A carbon emission flow analysis method for carbon emission assessment in multiple application scenarios

Abstract

The application discloses a carbon emission flow analysis method for carbon emission evaluation in multiple application scenarios, belongs to the technical field of carbon emission modeling and evaluation of power systems, and constructs the connection relationship among nodes, branches and power sources in the power system, collects output data, type information and power flow results under multiple operating conditions of the power generation source. The carbon emission of the power generation node is calculated and distributed to the load node according to the power flow path, the power tracking method is used to identify the carbon emission source of the load node, the carbon emission intensity and carbon potential index are calculated, the transmission structure of the carbon emission in the power grid is determined, the step-by-step distribution and flow tracking of the carbon emission are realized, the deficiency that the carbon emission path is unobservable in the prior art is made up, the identification accuracy of the carbon emission composition is improved, and a basis is provided for priority division of energy-saving and emission-reducing measures. The carbon emission path mapping matrix and the transition probability matrix are constructed, dynamic identification of path changes is realized, and it is helpful to find the key path and node.

Description

Technical Field

[0001] This invention relates to the field of carbon emission modeling and assessment technology for power systems, and specifically to a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios. Background Technology

[0002] Current carbon emission assessment methods in power systems mostly focus on statistically analyzing the total emissions from power generation sources, lacking systematic modeling and quantitative analysis of carbon emission transmission processes within the power grid. In multi-node, multi-branch structures, different types of power generation sources transmit emissions to load nodes via power flow paths, forming complex carbon emission flow relationships. Traditional methods fail to refine emission allocation to the node or path level, thus failing to accurately reflect the spatial distribution and transfer processes of carbon emissions within the power system. Furthermore, existing methods are mostly based on static, single operating conditions, failing to cover the dynamic evolution of carbon emission paths under complex operating conditions such as different load levels and fluctuations in renewable energy output, making it difficult to meet the comprehensive assessment needs of carbon emission flow characteristics under changing operating conditions.

[0003] Meanwhile, there is currently a lack of detailed analytical methods for the composition of carbon emissions received by load nodes. It is impossible to identify the type of power source from which carbon emissions originate, nor can a quantitative relationship be established between carbon emission intensity and the node's location within the network. Furthermore, there is a lack of mechanisms to identify the trends in carbon emission paths over time, and no indicator system has been established to assist in path optimization and node control. Therefore, there is an urgent need for a method based on the power source-node-branch structure, combined with power flow distribution and node power composition, capable of dynamically identifying carbon emission flow paths and source composition, and mining carbon emission analysis data from key transmission paths and key nodes, to support system-level operation scheduling and optimization management. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is: to propose a carbon emission flow analysis method applicable to multiple operating conditions and various power source types, addressing the shortcomings of existing technologies in tracking carbon emission paths, identifying carbon source contribution structures, and the poor control capabilities of critical paths and nodes. By constructing the connection relationships between power sources, nodes, and branches, carbon emissions are allocated to load nodes based on power flow results. Power tracking is used to identify carbon source contributions, calculate carbon emission intensity and carbon potential indices, extract carbon emission path evolution characteristics, and identify critical paths and nodes, thereby improving carbon emission scheduling and optimization capabilities and achieving dynamic analysis and accurate perception of grid carbon emissions.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios, comprising:

[0007] Construct the connection relationship between nodes, branches and power sources in the power system, and collect the output data of power generation sources, power source type information and power flow results under multiple operating conditions;

[0008] Based on the power output data of the generating power source and the corresponding unit carbon emission factor, the carbon emissions of the generating node are calculated, and the carbon emissions are allocated to the relevant load nodes according to the power flow transmission path.

[0009] Based on the carbon emission allocation results, the power tracking method is used to identify the carbon emission sources contributed by different types of power generation received by load nodes, and to calculate the carbon emission intensity and carbon potential index of load nodes.

[0010] By comparing and analyzing the carbon emission intensity and carbon potential index of load nodes under different operating scenarios, the transmission paths and change characteristics of carbon emissions are extracted to form a carbon emission flow map, and key carbon emission paths and key nodes are identified to support scheduling optimization.

[0011] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios described in this invention, the construction of the connection relationship between nodes, branches and power sources in the power system includes determining the bus node number of the power generation power source, obtaining the starting and ending node numbers of the branches in the power grid, and clarifying the electrical connection relationship between nodes; combining the access node information of the power generation power source and the connection relationship between nodes and branches, determining the correspondence between the power generation power source and the connected nodes and related branches.

[0012] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment under multiple application scenarios described in this invention, the calculation of carbon emissions of power generation nodes is based on the established correspondence between power generation sources and access nodes and branches, and the output data of power generation sources under multiple operating conditions are extracted. And determine the unit carbon emission factor EF according to the type of power generation source. i ; Calculate the carbon emissions of power generation source i during time period t. Represented as:

[0013]

[0014] Where i represents the generator number and t represents the time period number. EF represents the output value of the i-th generating power source in time period t. i This represents the unit carbon emission factor corresponding to the i-th power source. Let represent the carbon emissions of the i-th power source during time period t. This represents the total carbon emissions of power generation node j during time period t; j represents the power generation node.

[0015] Carbon emissions from power generation The total carbon emissions of power generation node j in the corresponding time period are obtained by summing the data according to its access node number. This serves as an input parameter when allocating carbon emissions to load nodes according to the power flow path.

[0016] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios described in this invention, the method of allocating carbon emissions to relevant load nodes according to the power flow transmission path includes: using power flow results to identify the power output path of power generation nodes under multiple operating conditions, and extracting the distribution of active power transmitted from power generation nodes to load nodes through branches; based on the proportional relationship of power transmission between power generation nodes and load nodes, allocating the total carbon emissions of power generation nodes to load nodes according to the power flow transmission path, and obtaining the carbon emissions received by load nodes in the corresponding time period.

[0017] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios described in this invention, the method of identifying the carbon emission sources contributed by different types of power generation sources received by load nodes using the power tracking method includes, according to the carbon emission amount allocated to load nodes, combining the path of power transmission from power generation nodes to load nodes in the power flow results, tracing back the composition of active power received by load nodes, and determining the proportion of power received by different power generation nodes.

[0018] Based on the proportion of power transmission received by the load node from the power generation node, the carbon emissions received by the load node are proportionally allocated to the power generation node source, and the carbon emission values ​​contributed by different types of power generation sources received by the load node are identified in combination with the power source type corresponding to the power generation node.

[0019] The carbon emissions contributed by different types of power generation received by load nodes at different times are expressed as follows:

[0020]

[0021] Where k represents the load node number and m represents the generator node number. This represents the total carbon emissions received by load node k at time t. This represents the active power transferred from generator node m to load node k at time t. This represents the total active power transferred from all generating nodes to load node k at time t. This represents the contribution of carbon emissions generated during the transmission process from power generation node m to load node k at time t.

[0022] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios described in this invention, the calculation of the carbon emission intensity and carbon potential index of the load node includes the carbon emission results contributed by different types of power generation received by the load node at different time periods, combined with the power consumption of the load node at different time periods, to calculate the carbon emission intensity of the node at different time periods, which is expressed as the carbon emission per unit of power.

[0023] Carbon emission intensity is expressed as:

[0024]

[0025] in, This represents the carbon emission intensity of load node k in time period t. This represents the sum of carbon emissions received by a node during that time period from the contribution of power generation node m. This indicates the electricity consumption of the node during that time period;

[0026] By combining the connection relationships between nodes, branches and power sources in the power system, the transmission path structure between nodes is clarified. Based on the carbon emission transmission relationship of nodes and the relative distribution between the carbon emissions received by load nodes and the system carbon emissions, a carbon potential index is constructed to characterize the position and potential impact of nodes in the carbon emission transmission path.

[0027] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment under multiple application scenarios described in this invention, the extraction of carbon emission transmission paths and variation characteristics includes: constructing a carbon emission path mapping matrix based on the carbon emission results contributed by different types of power generation sources received by load nodes at different time periods, combined with the carbon emission transmission path information allocated to load nodes according to power flow results, to represent the path distribution pattern of carbon emissions; and constructing a carbon emission transfer probability matrix between nodes for multiple operating conditions and different time periods to characterize the dynamic differences in path direction changes, path span evolution, and carbon emission source composition, thereby extracting carbon emission transmission paths and variation characteristics.

[0028] As a preferred embodiment of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios described in this invention, the identification of critical carbon emission paths and key nodes, supporting scheduling optimization, includes extracting a set of paths (i.e., critical paths) based on the carbon emission path mapping matrix and the carbon emission transfer probability matrix, where the frequency of path direction changes exceeds a threshold, the standard deviation of path length exceeds a threshold, and the range of changes in the proportion of carbon emission source composition exceeds a set range under multiple operating conditions and in different time periods; based on the nodes in the path set, the changes in the number of output paths, the changes in the proportion of path power, and the corresponding total amount of carbon emission transfer are statistically analyzed in different time periods, and key nodes with controllability in the path structure are identified based on the comparison results with preset thresholds.

[0029] Using critical paths and critical nodes as input data for carbon emission control decisions, this approach supports the optimization of carbon emission path scheduling under multiple operating conditions.

[0030] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios.

[0031] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios.

[0032] The beneficial effects of this invention are as follows: By constructing the connection relationship between power generation sources, nodes, and branches, this invention clarifies the transmission structure of carbon emissions in the power grid topology, enabling the hierarchical allocation and flow tracking of carbon emissions along the power flow path, thus overcoming the problem of unobservable carbon emission transmission paths in existing technologies. Through the power point-of-care method, the carbon emissions received by load nodes are traced back to specific power generation nodes and power source types, refining the carbon source contribution structure and improving the accuracy of carbon emission composition identification. The introduction of carbon emission intensity and carbon potential indices makes the carbon characteristics of load nodes quantifiable and comparable, providing a basis for prioritizing energy conservation and emission reduction measures. Simultaneously, the construction of a carbon emission path mapping matrix and a transition probability matrix enables dynamic identification of path direction changes, length evolution, and source composition changes, helping to accurately identify key paths and nodes with concentrated variations in carbon emission paths. Finally, the invention can be used to support the visualization and control optimization of carbon emissions in power grid operation, improving the real-time performance and accuracy of carbon emission management and control response. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 The above is a flowchart of a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios, provided as an embodiment of the present invention. Detailed Implementation

[0035] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0036] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios, including:

[0037] S1: Construct the connection relationships between nodes, branches, and power sources in the power system; collect power output data, power source type information, and power flow results under multiple operating conditions; such as... Figure 1 As shown in S1;

[0038] It should be noted that constructing the connection relationship between nodes, branches and power sources in a power system includes determining the bus node number of the power generation power source, obtaining the starting and ending node numbers of the branches in the power grid, and clarifying the electrical connection relationship between nodes; combining the power generation power source access node information and the connection relationship between nodes and branches, determining the correspondence between the power generation power source and the connected nodes and related branches.

[0039] Furthermore, to achieve dynamic assessment of carbon emission flows, it is necessary to collect power output data from power generation sources under various operating conditions. These diverse operating conditions can cover typical scenarios such as load changes, renewable energy fluctuations, and adjustments to dispatch strategies. For each operating condition, the active power output of each power generation source is extracted as the basis for calculating its carbon emissions. Simultaneously, the system also needs to record the type information of the power generation source, including but not limited to coal-fired units, gas-fired units, hydropower units, wind power units, and photovoltaic units, in order to establish a database of carbon emission characteristics for each type of power generation source under different conditions, based on pre-set unit carbon emission factors.

[0040] Furthermore, based on establishing connectivity and collecting source data, power flow simulations or historical power flow calculation results are performed to obtain the power distribution within the system under multiple operating conditions. The power flow results should include the injected power at each node, the active power transmission value of branches, and basic electrical parameters such as voltage and current. Particularly important is extracting the paths from the generation nodes, through several branches, to the load nodes and their corresponding power allocation ratios, serving as crucial inputs for subsequent carbon emission transmission and tracking within the network.

[0041] S2: Based on the power output data of the generating units and the corresponding unit carbon emission factor, calculate the carbon emissions of the generating nodes and distribute the carbon emissions to the relevant load nodes according to the power flow transmission path; such as Figure 1 As shown in S2;

[0042] It should be noted that establishing the correspondence between power generation sources and bus access nodes in the power system topology is crucial, and the branches connected to each power generation node and their power flow paths to load nodes must be clearly defined. Based on this, active power output data for each power generation source under multiple operating conditions is extracted. The data format is typically time series, covering the time-of-use output characteristics under the daily load curve. Then, according to the type of power generation source (e.g., coal-fired, gas-fired, electric, wind, and solar), the corresponding unit carbon emission factor is matched to construct a carbon emission calculation model.

[0043] It should also be noted that the calculation of carbon emissions from power generation nodes is based on the established correspondence between power generation sources and connected nodes and branches, and the output data of power generation sources under multiple operating conditions are extracted. And determine the unit carbon emission factor EF according to the type of power generation source. i ; Calculate the carbon emissions of power generation source i during time period t. When a node is connected to multiple power sources, the emissions of each power source at different time periods need to be calculated separately, and these emissions are aggregated at the node level based on the connection relationship between the power source and the node. The calculation process must consider the consistency of output units (MW or kW) and time standardization (e.g., 1 hour) to ensure uniformity of carbon emission units; expressed as:

[0044]

[0045] Where i represents the generator number and t represents the time period number. EF represents the output value of the i-th generating power source in time period t. i This represents the unit carbon emission factor corresponding to the i-th power source. Let represent the carbon emissions of the i-th power source during time period t. This represents the total carbon emissions of power generation node j during time period t; j represents the power generation node.

[0046] Furthermore, the carbon emissions from power generation... The total carbon emissions of power generation node j in the corresponding time period are obtained by summing the data according to its access node number. This data structure serves as the input parameter for subsequent allocation of carbon emissions to load nodes according to the power flow path. The aggregation operation here is implemented as an aggregation operation in the data structure, ensuring that the emissions of all generators connected to the same node can be summarized into the node's total output carbon load over time. This data structure will become the starting point for subsequent load node receiver path tracking and also the upstream input source for constructing the carbon flow path graph.

[0047] Furthermore, allocating carbon emissions to relevant load nodes according to power flow transmission paths includes using power flow results to identify the power output paths of generation nodes under multiple operating conditions and extracting the distribution of active power transmitted from generation nodes to load nodes through branches; based on the proportional relationship of power transmission between generation nodes and load nodes, the total carbon emissions of generation nodes are allocated to load nodes according to power flow transmission paths to obtain the carbon emissions received by load nodes in the corresponding time period; the power flow direction and magnitude of each branch under different operating scenarios are extracted through power flow calculation results to construct the power transmission path originating from the generation node. Paths can be analyzed using methods based on DFL (Depth-First Load Flow) or PTDF (Power Transfer Distribution Factor), and the load-bearing ratio of different paths under different operating conditions can be quantified. The active power information involved in the path is standardized as weights for subsequent carbon emission proportion allocation.

[0048] S3: Based on the carbon emission allocation results, the power point tracking method is used to identify the carbon emission sources contributed by different types of power generation received by load nodes, and the carbon emission intensity and carbon potential index of the load nodes are calculated; such as Figure 1 As shown in S3;

[0049] It should be noted that identifying the carbon emission sources contributed by different types of generators received by load nodes using the power tracking method involves tracing the composition of active power received by load nodes back to determine the proportion of power received by different generators, based on the carbon emissions already allocated to the load nodes and the hierarchical power transmission paths from generator nodes to load nodes in the power flow results. During the power tracking process, the transmission direction and amplitude of active power in each branch must first be obtained using power flow calculations. Based on the node-branch-source topology, the power flow is traced backwards along the power flow path to identify all paths from each generator node to the target load node. On this basis, the proportion of power received by each generator node in the transmission path is statistically analyzed to obtain the "source weight vector" for each load node. This weight vector not only reflects the composition of the power sources but also lays a quantitative foundation for subsequent carbon emission allocation, helping to accurately characterize the carbon source structure in multi-source power supply scenarios.

[0050] Based on the proportion of power transmission received by load nodes from power generation nodes, the carbon emissions received by load nodes are proportionally allocated to the sources of power generation nodes. Combined with the power source type corresponding to each power generation node, the carbon emission values ​​contributed by different types of power generation sources received by the load node are identified. After determining the proportion of power supply from each power generation node to the target load node, the total received carbon emissions can be proportionally allocated to obtain the carbon emission contribution from each source. Combining the "type label" of each power generation node (such as coal power, hydropower, wind power, etc.), a "source type - carbon emission" mapping can be further obtained. This mapping result not only reveals the source composition of carbon emissions from load nodes but also provides structural input for the subsequent development of differentiated scheduling strategies.

[0051] Specifically, the carbon emissions contributed by different types of power generation received by load nodes at different times are expressed as follows:

[0052]

[0053] Where k represents the load node number and m represents the generator node number. This represents the total carbon emissions received by load node k at time t. This represents the active power transferred from generator node m to load node k at time t. This represents the total active power transferred from all generating nodes to load node k at time t. This represents the contribution of carbon emissions generated during the transmission process from power generation node m to load node k at time t.

[0054] Furthermore, the calculation of the carbon emission intensity and carbon potential index of the load node includes the carbon emission results contributed by different types of power generation received by the load node at different times, combined with the power consumption of the load node at different times, to calculate the carbon emission intensity of the node at different times, expressed as the carbon emission per unit of power.

[0055] Specifically, carbon emission intensity is expressed as:

[0056]

[0057] in, This represents the carbon emission intensity of load node k in time period t. This represents the sum of carbon emissions received by a node during that time period from the contribution of power generation node m. This indicates the electricity consumption of the node during that time period.

[0058] Furthermore, by combining the connection relationships between nodes, branches and power sources in the power system, the transmission path structure between nodes is clarified. Based on the carbon emission transmission relationship of nodes and the relative distribution between the carbon emissions received by load nodes and the system carbon emissions, a carbon potential index is constructed to characterize the position and potential impact of nodes in the carbon emission transmission path.

[0059] It should also be noted that the carbon potential index is used to assess the centrality and regulatory potential of a node in a carbon emission transmission network; it is comprehensively evaluated by combining the following factors: the clustering intensity of the node as a carbon emission receiver; the degree of convergence of the node as a path transfer point; and the sensitivity of the impact of the carbon emissions received by the node on the total carbon load of the entire network. Commonly used modeling methods include the weighted PageRank index based on the number of paths and the amount of carbon transmitted, and carbon flow path centrality, etc.

[0060] S4: Compare and analyze the carbon emission intensity and carbon potential indicators of load nodes under different operating scenarios, extract the transmission paths and variation characteristics of carbon emissions, form a carbon emission flow map, and identify critical carbon emission paths and nodes to support scheduling optimization; such as Figure 1 Shown in S4;

[0061] It should be noted that extracting the transmission paths and variation characteristics of carbon emissions includes constructing a carbon emission path mapping matrix based on the carbon emission contributions from different types of power generation received by load nodes at different time periods, combined with the carbon emission transmission path information allocated to load nodes according to power flow results, to represent the path distribution pattern of carbon emissions; constructing a carbon emission transfer probability matrix between nodes for multiple operating conditions and different time periods to characterize changes in path direction, path span evolution, and dynamic differences in the composition of carbon emission sources, thereby extracting the transmission paths and variation characteristics of carbon emissions; the path mapping matrix is ​​constructed based on power flow data, quantifying the path distribution intensity of carbon emissions from source nodes through various branches to load nodes, supporting the spatial representation of carbon emission flow per unit time for each path; while the carbon emission transfer probability matrix introduces the Markov chain concept to describe the probability of carbon transfer from a node to adjacent nodes per unit time and its dynamic evolution trend, thereby reflecting the volatility, directionality, and source type composition changes of carbon emission paths in different operating states of the power grid structure; through dynamic analysis of these matrices, path behavior indicators reflecting the core characteristics of network carbon flow can be extracted, including path stability, carbon source structure offset rate, and evolution trajectory regularity.

[0062] Furthermore, the system identifies critical carbon emission pathways and nodes to support scheduling optimization. This includes extracting a set of paths—those where the frequency of path direction changes exceeds a threshold, the standard deviation of path length exceeds a threshold, and the range of carbon emission source composition changes exceeds a set range—based on the carbon emission pathway mapping matrix and the carbon emission transfer probability matrix across multiple operating conditions and different time periods. These are the critical pathways. Based on the nodes in the pathway set, the system statistically analyzes the changes in the number of output pathways, the magnitude of changes in path power ratios, and the corresponding total carbon emission transfers over different time periods. Comparison with preset thresholds identifies key nodes with regulatory influence within the pathway structure. Key node identification emphasizes the "hub" and "leverage" role of nodes in the carbon emission pathway network, i.e., the importance of nodes in controlling the total number of pathways, the proportion of carbon emissions, and dynamic adjustment capacity. Changes in the number of output pathways can determine whether the connectivity of a node fluctuates drastically under different operating conditions; changes in path power ratios reflect the flexibility of a node's load response capability during load transmission; and the total carbon emission transfer comprehensively reflects the node's actual capacity to handle changes in system carbon flow. Finally, by setting quantitative threshold standards, the system identifies "high-carbon-risk nodes" and "scheduling bottleneck nodes" that should be prioritized for control in multi-scenario scheduling.

[0063] Using critical paths and critical nodes as input data for carbon emission control decisions supports carbon emission path scheduling optimization under multiple operating conditions. The identified critical paths and nodes can be further input into the optimization scheduling model as constraints or part of the objective function, guiding the system to achieve power balance or economic operation while minimizing carbon emissions. By prioritizing power flow reduction on critical paths and implementing flexible load or source-end switching strategies for critical nodes, the risk of spatial accumulation of carbon emissions can be effectively reduced, enabling precise guidance of carbon migration in the system. Thus, without sacrificing scheduling flexibility and operational safety, the system's carbon efficiency and green operation level can be maximized.

[0064] Example 2 is an embodiment of the present invention, which provides a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0065] A simulation scenario was constructed in a typical 10kV distribution network simulation environment. The system scale includes 5 main load nodes (nodes A to E) and 3 power sources (coal-fired power plant, gas-fired power plant, and photovoltaic power plant). The connection relationships between nodes, branches, and power sources were constructed using a topology file, and power flow calculation tools (such as Matpower) were used to collect power flow data under multiple operating conditions. The output data of the power sources were recorded under each operating condition, and combined with their carbon emission factors (0.95 tCO2 / MWh for coal-fired power, 0.45 tCO2 / MWh for gas-fired power, and 0 for photovoltaic power), the carbon emissions were distributed to each load node along the path according to the power flow distribution results.

[0066] Taking a typical morning peak operation scenario as an example, the received power and carbon emission allocation values ​​of each node were collected, and a contribution relationship matrix from power source to load node was constructed based on the power point tracking method. The carbon emission intensity of each node was calculated by comparing the total carbon emissions corresponding to a unit received power of the load node. Simultaneously, a "carbon potential index" formed by integrating node connectivity centrality, power flow directionality, and carbon source proportion was used to measure the criticality of a node in the carbon emission flow. Furthermore, a source tracing algorithm was used to identify the critical paths upon which the carbon emissions received by each load node depend, and the contribution ratio of these paths in the overall carbon transport network was statistically analyzed to quantify the carbon intensity of the critical paths. The experimental data are shown in Table 1.

[0067] Table 1 Experimental Data on Carbon Emission Flow Path

[0068]

[0069]

[0070] The data above shows that, under the same system power flow conditions, although the power levels received by each load node are relatively similar, their corresponding carbon emission intensity and carbon potential indicators exhibit significant differences due to variations in power supply structure, path length, and topological location. For example, node C receives the largest power (140.1 MW), with a carbon emission intensity of 0.334 tCO2 / MW, higher than node B's 0.298 tCO2 / MW. This indicates that node C receives more electricity from high-carbon sources (such as coal-fired power units) in the power flow. Correspondingly, node C's carbon potential indicator is also as high as 0.910, further reflecting its position on a high-contribution upstream path in the carbon transport network.

[0071] Node C exhibits the highest contribution ratio and carbon intensity index of the critical path for carbon emissions (1.45% and 0.82 tCO2 / MW, respectively), indicating that this node is a critical load point in the high-carbon power transmission chain. Compared with the traditional power averaging method, the power flow tracing and path allocation method proposed in this invention can significantly reveal the non-uniform carbon emission transfer phenomenon caused by topological location, power source type, and path coupling effects.

[0072] This experiment demonstrates that it overcomes the mean masking effect of traditional static allocation methods, can accurately locate key carbon emission pathways, and reveals the weight of nodes in the carbon transmission network by introducing carbon potential indicators. This helps to establish regional carbon responsibility models, supports the identification of high carbon loads based on path tracing, and provides a decision-making basis for formulating regional scheduling optimization and low-carbon regulation.

[0073] Example 3 is an embodiment of the present invention. This embodiment provides an electronic device applicable to a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as proposed in the above embodiments.

[0074] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as proposed in the above embodiments.

[0075] The storage medium proposed in this embodiment and the carbon emission flow analysis method for realizing carbon emission assessment in multiple application scenarios proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0076] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0077] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A carbon emission flow analysis method for carbon emission assessment in multiple application scenarios, characterized in that: include: Construct the connection relationship between nodes, branches and power sources in the power system, and collect the output data of power generation sources, power source type information and power flow results under multiple operating conditions; Based on the power output data of the generating power source and the corresponding unit carbon emission factor, the carbon emissions of the generating node are calculated, and the carbon emissions are allocated to the relevant load nodes according to the power flow transmission path. Based on the carbon emission allocation results, the power tracking method is used to identify the carbon emission sources contributed by different types of power generation received by load nodes, and to calculate the carbon emission intensity and carbon potential index of load nodes. By comparing and analyzing the carbon emission intensity and carbon potential index of load nodes under different operating scenarios, the transmission path and change characteristics of carbon emissions are extracted, a carbon emission flow map is formed, and key carbon emission paths and key nodes are identified to support scheduling optimization. The method of identifying the carbon emission sources contributed by different types of power generation received by load nodes using the power tracking method includes, according to the carbon emission amount allocated to load nodes, combined with the hierarchical power transmission path from power generation nodes to load nodes in the power flow results, tracing back the composition of active power received by load nodes, and determining the proportion of power received by different power generation nodes. Based on the proportion of power transmission received by the load node from the power generation node, the carbon emissions received by the load node are proportionally allocated to the power generation node source, and the carbon emission values ​​contributed by different types of power generation sources received by the load node are identified in combination with the power source type corresponding to the power generation node. The carbon emissions contributed by different types of power generation received by load nodes at different times are expressed as follows: in, Indicates the load node number. Indicates the power generation node number. Indicates load node In time Total carbon emissions received Indicates time From power generation node To load nodes Transmitted active power, Indicates time All power generation nodes send to load nodes The sum of active power transmitted, Indicates time From power generation nodes To load nodes The contribution of carbon emissions generated during transmission; The carbon emission intensity and carbon potential index of the load node are calculated based on the carbon emission results contributed by different types of power generation received by the load node at different times, combined with the power consumption of the load node at different times, to calculate the carbon emission intensity of the node at different times, which is expressed as the carbon emission per unit of power. Carbon emission intensity is expressed as: in, Indicates load node During the period carbon emission intensity, This indicates the data received by the node from the power generation node during that time period. The sum of carbon emissions contributed, This indicates the electricity consumption of the node during that time period; By combining the connection relationships between nodes, branches and power sources in the power system, the transmission path structure between nodes is clarified. Based on the carbon emission transmission relationship of nodes and the relative distribution between the carbon emissions received by load nodes and the system carbon emissions, a carbon potential index is constructed to characterize the position and potential impact of nodes in the carbon emission transmission path.

2. The carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in claim 1, characterized in that: The construction of the connection relationship between nodes, branches and power sources in the power system includes determining the bus node number of the power generation power source, obtaining the start and end node numbers of the branches in the power grid, and clarifying the electrical connection relationship between nodes; combining the power generation power source access node information and the connection relationship between nodes and branches, determining the correspondence between the power generation power source and the connected nodes and related branches.

3. The carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in claim 2, characterized in that: The calculation of carbon emissions from power generation nodes is based on the established correspondence between power generation sources and connected nodes and branches, extracting power output data of power generation sources under multiple operating conditions. And determine the unit carbon emission factor according to the type of power generation source. ; Calculate the power generation source During the period carbon emissions , is represented as: in, Indicates the number of the power generation source. Indicates the time period number. Indicates the first Each power source during the time period The output value, Indicates the first The carbon emission factor per unit of power generation Indicates the first Each power source during the time period Carbon emissions; Carbon emissions from power generation The power generation nodes are obtained by grouping them according to their access node numbers and summing them up. Total carbon emissions during the corresponding period This serves as an input parameter when allocating carbon emissions to load nodes according to the power flow path.

4. The carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in claim 3, characterized in that: The process of allocating carbon emissions to relevant load nodes according to power flow transmission paths includes using power flow results to identify the power output paths of power generation nodes under multiple operating conditions, and extracting the distribution of active power transmitted from power generation nodes to load nodes through branches; based on the proportional relationship of power transmission between power generation nodes and load nodes, allocating the total carbon emissions of power generation nodes to load nodes according to power flow transmission paths, and obtaining the amount of carbon emissions received by load nodes in the corresponding time period.

5. The carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in claim 4, characterized in that: The extraction of carbon emission transmission paths and variation characteristics includes constructing a carbon emission path mapping matrix based on the carbon emission amounts contributed by different types of power generation sources received by load nodes at different time periods, combined with the carbon emission transmission path information allocated to load nodes according to power flow results, to represent the path distribution pattern of carbon emissions; and constructing a carbon emission transfer probability matrix between nodes for multiple operating conditions and different time periods to characterize the dynamic differences in path direction changes, path span evolution, and carbon emission source composition, thereby extracting carbon emission transmission paths and variation characteristics.

6. The carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in claim 5, characterized in that: The identification of critical carbon emission paths and key nodes supports scheduling optimization by extracting a set of paths—namely, critical paths—based on the carbon emission path mapping matrix and the carbon emission transfer probability matrix, where the frequency of path direction changes exceeds a threshold, the standard deviation of path length exceeds a threshold, and the range of changes in the proportion of carbon emission source composition exceeds a set range under multiple operating conditions and in different time periods. Based on the nodes in the path set, the changes in the number of output paths, the magnitude of changes in path power proportions, and the corresponding total carbon emission transfer are statistically analyzed in different time periods. Then, based on the comparison results with preset thresholds, key nodes with controllability in the path structure are identified. Using critical paths and critical nodes as input data for carbon emission control decisions, this approach supports the optimization of carbon emission path scheduling under multiple operating conditions.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the carbon emission flow analysis method for carbon emission assessment in multiple application scenarios as described in any one of claims 1 to 6.

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