A method for constructing a power grid carbon emission distribution network model

Abstract

The application discloses a kind of construction methods of power grid carbon emission distribution network model, comprising: obtaining the load data of each node of power grid, power generation data and each branch data;According to the original data set and power flow calculation result formed according to each data, then the network topology model is established according to original data set and power flow calculation result;Equivalent transplantation of the power loss of each branch in the network topology model is moved to the corresponding upstream node, while keeping the power flow size, direction of each branch unchanged, updating network topology model;Based on the updated network topology model, combined with the proportion sharing principle, a carbon emission distribution network model is constructed.The application can accurately quantify carbon emissions in the power system through real-time data acquisition, network topology modeling, carbon emission flow calculation and optimization solution, and provide a scientific basis for low-carbon dispatching and policy-making in the power industry.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power systems, and particularly relates to a construction method of a power grid carbon emission distribution network model. BACKGROUND

[0002] With the intensification of global climate change, optimizing the carbon emission management of power systems has become an urgent problem to be solved. In the context of addressing climate change, reducing carbon emissions is the core driving force for the sustainable development of enterprises. However, existing carbon emission calculation methods are mostly focused on static analysis, often ignoring the dynamic characteristics and spatial-temporal 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 effect of management measures.

[0003] Specifically, traditional carbon emission calculation models usually assume that the power system remains unchanged within a certain time period, failing to consider the influence of factors such as load fluctuation, renewable energy access, and power generation scheduling. This model lacking dynamic response makes carbon emission management unable to accurately interface with actual situations, resulting in waste of resources and low emission reduction effect. In addition, static models cannot capture the complexity of carbon emission flow in power networks, for example, under different time and space conditions, the source and distribution of carbon emissions will change significantly, which has an important influence on optimizing the operation of power systems.

[0004] In addition, due to the lack of dynamic monitoring of carbon emission flow, relevant decision-makers often lack the necessary information support when formulating carbon emission reduction strategies. This makes the formulation and implementation of policies often based on insufficient data, which may result in the effect of some measures failing to achieve the expected effect, or even producing the opposite effect. SUMMARY

[0005] (I) Invention purpose

[0006] The purpose of the present application is to provide a construction method of a power grid carbon emission distribution network model, which provides a new dynamic model that can more accurately reflect the characteristics of carbon emissions in the power grid. This model can integrate real-time data, consider load changes, access of renewable energy and power grid operation status and other factors to achieve comprehensive and accurate monitoring and analysis of carbon emission flow.

[0007] (II) Technical solution

[0008] To solve the above problems, the present application provides a construction method of a power grid carbon emission distribution network model, comprising:

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

[0010] Data cleaning is performed on the historical load of each node and the historical power generation of each generator set to form an original data set;

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

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

[0013] The branch loss power in the network topology model is equivalent to be transplanted to the corresponding upstream node, while the power flow size and direction of each branch remain unchanged, and the network topology model is updated;

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

[0015] Further, the construction of the carbon emission distribution network model based on the updated network topology model in combination with the proportion sharing principle comprises:

[0016] According to the branch power and the historical carbon emission data of each generator set, the carbon emission of each branch is calculated in combination with the branch power, the carbon emission of each branch and the proportion sharing principle; the power generation data further includes historical carbon emission data and real-time carbon emission data of each generator set;

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

[0018] Further, 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 load of node j, L j is the load of node j, ∑ w L w is the total load of all nodes, used to calculate the proportion of the load, and w is the node.

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

[0022]

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

[0024] Further, the method further comprises: solving the carbon emission distribution network model by using a linear programming model to obtain optimal carbon emission flow of each node, wherein the linear programming model comprises: an objective function and a constraint condition, the objective function comprises minimization of total carbon emission, and the constraint condition comprises power flow balance constraint, voltage constraint of each node, and power constraint of each branch.

[0025] Further, the solving the carbon emission distribution network model by using the linear programming model to obtain optimal carbon emission flow of each node comprises:

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

[0027] Performing iterative optimization in combination with the initial value, real-time load of each node, and real-time power generation of each generator unit to adjust the carbon emission distribution network model parameter;

[0028] Setting a convergence condition, determining whether iteration is terminated according to whether the iterative result meets the convergence condition, and outputting optimal carbon emission flow of each node.

[0029] Further, the calculation formula of the iteration is:

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

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

[0032] Further, the determining whether iteration is terminated according to whether the iterative result meets the convergence condition, and outputting optimal carbon emission flow of each node comprises:

[0033] If ΔC ij < ξ and k < T max , the iteration is terminated and the optimal carbon emission of each node is output, otherwise, k=k+1 is set and the iteration optimization process is returned to continue the loop;

[0034] If k=T max , the iteration is terminated; wherein, T max is the maximum iteration number, ΔC ij is the difference between the carbon emissions of two adjacent iterations, and k is the iteration number.

[0035] Further, the method further comprises: visualizing the carbon emission of the power grid by monitoring the optimal carbon emission flow of each node.

[0036] Further, the objective function is:

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

[0038] (Three) beneficial effects

[0039] The technical scheme of the present application has the following beneficial technical effects: The present application provides a method for constructing a power grid carbon emission distribution network model, which aims to dynamically analyze and calculate carbon emission flow under different time and space conditions. In this model, the 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, obtain the load data, power generation data and branch data of each node of the power grid, clean and organize the historical data, form the original data set to ensure its accuracy, and perform power flow calculation on the real-time data and branch data to obtain the power flow calculation result. Then, based on the power flow calculation result and the original data set, a network topology model is established, and the branch loss power in the network topology model is equivalent to the corresponding upstream node. This setting can more accurately calculate the carbon emissions of the entire power grid or system, and a carbon emission distribution network model is constructed based on the proportional sharing principle. The proportional sharing principle is used to calculate the carbon emission flow of each node and its contribution to the load, 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 carbon emission network model is solved by using a linear programming model to obtain the optimal carbon emission flow of each node. The model construction method of the present application makes the allocation of carbon emissions more scientific, and can reflect the actual emission level of each node under different time and conditions. In addition, during the peak period of power demand, the model can adjust the allocation of carbon emissions in real time to ensure the preferential use of low-carbon energy, thereby reducing the overall carbon emission level. The model can also provide data support for decision-makers to help them develop more effective carbon emission reduction policies and measures. Through dynamic updating of 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, the managers of the power system can more effectively develop and adjust carbon emission reduction strategies to achieve precise management of carbon emissions, thereby contributing more to global climate governance. Ultimately, this will help to promote the development of the power system towards low-carbon and sustainable direction, and provide practical support for mitigating global warming. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a flowchart of a method for constructing a power grid carbon emission distribution network model of the present application;

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

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

[0043] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the embodiments and drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present application. 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 application.

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

[0045] S1, obtaining node load data, power generation data and branch data of the power grid; wherein the node load data 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 node load data includes historical load and real-time load of each node, and the power generation data includes historical power generation and real-time power generation of each generator set; the power generation data also includes power generation capacity and type of the generator set, etc.

[0046] S2, data cleaning is performed on the historical load of each node and the historical power generation of each generator set to form an original data set, so as to improve the accuracy of subsequent analysis; the specific process of data cleaning is as follows: ① removing outliers: by setting a reasonable threshold, unreasonable extreme values and error data are identified and removed; ② filling missing values: for missing values in the data, interpolation method or mean filling method is used for filling to ensure the continuity of the data; ③ standardization processing: standardization processing is performed on various data to eliminate the influence of different dimensions; z-score standardization or Min-Max standardization method is used to normalize the data to the same range; ④ data integration: data from different sources are integrated to form a comprehensive data set (i.e. original data set) containing all necessary information, so as to facilitate subsequent modeling and analysis.

[0047] S3, power flow calculation is performed on the real-time load of each node, the real-time power generation of each generator set and the branch data to obtain 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 loss power of each branch, and the resistance, reactance, rated power and other parameters of each branch are recorded to ensure the accuracy of subsequent network topology model modeling;

[0048] S4, based on the original data set, the voltage of each node, the power of each branch and the loss power of each branch, a network topology model is established; in this step: based on the original data set and the power flow calculation results, a network topology model is established, in order to clearly describe the topology structure of each node in the power system, a node correlation matrix is introduced, assuming that the power system has p nodes, the size of the correlation matrix is p x p, a ij is the element of the matrix, aij value 1, indicating that there is an association between node j and node i; a ij value 0, indicating that there is no association between node j and node i, for example, for Figure 2 , the node association matrix of which is:

[0049]

[0050] S5, equivalent transplant the branch loss power in the network topology model to the corresponding upstream node while keeping the branch power flow size and direction unchanged, and update the network topology model. In this step: according to the power flow calculation result, the power loss of branch ij is equivalent to the upstream node i as the equivalent load of node i, and the branch power flow size and direction remain unchanged, that is, the original line end power flow size and direction. The branch power loss (branch loss power) is attributed to the upstream node, which can accurately reflect the total emission, that is, can more accurately calculate the carbon emission of the entire power grid or system, and ensure that all power losses are considered, reflecting the actual emission situation. Two, simplify 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, a carbon emission distribution network model is constructed based on the proportion sharing principle. The proportion sharing principle refers to the process of allocating resources or responsibilities according to the contribution or usage degree of each participant, which can reasonably reflect the actual influence and contribution of each participant. As shown in the total carbon emission of the power system, the carbon emission of the power generation side, the transmission line, the transformer substation and the distribution side, and each part is allocated according to the proportion sharing principle. Figure 3

[0052] Step S6 specifically includes:

[0053] S61, according to the historical carbon emission data of each branch power and each generator, combining the branch power, the carbon emission of each branch and the proportion sharing principle, the carbon emission of each branch is calculated. The power generation data also includes historical carbon emission data and real-time carbon emission data of each generator; wherein the carbon emission of each branch is calculated according to the following formula:

[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 load of node j, L j is the load of node j, and ∑ w L w ​Total load of all nodes, used to calculate the proportion of load, w is the node.

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

[0057]

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

[0059] Further, the method further comprises S7, solving the carbon emission network model by using a linear programming model to obtain the optimal carbon emission flow of each node. The linear programming model includes an objective function and a constraint condition, the objective function includes minimization of total carbon emission, and the constraint condition includes power flow balance constraint, node voltage constraint and branch power constraint. This step specifically includes:

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

[0061]

[0062] In formula (3), f min represents the minimum value of the total carbon emission of the power system, and N is the total number of nodes of the power system. At the same time, according to the operation characteristics of the power system, the corresponding constraint conditions are set, including power flow balance constraint, power balance constraint, node voltage constraint, and each constraint condition is specifically as follows:

[0063] ① Power flow balance constraint:

[0064]

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

[0066] ②Node voltage constraint:

[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] ③Branch power constraint:

[0070] S q ≤ S qmax ; (6)

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

[0072] S72, in combination with the initial value, the real-time load of each node, and the real-time power generation of each generator, iteratively optimizes and adjusts the carbon emission distribution network model parameters; in order to improve the solving efficiency, an iterative algorithm is used for calculation. Set the initial value: set the initial carbon emission flow value, the initial value is the optimal carbon emission flow of each node in the current state, and in the iterative optimization, the calculation formula of iteration 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, set the convergence condition, determine whether the iteration is terminated according to whether the iteration result meets the convergence condition, and output the optimal carbon emission flow of each node. This step specifically includes:

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

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

[0078] Further, the method further comprises S8: visualizing and analyzing the carbon emissions of the power grid by monitoring the optimal carbon emission flow of each node, which comprises:

[0079] (1) Spatiotemporal distribution characteristic analysis

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

[0081] ② 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 valley periods.

[0085] (2) Dynamic visualization

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

[0087] A method for constructing a power grid carbon emission distribution network model is proposed, providing a systematic and efficient solution for carbon emission management in power systems. Through real-time data acquisition, network topology modeling, carbon emission flow calculation, and optimization solving, this method can accurately quantify carbon emissions in power systems and analyze their temporal and spatial dynamic characteristics. The proportional sharing principle can reasonably allocate carbon emission responsibilities for each node, better reflecting the actual operation of the power system. Meanwhile, the combination of linear programming and iterative algorithms effectively improves computational efficiency, ensuring rapid convergence to the optimal solution in complex systems. This invention not only provides a scientific basis for low-carbon power dispatching but also offers a practical tool for policymakers in carbon emission reduction. Future research can further expand the model's applicability by considering more influencing factors, such as proportional fluctuations in renewable energy and market electricity price changes, to promote sustainable development and low-carbon transformation in the power industry.

[0088] It should be understood that the above specific embodiments of the present application are only used for illustrative or explanatory purposes of the principles of the present application, and do not constitute a limitation of the present application. Therefore, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present application shall be included in the protection scope of the present application. In addition, the appended claims of the present application are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

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

[0090] Although embodiments of the present application have been described in detail, it should be understood that various changes, substitutions and modifications can be made to the embodiments of the present application without departing from the spirit and scope of the present application.

[0091] Obviously, the above embodiments are only examples for clarity and do not limit the embodiments. Based on the above description, other different forms of changes or modifications can be made by those skilled in the art. Here, it is not necessary or possible to exhaust all embodiments. The obvious changes or modifications derived therefrom are still within the protection scope of the present application.

[0092] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0093] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0094] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0096] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a program, and the program can be stored in a computer readable storage medium. When the program is executed, the program includes the flow of the above-mentioned embodiment of each method. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc. The steps in the embodiment method of the present application can be adjusted, combined and deleted in sequence according to actual needs. The modules in the embodiment system of the present application

terminal or device

Claims

1. A method for constructing a grid carbon emission distribution network model, characterized in that, The method comprises the following steps: obtaining node load data, power generation data and branch data of a power grid; wherein the node load data comprises historical load and real-time load of each node, and the power generation data comprises historical power generation and real-time power generation of each generator set; performing data cleaning on the historical load of each node and the historical power generation of each generator set to form an original data set; performing power flow calculation on the real-time load of each node, the real-time power generation of each generator set and the branch data to obtain the voltage of each node, the power of each branch and the loss power 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 loss power of each branch; equivalent transplanting the loss power of each branch in the network topology model to the corresponding upstream node while keeping the size and direction of the power flow of each branch unchanged, and updating the network topology model; based on the updated network topology model, constructing a carbon emission distribution network model in combination with the proportion sharing principle, including: the carbon emission calculation formula of each branch is: where 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 load of node j, L j is the load of node j,∑ w L w is the total load of all nodes, w is the node; the power generation data further comprises historical carbon emission data and real-time carbon emission data of each generator set; based on the carbon emission of each branch, establishing a linear equation set, calculating the carbon emission of each node through the linear equation set, and constructing a carbon emission distribution network model based on the carbon emission of each node; the calculation formula of the linear equation set is: wherein C w is the total carbon emissions of the node w; is the carbon emissions of the previous node i flowing into the node w; is the carbon emissions of the node w flowing into the next node j; and ΔC is the carbon emissions generated by losses.

2. The method of claim 1, wherein, The method further comprises: solving the carbon emission distribution network model by using a linear programming model to obtain optimal carbon emission flow of each node; wherein the linear programming model comprises an objective function and a constraint condition, the objective function comprises minimization of total carbon emission, and the constraint condition comprises power flow balance constraint, node voltage constraint and branch power constraint.

3. The method of claim 2, wherein, The solving of the carbon emission distribution network model by using the linear programming model to obtain the optimal carbon emission flow of each node comprises: solving the carbon emission distribution network model by using the objective function and the constraint condition to generate an initial value; iterative optimization is performed in combination with 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; 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.

4. The method of claim 3, wherein, The calculation formula of the iteration is: C ij (k+1) = C ij (k) + a(C ij * - C ij (k) ); In the formula, C ij (k) Ckis the carbon emission flow for the kth iteration, ij * Ckis the optimal carbon emission flow, and a is the learning rate.

5. The method of claim 4, wherein, The determination of whether the iteration is terminated according to whether the iteration result meets the convergence condition and the output of the optimal carbon emission of each node comprise: If ΔC ij < ξ and k < T max the iteration is terminated and the optimal carbon emission of each node is output, otherwise, let k = k + 1 and return to the iteration optimization process to continue the loop. If k = T max , iteration is terminated; wherein, T max is the maximum number of iterations, ΔC ij is the difference between carbon emissions of two adjacent iterations, k is the iteration number, and ξ is the preset accuracy.

6. The method of claim 2, wherein, The method further comprises: visualizing the carbon emission of the power grid by monitoring the optimal carbon emission flow of each node.

7. The method of claim 2, wherein, The objective function is: In the formula, f min N is the total number of nodes in the power system.

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