Power grid risk assessment method, device and equipment under extreme weather and storage medium

Abstract

The invention relates to the technical field of power system risk assessment, in particular to a power grid risk assessment method, device and equipment under extreme weather and a storage medium, and the method comprises the steps: analyzing an abnormal action mechanism of an extreme weather factor on power grid equipment under a tropical cyclone, and obtaining a wind power abnormal output space-time matrix under the extreme weather; a line fault state space-time matrix is obtained through power transmission line fault probability sampling, and an abnormal state matrix of a power grid is obtained through integration; combining the abnormal state matrix with power flow redistribution to obtain a power grid abnormal coupling process considering the extreme weather and the power flow redistribution; a power grid abnormal propagation path is obtained through re-scheduling and island processing of a unit in a power grid abnormal coupling process, an abnormal propagation path evolution process is determined, and abnormal propagation paths of different power grids are obtained through simulation; and determining risk assessment indexes according to the abnormal propagation paths of different power grids, and calculating to obtain a risk assessment result considering the abnormal space-time evolution of the power grids.

Description

Technical Field

[0001] The present invention relates to the technical field of power system risk assessment, and in particular to a method, device, equipment and storage medium for power grid risk assessment under extreme weather conditions. Background Art

[0002] In recent years, global warming has led to frequent tropical cyclone events such as typhoons and extreme storm surges in my country's coastal areas. These events not only cause abnormal fluctuations in offshore wind power output, but may also cause transmission line disconnection accidents, seriously threatening the safety and reliable operation of the power grid. Unlike other extreme disasters, tropical cyclones usually have specific spatiotemporal trajectories. During the passage of tropical cyclones, the degree of impact on power grid equipment and its abnormal characteristics change over time, which makes the power grid anomaly show a complex spatiotemporal evolution trend, thereby increasing the difficulty of wind power assessment in the power grid.

[0003] The causes of grid operation risks mainly come from large-scale or severe disturbances. With the widespread application of distributed power sources in new power grids, grid operation risks have evolved from traditional power flow over-limit under single equipment failure to multi-dimensional operation indicator abnormalities under complex large-scale distributed power disturbances. The random volatility of flexible resources such as electric vehicles, distributed photovoltaics and wind power may amplify the operation risks of the grid and break through the safety and stability boundaries through the cluster effect after aggregation. In addition, large-scale disturbances also cause significant impacts on the grid, thereby causing operational risks. Due to its frequency and destructiveness, extreme meteorological disasters have received widespread attention in the field of grid risk assessment in recent years.

[0004] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for evaluating power grid risks under extreme weather conditions, thereby effectively solving the problems in the background technology.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a method for risk assessment of power grid under extreme weather conditions, comprising the following steps:

[0007] The abnormal action mechanism of extreme meteorological factors under tropical cyclones on power grid equipment is analyzed to obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions. The spatiotemporal matrix of line fault status is obtained by sampling the probability of transmission line faults, and the abnormal state matrix of the power grid is obtained by synthesis.

[0008] The abnormal state matrix is ​​combined with power flow redistribution to obtain a power grid abnormal coupling process considering extreme weather and power flow redistribution;

[0009] By re-dispatching the units and handling the islands during the abnormal coupling process of the power grid, the abnormal propagation path of the power grid is obtained, the evolution process of the abnormal propagation path is determined, and the abnormal propagation paths of different power grids are obtained through simulation;

[0010] According to the abnormal propagation paths of different power grids, the risk assessment indicators are determined, and the risk assessment results considering the spatiotemporal evolution of power grid anomalies are calculated.

[0011] Furthermore, the analysis of the abnormal action mechanism of extreme meteorological factors under tropical cyclones on power grid equipment includes:

[0012] Analyze the mechanism of extreme meteorological factors on wind power anomaly treatment and establish a wind turbine output model under tropical cyclone passage;

[0013] Analyze the abnormal effect mechanism of extreme meteorological factors on transmission lines, and establish the probability model of tower collapse and the probability model of line disconnection of unit span lines;

[0014] Transmission line i is composed of n towers and n-1 unit span lines in series, so the failure probability of transmission line i in the first j time periods is:

[0015]

[0016] In the formula, represents the failure probability of tower k in the first j periods; Represents the failure probability of the line per unit span.

[0017] Furthermore, the wind power abnormal output spatiotemporal matrix is ​​calculated by the wind turbine output model under the tropical cyclone passage, and the wind power abnormal output spatiotemporal matrix is:

[0018]

[0019] Where P wi,j represents the abnormal output of wind farm i in period j;

[0020] The line fault state space-time matrix is ​​jointly calculated by the tower collapse probability model and the line disconnection probability model of the unit span line, and is obtained by sampling different lines according to the fault probability. The line fault state space-time matrix is:

[0021]

[0022] In the formula, Indicates the operating status of line l to the jth period, 0 represents line closed, and 1 represents line open.

[0023] Furthermore, the abnormal state matrix is:

[0024] AE t = {P w,t ,S lic, t};

[0025] Where P w,t =[P w1,t ,…P wN,t ] represents the abnormal output vector of the wind farm in period t, Represents the operating state vector of the line in period t.

[0026] Further, combining the abnormal state matrix with power flow redistribution includes:

[0027] P f,t =F(AE t ,x t );

[0028] In the formula, AE t is the abnormal state matrix, x t represents the system parameter vector; F(·) represents the line transmission power function; P f,t represents the power vector of all lines transmitted by the system during period t, P f,t There may be a transmission power overload of the line in the vector;

[0029] For P f,t Possible overload lines in Calculate its failure probability at time t for:

[0030]

[0031] Where P fl,t represents the transmission power of the overloaded line l; represents the transmission power limit of the overloaded line l; Indicates the rated transmission power of the overload line l.

[0032] Furthermore, the abnormal coupling process of the power grid considering extreme weather and power flow redistribution includes:

[0033] For overloaded lines caused by power flow redistribution, sampling is performed based on their failure probability to obtain the abnormal transition state of the line under power flow redistribution.

[0034]

[0035] according to And the line fault vector Prob caused by extreme meteorological factors lic,t , and obtain the line fault status in period t+1:

[0036]

[0037] In the formula, G(·) is the following conditional function:

[0038]

[0039] Where Sa(·) represents the sampling function. When the transition state of line l in period t is disconnected, that is, When t+1 period is still disconnected; otherwise, sampling is performed according to the fault probability of t+1 period to establish the state;

[0040] Finally, combined with the abnormal wind power output vector P in period t+1 w，t+1 Get the system abnormal status at time t+1:

[0041] AE t+1 = {P w,t+1 ,S lic,t+1}.

[0042] Furthermore, the unit rescheduling comprises the following steps:

[0043] Step S11: unit adjustment and emergency machine or load shedding;

[0044] Assuming that the imbalance between supply and demand of the system caused by the fault is ΔP, for the units involved in the regulation If the power supply is less than the load demand ΔP>0 and the unbalance does not exceed the upper regulation total power ∑ i (P gmax,i -P g0,i ), the regulated power is expressed as:

[0045]

[0046] Where P gmax,i represents the maximum power of unit i; P g0,i represents the reference power of unit i;

[0047] If ΔP>∑ i (P gmax,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the load shedding operation should be performed at this time:

[0048] ΔP ld =ΔP-∑ i (P gmax,i -P g0,i );

[0049] If the power supply is greater than the load demand, that is, ΔP<0, and the unbalanced amount is not less than the total power ΔP≥∑ i (P gmin,i -Pg0,i ), the regulated power is expressed as:

[0050]

[0051] Where P min,i represents the minimum power of unit i;

[0052] If ΔP<∑ i (P gmin,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the unit should be cut off at this time:

[0053] ΔP gt =ΔP-∑ i (P gmin,i -P g0,i );

[0054] Step S12: power flow calculation, after the supply and demand are balanced by adjusting the units or cutting the units and loads, the line transmission power is obtained by power flow calculation;

[0055] Step S13: Overload line removal: According to the obtained transmission power, the overload line is cut off. The failure probability determines the overload line state;

[0056] Steps S11 to S13 are repeated until the system reaches a new equilibrium state.

[0057] Furthermore, the island processing comprises the following steps:

[0058] Step S21: If there is no generator in the island, the load to be abandoned is ΔP ld P is the total load of the island ds :

[0059] ΔP ld =P ds ;

[0060] If there is only one generator in the island and the total load demand P ds Less than the power generation P g0 , then the generator reduces the power ΔP gd for:

[0061] ΔP gd =max(P ds -P g0 ,P gmin -P g0 );

[0062] Where P gmin Indicates the minimum power of the generator.

[0063] Step S22: If the generator power reduction cannot balance the system supply-demand imbalance ΔP caused by the fault, the generator is cut off;

[0064] If there is only one generator in the island and the total load demand P ds Greater than the power generation P g0 , then the generator increases the power ΔP gu for:

[0065] ΔP gu =min(P ds -P g0 ,P gmax -P g0 );

[0066] Where P gmax Indicates the maximum power of the generator.

[0067] Step S23: if the generator power cannot be increased to balance ΔP, load shedding occurs;

[0068] For an island with multiple generators, the calculation process is the same as steps S21 to S23.

[0069] Furthermore, the abnormal propagation paths of different power grids are obtained through simulation, including:

[0070] Obtain different load curves ΔP d1 ,ΔP d2 ,…,ΔP dT Generate different supply and demand imbalance time series ΔP1, ΔP2, …, ΔP T ,Through re-dispatching, power flow redistribution and island processing, different grid anomaly propagation paths are finally formed;

[0071] In the simulation, the load shedding rate LSR is selected as the cutoff index in the cascading failure simulation. The load shedding rate includes:

[0072]

[0073] Where D and D′ are the sets of nodes with load in the original network and the island network after decoupling, respectively. D and P D ″ are the corresponding residual loads in each network respectively.

[0074] Furthermore, the risk assessment indicators include:

[0075] Load factor index:

[0076] Define α i is the ratio of the active power of branch i at stage t and t-1 to its limit active power, which reflects the load level of branch i under stable operating conditions:

[0077]

[0078] In the formula and are the power flows of branch i in the t and t-1 stages of the cascading failure, respectively. is the limiting power flow of branch i;

[0079] Coupling coefficient:

[0080] Defining β i is the ratio of the change in active power of branch i at stages t-1, t, and t+1 to the original active power of branch i:

[0081]

[0082] In the formula They represent the active power on line i at stages t-1, t, and t+1 of a cascading fault respectively;

[0083] Power outage expectations:

[0084] Using the expected power shortage EENS as an indicator to understand and quantify the impact of typhoons in specific fault chains:

[0085]

[0086] In the formula, R E is the expected power shortage of the system; P(s) is the system state probability; C(s) is the load reduction caused by the fault state s, T is the evaluation event frame, and F is the system state set.

[0087] The present invention also includes a device for evaluating power grid risk under extreme weather conditions, using the above method, the device comprising:

[0088] The abnormal state matrix unit is used to analyze the abnormal action mechanism of extreme meteorological factors on power grid equipment under tropical cyclones, obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions, obtain the spatiotemporal matrix of line fault states by sampling the probability of transmission line faults, and comprehensively obtain the abnormal state matrix of the power grid;

[0089] A coupling unit, used for combining the abnormal state matrix with power flow redistribution to obtain an abnormal coupling process of a power grid considering extreme weather and power flow redistribution;

[0090] A propagation path unit, used to obtain the abnormal propagation path of the power grid by re-dispatching the units and processing the islands during the abnormal coupling process of the power grid, determine the abnormal propagation path evolution process, and obtain the abnormal propagation paths of different power grids through simulation;

[0091] The evaluation unit is used to determine the risk assessment index for the abnormal propagation paths of different power grids, and calculate the risk assessment results considering the spatiotemporal evolution of power grid anomalies.

[0092] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0093] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0094] The beneficial effects of the present invention are as follows: by comprehensively considering the complex interaction between extreme meteorological factors caused by tropical cyclones and power grid tidal operation, a technical solution that can dynamically adjust the risk assessment model is proposed. By introducing the coupled interaction of time-varying extreme meteorological factors and tidal risk factors, the abnormal state change trend of power grid equipment is evaluated in real time. The present invention can not only improve the fault prediction accuracy of power grid equipment under extreme meteorological conditions, but also greatly reduce the errors caused by traditional evaluation methods that rely on historical weights or empirical formulas. In addition, the present invention ensures that the evaluation results are more timely and accurate by dynamically adjusting the quantitative model of equipment abnormality, thereby effectively improving the safety and reliability of power grid operation. This innovation greatly reduces the randomness of the evaluation results and provides important technical support for the formulation of power grid disaster emergency plans and wind power operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0096] Figure 1 is a flow chart of the method in Example 1;

[0097] Figure 2 It is a structural schematic diagram of the system in Example 1;

[0098] Figure 3 is a flow chart of the risk assessment method in Example 2;

[0099] Figure 4 This is a diagram of the abnormal coupling process of the power grid in Example 2;

[0100] Figure 5 This is a schematic diagram of the abnormal propagation path of the power grid in Example 2;

[0101] Figure 6 This is a flow chart of the abnormal propagation path evolution in Example 2;

[0102] Figure 7 It is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0103] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0104] Embodiment 1:

[0105] like Figure 1 As shown: A method for risk assessment of power grid under extreme weather conditions, comprising the following steps:

[0106] The abnormal action mechanism of extreme meteorological factors on power grid equipment under tropical cyclones is analyzed to obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions. The spatiotemporal matrix of line fault status is obtained by sampling the probability of transmission line faults, and the abnormal state matrix of the power grid is obtained by synthesis.

[0107] The abnormal state matrix is ​​combined with power flow redistribution to obtain the abnormal coupling process of the power grid considering extreme weather and power flow redistribution.

[0108] By re-dispatching the units and handling the islands during the abnormal coupling process of the power grid, the abnormal propagation path of the power grid is obtained, the evolution process of the abnormal propagation path is determined, and the abnormal propagation paths of different power grids are obtained through simulation;

[0109] According to the abnormal propagation paths of different power grids, the risk assessment indicators are determined, and the risk assessment results considering the spatiotemporal evolution of power grid anomalies are calculated.

[0110] By comprehensively considering the complex interaction between extreme meteorological factors caused by tropical cyclones and power grid tidal operation, a technical solution that can dynamically adjust the risk assessment model is proposed. By introducing the coupled interaction of time-varying extreme meteorological factors and tidal risk factors, the abnormal state change trend of power grid equipment is evaluated in real time. It can not only improve the accuracy of fault prediction of power grid equipment under extreme meteorological conditions, but also greatly reduce the errors caused by traditional evaluation methods that rely on historical weights or empirical formulas. In addition, by dynamically adjusting the quantitative model of equipment abnormal state, the evaluation results are ensured to be more timely and accurate, effectively improving the safety and reliability of power grid operation. This innovation greatly reduces the randomness of the evaluation results and provides important technical support for the formulation of power grid disaster emergency plans and wind power operation management.

[0111] In this embodiment, the abnormal action mechanism of extreme meteorological factors under tropical cyclones on power grid equipment is analyzed, including:

[0112] Analyze the mechanism of extreme meteorological factors on wind power anomaly treatment and establish a wind turbine output model under tropical cyclone passage;

[0113] Analyze the abnormal effect mechanism of extreme meteorological factors on transmission lines, and establish the probability model of tower collapse and the probability model of line disconnection of unit span lines;

[0114] Transmission line i is composed of n towers and n-1 unit span lines in series, so the failure probability of transmission line i in the first j time periods is:

[0115]

[0116] In the formula, represents the failure probability of tower k in the first j periods; Represents the failure probability of the line per unit span.

[0117] The spatiotemporal matrix of abnormal wind power output calculated by the wind turbine output model under tropical cyclone passage is:

[0118]

[0119] Where P wi,j represents the abnormal output of wind farm i in period j;

[0120] The failure probability of the line can be obtained by jointly calculating the tower collapse probability model and the line disconnection probability model of the unit span line. The line fault state space-time matrix is ​​obtained by sampling different lines according to their failure probabilities:

[0121]

[0122] In the formula, Indicates the operating status of line l to the jth period, 0 represents line closed, and 1 represents line open.

[0123] Among them, the abnormal state matrix is:

[0124] AE t = {P w,t ,S lic, t};

[0125] Where P w,t =[P w1,t ,…P wN,t ] represents the abnormal output vector of the wind farm in period t, Represents the operating state vector of the line in period t.

[0126] As a preferred embodiment of the above, the abnormal state matrix is ​​combined with power flow redistribution, including:

[0127] P f,t =F(AE t ,x t );

[0128] In the formula, AE t is the abnormal state matrix, x t represents the system parameter vector; F(·) represents the line transmission power function; P f,t represents the power vector of all lines transmitted by the system during period t, P f,t There may be a transmission power overload of the line in the vector;

[0129] For P f,t Possible overload lines in Calculate its failure probability at time t for:

[0130]

[0131] Where P fl,t represents the transmission power of the overloaded line l; represents the transmission power limit of the overloaded line l; Indicates the rated transmission power of the overload line l.

[0132] In this embodiment, the abnormal coupling process of the power grid considering extreme weather and power flow redistribution is obtained, including:

[0133] For overloaded lines caused by power flow redistribution, sampling is performed based on their fault probability to obtain the abnormal transition state of the line under power flow redistribution:

[0134]

[0135] according to And the line fault vector Prob caused by extreme meteorological factors lic,t , and obtain the line fault status in period t+1:

[0136]

[0137] In the formula, G(·) is the following conditional function:

[0138]

[0139] Where Sa(·) represents the sampling function. When the transition state of line l in period t is disconnected, that is, When t+1 period is still disconnected; otherwise, sampling is performed according to the fault probability of t+1 period to establish the state;

[0140] Finally, combined with the abnormal wind power output vector P in period t+1w，t+1 Get the system abnormal status at time t+1:

[0141] AE t+1 = {P w,t+1 ,S lic,t+1}.

[0142] The unit rescheduling includes the following steps:

[0143] Step S11: unit adjustment and emergency machine or load shedding;

[0144] Assuming that the imbalance between supply and demand of the system caused by the fault is ΔP, for the units involved in the regulation If the power supply is less than the load demand ΔP>0 and the unbalance does not exceed the upper regulation total power ∑ i (P gmax,i -P g0,i ), the regulated power is expressed as:

[0145]

[0146] Where P gmax,i represents the maximum power of unit i; P g0,i represents the reference power of unit i;

[0147] If ΔP>∑ i (P gmax,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the load shedding operation should be performed at this time:

[0148] ΔP ld =ΔP-∑ i (P gmax,i -P g0,i );

[0149] If the power supply is greater than the load demand, that is, ΔP<0, and the unbalanced amount is not less than the total power ΔP≥∑ i (P gmin,i -P g0,i ), the regulated power is expressed as:

[0150]

[0151] Where P min,i represents the minimum power of unit i;

[0152] If ΔP<∑ i (P gmin,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the unit should be cut off at this time:

[0153] ΔP gt=ΔP-∑ i (P gmin,i -P g0,i );

[0154] Step S12: power flow calculation, after the supply and demand are balanced by adjusting the units or cutting the units and loads, the line transmission power is obtained by power flow calculation;

[0155] Step S13: Overload line removal: According to the obtained transmission power, the overload line is cut off. The failure probability determines the overload line state;

[0156] Steps S11 to S13 are repeated until the system reaches a new equilibrium state.

[0157] Island processing includes the following steps:

[0158] Step S21: If there is no generator in the island, the load to be abandoned is ΔP ld P is the total load of the island ds :

[0159] ΔP ld =P ds ;

[0160] If there is only one generator in the island and the total load demand P ds Less than the power generation P g0 , then the generator reduces the power ΔP gd for:

[0161] ΔP gd =max(P ds -P g0 ,P gmin -P g0 );

[0162] Where P gmin Indicates the minimum power of the generator.

[0163] Step S22: If the generator power reduction cannot balance the system supply-demand imbalance ΔP caused by the fault, the generator is cut off;

[0164] If there is only one generator in the island and the total load demand P ds Greater than the power generation P g0 , then the generator increases the power ΔP gu for:

[0165] ΔP gu =min(P ds -P g0 ,P gmax -P g0 );

[0166] Where P gmax Indicates the maximum power of the generator.

[0167] Step S23: if the generator power cannot be increased to balance ΔP, load shedding occurs;

[0168] For an island with multiple generators, the calculation process is the same as steps S21 to S23.

[0169] As a preferred embodiment of the above, the abnormal propagation paths of different power grids are obtained through simulation, including:

[0170] Obtain different load curves ΔP d1 ,ΔP d2 ,…,ΔP dT Generate different supply and demand imbalance time series ΔP1, ΔP2, …, ΔP T ,Through re-dispatching, power flow redistribution and island processing, different grid anomaly propagation paths are finally formed;

[0171] In the simulation, the load shedding rate LSR is selected as the cutoff indicator in the cascading failure simulation. The load shedding rate includes:

[0172]

[0173] Where D and D′ are the sets of nodes with load in the original network and the island network after decoupling, respectively. D and P D ″ are the corresponding residual loads in each network respectively.

[0174] In this embodiment, the risk assessment indicators include:

[0175] Load factor index:

[0176] Define α i is the ratio of the active power of branch i at stage t and t-1 to its limit active power, which reflects the load level of branch i under stable operating conditions:

[0177]

[0178] In the formula and are the power flows of branch i in the t and t-1 stages of the cascading failure, respectively. is the limiting power flow of branch i;

[0179] Coupling coefficient:

[0180] Defining β i is the ratio of the change in active power of branch i at stages t-1, t, and t+1 to the original active power of branch i:

[0181]

[0182] In the formula They represent the active power on line i at stages t-1, t, and t+1 of a cascading fault respectively;

[0183] Power outage expectations:

[0184] Using the expected power shortage EENS as an indicator to understand and quantify the impact of typhoons in specific fault chains:

[0185]

[0186] In the formula, R E is the expected power shortage of the system; P(s) is the system state probability; C(s) is the load reduction caused by the fault state s, T is the evaluation event frame, and F is the system state set.

[0187] like Figure 2 As shown, this embodiment also includes a power grid risk assessment device under extreme weather conditions, using the above method, the device includes:

[0188] The abnormal state matrix unit is used to analyze the abnormal action mechanism of extreme meteorological factors on power grid equipment under tropical cyclones, obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions, obtain the spatiotemporal matrix of line fault states by sampling the probability of transmission line faults, and comprehensively obtain the abnormal state matrix of the power grid;

[0189] A coupling unit is used to combine the abnormal state matrix with the power flow redistribution to obtain the abnormal coupling process of the power grid considering extreme weather and power flow redistribution;

[0190] The propagation path unit is used to obtain the abnormal propagation path of the power grid through the re-dispatch and island processing of the units in the process of abnormal coupling of the power grid, determine the evolution process of the abnormal propagation path, and obtain the abnormal propagation paths of different power grids through simulation;

[0191] The evaluation unit is used to determine the risk assessment index for the abnormal propagation paths of different power grids, and calculate the risk assessment results considering the spatiotemporal evolution of power grid anomalies.

[0192] Embodiment 2:

[0193] like Figure 3 As shown, this embodiment provides a risk assessment method considering the abnormal spatiotemporal evolution of power grid under tropical cyclones, including the following steps:

[0194] Step S1: Before implementing the method of this embodiment, the predicted tropical cyclone route and intensity are predetermined. During the movement of the tropical cyclone, due to the continuous changes in extreme meteorological factors and their relative positions with the power grid equipment, the extreme meteorological factors perceived by the power grid equipment and the equipment abnormality characteristics caused by them present time-varying characteristics. According to the Rankine vortex wind field model, assuming that the distance from the power grid equipment i to the center of the tropical cyclone in the jth period is r ij , the maximum wind speed of a tropical cyclone is v max,j , the maximum wind speed radius of a tropical cyclone is R max,j , then the wind speed v sensed by device i in period j is i,j It can be expressed as:

[0195]

[0196] Where, the distance between device i and the center of the tropical cyclone is r i,j It can be expressed as:

[0197]

[0198] In the formula, (x i ,y i ) represents the horizontal and vertical coordinates of device i; (x o,j ,y o,j ) represents the horizontal and vertical coordinates of the tropical cyclone center in time period j.

[0199] The perceived wind speed v is calculated i,j , use the abnormal output model of the fan to calculate the output power P wi,j for:

[0200]

[0201] Similarly, through the wind speed v i,j , using the transmission line failure probability model to calculate the failure probability of transmission line i in the first j time periods is:

[0202]

[0203] Previously, the failure probability of the n towers and n-1 unit span lines that make up the transmission line needed to be calculated:

[0204] According to the Palmgren-Miner linear fatigue damage criterion, the cumulative fatigue damage D is obtained by superimposing the fatigue damage of several previous time periods. Let the single fatigue damage of tower i in time period j be D i,j , D i,j It can be expressed as:

[0205]

[0206] In the formula, vi,j represents the extreme wind speed sensed by tower i in period j, a and b are fatigue damage coefficients; v cr The critical wind speed that causes fatigue of the tower, v m It indicates the maximum wind speed corresponding to the damage and fracture caused by a tropical cyclone to the tower in a single period of time.

[0207] In fact, whether the tower collapses or not should be related to the cumulative fatigue damage during the entire period under the influence of the tropical cyclone. The probability that tower i will not collapse during period j is It can be expressed as:

[0208]

[0209] Where Δt is the length of each time period. The probability that the tower does not collapse in the first t time periods is It can be expressed as the product of the probability of no tower collapse from 1 to t:

[0210]

[0211] The probability that tower i does not collapse in the first t time periods can be calculated as for:

[0212]

[0213] The probability of tower i collapsing in the first t time periods for:

[0214]

[0215] Tropical cyclones affect short-term faults of unit span lines through wind pressure. Let the wind speed perceived by unit span line i in period j be v i,j , then the wind pressure it is subjected to is W Ai,j It can be expressed as:

[0216]

[0217] In the formula, α, β c ,γ,μ z , μ sc They represent wind pressure unevenness coefficient, wind load coefficient, load coefficient, wind pressure height change, and line type coefficient respectively. θ represents the angle between wind direction and conductor. The failure rate of unit span line i in time period j is λ i,j It can be expressed as:

[0218]

[0219] The probability of line disconnection of unit span line i in the first j time periods can be expressed as:

[0220]

[0221] Step S2: Assume that the wind farms affected by the tropical cyclone in the power grid are numbered from 1 to N, the affected lines are numbered from 1 to L, and the tropical cyclone transit phase is divided into T time periods. Considering only the abnormal evolution process of the power grid under extreme meteorological factors, it can be described as the binary AE = (P w ,Prob lic ). Element P w Represents the spatiotemporal matrix of abnormal output of N wind farms:

[0222]

[0223] Where P wi,j Represents the abnormal output of wind farm i in period j.

[0224] Element Prob li,c Represents the failure probability space-time matrix:

[0225]

[0226] In the formula represents the probability of failure of line l in the first j time periods. Since the above formula cannot reflect the actual operation status of the line, it is also necessary to determine the line fault state time-space matrix through sampling:

[0227]

[0228] In the formula, Indicates the operating status of line l to the jth period (0 represents line closed, 1 represents line open).

[0229] Step S3: During the period t when the tropical cyclone passes through, according to the above-mentioned power grid abnormal state deduction method, the abnormal state of the power grid during the period t can be expressed as:

[0230] AE t = {P w,t ,S lic, t};

[0231] Where P w,t =[P w1,t ,…P wN,t ] represents the abnormal output vector of the wind farm in period t, Represents the operating state vector of the line in period t. Combined with AE t Analysis of the power flow redistribution in period t:

[0232] P f,t =F(AE t ,x t );

[0233] Where P f,t represents the power vector of all lines transmitted by the system in period t; F(·) represents the line transmission power function; x t represents the system parameter vector. f,t There may be overload transmission power. Overload line The failure probability is:

[0234]

[0235] P fl,t represents the transmission power of the overloaded line l; represents the transmission power limit of the overloaded line l; Represents the rated transmission power of the overloaded line l. According to the above formula, sampling is performed to obtain the abnormal transition state of the line under power flow redistribution:

[0236]

[0237] according to And the line fault vector Prob caused by extreme meteorological factors lic,t The line fault status in the t+1 period can be obtained:

[0238]

[0239] In the formula, G(·) is the following conditional function:

[0240]

[0241] Where Sa(·) represents the sampling function. When the transient state of line l in period t is disconnected When t+1 is still disconnected, the fault probability of t+1 is sampled and the state is established. Finally, the abnormal system state AE of t+1 is obtained by combining the abnormal wind power output vector of t+1. t+1 = {P w,t+1 ,S lic,t+1}.

[0242] The system is in abnormal state AE t Convert to After Effects t+1 In addition to being affected by extreme meteorological factors in the t+1 period, it is also related to the line transition state caused by the power flow redistribution in the t period. Extreme meteorological factors and power flow redistribution alternately couple to act on the power grid and jointly promote the abnormal evolution of the system. The abnormal coupling process of the power grid considering extreme meteorological factors and power flow redistribution can be summarized as follows: Figure 4 shown.

[0243] Step S4: The conversion of the abnormal state of the power grid from the previous period to the next period is a complex process determined by the redistribution of power flow and extreme meteorological factors. In the T periods of tropical cyclone passage, each period corresponds to a different system state distribution, forming the following Figure 5 The abnormal propagation path shown in the figure. The above has already described in detail the calculation method of line failure probability and abnormal wind power output considering extreme meteorological factors. In order to obtain the abnormal propagation path, the key is to analyze the power flow redistribution process. The power flow redistribution process is explained in detail through the two aspects of unit rescheduling and island processing. Finally, the deduction process of the abnormal propagation path is given.

[0244] When the system is affected by a tropical cyclone and fails during period t, it is necessary to restore the supply and demand balance through rescheduling. The detailed steps are as follows:

[0245] Step 1: Unit adjustment and emergency load shedding or machine shedding;

[0246] The direct goal of unit ramping and load shedding is to restore the balance between supply and demand. Considering that the cost of load shedding is higher than the unit regulation cost, the scheduling priority of unit ramping is higher than that of emergency load shedding. Assuming that the imbalance between supply and demand of the system caused by the fault is ΔP, for the units involved in regulation For example, if the power supply is less than the load demand ΔP>0 and the unbalance does not exceed the upper regulation total power ∑ i (P gmax,i -P g0,i ), the regulated power can be expressed as:

[0247]

[0248] Where P gmax,i represents the maximum power of unit i; P g0,i represents the reference power of unit i. Further, if ΔP>∑ i (P gmax,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the load shedding operation should be performed at this time:

[0249] ΔP ld =ΔP-∑ i (P gmax,i -P g0,i );

[0250] If the power supply is greater than the load demand, that is, ΔP<0 and the unbalanced amount is not less than the lower adjusted total power ΔP≥∑ i (P gmin,i -P g0,i ), the regulated power can be expressed as:

[0251]

[0252] Where P min,i represents the minimum power of unit i. Further, if ΔP<∑ i (P gmin,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the unit should be cut off at this time:

[0253] ΔP gt =ΔP-∑ i (P gmin,i -P g0,i );

[0254] Step 2: Power flow calculation;

[0255] After the supply and demand are balanced by adjusting the units or cutting the machines and loads, the line transmission power is obtained through power flow calculation.

[0256] Step 3: Remove the overloaded line;

[0257] According to the transmission power obtained in step 2, according to the overload line The failure probability determines the overload line state.

[0258] Repeat steps 1 to 3 until the system reaches a new equilibrium state.

[0259] Step S5: When a tropical cyclone passes through, the power grid will form an abnormal propagation path due to the coupling of meteorology and tidal currents. According to the law of large numbers, the more samples are drawn, the higher the line status S. lic,t The more accurately the frequency reflects the probability Prob lic , the higher the accuracy of risk assessment. In addition to being affected by the number of line state sampling samples, different load curves ΔP d1 ,ΔP d2 ,…,ΔP dT This will generate different supply and demand imbalance time series ΔP1, ΔP2, …, ΔP T , different abnormal propagation paths of power grid are finally formed through re-dispatching, flow redistribution and island processing. Assuming that the total number of load curves to be evaluated is M, and the number of line state samples corresponding to each load curve is N, a total of M×N rounds of abnormal propagation path deduction need to be performed. In order to ensure the accurate characterization of the probability of line failure, the number of samples N cannot be reduced; therefore, the only way is to reduce the number of deductions by reducing M and improve the computational efficiency of risk assessment.

[0260] In order to evaluate the impact of abnormal propagation on the power grid, the load shedding rate (LSR) is selected as an important indicator in the cascading fault simulation. These indicators are used to simulate the cutoff during the development of the simulated cascading fault. Specifically, it is expressed as the ratio of the total load loss to the original power grid load.

[0261]

[0262] Where D and D′ are the sets of nodes with load in the original network and the island network after decoupling, respectively. D and P D ″ are the corresponding residual loads in each network respectively.

[0263] Based on the above content, the abnormal propagation path evolution process is as follows Figure 6 As shown:

[0264] Step S6: The purpose of quantitative risk assessment is to establish indicators that characterize system risks. Various indicators can be used for risk analysis, such as overload indicators, voltage indicators, line indicators, etc. Since the line risk indicator reflects the properties of line overload, and the line overload is caused by the flow transfer caused by line disconnection, this paper uses the line load rate indicator and coupling coefficient as risk assessment indicators. The economic benefits of power companies are related to the loss of user electricity, so this paper uses the expected value of insufficient electricity as a risk assessment indicator.

[0265] (1) Load factor index;

[0266] Define α i It is the ratio of the active power of branch i in stages t and t-1 to its limit active power, which reflects the load level of branch i under stable operating conditions.

[0267]

[0268] In the formula and are the power flows of branch i in the t and t-1 stages of the cascading failure respectively. is the limiting power flow of branch i.

[0269] 2) Coupling coefficient;

[0270] Since the continuous disconnection of the line has a superimposed effect on the remaining lines, β is defined as i It is the ratio of the change in active power of branch i in stages t-1, t, and t+1 to the original active power of branch i. The physical meaning is that the greater the power flow fluctuation caused by the withdrawal of the previous branch, the greater the coupling coefficient.

[0271]

[0272] In the formula They represent the active power on line i in stages t-1, t, and t+1 of a cascading fault, respectively.

[0273] 3) Expected Energy Not Supplied (EENS)

[0274] The expected value of the power shortage caused by the forced shutdown of the unit can indicate the severity of the fault. Therefore, EENS is used as an indicator to understand and quantify the impact of typhoons in a specific fault chain.

[0275]

[0276] In the formula, R E is the expected power shortage of the system; P(s) is the system state probability; C(s) is the load reduction caused by fault state s, T is the evaluation event frame (usually hours), and F is the system state set.

[0277] See also Figure 7 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0278] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0279] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0280] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0281] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0282] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0283] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0284] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0285] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0286] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0287] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for assessing power grid risk under extreme weather conditions, characterized in that: The steps include: The abnormal action mechanism of extreme meteorological factors under tropical cyclones on power grid equipment is analyzed to obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions. The spatiotemporal matrix of line fault status is obtained by sampling the probability of transmission line faults, and the abnormal state matrix of the power grid is obtained by synthesis. The abnormal state matrix is ​​combined with power flow redistribution to obtain a power grid abnormal coupling process considering extreme weather and power flow redistribution; By re-dispatching the units and handling the islands during the abnormal coupling process of the power grid, the abnormal propagation path of the power grid is obtained, the evolution process of the abnormal propagation path is determined, and the abnormal propagation paths of different power grids are obtained through simulation; According to the abnormal propagation paths of different power grids, the risk assessment indicators are determined, and the risk assessment results considering the spatiotemporal evolution of power grid anomalies are calculated.

2. The method for evaluating power grid risk under extreme weather conditions according to claim 1, characterized in that: The analysis of the abnormal action mechanism of extreme meteorological factors under tropical cyclones on power grid equipment includes: Analyze the mechanism of extreme meteorological factors on wind power anomaly treatment and establish a wind turbine output model under tropical cyclone passage; Analyze the abnormal effect mechanism of extreme meteorological factors on transmission lines, and establish the probability model of tower collapse and the probability model of line disconnection of unit span lines; Transmission line i is composed of n towers and n-1 unit span lines in series, so the failure probability of transmission line i in the first j time periods is: In the formula, represents the failure probability of tower k in the first j periods; Represents the failure probability of the line per unit span.

3. The method for evaluating power grid risk under extreme weather conditions according to claim 2, characterized in that: The abnormal wind power output spatiotemporal matrix is ​​calculated by the wind turbine output model under the tropical cyclone passage, and the abnormal wind power output spatiotemporal matrix is: Where P wi,j represents the abnormal output of wind farm i in period j; The line fault state space-time matrix is ​​jointly calculated by the tower collapse probability model and the line disconnection probability model of the unit span line, and is obtained by sampling different lines according to the fault probability. The line fault state space-time matrix is: In the formula, Indicates the operating status of line l to the jth period, 0 represents line closed, and 1 represents line open.

4. The method for evaluating power grid risk under extreme weather conditions according to claim 3, characterized in that: The abnormal state matrix is: AE t ={P w,t ,S lic, t}; Where P w,t =[P w1,t ,…P wN,t ] represents the abnormal output vector of the wind farm in period t, Represents the operating state vector of the line in period t.

5. The method for assessing power grid risk under extreme weather conditions according to claim 1, characterized in that: The combining of the abnormal state matrix with power flow redistribution includes: P f,t =F(AE t ,x t ); In the formula, AE t is the abnormal state matrix, x t represents the system parameter vector; F(·) represents the line transmission power function; P f,t represents the power vector of all lines transmitted by the system in period t, and P f,t There may be a transmission power overload of the line in the vector; For P f,t Possible overload lines in Calculate its failure probability at time t for: Where P fl,t represents the transmission power of the overloaded line l; represents the transmission power limit of the overloaded line l; Indicates the rated transmission power of the overload line l.

6. The method for evaluating power grid risk under extreme weather conditions according to claim 5, characterized in that: The abnormal coupling process of the power grid considering extreme weather and power flow redistribution includes: For overloaded lines caused by power flow redistribution, sampling is performed based on their fault probability to obtain the abnormal transition state of the line under power flow redistribution: according to And the line fault vector Prob caused by extreme meteorological factors lic,t , and obtain the line fault status in period t+1: In the formula, G(·) is the following conditional function: Where Sa(·) represents the sampling function. When the transition state of line l in period t is disconnected, that is, When t+1 period is still disconnected; otherwise, sampling is performed according to the fault probability of t+1 period to establish the state; Finally, combined with the abnormal wind power output vector P in period t+1 w，t+1 Get the system abnormal status at time t+1: AE t+1 ={P w,t+1 ,S lic,t+1 }。 7. The method for evaluating power grid risk under extreme weather conditions according to claim 1, characterized in that: The unit rescheduling comprises the following steps: Step S11: unit adjustment and emergency machine or load shedding; Assuming that the imbalance between supply and demand of the system caused by the fault is ΔP, for the units involved in the regulation If the power supply is less than the load demand, that is, ΔP>0 and the unbalanced amount does not exceed the upper adjustment total power ∑ i (P gmax,i -P g0,i ), the regulated power is expressed as: Where P gmax,i represents the maximum power of unit i; P g0,i represents the reference power of unit i; If ΔP>∑ i (P gmax,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the load shedding operation should be performed at this time: ΔP ld =ΔP-∑ i (P gmax,i -P g0,i ); If the power supply is greater than the load demand, that is, ΔP<0, and the unbalanced amount is not less than the total power ΔP≥∑ i (P gmin,i -P g0,i ), the regulated power is expressed as: Where P min,i represents the minimum power of unit i; If ΔP<∑ i (P gmin,i -P g0,i ), that is, the unit adjustment still cannot restore the supply and demand balance, and the unit should be cut off at this time: ΔP gt =ΔP-∑ i (P gmin,i -P g0,i ); Step S12: power flow calculation, after the supply and demand are balanced by adjusting the units or cutting the units and loads, the line transmission power is obtained by power flow calculation; Step S13: Overload line removal: According to the obtained transmission power, according to the overload line l, The failure probability determines the overload line state; Steps S11 to S13 are repeated until the system reaches a new equilibrium state.

8. The method for evaluating power grid risk under extreme weather conditions according to claim 1, characterized in that: The island processing comprises the following steps: Step S21: If there is no generator in the island, the load ΔP to be abandoned ld P is the total load of the island ds : ΔP ld =P ds ; If there is only one generator in the island and the total load demand P ds Less than the power generation P g0 , then the generator reduces the power ΔP gd for: ΔP gd =max(P ds -P g0 ,P gmin -P g0 ); Where P gmin Indicates the minimum power of the generator; Step S22: If the generator power reduction cannot balance the system supply-demand imbalance ΔP caused by the fault, the generator is cut off; If there is only one generator in the island and the total load demand P ds Greater than the power generation P g0 , then the generator increases the power ΔP gu for: ΔP gu =min(P ds -P g0 ,P gmax -P g0 ); Where P gmax Indicates the maximum power of the generator; Step S23: if the generator power cannot be increased to balance ΔP, load shedding occurs; For an island with multiple generators, the calculation process is the same as steps S21 to S23.

9. The method for evaluating power grid risk under extreme weather conditions according to claim 1, characterized in that: Through simulation, abnormal propagation paths of different power grids are obtained, including: Obtain different load curves ΔP d1 ,ΔP d2 ,…,ΔP dT Generate different supply and demand imbalance time series ΔP1, ΔP2, …, ΔP T ,Through re-dispatching, power flow redistribution and island processing, different grid anomaly propagation paths are finally formed; In the simulation, the load shedding rate LSR is selected as the cutoff index in the cascading failure simulation. The load shedding rate includes: Where D and D′ are the sets of nodes with load in the original network and the island network after decoupling, respectively. D and P′ D′ are the corresponding residual loads in each network respectively.

10. The method for evaluating power grid risk under extreme weather conditions according to claim 1, characterized in that: The risk assessment indicators include: Load factor index: Define the load factor index α i is the ratio of the active power of branch i at stage t and t-1 to its limit active power, which reflects the load level of branch i under stable operating conditions: In the formula and are the power flows of branch i in the tth and t-1th stages of the cascading failure, is the limiting power flow of branch i; Coupling coefficient: Defining the coupling coefficient β i is the ratio of the change in active power of branch i at stages t-1, t, and t+1 to the original active power of branch i: In the formula They represent the active power on line i in stages t-1, t, and t+1 of a cascading fault respectively; System power shortage expectation: Using the expected system power shortage EENS as an indicator to understand and quantify the impact of typhoons in specific fault chains: In the formula, R E is the expected power shortage of the system; P(s) is the system state probability; C(s) is the load reduction caused by the fault state s, T is the evaluation event frame, and F is the system state set.

11. A power grid risk assessment device under extreme weather conditions, characterized in that: Using the method according to any one of claims 1 to 10, the device comprises: The abnormal state matrix unit is used to analyze the abnormal action mechanism of extreme meteorological factors on power grid equipment under tropical cyclones, obtain the spatiotemporal matrix of abnormal wind power output under extreme weather conditions, obtain the spatiotemporal matrix of line fault states by sampling the probability of transmission line faults, and comprehensively obtain the abnormal state matrix of the power grid; A coupling unit, used for combining the abnormal state matrix with power flow redistribution to obtain an abnormal coupling process of a power grid considering extreme weather and power flow redistribution; A propagation path unit, used to obtain the abnormal propagation path of the power grid by re-dispatching the units and processing the islands during the abnormal coupling process of the power grid, determine the abnormal propagation path evolution process, and obtain the abnormal propagation paths of different power grids through simulation; The evaluation unit is used to determine the risk assessment index for the abnormal propagation paths of different power grids, and calculate the risk assessment results considering the spatiotemporal evolution of power grid anomalies.

12. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 10 is implemented.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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