Method and device for analyzing minimum inertia of power system in multiple space-time scenes

Abstract

The invention relates to the technical field of power systems, and discloses a system minimum inertia analysis method and device for a power system under multiple space-time scenes, and the method comprises the steps: describing the correlation between a source and a load through constructing a joint probability distribution model of source-load output data of the power system; scene sampling and reduction are carried out by using the joint probability distribution model, and the higher minimum inertia is determined as the required minimum inertia of the power system in the typical scene through the minimum inertia of the power system in the typical scene under the system disturbance constraint and the minimum inertia of the power system under the system frequency deviation constraint; the influence degree of each uncertainty factor on the minimum inertia demand of the system is accurately described, and the expected value of the minimum inertia of the power system is determined through the required minimum inertia and the occurrence probability corresponding to a plurality of typical scenes, so that the dynamic stability and safety of the power system in a complex and changeable environment are accurately controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for analyzing minimum inertia of a power system in multiple time and space scenarios. Background Art

[0002] In the context of the new power system, compared with the traditional architecture dominated by synchronous generators, today's power grid is moving towards a higher proportion of renewable energy grid connection and the continuous growth of DC transmission resources. A large number of traditional thermal power units have been replaced by new energy power generation facilities such as wind power and photovoltaic power generation, resulting in the terminal power grid showing significant power electronics characteristics. New energy power generation equipment such as wind turbines have small inertia, and photovoltaic power stations do not have mechanical rotation inertia. This transformation has overall reduced the total effective rotation inertia of the power system.

[0003] At the same time, since new energy power generation devices are usually connected to the grid through power electronic converters, they do not have the ability to directly contribute rotational inertia to the grid like traditional synchronous generators, further increasing the uncertainty of grid inertia. Rotational inertia plays a vital role in maintaining grid frequency stability when the power system is subject to sudden power disturbances. When the overall inertia of the system is low, it may cause a serious decline in the dynamic performance of the grid frequency when the power changes suddenly.

[0004] Faced with this challenge, research work at home and abroad has focused on how to evaluate the impact of large-scale access to renewable energy on the system inertia level, and actively carried out research on inertia monitoring technology to achieve real-time understanding and control of the inertia support status of the power grid.

[0005] However, when it comes to determining the minimum safe inertia value of the system, most existing methods only consider the minimum inertia of the system in a single time section, which makes it difficult to comprehensively and accurately characterize the impact of various uncertain factors on the minimum inertia requirement of the system. This makes it difficult to accurately control the dynamic stability and safety of the power system in a complex and changing environment. Summary of the invention

[0006] In view of this, the present invention provides a method and device for analyzing the minimum inertia of a power system in a multi-time and space scenario, which solves the technical problem that it is difficult to comprehensively and accurately characterize the influence of various uncertain factors on the minimum inertia requirement of the system when determining the minimum safe inertia value of the system, resulting in difficulty in accurately controlling the dynamic stability and safety of the power system in a complex and changeable environment.

[0007] A first aspect of the present invention provides a method for analyzing minimum inertia of a power system in multiple time and space scenarios, comprising:

[0008] Constructing a joint probability distribution model of the source-load output data according to the source-load output data of the power system;

[0009] Sampling scenes according to the joint probability distribution model, and reducing the sampled random scene set to obtain multiple typical scenes and the probability of occurrence of each of the typical scenes;

[0010] For each of the typical scenarios, the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in the typical scenario is obtained, and the higher minimum inertia is taken as the required minimum inertia of the power system in the typical scenario;

[0011] The expected value of the minimum inertia of the power system is determined according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios.

[0012] Preferably, the step of constructing a joint probability distribution model of the source-load output data according to the source-load output data of the power system comprises:

[0013] Determining a standard normal distribution of the source-load output data according to the source-load output data of the power system;

[0014] Constructing a covariance matrix of the source-load output data according to the standard normal distribution;

[0015] A joint probability distribution model of the source-load output data is established according to the covariance matrix of the source-load output data through a multivariate Gaussian Copula function.

[0016] Preferably, the step of sampling scenes according to the joint probability distribution model and reducing the random scene set obtained by sampling to obtain a plurality of typical scenes and the probability of occurrence of each of the typical scenes includes:

[0017] Discretizing the joint probability distribution model by random sampling using a sequential Monte Carlo method to obtain a plurality of random scenarios and form the random scenario set;

[0018] The random scene set is clustered using a K-means clustering method, and random scenes corresponding to a plurality of cluster centers are determined as typical scenes;

[0019] For each of the typical scenarios, the occurrence probability of the typical scenario is determined according to the typical scenario and the total number of scenarios.

[0020] Preferably, the process of obtaining the minimum inertia of the power system under the system disturbance constraint in the typical scenario includes:

[0021] Determining a first minimum inertia according to a maximum value of a frequency change rate after a disturbance occurs in the power system and a maximum disturbance power after a disturbance occurs in the power system in the typical scenario;

[0022] The initial minimum inertia is corrected by using the inertia change caused by the disturbance to obtain a second minimum inertia;

[0023] The maximum disturbance power after the disturbance of the power system in the second minimum inertia is corrected by using the static load voltage equivalent inertia, and a third minimum inertia is obtained as the minimum inertia of the power system under the system disturbance constraint in the typical scenario, wherein the static load voltage equivalent inertia is determined by the disturbance voltage measurement and the load.

[0024] Preferably, the process of obtaining the minimum inertia of the power system under the system frequency deviation constraint in the typical scenario includes:

[0025] When the maximum disturbance power occurs in the power system under the typical scenario, and after the safety control action time, the maximum frequency deviation is the system inertia corresponding to the maximum bus frequency of the power disturbance point. The minimum inertia of the power system under the system frequency deviation constraint in the typical scenario is determined. The minimum inertia under the system frequency deviation constraint is:

[0026]

[0027] In the formula, is the minimum inertia under the system frequency deviation constraint, is the load frequency regulation coefficient, To control the action time, is the maximum disturbance power after the power system is disturbed, is the static load voltage, is the maximum bus frequency deviation value at the power disturbance point, is the frequency distribution coefficient, where

[0028]

[0029] In the formula, , are the inertia center frequency corresponding to the disturbance point after the disturbance and the maximum value of the system frequency change rate after the disturbance, It is the maximum bus frequency deviation value of the system.

[0030] Preferably, the step of determining the expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios includes:

[0031] Determine an expected value of the minimum inertia of the power system under the typical scenario according to the required minimum inertia corresponding to each typical scenario and the probability of occurrence;

[0032] The expected value of the minimum inertia of the power system under the multiple typical scenarios is weighted averaged to obtain the expected value of the minimum inertia of the power system.

[0033] In a second aspect, the present invention further provides a system minimum inertia analysis device under multiple time and space scenarios of a power system, comprising:

[0034] A probability determination module, used to construct a joint probability distribution model of the source-load output data according to the source-load output data of the power system;

[0035] A scene sampling module, used to perform scene sampling according to the joint probability distribution model, and reduce the random scene set obtained by sampling to obtain multiple typical scenes and the probability of occurrence of each of the typical scenes;

[0036] An inertia comparison module is used to obtain, for each of the typical scenarios, the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in the typical scenario, and take the higher minimum inertia as the required minimum inertia of the power system in the typical scenario;

[0037] The expected inertia determination module is used to determine the expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios.

[0038] In a third aspect, the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the system minimum inertia analysis method under the multi-time and space scenario of the power system as described in the first aspect.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the method for analyzing the minimum inertia of a power system in a multi-time and space scenario as described in the first aspect.

[0040] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program comprises program instructions, wherein when the program instructions are executed by a computer, the computer executes the steps of the method for minimum inertia analysis of the power system in a multi-time and space scenario as described in the first aspect.

[0041] It can be seen from the above technical solutions that the present invention has the following advantages:

[0042] The present invention describes the correlation between source and load by constructing a joint probability distribution model of source-load output data of an electric power system, and uses the joint probability distribution model to perform scenario sampling and reduction. The minimum inertia of the electric power system under the system disturbance constraint and the system frequency deviation constraint in a typical scenario is used to determine the higher minimum inertia as the required minimum inertia of the electric power system in the typical scenario, so as to accurately characterize the influence of various uncertain factors on the minimum inertia demand of the system. The expected value of the minimum inertia of the electric power system is determined by the required minimum inertia and the occurrence probability corresponding to a plurality of typical scenarios, thereby combining the minimum inertia demand of the system with its performance in different typical scenarios, and realizing accurate control of the dynamic stability and safety of the electric power system in a complex and changeable environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of an application environment of a power system provided by an embodiment of the present invention;

[0044] Figure 2 A flowchart of a method for analyzing the minimum inertia of a power system in a multi-time and space scenario provided by an embodiment of the present invention;

[0045] Figure 3 An equivalent system diagram provided for an embodiment of the present invention;

[0046] Figure 4 A schematic diagram of the structure of a device for analyzing the minimum inertia of a power system in multiple time and space scenarios provided by an embodiment of the present invention;

[0047] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described 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, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The embodiment of the present application provides a method for analyzing the minimum inertia of a power system in a multi-time and space scenario, which can be applied to Figure 1In the application environment of the power system shown in FIG. 1 , the power system node communicates with the server 102. The data storage system can store data that the server 102 needs to process. The data storage system can be integrated on the server 102, or placed on the cloud or other network servers. The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0050] like Figure 2 As shown, the embodiment of the present application provides a method for analyzing the minimum inertia of a power system in a multi-time and space scenario. Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S1 to S4. Among them:

[0051] Step S1: construct a joint probability distribution model of source-load output data according to the source-load output data of the power system.

[0052] The source-load output data refers to the power output data and load demand data of the power system. The joint probability distribution model is a probability model used to describe the relationship between two or more random variables. In the power system, the relationship between power output and load demand can be described.

[0053] Step S2: Sampling scenes according to the joint probability distribution model, and reducing the sampled random scene set to obtain multiple typical scenes and the probability of occurrence of each typical scene.

[0054] Step S3: for each typical scenario, obtain the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint respectively in the typical scenario, and take the higher minimum inertia as the required minimum inertia of the power system in the typical scenario.

[0055] Among them, the embodiment of the present invention considers the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in a typical scenario. After the minimum inertia of the system is obtained according to the system disturbance constraint and the system frequency deviation constraint, the higher minimum inertia is taken as the required minimum inertia of the power system in the typical scenario, that is:

[0056]

[0057] In the formula, is the required minimum inertia of the power system, is the minimum inertia under system disturbance constraints, is the minimum inertia under the system frequency deviation constraint.

[0058] Step S4: determining an expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities corresponding to a plurality of typical scenarios.

[0059] It should be noted that the present invention describes the correlation between source and load by constructing a joint probability distribution model of the source-load output data of the power system, and uses the joint probability distribution model to perform scenario sampling and reduction. The higher minimum inertia is determined by the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in the typical scenario, so as to accurately characterize the influence of various uncertain factors on the minimum inertia demand of the system, and the expected value of the minimum inertia of the power system is determined by the required minimum inertia and occurrence probability corresponding to multiple typical scenarios, thereby combining the minimum inertia demand of the system with its performance in different typical scenarios, and realizing accurate control of the dynamic stability and safety of the power system in a complex and changeable environment.

[0060] In one example, constructing a joint probability distribution model of source-load output data according to source-load output data of the power system in step S1 includes:

[0061] Step S101: determining a standard normal distribution of source-load output data according to source-load output data of a power system.

[0062] Among them, the power output and load data are converted into distributions on a standardized unit interval, namely, standard normal distribution scores, so that the original data are mapped to a standard normal distribution with a mean of 0 and a standard deviation of 1.

[0063] Step S102: construct a covariance matrix of source-load output data according to a standard normal distribution.

[0064] Among them, the covariance matrix between power output and load data is calculated. The covariance matrix describes the linear relationship between different variables. For two variables, the covariance matrix is ​​a 2x2 matrix, where the main diagonal elements are the respective variances and the off-diagonal elements are the covariances.

[0065] Step S103: establishing a joint probability distribution model of the source-load output data through a multivariate Gaussian Copula function according to the covariance matrix of the source-load output data.

[0066] Among them, a multivariate Gaussian Copula is used to capture the dependency between the standardized power output and load data. As a method in multivariate analysis, Copula theory can accurately describe the correlation structure between multivariate variables and is widely used in modeling the dependency structure of two (or more) random variables. This method has been used in many fields of research, including wind farm correlation analysis, flood risk analysis, frequency analysis, etc. The dependency between random variables can be characterized by the Copula function, which is equivalent to a tool for mapping from marginal distribution to joint distribution, and can also be called the connection function between marginal distribution and joint distribution. The more commonly used correlation measure in correlation analysis is the linear correlation coefficient, but it can only reflect the linear correlation between variables, and cannot accurately describe nonlinearly related variables. In the case of non-normal distribution, it is necessary to introduce an indicator that can well measure the correlation of random variables. It is usually estimated through the theoretical relationship between parameters and empirical dependence measures, such as Kendall rank, Spearman rank, tail correlation coefficient, etc. Their correlation measures will not change under monotonically increasing transformations and will not affect the accuracy of the results.

[0067] The Gaussian copula is derived from the multivariate normal distribution Obtained through probability integral transformation. For a given covariance matrix (correlation matrix) , based on the parameter matrix The Gaussian Copula can be expressed as:

[0068]

[0069] In the formula, is the Gaussian Copula function, is the inverse cumulative distribution function of the standard normal distribution, is the joint cumulative distribution function of each element's normal distribution, its mean vector is 0, and the covariance matrix is .

[0070] In one example, to obtain a typical scenario, step S2 includes:

[0071] Step S201: discretize the joint probability distribution model by random sampling using the Sequential Monte Carlo method to obtain multiple random scenarios and form a random scenario set.

[0072] Among them, the uncertainties existing in modern power grids include not only traditional generators and loads, but also new energy generation such as wind power and photovoltaics. Their random volatility brings new challenges to the operation and dispatch of power systems. Using the sequential Monte Carlo simulation method, it is necessary to first obtain the total power situation, load situation and output situation of each micro-power source of the system at the specified simulation time. Based on the purpose of simplifying the difficulty of model solution, clear concepts and easy understanding, the uncertainties existing in modern power systems are analyzed and processed and the corresponding probability model is established, that is, the joint probability distribution model in the embodiment of the present invention.

[0073] The basic idea of ​​the sequential Monte Carlo simulation method is to establish a virtual system state cycle transfer process within a specified time period according to the time sequence, and to calculate and analyze each system state, and finally obtain the evaluation index through statistical laws. The calculation formula is:

[0074]

[0075] in: is the total duration of the simulation; The system is The operating status at the moment; is the test function of the system evaluation index; It is the estimated value of the system evaluation index.

[0076] System operating status The system state transfer changes according to the set calculation time granularity. Therefore, the system state transfer is a discretization process, which can be discretized into:

[0077]

[0078] in: is the total duration of the simulation, It is The system evaluation index results for 2018. According to the law of large numbers, the sample mean is the expected value of the system evaluation index The unbiased estimate of , its estimated value All meet

[0079]

[0080] Therefore, after a longer simulation time, , sample mean Converges to the expected value The probability of is 1, so the convergence of this method is guaranteed.

[0081] The sequential Monte Carlo simulation method generally performs multiple simulations until a statistical indicator with the slowest convergence converges, and then it is considered converged. The calculation formula for the convergence criterion is:

[0082]

[0083] Where: is an evaluation index with the slowest convergence, yes The standard deviation of yes The mean of is the number of simulation years, is the given value of the convergence criterion, which is generally taken as .

[0084] Step S202: clustering the random scene set using the K-means clustering method, and determining the random scenes corresponding to multiple cluster centers as typical scenes.

[0085] Among them, K-means clustering, also known as K-means algorithm, is a distance-based partitioning clustering method. Its basic idea is to use the distance between data objects as a similarity measure based on a given parameter k, and to divide objects with high similarity into the same cluster, thereby dividing all data objects into k clusters. K-means is an iterative process. Each time, the accuracy of the clustering results must be examined, and the cluster center is adjusted according to the results, and then the next operation is performed until all data are assigned to the correct cluster. The basic operation steps are as follows:

[0086] 1) According to the input parameters In size Randomly select from the dataset data points as initial cluster centers , in is the number of iterations.

[0087] 2) Calculate the Euclidean distance from each data point to the cluster center , if satisfied , , but .

[0088] 3) Calculate the mean of the data points in each cluster As the new cluster center , and use this to allocate new data points, the same as step 2), to form new clusters.

[0089] 4) The criterion function for calculating the sum of squared errors is:

[0090]

[0091] 5) If The operation ends and multiple optimal cluster centers are output as typical scenarios. Otherwise, the process returns to step 3) and the loop iteration begins, where: is the error threshold.

[0092] Among them, the number of cluster center points The impact on clustering results is huge. The present invention selects the elbow method to determine the optimal number of clusters. The core indicator of the elbow method is the error sum of squares. , whose size is the quality of clustering effect under different clustering conditions, and its calculation formula is:

[0093]

[0094] In the formula, The number of clusters formed for clustering; After clustering is completed clusters; is the data point in this cluster; For the The cluster centers of the clusters.

[0095] Step S203: for each typical scenario, determine the occurrence probability of the typical scenario according to the typical scenario and the total number of scenarios.

[0096] In one example, the frequency characteristics of the power system can be expressed by the equivalent generator rotor motion equation:

[0097]

[0098] In the formula, is the system inertia constant, s; is the center frequency of inertia; is the change in mechanical power of the equivalent generator; t is the time; is the disturbance power; is the load frequency adjustment coefficient. Except for the nominal value, the rest are standard values.

[0099] The process of obtaining the minimum inertia of the power system under the system disturbance constraint in a typical scenario includes:

[0100] Step S301: determining a first minimum inertia according to a maximum frequency change rate after a disturbance occurs in the power system and a maximum disturbance power after a disturbance occurs in the power system in a typical scenario.

[0101] Among them, if the disturbance RoCoF does not exceed a certain threshold value as a constraint, the disturbance instantaneous RoCoF should be used as the benchmark. At this time, there is no frequency deviation, that is, there is no adjustment effect of the generator speed regulator and the load frequency adjustment coefficient. For systems with a small proportion of voltage-sensitive loads, the frequency change only depends on the system inertia and the disturbance power, that is:

[0102]

[0103] In the formula, Indicates the change in system active power (or electromagnetic power change rate).

[0104] The calculation method of the minimum inertia of the power system based on disturbance constraints is:

[0105] In the formula, is the first minimum inertia, is the maximum frequency change rate after the disturbance occurs, is the maximum disturbance power after the disturbance occurs.

[0106] The maximum disturbance power occurs in the system After the disturbance, the system inertia shall not be less than , the greater the disturbance power, The smaller it is, the greater the minimum inertia is, and vice versa.

[0107] Step S302: correct the initial minimum inertia using the inertia change caused by the disturbance to obtain a second minimum inertia.

[0108] It should be noted that when considering the maximum disturbance power of the load voltage characteristic, The maximum disturbance power that may occur in the system, generally the tripping of the largest unit, the disconnection of the AC tie line or the DC interlocking, depends on the system operating conditions. Different disturbance types have different effects on the frequency. The DC interlocking fault only increases the unbalanced power and does not change the system inertia level. The disconnection of the AC tie line and the tripping of the synchronous unit will not only cause the unbalanced power but also reduce the system inertia, further deteriorating the frequency characteristics. Therefore, the minimum inertia must take into account the inertia change caused by the disturbance, that is, use the inertia change caused by the disturbance to correct the initial minimum inertia, and get:

[0109]

[0110] In the formula, is the second minimum inertia, is the change in inertia caused by the disturbance.

[0111] Due to the tripping of the synchronous machine and the disconnection of the AC tie line, the system inertia is reduced, so that under the same disturbance power, the disturbance is greater than the DC blocking fault. Therefore, when evaluating the minimum inertia, it is necessary to consider the dual effects of the disturbance on the unbalanced power and the system inertia.

[0112] Step S303: Use the static load voltage equivalent inertia to correct the maximum disturbance power after the disturbance of the power system in the second minimum inertia, and obtain the third minimum inertia as the minimum inertia of the power system under the system disturbance constraint in a typical scenario, wherein the static load voltage equivalent inertia is determined by the disturbance voltage measurement and the load.

[0113] Among them, for actual power grids, real-time inertia monitoring is usually obtained from the SCADA (Supervisory Control and Data Acquisition) system:

[0114]

[0115] Where: The start / stop status of the unit, with a value of 1 (on) or 0 (off); is the unit kinetic energy, It is the sum of the capacities of all units in the system. Real-time inertia monitoring only includes the inertia of the synchronous machine, excluding the voltage characteristics of the static load.

[0116] In order to make the minimum inertia correspond to the inertia monitoring value, the maximum frequency change rate after the disturbance occurs is calculated. Make corrections to remove the static load voltage equivalent inertia:

[0117]

[0118] In the formula After removing the static load voltage, the unbalanced power actually applied to the equivalent rotor of the system is the power supported by the inertia of the synchronous machine. is the minimum inertia of the system after the disturbance corresponding to the system inertia monitoring value. Therefore, the minimum inertia of the power system under the system disturbance constraint in a typical scenario is for:

[0119]

[0120] Among them, the static load voltage Through the disturbance voltage measurement and load composition, we can obtain:

[0121]

[0122] In the formula, and They are constant impedance, constant current and constant power load ratios respectively; is the voltage change value; is the load power before disturbance.

[0123] The frequency constraint is usually based on the maximum bus frequency or specific bus frequency of the system, which is generally located near the disturbance point. For small systems, the bus frequencies are basically the same, while larger systems usually have frequency space distribution characteristics, and the relationship between the maximum bus frequency and the inertia center frequency needs to be quantified. Therefore, in an embodiment of the present invention, the process of obtaining the minimum inertia of the power system under the system frequency deviation constraint in a typical scenario includes:

[0124] When the maximum disturbance power occurs in the power system in a typical scenario, and after the safety control action time, the maximum frequency deviation is the system inertia corresponding to the maximum bus frequency of the power disturbance point. The minimum inertia of the power system under the system frequency deviation constraint in the typical scenario is determined. The minimum inertia under the system frequency deviation constraint is:

[0125]

[0126] In the formula, is the minimum inertia under the system frequency deviation constraint, is the load frequency regulation coefficient, To control the action time, is the maximum disturbance power after the power system is disturbed, is the static load voltage, is the maximum bus frequency deviation value at the power disturbance point, is the frequency distribution coefficient.

[0127] It should be noted that the sub-area where the disturbance point is located and the sub-area where the system inertia center is located are respectively equal to two machines, which is equivalent to Figure 3 The system diagram shown in is the power disturbance point, It is the equivalent unit in the area near the disturbance point; It is the equivalent unit in the area where the center of inertia is located; are the equivalent reactances of the interconnection lines at the disturbance point and the corresponding area, respectively. According to the disturbance power distribution mechanism in the inertia response stage, the relationship between the inertia center frequency corresponding to the disturbance point after the disturbance and the maximum value of the system frequency change rate after the disturbance is obtained as follows:

[0128]

[0129] In the formula, are the power and inertia shared by the units in the sub-region where the disturbance point is located; The inertia center unit shares power and inertia respectively.

[0130] In the initial stage of disturbance when the speed control system has not yet taken action, the frequency regulation effect of small load can be ignored, and the frequency deviation has an approximately equivalent relationship with the inertia center frequency, and the frequency distribution coefficient for:

[0131]

[0132] In the formula, , are the inertia center frequency corresponding to the disturbance point after the disturbance and the maximum value of the system frequency change rate after the disturbance, It is the maximum bus frequency deviation value of the system.

[0133] If the frequency deviation at the moment of safety control action is used as a constraint, the speed control system has not yet acted. , the expression of inertia center frequency is:

[0134]

[0135] The above situation is applicable to the frequency constraint scenario under the DC blocking and sending-end system safety control action. If the frequency regulation effect of smaller loads is ignored, it can be converted into the minimum inertia evaluation based on disturbance constraints. In order to prevent power outages caused by low-frequency load reduction actions under large disturbances, the system inertia must be maintained at a certain level so that the lowest frequency point after the disturbance is not lower than the first round of low-frequency load reduction action value of the third line of defense, which is usually 49Hz.

[0136] In one example, the step of determining the expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities corresponding to a plurality of typical scenarios in step S4 includes:

[0137] Step S401: determining an expected value of the minimum inertia of the power system in a typical scenario according to the required minimum inertia corresponding to each typical scenario and the probability of occurrence.

[0138] Step S402: performing weighted averaging on the expected values ​​of the minimum inertia of the power system under multiple typical scenarios to obtain the expected value of the minimum inertia of the power system.

[0139] Among them, for discrete random variables, the expected value is the probability weighted sum of all possible results. That is, the sum of each result multiplied by its probability of occurrence. By taking a weighted average of the expected values ​​of the minimum inertia of the power system calculated under all typical scenarios, the expected value of the minimum inertia of the power system under different scenarios is obtained. This expected value combines the minimum inertia requirements and their probability of occurrence of each scenario, providing a more comprehensive and accurate reference for power system planning and operation.

[0140] Based on the same inventive concept, an embodiment of the present application also provides a device for analyzing the minimum inertia of a power system in multiple time and space scenarios, which is used to implement the method for analyzing the minimum inertia of a power system in multiple time and space scenarios as mentioned above.

[0141] The implementation solution to the problem provided by the device is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in the embodiments of the system minimum inertia analysis device under multi-time and space scenarios of one or more power systems provided below can be referred to the limitations of the system minimum inertia analysis method under multi-time and space scenarios of the power system above, and will not be repeated here.

[0142] like Figure 4 As shown, an embodiment of the present invention further provides a system minimum inertia analysis device in a multi-time and space scenario of a power system, comprising:

[0143] A probability determination module 100, for constructing a joint probability distribution model of source-load output data according to source-load output data of the power system;

[0144] The scene sampling module 200 is used to perform scene sampling according to the joint probability distribution model, and reduce the random scene set obtained by sampling to obtain multiple typical scenes and the probability of occurrence of each typical scene;

[0145] The inertia comparison module 300 is used to obtain the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in each typical scenario, and take the higher minimum inertia as the required minimum inertia of the power system in the typical scenario;

[0146] The expected inertia determination module 400 is used to determine an expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities respectively corresponding to a plurality of typical scenarios.

[0147] In some embodiments, the probability determination module 100 is specifically used to determine the standard normal distribution of source-load output data based on the source-load output data of the power system; construct the covariance matrix of the source-load output data based on the standard normal distribution; and establish a joint probability distribution model of the source-load output data through a multivariate Gaussian Copula function based on the covariance matrix of the source-load output data.

[0148] In some embodiments, the scene sampling module 200 is specifically used to discretize the joint probability distribution model through random sampling using the sequential Monte Carlo method to obtain multiple random scenes and form a random scene set; the random scene set is clustered using the K-means clustering method to obtain random scenes corresponding to multiple cluster centers and determine them as typical scenes; for each typical scene, the probability of occurrence of the typical scene is determined based on the typical scene and the total number of scenes.

[0149] In some embodiments, obtaining the minimum inertia of the power system under the system disturbance constraint in a typical scenario includes:

[0150] Determine the first minimum inertia according to the maximum value of the frequency change rate after the power system is disturbed and the maximum disturbance power after the power system is disturbed in a typical scenario;

[0151] The initial minimum inertia is corrected using the inertia change caused by the disturbance to obtain the second minimum inertia;

[0152] The maximum disturbance power after the disturbance of the power system in the second minimum inertia is corrected by using the static load voltage equivalent inertia, and the third minimum inertia is obtained as the minimum inertia of the power system under the system disturbance constraint in a typical scenario, wherein the static load voltage equivalent inertia is determined by the disturbance voltage measurement and the load.

[0153] In some embodiments, obtaining the minimum inertia of the power system under the system frequency deviation constraint in a typical scenario includes:

[0154] When the maximum disturbance power occurs in the power system in a typical scenario, and after the safety control action time, the maximum frequency deviation is the system inertia corresponding to the maximum bus frequency of the power disturbance point. The minimum inertia of the power system under the system frequency deviation constraint in the typical scenario is determined. The minimum inertia under the system frequency deviation constraint is:

[0155]

[0156] In the formula, is the minimum inertia under the system frequency deviation constraint, is the load frequency regulation coefficient, To control the action time, is the maximum disturbance power after the power system is disturbed, is the static load voltage, is the maximum bus frequency deviation value at the power disturbance point, is the frequency distribution coefficient, where

[0157]

[0158] In the formula, , are the inertia center frequency corresponding to the disturbance point after the disturbance and the maximum value of the system frequency change rate after the disturbance, It is the maximum bus frequency deviation value of the system.

[0159] In some embodiments, the expected inertia determination module 400 is specifically used to determine the expected value of the minimum inertia of the power system under a typical scenario based on the required minimum inertia and the probability of occurrence corresponding to each typical scenario; and to perform a weighted average on the expected values ​​of the minimum inertia of the power system under multiple typical scenarios to obtain the expected value of the minimum inertia of the power system.

[0160] like Figure 5 As shown, an embodiment of the present invention further provides an electronic device, the electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the system minimum inertia analysis method in a multi-time and space scenario of the power system in any of the above embodiments.

[0161] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the method for analyzing the minimum inertia of a power system in a multi-time and space scenario as described in any of the above embodiments are implemented.

[0162] An embodiment of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the system minimum inertia analysis method in a multi-time and space scenario of the power system in any of the above embodiments.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, computer storage media and computer program products can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0165] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and a part of a module, a program segment or a code includes one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0166] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the various embodiments of the method of the present invention through a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk, etc. Various media that can store program codes.

[0170] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the minimum inertia of a power system in multiple time and space scenarios, characterized in that: include: Constructing a joint probability distribution model of the source-load output data according to the source-load output data of the power system; Sampling scenes according to the joint probability distribution model, and reducing the sampled random scene set to obtain multiple typical scenes and the probability of occurrence of each of the typical scenes; For each of the typical scenarios, the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in the typical scenario is obtained, and the higher minimum inertia is taken as the required minimum inertia of the power system in the typical scenario; The expected value of the minimum inertia of the power system is determined according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios.

2. The method for analyzing the minimum inertia of a power system in multiple time and space scenarios according to claim 1 is characterized in that: The step of constructing a joint probability distribution model of the source-load output data according to the source-load output data of the power system comprises: Determining a standard normal distribution of the source-load output data according to the source-load output data of the power system; Constructing a covariance matrix of the source-load output data according to the standard normal distribution; A joint probability distribution model of the source-load output data is established according to the covariance matrix of the source-load output data through a multivariate Gaussian Copula function.

3. The method for analyzing the minimum inertia of a power system in multiple time and space scenarios according to claim 1 is characterized in that: The step of sampling scenes according to the joint probability distribution model and reducing the random scene set obtained by sampling to obtain multiple typical scenes and the probability of occurrence of each of the typical scenes includes: Discretizing the joint probability distribution model by random sampling using a sequential Monte Carlo method to obtain a plurality of random scenarios and form the random scenario set; The random scene set is clustered using a K-means clustering method, and random scenes corresponding to a plurality of cluster centers are determined as typical scenes; For each of the typical scenarios, the occurrence probability of the typical scenario is determined according to the typical scenario and the total number of scenarios.

4. The method for analyzing the minimum inertia of a power system in multiple time and space scenarios according to claim 1 is characterized in that: The process of obtaining the minimum inertia of the power system under the system disturbance constraint in the typical scenario includes: Determining a first minimum inertia according to a maximum value of a frequency change rate after a disturbance occurs in the power system and a maximum disturbance power after a disturbance occurs in the power system in the typical scenario; The initial minimum inertia is corrected by using the inertia change caused by the disturbance to obtain a second minimum inertia; The maximum disturbance power after the disturbance of the power system in the second minimum inertia is corrected by using the static load voltage equivalent inertia, and a third minimum inertia is obtained as the minimum inertia of the power system under the system disturbance constraint in the typical scenario, wherein the static load voltage equivalent inertia is determined by the disturbance voltage measurement and the load.

5. The method for analyzing the minimum inertia of a power system in multiple time and space scenarios according to claim 1 or 4, characterized in that: The process of obtaining the minimum inertia of the power system under the system frequency deviation constraint in the typical scenario includes: When the maximum disturbance power occurs in the power system under the typical scenario, and after the safety control action time, the maximum frequency deviation is the system inertia corresponding to the maximum bus frequency of the power disturbance point. The minimum inertia of the power system under the system frequency deviation constraint in the typical scenario is determined. The minimum inertia under the system frequency deviation constraint is: In the formula, is the minimum inertia under the system frequency deviation constraint, is the load frequency regulation coefficient, To control the action time, is the maximum disturbance power after the power system is disturbed, is the static load voltage, is the maximum bus frequency deviation value at the power disturbance point, is the frequency distribution coefficient, where In the formula, , are the inertia center frequency corresponding to the disturbance point after the disturbance and the maximum value of the system frequency change rate after the disturbance, It is the maximum bus frequency deviation value of the system.

6. The method for analyzing the minimum inertia of a power system in multiple time and space scenarios according to claim 1 is characterized in that: The step of determining the expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios comprises: Determine an expected value of the minimum inertia of the power system under the typical scenario according to the required minimum inertia corresponding to each typical scenario and the probability of occurrence; The expected value of the minimum inertia of the power system under the multiple typical scenarios is weighted averaged to obtain the expected value of the minimum inertia of the power system.

7. A system minimum inertia analysis device for a power system in multiple time and space scenarios, characterized in that: include: A probability determination module, used to construct a joint probability distribution model of the source-load output data according to the source-load output data of the power system; A scene sampling module, used to perform scene sampling according to the joint probability distribution model, and reduce the random scene set obtained by sampling to obtain multiple typical scenes and the probability of occurrence of each of the typical scenes; An inertia comparison module is used to obtain, for each of the typical scenarios, the minimum inertia of the power system under the system disturbance constraint and the system frequency deviation constraint in the typical scenario, and take the higher minimum inertia as the required minimum inertia of the power system in the typical scenario; The expected inertia determination module is used to determine the expected value of the minimum inertia of the power system according to the required minimum inertias and occurrence probabilities respectively corresponding to the multiple typical scenarios.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for minimum inertia analysis of the power system in a multi-time and space scenario as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the method for analyzing the minimum inertia of a power system in a multi-time and space scenario are implemented as described in any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to perform the steps of the method for minimum inertia analysis of a power system in a multi-time and space scenario as described in any one of claims 1 to 6.

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