Power system scheduling method, device and equipment based on adaptive step length and small interference stability constraint, and medium

Abstract

The invention discloses a power system scheduling method, device, equipment and medium based on adaptive step length and small-interference stability constraint, and relates to the field of power systems and automation thereof, and the method comprises the steps: judging whether an initial power scheduling result outputted by an original optimal power flow model of a power system meets the small-interference stability constraint or not; if the obtained first judgment result is negative, performing iterative solution in a neighborhood of the initial power dispatching result based on a damping ratio threshold, a fluctuation interval of renewable energy output and a preset adaptive step iterative solution strategy to obtain a target solution result; carrying out robust small interference stability constrained boundary key local positioning based on a target solving result, and collecting sample data according to a positioning result; and performing explicit equivalent modeling based on the sample data, the linear kernel function, the penalty factor and the positioning result, and determining a target power dispatching result by using a modeling result and an original optimal power flow model. And the robust small-interference stability of the power system under renewable energy fluctuation can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation thereof, and in particular to a power system dispatching method, device, equipment and medium based on adaptive step size and small disturbance stability constraints. Background Art

[0002] Traditional power system dispatch methods usually calculate economic dispatch plans through optimal power flow, followed by safety assessment and correction. Although this step-by-step approach is intuitive, it faces significant bottlenecks under high renewable energy penetration.

[0003] To solve this problem, current technicians often use sensitivity-based methods to expand the Taylor expansion of the initial point, or data-driven methods to construct a mapping relationship between state variables and small disturbance stability results to construct a small disturbance stability constraint equivalent model and directly embed it into the optimal power flow model. However, the former of these two solutions usually requires a large number of iterations, and each step involves time domain simulation and operation optimization model solution, which makes it difficult to achieve computational efficiency while ensuring convergence; while the latter usually relies on extensive full-space sampling, faces the "dimensionality curse" problem, and the generated stability constraint model is often more complex, which leads to computational difficulties in subsequent power optimization. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a power system dispatching method, device, equipment and medium based on adaptive step size and small disturbance stability constraints, which can improve the convergence of the equivalent modeling of the robust small disturbance stability constraints of the main grid under the condition of renewable energy uncertainty, thereby ensuring the robust small disturbance stability of the power system under the fluctuation of renewable energy and improving the accuracy of power dispatching. The specific scheme is as follows:

[0005] In a first aspect, the present application provides a power system scheduling method based on adaptive step size and small disturbance stability constraints, comprising:

[0006] Acquire an original optimal power flow model of the power system, and determine whether an initial power dispatch result output by the original optimal power flow model satisfies a small disturbance stability constraint, so as to obtain a first determination result;

[0007] If the first judgment result is no, then performing an iterative solution in the neighborhood of the initial power dispatch result based on the damping ratio threshold, the fluctuation range of the renewable energy output and a preset adaptive step size iterative solution strategy to obtain a target solution result;

[0008] Based on the target solution result, a key local positioning of the boundary of the robust small disturbance stability constraint is performed, and a corresponding sample collection operation is triggered according to the positioning result to obtain sample data;

[0009] Explicit equivalent modeling is performed based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and the target power dispatch result is determined using the modeling result and the original optimal power flow model.

[0010] Optionally, the iterative solution strategy based on the damping ratio threshold, the fluctuation range of the renewable energy output and the preset adaptive step size is iteratively solved in the neighborhood of the initial power dispatch result, including:

[0011] Performing a bi-norm measurement on the solution space based on the distance threshold and the initial power dispatch result to obtain a measurement result;

[0012] The current renewable energy output is adjusted by the fluctuation range of the renewable energy output, so as to iteratively search for a feasible solution in the neighborhood of the initial power dispatch result based on the measurement result and the damping ratio threshold so as to determine the target solution result; the feasible solution is the solution with the smallest difference between the minimum damping ratio of the corresponding small disturbance stable modal analysis and the damping ratio threshold.

[0013] Optionally, iteratively searching for a feasible solution in a neighborhood of the initial power dispatch result based on the measurement result and the damping ratio threshold so as to determine a target solution result includes:

[0014] For any iterative solution round, by adjusting the initial power dispatch result, under the condition of the given initial power dispatch result, the current renewable energy output is adjusted through the fluctuation range to determine the corresponding minimum damping ratio solution result;

[0015] Triggering a corresponding convergence evaluation operation based on the minimum damping ratio solution result to obtain an evaluation result of the current round;

[0016] If the evaluation result indicates that the convergence of the current round is reduced, triggering a corresponding first damping ratio threshold update operation to obtain a new damping ratio threshold;

[0017] A feasible solution is searched based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result, and based on the search result and the new damping ratio threshold, the step of jumping again to the step of adjusting the initial power dispatch result to determine the corresponding minimum damping ratio solution result by adjusting the current renewable energy output through the fluctuation range under the condition of the given initial power dispatch result is performed, until the preset termination condition is met, and the target solution result is determined.

[0018] Optionally, after obtaining the evaluation result of the current round, the step further includes:

[0019] If the evaluation result indicates that the convergence of the current round is improved, judging whether the difference between the target minimum damping ratio of the current round and the damping ratio threshold is greater than a preset error value based on the minimum damping ratio solution result, so as to obtain a second judgment result;

[0020] If the second judgment result indicates that it is greater than the preset error value, searching for a feasible solution based on the damping ratio threshold value of the current round, the minimum damping ratio solution result and the measurement result;

[0021] If the second judgment result indicates that the value is not greater than the preset error value, a corresponding damping ratio threshold change judgment operation is triggered to obtain a third judgment result;

[0022] If the third judgment result indicates that the damping ratio threshold of the current round is not greater than the initial damping ratio threshold, the corresponding second damping ratio threshold update operation is triggered to obtain a new damping ratio threshold, and a feasible solution is searched based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result.

[0023] Optionally, after obtaining the third judgment result, the method further includes:

[0024] If the third judgment result indicates that the damping ratio threshold of the current round is greater than the initial damping ratio threshold, it is determined that the preset termination condition is currently met, and the target solution result is determined based on the minimum damping ratio solution result.

[0025] Optionally, performing explicit equivalent modeling based on the sample data, the linear kernel function, the penalty factor and the positioning result to determine a modeling result, and determining a target power dispatch result using the modeling result and the original optimal power flow model includes:

[0026] Performing explicit modeling based on the sample data, the linear kernel function and the positioning result to obtain a target classification hyperplane;

[0027] The optimization model of the soft margin support vector machine is solved by using the penalty factor and the sample data to obtain the equivalent constraint corresponding to the target classification hyperplane, the equivalent constraint is embedded into the original optimal power flow model, and the target power dispatch result is determined based on the obtained embedded model.

[0028] Optionally, solving the optimization model of the soft margin support vector machine by using the penalty factor and the sample data includes:

[0029] Perform Lagrangian dual transformation on the optimization model of soft margin support vector machine to obtain the transformation result;

[0030] The equivalue constraint corresponding to the target classification hyperplane is determined based on the penalty factor, the conversion result, and the slack variables respectively corresponding to each target sample in the sample data.

[0031] In a second aspect, the present application provides a power system dispatching device based on adaptive step size and small disturbance stability constraints, comprising:

[0032] A constraint judgment module, used to obtain an original optimal power flow model of the power system, and judge whether an initial power dispatch result output by the original optimal power flow model satisfies a small disturbance stability constraint, so as to obtain a first judgment result;

[0033] an iterative solution module, configured to perform iterative solution in the neighborhood of the initial power dispatch result based on a damping ratio threshold, a fluctuation range of renewable energy output, and a preset adaptive step size iterative solution strategy if the first judgment result is no, so as to obtain a target solution result;

[0034] A local positioning module is used to perform a key local positioning of a boundary of a robust small disturbance stability constraint based on the target solution result, and trigger a corresponding sample collection operation according to the positioning result to obtain sample data;

[0035] The equivalent modeling module is used to perform explicit equivalent modeling based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and use the modeling result and the original optimal power flow model to determine the target power dispatch result.

[0036] In a third aspect, the present application provides an electronic device, including:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the steps of the aforementioned power system scheduling method based on adaptive step size and small disturbance stability constraints.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned power system scheduling method based on adaptive step size and small disturbance stability constraints.

[0040] It can be seen that in the present application, the original optimal power flow model of the power system is obtained, and it is determined whether the initial power dispatching result output by the original optimal power flow model satisfies the small disturbance stability constraint to obtain a first judgment result; if the first judgment result is no, an iterative solution is performed in the neighborhood of the initial power dispatching result based on the damping ratio threshold, the fluctuation range of renewable energy output and the preset adaptive step size iterative solution strategy to obtain the target solution result; based on the target solution result, the boundary key local positioning of the robust small disturbance stability constraint is performed, and the corresponding sample collection operation is triggered according to the positioning result to obtain sample data; based on the sample data, linear kernel function, penalty factor and the positioning result, explicit equivalent modeling is performed to determine the modeling result, and the target power dispatching result is determined using the modeling result and the original optimal power flow model. That is, the present application first obtains the original optimal power flow model of the power system, and then when the initial power dispatch result output by the original optimal power flow model does not meet the small disturbance stability constraint, an adaptive step size iterative solution is performed in the neighborhood of the initial power dispatch result based on the damping ratio threshold, and then the target solution result is used to achieve the key local positioning of the boundary of the robust small disturbance stability constraint, and the positioning result and the linear kernel function are used to perform explicit equivalent modeling, so as to use the obtained modeling results and the original optimal power flow model to determine the target power dispatch result and complete the power dispatch. In this way, the convergence of the equivalent modeling of the robust small disturbance stability constraint of the main grid under the uncertainty of renewable energy can be improved, thereby ensuring the robust small disturbance stability of the power system under the fluctuation of renewable energy and improving the accuracy of power dispatch. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0042] Figure 1 A flow chart of a power system dispatching method based on adaptive step size and small disturbance stability constraints provided in this application;

[0043] Figure 2 A schematic diagram of an iterative solution framework process provided for this application;

[0044] Figure 3 A schematic diagram of an iterative solution framework flow chart for an adaptive step size provided in this application;

[0045] Figure 4 A schematic diagram for comparing the minimum damping ratios of different scheduling time intervals provided in this application;

[0046] Figure 5 A schematic diagram of the comparison of calculation time for different scheduling time intervals provided in this application;

[0047] Figure 6 A schematic diagram of the structure of a power system dispatching device based on adaptive step size and small disturbance stability constraints provided in this application;

[0048] Figure 7 A structural diagram of an electronic device provided for this application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] At present, technicians often use sensitivity-based methods to expand the Taylor of the initial point, or data-driven methods to construct a mapping relationship between state variables and small disturbance stability results to construct a small disturbance stability constraint equivalent model and directly embed it into the optimal power flow model. However, the former of these two schemes usually requires a large number of iterations, and each step involves time domain simulation and operation optimization model solution, which makes it difficult to achieve computational efficiency while ensuring convergence; while the latter usually relies on extensive full-space sampling, faces the "dimensionality curse" problem, and the generated stability constraint model is often more complex, which leads to subsequent computational difficulties in power optimization. To this end, the present application provides a power system scheduling scheme based on adaptive step size and small disturbance stability constraints, which can improve the convergence of the equivalent modeling of the robust small disturbance stability constraints of the main grid under the uncertainty of renewable energy, thereby ensuring the robust small disturbance stability of the power system under the fluctuation of renewable energy and improving the accuracy of power scheduling.

[0051] See also Figure 1 As shown, an embodiment of the present invention discloses a power system scheduling method based on adaptive step size and small disturbance stability constraint, including:

[0052] Step S11, obtaining an original optimal power flow model of the power system, and determining whether an initial power dispatch result output by the original optimal power flow model satisfies a small disturbance stability constraint, so as to obtain a first determination result.

[0053] Specifically, in this embodiment, a DC power flow model is selected to verify the proposed method framework. It should be emphasized that the method proposed in this embodiment has strong adaptability and is applicable to a variety of power flow models. In order to achieve simplicity of calculation during the verification process, this embodiment selects a DC power flow model as an example, and its DC optimal power flow model with embedded robust small disturbance stability constraints is as follows:

[0054] (1);

[0055] (2);

[0056] (3);

[0057] (4);

[0058] (5);

[0059] (6);

[0060] Wherein, formula (1) is the objective function that characterizes the operating cost of the power system. , and is the coefficient matrix related to the operating cost; T represents the transpose; represents the generator output. Equation (2) represents the power balance constraint, and Equations (3) and (4) represent the generator output constraint and branch power constraint, respectively. , and They represent renewable energy output, load demand, and branch power respectively; , and is an “all-one” vector; , , They are , , The coefficient matrix of represents the power transfer distribution factor matrix; Indicates the minimum output of the generator; Indicates the maximum output of the generator; Indicates the minimum branch power; represents the maximum branch power. Equation (6) represents the robust small disturbance stability constraint, which needs to ensure that the renewable energy is in the interval (The fluctuation range of renewable energy output, Indicates the minimum output value, The stability requirements can still be met in extreme scenarios with fluctuations (maximum output). Represents the minimum damping ratio in small-disturbance stable modal analysis. represents the damping ratio threshold that satisfies the small disturbance stability constraint.

[0061] It should be understood that when performing power system dispatch, the original OPF (Optimal Power Flow) model (i.e., the optimal power flow model without equation (6)) can be solved first without considering the robust small disturbance stability constraint. In some cases, the result obtained may already satisfy the disturbance stability. Therefore, the small disturbance stability of the obtained power dispatch result (i.e., the initial power dispatch result) is checked without equation (6). If the first judgment result obtained determines that the power dispatch result directly meets the small disturbance stability requirement, then equation (6) has no effect on the optimality. The result obtained is the optimal solution of the proposed OPF. Otherwise, the initial power dispatch result obtained is used as the initial point Initially, the critical local region of the robust small disturbance stability constraint is searched.

[0062] Step S12: If the first judgment result is no, an iterative solution is performed in the neighborhood of the initial power dispatch result based on the damping ratio threshold, the fluctuation range of renewable energy output and a preset adaptive step size iterative solution strategy to obtain a target solution result.

[0063] In this embodiment, when it is determined that the initial power dispatch result does not meet the small disturbance stability constraint, the initial power dispatch result is used as the initial point Start searching for the key local part of the robust small disturbance stability constraint. Specifically, it is necessary to perform a two-norm measurement on the solution space based on the distance threshold and the initial power dispatch result to obtain a measurement result; adjust the current renewable energy output according to the fluctuation range of the renewable energy output to obtain a stable power consumption based on the measurement result and the damping ratio threshold ( ) iteratively searches for a feasible solution in the neighborhood of the initial power dispatch result so as to determine a target solution result; the feasible solution is a solution with the smallest difference between the minimum damping ratio of the corresponding small disturbance stable modal analysis and the damping ratio threshold.

[0064] Furthermore, in the process of determining the target solution result, for any iterative solution round, the initial power dispatch result is adjusted to adjust the current renewable energy output through the fluctuation range under the condition of the given initial power dispatch result to determine the corresponding minimum damping ratio solution result; based on the minimum damping ratio solution result, a corresponding convergence evaluation operation is triggered to obtain the evaluation result of the current round; if the evaluation result shows that the convergence of the current round is reduced, the corresponding first damping ratio threshold update operation is triggered to obtain the new damping ratio threshold; based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result, a feasible solution search is performed, and based on the search result and the new damping ratio threshold, the step of adjusting the initial power dispatch result to adjust the current renewable energy output through the fluctuation range under the condition of the given initial power dispatch result to determine the corresponding minimum damping ratio solution result is jumped again, until the preset termination condition is met, the target solution result is determined.

[0065] It should be understood that after obtaining the evaluation result of the current round, if the evaluation result shows that the convergence of the current round is improved, then based on the minimum damping ratio solution result, it is judged whether the difference between the target minimum damping ratio of the current round and the damping ratio threshold is greater than the preset error value to obtain a second judgment result; if the second judgment result shows that it is greater than the preset error value, then a feasible solution is searched based on the damping ratio threshold of the current round, the minimum damping ratio solution result and the measurement result; if the second judgment result shows that it is not greater than the preset error value, then the corresponding damping ratio threshold change judgment operation is triggered to obtain a third judgment result; if the third judgment result shows that the damping ratio threshold of the current round is not greater than the initial damping ratio threshold, then the corresponding second damping ratio threshold update operation is triggered to obtain a new damping ratio threshold, and a feasible solution is searched based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result. At the same time, after obtaining the third judgment result, if the third judgment result indicates that the damping ratio threshold of the current round is greater than the initial damping ratio threshold, it is determined that the preset termination condition is currently met, and the target solution result is determined based on the minimum damping ratio solution result.

[0066] Regarding the specific implementation of iterative solution, As a reference point for key local positioning, Search for a feasible solution in the neighborhood of . Use the two-norm metric to limit the search space as shown below:

[0067] (7);

[0068] In the formula, Represents a feasible solution With the initial point The maximum distance. Formula (7) can ensure that the feasible solution With the initial point The distance between them is controllable, and the optimization process of key local positioning can be described as an optimization problem, as shown in the following formula:

[0069] (8);

[0070] The purpose of formula (8) is to minimize Minimum value and damping ratio requirements The gap between them is reduced, thus guiding the solution of the problem to approach the boundary of the robust small disturbance stability constraint. The lower bound of is difficult to solve. The proposed method first decomposes equation (8) into two optimization sub-problems:

[0071] (9);

[0072] Subproblem 1 (SP1 for short) shown in formula (9) is to solve the problem of (k represents the number of iterations of the power dispatch results), by changing the output of renewable energy output to minimize the damping ratio. SP1 can be solved by the gradient method. However, due to the output of renewable energy The gradient with respect to the damping ratio is difficult to analyze analytically, so it can be solved using numerical methods such as the perturbation method.

[0073] (10);

[0074] Subproblem 2 (SP2 for short) shown in equation (10) is to find a feasible solution in a specific scenario, where the output of renewable energy is It can be obtained based on the solution of SP1, and then SP2 can be solved by the exterior point method.

[0075] like Figure 2 As shown, decomposing equation (8) and iteratively solving SP1 and SP2 may result in a feasible solution. However, if the step size in SP2 is too large, the extreme cases identified by SP1 may no longer be consistent with the feasible region of SP2, resulting in convergence problems. Therefore, this embodiment introduces an improved subproblem and smaller step size optimization method to improve SP2, then the improved :

[0076] (11);

[0077] In the formula, i represents The number of iterations (i.e. updates), represents the damping ratio threshold set in the i-th step to satisfy the small disturbance stability constraint; represents the damping ratio threshold set in the i+1th step to satisfy the small disturbance stability constraint; When the second damping ratio threshold update operation is triggered, update The updated value at time; represents the output of the generator set at step k+1. (Here k represents The number of iterations; i represents the number of iterations in the figure In addition, Figure 2 In The permissible error value of the stability margin (i.e., the permissible error value between the iteratively calculated stability margin and the target set stability margin); Indicates the generator output at step 0 (i.e. the very beginning); Figure 3 Solve in it means solving.

[0078] To ensure In order to improve the convergence of the system, this embodiment also introduces an adaptive adjustment mechanism consisting of two modules:

[0079] Module ①: Determine whether the iteration step size is appropriate;

[0080] Module ②: Adjust the target value of the damping ratio.

[0081] Specifically, module ① evaluates whether the small disturbance stability performance of the optimization result deteriorates after one iteration cycle. If so, it indicates that the iteration step size may be too large, thus affecting the accuracy of the gradient estimation. To solve this problem, a smaller step size can be selected to improve the gradient accuracy. Therefore, module ② is introduced to further reduce the step size to adjust The target value is obtained as Figure 3 The adaptive step size iterative solution framework shown in FIG. Figure 3 The iterative solution framework shown in the figure determines the target solution result. Figure 3 In the process of reducing the step size, that is, triggering the first damping ratio threshold update operation, the damping ratio threshold of the previous step will be obtained ( ), and compare it with the damping ratio threshold of the current round ( ) and divide by 2, then add Add them together to get the new damping ratio threshold for the current round, and then continue to solve .

[0082] Step S13: performing boundary key local positioning of robust small disturbance stability constraints based on the target solution result, and triggering corresponding sample collection operations according to the positioning result to obtain sample data.

[0083] In this embodiment, the feasible solution obtained in the previous step can be , that is, the target solution result, locates the key part of the robust small disturbance stability boundary, and then, in the feasible solution The neighborhood of is randomly sampled to obtain sample data.

[0084] Step S14, performing explicit equivalent modeling based on the sample data, linear kernel function, penalty factor and positioning result to determine a modeling result, and using the modeling result and the original optimal power flow model to determine a target power dispatch result.

[0085] Specifically, in this embodiment, after sampling is completed, explicit equivalent modeling is performed based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and the target power dispatch result is determined by using the original optimal power flow model of the modeling result. Specifically, explicit modeling is performed based on the sample data, linear kernel function and positioning result to obtain the target classification hyperplane; the optimization model of the soft margin support vector machine is solved by using the penalty factor and the sample data to obtain the equivalent constraint corresponding to the target classification hyperplane, the equivalent constraint is embedded into the original optimal power flow model, and the target power dispatch result is determined based on the embedded model obtained. In addition, regarding solving the optimization model of the soft margin support vector machine by using the penalty factor and the sample data, the optimization model of the soft margin support vector machine is first transformed in the Lagrangian dual form to obtain the transformation result; then the equivalent constraint corresponding to the target classification hyperplane is determined based on the penalty factor, the transformation result and the slack variables corresponding to each target sample in the sample data. That is, based on the obtained sample data, the key local parts of the stability constraint can be explicitly modeled through the linear kernel function support vector machine, and a linear classification hyperplane can be obtained. This hyperplane can be directly applied to the existing OPF framework. The expression of the linear hyperplane is as follows:

[0086] (12);

[0087] In the formula, x represents the independent variable of the linear hyperplane, and w and b represent the parameters of the linear hyperplane. In order to solve the parameters of the hyperplane, the following soft margin support vector machine optimization model is required:

[0088] (13);

[0089] In the formula, is a slack variable, representing the degree of violation of the constraint by sample i, and the subscript i represents the number of the sample; represents the label of sample i; n represents the number of samples; represents sample i; C is the penalty factor used to penalize classification errors. The above optimization model is usually converted into the Lagrangian dual form for solution, and the equivalence constraint of formula (6) is finally obtained as:

[0090] (14).

[0091] The equivalent constraint is embedded into the original optimal power flow model, and the target power dispatch result is determined based on the obtained embedded model. In this way, by modeling only the key parts of the robust small disturbance stability constraint, the complexity of building a complete model is avoided, and the convergence of the equivalent modeling is significantly improved by introducing an adaptive step size, making it easier to embed directly into the model, that is, it can provide a simplified but accurate stability constraint model without significantly increasing the computational complexity, while taking into account the computational efficiency and convergence of the equivalent modeling, and thus effectively deal with the uncertainty and complexity problems brought about by the increase in the penetration rate of renewable energy while taking into account both economy and stability.

[0092] It can be seen that the present application first obtains the original optimal power flow model of the power system, and then when the initial power dispatch result output by the original optimal power flow model does not meet the small disturbance stability constraint, an adaptive step size iterative solution is performed in the neighborhood of the initial power dispatch result based on the damping ratio threshold, and then the target solution result is used to realize the key local positioning of the boundary of the robust small disturbance stability constraint, and the positioning result and the linear kernel function are used for explicit equivalent modeling, so as to determine the target power dispatch result using the obtained modeling result and the original optimal power flow model, and complete the power dispatch. In this way, the convergence of the equivalent modeling of the robust small disturbance stability constraint of the main grid under the uncertainty of renewable energy can be improved, thereby ensuring the robust small disturbance stability of the power system under the fluctuation of renewable energy and improving the accuracy of power dispatch.

[0093] Combine the following Figure 4 and Figure 5 The timing diagram disclosed in the specification specifically illustrates the technical solution of the embodiment of the present application.

[0094] In a specific implementation, an improved IEEE-118 node system is used as a test platform, which includes 54 synchronous generators and 5 types of renewable energy. To verify the effectiveness of the method proposed in the present invention, an optimal power flow calculation scheme for 24 time intervals per day is designed based on a typical load curve. The solution process is implemented using Python 3.4, and DIgSILENT / PowerFactory (a comprehensive power system simulation software) is used to calculate the damping ratio of small disturbance stability. The critical damping ratio of robust small disturbance stability is set to 5%.

[0095] In order to verify the convergence improvement brought by the adaptive step size iterative solution framework proposed in this invention, three comparison methods are defined:

[0096] M1: initial scheduling result without stability constraints;

[0097] M2: SP1 and SP2 iterative solution without adaptive step size adjustment;

[0098] M3: The method proposed by the present invention;

[0099] exist Figure 4 In the figure, the minimum damping ratios of the three methods under extreme scenarios within 24 dispatch intervals are depicted ( ). It can be seen that at the 3rd, 5th, 6th and 7th hours, The power dispatch results of meet the robust small disturbance stability requirements by default. Therefore, the small disturbance constraints become invalid in these time periods, so there is no need to construct new equivalent constraints. In other time periods, all results of the proposed method meet the stability requirements. Although M2 without the adaptive step size iterative solution framework meets the stability requirements in most cases, it fails to converge and the stability boundary cannot be determined in some cases. For those scenarios that do not converge, Figure 4 The best result of the M2 iteration process is shown in .

[0100] At the same time, Figure 5 In the figure, the calculation time of each time period is shown. Figure 5 As shown in Table 1, the computation time of M2 varies greatly in different scenarios, indicating that the convergence is unstable and the computational efficiency has high uncertainty. In contrast, although the computational efficiency of the method proposed in the present invention may not always be the fastest in every case, it remains stable overall, highlighting the good convergence and robustness of the method, in which the improvement of the adaptive step size plays a vital role. The details are shown in Table 1.

[0101] Table 1

[0102]

[0103] Table 1 shows the performance results under different uncertainty levels of renewable energy, where the uncertainty level is represented by the difference between the maximum and minimum values ​​of renewable energy output. ) is set to 200MW, the initial solution can meet the small disturbance stability requirements. However, with the increase of renewable energy uncertainty, the convergence of the M2 method gradually deteriorates, resulting in a significant increase in calculation time. When the uncertainty interval reaches 700MW, the M2 method cannot find a feasible solution. In contrast, even when the uncertainty increases significantly, the method proposed in the present invention still shows high robustness. Convergence is always maintained, and the calculation time only increases slightly and is still within an acceptable range. In addition, while ensuring convergence, the method proposed in the present invention has little impact on economic performance, and the cost increase is always controlled below 5%. This shows that the method proposed in the present invention has significant advantages in dealing with renewable energy uncertainties, and can effectively solve large-scale operation optimization problems while maintaining system stability and economic feasibility.

[0104] In summary, the present invention proposes a small disturbance stability constraint equivalent modeling method for high-penetration renewable energy power systems, which introduces an adaptive step size optimization strategy to greatly improve convergence and computational efficiency. This method constructs a small disturbance stability constraint equivalent model and embeds it into the optimal power flow to adjust the generator output, ensuring that the system achieves robust stability under different load and uncertainty conditions, while making only minimal compromises on economic performance, providing an effective solution for operation optimization in complex scenarios.

[0105] See also Figure 6 As shown, the embodiment of the present application also discloses a power system dispatching device based on adaptive step size and small disturbance stability constraint, including:

[0106] The constraint judgment module 11 is used to obtain an original optimal power flow model of the power system and judge whether the initial power dispatch result output by the original optimal power flow model satisfies the small disturbance stability constraint to obtain a first judgment result;

[0107] An iterative solution module 12, configured to perform iterative solution in the neighborhood of the initial power dispatch result based on the damping ratio threshold, the fluctuation range of renewable energy output and a preset adaptive step size iterative solution strategy to obtain a target solution result if the first judgment result is no;

[0108] A local positioning module 13 is used to perform a critical local positioning of a boundary of a robust small disturbance stability constraint based on the target solution result, and trigger a corresponding sample collection operation according to the positioning result to obtain sample data;

[0109] The equivalent modeling module 14 is used to perform explicit equivalent modeling based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and use the modeling result and the original optimal power flow model to determine the target power dispatch result.

[0110] Among them, for more specific working processes of the above-mentioned modules, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0111] It can be seen that the present application first obtains the original optimal power flow model of the power system, and then when the initial power dispatch result output by the original optimal power flow model does not meet the small disturbance stability constraint, an adaptive step size iterative solution is performed in the neighborhood of the initial power dispatch result based on the damping ratio threshold, and then the target solution result is used to realize the key local positioning of the boundary of the robust small disturbance stability constraint, and the positioning result and the linear kernel function are used for explicit equivalent modeling, so as to determine the target power dispatch result using the obtained modeling result and the original optimal power flow model, and complete the power dispatch. In this way, the convergence of the equivalent modeling of the robust small disturbance stability constraint of the main grid under the uncertainty of renewable energy can be improved, thereby ensuring the robust small disturbance stability of the power system under the fluctuation of renewable energy and improving the accuracy of power dispatch.

[0112] In some specific embodiments, the iterative solution module 12 may specifically include:

[0113] A measurement result acquisition unit, used for performing a two-norm measurement on the solution space based on the distance threshold and the initial power dispatch result to obtain a measurement result;

[0114] The solution result determination submodule is used to adjust the current renewable energy output according to the fluctuation range of the renewable energy output, so as to iteratively search for a feasible solution in the neighborhood of the initial power dispatch result based on the measurement result and the damping ratio threshold, so as to determine the target solution result; the feasible solution is the solution with the smallest difference between the minimum damping ratio of the corresponding small disturbance stable modal analysis and the damping ratio threshold.

[0115] In some specific embodiments, the solution result determination submodule may specifically include:

[0116] A minimum damping ratio solving unit, for any iterative solving round, by adjusting the initial power dispatching result, so as to adjust the current renewable energy output through the fluctuation range to determine the corresponding minimum damping ratio solving result under the condition of the given initial power dispatching result;

[0117] A convergence evaluation unit, configured to trigger a corresponding convergence evaluation operation based on the minimum damping ratio solution result to obtain an evaluation result of a current round;

[0118] A first damping ratio threshold updating unit, configured to trigger a corresponding first damping ratio threshold updating operation if the evaluation result indicates that the convergence of the current round is reduced, so as to obtain a new damping ratio threshold;

[0119] A step jump unit is used to search for a feasible solution based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result, and based on the search result and the new damping ratio threshold, jump again to the step of adjusting the initial power dispatch result to adjust the current renewable energy output through the fluctuation range to determine the corresponding minimum damping ratio solution result under the condition of the given initial power dispatch result, until the preset termination condition is met, and the target solution result is determined.

[0120] In some specific embodiments, the power system dispatching device based on adaptive step size and small disturbance stability constraint may further include:

[0121] a damping ratio judgment unit, configured to judge whether the difference between the target minimum damping ratio of the current round and the damping ratio threshold is greater than a preset error value based on the minimum damping ratio solution result if the evaluation result indicates that the convergence of the current round is improved, so as to obtain a second judgment result;

[0122] A feasible solution searching unit, configured to search for a feasible solution based on the damping ratio threshold value of the current round, the minimum damping ratio solution result and the measurement result if the second judgment result indicates that the value is greater than the preset error value;

[0123] A threshold change judgment unit, configured to trigger a corresponding damping ratio threshold change judgment operation if the second judgment result indicates that the damping ratio is not greater than the preset error value, so as to obtain a third judgment result;

[0124] A second damping ratio threshold updating unit is configured to trigger a corresponding second damping ratio threshold updating operation to obtain a new damping ratio threshold if the third judgment result indicates that the damping ratio threshold of the current round is not greater than the initial damping ratio threshold, and to search for a feasible solution based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result.

[0125] In some specific embodiments, the power system dispatching device based on adaptive step size and small disturbance stability constraint may further include:

[0126] The termination condition satisfaction unit is used to determine that the preset termination condition is currently satisfied if the third judgment result indicates that the damping ratio threshold of the current round is greater than the initial damping ratio threshold, and determine the target solution result based on the minimum damping ratio solution result.

[0127] In some specific embodiments, the equivalent modeling module 14 may specifically include:

[0128] An explicit modeling unit, configured to perform explicit modeling based on the sample data, the linear kernel function and the positioning result to obtain a target classification hyperplane;

[0129] The scheduling result determination submodule is used to solve the optimization model of the soft margin support vector machine by using the penalty factor and the sample data to obtain the equivalent constraint corresponding to the target classification hyperplane, embed the equivalent constraint into the original optimal power flow model, and determine the target power scheduling result based on the obtained embedded model.

[0130] In some specific embodiments, the scheduling result determination submodule may specifically include:

[0131] A form conversion unit, used for converting the optimization model of the soft margin support vector machine into a Lagrangian dual form to obtain a conversion result;

[0132] An equivalue constraint determination unit is used to determine the equivalue constraint corresponding to the target classification hyperplane based on the penalty factor, the conversion result and the slack variables corresponding to each target sample in the sample data.

[0133] Furthermore, the present application also discloses an electronic device. Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be considered as any limitation on the scope of use of the present application.

[0134] Figure 7 A schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the power system scheduling method based on adaptive step size and small disturbance stability constraints disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0135] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0136] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0137] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the power system dispatching method based on adaptive step size and small disturbance stability constraints performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.

[0138] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the power system dispatching method based on adaptive step size and small disturbance stability constraint disclosed above is implemented. For the specific steps of the method, reference can be made to the corresponding contents disclosed in the above embodiments, and no further description will be given here.

[0139] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0140] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0141] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0142] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0143] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A power system dispatching method based on adaptive step size and small disturbance stability constraints, characterized in that: include: Acquire an original optimal power flow model of the power system, and determine whether an initial power dispatch result output by the original optimal power flow model satisfies a small disturbance stability constraint, so as to obtain a first determination result; If the first judgment result is no, then performing an iterative solution in the neighborhood of the initial power dispatch result based on the damping ratio threshold, the fluctuation range of the renewable energy output and a preset adaptive step size iterative solution strategy to obtain a target solution result; Based on the target solution result, a key local positioning of the boundary of the robust small disturbance stability constraint is performed, and a corresponding sample collection operation is triggered according to the positioning result to obtain sample data; Explicit equivalent modeling is performed based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and the target power dispatch result is determined using the modeling result and the original optimal power flow model.

2. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 1, characterized in that: The iterative solution strategy based on the damping ratio threshold, the fluctuation range of the renewable energy output and the preset adaptive step size is iteratively solved in the neighborhood of the initial power dispatch result, including: Performing a bi-norm measurement on the solution space based on the distance threshold and the initial power dispatch result to obtain a measurement result; The current renewable energy output is adjusted by the fluctuation range of the renewable energy output, so as to iteratively search for a feasible solution in the neighborhood of the initial power dispatch result based on the measurement result and the damping ratio threshold so as to determine the target solution result; the feasible solution is the solution with the smallest difference between the minimum damping ratio of the corresponding small disturbance stable modal analysis and the damping ratio threshold.

3. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 2, characterized in that: The iterative search for a feasible solution in the neighborhood of the initial power dispatch result based on the measurement result and the damping ratio threshold so as to determine a target solution result includes: For any iterative solution round, by adjusting the initial power dispatch result, under the condition of the given initial power dispatch result, the current renewable energy output is adjusted through the fluctuation range to determine the corresponding minimum damping ratio solution result; triggering a corresponding convergence evaluation operation based on the minimum damping ratio solution result to obtain an evaluation result of the current round; If the evaluation result indicates that the convergence of the current round is reduced, triggering a corresponding first damping ratio threshold update operation to obtain a new damping ratio threshold; A feasible solution is searched based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result, and based on the search result and the new damping ratio threshold, the step of jumping again to the step of adjusting the initial power dispatch result to determine the corresponding minimum damping ratio solution result by adjusting the current renewable energy output through the fluctuation range under the condition of the given initial power dispatch result is performed, until the preset termination condition is met, and the target solution result is determined.

4. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 3 is characterized in that: After obtaining the evaluation result of the current round, the following steps are also included: If the evaluation result indicates that the convergence of the current round is improved, judging whether the difference between the target minimum damping ratio of the current round and the damping ratio threshold is greater than a preset error value based on the minimum damping ratio solution result, so as to obtain a second judgment result; If the second judgment result indicates that it is greater than the preset error value, searching for a feasible solution based on the damping ratio threshold value of the current round, the minimum damping ratio solution result and the measurement result; If the second judgment result indicates that the value is not greater than the preset error value, a corresponding damping ratio threshold change judgment operation is triggered to obtain a third judgment result; If the third judgment result indicates that the damping ratio threshold of the current round is not greater than the initial damping ratio threshold, the corresponding second damping ratio threshold update operation is triggered to obtain a new damping ratio threshold, and a feasible solution is searched based on the new damping ratio threshold, the minimum damping ratio solution result and the measurement result.

5. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 4, characterized in that: After obtaining the third judgment result, the method further includes: If the third judgment result indicates that the damping ratio threshold of the current round is greater than the initial damping ratio threshold, it is determined that the preset termination condition is currently met, and the target solution result is determined based on the minimum damping ratio solution result.

6. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 1, characterized in that: The explicit equivalent modeling is performed based on the sample data, the linear kernel function, the penalty factor and the positioning result to determine the modeling result, and the target power dispatching result is determined by using the modeling result and the original optimal power flow model, including: Performing explicit modeling based on the sample data, the linear kernel function and the positioning result to obtain a target classification hyperplane; The optimization model of the soft margin support vector machine is solved by using the penalty factor and the sample data to obtain the equivalent constraint corresponding to the target classification hyperplane, the equivalent constraint is embedded into the original optimal power flow model, and the target power dispatch result is determined based on the obtained embedded model.

7. The power system dispatching method based on adaptive step size and small disturbance stability constraint according to claim 6, characterized in that: The step of solving the optimization model of the soft margin support vector machine by using the penalty factor and the sample data includes: Perform Lagrangian dual transformation on the optimization model of soft margin support vector machine to obtain the transformation result; The equivalue constraint corresponding to the target classification hyperplane is determined based on the penalty factor, the conversion result, and the slack variables respectively corresponding to each target sample in the sample data.

8. A power system dispatching device based on adaptive step size and small disturbance stability constraints, characterized in that: include: A constraint judgment module, used to obtain an original optimal power flow model of the power system, and judge whether an initial power dispatch result output by the original optimal power flow model satisfies a small disturbance stability constraint, so as to obtain a first judgment result; an iterative solution module, configured to perform iterative solution in the neighborhood of the initial power dispatch result based on a damping ratio threshold, a fluctuation range of renewable energy output, and a preset adaptive step size iterative solution strategy if the first judgment result is no, so as to obtain a target solution result; A local positioning module is used to perform a key local positioning of the boundary of the robust small disturbance stability constraint based on the target solution result, and trigger a corresponding sample collection operation according to the positioning result to obtain sample data; The equivalent modeling module is used to perform explicit equivalent modeling based on the sample data, linear kernel function, penalty factor and positioning result to determine the modeling result, and use the modeling result and the original optimal power flow model to determine the target power dispatch result.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the power system scheduling method based on adaptive step size and small disturbance stability constraints as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the power system scheduling method based on adaptive step size and small disturbance stability constraints as described in any one of claims 1 to 7.

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