Electric power real-time dynamic carbon factor metering and monitoring system

Abstract

The invention discloses an electric power real-time dynamic carbon factor metering and monitoring system, which belongs to the technical field of electric power systems, and comprises a data acquisition module used for acquiring multi-source data of a power generation side, a power grid side and a user side of an electric power system in real time, verifying the multi-source data, generating high-frequency data streams, and transmitting the high-frequency data streams to a data processing module; the high-frequency data stream comprises renewable energy output fluctuation data, load change data and power grid topological structure data; the dynamic calculation module is used for generating time-sharing and partitioned dynamic carbon factors based on the high-frequency data stream; the monitoring feedback module is used for generating a carbon flow path track based on the dynamic carbon factors and generating a calibration instruction set in combination with deviation records in the historical dynamic carbon factors; and the optimization output module is used for recalculating the dynamic carbon factor based on the calibration instruction set and the real-time multi-source data, and outputting the optimized dynamic carbon factor of the time-sharing partition to an external carbon accounting system. According to the method, the problem that the traditional annual average factor cannot reflect the space-time dynamism is solved by calculating the carbon factor of the time-sharing partition.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a real-time dynamic carbon factor metering and monitoring system for power. BACKGROUND

[0002] The dynamic carbon factor refers to a dynamic index reflecting the carbon emission intensity corresponding to the unit power generation of the power system in real time. The core is to capture the dynamic change characteristics of the carbon emission intensity in time (such as the time scale) and space (such as the regional scale) due to the renewable energy output fluctuation, load change and power grid topology difference. This factor quantifies the real carbon intensity of the power grid through the integration of multi-source data on the generation side, the grid side and the user side, and provides a basis for accurate carbon accounting through high-frequency updating.

[0003] In the prior art, the metering of the power carbon factor is mostly based on the static or quasi-static method based on historical data. For example, through the provincial annual average carbon emission factor or the accounting model of fixed time interval (such as monthly), the working principle thereof depends on the statistical average of the historical power generation, fuel consumption and emission factor, a unified carbon intensity value covering a large time and space range is calculated, and is applied to the user side carbon footprint accounting. Such method usually generates a carbon factor through a standard formula (such as the total life cycle emission divided by the total power generation), and relies on the electric meter data and fixed emission coefficient for indirect carbon emission estimation.

[0004] The prior art has the following defects: the traditional average carbon emission factor of the power grid is mostly the annual average value, which cannot reflect the carbon intensity difference within a day or within a time caused by the renewable energy output fluctuation and load change in the power system, resulting in insufficient accuracy of the enterprise carbon footprint accounting and affecting the efficiency of green investment. Therefore, it is urgent to provide a real-time dynamic carbon factor metering and monitoring system for power to solve the above problems. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art that the traditional average carbon emission factor of the power grid is mostly the annual average value, which cannot reflect the carbon intensity difference within a day or within a time caused by the renewable energy output fluctuation and load change in the power system, resulting in insufficient accuracy of the enterprise carbon footprint accounting and affecting the efficiency of green investment, and to provide a real-time dynamic carbon factor metering and monitoring system for power.

[0006] To solve the above technical problems, one technical solution adopted by the present application is to provide a real-time dynamic carbon factor metering and monitoring system for power, comprising a data acquisition module, a dynamic calculation module, a monitoring feedback module and an optimization output module.

[0007] A data acquisition module is configured to acquire multi-source data of a power system in real time, including output data of thermal power generating units and new energy units, power flow data of a power transmission line, power data of a power distribution node, consumption data of a user electric meter, and power grid topology data.

[0008] A dynamic calculation module is configured to analyze the renewable energy output fluctuation data, the load change data, and the power grid topology data based on the high-frequency data stream through a preset dynamic calculation rule to generate dynamic carbon factors in different time zones and different regions.

[0009] A monitoring feedback module is configured to generate a carbon flow path trajectory through a preset analysis unit based on the dynamic carbon factors, receive accuracy feedback information generated by the user electric meter consumption data, and generate a calibration instruction set in combination with a deviation record in the dynamic carbon factors.

[0010] An optimization output module is configured to adjust associated parameters in the dynamic calculation rule based on the calibration instruction set and real-time multi-source data, recalculate the dynamic carbon factors, and output the optimized dynamic carbon factors in different time zones and different regions to an external carbon accounting system.

[0011] The application further provides that the multi-source data of the data acquisition module is acquired in real time by an electric carbon meter and a sensor network deployed at the power generation side, the power grid side, and the user side.

[0012] The power grid topology data includes substation connection relationships and line impedance parameters.

[0013] The generation step of the high-frequency data stream is as follows:

[0014] S1, receiving the multi-source data, performing timestamp synchronization and data format consistency verification on the multi-source data, and generating an initial multi-source data stream;

[0015] S2, based on the initial multi-source data stream, extracting the output data of the thermal power generating units and the new energy units to generate renewable energy output fluctuation data, extracting the user electric meter consumption data and the power data of the power distribution node to generate load change data, and inserting the power flow data of the power transmission line into the initial multi-source data stream according to the timestamp to generate an intermediate data stream;

[0016] S3, by verifying the spatiotemporal continuity of the renewable energy output fluctuation data, the load change data, the power flow data of the power transmission line, and the power grid topology data in the intermediate data stream, filling in the data missing segments caused by transmission packet loss, and compressing the volume of the intermediate data stream, a high-frequency data stream is generated.

[0017] The application is further provided that: the generation step of the dynamic carbon factor in the dynamic calculation module is as follows:

[0018] Q1, extract the fluctuation amplitude of the renewable energy output fluctuation data and the change trend of the load change data within a preset sampling period, and spatially align the fluctuation amplitude and the change trend according to the connection relationship in the power grid topology data to generate an association feature set;

[0019] Q2, based on the output data of the power generation side and the power flow data of the transmission line, match the unit carbon emission intensity reference value through a preset emission factor database, and calculate the line loss coefficient combined with the power flow data of the transmission line, fuse the unit carbon emission intensity reference value and the line loss coefficient according to the power transmission path in the power grid topology, and generate an initial carbon emission intensity distribution map;

[0020] Q3, superimpose the association feature set and the initial carbon emission intensity distribution map, and introduce the fluctuation amplitude in the renewable energy output fluctuation data to real-time correct the initial carbon emission intensity distribution map, and slice the corrected initial carbon emission intensity distribution map according to a preset time interval to generate a dynamic carbon factor in a time and partition manner.

[0021] The application is further provided that: the calculation step of the line loss coefficient in the step Q2 is:

[0022] Q201, based on the power flow data of the transmission line, extract the current intensity data of each transmission line, and obtain the unit length resistance value combined with the line impedance parameter in the power grid topology data, calculate the instantaneous active power loss value of each transmission line by multiplying the square value of the current intensity data and the unit length resistance value, and introducing a preset line length coefficient;

[0023] Q202, based on the power transmission path in the power grid topology, accumulate the instantaneous active power loss value of each transmission line according to the connection order of the power transmission path, and introduce a preset path load weight factor to adjust the accumulated value of the instantaneous active power loss value of each transmission line, to generate a total active power loss value of the power transmission path;

[0024] Q203, based on the reference active power value in the output data of the power generation side, calculate the ratio of the total active power loss value and the reference active power value to generate a ratio value, and map the ratio value to a standardized interval of 0 to 1 through a linear normalization function to generate a line loss coefficient.

[0025] The present invention is further configured such that: the specific content of step Q2, which involves fusing the unit carbon emission intensity benchmark value and the line loss coefficient according to the power transmission path in the power grid topology to generate an initial carbon emission intensity distribution map, is as follows:

[0026] Q204. Based on the power transmission path in the power grid topology data, extract the key node sequence and connection line relationship on the power transmission path, and initially allocate the unit carbon emission intensity benchmark value according to the power node position in the key node sequence to generate the node carbon emission intensity initial value sequence.

[0027] Q205. Based on the line loss coefficient, and combined with the transmission distance and load characteristics of each connecting line in the power transmission path, calculate the loss adjustment factor for each connecting line, and multiply the loss adjustment factor by the initial value of the node carbon emission intensity of the corresponding connecting line to generate a node carbon emission intensity correction value sequence.

[0028] Q206. Based on the node carbon emission intensity correction value sequence, interpolate the node carbon emission intensity correction values ​​to the entire geographical area covered by the power grid topology data to generate an initial carbon emission intensity distribution map.

[0029] The initial carbon emission intensity distribution map stores the carbon emission intensity values ​​of each data grid cell in the power grid topology data in the form of a two-dimensional matrix.

[0030] The present invention is further configured such that the generation steps of the carbon flow path trajectory in the monitoring feedback module are as follows:

[0031] W1. Based on the dynamic carbon factor, extract carbon emission intensity data under different time slices, and combine the power transmission path and connection line relationship in the power grid topology data to spatially allocate the carbon emission intensity data according to the circuit transmission path to generate a preliminary carbon flow path sequence.

[0032] W2. Based on the accuracy feedback information generated from the user's electricity meter consumption data, the preliminary carbon flow path sequence is calibrated to generate a calibrated carbon flow path sequence.

[0033] W3. The calibrated carbon flow path sequence is fused with the geographic coordinate information in the power grid topology data to generate a carbon flow path trajectory.

[0034] The present invention is further configured such that the step of generating the calibration instruction set in the monitoring feedback module is as follows:

[0035] W4. Extract the carbon intensity anomaly segment and path deviation features in the carbon flow path trajectory, and combine them with the error index in the accuracy feedback information generated by the user's electricity meter consumption data to calculate the deviation of the carbon flow path trajectory from the expected path, and generate a preliminary calibration parameter set, which includes a path correction coefficient and an intensity adjustment factor.

[0036] W5. Based on the preliminary calibration parameter set, and combined with the deviation records in the historical dynamic carbon factor, identify the deviation pattern characteristics from the historical deviation records, generate a historical deviation pattern set, match the path correction coefficient and intensity adjustment factor in the preliminary calibration parameter set with the historical deviation pattern set, and introduce a preset time decay weight to weight the deviation values ​​in the historical deviation records to generate a calibration instruction set.

[0037] The present invention is further configured such that: the specific steps in step W4 for calculating the deviation between the carbon flow path trajectory and the expected path to generate a preliminary calibration parameter set are as follows:

[0038] W401. Based on the carbon flow path trajectory, extract the carbon intensity anomaly segment and path offset features in the carbon flow path trajectory, and combine the power transmission path in the power grid topology data as the expected path to calculate the spatial distance deviation value and carbon intensity difference value between the actual carbon flow path and the expected path, and generate a preliminary deviation index set.

[0039] W402. Based on the preliminary deviation index set, and combined with the error index in the accuracy feedback information generated from the user's electricity consumption data, the spatial distance deviation value and the carbon intensity difference value are multiplied by a preset adaptive weighting factor to generate a path correction coefficient and an intensity adjustment factor, and integrated into a preliminary calibration parameter set.

[0040] The present invention is further configured such that: the optimized output module specifically comprises: extracting the path correction coefficient and the intensity adjustment factor from the calibration instruction set and the real-time multi-source data, adjusting the correlation parameters in the dynamic calculation rules, wherein the correlation parameters include the carbon emission intensity weight derived from the unit carbon emission intensity benchmark value and the correlation coefficient calculated based on the substation connection relationship and line impedance parameters in the power grid topology data; mapping the adjusted correlation parameters to the generation process of the initial carbon emission intensity distribution map, recalculating the node carbon emission intensity correction value sequence, and performing real-time verification of the recalculated node carbon emission intensity correction value sequence based on the renewable energy output fluctuation data and the load change data in the real-time multi-source data, generating the optimized dynamic carbon factor, and outputting the optimized time-division and zone-division dynamic carbon factor to an external carbon accounting system.

[0041] The beneficial effects of this invention are as follows:

[0042] 1. This invention generates high-frequency data streams by collecting multi-source data from the power generation side, the grid side, and the user side in real time. It then dynamically calculates the carbon factor in different time zones based on the grid topology, accurately capturing the carbon intensity differences caused by renewable energy fluctuations and load changes. This solves the problem that traditional annual average factors cannot reflect spatiotemporal dynamics, significantly improves the accuracy of carbon footprint accounting, and supports green investment decisions.

[0043] 2. This invention generates carbon flow path trajectories through a monitoring feedback module, dynamically calibrates carbon factors by combining user-side feedback and historical deviation records, and uses an optimization output module to adjust parameters in a closed loop, thereby achieving high-frequency updates and continuous optimization of carbon factors, enhancing the system's adaptive capabilities, and providing timely data support for carbon market trading and low-carbon grid dispatch. Attached Figure Description

[0044] Figure 1 This is a system flowchart of the present invention;

[0045] Figure 2 This is a flowchart of the steps for generating the dynamic carbon factor according to the present invention.

[0046] Figure 3 This is a flowchart illustrating the calculation steps for the line loss coefficient of the present invention. Detailed Implementation

[0047] The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0048] Please see Figure 1 - Figure 3 A real-time dynamic carbon factor metering and monitoring system for electricity includes a data acquisition module, a dynamic calculation module, a monitoring feedback module, and an optimization output module.

[0049] The data acquisition module is used to collect multi-source data from the power generation side, grid side, and user side of the power system in real time. The multi-source data includes output data of thermal power generating units and new energy generating units, power flow data of transmission lines, power data of distribution nodes, power consumption data of user meters, and grid topology data. The module verifies the multi-source data and generates a high-frequency data stream, which includes renewable energy output fluctuation data, load change data, and grid topology data.

[0050] The dynamic calculation module, based on high-frequency data streams, analyzes renewable energy output fluctuation data, load change data, and grid topology data through preset dynamic calculation rules to generate time- and zone-specific dynamic carbon factors.

[0051] The monitoring and feedback module, based on the dynamic carbon factor, generates a carbon flow path trajectory through a preset analysis unit, receives accuracy feedback information generated from user electricity meter consumption data, and generates a calibration instruction set by combining deviation records in the historical dynamic carbon factor.

[0052] The output module is optimized by adjusting the correlation parameters in the dynamic calculation rules based on the calibration instruction set and real-time multi-source data. The dynamic carbon factor is recalculated, and the optimized time-sharing and zone-specific dynamic carbon factor is output to the external carbon accounting system. This external carbon accounting system serves as a powerful "command center," utilizing real-time data provided by radar, combined with other enterprise information, for strategic deployment, compliance management, and asset operation. Working in conjunction with this system, it provides a solid data foundation for the enterprise's green and low-carbon transformation.

[0053] This system forms a closed loop through data acquisition, dynamic calculation, monitoring feedback, and optimized output, enabling high-frequency updates and accurate measurement of the carbon factor in electricity.

[0054] One embodiment of the present invention is as follows: multi-source data from the data acquisition module is acquired in real time through carbon meters and sensor networks deployed on the power generation side, the grid side, and the user side;

[0055] Sensor networks include pressure sensors, temperature sensors, flow sensors, carbon meters, and smart meters deployed on the power generation side, grid side, and user side, used to monitor in real time the output parameters of thermal power generating units and new energy generating units, power flow data of transmission lines, power consumption at distribution nodes, and user consumption data.

[0056] Power grid topology data includes substation connection relationships and line impedance parameters;

[0057] The substation connection relationship and line impedance parameters are as follows: The substation connection relationship refers to the electrical interconnection structure of equipment (such as circuit breakers and busbars) within the substation and the connection method of transmission lines between substations; the line impedance parameters include resistance per unit length and reactance value, which are used to characterize the electrical characteristics of the line and calculate transmission losses.

[0058] The steps for generating a high-frequency data stream are as follows:

[0059] S1. Receive multi-source data, perform timestamp synchronization and data format consistency verification on the multi-source data, eliminate timing misalignment and protocol differences caused by device heterogeneity, and generate an initial multi-source data stream.

[0060] S2. Based on the initial multi-source data stream, extract the output data of thermal power generating units and new energy generating units to generate renewable energy output fluctuation data, extract the user meter consumption data and the power data of distribution nodes to generate load change data, and insert the power flow data of transmission lines into the initial multi-source data stream according to the timestamp to generate intermediate data stream.

[0061] The steps for generating renewable energy output fluctuation data are as follows: Based on multi-source data streams, extract real-time output data of thermal power generating units and new energy units (such as wind power and photovoltaic), calculate their fluctuation amplitude (such as the ratio of the output difference between adjacent time points to the benchmark output), and identify the fluctuation trend through time series analysis to generate renewable energy output fluctuation data, which is used to characterize the instability of renewable energy output.

[0062] The steps for generating load change data are as follows: Based on multi-source data streams, extract user meter consumption data and power distribution node power data, calculate the load change rate (such as the ratio of load difference to baseline load per unit time) and analyze its changing trend (such as peak and valley characteristics) to generate load change data, reflecting the dynamic fluctuation of user-side electricity demand.

[0063] S3. By verifying the spatiotemporal continuity of renewable energy output fluctuation data, load change data, power flow data of transmission lines, and power grid topology data in the intermediate data stream, the missing data segments caused by transmission packet loss are filled in, and the volume of the intermediate data stream is compressed to generate a high-frequency data stream.

[0064] The filling of missing data segments is based on the spatial correlation in the power grid topology data. Spatiotemporal interpolation methods (such as weighted average of neighboring sensor data or historical data from the same period) are used, combined with geographic coordinate information to ensure the continuity between the missing point and the surrounding data, and to fill in the data interruption caused by packet loss during transmission.

[0065] The volume of the intermediate data stream is compressed using a pre-defined lightweight data compression algorithm. This algorithm is based on the characteristics of time-series data (such as the spatiotemporal continuity of renewable energy output fluctuation data, load change data, power flow data, and grid topology data). It adopts a combination of differential coding and Run-Length Encoding (RLE). First, the data is processed by temporal differential to reduce redundancy. Then, continuous repetitive values ​​are compressed and encoded, thereby significantly reducing the data volume while ensuring that the data integrity is not compromised. This compression process also introduces an adaptive threshold mechanism to dynamically adjust the compression rate according to the data fluctuation amplitude, avoiding the loss of key information. Finally, a high-frequency data stream with optimized volume is generated to support the efficient processing and real-time transmission of subsequent dynamic carbon factor calculations.

[0066] This embodiment collects multi-source data in real time through a sensor network, and generates a high-frequency data stream through timestamp synchronization, format consistency verification, and spatiotemporal continuity processing. This effectively eliminates timing misalignments and protocol differences caused by device heterogeneity, and dynamically fills in transmission packet loss gaps, significantly improving data integrity, real-time performance, and consistency. It provides high-precision input for dynamic carbon factor measurement and enhances the accuracy and reliability of carbon footprint tracking.

[0067] One embodiment of the present invention is as follows: the generation steps of the dynamic carbon factor in the dynamic calculation module are as follows:

[0068] Q1. Extract the fluctuation amplitude of renewable energy output fluctuation data and the trend of load change data within the preset sampling period, and spatially align the fluctuation amplitude and trend according to the connection relationship in the power grid topology data to generate a set of related features.

[0069] The specific steps of spatial alignment are as follows: Based on the connection relationship in the power grid topology data, establish a node association matrix, map the fluctuation amplitude of renewable energy output fluctuation data and the change trend of load change data according to the node position, and match the discrete renewable energy output fluctuation data points and load change data points to the data nodes of the power grid topology through a spatial interpolation algorithm (existing technology) to generate a spatially continuous association feature set;

[0070] Q2. Based on the power output data of the generation side and the power flow data of the transmission line, the benchmark value of the carbon emission intensity of the unit is obtained by matching with the preset emission factor database, and the line loss coefficient is calculated by combining the power flow data of the transmission line. The benchmark value of the carbon emission intensity of the unit and the line loss coefficient are fused according to the power transmission path in the power grid topology to generate an initial carbon emission intensity distribution map.

[0071] The preset emission factor database contains standard carbon emission intensity values ​​for thermal power generating units and new energy generating units. It is linked with the power output data on the generation side by the unit type, fuel characteristics and operating status parameters, and dynamically adjusts the emission factor values ​​in combination with the power transmission direction in the power flow data of the transmission line.

[0072] Q3. Overlay the associated feature set with the initial carbon emission intensity distribution map, and introduce the fluctuation amplitude in the renewable energy output fluctuation data to correct the initial carbon emission intensity distribution map in real time. The corrected initial carbon emission intensity distribution map is sliced ​​at preset time intervals to generate time-division dynamic carbon factors. The dynamic carbon factors are updated at preset time intervals, and the dynamic carbon factor sequence is used to characterize the spatiotemporal evolution of the grid carbon intensity.

[0073] The specific content of real-time correction of the initial carbon emission intensity distribution map is as follows: the associated feature set is superimposed with the initial carbon emission intensity distribution map in a weighted manner, the fluctuation amplitude in the renewable energy output fluctuation data is introduced as a dynamic correction coefficient, and the carbon emission intensity value of each grid cell in the distribution map is adjusted in real time to generate the corrected carbon emission intensity distribution map.

[0074] The optimal value for the preset time interval is 15 minutes. This value is determined based on the balance between the grid dispatch cycle and the carbon factor fluctuation characteristics. It can capture the dynamic characteristics of renewable energy output and load changes, while avoiding system resource overload due to frequent updates.

[0075] Preferably, the calculation steps for the line loss coefficient in step Q2 are as follows:

[0076] Q201. Based on the power flow data of transmission lines, extract the current intensity data of each transmission line, and obtain the resistance value per unit length by combining the line impedance parameters in the power grid topology data. Calculate the instantaneous active power loss value of each transmission line by multiplying the square of the current intensity data with the resistance value per unit length and introducing a preset line length coefficient.

[0077] The calculation steps for the preset line length coefficient are as follows: Based on the line spatial coordinate data in the power grid topology, calculate the Euclidean distance between the starting point and the ending point of the transmission line, and correct it by combining the actual meandering coefficient of the transmission line to generate a proportional coefficient that represents the actual length of the line.

[0078] The steps for calculating the instantaneous active power loss value of each transmission line are as follows: extract the current intensity value from the power flow data of the transmission line, combine it with the resistance value per unit length from the power grid topology data, calculate the base loss by multiplying the square value of the current intensity data with the resistance value per unit length, and then multiply it by the line length coefficient to obtain the instantaneous active power loss value.

[0079] Q202. Based on the power transmission path in the power grid topology, the instantaneous active power loss value of each transmission line is accumulated according to the connection order of the power transmission path, and a preset path load weight factor is introduced to adjust the accumulated value of the instantaneous active power loss value of each transmission line, so as to generate the total active power loss value of the power transmission path.

[0080] The steps for obtaining the preset path load weight factor are as follows: Based on the proportion of power transmission volume of each line to the total transmission volume in the power flow data, the weight factor is generated through normalization processing. High-load lines are assigned larger weights, and low-load lines are assigned smaller weights.

[0081] The specific steps for adjusting the accumulated value to generate the total active power loss value are as follows: arrange the instantaneous active power loss values ​​of each transmission line in the order of power transmission path, multiply them by the corresponding path load weight factor, and then perform weighted summation to generate the total active power loss value of the power transmission path.

[0082] Q203. Based on the baseline active power value in the power output data of the power generation side, the ratio of the total active power loss value to the baseline active power value is calculated to generate a ratio value. The ratio value is then mapped to a standardized range of 0 to 1 through a linear normalization function to generate the line loss coefficient. The line loss coefficient is used for the subsequent fusion calculation of the initial carbon emission intensity distribution map.

[0083] Preferably, the specific content of step Q2, which involves fusing the unit's carbon emission intensity benchmark value with the line loss coefficient according to the power transmission path in the power grid topology to generate the initial carbon emission intensity distribution map, is as follows:

[0084] Q204. Based on the power transmission path in the power grid topology data, extract the key node sequence and connection line relationship on the power transmission path, initially allocate the unit carbon emission intensity benchmark value according to the power node position in the key node sequence, generate the node carbon emission intensity initial value sequence, and the node carbon emission intensity initial value sequence characterizes the initial carbon intensity distribution from the start point to the end point of the power transmission path.

[0085] The initial allocation rule is as follows: the base value of the unit's carbon emission intensity is linearly allocated according to the distance between the nodes in the power transmission path from the power node to the load node. The closer to the power node, the higher the allocation value, and the closer to the load node, the lower the allocation value.

[0086] Q205. Based on the line loss coefficient, combined with the transmission distance and load characteristics of each connecting line in the power transmission path, calculate the loss adjustment factor of each connecting line, multiply the loss adjustment factor by the initial value of the node carbon emission intensity of the corresponding connecting line, and generate a node carbon emission intensity correction value sequence. The node carbon emission intensity correction value sequence reflects the cumulative impact of transmission loss on carbon intensity.

[0087] The formula for calculating the loss adjustment factor is described as follows: based on the product of the line loss coefficient and the transmission distance of the connecting line, and then divided by the preset reference transmission distance of the connecting line, the loss adjustment factor characterizing a specific line is obtained.

[0088] Q206. Based on the node carbon emission intensity correction value sequence, interpolate the node carbon emission intensity correction values ​​to the entire geographical area covered by the power grid topology data to generate an initial carbon emission intensity distribution map.

[0089] The specific steps for interpolating the node carbon emission intensity correction values ​​to the entire geographic area covered by the power grid topology data are as follows: Based on the node carbon emission intensity correction value sequence, an inverse distance weighted interpolation algorithm is used to calculate the weights according to the spatial distance between the grid cells and the nodes, and the node values ​​are interpolated to the grid of the entire geographic area to generate a continuously distributed initial carbon emission intensity distribution map.

[0090] The initial carbon emission intensity distribution map stores the carbon emission intensity values ​​of each data grid cell in the power grid topology data in the form of a two-dimensional matrix, which is used for subsequent overlay and correction with the associated feature set.

[0091] Example: Taking a regional power grid as an example, based on a 15-minute sampling period, extract the fluctuation amplitude (e.g., ±15MW) of wind power and photovoltaic output fluctuation data and the trend of load change data (e.g., ±8% per hour). Spatial alignment is performed using the connection relationships of 100 nodes in the power grid topology data to generate a related feature set. Combining thermal power unit output data (baseline value 800MW) and transmission line power flow data (current intensity 200A), calculate the line loss coefficient (e.g., 0.03), and then set the base value of the unit carbon emission intensity (0.6...). The initial carbon emission intensity distribution map is generated by fusing the loss coefficient with the power transmission path. Through the overlay of associated feature sets and real-time correction of fluctuation amplitude, the distribution map is sliced ​​to output a time- and zone-specific dynamic carbon factor (e.g., X = 0.9 for peak period regions). The Y value for the flat section is 0.5. The sequence of dynamic carbon factors is updated every 15 minutes.

[0092] This example generates accurate spatiotemporal carbon factors by dynamically fusing high-frequency sampling and multi-source data, effectively capturing the impact of renewable energy fluctuations and load changes, improving the accuracy and timeliness of carbon accounting, and supporting corporate emission reduction decisions, grid optimization scheduling, and carbon market trading efficiency.

[0093] One embodiment of the present invention is as follows: the steps for generating the carbon flow path trajectory in the monitoring feedback module are as follows:

[0094] W1. Based on dynamic carbon factors, carbon emission intensity data under different time slices are extracted, and combined with the power transmission path and connection line relationship in the power grid topology data, the carbon emission intensity data is spatially allocated according to the circuit transmission path to generate a preliminary carbon flow path sequence. The preliminary carbon flow path sequence includes path direction, carbon intensity value and timestamp information.

[0095] The steps for extracting carbon emission intensity data are as follows: Based on the dynamic carbon factor sequence, carbon emission intensity data at different time points are extracted by slicing at preset time intervals (such as 15 minutes). Combined with timestamp information and node identifiers in the power grid topology, a time-series carbon emission intensity dataset is generated for subsequent spatial allocation and path generation.

[0096] The specific steps for spatially allocating carbon emission intensity data according to circuit transmission paths to generate a preliminary carbon flow path sequence are as follows:

[0097] Based on the relationship between power transmission paths and connecting lines in the power grid topology data, the time-series carbon emission intensity data is spatially mapped according to path nodes. The intensity value is allocated to each line segment through an inverse distance weighted interpolation algorithm, generating a preliminary carbon flow path sequence containing path direction, carbon intensity value and timestamp information, ensuring the spatiotemporal consistency between data and physical paths.

[0098] W2. Based on the accuracy feedback information generated from the user's electricity meter consumption data, the preliminary carbon flow path sequence is calibrated. The error part in the carbon intensity value is adjusted through the preset deviation correction model. The deviation correction model calculates the correction coefficient based on the deviation record in the historical dynamic carbon factor and applies the correction coefficient to the carbon intensity value in the preliminary carbon flow path sequence to generate the calibrated carbon flow path sequence.

[0099] The deviation correction model is specifically the adaptive weighted correction model in the existing technology. By analyzing the deviation records in the historical dynamic carbon factor, the correction coefficient based on the time series and spatial distribution is calculated, and the correction coefficient is applied to the carbon intensity value in the preliminary carbon flow path sequence to reduce the deviation caused by measurement error and transmission loss.

[0100] W3. The calibrated carbon flow path sequence is fused with the geographic coordinate information in the power grid topology data to generate a carbon flow path trajectory. The carbon flow path trajectory graphically displays the transmission path of carbon intensity values ​​in the power grid, real-time change trends, and carbon flow density at key nodes.

[0101] The specific content of step W3 is as follows: the calibrated carbon flow path sequence is fused with the geographic coordinate information in the power grid topology data, and the path sequence is mapped to the geographic coordinate system through spatial rendering technology to generate a graphical carbon flow path trajectory, dynamically displaying the transmission path of carbon intensity value in the power grid, real-time change trend and carbon flow density at key nodes.

[0102] Specifically, the steps for generating the calibration instruction set in the monitoring feedback module are as follows:

[0103] W4. Extract the abnormal carbon intensity segments and path deviation features in the carbon flow path trajectory, and combine them with the error index in the accuracy feedback information generated from the user's electricity meter consumption data to calculate the deviation between the carbon flow path trajectory and the expected path, and generate a preliminary calibration parameter set. The preliminary calibration parameter set includes path correction coefficients and intensity adjustment factors.

[0104] The steps for extracting carbon intensity anomaly segments and path offset features in the carbon flow path trajectory are as follows: Based on the carbon flow path trajectory, the distribution characteristics of carbon intensity values ​​are analyzed using the sliding window statistical method, anomaly segments exceeding the dynamic threshold (such as the range of ±2 standard deviations based on historical deviation records) are identified, and offset features are detected by combining the path curvature change of the carbon flow path trajectory. The coordinates and offset of the anomaly segments are extracted to generate a dataset of carbon intensity anomaly segments and path offset features.

[0105] W5. Based on the preliminary calibration parameter set and combined with the deviation records in the historical dynamic carbon factor, identify the deviation pattern characteristics (such as time periodicity and spatial correlation) from the historical deviation records, generate a historical deviation pattern set, match the path correction coefficient and intensity adjustment factor in the preliminary calibration parameter set with the historical deviation pattern set, and introduce a preset time decay weight to weight the deviation values ​​in the historical deviation records to generate a calibration instruction set.

[0106] The specific content of identifying deviation patterns and generating a historical deviation pattern set from historical deviation records is as follows: Based on deviation records in historical dynamic carbon factors, periodic features (such as deviation peaks during daily load peak periods) are extracted through time series decomposition, and regional clustering patterns (such as coordinated changes in deviations of adjacent nodes) are identified through spatial correlation analysis, thereby generating a historical deviation pattern set that integrates time periodicity and spatial correlation.

[0107] Preset time decay weight: The path correction coefficient and intensity adjustment factor in the initial calibration parameter set are matched with the historical deviation pattern set. The best matching pattern is determined by cosine similarity calculation. A time decay weight (the optimal value is 0.95, based on the exponential decay model to enhance the influence of recent deviations) is introduced to dynamically weight the deviation values ​​in the historical deviation records, generating a calibration instruction set for optimizing the parameter adjustment of the output module.

[0108] Preferably, the specific steps in step W4 for calculating the deviation between the carbon flow path trajectory and the expected path to generate the preliminary calibration parameter set are as follows:

[0109] W401. Based on the carbon flow path trajectory, extract the carbon intensity anomaly segment and path deviation features in the carbon flow path trajectory, and combine the power transmission path in the power grid topology data as the expected path to calculate the spatial distance deviation value and carbon intensity difference value between the actual carbon flow path and the expected path, and generate a preliminary deviation index set.

[0110] The spatial distance deviation value is obtained by calculating the Euclidean distance between the coordinate points in the actual carbon flow path trajectory and the corresponding points in the expected path (derived from the power transmission path in the power grid topology). The carbon intensity difference value is calculated by the absolute difference between the actual carbon intensity value and the expected carbon intensity value (based on historical benchmarks), and normalization is performed to eliminate the influence of dimensions.

[0111] W402. Based on the preliminary deviation index set, and combined with the error index in the accuracy feedback information generated from the user's electricity meter consumption data, the spatial distance deviation value and the carbon intensity difference value are multiplied by the preset adaptive weighting factor to generate the path correction coefficient and intensity adjustment factor, and integrated into the preliminary calibration parameter set.

[0112] The adaptive weighting factor is dynamically adjusted based on the error magnitude in the error index (such as the ratio of the absolute value of the error to the preset threshold) and the fluctuation frequency in the historical deviation record (such as the number of deviations occurring per unit time). The error magnitude and fluctuation frequency are linearly combined through a weighted fusion algorithm. When the error is large or the frequency is high, the weighting factor increases, and vice versa, to ensure that the weight adapts to the real-time state of the system.

[0113] Specific example: Taking a regional power grid as an example, carbon emission intensity data for each node is extracted based on a dynamic carbon factor sequence sliced ​​at 15-minute time intervals (e.g., the intensity value of node X at timestamp 2024-10-29 10:00:00 is 0.65). Combining power transmission paths (such as paths ABC) and connecting lines in the power grid topology data, an inverse distance weighted interpolation algorithm is used to allocate intensity values ​​to each line segment, generating a preliminary carbon flow path sequence containing path direction, carbon intensity value, and timestamp. Based on accuracy feedback information generated from user meter consumption data (such as an error index of 0.05), an adaptive weighted correction model is used to adjust the carbon intensity values ​​in the preliminary sequence (such as correcting the intensity of node B from 0.70 to 0.67), generating a calibrated carbon flow path sequence. This sequence is then fused with geographical coordinates (such as latitude and longitude coordinates) in the power grid topology, and a graphical carbon flow path trajectory is generated using spatial rendering technology. Take the abnormal carbon intensity segments in the trajectory (such as path segments with intensity continuously exceeding 0.75) and path deviation characteristics (such as the actual path deviating from the expected path by 50 meters). Combine the user feedback error index to calculate the spatial distance deviation value (such as an average deviation of 40 meters) and the carbon intensity difference value (such as an average difference of 0.08). Generate the path correction coefficient (such as 1.2) and intensity adjustment factor (such as 0.9) through an adaptive weight factor (such as 0.8), and integrate them into a preliminary calibration parameter set. Finally, combine the historical dynamic carbon factor deviation records (such as the daily peak deviation peak of 0.12) and match the historical deviation pattern set through a time decay weight (0.95) to generate a calibration instruction set for system optimization.

[0114] This example achieves accurate generation and dynamic calibration of carbon flow paths through high-frequency data extraction and multi-source fusion, effectively improving the spatiotemporal accuracy of carbon intensity monitoring. Through deviation adaptive correction and historical pattern matching, it enhances the system's ability to identify abnormal paths and its response speed, supporting the real-time performance and reliability of power grid carbon management, and providing a highly reliable data foundation for enterprise emission reduction decisions and carbon market trading.

[0115] One embodiment of the present invention is as follows: The specific content of the optimized output module is as follows: Based on the calibration instruction set and real-time multi-source data, the path correction coefficient and intensity adjustment factor in the calibration instruction set are extracted, and the correlation parameters in the dynamic calculation rules are adjusted. The correlation parameters include the carbon emission intensity weight derived from the unit carbon emission intensity benchmark value and the correlation coefficient calculated based on the substation connection relationship and line impedance parameters in the power grid topology data. The adjusted correlation parameters are mapped to the initial carbon emission intensity distribution map generation process, the node carbon emission intensity correction value sequence is recalculated, and the recalculated node carbon emission intensity correction value sequence is verified in real time based on the renewable energy output fluctuation data and load change data in the real-time multi-source data to generate the optimized dynamic carbon factor. The optimized time-division and zone-based dynamic carbon factor is output to the external carbon accounting system to complete the closed-loop feedback from parameter adjustment to carbon factor optimization.

[0116] The derivation steps of carbon emission intensity weighting:

[0117] Based on the benchmark value of carbon emission intensity of generating units, historical and real-time carbon emission intensity data of each generating unit are extracted. The intensity data is mapped to a standardized range of 0-1 according to the numerical value through a dynamic normalization algorithm. The normalization result is then corrected by combining the intensity adjustment factor in the calibration instruction set to generate a preliminary weight sequence. Further, based on load change data and renewable energy output fluctuation data from multi-source data, the weight values ​​in the preliminary weight sequence are adjusted through an adaptive weighting allocation mechanism. Higher weights are allocated during periods of high fluctuation to enhance sensitivity. Finally, carbon emission intensity weights are generated for intensity weighting calculation in the associated parameters.

[0118] Steps for generating correlation coefficients:

[0119] Based on the substation connection relationships and line impedance parameters in the power grid topology data, and combined with carbon emission intensity weights, the spatial connection topology and electrical impedance values ​​of substation nodes are extracted. The electrical distance between nodes is calculated through a topology association algorithm. The electrical distance is multiplied by the line impedance parameters to generate a basic association value. The carbon emission intensity weight is introduced as an adjustment coefficient to perform a weighted correction on the basic association value. The corrected association value is then processed through spatial smoothing to eliminate local anomalies, and finally, an association coefficient matrix is ​​generated to characterize the dynamic coupling strength between power grid nodes.

[0120] This embodiment dynamically adjusts the carbon emission intensity weight and correlation coefficient through a calibration instruction set, enabling high-frequency updates and accurate measurement of dynamic carbon factors. This significantly improves the timeliness and accuracy of carbon footprint accounting. The closed-loop feedback mechanism enhances the system's adaptive capability and provides reliable support for external carbon accounting.

[0121] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A real-time dynamic carbon factor metering and monitoring system for electricity, characterized in that: It includes a data acquisition module, a dynamic calculation module, a monitoring and feedback module, and an optimization output module; The data acquisition module is used to collect multi-source data from the power generation side, grid side, and user side of the power system in real time. The multi-source data includes output data of thermal power generating units and new energy generating units, power flow data of transmission lines, power data of distribution nodes, power consumption data of user meters, and grid topology data. The module verifies the multi-source data and generates a high-frequency data stream, which includes renewable energy output fluctuation data, load change data, and grid topology data. The dynamic calculation module, based on the high-frequency data stream, analyzes renewable energy output fluctuation data, load change data, and power grid topology data through preset dynamic calculation rules to generate time-division and zone-based dynamic carbon factors. The monitoring and feedback module generates a carbon flow path trajectory based on the dynamic carbon factor through a preset analysis unit, receives accuracy feedback information generated from the user's electricity meter consumption data, and generates a calibration instruction set by combining the deviation records in the historical dynamic carbon factor. The optimized output module adjusts the correlation parameters in the dynamic calculation rules based on the calibration instruction set and the real-time multi-source data, recalculates the dynamic carbon factor, and outputs the optimized time-division and partitioned dynamic carbon factor to the external carbon accounting system.

2. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 1, characterized in that: The multi-source data of the data acquisition module is acquired in real time through carbon meters and sensor networks deployed on the power generation side, the grid side, and the user side. The power grid topology data includes substation connection relationships and line impedance parameters; The steps for generating the high-frequency data stream are as follows: S1. Receive the multi-source data, perform timestamp synchronization and data format consistency verification on the multi-source data, and generate an initial multi-source data stream; S2. Based on the initial multi-source data stream, extract the output data of the thermal power generating unit and the new energy generating unit to generate renewable energy output fluctuation data, extract the user electricity meter consumption data and the power data of the distribution node to generate load change data, and insert the power flow data of the transmission line into the initial multi-source data stream according to the timestamp to generate an intermediate data stream. S3. By verifying the spatiotemporal continuity of the renewable energy output fluctuation data, the load change data, the power flow data of the transmission lines, and the power grid topology data in the intermediate data stream, the missing data segments caused by transmission packet loss are filled in, and the volume of the intermediate data stream is compressed to generate a high-frequency data stream.

3. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 2, characterized in that: The steps for generating the dynamic carbon factor in the dynamic calculation module are as follows: Q1. Extract the fluctuation amplitude of the renewable energy output fluctuation data and the change trend of the load change data within a preset sampling period, and spatially align the fluctuation amplitude and the change trend according to the connection relationship in the power grid topology data to generate an associated feature set; Q2. Based on the power output data of the power generation side and the power flow data of the transmission line, the unit carbon emission intensity benchmark value is obtained by matching with the preset emission factor database, and the line loss coefficient is calculated by combining the power flow data of the transmission line. The unit carbon emission intensity benchmark value and the line loss coefficient are fused according to the power transmission path in the power grid topology to generate an initial carbon emission intensity distribution map. Q3. Overlay the associated feature set with the initial carbon emission intensity distribution map, and introduce the fluctuation amplitude in the renewable energy output fluctuation data to correct the initial carbon emission intensity distribution map in real time. The corrected initial carbon emission intensity distribution map is sliced ​​at preset time intervals to generate time-divided dynamic carbon factors.

4. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 3, characterized in that: The calculation steps for the line loss coefficient in step Q2 are as follows: Q201. Based on the power flow data of the transmission lines, extract the current intensity data of each transmission line, and obtain the resistance value per unit length by combining the line impedance parameters in the power grid topology data. Multiply the square of the current intensity data with the resistance value per unit length, and introduce a preset line length coefficient to calculate the instantaneous active power loss value of each transmission line. Q202. Based on the power transmission paths in the power grid topology, the instantaneous active power loss values ​​of each transmission line are accumulated according to the connection order of the power transmission paths, and a preset path load weighting factor is introduced to adjust the accumulated value of the instantaneous active power loss values ​​of each transmission line, thereby generating the total active power loss value of the power transmission paths. Q203. Based on the reference active power value in the power output data of the power generation side, calculate the ratio between the total active power loss value and the reference active power value to generate a ratio value, and map the ratio value to a standardized range of 0 to 1 through a linear normalization function to generate the line loss coefficient.

5. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 4, characterized in that: The specific content of step Q2, which involves fusing the unit carbon emission intensity benchmark value with the line loss coefficient according to the power transmission path in the power grid topology to generate an initial carbon emission intensity distribution map, is as follows: Q204. Based on the power transmission path in the power grid topology data, extract the key node sequence and connection line relationship on the power transmission path, and initially allocate the unit carbon emission intensity benchmark value according to the power node position in the key node sequence to generate the node carbon emission intensity initial value sequence. Q205. Based on the line loss coefficient, and combined with the transmission distance and load characteristics of each connecting line in the power transmission path, calculate the loss adjustment factor for each connecting line, and multiply the loss adjustment factor by the initial value of the node carbon emission intensity of the corresponding connecting line to generate a node carbon emission intensity correction value sequence. Q206. Based on the node carbon emission intensity correction value sequence, interpolate the node carbon emission intensity correction values ​​to the entire geographical area covered by the power grid topology data to generate an initial carbon emission intensity distribution map. The initial carbon emission intensity distribution map stores the carbon emission intensity values ​​of each data grid cell in the power grid topology data in the form of a two-dimensional matrix.

6. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 5, characterized in that: The steps for generating the carbon flow path trajectory in the monitoring feedback module are as follows: W1. Based on the dynamic carbon factor, extract carbon emission intensity data under different time slices, and combine the power transmission path and connection line relationship in the power grid topology data to spatially allocate the carbon emission intensity data according to the circuit transmission path to generate a preliminary carbon flow path sequence. W2. Based on the accuracy feedback information generated from the user's electricity meter consumption data, the preliminary carbon flow path sequence is calibrated to generate a calibrated carbon flow path sequence. W3. The calibrated carbon flow path sequence is fused with the geographic coordinate information in the power grid topology data to generate a carbon flow path trajectory.

7. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 6, characterized in that: The steps for generating the calibration instruction set in the monitoring feedback module are as follows: W4. Extract the carbon intensity anomaly segment and path deviation features in the carbon flow path trajectory, and combine them with the error index in the accuracy feedback information generated by the user's electricity meter consumption data to calculate the deviation of the carbon flow path trajectory from the expected path, and generate a preliminary calibration parameter set, which includes a path correction coefficient and an intensity adjustment factor. W5. Based on the preliminary calibration parameter set, and combined with the deviation records in the historical dynamic carbon factor, identify the deviation pattern characteristics from the historical deviation records, generate a historical deviation pattern set, match the path correction coefficient and intensity adjustment factor in the preliminary calibration parameter set with the historical deviation pattern set, and introduce a preset time decay weight to weight the deviation values ​​in the historical deviation records to generate a calibration instruction set.

8. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 7, characterized in that: The specific steps in step W4 for calculating the deviation between the carbon flow path trajectory and the expected path to generate a preliminary calibration parameter set are as follows: W401. Based on the carbon flow path trajectory, extract the carbon intensity anomaly segment and path offset features in the carbon flow path trajectory, and combine the power transmission path in the power grid topology data as the expected path to calculate the spatial distance deviation value and carbon intensity difference value between the actual carbon flow path and the expected path, and generate a preliminary deviation index set. W402. Based on the preliminary deviation index set, and combined with the error index in the accuracy feedback information generated from the user's electricity consumption data, the spatial distance deviation value and the carbon intensity difference value are multiplied by a preset adaptive weighting factor to generate a path correction coefficient and an intensity adjustment factor, and integrated into a preliminary calibration parameter set.

9. The real-time dynamic carbon factor metering and monitoring system for electricity according to claim 8, characterized in that: The specific content of the optimized output module is as follows: Based on the calibration instruction set and the real-time multi-source data, the path correction coefficient and the intensity adjustment factor in the calibration instruction set are extracted, and the correlation parameters in the dynamic calculation rules are adjusted. The correlation parameters include the carbon emission intensity weight derived from the unit carbon emission intensity benchmark value and the correlation coefficient calculated based on the substation connection relationship and line impedance parameters in the power grid topology data. The adjusted correlation parameters are mapped to the generation process of the initial carbon emission intensity distribution map, the node carbon emission intensity correction value sequence is recalculated, and the recalculated node carbon emission intensity correction value sequence is verified in real time based on the renewable energy output fluctuation data and the load change data in the real-time multi-source data to generate the optimized dynamic carbon factor. The optimized time-division and zone-based dynamic carbon factor is output to the external carbon accounting system.

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