A novel method and system for identifying high-carbon nodes in power systems based on carbon flow tracing.

Abstract

This application provides a novel method and system for identifying high-carbon nodes in a power system based on carbon flow tracing, relating to the field of carbon emission management technology. The method includes: obtaining operating condition data of the power system during its current operating cycle and performing power flow calculations to determine the transmission path of carbon flow in the power grid topology and the carbon conduction relationship between nodes. First, the initial carbon emissions of each carbon-emitting node are calculated, and nodes exceeding a first threshold are designated as candidate carbon-emitting nodes. Then, a first carbon emission index is calculated based on the historical carbon emission data and power flow ratio of the candidate nodes' adjacent nodes; simultaneously, a second carbon emission index is calculated using the candidate nodes' own historical carbon emission data and power flow distribution. Finally, the initial carbon emissions are corrected by combining the two indices to obtain a corrected carbon emission amount, which is then compared with the second threshold to determine high-carbon-emitting nodes, achieving accurate identification of key carbon-emitting nodes. This application aims to improve the problem of inaccurate high-carbon node identification.

Description

Technical Field

[0001] This application relates to the field of carbon emission management technology, and in particular to a novel method and system for identifying high-carbon nodes in a power system based on carbon flow tracing. Background Technology

[0002] As a key area of ​​greenhouse gas emissions, the power system urgently needs to achieve low-carbon operation through scientific carbon emission monitoring and management. In this process, high-carbon node identification becomes a crucial step. High-carbon node identification refers to the process of analyzing the coupling relationship between electricity flow and carbon emission flow during power system operation to locate and identify key nodes that account for a high proportion of carbon emissions and contribute significantly to the overall carbon emission level of the system. These high-carbon nodes often correspond to high-carbon power source access points, regional power supply nodes mainly based on fossil fuels, or hub nodes in power flow that bear significant carbon emission transfer responsibilities.

[0003] Existing methods for identifying high-carbon nodes mostly rely on static statistics and empirical emission factors, which are insufficient to reflect the dynamic carbon emission distribution during power flow, resulting in inadequate identification accuracy and decision support capabilities. Summary of the Invention

[0004] This application provides a novel method for identifying high-carbon nodes in a power system based on carbon flow tracing. The embodiments of this application adopt the following technical solutions:

[0005] In a first aspect, embodiments of this application provide a novel method for identifying high-carbon nodes in a power system based on carbon flow tracing, the method comprising:

[0006] The system acquires operating condition data for the current operating cycle of the power system and performs power flow calculations based on the operating condition data to determine the carbon flow transmission path in the power system topology. The carbon flow transmission path includes the carbon conduction relationship between multiple different carbon emission nodes.

[0007] Calculate the initial carbon emissions of each carbon emission node, and identify carbon emission nodes with initial carbon emissions greater than the first threshold as candidate carbon emission nodes;

[0008] Based on the historical carbon emission data and power flow ratio of the adjacent carbon emission nodes of the candidate carbon emission node, the first carbon emission index of the candidate carbon emission node is calculated. The first carbon emission index is used to characterize the impact of the adjacent carbon emission nodes on the candidate carbon emission node.

[0009] Based on the historical carbon emission data of the candidate carbon emission node and its power flow distribution with neighboring carbon emission nodes, the second carbon emission index of the candidate carbon emission node is calculated. The second carbon emission index is used to characterize the impact of the candidate carbon emission node on neighboring carbon emission nodes.

[0010] The initial carbon emissions of the candidate carbon emission nodes are adjusted according to the first carbon emission index and the second carbon emission index to obtain the corrected carbon emissions of the candidate carbon emission nodes. Based on the relationship between the corrected carbon emissions of each candidate carbon emission node and the second threshold, high carbon emission nodes are determined.

[0011] In one optional implementation, power flow calculations are performed based on operating condition data to determine the carbon flow transport path in the power system topology, including:

[0012] Obtain the topology model of the power system nodes and branches;

[0013] Based on the topology model and operating condition data, perform power flow calculations to obtain the power flow calculation results;

[0014] The carbon emission coefficients of different types of power sources are mapped to the power flow calculation results, and the carbon flow transfer relationship between nodes is determined according to the power flow direction and power ratio.

[0015] Based on the carbon flow transport relationship, carbon flow transport paths are generated.

[0016] In one alternative implementation, the initial carbon emissions at each carbon emission node are calculated, including:

[0017] Determine the node type for each carbon emission node, including power generation nodes and load nodes;

[0018] When the node type of the carbon emission node is a power generation node, the initial carbon emissions of the carbon emission node are calculated based on the actual power generation capacity of the power generation node and the carbon emission factor of the corresponding power source type.

[0019] When the node type of the carbon emission node is a load node, the initial carbon emission of the carbon emission node is calculated based on the actual load power of the load node and the carbon emission factor of the regional power grid.

[0020] In one optional implementation, a first carbon emission index for the candidate carbon emission node is calculated based on historical carbon emission data and power flow ratios of neighboring carbon emission nodes, including:

[0021] Obtain historical carbon emissions and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission nodes;

[0022] The carbon transmission contribution of the adjacent carbon emission nodes to the candidate carbon emission nodes is calculated based on the power flow ratio between the adjacent carbon emission nodes and the candidate carbon emission nodes.

[0023] The carbon transmission contributions of all adjacent carbon emission nodes are aggregated to obtain the first carbon emission index for the candidate carbon emission nodes.

[0024] In one optional implementation, a second carbon emission index for the candidate carbon emission node is calculated based on its own historical carbon emission data and its power flow distribution with neighboring carbon emission nodes, including:

[0025] Obtain the candidate carbon emission node's own historical carbon emission data and the power flow ratio between it and its neighboring carbon emission nodes;

[0026] Based on the power flow ratio, the carbon emissions of the candidate carbon emission node are proportionally allocated to the adjacent carbon emission nodes, and the carbon transmission contribution of the candidate carbon emission node to the adjacent carbon emission nodes is calculated.

[0027] The carbon transmission contribution of the candidate carbon emission nodes to adjacent carbon emission nodes is summarized to obtain the second carbon emission index of the candidate carbon emission nodes.

[0028] In one optional implementation, adjusting the initial carbon emissions of the candidate carbon emission nodes according to a first carbon emission index and a second carbon emission index to obtain the corrected carbon emissions of the candidate carbon emission nodes includes:

[0029] Based on the first carbon emission index, determine the transmission impact value of adjacent carbon emission nodes on the selected carbon emission node;

[0030] Based on the second carbon emission index, determine the spillover contribution value of the candidate carbon emission node to the adjacent carbon emission node;

[0031] The transmission impact value and spillover contribution value are respectively weighted and coupled with the initial carbon emissions of the candidate carbon emission nodes;

[0032] Based on the weighted coupling results, the initial carbon emissions of the candidate carbon emission nodes are corrected to obtain the corrected carbon emissions of the candidate carbon emission nodes.

[0033] In one optional implementation, high carbon emission nodes are determined based on the relationship between the corrected carbon emissions of each candidate carbon emission node and the second threshold, including:

[0034] Obtain the corrected carbon emissions of each candidate carbon emission node over multiple verification periods;

[0035] If the corrected carbon emissions for at least two verification cycles are greater than the second threshold, the candidate carbon emission node will be identified as a high carbon emission node.

[0036] In one alternative implementation, the method further includes:

[0037] The remote server determines the target edge computing device based on the high carbon node identification requirements and carbon flow transmission path. The higher the degree of coupling between carbon emission nodes and other carbon emission nodes, the higher the priority of the edge computing device corresponding to the carbon emission node in being selected as the target edge computing device.

[0038] Secondly, embodiments of this application provide a novel high-carbon node identification system for power systems based on carbon flow tracing, the system comprising:

[0039] The acquisition module is used to acquire the operating condition data of the power system during the current operating cycle, and to perform power flow calculation based on the operating condition data to determine the carbon flow transmission path in the power system topology. The carbon flow transmission path includes the carbon conduction relationship between carbon emission nodes of multiple different carbon emission nodes.

[0040] The first screening module is used to calculate the initial carbon emissions of each carbon emission node and identify carbon emission nodes with initial carbon emissions greater than a first threshold as candidate carbon emission nodes.

[0041] The first calculation module is used to calculate the first carbon emission index of the candidate carbon emission node based on the historical carbon emission data and power flow ratio of the adjacent carbon emission nodes of the candidate carbon emission node. The first carbon emission index is used to characterize the impact of the adjacent carbon emission nodes on the candidate carbon emission node.

[0042] The second calculation module is used to calculate the second carbon emission index of the candidate carbon emission node based on its own historical carbon emission data and its power flow distribution with neighboring carbon emission nodes. The second carbon emission index is used to characterize the impact of the candidate carbon emission node on neighboring carbon emission nodes.

[0043] The second screening module is used to adjust the initial carbon emissions of the candidate carbon emission nodes according to the first carbon emission index and the second carbon emission index, obtain the corrected carbon emissions of the candidate carbon emission nodes, and determine the high carbon emission nodes according to the relationship between the corrected carbon emissions of each candidate carbon emission node and the second threshold.

[0044] In one alternative implementation, the determining module includes:

[0045] The acquisition submodule is used to acquire the topology model of the power system nodes and branches;

[0046] The calculation submodule is used to perform power flow calculations based on the topology model and operating condition data, and obtain the power flow calculation results.

[0047] The mapping submodule is used to map the carbon emission coefficients of different types of power sources to the power flow calculation results, and determine the carbon flow transfer relationship between nodes according to the power flow direction and power ratio.

[0048] The generation submodule is used to generate carbon flow transport paths based on carbon flow transport relationships.

[0049] In one alternative implementation, the first filtering module includes:

[0050] The node type determination submodule is used to determine the node type of each carbon emission node, including power generation nodes and load nodes;

[0051] The first calculation submodule is used to calculate the initial carbon emissions of a carbon emission node based on the actual power generation of the power generation node and the carbon emission factor of the corresponding power source type when the node type of the carbon emission node is a power generation node.

[0052] The second calculation submodule is used to calculate the initial carbon emissions of the carbon emission node based on the actual load power of the load node and the carbon emission factor of the regional power grid when the node type of the carbon emission node is a load node.

[0053] In one alternative implementation, the first computing module includes:

[0054] The first acquisition submodule is used to acquire historical carbon emissions and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission nodes.

[0055] The first carbon transmission contribution calculation submodule is used to calculate the carbon transmission contribution of the adjacent carbon emission nodes to the candidate carbon emission nodes based on the power flow ratio between the adjacent carbon emission nodes and the candidate carbon emission nodes.

[0056] The first aggregation submodule is used to aggregate the carbon transmission contributions of all adjacent carbon emission nodes to obtain the first carbon emission index of the candidate carbon emission nodes.

[0057] In one alternative implementation, the first computing module includes:

[0058] The second acquisition submodule is used to acquire the candidate carbon emission node's own historical carbon emission data and the power flow ratio between it and its neighboring carbon emission nodes.

[0059] The second carbon transmission contribution calculation submodule is used to allocate the carbon emissions of the candidate carbon emission node to the adjacent carbon emission nodes according to the power flow ratio, and calculate the carbon transmission contribution of the candidate carbon emission node to the adjacent carbon emission nodes.

[0060] The second aggregation submodule is used to aggregate the carbon transmission contribution of the candidate carbon emission nodes to adjacent carbon emission nodes to obtain the second carbon emission index of the candidate carbon emission nodes.

[0061] In one alternative implementation, the second computing module includes:

[0062] The first correction submodule is used to determine the transmission impact value of adjacent carbon emission nodes on the candidate carbon emission node based on the first carbon emission index.

[0063] The second correction submodule is used to determine the spillover contribution value of the candidate carbon emission node to the adjacent carbon emission node based on the second carbon emission index.

[0064] The third correction submodule is used to perform weighted coupling calculations on the transmission impact value and the spillover contribution value with the initial carbon emissions of the candidate carbon emission nodes, respectively.

[0065] The determination submodule is used to correct the initial carbon emissions of the candidate carbon emission nodes based on the weighted coupling results, and obtain the corrected carbon emissions of the candidate carbon emission nodes.

[0066] Thirdly, this application also provides an electronic device, which includes: a memory and one or more processors, the memory being coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the method in any of the possible design embodiments of the first aspect described above.

[0067] Fourthly, this application provides a computer-readable storage medium including computer instructions; when the computer instructions are executed on an electronic device, they cause the electronic device to perform the method described in the first aspect above and any possible design of the above.

[0068] Fifthly, this application provides a computer program product that, when run on an electronic device, causes the electronic device to perform the method described in the first aspect above and any possible design of the above.

[0069] This application provides a novel method for identifying high-carbon nodes in power systems based on carbon flow tracing. By acquiring operating data of the power system during its current operating cycle and combining this with power flow calculations to determine the transmission path of carbon flow in the power system topology, it achieves a dynamic characterization of the carbon emission flow pattern in the power grid. Compared to traditional methods that rely solely on static statistical data or empirical emission factors, this application can fully reflect the energy coupling and carbon flow transmission relationship between nodes, thereby improving the physical rationality and accuracy of carbon emission calculations. In specific implementation, this application first calculates the initial carbon emissions of each carbon emission node and then filters out candidate carbon emission nodes by setting thresholds, ensuring that the focus of analysis is on key nodes that may contribute significantly to system carbon emissions. Subsequently, by introducing a first carbon emission index and a second carbon emission index, the transmission influence of adjacent nodes on the target node and the carbon transmission effect of the target node on adjacent nodes are quantified, respectively, achieving a two-way characterization of the node's carbon emission impact. This method can overcome the problem of traditional methods neglecting node coupling effects, enabling even nodes with low carbon emissions but located on high-carbon power flow paths to be reasonably identified as actual high-carbon nodes, thus significantly improving the accuracy and comprehensiveness of high-carbon node identification.

[0070] Furthermore, by correcting the initial carbon emissions of nodes based on the first and second carbon emission indicators, this application can obtain corrected carbon emissions that more closely approximate the actual operating conditions, providing a reliable basis for subsequent high-carbon node identification and thus enabling precise monitoring and management of key emission sources. This method not only reflects the node's own emissions but also comprehensively considers the transmission effect of carbon emissions in the power grid topology and the dynamic coupling between nodes, offering numerous benefits such as improving the accuracy of carbon emission monitoring, optimizing low-carbon dispatch strategies, and assisting energy planning.

[0071] The technical effects of the second to fifth aspects refer to the technical effects of the first aspect and any of its embodiments, and will not be repeated here. Attached Figure Description

[0072] Figure 1 A flowchart illustrating the steps of a novel high-carbon node identification method for power systems based on carbon flow tracing, as provided in this application embodiment;

[0073] Figure 2 This is a schematic diagram of a novel high-carbon node identification system for power systems based on carbon flow tracing, provided as an embodiment of this application. Detailed Implementation

[0074] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one or more (including two). The character “ / ” generally indicates that the preceding and following objects are in an “or” relationship.

[0075] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0076] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more. For example, multiple processing units refer to two or more processing units.

[0077] Existing methods for identifying high-carbon nodes mostly rely on static statistical data and empirical emission factors for estimation. They often simply attribute carbon emissions to individual nodes, lacking responsiveness to dynamic changes in power flow and failing to accurately depict the transmission path of carbon emissions within the power grid topology. Furthermore, they neglect the coupling effects between nodes and do not adequately consider the transmission impact of adjacent carbon-emitting nodes on the target node. For example, even if a node has a low carbon emission level, it may still bear the emission transfer from high-carbon nodes because it is in the power flow transmission path, thus appearing as a high-carbon node in the system's operational behavior, which can easily lead to biased identification results.

[0078] Based on this, the inventors proposed the inventive concept of this application: by introducing a carbon flow tracing method, the power flow and carbon emission flow are dynamically coupled and modeled, and a carbon emission transmission path is constructed in the power grid topology. By combining the node's own emission level and the influence of external transmission, high-carbon nodes in the power system can be accurately identified.

[0079] Reference Figure 1 The present invention provides a novel method for identifying high-carbon nodes in a power system based on carbon flow tracing, which may specifically include the following steps:

[0080] S101: Obtain the operating condition data of the power system during the current operating cycle, and perform power flow calculation based on the operating condition data to determine the carbon flow transmission path in the power system topology.

[0081] In this embodiment, the target edge computing device is first determined based on the high-carbon node identification requirements and the power system topology. The target edge computing device is used to perform carbon flow decomposition and carbon emission factor calculation tasks within its assigned local power grid area, thereby achieving accurate tracking of local carbon flows and dynamic calculation of node carbon emission contributions. Edge computing devices may include, but are not limited to, intelligent measurement and control units, distributed computing nodes, micro data centers, or distribution automation terminals with computing and communication functions. The selection of edge computing devices prioritizes the degree of coupling between the node and surrounding carbon-emitting nodes; that is, the stronger the coupling relationship between the node and its corresponding edge computing device, the more suitable it is for undertaking the carbon flow decomposition and factor calculation tasks, ensuring the comprehensiveness and accuracy of high-carbon node identification. Specific steps may include:

[0082] S1011: Obtain the topology model of power system nodes and branches;

[0083] S1012: Perform power flow calculation based on the topology model and operating condition data, and obtain the power flow calculation results;

[0084] S1013: Map the carbon emission coefficients of different types of power sources to the power flow calculation results, and determine the carbon flow transfer relationship between nodes according to the power flow direction and power ratio;

[0085] S1014: Generate carbon flow transport paths based on carbon flow transport relationships.

[0086] In the implementations of S1011 to S1014, the node and branch topology model of the power system is first obtained to clarify each node and its connection relationship, including node type, branch parameters, power supply access information, and load distribution, providing basic data for subsequent power flow calculation and carbon flow transfer analysis. Subsequently, power flow calculation is performed based on the topology model and operating condition data to obtain the voltage, current, and power flow distribution information of each node, thereby determining the flow path of electrical energy in the system and quantifying the power transfer ratio and direction between nodes, providing a dynamic basis for carbon flow decomposition and transfer modeling. Next, the carbon emission coefficients of different types of power sources are mapped to the power flow calculation results, and the carbon flow transfer relationship between nodes is determined according to the power flow direction and power ratio, achieving accurate distribution of carbon emissions along the power flow path. Finally, carbon flow transfer paths are generated based on the carbon flow transfer relationships, describing the dynamic distribution of carbon emissions in the power grid topology in a structured manner, providing a direct basis for subsequent calculation of node carbon emission contributions and identification of high-carbon nodes.

[0087] S102: Calculate the initial carbon emissions for each carbon emission node.

[0088] In this embodiment, after acquiring the operating condition information of the power generation units, load units, and energy exchange units in its region, the target edge computing device calculates the initial carbon emissions of the node based on the carbon emission factors of various energy sources and the corresponding energy conversion relationships. This initial carbon emission reflects the node's direct emission contribution before carbon flow decomposition and transmission correction. The specific steps include:

[0089] S1021: Determine the node type for each carbon emission node, including power generation nodes and load nodes;

[0090] S1022: When the node type of the carbon emission node is a power generation node, calculate the initial carbon emissions of the carbon emission node based on the actual power generation capacity of the power generation node and the carbon emission factor of the corresponding power source type.

[0091] S1023: When the node type of the carbon emission node is a load node, calculate the initial carbon emission of the carbon emission node based on the actual load power of the load node and the carbon emission factor of the regional power grid.

[0092] In the implementation schemes S1021 to S1023, different types of nodes exhibit fundamental differences in their carbon emission formation mechanisms during power system carbon emission accounting. Power generation nodes are the direct source of carbon emissions, while load nodes themselves do not generate carbon emissions, but their electricity consumption indirectly corresponds to certain carbon emissions. Therefore, it is necessary to first classify the nodes. This classification lays the foundation for subsequent use of differentiated calculation models, thereby improving the accuracy of carbon emission factor detection. Carbon emissions from power generation nodes primarily originate from the combustion of fossil fuels. Different energy types (such as coal, gas, and oil) have different carbon emission levels per unit of electricity, while renewable energy sources (such as photovoltaic and wind power) theoretically have carbon emission factors approaching zero. Multiplying the actual power generation by this carbon emission factor yields the direct carbon emissions of the power generation node.

[0093] While load nodes themselves do not directly emit carbon, the electrical energy they consume corresponds to carbon emissions from upstream power generation. The comprehensive carbon emission factor of a regional power grid reflects the region's power structure and power flow distribution characteristics. By multiplying the power consumption by this factor, system-level carbon emissions can be reasonably allocated to each power-consuming node. By first distinguishing node types, carbon emissions can be characterized from both the source and consumption ends. This not only ensures the physical rationality of the calculation but also avoids double counting or omissions.

[0094] S103: Calculate the first carbon emission index of the candidate carbon emission node based on the historical carbon emission data and power flow ratio of the adjacent carbon emission nodes.

[0095] In this embodiment, the first carbon emission index is used to characterize the impact of adjacent carbon emission nodes on the candidate carbon emission node. Power system nodes are tightly coupled through power flow, and carbon emissions not only originate from the node's direct emissions but also indirectly share them due to power flow transmission. For example, when a node's power consumption is mainly supplied by upstream thermal power nodes, the actual carbon emission level corresponding to that node will inevitably be affected by the upstream thermal power emissions. By introducing historical carbon emission data, the long-term carbon emission characteristics of adjacent carbon emission nodes can be reflected, avoiding the uncertainty caused by short-term data fluctuations. The power flow ratio is used to characterize the transmission weight of carbon emissions between nodes, ensuring that the allocation result conforms to the physical laws of the power grid. The combination of these two methods enables a reasonable correction of the carbon emission level of the candidate node, resulting in a first carbon emission index that truly reflects the coupling effect. This aims to compensate for the shortcomings of relying solely on initial carbon emission calculations, ensuring that the result includes not only the node's direct emissions but also the indirect emissions from network transmission, thus providing a more comprehensive and accurate input basis for subsequent carbon flow tracking and high-carbon node identification. The specific implementation steps include:

[0096] S1031: Obtain historical carbon emissions and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission nodes;

[0097] S1032: Calculate the carbon transmission contribution of the adjacent carbon emission node to the candidate carbon emission node based on the power flow ratio between the adjacent carbon emission node and the candidate carbon emission node.

[0098] S1033: Summarize the carbon transmission contributions of all adjacent carbon emission nodes to obtain the first carbon emission index of the candidate carbon emission nodes.

[0099] In the implementation schemes S1031 to S1033, firstly, by acquiring historical carbon emissions and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission node, the long-term emission levels and energy transmission characteristics of adjacent carbon emission nodes can be comprehensively understood, thus providing a reliable input basis for subsequent carbon flow transmission calculations. Subsequently, based on the power flow ratio between adjacent carbon emission nodes and the candidate carbon emission node, the carbon emission transmission impact of adjacent carbon emission nodes on the candidate node can be quantified. That is, the higher the power flow ratio, the greater the energy contribution of adjacent carbon emission nodes to the candidate node, and their carbon emission impact should also be proportionally aggravated. This calculation method ensures that the carbon emission allocation process conforms to the physical power flow law of the power grid. On this basis, by weighted summarizing the carbon transmission contributions of all adjacent carbon emission nodes, the first carbon emission index of the candidate carbon emission node can be obtained. This index not only reflects the emission characteristics of the node itself, but also comprehensively considers the indirect emission effects of adjacent carbon emission nodes. This process effectively overcomes the limitations of relying solely on the initial emissions of nodes while ignoring network coupling relationships, enabling a more scientific and comprehensive characterization of node carbon emission levels and providing solid data support for subsequent carbon emission factor correction and high-carbon node identification.

[0100] S104: Calculate the second carbon emission index of the candidate carbon emission node based on its own historical carbon emission data and its power flow distribution with adjacent carbon emission nodes.

[0101] In this embodiment, in addition to considering the impact of other adjacent carbon emission nodes on the candidate carbon emission node, it is also necessary to comprehensively consider the candidate node's own historical carbon emission level to ensure that its carbon emission characteristics can be fully reflected. Since a node's carbon emissions are not entirely formed by external transmission, its own energy structure, power generation method, or electricity consumption characteristics directly determine its direct emissions. By introducing historical carbon emission data, its long-term stable emission characteristics can be characterized. Simultaneously, incorporating the power flow distribution between the node and its neighboring nodes into the calculation reflects its position and transmission relationship in the grid energy exchange process, thereby reasonably decomposing and weighting its own emissions and externally transmitted emissions. The resulting second carbon emission index can effectively distinguish between the node's direct emission contribution and the indirect emissions from network coupling. The second carbon emission index is used to characterize the impact of the candidate carbon emission node on its neighboring carbon emission nodes.

[0102] The specific implementation steps include:

[0103] S1041: Obtain the candidate carbon emission node's own historical carbon emission data and the power flow ratio between it and its neighboring carbon emission nodes;

[0104] S1042: Based on the power flow ratio, the carbon emissions of the candidate carbon emission node are proportionally allocated to the adjacent carbon emission nodes, and the carbon transmission contribution of the candidate carbon emission node to the adjacent carbon emission nodes is calculated.

[0105] S1043: Summarize the carbon transmission contributions of the candidate carbon emission nodes to adjacent carbon emission nodes to obtain the second carbon emission index of the candidate carbon emission nodes.

[0106] In the implementations of S1041 to S1043, firstly, by acquiring the historical carbon emission data of the candidate carbon emission node and the power flow ratio between it and its neighboring carbon emission nodes, the emission characteristics of the node during long-term operation can be comprehensively reflected. Combined with the power flow distribution relationship, the transmission path of its emissions in the network is determined. Subsequently, based on the power flow ratio, the carbon emissions of the candidate node are rationally allocated to neighboring nodes, thereby quantifying its indirect impact on surrounding nodes as a carbon emission source. This allocation method can realistically depict the physical laws of the synchronous transfer of energy and carbon emissions in the power system. Finally, the carbon transmission contribution of the candidate node to all neighboring nodes is weighted and summarized to obtain its second carbon emission index. This index not only reflects the node's own direct emission level but also measures its outward transmission of carbon emissions. Through this calculation process, the one-sidedness of examining node carbon emissions solely from an external perspective can be avoided. A unified quantification of the node's dual role in the power grid—both being affected and exerting influence—is achieved, making the carbon emission factor calculation results more comprehensive and accurate, providing solid data support for the subsequent formation of the final carbon emission factor.

[0107] S105: Adjust the initial carbon emissions of the candidate carbon emission nodes according to the first carbon emission index and the second carbon emission index to obtain the corrected carbon emissions of the candidate carbon emission nodes, and determine the high carbon emission nodes according to the relationship between the corrected carbon emissions of each candidate carbon emission node and the second threshold.

[0108] In this embodiment, after determining the first and second carbon emission indicators, the edge computing device combines these indicators with the initial carbon emission amount to correct the carbon emission amount of the selected carbon emission nodes. This corrects the carbon emission amount of the node itself and the carbon emission effect transmitted from neighboring nodes. By comparing the corrected carbon emission amount with a preset second threshold, high-carbon nodes that significantly impact the overall carbon emission level during system operation can be effectively identified. The specific implementation steps include:

[0109] S1051: Based on the first carbon emission index, determine the transmission impact value of adjacent carbon emission nodes on the selected carbon emission node;

[0110] S1052: Determine the spillover contribution of the candidate carbon emission node to the adjacent carbon emission node based on the second carbon emission index;

[0111] S1053: The transmission impact value and spillover contribution value are weighted and coupled with the initial carbon emissions of the candidate carbon emission nodes, respectively.

[0112] S1054: Based on the weighted coupling results, correct the initial carbon emissions of the candidate carbon emission nodes to obtain the corrected carbon emissions of the candidate carbon emission nodes.

[0113] In the implementations of S1051 to S1054, firstly, based on a first carbon emission index, the transmission influence value of adjacent carbon emission nodes on the candidate carbon emission node is quantified to reflect the actual contribution of carbon emission transmission effects from surrounding nodes to the target node. Then, based on a second carbon emission index, the spillover contribution value of the candidate carbon emission node to adjacent carbon emission nodes is calculated to characterize the transmission influence of the target node's own carbon emissions to surrounding nodes. Next, the transmission influence value and the spillover contribution value are weighted and coupled with the initial carbon emissions of the candidate carbon emission node to comprehensively consider the influence of the node's own carbon emissions and the mutual coupling between nodes. Finally, the initial carbon emissions are corrected based on the weighted coupling result to obtain the corrected carbon emissions of the candidate carbon emission node, thereby accurately characterizing the node's true contribution to the entire power system's carbon emission network. In this implementation, this step effectively reflects the coupling effect between nodes, avoids the high-carbon node identification bias caused by ignoring the transmission influence of adjacent nodes, and provides a reliable basis for system-level low-carbon scheduling and carbon emission optimization.

[0114] As an example, suppose the candidate carbon emission node is node A, with an initial carbon emission of 100 tons. The adjacent carbon emission nodes are node B and node C. First, the transmission impact of adjacent nodes on node A is determined based on the first carbon emission index, where the transmission impact of node B on node A is 20 tons. The conduction effect of node C on node A is 10 tons. This is used to characterize the carbon emission transmission effect from neighboring nodes. Subsequently, the spillover contribution of node A to neighboring nodes is determined based on the second carbon emission index, where the spillover contribution of node A to node B is 15 tons. The spillover contribution of node A to node C is 5 tons. This is used to characterize the transmission effect of carbon emissions from node A to surrounding nodes. Next, the transmission effect value and spillover contribution value are weighted and coupled with the initial carbon emissions, for example, assigning a weight of 0.6 to the transmission effect and 0.4 to the spillover contribution, resulting in a weighted transmission contribution of 18 tons for node A. The weighted spillover contribution was 8 tons. Finally, the initial carbon emissions were corrected based on the weighted coupling results, resulting in a corrected carbon emission of 110 tons for node A. This is used for subsequent comparison with the second threshold to determine whether node A belongs to a high-carbon emission node.

[0115] The identification of high carbon emission nodes includes:

[0116] S1055: Obtain the corrected carbon emissions of each candidate carbon emission node over multiple verification periods;

[0117] S1056: If the corrected carbon emissions for at least two verification cycles are greater than the second threshold, the candidate carbon emission node will be identified as a high carbon emission node.

[0118] In this implementation, to ensure the stability and reliability of the high-carbon emission node identification results, the corrected carbon emissions of each candidate carbon emission node are first obtained over multiple verification periods. By comparing the corrected carbon emissions of each node in consecutive periods with a preset second threshold, it is determined whether its carbon emission level remains consistently high. Specifically, if the corrected carbon emissions of a candidate carbon emission node are greater than the second threshold for at least two verification periods, the node is identified as a high-carbon emission node, thus avoiding misjudgments caused by abnormal data in a single period or short-term fluctuations. This multi-period verification mechanism can effectively reflect the long-term carbon emission characteristics of nodes in the power system, ensuring that the identified high-carbon nodes not only exhibit high carbon emissions instantaneously but also make continuous contributions during dynamic operation, thereby providing reliable data support and decision-making basis for system-level low-carbon scheduling and carbon emission optimization.

[0119] As an example, when a node's carbon emissions exceed the second threshold in three consecutive verification cycles, the node can be stably identified as a high-carbon-emission node.

[0120] In one feasible implementation, the method further includes:

[0121] The remote server determines the target edge computing device based on the high carbon node identification requirements and carbon flow transmission path. The higher the degree of coupling between carbon emission nodes and other carbon emission nodes, the higher the priority of the edge computing device corresponding to the carbon emission node in being selected as the target edge computing device.

[0122] In this embodiment, when selecting edge computing devices, the remote server can also comprehensively consider factors such as the computing power, storage resources and communication bandwidth of the edge devices, and give priority to edge devices that can efficiently perform carbon flow decomposition and carbon emission factor calculation tasks, thereby achieving load balancing of task allocation and optimization of computing efficiency.

[0123] This application provides a novel method for identifying high-carbon nodes in power systems based on carbon flow tracing. By acquiring operating data of the power system during its current operating cycle and combining this with power flow calculations to determine the transmission path of carbon flow in the power system topology, it achieves a dynamic characterization of the carbon emission flow pattern in the power grid. Compared to traditional methods that rely solely on static statistical data or empirical emission factors, this application can fully reflect the energy coupling and carbon flow transmission relationship between nodes, thereby improving the physical rationality and accuracy of carbon emission calculations. In specific implementation, this application first calculates the initial carbon emissions of each carbon emission node and then filters out candidate carbon emission nodes by setting thresholds, ensuring that the focus of analysis is on key nodes that may contribute significantly to system carbon emissions. Subsequently, by introducing a first carbon emission index and a second carbon emission index, the transmission influence of adjacent nodes on the target node and the carbon transmission effect of the target node on adjacent nodes are quantified, respectively, achieving a two-way characterization of the node's carbon emission impact. This method can overcome the problem of traditional methods neglecting node coupling effects, enabling even nodes with low carbon emissions but located on high-carbon power flow paths to be reasonably identified as actual high-carbon nodes, thus significantly improving the accuracy and comprehensiveness of high-carbon node identification.

[0124] Furthermore, by correcting the initial carbon emissions of nodes based on the first and second carbon emission indicators, this application can obtain corrected carbon emissions that more closely approximate the actual operating conditions, providing a reliable basis for subsequent high-carbon node identification. This enables precise monitoring and management of key emission sources within the context of power system carbon neutrality and energy transition. This method not only reflects the node's own emissions but also comprehensively considers the transmission effect of carbon emissions in the grid topology and the dynamic coupling between nodes. It has multiple beneficial effects, including improving the accuracy of carbon emission monitoring, optimizing low-carbon dispatch strategies, and assisting energy planning, thus providing scientific support for the safe, economical, and low-carbon operation of the power system.

[0125] This application also provides a novel high-carbon node identification system for power systems based on carbon flow tracing, referring to... Figure 2 The diagram illustrates a functional block diagram of a novel high-carbon node identification system 200 for power systems based on carbon flow tracing, which may include the following modules:

[0126] The acquisition module 201 is used to acquire the operating condition data of the power system during the current operating cycle, and to perform power flow calculation based on the operating condition data to determine the carbon flow transmission path in the power system topology. The carbon flow transmission path includes the carbon conduction relationship between carbon emission nodes of multiple different carbon emission nodes.

[0127] The first screening module 202 is used to calculate the initial carbon emissions of each carbon emission node and identify carbon emission nodes with initial carbon emissions greater than a first threshold as candidate carbon emission nodes.

[0128] The first calculation module 203 is used to calculate the first carbon emission index of the candidate carbon emission node based on the historical carbon emission data and power flow ratio of the adjacent carbon emission nodes of the candidate carbon emission node. The first carbon emission index is used to characterize the impact of the adjacent carbon emission nodes on the candidate carbon emission node.

[0129] The second calculation module 204 is used to calculate the second carbon emission index of the candidate carbon emission node based on its own historical carbon emission data and its power flow distribution with adjacent carbon emission nodes. The second carbon emission index is used to characterize the impact of the candidate carbon emission node on adjacent carbon emission nodes.

[0130] The second screening module 205 is used to adjust the initial carbon emissions of the candidate carbon emission nodes according to the first carbon emission index and the second carbon emission index, obtain the corrected carbon emissions of the candidate carbon emission nodes, and determine the high carbon emission nodes according to the relationship between the corrected carbon emissions of each candidate carbon emission node and the second threshold.

[0131] In one alternative implementation, the determining module includes:

[0132] The acquisition submodule is used to acquire the topology model of the power system nodes and branches;

[0133] The calculation submodule is used to perform power flow calculations based on the topology model and operating condition data, and obtain the power flow calculation results.

[0134] The mapping submodule is used to map the carbon emission coefficients of different types of power sources to the power flow calculation results, and determine the carbon flow transfer relationship between nodes according to the power flow direction and power ratio.

[0135] The generation submodule is used to generate carbon flow transport paths based on carbon flow transport relationships.

[0136] In one alternative implementation, the first filtering module includes:

[0137] The node type determination submodule is used to determine the node type of each carbon emission node, including power generation nodes and load nodes;

[0138] The first calculation submodule is used to calculate the initial carbon emissions of a carbon emission node based on the actual power generation of the power generation node and the carbon emission factor of the corresponding power source type when the node type of the carbon emission node is a power generation node.

[0139] The second calculation submodule is used to calculate the initial carbon emissions of the carbon emission node based on the actual load power of the load node and the carbon emission factor of the regional power grid when the node type of the carbon emission node is a load node.

[0140] In one alternative implementation, the first computing module includes:

[0141] The first acquisition submodule is used to acquire historical carbon emissions and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission nodes.

[0142] The first carbon transmission contribution calculation submodule is used to calculate the carbon transmission contribution of the adjacent carbon emission nodes to the candidate carbon emission nodes based on the power flow ratio between the adjacent carbon emission nodes and the candidate carbon emission nodes.

[0143] The first aggregation submodule is used to aggregate the carbon transmission contributions of all adjacent carbon emission nodes to obtain the first carbon emission index of the candidate carbon emission nodes.

[0144] In one alternative implementation, the first computing module includes:

[0145] The second acquisition submodule is used to acquire the candidate carbon emission node's own historical carbon emission data and the power flow ratio between it and its neighboring carbon emission nodes.

[0146] The second carbon transmission contribution calculation submodule is used to allocate the carbon emissions of the candidate carbon emission node to the adjacent carbon emission nodes according to the power flow ratio, and calculate the carbon transmission contribution of the candidate carbon emission node to the adjacent carbon emission nodes.

[0147] The second aggregation submodule is used to aggregate the carbon transmission contribution of the candidate carbon emission nodes to adjacent carbon emission nodes to obtain the second carbon emission index of the candidate carbon emission nodes.

[0148] In one alternative implementation, the second computing module includes:

[0149] The first correction submodule is used to determine the transmission impact value of adjacent carbon emission nodes on the candidate carbon emission node based on the first carbon emission index.

[0150] The second correction submodule is used to determine the spillover contribution value of the candidate carbon emission node to the adjacent carbon emission node based on the second carbon emission index.

[0151] The third correction submodule is used to perform weighted coupling calculations on the transmission impact value and the spillover contribution value with the initial carbon emissions of the candidate carbon emission nodes, respectively.

[0152] The determination submodule is used to correct the initial carbon emissions of the candidate carbon emission nodes based on the weighted coupling results, and obtain the corrected carbon emissions of the candidate carbon emission nodes.

[0153] In this embodiment, the present application also provides an electronic device, which may include a memory and one or more processors. The memory and processors are coupled. The memory is used to store computer program code, which includes computer instructions. When the processor executes the computer instructions, the electronic device can perform various functions or steps in the above method embodiments.

[0154] This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed on an electronic device, cause the electronic device to perform the various functions or steps described in the above method embodiments.

[0155] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the various functions or steps in the above method embodiments.

[0156] In this embodiment, the electronic device, computer-readable storage medium, and computer program product are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0157] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, a computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0158] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0159] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, units, and processes described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0161] In the embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0162] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0163] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0164] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0165] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying high-carbon nodes of a new power system according to carbon flow tracking, characterized in that, Applied to a target edge side computing device, the method comprises: Obtaining the working condition data of the current operation cycle of the power system, and performing power flow calculation according to the working condition data to determine the carbon flow transmission path of the carbon flow in the power system topology, the carbon flow transmission path including the carbon conduction relationship between a plurality of different carbon emission nodes; Calculating the initial carbon emission amount of each carbon emission node, and determining the carbon emission node with an initial carbon emission amount greater than a first threshold value as a candidate carbon emission node; According to the historical carbon emission data and power flow proportion of the adjacent carbon emission nodes of the candidate carbon emission node, a first carbon emission index of the candidate carbon emission node is calculated, which is used to represent the influence of the adjacent carbon emission nodes on the candidate carbon emission node; According to the historical carbon emission data of the candidate carbon emission node itself and the power flow distribution between the candidate carbon emission node and the adjacent carbon emission nodes, a second carbon emission index of the candidate carbon emission node is calculated, including: Obtaining the historical carbon emission data of the candidate carbon emission node itself and the power flow proportion between the candidate carbon emission node and the adjacent carbon emission nodes; According to the power flow proportion, the carbon emission amount of the candidate carbon emission node is proportionally distributed to the adjacent carbon emission nodes, and the carbon conduction contribution of the candidate carbon emission node to the adjacent carbon emission nodes is calculated; The carbon conduction contributions of the candidate carbon emission node to the adjacent carbon emission nodes are summarized to obtain the second carbon emission index of the candidate carbon emission node; The second carbon emission index is used to represent the influence of the candidate carbon emission node on the adjacent carbon emission nodes; According to the first carbon emission index and the second carbon emission index, the initial carbon emission amount of the candidate carbon emission node is adjusted to obtain a corrected carbon emission amount of the candidate carbon emission node, and according to the size relationship between the corrected carbon emission amount of each candidate carbon emission node and a second threshold value, a high carbon emission node is determined.

2. The method of claim 1, wherein the method is characterized by, According to the working condition data, the power flow calculation is performed to determine the carbon flow transmission path of the carbon flow in the power system topology, including: Obtaining the topology model of the nodes and branches of the power system; According to the topology model and the operating condition data, the power flow calculation is performed to obtain the power flow calculation result; The carbon emission coefficients of different types of power sources are mapped to the power flow calculation result, and the carbon flow transmission relationship between nodes is determined according to the power flow direction and the power proportion; According to the carbon flow transmission relationship, the carbon flow transmission path is generated. 3.The method of claim 1, wherein, The initial carbon emission amount of each carbon emission node is calculated, including: Determining the node type of each carbon emission node, the node type including a power generation node and a load node; In the case where the node type of the carbon emission node is the power generation node, the initial carbon emission amount of the carbon emission node is calculated according to the actual power generation power of the power generation node and the carbon emission factor of the corresponding power source type; In the case where the node type of the carbon emission node is the load node, the initial carbon emission amount of the carbon emission node is calculated according to the actual load power of the load node and the carbon emission factor of the regional power grid. 4.The method of claim 1, wherein, According to the historical carbon emission data and power flow proportion of the adjacent carbon emission nodes of the candidate carbon emission node, the first carbon emission index of the candidate carbon emission node is calculated, including: obtaining historical carbon emission and historical power flow data of carbon emission nodes adjacent to the candidate carbon emission node; calculating carbon conduction contribution of the adjacent carbon emission nodes to the candidate carbon emission node according to power flow proportion between the adjacent carbon emission nodes and the candidate carbon emission node; summing up carbon conduction contribution of all adjacent carbon emission nodes to obtain first carbon emission index of the candidate carbon emission node. 5.The method of claim 1, wherein, adjusting initial carbon emission of the candidate carbon emission node according to the first carbon emission index and the second carbon emission index to obtain corrected carbon emission of the candidate carbon emission node, including: determining conduction influence value of adjacent carbon emission nodes to the candidate carbon emission node according to the first carbon emission index; determining outflow contribution value of the candidate carbon emission node to adjacent carbon emission nodes according to the second carbon emission index; performing weighted coupling operation on the conduction influence value and the outflow contribution value and the initial carbon emission of the candidate carbon emission node respectively; correcting the initial carbon emission of the candidate carbon emission node according to the weighted coupling result to obtain the corrected carbon emission of the candidate carbon emission node.

6. The method for identifying high carbon emission nodes of a new power system according to carbon flow tracking according to claim 1, characterized in that, determining high carbon emission nodes according to size relationship between the corrected carbon emission of each candidate carbon emission node and a second threshold value, including: obtaining corrected carbon emission of each candidate carbon emission node in multiple check periods; determining the candidate carbon emission node as the high carbon emission node in the case that corrected carbon emission of at least two check periods is greater than the second threshold value.

7. The method according to any one of claims 1-6, wherein, The method further includes: determining the target edge side computing device according to high carbon node identification demand and carbon flow transmission path by the remote server, wherein the higher the coupling degree of the carbon emission node with other carbon emission nodes, the higher the priority of the edge side computing device corresponding to the carbon emission node to be selected as the target edge side computing device.

8. A new power system high carbon node identification system according to carbon flow tracking, characterized by, The system for implementing the method of any one of claims 1-7 includes: an acquisition module for acquiring working condition data of a current operation period of a power system, and performing power flow calculation according to the working condition data to determine carbon flow transmission path of carbon flow in a power system topology, the carbon flow transmission path including carbon conduction relationship between multiple different carbon emission nodes; a first screening module for calculating initial carbon emission of each carbon emission node, and determining carbon emission nodes with initial carbon emission greater than a first threshold value as candidate carbon emission nodes; a first calculation module for calculating first carbon emission index of the candidate carbon emission node according to historical carbon emission data and power flow proportion of adjacent carbon emission nodes of the candidate carbon emission node, the first carbon emission index being used to represent influence of the adjacent carbon emission nodes on the candidate carbon emission node; a second calculation module for calculating second carbon emission index of the candidate carbon emission node according to historical carbon emission data of the candidate carbon emission node itself and power flow distribution of the candidate carbon emission node and adjacent carbon emission nodes, including: obtain historical carbon emission data of the candidate carbon emission node itself and a power flow proportion between the candidate carbon emission node and adjacent carbon emission nodes; distribute the carbon emission of the candidate carbon emission node to the adjacent carbon emission nodes according to the power flow proportion, and calculate carbon conduction contribution of the candidate carbon emission node to the adjacent carbon emission nodes; obtain a second carbon emission index of the candidate carbon emission node by summarizing the carbon conduction contribution of the candidate carbon emission node to the adjacent carbon emission nodes; the second carbon emission index is used to represent the influence of the candidate carbon emission node on the adjacent carbon emission nodes; a second screening module is configured to adjust the initial carbon emission of the candidate carbon emission node according to the first carbon emission index and the second carbon emission index, obtain a corrected carbon emission of the candidate carbon emission node, and determine the high carbon emission node according to the size relationship between the corrected carbon emission of each candidate carbon emission node and a second threshold. 9.The new power system high carbon node identification system according to carbon flow tracing of claim 8, wherein, The acquisition module comprises: an acquisition submodule configured to acquire a topological model of nodes and branches of the power system; a calculation submodule configured to perform power flow calculation according to the topological model and operating condition data, and obtain a power flow calculation result; a mapping submodule configured to map carbon emission coefficients of different types of power sources to the power flow calculation result, determine carbon flow transmission relationships between nodes according to power flow directions and power proportions, and a generation submodule configured to generate the carbon flow transmission path according to the carbon flow transmission relationships.

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