supply caused by low output of new energy sources, the literature proposes a risk assessment method for monthly power supply and demand imbalance of new power systems under extreme weather scenarios without wind and light, and uses the IEEE RTS-79 standard system to demonstrate the effectiveness of this method; on the grid side, focusing on the risk of insufficient power supply caused by changes in system status after power grid equipment failures caused by various weather conditions, the literature simulates wind speed and path based on Batts distribution, establishes a probability model for transmission line failure under the influence of typhoon time and space, and takes the 2016 strong typhoon "Nida" No. 4 as an example to evaluate the component vulnerability and system resilience; the literature takes into account the increase in ice coverage leading to insulator flashover, line disconnection, and pole collapse, and considers the impact of system response and recovery measures on the "resilience curve" to evaluate system performance under ice storms. On the load side, the literature establishes an uncertainty model of load and response under extreme high temperatures, and evaluates the reliable power supply capacity of the power grid. Furthermore, some studies consider the dual uncertainty of source-grid / source-load for evaluation. The literature considers the impact of typhoons on wind power output and line failures to establish a component state transfer probability model and a cascading failure model, and takes "2019 Lekima" as an example to evaluate the IEEE-39 system; the literature establishes a failure probability model of lines and generators under load change disturbances caused by high temperatures, and then evaluates the cascading failure and voltage instability risks of the IEEE RTS-96 system.

[0004] The above-mentioned literature only evaluates the current power grid based on a certain historical extreme weather and takes into account the partial uncertainty of the source-grid load under its influence. It does not take into account the actual climate characteristics of the target area and the comprehensive impact of extreme weather on the source-grid load, and ignores the fact that the actual power grid may face multiple types of extreme weather and multiple uncertainties. Therefore, current research is difficult to fully characterize the ability of new power system planning schemes to respond to various types of extreme weather, and it is also impossible to reasonably guide the effective investment of risk avoidance funds in the planning stage. Summary of the invention

[0005] In view of the above problems, an object of the present invention is to provide a method for risk assessment of power systems for multiple types of extreme weather.

[0006] The second object of the present invention is to provide a power system risk assessment system for multiple types of extreme weather.

[0007] The first technical solution adopted by the present invention is: S1, defining risk scenario elements, and determining a risk scenario judgment confidence value based on a threshold value under normal power grid operation conditions;

[0008] S2. Constructing a comprehensive evaluation framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements;

[0009] S3. Establishing an extreme weather occurrence probability model based on the comprehensive evaluation framework of the entire scenario;

[0010] S4. Based on the risk scenario elements and the historical source and load data of the power grid, an initial source and load power set and a feature vector set are obtained, and the approximate distance method is used to screen to obtain the optimal source and load power set and the corresponding system source and load state probability, and then determine the source and load power probability model;

[0011] S5. Based on the failure factors of power grid equipment, multiple failure probability models under the influence of different factors are constructed, and a comprehensive time-varying failure probability model is determined by series connection, and then a probability model of the occurrence of power grid failure state is obtained by sampling;

[0012] S6. Perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model;

[0013] S7. Establish a risk consequence indicator system based on the potential risks of power grid system operation;

[0014] S8. Input the target power grid data into the risk scenario probability model, and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

[0015] Optionally, the S4 includes:

[0016] S41, obtaining the historical source and load data of the power grid;

[0017] S42, based on the risk scenario elements and the historical source and load data of the power grid, using an approximate daily search method to obtain an initial source and load power set and a feature vector set;

[0018] S43. Using an approximate distance method, the initial source-load power set and the feature vector set are screened to obtain an optimal source-load power set and a corresponding system source-load state probability, and then determine the source-load power probability model.

[0019] Optionally, the S5 includes:

[0020] S51, determining the power grid equipment failure factor data, including equipment service life data, short-term external environment data and operation status data;

[0021] S52. Based on the long-term service life data, the basic failure rate and basic failure probability of all types of equipment are calculated using the bathtub curve and the exponential model to obtain a basic failure probability model of equipment;

[0022] S53, based on the short-term external environment data, using the exponential model to describe the failure probability under different environmental conditions, and obtain an environment-dependent equipment failure probability model;

[0023] S54, fitting the operating state data using a linear model to obtain a failure probability model of the operating state;

[0024] S55, obtaining the comprehensive time-varying fault probability model by using a series model according to the equipment basic fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model;

[0025] S56. According to the comprehensive time-varying fault probability model, sampling is performed by using the Monte Carlo method to obtain the grid fault state occurrence probability model.

[0026] Optionally, the S53 includes:

[0027] S531, obtaining historical life data and operating temperature data of power grid lines and transformers, analyzing them, and obtaining line life parameters and transformer life parameters;

[0028] S532, based on the line life parameter and the transformer life parameter, using the basic failure rate formula of the equipment to calculate and obtain the line failure rate and transformer failure rate that are dependent on the temperature condition;

[0029] S533, calculating the line failure rate and transformer failure rate that are dependent on the temperature condition by using the exponential model to obtain a temperature failure probability model;

[0030] S534, obtaining historical ice and wind meteorological factor data, and constructing a line ice growth model and a line stress model;

[0031] S535. According to metal deformation theory, the line ice growth model and the line stress model are analyzed to obtain a line failure probability model under ice-wind load.

[0032] Optionally, the risk scenario probability model is:

[0033]

[0034] In the formula, p s is the probability of occurrence of the risk scenario, p s,X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

[0035] The second technical solution adopted by the present invention is: a power system risk assessment system for multiple types of extreme weather, comprising:

[0036] A confidence value determination module is used to define risk scenario elements and determine the confidence value of risk scenario judgment based on thresholds under normal power grid operation conditions;

[0037] A framework construction module, used to construct a comprehensive assessment framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements;

[0038] A weather probability model building module is used to build an extreme weather occurrence probability model based on the full-scenario comprehensive evaluation framework;

[0039] A source-load power probability model construction module is used to obtain an initial source-load power set and a feature vector set based on the risk scenario elements and the historical source-load data of the power grid, and to screen them using the approximate distance method to obtain the optimal source-load power set and the corresponding system source-load state probability, thereby determining the source-load power probability model;

[0040] The fault probability model building module is used to build multiple fault probability models under the influence of different factors based on the fault factors of power grid equipment, determine the comprehensive time-varying fault probability model through series connection, and then obtain the probability model of power grid fault state through sampling;

[0041] A risk scenario probability model building module is used to perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model;

[0042] The indicator system construction module is used to establish a risk consequence indicator system based on the potential risks of power grid system operation;

[0043] The risk assessment module is used to input the target power grid data into the risk scenario probability model and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

[0044] Optionally, the source-load power probability model building module includes:

[0045] A source-load data acquisition module, used to acquire the historical source-load data of the power grid;

[0046] A data search module, configured to obtain an initial source-load power set and a feature vector set by using an approximate daily search method based on the risk scenario elements and the historical source-load data of the power grid;

[0047] The data screening module is used to screen the initial source load power set and the feature vector set using the approximate distance method to obtain the optimal source load power set and the corresponding system source load state probability, and then determine the source load power probability model.

[0048] Optionally, the fault probability model building module includes:

[0049] A fault factor determination module is used to determine the fault factor data of power grid equipment, including equipment service life data, short-term external environment data and operation status data;

[0050] A basic failure calculation module is used to calculate the basic failure rate and basic failure probability of all types of equipment based on the long-term service life data, using the bathtub curve and the exponential model, to obtain a basic failure probability model of the equipment;

[0051] An environmental impact failure calculation module is used to describe the failure probability of different environmental conditions based on the short-term external environmental data and use the exponential model to obtain an environment-dependent equipment failure probability model;

[0052] An operation fault calculation module is used to obtain a fault probability model of the operation state by fitting a linear model based on the operation state data;

[0053] A comprehensive time-varying fault probability calculation module, used to obtain the comprehensive time-varying fault probability model by using a series model according to the basic equipment fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model;

[0054] The comprehensive fault calculation module is used to obtain the probability model of the power grid fault state by sampling through the Monte Carlo method according to the comprehensive time-varying fault probability model.

[0055] Optionally, the environmental impact fault calculation module includes:

[0056] The parameter analysis module is used to obtain the historical life data and operating temperature data of the power grid lines and transformers, and analyze them to obtain the line life parameters and transformer life parameters;

[0057] A failure rate calculation module, used to calculate based on the line life parameters and the transformer life parameters using the basic failure rate formula of the device to obtain the line failure rate and transformer failure rate that are dependent on the temperature condition;

[0058] A temperature-affected failure rate calculation module, used to calculate the line failure rate and transformer failure rate that are dependent on the temperature condition using the exponential model to obtain a temperature failure probability model;

[0059] Ice-wind impact model building module, used to obtain historical ice-wind meteorological factor data, build line ice growth model and line stress model;

[0060] The load analysis module is used to analyze the line ice growth model and the line stress model based on metal deformation theory to obtain a line failure probability model of ice-wind load.

[0061] Optionally, the risk scenario probability model is:

[0062]

[0063] In the formula, p s is the probability of occurrence of the risk scenario, p s,X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

[0064] Beneficial effects of the above technical solution:

[0065] The present invention identifies regional power grid risk scenarios based on set extreme meteorological conditions, comprehensively quantifies the uncertainty of system source, grid and load under extreme weather conditions, and establishes a comprehensive risk consequence indicator system from the perspectives of adequacy, flexibility, safety and stability. Finally, simulation is carried out based on the planning platform to provide a reference for the rational formulation of actual power grid risk avoidance strategies, which is conducive to improving the disaster response capabilities of various aspects of future power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1A flow chart of a power system risk assessment method for multiple types of extreme weather provided by the present invention;

[0067] Figure 2 A schematic diagram of the structure of a power system risk assessment system for multiple types of extreme weather provided by the present invention;

[0068] Figure 3 This is a statistical chart of the number of extreme weather events of various types in Northeast China in each season from 2003 to 2023;

[0069] Figure 4 Schematic diagram of the research framework for risk scenario composition and planning scheme risk assessment. DETAILED DESCRIPTION

[0070] The following detailed description of the embodiments of the present invention is further described in detail in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present invention, but cannot be used to limit the scope of the present invention, that is, the present invention is not limited to the preferred embodiments described, and the scope of the present invention is defined by the claims.

[0071] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more than two; the terms “first”, “second”, etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance; for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0072] Embodiment 1

[0073] An embodiment of the present invention provides a method for risk assessment of power system for multiple types of extreme weather, such as Figure 1 As shown,

[0074] S1. Define risk scenario elements and determine the confidence value of risk scenario judgment based on the threshold value under normal operation of the power grid;

[0075] The risk level of the power grid depends on the frequency of extreme events and their consequences. This article takes the Northeast region as an example and counts the number of extreme weather events in the past 20 years based on the yearbooks of the provincial meteorological bureaus. Figure 3 As shown in Table 1, further analysis of power outage accident reports under extreme weather conditions by power grid companies revealed two types of key potential extreme weather conditions with the characteristics of high frequency, long duration, and easy to cause source-load imbalance and equipment failure: high temperature and no wind in summer and blizzard in winter, as shown in Table 1.

[0076] Table 1 Historical records of extreme weather accidents in Northeast China Power Grid

[0077]

[0078] The above two types of extreme weather combined affect the source, network, and load sides of the system with a high proportion of new energy and cause it to deviate from the normal operating threshold, causing system risks. In hot and windless weather, high temperature causes the cold load to rise, and the failure rate of transmission lines and transformers increases due to long-term heavy load and high temperature; the wind speed is continuously lower than the cut-in wind speed, resulting in continuous insufficient output of large-scale wind power, and then insufficient system redundancy, and the risk of power outage increases. In blizzard weather, extreme low temperature and heavy snow simultaneously cause a sudden increase in heat load, line ice breakage, and low temperature protection of wind turbines; high wind speed causes wind turbine shutdown, wind power output decreases, and the system faces risks such as voltage and current limit violations caused by insufficient redundancy and large-scale failures.

[0079] It can be seen that extreme meteorological factors are the source of risks. When meteorological factors exceed the range of normal equipment operation / normal load growth, the source, grid, and load sides of the power system will be in an abnormal operating state. Various combinations of abnormal operating states will cause risks to the system.

[0080] First, the elements of the risk scenario are defined, namely the meteorological factor matrix X and the power grid state matrix Y, as shown in equations (1)-(2). Then, taking the normal operation threshold of the power grid state as the boundary, the extreme meteorological conditions under a certain confidence value are defined and used as the criterion for identifying risk scenarios, as shown in equations (3)-(5).

[0081]

[0082] Where: The dimension of the meteorological factor matrix X is T×3, including the temperature at time t Wind speed and snowfall The dimension of the grid state matrix Y is T×5, including the low output of wind power at time t Abnormal load demand Line fault status Transformer fault status and thermal power unit fault status T is the duration of extreme weather; σ is the confidence value corresponding to extreme meteorological conditions; Indicates the boundary value of the i-th meteorological factor corresponding to the j-th power grid state, which has different meanings under different conditions, where: ΔP L,pred is the unit increment threshold of load power changing with temperature; θ WT,pro ,θ line,rate and θ transf,rate are the temperature design values ​​of fan protection, line and transformer respectively; v WT,cut-out and v WT,cut-in are the cut-out and cut-in wind speeds of the fan respectively; v line,rate is the design value of wind speed that the line can withstand; s line,rate It is the design value of snow cover that the line can withstand.

[0083] Taking formula (3) as an example, the maximum value of the load unit increment threshold and the equipment temperature design value under the confidence value σ is the temperature boundary value. When the ambient temperature exceeds this value, it is a grid risk scenario.

[0084] It should be noted that the confidence value is the boundary value of the confidence interval. In statistics, the confidence interval of a probability sample is an interval estimate of a population parameter of the sample. The confidence interval shows the degree to which the true value of the parameter has a certain probability of falling around the measurement result. The confidence interval gives the range of credibility of the measured value of the measured parameter, that is, the "certain probability" required above.

[0085] S2. Constructing a comprehensive evaluation framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements;

[0086] Taking into account all the potential extreme weather and multiple uncertainties in the region, a more comprehensive and detailed planning scheme risk assessment research framework was established, such as Figure 4 As shown in Figure 2, when the power grid is in risk scenario s, the corresponding meteorological factor X, power grid state Y and power grid consequence Ω appear at time t. C Specifically, the mathematical models of various risks of the power grid at time t are shown in equations (6)-(7).

[0087]

[0088] p s =p s,X ×p s,Y|X (7)

[0089] In the formula, R o,t is the value of the system operation risk o at time t; Ω S is a set of risk scenarios; p s is the probability of occurrence of risk scenario s; C s,o,t is the value of the grid consequence index o at time t under risk scenario s; Ω T is the simulation time set; Ω C is the set of power grid consequence indicators; p s,X is the probability of meteorological factor X occurring under risk scenario s; p s,Y|X is the probability of grid state Y occurring under extreme weather X.

[0090] S3. Establishing an extreme weather occurrence probability model based on the comprehensive evaluation framework of the entire scenario;

[0091] The probability of extreme weather occurring is p s,X It can be expressed by the probability of various meteorological factors occurring simultaneously, as shown in formula (8).

[0092]

[0093] Where: represents the i-th meteorological factor at time t; is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s. This probability can be calculated based on the meteorological factor The probability density function of And the integral of the value interval corresponding to the extreme meteorological conditions is obtained, as shown in formula (9).

[0094]

[0095] Where: Due to meteorological factors The relevant grid state extreme boundary values ​​are For example, it can be expressed as

[0096] S4. Based on the risk scenario elements and the historical source and load data of the power grid, an initial source and load power set and a feature vector set are obtained, and the approximate distance method is used to screen to obtain the optimal source and load power set and the corresponding system source and load state probability, and then determine the source and load power probability model;

[0097] S41, obtaining the historical source and load data of the power grid;

[0098] S42, based on the risk scenario elements and the historical source and load data of the power grid, using an approximate daily search method to obtain an initial source and load power set and a feature vector set;

[0099] The approximate daily search method is used to generate the source load power under the corresponding extreme meteorological conditions. X is used as the characteristic matrix of the prediction period, and the initial source load power set Ω1 of the approximate period is established based on historical data Y,apx and the eigenvector set Ω1 X,apx , as shown in formulas (10)-(11).

[0100]

[0101] Where: Y1 apx , and are the source-load power vectors of the 1st, dth and D1th historical approximate time periods respectively; and are the new energy output and load demand at time t in the historical approximate period d; and are the meteorological factor vectors of the 1st, dth and D1th historical approximate periods respectively; and are the temperature, wind speed and snowfall at time t in the historical approximate period d; D1 is the initial number of the approximate period.

[0102] S43. Using an approximate distance method, the initial source-load power set and the feature vector set are screened to obtain an optimal source-load power set and a corresponding system source-load state probability, and then determine the source-load power probability model.

[0103] Use approximate distance method to filter historical dataΩ1 Y,apx The eigenvector Ω1 X,apx The approximate time period with the greatest similarity to X is shown in formula (12). The optimal source-load power set after screening is Ω1 Y,apx , the corresponding system source-load state probability As shown in formula (13).

[0104]

[0105] Where: d,t is the approximate distance between the characteristic parameters at time t in the approximate time period d and the time period to be predicted; ε is the judgment threshold; D2 is the number of approximate time periods after screening.

[0106] S5. Based on the failure factors of power grid equipment, multiple failure probability models under the influence of different factors are constructed, and a comprehensive time-varying failure probability model is determined by series connection, and then a probability model of the occurrence of power grid failure state is obtained by sampling;

[0107] The equipment failure probability of the power grid under extreme weather conditions presents a time-varying characteristic due to the combined effects of long-term service life, short-term external environment and operating status. In order to accurately reflect the time-varying failure probability of equipment, a comprehensive time-varying failure probability model for all types of equipment such as transmission lines, transformers and generators is established by comprehensively considering the above three types of influencing factors.

[0108] S51, determining the power grid equipment failure factor data, including equipment service life data, short-term external environment data and operation status data;

[0109] S52. Based on the long-term service life data, the basic failure rate and basic failure probability of all types of equipment are calculated using the bathtub curve and the exponential model to obtain a basic failure probability model of equipment;

[0110] The basic failure rate of equipment shows a bathtub curve as the service life increases, and rises sharply during the wear-out failure period. Since a large number of equipment in the planned power grid may be in this period, the service life factor is considered, and it is assumed that the basic failure rate of equipment remains unchanged during the evaluation period. The bathtub curve and exponential model are used to describe the basic failure rate and basic failure probability of all types of equipment respectively.

[0111]

[0112] Where: is the service life of equipment j; and are the basic failure rate and basic failure probability of equipment j at time t; α j and β j They represent the life parameter and shape parameter of device j respectively; Δt is the time step.

[0113] S53, based on the short-term external environment data, using the exponential model to describe the failure probability under different environmental conditions, and obtain an environment-dependent equipment failure probability model;

[0114] At the environmental level, different extreme weather conditions have different impact mechanisms on different equipment. In continuous high temperature weather, when the equipment temperature reaches its hot spot temperature, the line tensile life and transformer insulation life will be greatly shortened, and the equipment will age faster; in blizzard weather, the transmission line will experience ice and withstand the dual effects of wind and ice. When the force increases with the thickness of the ice and exceeds the mechanical limit, metal deformation or even line breakage will occur. Therefore, a temperature-dependent equipment failure probability model and a line failure probability model based on ice and wind loads are established respectively.

[0115] S531, obtaining historical life data and operating temperature data of power grid lines and transformers, analyzing them, and obtaining line life parameters and transformer life parameters;

[0116] The relationship between the life of the line and transformer and the operating temperature is shown in equations (16) and (17) respectively.

[0117]

[0118] Where: and are the operating life of line j and transformer j at time t respectively; and They are the operating temperatures of line j and transformer j at time t, which can be determined according to the external temperature. Calculation; Coefficient γ = b / m, where σ1 is the maximum tensile strength loss ratio of the wire, σ2 is the tensile strength loss value of the wire when it is fully annealed at high temperature, a~e are constants related to the material properties of the conductor; m is the line quality; f and g are empirical constants.

[0119] S532, based on the line life parameter and the transformer life parameter, using the basic failure rate formula of the equipment to calculate and obtain the line failure rate and transformer failure rate that are dependent on the temperature condition;

[0120] S533, calculating the line failure rate and transformer failure rate that are dependent on the temperature condition by using the exponential model to obtain a temperature failure probability model;

[0121] Equipment life parameters and Substituting into equation (14) respectively, we can get the line failure rate dependent on temperature condition: and transformer failure rate Similarly, an exponential model is used to describe the failure probability, as shown in equations (18)-(19).

[0122]

[0123] Where: and is the failure probability of line j and transformer j affected by extreme weather at time t.

[0124] S534, obtaining historical ice and wind meteorological factor data, and constructing a line ice growth model and a line stress model;

[0125] Since the snow cover of the line is related to its location, the meteorological factors of wind speed, temperature and snowfall are first considered to establish a line ice cover growth model related to the geographical location, as shown in Equations (20)-(22).

[0126]

[0127] Where: are the ice thickness, ice speed and snowfall rate at line j at time t respectively; k1 is the conversion coefficient between maximum wind speed and average wind speed; ρ is the density of ice; are the maximum snowfall rate at time t and the coordinates of the corresponding geographical location; and is the coordinate of the geographical location of line j; k2 is the attenuation coefficient.

[0128] Secondly, considering the combined effects of ice load and wind load, the line stress model is established, such as equations (23)-(25).

[0129]

[0130] Where: are the ice-wind load, ice load and wind load borne by line j at time t respectively; D j and S j are the conductor diameter and span factor of line j respectively; is the wind speed of line j at time t; C is a constant.

[0131] S535. According to metal deformation theory, the line ice growth model and the line stress model are analyzed to obtain a line failure probability model under ice-wind load.

[0132] According to metal deformation theory, the probability of line failure increases exponentially with the increase of stress. When the mechanical force limit is exceeded, the line will be directly broken, as shown in formula (26).

[0133]

[0134]

[0135] Where: and are the lower and upper limits of ice-wind loads borne by line j; and are the wind forces borne by the line when the wind speed is 1 and 2 times the design value of line j respectively; and When the ice thickness is 1 and 5 times the design value of line j, the ice force on the line; θ t is the angle between the ice wind force and the vertical ground at time t, which can be expressed as

[0136] S54, fitting the operating state data using a linear model to obtain a failure probability model of the operating state;

[0137] When extreme weather causes a power grid failure, the power flow shifts, which can easily cause the line power flow, transformer load factor, and generator voltage to exceed the limit, resulting in an increase in the probability of equipment failure. A linear model is used to fit the probability of equipment failure for the three types of equipment, as shown in equations (29)-(31).

[0138]

[0139] Where: and are the operating failure probabilities of line j, transformer j and generator j at time t respectively; and are the load currents of line j and transformer j at time t respectively; and are the rated values ​​of load current of line j and transformer j respectively; and The setting values ​​of overcurrent protection of line j and transformer j respectively; is the terminal voltage of generator j at time t; and They are the upper and lower limits of the normal value of the terminal voltage of generator j respectively; and They are the setting values ​​of low voltage protection and overvoltage protection of generator j respectively.

[0140] S55, obtaining the comprehensive time-varying fault probability model by using a series model according to the equipment basic fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model;

[0141] Considering that the three factors affecting equipment failure are independent of each other, a series model is used to describe the comprehensive time-varying failure probability of the three types of equipment. As shown in formula (32).

[0142]

[0143] S56. According to the comprehensive time-varying fault probability model, sampling is performed by using the Monte Carlo method to obtain the grid fault state occurrence probability model.

[0144] Based on the comprehensive failure probability The Monte Carlo method is used to sample the equipment state, as shown in formula (33). Assuming the number of sampling is M, the probability of the grid fault state is shown in formula (34).

[0145]

[0146]

[0147] Where: and are the fault states of the line, transformer and thermal power unit j at time t respectively. otherwise ε is a random number that follows a uniform distribution between [0,1].

[0148] S6. Perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model;

[0149] The risk scenario probability model is:

[0150]

[0151] In the formula, p s is the probability of occurrence of the risk scenario, p s,X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

[0152] S7. Establish a risk consequence indicator system based on the potential risks of power grid system operation;

[0153] In order to comprehensively improve the disaster prevention capability of the planned power grid, this section fully considers the future characteristics of the power grid and the impact of extreme weather, extracts the potential risks of insufficient adequacy, insufficient flexibility, and insufficient safety and stability during system operation, and establishes a set of risk consequence indicator systems for planning schemes based on this, including adequacy indicators, flexibility indicators, and safety and stability indicators.

[0154] Adequacy index

[0155] Adequacy refers to the system's generation, transmission and power supply capabilities after considering factors such as the shutdown of some equipment or insufficient output. The two consequence indicators of power shortage and power shortage are used to evaluate the adequacy of the planning scheme, as shown in equations (36)-(37) respectively.

[0156]

[0157] Where: C s,1,t is the power shortage of the system at time t under risk scenario s; Ω N is a node set; is the load reduction of node i at time t; is the total load demand at node i at time t; C s,2,t is the power shortage of the system under risk scenario s.

[0158] Flexibility Index

[0159] Flexibility refers to the system's ability to respond quickly to disturbance events. As the proportion of conventional units in the new power system gradually decreases, its regulation ability in extreme weather conditions decreases, and the system faces the risk of insufficient flexibility. The flexibility index is incorporated into the evaluation system, and the spinning reserve deficiency consequence index is used to evaluate the flexibility of the planning scheme, as shown in formula (38).

[0160]

[0161] Where: C s,3,t is the spinning reserve shortage of the system at time t under risk scenario s; is the spinning reserve of the conventional unit at time t, are the sum of the outputs of each type of units at node i.

[0162] Safety and stability indicators

[0163] Safety and stability refer to the ability of the system to avoid loss of control and blackouts in the event of an accident. The safety and stability of the planning scheme are evaluated using indicators such as voltage over-limit, power flow over-limit, transformer over-limit, and frequency over-limit, as shown in equations (39)-(42), respectively.

[0164]

[0165] S8. Input the target power grid data into the risk scenario probability model, and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

[0166] The historical weather data and power grid accident records in Northeast my country were investigated, two types of potential extreme weather were screened out, and risk scenarios were identified and generated. The method proposed in the present invention was used to calculate the probabilities of extreme weather and multiple uncertain factors such as source, network, and load in the generated risk scenarios in MATLAB. Finally, the evaluation index system established by the present invention was used to conduct a multi-dimensional risk assessment of the Northeast Power Grid, verifying the effectiveness of the proposed method.

[0167] This embodiment is a method for risk assessment of power system for multiple types of extreme weather, including defining risk scenario elements, determining risk scenario judgment confidence values ​​based on thresholds under normal power grid operation conditions, constructing a comprehensive assessment framework for all scenarios based on the confidence values ​​and risk scenario elements, establishing an extreme weather occurrence probability model based on the comprehensive assessment framework for all scenarios, generating and screening based on the risk scenario elements and historical power grid source and load data using approximate daily search method and approximate distance method to obtain a source and load power probability model, constructing a failure probability model under the influence of different factors based on power grid equipment failure factors, and determining a comprehensive time-varying failure probability model using a series model to obtain a power grid failure state occurrence probability model, performing a comprehensive analysis based on the extreme weather occurrence probability model and the power grid failure state occurrence probability model to obtain a risk scenario probability model, establishing a risk consequence indicator system based on the potential risks of power grid system operation, inputting target power grid data into the risk scenario probability model, and using the risk consequence indicator system for evaluation to obtain a risk assessment result. This provides a reference for the reasonable formulation of actual power grid risk avoidance strategies, which is conducive to improving the disaster response capabilities of various aspects of future power systems.

[0168] Embodiment 2

[0169] An embodiment of the present invention provides a power system risk assessment system 200 for multiple types of extreme weather, such as Figure 2 As shown, including:

[0170] The confidence value determination module 201 is used to define risk scenario elements and determine the confidence value of risk scenario judgment based on the threshold value under the normal operation condition of the power grid;

[0171] A framework construction module 202 is used to construct a comprehensive evaluation framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements;

[0172] A weather probability model building module 203 is used to build an extreme weather occurrence probability model based on the full-scenario comprehensive evaluation framework;

[0173] The source load power probability model building module 204 is used to obtain an initial source load power set and a feature vector set based on the risk scenario elements and the historical source load data of the power grid, and use the approximate distance method to screen to obtain the optimal source load power set and the corresponding system source load state probability, and then determine the source load power probability model;

[0174] The fault probability model building module 205 is used to build multiple fault probability models under the influence of different factors based on the fault factors of the power grid equipment, and determine the comprehensive time-varying fault probability model by series connection, and then obtain the power grid fault state occurrence probability model by sampling;

[0175] A risk scenario probability model building module 206 is used to perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model;

[0176] An indicator system construction module 207 is used to establish a risk consequence indicator system based on the potential risks of power grid system operation;

[0177] The risk assessment module 208 is used to input the target power grid data into the risk scenario probability model, and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

[0178] Furthermore, the source-load power probability model building module 204 includes:

[0179] The source load data acquisition module 2041 is used to acquire the historical source load data of the power grid;

[0180] A data search module 2042 is used to obtain an initial source-load power set and a feature vector set by using an approximate daily search method based on the risk scenario elements and the historical source-load data of the power grid;

[0181] The data screening module 2043 is used to screen the initial source load power set and the feature vector set using the approximate distance method to obtain the optimal source load power set and the corresponding system source load state probability, and then determine the source load power probability model.

[0182] Furthermore, the fault probability model building module 205 includes:

[0183] The fault factor determination module 2051 is used to determine the fault factor data of the power grid equipment, including the service life data of the equipment, the short-term external environment data and the operation status data;

[0184] A basic failure calculation module 2052 is used to calculate the basic failure rate and basic failure probability of all types of equipment based on the long-term service life data using a bathtub curve and an exponential model to obtain a basic failure probability model of equipment;

[0185] An environmental impact failure calculation module 2053 is used to describe the failure probability of different environmental conditions based on the short-term external environmental data and use the exponential model to obtain an environment-dependent equipment failure probability model;

[0186] An operation fault calculation module 2054 is used to obtain a fault probability model of the operation state by fitting a linear model based on the operation state data;

[0187] The comprehensive time-varying fault probability calculation module 2055 is used to obtain the comprehensive time-varying fault probability model by using a series model according to the equipment basic fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model;

[0188] The comprehensive fault calculation module 2056 is used to obtain the power grid fault state occurrence probability model by sampling through the Monte Carlo method according to the comprehensive time-varying fault probability model.

[0189] Furthermore, the environmental impact fault calculation module 2053 includes:

[0190] The parameter analysis module 20531 is used to obtain the historical life data and operating temperature data of the power grid line and transformer, and analyze them to obtain the line life parameters and transformer life parameters;

[0191] A failure rate calculation module 20532 is used to calculate the line failure rate and transformer failure rate that are dependent on the temperature condition based on the line life parameter and the transformer life parameter using the basic failure rate formula of the device;

[0192] The temperature-affected failure rate calculation module 20533 is used to calculate the line failure rate and transformer failure rate that are dependent on the temperature condition using the exponential model to obtain a temperature failure probability model;

[0193] Ice and wind impact model building module 20534, used to obtain historical ice and wind meteorological factor data, and build line ice growth model and line stress model;

[0194] The load analysis module 20535 is used to analyze the line ice growth model and the line stress model based on the metal deformation theory to obtain a line failure probability model of ice-wind load.

[0195] Furthermore, the risk scenario probability model is:

[0196]

[0197] In the formula, p s is the probability of occurrence of the risk scenario, p s,X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

[0198] In this embodiment, the present invention uses a confidence value determination module to define risk scenario elements, and determines the risk scenario judgment confidence value based on the threshold value under the normal operation condition of the power grid; a framework construction module is used to construct a full-scenario comprehensive evaluation framework based on the confidence value and the risk scenario elements; a weather probability model construction module is used to establish an extreme weather occurrence probability model according to the full-scenario comprehensive evaluation framework; a source-load power probability model construction module is used to generate and filter based on the risk scenario elements and the historical source-load data of the power grid using the approximate daily search method and the approximate distance method to obtain a source-load power probability model; a fault probability model construction module is used to construct a full-scenario comprehensive evaluation framework based on the confidence value and the risk scenario elements; a weather probability model construction module is used to construct an extreme weather occurrence probability model based on the full-scenario comprehensive evaluation framework; a source-load power probability model construction module is used to construct an extreme weather occurrence probability model based on the risk scenario elements and the historical source-load data of the power grid using the approximate daily search method and the approximate distance method; a fault probability model construction module is used to construct an extreme weather occurrence probability model based on the power grid; a source-load power probability model construction module is used to construct an extreme weather occurrence probability model based on the confidence value and the risk scenario elements ... The failure factors of network equipment are used to construct the failure probability model under the influence of different factors, and the series model is used to determine the comprehensive time-varying failure probability model, and then the probability model of the occurrence of power grid fault state is obtained; the risk scenario probability model construction module is used to conduct a comprehensive analysis based on the extreme weather probability model and the probability model of the occurrence of power grid fault state, and obtain the risk scenario probability model; the indicator system construction module is used to establish a risk consequence indicator system based on the potential risks of power grid system operation; the risk assessment module is used to input the target power grid data into the risk scenario probability model, and use the risk consequence indicator system for assessment to obtain the risk assessment result. It provides a reference for the reasonable formulation of the actual power grid risk avoidance strategy, which is conducive to improving the disaster response capabilities of various aspects of the future power system.

[0199] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A power system risk assessment method for multiple types of extreme weather, characterized in that: include: S1. Define risk scenario elements and determine the confidence value of risk scenario judgment based on the threshold value under normal operation of the power grid; S2. Constructing a comprehensive evaluation framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements; S3. Establishing an extreme weather occurrence probability model based on the comprehensive evaluation framework of the entire scenario; S4. Based on the risk scenario elements and the historical source and load data of the power grid, an initial source and load power set and a feature vector set are obtained, and the approximate distance method is used to screen to obtain the optimal source and load power set and the corresponding system source and load state probability, and then determine the source and load power probability model; S5. Based on the failure factors of power grid equipment, multiple failure probability models under the influence of different factors are constructed, and a comprehensive time-varying failure probability model is determined by series connection, and then a probability model of the occurrence of power grid failure state is obtained by sampling; S6. Perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model; S7. Establish a risk consequence indicator system based on the potential risks of power grid system operation; S8. Input the target power grid data into the risk scenario probability model, and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

2. The power system risk assessment method for multiple types of extreme weather according to claim 1 is characterized in that: The S4 includes: S41, obtaining the historical source and load data of the power grid; S42, based on the risk scenario elements and the historical source and load data of the power grid, using an approximate daily search method to obtain an initial source and load power set and a feature vector set; S43. Using an approximate distance method, the initial source-load power set and the feature vector set are screened to obtain an optimal source-load power set and a corresponding system source-load state probability, and then determine the source-load power probability model.

3. The power system risk assessment method for multiple types of extreme weather according to claim 1 is characterized in that: The S5 includes: S51, determining the power grid equipment failure factor data, including equipment service life data, short-term external environment data and operation status data; S52. Based on the long-term service life data, the basic failure rate and basic failure probability of all types of equipment are calculated using the bathtub curve and the exponential model to obtain a basic failure probability model of equipment; S53, based on the short-term external environment data, using the exponential model to describe the failure probability under different environmental conditions, and obtain an environment-dependent equipment failure probability model; S54, fitting the operating state data using a linear model to obtain a failure probability model of the operating state; S55, obtaining the comprehensive time-varying fault probability model by using a series model according to the equipment basic fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model; S56. According to the comprehensive time-varying fault probability model, sampling is performed by Monte Carlo method to obtain the grid fault state occurrence probability model.

4. The power system risk assessment method for multiple types of extreme weather according to claim 3 is characterized in that: The S53 includes: S531, obtaining historical life data and operating temperature data of power grid lines and transformers, analyzing them, and obtaining line life parameters and transformer life parameters; S532, based on the line life parameter and the transformer life parameter, using the basic failure rate formula of the equipment to calculate and obtain the line failure rate and transformer failure rate that are dependent on the temperature condition; S533, calculating the line failure rate and transformer failure rate that are dependent on the temperature condition by using the exponential model to obtain a temperature failure probability model; S534, obtaining historical ice and wind meteorological factor data, and constructing a line ice growth model and a line stress model; S535. According to metal deformation theory, the line ice growth model and the line stress model are analyzed to obtain a line failure probability model under ice-wind load.

5. The power system risk assessment method for multiple types of extreme weather according to claim 1 is characterized in that: In step S6, the risk scenario probability model is: In the formula, p s is the probability of occurrence of the risk scenario, p s, X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

6. A power system risk assessment system for multiple types of extreme weather, characterized in that: include: A confidence value determination module is used to define risk scenario elements and determine the confidence value of risk scenario judgment based on thresholds under normal power grid operation conditions; A framework construction module, used to construct a comprehensive assessment framework for the entire scenario based on the risk scenario judgment confidence value and the risk scenario elements; A weather probability model building module is used to build an extreme weather occurrence probability model based on the full-scenario comprehensive evaluation framework; A source-load power probability model construction module is used to obtain an initial source-load power set and a feature vector set based on the risk scenario elements and the historical source-load data of the power grid, and to screen them using the approximate distance method to obtain the optimal source-load power set and the corresponding system source-load state probability, thereby determining the source-load power probability model; The fault probability model building module is used to build multiple fault probability models under the influence of different factors based on the fault factors of power grid equipment, determine the comprehensive time-varying fault probability model through series connection, and then obtain the probability model of power grid fault state through sampling; A risk scenario probability model building module is used to perform a comprehensive analysis based on the extreme weather occurrence probability model and the power grid fault state occurrence probability model to obtain a risk scenario probability model; The indicator system construction module is used to establish a risk consequence indicator system based on the potential risks of power grid system operation; The risk assessment module is used to input the target power grid data into the risk scenario probability model and use the risk consequence indicator system to perform an assessment to obtain a risk assessment result.

7. The power system risk assessment system for multiple types of extreme weather according to claim 6 is characterized in that: The source load power probability model building module includes: A source-load data acquisition module, used to acquire the historical source-load data of the power grid; A data search module, configured to obtain an initial source-load power set and a feature vector set by using an approximate daily search method based on the risk scenario elements and the historical source-load data of the power grid; The data screening module is used to screen the initial source load power set and the feature vector set using the approximate distance method to obtain the optimal source load power set and the corresponding system source load state probability, and then determine the source load power probability model.

8. The power system risk assessment system for multiple types of extreme weather according to claim 6 is characterized in that: The fault probability model building module includes: A fault factor determination module is used to determine the fault factor data of power grid equipment, including equipment service life data, short-term external environment data and operation status data; A basic failure calculation module is used to calculate the basic failure rate and basic failure probability of all types of equipment based on the long-term service life data, using a bathtub curve and an exponential model, to obtain a basic failure probability model of equipment; An environmental impact failure calculation module is used to describe the failure probability of different environmental conditions based on the short-term external environmental data and use the exponential model to obtain an environment-dependent equipment failure probability model; An operation fault calculation module is used to obtain a fault probability model of the operation state by fitting a linear model based on the operation state data; A comprehensive time-varying fault probability calculation module, used to obtain the comprehensive time-varying fault probability model by using a series model according to the basic equipment fault probability model, the environment-dependent equipment fault probability model and the operating state fault probability model; The comprehensive fault calculation module is used to obtain the probability model of the power grid fault state by sampling through the Monte Carlo method according to the comprehensive time-varying fault probability model.

9. The power system risk assessment system for multiple types of extreme weather according to claim 8, characterized in that: The environmental impact fault calculation module includes: The parameter analysis module is used to obtain the historical life data and operating temperature data of the power grid lines and transformers, and analyze them to obtain the line life parameters and transformer life parameters; A failure rate calculation module, used to calculate based on the line life parameters and the transformer life parameters using the basic failure rate formula of the device to obtain the line failure rate and transformer failure rate that are dependent on the temperature condition; A temperature-affected failure rate calculation module, used to calculate the line failure rate and transformer failure rate that are dependent on the temperature condition using the exponential model to obtain a temperature failure probability model; Ice-wind impact model building module, used to obtain historical ice-wind meteorological factor data, build line ice growth model and line stress model; The load analysis module is used to analyze the line ice growth model and the line stress model based on metal deformation theory to obtain a line failure probability model of ice-wind load.

10. The power system risk assessment system for multiple types of extreme weather according to claim 6, characterized in that: In the fault probability model construction module, the risk scenario probability model is: In the formula, p s is the probability of occurrence of the risk scenario, p s, X is the probability of meteorological factor X occurring under risk scenario s, Y is the power grid state matrix, is the probability of the occurrence of the i-th meteorological factor at time t under risk scenario s, is the system source-load state probability, is the meteorological factor, D2 is the conductor diameter of line 2, and M is the number of sampling times.

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