Power transmission capacity evaluation method, device and equipment of power transmission network, medium and product

Abstract

The invention discloses a power transmission capacity evaluation method, device and equipment of a power transmission network, a medium and a product. The method comprises the following steps: acquiring operation data of each power flow node of the power transmission network; and based on each operation data, establishing a full-pure embedded affine power flow model corresponding to each power flow node, and solving each full-pure embedded affine power flow model through a power series coefficient recursion method until a preset convergence condition is met, and obtaining a first voltage affine solution of a balance node, a second voltage affine solution of a PQ node, and a third voltage affine solution and a reactive power affine solution of a PV node, inputting the first voltage affine solution, the second voltage affine solution, the third voltage affine solution and the reactive power affine solution into an evaluation model, and taking maximization of the power transmission capacity of the power transmission line as a target function. Solving under constraint conditions to obtain a plurality of solving results; and evaluating the power transmission capability of the power transmission network based on each solving result. The evaluation efficiency and accuracy of the power transmission capacity of the power transmission network can be improved.

Description

Technical Field

[0001] This invention relates to the field of power transmission networks, and more particularly to methods, apparatus, equipment, media, and products for assessing the transmission capacity of power transmission networks. Background Technology

[0002] With the deepening of my country's power market reform and the continuous increase in the proportion of renewable energy generation, the power flow in the actual operation of the transmission network exhibits unprecedented volatility and uncertainty. Traditional deterministic power flow predictions are prone to significant deviations from the actual operating state of the grid, leading to a series of operational risks such as transmission congestion and insufficient system safety margins. Therefore, in the planning and operation of the power grid, the ability to efficiently and accurately assess the maximum power level that the transmission network can safely and stably transmit under uncertain conditions—that is, the available transmission capacity—has become a key technical prerequisite for ensuring the safe and stable operation of the power system, improving the utilization efficiency of transmission assets, and supporting fair and transparent electricity market transactions.

[0003] In existing methods for assessing the transmission capacity of power grids, mainstream technologies rely on optimal power flow models based on deterministic parameters and employ nonlinear numerical iterative algorithms such as the Newton-Raphson method for solution. However, these methods suffer from significant bottlenecks in assessment accuracy and computational efficiency: Regarding accuracy, their deterministic model framework struggles to effectively characterize and quantify uncertainties such as power fluctuations and load forecasting errors on both the source and load sides, leading to frequent deviations between assessment results and actual physical limits, failing to meet the precise assessment requirements under conditions of high-proportion renewable energy integration into the grid; in terms of computational efficiency, these methods require reconstructing and decomposing high-dimensional Jacobian matrices in each iteration, resulting in complex and time-consuming calculations. They are also sensitive to initial values ​​and prone to divergence under heavy system loads, failing to meet the urgent computational efficiency requirements of real-time online analysis of large-scale power grids. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for assessing the transmission capacity of a power transmission network, which can improve the efficiency and accuracy of assessing the transmission capacity of a power transmission network.

[0005] In a first aspect, an embodiment of the present invention provides a method for assessing the transmission capacity of a power transmission network, comprising:

[0006] The operation data of each power flow node in the power transmission network are obtained, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes;

[0007] Based on the aforementioned operational data, a fully embedded affine power flow model corresponding to each power flow node is established. The fully embedded affine power flow model is solved using a power series coefficient recursion method until a preset convergence condition is met, yielding the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution for the PV node. These solutions are then input into a preset evaluation model, with the objective function being the maximization of the transmission line's transmission capacity. The model is then solved under constraints, yielding several solution results. The connection relationship between the transmission line and each power flow node is determined.

[0008] The power transmission capacity of the power grid is evaluated based on the solution results.

[0009] Obtaining real operational data from each power flow node in the transmission network in this way can avoid evaluation bias caused by inaccurate data from the source, while eliminating redundant steps in secondary data processing, thus improving evaluation efficiency and accuracy. Establishing fully embedded affine power flow models for each power flow node combines the advantages of fully embedded methods and affine modeling, avoiding complex traditional solution logic while characterizing uncertainties in the power system, laying a model foundation for subsequent efficient and accurate evaluation. Solving each fully embedded affine power flow model using a power series coefficient recursion method yields the voltage affine solutions for the slack node, PQ node, and PV node, as well as the reactive power affine solution for the PV node. The power series coefficient recursion method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship, and then through successive solutions satisfying convergence conditions, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained, significantly reducing the computational load and improving solution efficiency. Furthermore, through rigorous... To ensure convergence and guarantee accurate and reliable output results, this method improves the efficiency and accuracy of subsequent transmission capacity assessments. The obtained voltage affine solutions for each power flow node and reactive power affine solutions for PV nodes are directly input into a pre-defined assessment model. The model uses maximizing the transmission capacity of transmission lines as the objective function and solves the model under constraints. Various high-precision affine solutions provide the assessment model with complete input containing uncertainty information. Combined with the objective function of maximizing transmission capacity and the constraints, the solution results accurately match the physical laws of actual transmission network operation, avoiding error accumulation in traditional linearization methods or the conservatism of convex relaxation models. Furthermore, the assessment model can directly output the solution results for each line without additional iterative verification, improving assessment efficiency and accuracy. Based on the solution results, the transmission capacity of the transmission network is assessed, ensuring the scientific validity of the assessment conclusions and simplifying the assessment process, ultimately achieving a dual improvement in the efficiency and accuracy of transmission capacity assessment. This application can improve the efficiency and accuracy of transmission network capacity assessment.

[0010] Furthermore, the step of establishing a target holomorphic embedded affine current model corresponding to each current flow node based on the aforementioned operational data specifically includes:

[0011] An initial affine power flow model of the power system is constructed based on the aforementioned operational data.

[0012] Based on the preset first embedding factor and the first voltage reference value of the equilibrium node, the initial affine power flow model is subjected to pure embedding processing to obtain the first pure embedded affine power flow model of the equilibrium node.

[0013] Based on the preset second embedding factor, the first affine active power, the first affine reactive power, and the first admittance of the PQ node, the initial affine power flow model is subjected to full pure embedding processing to obtain the second full pure embedded affine power flow model of the PQ node.

[0014] Based on the preset third embedding factor, the second affine active power of the PV node, the second voltage reference value, and the second admittance, the initial affine power flow model is subjected to full pure embedding processing to obtain the third full pure embedded affine power flow model of the PV node.

[0015] Based on the first fully embedded affine current model, the second fully embedded affine current model, and the third fully embedded affine current model, the target fully embedded affine current model is determined.

[0016] By establishing fully embedded affine power flow models for each power flow node, the advantages of fully embedded methods and affine modeling can be combined. This avoids the complex traditional solution logic and can characterize the uncertainties in the power system, laying a model foundation for subsequent efficient and accurate evaluation.

[0017] Furthermore, the step of solving each of the target holomorphic embedded affine power flow models using a power series coefficient recursive method until a preset convergence condition is met, yielding the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution, specifically includes:

[0018] Using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV node, the power series expansion of each target holomorphic embedded affine power flow model is performed to obtain the power series coefficient recursive relationship of each power flow node. The initial voltage values ​​and the initial reactive power values ​​are obtained using preset embedding values.

[0019] The power series coefficients of each power flow node are obtained by iteratively solving the recursive relationship of each power series using the initial voltage values ​​until the preset convergence condition is met.

[0020] The coefficients of each power series are summed to obtain the first voltage affine solution of the equilibrium node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution.

[0021] By solving each fully embedded affine power flow model using a power series coefficient recursive method, the voltage affine solutions for the slack node, PQ node, and PV node are obtained, as well as the reactive power affine solution for the PV node. The power series coefficient recursive method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship. Then, through successive solutions and satisfying the convergence condition, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained. This significantly reduces the amount of computation and improves the solution efficiency. Furthermore, by strictly guaranteeing convergence, the output results are ensured to be accurate and reliable, thus improving the efficiency and accuracy of subsequent transmission capacity assessment.

[0022] Furthermore, the step of using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV nodes to perform a power series expansion on each of the target holomorphic embedded affine power flow models to obtain the recursive formulas for the power series coefficients of each power flow node specifically includes:

[0023] Based on the initial voltage values ​​of each power flow node, determine the first power series expansion of the voltage affine quantity of each power flow voltage;

[0024] Based on the initial value of the reactive power of the PV node, the second power series expansion form of the reactive power affine quantity of the PV node is determined.

[0025] Based on the first power series expansion form, the second power series expansion form, and the target holomorphic embedded affine power flow model, the recursive relationship of the power series coefficients of each power flow node is determined.

[0026] This approach, which constructs two types of power series expansions based on initial voltage and reactive power values, directly incorporates the initial foundational data, avoiding errors caused by data disconnect. By combining the expansion with the target model to derive the recursive relationship, the uniqueness and convergence of the expansion are guaranteed by the analyticity of holomorphic functions. Furthermore, the uncertainty propagation path is fully preserved through affine quantities, ensuring that the recursive relationship accurately reflects the intrinsic correlation of nodal electrical quantities. This process requires no complex preprocessing, simplifies the solution logic, improves computational efficiency, and ensures the reliability of the recursive results. This provides a precise basis for subsequent step-by-step solutions, thereby improving the accuracy of the evaluation.

[0027] Furthermore, the evaluation of the power transmission capacity of the power grid based on the solution results specifically includes:

[0028] Statistical analysis was performed on the solution results to obtain the power transmission capacity distribution range;

[0029] Based on the power transmission capacity distribution range and the obtained power transmission network topology, the hub areas and edge areas of the power transmission lines are identified, so as to evaluate the differences in power transmission capacity of lines at different topological locations based on the hub areas and the edge areas.

[0030] By statistically analyzing the solution results, the distribution range of transmission capacity is determined. Combined with the transmission network topology, hubs and edge areas are identified. The transmission capacity value is associated with the physical location of the line and the network function, avoiding one-sided judgment based solely on numerical values. This can accurately depict the capacity differences of lines in different topological locations. At the same time, the structured assessment does not require redundant data interpretation, quickly locates core channels and weak links, and balances assessment efficiency, achieving a dual optimization of assessment efficiency and accuracy.

[0031] Furthermore, after obtaining several solution results, the process also includes:

[0032] Several candidate bottleneck transmission lines are selected from the solution results, wherein the solution results are the transmission capacity values ​​of the corresponding transmission lines;

[0033] Obtain the actual transmission power of each of the candidate bottleneck transmission lines;

[0034] Based on the transmission capacity value and the actual transmission power of each candidate bottleneck transmission line, several transmission differences are obtained.

[0035] Each transmission difference is determined to be less than a preset transmission threshold. If the threshold is met, the corresponding candidate bottleneck transmission line is determined as the target bottleneck transmission line, so as to monitor the operation status of the target bottleneck transmission line.

[0036] This method involves selectively identifying candidate bottleneck lines from the solution results, quantifying the difference between the transmission capacity and the actual transmission power, and using threshold judgments to determine the target bottleneck. By monitoring the operational status of the target bottleneck line, it is possible to eliminate the need for comprehensive analysis of all lines, thus improving evaluation efficiency. Quantitative calculations and threshold judgments replace the subjective biases of traditional experience-based judgments, ensuring the accuracy of bottleneck identification and achieving a dual optimization of evaluation efficiency and accuracy.

[0037] In a second aspect, an embodiment of the present invention provides a power transmission capacity assessment device for a power transmission network, characterized in that it includes a first module, a second module and a third module;

[0038] The first module is used to acquire the operating data of each power flow node in the power transmission network, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes;

[0039] The second module is used to establish a fully embedded affine power flow model corresponding to each power flow node based on the aforementioned operational data. It solves each fully embedded affine power flow model using a power series coefficient recursive method until a preset convergence condition is met, obtaining the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution. These solutions are then input into a preset evaluation model, with the objective function of maximizing the transmission capacity of the transmission line, and solved under constraints to obtain several solution results. The connection relationship between the transmission line and each power flow node is determined.

[0040] The third module is used to evaluate the power transmission capacity of the power grid based on the solution results.

[0041] By acquiring real operational data of each power flow node in the transmission network through the first module, assessment bias caused by inaccurate data can be avoided from the source, while eliminating redundant steps in secondary data processing, thus improving assessment efficiency and accuracy. The second module establishes fully embedded affine power flow models for each node, combining the advantages of fully embedded methods and affine modeling. This avoids complex traditional solution logic while characterizing uncertainties in the power system, laying a model foundation for subsequent efficient and accurate assessments. Solving each fully embedded affine power flow model using a power series coefficient recursion method yields voltage affine solutions for the slack node, PQ node, and PV node, as well as the reactive power affine solution for the PV node. The power series coefficient recursion method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship, and then through successive solutions satisfying convergence conditions, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained, significantly reducing computational load and improving solution efficiency. Strict convergence is guaranteed to ensure accurate and reliable output results, improving the efficiency and accuracy of subsequent transmission capacity assessments. The obtained voltage affine solutions for each power flow node and reactive power affine solutions for PV nodes are directly input into a pre-defined assessment model. The model uses maximizing the transmission capacity of transmission lines as the objective function, and solves the model under constraints to obtain the results. Various high-precision affine solutions provide the assessment model with complete input containing uncertainty information. Combined with the objective function of maximizing transmission capacity and the constraints, the solution results accurately match the physical laws of actual transmission network operation, avoiding the error accumulation problems of traditional linearization methods or the conservatism of convex relaxation models. Furthermore, the assessment model can directly output the solution results for each line without additional iterative verification, improving assessment efficiency and accuracy. Through the third module, the transmission capacity of the transmission network is assessed based on the solution results, ensuring the scientific validity of the assessment conclusions while simplifying the assessment process, ultimately achieving a dual improvement in the efficiency and accuracy of transmission capacity assessment.

[0042] Thirdly, another embodiment of the present invention also provides a terminal device, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0043] The memory is used to store at least one executable instruction that causes the processor to perform an operation of a power transmission capacity assessment method for a power transmission network.

[0044] Fourthly, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus where the computer-readable storage medium is located to perform a method for assessing the transmission capacity of a power transmission network.

[0045] Fifthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implement a method for evaluating the transmission capacity of a power grid. Attached Figure Description

[0046] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating one embodiment of a method for assessing the transmission capacity of a power transmission network provided in this application;

[0048] Figure 2 This is a schematic diagram of the IEEE-30 node transmission network topology provided in this application;

[0049] Figure 3 This is a flowchart illustrating steps S201 to S203 provided in this application;

[0050] Figure 4 This is a schematic flowchart of another embodiment of the power transmission capacity assessment method for a power transmission network provided in this application;

[0051] Figure 5 This is a schematic diagram of the structure of a power transmission capacity assessment device for a power transmission network provided in this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0054] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0057] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0058] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0059] In the field of power transmission networks, the high proportion of renewable energy integration and the deepening of the electricity market have led to increased power flow fluctuations and significantly enhanced uncertainty, placing extremely high demands on the accuracy and computational efficiency of transmission capacity assessment. While existing assessment methods based on deterministic optimal power flow models and the Newton-Raphson method are widely used, they still have significant shortcomings: First, the models cannot effectively characterize uncertainties such as power fluctuations on both the source and load sides and load forecasting errors, causing assessment results to deviate from actual physical limits and making it difficult to support high-precision assessment requirements; second, the algorithms require repeated construction and decomposition of high-dimensional Ajaxamethylene matrices, resulting in complex and time-consuming calculations, sensitivity to initial values, and a tendency to diverge under heavy system loads, failing to meet the urgent efficiency requirements of real-time online analysis of large-scale power transmission networks.

[0060] See Figure 1 To improve the efficiency and accuracy of power transmission capacity assessment, an embodiment of the present invention provides a method for assessing the power transmission capacity of a power transmission network, including steps S101 to S103.

[0061] See Figure 2 , Figure 2 This is a schematic diagram of the IEEE-30 node transmission network topology provided in this application. The transmission capacity assessment method of the transmission network in this application is... Figure 2 This was carried out on the IEEE-30 node transmission network shown.

[0062] Step S101: Obtain the operating data of each power flow node in the power transmission network, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes;

[0063] In some embodiments, acquiring the operating data of each power flow node in the transmission network specifically includes: acquiring the operating parameters of each power flow node through the data acquisition terminal of the transmission network; acquiring the preset first voltage reference value (including amplitude and phase angle, with the phase angle typically set to 0° as the system reference) of the balancing node; acquiring the first affine active power and the first affine reactive power injected by the PQ node; acquiring the second affine active power injected by the PV node and the preset second voltage reference value; simultaneously acquiring the topology correlation data of the transmission network; acquiring the line connection relationship between each power flow node; acquiring the series admittance and parallel admittance parameters (admittance includes conductance and susceptance components) of the lines and transformers; and simultaneously initializing the system reference capacity (e.g., setting the reference capacity of the IEEE-30 node transmission network to 100MVA) and the maximum transmission capacity of the lines (e.g., 40MW); thereby obtaining the operating data, topology correlation data, and initial capacity data of each power flow node in the transmission network.

[0064] It should be noted that power flow nodes include slack nodes, PQ nodes, and PV nodes. A PQ node is a node type for which the active power P and reactive power Q injected into the node are known, and the magnitude V and phase angle of the node voltage to be solved are unknown. A PV node is a node type for which the active power P injected into the node and the magnitude V of the node voltage are known, and the reactive power Q injected into the node and the voltage phase angle δ to be solved are unknown. A slack node is a node type for which the magnitude V and phase angle of the node voltage are given (usually the phase angle is set to 0° as the system phase reference), and the active power P and reactive power Q injected into the node to be solved are unknown.

[0065] Step S102: Based on the operational data, establish a fully embedded affine power flow model corresponding to each power flow node. Solve each fully embedded affine power flow model using a power series coefficient recursion method until a preset convergence condition is met. Obtain the first voltage affine solution of the equilibrium node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution of the PV node. Input the first voltage affine solution, the second voltage affine solution, the third voltage affine solution, and the reactive power affine solution into a preset evaluation model. With maximizing the transmission capacity of the transmission line as the objective function, solve the model under constraints to obtain several solution results. The connection relationship of the transmission line to each power flow node is determined.

[0066] In some embodiments, establishing a target fully embedded affine power flow model corresponding to each power flow node based on the operational data specifically includes: constructing an initial affine power flow model of the power system based on the operational data; performing fully embedded processing on the initial affine power flow model based on a preset first embedding factor and a first voltage reference value of the slack node to obtain a first fully embedded affine power flow model of the slack node; performing fully embedded processing on the initial affine power flow model based on a preset second embedding factor, a first affine active power, a first affine reactive power, and a first admittance of the PQ node to obtain a second fully embedded affine power flow model of the PQ node; performing fully embedded processing on the initial affine power flow model based on a preset third embedding factor, a second affine active power, a second voltage reference value, and a second admittance of the PV node to obtain a third fully embedded affine power flow model of the PV node; and determining the target fully embedded affine power flow model based on the first fully embedded affine power flow model, the second fully embedded affine power flow model, and the third fully embedded affine power flow model.

[0067] Specifically, affine arithmetic theory is used to describe physical quantities with uncertainties, such as node power and voltage. Node affine power and affine voltage are expressed as affine quantities containing center values ​​and noise elements. Combined with inter-node admittance parameters, an initial affine power flow equation is established. Using a first embedding factor and the first voltage reference value of the slack node, the initial affine power flow model is processed to obtain a first fully embedded affine power flow model for the slack node. A preset second embedding factor is introduced, and the first affine active power, first affine reactive power, and first admittance parameter of the PQ node are integrated into the initial affine power flow model to form a second fully embedded affine power flow model. A preset third embedding factor is introduced, and the second affine active power, second voltage reference value, and second admittance parameter of the PV node are integrated into the initial affine power flow model to form a third fully embedded affine power flow model. The first, second, and third fully embedded affine power flow models are integrated to form a target fully embedded affine power flow model.

[0068] In some embodiments, the step of establishing the relevant formulas for the target holomorphic embedding affine current model corresponding to each current node based on the operational data specifically includes:

[0069] Initial affine power flow model:

[0070]

[0071] In the formula, i and l are both node indices; Let be the affine power of node i; the symbol “^” represents an uncertain affine quantity. Let be the affine voltage at node i; Y is the voltage reference value for node i; il Let Y represent the admittance of branch il, where Y il,tr For the series admittance of branch il, Y i,sh Let N be the parallel admittance of node i; N represents the total number of nodes; O PQ O PV and O PH These represent the sets of PQ nodes, PV nodes, and balanced nodes, respectively. Let be the complex conjugate of the affine power of node i; Let be the conjugate complex number of the affine voltage at node i.

[0072] Formula for calculating nodal affine power:

[0073]

[0074] In the formula, Let be the affine active power of node i; Let be the affine reactive power of node i; the symbol "^" represents the uncertain affine quantity; j is the imaginary unit;

[0075] The first fully virtually embedded affine power flow model of the equilibrium node:

[0076]

[0077] In the formula, s is the first embedding factor; The first voltage reference value for the balancing node; i is the node index; O PH This is the set of balanced nodes.

[0078] The second fully pure embedded affine power flow model of PQ nodes:

[0079]

[0080] In the formula, i and l are both node indices; O PQ Let be the set of PQ nodes; s is the second embedding factor; N represents the total number of nodes; Y il Let Y represent the first admittance of branch il, where Y il,tr For the series admittance of branch il, Y i,sh Let be the parallel admittance of node i; Let be the affine voltage at node i; The affine power of node i The conjugate complex number, where, Let be the affine power of node i, which is obtained from the first affine active power and the first affine reactive power; s is the conjugate complex number of the affine voltage at node i; * is the conjugate complex number of the second embedding factor;

[0081] The third fully pure embedded affine power flow model for PV nodes:

[0082]

[0083] In the formula, s is the third embedding factor; i and l are node indices; O PV Y represents the set of PV nodes; N represents the total number of nodes; Y represents the set of PV nodes. il Let Y represent the second admittance of branch il, where Y il,tr For the series admittance of branch il, Y i,sh Let be the parallel admittance of node i; Let be the affine voltage at node i; This is the second voltage reference value for node i; Let be the second affine active power of node i; s is the conjugate complex number of the affine voltage at node i; * It is the conjugate complex number of the third embedding factor.

[0084] By establishing fully embedded affine power flow models for each power flow node, the advantages of fully embedded methods and affine modeling can be combined. This avoids the complex traditional solution logic and can characterize the uncertainties in the power system, laying a model foundation for subsequent efficient and accurate evaluation.

[0085] See Figure 3 In some embodiments, the step of solving each of the target holomorphic embedded affine power flow models by power series coefficient recursion method until the preset convergence condition is met, and obtaining the first voltage affine solution of the balance node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node and the reactive power affine solution, includes steps S201 to S203.

[0086] Step S201: Using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV node, perform power series expansion on each target fully embedded affine power flow model to obtain the power series coefficient recursive relationship of each power flow node. The initial voltage values ​​and the initial reactive power values ​​are obtained using preset embedding values.

[0087] In some embodiments, the step of using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV nodes to perform power series expansion processing on each target holomorphic embedded affine power flow model to obtain the power series coefficient recursive relation of each power flow node specifically includes: determining a first power series expansion form of the voltage affine quantity of each power flow voltage based on the initial voltage values ​​of each power flow node; determining a second power series expansion form of the reactive power affine quantity of the PV nodes based on the initial reactive power values ​​of the PV nodes; and determining the power series coefficient recursive relation of each power flow node based on each of the first power series expansion forms, the second power series expansion forms, and each target holomorphic embedded affine power flow model.

[0088] Specifically, a preset embedding value (s=0) is first set. This preset embedding value is then substituted into the target holomorphic affine power flow model as the values ​​of the second and third embedding factors to derive the joint equations. Finally, the joint equations are solved simultaneously to obtain the initial voltage values ​​of each power flow node. Initial values ​​of reactive power at PV nodes Simultaneously, the noise element coefficients (all 0) are obtained; then, based on the noise element and the initial voltage values ​​of each power flow node, the voltage affine of each power flow node is expressed as a first power series expansion containing power series coefficients; based on the noise element and the initial reactive power values ​​of the PV node, the reactive power affine of the PV node is expressed as a second power series expansion containing power series coefficients; substituting the two expansion forms into the corresponding models, and based on the principle that coefficients of the same power series are equal, the recursive relationship of the power series coefficients of the slack node, PQ node, and PV node is derived; then, an auxiliary variable (using...) is established. This indicates that the voltage is an affine voltage. The reciprocal of, that is, satisfying ), and based on the relationship between auxiliary variables and affine voltage The formula for calculating auxiliary variables is obtained by using the principle of equal coefficients of the same power series. Then, the voltage of the node is decomposed into real and imaginary parts, and the admittance of the line and transformer is expressed as real and imaginary parts. Based on the first power series expansion form, the second power series expansion form, the target holomorphic embedded affine power flow model, the formula for calculating auxiliary variables, the real and imaginary parts of voltage and admittance, the recursive formulas for the power series coefficients of the slack node, the power series coefficients of the PV node, and the power series coefficients of the PQ node are obtained.

[0089] In some embodiments, the step of using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV nodes to perform power series expansion on each of the target holomorphic embedded affine power flow models to obtain the relevant formulas for the recursive relationships of the power series coefficients of each power flow node specifically includes:

[0090] Joint equations:

[0091]

[0092] In the formula, i and l are both node indices; the symbol "^" represents an uncertain affine quantity; Y is the affine voltage at node i; il Let Y represent the admittance of branch il, where Y il,tr For the series admittance of branch il, Y i,sh Let N be the parallel admittance of node i; N represents the total number of nodes; O PQ O PV and O PH These represent the sets of PQ nodes, PV nodes, and balanced nodes, respectively. Let be the complex conjugate of the affine voltage at node i; Let be the affine reactive power of node i;

[0093] First power series expansion:

[0094]

[0095] In the formula, j represents the noise meta-index; M i ε represents the total number of noise elements. j Indicates a noise element; n represents the order term index; U represents the coefficients of the nth-order affine power series of the voltage at node i; i,0 [n] is The center value of ; s is the second embedding factor; U i,j [n] represents The j-th noise element coefficient;

[0096] Second power series expansion:

[0097]

[0098] In the formula, j represents the noise meta-index; M i ε represents the total number of noise elements. j The noisy element is represented by n; the order term index is represented by s; and the third embedding factor is represented by s. Q represents the coefficient of the nth-order affine power series of reactive power at node i; i,0 [n] is The central value; express The j-th noise element coefficient;

[0099] Formula for calculating auxiliary variables:

[0100]

[0101] In the formula, n and k both represent the index of the order term; The coefficients of the nth affine power series of the auxiliary variable are represented; s is the embedding factor; Represents the coefficients of the nth order affine power series of the affine voltage;

[0102] Formula for decomposing voltage into real and imaginary parts:

[0103]

[0104] In the formula, n represents the index of the order term; and Let be the real and imaginary parts of the voltage affine solution of node i at order n, respectively. The nth-order affine power series coefficients of the voltage at node i; j is the imaginary unit;

[0105] Formula for decomposing admittance into real and imaginary parts:

[0106] Y il,tr =G il,tr +jB il,tr ;

[0107] In the formula, Y il,tr For the admittance of the series branch il; G il,tr and B il,tr λ and sqrt(il) represent the conductance and susceptance of the series branch il, respectively; j is the imaginary unit.

[0108] The recurrence relation for the power series coefficients of the equilibrium nodes:

[0109]

[0110] In the formula, δ n0 This is an impulse function, taking the value 1 when n = 0, and 0 otherwise; δ n1 This is an impulse function, taking the value 1 when n=1 and 0 otherwise; Re() takes the real part; Im() takes the imaginary part. This is the first voltage reference value for the balancing node;

[0111] The recurrence relation for the power series coefficients of PV nodes:

[0112]

[0113] In the formula, l is the node index; N represents the total number of nodes; and G represents the real and imaginary parts of the nth-order voltage affine solution at PV node l, respectively; il,tr and B il,tr These are the conductance and susceptance of the series branch il, respectively; The affine power of PV node i The conjugate of the complex number; Re() takes the real part; Im() takes the imaginary part; Y represents the nth-order affine power series coefficient of the voltage at PV node i; i,sh Let i be the parallel admittance of PV node i; Let be the conjugate complex number of the auxiliary variable of PV node i;

[0114] The recurrence relation of the power series coefficients of nodes PQ:

[0115]

[0116] In the formula, l is the node index; N represents the total number of nodes; n and k both represent the order item index; and These are the real and imaginary parts of the nth-order voltage affine solution at node l of PQ, respectively; G il,tr and B il,tr These are the conductance and susceptance of the series branch il, respectively; Let be the nth order affine power series coefficients of the reactive power at node i in PQ; Re() is for taking the real part; Im() is for taking the imaginary part; Let be the first affine active power of node i in PQ; Y is the conjugate complex number of the auxiliary variable of node i in PQ; j is the imaginary unit; i,sh Let i be the parallel admittance of node i in PQ; The coefficients of the nth order affine power series of the voltage at node i; δ is the conjugate complex number of the affine voltage at node i in PQ; n1 This is an impulse function, taking the value 1 when n = 1, and 0 otherwise; This is the second voltage reference value for the PQ node.

[0117] This approach, which constructs two types of power series expansions based on initial voltage and reactive power values, directly incorporates the initial foundational data, avoiding errors caused by data disconnect. By combining the expansion with the target model to derive the recursive relationship, the uniqueness and convergence of the expansion are guaranteed by the analyticity of holomorphic functions. Furthermore, the uncertainty propagation path is fully preserved through affine quantities, ensuring that the recursive relationship accurately reflects the intrinsic correlation of nodal electrical quantities. This process requires no complex preprocessing, simplifies the solution logic, improves computational efficiency, and ensures the reliability of the recursive results. This provides a precise basis for subsequent step-by-step solutions, thereby improving the accuracy of the evaluation.

[0118] Step S202: Using the initial voltage values, the recursive formulas of the power series coefficients are iteratively solved step by step until the preset convergence condition is met, so as to obtain the power series coefficients of several orders of each power flow node.

[0119] In some embodiments, the recursive formulas for the power series coefficients are iteratively solved using the initial voltage values ​​until a preset convergence condition is met, yielding power series coefficients of several orders for each power flow node. Specifically, this includes: using the initial voltage values ​​and the initial reactive power of the PV node as the initial iteration values; substituting the coefficients from order 0 to n-1 into the recursive formula in the order n = 1, 2, 3, ..., transforming them into a system of linear equations for the current order coefficients; solving the system of linear equations to obtain the current order power series coefficients; and determining whether the current order coefficients meet the preset convergence condition (the convergence accuracy α can be set to 10). -6 If the condition is met, the iteration stops, and the power series coefficients of several orders for each power flow node are obtained.

[0120] In some embodiments, the recursive formulas for the power series coefficients of each power series node are iteratively solved using the initial voltage values ​​until a preset convergence condition is met, thereby obtaining the relevant formulas for the power series coefficients of each power flow node, specifically including:

[0121] Linear equation system:

[0122]

[0123] In the formula, A is a known constant matrix, which is formed by the conductance G of the lines and transformers in the system. il,tr and susceptance B il,tr and voltage U i It consists of the initial value of the reciprocal of the voltage; The vector consisting of the coefficients of the unknown nth power series to be solved, represented by the affine voltage. and affine reactive power composition; A known vector calculated from the coefficients of the first 1 to n-1 power series;

[0124] Convergence condition:

[0125]

[0126] In the formula, α represents the convergence accuracy; max{} represents the maximum value; and i represents the vector. The i-th term in the equation; sup() and inf() take the maximum and minimum values ​​of the affine quantity range, respectively; k is the iteration order index.

[0127] It should be noted that, after solving for the coefficients of the k-th power series... When doing so, it is necessary to determine whether the convergence condition is met.

[0128] Step S203: The coefficients of each power series are accumulated to obtain the first voltage affine solution of the balance node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution.

[0129] In some embodiments, the power series coefficients of each of the above are summed to obtain the first voltage affine solution of the slack node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution. Specifically, this includes: summing the voltage power series coefficients of each order of the slack node to obtain the first voltage affine solution; summing the voltage power series coefficients of each order of the PQ node to obtain the second voltage affine solution; summing the voltage power series coefficients of each order of the PV node to obtain the third voltage affine solution; and summing the reactive power power series coefficients of each order of the PV node to obtain the reactive power affine solution.

[0130] In some embodiments, the coefficients of each power series are accumulated to obtain the relevant formulas for the first voltage affine solution of the slack node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution, specifically including:

[0131] Voltage affine solution:

[0132]

[0133] In the formula, k is the highest order that satisfies the convergence condition; i is the order index of the power series; The coefficients are the i-th power series coefficients of the voltage.

[0134] Affine solution for reactive power:

[0135]

[0136] In the formula, k is the highest order that satisfies the convergence condition; i is the order index of the power series; is the coefficient of the i-th power series of reactive power.

[0137] By solving each fully embedded affine power flow model using a power series coefficient recursive method, the voltage affine solutions for the slack node, PQ node, and PV node are obtained, as well as the reactive power affine solution for the PV node. The power series coefficient recursive method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship. Then, through successive solutions and satisfying the convergence condition, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained. This significantly reduces the amount of computation and improves the solution efficiency. Furthermore, by strictly guaranteeing convergence, the output results are ensured to be accurate and reliable, thus improving the efficiency and accuracy of subsequent transmission capacity assessment.

[0138] In some embodiments, the first voltage affine solution, the second voltage affine solution, the third voltage affine solution, and the reactive power affine solution are input into a preset evaluation model. The objective function is to maximize the transmission capacity of the transmission line, and the model is solved under constraints to obtain several solution results. Specifically, the connection relationship between the transmission line and each power flow node is determined. This includes inputting the first voltage affine solution, the second voltage affine solution, the third voltage affine solution, and the reactive power affine solution into a preset evaluation model. The objective function is to maximize the transmission capacity of the transmission line. The model is then solved using a solver under power flow constraints, node voltage constraints, and line transmission capacity constraints to obtain multiple solution results.

[0139] It should be noted that the evaluation model can be solved using the Gurobi solver; the evaluation model is constructed from an objective function and three constraints; the transmission lines are determined by the connection relationships of each power flow node; the solution result is the maximum remaining power margin that each transmission line can safely transmit, that is, the transmission capacity of the line that can currently be used for new power trading (for example, the solution result for the transmission line composed of node 1 and node 2 is a transmission capacity of 27.330MW).

[0140] In some embodiments, the first voltage affine solution, the second voltage affine solution, the third voltage affine solution, and the reactive power affine solution are input into a preset evaluation model. The objective function is to maximize the transmission capacity of the transmission line, and the model is solved under constraints to obtain several solution results. Specifically, the formulas for determining the connection relationships of each power flow node of the transmission line include:

[0141] Objective function:

[0142]

[0143] In the formula, x represents the line; O X A collection of routes; This represents the transmission capacity of line x; Let be the magnitude of the affine voltage at node i; G represents the phase angle of the affine voltage at node i; il and B il ...

[0144] Current constraints:

[0145]

[0146] In the formula, P i,min and P i,max Let Q be the lower and upper bounds of the active power fluctuation range at node i;i,min and Q i,max G represents the lower and upper bounds of the reactive power fluctuation range at node i; il and B il These are the conductance and susceptance of branch il, respectively; and Let O represent the real and imaginary parts of the voltage affine solution at node i, respectively; PQ O PV and O PH These represent the sets of PQ nodes, PV nodes, and balanced nodes, respectively.

[0147] Node voltage constraints:

[0148]

[0149] In the formula, U i,min and U i,max These are the lower and upper bounds of the voltage at node i, respectively; Let be the affine voltage at node i;

[0150] Tributary transmission capacity constraints:

[0151]

[0152] In the formula, and G represents the real and imaginary parts of the voltage affine solution at node i, respectively; il and B il These are the conductance and susceptance of branch il, respectively; and These represent the active power transmission value and reactive power transmission value of branch il, respectively; s il,max This represents the maximum transmission capacity of the tributary I1.

[0153] In some embodiments, after obtaining several solution results, the method further includes: selecting several candidate bottleneck transmission lines from the solution results, wherein the solution result is the transmission capacity value of the corresponding transmission line; obtaining the actual transmission power of each candidate bottleneck transmission line; obtaining several transmission differences based on the transmission capacity value and the actual transmission power of each candidate bottleneck transmission line; determining whether each transmission difference is less than a preset transmission threshold, and if so, determining the corresponding candidate bottleneck transmission line as the target bottleneck transmission line, so as to monitor the operating status of the target bottleneck transmission line. Specifically, the solution yields the Available Transfer Capability (ATC) of the transmission lines. All transmission lines are then ranked by their ATC values. Lines with ATC values ​​in the bottom 30% are selected as candidate bottleneck transmission lines. The actual transmission power of each candidate bottleneck transmission line is obtained through a real-time power grid monitoring system. The transmission difference between each candidate bottleneck transmission line is calculated. It is then determined whether each transmission difference is less than a preset transmission threshold. Candidate bottleneck transmission lines with transmission differences less than the preset threshold are identified as target bottleneck transmission lines, allowing for monitoring of their power variations, voltage fluctuations, equipment temperatures, and other operational status.

[0154] This method involves selectively identifying candidate bottleneck lines from the solution results, quantifying the difference between the transmission capacity and the actual transmission power, and using threshold judgments to determine the target bottleneck. By monitoring the operational status of the target bottleneck line, it is possible to eliminate the need for comprehensive analysis of all lines, thus improving evaluation efficiency. Quantitative calculations and threshold judgments replace the subjective biases of traditional experience-based judgments, ensuring the accuracy of bottleneck identification and achieving a dual optimization of evaluation efficiency and accuracy.

[0155] For example, Table 1 shows the transmission capacity (i.e. partial solution results) of some lines in the IEEE-30 node transmission network according to another embodiment of the present invention. The transmission capacity of critical lines in the IEEE-30 node transmission network is distributed between 27.330MW and 37.564MW. For example, the transmission capacity of line 7 is 27.330MW and that of line 16 is 37.564MW, indicating that there are significant differences in the transmission capacity of lines at different topological locations.

[0156] Table 1 Transmission Capacity of Some Lines in the IEEE-30 Node Transmission Network

[0157] line From node To the node Transmission capacity / MW 7 4 6 27.331 10 6 8 33.130 16 12 13 37.563 29 21 22 33.058

[0158] Step S103: Evaluate the power transmission capacity of the power grid based on the solution results.

[0159] In some embodiments, the evaluation of the transmission capacity of the power grid based on the solution results specifically includes: performing statistical analysis on the solution results to obtain the transmission capacity distribution range; identifying the hub region and edge region of the transmission lines based on the transmission capacity distribution range and the obtained power grid topology, and evaluating the transmission capacity differences of lines at different topological locations based on the hub region and the edge region. Specifically, statistical processing is performed on the solution results of all transmission lines to determine the transmission capacity distribution range (e.g., in Table 1, the transmission capacity value of line 7 is 27.330MW and the transmission capacity value of line 16 is 37.564MW, so the transmission capacity distribution range of the region from node 4 to node 8 is 27.330MW-37.563MW); combining the power grid topology, identifying the hub region and edge region of the transmission lines; comparing the transmission capacity distribution ranges of lines in the hub region and edge region to analyze the transmission capacity differences of lines at different topological locations.

[0160] By statistically analyzing the solution results, the distribution range of transmission capacity is determined. Combined with the transmission network topology, hubs and edge areas are identified. The transmission capacity value is associated with the physical location of the line and the network function, avoiding one-sided judgment based solely on numerical values. This can accurately depict the capacity differences of lines in different topological locations. At the same time, the structured assessment does not require redundant data interpretation, quickly locates core channels and weak links, and balances assessment efficiency, achieving a dual optimization of assessment efficiency and accuracy.

[0161] For example, Table 2 shows the transmission capacity of some lines in the IEEE-30 node power grid under different fluctuation ranges according to another embodiment of the present invention. It can be seen that the expansion of the node input power fluctuation range has a systematic inhibitory effect on the available transmission capacity of the power grid. When the power fluctuation range increases from 10% to 50%, the available transmission capacity of typical lines all decreases: the transmission capacity of line 7 decreases from 27.331133MW to 27.331093MW, a decrease of 0.014%, and the transmission capacity of line 29 decreases from 33.058088MW to 33.057948MW, a decrease of 0.14%. This reflects the negative correlation between fluctuation and available transmission capacity.

[0162] Table 2 Transmission Capacity of Some Lines in the IEEE-30 Node Transmission Network under Different Fluctuation Ranges

[0163]

[0164] For example, the overall power grid capacity matching can be assessed based on the solution results. First, the solution results are correlated with the power grid topology (such as the line connection relationship of IEEE-30 nodes) to determine the capacity balance of the lines in the core area (the hub area where multiple nodes converge). That is, if the line capacity difference in the core area is too large, local power congestion may occur, affecting the overall power dispatch efficiency.

[0165] For example, the safety margin of transmission lines in a power grid can be assessed based on the solution results. First, the difference between the solution result of the transmission line to be assessed and the actual transmission power of the transmission line is calculated to obtain the remaining margin value. Lines with a remaining margin value less than a preset margin threshold have lower safety redundancy and require strict power increment control to avoid overloading.

[0166] For further explanation, please refer to Figure 4 , Figure 4 This is a flowchart illustrating another embodiment of the power transmission capacity assessment method for a power transmission network provided in this application. First, the initial data of the power flow model of the power transmission network is input, i.e., the operating data obtained in step S101 of this application is input. Then, a fully embedded affine optimization power flow model is established, and the embedding factor is set to a preset embedding value (i.e., s = 0). The initial voltage and reactive power values ​​are obtained by substituting these values ​​into the model. The higher-order power series coefficients of the node voltage and reactive power are iteratively solved multiple times according to the derived formula until the preset convergence condition is met, obtaining the affine solution of the node voltage and the affine solution of the PV node reactive power. Then, the objective function and constraints of the available power transmission capacity of the power transmission network are established as the assessment model. The Gurobi solver is used to solve the assessment model, obtaining the final solution result, i.e., step S102 of this application.

[0167] It should be noted that this application adopts a recursive power flow calculation method called the fully embedded power flow method, which avoids the tedious process of repeatedly constructing and decomposing the Jacobian matrix in the traditional Newton-Raphson method. It can be completed by recursively solving the linear equation system. When the system has a power flow solution, the fully embedded method can ensure that it converges within the reliable domain, avoiding the dilemma of iterative non-convergence during the optimization process. By introducing affine arithmetic to model the input power fluctuation, the propagation mechanism of uncertainty factors can be effectively characterized, thereby accurately quantifying the negative correlation between the power fluctuation range and the available transmission capacity, and solving the limitation of traditional deterministic methods that cannot reflect the impact of fluctuations.

[0168] Obtaining real operational data from each power flow node in the transmission network in this way can avoid evaluation bias caused by inaccurate data from the source, while eliminating redundant steps in secondary data processing, thus improving evaluation efficiency and accuracy. Establishing fully embedded affine power flow models for each power flow node combines the advantages of fully embedded methods and affine modeling, avoiding complex traditional solution logic while characterizing uncertainties in the power system, laying a model foundation for subsequent efficient and accurate evaluation. Solving each fully embedded affine power flow model using a power series coefficient recursion method yields the voltage affine solutions for the slack node, PQ node, and PV node, as well as the reactive power affine solution for the PV node. The power series coefficient recursion method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship, and then through successive solutions satisfying convergence conditions, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained, significantly reducing the computational load and improving solution efficiency. Furthermore, through rigorous... To ensure convergence and guarantee accurate and reliable output results, this method improves the efficiency and accuracy of subsequent transmission capacity assessments. The obtained voltage affine solutions for each power flow node and reactive power affine solutions for PV nodes are directly input into a pre-defined assessment model. The model uses maximizing the transmission capacity of transmission lines as the objective function and solves the model under constraints. Various high-precision affine solutions provide the assessment model with complete input containing uncertainty information. Combined with the objective function of maximizing transmission capacity and the constraints, the solution results accurately match the physical laws of actual transmission network operation, avoiding error accumulation in traditional linearization methods or the conservatism of convex relaxation models. Furthermore, the assessment model can directly output the solution results for each line without additional iterative verification, improving assessment efficiency and accuracy. Based on the solution results, the transmission capacity of the transmission network is assessed, ensuring the scientific validity of the assessment conclusions and simplifying the assessment process, ultimately achieving a dual improvement in the efficiency and accuracy of transmission capacity assessment. This application can improve the efficiency and accuracy of transmission network capacity assessment.

[0169] See Figure 2 Based on the above method embodiments, corresponding device embodiments are provided;

[0170] An embodiment of the present invention provides a power transmission capacity assessment device for a power transmission network, characterized in that it includes a first module 100, a second module 200 and a third module 300;

[0171] The first module 100 is used to acquire the operating data of each power flow node in the power transmission network, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes;

[0172] The second module 200 is used to establish a fully embedded affine power flow model corresponding to each power flow node based on the aforementioned operational data. It solves each fully embedded affine power flow model using a power series coefficient recursion method until a preset convergence condition is met, obtaining the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution. These solutions are then input into a preset evaluation model, with the goal of maximizing the transmission capacity of the transmission line, and solved under constraints to obtain several solution results. The connection relationship between the transmission line and each power flow node is determined.

[0173] The third module 300 is used to evaluate the power transmission capacity of the power grid based on the solution results.

[0174] By acquiring real operational data of each power flow node in the transmission network through the first module, assessment bias caused by inaccurate data can be avoided from the source, while eliminating redundant steps in secondary data processing, thus improving assessment efficiency and accuracy. The second module establishes fully embedded affine power flow models for each node, combining the advantages of fully embedded methods and affine modeling. This avoids complex traditional solution logic while characterizing uncertainties in the power system, laying a model foundation for subsequent efficient and accurate assessments. Solving each fully embedded affine power flow model using a power series coefficient recursion method yields voltage affine solutions for the slack node, PQ node, and PV node, as well as the reactive power affine solution for the PV node. The power series coefficient recursion method can be used to first transform the nonlinear fully embedded affine power flow model into a linear recursive relationship, and then through successive solutions satisfying convergence conditions, the voltage affine solutions for each power flow node and the reactive power affine solutions for the PV node are obtained, significantly reducing computational load and improving solution efficiency. Strict convergence is guaranteed to ensure accurate and reliable output results, improving the efficiency and accuracy of subsequent transmission capacity assessments. The obtained voltage affine solutions for each power flow node and reactive power affine solutions for PV nodes are directly input into a pre-defined assessment model. The model uses maximizing the transmission capacity of transmission lines as the objective function, and solves the model under constraints to obtain the results. Various high-precision affine solutions provide the assessment model with complete input containing uncertainty information. Combined with the objective function of maximizing transmission capacity and the constraints, the solution results accurately match the physical laws of actual transmission network operation, avoiding the error accumulation problems of traditional linearization methods or the conservatism of convex relaxation models. Furthermore, the assessment model can directly output the solution results for each line without additional iterative verification, improving assessment efficiency and accuracy. Through the third module, the transmission capacity of the transmission network is assessed based on the solution results, ensuring the scientific validity of the assessment conclusions while simplifying the assessment process, ultimately achieving a dual improvement in the efficiency and accuracy of transmission capacity assessment.

[0175] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power transmission capacity assessment method for power transmission networks provided by any of the above-described method embodiments of the present invention.

[0176] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0177] Based on the above-described embodiment of a method for assessing the transmission capacity of a power grid, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for assessing the transmission capacity of a power grid according to any embodiment of the present invention.

[0178] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0179] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0180] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0181] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power transmission capacity assessment method of any of the above-described method embodiments of the present invention.

[0182] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0183] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a power transmission capacity assessment method for a power transmission network according to any embodiment of the present invention.

[0184] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for assessing the transmission capacity of a power transmission network, characterized in that, include: The operation data of each power flow node in the power transmission network are obtained, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes; Based on the aforementioned operational data, a target holomorphic embedded affine power flow model is established for each power flow node. The target holomorphic embedded affine power flow model is solved using a power series coefficient recursive method until a preset convergence condition is met, yielding the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution. These solutions are then input into a preset evaluation model, with the goal of maximizing the transmission capacity of the transmission line, and solved under constraints to obtain several solution results. The connection relationship between the transmission line and each power flow node is determined. The power transmission capacity of the power grid is evaluated based on the solution results.

2. The method for assessing the transmission capacity of a power transmission network as described in claim 1, characterized in that, The step of establishing a target holomorphic embedding affine current model corresponding to each current node based on the aforementioned operational data specifically includes: An initial affine power flow model of the power system is constructed based on the aforementioned operational data. Based on the preset first embedding factor and the first voltage reference value of the equilibrium node, the initial affine power flow model is subjected to pure embedding processing to obtain the first pure embedded affine power flow model of the equilibrium node. Based on the preset second embedding factor, the first affine active power, the first affine reactive power, and the first admittance of the PQ node, the initial affine power flow model is subjected to full pure embedding processing to obtain the second full pure embedded affine power flow model of the PQ node. Based on the preset third embedding factor, the second affine active power of the PV node, the second voltage reference value, and the second admittance, the initial affine power flow model is subjected to full pure embedding processing to obtain the third full pure embedded affine power flow model of the PV node. Based on the first fully embedded affine current model, the second fully embedded affine current model, and the third fully embedded affine current model, the target fully embedded affine current model is determined.

3. The method for assessing the transmission capacity of a power transmission network as described in claim 1, characterized in that, The process involves solving the holomorphic embedded affine power flow model for each target using a power series coefficient recursive method until a preset convergence condition is met, yielding the first voltage affine solution for the slack node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution. Specifically, this includes: Using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV node, the power series expansion of each target holomorphic embedded affine power flow model is performed to obtain the power series coefficient recursive relationship of each power flow node. The initial voltage values ​​and the initial reactive power values ​​are obtained using preset embedding values. The power series coefficients of each power flow node are obtained by iteratively solving the recursive relationship of each power series using the initial voltage values ​​until the preset convergence condition is met. The coefficients of each power series are summed to obtain the first voltage affine solution of the equilibrium node, the second voltage affine solution of the PQ node, the third voltage affine solution of the PV node, and the reactive power affine solution.

4. The method for assessing the transmission capacity of a power transmission network as described in claim 3, characterized in that, The process involves using the initial voltage values ​​of each power flow node and the initial reactive power values ​​of the PV nodes to perform a power series expansion on each target holomorphic embedded affine power flow model, thereby obtaining the recursive formulas for the power series coefficients of each power flow node. Specifically, this includes: Based on the initial voltage values ​​of each power flow node, determine the first power series expansion of the voltage affine quantity of each power flow voltage; Based on the initial value of the reactive power of the PV node, the second power series expansion form of the reactive power affine quantity of the PV node is determined; Based on the first power series expansion form, the second power series expansion form, and the target holomorphic embedded affine power flow model, the recursive relationship of the power series coefficients of each power flow node is determined.

5. The method for assessing the transmission capacity of a power transmission network as described in claim 1, characterized in that, The evaluation of the power transmission capacity of the power grid based on the solution results specifically includes: Statistical analysis was performed on the solution results to obtain the power transmission capacity distribution range; Based on the power transmission capacity distribution range and the obtained power transmission network topology, the hub areas and edge areas of the power transmission lines are identified, so as to evaluate the differences in power transmission capacity of lines at different topological locations based on the hub areas and the edge areas.

6. The method for assessing the transmission capacity of a power transmission network as described in claims 1-4, characterized in that, After obtaining several solution results, the process also includes: Several candidate bottleneck transmission lines are selected from the solution results, wherein the solution results are the transmission capacity values ​​of the corresponding transmission lines; Obtain the actual transmission power of each of the candidate bottleneck transmission lines; Based on the transmission capacity value and the actual transmission power of each candidate bottleneck transmission line, several transmission differences are obtained. Each transmission difference is determined to be less than a preset transmission threshold. If the threshold is met, the corresponding candidate bottleneck transmission line is determined as the target bottleneck transmission line, so as to monitor the operation status of the target bottleneck transmission line.

7. A device for evaluating the transmission capacity of a power transmission network, characterized in that, It includes Module 1, Module 2, and Module 3; The first module is used to acquire the operating data of each power flow node in the power transmission network, wherein the power flow nodes include balancing nodes, PQ nodes and PV nodes; The second module is used to establish a fully embedded affine power flow model corresponding to each power flow node based on the aforementioned operational data. It solves each fully embedded affine power flow model using a power series coefficient recursive method until a preset convergence condition is met, obtaining the first voltage affine solution for the equilibrium node, the second voltage affine solution for the PQ node, the third voltage affine solution for the PV node, and the reactive power affine solution. These solutions are then input into a preset evaluation model, with the objective function of maximizing the transmission capacity of the transmission line, and solved under constraints to obtain several solution results. The connection relationship between the transmission line and each power flow node is determined. The third module is used to evaluate the power transmission capacity of the power grid based on the solution results.

8. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the power transmission capacity assessment method for the power transmission network as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the power transmission capacity assessment method for a power transmission network as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, they implement the power transmission capacity assessment method for the power transmission network as described in any one of claims 1 to 6.

Images