Methods, apparatus, and electronic equipment for generating frequency control measures for split-blown bars

Abstract

This invention provides a method, apparatus, and electronic device for generating a sub-Brønsted bar frequency control scheme, relating to the field of power system technology. The method for generating the sub-Brønsted bar frequency control scheme includes: processing the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency security constraint; substituting the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint; and solving the objective function based on the dynamic equation of the target power grid frequency and the second frequency security constraint to obtain the target control measure quantity that minimizes the control measure cost of the target power grid. This invention can provide an efficient, accurate, and highly adaptable emergency frequency control scheme to perceive the power grid status in real time, quickly respond to various disturbances and faults, and effectively coordinate various frequency regulation resources to ensure the frequency security of the power grid.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, apparatus, and electronic device for generating a frequency control scheme using a split-blower rod. Background Technology

[0002] In the process of building a new power system driven by the "dual carbon" goal, my country's power system is gradually transforming into a hybrid AC / DC grid that includes a high proportion of renewable energy. This transformation has brought about profound changes in the grid structure, with a significant increase in the proportion of DC power received, while the system's rotational inertia has decreased accordingly. Under this new grid configuration, faults such as DC blocking may trigger significant power deficits, posing a severe challenge to the frequency security of the receiving-end grid.

[0003] Furthermore, the operation of new power systems is complex and variable, with disturbances and faults exhibiting significant uncertainty, further increasing the difficulty of frequency security control. Traditional pre-planned control strategies may fail to operate or malfunction in the face of this complex and ever-changing grid environment, failing to meet the needs of emergency frequency control. Simultaneously, with the large-scale integration of renewable energy and the gradual opening of the electricity market, resources providing frequency regulation services are becoming increasingly abundant, making the mathematical models of the power grid more complex and unknown, exhibiting nonlinear characteristics. This makes the modeling process for traditional pre-planned control strategies extremely difficult, increasing model complexity and compromising timeliness.

[0004] Therefore, how to provide an efficient, accurate, and adaptable emergency frequency control scheme to perceive the power grid status in real time, respond quickly to various disturbances and faults, and effectively coordinate various frequency regulation resources to ensure the frequency security of the power grid is an urgent technical problem to be solved. Summary of the Invention

[0005] To address the aforementioned problems in the existing technology, this invention provides a method, apparatus, and electronic device for generating a frequency control scheme using a multi-stage frequency control mechanism. This scheme provides an efficient, accurate, and highly adaptable emergency frequency control solution to perceive the power grid status in real time, respond quickly to various disturbances and faults, and effectively coordinate various frequency regulation resources to ensure the frequency security of the power grid.

[0006] This invention provides a method for generating a frequency control scheme for a split-bulb rod, comprising the following steps.

[0007] A sub-Browman emergency frequency control framework for a target power grid is obtained; wherein the sub-Browman emergency frequency control framework includes an objective function, a dynamic equation for the frequency of the target power grid as a constraint, and a first frequency security constraint; wherein the objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of control measures as weights; wherein the dynamic equation for the frequency of the target power grid uses a time-varying Koopman observation function to describe the impact of the control measures on the frequency of the target power grid; wherein the first frequency security constraint stipulates that at each time point, the difference between the predicted value and the safe value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst distribution in the uncertainty set at the significance level, wherein the uncertainty... The uncertainty set is the set of distributions of the differences between the predicted and actual frequency values ​​estimated from historical data of the target power grid. Using a Gaussian mixture model and Wasserstein distance, the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint is processed to obtain the lower limit of the first frequency security constraint. Substituting the lower limit of the first frequency security constraint into the first frequency security constraint yields the second frequency security constraint. Based on the system state data of the target power grid, the dynamic equation of the target power grid's frequency, and the second frequency security constraint, the objective function is solved to obtain the target control measure quantity that minimizes the cost of control measures for the target power grid. Emergency frequency control is then performed on the target power grid according to the target control measure quantity.

[0008] According to a method for generating a frequency control scheme for a multi-stage blobs provided by the present invention, the step of processing the uncertain set and the inverse cumulative distribution function in the first frequency safety constraint using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency safety constraint includes: obtaining a quantitative description of the uncertain set using a Gaussian mixture model and Wasserstein distance; approximating the inverse cumulative distribution function in the first frequency safety constraint using a Gaussian mixture model to obtain an analytical expression of the inverse cumulative distribution function; and solving the analytical expression of the inverse cumulative distribution function based on the quantitative description of the uncertain set to obtain the lower limit value of the first frequency safety constraint.

[0009] According to the present invention, a method for generating a frequency control scheme for a split-bulb rod is provided. The step of obtaining a quantitative description of the uncertain set using a Gaussian mixture model and the Wasserstein distance includes: describing the reference distribution of the uncertain set using a Gaussian mixture model to obtain a probability distribution model composed of Gaussian components; determining the Wasserstein distance between the two probability distribution models using the Wasserstein distance; and obtaining a quantitative description of the uncertain set based on the Wasserstein distance.

[0010] According to a method for generating a frequency control measure scheme based on a Gaussian mixture model, the method for approximating the inverse cumulative distribution function in the first frequency security constraint using a Gaussian mixture model to obtain an analytical expression for the inverse cumulative distribution function includes: approximating the inverse cumulative distribution function as a weighted sum of the 1-α quantiles calculated for each Gaussian component in the uncertainty set according to the Gaussian mixture weights at a probability level of 1-α, thereby obtaining an analytical expression for the inverse cumulative distribution function; wherein the 1-α probability is the confidence level at which the difference between the predicted frequency value and the safe value of the target power grid satisfies the frequency security constraint at a set significance level α.

[0011] According to the method for generating a frequency control scheme for a split-bulb rod provided by the present invention, the objective function is expressed by the following formula:

[0012] ;

[0013] Where R represents the positive definite matrix of control measure costs, and u is the control measure quantity; the frequency of the target power grid... The dynamic equation is expressed by the following formula:

[0014] ;

[0015] in, It is a set of Koopman observation functions starting from the initial time; , , These represent the transition matrix, control matrix, and output matrix of the Koopman observables, respectively; t is the total control time. k To control the numbering of time, For time k The control measures for the time period; the first frequency safety constraint is expressed by the following formula:

[0016] ;

[0017] ;

[0018] It is the safe value of the frequency of the target power grid; It belongs to the indeterminate set The unknown distribution; icdf is the inverse cumulative distribution function. Represents the significance level; This represents a reference probability distribution indicating the difference between the predicted and actual values ​​of the frequency estimated from historical data of the target power grid.

[0019] According to the method for generating a sub-bulb frequency control scheme provided by the present invention, the quantitative description of the uncertainty set is expressed by the following formula:

[0020] ;

[0021] ;

[0022] ;

[0023] in, It is a decision variable; This is a preset value representing the upper limit of the size of the fuzzy set. The first probability density function is the first... l The mean of a reference probability distribution, The second probability density function k The mean of a reference probability distribution, the first probability density function and the second probability density function are both probability density functions that conform to a Gaussian mixture model; The first probability density function is the first... l The standard deviation of each Gaussian component For the second probability density function k The standard deviation of each Gaussian component; l and k These represent the indices of the Gaussian components of the first and second probability density functions, respectively.

[0024] The analytical expression of the inverse cumulative distribution function can be expressed by the following formula:

[0025] ;

[0026] in, The first probability density function is the first... k Gaussian mixture weights; For the standard normal distribution ( ) Quantiles.

[0027] The present invention also provides a device for generating a frequency control scheme for a split-bulb rod, comprising the following modules:

[0028] A construction module is used to obtain a sub-Browman emergency frequency control framework for a target power grid. The sub-Browman emergency frequency control framework includes an objective function, a dynamic equation for the frequency of the target power grid as constraints, and a first frequency security constraint. The objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of control measures as weights. The dynamic equation for the frequency of the target power grid uses a time-varying Koopman observation function to describe the impact of the control measures on the frequency of the target power grid. The first frequency security constraint stipulates that at each time point, the difference between the predicted and safe values ​​of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst-case distribution in the uncertainty set at the significance level. It is a set of distributions of differences between predicted and actual frequency values ​​estimated from historical data of the target power grid; a first acquisition module is used to process the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency security constraint; a second acquisition module is used to substitute the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint; a third acquisition module is used to solve the objective function based on the dynamic equation of the frequency of the target power grid and the second frequency security constraint to obtain the target control measure quantity that minimizes the cost of the control measures for the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure quantity.

[0029] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for generating a split-brush frequency control scheme as described above.

[0030] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for generating a sub-blob frequency control scheme as described above.

[0031] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a method for generating a sub-blue bar frequency control scheme as described above.

[0032] The present invention provides a method, apparatus, and electronic device for generating frequency control measures using a multi-stage bar frequency control scheme. Utilizing the dynamic equation of the target power grid's frequency as a constraint and a first frequency security constraint, it can ensure the frequency security of the power grid system under most operating modes and fault conditions, while significantly reducing control costs and avoiding the enormous control costs incurred due to emergency frequency control strategies, such as large load shedding. By using a Gaussian mixture model and Wasserstein distance to process the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint, a lower limit value for the first frequency security constraint is obtained. Substituting this lower limit value into the first frequency security constraint yields a second frequency security constraint. Based on the dynamic equation of the target power grid's frequency and the second frequency security constraint, the objective function is solved to obtain the target control measure quantity that minimizes the control cost of the target power grid. This simplifies a complex nonlinear problem requiring extensive computation into an optimization problem of linear and quadratic equations, effectively improving the computational speed of the control strategy and enabling it to meet the timeliness requirements of emergency frequency strategy formulation while ensuring the economy of the control strategy. Therefore, it provides an efficient, accurate, and highly adaptable emergency frequency control scheme. It can sense the power grid status in real time, respond quickly to various disturbances and faults, and effectively coordinate various frequency regulation resources to ensure the frequency security of the power grid. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0034] Figure 1 This is a flowchart illustrating the method for generating the frequency control measures scheme for the split-blown bar provided by the present invention.

[0035] Figure 2 This is a flowchart illustrating the method for obtaining the lower limit value of the first frequency security constraint provided by the present invention.

[0036] Figure 3 This is a schematic diagram of the structure of the device for generating the frequency control measures scheme of the split-blob bar provided by the present invention.

[0037] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0039] The following is combined with Figures 1-2 The present invention describes a method for generating a frequency control scheme for a split-bulb rod.

[0040] Figure 1 This is a flowchart illustrating the method for generating the frequency control scheme for the split-bulb rod provided by the present invention, as shown below. Figure 1 As shown, the method includes the following:

[0041] Step 101: Obtain the sub-Bluerg emergency frequency control framework of the target power grid; wherein, the sub-Bluerg emergency frequency control framework includes the objective function, the dynamic equation of the frequency of the target power grid as a constraint condition, and the first frequency security constraint.

[0042] The target power grid is one that requires emergency frequency control measures, such as a hybrid AC / DC grid with a high proportion of renewable energy. The objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of the control measures as weights. As an example, the objective function can be expressed using the following formula:

[0043] (1)

[0044] Where R represents the positive definite matrix of control measure costs; u is the control measure quantity. , representing a control vector composed of control quantities at different times.

[0045] A control measure quantity *u* represents a series of control measures or inputs applied to the target power grid to achieve a desired effect. As an example only, a control measure quantity *u* can include various elements such as: output power adjustment of generator 1 (increase or decrease); output power adjustment of generator 2; ...; output power adjustment of generator n; load shedding; the number of capacitors or reactors switched on or off, etc.

[0046] The dynamic equation for the target power grid's frequency uses a time-varying Koopman observation function to describe the impact of control measures on the target power grid's frequency. (This is merely an example; the target power grid's frequency...) The dynamic equation is expressed by the following formula:

[0047] (2)

[0048] in, It is a set of Koopman observation functions starting from the initial time; , , These represent the transition matrix, control matrix, and output matrix of the Koopman observables, respectively; t is the total control time. k To control the numbering of time, For time k The amount of control measures at that time.

[0049] The first frequency security constraint stipulates that at each time point, the difference between the predicted and safe frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst-case distribution in the uncertainty set at the significance level. The uncertainty set is the set of distributions of the differences between the predicted and actual frequency values ​​estimated from historical data of the target power grid. As an example only, the first frequency security constraint is expressed by the following formula:

[0050] (3)

[0051] (4)

[0052] in, It is the safe value for the frequency of the target power grid; It belongs to the indeterminate set The unknown distribution; icdf is the inverse cumulative distribution function. Represents the significance level; This represents the reference probability distribution of the difference between the predicted and actual values ​​of the frequency estimated from historical data of the target power grid.

[0053] First frequency security constraints are used to find uncertain sets. The worst Make Maximum. Therefore, when the probability distribution of the frequency prediction error of the target power grid is... At that time, the first frequency security constraint can ensure the security probability of the target power grid frequency is . .

[0054] Step 102: Using the Gaussian mixture model and Wasserstein distance, process the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint to obtain the lower limit value of the first frequency safety constraint.

[0055] In the specific implementation process, Gaussian mixture models and Wasserstein distances are used to process the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint, thereby obtaining the lower limit value of the first frequency safety constraint. This divides the subbulb emergency frequency control framework into upper and lower two-level optimization problems: the lower-level optimization problem is how to determine the lower limit value of the first frequency safety constraint; the upper-level optimization problem is how to obtain the optimal solution of the objective function based on the dynamic equation of the target power grid frequency under the constraint of the lower limit value of the first frequency safety constraint.

[0056] For a detailed description of how to process the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint using Gaussian mixture models and Wasserstein distance to obtain the lower limit value of the first frequency safety constraint, please refer to [link to relevant documentation]. Figure 2 The relevant content will not be repeated here.

[0057] Step 103: Substitute the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint.

[0058] As an example only, the second frequency security constraint can be expressed as the following formula:

[0059] (5)

[0060] in, This represents the optimal value of the objective function in the lower-level optimization problem, i.e., the lower limit of the first frequency security constraint.

[0061] Step 104: Based on the system state data of the target power grid, the objective function is solved according to the dynamic equation of the frequency of the target power grid and the second frequency security constraint. The target control measure quantity that minimizes the cost of the control measures of the target power grid is obtained. Emergency frequency control is then performed on the target power grid according to the target control measure quantity.

[0062] The system state data of the target power grid is a set of state variables of the target power grid at a specific point in time. These state variables can comprehensively describe the current state of the target power grid system. For example, the system state data of the target power grid may include: the voltage amplitude and phase of generators at each node, the magnitude and direction of load current, the transformer turns ratio, and the frequency of the power grid, etc.

[0063] In the specific implementation process, based on the system state data of the target power grid, the objective function described by formula (1) can be solved according to the second frequency security constraint described by formula (5) and the dynamic equation of the frequency of the target power grid described by formula (2), so as to obtain the target control measure quantity that minimizes the cost of the control measures of the target power grid.

[0064] Since the second frequency security constraint and the frequency dynamic equation are both linear equations and the objective function is quadratic, the upper-level optimization problem is solvable and computationally efficient. Various algorithms can be used to solve the above upper-level optimization problem, and it is not limited by the description in this specification.

[0065] In the embodiments provided by this invention, a Gaussian mixture model and Wasserstein distance are used to process the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint to obtain the lower limit value of the first frequency safety constraint. This divides the subbulb emergency frequency control framework into an upper and lower bilayer optimization problem.

[0066] Figure 2 This is a flowchart illustrating the method for obtaining the lower limit value of the first frequency security constraint provided by the present invention, as shown below. Figure 2 As shown, the method includes the following:

[0067] Step 201: Use the Gaussian mixture model and Wasserstein distance to obtain a quantitative description of the uncertain set.

[0068] The probability density function (PDF) of the Gaussian mixture model is:

[0069] (6)

[0070] in, It has a mean Covariance Matrix The Gaussian distribution is the first of the Gaussian mixture models (GMMs). k One Gaussian component; yes The mixing weights of Gaussian components; It is in vector form and contains .

[0071] Determine the parameter set of GMM This is a typical parameter estimation problem. Based on Given the data, the parameter set of the GMM can be obtained using the maximum likelihood estimation technique. A typical algorithm includes the Expectation Maximization (EM) algorithm.

[0072] In practical implementation, a Gaussian mixture model can be used to describe the reference distribution of the uncertain set, resulting in a probability distribution model composed of Gaussian components; the Wasserstein distance between the two probability distribution models can be determined using the Wasserstein distance; and based on the Wasserstein distance, a quantitative description of the uncertain set can be obtained. As an example, the quantitative description of the uncertain set can be expressed using the following formula:

[0073] (6)

[0074] (7)

[0075] (8)

[0076] in, It is a decision variable; This is a preset value representing the upper limit of the size of the fuzzy set. The first probability density function is the first... l The mean of a reference probability distribution, The second probability density function k The mean of a reference probability distribution, the first probability density function and the second probability density function are both probability density functions that conform to a Gaussian mixture model; The first probability density function is the first... l The standard deviation of each Gaussian component For the second probability density function k The standard deviation of each Gaussian component; l and k These represent the indices of the Gaussian components of the first and second probability density functions, respectively.

[0077] The probability density function Second probability density function , can be represented as follows:

[0078] (9)

[0079] (10)

[0080] Step 202: Using the Gaussian mixture model, the inverse cumulative distribution function in the first frequency safety constraint is approximated to obtain the analytical expression of the inverse cumulative distribution function.

[0081] In practical implementation, the inverse cumulative distribution function can be approximated as... At the probability level, the Gaussian components in the uncertainty set are calculated separately according to the Gaussian mixture weights. The weighted sum of the quantiles yields the analytical expression for the inverse cumulative distribution function; where, The probability is the confidence level at which the difference between the predicted and safe frequency values ​​of the target power grid satisfies the frequency security constraints, given a set significance level α. As an example only, the analytical expression of the inverse cumulative distribution function can be expressed using the following formula:

[0082] (11)

[0083] in, The first probability density function is the first... k Gaussian mixture weights; For the standard normal distribution ( ) Quantiles.

[0084] Replace the part in formula (3) with formula (11) , making It becomes an analyzable expression determined by the parameters of the Gaussian mixture model.

[0085] Step 203: Based on the quantitative description of the uncertainty set, solve the analytical expression of the inverse cumulative distribution function to obtain the lower limit of the first frequency safety constraint.

[0086] In the specific implementation process, the analytical expression of the inverse cumulative distribution function (formula (11)) can be solved based on the quantitative description of the uncertainty set (formula (6) ~ formula (8)) to obtain the lower limit value of the first frequency security constraint, that is, the solution of the lower-level optimization problem.

[0087] Although the computational complexity of the lower-level optimization problem will increase... k and l The number of Gaussian components (i.e., the number of Gaussian components in a Gaussian Mixture Model, GMM) increases exponentially, but in practical applications, due to... k and l The value of is usually no more than 10, so its computation time remains within a controllable range. The overall computation time of the sub-Bruker emergency frequency control framework provided by this invention mainly consists of three parts: the time for fitting the GMM model, the time for solving the lower-level optimization problem, and the time for solving the upper-level optimization problem. Since the increase in the amount of historical data does not change the number of parameters in the GMM model, the computation time of the lower-level optimization problem and the upper-level optimization problem is basically unaffected by the data scale. At the same time, the process of learning GMM parameters using historical data is usually very efficient and requires very little time. Therefore, the sub-Bruker emergency frequency control framework provided by this invention can maintain stable computational performance on historical data of different scales, with the overall computation time controlled within 500 milliseconds, demonstrating excellent computational efficiency.

[0088] The apparatus for generating the frequency control measure scheme of the split-blob bar provided by the present invention will be described below. The apparatus for generating the frequency control measure scheme of the split-blob bar described below can be referred to in correspondence with the method for generating the frequency control measure scheme of the split-blob bar described above.

[0089] Figure 3 This is a schematic diagram of the structure of the device for generating the frequency control measure scheme of the split-blown bar provided by the present invention. Figure 3 As shown, the device 300 includes the following modules.

[0090] Module 310 is used to obtain a sub-Browser emergency frequency control framework for a target power grid. The sub-Browser emergency frequency control framework includes an objective function, a dynamic equation for the frequency of the target power grid as a constraint, and a first frequency security constraint. The objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of control measures as weights. The dynamic equation for the frequency of the target power grid uses a time-varying Koopman observation function to describe the impact of the control measures on the frequency of the target power grid. The first frequency security constraint stipulates that at each time point, the difference between the predicted and safe values ​​of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst-case distribution in the uncertainty set at the significance level. The uncertainty set is the set of distributions of the differences between the predicted and actual frequency values ​​estimated from historical data of the target power grid.

[0091] The first acquisition module 320 is used to process the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency security constraint.

[0092] The second acquisition module 330 is used to substitute the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint.

[0093] The third acquisition module 340 is used to solve the objective function based on the dynamic equation of the frequency of the target power grid and the second frequency security constraint, to obtain the target control measure quantity that minimizes the cost of the control measures for the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure quantity.

[0094] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a method for generating a sub-Browser frequency control measure scheme. This method includes: obtaining a sub-Browser emergency frequency control framework for the target power grid; wherein the sub-Browser emergency frequency control framework includes an objective function, a dynamic equation of the target power grid's frequency as a constraint, and a first frequency safety constraint; wherein the objective function minimizes the weighted sum of squares of the control measure quantities using a positive definite matrix representing the cost of the control measures as weights; wherein the dynamic equation of the target power grid's frequency uses a time-varying Koopman observation function to describe the impact of the control measure quantities on the target power grid's frequency; wherein the first frequency safety constraint stipulates that at each time point, the difference between the predicted value and the safe value of the target power grid's frequency is not less than the worst-case scenario in the uncertainty set. The inverse cumulative distribution function at the significance level is defined as follows: the uncertainty set is the set of differences between the predicted and actual frequency values ​​estimated from historical data of the target power grid; the uncertainty set and the inverse cumulative distribution function in the first frequency security constraint are processed using a Gaussian mixture model and Wasserstein distance to obtain the lower limit of the first frequency security constraint; the lower limit of the first frequency security constraint is substituted into the first frequency security constraint to obtain the second frequency security constraint; based on the system state data of the target power grid, the dynamic equation of the frequency of the target power grid, and the second frequency security constraint, the objective function is solved to obtain the target control measure quantity that minimizes the cost of the control measures for the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure quantity.

[0095] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the generation method of the sub-Browman frequency control measure scheme provided by the above methods. The method includes: obtaining a sub-Browman emergency frequency control framework for a target power grid; wherein the sub-Browman emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint, and a first frequency security constraint; wherein the objective function minimizes the weighted sum of squares of the control measure quantities using a positive definite matrix representing the cost of the control measure as weights; wherein the dynamic equation of the frequency of the target power grid describes the impact of the control measure quantities on the frequency of the target power grid using a time-varying Koopman observation function; wherein the first frequency security constraint is limited at each time point... The difference between the predicted and safe frequency values ​​of the target power grid is not less than the value of the inverse cumulative distribution function under the worst-case distribution in the uncertainty set at the significance level. The uncertainty set is the set of distributions of the differences between the predicted and actual frequency values ​​estimated from historical data of the target power grid. Using a Gaussian mixture model and Wasserstein distance, the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint are processed to obtain the lower limit of the first frequency safety constraint. Substituting the lower limit of the first frequency safety constraint into the first frequency safety constraint, a second frequency safety constraint is obtained. Based on the system state data of the target power grid, the objective function is solved according to the dynamic equation of the target power grid's frequency and the second frequency safety constraint to obtain the target control measure quantity that minimizes the cost of control measures for the target power grid. Emergency frequency control is then performed on the target power grid according to the target control measure quantity.

[0097] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for generating a sub-Browman frequency control measure scheme provided by the methods described above. This method includes: obtaining a sub-Browman emergency frequency control framework for a target power grid; wherein the sub-Browman emergency frequency control framework includes an objective function, a dynamic equation of the target power grid's frequency as a constraint, and a first frequency security constraint; wherein the objective function minimizes the weighted sum of squares of control measure quantities, using a positive definite matrix representing the cost of control measures as weights; wherein the dynamic equation of the target power grid's frequency uses a time-varying Koopman observation function to describe the impact of control measure quantities on the target power grid's frequency; and wherein the first frequency security constraint limits the predicted value of the target power grid's frequency at each time point to a safe frequency. The difference between the values ​​is not less than the value of the inverse cumulative distribution function under the worst distribution in the uncertainty set at the significance level. The uncertainty set is the set of distributions of the differences between the predicted and actual frequency values ​​estimated from the historical data of the target power grid. Using a Gaussian mixture model and Wasserstein distance, the uncertainty set and the inverse cumulative distribution function in the first frequency security constraint are processed to obtain the lower limit of the first frequency security constraint. The lower limit of the first frequency security constraint is substituted into the first frequency security constraint to obtain the second frequency security constraint. Based on the system state data of the target power grid, the objective function is solved based on the dynamic equation of the frequency of the target power grid and the second frequency security constraint to obtain the target control measure quantity that minimizes the cost of the control measures of the target power grid. Emergency frequency control is then performed on the target power grid according to the target control measure quantity.

[0098] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0099] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating a frequency control scheme for a split-bulb rod, characterized in that, include: Obtain the sub-Bluerg emergency frequency control framework of the target power grid; wherein, the sub-Bluerg emergency frequency control framework includes an objective function, a dynamic equation of the frequency of the target power grid as a constraint, and a first frequency safety constraint; The objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of the control measures as weights. The dynamic equation for the frequency of the target power grid uses a time-varying Koopman observation function to describe the impact of control measures on the frequency of the target power grid. Wherein, the first frequency security constraint is limited to the fact that at each time point, the difference between the predicted value and the safe value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst distribution in the uncertainty set at the significance level, wherein the uncertainty set is the set of distributions of the difference between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; By using a Gaussian mixture model and Wasserstein distance, the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint is processed to obtain the lower limit value of the first frequency security constraint; Substitute the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint; Based on the system state data of the target power grid, the objective function is solved according to the dynamic equation of the frequency of the target power grid and the second frequency security constraint to obtain the target control measure quantity that minimizes the cost of the control measures of the target power grid. Emergency frequency control is then performed on the target power grid according to the target control measure quantity.

2. The method for generating the frequency control scheme of the split-bulb rod according to claim 1, characterized in that, The process of using a Gaussian mixture model and Wasserstein distance to process the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint to obtain the lower limit value of the first frequency security constraint includes: A quantitative description of the uncertain set is obtained using a Gaussian mixture model and Wasserstein distance; Using a Gaussian mixture model, the inverse cumulative distribution function in the first frequency safety constraint is approximated, and the analytical expression of the inverse cumulative distribution function is obtained; Based on the quantitative description of the uncertainty set, the analytical expression of the inverse cumulative distribution function is solved to obtain the lower limit of the first frequency security constraint.

3. The method for generating the frequency control scheme of the split-bulb rod according to claim 2, characterized in that, The process of obtaining a quantitative description of the uncertain set using a Gaussian mixture model and Wasserstein distance includes: The reference distribution of the uncertain set is described using a Gaussian mixture model, resulting in a probability distribution model composed of Gaussian components; The Wasserstein distance between the two probability distribution models is determined using the Wasserstein distance. Based on the Wasserstein distance, a quantitative description of the uncertain set is obtained.

4. The method for generating the frequency control scheme of the split-bulb rod according to claim 3, characterized in that, The method of using a Gaussian mixture model to approximate the inverse cumulative distribution function in the first frequency safety constraint, thereby obtaining an analytical expression for the inverse cumulative distribution function, includes: The inverse cumulative distribution function is approximated as the weighted sum of the 1-α quantiles calculated for each Gaussian component in the uncertainty set according to the Gaussian mixture weights at the 1-α probability level, thus obtaining the analytical expression of the inverse cumulative distribution function; wherein, the 1-α probability is the confidence level at which the difference between the predicted frequency value and the safe value of the target power grid satisfies the frequency security constraint at the set significance level α.

5. The method for generating a frequency control scheme for a split-bulb rod according to any one of claims 1 to 4, characterized in that, The objective function is expressed by the following formula: ； Where R represents the positive definite matrix of control measures costs, and u is the amount of control measures; The frequency of the target power grid The dynamic equation is expressed by the following formula: ； in, It is a set of Koopman observation functions starting from the initial time; , , These represent the transition matrix, control matrix, and output matrix of the Koopman observables, respectively; t is the total control time. k To control the numbering of time, For time k The amount of control measures at that time; The first frequency security constraint is expressed by the following formula: ； ； It is the safe value of the frequency of the target power grid; It belongs to the indeterminate set The unknown distribution; icdf is the inverse cumulative distribution function. Represents the significance level; This represents a reference probability distribution indicating the difference between the predicted and actual values ​​of the frequency estimated from historical data of the target power grid.

6. The method for generating the frequency control scheme of the split-bulb rod according to claim 4, characterized in that, The quantitative description of the uncertain set can be expressed using the following formula: ； ； ； in, It is a decision variable; This is a preset value representing the upper limit of the size of the fuzzy set. The first probability density function is the first... l The mean of a reference probability distribution, The second probability density function k The mean of a reference probability distribution, the first probability density function and the second probability density function are both probability density functions that conform to a Gaussian mixture model; The first probability density function is the first... l The standard deviation of each Gaussian component For the second probability density function k The standard deviation of each Gaussian component; l and k These represent the indices of the Gaussian components of the first and second probability density functions, respectively. The analytical expression of the inverse cumulative distribution function can be expressed by the following formula: ； in, The first probability density function is the first... k Gaussian mixture weights; For the standard normal distribution ( Quantities.

7. A device for generating a frequency control scheme for a split-blown bar, characterized in that, include: A construction module is used to obtain the sub-Bluerg emergency frequency control framework of the target power grid; wherein, the sub-Bluerg emergency frequency control framework includes an objective function, a dynamic equation of the frequency of the target power grid as a constraint, and a first frequency safety constraint; The objective function minimizes the weighted sum of squares of the control measures, using a positive definite matrix representing the cost of the control measures as weights. The dynamic equation for the frequency of the target power grid uses a time-varying Koopman observation function to describe the impact of control measures on the frequency of the target power grid. Wherein, the first frequency security constraint is limited to the fact that at each time point, the difference between the predicted value and the safe value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function under the worst distribution in the uncertainty set at the significance level, wherein the uncertainty set is the set of distributions of the difference between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; The first acquisition module is used to process the inverse cumulative distribution function in the uncertainty set and the first frequency security constraint using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency security constraint. The second acquisition module is used to substitute the lower limit value of the first frequency security constraint into the first frequency security constraint to obtain the second frequency security constraint. The third acquisition module is used to solve the objective function based on the dynamic equation of the frequency of the target power grid and the second frequency security constraint, and obtain the target control measure quantity that minimizes the cost of the control measures for the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure quantity.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for generating the sub-bulb frequency control scheme as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for generating the sub-bulb frequency control measure scheme as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for generating the sub-bulb frequency control measure scheme as described in any one of claims 1 to 6.

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