Carbon emission flow iterative calculation method and system of reference power transmission system, medium and processor

Abstract

The invention provides a carbon emission flow iterative calculation method of a reference power transmission system. The method comprises the following steps: constructing a carbon emission factor and carbon intensity correlation model in each node in the reference power transmission system and a carbon emission factor correlation model between the nodes; converting the correlation model of the carbon emission factors among the nodes to obtain a linear equation of the carbon emission factors among the nodes; obtaining a carbon emission factor linear equation set according to the inter-node carbon emission factor linear equation set; converting the carbon emission factor linear equation set into a corresponding carbon emission factor matrix; decomposing the carbon emission factor matrix coefficient to obtain an iterative matrix of the Jacobi method; obtaining a carbon emission factor iteration equation matrix and a carbon emission factor iteration equation set according to the iteration matrix; and after iterative calculation convergence is performed on the carbon emission factor iterative equation set, the carbon intensity of each node is obtained in combination with the carbon emission factor and carbon intensity correlation model. A carbon emission flow theoretical model based on power flow is expanded, and the method has high adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission calculation of power systems, and in particular to a method, system, medium and processor for iterative calculation of carbon emission flow of a benchmark power transmission system. Background Art

[0002] In recent years, with the rapid development of industrialization and urbanization, human activities have continued to increase, leading to a continuous increase in the concentration of carbon dioxide in the atmosphere. This situation has attracted attention from global climate change, because the increase of greenhouse gases such as carbon dioxide will lead to the intensification of the greenhouse effect of the earth, exacerbating problems such as climate warming. Through monitoring and simulation analysis, carbon emissions in different regions can be well tracked. Studies have found that developed countries such as the United States, European countries and other regions with a high degree of industrialization have large carbon dioxide emissions, while some developing countries such as China and India are also growing rapidly, resulting in a continuous increase in the total amount of greenhouse gas emissions. In addition, carbon emission flow (CEF) is also affected by factors such as topography and climate, and there are differences in the flow of carbon dioxide under different seasons and climate conditions.

[0003] The most commonly used CEF models include equilibrium models, input-output models, system dynamics models, and life cycle assessment models. Chan et al. proposed a dynamic, stochastic general equilibrium framework for carbon emissions. It integrates carbon emissions at different stages of the production chain and under various industrial shocks. It not only addresses the impact of productivity improvement and structural changes on the environment, but also determines the optimal carbon tax policy under different shocks, solving key issues in carbon policy making. Lu et al. used a dynamic life cycle assessment framework to integrate the carbon footprint of solar photovoltaic panels with spatiotemporal changes. The supply chain of a carbon-neutral energy system was simulated to maximize environmental benefits. By introducing a variety of manufacturing scenarios, the assessment of indirect greenhouse gas emissions affected by future energy structure changes was solved, thereby improving the accuracy and applicability of the model.

[0004] However, the above methods lack an accurate description of the dynamic flow and spatiotemporal distribution of carbon emissions, and do not fully consider the comprehensive factors that affect the carbon emission path within the system. Yang et al. proposed an improved power flow modeling method based on CEF in the power sector. The proposed method accurately quantifies the impact of producers and consumers on carbon emissions from three aspects: carbon emission sources, network flows, and indirect carbon emissions. Wang et al. introduced a data-driven approach that addresses the limitations of traditional emission calculations and optimizes energy consumption. By adopting the CEF model, the reliance on high-carbon emission sources was successfully reduced and carbon emissions were minimized.

[0005] The above literature describes the calculation method of carbon emissions, especially emphasizing the CEF calculation model based on power flow theory, which comprehensively describes the power flow carbon intensity of units, lines and nodes. However, the scalability and effectiveness of this method for different transmission grid systems still need to be verified and analyzed. Different types of power systems (such as transmission grids, distribution grids, microgrids, etc.) have different characteristics and constraints, and the CEF theory needs to be adjusted and modified according to specific circumstances.

[0006] In view of this, a method, system, medium and processor for iteratively calculating the carbon emission flow of a benchmark transmission system are needed. Summary of the invention

[0007] In view of the problem that the existing CEF theory in the prior art has poor scalability and effectiveness universality for different transmission network systems, the present invention provides an iterative calculation method, system, medium and processor for the carbon emission flow of a benchmark transmission system, which can have strong adaptability and solve the problem of poor scalability and effectiveness universality. The specific technical solution is as follows:

[0008] A method for iteratively calculating carbon emission flows of a benchmark transmission system, comprising:

[0009] Construct a correlation model between carbon emission factors and carbon intensity within each node in the benchmark transmission system and a correlation model between carbon emission factors among nodes;

[0010] Transform the carbon emission factor correlation model between nodes to obtain the linear equation of carbon emission factors between nodes;

[0011] The linear equations of carbon emission factors of the benchmark transmission system are obtained from the linear equations of carbon emission factors between nodes;

[0012] Convert the carbon emission factor linear equations into the corresponding carbon emission factor matrix;

[0013] Decompose the carbon emission factor matrix coefficients according to the Jacobi method to obtain the Jacobi method iteration matrix;

[0014] According to the iteration matrix, the carbon emission factor iteration equation matrix and the carbon emission factor iteration equation group are obtained;

[0015] After the iterative calculation of the iterative equation group of the carbon emission factor converges, the carbon emission factor of each node is obtained, and the carbon intensity of each node is obtained by combining the carbon emission factor with the carbon intensity association model.

[0016] Furthermore, the carbon emission factor and carbon intensity association model is as follows:

[0017]

[0018] In the formula, l + , l- represents the branch set flowing into node i and the branch set flowing out of node i respectively; E N，i represents the carbon intensity at node i; E G，i Represents the carbon emission intensity of the generator; P B，l Indicates the active power flow of branch l; P G，i represents the generator injected power; ρ l Represents the carbon flow density of branch 1.

[0019] Furthermore, the carbon emission factor correlation model is as follows:

[0020]

[0021] In the above formula, e i is the carbon emission factor of node i; P j→i represents the active power injected from node j to node i; e j represents the carbon emission factor injected into node j; G s Indicates the active power output of generator set s; e s represents the carbon emission factor of the generator set s; I + , T + They represent the set of all branches that inject active power into node i and the set of generator sets connected to node i respectively.

[0022] Furthermore, the linear equations of the carbon emission factor are as follows:

[0023]

[0024] Furthermore, the carbon emission factor matrix is ​​as follows:

[0025]

[0026] In the above formula, P N represents the node active power flow matrix, which is an N-order diagonal matrix, and the diagonal element P in the ith row Nii represents the total active power injected into node i by branches and generators; P B represents the branch power flow distribution matrix, which is an N-order square matrix. YesP B The transpose of the non-diagonal element P Bij represents the active power injected from node j to node i; E N represents the node carbon emission factor vector; and P G represents the power distribution matrix injected by the generator set, YesP G The transpose of E G is the carbon emission factor vector of the generator set; is represented as the coefficient matrix of a linear system.

[0027] Furthermore, the carbon emission factor iteration equation matrix is ​​as follows:

[0028]

[0029] In the formula, represents the node carbon emission factor vector after k iterations; from the perspective of the entire power system, with flow distribution, generator injection distribution and carbon emission factor as boundary conditions, iterative calculation from any initial vector At the beginning, the node carbon emission factor vector generated after each iteration is used as the initial value of the next iteration, and k+1 linear iterations are performed until the result converges to

[0030] Furthermore, the carbon emission factor iteration equation group is as follows:

[0031]

[0032] In the above formula, Represents the carbon emission factor of node n calculated from k+1 iterations.

[0033] A carbon emission flow iterative calculation system for a benchmark power transmission system, applied to the carbon emission flow iterative calculation method for the benchmark power transmission system described above, comprising:

[0034] A first construction module is used to construct a carbon emission factor and carbon intensity association model within each node in the benchmark transmission system and a carbon emission factor association model between each node;

[0035] A first conversion module is used to convert the carbon emission factor correlation model between nodes to obtain a linear equation of the carbon emission factor between nodes;

[0036] A second building block is used to obtain a linear equation group of carbon emission factors of a benchmark transmission system from linear equations of carbon emission factors between nodes;

[0037] A second conversion module, which is used to convert the carbon emission factor linear equation group into a corresponding carbon emission factor matrix;

[0038] A decomposition module, which is used to decompose the carbon emission factor matrix coefficients according to the Jacobi method to obtain an iterative matrix of the Jacobi method;

[0039] A third construction module is used to obtain a carbon emission factor iteration equation matrix and a carbon emission factor iteration equation group according to the iteration matrix;

[0040] The calculation module is used to obtain the carbon emission factor of each node after iterative calculation of the iterative equation group of the carbon emission factor converges, and to obtain the carbon intensity of each node by combining the carbon emission factor with the carbon intensity association model.

[0041] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned iterative calculation method for carbon emission flow of a benchmark power transmission system.

[0042] A processor is used to run a program, wherein the program executes the above-mentioned iterative calculation method for carbon emission flow of a benchmark power transmission system when running.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The theoretical model of carbon emission flow based on power flow has been expanded. Based on the concepts of node active flux, node carbon potential, load carbon flow, etc., a carbon emission flow iteration method is proposed, and its convergence is verified. This method has strong adaptability and solves the problem of poor scalability and effectiveness of the existing CEF theory. There is no need to modify the model parameters separately for different power grid systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0046] Figure 1 A flowchart of an iterative calculation method for carbon emission flow of a benchmark transmission system is shown;

[0047] Figure 2 is a schematic diagram of the structure of a benchmark power transmission system;

[0048] Figure 3 It is a schematic diagram of active power flow of nodes;

[0049] Figure 4 Schematic diagram of node carbon intensity and load carbon emission rate. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0052] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0053] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] Embodiment 1

[0055] like Figure 1 and Figure 2 As shown, Figure 1 A schematic diagram of an iterative calculation method for carbon emission flow of a benchmark transmission system, including the following steps:

[0056] S1: Construct the carbon emission factor and carbon intensity correlation model within each node in the benchmark transmission system and the carbon emission factor correlation model between nodes. Calculate the carbon emission factor e of node i in the system i , defined by the carbon intensity of the node, the carbon emission factor and carbon intensity association model formula is as follows:

[0057]

[0058] In the formula, l + , l - represents the branch set flowing into node i and the branch set flowing out of node i respectively; E N，i represents the carbon intensity at node i; E G，i Represents the carbon emission intensity of the generator; P B，l Indicates the active power flow of branch l; P G，i represents the generator injected power; ρ l Represents the carbon flow density of branch 1.

[0059] S2: The carbon flow density of all injection branches b1,...,n of node i is equal to the carbon emission coefficient of its injection node j1,...,n. Therefore, the carbon emission factor of node i can be expressed by the carbon emission factor of its injection node. The calculation formula of the carbon emission factor association model between nodes is as follows:

[0060]

[0061] In the above formula, e i is the carbon emission factor of node i; P j→i represents the active power injected from node j to node i; e j represents the carbon emission factor injected into node j; G s Indicates the active power output of generator set s; e s represents the carbon emission factor of the generator set s; I + , T + They represent the set of all branches that inject active power into node i and the set of generator sets connected to node i respectively.

[0062] S3: Transform the carbon emission factor correlation model between nodes to obtain the linear equation of carbon emission factors between nodes. The formula is as follows:

[0063]

[0064] S4: The linear equations of carbon emission factors between nodes are used to obtain the linear equations of carbon emission factors of the benchmark transmission system. For a power system consisting of n nodes, the linear equations of the calculation formula of the carbon emission factor of each node are as follows:

[0065]

[0066] S5: Convert the carbon emission factor linear equations into the corresponding carbon emission factor matrix, which can be expressed as follows:

[0067]

[0068] In the above formula, P N represents the node active power flow matrix, which is an N-order diagonal matrix, and the diagonal element P in the ith row Nii represents the total active power injected into node i by branches and generators; P B represents the branch power flow distribution matrix, which is an N-order square matrix. YesP B The transpose of the non-diagonal element P Bij represents the active power injected from node j to node i; E N represents the node carbon emission factor vector; and PG represents the power distribution matrix injected by the generator set, YesP G The transpose of E G is the carbon emission factor vector of the generator set; is represented as the coefficient matrix of a linear system.

[0069] S6: According to the Jacobi method, the coefficient matrix is ​​decomposed into the sum of the following three parts:

[0070]

[0071] In the above formula, D is a diagonal matrix, L is a strict lower triangular matrix, and U is a strict upper triangular matrix.

[0072] S7: The iteration matrix of the Jacobi method can be obtained as follows:

[0073]

[0074] In the above formula, P N is a diagonal matrix, equal to D; Equal to -(L+U).

[0075] S8: There is no traffic on the lines between the suspended nodes and other nodes in the power grid, so it will not affect the calculation of CEF in the power system. If all suspended nodes are excluded when calculating CEF, the power grid becomes a connected network, then P N The diagonal elements of do not contain non-zero elements. Therefore, P N The reversibility is guaranteed. The matrix form of the carbon emission factor iteration equation is as follows:

[0076]

[0077] In the formula, represents the node carbon emission factor vector after k iterations; from the perspective of the entire power system, with flow distribution, generator injection distribution and carbon emission factor as boundary conditions, iterative calculation from any initial vector The node carbon emission factor vector generated after each iteration is used as the initial value of the next iteration, and k+1 linear iterations are performed until the result converges to In essence, each iteration process is gradually approaching the convergent node carbon emission factor; P N -1 ·P B T ·E N (k) It is the product of the iteration matrix B and the carbon emission factor vector of the current iteration, which represents the iterative update of the known terms in the linear equation system. N-1 ·P G T ·E G is a correction term, which represents the impact of the carbon emission factor of the generator set on the carbon emission factor of the node.

[0078] S9: Further, the iterative equations of carbon emission factors can be obtained in the following form:

[0079]

[0080] In the above formula, Represents the carbon emission factor of node n calculated from k+1 iterations.

[0081] Formula (7) is the matrix form of the iterative equation of the node carbon emission factor, and formula (8) is the component form of the iterative equation of the node carbon emission factor. Formula (7) in matrix form is usually used for fast iteration and calculation in computer programs because it can utilize efficient algorithms for matrix operations. Formula (8) in component form is more suitable for theoretical analysis and understanding the specific calculation process of each node. Both have their own advantages in application. The matrix form is convenient for handling large-scale problems, while the component form is convenient for detailed analysis and understanding. The final calculation is calculated by formula (7).

[0082] S10: After the iterative calculation of the carbon emission factor iterative equation group converges, the carbon emission factor of each node is obtained, and the carbon intensity of each node is obtained by combining the carbon emission factor with the carbon intensity association model.

[0083] Effect verification:

[0084] In order to verify the reliability and scalability of the iterative calculation method of carbon emission factors proposed in this paper, IEEE-6, IEEE-14 and IEEE-39 node systems are used for verification. The scale of the IEEE-6 node system is relatively small, which is suitable for preliminary verification of the effectiveness and accuracy of the iterative calculation method of carbon emission flow based on power flow. As a medium-sized power system test platform, the IEEE-14 node system has a moderate installed capacity, generally ranging from hundreds of megawatts to thousands of megawatts. As for the IEEE-39 node system, it is used to test larger and more complex power systems. Due to the relatively large load scale, it usually contains more load nodes and more power equipment, reflecting a more realistic power system scenario.

[0085] Figure 3The active power flux at the nodes of the three test systems is described. In the carbon emission analysis of the power system, the node active power flux is a key parameter because it is directly related to the energy consumption of the node and the corresponding carbon emissions. The node active power flux is a key indicator for evaluating and optimizing the carbon emissions of the power system. Combined with the node carbon emission factor, it can provide a detailed view of the carbon footprint of the power system and provide a basis for emission reduction measures. Figure 3 In (a) (IEEE-6-node system simulation results), the maximum active power flow occurs at node 2, indicating that the generator sets contribute greatly to the carbon potential of nodes 2 and 1. Figure 3 In (b) (IEEE-14 bus system simulation results), the highest active power flow occurs at node 1. The active power flow represents the "absolute amount" of active power flowing into the node in the flow direction. Generator unit 1 outputs the active power flowing into node 1, resulting in a significant increase in the power of node 1. Figure 3 In (c) (IEEE-39 node system simulation results), the active power flow of node 39 is the highest. This is because a generator with a maximum output power of 1000 MW is installed, resulting in the maximum active power flowing into node 39.

[0086] Figure 4 The carbon intensity (carbon intensity is positively correlated with carbon emission factor) and load carbon flow of each node in the three test systems are shown. It is obvious that the carbon intensity of the node is significantly higher than the load carbon flow. Node carbon intensity refers to the carbon emission potential or capacity of each node in the CEF model. It reflects the degree of influence of the node on carbon emissions and is usually positively correlated with the power load and carbon emission factor of the node. The size of the node carbon intensity will affect the distribution and flow of carbon emissions in the power system. On the other hand, the load carbon flow refers to the carbon flux related to the power load of each node in the CEF model. It represents the carbon emissions corresponding to the unit power load and can be used to evaluate the contribution of different loads to the carbon emissions of the power system. Node carbon intensity: reflects the carbon emissions generated by each unit power consumption of each node, and is a direct reflection of the node carbon emission factor. Load carbon flow: reflects the total carbon emissions caused by the power flow through the node, which is the cumulative effect of the node carbon emission factor when the power flows through the node.

[0087] Therefore, the verification results show that in the power system, the carbon emission factor directly reflects the carbon intensity and active power flux of the node. The carbon emission factor of the calculated node can reflect the carbon emissions generated by each node's unit electricity consumption.

[0088] This paper extends the theoretical model of carbon emission flow based on power flow. Based on the concepts of node active flux, node carbon intensity, load carbon flow, etc., a carbon emission flow iteration method is proposed, and its convergence is verified. In order to apply the theoretical model of carbon emission flow to actual carbon accounting, this paper conducts a comprehensive simulation verification of different node test systems to test its versatility. Various types and capacities of systems are selected from the IEEE benchmark node system to verify the proposed method, which proves that the method has strong adaptability.

[0089] Embodiment 2

[0090] A carbon emission flow iterative calculation system for a benchmark power transmission system, applied to the carbon emission flow iterative calculation method for the benchmark power transmission system described above, comprising:

[0091] A first construction module is used to construct a carbon emission factor and carbon intensity association model within each node in the benchmark transmission system and a carbon emission factor association model between each node;

[0092] A first conversion module is used to convert the carbon emission factor correlation model between nodes to obtain a linear equation of the carbon emission factor between nodes;

[0093] A second building block is used to obtain a linear equation group of carbon emission factors of a benchmark transmission system from linear equations of carbon emission factors between nodes;

[0094] A second conversion module, which is used to convert the carbon emission factor linear equation group into a corresponding carbon emission factor matrix;

[0095] A decomposition module, which is used to decompose the carbon emission factor matrix coefficients according to the Jacobi method to obtain an iterative matrix of the Jacobi method;

[0096] A third construction module is used to obtain a carbon emission factor iteration equation matrix and a carbon emission factor iteration equation group according to the iteration matrix;

[0097] The calculation module is used to obtain the carbon emission factor of each node after iterative calculation of the iterative equation group of the carbon emission factor converges, and to obtain the carbon intensity of each node by combining the carbon emission factor with the carbon intensity association model.

[0098] Embodiment 3

[0099] A computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned iterative calculation method for carbon emission flow of a benchmark power transmission system.

[0100] Embodiment 4

[0101] A processor is used to run a program, wherein the program executes the above-mentioned iterative calculation method for carbon emission flow of a benchmark power transmission system when running.

[0102] The present application provides an iterative calculation method for the carbon emission flow of a benchmark transmission system, including: constructing a carbon emission factor and carbon intensity correlation model within each node in the benchmark transmission system and a carbon emission factor correlation model between each node; converting the carbon emission factor correlation model between each node to obtain a linear equation for the carbon emission factor between nodes; obtaining a linear equation group of carbon emission factors of the benchmark transmission system from the linear equation of carbon emission factors between nodes; converting the linear equation group of carbon emission factors into a corresponding carbon emission factor matrix; decomposing the coefficients of the above carbon emission factor matrix according to the Jacobi method to obtain an iterative matrix of the Jacobi method; obtaining a carbon emission factor iterative equation matrix and a carbon emission factor iterative equation group according to the iterative matrix; after iterative calculation of the iterative equation group of carbon emission factors converges, the carbon emission factor of each node is obtained, and the carbon intensity of each node is obtained by combining the carbon emission factor and the carbon intensity correlation model. This paper expands the theoretical model of carbon emission flow based on power flow. Based on the concepts of node active flux, node carbon intensity, load carbon flow, etc., a carbon emission flow iteration method is proposed, and its convergence is verified. In order to apply the theoretical model of carbon emission flow to actual carbon accounting, this paper conducts comprehensive simulation verification on different node test systems to test its universality. Various types and capacities of systems are selected from the IEEE benchmark node system to verify the proposed method, which proves that the method has strong adaptability.

[0103] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0104] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0105] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0106] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for iteratively calculating carbon emission flow of a benchmark transmission system, characterized in that: include: Construct a correlation model between carbon emission factors and carbon intensity within each node in the benchmark transmission system and a correlation model between carbon emission factors among nodes; Transform the carbon emission factor correlation model between nodes to obtain the linear equation of carbon emission factors between nodes; The linear equations of carbon emission factors of the benchmark transmission system are obtained from the linear equations of carbon emission factors between nodes; Convert the carbon emission factor linear equations into the corresponding carbon emission factor matrix; Decompose the carbon emission factor matrix coefficients according to the Jacobi method to obtain the Jacobi method iteration matrix; According to the iteration matrix, the carbon emission factor iteration equation matrix and the carbon emission factor iteration equation group are obtained; After the iterative calculation of the iterative equation group of the carbon emission factor converges, the carbon emission factor of each node is obtained, and the carbon intensity of each node is obtained by combining the carbon emission factor with the carbon intensity association model.

2. The iterative calculation method for carbon emission flow of the benchmark power transmission system according to claim 1, characterized in that: The carbon emission factor and carbon intensity association model are as follows: In the formula, l + , l - represents the branch set flowing into node i and the branch set flowing out of node i respectively; E N，i represents the carbon intensity at node i; E G，i Represents the carbon emission intensity of the generator; P B，l Indicates the active power flow of branch 1; P G，i represents the generator injected power; ρ l Represents the carbon flow density of branch 1.

3. The iterative calculation method for carbon emission flow of the benchmark power transmission system according to claim 2, characterized in that: The carbon emission factor association model is as follows: In the above formula, e i is the carbon emission factor of node i; P j→i represents the active power injected from node j to node i; e j represents the carbon emission factor injected into node j; G s Indicates the active power output of generator set s; e s represents the carbon emission factor of the generator set s; I + , T + They represent the set of all branches that inject active power into node i and the set of generator sets connected to node i respectively.

4. The iterative calculation method for carbon emission flow of the benchmark power transmission system according to claim 3, characterized in that: The linear equations of carbon emission factors are as follows:

5. The iterative calculation method for carbon emission flow of a benchmark power transmission system according to claim 1, characterized in that: The carbon emission factor matrix is ​​as follows: In the above formula, P N represents the node active power flow matrix, which is an N-order diagonal matrix, and the diagonal element P in the ith row Nii represents the total active power injected into node i by branches and generators; P B represents the branch power flow distribution matrix, which is an N-order square matrix. YesP B The transpose of the non-diagonal element P Bij represents the active power injected from node j into node i; E N represents the node carbon emission factor vector; and P G represents the power distribution matrix injected by the generator set, YesP G The transpose of E G is the carbon emission factor vector of the generator set; is represented as the coefficient matrix of a linear system.

6. The iterative calculation method for carbon emission flow of the benchmark power transmission system according to claim 5, characterized in that: The carbon emission factor iteration equation matrix is ​​as follows: In the formula, represents the node carbon emission factor vector after k iterations; from the perspective of the entire power system, with flow distribution, generator injection distribution and carbon emission factor as boundary conditions, iterative calculation from any initial vector At the beginning, the node carbon emission factor vector generated after each iteration is used as the initial value of the next iteration, and k+1 linear iterations are performed until the result converges to 7. The iterative calculation method for carbon emission flow of the benchmark power transmission system according to claim 6, characterized in that: The carbon emission factor iteration equations are as follows: In the above formula, Represents the carbon emission factor of node n calculated from k+1 iterations.

8. A carbon emission flow iterative calculation system for a benchmark power transmission system, characterized in that: The iterative calculation method for carbon emission flow of the benchmark power transmission system applied to any one of claims 1 to 7 comprises: A first construction module is used to construct a carbon emission factor and carbon intensity association model within each node in the benchmark transmission system and a carbon emission factor association model between each node; A first conversion module is used to convert the carbon emission factor correlation model between nodes to obtain a linear equation of the carbon emission factor between nodes; A second building block is used to obtain a linear equation group of carbon emission factors of a benchmark transmission system from linear equations of carbon emission factors between nodes; A second conversion module, which is used to convert the carbon emission factor linear equation group into a corresponding carbon emission factor matrix; A decomposition module, which is used to decompose the carbon emission factor matrix coefficients according to the Jacobi method to obtain an iterative matrix of the Jacobi method; A third construction module is used to obtain a carbon emission factor iteration equation matrix and a carbon emission factor iteration equation group according to the iteration matrix; The calculation module is used to obtain the carbon emission factor of each node after iterative calculation of the iterative equation group of the carbon emission factor converges, and to obtain the carbon intensity of each node by combining the carbon emission factor with the carbon intensity association model.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the iterative calculation method for the carbon emission flow of the benchmark power transmission system according to any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the iterative calculation method for carbon emission flow of the benchmark power transmission system as described in any one of claims 1 to 7.

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