Power grid operation safety analysis method, device, equipment and medium

Abstract

The present application discloses a method, device, equipment and medium for analyzing the operation safety of a power grid, and relates to the field of the operation safety analysis of a power grid. The method comprises: obtaining historical wind speed data of wind farms included in the area where a target power grid is located; determining a wind speed data sequence with dynamic correlation characteristics between wind farms from the historical wind speed data, and calculating the actual output of the wind farms according to the wind speed data sequence; constructing an optimal probability flow calculation model for the target power grid with the minimum power generation cost of the target power grid as the objective function; wherein the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost for underestimation of wind power output and the penalty cost for overestimation of wind power output; solving the optimal probability flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to node voltage crossing and branch overload; and analyzing the operation safety of the power grid in combination with the probability density distribution and numerical characteristics of the state quantity in all time periods.

Description

Technical Field

[0001] The present application relates to the field of power grid operation safety analysis, and more specifically, it relates to power grid operation safety analysis methods, devices, equipment and media. Background Art

[0002] At present, existing technologies have been used to analyze the wind speed correlation of wind farms. For example, a static Copula function is used to fit the wind speed correlation. It is proposed to mix several static Copula functions that describe different correlation structures to describe the correlation structure. The matrix transformation method is studied and wind speed correlation is introduced into the reliability assessment of the power system. By linearly transforming multidimensional independent random variables, multidimensional wind speed samples with correlation are obtained, so as to calculate the probabilistic flow considering wind speed correlation. The impact of wind speed correlation on the optimal rotating reserve capacity of the power grid is studied. The above method takes into account the impact of wind speed correlation on the power grid, in which the correlation structure is obtained by static simulation of a large amount of historical wind speed data.

[0003] However, in actual operation, multiple wind farms are connected to the power grid in the same area. When the wind speed is faster at night, the wind speed correlation effect between wind farms is more obvious, and the output power of wind farms increases and decreases at the same time. At this time, the load on the power grid is small, which has a greater impact on the safe operation of the power grid. When the wind speed is relatively weak during the day, the wind speed correlation effect between wind farms is not obvious. At this time, the load on the power grid is large, which has a smaller impact on the safe operation of the power grid.

[0004] Therefore, the correlation measure of wind speed fluctuates dynamically over time, so the static Copula function cannot reflect the changes in wind speed correlation, which affects the accuracy of wind speed correlation analysis on regional power grid operation safety. Summary of the invention

[0005] The present application provides a power grid operation safety analysis method, device, equipment and medium to solve the problem that the analysis method provided by the prior art has low accuracy in regional power grid operation safety analysis.

[0006] In a first aspect of the present application, a method for analyzing power grid operation safety is provided, the method comprising:

[0007] Obtain historical wind speed data of wind farms in the target power grid area;

[0008] Determine a wind speed data sequence with dynamic correlation characteristics between wind farms from historical wind speed data, and calculate the actual output of the wind farm based on the wind speed data sequence;

[0009] The optimal probability power flow calculation model of the target power grid is constructed with the minimum power generation cost of the target power grid as the objective function; the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimation of wind power output and the penalty cost of overestimation of wind power output; the penalty cost of underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the underestimation penalty coefficient; the penalty cost of overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the overestimation penalty coefficient;

[0010] Solve the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to node voltage crossing the line and branch overload;

[0011] The power grid operation safety is analyzed by combining the probability density distribution and numerical characteristics of the state quantities in all time periods.

[0012] In one implementation, a wind speed data sequence having dynamic correlation characteristics between wind farms is determined by: determining an optimal time-varying Copula function based on the historical wind speed data;

[0013] Calling a discrete grey prediction model to predict the time-varying correlation coefficient of the optimal time-varying Copula function, and determining a random number sequence of the historical wind speed data according to the time-varying correlation coefficient;

[0014] The random number sequence is inversely transformed with equal probability to determine the wind speed data sequence with dynamic correlation characteristics between wind farms.

[0015] In one implementation, the optimal time-varying Copula function is determined by: calculating parameter estimation results of historical wind speed data of at least two wind farms according to different types of time-varying Copula functions;

[0016] According to the parameter estimation results, the Akaike Information Criterion index used to characterize the goodness of fit of different types of time-varying Copula functions is calculated;

[0017] The time-varying Copula function corresponding to the smallest Akaike information criterion indicator is selected as the optimal time-varying Copula function.

[0018] In one implementation, the actual output of the wind farm is calculated according to the wind speed data sequence, specifically: the corresponding wind power output is calculated according to the wind speed data sequence of each wind farm;

[0019] The wind power output of each wind farm is summed up to obtain the actual output of the wind farm.

[0020] In one implementation, the construction of the optimal probabilistic power flow calculation model of the target power grid further includes: determining the constraints of the target power grid; wherein the constraints include active power balance constraints, reactive power balance constraints, upper and lower limits of active power output of thermal power units, upper and lower limits of active power output of wind power units, upper and lower limits of reactive power sources, and node voltage constraints;

[0021] The optimal probabilistic power flow calculation model of the target power grid is constructed according to the constraints and objective functions.

[0022] In one implementation, the power grid operation safety is analyzed by combining the probability density distribution and numerical characteristics of the state quantities in all time periods, specifically:

[0023] Calculate the severity of branch overload according to the branch overload of the target power grid;

[0024] According to the probability density function of the branch power flow and the severity of the branch overload, the overload risk index of the branch of the target power grid is calculated;

[0025] Calculate the severity of node voltage crossing according to the node voltage crossing amount of the target power grid;

[0026] According to the probability density function of the node voltage and the severity of the node voltage crossing the line, the node voltage crossing risk index of the target power grid is calculated;

[0027] The overload risk index of the branch and the voltage over-line risk index of the node are weighted and summed to determine the global risk index for the safe operation of the power grid.

[0028] In one implementation, the node voltage over-line amount is calculated in the following manner: the node voltage over-line amount is calculated based on the node voltage amplitude of the target power grid and the upper and lower limits of the node voltage amplitude.

[0029] In a second aspect of the present application, a power grid operation safety analysis device is further provided, the device comprising:

[0030] A data acquisition module is used to obtain historical wind speed data of wind farms included in the target power grid area;

[0031] The actual output calculation module is used to determine the wind speed data sequence with dynamic correlation characteristics between wind farms from the historical wind speed data, and calculate the actual output of the wind farm according to the wind speed data sequence;

[0032] The model building module is used to build an optimal probability power flow calculation model of the target power grid with the minimum power generation cost of the target power grid as the objective function; the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimation of wind power output and the penalty cost of overestimation of wind power output; the penalty cost of underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the underestimation penalty coefficient; the penalty cost of overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the overestimation penalty coefficient;

[0033] A solution module is used to solve the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to the node voltage crossing the line and the branch overload;

[0034] The analysis module is used to analyze the safety of power grid operation by combining the probability density distribution and numerical characteristics of the state quantities in all time periods.

[0035] The third aspect of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a power grid operation safety analysis method provided in the first aspect of the present application are implemented.

[0036] The fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of a power grid operation safety analysis method provided in the first aspect of the present application are implemented.

[0037] Compared with the prior art, this application has the following beneficial effects:

[0038] In a power grid operation safety analysis method provided in the present application, first, a wind speed data sequence with dynamic correlation characteristics between wind farms is determined from historical wind speed data, and the actual output of each wind farm is calculated in combination with the wind speed data sequence, thereby determining the correlation of the output of each wind farm in the corresponding period. Then, the power generation cost of the target power grid is determined by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimating wind power output, and the penalty cost of overestimating wind power output. The optimal probability flow calculation model is constructed with the minimum power generation cost as the objective function. Then, the optimal probability flow meter model is solved based on Monte Carlo simulation and genetic algorithm and other solution methods, so that the node voltage over-line and branch overload probability density distribution and numerical characteristics of the target power grid in all time periods can be obtained. In order to consider the impact of the time-varying correlation of wind speed on the safety of power grid operation, the power grid operation safety is finally analyzed in combination with the probability density distribution and numerical characteristics of the state quantity in all time periods. In summary, this application proposes to apply wind speed data sequences with dynamic correlation characteristics to the security assessment of regional power grids containing wind farms. When the grid load is large, key grid lines change and the grid operation risk increases. Therefore, it is necessary to remind dispatchers to pay close attention. This application provides certain engineering value for the security assessment of regional power grids with large-scale wind power access. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0040] Figure 1 A flowchart of a power grid operation safety analysis method provided in an embodiment of the present application;

[0041] Figure 2 A functional block diagram of a power grid operation safety analysis device provided in an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0043] Reference numerals and description of the drawings:

[0044] 210, data acquisition module; 220, actual output calculation module; 230, model building module; 240, solution module; 250, analysis module; 300, electronic device; 310, processor; 320, memory; 321, one or more programs; 330, communication interface. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0046] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0047] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a power grid operation safety analysis method provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0048] S101, obtaining historical wind speed data of wind farms included in the area where the target power grid is located.

[0049] In this embodiment, it can be understood that the target power grid area may include two, three, etc. wind farms. Since the wind speeds of adjacent wind farms change over time, the correlation characteristics between the wind speeds of wind farms are also constantly changing. The correlation measures between the wind speeds of each wind farm are different depending on the selected sampling points. For example, taking two adjacent wind farms as an example, the correlation characteristics of the wind speeds of the two adjacent wind farms also change with the number of samplings. For example, in the first 400 samples, the wind speeds have a strong negative correlation, but after 400 samples, the wind speeds have a strong positive correlation.

[0050] S102: determining a wind speed data sequence having dynamic correlation characteristics between wind farms from historical wind speed data, and calculating actual output of the wind farm according to the wind speed data sequence.

[0051] In this embodiment, the traditional static Copula function belongs to the static correlation measurement between variables, and cannot accurately describe the dynamic correlation characteristics between wind speeds in a wind farm. Therefore, it is necessary to model the dynamic correlation characteristics of wind speeds.

[0052] In some embodiments, the wind speed data sequence with dynamic correlation characteristics between wind farms is determined by:

[0053] S1021, determining an optimal time-varying Copula function according to the historical wind speed data.

[0054] Specifically, the common time-varying Copula functions are the following three categories, namely, the time-varying Normal-Copula function, the time-varying Gumbel-Copula function, and the time-varying Clayton-Copula function. Among them, the time-varying Normal-Copula density function can better describe the correlation characteristics with symmetric characteristics. The time-varying Gumbel-Copula density function can better describe the correlation characteristics with lower tail characteristics. The time-varying Clayton-Copula function can better describe the correlation characteristics with upper tail and lower tail at the same time.

[0055] Among them, (1) the expression of the time-varying Normal-Copula function is:

[0056] , where: ρ is the time-varying coefficient of the Normal-Copula function, u , v are the normalized wind speed variables of the two wind farms, is the inverse function of the standard normal distribution, s , t are the wind speed variables of the two wind farms respectively.

[0057] (2) The expression of the time-varying Gumbel-Copula function is:

[0058] , where: ρ is the time-varying coefficient of the time-varying Gumbel-Copula function.

[0059] (3) The expression of the time-varying Clayton-Copula function is:

[0060] , where: , , and is the time-varying coefficient of the Clayton-Copula function, which characterizes the upper and lower tail correlations respectively, so it can better describe the correlation characteristics with upper and lower tails at the same time; U , L They are used to distinguish the upper tail correlation coefficient from the lower tail correlation coefficient.

[0061] In some embodiments, the optimal time-varying Copula function is determined in the following manner: parameter estimation results of historical wind speed data of at least two wind farms are calculated based on different types of time-varying Copula functions; Akaike information criterion index used to characterize the goodness of fit of different types of time-varying Copula functions is calculated based on the parameter estimation results; and the time-varying Copula function corresponding to the smallest Akaike information criterion index is selected as the optimal time-varying Copula function.

[0062] Specifically, 8,000 samples were taken based on the wind speed of the actual wind farms, with a sampling interval of 30 seconds, and a binary frequency histogram of the wind speed of the three wind farms A, B, and C was drawn. From each frequency histogram, it can be preliminarily determined whether the correlation between the wind speeds has symmetry or tail characteristics. For example, there is a symmetrical correlation characteristic between the wind speeds of wind farms A and B, that is, the probability of the wind speeds being large or small at the same time is high. However, there is an asymmetric tail correlation characteristic between the wind speeds of wind farms B and C, with a stronger upper tail correlation, that is, the probability of the wind speeds being large at the same time is high; the lower tail correlation is relatively weak, that is, the probability of the wind speeds being small at the same time is low.

[0063] The Akaike's information criterion (AIC) is used to evaluate the goodness of fit of the time-varying Copula function. The smaller the AIC value, the higher the goodness of fit. As shown in Table 1 below, wind farms A and B have a strong symmetrical correlation, so the time-varying Normal-Copula function describes the wind speeds of wind farms A and B better. As shown in Table 2 below, the time-varying Clayton-Copula function describes the wind speeds of wind farms B and C better. At the same time, the comparison shows that the time-varying Copula function has a better goodness of fit than the static Copula function.

[0064] Table 1 Parameter estimation results of time-varying Copula functions of wind farms A and B

[0065]

[0066] Table 2 Estimation results of time-varying Copula function parameters for wind farms B and C

[0067]

[0068] S1022, calling a discrete grey prediction model to predict the time-varying correlation coefficient of the optimal time-varying Copula function, and determining a random number sequence of the historical wind speed data according to the time-varying correlation coefficient.

[0069] In this embodiment, in order to improve the prediction accuracy, the time-varying correlation coefficient of the optimal time-varying Copula function is predicted by the Metabolism Discrete Grey Forecasting Model (MDGM), as follows:

[0070] The time series observation value of the correlation coefficient in the time-varying Copula function is:

[0071] ,in, is the original observed value of the time-varying correlation coefficient at time t;

[0072] The cumulative generated sequence is: ,in, is the observed value of the time-varying correlation coefficient after one accumulation up to time t;

[0073] in, , i Number all the original observations preceding time t.

[0074] The mean generating sequence is: ;

[0075] in, ; is the mean of the time-varying correlation coefficient at time t after transformation.

[0076] , the above formula is the basic form of discrete GM(1,1), where a , b is the time-varying equation parameter.

[0077] When the metabolic discrete grey prediction model (MDGM) is used for prediction, the MDGM model established by the known initial data sequence predicts a value, and then this predicted value is added to the known sequence, while removing the earliest data to keep the sequence equal in dimension. Then, the MDGM model is established to predict the next data, and then the new predicted data is added to the sequence, while removing the earliest data in the sequence. In this way, metabolism is predicted one by one and supplemented in sequence until the prediction target is achieved.

[0078] The predicted value of the time-varying coefficient is then logistically transformed in order to make the time-varying coefficient fall within the value range of the corresponding Copula coefficient. is the logistic transformation function. In the time-varying Normal-Copula function, , the purpose is to ensure that the correlation coefficient is within the value range ; In the time-varying Gumbel-Copula function, , the purpose is to ensure that the correlation coefficient is within the value range .

[0079] In the time-varying Clayton-Copula function, , the purpose is to ensure that the correlation coefficient is within the value range .

[0080] The function value at the original sample point is obtained by using the predicted time-varying correlation coefficient of the time-varying Copula function.

[0081] S1023, performing an equal-probability inverse transformation on the random number sequence to determine a wind speed data sequence having a dynamic correlation characteristic between wind farms.

[0082] In this embodiment, according to a large amount of measured data, the annual average wind speed distribution in most regions obeys the Weibull probability distribution. When the wind speed sample of the wind farm is Weibull distributed, Approximately satisfies the following relationship:

[0083] , where , The corresponding random variables are X The mean and standard deviation of .

[0084] The correlation matrix was determined Then, perform Cholesky decomposition: , where for is the lower triangular decomposition matrix of , and T is the transpose of the matrix.

[0085] The lower triangular matrix can be used to transform correlated standard normal distributed variables into independent standard normal distributed variables: , , according to the inverse transformation of the equal probability principle, the wind speed sample space of the wind farm with the correlation coefficient matrix can be obtained: , where: v i is the wind speed sample of the wind farm with time-varying correlation characteristics, wind speed; k is the shape parameter; c is the scale parameter, y i is the normalized wind speed sample with relevant characteristics, and m is the number of samples.

[0086] S103, constructing an optimal probabilistic power flow calculation model for the target power grid with minimizing the power generation cost of the target power grid as the objective function; wherein the power generation cost is obtained by summing up the energy consumption cost of the thermal power units of the target power grid, the penalty cost for underestimation of wind power output and the penalty cost for overestimation of wind power output; wherein the penalty cost for underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the preset predicted output of the wind farm by the underestimation penalty coefficient; and the penalty cost for overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the preset predicted output of the wind farm by the overestimation penalty coefficient.

[0087] In this embodiment, the objective function is to minimize the power generation cost, wherein the first term represents the energy consumption cost of the thermal power unit, the second term represents the penalty cost for underestimating the wind power output, and the third term represents the penalty cost for overestimating the wind power output.

[0088] , where: It is the active output of the adjustable generator; a i , b i and c i They are the energy consumption curve parameters of thermal power units; C u To underestimate the penalty coefficient; C o Overestimation of penalty coefficient; The actual output of the wind farm; Contribute to wind farm forecasting; S G A collection of adjustable generator sets.

[0089] In some embodiments, the actual output of the wind farm is calculated based on the wind speed data sequence, specifically: the corresponding wind power output is calculated based on the wind speed data sequence of each wind farm; and the wind power output of each wind farm is summed to obtain the actual output of the wind farm.

[0090] Specifically, based on the above description, it can be known that according to the historical wind speed data of each wind farm, a wind speed data sequence with dynamic correlation characteristics can be determined. Then, based on the wind speed data sequence, the wind power output of the wind turbines in the corresponding wind farm can be calculated. It should be noted here that the calculation of wind power output is a conventional method in this technical field, and this embodiment does not make redundant descriptions. Since this application is to analyze and evaluate the operating safety of the target power grid, the parameters It is obtained by summing the wind power output of each wind farm.

[0091] In some embodiments, the construction of the optimal probabilistic power flow calculation model of the target power grid also includes: determining the constraints of the target power grid; wherein the constraints include active power balance constraints of the nodes, reactive power balance constraints, upper and lower limit constraints of active power output of thermal power units, upper and lower limit constraints of active power output of wind power units, upper and lower limit constraints of reactive power sources, and node voltage constraints; constructing the optimal probabilistic power flow calculation model of the target power grid according to the constraints and the objective function.

[0092] The constraint expression is: , Where: S B is the set of all nodes of the target power grid; S w Assemble for wind turbines; S R is a collection of reactive power sources;

[0093] , is the active power and reactive power of the thermal power unit, , are the active power and reactive power of the wind farm, , are the load active power and reactive power, For the i Nodes t

[0094] Voltage at the moment, is the voltage of the jth node at time t, , are the real and imaginary parts of the node admittance matrix, is the voltage phase angle difference between nodes i and j, , are the lower and upper limits of the active power of the wind farm,

[0095] , are the lower and upper limits of the reactive power of thermal power units, , are the lower and upper limits of the node voltage; , is the up and down climbing rate of the thermal power unit, is the active power of the thermal power unit at the moment t-1.

[0096] S104, solving the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to node voltage crossing the line and branch overload.

[0097] In order to quickly evaluate the safety of the power grid, a probabilistic power flow method based on sorted set sampling is used. Its advantage is that it can quickly and comprehensively sample sample features. Compared with simple random sampling, RS sampling greatly reduces the sampling scale and has the characteristics of high sampling efficiency and good stability.

[0098] a) Sampling, the sampling steps are: first input random variables The cumulative distribution function of Y i Randomly select from the value range [0,1] Individuals are divided into k groups, each with k individuals; then the individuals in each group are sorted from small to large; finally, the first individual in the sorted samples of the first group is selected and recorded as , select the second individual from the second group, denoted as ; Select the third individual from the third group, record it as ; and so on, until the last individual in the order is drawn from the group, recorded as. The above process is a cycle, and finally from The capacity is selected from random individuals k A set of samples, then The sampling value of is obtained by the inverse function. Arrange the sampling values ​​of each random variable in a row, and finally form an m*k sampling matrix .

[0099] b) Sorting, the sorting steps are: under the condition that the mean, variance and correlation coefficient matrix of the input random variables are known, a sample matrix Y of the standard normal distribution with the correlation coefficient matrix R0 can be generated, and the sample matrix X of the correlation coefficient matrix can be generated by transforming the sample matrix Y, thereby obtaining the order matrix B .

[0100] At the same time, the improved niche genetic algorithm (INGA) is used to solve the optimal probability flow calculation model. The improved niche genetic algorithm can better maintain the diversity of the population and has a high global optimization ability and convergence speed. The main differences between it and the traditional genetic algorithm are: (1) The population generated by the pre-selection M and the size generated by selection, crossover and mutation is N The new population is combined together and the Hamming distance between any two individuals is calculated: , , are two different individuals in the population, M is the original population, M+N is the new population, and the radius of the microhabitat is L, if the Hamming distance between two individuals is smaller than the Hamming distance, the individual with smaller fitness will be punished and its fitness value will be reduced. After all individuals are calculated, the M+N individuals with higher fitness are selected.

[0101] (2) Adaptive hybridization probability and mutation probability

[0102]

[0103]

[0104] in, k 1, k 2 are both crossover operators, k 3. k 4 are all mutation operators. And , f max is the maximum fitness of the group, f arg is the average fitness of the group. is the larger fitness value of the two chromosomes being hybridized. f m is the fitness value of the mutant string.

[0105] S105, analyzing the safety of power grid operation based on the probability density distribution and numerical characteristics of the state quantities in all time periods.

[0106] In this embodiment, in order to consider the impact of the time-varying correlation of wind speed on the safety of the power grid, the following indicators are cited to evaluate the safety of the power grid, including the overload risk indicator of the branch and the node voltage over-line risk indicator. The overload risk indicator of the branch and the node voltage over-line risk indicator are then weighted and summed to determine the global risk indicator for the safety of power grid operation.

[0107] In some embodiments, the power grid operation safety is analyzed in combination with the probability density distribution and numerical characteristics of the state quantities of all time periods, specifically: the severity of the branch overload is calculated according to the overload of the branch of the target power grid; the overload risk index of the branch of the target power grid is calculated according to the probability density function of the branch flow and the severity of the branch overload;

[0108] The severity of node voltage crossing is calculated according to the node voltage crossing amount of the target power grid; and the node voltage crossing risk index of the target power grid is calculated according to the probability density function of the node voltage and the severity of the node voltage crossing.

[0109] The branch overload risk index is defined as: , where: g m (L) is the probability density function of the branch power flow, Sev ( L m ) is the severity of the branch overload consequence, and its expression is:

[0110] ,in , where: a is a positive number, w ( L m ) is the overload of branch m; L m is the ratio of the actual transmission power of branch m to the power limit; L The value of 0 is set to 0.9. L m Less than L 0, then the overload risk is considered to be 0.

[0111] The node voltage crossing risk index is defined as: ,in: U nmin , U nmax are the lower and upper limits of the node voltage, f n (U) is the probability density function of the node voltage, S ev ( U n ) is the severity of the node voltage crossing the line, and its expression is: .

[0112] Specifically, the node voltage over-line amount is calculated in the following manner: the node voltage over-line amount is calculated according to the node voltage amplitude of the target power grid and the upper and lower limits of the node voltage amplitude.

[0113] The expression for calculating the node voltage over-line amount is: ; Where: w ( U n ) is the voltage over-line value; U n is the voltage amplitude at node n. U n If the voltage is within the allowable range, the risk of voltage crossing the line is 0.

[0114] The target grid j After a line is disconnected, the overload risk index of the branch and the node voltage over-line risk index are weighted and summed to determine the global risk index for power grid operation safety. SI j The expression is: , where: α andβ is the weight coefficient and satisfies α + β= 1.

[0115] Please refer to Figure 2 , Figure 2 A schematic diagram of a power grid operation safety analysis device provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the device comprises:

[0116] The data acquisition module 210 is used to acquire the historical wind speed data of the wind farms included in the area where the target power grid is located;

[0117] The actual output calculation module 220 is used to determine a wind speed data sequence with dynamic correlation characteristics between wind farms from historical wind speed data, and calculate the actual output of the wind farm according to the wind speed data sequence;

[0118] The model building module 230 is used to build an optimal probability power flow calculation model of the target power grid with the minimum power generation cost of the target power grid as the objective function; wherein the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimating wind power output, and the penalty cost of overestimating wind power output; wherein the penalty cost of underestimating wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the underestimation penalty coefficient; the penalty cost of overestimating wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the overestimation penalty coefficient;

[0119] A solution module 240 is used to solve the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to the node voltage crossing the line and the branch overload;

[0120] The analysis module 250 is used to analyze the safety of power grid operation by combining the probability density distribution and numerical characteristics of the state quantities in all time periods.

[0121] It can be seen that the embodiment of the present application provides a power grid operation safety analysis device. First, a wind speed data sequence with dynamic correlation characteristics between wind farms is determined from historical wind speed data, and the actual output of each wind farm is calculated in combination with the wind speed data sequence, thereby determining the correlation of the output of each wind farm in the corresponding period. Then, the power generation cost of the target power grid is determined by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimating the wind power output, and the penalty cost of overestimating the wind power output. The optimal probability flow calculation model is constructed with the minimum power generation cost as the objective function. Then, the optimal probability flow meter model is solved based on Monte Carlo simulation and genetic algorithm and other solution methods, so that the node voltage over-line and branch overload probability density distribution and numerical characteristics of the target power grid in all time periods can be obtained. In order to consider the impact of the time-varying correlation of wind speed on the safety of power grid operation, the power grid operation safety is finally analyzed in combination with the probability density distribution and numerical characteristics of the state quantity in all time periods. In summary, this application proposes to apply wind speed data sequences with dynamic correlation characteristics to the security assessment of regional power grids containing wind farms. When the grid load is large, key grid lines change and the grid operation risk increases. Therefore, it is necessary to remind dispatchers to pay close attention. This application provides certain engineering value for the security assessment of regional power grids with large-scale wind power access.

[0122] Please refer to Figure 3 , Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 300 includes a processor 310, a memory 320, a communication interface 330, and at least one communication bus for connecting the processor 310, the memory 320, and the communication interface 330. The memory 320 includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (PROM), or a portable CD-ROM, and the memory 320 is used for related instructions and data.

[0123] The communication interface 330 is used to receive and send data. The processor 310 can be one or more CPUs. When the processor 310 is a CPU, the CPU can be a single-core CPU or a multi-core CPU. The processor 310 in the electronic device 300 is used to read one or more programs 321 stored in the memory 320, and perform the following operations: obtain the historical wind speed data of the wind farms included in the area where the target power grid is located; determine the wind speed data sequence with dynamic correlation characteristics between the wind farms from the historical wind speed data, and calculate the actual output of the wind farm based on the wind speed data sequence; construct an optimal probabilistic power flow calculation model for the target power grid with the minimum power generation cost of the target power grid as the objective function; wherein the power generation cost is composed of the energy consumption cost of the thermal power units of the target power grid, the penalty cost for underestimating the wind power output, and the penalty cost for overestimating the wind power output. The cost is summed up; the penalty cost for underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the preset predicted output of the wind farm by the underestimation penalty coefficient; the penalty cost for overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the preset predicted output of the wind farm by the overestimation penalty coefficient; the optimal probability flow calculation model is solved to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; the state quantity refers to the node voltage crossing the line and the branch overload; the power grid operation safety is analyzed in combination with the probability density distribution and numerical characteristics of the state quantity in all time periods.

[0124] It should be noted that the specific implementation of each operation can be Figure 1 The corresponding description of the method embodiment shown, the electronic device 300 can be used to execute a power grid operation safety analysis method of the above method embodiment of the present application, which will not be described in detail here.

[0125] The embodiment of the present application also provides a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a power grid operation safety analysis method in the above embodiment. Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0126] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for analyzing power grid operation safety, characterized in that: include: Obtain historical wind speed data of wind farms in the target power grid area; A wind speed data sequence with dynamic correlation characteristics between wind farms is determined from historical wind speed data, and the actual output of the wind farm is calculated based on the wind speed data sequence; wherein the wind speed data sequence with dynamic correlation characteristics between wind farms is determined in the following manner: an optimal time-varying Copula function is determined based on the historical wind speed data; a discrete grey prediction model is called to predict the time-varying correlation coefficient of the optimal time-varying Copula function, and a random number sequence of the historical wind speed data is determined based on the time-varying correlation coefficient; an equal-probability inverse transformation is performed on the random number sequence to determine a wind speed data sequence with dynamic correlation characteristics between wind farms; wherein the optimal time-varying Copula function is determined in the following manner: parameter estimation results of historical wind speed data of at least two wind farms are calculated based on different types of time-varying Copula functions; an Akaike information criterion index used to characterize the goodness of fit of different types of time-varying Copula functions is calculated based on the parameter estimation results; a time-varying Copula function corresponding to the smallest Akaike information criterion index is selected as the optimal time-varying Copula function; The optimal probability power flow calculation model of the target power grid is constructed with the minimum power generation cost of the target power grid as the objective function; the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimation of wind power output and the penalty cost of overestimation of wind power output; the penalty cost of underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the underestimation penalty coefficient; the penalty cost of overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the overestimation penalty coefficient; Solve the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to node voltage crossing the line and branch overload; The power grid operation safety is analyzed by combining the probability density distribution and numerical characteristics of the state quantities in all time periods.

2. A power grid operation safety analysis method according to claim 1, characterized in that: The actual output of the wind farm is calculated based on the wind speed data sequence, specifically: Calculate the corresponding wind power output according to the wind speed data sequence of each wind farm; The wind power output of each wind farm is summed up to obtain the actual output of the wind farm.

3. A method for analyzing power grid operation safety according to claim 1, characterized in that: The construction of the optimal probabilistic power flow calculation model of the target power grid also includes: determining the constraints of the target power grid; wherein the constraints include active power balance constraints, reactive power balance constraints, upper and lower limits of active power output of thermal power units, upper and lower limits of active power output of wind power units, upper and lower limits of reactive power sources, and node voltage constraints; The optimal probabilistic power flow calculation model of the target power grid is constructed according to the constraints and objective functions.

4. A method for analyzing power grid operation safety according to claim 1, characterized in that: The power grid operation safety is analyzed by combining the probability density distribution and numerical characteristics of the state quantities in all time periods, specifically: Calculate the severity of branch overload according to the branch overload of the target power grid; According to the probability density function of the branch power flow and the severity of the branch overload, the overload risk index of the branch of the target power grid is calculated; Calculate the severity of node voltage crossing according to the node voltage crossing amount of the target power grid; According to the probability density function of the node voltage and the severity of the node voltage crossing the line, the node voltage crossing risk index of the target power grid is calculated; The overload risk index of the branch and the voltage over-line risk index of the node are weighted and summed to determine the global risk index for the safe operation of the power grid.

5. A method for analyzing power grid operation safety according to claim 4, characterized in that: The node voltage over-line amount is calculated in the following manner: the node voltage over-line amount is calculated based on the node voltage amplitude of the target power grid and the upper and lower limits of the node voltage amplitude.

6. A power grid operation safety analysis device, characterized in that: The device includes: A data acquisition module is used to obtain historical wind speed data of wind farms included in the target power grid area; An actual output calculation module is used to determine a wind speed data sequence with dynamic correlation characteristics between wind farms from historical wind speed data, and calculate the actual output of the wind farm based on the wind speed data sequence; wherein the wind speed data sequence with dynamic correlation characteristics between wind farms is determined in the following manner: determining an optimal time-varying Copula function based on the historical wind speed data; calling a discrete grey prediction model to predict the time-varying correlation coefficient of the optimal time-varying Copula function, and determining a random number sequence of the historical wind speed data based on the time-varying correlation coefficient; performing an equal probability inverse transformation on the random number sequence to determine a wind speed data sequence with dynamic correlation characteristics between wind farms; wherein the optimal time-varying Copula function is determined in the following manner: calculating parameter estimation results of historical wind speed data of at least two wind farms based on different types of time-varying Copula functions; calculating the Akaike information criterion index used to characterize the goodness of fit of different types of time-varying Copula functions based on the parameter estimation results; selecting the time-varying Copula function corresponding to the smallest Akaike information criterion index as the optimal time-varying Copula function; The model building module is used to build an optimal probability power flow calculation model of the target power grid with the minimum power generation cost of the target power grid as the objective function; the power generation cost is obtained by summing the energy consumption cost of the thermal power units of the target power grid, the penalty cost of underestimation of wind power output and the penalty cost of overestimation of wind power output; the penalty cost of underestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the underestimation penalty coefficient; the penalty cost of overestimation of wind power output is obtained by multiplying the difference between the actual output of the wind farm and the predicted output of the preset wind farm by the overestimation penalty coefficient; A solution module is used to solve the optimal probability power flow calculation model to obtain the probability density distribution and numerical characteristics of the state quantity of the target power grid in all time periods; wherein the state quantity refers to the node voltage crossing the line and the branch overload; The analysis module is used to analyze the safety of power grid operation by combining the probability density distribution and numerical characteristics of the state quantities in all time periods.

7. An electronic device, characterized in that: The electronic device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a power grid operation safety analysis method as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of a power grid operation safety analysis method according to any one of claims 1 to 5 are implemented.

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