A method and system for tracing the source of carbon dioxide in a power distribution system

Abstract

The present application relates to the technical field of carbon emission, and discloses a method and system for tracing the carbon source of a power distribution system, including obtaining real-time operation data of a distribution network; wherein the real-time operation data of the distribution network includes interconnection switch change data, feeder connection relationship, and output data of each power source; based on the changes in the interconnection switch change data and the feeder connection relationship in a preset time window, obtaining and updating the line topology of the distribution network, and determining the updated power flow distribution data corresponding to the updated line topology of the distribution network; based on the updated power flow distribution data, determining the target carbon reduction data; based on the output of each power source, determining the carbon flow contribution value of each power source to the load carbon flow; based on the target carbon reduction data and the carbon flow contribution value, extracting the characteristics of the carbon flow distribution law under different distribution network architectures, and establishing a carbon flow distribution knowledge base to achieve carbon flow tracing. Its beneficial effect is the real-time optimization and precise tracking of the carbon flow distribution of the distribution network.

Description

Technical Field

[0001] The present application relates to the field of carbon emission technology, and in particular to a method and system for tracing the carbon emissions of electricity in a power distribution system. Background Art

[0002] In the multi-feeder ring network structure of the distribution network, carbon flow allocation faces complex challenges, mainly due to the dynamic changes in the distribution network architecture and the nonlinear characteristics of carbon flow reconstruction. The action of the interconnecting switch causes real-time changes in the distribution network topology, making the carbon flow allocation subject to the interaction of multiple factors such as load, output, and line parameters. In addition, there is a lack of a clear mapping relationship between carbon flow and the distribution network architecture, making it difficult for traditional carbon flow allocation methods to adapt to all scenarios.

[0003] Therefore, how to solve the problems faced by carbon flow distribution under the multi-feeder ring network structure, such as dynamic topological changes, unclear carbon flow distribution rules, and lack of effective carbon flow and architecture mapping mechanism, has become a key challenge that needs to be solved urgently. Summary of the invention

[0004] The present application provides a method and system for tracing the carbon tracing of a power distribution system, which solves the technical problems faced by the carbon flow distribution under the multi-feeder ring network structure of the distribution network, such as dynamic topological changes, unclear carbon flow distribution rules, and lack of effective carbon flow and architecture mapping mechanism, and achieves the technical effect of real-time optimization and precise tracking of carbon flow distribution in the distribution network.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for tracing the source of carbon dioxide in a power distribution system, the method comprising:

[0007] Acquire real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source;

[0008] Based on the tie switch change data and the change of the feeder connection relationship in a preset time window, obtaining an updated distribution network line topology structure, and determining updated power flow distribution data corresponding to the updated distribution network line topology structure;

[0009] Determining target carbon reduction data based on the updated power flow distribution data;

[0010] Based on the output of each power source, determining the carbon flow contribution value of each power source to the load carbon flow;

[0011] Based on the target carbon reduction data and the carbon flow contribution value, the carbon flow distribution law characteristics under different distribution network architectures are extracted, and a carbon flow distribution knowledge base is established to achieve carbon flow traceability; wherein, the carbon flow distribution knowledge base is used for graphical display.

[0012] In a second aspect, an embodiment of the present application provides a carbon traceability system for a power distribution system, the system comprising:

[0013] A data acquisition unit, used to acquire real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source;

[0014] A data updating unit, configured to obtain an updated distribution network line topology structure based on the tie switch change data and the change of the feeder connection relationship in a preset time window, and determine updated power flow distribution data corresponding to the updated distribution network line topology structure;

[0015] A carbon flow allocation determination unit, configured to determine target carbon reduction amount data based on the updated tidal current distribution data;

[0016] A carbon flow contribution determination unit, configured to determine a carbon flow contribution value of each power source to a load carbon flow based on the output of each power source;

[0017] A knowledge base construction unit is used to extract the characteristics of carbon flow allocation rules under different distribution network architectures based on the target carbon reduction data and the carbon flow contribution value, and establish a carbon flow allocation knowledge base to achieve carbon flow traceability; wherein the carbon flow allocation knowledge base is used for graphical display. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of a method for tracing the source of carbon dioxide in a power distribution system provided in an embodiment of the present application;

[0020] Figure 2 A flowchart of step S3 provided in an embodiment of the present application;

[0021] Figure 3 A flowchart of step S5 provided in an embodiment of the present application;

[0022] Figure 4A flowchart of a training method for a pre-trained support vector machine model provided in an embodiment of the present application;

[0023] Figure 5 A flowchart of step S7 provided in an embodiment of the present application;

[0024] Figure 6 A flowchart of step S9 provided in an embodiment of the present application;

[0025] Figure 7 A flow chart after constructing a data association matrix provided in an embodiment of the present application;

[0026] Figure 8 A flow chart of another method for tracing the source of carbon dioxide in a power distribution system provided in an embodiment of the present application;

[0027] Fig. 9 A block diagram of a carbon traceability system for a power distribution system provided in an embodiment of the present application;

[0028] Fig.10 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0030] According to an embodiment of the present application, an embodiment of a method for tracing the carbon traceability of electricity in a distribution and utilization system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0031] In this embodiment, a method for tracing the source of carbon dioxide in a power distribution system is provided. Figure 1 A flow chart of a method for tracing the source of carbon dioxide in a power distribution system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the process includes the following steps:

[0032] Step S1, obtaining real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source.

[0033] Specifically, the change data of the interconnection switch can be obtained through the real-time data monitoring and early warning system. The system locates the corresponding interconnection switch in the single-line diagram based on GIS data, and collects its status information in real time. The feeder connection relationship can be determined by the multi-source data management system of the distribution network line interconnection switch. The system uses GIS map data to establish the association between the interconnection switch and the feeder, and associates the real-time collected interconnection switch status information with the corresponding feeder and switch. In this way, the real-time connection status and topology of the feeder can be obtained. The output data of each power supply can be obtained through the intelligent power monitoring and management system, which is based on the real-time data transmission of the MQTT protocol, combined with React and D3.js to realize data visualization. In this way, the operating status of the power equipment can be monitored in real time, including the output data of each power supply.

[0034] Step S3, based on the tie switch change data and the change of the feeder connection relationship in the preset time window, obtain the updated distribution network line topology structure, and determine the updated power flow distribution data corresponding to the updated distribution network line topology structure.

[0035] Specifically, a preset time window, such as 1 hour, is used to perform real-time statistics on the number of action changes corresponding to the tie switch change data and the changes in the feeder connection relationship, and a global timestamp is added to the collected data to facilitate the subsequent analysis of the time sequence and synchronization of the data. The distribution network line topology is updated according to the changes, and the forward-backward method is used to utilize the radial characteristics of the distribution network, and the node voltage and line power are iteratively calculated, or a power flow calculation program is written in a software environment such as MATLAB, the distribution network model and parameters are input, and the program is run to perform power flow calculation to obtain the corresponding updated power flow distribution data.

[0036] Step S5: determining target carbon reduction data based on the updated power flow distribution data.

[0037] Specifically, by updating the power flow distribution data, we can obtain data such as branch power flow, generator injection power, node injection power and load size, which will be used to calculate the carbon reduction amount. The generator set carbon emission intensity vector is a K-dimensional column vector, which represents the carbon emission intensity of each generator set. The node carbon potential vector is an N-dimensional column vector, which is expressed by the formula EN=(PN-PTB) -1PTG×EG is calculated, where EN represents the node carbon potential vector, PN represents the node flux matrix, PTB represents the transpose of the branch flow distribution matrix, PTG represents the generator injection power matrix, and EG represents the carbon emission intensity vector of the generator set. The branch flow distribution matrix describes the active power flow distribution of the power system, while the node flux matrix describes the active flux of the node. Through the node carbon potential vector and the branch flow distribution matrix, the carbon flow rate of each branch of the system can be calculated. The branch carbon flow rate distribution matrix is ​​defined as an N-order square matrix, represented by RB, and the calculation formula is RB=PBdiag(EN). At the same time, combined with the load distribution matrix, the carbon flow rate corresponding to all loads can be obtained, that is, the load carbon flow rate vector RL, and the calculation formula is RL=PLEN. From the carbon flow rate data of the branch and the load, the carbon emission contribution of each power system component is comprehensively considered. According to the system's goals, such as reducing a certain proportion of carbon emissions, formulate corresponding carbon reduction targets. For example, if you want to reduce carbon emissions by 20%, calculate the new emissions as 80% of the original emissions. By analyzing and optimizing the carbon flow rate of each part of the system, the carbon reduction data obtained can be used to evaluate the emission reduction effect under different operation strategies. Common carbon reduction targets include: the reduction of total carbon emissions in a certain period of time (such as one year). Changes in carbon emissions in certain specific areas or branches. Carbon emission allocation based on different power generation methods (such as renewable energy, traditional thermal power generation, etc.). It is also possible to use a model pre-trained with vertical data to infer the target carbon reduction data. For example, support vector machine.

[0038] Step S7: determining the initial carbon flow contribution value of each power source to the load carbon flow based on the output of each power source.

[0039] Specifically, by determining the carbon emission factor according to the current output of the coal-fired power unit, the carbon dioxide emissions generated by the coal-fired power unit per unit of electricity production are reflected. And the carbon emission factor of the gas unit is determined according to the output of the gas unit. Finally, combined with the current combustion volume, the carbon flow contribution values ​​of the coal-fired power unit and the gas unit are calculated respectively. The accurate quantification and comparison of carbon emissions of different power units are achieved, providing a scientific basis for subsequent carbon emission management and optimization.

[0040] Step S9, based on the target carbon reduction data and the carbon flow contribution value, extract the carbon flow distribution law characteristics under different distribution network architectures, and establish a carbon flow distribution knowledge base to achieve carbon flow traceability; wherein the carbon flow distribution knowledge base is used for graphical display.

[0041] Specifically, a data association matrix is ​​constructed based on the distribution network line topology, target carbon reduction data and carbon flow contribution value. The data association matrix is ​​labeled according to the preset coding rules. This may include the encoding of the main line, branch line and tie switch, as well as other related topological information. Through this labeling, an architecture identifier can be obtained, which can uniquely represent the specific topology in the distribution network. For each period corresponding to the architecture identifier, the changes in its carbon reduction data are compared. This includes analyzing the trend of the change in carbon flow values, the fluctuation range and any significant change points. By comparing the carbon reduction data under different architecture identifiers, the impact of architecture changes on carbon flow allocation can be identified. Based on the comparison results, a carbon flow allocation knowledge base is established. This carbon flow allocation knowledge base will contain carbon reduction data under different architecture identifiers and the changes of these data over time. The carbon flow allocation knowledge base can be used to store historical data, analyze trends, predict future carbon flow allocation, and provide support for the optimization and decision-making of the distribution network. Visualization tools such as Sankey diagrams are used to display the distribution of carbon flows in the distribution network. Sankey diagrams use directed arrows to connect different nodes to show the path and magnitude of flow. The width of the arrows indicates the size of the flow or quantity. This graphical display method can help users intuitively understand and analyze complex carbon flow distribution and relationships, thereby supporting decision-making and planning processes.

[0042] The present embodiment provides a method for tracing the carbon source of a power distribution system, which obtains real-time operating data of the distribution network, including changes in interconnecting switches, feeder connection relationships, and power output data, to provide necessary basic information for subsequent analysis. The line topology of the distribution network is updated using the time-varying data of interconnecting switch changes and feeder connection relationships, and the flow distribution data under the topology is further determined to provide real-time network status for carbon flow analysis. Based on the updated flow distribution data, the target carbon reduction data is determined. Based on the output data of each power source, the initial carbon flow contribution value of each power source to the load carbon flow is calculated to reflect the impact of different power sources on the overall carbon flow. Finally, combined with the target carbon reduction data and the carbon flow contribution value, the characteristics of the carbon flow distribution law under different distribution network architectures are extracted, and a carbon flow distribution knowledge base is established to support the tracing of carbon flows. The present embodiment can accurately track and optimize the distribution of carbon flows to achieve green and low-carbon operation of the distribution network.

[0043] Figure 2 The flowchart of step S3 provided in the embodiment of the present application may include the following steps:

[0044] Step S31, determining the number of action changes corresponding to the tie switch change data and the change of the feeder connection relationship within a preset time window.

[0045] Step S33, when the number of action changes exceeds a preset number of change thresholds, the original distribution network line topology structure is updated according to the change time series corresponding to the tie switch change data and the change of the feeder connection relationship to obtain an updated distribution network line topology structure.

[0046] Specifically, a preset time window, such as 1 hour, is used to perform real-time statistics on the number of action changes corresponding to the tie switch change data and the changes in the feeder connection relationship, and a global timestamp is added to the collected data to facilitate subsequent analysis of the time sequence and synchronization of the data. Then, according to the preset number of change thresholds, for example, if the number of action changes exceeds 3 times within 1 hour, the tie switch change data exceeding 3 times is marked, and the change time series of the switch on-off state is obtained through the switch action record, and compared with the feeder connection relationship. If the switch position quantity changes at the moment corresponding to the feeder connection relationship change, the new change data is written into the topology update buffer area, and the feeder node connection data with a timestamp is read from the topology update buffer area. The node connection relationship of the new and old topological structures is verified, and the connection status of each node is checked to confirm whether the connection of each node is consistent with the actual switch status. This usually involves traversing all nodes and edges in the topological graph to ensure that the adjacency list (or connection relationship) of each node matches the actual power grid structure. The verified topological graph data is written into the formal library to complete the update of the distribution network line topology structure, and the updated distribution network line topology structure is obtained.

[0047] For example, the tie switch change data has 4 action changes within 1 hour, exceeding the preset change threshold 3 times, indicating that the line topology of this section may change. The timestamps of each action change are recorded at 10:00, 10:15, 10:30, and 10:45, respectively, with an interval of 15 minutes, which is much lower than the 90-minute action interval during normal operation. According to the switch control logic truth table, it is verified that the switch position is switched from the open position to the closed position and then back to the open position, indicating that there is a fault removal and recovery process in this section of the line. In the original distribution network line topology, feeder A is connected to feeder B through a tie switch to form a hand-in-hand power supply structure. After the tie switch action changes, the 8 load nodes at the end of feeder A are powered by feeder B, and the topology update buffer temporarily stores the node connection relationship after the change. Through node traversal, it is found that the load of feeder A is transferred to feeder B, and the topology change is verified. In the original distribution network line topology, feeder B adds 8 load nodes to obtain an updated distribution network line topology.

[0048] and Figure 1Compared with the embodiment shown in the figure, this embodiment can dynamically update the line topology of the distribution network by real-time monitoring of the changes in the connection relationship between the tie switches and the feeders in the distribution network, combined with the analysis of the number of action changes. When the number of changes detected exceeds the preset threshold, the topology will be automatically adjusted to ensure that it is consistent with the actual operating status. This automated update not only improves the intelligence level of the distribution network, but also can reflect the network status in real time, optimize the power dispatching and fault recovery process, enhance the reliability and stability of the distribution network, reduce manual intervention, and improve the overall operating efficiency.

[0049] Figure 3 The flowchart of step S5 provided in the embodiment of the present application may include the following steps:

[0050] Step S51, determining the branch resistance loss power of each branch according to the updated power flow distribution data.

[0051] Specifically, according to the updated power flow distribution data, the current value of each branch and the corresponding conductor resistance value are statistically divided into sections according to the line length of every 500 meters, and the branch resistance loss power is obtained by multiplying the square value of the current and the conductor resistance. The specific calculation formula is: P loss =I 2 ×R, where P loss is the power loss of branch resistance, I is the branch current, and R is the branch resistance.

[0052] Step S53, based on the branch resistance power loss, using a pre-trained support vector machine model to generate target carbon reduction data.

[0053] Specifically, we can use the data of the specified time period to obtain the load rate data from the power load data download warehouse, obtain the power consumption and power generation data of each power in the power industry, and train the support vector machine model in combination with the branch resistance loss power. The model can generate the target carbon reduction data corresponding to the branch resistance loss power under the distribution network data architecture. After training and parameter adjustment, the obtained model is a pre-trained support vector machine model, which can be used to predict new data.

[0054] and Figure 1 Compared with the embodiment shown in the figure, this embodiment first calculates the branch resistance loss power of each branch based on the updated power flow distribution data, reflecting the energy loss caused by resistance when the current passes through each branch. These power losses provide important basic information for the subsequent carbon flow distribution. Then, using the pre-trained support vector machine (SVM) model, the target carbon reduction data is generated according to the branch resistance loss power, so as to achieve the reasonable distribution and optimization of carbon flow. Through this process, the distribution of carbon flow between the branches of the distribution network can be accurately predicted, providing support for reducing carbon emissions and improving the energy utilization efficiency of the distribution network.

[0055] Figure 4 A flowchart of a training method for a pre-trained support vector machine model provided in an embodiment of the present application, the process may include the following steps:

[0056] Step S571, constructing a mapping relationship between power loss-load rate-power output data and carbon flow value through a Gaussian kernel function.

[0057] Step S573, using a support vector machine model to analyze the mapping relationship to obtain a pre-trained support vector machine model.

[0058] Specifically, the branch resistance power loss, carbon reduction data, load rate and power output data are extracted from the database for 30 consecutive days, and the data are annotated with the load rate value at the corresponding moment. The data is preprocessed by Gaussian kernel function to construct the mapping relationship between power loss-load rate-power output data and carbon reduction. Gaussian kernel function helps to transform nonlinear problems into linear problems so that the support vector machine model can handle them. Cross-validation is used to divide all samples into 80% training sets and 20% test sets, and the carbon reduction data is calculated until the model converges to obtain a trained support vector machine model.

[0059] For example, by analyzing the carbon reduction data sampled every hour within 30 days in the distribution network database, it is found that the load rate and photovoltaic output have a significant impact on the carbon reduction. A mapping relationship is established through the Gaussian kernel function to handle the nonlinear characteristics of the carbon flow weight parameter. The carbon flow weight parameter shows nonlinear changes when the load rate of the distribution line changes. For example, when the load rate changes from 50% to 90%, the carbon reduction decreases. An increase in photovoltaic output will also lead to an increase in carbon reduction.

[0060] and Figure 3 Compared with the embodiment shown in the figure, this embodiment provides the necessary basic information for subsequent modeling by obtaining branch resistance power loss, carbon reduction data, load rate and power output data. These data help the system capture the energy efficiency and carbon flow characteristics of the distribution network under different working conditions. Based on these data, the Gaussian kernel function is used to construct the mapping relationship between power loss, load rate and power output data and carbon reduction, thereby realizing the correlation analysis between carbon flow and other key factors. Finally, the support vector machine (SVM) model is used to analyze the above mapping relationship, and a pre-trained support vector machine model is obtained. The model can accurately predict the carbon reduction under different operating conditions, thereby providing strong technical support for the green and low-carbon operation and decision optimization of the distribution network.

[0061] Figure 5In the flowchart of step S7 provided in the embodiment of the present application, each power source output includes the coal power output of the coal-fired power unit and the gas output of the gas-fired power unit; the carbon flow contribution value includes the coal power carbon flow contribution value and the gas carbon flow contribution value; the process may include the following steps:

[0062] Step S71, determining the current coal-fired power carbon emission factor according to the coal-fired power output.

[0063] Specifically, we first need to calculate the amount of coal consumed per hour by coal-fired power units. Assume that the calorific value of coal-fired power fuel is 29,000 kilojoules per kilogram, the heat consumption rate is 290 grams of standard coal per kilowatt-hour, the coal-fired power output is 300 megawatts (MW), that is, 300,000 kilowatts (kW), and the carbon content per unit calorific value of coal-fired power is 0.785 kilograms. The amount of coal consumed per hour FC = 300,000 kW × 1h × 290g / kWh × 1kg ÷ 1000g = 87,000 kg. The gas carbon emission factor refers to the amount of carbon dioxide produced per unit energy, and the calculation formula is: EF = CC × 44 / 12, where 44 / 12 is the molar mass conversion coefficient of carbon (C) to carbon dioxide (CO2), EF is the carbon dioxide emission factor, and CC is the carbon content per unit calorific value of coal-fired power.

[0064] EF=0.785×44 / 12=2.86 kgCO2 / kg, total carbon dioxide emissions E=87000×2.86=248820kgCO2, power generation per hour=300000kW×1h=300000kWh, coal-fired power carbon emission factor=248820 / 300000=0.829 kgCO2 / kWh.

[0065] Step S73, determining the current gas carbon emission factor according to the gas output.

[0066] Specifically, the emission factor refers to the amount of carbon dioxide emissions generated per unit of electricity. For the gas output, it is assumed to be 200,000 kilowatts, the gas fuel consumes 45,000 cubic meters of natural gas per hour, the carbon content of the gas unit calorific value is 0.553 kg / cubic meter, and the calorific value of the gas fuel reaches 36,000 kilojoules per cubic meter. The gas carbon emission factor refers to the amount of carbon dioxide generated per unit of energy. The calculation formula is: EF=CC×44 / 12, where 44 / 12 is the molar mass conversion factor of carbon (C) to carbon dioxide (CO2), EF is the carbon dioxide emission factor, and CC is the carbon content of the gas unit calorific value.

[0067] EF = 0.553 × 44 / 12 = 1.935 kgCO2 / m 3, total carbon dioxide emissions E = 45000 × 1.935 = 86925 kgCO2, power generation per hour = 200000 kW × 1h = 200000 kWh, gas carbon emission factor = 86925 / 200000 = 0.4346 kgCO2 / kWh.

[0068] Step S75, determining the coal-fired power carbon flow contribution value corresponding to the coal-fired power unit according to the current coal combustion amount and the current coal-fired power carbon emission factor, and determining the gas carbon flow contribution value corresponding to the gas unit according to the current gas combustion amount and the current gas carbon emission factor.

[0069] Specifically, the carbon flow contribution of coal-fired power = current coal combustion volume × coal-fired power carbon emission factor; the carbon flow contribution of gas = current gas combustion volume × gas carbon emission factor. The current coal combustion volume and the current gas combustion volume can be calculated through consumption records, flow meters, weighing and other methods of each combustion equipment.

[0070] and Figure 1 Compared with the embodiment shown in the figure, this embodiment reflects the carbon dioxide emissions generated by the coal-fired power unit in the production process of each unit of electricity by determining its carbon emission factor according to the current output of the coal-fired power unit. Next, the carbon emission factor of the gas unit is determined according to the output of the gas unit, indicating the carbon emissions per unit of electricity of the gas unit. Finally, combined with the current combustion amount, the carbon flow contribution values ​​of the coal-fired power unit and the gas unit are calculated respectively. The accurate quantification and comparison of carbon emissions of different power units are achieved, providing a scientific basis for subsequent carbon emission management and optimization.

[0071] Figure 6 The flowchart of step S9 provided in the embodiment of the present application may include the following steps:

[0072] Step S91, constructing a data association matrix based on the updated distribution network line topology, target carbon reduction data and carbon flow contribution value.

[0073] Step S93: annotate the data association matrix according to a preset encoding rule to obtain a schema identifier.

[0074] In some preferred embodiments, the data in the data association matrix are labeled according to the rule of 4-bit trunk line coding, 4-bit branch line coding, and 2-bit tie switch coding to obtain a 10-bit architecture identifier.

[0075] Step S95, comparing the changes in the carbon reduction data for the time period corresponding to the architecture identifier, and establishing a carbon flow allocation knowledge base according to the change results.

[0076] Specifically, the latest distribution network line topology data is obtained, including the information of newly added or removed lines, transformers and other equipment. The target carbon reduction data related to the distribution network lines are collected, and the target carbon reduction data are obtained from the pre-trained support vector machine model. Based on the output of each power source, the carbon flow contribution value of each power source to the load carbon flow is determined. The above data are integrated into a matrix, in which each row represents a branch and each column represents a specific parameter (such as resistance loss power, carbon reduction data, carbon flow contribution value, etc.). Then, according to the rules of 4-bit encoding of the main line, 4-bit encoding of the branch line, and 2-bit encoding of the tie switch, the data in the data association matrix are labeled. According to the encoding rules, a 10-bit architecture identifier is generated for each branch, and this identifier will uniquely identify each branch. By comparing the carbon reduction data corresponding to the architecture identifiers in different time periods, the changing trend and pattern of carbon reduction are analyzed. According to the change results, a carbon flow allocation knowledge base containing carbon reduction data and related analysis is established. This carbon flow allocation knowledge base can be used to guide future carbon flow allocation and carbon emission management. Therefore, this embodiment can effectively manage and optimize the carbon flow distribution of the distribution network, and at the same time provide a scientific basis for achieving carbon emission targets.

[0077] and Figure 1 Compared with the embodiment shown, this embodiment constructs a data association matrix by updating the distribution network line topology, target carbon reduction and carbon flow contribution value, laying the foundation for subsequent carbon flow optimization analysis. The matrix can reflect the carbon flow distribution and mutual relationship of each part of the distribution network. Then, the data association matrix is ​​labeled according to the preset coding rules to generate an architecture identifier so that data from different time periods can be effectively compared and tracked. Finally, by comparing the changes in carbon reduction data in the corresponding time period of the architecture identifier, a carbon flow allocation knowledge base can be established based on these changes, and the relationship between carbon flow and flow emissions can be accumulated and optimized, thereby providing intelligent decision-making support for future green energy scheduling and carbon emission control of distribution networks.

[0078] Figure 7 The flowchart after constructing the data association matrix provided in the embodiment of the present application may include the following steps:

[0079] Step S97, establishing a double-layer index directory in the carbon flow distribution knowledge base; wherein the double-layer index directory includes a first layer retrieval by architecture identifier and a second layer retrieval by timestamp;

[0080] Step S99, retrieve data records in the carbon flow distribution knowledge base through the double-layer index directory to achieve carbon flow traceability tracking.

[0081] Specifically, the schema identifier is used as the first-level index, and based on the first-level index, the timestamp is used as the second-level index. This means that the data records under each schema identifier will be indexed in chronological order to quickly retrieve data at a specific time point or time period. Using the established double-layer index directory, specific data records can be quickly located through the schema identifier and timestamp. For example, if you need to retrieve the carbon reduction data of a specific distribution network line in a specific time period, you can quickly access it directly through the index. Through the retrieved data records, you can track the source and destination of the carbon flow and realize carbon flow traceability. This includes analyzing changes in carbon reduction data, as well as other information related to carbon flow, such as the operating status of power equipment, the load conditions of the power grid, etc., to provide support for carbon emission management and optimization.

[0082] and Figure 6 Compared with the embodiment shown, this embodiment establishes a double-layer index directory in the carbon flow allocation knowledge base, in which the first-layer index is retrieved by the architecture identifier, and the second-layer index is retrieved by the timestamp. This double-layer index structure can realize multi-dimensional and efficient retrieval of data, ensuring that the carbon flow allocation information related to a specific time period and a specific architecture can be quickly located in massive data. Then, using this double-layer index directory, the relevant data records are quickly retrieved in the carbon flow allocation knowledge base to support carbon flow traceability. This process can accurately track the source and destination of carbon flows, thereby providing accurate data support for carbon emission monitoring, carbon trading, and energy optimization, and promoting the realization of green and low-carbon goals. Through this indexing and retrieval mechanism, the system not only improves the efficiency of data processing, but also enhances the transparency and traceability of carbon flow management.

[0083] Figure 8 A flowchart of another method for tracing the source of carbon dioxide in a power distribution system provided in an embodiment of the present application, wherein the real-time operation data of the distribution network includes three-phase voltage and three-phase current; the process may include the following steps:

[0084] Step S111, calculating the angle between the three-phase voltage and the three-phase current through vector operation to obtain the power factor.

[0085] Specifically,

[0086]

[0087] Among them, P is active power, S is apparent power, V line is the line voltage, I line is the line current.

[0088] Step S113, determining the voltage imbalance degree according to the maximum voltage and the minimum voltage among the three-phase voltages.

[0089] Specifically,

[0090]

[0091] Step S115, determining the current imbalance degree according to the maximum current value and the minimum current value in the three-phase current.

[0092] Specifically,

[0093]

[0094] Step S117, within the preset time window, when the power factor drops to the first warning threshold, and / or the voltage imbalance exceeds the second warning threshold, and / or the current imbalance exceeds the third warning threshold, a warning signal is issued.

[0095] Specifically, the first warning threshold here is preferably 0.6; the second warning threshold and the third warning threshold are preferably 3%.

[0096] and Figure 1 Compared with the embodiment shown in the figure, the present embodiment calculates the angle between the three-phase voltage and the three-phase current through vector operation to obtain the power factor. The voltage imbalance is determined by comparing the maximum and minimum values ​​of the three-phase voltage. The voltage imbalance can be monitored in real time, and voltage fluctuations and imbalance phenomena can be identified, thereby providing an early warning basis for the safe operation of the system to avoid equipment damage or power loss caused by voltage imbalance. The current imbalance is determined based on the maximum and minimum values ​​of the three-phase current. The current imbalance can be accurately reflected, the load balance of the power system can be evaluated, potential overload or current imbalance problems can be discovered in advance, and the stable operation of the power system can be guaranteed. Based on the monitoring results of the power factor, voltage imbalance and current imbalance, when the power factor drops to the first warning threshold, or the voltage imbalance exceeds the second warning threshold, or the current imbalance exceeds the third warning threshold, a warning signal is issued in time. Early fault detection and early warning of the power system are realized. Through real-time monitoring and multi-dimensional abnormal judgment, the system can effectively prevent safety hazards caused by imbalance or reduced efficiency, and improve the operation stability and reliability of the power system.

[0097] Another method for tracing the source of carbon dioxide in a power distribution system provided in an embodiment of the present application includes the following steps:

[0098] Step S1, obtaining real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source.

[0099] Step S3, based on the tie switch change data and the change of the feeder connection relationship in the preset time window, obtain the updated distribution network line topology structure, and determine the updated power flow distribution data corresponding to the updated distribution network line topology structure.

[0100] Step S4, based on the updated power flow distribution data, the carbon emissions are allocated according to the preset initial carbon flow allocation weight parameters to determine the target carbon flow allocation data.

[0101] Specifically, the carbon emissions of each power source in the distribution network are first calculated. This can be achieved through the carbon dioxide emission accounting method, such as using the calculation formula for coal-fired emissions, fuel oil emissions and gas emissions: carbon dioxide emissions = coal-fired emissions + fuel oil emissions + gas emissions, where the calculation formula for each emission is: coal-fired emissions = coal consumption in the current year × coal-fired comprehensive emission factor, fuel oil emissions = oil product consumption in the current year × fuel oil comprehensive emission factor, gas emissions = natural gas consumption in the current year × gas comprehensive emission factor. According to the preset initial carbon flow allocation weight parameters, these parameters can be set based on historical data, policy orientation or industry standards. The weight parameter is used to determine the proportion or share of each power source in the total carbon emissions. Using the above weight parameters, combined with the carbon emissions of each power source, the target carbon flow allocation data is calculated. This can be achieved by the following formula: target carbon flow allocation data = carbon emissions × weight parameter. For example, if the weight parameter of a power source is 0.3, then its target carbon flow allocation data in the total carbon emissions will be 30% of the carbon emissions of the power source.

[0102] The resistance loss power of the branch can also be calculated based on the updated power flow distribution data and branch conductance data, and a detailed resistance loss distribution diagram can be obtained, and the mapping relationship between carbon flow distribution and distribution network architecture can be established to determine the target carbon flow distribution data. That is, based on the updated power flow distribution data, the current and voltage conditions of each branch can be known, and the resistance loss power of each branch can be calculated in combination with the branch conductance data. The calculation formula for resistance loss power is: P loss =I 2 ×R, where P loss is the branch resistance loss power, I is the branch current, and R is the branch resistance. According to the calculated branch resistance loss power, a resistance loss distribution diagram is drawn, which helps to intuitively understand the energy loss in the distribution network. Carbon flow allocation refers to the process of allocating total carbon emissions in the distribution network according to the carbon emission intensity and output of each power source. Establishing a mapping relationship between carbon flow allocation and the distribution network architecture means that the carbon emissions of each node or branch need to correspond to its position in the power grid. This can be achieved through the carbon emission flow theory, which regards carbon emissions as a virtual network flow that depends on the existence of active power flow in the power grid. According to the updated distribution network line topology and the updated power flow distribution data, the carbon flow allocation parameters need to be initialized. This includes determining the initial carbon emission weights of each power source to ensure the reasonable distribution of carbon emissions.

[0103] Step S6: determining the initial carbon flow contribution value of each power source to the load carbon flow based on the output of each power source.

[0104] Specifically, by determining the carbon emission factor according to the current output of coal-fired power units, the carbon dioxide emissions generated by coal-fired power units during the production of each unit of electricity are reflected. The carbon emission factor of the gas unit is determined according to the output of the gas unit, and the carbon emission factors of coal-fired power and gas are normalized to obtain the carbon emission weights of coal-fired power and gas, ensuring that their respective contribution ratios can be properly reflected in the overall carbon emission calculation. Finally, the carbon flow contribution values ​​of coal-fired power units and gas units are calculated respectively, combining the output of coal-fired power and gas and the corresponding carbon emission weights. The accurate quantification and comparison of carbon emissions of different power units are achieved, providing a scientific basis for subsequent carbon emission management and optimization.

[0105] Step S8, based on the target carbon flow allocation data, adjust each power source until the initial carbon flow contribution value and the target carbon flow allocation data meet the preset conditions, and obtain the target carbon flow contribution value to achieve carbon traceability tracking.

[0106] Specifically, the initial carbon flow contribution value refers to the actual carbon emissions generated by each power source at a certain moment or in a certain period. The initial carbon flow contribution value may not be equal to the target carbon flow allocation data, especially when the energy supply structure is uneven or the power load changes. Therefore, the power source may need to be adjusted to achieve the goal of carbon flow allocation. The power system usually contains multiple power sources (such as thermal power, hydropower, wind power, solar power, etc.), and the carbon emission characteristics of each power source are different. In order to meet the target carbon flow allocation data, it may be necessary to adjust the power generation ratio or operation strategy of each power source. For example, reduce the proportion of thermal power with high carbon emissions, increase the proportion of low-carbon or zero-carbon energy (such as wind power, solar power, etc.), or upgrade the technology of high-carbon emission equipment (improve combustion efficiency and reduce carbon emissions). The goal of the adjustment is to match the final carbon flow contribution value (that is, the actual carbon emissions of each power source) with the target carbon flow allocation data (that is, the predetermined carbon emission target). This process requires multiple adjustments and iterative calculations to gradually approach the target value. Once the carbon flow contribution value of each power source and the target carbon flow allocation data meet the preset conditions, the final target carbon flow contribution value is determined. This value is the adjusted carbon emission status of the system, which can be used to evaluate the effectiveness of carbon emission control and provide data support for subsequent carbon traceability tracking.

[0107] This embodiment provides another method for tracing the source of carbon in the power distribution system. By acquiring the real-time operation data of the distribution network, the current operation status of the distribution network and the output of the power supply can be understood in real time. Based on the change data of the tie switch and the change of the feeder connection relationship within the preset time window, the updated distribution network line topology can be obtained, and the updated power flow distribution data corresponding to the topology can be determined. It can dynamically adapt to the changes in the distribution network structure and provide real-time and accurate power grid topology and power flow data for carbon flow allocation and traceability. According to the updated power flow distribution data, the carbon emissions are allocated according to the preset initial carbon flow allocation weight parameters to determine the target carbon flow allocation data. By optimizing the setting of the weight parameters, the reasonable allocation of carbon emissions is ensured, and the carbon emission targets of each power supply in the distribution network are achieved. This provides clear target data for subsequent carbon emission reduction strategies. By determining the initial carbon flow contribution value of each power supply to the load based on the output data of each power supply. It can accurately evaluate the carbon emission contribution of each power supply in the load power supply, and provide a quantitative basis for adjusting the carbon flow allocation of each power supply. Finally, by adjusting each power source based on the target carbon flow allocation data, until the initial carbon flow contribution value and the target carbon flow allocation data meet the preset conditions, the final target carbon flow contribution value is obtained, and carbon traceability is achieved. Ensure that the predetermined carbon emission target is achieved by dynamically adjusting the power generation strategy of each power source. At the same time, the carbon traceability tracking system is used to achieve accurate tracking and transparent monitoring of carbon emissions, providing a basis for carbon emission management.

[0108] The sub-steps of step S4 provided in the embodiment of the present application include the following steps:

[0109] Step S41, determining the branch resistance loss power of each branch according to the updated power flow distribution data, calibrating the branch resistance loss power, and generating a resistance loss distribution diagram.

[0110] Specifically, according to the updated power flow distribution data, the current value of each branch and the corresponding conductor resistance value are statistically divided into sections according to the line length of every 500 meters, and the branch resistance loss power is obtained by multiplying the square value of the current and the conductor resistance. The specific calculation formula is: P loss =I 2 ×R, where P loss is the branch resistance power loss, I is the branch current, and R is the branch resistance. Then the electronic map coordinate system is used to calibrate the branch resistance power loss, and a resistance loss distribution map is established according to the three-dimensional space coordinates. The resistance loss distribution map is divided into three display intervals of red, yellow and green. The red interval corresponds to a branch resistance power loss greater than 100 kilowatts, the yellow interval corresponds to 50 to 100 kilowatts, and the green interval corresponds to less than 50 kilowatts.

[0111] Step S43, generating an initial carbon flow distribution map according to the resistance loss distribution map and the pre-constructed carbon emission linear relationship matrix.

[0112] Specifically, first, based on the branch resistance loss power and the preset carbon emission mapping ratio (0.785 kg of carbon emissions per kilowatt of power loss), a carbon emission linear relationship matrix is ​​constructed. This matrix describes the linear relationship between the branch resistance loss power and carbon emissions of each branch in the distribution network line topology. Using the resistance loss distribution map and the carbon emission linear relationship matrix, that is, on the resistance loss distribution map, according to the corresponding values ​​in the carbon emission linear relationship matrix, mark the carbon emissions of each branch or area to obtain the initial carbon flow distribution map.

[0113] Step S45, according to the carbon flow initial distribution map, the carbon emissions are allocated according to the preset initial carbon flow allocation weight parameters to obtain initial carbon flow allocation data.

[0114] Specifically, the preset initial carbon flow allocation weight parameter can be set to feeder end carbon emissions account for 20% of the total carbon emissions, intermediate connecting line carbon emissions account for 30% of the total carbon emissions, and trunk line carbon emissions account for 50% of the total carbon emissions. The total carbon emissions can be obtained according to the initial carbon flow distribution map, and the total carbon emissions are multiplied by the corresponding initial carbon flow allocation weight parameter to obtain the initial carbon flow allocation data. For example, if the total carbon emissions are 1,884 kg, the feeder end carbon emissions are 376.8 kg, the intermediate connecting line carbon emissions are 565.2 kg, and the trunk line carbon emissions are 942 kg.

[0115] Step S47, based on the load rate and the output data of each power source, the current carbon flow distribution data is generated using a pre-trained support vector machine model.

[0116] Specifically, according to the trained support vector machine model, the current load rate and the output data of each power source are input, and the model will output the current carbon flow distribution data. This output is based on the nonlinear relationship learned in the model training, and can predict the carbon emission distribution under given load rate and output conditions.

[0117] Step S49: based on the initial carbon flow distribution data, iteratively optimize the current carbon flow distribution data until the target carbon flow distribution data is obtained.

[0118] Specifically, the initial carbon flow allocation data is compared with the current carbon flow allocation data. If there is a significant difference between the current carbon flow allocation data and the initial carbon flow allocation data, the current carbon flow allocation data needs to be corrected to make it closer to the initial carbon flow allocation data. Therefore, by using the initial carbon flow allocation data as a reference, it can be ensured that the current carbon flow allocation data more accurately reflects the actual power grid operation.

[0119] This embodiment first determines the resistance loss power of each branch by updating the flow distribution data, and calibrates it to generate a resistance loss distribution map, which can accurately reflect the power loss of each branch in the distribution network line topology structure, and provide basic data for the subsequent carbon emission allocation. Then, a preliminary carbon flow distribution map is generated using the resistance loss distribution map and the preset carbon emission linear relationship matrix, and the carbon emissions are further preliminarily allocated in combination with the initial carbon flow allocation weight parameters to obtain the initial carbon flow allocation data. On this basis, combined with the load rate and the output data of each power source, the current carbon flow allocation data is generated with the help of a pre-trained support vector machine model. Through continuous adjustment and optimization, the carbon flow allocation data is finally optimized through an iterative process to obtain the target carbon flow allocation data that meets the requirements. This method can not only accurately adjust the carbon flow allocation, so that the carbon emissions in the distribution network line topology structure are scientifically quantified and reasonably optimized, but also improve the flexibility and accuracy of carbon emission management.

[0120] The sub-steps of the training method of the pre-trained support vector machine model provided in the embodiment of the present application include the following steps:

[0121] Step S471, obtaining historical carbon flow distribution data, historical load rate and historical power output data of each power source.

[0122] Step S473, based on the historical carbon flow distribution data, the historical load rate and the historical power output data, a mapping relationship between the load rate-power output data and the carbon flow value is constructed through a Gaussian kernel function.

[0123] Step S475, using a support vector machine model to analyze the mapping relationship to obtain a pre-trained support vector machine model.

[0124] Specifically, historical carbon flow allocation data and historical load rates are extracted from a 30-day historical database, and the load rate values ​​at the corresponding moments are annotated for the historical power output data. The data is preprocessed using the Gaussian kernel function to construct a mapping relationship between load rate-power output data and carbon flow values. The Gaussian kernel function helps to transform nonlinear problems into linear problems, so that the support vector machine model can handle them. Cross-validation is used to divide all samples into 80% training sets and 20% test sets, and the carbon flow allocation data is calculated until the model converges to obtain a trained support vector machine model.

[0125] For example, by analyzing the carbon flow allocation data sampled every hour within 30 days in the distribution network history database, it is found that the load rate and photovoltaic output have a significant impact on the carbon flow value. The mapping relationship between load rate and carbon flow is established through the Gaussian kernel function to deal with the nonlinear characteristics of the carbon flow weight parameter. The carbon flow weight parameter shows nonlinear changes when the load rate of the distribution line changes. For example, when the load rate changes from 50% to 90%, the carbon flow weight increases from 0.3 to 0.7. The increase in photovoltaic output leads to a decrease in the carbon flow weight. For example, when the carbon flow weight decreases from 0 to 50% of the rated power, it decreases from 0.6 to 0.3. The changes in branch power factor, voltage and load rate jointly determine the correction direction and numerical value of carbon flow allocation. During the iterative calculation process, the parameters are corrected according to the relative error and the preset threshold. For example, the carbon flow allocation data is 80 kilograms per hour for the first calculation, and 82 kilograms per hour for the second calculation, with a relative error of 2.5%. After the parameter correction is triggered, the carbon flow allocation data is adjusted to 81 kilograms per hour, and the relative error is reduced to 1.2%. When the load demand and photovoltaic output of the photovoltaic grid-connected distribution line change within 1 hour, the initial value of the carbon flow distribution data is optimized from 90 kg per hour to 75 kg per hour. The power loss measured at the feeder end switch station is 100 kWh. The carbon flow distribution weight is 0.4 calculated by the Gaussian kernel function. The initial value of the carbon flow is 40 kg per hour. Considering the current load rate, power factor, voltage and other characteristic quantities, the corrected carbon flow distribution data is 45 kg per hour. The distribution automation master station immediately starts the carbon flow optimization program when a sudden load change is detected. After 5 rounds of iterative optimization, it is finally stabilized at 115 kg per hour, and the relative error is less than 0.1% for 5 consecutive times. Through this process, the distribution network can realize the accurate calculation and optimization of carbon flow distribution, ensure the accuracy and real-time performance of carbon flow distribution data, and at the same time reflect the reduction effect of new energy output on carbon flow distribution.

[0126] This embodiment provides a data basis for subsequent analysis by obtaining historical carbon flow distribution data, historical load rates, and historical power output data. Based on these historical data, the Gaussian kernel function is used to construct a mapping relationship between load rates, power output data, and carbon flow values, thereby capturing the regularity of carbon flow distribution under different loads and power output conditions. The mapping relationship is analyzed using a support vector machine model to obtain a pre-trained support vector machine model. This model can accurately reflect the complex relationship between carbon flow distribution and power system load and power output, and provide an effective prediction tool for subsequent carbon flow optimization, further improving the accuracy and flexibility of carbon emission distribution.

[0127] The sub-steps of step S49 provided in the embodiment of the present application are as follows:

[0128] Step S491, first iterative optimization step: determining the relative error between the initial carbon flow distribution data and the current carbon flow distribution data.

[0129] Step S493, when the relative error is greater than the set error threshold, the branch voltage and branch current of each branch are adjusted, and the current carbon flow distribution data is updated to obtain updated carbon flow distribution data.

[0130] Step S495, repeat the first iterative optimization step until the relative error is less than or equal to the set error threshold, and obtain the target carbon flow distribution data.

[0131] Specifically, determine the relative error between the initial carbon flow allocation data and the current carbon flow allocation data. This is the starting point of the iterative optimization, which is used to evaluate the difference between the current carbon flow allocation data and the initial value. If the relative error is greater than the set error threshold, the error threshold is preferably set to 0.1% here, indicating that the current carbon flow allocation data needs to be further optimized. In this case, adjust the branch voltage and branch current of each branch to obtain the adjusted load rate and each power output data. Using the trained support vector machine model, based on the adjusted load rate and each power output data, obtain the updated carbon flow allocation data. Repeat the first iterative optimization step until the relative error is less than or equal to the set error threshold, thereby obtaining the target carbon flow allocation data. The carbon flow allocation data in the distribution network line topology can be accurately adjusted and optimized to adapt to the actual operating conditions of the power grid.

[0132] This embodiment uses the first iterative optimization step to determine the relative error between the initial carbon flow allocation data and the current carbon flow allocation data. When the error is greater than the set error threshold, the voltage and current of each branch are adjusted to update the carbon flow allocation data. This process continues to iterate until the relative error is less than or equal to the set error threshold, and finally the target carbon flow allocation data is obtained. By continuously optimizing the carbon flow allocation, the accuracy of the carbon emission data is ensured to be highly consistent with the actual operation of the power system, thereby improving the accuracy and flexibility of carbon emission management, and helping to achieve more efficient green power dispatch and carbon emission reduction goals.

[0133] The sub-steps of step S6 provided in the embodiment of the present application, wherein each power source output includes the coal power output of the coal-fired power unit and the gas output of the gas-fired power unit; and the carbon flow contribution value includes the coal power carbon flow contribution value and the gas carbon flow contribution value; include the following steps:

[0134] Step S61, determining the current coal-fired power carbon emission factor according to the coal-fired power output.

[0135] Specifically, we first need to calculate the amount of coal consumed per hour by coal-fired power units. Assume that the calorific value of coal-fired power fuel is 29,000 kilojoules per kilogram, the heat consumption rate is 290 grams of standard coal per kilowatt-hour, the coal-fired power output is 300 megawatts (MW), that is, 300,000 kilowatts (kW), and the carbon content per unit calorific value of coal-fired power is 0.785 kilograms. The amount of coal consumed per hour FC = 300,000 kW × 1h × 290g / kWh × 1kg ÷ 1000g = 87,000 kg. The gas carbon emission factor refers to the amount of carbon dioxide produced per unit energy, and the calculation formula is: EF = CC × 44 / 12, where 44 / 12 is the molar mass conversion coefficient of carbon (C) to carbon dioxide (CO2), EF is the carbon dioxide emission factor, and CC is the carbon content per unit calorific value of coal-fired power.

[0136] EF=0.785×44 / 12=2.86 kgCO2 / kg, total carbon dioxide emissions E=87000×2.86=248820kgCO2, power generation per hour=300000kW×1h=300000kWh, coal-fired power carbon emission factor=248820 / 300000=0.829 kgCO2 / kWh.

[0137] Step S63, determining the current gas carbon emission factor according to the gas output.

[0138] Specifically, the emission factor refers to the amount of carbon dioxide emissions generated per unit of electricity. For the gas output, it is assumed to be 200,000 kilowatts, the gas fuel consumes 45,000 cubic meters of natural gas per hour, the carbon content of the gas unit calorific value is 0.553 kg / cubic meter, and the calorific value of the gas fuel reaches 36,000 kilojoules per cubic meter. The gas carbon emission factor refers to the amount of carbon dioxide generated per unit of energy. The calculation formula is: EF=CC×44 / 12, where 44 / 12 is the molar mass conversion factor of carbon (C) to carbon dioxide (CO2), EF is the carbon dioxide emission factor, and CC is the carbon content of the gas unit calorific value.

[0139] EF = 0.553 × 44 / 12 = 1.935 kgCO2 / m 3 , total carbon dioxide emissions E = 45000 × 1.935 = 86925 kgCO2, power generation per hour = 200000 kW × 1h = 200000 kWh, gas carbon emission factor = 86925 / 200000 = 0.4346 kgCO2 / kWh.

[0140] Step S65, normalizing the current coal-fired power carbon emission factor and the current gas carbon emission factor to obtain the coal-fired power carbon emission weight and the gas carbon emission weight.

[0141] Specifically, the carbon emission weight of coal-fired power generation = coal-fired power generation carbon emission factor / (coal-fired power generation carbon emission factor + gas carbon emission factor); the carbon emission weight of gas generation = gas carbon emission factor / (coal-fired power generation carbon emission factor + gas carbon emission factor).

[0142] Step S67, determining the coal-fired power carbon flow contribution value corresponding to the coal-fired power unit according to the coal-fired power output and the coal-fired power carbon emission weight, and determining the gas carbon flow contribution value corresponding to the gas-fired unit according to the gas output and the gas carbon emission weight.

[0143] Specifically, the carbon flow contribution value of coal-fired power = coal-fired power output × coal-fired power carbon emission weight × coal-fired power carbon emission factor; the carbon flow contribution value of gas = gas output × gas carbon emission weight × gas carbon emission factor.

[0144] This embodiment reflects the carbon dioxide emissions generated by the coal-fired power unit during the production of each unit of electricity by determining its carbon emission factor according to the output of the current coal-fired power unit. Next, the carbon emission factor of the gas-fired unit is determined according to the output of the gas-fired unit, indicating the carbon emissions of the gas-fired unit per unit of electricity. The carbon emission factors of coal-fired power and gas are normalized to obtain the carbon emission weights of coal-fired power and gas, ensuring that their respective contribution ratios can be properly reflected in the overall carbon emission calculation. Finally, the carbon flow contribution values ​​of the coal-fired power unit and the gas-fired unit are calculated respectively based on the output of coal-fired power and gas and the corresponding carbon emission weights. Accurate quantification and comparison of carbon emissions from different power units are achieved, providing a scientific basis for subsequent carbon emission management and optimization.

[0145] The sub-steps of step S8 provided in the embodiment of the present application include the following steps:

[0146] Step S81, determining the difference between the target carbon flow distribution data and the initial carbon flow contribution value.

[0147] Step S83, updating the contribution value: when the difference is greater than the preset difference, adjusting the output data of each power source to obtain the adjusted output data of each power source.

[0148] Step S85: determining an updated carbon flow contribution value of each power source to the load carbon flow based on the adjusted output data of each power source.

[0149] Step S87, repeatedly updating the contribution value until the difference is less than or equal to the preset difference, and obtaining the target carbon flow contribution value.

[0150] Specifically, the difference between the target carbon flow allocation data and the initial carbon flow contribution value is evaluated. If the calculated difference is greater than the preset difference. The output data of each power source is adjusted to reduce the difference. Based on the adjusted power output data, the contribution value of each power source to the load carbon flow is recalculated. If the difference is still greater than the preset difference. Repeat steps S83 and S85 until the difference is less than or equal to the preset difference. The final power output data and the corresponding carbon flow contribution value, these values ​​meet the requirements of the target carbon flow allocation. This process is an iterative optimization process, the purpose of which is to optimize carbon emissions in the power system by adjusting the power output data. To ensure that carbon emissions are minimized while meeting electricity demand.

[0151] This embodiment can provide a quantitative basis for subsequent adjustments by determining the difference between the target carbon flow allocation data and the initial carbon flow contribution value, thereby ensuring the consistency of the carbon flow calculation with the target allocation. If the difference is greater than the preset difference, the output data of each power source is adjusted to effectively change the power output structure, reduce the deviation of carbon emissions, and ensure that the carbon flow contribution is more accurate. Recalculating the carbon flow contribution value based on the adjusted output data of each power source helps to redistribute carbon emissions according to the new output situation and optimize the overall carbon flow allocation of the system. Continuously repeat the update steps until the difference is less than or equal to the preset difference, thereby ensuring the accurate convergence of the carbon flow contribution value. This series of steps can dynamically adjust the output data and carbon flow contribution value of each power source, ultimately achieving accurate matching of the target carbon flow contribution value, optimizing the carbon emission allocation of the power system, and providing a scientific basis and data support for achieving low-carbon emission targets.

[0152] The flowchart after determining the target carbon flow distribution data provided in the embodiment of the present application may include the following steps:

[0153] Step S11, based on the target carbon flow distribution data, the tidal flow distribution data is calculated by forward and backward operation to obtain the current tidal flow distribution data.

[0154] Step S12, based on the updated power flow distribution data and the current power flow distribution data, a weight coefficient is calculated according to the ratio of the carbon flow change of the adjacent branch to the initial carbon flow distribution data before the change.

[0155] Step S13, generating reduced carbon flow distribution data under the current distribution network line topology structure according to the weight coefficient and the initial carbon flow distribution data.

[0156] Step S14, second iterative optimization step: determining the relative error between the reduced carbon flow distribution data and the target carbon flow distribution data.

[0157] Step S15, when the relative error is greater than the set error threshold, the branch voltage and branch current of each branch are adjusted, and the target carbon flow distribution data is updated to obtain the latest carbon flow distribution data.

[0158] Step S16, repeat the iterative optimization until the relative error is less than or equal to the set error threshold, and obtain the latest carbon flow distribution data.

[0159] Specifically, since the power flow distribution data will be updated after multiple rounds of iterative optimization calculations in the early stage, the forward-backward algorithm is used to calculate the power flow distribution data to obtain the current power flow distribution data. The current power flow distribution data represents the new state of the power grid after certain operations or conditions change. By comparing the updated power flow distribution data with the current power flow distribution data, the change of power flow in the power grid can be quantified. Among them, the change in carbon flow of adjacent branches refers to the difference in power flow (carbon flow) between adjacent branches after the power grid state changes. This change can be used to evaluate the impact of the change in power grid state on carbon flow allocation. The weight coefficient is calculated based on the ratio of the carbon flow change to the initial carbon flow allocation data before the change. The weight coefficient reflects the degree to which the carbon flow allocation data needs to be adjusted to adapt to the change in the power grid state. Carbon flow allocation data restoration is to adjust the carbon flow allocation based on the weight coefficient and the initial carbon flow allocation data to match the actual operating state of the power grid. This process takes into account the changes in the topology of the power grid and ensures the accuracy of the carbon flow allocation data. The relative error evaluation is to determine whether the difference between the current carbon flow allocation data and the target carbon flow allocation data is within an acceptable range. If the error is large, it is necessary to update the carbon flow allocation data by adjusting the branch voltage and current to reduce the error. The branch voltage and current are adjusted to change the distribution of power flow, thereby affecting the distribution of carbon flow. This adjustment is based on the physical characteristics and operating constraints of the power system. The iterative process will continue until the relative error is less than or equal to the set error threshold, which means that a solution close enough to the target solution has been found. This condition ensures the termination of the optimization process and that the quality of the solution meets the predetermined requirements.

[0160] This embodiment calculates the flow distribution data by using forward push-back operation, and can obtain the current flow distribution data according to the target carbon flow distribution data, providing accurate basic data for subsequent optimization. The weight coefficient is obtained by calculating the ratio of the carbon flow change of the adjacent branch to the initial carbon flow distribution data before the change. It ensures that the carbon flow contribution of each branch is dynamically adjusted according to the actual changes of different branches, so as to accurately control the influence of each branch on the carbon flow distribution, thereby improving the accuracy of the distribution. According to the weight coefficient and the initial carbon flow distribution data, the reduced carbon flow distribution data under the current distribution network line topology is generated. By reversely deducing the distribution network topology, the actual operation status of the power grid is ensured to be connected with the carbon flow distribution target, thereby ensuring the accuracy and practical feasibility of the optimized carbon flow data. Further optimize the carbon flow distribution, when calculating the relative error between the reduced carbon flow distribution data and the target carbon flow distribution data, the gap between the two can be quantified, providing a specific basis for subsequent adjustments, so as to more accurately determine the optimization direction. By adjusting the branch voltage and branch current, the target carbon flow distribution data is updated, and timely adjustments can be made when the error is large to ensure that the carbon flow distribution data is as close to the target as possible. This adjustment helps to dynamically correct the power output of each branch, avoid carbon emission deviations caused by system fluctuations, and push the system closer to a more accurate carbon flow allocation target. Through repeated iterative optimization, until the relative error is less than or equal to the set error threshold, it is ensured that the carbon flow allocation data can ultimately accurately match the target carbon flow allocation data. Through this repeated correction and optimization process, an accurate balance between the power flow and carbon emission allocation of each branch in the system is achieved, providing scientific support and refined management methods for achieving low-carbon emission targets.

[0161] Accordingly, please refer to Fig. 9 A block diagram of a carbon traceability system for a power distribution system provided in an embodiment of the present application, the system comprising:

[0162] The data acquisition unit S101 is used to acquire the real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes the change data of the tie switch, the connection relationship of the feeder and the output data of each power source;

[0163] The data updating unit S103 is used to obtain and update the distribution network line topology structure based on the tie switch change data and the change of the feeder connection relationship in the preset time window, and determine the updated power flow distribution data corresponding to the updated distribution network line topology structure;

[0164] A carbon flow allocation determination unit S105 is used to determine target carbon reduction data based on the updated tidal flow distribution data;

[0165] A carbon flow contribution determination unit S107, used to determine the carbon flow contribution value of each power source to the load carbon flow based on the output of each power source;

[0166] The knowledge base construction unit S109 is used to extract the characteristics of carbon flow allocation rules under different distribution network architectures based on the target carbon reduction data and the carbon flow contribution value, and establish a carbon flow allocation knowledge base to achieve carbon flow traceability; wherein the carbon flow allocation knowledge base is used for graphical display.

[0167] In some optional implementations, the data updating unit S103 includes:

[0168] Determine the number of action changes corresponding to the tie switch change data and the change of the feeder connection relationship within the preset time window;

[0169] When the number of action changes exceeds a preset change threshold, the change time series corresponding to the tie switch change data and the change of the feeder connection relationship are used to update the original distribution network line topology structure to obtain an updated distribution network line topology structure.

[0170] In some optional embodiments, the carbon flow allocation determination unit S105 includes:

[0171] Determine the branch resistance power loss of each branch according to the updated power flow distribution data;

[0172] Based on the branch resistance power loss, the target carbon reduction data is generated using a pre-trained support vector machine model.

[0173] In some optional implementations, the training method of the pre-trained support vector machine model includes:

[0174] The mapping relationship between power loss, load rate, power output data and carbon reduction is constructed through Gaussian kernel function;

[0175] The mapping relationship is analyzed using a support vector machine model to obtain a pre-trained support vector machine model.

[0176] In some optional implementations, each power source output includes the coal power output of the coal-fired power unit and the gas output of the gas-fired power unit; the carbon flow contribution value includes the coal power carbon flow contribution value and the gas carbon flow contribution value; and the carbon flow contribution determination unit S107 includes:

[0177] Determine the current coal-fired power carbon emission factor based on coal-fired power output;

[0178] Determine the current gas carbon emission factor based on gas output;

[0179] The coal-fired power carbon flow contribution value corresponding to the coal-fired power unit is determined according to the current coal combustion amount and the current coal-fired power carbon emission factor, and the gas carbon flow contribution value corresponding to the gas-fired unit is determined according to the current gas combustion amount and the current gas carbon emission factor.

[0180] In some optional implementations, the knowledge base construction unit S109 includes:

[0181] Construct a data association matrix based on the updated distribution network line topology, target carbon reduction data and carbon flow contribution value;

[0182] The data association matrix is ​​labeled according to the preset coding rules to obtain the architecture identifier;

[0183] Compare the changes in carbon reduction data for the corresponding time periods of the architecture identifiers, and establish a carbon flow allocation knowledge base based on the change results.

[0184] In some optional implementations, the data association matrix is ​​labeled according to a preset encoding rule to obtain a schema identifier, including:

[0185] The data in the data association matrix are labeled according to the rule of 4-digit trunk line code, 4-digit branch line code, and 2-digit tie switch code to obtain a 10-digit architecture identifier.

[0186] In some optional embodiments, the system further comprises:

[0187] A double-layer index directory is established in the carbon flow allocation knowledge base; wherein the double-layer index directory includes a first layer retrieval through a schema identifier and a second layer retrieval through a timestamp;

[0188] Data records are retrieved from the carbon flow distribution knowledge base through a double-layer index directory to achieve carbon flow traceability.

[0189] In some optional implementations, the real-time operation data of the distribution network includes three-phase voltage and three-phase current, and the system further includes:

[0190] Through vector operation, the angle between the three-phase voltage and the three-phase current is calculated to obtain the power factor;

[0191] Determine the voltage unbalance degree according to the maximum voltage and the minimum voltage among the three-phase voltages;

[0192] Determine the current unbalance degree according to the maximum current value and the minimum current value in the three-phase current;

[0193] In the preset time window, when the power factor drops to the first warning threshold, and / or the voltage imbalance exceeds the second warning threshold, and / or the current imbalance exceeds the third warning threshold, a warning signal is issued.

[0194] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0195] In this embodiment, a power distribution system carbon traceability tracking system is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0196] See also Fig.10 , Fig.10 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0197] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0198] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0199] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0200] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0201] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0202] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0203] The systems and units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0204] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0205] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and systems. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0206] The present application is described with reference to the flowcharts and / or block diagrams of the methods and systems according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0207] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0208] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0209] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0210] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0211] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0212] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for tracing the source of carbon dioxide in a power distribution system, characterized in that: The method comprises: Acquire real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source; Based on the tie switch change data and the change of the feeder connection relationship in a preset time window, obtaining an updated distribution network line topology structure, and determining updated power flow distribution data corresponding to the updated distribution network line topology structure; Wherein, obtaining and updating the distribution network line topology structure based on the tie switch change data and the change of the feeder connection relationship in a preset time window includes: Determine the number of action changes corresponding to the tie switch change data and the change of the feeder connection relationship within the preset time window; When the number of action changes exceeds a preset number of change thresholds, the original distribution network line topology structure is updated according to the change time series corresponding to the tie switch change data and the change of the feeder connection relationship to obtain the updated distribution network line topology structure; Determining target carbon reduction data based on the updated power flow distribution data; Based on the output of each power source, determining the carbon flow contribution value of each power source to the load carbon flow; Based on the target carbon reduction data and the carbon flow contribution value, the carbon flow distribution law characteristics under different distribution network architectures are extracted, and a carbon flow distribution knowledge base is established to achieve carbon flow traceability; wherein, the carbon flow distribution knowledge base is used for graphical display.

2. The method according to claim 1, characterized in that The step of determining target carbon reduction data based on the updated power flow distribution data includes: Determining the branch resistance power loss of each branch according to the updated power flow distribution data; Based on the branch resistance power loss, the target carbon reduction data is generated using a pre-trained support vector machine model.

3. The method according to claim 2, characterized in that The training method of the pre-trained support vector machine model includes: The mapping relationship between power loss, load rate, power output data and carbon reduction is constructed through Gaussian kernel function; The mapping relationship is analyzed using a support vector machine model to obtain the pre-trained support vector machine model.

4. The method according to claim 1, characterized in that: The output of each power source includes the coal-fired power output of the coal-fired power unit and the gas output of the gas-fired power unit; the carbon flow contribution value includes the coal-fired power carbon flow contribution value and the gas carbon flow contribution value; based on the output of each power source, determining the carbon flow contribution value of each power source to the load carbon flow includes: Determine the current coal-fired power carbon emission factor based on the coal-fired power output; Determining a current gas carbon emission factor according to the gas output; The coal-fired power carbon flow contribution value corresponding to the coal-fired power unit is determined according to the current coal combustion amount and the current coal-fired power carbon emission factor, and the gas carbon flow contribution value corresponding to the gas unit is determined according to the current gas combustion amount and the current gas carbon emission factor.

5. The method according to claim 1, characterized in that The method of extracting the carbon flow distribution law characteristics under different distribution network architectures based on the target carbon reduction data and the carbon flow contribution value, and establishing a carbon flow distribution knowledge base includes: Constructing a data association matrix according to the updated distribution network line topology, the target carbon reduction data and the carbon flow contribution value; Annotating the data association matrix according to a preset coding rule to obtain an architecture identifier; The changes in the carbon reduction data in the time period corresponding to the architecture identifier are compared, and the carbon flow allocation knowledge base is established according to the change results.

6. The method according to claim 5, characterized in that The step of labeling the data association matrix according to a preset coding rule to obtain a schema identifier includes: The data in the data association matrix are labeled according to the rule of 4-digit trunk line code, 4-digit branch line code, and 2-digit tie switch code to obtain a 10-digit architecture identifier.

7. The method according to claim 1, characterized in that The method further comprises: Establishing a double-layer index directory in the carbon flow allocation knowledge base; wherein the double-layer index directory includes a first layer retrieval by architecture identifier and a second layer retrieval by timestamp; The data records are retrieved and obtained in the carbon flow allocation knowledge base through the double-layer index directory to realize carbon flow traceability tracking.

8. The method according to claim 1, characterized in that The real-time operation data of the distribution network includes three-phase voltage and three-phase current, and the method further includes: By vector operation, the angle between the three-phase voltage and the three-phase current is calculated to obtain the power factor; Determine the voltage imbalance according to the maximum voltage and the minimum voltage among the three-phase voltages; Determine the current imbalance according to the maximum current value and the minimum current value in the three-phase current; In the preset time window, when the power factor drops to a first warning threshold, and / or the voltage imbalance exceeds a second warning threshold, and / or the current imbalance exceeds a third warning threshold, a warning signal is issued.

9. A carbon traceability system for power distribution and utilization system, characterized in that: The system comprises: A data acquisition unit, used to acquire real-time operation data of the distribution network; wherein the real-time operation data of the distribution network includes tie switch change data, feeder connection relationship and output data of each power source; A data updating unit, configured to obtain an updated distribution network line topology structure based on the tie switch change data and the change of the feeder connection relationship in a preset time window, and determine updated power flow distribution data corresponding to the updated distribution network line topology structure; Wherein, obtaining and updating the distribution network line topology structure based on the tie switch change data and the change of the feeder connection relationship in a preset time window includes: Determine the number of action changes corresponding to the tie switch change data and the change of the feeder connection relationship within the preset time window; When the number of action changes exceeds a preset number of change thresholds, the original distribution network line topology structure is updated according to the change time series corresponding to the tie switch change data and the change of the feeder connection relationship to obtain the updated distribution network line topology structure; A carbon flow allocation determination unit, configured to determine target carbon reduction amount data based on the updated tidal current distribution data; A carbon flow contribution determination unit, configured to determine a carbon flow contribution value of each power source to a load carbon flow based on the output of each power source; A knowledge base construction unit is used to extract the characteristics of carbon flow allocation rules under different distribution network architectures based on the target carbon reduction data and the carbon flow contribution value, and establish a carbon flow allocation knowledge base to achieve carbon flow traceability; wherein the carbon flow allocation knowledge base is used for graphical display.

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