Construction method of power grid carbon emission distribution network model

Abstract

The invention discloses a construction method of a power grid carbon emission distribution network model. The construction method comprises the steps of obtaining node load data, generating capacity data and branch data of a power grid; forming an original data set and a load flow calculation result according to the data, and establishing a network topology model according to the original data set and the load flow calculation result; equivalently transplanting the loss power of each branch in the network topology model to a corresponding upstream node, keeping the size and direction of the power flow of each branch unchanged, and updating the network topology model; and based on the updated network topology model, constructing a carbon emission distribution network model in combination with a proportion sharing principle. According to the method, through real-time data acquisition, network topology modeling, carbon emission flow calculation and optimization solution, the carbon emission in the power system can be accurately quantified, and a scientific basis is provided for low-carbon scheduling and policy making of the power industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and in particular relates to a method for constructing a carbon emission distribution network model of a power grid. Background Art

[0002] As global climate change intensifies, optimizing carbon emission management in the power system has become an urgent issue to be addressed. In the context of addressing climate change, reducing carbon emissions is not only a policy goal of governments, but also a core driving force for sustainable development of enterprises. However, existing carbon emission calculation methods mostly focus on static analysis, often ignoring the dynamic characteristics and spatiotemporal distribution of carbon emissions in power transmission and distribution networks. This static analysis method mainly relies on historical data and cannot reflect the changes in carbon emissions in the power grid in real time, resulting in poor implementation of management measures.

[0003] Specifically, traditional carbon emission calculation models usually assume that the power system remains unchanged over a specific period of time, and fail to take into account the impact of factors such as load fluctuations, renewable energy access, and power generation scheduling. This lack of dynamic response makes it impossible for carbon emission management to accurately match the actual situation, resulting in waste of resources and low emission reduction effects. In addition, static models cannot capture the complexity of carbon emission flows in power networks. For example, under different time and space conditions, the source and distribution of carbon emissions will change significantly, and this change has an important impact on optimizing the operation of the power system.

[0004] In addition, due to the lack of dynamic monitoring of carbon emission flows, relevant decision makers often lack the necessary information support when formulating carbon emission reduction strategies. This means that policy formulation and implementation are often based on insufficient data, which may lead to some measures failing to achieve the expected results or even producing counterproductive effects. Summary of the invention

[0005] (I) Purpose of the invention

[0006] The purpose of the present invention is to provide a method for constructing a carbon emission distribution network model of a power grid. The invention provides a new dynamic model that can more accurately reflect the carbon emission characteristics in the power grid. The model can integrate real-time data, consider multiple factors such as load changes, the access of renewable energy and the operation status of the power grid, so as to achieve comprehensive and accurate monitoring and analysis of carbon emission flows.

[0007] (II) Technical solution

[0008] In order to solve the above problems, the present invention provides a method for constructing a grid carbon emission distribution network model, comprising:

[0009] Obtain the load data, power generation data and branch data of each node of the power grid; wherein the load data of each node includes the historical load and real-time load of each node, and the power generation data includes the historical power generation and real-time power generation of each generator set;

[0010] Clean the historical load of each node and the historical power generation of each generator set to form the original data set;

[0011] Perform power flow calculation on the real-time load of each node, the real-time power generation of each generator set and the data of each branch to obtain the voltage of each node, the power of each branch and the power loss of each branch;

[0012] Establishing a network topology model based on the original data set, the voltage of each node, the power of each branch and the power loss of each branch;

[0013] Equivalently transplant the power loss of each branch in the network topology model to the corresponding upstream node, while keeping the power flow size and direction of each branch unchanged, and updating the network topology model;

[0014] Based on the updated network topology model, a carbon emission distribution network model is constructed in combination with the proportional sharing principle.

[0015] Furthermore, the carbon emission distribution network model is constructed based on the updated network topology model and in combination with the proportional sharing principle, including:

[0016] The carbon emissions of each branch are calculated based on the power of each branch and the historical carbon emission data of each generator set, combined with the power of each branch, the carbon emissions of each branch and the proportional sharing principle; the power generation data also includes the historical carbon emission data and real-time carbon emission data of each generator set;

[0017] According to the carbon emissions of each branch, a linear equation group is established, the carbon emissions of each node are calculated by the linear equation group, and a carbon emission distribution network model is constructed based on the carbon emissions of each node.

[0018] Furthermore, the carbon emission calculation formula of each branch is:

[0019]

[0020] In the formula, C ij is the carbon emission contribution of node i to node j, P ij is the power from node i to node j, C f is the carbon emission factor corresponding to the node j load, L j is the load of node j, ∑ w L w is the total load of all nodes, used to calculate the load ratio, and w is the node.

[0021] Furthermore, the calculation formula of the linear equation group is:

[0022]

[0023] In the formula, C w is the total carbon emission of node w; is the carbon emissions from the previous node i flowing into node w; is the carbon emission from w to the next node j; ΔC is the carbon emission caused by loss.

[0024] Furthermore, the method also includes: solving the carbon emission network model using a linear programming model to obtain the optimal carbon emission flow of each node, wherein the linear programming model includes: an objective function and constraints, the objective function includes minimizing the total carbon emissions, and the constraints include flow balance constraints, node voltage constraints, and branch power constraints.

[0025] Furthermore, the use of a linear programming model to solve the carbon emission distribution network model to obtain the optimal carbon emission flow of each node includes:

[0026] Solving the carbon emission distribution network model through the objective function and the constraint conditions to generate an initial value;

[0027] Iterative optimization is performed based on the initial value, the real-time load of each node, and the real-time power generation of each generator set to adjust the parameters of the carbon emission distribution network model;

[0028] Set the convergence condition, determine whether the iteration is terminated based on whether the iteration result meets the convergence condition, and output the optimal carbon emission flow of each node.

[0029] Furthermore, the iterative calculation formula is:

[0030] C ij (k+1) =C ij (k) +α(C ij * -C ij (k) );

[0031] In the formula, C ij (k) is the carbon emission flow of the kth iteration, C ij * is the optimal carbon emission flow, and α is the learning rate.

[0032] Furthermore, determining whether the iteration is terminated according to whether the iteration result satisfies the convergence condition and outputting the optimal emission of each node includes:

[0033] If ΔC ij <ξ and k<T max When this condition is met, the iteration terminates, and the optimal carbon emissions of each node are output. Otherwise, let k = k + 1, and return to the iterative optimization process to continue the loop;

[0034] If k = T max When this condition is met, the iteration terminates; where T max is the maximum number of iterations, ΔC ij is the difference in carbon emissions between two adjacent iterations, and k is the number of iterations.

[0035] Furthermore, the method further includes: visualizing and analyzing the carbon emissions of the power grid by monitoring the optimal carbon emission flow of each node.

[0036] Furthermore, the objective function is:

[0037] In the formula, f min represents the minimum value of the total carbon emissions of the power system, N is the total number of nodes in the power system,

[0038] (III) Beneficial effects

[0039] The above technical scheme of the present invention has the following beneficial technical effects: the present invention provides a method for constructing a carbon emission distribution network model of a power grid, which aims to dynamically analyze and calculate the flow of carbon emissions under different time and space conditions. In this model, a power grid topology structure is introduced, which combines the load and power generation of each node, and adopts the proportional sharing principle to reasonably allocate carbon emissions, significantly improving the accuracy of carbon emission management. The specific principle is as follows: first, the load data, power generation data and branch data of each node of the power grid are obtained, and the historical data are cleaned and sorted to form an original data set to ensure its accuracy, and the flow calculation is performed on each real-time data and each branch data to obtain the flow calculation result, and then a network topology model is established based on the flow calculation result and the original data set, and the power loss of each branch in the network topology model is equivalently transplanted to the corresponding upstream node. This setting can more accurately calculate the carbon emissions of the entire power grid or system, and at the same time, a carbon emission distribution network model is constructed in combination with the proportional sharing principle, and the carbon emission flow of each node is calculated using the proportional sharing principle, and its contribution to the load is clarified, and the carbon emissions are scientifically and reasonably allocated. The model based on the proportional sharing principle also supports flexible scheduling and optimization strategies. Finally, the linear programming model is used to solve the carbon emission network model to obtain the optimal carbon emission flow of each node. The model construction method of the present invention makes the allocation of carbon emissions more scientific and can reflect the actual emission level of each node under different times and conditions. In addition, during the peak period of electricity demand, the model can adjust the allocation of carbon emissions in real time to ensure that low-carbon energy is used first, thereby reducing the overall carbon emission level. The model can also provide data support for decision makers to help them formulate more effective carbon emission reduction policies and measures. Through dynamically updated carbon emission flow information, managers can respond to market changes in a timely manner and optimize the operation of the power system. Through this dynamic model, managers of the power system can more effectively formulate and adjust carbon emission reduction strategies, achieve precise management of carbon emissions, and thus contribute more to global climate governance. Ultimately, this will help promote the development of the power system in a low-carbon and sustainable direction and provide practical support for mitigating global warming. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of a method for constructing a power grid carbon emission distribution network model of the present invention;

[0041] Figure 2 4 is an example diagram of a node association matrix according to a specific embodiment of the present invention.

[0042] Figure 3 This is an example diagram of total carbon emissions of a power system according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0044] like Figure 1 As shown, the present invention provides a method for constructing a power grid carbon emission distribution network model, comprising:

[0045] S1, obtain the load data, power generation data and branch data of each node of the power grid; among them, the load data of each node is obtained through smart meters and monitoring equipment, and the power generation data of each generator set is obtained through the dispatching system of the power generation enterprise. The load data of each node includes the historical load and real-time load of each node, and the power generation data includes the historical power generation and real-time power generation of each generator set; the power generation data also includes the power generation capacity and the type of the generator set.

[0046] S2, the historical load of each node and the historical power generation of each generator set are cleaned to form the original data set to improve the accuracy of subsequent analysis; the specific process of data cleaning is as follows: ① Remove outliers: By setting reasonable thresholds, identify and eliminate unreasonable extreme values ​​and erroneous data; ② Fill missing values: For missing values ​​in the data, interpolation or mean filling methods are used to fill in to ensure the continuity of the data; ③ Standardization: Standardize all types of data to eliminate the impact of different dimensions. Use methods such as z-score standardization or Min-Max standardization to normalize the data to the same range; ④ Data integration: Integrate data from different sources to form a comprehensive data set (i.e., original data set) containing all necessary information for subsequent modeling and analysis.

[0047] S3, perform power flow calculation on the real-time load of each node, the real-time power generation of each generator set and the data of each branch to obtain the power flow calculation results, which are convenient for subsequent carbon emission tracking. The power flow calculation results include the voltage of each node, the power of each branch and the power loss of each branch, and record the resistance, reactance, rated power and other parameters of each branch to ensure the accuracy of subsequent network topology modeling;

[0048] S4, based on the original data set, the voltage of each node, the power of each branch and the power loss of each branch, establish a network topology model; in this step: based on the original data set and the power flow calculation results, establish a network topology model. In order to clearly describe the topological structure of each node in the power system, introduce a node association matrix. Assuming that the power system has p nodes, the size of the association matrix is ​​p×p, a ij is the element of the matrix, aij The value is 1, indicating that there is a relationship between node j and node i; a ij The value is 0, indicating that there is no association between node j and node i. For example, Figure 2 , its node association matrix is:

[0049]

[0050] S5, transplant the power loss of each branch in the network topology model to the corresponding upstream node, while keeping the power flow size and direction of each branch unchanged, and update the network topology model. In this step: according to the power flow calculation results, transplant the power loss of branch ij to its upstream node i as the equivalent load of node i, and the power flow size and direction of branch ij remain unchanged, that is, the power flow size and direction at the end of the original line are taken. Attributing the branch power loss (branch loss power) to the upstream node can accurately reflect the total emissions, that is, it can more accurately calculate the carbon emissions of the entire power grid or system, ensure that all power losses are taken into account, and reflect the actual emissions. Second, it simplifies the calculation process: by concentrating the branch power loss to the upstream node, the complexity of the calculation can be reduced and the model can be simplified.

[0051] S6, based on the updated network topology model, combined with the proportional sharing principle, builds a carbon emission distribution network model. The proportional sharing principle means that in the process of resource allocation and responsibility, each participant allocates resources or responsibilities in proportion to their contribution or usage, which can reasonably reflect the actual impact and contribution of each participant. Figure 3 As shown, it includes carbon emissions from the power generation side, transmission lines, substations and distribution side, and each part allocates carbon emissions according to the principle of proportional sharing.

[0052] Step S6 specifically includes:

[0053] S61, based on the power of each branch and the historical carbon emission data of each generator set, combined with the power of each branch, the carbon emission of each branch and the proportional sharing principle, calculate the carbon emission of each branch. The power generation data also includes the historical carbon emission data and real-time carbon emission data of each generator set; the carbon emission calculation formula of each branch is:

[0054]

[0055] In formula (1), C ij is the carbon emission contribution of node i to node j, P ij is the power from node i to node j, C f is the carbon emission factor corresponding to the node j load, L j is the load of node j, ∑ w L wis the total load of all nodes, used to calculate the load ratio, and w is the node.

[0056] S62: According to the carbon emissions of each branch, a linear equation group is established, the carbon emissions of each node are calculated by the linear equation group, and a carbon emission distribution network model is constructed based on the carbon emissions of each node. The calculation formula of the linear equation group is:

[0057]

[0058] In formula (2), C w is the total carbon emission of node w; is the carbon emissions from the previous node i flowing into node w; is the carbon emission from w to the next node j; ΔC is the carbon emission caused by loss.

[0059] Furthermore, the method further includes S7, using a linear programming model to solve the carbon emission network model to obtain the optimal carbon emission flow of each node. The linear programming model includes: an objective function and constraints, the objective function includes minimization of total carbon emissions, and the constraints include power flow balance constraints, voltage constraints of each node, and power constraints of each branch. This step specifically includes:

[0060] S71, solving the carbon emission distribution network model through the objective function and the constraint conditions to generate initial values; inputting the objective function and the constraint conditions into the linear programming solver CPLEX, and using the solver to solve the carbon emission distribution network model to obtain the optimal carbon emission allocation scheme C for each node in the current state; ij * , the initial value is the optimal carbon emission flow of each node under the current state; the objective function is:

[0061]

[0062] In formula (3), f min It represents the minimum value of the total carbon emissions of the power system, and N is the total number of nodes in the power system. At the same time, according to the operating characteristics of the power system, corresponding constraints are set, including power balance constraints, power balance constraints, and node voltage constraints. The specific constraints are as follows:

[0063] ① Flow balance constraints:

[0064]

[0065] In formula (4): P Gi and Q Gi They represent the active power and reactive power injected by the power source into node i respectively; U i represents the voltage amplitude at node i; Gij and B ij Respectively represent the conductance and susceptance between nodes i and j; θ ij Represents the phase difference between the voltages at nodes i and j, ΔP i is the active power difference, ΔQ i is the reactive power difference.

[0066] ②Node voltage constraints:

[0067] U min ≤U i ≤U max ; (5)

[0068] In formula (5): U min and U max Represent the minimum and maximum values ​​of the node voltage respectively.

[0069] ③ Branch power constraint:

[0070] S q ≤S qmax ; (6)

[0071] In formula (6): S q represents the apparent power flowing through branch q; S qmax It represents the maximum value of the apparent power allowed to flow through branch q.

[0072] S72, perform iterative optimization based on the initial value, the real-time load of each node, and the real-time power generation of each generator set, and adjust the parameters of the carbon emission distribution network model; in order to improve the solution efficiency, an iterative algorithm is used for calculation. Set the initial value: Set the initial carbon emission flow value, which is the optimal carbon emission flow of each node in the current state. In iterative optimization, the iterative calculation formula is:

[0073] C ij (k+1) =C ij (k) +α(C ij * -C ij (k) ); (7)

[0074] In formula (7), C ij (k) is the carbon emission flow of the kth iteration, C ij * is the optimal carbon emission flow, and α is the learning rate.

[0075] S73, setting a convergence condition, determining whether the iteration is terminated according to whether the iteration result meets the convergence condition, and outputting the optimal carbon emission flow of each node. This step specifically includes:

[0076] If ΔC ij <ξ and k<T max then the iteration terminates, and the optimal carbon emissions of each node are output. Otherwise, let k = k + 1, and return to the iterative optimization process to continue the loop;

[0077] If k = T max then the iteration terminates; where T max is the maximum number of iterations, ΔC ij is the difference in carbon emissions between two adjacent iterations, and k is the number of iterations.

[0078] Furthermore, the method further includes S8: performing visual analysis on the carbon emissions of the power grid by monitoring the optimal carbon emission flow of each node, and this step includes:

[0079] (1) Analysis of spatio-temporal distribution characteristics

[0080] ① Organize the obtained optimal carbon emission flow data: Integrate the carbon emissions of each node in the power system with data such as time, load, and power generation to form a time series data set.

[0081] ② Perform feature analysis on the time series data set:

[0082] Time period division: Divide the time series data set into different time periods (such as hours, days, weeks), and analyze the carbon emission characteristics of each time period.

[0083] Correlation analysis: Use statistical methods (such as Pearson correlation coefficient) to explore the relationship between carbon emissions and load, power generation, and identify influencing factors.

[0084] Summarize the carbon emission characteristics of different time periods, form a report, and point out the carbon emission characteristics of peak and trough periods.

[0085] (2) Dynamic visualization

[0086] Develop a dynamic visualization tool using a JavaScript library to real-time display the change process of carbon emission flow, and support decision-making and analysis.

[0087] This paper proposes a method for constructing a grid carbon emission distribution network model, which provides a systematic and efficient solution for carbon emission management of power systems. Through real-time data acquisition, network topology modeling, carbon emission flow calculation and optimization solution, this method can accurately quantify carbon emissions in the power system and analyze its spatiotemporal dynamic change characteristics. The adoption of the proportional sharing principle can reasonably allocate the carbon emission responsibilities of each node, thereby better reflecting the actual operation of the power system. At the same time, the combination of linear programming and iterative algorithms effectively improves the calculation efficiency and ensures rapid convergence to the optimal solution in complex systems. The present invention not only provides a scientific basis for low-carbon power dispatching, but also provides policymakers with practical tools for carbon emission reduction. Future research can further expand the applicability of the model and consider more influencing factors, such as the fluctuation of the proportion of renewable energy and changes in market electricity prices, to promote the sustainable development and low-carbon transformation of the power industry.

[0088] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

[0089] The present invention has been described above with reference to the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, a person skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

[0090] Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.

[0091] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] A person of ordinary skill in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes the following process of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (Read-Only Memory, referred to as ROM) or a random access memory (Random Access Memory, referred to as RAM), etc. The steps in the method of the embodiment of the present invention can be adjusted in order, merged and deleted according to actual needs. The modules [units] in the system [terminal or device] of the embodiment of the present invention can be merged, divided and deleted according to actual needs.

Claims

1. A method for constructing a power grid carbon emission distribution network model, characterized in that: include: Obtain the load data, power generation data and branch data of each node of the power grid; wherein the load data of each node includes the historical load and real-time load of each node, and the power generation data includes the historical power generation and real-time power generation of each generator set; Clean the historical load of each node and the historical power generation of each generator set to form the original data set; Perform power flow calculation on the real-time load of each node, the real-time power generation of each generator set and the data of each branch to obtain the voltage of each node, the power of each branch and the power loss of each branch; Establishing a network topology model based on the original data set, the voltage of each node, the power of each branch and the power loss of each branch; Equivalently transplant the power loss of each branch in the network topology model to the corresponding upstream node, while keeping the power flow size and direction of each branch unchanged, and updating the network topology model; Based on the updated network topology model, a carbon emission distribution network model is constructed in combination with the proportional sharing principle.

2. The method for constructing a grid carbon emission distribution network model according to claim 1, characterized in that: The carbon emission distribution network model constructed based on the updated network topology model and the proportional sharing principle includes: The carbon emissions of each branch are calculated based on the power of each branch and the historical carbon emission data of each generator set, combined with the power of each branch, the carbon emissions of each branch and the proportional sharing principle; the power generation data also includes the historical carbon emission data and real-time carbon emission data of each generator set; According to the carbon emissions of each branch, a linear equation group is established, the carbon emissions of each node are calculated by the linear equation group, and a carbon emission distribution network model is constructed based on the carbon emissions of each node.

3. The method for constructing a grid carbon emission distribution network model according to claim 2, characterized in that: The carbon emission calculation formula of each branch is: In the formula, C ij is the carbon emission contribution of node i to node j, P ij is the power from node i to node j, C f is the carbon emission factor corresponding to the node j load, L j is the load of node j, ∑ w L w is the total load of all nodes, and w is the node.

4. The method for constructing a grid carbon emission distribution network model according to claim 2, characterized in that: The calculation formula of the linear equation system is: In the formula, C w is the total carbon emission of node w; is the carbon emissions from the previous node i flowing into node w; is the carbon emission from w to the next node j; ΔC is the carbon emission caused by loss.

5. The method for constructing a grid carbon emission distribution network model according to claim 1, characterized in that: The method further comprises: The carbon emission network model is solved using a linear programming model to obtain the optimal carbon emission flow of each node; wherein the linear programming model includes: an objective function and constraints, the objective function includes minimization of total carbon emissions, and the constraints include flow balance constraints, voltage constraints of each node, and power constraints of each branch.

6. The method for constructing a grid carbon emission distribution network model according to claim 5, characterized in that: The method of solving the carbon emission distribution network model by using a linear programming model to obtain the optimal carbon emission flow of each node includes: Solving the carbon emission distribution network model through the objective function and the constraint conditions to generate an initial value; Iterative optimization is performed based on the initial value, the real-time load of each node, and the real-time power generation of each generator set to adjust the parameters of the carbon emission distribution network model; Set the convergence condition, determine whether the iteration is terminated based on whether the iteration result meets the convergence condition, and output the optimal carbon emission flow of each node.

7. The method for constructing a grid carbon emission distribution network model according to claim 6, characterized in that: The calculation formula of the iteration is: C ij (k+1) =C ij (k) +α(C ij * -C ij (k) ); In the formula, C ij (k) is the carbon emission flow of the kth iteration, C ij * is the optimal carbon emission flow, and α is the learning rate.

8. The method for constructing a grid carbon emission distribution network model according to claim 7, characterized in that: The step of determining whether the iteration is terminated according to whether the iteration result satisfies the convergence condition and outputting the optimal emission of each node includes: If ΔC ij < ξ and k < T max the iteration terminates, and the optimal carbon emissions of each node are output. Otherwise, let k = k + 1, and return to the iterative optimization process to continue the loop; If k = T max When , the iteration terminates; where T max is the maximum number of iterations, ΔC ij is the difference between the carbon emissions of two adjacent iterations, k is the number of iterations, and ξ is the preset accuracy.

9. The method for constructing a grid carbon emission distribution network model according to claim 5, characterized in that: The method also includes: visually analyzing the carbon emissions of the power grid by monitoring the optimal carbon emission flow of each node.

10. The method for constructing a grid carbon emission distribution network model according to claim 5, characterized in that: The objective function is: In the formula, f min To represent the total power system The minimum value of carbon emissions, N is the total number of nodes in the power system.

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