Multi-dimensional carbon emission monitoring method and system based on spatial-temporal distribution

Abstract

The invention is suitable for the technical field of carbon emission, and provides a multi-dimensional carbon emission monitoring method and system based on spatial and temporal distribution, and the method comprises the following steps: collecting time series data, spatial topological data and carbon intensity data, binding real-time carbon intensity for each generator set, and building a set-node mapping table; a carbon topology matrix Tc is constructed, the carbon conduction weight of each connection line is calculated, a dynamic carbon potential field is constructed, and node carbon potential = (local power generation carbon emission + carbon emission carried in the line) / (local net power generation amount + total output power); line-level carbon flow is monitored, real-time carbon flow of each line is calculated, user-level carbon emission is monitored according to the carbon flow, and regional net carbon transfer is analyzed. According to the method, the carbon emission condition of each node and each line can be accurately calculated, the distribution condition of the carbon emission at different time and different spatial positions can be reflected, the hot spot region and peak period of the carbon emission can be found, and the refined monitoring of the carbon emission can be realized.

Description

Technical Field

[0001] This invention relates to the field of carbon emission technology, specifically to a multi-dimensional carbon emission monitoring method and system based on spatiotemporal distribution. Background Technology

[0002] Carbon emission monitoring is a key task in achieving the Sustainable Development Goals. As one of the major sources of carbon emissions, the accurate monitoring of carbon emissions from the power industry is crucial for formulating effective emission reduction strategies. Traditional carbon emission monitoring methods mainly focus on macro-level statistics and estimations, often calculating carbon emissions based on industry average data or overall power generation, lacking a detailed characterization of the spatiotemporal distribution of carbon emissions during power system operation.

[0003] With the continuous expansion of power system scale, the large-scale integration of new energy sources, and the increasing complexity of electricity market transactions, the operation mode of power systems has become more flexible and variable, resulting in significant differences in carbon emissions across different regions and time periods. While existing power system monitoring technologies can acquire a large amount of operational data, such as unit output, load power, and line power flow, this data is mostly used only for analyzing the safe and stable operation of the power system, failing to fully explore its potential in carbon emission monitoring. Furthermore, due to the lack of effective data integration and analysis methods, it is difficult to accurately grasp the dynamic changes and transfers of carbon emissions across different nodes, lines, and users, thus failing to provide strong support for the low-carbon operation and precise emission reduction of the power system. Therefore, there is a need to provide a multi-dimensional carbon emission monitoring method and system based on spatiotemporal distribution to address the aforementioned problems. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a multi-dimensional carbon emission monitoring method and system based on spatiotemporal distribution, so as to solve the problems existing in the above-mentioned background technology.

[0005] This invention is implemented as follows: a multi-dimensional carbon emission monitoring method based on spatiotemporal distribution, the method comprising the following steps:

[0006] Time-series data, spatial topology data, and carbon intensity data were collected. The time-series data included unit output data, load data at each node, and tie-line power data.

[0007] Spatial topology data, including node coordinates and line parameters, is imported into the power grid geographic information system model to construct a node-line connection table.

[0008] Bind real-time carbon intensity to each generator set and establish a generator set-node mapping table;

[0009] Construct a carbon topology matrix Tc and calculate the carbon conduction weight Tc for each interconnection line.ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc.

[0010] Construct a dynamic carbon potential field, where nodal carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (net local power generation + total output power).

[0011] Monitor line-level carbon flow and calculate the real-time carbon flow rate for each line. φi is the initial carbon potential, and φj is the final carbon potential.

[0012] Based on carbon flow monitoring of user-level carbon emissions, regional net carbon transfer is analyzed.

[0013] As a further aspect of the present invention: the step of calculating the carbon conduction weight of each interconnection line specifically includes:

[0014] Calculate the electrical impedance of the line, which is the vector magnitude of resistance and reactance. The line parameters include resistance, reactance, and rated capacity.

[0015] The ratio of the line's rated capacity to its baseline capacity is used as the capacity factor to calculate the carbon conduction weight Tc. ij Tc ij = (1 / electrical impedance) × capacity factor.

[0016] As a further aspect of the present invention: the step of constructing the dynamic carbon potential field specifically includes:

[0017] Calculate local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node.

[0018] The local net power generation is determined based on the total power generation and load data of this node, and the total output power is determined based on the sum of the power of all output lines. The interconnection lines include output lines and input lines, and the node carbon potential is obtained.

[0019] The carbon potential of all nodes is iteratively updated to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

[0020] As a further aspect of the present invention: the step of analyzing regional net carbon transfer based on monitoring user-level carbon emissions using carbon flow specifically includes:

[0021] Obtain the real-time carbon potential of the node where the user is located, and calculate the user's carbon emissions. User carbon emissions = user electricity consumption × node carbon potential.

[0022] The calculation area includes net input carbon emissions and net output carbon emissions. Net input carbon emissions are the sum of carbon flows of all input lines, and net output carbon emissions are the sum of carbon flows of all output lines.

[0023] The net carbon transfer amount of the region is obtained. Net carbon transfer amount = net input carbon emissions - net output carbon emissions.

[0024] As a further aspect of the present invention: the method further includes spatiotemporal carbon hotspot diagnosis and visualization, the specific steps of which are as follows:

[0025] Spatial mapping is performed to map the nodal carbon potential to the nodal coordinate map, and a dynamic color temperature map is generated based on the carbon potential value.

[0026] Carbon hotspot diagnosis is performed to screen out the lines with the top M% carbon flow, trace back along the power to the power generation node, and mark the node whose carbon potential continuously exceeds the threshold as the carbon source point.

[0027] As a further aspect of the present invention: the line carbon loss coefficient is determined by the line resistance R ij Line starting node voltage V i And the baseline capacity SA is determined, F ij (t) represents the line power from node i to j at time t, η ij This represents the carbon loss coefficient of the line from node i to j.

[0028] Another object of the present invention is to provide a multi-dimensional carbon emission monitoring system based on spatiotemporal distribution, the system comprising:

[0029] The data upload and acquisition module is used to collect time-series data, spatial topology data, and carbon intensity data. The time-series data includes unit output data, load data of each node, and tie-line power data.

[0030] The spatial data import module is used to import spatial topology data into the power grid geographic information system model. The spatial topology data includes node coordinates and line parameters, and constructs a node-line connection relationship table.

[0031] The carbon intensity binding module is used to bind real-time carbon intensity to each generator set and establish a generator set-node mapping table;

[0032] The carbon conduction weighting module is used to construct the carbon topology matrix Tc and calculate the carbon conduction weight Tc for each interconnection line. ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc.

[0033] The nodal carbon potential calculation module is used to construct a dynamic carbon potential field. Nodal carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (local net power generation + total output power).

[0034] The line carbon flow monitoring module is used to monitor line-level carbon flow and calculate the real-time carbon flow rate for each line. φi is the initial carbon potential, and φj is the final carbon potential.

[0035] The Net Carbon Transfer Analysis module is used to monitor user-level carbon emissions based on carbon flow and analyze regional net carbon transfer.

[0036] As a further aspect of the present invention: the carbon conduction weighting module includes:

[0037] An electrical impedance calculation unit is used to calculate the electrical impedance of a line. The electrical impedance is the vector magnitude of resistance and reactance. The line parameters include resistance, reactance, and rated capacity.

[0038] The carbon conduction weighting unit is used to calculate the carbon conduction weight Tc by using the ratio of the line's rated capacity to the baseline capacity as a capacity factor. ij Tc ij = (1 / electrical impedance) × capacity factor.

[0039] As a further aspect of the present invention: the node carbon potential calculation module includes:

[0040] The carbon emission calculation unit is used to calculate the local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; and to determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node.

[0041] The node carbon potential determination unit is used to determine the local net power generation based on the total power generation and load data of this node, and to determine the total output power based on the sum of the power of all output lines. The interconnection lines include output lines and input lines, and thus obtain the node carbon potential.

[0042] The carbon potential iteration update unit is used to iteratively update the carbon potential of all nodes to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

[0043] As a further aspect of the present invention: the net carbon transfer analysis module includes:

[0044] The user carbon emission unit is used to obtain the real-time carbon potential of the node where the user is located and calculate the user's carbon emissions. User carbon emissions = user electricity consumption × node carbon potential.

[0045] The net carbon emission calculation unit is used to calculate the net input carbon emission and net output carbon emission of the region. The net input carbon emission is the sum of the carbon flow of all input lines, and the net output carbon emission is the sum of the carbon flow of all output lines.

[0046] The net carbon transfer unit is used to obtain the net carbon transfer amount of a region. Net carbon transfer amount = net input carbon emissions - net output carbon emissions.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] This invention, by collecting time-series data, spatial topology data, and carbon intensity data, and constructing a carbon topology matrix and a dynamic carbon potential field model, can accurately calculate the carbon emissions of each node and each power line. It reflects the real-time distribution of carbon emissions at different times and spatial locations, helping to identify carbon emission hotspots and peak periods, achieving refined carbon emission monitoring, and providing accurate decision-making basis for the low-carbon operation of the power system. Furthermore, by monitoring line-level carbon flow, it can accurately calculate the real-time carbon flow of each line, thereby analyzing the regional net carbon transfer situation, providing detailed spatiotemporal distribution and carbon transfer information of carbon emissions, and providing a scientific basis for formulating carbon reduction and energy policies. Attached Figure Description

[0049] Figure 1 A flowchart of a multi-dimensional carbon emission monitoring method based on spatiotemporal distribution;

[0050] Figure 2 This is a flowchart illustrating the calculation of carbon transmission weights in a multi-dimensional carbon emission monitoring method based on spatiotemporal distribution.

[0051] Figure 3 A flowchart for constructing a dynamic carbon potential field in a multidimensional carbon emission monitoring method based on spatiotemporal distribution;

[0052] Figure 4 This is a flowchart of carbon emission monitoring in a multi-dimensional carbon emission monitoring method based on spatiotemporal distribution;

[0053] Figure 5 A flowchart illustrating carbon potential visualization in a multidimensional carbon emission monitoring method based on spatiotemporal distribution;

[0054] Figure 6 This is a schematic diagram of a multi-dimensional carbon emission monitoring system based on spatiotemporal distribution. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0056] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0057] like Figure 1 As shown in the figure, this invention provides a multi-dimensional carbon emission monitoring method based on spatiotemporal distribution, the method comprising the following steps:

[0058] S100 collects time-series data, spatial topology data, and carbon intensity data. The time-series data includes unit output data, load data for each node, and tie-line power data.

[0059] S200 imports spatial topology data into the power grid geographic information system model. Spatial topology data includes node coordinates and line parameters.

[0060] S300, which binds real-time carbon intensity to each generator set;

[0061] S400, construct the carbon topology matrix Tc, and calculate the carbon conduction weight Tc for each interconnection line. ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc.

[0062] S500 constructs a dynamic carbon potential field, where nodal carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (local net power generation + total output power).

[0063] The S600 monitors line-level carbon flow and calculates the real-time carbon flow rate for each line.

[0064] The S700 monitors user-level carbon emissions based on carbon flow and analyzes regional net carbon transfer.

[0065] It should be noted that traditional methods cannot accurately reflect the carbon emissions of the power system at different times and locations, and can only provide macroscopic carbon emission estimates. Existing technologies lack effective means to monitor the spatiotemporal distribution of carbon emissions, and cannot accurately grasp the dynamic changes of carbon emissions at different nodes and along different lines. Currently, it is difficult to accurately monitor and analyze the transfer of carbon emissions between different regions and different users, which is detrimental to the formulation of fair and reasonable carbon emission responsibility-sharing mechanisms and inter-regional carbon trading strategies. The embodiments of this invention aim to solve the above problems.

[0066] In this embodiment of the invention, it is first necessary to collect time series data, spatial topology data, and carbon intensity data. The time series data includes power output data for all generating units (unit: kW), load data for each node (unit: kW), and tie-line power data (unit: kW). The tie-line power data contains a large amount of F... ij Fij This represents the line power from node i to j. Then, the spatial topology data is imported into the power grid geographic information system (GIS) model. The spatial topology data includes node coordinates and line parameters. A node-line connection table is constructed, and real-time carbon intensity is bound to each generator unit. A unit-node mapping table is established to ensure spatial location matching. Different types of units have different carbon intensities; for example, coal-fired units use a fixed value (e.g., 800 gCO2 / kWh), gas-fired units use a dynamic value (determined by the efficiency curve, ranging from 400-600 gCO2 / kWh), and wind and solar units have zero carbon emissions. Next, the carbon transmission weight Tc for each interconnection line needs to be calculated. ij Tc ij This represents the carbon conduction weight from node i to node j. Then, a carbon admittance matrix Bc is generated based on the carbon topology matrix Tc. The carbon conduction weight represents the efficiency of carbon emission transmission per unit power. During the process of obtaining Bc from Tc, the diagonal element Bc... ii =∑ j Tc ij (Sum of carbon admittances of all outgoing lines at node i), off-diagonal element Bc ij =-Tc ij (Negative carbon admittance from node i to j). Next, a dynamic carbon potential field will be constructed. The carbon potential represents the total carbon emissions the entire network must bear when 1 kWh of electricity is injected at node i. Node carbon potential = (local generation carbon emissions + carbon emissions carried by inflow lines) / (net local generation + total output power). Then, multi-dimensional carbon emission monitoring will be performed. First, line-level carbon flow will be monitored, and the real-time carbon flow for each line (i→j) will be calculated. φi represents the initial carbon potential, and φj represents the final carbon potential. User-level carbon emissions need to be monitored based on carbon flow, and regional net carbon transfer needs to be analyzed. The line carbon loss coefficient is determined by the line resistance R. ij Line starting node voltage V i And the baseline capacity SA is determined, F ij (t) represents the line power from node i to j at time t, η ij This represents the carbon loss coefficient of the line from node i to j. Thus, by collecting time-series data, spatial topology data, and carbon intensity data, and constructing models such as the carbon topology matrix and dynamic carbon potential field, the carbon emissions of each node and each line can be accurately calculated. This allows for real-time reflection of the distribution of carbon emissions at different times and spatial locations, helping to identify carbon emission hotspots and peak periods, achieving refined carbon emission monitoring, and providing accurate decision-making basis for the low-carbon operation of the power system.

[0067] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of calculating the carbon conduction weight of each interconnection line specifically includes:

[0068] S401, calculate the electrical impedance of the line, where electrical impedance is the vector magnitude of resistance and reactance;

[0069] S402 uses the ratio of the line's rated capacity to the baseline capacity as the capacity factor to calculate the carbon conduction weight Tc. ij Tc ij = (1 / electrical impedance) × capacity factor.

[0070] In this embodiment of the invention, the line parameters include resistance, reactance, and rated capacity. The electrical impedance of the line is obtained based on the vector magnitude of the resistance and reactance, and the ratio of the rated capacity to the base capacity is used as the capacity factor. For example, a regional power grid typically uses 100MVA as the base capacity. Thus, the carbon conduction weight, Tc, can be obtained. ij = (1 / electrical impedance) × capacity factor. The smaller the impedance and the larger the capacity of the line, the higher the efficiency of carbon flow transmission. Finally, construct the carbon conduction weight Tc (N×N, where N is the total number of nodes): diagonal elements are set to zero; for off-diagonal elements, if nodes i and j are directly connected, fill in the carbon conduction weight, otherwise set to zero.

[0071] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of constructing a dynamic carbon potential field specifically includes:

[0072] S501: Calculate local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node.

[0073] S502, determine the local net power generation based on the total power generation and load data of this node, and determine the total output power based on the sum of the power of all output lines;

[0074] S503 iteratively updates the carbon potential of all nodes to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

[0075] In this embodiment of the invention, to obtain the node carbon potential, the local power generation carbon emissions are first obtained by multiplying the output power of all units at this node by their respective carbon intensity, and then summing them up. Then, based on the interconnection line power data, all adjacent nodes flowing into this node are determined. ij A value greater than 0 indicates that power flows from i to j, F ijIn other words, the carbon potential is calculated by multiplying the carbon potential of all adjacent nodes by the power flowing into the current node, and then summing the results to obtain the carbon emissions carried by the inflow lines. Next, the total power generation of the current node is subtracted from the load data to determine the local net power generation. The total output power is then determined based on the sum of the power of all output lines. Connecting lines include output lines and input lines. Input lines are lines where power flows from outside the region into the region, and output lines are lines where power flows from the current region to outside the region. Then, the node carbon potential can be calculated. The node carbon potential is iteratively updated every minute to construct a dynamic carbon potential field. Furthermore, in the first iteration, the carbon potential of adjacent nodes is the initial carbon potential of that node. The initial carbon potential of a pure power generation node = the weighted average of the carbon intensity of the units at that node; the initial carbon potential of a pure load node is 0; and the initial carbon potential of a mixed node = local power generation carbon emissions / local net power generation.

[0076] like Figure 4 As shown, in a preferred embodiment of the present invention, the step of analyzing regional net carbon transfer based on monitoring user-level carbon emissions using carbon flow data specifically includes:

[0077] S701: Obtain the real-time carbon potential of the user's node and calculate the user's carbon emissions.

[0078] S702, net input carbon emissions and net output carbon emissions of the computational region;

[0079] S703, obtain the net carbon transfer amount of the region, net carbon transfer amount = net input carbon emissions - net output carbon emissions.

[0080] In this embodiment of the invention, when calculating user-level carbon emissions, the real-time carbon potential of the node where the user is located is first obtained, and the user's carbon emissions are calculated. User carbon emissions = user electricity consumption × node carbon potential. When multiple users share a node: the total carbon emissions of the node are allocated according to the proportion of electricity consumption. When analyzing regional net carbon transfer, the net input carbon emissions and net output carbon emissions of the region are calculated. The net input carbon emissions are the sum of carbon flows of all input lines, and the net output carbon emissions are the sum of carbon flows of all output lines. The net carbon transfer amount is obtained by subtracting the net output carbon emissions from the net input carbon emissions.

[0081] like Figure 5 As shown in the preferred embodiment of the present invention, the method further includes spatiotemporal carbon hotspot diagnosis and visualization, the specific steps of which are as follows:

[0082] S801 performs spatial mapping, mapping the nodal carbon potential to the nodal coordinate map, and generates a dynamic color temperature map based on the carbon potential value.

[0083] S802 performs carbon hotspot diagnosis, selects the lines with the highest carbon flow in the top M%, traces back along the power to the power generation node, and marks the node whose carbon potential continuously exceeds the threshold as the carbon source point.

[0084] In this embodiment of the invention, a node coordinate map is also established. By mapping the node carbon potential onto the node coordinate map, a dynamic color temperature map can be generated based on the carbon potential value. Red indicates a high carbon potential area, where the carbon potential is greater than 150% of the network average; yellow indicates a medium carbon potential area, where the carbon potential is 80% to 150% of the network average; and green indicates a low carbon potential area, where the carbon potential is less than 80% of the network average. In addition, carbon hotspot diagnosis is performed, and lines with carbon flow ranking in the top M percent are selected, for example, M is 10. The power flow is traced backward to the power generation node, and nodes whose carbon potential continuously exceeds the threshold are marked as carbon source points. For example, nodes with a carbon potential > 700gCO2 / kWh for more than 30 minutes are carbon source points.

[0085] like Figure 6 As shown, this embodiment of the invention also provides a multi-dimensional carbon emission monitoring system based on spatiotemporal distribution, the system comprising:

[0086] The data upload and acquisition module 100 is used to collect time-series data, spatial topology data, and carbon intensity data. The time-series data includes unit output data, load data of each node, and power data of tie lines.

[0087] The spatial data import module 200 is used to import spatial topology data into the power grid geographic information system model. The spatial topology data includes node coordinates and line parameters, and constructs a node-line connection relationship table.

[0088] The carbon intensity binding module 300 is used to bind real-time carbon intensity to each generator set and establish a generator set-node mapping table;

[0089] The carbon conduction weighting module 400 is used to construct the carbon topology matrix Tc and calculate the carbon conduction weight Tc for each interconnection line. ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc.

[0090] The node carbon potential calculation module 500 is used to construct a dynamic carbon potential field. The node carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (local net power generation + total output power).

[0091] The Line Carbon Flow Monitoring Module 600 is used to monitor line-level carbon flow and calculate the real-time carbon flow rate for each line. φi is the initial carbon potential, and φj is the final carbon potential.

[0092] The Net Carbon Transfer Analysis Module 700 is used to monitor user-level carbon emissions based on carbon flow and analyze regional net carbon transfer.

[0093] In a preferred embodiment of the present invention, the carbon conduction weighting module 400 includes:

[0094] An electrical impedance calculation unit is used to calculate the electrical impedance of a line. The electrical impedance is the vector magnitude of resistance and reactance. The line parameters include resistance, reactance, and rated capacity.

[0095] The carbon conduction weighting unit is used to calculate the carbon conduction weight Tc by using the ratio of the line's rated capacity to the baseline capacity as a capacity factor. ij Tc ij = (1 / electrical impedance) × capacity factor.

[0096] In a preferred embodiment of the present invention, the nodal carbon potential calculation module 500 includes:

[0097] The carbon emission calculation unit is used to calculate the local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; and to determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node.

[0098] The node carbon potential determination unit is used to determine the local net power generation based on the total power generation and load data of this node, and to determine the total output power based on the sum of the power of all output lines. The interconnection lines include output lines and input lines, and thus obtain the node carbon potential.

[0099] The carbon potential iteration update unit is used to iteratively update the carbon potential of all nodes to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

[0100] In a preferred embodiment of the present invention, the net carbon transfer analysis module 700 includes:

[0101] The user carbon emission unit is used to obtain the real-time carbon potential of the node where the user is located and calculate the user's carbon emissions. User carbon emissions = user electricity consumption × node carbon potential.

[0102] The net carbon emission calculation unit is used to calculate the net input carbon emission and net output carbon emission of the region. The net input carbon emission is the sum of the carbon flow of all input lines, and the net output carbon emission is the sum of the carbon flow of all output lines.

[0103] The net carbon transfer unit is used to obtain the net carbon transfer amount of a region. Net carbon transfer amount = net input carbon emissions - net output carbon emissions.

[0104] The above description only details the preferred embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0105] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0106] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0107] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A multi-dimensional carbon emission monitoring method based on spatiotemporal distribution, characterized in that, The method includes the following steps: Time-series data, spatial topology data, and carbon intensity data were collected. The time-series data included unit output data, load data at each node, and tie-line power data. Spatial topology data, including node coordinates and line parameters, is imported into the power grid geographic information system model to construct a node-line connection table. Bind real-time carbon intensity to each generator set and establish a generator set-node mapping table; Construct a carbon topology matrix Tc and calculate the carbon conduction weight Tc for each interconnection line. ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc. Construct a dynamic carbon potential field, where nodal carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (net local power generation + total output power). Monitor line-level carbon flow and calculate the real-time carbon flow rate for each line. φi is the initial carbon potential, and φj is the final carbon potential. Based on carbon flow monitoring of user-level carbon emissions, regional net carbon transfer is analyzed.

2. The multi-dimensional carbon emission monitoring method based on spatiotemporal distribution according to claim 1, characterized in that, The steps for calculating the carbon conduction weight of each interconnection line specifically include: Calculate the electrical impedance of the line, which is the vector magnitude of resistance and reactance. The line parameters include resistance, reactance, and rated capacity. The ratio of the line's rated capacity to its baseline capacity is used as the capacity factor to calculate the carbon conduction weight Tc. ij Tc ij = (1 / electrical impedance) × capacity factor.

3. The multi-dimensional carbon emission monitoring method based on spatiotemporal distribution according to claim 1, characterized in that, The steps for constructing the dynamic carbon potential field specifically include: Calculate local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node. The local net power generation is determined based on the total power generation and load data of this node, and the total output power is determined based on the sum of the power of all output lines. The interconnection lines include output lines and input lines, and the node carbon potential is obtained. The carbon potential of all nodes is iteratively updated to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

4. The multi-dimensional carbon emission monitoring method based on spatiotemporal distribution according to claim 1, characterized in that, The steps for analyzing regional net carbon transfer based on user-level carbon emissions monitoring using carbon flow data specifically include: Obtain the real-time carbon potential of the node where the user is located, and calculate the user's carbon emissions. User carbon emissions = user electricity consumption × node carbon potential. The calculation area includes net input carbon emissions and net output carbon emissions. Net input carbon emissions are the sum of carbon flows of all input lines, and net output carbon emissions are the sum of carbon flows of all output lines. The net carbon transfer amount of the region is obtained. Net carbon transfer amount = net input carbon emissions - net output carbon emissions.

5. The multi-dimensional carbon emission monitoring method based on spatiotemporal distribution according to claim 1, characterized in that, The method also includes spatiotemporal carbon hotspot diagnosis and visualization, with the following specific steps: Spatial mapping is performed to map the nodal carbon potential to the nodal coordinate map, and a dynamic color temperature map is generated based on the carbon potential value. Carbon hotspot diagnosis is performed to screen out the lines with the top M% carbon flow, trace back along the power to the power generation node, and mark the node whose carbon potential continuously exceeds the threshold as the carbon source point.

6. The multi-dimensional carbon emission monitoring method based on spatiotemporal distribution according to claim 1, characterized in that, The carbon loss coefficient of the line is determined by the line resistance R. ij Line starting node voltage V i And the baseline capacity SA is determined, F ij (t) represents the line power from node i to j at time t, η ij This represents the carbon loss coefficient of the line from node i to j.

7. A multi-dimensional carbon emission monitoring system based on spatiotemporal distribution, characterized in that, The system includes: The data upload and acquisition module is used to collect time-series data, spatial topology data, and carbon intensity data. The time-series data includes unit output data, load data of each node, and tie-line power data. The spatial data import module is used to import spatial topology data into the power grid geographic information system model. The spatial topology data includes node coordinates and line parameters, and constructs a node-line connection relationship table. The carbon intensity binding module is used to bind real-time carbon intensity to each generator set and establish a generator set-node mapping table; The carbon conduction weighting module is used to construct the carbon topology matrix Tc and calculate the carbon conduction weight Tc for each interconnection line. ij Tc ij The carbon transport weight from node i to node j is represented by Tc, and the carbon admittance matrix Bc is generated based on Tc. The nodal carbon potential calculation module is used to construct a dynamic carbon potential field. Nodal carbon potential = (local power generation carbon emissions + carbon emissions carried by inflow lines) / (local net power generation + total output power). The line carbon flow monitoring module is used to monitor line-level carbon flow and calculate the real-time carbon flow rate for each line. φi is the initial carbon potential, and φj is the final carbon potential. The Net Carbon Transfer Analysis module is used to monitor user-level carbon emissions based on carbon flow and analyze regional net carbon transfer.

8. The multi-dimensional carbon emission monitoring system based on spatiotemporal distribution according to claim 7, characterized in that, The carbon conduction weighting module includes: An electrical impedance calculation unit is used to calculate the electrical impedance of a line. The electrical impedance is the vector magnitude of resistance and reactance. The line parameters include resistance, reactance, and rated capacity. The carbon conduction weighting unit is used to calculate the carbon conduction weight Tc by using the ratio of the line's rated capacity to the baseline capacity as a capacity factor. ij Tc ij = (1 / electrical impedance) × capacity factor.

9. The multi-dimensional carbon emission monitoring system based on spatiotemporal distribution according to claim 7, characterized in that, The node carbon potential calculation module includes: The carbon emission calculation unit is used to calculate the local power generation carbon emissions based on the unit output data and corresponding carbon intensity of this node; and to determine the amount of carbon emissions carried by the inflow lines based on the carbon potential of adjacent nodes and the power flowing into this node. The node carbon potential determination unit is used to determine the local net power generation based on the total power generation and load data of this node, and to determine the total output power based on the sum of the power of all output lines. The interconnection lines include output lines and input lines, and thus obtain the node carbon potential. The carbon potential iteration update unit is used to iteratively update the carbon potential of all nodes to construct a dynamic carbon potential field. In the first iteration, the carbon potential of adjacent nodes is determined by the initial carbon potential.

10. The multi-dimensional carbon emission monitoring system based on spatiotemporal distribution according to claim 7, characterized in that, The net carbon transfer analysis module includes: The user carbon emission unit is used to obtain the real-time carbon potential of the node where the user is located and calculate the user's carbon emissions. User carbon emissions = user electricity consumption × node carbon potential. The net carbon emission calculation unit is used to calculate the net input carbon emission and net output carbon emission of the region. The net input carbon emission is the sum of the carbon flow of all input lines, and the net output carbon emission is the sum of the carbon flow of all output lines. The net carbon transfer unit is used to obtain the net carbon transfer amount of a region. Net carbon transfer amount = net input carbon emissions - net output carbon emissions.

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