Multi-operation-mode section power transmission capacity evaluation method, device, equipment and medium

Abstract

The invention provides a multi-operation-mode section power transmission capacity evaluation method, device and equipment and a medium, and relates to the technical field of power systems, and the method comprises the steps: building a multi-operation-mode model based on the operation parameters of power equipment; according to the multi-operation mode model, constructing a main problem optimization model which aims at minimizing the operation cost of the power system and maximizing the power transmission capacity of the section to be evaluated; according to the optimization target of the main problem optimization model, designing a double-layer robust sub-problem model for quantifying the reliability margin of the to-be-evaluated section; iteratively solving the main problem optimization model and the double-layer robust sub-problem model to obtain an available power transmission capacity evaluation model; a multi-target clustering algorithm is adopted to classify the multi-operation-mode sample set, and clustering agent points and probability weights of the clustering agent points are obtained; and integrating the evaluation model and the clustering result, and outputting an evaluation result of the to-be-evaluated section. According to the method, the hostile scene is captured through the double-layer robust sub-problem model, and the accuracy of the obtained evaluation result is improved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, equipment and medium for evaluating cross-sectional transmission capacity under multiple operating modes. Background Technology

[0002] With the development of new energy power generation technologies, wind power, which is characterized by high volatility and uncertainty, has been connected to the grid on a large scale. Simultaneously, the load side is also affected by large-scale power loads such as new energy electric vehicles, increasing uncertainty and leading to increasingly complex system operation modes. Furthermore, considering the political and environmental restrictions on transmission line operation, a large number of transmission lines operate at marginal rated values. Against this backdrop, grid dispatching departments face a severe challenge in accurately assessing the available transmission capacity of transmission lines. Therefore, it is urgent to assess the available transmission capacity of transmission lines operating under multiple modes to ensure that transmission lines operate at reliable rated values.

[0003] Currently, most industry practices assess cross-sectional transmission capacity using numerical optimization methods and machine learning models, with few considerations for multiple operating modes. Furthermore, common transmission capacity assessment methods (such as Monte Carlo simulation (MCS)) are computationally intensive and inefficient, struggling to respond to assessment needs in real time. They also fail to quantify the uncertainties of wind power and load, and do not adequately consider scenarios of engine shutdown and severe load fluctuations, leading to overly optimistic assessment results and increasing the risk of line overload. Traditional clustering methods (such as k-means clustering) require manual setting of the number of clusters, demanding high skill levels from the operator. While traditional analytical approximation methods offer low computational cost, they neglect nonlinear constraints and underestimate tail risks. Deep learning-based black-box models, while improving computational speed, lack reliability and are difficult for dispatchers to trust. Additionally, traditional static line rating methods ignore weather effects, resulting in a significant underestimation of actual available capacity.

[0004] Therefore, there is an urgent need for a method that can accurately assess the cross-sectional transmission capacity under multiple operating modes. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for evaluating the cross-sectional transmission capacity under multiple operating modes, mainly to solve the problem of low accuracy in the evaluation results of existing methods for evaluating the cross-sectional transmission capacity under multiple operating modes. The technical solution is as follows: Firstly, a method for assessing the cross-sectional transmission capacity under multiple operating modes is provided, including: A multi-mode model of the power system is constructed based on the operating parameters of the power equipment. The multi-mode model is used to reflect the uncertainty of the power system. Based on the multi-operation mode model, a main problem optimization model is constructed with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated. The Benders decomposition method is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model to obtain the available transmission capacity assessment model of the section to be evaluated. A preset multi-objective clustering algorithm is used to process the sample set generated by the multi-operation mode model to obtain clustering surrogate points and their probability weights that represent typical operation modes. Based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, the expected value and probability distribution of the available transmission capacity of the section to be evaluated are calculated, and the expected value and probability distribution of the available transmission capacity are output as the evaluation result.

[0006] Secondly, a multi-mode cross-sectional transmission capacity assessment device is provided, comprising: The first construction module is used to construct a multi-operation mode model of the power system based on the operating parameters of the power equipment. The multi-operation mode model is used to reflect the uncertainty of the power system. The second construction module is used to construct a main problem optimization model based on the multi-operation mode model, with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated. The third construction module is used to design a two-layer robust sub-problem model for quantifying the reliability margin of the cross section to be evaluated, based on the optimization objective of the main problem optimization model. The fourth construction module is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model using the Benders decomposition method to obtain the available transmission capacity assessment model of the section to be evaluated. The data classification module is used to process the sample set generated by the multi-operation mode model using a preset multi-objective clustering algorithm to obtain cluster surrogate points and their probability weights that represent typical operation modes. The result generation module is used to calculate the expected value and probability distribution of the available transmission capacity of the section to be evaluated based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, and output the expected value and probability distribution of the available transmission capacity as the evaluation result.

[0007] Thirdly, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the multi-operation mode cross-sectional transmission capacity assessment method described in any of the preceding claims.

[0008] Fourthly, a storage medium is provided that stores a computer program, wherein the computer program is configured to execute the cross-sectional transmission capacity assessment method with multiple operating modes as described above when running.

[0009] The technical solutions provided in this application can achieve the following technical effects: (1) First, this application constructs a multi-operation mode model of the power system by considering various operating modes such as the uncertainty of wind turbine output and load fluctuation, so as to comprehensively characterize the uncertainty of power system operation. This model provides a realistic and diverse operating scenario basis for subsequent refined evaluation.

[0010] (2) Secondly, various adverse scenarios are captured by a two-layer robust sub-problem model, and the transmission reliability margin (TRM) and capacity reliability margin (CBM) are quantified accordingly to improve the robustness of the available transmission capacity model of the section to be evaluated, thereby improving the accuracy of the evaluation results.

[0011] (3) Finally, by employing a sequential game-based multi-objective clustering algorithm, the optimal clustering scheme is automatically determined, and a single-step non-zero-sum game is constructed to exchange low-similarity samples to further optimize the multi-objective clustering algorithm. This avoids the blindness of manually setting clustering schemes and further improves the accuracy of the obtained evaluation results. At the same time, the use of the multi-objective clustering algorithm also reduces the time consumed in cross-sectional evaluation, making the evaluation method of this application more efficient than traditional evaluation methods. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. In the drawings: Figure 1 This is a flowchart of a method for evaluating the cross-sectional power transmission capacity under multiple operating modes according to an embodiment of this application; Figure 2 This is an example diagram of the test system for scenario 1 in the method embodiments of this application; Figure 3 yes Figure 2 Wind speed data graphs for two typical wind farm sites in the scenario; Figure 4 yes Figure 2 Example diagram of ATC convergence when using the MCS method to solve a scenario; Figure 5 yes Figure 2 Example diagram showing the impact of CBM robust budget on the expected cross-sectional available transmission capacity ATC and CBM values ​​in a scenario; Figure 6 yes Figure 2Example diagram showing the impact of fixing the CBM robust budget to 1 on the expected cross-sectional available transmission capacity ATC and TRM values ​​in a scenario; Figure 7 yes Figure 2 Example diagram of ATC convergence of cross-section available transmission capacity after 3000 iterations using the MCS method in a scenario; Figure 8 yes Figure 2 The cumulative distribution function curve obtained from the static line model in the application scenario; Figure 9 yes Figure 2 The cumulative distribution function curve obtained from the application dynamic circuit model in the scenario; Figure 10 This is an example diagram of the test system in scenario 2 of the method embodiments of this application; Figure 11 yes Figure 10 The probability map of available transmission capacity for cross-sections in the scenario; Figure 12 This is a structural block diagram of a cross-sectional power transmission capacity assessment device with multiple operating modes according to an embodiment of this application; Figure 13 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0013] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0015] This application provides a method for evaluating the cross-sectional transmission capacity under multiple operating modes, such as... Figure 1 As shown, the method includes the following steps S101 to S106: Step S101: Construct a multi-operation mode model of the power system based on the operating parameters of the power equipment. The multi-operation mode model is used to reflect the uncertainty of the power system.

[0016] In this embodiment, wind turbines and loads are collectively referred to as power equipment. Therefore, the operating parameters of power equipment include the operating parameters of wind turbines and loads. The operating parameters of wind turbines include the actual output of the wind turbine unit, power generation, cut-in wind speed, and slope and intercept when the power generation exceeds the cut-in wind speed. The operating parameters of loads include load power, load variance, and expected load value. These parameters can be obtained by real-time data acquisition and processing from power equipment, or they can be obtained from a database specifically designed to manage power equipment-related data. This database is pre-established and used to store data related to power equipment.

[0017] First, a real-time power output sub-model of the wind turbine is constructed based on its operating parameters, specifically as follows: (1) (2) in, Represents the actual output of the wind turbine unit. For power generation, To cut into wind speed, The slope of the power output curve when the power generation is greater than the cut-in wind speed. This is the intercept of the power output curve when the power generation is greater than the cut-in wind speed.

[0018] Simultaneously, a stochastic load probability density sub-model is constructed based on the load's operating parameters, specifically as follows: (3) in, Indicates the load variance. Indicates load power. This represents the expected load value.

[0019] By combining the above-mentioned wind turbine real-time output sub-model and stochastic load probability density sub-model, a multi-operation mode model is obtained. This multi-operation mode model is used to reflect the uncertainty of the power system, that is, the uncertainty of wind turbine output and the fluctuation of load are all included in the model. This model represents various operating scenarios of the power system.

[0020] The aforementioned power system refers to the power grid that includes wind turbines, loads, and transmission lines.

[0021] Step S102: Construct a main problem optimization model based on the multi-operation mode model, with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated.

[0022] First, to ensure the stability of the power system, a multi-mode operation model is used to simulate the actual operation of the power system, obtaining the conventional generator output limit, node power balance, line static capacity, and line dynamic capacity based on transmission line tension values. Among these, the conventional generator output limit is: (4) in, , These represent the minimum and maximum output values ​​of a conventional generator, respectively. This represents the reference output value of a conventional generator. This represents the actual output value obtained by adding the output adjustment value Δ to the baseline output value. The output adjustment value Δ is set to balance the fluctuations of the fan and the load.

[0023] Node power balance is as follows: (5) in, , They respectively represent connections to the first The reference output and actual output of the generator on the busbar Indicates connection to the first The total output of the wind farm on the busbar, For the position located at the The output value of the load on the busbar, For the position located at the The output value of the load in the maximum transfer box on the busbar. This represents the power output of the transmission line under normal conditions. This represents the maximum power of the transmission box in the transmission line.

[0024] The static capacity of the line is: (6) in, It is used for constraints and The values ​​used are pre-set values.

[0025] The dynamic capacity of the transmission line based on the transmission line tension value is: (7) (8) in, This indicates that the tension value H of the sp-th span of the l-th transmission line can be obtained. This represents the coefficient of length expansion, where S and A represent the span length and conductor area, respectively. For the weight of the conductor, w, , These represent the weight of the conductor, the wind speed at the sp-th span, and the wind direction at that point, respectively. Furthermore, based on the line resistance versus temperature curve, the multi-operation mode model provides a mathematical model of the resistance of the l-th transmission line at the sp-th span. These are heat transferred by convection, heat radiated to the surrounding air, and heat gained from solar radiation. All are related to weather conditions across the span and are calculated using the IEEE thermal balance standard. In other words, formula (8) is used to limit the tension of each critical span of the transmission line.

[0026] Based on the above constraints, the main problem optimization model is constructed as follows: (9) in, , All represent positive factors. It is designed to minimize the operating costs of the power system. It is set with the consideration of maximizing the transmission capacity of the section to be evaluated. ATC means the available transmission capacity that the section to be evaluated should achieve under the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated.

[0027] Step S103: Based on the optimization objective of the main problem optimization model, design a two-layer robust subproblem model to quantify the reliability margin of the section to be evaluated.

[0028] To achieve the goals of minimizing power system operating costs and maximizing the transmission capacity of the section under evaluation, it is necessary to ensure that the available transmission capacity of the section under evaluation is maximized. Specifically, the relationship between the available transmission capacity (ATC) and the transmission capacity (TTC), transmission reliability margin (TRM), and capacity reliability margin (CBM) of the section under evaluation is as follows: (10) Among them, the transmission capacity TTC meets the following requirements: (11) It is evident that maximizing available transmission capacity (ATC) is influenced not only by transmission capacity (TTC) but also by transmission reliability margin (TRM) and capacity reliability margin (CBM). This embodiment designs a two-layer robust subproblem model in order to select appropriate transmission reliability margin (TRM) and capacity reliability margin (CBM).

[0029] The two-layer robust subproblem model includes the CBM evaluation subproblem model and the TRM evaluation subproblem model. The CBM evaluation subproblem model is designed as follows: Upper layer: Generate the worst-case generator outage scenario:

[0030] Lower layer: Minimize load shedding and ensure power supply reliability at the load end:

[0031] in, Representing the Bus load reduction value, robust budget Represents the maximum number of generators that can be shut down; a binary variable. Representative of the regional generator Out of service status (0 = out of service).

[0032] The TRM assessment sub-problem model is designed as follows: Upper layer: Generate the worst-case load fluctuation scenario:

[0033] Lower layer: Optimize scheduling to reduce the risk of load shedding.

[0034] in, For load fluctuation scaling factor, robust budget This represents the total allowable load fluctuation.

[0035] Step S104: The Benders decomposition method is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model to obtain the available transmission capacity assessment model of the section to be evaluated.

[0036] Benders decomposition is an efficient mathematical programming method for handling complex optimization problems. Its core idea is to decompose the original problem into a main problem and one or more subproblems. The main problem is responsible for deciding the core objective and providing preliminary solutions, while the subproblems verify feasibility or calculate to assist decision-making based on the solution of the main problem. They also feed back linear constraints called "Benders cut sets" to the main problem, thus iteratively approximating the global optimum.

[0037] In this embodiment, the Benders decomposition method aims to minimize operating costs and maximize cross-sectional transmission capacity in the main problem optimization model, providing an initial available transmission capacity value. Subsequently, a two-layer robust subproblem model is activated. Upon activation, the upper layer of the CBM (Continuous Benchmarking Model) evaluates the worst-case scenarios, such as wind turbine fluctuations, and the upper layer of the TRM (Total Reliability Margin) evaluates the worst-case scenarios, such as load changes. The lower layers of both verify the safety and stability of the power system under these worst-case scenarios and calculate the actual reliability margin. The two-layer robust subproblem model feeds back the verification results to the main problem optimization model in the form of Benders cut sets, which the main problem optimization model then uses to adjust its model parameters. This process iterates until both the transmission capacity value given by the main problem optimization model and the reliability margin calculated by the two-layer robust subproblem model converge stably.

[0038] The available transmission capacity assessment model obtained by iteratively solving the above Benders decomposition method no longer provides a single, deterministic transmission capacity value, but rather an optimal decision embedded in dealing with uncertain scenarios. That is, it can ensure the maximization of the transmission capacity of the section to be evaluated under all possible worst operating scenarios, thus achieving a balance between economy and robustness.

[0039] Step S105: Using a preset multi-objective clustering algorithm, the sample set generated by the multi-operation mode model is processed to obtain clustering surrogate points and their probability weights that represent typical operation modes.

[0040] First, a multi-objective clustering algorithm is established in advance. The process of establishing this algorithm is as follows: Initialize clustering. This includes setting the initial number of clusters. , where n represents the sample size, and then based on the dissimilarity index Select cluster proxy point:

[0041] in, For the sample The dissimilarity index is mainly used for selecting initial cluster centers. For clusters The surrogate point, i.e., the cluster center. That is, the turbine output and load demand in the multi-mode operation model are used as a sample set, and the elements in the sample set are then categorized. In this embodiment, the categorization is performed by assigning elements in the sample set to the nearest surrogate point using a threshold parameter, specifically:

[0042] in, For clusters The set of neighboring clusters.

[0043] Based on the multi-objective clustering algorithm described above, this embodiment employs sequential game theory to resolve clustering conflicts. First, a conflict cluster set is constructed:

[0044] in, A set of conflict clusters, that is, clusters that compete for the same sample, also known as conflict cluster set.

[0045] Next, a multi-objective game is designed, which includes players, strategies, and payoff functions. Players are the decision-makers, strategies are the players' decision variables and feasible means, and payoff functions are the players' value guides. In this embodiment, by setting clustering proxy points as players and assigning them strategies to compete for samples, and designing a vector payoff function that balances separation and connectivity, clustering conflicts can be resolved intelligently and automatically, finding the optimal sample allocation scheme that achieves a balance among multiple clustering objectives. The players, strategies, and payoff functions are introduced below.

[0046] Players: Clustering proxy points for competing samples.

[0047]

[0048] in, Gathering for players, For clusters A set of competing samples.

[0049] Strategy: Compete for boundary samples to optimize the objective function. The objective function is as follows:

[0050] in, For cluster separation With connectivity A multi-objective function.

[0051] Payment function: Synchronously maximize cluster connectivity With resolution Specifically:

[0052] in, This is the player's payment function vector.

[0053] Finally, using Nash equilibrium, the competition among players for boundary samples is simulated. Each player evaluates the payoff of different strategies based on their multi-objective payoff function (i.e., considering both improving cluster separation and connectivity), specifically the payoff for competing for or abandoning a sample. When the iterative simulation reaches a state where all players find that, under the current sample allocation scheme, competing for or abandoning any remaining samples no longer improves their overall payoff, the final sample allocation scheme is determined. This final allocation scheme represents the optimal balance between multiple clustering objectives, such as separation and connectivity.

[0054] Furthermore, cluster homogeneity optimization is achieved by constructing a single-step non-zero-sum game to exchange low-similarity samples. Specifically, the single-step non-zero-sum game to exchange low-similarity samples is as follows:

[0055] in, The set of samples to be exchanged refers to samples whose distance to neighboring clusters is less than their distance to the current cluster.

[0056] The goal of this study is to minimize intra-cluster inertia by exchanging low-similarity samples using a single-step non-zero-sum game. , It is a metric for measuring the compactness within a cluster. The smaller the value, the more similar the samples within the same cluster are to each other, and the closer they are to the cluster center, indicating better homogeneity and higher quality of the cluster. Specifically, cluster inertia... The calculation formula is:

[0057] in, This indicates the sample set during t iterations. Samples in 、 The distance between them.

[0058] This embodiment uses a multi-objective clustering algorithm to process the sample set to obtain... There are *n* cluster surrogate points, and the probability weight of each cluster surrogate point is:

[0059] in, For clusters The probability weights.

[0060] Therefore, it can be seen that by using multi-objective clustering algorithms, a few representative typical scenarios can be intelligently extracted from the massive random operating scenarios covered by the multi-operation mode model, and the probability of each typical scenario occurring can be calculated, thereby simplifying the subsequent evaluation work. By conducting in-depth analysis of these typical scenarios, the probabilistic characteristics of the entire power system can be grasped.

[0061] Step S106: Based on the available transmission capacity assessment model and cluster surrogate points and their probability weights, calculate the expected value and probability distribution of the available transmission capacity of the section to be assessed, and output the expected value and probability distribution of the available transmission capacity as the assessment result.

[0062] Based on the results obtained in step S105 For each cluster surrogate point, perform the following operations: First, the two-layer robust subproblem in the available transmission capacity assessment model is transformed into a single-layer optimization, where the capacity reliability margin (CBM) and transmission reliability margin (TRM) need to satisfy the following:

[0063]

[0064] Among them, dual variables , The KKT multiplier is a power balance and load reduction constraint.

[0065] Under the premise that the two-layer robust subproblem satisfies the above formulas (25) and (26), a definite main problem optimization model is obtained. Then, by combining the main problem optimization model and the two-layer robust subproblem model, an evaluation model for the available transmission capacity of the section to be evaluated under each typical scenario is obtained.

[0066] Then, the Benders decomposition method is used to iteratively solve the available transmission capacity assessment model for each typical scenario, obtaining the expected value and probability distribution of the available transmission capacity of the section to be assessed, specifically: First, calculate the expected value of available transmission capacity using the following formula:

[0067] in, This is the expected value of available transmission capacity, which is the most likely available transmission capacity of the section to be evaluated after considering all typical operating modes and their probabilities of occurrence. For the first The available power transmission capacity under typical operating conditions represented by each cluster agent point.

[0068] Then, the standard deviation of the available transmission capacity and the expected value of the available transmission capacity for each cluster surrogate point is calculated using the following formula:

[0069] in, For the first The standard deviation of the available transmission capacity and the expected value of the available transmission capacity of each cluster agent point.

[0070] Therefore, the calculated expected value of available transmission capacity is used to represent the average or most likely level of transmission capacity of the section to be evaluated, while the standard deviation quantifies the range of fluctuation or degree of uncertainty around this expected value and is a key indicator for risk assessment. Typically, when assessing the transmission capacity of a section to be evaluated, this standard deviation is used to define the probability distribution of the transmission capacity: a small standard deviation indicates that the assessment results are stable and reliable, and the transmission capacity is likely close to the expected value; a large standard deviation indicates that under the influence of uncertainty, the actual transmission capacity may deviate significantly from the expected value, posing a higher operational risk.

[0071] In this embodiment, based on the obtained expected value and standard deviation of available transmission capacity, a cumulative distribution function curve of available transmission capacity based on clustered surrogate points and their probability weights is plotted. This allows assessors to intuitively view the distribution of transmission capacity under typical operating modes represented by each clustered surrogate point, and to directly read the robust transmission capacity value of the section at any confidence level, thereby realizing a visual assessment of the transmission risk of the section.

[0072] To facilitate the explanation of the cross-sectional transmission capacity assessment method for multiple operating modes in the embodiments of this application, and to illustrate the refined assessment capability of traditional methods in multiple operating mode scenarios, the following uses two actual scenarios as examples.

[0073] Scene 1: like Figure 2 As shown, this scenario uses an improved IEEE 118 bus test system, which includes three areas. Area 1 and Area 3 are the source areas, and Area 2 is the sink area. Additionally, five wind farms, W1, W2, W3, W4, and W5, are connected to nodes 17, 44, 54, 82, and 86 respectively. The expected load values ​​for the remaining nodes are the original system data. Each wind farm consists of 100 wind turbines, and the cut-in wind speed for each turbine is Ws = 5 m / s, k1 = 200000, b = -1000000, and the load variance is... =0.05. Meanwhile, 1000 hours of wind speed data recorded at two typical wind farm sites were used as real-time random wind speed data from nature, and this data was applied to model the real-time random power output of the wind turbines. The 1000 hours of wind speed data used are as follows: Figure 3 As shown.

[0074] In this scenario, varying wind farm output power and load demand constitute multiple operating modes. It is assumed that the transmission line capacity equals the static line rated capacity. The first step is to evaluate the available transmission capacity (ATC) of the cross-section using Monte Carlo simulation (MCS). This evaluation is to calculate the effectiveness of the proposed method and other clustering techniques. Figure 4 The ATC convergence results for the available transmission capacity of a cross section after 3000 iterations using the MCS method are shown. Figure 4 It can be seen that the expected value of the available transmission capacity of the section obtained by the MCS method is 668MW. In the evaluation of the expected value of the available transmission capacity of the section, it is assumed that the objective function factor is α1=α2=0.5. In addition, the CBM robust budget and the TRM robust budget are fixed at 1% and 20% of the total load demand, respectively. By solving the proposed ATC model and considering that the value of the TRM robust budget is 20% of the total load demand, the impact of the CBM robust budget on the expected available transmission capacity of the section ATC and CBM values ​​is obtained, and thus the following is obtained. Figure 5 By fixing the CBM robust budget to 1, the impact of the TRM robust budget on the expected cross-sectional available transmission capacity (ATC) and TRM values ​​was obtained, thereby yielding... Figure 6 .according to Figure 5 and Figure 6 It is evident that increasing the robust budget can reduce the expected cross-sectional available transmission capacity (ATC) and risk. In the probabilistic cross-sectional available transmission capacity (ATC) assessment based on the MCS method, the expected value of the cross-sectional available transmission capacity obtained using the proposed cross-sectional available transmission capacity model is 663.34 MW.

[0075] If the transmission line capacity connecting the wind farm to the source area system and the transmission line capacity connecting the source area to the sink area are further defined as dynamic line capacity, then the number of uncertain parameters increases compared to the above. Figure 7 The convergence of the available transmission capacity (ATC) of the cross-section using the MCS method for 3000 iterations is described. The expected value of the available transmission capacity after convergence is 776MW, which is 108MW higher than the previously mentioned value. In the ATC assessment of the available transmission capacity of the cross-section, the objective function factors are assumed to be α1 = α2 = 0.5. Furthermore, the robust budgets for CBM and TRM account for 1% and 20% of the total grid area demand, respectively. A tension-based dynamic line model is used to highlight the impact of the dynamic line model on the expected value of the available transmission capacity of the cross-section. The results are compared with those of a dynamic line model based on thermal balance and a prediction-based dynamic line model. The comparison results are shown in Table 1.

[0076] As shown in Table 1, the dynamic circuit model based on tension used in this application has higher accuracy in calculation results than the general dynamic circuit model.

[0077] Finally, to verify the performance of the multi-operation mode cross-sectional transmission capacity assessment method of this application, static line models and dynamic line models were applied respectively, and their effects were compared with the ATC assessment methods based on the self-organizing map network (SOM) method and the Latin hypercube sampling (LHS) method. The comparison results are shown in Table 2.

[0078] As can be seen from Table 2, the evaluation method of this application is more accurate than other evaluation methods in evaluating probabilistic ATC. Furthermore, compared with other evaluation methods, the method of this application requires less computation time to obtain the expected value of the available transmission capacity of a section. Finally, the cumulative distribution function curves obtained by performing the evaluation method of this application and the other evaluation methods are shown in Table 2. Figure 8 , Figure 9 As shown. Among them, Figure 8 The cumulative distribution function curve obtained by applying the static line model is shown in the figure. Figure 9 To apply the cumulative distribution function curve obtained from the dynamic line model, analysis was performed. Figure 8 and Figure 9 It can be seen that, compared with other methods, the cumulative distribution function curve obtained by applying the evaluation method of this application has the best agreement with the MCS.

[0079] Scene 2: like Figure 10 As shown, this scenario uses an improved IEEE 39-node test system. This system connects nodes 3, 4, 8, 16, 20, 24, 27, and 36 to wind farms. Each wind farm has 100 wind turbines, and the cut-in wind speed for each turbine is 3.5 m / s, k1 = 200000, b = -700000. The transmission section consists of lines 2-3, 5-4, 13-14, 19-16, 23-24, 22-21, and 25-26. The available transmission capacity (ATC) of the section is the sum of the available transmission capacities of the above lines. The system is divided into Area 1 and Area 2, where Area 1 is the source area and Area 2 is the sink area. The expected load data for the remaining nodes in this system are the same as those in the original system, with a load variance of 0.1. Furthermore, the wind speed data for wind farms connected to nodes 3 and 4 are the same as the wind speed data for W1 in scenario 1; the wind speed data for wind farms connected to nodes 8 and 16 are the same as the wind speed data for W2 in scenario 1; the wind speed data for wind farms connected to nodes 20 and 24 are the same as the wind speed data for W3 in scenario 1; the wind speed data for wind farm connected to node 27 is the same as the wind speed data for W4 in scenario 1; and the wind speed data for wind farm connected to node 36 is the same as the wind speed data for W5 in scenario 1. All other parameter settings are also the same as in scenario 1.

[0080] In scenario 2, the evaluation method of this application can be used to obtain a probability map of the available transmission capacity of the cross section, as shown below. Figure 11 As shown, by Figure 11 It is understood that the systems applicable to this application are not limited to the IEEE 118-node system, but are also applicable to the IEEE 39-node system, thus having a wide range of applications.

[0081] In summary, the implementation principle of the multi-operation mode cross-sectional transmission capacity assessment method provided in this application is as follows: Considering the actual impact of wind turbine output uncertainty and load fluctuation on the power system, a multi-operation mode model of the power system is modeled. Then, a main problem optimization model is established with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the cross-section to be assessed. Based on the main problem optimization model, a two-level robust sub-problem model is designed to quantify the reliability margin of the cross-section to be assessed. By iteratively solving the main problem optimization model and the two-level robust sub-problem model, the available transmission capacity assessment model of the cross-section to be assessed is obtained. Simultaneously, a sequential game multi-objective clustering algorithm is constructed. This algorithm is used to classify the sample set output by the multi-operation mode model, and then the distribution of the available transmission capacity of the cross-section under the typical operation mode represented by each cluster surrogate point after classification is calculated, thereby achieving an accurate assessment of the available transmission capacity of the cross-section.

[0082] It should be noted that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, all the above possible implementation methods can be arbitrarily combined in a combined manner to form possible embodiments of this application, which will not be described in detail here.

[0083] Based on the above embodiments, which provide a method for evaluating the cross-sectional power transmission capacity under multiple operating modes, this application also provides a device for evaluating the cross-sectional power transmission capacity under multiple operating modes, based on the same inventive concept.

[0084] Figure 12 This is a structural block diagram of a cross-sectional power transmission capacity assessment device with multiple operating modes provided in an embodiment of this application. For example... Figure 12 As shown, the device may specifically include a first building module, a second building module, a third building module, a fourth building module, a data classification module, and a result generation module.

[0085] The first construction module is used to build a multi-operation mode model of the power system based on the operating parameters of the power equipment. The multi-operation mode model is used to reflect the uncertainty of the power system.

[0086] The second building module is used to construct a main problem optimization model based on the multi-operation mode model, with the goal of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated.

[0087] The third building module is used to design a two-layer robust subproblem model for quantifying the reliability margin of the cross section to be evaluated, based on the optimization objective of the main problem optimization model.

[0088] The fourth module is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model using the Benders decomposition method to obtain the available transmission capacity assessment model of the section to be evaluated.

[0089] The data classification module is used to process the sample set generated by the multi-operation mode model using a preset multi-objective clustering algorithm to obtain cluster surrogate points and their probability weights that represent typical operation modes.

[0090] The results generation module is used to calculate the expected value and probability distribution of the available transmission capacity of the section to be evaluated based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, and output the expected value and probability distribution of the available transmission capacity as the assessment result.

[0091] This embodiment provides a cross-sectional power transmission capacity assessment device with multiple operating modes, used to execute the cross-sectional power transmission capacity assessment method with multiple operating modes provided in the above embodiment. Its implementation method and principle are the same. For details of the implementation method of each module, please refer to the relevant description of the above method embodiment, which will not be repeated here.

[0092] Based on the same inventive concept, this application also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a method for evaluating cross-sectional power transmission capacity in multiple operating modes according to any of the above embodiments.

[0093] In an exemplary embodiment, an electronic device is provided, such as Figure 13 As shown, Figure 13 The illustrated electronic device 1300 includes a processor 1301 and a memory 1303. The processor 1301 and the memory 1303 are connected, for example, via a bus 1302. Optionally, the electronic device 1300 may also include a transceiver 1304. It should be noted that in practical applications, the transceiver 1304 is not limited to one type, and the structure of this electronic device 1300 does not constitute a limitation on the embodiments of this application.

[0094] Processor 1301 may be a CPU (Central Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 1301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0095] Bus 1302 may include a pathway for transmitting information between the aforementioned components. Bus 1302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 1302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 13 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0096] The memory 1303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0097] The memory 1303 is used to store computer program code that executes the scheme of this application, and its execution is controlled by the processor 1301. The processor 1301 is used to execute the computer program code stored in the memory 1303 to implement the content shown in the foregoing method embodiments.

[0098] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 13 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0099] Based on the same inventive concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute a multi-operation mode cross-sectional power transmission capacity evaluation method of any of the above embodiments when running.

[0100] Those skilled in the art will clearly understand that the specific working process of the systems, devices, and modules described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0101] Those skilled in the art will understand that the technical solution of this application, or all or part of it, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several program instructions to cause an electronic device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of this application when running the program instructions. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0102] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as electronic devices like personal computers, servers, or network devices) associated with program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0103] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of this application, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to leave the protection scope of this application.

Claims

1. A method for evaluating the cross-sectional transmission capacity under multiple operating modes, characterized in that, include: A multi-mode model of the power system is constructed based on the operating parameters of the power equipment. The multi-mode model is used to reflect the uncertainty of the power system. Based on the multi-operation mode model, a main problem optimization model is constructed with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated. Based on the optimization objective of the main problem optimization model, a two-layer robust sub-problem model is designed to quantify the reliability margin of the cross section to be evaluated. The Benders decomposition method is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model to obtain the available transmission capacity assessment model of the section to be evaluated. A preset multi-objective clustering algorithm is used to process the sample set generated by the multi-operation mode model to obtain clustering surrogate points and their probability weights that represent typical operation modes. Based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, the expected value and probability distribution of the available transmission capacity of the section to be evaluated are calculated, and the expected value and probability distribution of the available transmission capacity are output as the evaluation result.

2. The method according to claim 1, characterized in that, The operating parameters of the power equipment include the operating parameters of the wind turbine and the operating parameters of the load. The construction of a multi-mode power system model based on these operating parameters includes: A real-time power output sub-model of the wind turbine is constructed based on the operating parameters of the wind turbine; Construct a random load probability density sub-model based on the operating parameters of the load; The multi-operation mode model is obtained by combining the real-time output sub-model of the wind turbine and the probability density sub-model of the random load.

3. The method according to claim 2, characterized in that, The real-time output sub-model of the wind turbine is as follows: ； ； in, Represents the actual output of the wind turbine unit. For power generation, To cut into wind speed, The slope of the power output curve when the power generation is greater than the cut-in wind speed. This is the intercept of the power output curve when the power generation is greater than the cut-in wind speed.

4. The method according to claim 2, characterized in that, The probability density sub-model for the random load is: ； in, Indicates the load variance. Indicates load power. This represents the expected load value.

5. The method according to claim 1, characterized in that, Based on the aforementioned multi-operation mode model, a master problem optimization model is constructed with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated. This model includes: The constraints are obtained through the multi-operation mode model, including conventional generator output limits, node power balance, line static capacity, and line dynamic capacity based on transmission line tension values. Based on the constraints, a master problem optimization model is constructed with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated.

6. The method according to claim 1, characterized in that, The two-layer robust subproblem model includes a CBM evaluation subproblem model and a TRM evaluation subproblem model. The two-layer robust subproblem model designed to quantify the reliability margin of the cross section to be evaluated includes: The CBM evaluation sub-problem model is constructed. The upper-level optimization objective of the model is to generate the worst-case generator outage scenario under the preset robust budget constraint. The lower-level optimization objective is to minimize the load reduction by adjusting the generator output under the worst-case generator outage scenario. The TRM evaluation sub-problem model is constructed. The upper-level optimization objective of this model is to generate the worst load fluctuation scenario under the preset robust budget constraint. The lower-level optimization objective is to minimize the load reduction risk by optimizing scheduling under the worst load fluctuation scenario.

7. The method according to claim 6, characterized in that, The Benders decomposition method is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model to obtain the available transmission capacity assessment model for the section to be evaluated, including: The capacity reliability margin CBM is obtained through the CBM evaluation sub-problem model. The transmission reliability margin (TRM) is obtained through the TRM evaluation sub-problem model. Based on the capacity reliability margin (CBM) and the transmission reliability margin (TRM), an assessment model for available transmission capacity is constructed.

8. The method according to claim 7, characterized in that, The available transmission capacity assessment model is as follows: ； Where ATC represents the available transmission capacity output by the available transmission capacity assessment model, and TTC represents the transmission capacity of the section to be assessed, and , , They respectively represent connections to the first The reference output and actual output of the generators on the busbar, TRM represents the transmission reliability margin, and CBM represents the capacity reliability margin.

9. The method according to claim 6, characterized in that, The CBM evaluation sub-problem model is as follows: The upper level represents the worst-case scenario of generator outage: ； The lower layer minimizes load reduction: ； in, Representing the Bus load reduction value, robust budget Represents the maximum number of generators that can be shut down; a binary variable. Representative of the regional generator The service is currently suspended.

10. The method according to claim 6, characterized in that, The TRM evaluation sub-problem model is as follows: The upper layer represents the worst-case load fluctuation scenario: ； The lower layer needs to mitigate the risk of load shedding: ； in, For load fluctuation scaling factor, robust budget This represents the total allowable load fluctuation.

11. The method according to claim 1, characterized in that, The process employs a pre-defined multi-objective clustering algorithm to process the sample set generated by the multi-operation mode model, obtaining clustering surrogate points representing typical operation modes and their probability weights, including: The multi-objective clustering algorithm is invoked to classify the elements in the sample set; The allocation scheme of the multi-objective clustering algorithm is optimized using multi-objective game theory; A single-step non-zero-sum game is constructed to exchange low-similarity samples, and the allocation scheme of the multi-objective clustering algorithm is optimized again using these samples to obtain cluster surrogate points and their probability weights.

12. The method according to claim 11, characterized in that, The multi-objective game includes players, strategies, and payoff functions; We use Nash equilibrium to solve the allocation scheme of a multi-objective clustering algorithm in a multi-objective game.

13. The method according to claim 1, characterized in that, Based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, including: The two-layer robust subproblem in the available transmission capacity assessment model is transformed into a single-layer optimization, resulting in an available transmission capacity assessment model for the section to be assessed under each typical scenario. The Benders decomposition method is used to iteratively solve the available transmission capacity assessment model of the section to be evaluated under each typical scenario, so as to obtain the expected value and probability distribution of the available transmission capacity of the section to be evaluated.

14. The method according to claim 13, characterized in that, The formula for calculating the expected value of the available transmission capacity is as follows: ； in, This represents the expected value of available transmission capacity. For the first The available transmission capacity under typical operating conditions represented by each cluster proxy point. For the number of cluster surrogate points, For the first The probability weights of each cluster surrogate point.

15. The method according to claim 13, characterized in that, The method further includes: Calculate the available transmission capacity and the standard deviation of the expected available transmission capacity for each clustered agent point; The probability distribution of the section to be evaluated is obtained based on the standard deviation.

16. A cross-sectional transmission capacity assessment device with multiple operating modes, characterized in that, include: The first construction module is used to construct a multi-operation mode model of the power system based on the operating parameters of the power equipment. The multi-operation mode model is used to reflect the uncertainty of the power system. The second construction module is used to construct a main problem optimization model based on the multi-operation mode model, with the objectives of minimizing the operating cost of the power system and maximizing the transmission capacity of the section to be evaluated. The third construction module is used to design a two-layer robust sub-problem model for quantifying the reliability margin of the cross section to be evaluated, based on the optimization objective of the main problem optimization model. The fourth construction module is used to iteratively solve the main problem optimization model and the two-layer robust subproblem model using the Benders decomposition method to obtain the available transmission capacity assessment model of the section to be evaluated. The data classification module is used to process the sample set generated by the multi-operation mode model using a preset multi-objective clustering algorithm to obtain cluster surrogate points and their probability weights that represent typical operation modes. The result generation module is used to calculate the expected value and probability distribution of the available transmission capacity of the section to be evaluated based on the available transmission capacity assessment model and the clustering surrogate points and their probability weights, and output the expected value and probability distribution of the available transmission capacity as the evaluation result.

17. An electronic device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the cross-sectional transmission capacity assessment method of any one of claims 1 to 15.

18. A storage medium, characterized in that, The storage medium stores a computer program, wherein the computer program is configured to execute the cross-sectional transmission capacity assessment method of any one of claims 1 to 15 when running.

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