Generation method and device of distributed robust frequency control measure scheme and electronic equipment

Abstract

The invention provides a method and device for generating a distributed robust frequency control measure scheme and electronic equipment, and relates to the technical field of power systems. The generation method of the distributed robust frequency control measure scheme comprises the following steps: processing an uncertain set and an inverse cumulative distribution function in a first frequency security constraint by using a Gaussian mixture model and a Wasserstein distance to obtain a 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 a second frequency security constraint; and based on the dynamic equation of the frequency of the target power grid and the second frequency security constraint, solving the target function to obtain a target control measure quantity which enables the control measure cost of the target power grid to be minimum. The invention can provide an efficient, accurate and high-adaptability emergency frequency control scheme so as to sense the state of the power grid in real time, quickly respond to various disturbance faults, effectively coordinate various frequency modulation resources and ensure the frequency safety of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, device and electronic equipment for generating a distributed robust frequency control measure scheme. Background Art

[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 an AC / DC hybrid power grid with 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 receiving and a corresponding decrease in the system's rotational inertia. Under this new grid form, faults such as DC interlocking may cause significant power shortage impacts, posing a severe challenge to the frequency security of the receiving-end power grid.

[0003] In addition, the operation mode of the new power system is complex and changeable, and the disturbance faults have strong uncertainty, which further increases the difficulty of frequency safety control. When faced with such a complex and changeable power grid environment, the traditional plan-based control strategy may refuse to operate or malfunction, and cannot meet the needs of emergency frequency control. At the same time, with the large-scale access of renewable energy and the gradual opening of the power market, the resources that can provide frequency regulation services are becoming increasingly abundant, and the mathematical model of the power grid has become more complex and unknown, showing nonlinear characteristics. This makes the modeling process of the traditional plan-based control strategy difficult, the model complexity increases, and the timeliness cannot be guaranteed.

[0004] Therefore, how to provide an efficient, accurate and adaptable emergency frequency control solution to perceive the power grid status in real time, quickly respond to various disturbance faults, and effectively coordinate various frequency regulation resources to ensure the frequency safety of the power grid is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above-mentioned problems existing in the prior art, the present invention provides a method, device and electronic equipment for generating a distributed robust frequency control measure scheme, which is used to provide an efficient, accurate and adaptable emergency frequency control scheme to sense the power grid status in real time, respond quickly to various disturbance faults, and effectively coordinate various frequency modulation resources to ensure the frequency safety of the power grid.

[0006] The present invention provides a method for generating a distributed robust frequency control measure scheme, comprising the following steps.

[0007] Obtain a distributed robust emergency frequency control framework for a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as constraints and a first frequency safety constraint; wherein 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; wherein the dynamic equation of the frequency of the target power grid describes the influence of the control measures on the frequency of the target power grid using a time-varying Koopman observation function; wherein the first frequency safety constraint specifies that at each time point, the difference between the predicted value and the safety value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function at the significance level under the worst distribution in the uncertainty set, and the uncertainty A set is a set of difference distributions between a predicted value and an actual value of a frequency estimated from historical data of the target power grid; the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint is processed using a Gaussian mixture model and a Wasserstein distance to obtain a lower limit value of the first frequency safety constraint; the lower limit value of the first frequency safety constraint is substituted into the first frequency safety constraint to obtain a second frequency safety constraint; according to the system state data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain a target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0008] According to a method for generating a distributed robust frequency control measure scheme provided by the present invention, the Gaussian mixture model and the Wasserstein distance are used to process the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint to obtain the lower limit value of the first frequency safety constraint, including: using the Gaussian mixture model and the Wasserstein distance to obtain a quantitative description of the uncertainty set; using the Gaussian mixture model to approximate the inverse cumulative distribution function in the first frequency safety constraint to obtain an analytical expression of the inverse cumulative distribution function; based on the quantitative description of the uncertainty set, solving the analytical expression of the inverse cumulative distribution function to obtain the lower limit value of the first frequency safety constraint.

[0009] According to a method for generating a distributed robust frequency control measure scheme provided by the present invention, the Gaussian mixture model and Wasserstein distance are used to obtain a quantitative description of the uncertainty set, including: using a Gaussian mixture model to describe the reference distribution of the uncertainty set to obtain a probability distribution model composed of Gaussian components; using the Wasserstein distance to determine the Wasserstein distance between two probability distribution models; and based on the Wasserstein distance, obtaining a quantitative description of the uncertainty set.

[0010] According to a method for generating a distributed robust frequency control measure scheme provided by the present invention, the Gaussian mixture model is used to approximate the inverse cumulative distribution function in the first frequency safety constraint to obtain an analytical expression of the inverse cumulative distribution function, including: approximating the inverse cumulative distribution function as a weighted sum of 1-α quantiles calculated for each Gaussian component in the uncertainty set at a 1-α probability level according to the Gaussian mixture weights to obtain an analytical expression of the inverse cumulative distribution function; wherein the 1-α probability is the confidence level at which the difference between the frequency prediction value and the safety value of the target power grid satisfies the frequency safety constraint at a set significance level α.

[0011] According to a method for generating a distributed robust frequency control measure scheme provided by the present invention, the objective function is expressed by the following formula: ; Where R represents the positive definite matrix of control measure cost, u is the control measure quantity; the frequency of the target power grid The dynamic equation of is expressed by the following formula: ; in, is a set of Koopman observation functions starting from the start time; , , are the transfer matrix, control matrix and output matrix of Koopman observable variables respectively; t is the total control time, k To control the time number, For time k The first frequency safety constraint is expressed by the following formula: ; ; is the safe value of the frequency of the target power grid; It belongs to the uncertain set The unknown distribution of ; icdf is the inverse cumulative distribution function, represents the significance level; A reference probability distribution representing a difference between a predicted value and an actual value of a frequency estimated from historical data of the target power grid.

[0012] According to a method for generating a distributed robust frequency control measure scheme provided by the present invention, the following formula is used to express the quantitative description of the uncertainty set: ; ; ; in, is the decision variable; is a preset value indicating the upper limit of the size of the fuzzy set, is the first probability density function l The mean of the reference probability distribution, is the second probability density function k The first probability density function and the second probability density function are both probability density functions that conform to the Gaussian mixture model; is the first probability density function l The standard deviation of the Gaussian components, is the second probability density function k The standard deviation of the Gaussian components; l and k The numbers of the Gaussian components of the first probability density function and the second probability density function respectively; The analytical expression of the inverse cumulative distribution function is expressed by the following formula: ; in, is the first probability density function k Gaussian mixture weights; For the standard normal distribution ( ) quantile.

[0013] The present invention also provides a device for generating a distributed rod frequency control measure scheme, comprising the following modules: A construction module is used to obtain a distributed robust emergency frequency control framework for a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint; wherein 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; wherein the dynamic equation of the frequency of the target power grid describes the influence of the control measures on the frequency of the target power grid using a time-varying Koopman observation function; wherein the first frequency safety constraint is defined as a difference between the predicted value and the safety value of the frequency of the target power grid at each time point, which is not less than the value of the inverse cumulative distribution function under the worst distribution in the uncertainty set at the significance level, and the uncertainty set It is a set of difference distributions between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; a first acquisition module is used to process the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint by using a Gaussian mixture model and Wasserstein distance to obtain the lower limit value of the first frequency safety constraint; a second acquisition module is used to substitute the lower limit value of the first frequency safety constraint into the first frequency safety constraint to obtain a second frequency safety constraint; a third acquisition module is used to solve the objective function for the dynamic equation of the frequency of the target power grid and the second frequency safety constraint to obtain a target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for generating a distributed robust frequency control measure scheme as described in any one of the above-mentioned methods is implemented.

[0015] 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 distributed robust frequency control measure scheme as described in any one of the above.

[0016] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements a method for generating a distributed robust frequency control measure scheme as described in any one of the above.

[0017] The method, device and electronic device for generating a distributed robust frequency control measure scheme provided by the present invention utilize the dynamic equation of the frequency of the target power grid as a constraint condition and the first frequency safety constraint to ensure the frequency safety of the power grid system under the operation mode and fault of most power grid systems, and significantly reduce the control cost, so as to avoid paying a huge control cost due to the emergency frequency control strategy, such as large load shedding. The inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint is processed by using the Gaussian mixture model and the Wasserstein distance to obtain the lower limit value of the first frequency safety constraint; the lower limit value of the first frequency safety constraint is substituted into the first frequency safety constraint to obtain the second frequency safety constraint; based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain the target control measure amount that minimizes the control measure cost of the target power grid, thereby simplifying a complex nonlinear problem requiring a large amount of calculation into an optimization problem of linear equations and quadratic equations, effectively improving the calculation speed of the control strategy, enabling it to meet the timeliness requirements of the formulation of the emergency frequency strategy, and ensuring the economy of the control strategy. Thus, an efficient, accurate and adaptable emergency frequency control scheme can be provided. It can sense the status of the power grid in real time, respond quickly to various disturbances and faults, and effectively coordinate various frequency regulation resources to ensure the frequency safety of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a method for generating a distributed rod frequency control measure scheme provided by the present invention.

[0020] Figure 2 It is a flowchart of a method for obtaining a lower limit value of a first frequency safety constraint provided by the present invention.

[0021] Figure 3 It is a structural schematic diagram of a device for generating a distributed rod frequency control measure scheme provided by the present invention.

[0022] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0024] Combine the following Figure 1-Figure 2 The method for generating the distributed robust frequency control measure scheme of the present invention is described.

[0025] Figure 1 is a flow chart of a method for generating a distributed rod frequency control measure scheme provided by the present invention, such as Figure 1 As shown, the method includes the following: Step 101: Obtain a distributed robust emergency frequency control framework of a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint.

[0026] The target grid is a grid that needs to take emergency frequency control measures, such as an AC / DC hybrid grid with a high proportion of renewable energy. The objective function minimizes the weighted sum of squares of the control measures, with the positive definite matrix representing the cost of the control measures as the weight. As an example only, the objective function can be expressed using the following formula: (1)

[0027] Where R represents the positive definite matrix of control measure cost; u is the amount of control measures, , represents the control vector composed of control quantities at different times.

[0028] The control measure u represents a series of control measures or control inputs that are applied to the target power grid to achieve the desired effect. As an example, the control measure u may include the following elements: output power adjustment of generator 1 (increase or decrease); output power adjustment of generator 2; ...; output power adjustment of generator n; load reduction; number of switched capacitors or reactors, etc.

[0029] The dynamic equation of the frequency of the target power grid uses the Koopman observation function that changes with time to describe the impact of the control measure on the frequency of the target power grid. As an example only, the frequency of the target power grid The dynamic equation of is expressed by the following formula: (2)

[0030] in, is a set of Koopman observation functions starting from the start time; , , are the transfer matrix, control matrix and output matrix of Koopman observable variables respectively; t is the total control time, k To control the time number, For time k The amount of control measures at the time.

[0031] The first frequency safety constraint is defined as follows: at each time point, the difference between the predicted value and the safety value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function at the significance level under the worst distribution in the uncertainty set, and the uncertainty set is a 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. As an example only, the first frequency safety constraint is expressed using the following formula: (3)

[0032] (4)

[0033] in, is the safe value of the frequency of the target power grid; It belongs to the uncertain set The unknown distribution of ; icdf is the inverse cumulative distribution function, represents the significance level; A reference probability distribution representing the difference between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid.

[0034] First frequency safety constraint to find uncertainty set The worst Make Maximum. Therefore, when the probability distribution of the frequency prediction error of the target power grid is When the first frequency security constraint can ensure the safety probability of the target power grid frequency is .

[0035] Step 102: Using the Gaussian mixture model and the Wasserstein distance, the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint are processed to obtain a lower limit value of the first frequency safety constraint.

[0036] In the specific implementation process, the 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 of the first frequency safety constraint. The distributed robust emergency frequency control framework is thus divided into an upper and lower double-layer optimization problem: the lower optimization problem is how to determine the lower limit of the first frequency safety constraint; the upper optimization problem is how to obtain the optimal solution of the objective function based on the dynamic equation of the frequency of the target power grid under the constraint of the lower limit of the first frequency safety constraint.

[0037] For a detailed description of using the Gaussian mixture model and Wasserstein distance to process the uncertainty set and the inverse cumulative distribution function in the first-frequency safety constraint to obtain the lower limit of the first-frequency safety constraint, see Figure 2 The relevant content in will not be repeated here.

[0038] Step 103: Substitute the lower limit value of the first frequency safety constraint into the first frequency safety constraint to obtain a second frequency safety constraint.

[0039] As an example only, the second frequency safety constraint may be expressed as follows: (5)

[0040] in, It is the optimal value of the objective function in the lower-level optimization problem, that is, the lower limit value of the first frequency safety constraint.

[0041] Step 104: Solve the objective function according to the system status data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, and obtain the target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0042] The system state data of the target power grid is a set of state variables of the target power grid at a specific time point. These state variables can fully describe the current status 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 each node generator, the current size and direction of the load, the transformation ratio of the transformer, and the frequency of the power grid.

[0043] In the specific implementation process, the target control measure amount that minimizes the control measure cost of the target power grid can be obtained by solving the objective function described in formula (1) based on the system status data of the target power grid, the second frequency safety constraint described in formula (5), and the dynamic equation of the frequency of the target power grid described in formula (2).

[0044] Since the second frequency safety constraint and the dynamic equation of the frequency are both linear equations and the objective function is a quadratic form, the upper-level optimization problem can be solved with high computational efficiency. A variety of algorithms can be used to solve the above-mentioned upper-level optimization problem without being limited by the description of this specification.

[0045] In the embodiment provided by the present invention, the 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.

[0046] Figure 2 is a flow chart of a method for obtaining a lower limit value of a first frequency safety constraint provided by the present invention, such as Figure 2 As shown, the method includes the following: Step 201: Use the Gaussian mixture model and Wasserstein distance to obtain a quantitative description of the uncertainty set.

[0047] The probability density function (PDF) of the Gaussian mixture model is: (6)

[0048] in, is a mean and the covariance matrix The Gaussian distribution of GMM is k Gaussian components; yes The mixture weights of the Gaussian components; is in vector form, containing .

[0049] Determine the parameter set of GMM This is a typical parameter estimation problem. The maximum likelihood estimation technique can be used to obtain the parameter set of GMM for the data. Typical algorithms include the "Expectation Maximization (EM)" algorithm.

[0050] In the specific implementation process, the Gaussian mixture model can be used to describe the reference distribution of the uncertainty set to obtain a probability distribution model composed of Gaussian components; the Wasserstein distance between the two probability distribution models is determined by the Wasserstein distance; based on the Wasserstein distance, a quantitative description of the uncertainty set is obtained. As an example only, the following formula can be used to express the quantitative description of the uncertainty set: (6)

[0051] (7)

[0052] (8)

[0053] in, is the decision variable; is a preset value indicating the upper limit of the size of the fuzzy set, is the first probability density function l The mean of the reference probability distribution, is the second probability density function k The first probability density function and the second probability density function are both probability density functions that conform to the Gaussian mixture model; is the first probability density function l The standard deviation of the Gaussian components, is the second probability density function k The standard deviation of the Gaussian components; l and k Represent the numbers of the Gaussian components of the first probability density function and the second probability density function respectively.

[0054] Probability density function and the second probability density function , which can be expressed as follows: (9)

[0055] (10)

[0056] Step 202: Use a Gaussian mixture model to approximate the inverse cumulative distribution function in the first frequency safety constraint, and obtain an analytical expression of the inverse cumulative distribution function.

[0057] In the specific implementation process, the inverse cumulative distribution function can be approximated as At the probability level, the Gaussian mixture weights are calculated for each Gaussian component in the uncertainty set. The weighted sum of the quantiles gives the analytical expression for the inverse cumulative distribution function; where The probability is the confidence level that the difference between the frequency prediction value and the safety value of the target power grid meets the frequency safety constraint under the set significance level α. As an example only, the analytical expression of the inverse cumulative distribution function can be expressed by the following formula: (11)

[0058] in, is the first probability density function kGaussian mixture weights; For the standard normal distribution ( ) quantile.

[0059] Use formula (11) to replace the , so that becomes an expression that can be resolved and is determined by the parameters of the Gaussian mixture model.

[0060] 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 value of the first frequency safety constraint.

[0061] In the specific implementation process, based on the quantitative description of the uncertainty set (Formula (6) to Formula (8)), the analytical expression of the inverse cumulative distribution function (Formula (11)) can be solved to obtain the lower limit value of the first frequency safety constraint, that is, the solution to the lower-level optimization problem.

[0062] Although the computational complexity of the underlying optimization problem increases with k and l (i.e., the number of Gaussian components in the 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 calculation time is still within a controllable range. The overall calculation time of the distributed robust emergency frequency control framework provided by the present invention mainly includes 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 calculation time of the lower-level optimization problem and the upper-level optimization problem is basically not affected by the data scale. At the same time, the process of using historical data to learn GMM parameters is usually very efficient and the time required is extremely short. Therefore, the distributed robust emergency frequency control framework provided by the present invention can maintain stable computing performance on historical data of different scales, and the overall calculation time is controlled within 500 milliseconds, showing excellent computing efficiency.

[0063] The following is a description of a device for generating a distributed R-rod frequency control measure scheme provided by the present invention. The device for generating a distributed R-rod frequency control measure scheme described below and the method for generating a distributed R-rod frequency control measure scheme described above can be referenced to each other.

[0064] Figure 3 Schematic diagram of the structure of the device for generating the distributed rod frequency control measure scheme provided by the present invention. Figure 3 As shown, the device 300 includes the following modules.

[0065] A construction module 310 is used to obtain a distributed robust emergency frequency control framework for a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as constraints and a first frequency safety constraint; wherein 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; wherein the dynamic equation of the frequency of the target power grid describes the influence of the control measures on the frequency of the target power grid using a time-varying Koopman observation function; wherein the first frequency safety constraint specifies that at each time point, the difference between the predicted value and the safety value of the frequency of the target power grid is not less than the value of the inverse cumulative distribution function at the significance level under the worst distribution in an uncertainty set, and the uncertainty set is a collection 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.

[0066] The first acquisition module 320 is used to process the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint by using a Gaussian mixture model and Wasserstein distance to obtain a lower limit value of the first frequency safety constraint.

[0067] The second acquisition module 330 is configured to substitute the lower limit value of the first frequency safety constraint into the first frequency safety constraint to obtain a second frequency safety constraint.

[0068] 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 safety constraint, and obtain the target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0069] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. 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 may call the logic instructions in the memory 430 to execute the method for generating a distributed robust frequency control measure scheme, the method comprising: obtaining a distributed robust emergency frequency control framework of the target power grid; wherein the distributed robust emergency frequency control framework comprises an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint; wherein the objective function minimizes the weighted sum of squares of the control measure quantity with a positive definite matrix representing the cost of the control measure as a weight; wherein the dynamic equation of the frequency of the target power grid uses a time-varying Koopman observation function to describe the influence of the control measure quantity on the frequency of the target power grid; wherein the first frequency safety constraint is defined as the difference between the predicted value and the safety value of the frequency of the target power grid at each time point, which is not less than the worst distribution in the uncertainty set. The value of the inverse cumulative distribution function at the significance level is obtained, and the uncertainty set is a set of difference distributions between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint is processed by using a Gaussian mixture model and a Wasserstein distance to obtain a lower limit value of the first frequency safety constraint; the lower limit value of the first frequency safety constraint is substituted into the first frequency safety constraint to obtain a second frequency safety constraint; according to the system state data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain a target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0070] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the 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 a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0071] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program 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 method for generating a distributed robust frequency control measure scheme provided by the above-mentioned methods, the method including: obtaining a distributed robust emergency frequency control framework of the target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint; wherein the objective function minimizes the weighted sum of squares of the control measure quantity with a positive definite matrix representing the cost of the control measure as a weight; wherein the dynamic equation of the frequency of the target power grid uses a time-varying Koopman observation function to describe the influence of the control measure quantity on the frequency of the target power grid; wherein the first frequency safety constraint is limited to, at each time point, the The difference between the predicted value and the safety 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, and the uncertainty set is a set of difference distributions between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint is processed by using a Gaussian mixture model and a Wasserstein distance to obtain a lower limit value of the first frequency safety constraint; the lower limit value of the first frequency safety constraint is substituted into the first frequency safety constraint to obtain a second frequency safety constraint; according to the system state data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain a target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0072] On the other hand, 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 distributed robust frequency control measure scheme provided by the above-mentioned methods, the method comprising: obtaining a distributed robust emergency frequency control framework of a target power grid; wherein the distributed robust emergency frequency control framework comprises an objective function and a dynamic equation of the frequency of the target power grid as constraints and a first frequency safety constraint; wherein the objective function minimizes the weighted sum of squares of the control measure quantities with a positive definite matrix representing the cost of the control measures as weights; wherein the dynamic equation of the frequency of the target power grid describes the influence of the control measure quantities on the frequency of the target power grid using a time-varying Koopman observation function; wherein the first frequency safety constraint is limited to, at each time point, the predicted value of the frequency of the target power grid and the safety 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, wherein the uncertainty set is a set of difference distributions between the predicted value and the actual value of the frequency estimated from the historical data of the target power grid; the inverse cumulative distribution function in the uncertainty set and the first frequency safety constraint is processed by using a Gaussian mixture model and a Wasserstein distance to obtain a lower limit value of the first frequency safety constraint; the lower limit value of the first frequency safety constraint is substituted into the first frequency safety constraint to obtain a second frequency safety constraint; according to the system state data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain a target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

[0073] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0074] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. 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 generating a distributed robust frequency control measure scheme, characterized in that: include: Acquire a distributed robust emergency frequency control framework of a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint; The objective function minimizes the weighted sum of squares of the control measures, taking the positive definite matrix representing the cost of the control measures as weights; The dynamic equation of the frequency of the target power grid uses a Koopman observation function that varies with time to describe the influence of the control measure on the frequency of the target power grid; The first frequency safety constraint is defined as follows: at each time point, the difference between the predicted value and the safety 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, and the uncertainty set is a 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; 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 a lower limit value of the first frequency safety constraint; Substituting the lower limit value of the first frequency safety constraint into the first frequency safety constraint to obtain a second frequency safety constraint; According to the system status data of the target power grid, based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint, the objective function is solved to obtain the target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

2. The method for generating a distributed robust frequency control measure scheme according to claim 1, characterized in that: The using of the Gaussian mixture model and the Wasserstein distance to process the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint to obtain the lower limit value of the first frequency safety constraint includes: Using a Gaussian mixture model and Wasserstein distance, a quantitative description of the uncertainty set is obtained; Using a Gaussian mixture model, an inverse cumulative distribution function in the first frequency safety constraint is approximately described to obtain an analytical expression of the inverse cumulative distribution function; 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 value of the first frequency safety constraint.

3. The method for generating a distributed robust frequency control measure scheme according to claim 2, characterized in that: The method of using the Gaussian mixture model and the Wasserstein distance to obtain a quantitative description of the uncertainty set includes: Using a Gaussian mixture model to describe the reference distribution of the uncertainty set, a probability distribution model composed of Gaussian components is obtained; Determining the Wasserstein distance between the two probability distribution models using the Wasserstein distance; Based on the Wasserstein distance, a quantitative description of the uncertainty set is obtained.

4. The method for generating a distributed robust frequency control measure scheme according to claim 3, characterized in that: The Gaussian mixture model is used to approximate the inverse cumulative distribution function in the first frequency safety constraint to obtain an analytical expression of the inverse cumulative distribution function, including: 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, to obtain an analytical expression for the inverse cumulative distribution function; wherein the 1-α probability is the confidence level at which the difference between the frequency prediction value and the safety value of the target power grid satisfies the frequency safety constraint at the set significance level α.

5. The method for generating a distributed rod frequency control measure scheme according to any one of claims 1 to 4, characterized in that: The objective function is expressed using the following formula: ； Among them, R represents the positive definite matrix of control measure cost, and u is the amount of control measures; The frequency of the target grid The dynamic equation of is expressed by the following formula: ； in, is a set of Koopman observation functions starting from the start time; , , are the transfer matrix, control matrix and output matrix of Koopman observable variables respectively; t is the total control time, k To control the time number, For time k The amount of control measures taken at the time The first frequency safety constraint is expressed by the following formula: ； ； is the safe value of the frequency of the target power grid; It belongs to the uncertain set The unknown distribution of ; icdf is the inverse cumulative distribution function, represents the significance level; A reference probability distribution representing a difference between a predicted value and an actual value of a frequency estimated from historical data of the target power grid.

6. The method for generating a distributed robust frequency control measure scheme according to claim 4, characterized in that: The quantitative description of the uncertainty set is expressed by the following formula: ； ； ； in, is the decision variable; is a preset value indicating the upper limit of the size of the fuzzy set, is the first probability density function l The mean of the reference probability distribution, is the second probability density function k The first probability density function and the second probability density function are both probability density functions that conform to the Gaussian mixture model; is the first probability density function l The standard deviation of the Gaussian components, is the second probability density function k The standard deviation of the Gaussian components; l and k The numbers of the Gaussian components of the first probability density function and the second probability density function respectively; The analytical expression of the inverse cumulative distribution function is expressed by the following formula: ； in, is the first probability density function k Gaussian mixture weights; For the standard normal distribution ( ) quantile.

7. A device for generating a distributed rod frequency control measure scheme, characterized in that: include: A construction module is used to obtain a distributed robust emergency frequency control framework of a target power grid; wherein the distributed robust emergency frequency control framework includes an objective function and a dynamic equation of the frequency of the target power grid as a constraint condition and a first frequency safety constraint; The objective function minimizes the weighted sum of squares of the control measures, taking the positive definite matrix representing the cost of the control measures as weights; The dynamic equation of the frequency of the target power grid uses a Koopman observation function that varies with time to describe the influence of the control measure on the frequency of the target power grid; The first frequency safety constraint is defined as follows: at each time point, the difference between the predicted value and the safety 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, and the uncertainty set is a 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; A first acquisition module is used to process the uncertainty set and the inverse cumulative distribution function in the first frequency safety constraint by using a Gaussian mixture model and a Wasserstein distance to obtain a lower limit value of the first frequency safety constraint; A second acquisition module, used for substituting the lower limit value of the first frequency safety constraint into the first frequency safety constraint to obtain a second frequency safety constraint; The third acquisition module is used for solving the objective function based on the dynamic equation of the frequency of the target power grid and the second frequency safety constraint to obtain the target control measure amount that minimizes the control measure cost of the target power grid, so as to perform emergency frequency control on the target power grid according to the target control measure amount.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the method for generating the distributed robust frequency control measure 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 a processor, the method for generating a distributed robust frequency control measure scheme as described in any one of claims 1 to 6 is implemented.

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

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