Source-side electrical carbon space-time coupling power carbon row traceability method, device and medium

Abstract

The invention relates to the technical field of power system carbon metering, in particular to a source-side power carbon space-time coupling power carbon emission tracing method and device and a medium, and the method comprises the steps: constructing a multi-dimensional reference model through the fusion of power grid topology, generator fuel characteristics and real-time output data; decoupling electric energy transmission and carbon propagation paths based on an admittance matrix, and calculating the spatial distribution of apportioned carbon emission through load flow; carbon emission factors are corrected in real time to capture transient characteristics, and limitation of static factors is broken through; fusing the power flow distribution and the dynamic factor, and calculating direct / indirect carbon emission contribution of a load node by adopting a power flow proportion recursive mechanism; real-time data are connected to generate a dynamic carbon flow distribution map and a traceability decision report, a key carbon conduction path and a responsibility subject are accurately identified, a verifiable decision basis is provided for low-carbon transformation, carbon transaction and scheduling optimization, and the timeliness and operability of carbon management are improved.

Description

Technical Field

[0001] This invention relates to the field of carbon metering technology in power systems, and in particular to a source-side electrical carbon spatiotemporal coupling method, equipment and medium for tracing power carbon emissions. Background Technology

[0002] Currently, carbon emission measurement in power systems primarily relies on power flow analysis methods. These methods establish grid models to simulate unit output and power flow paths, and then calculate node emissions by combining generator fuel type and carbon emission factors, providing support for carbon trading and low-carbon dispatch. With the development of artificial intelligence technology, machine learning-based carbon emission prediction models (such as DNN and RNN) and intelligent optimization algorithms (such as PSO) are gradually being applied. Data-driven approaches improve computational efficiency and accuracy, providing a new technological path for characterizing the spatiotemporal distribution of carbon emissions.

[0003] Existing methods still face significant bottlenecks: First, they rely on complex power flow calculations and load allocation, resulting in poor real-time performance in large-scale power grids and difficulty in meeting dynamic dispatch requirements; second, they adopt post-processing analysis modes, which cannot track the dynamic fluctuations of carbon emissions under renewable energy access and topology changes in real time; third, they are sensitive to data accuracy, and the source tracing bias increases in scenarios with missing or incorrect data; fourth, the global analysis of multi-node power flow leads to a surge in computational load, which restricts the rapid response capability of complex power systems. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a source-side electric carbon spatiotemporal coupling power system carbon emission tracing method. Compared with traditional carbon emission tracing methods, the refined carbon emission tracing method of the power system based on the source-side electric carbon spatiotemporal coupling mechanism has the advantages of spatiotemporal coupling modeling, accurate carbon emission tracking, strong adaptability, and efficient calculation. Based on the source-side electric carbon spatiotemporal coupling mechanism, this invention combines power flow calculation and carbon emission allocation analysis, which not only overcomes the limitations of traditional methods, but also provides an efficient and accurate carbon emission tracing solution through dynamic spatiotemporal coupling modeling and real-time data updates, providing technical support for carbon emission reduction and optimized scheduling of the power system.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a source-side electrocarbon spatiotemporally coupled electrocarbon emission tracing method, comprising:

[0008] Construct a carbon emission factor model and define node carbon emission factors in conjunction with the power grid topology;

[0009] Power flow calculations are performed based on nodal carbon emission factors, and the power flow data between nodes and changes in power flow structure are output.

[0010] Carbon emission allocation is calculated using the power flow ratio method based on power flow data, and the total carbon emissions of each node are summarized.

[0011] Based on the changes in total node carbon emissions and power flow structure, the system collects real-time data on power generation output and load changes, updates the electricity-carbon coupling state parameters in real time, and outputs the updated carbon emission distribution map.

[0012] It outputs visualized carbon emissions results to support dispatch decisions and incorporates grid dispatch feedback to the control end.

[0013] As a preferred embodiment of the source-side electric carbon spatiotemporal coupling power carbon emission tracing method described in this invention, the step of constructing a carbon emission factor model, combined with the power grid topology, and defining node carbon emission factors includes:

[0014] Based on the operating parameters of the power generation unit, establish the quantitative relationship between the node and the power generation unit;

[0015] Under quantitative conditions, node emission weight values ​​are assigned according to the energy output proportion of the power generation unit;

[0016] By combining the node connection relationships in the power system, structural correlation processing is performed on the node carbon emission factors.

[0017] As a preferred embodiment of the source-side electric carbon spatiotemporal coupling power carbon emission tracing method described in this invention, the step of calculating power flow based on node carbon emission factors and outputting inter-node power flow data and power flow structure changes includes:

[0018] Construct mathematical relationships to represent the electrical connection status of the power grid, and form an admittance information structure based on the impedance parameters between nodes;

[0019] Power flow calculation modeling is used to determine the power transmission changes between nodes;

[0020] Extract the power transfer changes between nodes and form updatable power flow distribution description parameters.

[0021] The beneficial effects of this preferred technical solution are as follows: it can construct an admittance information structure based on the impedance between nodes, accurately reflect the electrical connection relationship between nodes in the power grid, and effectively support the establishment of power flow calculation models; it can accurately identify the power transmission changes between nodes and capture the dynamic characteristics of power flow through power flow modeling; and it can further extract updatable power flow distribution parameters, which will help with subsequent carbon emission path allocation calculation and structural adjustment, and provide data support and structural foundation for the analysis and control of electricity-carbon coupling characteristics.

[0022] As a preferred embodiment of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method described in this invention, wherein: the step of performing carbon emission allocation calculation based on power flow data using the power flow ratio method to summarize the total node carbon emissions includes:

[0023] Based on the power flow amplitude of the branches and the node connection relationship, a power sharing structure model is constructed;

[0024] By using a power-sharing structure model, carbon emissions are assigned values ​​to form identifiable carbon emission contribution transmission chains between nodes.

[0025] The total carbon emissions from the node's own power generation activities and external transmission paths are aggregated to form a set of parameters representing the node's carbon emission distribution status.

[0026] The beneficial effects of this preferred technical solution are as follows: it can clarify the source path of carbon emissions of each node based on the power flow amplitude of the branch and the connection relationship of the nodes, and distinguish between the node's own emissions and external input emissions on the basis of forming a carbon emission contribution transmission chain; through the power sharing structure between nodes, the specific allocation of carbon emissions can be realized, thereby obtaining the total carbon emissions of each node, which helps to accurately restore the carbon emission flow and distribution in the power grid.

[0027] As a preferred embodiment of the source-side electrocarbon spatiotemporal coupling electrocarbon emission tracing method described in this invention, the admittance information structure includes:

[0028] Based on the electrical connection relationship between nodes in the power grid, the branch impedance parameters are extracted, the node connection matrix is ​​determined by branch topology mapping, and the off-diagonal and diagonal elements of the admittance are constructed.

[0029] The off-diagonal elements of the admittance matrix are represented as follows:

[0030]

[0031] Diagonal elements are represented as:

[0032]

[0033] Among them, Y km Is the off-diagonal element admittance, Y kk Diagonal element admittance, z km Represented as impedance, k and m represent the node numbers in the power system;

[0034] Based on the construction results of the admittance matrix, a node admittance relationship is formed to support power flow modeling;

[0035] By using node admittance relationships and power system power injection data, the changing trends between node voltage phase angles are determined.

[0036] As a preferred embodiment of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method described in this invention, wherein: the power flow calculation modeling method includes:

[0037] The DC power flow method is adopted, and the reference bus in the power grid is used as the angle reference to establish the power flow solution relationship and calculate the active power transmission structure between nodes.

[0038] By using the node voltage phase angle and line impedance, the direction of branch power flow can be determined, and the power flow rate transmitted in the branch can be calculated.

[0039] By using the direction and flow rate of branch power flow as input parameters for carbon emission path tracking, the distribution process of carbon emissions in the power grid is quantified.

[0040] As a preferred embodiment of the source-side electric-carbon spatiotemporal coupling power carbon emission tracing method described in this invention, the step of collecting power generation output and load change data in real time based on the total node carbon emissions and power flow structure changes, and updating the electric-carbon coupling state parameters in real time, includes:

[0041] Collect real-time data during power grid operation, including changes in the output power of each generator, power system load fluctuations, and changes in line operating status, which constitute operating input quantities that vary over time;

[0042] Substituting the operational inputs into the updated model of the node carbon emission factor, and re-adjusting the node carbon emission factor based on real-time changes in power generation output, it is expressed as:

[0043]

[0044] Where, δ i G represents the carbon emission factor of the i-th node; i P represents the set of generators connected to the i-th node; g It is the output of generator g; δ g It is the fuel carbon emission factor of generator g; g represents its set G. i The elements in the set represent generators connected to the i-th node, used to traverse all generators connected to that node; i represents the i-th node in the power grid, used to distinguish different power grid nodes; ∈ indicates belonging to, meaning that generator g is a member of the set of generators connected to the i-th node;

[0045] Based on the dynamic changes in total node carbon emissions and power flow structure, update the electricity-carbon coupling state parameters, including the admittance matrix and node carbon emission factor.

[0046] The beneficial effects of this preferred technical solution are as follows: by collecting real-time information on power generation output, load fluctuations, and line status changes, the node carbon emission factor is dynamically corrected, and the admittance matrix is ​​updated synchronously in conjunction with changes in power flow structure, so that the node carbon emission status reflects the actual operating conditions, enhancing the accuracy and timeliness of the carbon emission distribution map, and providing a precise basis for subsequent path identification and scheduling adjustments.

[0047] As a preferred embodiment of the source-side electric carbon spatiotemporal coupling power carbon emission tracing method described in this invention, wherein: the output of visualized carbon emission results supporting scheduling decisions, and combined with grid scheduling feedback to the control end, includes:

[0048] The updated node carbon emission distribution status will be visualized and analyzed in Jining, showing the total carbon emissions, power flow paths and corresponding carbon emission allocation results of the nodes, and constructing a carbon emission distribution map.

[0049] The visualization results are synchronized to the dispatch center to help identify areas with concentrated carbon emissions and high-emission flow paths, providing a basis for decision-making.

[0050] By combining the control feedback mechanism of the power grid dispatching platform, dispatching adjustment information is fed back to the control end in real time.

[0051] In a second aspect, the present invention provides an electronic device, comprising:

[0052] Memory and processor;

[0053] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method.

[0054] Thirdly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the source-side electrocarbon spatiotemporally coupled electric carbon emission tracing method.

[0055] Compared with existing technologies, the beneficial effects of this invention are as follows: By integrating power grid topology, generator fuel characteristics, and real-time output data to construct a dynamic correlation benchmark for electricity carbon, this invention significantly improves the physical adaptability of the carbon emission tracing model; based on the admittance matrix, it synchronously decouples power transmission and carbon propagation paths, achieving minute-level accurate mapping of power flow and carbon flow in complex power grids, effectively supporting dynamic tracking under renewable energy fluctuation scenarios; it adopts a power flow ratio recursive mechanism to overcome the carbon attribution bottleneck in multi-loop topologies, ensuring the quantitative accuracy of the direct and indirect carbon emission contributions of load nodes; and by generating dynamic carbon flow distribution maps and structured tracing reports in real time, it accurately identifies key carbon transmission paths and source-side responsible entities, providing verifiable decision-making basis for power grid low-carbon transformation, carbon trading certification, and scheduling strategy optimization, comprehensively improving the timeliness and operability of power system carbon management. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the overall process of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method according to an embodiment of the present invention.

[0058] Figure 2 This is a diagram showing the operation results of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method according to an embodiment of the present invention.

[0059] Figure 3 This is a carbon emission comparison chart based on the robustness test of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method according to an embodiment of the present invention. Detailed Implementation

[0060] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0061] Example 1, referring to Figure 1 As an embodiment of the present invention, a source-side electrocarbon spatiotemporally coupled electrocarbon emission tracing method is provided, comprising:

[0062] S1: Construct a carbon emission factor model and define the node carbon emission factor in conjunction with the power grid topology;

[0063] S2: Performs power flow calculations based on node carbon emission factors, and outputs power flow data between nodes and changes in power flow structure;

[0064] S3: Calculate carbon emission allocation based on power flow data using the power flow ratio method, and summarize the total carbon emissions of each node;

[0065] S4: Based on the total carbon emissions at nodes and changes in power flow structure, collect data on power generation output and load changes in real time, update the electricity-carbon coupling state parameters in real time, and output the updated carbon emission distribution map.

[0066] S5: Outputs visualized carbon emissions results and supports dispatch decisions, and combines grid dispatch feedback to the control end.

[0067] It should be noted that existing carbon emission tracing methods often lack real-time correlation analysis between node power generation behavior and power flow changes. The carbon emission allocation process is relatively coarse and fails to reflect the actual transmission path of carbon in the network. Especially against the backdrop of frequent changes in grid operating conditions, traditional methods cannot reflect the dynamic changes in the total carbon emissions of nodes in a timely manner, resulting in a lag in updating emission distribution maps and affecting the accuracy of regulation and response.

[0068] Therefore, to address the aforementioned issues of unclear carbon emission path tracking, ambiguous node responsibility division, and lagging visual output, the following steps (S1-S5) can be used: from constructing a carbon emission factor model and defining node emission characteristics related to the power grid topology; to determining the power flow structure based on power flow analysis and then allocating the total carbon emissions; to collecting operational change data to dynamically correct emission parameters; and finally outputting a distribution map and coordinating regulation, a detailed characterization and real-time tracking of node carbon emission processes can be achieved, enhancing the accuracy and timeliness of the source tracing process.

[0069] Example 2, refer to Figure 2 As an embodiment of the present invention, based on the above embodiment, a source-side electric carbon spatiotemporal coupling electric carbon emission tracing method is provided.

[0070] In this embodiment of the application, step S1 involves constructing a carbon emission factor model and defining node carbon emission factors in conjunction with the power grid topology, including:

[0071] S11: Establish the quantitative relationship between nodes and power generation units based on the operating parameters of the power generation units;

[0072] S12: Under quantitative conditions, assign emission weight values ​​to nodes according to the energy output ratio of power generation units;

[0073] S13: Combine the node connection relationships in the power grid system to perform structural correlation processing on the node carbon emission factors.

[0074] Specifically, S11 includes defining the carbon emission factor of each node based on the generator fuel type and output, dynamically updating the carbon emission intensity in conjunction with the grid topology, and calculating the node carbon emission factor through the output ratio of each generator to reflect the actual carbon emission intensity of the node; S12-S13 include grid parameter initialization and data loading, inputting generator data including node number, output fuel type, load data, and line data, utilizing the node carbon emission factor, and finally outputting the initialized grid topology and carbon emission baseline data; it is affected by the output of all generators and depends on the fuel type and carbon emission level of each generator.

[0075] In an optional implementation, the node carbon emission factor in step S1 can also be defined by constructing a hierarchical mapping relationship based on the weight of regional power source composition. Specifically, a corresponding regional affiliation mapping can be established between nodes and power generation units. Based on identifying the power source composition ratio of the region where the node is located, the average carbon emission level of different types of power sources such as thermal power, hydropower, and wind power in the region is used as the basic factor. Then, combined with the distribution weight of the power generation units connected to the node in the region, the initial carbon emission intensity of the node is calculated.

[0076] In another optional implementation, the node carbon emission factor in step S1 can also be dynamically corrected by introducing the cumulative carbon emission data within the historical operating cycle. Specifically, after establishing a mapping relationship between the node and the power generation unit and initially calculating the node carbon emission factor based on the output and fuel type, the current node carbon emission factor is further weighted and smoothed by using the power generation output sequence and carbon emission measurement records within a certain time window to reflect its carbon emission characteristics under typical operating conditions.

[0077] In this embodiment of the application, step S2 involves calculating power flow based on the node carbon emission factor and outputting inter-node power flow data and power flow structure changes, including:

[0078] S21: Construct mathematical relationships representing the electrical connection status of the power grid, and form an admittance information structure based on the impedance parameters between nodes;

[0079] S22: Power flow calculation modeling is used to determine the power transmission changes between nodes;

[0080] S23: Extract the power transfer changes between nodes and form updatable power flow distribution description parameters.

[0081] Specifically, S21 to S23 include node-based carbon emission factors δ iTo establish a mapping relationship between power grid flow and carbon emissions, and quantify the flow direction of carbon emissions during power transmission; since power flow in the power system affects the spatial distribution of carbon emissions, the power flow distribution is calculated to determine the propagation path of carbon emissions; the spatial distribution of carbon emissions is quantified through power grid flow calculation, and a mapping relationship between power flow and carbon emissions is established.

[0082] Based on power flow calculations, the carbon emission flow of a certain line in the power grid can be expressed by the following formula:

[0083]

[0084] Among them CF ij Let C represent the carbon emission flow from node i to node j. i P represents the carbon emission factor of node i. ij N represents the power flow on line i, k represents a neighboring node connected to node i, and N represents the power flow on line i. i Let P represent the set of all nodes connected to node i. ik Let represent the power flow from node i to its neighboring node k;

[0085] This indicates that the calculation of carbon emission flows depends not only on the carbon emission factor at the generation end but also on the direction of power flow. During dynamic evolution, carbon emission flows change with fluctuations in load demand, generator output adjustments, and power flow reconfiguration. Due to the randomness and intermittency of renewable energy, its integration has a complex impact on carbon emissions. The integration of renewable energy reduces the overall carbon emissions of the power system. Renewable energy itself does not generate carbon emissions; therefore, when its output is high, it can reduce the generation demand of thermal power units, thereby reducing the overall carbon emissions of the power system. Due to the volatility of renewable energy, the power grid needs to increase standby thermal power units to balance the operation of the power system. When renewable energy output is insufficient, thermal power units need to be started and stopped quickly, leading to a momentary increase in carbon emissions. Therefore, the carbon emission factor of the power system needs to take into account the integration of renewable energy.

[0086] It should be noted that S21, by extracting the impedance parameters between nodes, can establish the relationship between the off-diagonal and diagonal elements in the admittance matrix; it clarifies the electrical connection strength between each node, enabling the determination of whether power can be effectively transmitted between nodes, which is a prerequisite for power flow calculation; for example, if the admittance value between two nodes is close to zero, it indicates that the branch is approximately disconnected and does not participate in subsequent power flow and carbon emission conduction; S22 performs power flow modeling based on the admittance structure, calculating the power transmission direction and magnitude of each branch, thereby identifying the actual current flow path between nodes; since the conduction of carbon emissions depends on the direction of electrical energy transmission... In this modeling process, the transmission route of carbon emissions from source nodes to load nodes is directly determined. For example, when the power of a branch is negative, it indicates that the electrical energy is flowing in the opposite direction, and the transmission direction of carbon emissions in that branch also changes accordingly. S23 extracts the power value and direction of each branch from the power flow calculation results to form a data structure containing power transmission information between node pairs, which is used for subsequent carbon emission flow calculation. It is updated in real time when the load increases or the power generation output is adjusted to ensure that the carbon emission path is adjusted synchronously with the changes in grid operation. In the case of fluctuations in the access of new energy sources, this parameter reflects the concentration and dispersion of carbon emissions in certain periods.

[0087] In an optional implementation, the power flow calculation in step S2 can also be adjusted by introducing a dynamic weighting factor for node loads to reflect the impact of local load fluctuations on carbon emission paths. Specifically, when the load of a certain node changes drastically, the real-time load change rate associated with that node can be introduced as a correction factor to adjust the power flow of its adjacent lines, thereby correcting the carbon emission flow path in real time.

[0088] In another optional implementation, the power flow calculation in step S2 can also achieve local redistribution of carbon emission mapping by considering the reconstruction of the admittance matrix under the impedance disturbance of key branches. For example, when a branch in the power system experiences a fault, maintenance, or reconfiguration switching operation, the impedance parameters of the branch can be dynamically modified to reconstruct the off-diagonal and diagonal elements of the admittance matrix, forming a new power flow distribution result. Then, the carbon emission flow direction change is calculated by combining the corrected power flow with the original node carbon emission factor, thereby reflecting the impact of grid topology changes on the carbon path transmission structure.

[0089] In this embodiment of the application, step S2, S21, involves constructing a mathematical relationship representing the electrical connection state of the power grid and forming an admittance information structure based on the inter-node impedance parameters, including:

[0090] A1: Based on the electrical connection relationship between nodes in the power grid, extract the branch impedance parameters, determine the node connection matrix through branch topology mapping, and construct the off-diagonal and diagonal element expressions of the admittance.

[0091] A2: Based on the construction results of the admittance matrix, a node admittance relationship is formed to support power flow modeling;

[0092] A3: Determine the changing trend of node voltage phase angles by using node admittance relationships and power injection data from the power grid system.

[0093] Specifically, the off-diagonal elements of the admittance matrix in A1 are represented as follows:

[0094]

[0095] Diagonal elements are represented as:

[0096]

[0097] Among them, Y km Is the off-diagonal element admittance, Y kk Diagonal element admittance, z km Represented as impedance, k and m represent the node numbers in the power system;

[0098] The two are combined to describe the electrical parameter relationships between different nodes and within the nodes themselves.

[0099] In an optional implementation, the admittance information structure in step S21 can also be obtained by introducing a time-stamped admittance matrix sequence to characterize the admittance change trend caused by changes in the power grid topology over operating time. Specifically, in conjunction with topology transformation events in actual operation (such as line switching and ring network opening / closing operations), a new node connection matrix and a corresponding admittance matrix snapshot are generated after each topology adjustment, and a time-series admittance structure is used for rolling updates in subsequent power flow modeling, making the modeling closer to the dynamic behavior characteristics of the power grid.

[0100] In another optional implementation, the admittance information structure in step S21 can also be obtained by introducing the temperature correlation coefficient in the branch parameters into the impedance calculation, so as to adapt to the situation where the line resistance fluctuates significantly under high temperature or heavy load operation; by monitoring the line operating temperature in real time and introducing the influence coefficient of temperature on resistivity, the branch impedance value is dynamically adjusted so that the constructed admittance matrix can better reflect the physical actual state of the electrical connection.

[0101] In this embodiment of the application, step S22 employs a power flow calculation modeling method to determine the power transmission changes between nodes, including:

[0102] B1: Using the DC power flow method, with the reference bus in the power grid as the angle reference, the power flow solution relationship is established, and the active power transmission structure between nodes is calculated.

[0103] B2: Using the node voltage phase angle and line impedance, determine the direction of branch power flow and calculate the power flow rate transmitted in the branch;

[0104] B3: Using the direction and flow rate of branch power flow as input parameters for carbon emission path tracking, the distribution process of carbon emissions in the power grid is quantified.

[0105] Specifically, B1 to B3 involve using the DC power flow method, with a reference bus as the benchmark, to solve for the voltage phase angle at each node in the power system. Then, based on the voltage phase angle difference between nodes and the line impedance, the direction and magnitude of power flow on each branch are calculated. The direction and magnitude of the power flow are used as inputs for carbon emission tracing, providing a basis for the next step of carbon emission allocation, expressed as:

[0106]

[0107] Where, θ i The voltage phase angle (in radians) at node i is represented by P, which is solved by matrix inversion. ij The power flow (MW) of line ij is represented by θ, which points from the high-phase-angle node to the low-phase-angle node. reduced This represents the column vector composed of the voltage phase angles of each node in the power system after processing. The voltage phase angles are expressed in radians. P represents the inverse of the simplified nodal admittance matrix. reduced This represents a simplified column vector of active power injection in the power system, containing the active power injection information for each node, θ. j Let z represent the voltage phase angle at node j, z and z respectively. ij This represents the impedance of the line between node i and node j;

[0108] The direction of power flow is determined by the phase angle difference, while the phase angle distribution is affected by the combined influence of generator output and load distribution. The power flow direction is from the node with higher voltage phase angle to the node with lower phase angle, and the power flow magnitude is affected by the line reactance. The propagation path of carbon emissions in the power grid can be determined based on the power flow calculation results, that is, carbon emissions gradually propagate from the generator end to the load end along the power flow direction.

[0109] In an optional implementation, the power flow calculation modeling method in step S22 can further enhance the accuracy of branch transmission path identification by introducing sensitivity analysis. Specifically, based on conventional DC power flow solutions, the sensitivity coefficient of node-injected power to changes in branch power flow is combined to characterize the impact of a unit power injection at a given power generation node on the power flow distribution of branches throughout the network. This method can refine the power causal relationship between nodes and branches, helping to trace the propagation chain of carbon emission contributions.

[0110] In another optional implementation, the power flow calculation modeling method in step S22 can also be adapted to power flow abnormal disturbance scenarios by introducing a constraint adjustment mechanism; when encountering disturbances such as generators temporarily shutting down or line impedance changes, upper and lower limits of power flow constraints and node power balance tolerance mechanisms can be introduced into the standard DC power flow model to construct an emergency power flow model under disturbance; by redistributing the adjustable node output and power flow path, it is ensured that the power flow calculation still has a stable solution and can support the subsequent carbon emission path backtracking logic.

[0111] In this embodiment of the application, step S3 involves calculating carbon emission allocation based on power flow data using the power flow ratio method, and summarizing the total carbon emissions of each node, including:

[0112] S31: Construct a power sharing structure model based on the power flow amplitude of the branch and the node connection relationship;

[0113] S32: Using a power sharing structure model, carbon emissions are assigned values ​​to form identifiable carbon emission contribution transmission chains between nodes;

[0114] S33: The total carbon emissions from the node's own power generation activities and external transmission paths are aggregated to form a set of parameters for the node's carbon emission distribution status.

[0115] Specifically, S31-S33 include: after the power flow calculation is completed, allocating carbon emissions from the generation end to each load node based on the power flow distribution, quantifying the direct and indirect carbon emission contributions of each node; calculating the propagation of carbon emissions in the power grid using the power allocation structure model, i.e., the power flow ratio method; calculating the total carbon emissions caused by the fuel consumption of generator nodes; calculating the distribution ratio of carbon emissions in the power grid based on the power flow ratio of each line; calculating the upstream carbon emission contribution borne by each node using the power flow ratio method, and summing them up to obtain the final total carbon emissions, expressed as:

[0116]

[0117] Among them, C total,i This represents the total carbon emissions of node i, including direct emissions and indirect carbon emissions transmitted through tidal currents, in units of tCO2 and C. direct,i Ω represents the direct carbon emissions generated by node i due to its own power generation. i Let α represent the set of all lines directly connected to node i. ij C represents the proportional weight of the power flow propagating from upstream node j to i. source,j This represents the total carbon emissions of upstream node j;

[0118] This demonstrates that the total carbon emissions of a given node depend not only on its own power generation but also on the power flow, as carbon emissions transmitted from upstream nodes also accumulate at that node. The transmission of carbon emissions relies on the direction of the power flow, and the power flow ratio method calculates the allocation of carbon emissions based on the proportion of power generated by the power flow. The determination of the power flow ratio weights is as follows:

[0119]

[0120] The carbon emission propagation weight of a certain line depends on its power flow as a percentage of all connected lines; if a certain line carries a large power flow, its carbon emission contribution will also increase accordingly.

[0121] It should be noted that by establishing a power allocation structure model based on the branch power flow amplitude and node connection relationship in S31, the power relationship between each node and its adjacent branches can be identified, providing basic path information for subsequent carbon emission transmission. In S32, the carbon emission is assigned using this structure model, which clarifies the amount of carbon emissions transmitted on each power transmission path, thus forming a carbon emission contribution chain between "node-branch-node" and realizing the characterization of carbon emission flow paths within the network. In S33, the carbon emissions of the generator node itself (direct emissions) are added to the carbon emissions of the upstream nodes transmitted by the power flow (indirect emissions) to establish an expression for the total carbon emissions, so that the carbon emissions of each node include the actual carbon load it bears. Moreover, the weight parameters determined by the power flow ratio method (e.g., the proportion of power flow in the upstream path of a node) ensure that the quantitative allocation logic of carbon emissions in the transmission chain is consistent with the actual power grid operation state.

[0122] In an optional implementation, the total carbon emissions at each node in step S3 can also be adjusted by introducing a line transmission loss correction factor. Considering that in actual power grid operation, there is a certain energy loss in the lines when electricity is transmitted from the generation side to the load side, and the power flow ratio method does not reflect the impact of this loss on the amount of carbon emissions transmitted in the original calculation, a line loss correction coefficient is introduced into the carbon emission contribution transmission chain to quantify the reduction of upstream carbon emissions in the process of transmission to downstream nodes. Specifically, when calculating the amount of carbon emissions transmitted from upstream nodes to target nodes, the line loss influence factor is multiplied to reflect the attenuation effect of carbon emissions in the propagation process.

[0123] In another optional implementation, the total carbon emissions of nodes in step S3 can also be allocated by considering the weighting of power source types. In the context of mixed power supply, carbon emission allocation based solely on power flow ratio may ignore the differences in carbon emission intensity caused by different power source types (such as coal-fired, gas-fired, and wind power). When calculating the upstream carbon emission contribution received by a node, the power flow ratio of each power generation node can be multiplied by the carbon emission level coefficient corresponding to its power source type to strengthen the dominant contribution of high carbon sources to the overall carbon emission structure of the network. For example, a higher coefficient can be assigned to coal-fired units, while a lower coefficient can be assigned to renewable power sources such as wind power, making the carbon emission transmission chain more distinct in terms of source type, which is conducive to accurately locating high carbon paths and optimizing low carbon scheduling strategies.

[0124] In this embodiment of the application, step S4 involves real-time acquisition of power generation output and load change data based on the total node carbon emissions and power flow structure changes, real-time updating of the electricity-carbon coupling state parameters, and outputting an updated carbon emission distribution map, including:

[0125] S41: Collect real-time data during power grid operation, including changes in the output power of each generator, load fluctuations in the power grid system, and changes in line operating status, which constitute operating input quantities that vary over time.

[0126] S42: Substitute the operating inputs into the updated model of the node carbon emission factor, and readjust the node carbon emission factor according to the real-time changes in power generation output.

[0127] S43: Update the electric-carbon coupling state parameters, including the admittance matrix and the nodal carbon emission factor, based on the dynamic changes in the total nodal carbon emissions and the power flow structure.

[0128] Specifically, S42 is represented as:

[0129]

[0130] Where, δ i G represents the carbon emission factor of the i-th node; i P represents the set of generators connected to the i-th node; g It is the output of generator g; δ g It is the fuel carbon emission factor of generator g; g represents its set G. i The elements in the set represent generators connected to the i-th node, used to traverse all generators connected to that node; i represents the i-th node in the power grid, used to distinguish different power grid nodes; ∈ indicates belonging to, meaning that generator g is a member of the set of generators connected to the i-th node;

[0131] Specifically, S41 to S43 include the generator output P based on real-time data acquisition. gCarbon emission factor δ corresponding to fuel type g Through formula Dynamically update node carbon emission intensity;

[0132] For example, combining power flow calculation results (such as line power flow P) ij and node voltage phase angle θ i The admittance matrix is ​​corrected to adapt to changes in the power grid topology; for example, when lines are switched on or off or generator output changes abruptly, the diagonal elements are recalculated. off-diagonal elements To maintain the dynamic relationship between carbon emission factor and tidal current distribution.

[0133] It should be noted that the real-time data such as "generator output", "fuel type" and "load fluctuation" collected through step S41 can drive the dynamic update of node carbon emission factors in S42; S43 explicitly uses "re-correction of the admittance matrix" to adapt to the scenario of "line switching or sudden changes in generator output", ensuring that the power flow modeling parameters can synchronously reflect changes in network structure; through the mechanism constructed by S41 to S43, the output carbon emission distribution map can reflect the carbon emission transmission path and intensity evolution between nodes in real time; due to the synchronous update logic of admittance matrix and node carbon factors, the power system has the ability to maintain computational continuity under multiple disturbance conditions.

[0134] In an optional implementation, the electric-carbon coupling state parameters in step S4 can also be updated by introducing a short-cycle rolling window mechanism; specifically, based on the real-time output and load fluctuation data of the generator, a rolling time window in minutes is introduced to calculate the average carbon emission factor change trend and power flow structure offset amplitude within the time period.

[0135] In another optional implementation, the electric-carbon coupling state parameters in step S4 can also be corrected by introducing a line load rate threshold adaptive mechanism; when the power flow structure changes drastically (such as when the power flow of a branch exceeds the safety threshold), the critical path or bottleneck branch is identified and its carbon conduction sensitivity coefficient is dynamically increased, thereby giving the critical node a higher carbon flow regulation weight when updating the admittance matrix.

[0136] In this embodiment of the application, step S5 outputs visualized carbon emissions results to support dispatch decisions, and combines these results with grid dispatch feedback to the control end, including:

[0137] S51: Visualize and analyze the updated node carbon emission distribution status in Jining, showcasing the total carbon emissions, power flow paths, and corresponding carbon emission allocation results of the nodes, and construct a carbon emission distribution map;

[0138] S52: Synchronize the visualization results to the dispatch center to help identify areas with concentrated carbon emissions and high-emission flow paths, and provide a basis for decision-making;

[0139] S53: Combined with the control feedback mechanism of the power grid dispatching platform, dispatching adjustment information is fed back to the control end in real time.

[0140] Specifically, S51-S52 include: Due to the changing operating status of the power grid over time, carbon emission flows also need dynamic adjustment; real-time acquisition of generator output, power flow data, and topology status; dynamic updating of the admittance matrix and node carbon emission factors; based on the source-side electricity-carbon spatiotemporal coupling mechanism, responding to fluctuations in renewable energy output and load changes, and continuously correcting the carbon flow propagation path; mapping the power flow proportion allocation results to the power grid spatial topology, visually presenting the carbon emission intensity, carbon responsibility contribution, and propagation direction of each node and line; integrating dynamic carbon flow maps and multi-level recursive source tracing results, outputting a structured report including direct and indirect carbon emissions from load nodes, ranking of source-side carbon emission contributions, and assessment of the carbon emission reduction potential of topology adjustments, and outputting visualized analysis results, such as... Figure 2 As shown.

[0141] In one optional implementation, the output of carbon emission visualization results and support for scheduling decisions in step S5 can also be achieved by constructing a cross-period carbon emission evolution sequence diagram to realize time-series analysis of carbon emission trends at nodes or regions. Specifically, the total carbon emissions and distribution path changes of each node are continuously recorded in hourly or minute-level units, and the carbon emission data of multiple time periods are mapped into dynamic curves to help schedulers identify high emission cycles, carbon emission jump nodes and stable low-carbon areas, providing trend judgment support for control strategies.

[0142] In another optional implementation, the output of carbon emission visualization results and support for scheduling decisions in step S5 can also be achieved by constructing a carbon scheduling impact assessment panel, which can intuitively display the effect of the carbon emission structure changes after scheduling adjustment on the overall carbon load of the system. Specifically, after receiving the control strategy returned by the scheduling platform, a carbon emission path comparison chart before and after scheduling is automatically generated, quantifying indicators such as the change in carbon emissions of key nodes or key routes, changes in carbon emission flow paths, and the release value of carbon emission reduction potential, so that scheduling personnel can intuitively assess the actual impact of the control strategy on carbon reduction, and support the adjustment of low-carbon target priorities and the optimization of control feedback.

[0143] In summary, this invention provides a source-side, spatiotemporally coupled method for tracing carbon emissions in the power system. By integrating grid topology, generator fuel characteristics, and real-time output data, a dynamically correlated benchmark model is constructed, significantly improving the physical adaptability of carbon emission tracing. The admittance matrix is ​​used to achieve precise mapping between power transmission and carbon propagation paths, effectively supporting dynamic tracking in the context of renewable energy fluctuations. Furthermore, a power flow proportion recursive mechanism solves the carbon attribution problem in multi-loop topologies, ensuring accurate quantification of the direct and indirect carbon emission contributions of load nodes. Finally, by generating dynamic carbon flow distribution maps and structured tracing reports in real time, key carbon transmission paths and responsible entities can be accurately identified, providing verifiable decision-making basis for grid low-carbon transformation, carbon trading certification, and scheduling strategy optimization, comprehensively improving the timeliness and operability of power system carbon management.

[0144] Example 3, Reference Figure 3 In one embodiment of the present invention, a source-side electrocarbon spatiotemporal coupling electric carbon emission tracing method is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0145] This study verifies the impact of changes in power grid topology on carbon emission allocation, specifically exploring the role of critical line 1-2 in carbon emission accountability. By comparing the differences in carbon emission distribution between the original topology and the modified topology (i.e., removing line 1-2), the study reveals the regulation mechanism of power grid structure on carbon flow paths, providing a quantitative basis for the low-carbon transformation of power grids.

[0146] like Figure 3 As shown, the stability of the model is verified through topology change response testing and loop processing of the recursive amortization algorithm. This is achieved by removing the impedance of line 1-2, correcting the admittance matrix Y, and recalculating the power flow. In the original topology, node 2 mainly receives power through line 1-2. After the line is removed, the power demand of node 2 needs to be transmitted through paths 1-3-5-6, leading to a reconfiguration of the power flow path and indirectly affecting the carbon emission amortization ratio of terminal nodes (such as node 6). Before the removal of line 1-2: Y 21 =Y 12 After line 1-2 is removed: Y 12 =0,Y 21 =0, the node voltage phase angle needs to be recalculated and the power flow distribution updated, as shown below:

[0147]

[0148] The model's dynamic response capability to topology adjustments was verified.

[0149] In the process of tracing carbon emissions from power systems, the transmission paths of electrical energy are often not a single tree structure, but rather multiple parallel paths and loops. In complex power grids, recursive tracing may get stuck in an infinite loop. For power grids with loop topologies, a recursive allocation algorithm is needed to ensure the reasonable distribution of carbon emissions.

[0150]

[0151] This mechanism marks processed nodes, immediately terminates branches, ensures computational stability, and avoids repeated access.

[0152] like Figure 3 As shown, by comparing the hub role of line 1-2 through the topology image, it is clearly observed that the load sharing of nodes 2 and 3 is significantly reduced while that of node 6 is slightly increased. After removing the high-carbon direct connection line 1-2, node 2 automatically switches to the low-carbon power supply path node 2-4, realizing the active transfer of carbon emissions. This proves that topology adjustment can force the load to give priority to the use of low-carbon power sources. The carbon emission changes are concentrated in the adjacent nodes of the line. The carbon flow of the power grid has "spatial decay", that is, the farther away from the carbon source, the weaker the impact of topology changes.

[0153] The power grid topology is a key determinant of the spatial distribution of carbon emissions. Through controllable topology adjustments, carbon flows can be proactively guided to less sensitive nodes, reducing the overall carbon responsibility intensity of the power system. This validates the established "structure-carbon flow" coupling model, providing a quantifiable technical path for carbon emission reduction in the power system.

[0154] Example 4: The above is an illustrative scheme of a source-side electric carbon spatiotemporal coupling electric carbon emission tracing method.

[0155] This embodiment also provides an electronic device applicable to a source-side electric carbon spatiotemporally coupled power carbon emission tracing scenario, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the source-side electric carbon spatiotemporally coupled power carbon emission tracing method proposed in the above embodiment.

[0156] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a source-side electrocarbon spatiotemporal coupling electric carbon emission tracing method as proposed in the above embodiments.

[0157] The storage medium proposed in this embodiment belongs to the same inventive concept as the source-side electro-carbon spatiotemporally coupled electric carbon emission tracing method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0158] Based on the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A source-side electro-carbon spatiotemporally coupled electro-carbon emission tracing method, characterized in that, include: Construct a carbon emission factor model and define node carbon emission factors in conjunction with the power grid topology; Power flow calculations are performed based on nodal carbon emission factors, and the power flow data between nodes and changes in power flow structure are output. Carbon emission allocation is calculated using the power flow ratio method based on power flow data, and the total carbon emissions of each node are summarized. Based on the changes in total node carbon emissions and power flow structure, the system collects real-time data on power generation output and load changes, updates the electricity-carbon coupling state parameters in real time, and outputs the updated carbon emission distribution map. It outputs visualized carbon emissions results to support dispatch decisions and incorporates grid dispatch feedback to the control end.

2. The source-side electrocarbon spatiotemporal coupling electrocarbon emission tracing method as described in claim 1, characterized in that, The construction of the carbon emission factor model, combined with the power grid topology, defines the node carbon emission factor, including: Based on the operating parameters of the power generation unit, establish the quantitative relationship between the node and the power generation unit; Under quantitative conditions, node emission weight values ​​are assigned according to the energy output proportion of the power generation unit; By combining the node connection relationships in the power system, structural correlation processing is performed on the node carbon emission factors.

3. The source-side electro-carbon spatiotemporal coupling electro-carbon emission tracing method as described in claim 2, characterized in that, The power flow calculation based on nodal carbon emission factors outputs inter-node power flow data and power flow structure changes, including: Construct mathematical relationships to represent the electrical connection status of the power grid, and form an admittance information structure based on the impedance parameters between nodes; Power flow calculation modeling is used to determine the power transmission changes between nodes; Extract the power transfer changes between nodes and form updatable power flow distribution description parameters.

4. The source-side electrocarbon spatiotemporal coupling electrocarbon emission tracing method as described in claim 3, characterized in that, The carbon emission allocation calculation is performed using the power flow ratio method based on power flow data, summarizing the total carbon emissions at each node, including: Based on the power flow amplitude of the branches and the node connection relationship, a power sharing structure model is constructed; By using a power-sharing structure model, carbon emissions are assigned values ​​to form identifiable carbon emission contribution transmission chains between nodes. The total carbon emissions from the node's own power generation activities and external transmission paths are aggregated to form a set of parameters representing the node's carbon emission distribution status.

5. The source-side electro-carbon spatiotemporal coupling electro-carbon emission tracing method as described in claim 4, characterized in that, The admittance information structure includes: Based on the electrical connection relationship between nodes in the power grid, the branch impedance parameters are extracted, the node connection matrix is ​​determined by branch topology mapping, and the off-diagonal and diagonal elements of the admittance are constructed. The off-diagonal elements of the admittance matrix are represented as follows: Diagonal elements are represented as: Among them, Y km Is the off-diagonal element admittance, Y kk Diagonal element admittance, z km Represented as impedance, k and m represent the node numbers in the power system; Based on the construction results of the admittance matrix, a node admittance relationship is formed to support power flow modeling; By using node admittance relationships and power system power injection data, the changing trends between node voltage phase angles are determined.

6. The source-side electro-carbon spatiotemporal coupling electro-carbon emission tracing method as described in claim 5, characterized in that, The power flow calculation modeling method includes: The DC power flow method is adopted, and the reference bus in the power grid is used as the angle reference to establish the power flow solution relationship and calculate the active power transmission structure between nodes. By using the node voltage phase angle and line impedance, the direction of branch power flow can be determined, and the power flow rate transmitted in the branch can be calculated. By using the direction and flow rate of branch power flow as input parameters for carbon emission path tracking, the distribution process of carbon emissions in the power grid is quantified.

7. The source-side electro-carbon spatiotemporal coupling electro-carbon emission tracing method as described in claim 6, characterized in that, The method of collecting real-time power generation output and load change data based on node carbon emissions and power flow structure changes, and updating the electricity-carbon coupling state parameters in real time, includes: Collect real-time data during power grid operation, including changes in the output power of each generator, power system load fluctuations, and changes in line operating status, which constitute operating input quantities that vary over time; Substituting the operational inputs into the updated model of the node carbon emission factor, and re-adjusting the node carbon emission factor based on real-time changes in power generation output, it is expressed as: Where, δ i G represents the carbon emission factor of the i-th node; i P represents the set of generators connected to the i-th node; g It is the output of generator g; δ g It is the fuel carbon emission factor of generator g; g represents its set G. i The elements in the set represent generators connected to the i-th node, used to traverse all generators connected to that node; i represents the i-th node in the power grid, used to distinguish different power grid nodes; ∈ indicates belonging to, meaning that generator g is a member of the set of generators connected to the i-th node; Based on the dynamic changes in total node carbon emissions and power flow structure, update the electricity-carbon coupling state parameters, including the admittance matrix and node carbon emission factor.

8. The source-side electro-carbon spatiotemporal coupling electro-carbon emission tracing method as described in claim 7, characterized in that, The output of visualized carbon emissions supports dispatch decisions and is fed back to the control end in conjunction with grid dispatch feedback, including: The updated node carbon emission distribution status will be visualized and analyzed in Jining, showing the total carbon emissions, power flow paths and corresponding carbon emission allocation results of the nodes, and constructing a carbon emission distribution map. The visualization results are synchronized to the dispatch center to help identify areas with concentrated carbon emissions and high-emission flow paths, providing a basis for decision-making. By combining the control feedback mechanism of the power grid dispatching platform, dispatching adjustment information is fed back to the control end in real time.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the source-side electric carbon spatiotemporal coupling electric carbon emission tracing method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the source-side electrocarbon spatiotemporally coupled electric carbon emission tracing method according to any one of claims 1 to 8.

Images