A power grid dispatching feasibility determination method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, equipment, and storage medium for determining the feasibility of power grid dispatching, relating to the field of power dispatching. The method includes: determining a security-constrained economic dispatching model for the target power grid; determining a target capacity set based on the initial security constraints of the security-constrained economic dispatching model; using the security-constrained economic dispatching model to eliminate redundant constraints in the initial security constraints to determine a first target constraint; determining an initial target approximation region corresponding to the target capacity set and the corresponding second target constraint, thereby determining a target constraint set and updating the target approximation region; when the updated target approximation region satisfies the target convergence condition, determining target feasibility conditions based on the target constraint set and the target approximation region; and judging the feasibility of the target power grid's test operating point based on the target feasibility conditions to determine whether to trigger a dispatching operation on the target power grid. This application enables rapid feasibility assessment of power grid dispatching operations corresponding to any given operating point.

Description

Technical Field

[0001] This invention relates to the field of power dispatching, and in particular to a method, apparatus, equipment, and storage medium for determining the feasibility of power grid dispatching. Background Technology

[0002] Existing methods for handling the infeasibility of economic dispatching under security constraints are mainly divided into two categories: "passive response" and "active prevention." Passive response methods (such as constraint relaxation and reduction) only initiate after infeasibility occurs. Their adjustment strategies often lack systematic optimization guidance, and the identification process involves a huge computational load, making it difficult to meet real-time requirements. Active prevention methods (such as robust optimization) are mainly used in the day-ahead planning stage, aiming to formulate dispatching plans that can resist uncertainty. Their structure is not suitable for rapid feasibility assessment of any given real-time forecast point. Neither of these methods can meet the core requirements of "rapid diagnosis and optimal response" in real-time power grid dispatching. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide a method, apparatus, device, and storage medium for determining the feasibility of power grid dispatching, enabling real-time and rapid assessment of the feasibility of power grid dispatching operations. The specific solution is as follows: In a first aspect, this application discloses a method for determining the feasibility of power grid dispatching, including: A security-constrained economic dispatch model corresponding to the target power grid is determined, and a target capacity set corresponding to the security-constrained economic dispatch model is determined based on the initial security constraints corresponding to the security-constrained economic dispatch model; the target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space. The redundant constraints in the initial security constraints are determined using the security constraint economic scheduling model, and the redundant constraints are removed to determine the first target constraint corresponding to the security constraint economic scheduling model. Determine the initial target approximation region corresponding to the target capacity set, determine the second target constraint corresponding to the target approximation region through heuristic vertex sampling, and determine the target constraint set based on the first target constraint and the second target constraint; The target approximate region is updated using the target constraint set. When the updated target approximate region satisfies the target convergence condition, the target feasibility condition corresponding to the target capacity set is determined based on the target constraint set and the target approximate region. The feasibility of the test operation point of the target power grid is judged based on the target feasibility condition. Based on the feasibility judgment result, it is determined whether to trigger the power grid dispatch control center to perform dispatch operation on the target power grid.

[0004] Optionally, the initial safety constraints include power balance constraints, generator output upper and lower limit constraints, and line power flow constraints.

[0005] Optionally, determining the initial approximate target region corresponding to the target capacity set includes: The number of target search directions is determined based on the load quantity of the security-constrained economic scheduling model, and the target approximate region corresponding to the target capacity set is determined based on the number of target search directions.

[0006] Optionally, determining the redundancy constraints in the initial security constraints using the security-constrained economic scheduling model includes: Using the aforementioned security constraint economic scheduling model, redundant constraints in the initial security constraints are determined in parallel on several CPU cores based on a preset parallel umbrella constraint identification method.

[0007] Optionally, the method for determining the feasibility of power grid dispatching further includes: If the updated target approximation region does not satisfy the target convergence condition, then proceed to the step of determining the second target constraint corresponding to the target approximation region through heuristic vertex sampling.

[0008] Optionally, the feasibility assessment of the test operation point of the target power grid based on the target feasibility conditions includes: The system obtains the predicted renewable energy output and target load demand of the target power grid, determines the test operation point corresponding to the target power grid based on the predicted renewable energy output and target load demand, and judges the feasibility of the test operation point of the target power grid based on the target feasibility conditions.

[0009] Optionally, after determining the feasibility of the test operation point of the target power grid based on the target feasibility conditions, the method further includes: If the feasibility judgment result includes that the test running point does not satisfy at least one objective constraint in the objective constraint set, then the adjustment result corresponding to the test running point is determined using the objective optimization function.

[0010] Secondly, this application discloses a device for determining the feasibility of power grid dispatching, comprising: A capacity set construction module is used to determine the security-constrained economic dispatch model corresponding to the target power grid, and to determine the target capacity set corresponding to the security-constrained economic dispatch model based on the initial security constraints corresponding to the security-constrained economic dispatch model; the target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space; The constraint elimination module is used to determine redundant constraints in the initial security constraints using the security constraint economic scheduling model, and to eliminate the redundant constraints in order to determine the first target constraint corresponding to the security constraint economic scheduling model. The constraint set construction module is used to determine the initial target approximation region corresponding to the target capacity set, determine the second target constraint corresponding to the target approximation region through heuristic vertex sampling, and determine the target constraint set based on the first target constraint and the second target constraint; The feasibility assessment module is used to update the target approximate region using the target constraint set, and when the updated target approximate region satisfies the target convergence condition, to determine the target feasibility condition corresponding to the target capacity set based on the target constraint set and the target approximate region, and to assess the feasibility of the test operation point of the target power grid based on the target feasibility condition, so as to determine whether to trigger the power grid dispatch control center to perform dispatch operations on the target power grid based on the feasibility assessment result.

[0011] Thirdly, this application discloses an electronic device, including: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for determining the feasibility of power grid dispatching.

[0012] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned method for determining the feasibility of power grid dispatching.

[0013] In this application, when determining the feasibility of power grid dispatch, a security-constrained economic dispatch model corresponding to the target power grid is determined, and a target capacity set corresponding to the security-constrained economic dispatch model is determined based on the initial security constraints corresponding to the security-constrained economic dispatch model. The target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space. Redundant constraints in the initial security constraints are determined using the security-constrained economic dispatch model, and the redundant constraints are eliminated to determine the first target constraint corresponding to the security-constrained economic dispatch model. An initial target approximation region corresponding to the target capacity set is determined, and a second target constraint corresponding to the target approximation region is determined through heuristic vertex sampling. A target constraint set is determined based on the first target constraint and the second target constraint. The target approximation region is updated using the target constraint set, and when the updated target approximation region satisfies the target convergence condition, a target feasibility condition corresponding to the target capacity set is determined based on the target constraint set and the target approximation region. The feasibility of the test operation point of the target power grid is judged based on the target feasibility condition, and a decision on whether to trigger the dispatch operation of the target power grid by the power grid dispatch control center is made based on the feasibility judgment result. As can be seen, this application systematically applies a projection-based method to solve the infeasibility problem of real-time safety-constrained economic scheduling, proposing a complete closed-loop framework from identification to solution. Based on heuristic vertex sampling, this application cleverly avoids the dependence on mixed-integer programming by utilizing the target approximation region and target constraint set. Leveraging the high maturity and scalability of linear programming techniques, combined with efficient parallel umbrella constraint identification preprocessing, this application can easily handle real-world large-scale power grid systems, improving the scalability and robustness of the scheduling system. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0015] Figure 1 This is a flowchart of a method for determining the feasibility of power grid dispatching disclosed in this application; Figure 2 This is a schematic diagram of a specific method for determining the feasibility of power grid dispatching disclosed in this application; Figure 3 This is a schematic diagram of a specific renewable energy-load capacity set construction method disclosed in this application; Figure 4This is a schematic diagram illustrating the performance comparison of a specific online infeasibility solution disclosed in this application, wherein... Figure 4 (a) in the text selects the IEEE 39-node system. Figure 4 (b) in the text corresponds to the IEEE 118-node system; Figure 5 This is a schematic diagram of the structure of a power grid dispatching feasibility determination device disclosed in this application; Figure 6 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Existing methods for handling the infeasibility of economic dispatching under security constraints are mainly divided into two categories: "passive response" and "active prevention." Passive response methods (such as constraint relaxation and reduction) are only initiated after infeasibility occurs. Their adjustment strategies often lack systematic optimization guidance, and the identification process involves huge computational loads, making it difficult to meet real-time requirements. Active prevention methods (such as robust optimization) are mainly used in the day-ahead planning stage, aiming to formulate dispatching plans that can resist uncertainty. Their structure is not suitable for rapid feasibility assessment of any given real-time forecast point. Neither of these methods can meet the core requirements of "rapid diagnosis and optimal response" in real-time power grid dispatching. To solve the above technical problems, this application discloses a method for determining the feasibility of power grid dispatching, which can realize real-time and rapid assessment of the feasibility of power grid dispatching operations.

[0018] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for determining the feasibility of power grid dispatching, including: Step S11: Determine the security-constrained economic dispatch model corresponding to the target power grid, and determine the target capacity set corresponding to the security-constrained economic dispatch model based on the initial security constraints corresponding to the security-constrained economic dispatch model; the target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space.

[0019] In this embodiment, as Figure 2As shown, a security-constrained economic dispatch model for the target power grid is first established, and the target capacity set corresponding to the security-constrained economic dispatch model is determined based on the initial security constraints. The initial security constraints include power balance constraints, generator output upper and lower limit constraints, and line power flow constraints. This security-constrained economic dispatch model can be a mathematical model of a single-cycle security-constrained economic dispatch problem, and its objective function is to minimize the total system operating cost. ; In the formula, It is a generator The cost function, It is its generator Those who have made contributions This represents the total number of generators in the system.

[0020] The power balance constraint can be expressed as: ; In the formula, It is a renewable energy unit of efforts, It is a load demand, and These refer to the number of renewable energy units and loads in the system, respectively.

[0021] The upper and lower limits of generator output can be expressed as: ; In the formula, and These are generators Minimum and maximum output limits.

[0022] Line power flow constraints can be expressed as: ; In the formula, It refers to the number of transmission lines in the system. It is a transmission line Maximum transmission capacity limit, , , They represent generators Renewable energy unit and load With transmission lines The relevant power transfer distribution factor.

[0023] It is understandable that, for ease of expression, all constraints corresponding to the safety-constrained economic scheduling model in this application can be written in a unified matrix form: ; In the formula, Power is generated by a generator The resulting vector of decision variables; Powered by renewable energy and load The resulting vector of uncertainties / parameter variables; , , These are the coefficient matrix and constant vector corresponding to the constraints.

[0024] In this embodiment, the target capacity set (i.e., the renewable energy-load capacity set) is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space. In other words, the target capacity set is defined as the feasible region of the high-dimensional security-constrained economic dispatch problem within the renewable energy-load space. The projection onto the space where the variable resides (i.e., the renewable energy-load space). For any given... Value, if a Able to satisfy constraints So this The point lies within the renewable energy-load capacity set. The renewable energy-load capacity set can be expressed mathematically as: ; Since the constraints constituting the security-constrained economic dispatch problem are all linear constraints, the renewable energy-load capacity set is a convex polyhedron, which can be represented by a set of linear inequalities. To describe it precisely. The ultimate goal of the offline construction phase is to solve this crucial matrix. sum vector .

[0025] Step S12: Use the security constraint economic scheduling model to determine the redundant constraints in the initial security constraints, and remove the redundant constraints to determine the first target constraint corresponding to the security constraint economic scheduling model.

[0026] In this embodiment, the large-scale security-constrained economic scheduling model contains numerous redundant constraints, and direct processing would severely slow down the computation speed. Determining redundant constraints in the initial security constraints using the security-constrained economic scheduling model includes: using the security-constrained economic scheduling model, based on a preset parallel umbrella constraint identification method, to determine the redundant constraints in the initial security constraints in parallel across several CPU cores. Specifically, for a constraint... The constraint is valid if and only if removing it does not change the feasible region of the problem. These are called non-umbrella constraints, or redundant constraints. In this embodiment, all umbrella constraints are identified by solving a series of parallel linear programming problems. For each constraint... Solve the following problem: ; In the formula, This means that, under the condition that all other constraints are satisfied, the constraint... The minimum normal distance between the boundary and the feasible region. If Then the constraint Redundant constraints can be safely removed; if Then the constraint As umbrella constraints, they need to be retained. Because each constraint... It can be computed independently, and this process can be distributed across multiple CPU cores for parallel execution, greatly reducing the time required for dimensionality reduction of large-scale models. Besides parallelized umbrella constraint identification, there are other methods for identifying redundant constraints. However, the parallelized umbrella constraint identification method has a strong theoretical foundation and is extremely efficient in handling large-scale safety-constrained economic scheduling problems, which is unmatched by other serial methods.

[0027] Step S13: Determine the initial target approximation region corresponding to the target capacity set, determine the second target constraint corresponding to the target approximation region through heuristic vertex sampling, and determine the target constraint set based on the first target constraint and the second target constraint.

[0028] In this embodiment, as Figure 3 As shown, after simplifying the initial security constraints, the first objective constraint can be obtained. The set of objective constraints, including the first objective constraint and the second objective constraint, is denoted as... This paper employs an innovative external approximation algorithm based on heuristic vertex sampling to solve for the boundary of the renewable energy-load capacity set. First, it determines the initial target approximation region corresponding to the target capacity set, including: determining the number of target search directions based on the load quantity of the security-constrained economic dispatch model, and then determining the target approximation region corresponding to the target capacity set based on the number of target search directions. Specifically, it constructs an initial external approximation region (i.e., the target approximation region) containing the actual renewable energy-load capacity set. This is achieved through... Select a set of dispersed direction vectors in space This is achieved by solving a linear programming problem multiple times, ensuring that the initial region is as compact as possible. The specific formula is as follows: ; In the formula, This indicates that, given all other constraints, the feasible region lies in the direction vector. The maximum value on. The initial approximate target region. The quality of the initial search directions directly affects the convergence speed and efficiency of subsequent iterations. Simulation analysis of systems of different scales shows that the number of initial search directions... Set as the number of uncertain variables twice as much, of which This approach achieves an optimal balance between the compactness of the initial region and computational efficiency. Therefore, this embodiment selects this strategy for construction. Number of initial search directions Set as .

[0029] Subsequently, the preset constraint set determination algorithm (also known as the heuristic vertex sampling-based external approximation algorithm) adopted in this embodiment enters the iterative process, and in the... In this iteration, the following three steps are performed: a) Heuristic vertex sampling: This is the core innovation of the preset constraint set determination algorithm in this embodiment. To find the current approximate region... The point that most violates the constraints of the real renewable energy-load capacity set is addressed in this embodiment by solving the dual problem of the security-constrained economic dispatch problem, thus avoiding the difficulty of solving a complex bilinear problem in traditional methods. It is understood that the feasible region of the dual problem is also a polyhedron, whose vertices define all boundaries of the renewable energy-load capacity set. This embodiment does not need to enumerate all vertices, but instead uses a heuristic sampling strategy to efficiently find the most critical vertices. Specifically, a series of linear programming problems are solved along the coordinate axes and other directions in the dual space, with the following specific formulas: ; In the formula, It is a dual variable. It is a search direction composed of different unit vectors in the dual space. The polyhedron representing the dual space is in The farthest distance in a direction, i.e., the vertex of the dual polyhedron. For a series of search directions... Solving the above linear programming problem yields a representative set of vertices on the dual polyhedron. .

[0030] b) Generation of feasible cutting planes: The core basis of this step is the strong duality theorem of linear programming. According to this theorem, given a running point... Located within the real renewable energy-load capacity set, if and only if for all vertices of the security-constrained economic dispatch dual problem ,inequality All are true. In this embodiment, for each sampled dual vertex... Solve a separating linear programming problem to find the solution in the current approximate region. Searching for the most violating reason Points with defined constraints The specific formula is as follows: ; If the largest (recorded as) () greater than a small positive tolerance Explanation Within the current approximate region, but not within the true renewable energy-load capacity set, because it does not satisfy the necessary conditions defined by all dual vertices. At this point, according to the dual vertices... Generate a new constraint as the second objective constraint, i.e., a feasible cutting plane: This new constraint will cut off points containing infeasible points. The region is defined, and since all points in the real feasible region satisfy this inequality, it is guaranteed that no real feasible region will be cut in error. The first objective constraint and the second objective constraint are used to construct the objective constraint set, thereby achieving accurate optimization of the approximate region of the objective.

[0031] It is understandable that, in addition to the aforementioned heuristic vertex sampling-based external approximation algorithm, other polyhedral projection algorithms can also be used in this embodiment. For example, the renewable energy-load capacity set can be constructed by completely enumerating all vertices of the dual polyhedron, but this faces the combinatorial explosion problem at slightly higher dimensions, making it computationally infeasible. An approximation method based on mixed-integer linear programming can also be used to handle the bilinear problem, but its computational speed and robustness are inferior to the heuristic vertex sampling-based external approximation algorithm proposed in this embodiment.

[0032] Step S14: Update the target approximate region using the target constraint set, and when the updated target approximate region satisfies the target convergence condition, determine the target feasibility condition corresponding to the target capacity set based on the target constraint set and the target approximate region, and judge the feasibility of the test operation point of the target power grid based on the target feasibility condition, so as to determine whether to trigger the power grid dispatch control center to perform dispatch operation on the target power grid based on the feasibility judgment result.

[0033] In this embodiment, the newly generated cutting plane After being added to the existing set of target constraints, a smaller and more accurate approximate region can be formed using the target constraint set. This update re-approximates the target region. It then determines whether the updated target region satisfies the target convergence condition, which can be set to find no points violating the constraints within the current target region (i.e.,...). If the current target approximation region satisfies the target convergence condition, the target feasibility condition corresponding to the target capacity set is determined based on the target constraint set and the target approximation region. The feasibility of the test operation point of the target power grid is then judged based on the target feasibility condition, and the decision on whether to trigger the power grid dispatch control center's dispatch operation on the target power grid is based on the feasibility judgment result. If the updated target approximation region does not satisfy the target convergence condition, the process jumps to the step of determining the second target constraint corresponding to the target approximation region through heuristic vertex sampling. It is understood that the above iterative process for the target approximation region can be implemented in the offline construction phase.

[0034] In this embodiment, the feasibility of the test operation point of the target power grid is judged based on the target feasible conditions. This includes: obtaining the predicted renewable energy output and target load demand of the target power grid; determining the test operation point of the target power grid based on the predicted renewable energy output and target load demand; judging the feasibility of the test operation point of the target power grid based on the target feasible conditions; and then determining whether to trigger the grid dispatch control center to perform dispatch operations on the target power grid based on the feasibility judgment result. Specifically, during the real-time dispatch cycle, the dispatch system (i.e., the grid dispatch control center) obtains the current predicted renewable energy output. and load demand This forms a running point vector (i.e., the running point to be tested). At this point, there is no need to solve complex safety-constrained economic scheduling problems; only a simple matrix-vector multiplication and comparison operation is required to verify the results. Check if the inequalities are true. If all inequalities are true, then the run point is correct. Located within the renewable energy-load capacity set, the scheduling operation corresponding to the tested operating point is feasible; if at least one inequality is not true, it indicates that the operating point... Located outside the renewable energy-load capacity set, the scheduling operation is deemed infeasible, requiring immediate adjustment measures. In practical applications, this identification process involves extremely low computational cost and can be completed within milliseconds.

[0035] Furthermore, in this embodiment, after judging the feasibility of the test operation point of the target power grid based on the target feasibility conditions, it further includes: if the feasibility judgment result includes that the test operation point does not satisfy at least one target constraint in the target constraint set, then the adjustment result corresponding to the test operation point is determined using the target optimization function. Specifically, once the scheduling operation is identified as infeasible, this embodiment will immediately initiate an optimal adjustment strategy. It should be noted that the goal of this strategy is to provide a computationally extremely fast and engineeringally simple and effective adjustment scheme. Therefore, this embodiment uses minimizing the adjustment amount (Manhattan distance) as the objective. This online solution based on the optimal adjustment model of minimum Manhattan distance can quickly provide specific and quantitative reduction strategies, providing high-quality and executable decision support for power dispatch. In practical applications, if the accurate cost function of wind and solar curtailment and load shedding can be obtained, the objective optimization function can be replaced by minimizing the total adjustment cost, thereby obtaining the economically optimal solution. Specifically, this embodiment calculates the distance from the current infeasible point by solving the following linear programming problem. The most recent feasible point To achieve an exemplary optimal adjustment: ; The objective function described above seeks to minimize the Manhattan distance between the new operating point and the original operating point, while ensuring the new operating point remains within the renewable energy-load capacity set. Furthermore, the constraints stipulate that adjustment measures can only be reductions (wind and solar curtailment, load shedding), not increases. This linear programming problem has few variables and a simple structure, allowing for rapid solution. Its optimal solution... The specific adjustment plan was given, namely, the total amount of renewable energy that needs to be reduced is The total load that needs to be reduced is It is understandable that the process of judging and adjusting the feasibility of the target power grid's operating point based on the target's feasible conditions all belong to the online application stage. Through the innovative "offline-online" decoupling framework, the computational burden is successfully shifted from the time-critical online environment to the time-amplified offline environment. This minimizes the computational load in the online application stage, resulting in an order-of-magnitude improvement in computational efficiency and demonstrating practical engineering value.

[0036] Understandably, a cost-optimal objective can be used instead of the aforementioned objective function based on minimizing the Manhattan distance, with the unit costs of wind curtailment and load shedding as weights, or a quadratic function can be used to simulate the increasing marginal cost effect, thereby transforming the problem into a weighted linear programming or quadratic programming problem, respectively. While these alternatives can provide more economically efficient solutions, the objective function of minimizing the Manhattan distance chosen in this embodiment has advantages in computational speed and solver universality, making it more suitable for scenarios with high requirements for response time.

[0037] As can be seen, this application systematically applies a projection-based method to solve the infeasibility problem of real-time safety-constrained economic scheduling, proposing a complete closed-loop framework from identification to solution. Based on heuristic vertex sampling, this application cleverly avoids the dependence on mixed-integer programming by utilizing the target approximation region and target constraint set. Leveraging the high maturity and scalability of linear programming techniques, combined with efficient parallel umbrella constraint identification preprocessing, this application can easily handle real-world large-scale power grid systems, improving the scalability and robustness of the scheduling system.

[0038] As can be seen from the previous embodiment, this application discloses a method for determining the feasibility of power grid dispatching, which can realize real-time and rapid assessment of the feasibility of power grid dispatching operations. Next, specific calculation examples will be used to illustrate the implementation effect of the method for determining the feasibility of power grid dispatching proposed in this application.

[0039] The first experiment uses the IEEE 39-bus standard test system as a case study to illustrate the superiority of this application in the offline construction process of renewable energy-load capacity sets. In the system and model initialization phase, a modified IEEE 39-bus test system was selected, which includes 10 generator units, 46 transmission lines, 3 renewable energy injection points, and 5 loads. A mathematical model for the safe and economical dispatch of this system was established. After the model was established, there were a total of 114 initial constraints. Then, a parallelized umbrella constraint identification algorithm was used to filter these 114 constraints. The calculation results showed that only 32 constraints were valid (i.e., umbrella constraints), while the remaining 82 were redundant constraints. This step successfully reduced the number of constraints to be processed in subsequent calculations from 114 to 32, greatly simplifying the initial safety constraints. Then, a... (in this system) That is, 16 search directions are used to construct the initial approximate target region. Sixteen initial constraints are generated. Subsequently, an iterative solution is performed based on the core algorithm of heuristic vertex sampling and external approximation. The algorithm converges after nine iterations, generating eight linear inequality constraints as feasible cuts. These eight feasible cuts, combined with the 16 constraints generated from the initial external approximation region, constitute the 24 linear constraints defining the renewable energy-load capacity set of the system. The process of constructing the renewable energy-load capacity set in this embodiment is compared with two existing advanced projection algorithms, denoted as: M1, an asymptotic external approximation method based on mixed integer programming; M2, an asymptotic vertex enumeration method; and M3, an external approximation algorithm based on heuristic vertex sampling.

[0040] Under the same computational environment, the computation time required to construct the renewable energy-load capacity set and the volumetric accuracy error of the final renewable energy-load capacity set are compared. The specific computation time and accuracy results for the three methods are shown in Table 1 below: Table 1 Comparison of computation time and accuracy of various algorithms

[0041] As shown in Table 1, while method M1 yields results, it is time-consuming, taking 6.58 seconds, and the final capacity set has a high volume accuracy error. Method M2, due to its inherent combinatorial explosion problem, fails to converge within a computation time exceeding 1000 seconds. In contrast, method M3 proposed in this embodiment completes the high-precision capacity set construction in less than 0.5 seconds, with an accuracy error of only 2.39%. This embodiment fully demonstrates that the offline construction method for renewable energy-load capacity sets in the power grid dispatch feasibility determination method proposed in this application comprehensively surpasses existing technologies in both computational efficiency and solution accuracy.

[0042] The second experiment used IEEE 39-node and IEEE 118-node systems to compare the computational performance of this application (denoted as M2) with the traditional slack variable-based optimization method (denoted as M1) in handling online infeasibility scenarios. For both test systems, their renewable energy-load capacity sets were pre-constructed using the offline construction method for renewable energy-load capacity sets proposed in the previous embodiment. During the online scenario simulation phase, random perturbations were introduced into the system baseline load and renewable energy output data to generate 100 different infeasibility scenarios. For each infeasibility scenario, the method of this application and the traditional method were used for solution. The method of this application is denoted as M2, and the traditional slack variable-based optimization method is denoted as M1. M2 is solved using inequalities... The test quickly determines that the problem is infeasible, and then solves the linear programming model based on the minimum Manhattan distance to obtain the optimal adjustment strategy. M1 introduces slack variables into the original safety-constrained economic scheduling model and adds them as penalty terms to the objective function, and then solves the entire large-scale optimization problem online.

[0043] It should be noted that the reason why the online phase of this application is so efficient is that the number of variables in the problem is greatly reduced, thus significantly reducing the dimensionality of the problem. Figure 4 This visually demonstrates the effect of dimensionality reduction. Figure 4 In (a) of this application, the IEEE 39-node system is selected. It can be seen that the original security-constrained economic scheduling problem requires handling 114 constraints and 18 variables, while in the online phase of this application, only 24 constraints and 8 variables defined by the final renewable energy-load capacity set need to be handled. Figure 4(b) corresponds to the IEEE 118-node system. The original problem contains 482 constraints and 154 variables, while the method in this application only needs to handle 356 constraints and 100 variables in the online phase. The number of uncertainties constituting the renewable energy-load capacity set of this system. The value is 100. This application follows the initialization phase... The strategy for each search direction necessarily requires 200 search directions to construct an effective and compact initial external approximation region during the initialization process. The final 356 constraints are composed of these 200 initial constraints and the 156 feasible cuts generated in subsequent iterations. The computational performance of this application compared with traditional slack variable-based optimization methods in handling online infeasibility scenarios is shown in Table 2 below, based on the average solution time for 100 random infeasibility scenarios. Table 2 Comparison of computing performance of each system

[0044] As can be seen from the table above, this application (M2) requires only the handling of a small number of variables and constraints related to the capacity set boundary, resulting in a significantly faster online solution speed than traditional methods. On a 39-node system, the speed improvement is approximately 3 times; on the more complex 118-node system, the speed improvement exceeds 2.2 times. This embodiment fully demonstrates the significant real-time advantages of the online application method of this application, enabling it to provide effective and immediate decision support for power grid dispatching.

[0045] As can be seen, this application systematically applies a projection-based method to solve the infeasibility problem of real-time safety-constrained economic scheduling, proposing a complete closed-loop framework from identification to solution. Based on heuristic vertex sampling, this application cleverly avoids the dependence on mixed-integer programming by utilizing the target approximation region and target constraint set. Leveraging the high maturity and scalability of linear programming techniques, combined with efficient parallel umbrella constraint identification preprocessing, this application can easily handle real-world large-scale power grid systems, improving the scalability and robustness of the scheduling system.

[0046] See Figure 5 As shown, this application discloses a device for determining the feasibility of power grid dispatching, comprising: The capacity set construction module 11 is used to determine the security-constrained economic dispatch model corresponding to the target power grid, and to determine the target capacity set corresponding to the security-constrained economic dispatch model based on the initial security constraints corresponding to the security-constrained economic dispatch model; the target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space; The constraint elimination module 12 is used to determine redundant constraints in the initial security constraints using the security constraint economic scheduling model, and to eliminate the redundant constraints in order to determine the first target constraint corresponding to the security constraint economic scheduling model. The constraint set construction module 13 is used to determine the initial target approximation region corresponding to the target capacity set, determine the second target constraint corresponding to the target approximation region through heuristic vertex sampling, and determine the target constraint set based on the first target constraint and the second target constraint; The feasibility judgment module 14 is used to update the target approximate region using the target constraint set, and when the updated target approximate region satisfies the target convergence condition, to determine the target feasibility condition corresponding to the target capacity set based on the target constraint set and the target approximate region, and to judge the feasibility of the test operation point of the target power grid based on the target feasibility condition, so as to determine whether to trigger the power grid dispatch control center to perform dispatch operation on the target power grid based on the feasibility judgment result.

[0047] As can be seen, this application systematically applies a projection-based method to solve the infeasibility problem of real-time safety-constrained economic scheduling, proposing a complete closed-loop framework from identification to solution. Based on heuristic vertex sampling, this application cleverly avoids the dependence on mixed-integer programming by utilizing the target approximation region and target constraint set. Leveraging the high maturity and scalability of linear programming techniques, combined with efficient parallel umbrella constraint identification preprocessing, this application can easily handle real-world large-scale power grid systems, improving the scalability and robustness of the scheduling system.

[0048] In one specific implementation, the constraint set construction module 13 may include: An approximate region determination unit is used to determine the number of target search directions based on the load quantity of the security-constrained economic scheduling model, and to determine the target approximate region corresponding to the target capacity set based on the number of target search directions.

[0049] In one specific implementation, the constraint removal module 12 may include: The redundancy constraint determination unit is used to determine the redundancy constraints in the initial security constraints in parallel on several CPU cores using the security constraint economic scheduling model and based on a preset parallel umbrella constraint identification method.

[0050] In one specific embodiment, the device may further include: The constraint re-determination module is used to jump to the step of determining the second target constraint corresponding to the target approximation region through heuristic vertex sampling if the updated target approximation region does not meet the target convergence condition.

[0051] In one specific implementation, the feasibility assessment module 14 may include: The feasibility assessment unit is used to obtain the predicted renewable energy output and target load demand of the target power grid, determine the test operation point corresponding to the target power grid based on the predicted renewable energy output and target load demand, and assess the feasibility of the test operation point of the target power grid based on the target feasibility conditions.

[0052] In one specific embodiment, the device may further include: The test run point adjustment module is used to determine the adjustment result corresponding to the test run point by using the objective optimization function if the feasibility judgment result includes that the test run point does not satisfy at least one objective constraint in the objective constraint set.

[0053] Furthermore, embodiments of this application also disclose an electronic device, Figure 6 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0054] Figure 6 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may 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 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the power grid dispatch feasibility determination method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0055] In this embodiment, the power supply 23 is used to provide operating 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 external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire 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.

[0056] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored thereon can include an operating system 221, computer programs 222, etc., and the storage method can be temporary storage or permanent storage.

[0057] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the power grid dispatch feasibility determination method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0058] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned method for determining the feasibility of power grid dispatch. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0059] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0060] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0061] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0062] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0063] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for determining grid dispatchability feasibility, the method comprising: receiving a plurality of grid dispatchability feasibility parameters; and determining a grid dispatchability feasibility based on the plurality of grid dispatchability feasibility parameters. include: Determine the security-constrained economic dispatch model corresponding to the target power grid, and determine the target capacity set corresponding to the security-constrained economic dispatch model based on the initial security constraints corresponding to the security-constrained economic dispatch model; The target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space; The redundant constraints in the initial security constraints are determined using the security constraint economic scheduling model, and the redundant constraints are removed to determine the first target constraint corresponding to the security constraint economic scheduling model. An initial target approximation region corresponding to the target capacity set is determined. A second target constraint corresponding to the target approximation region is determined through heuristic vertex sampling. A target constraint set is then determined based on the first and second target constraints. The heuristic vertex sampling process includes: solving a linear programming problem in the dual space corresponding to the dual problem of the safety-constrained economic scheduling problem using a heuristic sampling strategy, obtaining representative vertices on the dual polyhedron; the target search direction is a search direction composed of different unit vectors in the dual space. The target approximate region is updated using the target constraint set. When the updated target approximate region satisfies the target convergence condition, the target feasibility condition corresponding to the target capacity set is determined based on the target constraint set and the target approximate region. The feasibility of the test operation point of the target power grid is judged based on the target feasibility condition. Based on the feasibility judgment result, it is determined whether to trigger the power grid dispatch control center to perform dispatch operation on the target power grid.

2. The method for determining the feasibility of power grid dispatching according to claim 1, characterized in that, The initial safety constraints include power balance constraints, generator output upper and lower limit constraints, and line power flow constraints.

3. The method for determining the feasibility of power grid dispatching according to claim 1, characterized in that, Determining the initial approximate target region corresponding to the target capacity set includes: The number of target search directions is determined based on the load quantity of the security-constrained economic scheduling model, and the target approximate region corresponding to the target capacity set is determined based on the number of target search directions.

4. The method for determining the feasibility of power grid dispatching according to claim 1, characterized in that, The step of determining redundant constraints in the initial security constraints using the security-constrained economic scheduling model includes: Using the aforementioned security constraint economic scheduling model, redundant constraints in the initial security constraints are determined in parallel on several CPU cores based on a preset parallel umbrella constraint identification method.

5. The method for determining the feasibility of power grid dispatching according to claim 1, characterized in that, Also includes: If the updated target approximation region does not satisfy the target convergence condition, then proceed to the step of determining the second target constraint corresponding to the target approximation region through heuristic vertex sampling.

6. The method for determining the feasibility of power grid dispatching according to claim 1, characterized in that, The assessment of the feasibility of the test operation point of the target power grid based on the target feasibility conditions includes: The system obtains the predicted renewable energy output and target load demand of the target power grid, determines the test operation point corresponding to the target power grid based on the predicted renewable energy output and target load demand, and judges the feasibility of the test operation point of the target power grid based on the target feasibility conditions.

7. The method for determining the feasibility of power grid dispatching according to any one of claims 1 to 6, characterized in that, After determining the feasibility of the test operation point of the target power grid based on the target feasibility conditions, the method further includes: If the feasibility judgment result includes that the test running point does not satisfy at least one objective constraint in the objective constraint set, then the adjustment result corresponding to the test running point is determined using the objective optimization function.

8. A device for determining the feasibility of power grid dispatching, characterized in that, include: A capacity set construction module is used to determine the security-constrained economic dispatch model corresponding to the target power grid, and to determine the target capacity set corresponding to the security-constrained economic dispatch model based on the initial security constraints corresponding to the security-constrained economic dispatch model. The target capacity set is the projection of the feasible region corresponding to the security-constrained economic dispatch model onto the renewable energy-load space; The constraint elimination module is used to determine redundant constraints in the initial security constraints using the security constraint economic scheduling model, and to eliminate the redundant constraints in order to determine the first target constraint corresponding to the security constraint economic scheduling model. A constraint set construction module is used to determine the initial target approximation region corresponding to the target capacity set, determine the second target constraint corresponding to the target approximation region through heuristic vertex sampling, and determine the target constraint set based on the first target constraint and the second target constraint; wherein, the heuristic vertex sampling process includes: solving a linear programming problem in the dual space corresponding to the dual problem of the safety-constrained economic scheduling problem through a heuristic sampling strategy, and obtaining representative vertices on the dual polyhedron; the target search direction is a search direction composed of different unit vectors in the dual space; The feasibility assessment module is used to update the target approximate region using the target constraint set, and when the updated target approximate region satisfies the target convergence condition, to determine the target feasibility condition corresponding to the target capacity set based on the target constraint set and the target approximate region, and to assess the feasibility of the test operation point of the target power grid based on the target feasibility condition, so as to determine whether to trigger the power grid dispatch control center to perform dispatch operations on the target power grid based on the feasibility assessment result.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the power grid dispatch feasibility determination method 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, wherein the computer program, when executed by a processor, implements the power grid dispatch feasibility determination method as described in any one of claims 1 to 7.

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